Security Data Lake Market Outlook

According to our latest research, the global Security Data Lake market size reached USD 2.8 billion in 2024, demonstrating robust momentum driven by escalating cyber threats and the proliferation of data-intensive security operations. The market is projected to expand at a CAGR of 21.7% from 2025 to 2033, reaching an estimated value of USD 20.1 billion by 2033. This remarkable growth trajectory is primarily fueled by the increasing adoption of advanced security analytics, regulatory compliance requirements, and the rapid shift toward cloud-based security infrastructure.

A key growth driver for the Security Data Lake market is the exponential increase in cyberattacks and sophisticated threat vectors targeting enterprises across all verticals. Organizations are increasingly recognizing the need for scalable, centralized repositories that can ingest, store, and analyze massive volumes of security data from diverse sources in real time. Security data lakes are uniquely positioned to address these challenges by enabling holistic threat detection, rapid incident response, and comprehensive forensic investigations. The ability to correlate disparate data points and leverage artificial intelligence and machine learning for advanced analytics further enhances the value proposition, making security data lakes an indispensable element of modern cybersecurity strategies.

Another significant factor propelling the market is the growing complexity of regulatory mandates governing data privacy, security, and retention. Industries such as BFSI, healthcare, and government are subject to stringent compliance frameworks like GDPR, HIPAA, and PCI DSS, necessitating robust data management and audit capabilities. Security data lakes offer organizations the flexibility to retain large volumes of historical data, maintain detailed audit trails, and generate compliance reports on demand. The integration of automated compliance management tools within data lakes streamlines regulatory adherence, reduces operational overhead, and minimizes the risk of non-compliance penalties, further driving market adoption.

The digital transformation wave, characterized by cloud migration, IoT proliferation, and the adoption of hybrid IT environments, has created new attack surfaces and increased the complexity of enterprise security ecosystems. Security data lakes provide a unified platform for aggregating security telemetry from on-premises, cloud, and edge environments, enabling organizations to gain end-to-end visibility and actionable insights. As enterprises prioritize zero-trust architectures and real-time security analytics, the demand for scalable, cloud-native data lake solutions is expected to surge, fostering sustained market expansion over the forecast period.

Regionally, North America continues to dominate the Security Data Lake market, accounting for the largest revenue share in 2024, driven by high cybersecurity spending, early technology adoption, and a mature regulatory landscape. However, Asia Pacific is emerging as the fastest-growing region, with organizations across China, India, and Southeast Asia ramping up investments in advanced security infrastructure to combat rising cyber threats. Europe also exhibits strong growth potential, particularly in sectors like BFSI and healthcare, where data privacy and compliance are paramount. The Middle East & Africa and Latin America are witnessing increased market traction as governments and enterprises recognize the strategic importance of robust security data management.

Component Analysis

The Security Data Lake market is segmented by component into Solutions and Services. Solutions represent the core technology stack, including data storage, processing, analytics engines, and integration frameworks that form the backbone of security data lakes. These solutions are designed to handle structured and unstructured security data at scale, offering features such as high-speed ingestion, schema-on-read capabilities, and advanced visualization tools. The growing sophistication of cyber threats has led organizations to prioritize investments in comprehensive security data lake solutions that can facilitate real-time threat detection, automated response, and deep-dive forensic analysis. Vendors are increasingly focusing on enhancing interoperability, scalability, and AI-driven analytics to differentiate their offerings in a highly competi

Insider Threat Narrative Analytics Market Outlook

According to our latest research, the global Insider Threat Narrative Analytics market size reached USD 2.19 billion in 2024, reflecting robust demand for advanced insider threat detection and mitigation solutions across industries. The market is projected to expand at a CAGR of 16.8% during the forecast period, with revenues forecasted to reach USD 10.12 billion by 2033. This impressive growth trajectory is primarily driven by the escalating frequency and sophistication of insider threats, increasing regulatory compliance requirements, and the rapid digital transformation of enterprises worldwide.

The growth of the Insider Threat Narrative Analytics market is fundamentally fueled by the rising awareness among organizations about the potentially devastating impact of insider threats. High-profile breaches and data leaks have highlighted the limitations of traditional security approaches, prompting enterprises to adopt narrative analytics for a holistic and context-driven understanding of user behavior. The proliferation of hybrid and remote work environments has further complicated the security landscape, making it imperative for organizations to deploy advanced analytics solutions capable of correlating disparate data points and identifying subtle patterns indicative of insider risks. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in narrative analytics platforms empowers security teams to predict, detect, and respond to threats in real-time, significantly reducing response times and minimizing potential damages.

Another key driver for market expansion is the tightening regulatory landscape across various sectors, particularly in finance, healthcare, and government. Stringent data privacy and security mandates such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and other regional compliance requirements are compelling organizations to invest in advanced insider threat analytics. These regulations demand comprehensive monitoring, reporting, and incident response capabilities, which narrative analytics platforms are uniquely positioned to deliver. The ability to provide actionable insights and detailed audit trails not only ensures compliance but also enhances organizational resilience against evolving insider threats.

Furthermore, the surge in digital transformation initiatives and the increasing adoption of cloud-based infrastructure are amplifying the complexity of enterprise IT environments. As organizations migrate critical workloads to the cloud and embrace interconnected digital ecosystems, the attack surface for insider threats expands significantly. This has necessitated the deployment of scalable and adaptive narrative analytics solutions that can operate seamlessly across on-premises and cloud environments. The growing emphasis on proactive threat intelligence, coupled with the need for automated and context-aware security operations, is expected to sustain the high growth momentum of the Insider Threat Narrative Analytics market over the coming years.

From a regional perspective, North America continues to dominate the global market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology vendors, early adoption of advanced security solutions, and a highly regulated business environment contribute to North America's leadership. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing cyber threats, and heightened regulatory scrutiny in countries such as China, India, and Japan. Europe also demonstrates strong growth potential, fueled by strict data protection regulations and a rising focus on cybersecurity investments across key verticals. The competitive dynamics and evolving threat landscape in these regions are expected to shape the future trajectory of the global market.

Component Analysis

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Law Enforcement Analytics Market Outlook

According to our latest research, the global law enforcement analytics market size reached USD 13.4 billion in 2024, driven by increasing adoption of advanced analytics and AI technologies in public safety operations. The market is expected to grow at a robust CAGR of 10.2% from 2025 to 2033, reaching a projected value of USD 34.3 billion by 2033. This impressive growth trajectory is underpinned by the rising need for data-driven decision-making in law enforcement, the proliferation of digital evidence, and the continuous evolution of cyber and physical crime landscapes.

A key growth factor for the law enforcement analytics market is the exponential increase in digital data generated from diverse sources, including surveillance cameras, social media, sensors, and mobile devices. Law enforcement agencies are increasingly leveraging analytics to process and interpret this massive influx of data, enabling them to identify patterns, predict criminal activity, and allocate resources more efficiently. The integration of artificial intelligence and machine learning into analytics platforms has further enhanced the ability to detect anomalies and correlate disparate data points, resulting in more proactive and informed policing. As criminal tactics become more sophisticated, the demand for advanced analytics tools that can quickly process and analyze complex datasets is expected to rise significantly, fueling market expansion.

Another major driver is the growing emphasis on public safety and homeland security, especially in urban areas experiencing rapid population growth and urbanization. Governments worldwide are investing heavily in upgrading law enforcement infrastructure, including digital forensics, predictive policing, and real-time surveillance systems. The adoption of cloud-based analytics solutions is also accelerating, as agencies seek scalable and flexible platforms that can support remote operations and facilitate inter-agency collaboration. Additionally, the increasing frequency of cybercrimes, terrorism, and organized crime has underscored the importance of timely intelligence and data sharing, further boosting the adoption of law enforcement analytics solutions across various jurisdictions.

Furthermore, regulatory mandates and compliance requirements are compelling law enforcement agencies to modernize their data management and reporting capabilities. The need to adhere to privacy laws, maintain accurate records, and ensure transparency in investigations is driving investments in comprehensive analytics platforms. These solutions not only streamline case management and evidence handling but also support accountability and public trust by providing auditable and traceable data trails. As public scrutiny of law enforcement practices intensifies, agencies are increasingly turning to analytics to demonstrate compliance, improve operational efficiency, and deliver measurable outcomes.

From a regional perspective, North America continues to dominate the law enforcement analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has been at the forefront of adopting advanced analytics technologies, driven by substantial government funding, strong technology infrastructure, and a high incidence of both cyber and physical crimes. Europe is also witnessing steady growth, propelled by cross-border security initiatives and stringent regulatory frameworks. Meanwhile, Asia Pacific is emerging as a high-growth region, supported by rapid urbanization, increasing government investments in smart city projects, and a heightened focus on public safety and surveillance. Latin America and the Middle East & Africa are gradually catching up, with growing awareness of the benefits of analytics in combating organized crime and terrorism.

Component Analysis

The law enforcement analytics market is segmented by component into software and services, each playing a pivotal role in enhancing the capabilities of law enforcement agencies. The software segment encompasses a wide range of analytics platforms, including crime mapping, predictive analytics, social network analysis, and digital forensics tools. These software solutions are designed to aggregate, process, and analyze vast amounts of structured and unstructured data from multiple sources, providing actionable insights to investigators and decision-makers. The growing complexity of criminal activities and the increasing volu

Log Pipeline Transformation for SIEM Market Outlook

According to our latest research, the global Log Pipeline Transformation for SIEM market size in 2024 stands at USD 4.8 billion, reflecting robust adoption across diverse industries. The market is projected to expand at a Compound Annual Growth Rate (CAGR) of 13.2% from 2025 to 2033, reaching a forecasted value of USD 14.1 billion by 2033. This remarkable growth trajectory is fueled by the increasing complexity of security threats and the critical need for advanced Security Information and Event Management (SIEM) solutions that can efficiently handle, transform, and analyze massive log data streams in real time.

The primary growth factor for the Log Pipeline Transformation for SIEM market is the exponential surge in data generation and the sophistication of cyber threats. Organizations are now dealing with unprecedented volumes of log data emanating from a multitude of sources, including cloud services, IoT devices, and remote endpoints. This escalation in data complexity necessitates advanced log pipeline transformation tools that can preprocess, normalize, and enrich raw logs before feeding them into SIEM platforms. As a result, enterprises are investing heavily in next-generation solutions that offer real-time log parsing, context enrichment, and noise reduction, which significantly enhance threat detection capabilities and reduce false positives.

Another critical driver behind the market’s expansion is the tightening regulatory landscape and the growing emphasis on compliance management. Industries such as BFSI, healthcare, and government are subject to stringent regulations like GDPR, HIPAA, and PCI DSS, which require comprehensive log management and audit trails. Log pipeline transformation solutions are increasingly being adopted to automate compliance reporting, ensure data integrity, and facilitate faster incident response. The ability of these solutions to seamlessly integrate with existing SIEM platforms, automate log retention, and provide granular visibility into security events is making them indispensable for organizations aiming to achieve continuous compliance while minimizing operational overhead.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also propelling the Log Pipeline Transformation for SIEM market forward. Modern log transformation pipelines leverage AI-driven analytics to identify anomalies, correlate disparate data points, and prioritize security alerts based on risk context. This intelligent automation not only accelerates incident response times but also empowers security teams to focus on high-value tasks. Furthermore, the proliferation of cloud-native SIEM platforms and the adoption of microservices architectures are driving demand for scalable and flexible log pipeline solutions capable of supporting dynamic, distributed IT environments. This fusion of AI, cloud, and automation is expected to further amplify market growth over the forecast period.

From a regional perspective, North America continues to dominate the Log Pipeline Transformation for SIEM market due to its mature cybersecurity ecosystem, high adoption of advanced security solutions, and the presence of leading technology vendors. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid digital transformation, increasing cyber incidents, and growing regulatory scrutiny. Europe also holds a significant market share, underpinned by its strong focus on data privacy and compliance. Collectively, these regional trends underscore the global imperative for robust log pipeline transformation solutions as organizations seek to fortify their security posture and navigate an increasingly complex threat landscape.

Component Analysis

The Component segment of the Log Pipeline Transformation for SIEM market is categorized into software, hardware, and services. Software solutions form the backbone of log pipeline transformation, offering functionalities such as log parsing, normalization,

Graph-Based Security Analytics Market Outlook

According to our latest research, the global Graph-Based Security Analytics market size in 2024 stands at USD 2.46 billion, with a robust compound annual growth rate (CAGR) of 21.8% expected throughout the forecast period. By 2033, the market is projected to reach USD 17.38 billion, propelled by the rapid adoption of advanced analytics solutions in cybersecurity. The primary growth factor driving this market is the increasing sophistication of cyber threats, which has compelled organizations to adopt more dynamic, interconnected, and real-time security analytics frameworks. The integration of graph-based analytics enables enterprises to map complex relationships, detect anomalies, and respond to threats with greater precision and speed, making it a cornerstone of modern security architectures.

One of the most significant growth drivers for the Graph-Based Security Analytics market is the surge in advanced persistent threats (APTs) and multi-stage cyber-attacks targeting critical infrastructure and enterprise assets. Traditional security solutions often struggle to correlate disparate data points and identify hidden patterns indicative of sophisticated attacks. Graph-based analytics, leveraging the power of graph databases and advanced algorithms, can map intricate relationships among users, devices, and events, thereby enabling security teams to uncover lateral movement, privilege escalation, and other complex attack vectors. The ability to visualize and analyze these relationships in real time allows organizations to proactively mitigate risks, reducing the mean time to detect (MTTD) and respond (MTTR) to incidents. As cyber adversaries continue to evolve, the demand for such advanced analytics is set to rise exponentially across all industry verticals.

Another key factor fueling the expansion of the Graph-Based Security Analytics market is the proliferation of digital transformation initiatives and the exponential growth of enterprise data. As organizations migrate to cloud environments, adopt IoT devices, and enable remote workforces, the attack surface expands dramatically, creating new vulnerabilities and increasing the complexity of security management. Graph-based security analytics solutions are uniquely positioned to address these challenges by integrating data from diverse sources, including network logs, endpoint telemetry, and user behavior analytics, into a unified graph structure. This holistic approach not only enhances threat detection and investigation capabilities but also supports compliance with stringent regulatory requirements, such as GDPR, HIPAA, and PCI DSS. Enterprises are increasingly investing in these solutions to ensure robust security postures while maintaining agility and scalability.

The growing emphasis on regulatory compliance and risk management is also shaping the trajectory of the Graph-Based Security Analytics market. Regulatory bodies worldwide are imposing stricter data protection mandates, compelling organizations to implement advanced monitoring and reporting mechanisms. Graph-based analytics platforms facilitate comprehensive risk assessments by mapping out dependencies, identifying critical assets, and quantifying potential impacts of security incidents. This capability is particularly valuable for sectors such as BFSI, healthcare, and government, where data breaches can have far-reaching legal and financial consequences. As compliance requirements continue to evolve, the adoption of graph-based security analytics is expected to become a standard practice among organizations seeking to safeguard their digital assets and maintain regulatory alignment.

From a regional perspective, North America currently dominates the Graph-Based Security Analytics market, accounting for the largest share in 2024. This leadership is attributed to the high concentration of technology-driven enterprises, early adoption of advanced security solutions, and the presence of leading market players. However, the Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, driven by rapid digitalization, increasing cyber threats, and rising investments in cybersecurity infrastructure across emerging economies such as China, India, and Southeast Asia. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, fueled by heightened awareness of cyber risks and the implementation of robust regulatory frameworks. The global market landscape is thus characterized by dynam

Trauma means an emotional response to a deeply distressing or disturbing event like loss of a loved one or an accident.A trauma patient will get data from multiple sources like neurocognitive, physiologic data from various medical tests.The neuro cognitive data comprises of EEG signal data like Amplitude,Delta,Theta,Alpha and Beta Values.The physiologic data comprises of heart_rate,skin_conductance,skin_temperature,cortisol_level, Systolic_BP and Diastolic_BP. The In the existing system, all thoses data are analyzed by medical experts in order to arrive at the conditional severity of the Trauma patient. But, it is difficult for the experts to correlate data from multiple sources, and arrive at a decision on severity. This dataset is useful at classifying the Severity of Trauma patients.

Operator Identity Graph Market Outlook

According to our latest research, the global Operator Identity Graph market size reached USD 1.76 billion in 2024, reflecting robust momentum driven by the surge in digital transformation and heightened security requirements across various industries. The market is expected to grow at a CAGR of 14.2% from 2025 to 2033, resulting in a projected market size of USD 5.02 billion by 2033. This strong growth trajectory is primarily fueled by the increasing demand for sophisticated identity resolution solutions to combat fraud, enhance customer experience, and support regulatory compliance in an evolving digital ecosystem.

The primary growth factor for the Operator Identity Graph market is the exponential rise in digital interactions across sectors such as telecommunications, BFSI, retail, and healthcare. As organizations increasingly adopt omnichannel strategies, the need for a unified and real-time view of user identities has become paramount. Operator Identity Graph solutions enable enterprises to aggregate and correlate disparate data points, such as device identifiers, behavioral attributes, and transactional histories, into a single, comprehensive identity profile. This holistic approach not only strengthens security and fraud prevention efforts but also empowers businesses to deliver personalized customer experiences, thus driving adoption rates globally.

Another significant driver is the intensifying regulatory landscape, especially in regions like North America and Europe, where data privacy and security standards such as GDPR, CCPA, and PSD2 are strictly enforced. Operator Identity Graph solutions facilitate compliance by enabling accurate identity verification, consent management, and data lineage tracking. Enterprises are increasingly investing in these technologies to mitigate risks associated with data breaches and non-compliance penalties. Moreover, the proliferation of mobile devices and IoT endpoints has created complex identity ecosystems, further amplifying the need for advanced identity resolution and management capabilities.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are also propelling the Operator Identity Graph market forward. Modern solutions leverage AI-driven algorithms to continuously update and enrich identity graphs with real-time data, ensuring high accuracy and adaptability in dynamic environments. These innovations are particularly valuable in fraud detection and prevention, where rapid identification of suspicious patterns is crucial. Additionally, the integration of operator identity graphs with existing security and marketing technology stacks enhances interoperability and operational efficiency, making them indispensable for digital-first enterprises seeking competitive differentiation.

From a regional perspective, North America currently dominates the Operator Identity Graph market, accounting for over 38% of the global revenue in 2024. The region’s leadership is attributed to early technology adoption, a mature digital infrastructure, and stringent regulatory frameworks. However, Asia Pacific is expected to exhibit the fastest growth over the forecast period, supported by rapid digitalization, increasing mobile penetration, and expanding e-commerce ecosystems. Europe remains a key market due to its focus on data protection and privacy, while Latin America and the Middle East & Africa are gradually emerging as attractive markets, driven by growing investments in digital security and identity management solutions.

Component Analysis

The Operator Identity Graph market is segmented by component into software and services, each playing a crucial role in shaping the overall market dynamics. The software segment currently holds the largest market share, driven by the increasing adoption of advanced identity resolution platforms that integrate seamlessly with enterprise IT architectures. These software solutions are designed to ingest, process, and correlate vast volumes of identity data from multiple sources, providing organizations with a unified, real-time view of users and devices. The demand for scalable, cloud-native software platforms is particularly strong among large enterprises and digitally native businesses seeking rapid deployment and flexible integration capabilities.

Within the software segment, vendors are focusing o

IntroductionWe use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.MethodsWe develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.ResultsWe find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.DiscussionOur results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

This code analyses behavioural data from a group of 16 Parkinson patients and 15 healthy control participants performing an action adaptation tasks, in which participants need to continuously adapt the applied force based on the feedback they receive. The first feedback ranges from 0 (worst) to 10 (best) points depending on the error between actual force and target force (Value-cue) and the second feedback indicates whether the force had been too low or too high (Direction-feedback). The main behavioural outcomes are measures of force production and force adaptation (folder 1, used for figure 1 in the published article). In patients local field potentials were recorded during the task and corresponding code is stored in folder 2 (figure 2&3). In 14 patients burst deep brain stimulation was applied during a second session. Its effects on behaviour and local field potentials are analysed with code from folder 3 and 4 (figures 4&5). The results have been published in a paper entitled ‘Neural underpinnings of action adaptation in the subthalamic nucleus’ by Herz et al.

The code has been tested on a MacBook Pro, macOS Mojave 10.14.6. All data were analysed in Matlab (2019a, requires a software license) and FieldTrip. Installation guides can be found on: https://matlab.mathworks.com/ and https://www.fieldtriptoolbox.org/download/. Run times of the different scripts is usually short (from < 1 minutes to ~ 5 minutes) for most analyses except for cluster-based permutation tests of linear mixed effects models, which take a few hours.

Example data is provided for the behavioural analysis for 2 healthy control (HC) participants. Of note, this is not the actual data from HC01 & HC02 from the study, but it allows testing the behavioural scripts if applicable (see below for instructions).

(i) Behavioral data:

Scripts: ‘CompareLevodopaDemographicsMVC’: this script compares demographics and the maximum voluntary contraction (MVC) between patients and HC, and tests the effect of levodopa on the Unified Parkinson’s Disease Rating Scale (UPDRS). ‘GetEvents’ (_PD & _HC): Imports the events file from PsychoPy using ‘ExtractData’ (& _HC) and saves it as a mat-file. ‘GetForce’ (_PD & _HC): Computes several variables reflecting force production and adaptation using several functions: ‘Forceparameters’ computes measures of force production and the time of peak force, and allows illustrating single trials, ‘Forces_within’ computes mean and standard error of mean (SEM) of single subject force traces, ’Stat_within’ computes several single subject correlations and measures of force adaptation, ‘Forces_across’ computes mean and SEM of group force traces, ‘stat_across’ computes group-mean trajectories of actual force and target force. The results are saved as a mat-file for subject-averaged data and a csv-file for single subject data. ‘Plot_Stats’ computes statistics of these measures of force production and adaptation and plots the results.

(ii) Local field potential data:

Scripts: ‘GetLFP_FirstLevel.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip. It uses the custom-written functions. ‘MakeMontage_AllBipolar’ (which creates a bipolar montage from the monopolar data) and ‘EpochData_TF’ (which epochs the continuous data aligned to the feedback cue and peak force). The epoched spectra are saved. ‘GetLFP_SecondLevel_PlotSpectra.m’: Loads the spectra from first level analysis and plots the grand average as well as group-averaged beta, alpha (for feedback-aligned data) and gamma traces (for movement-aligned data).
‘GetLFP_SecondLevel_LME.m’: Loads the spectra from first level analysis and computes LME analyses with variables of interest using moving windows of single trial beta power.For cluster-based permutation tests (which take several hours) the function ‘PermTests_LME’ is used. ‘GetLFP_SecondLevel_controlLME.m’: Loads the spectra from first level analysis and computes control LME analyses: Effect of Value and Direction on Alpha power in the feedback period (where it showed an increase), effect of change in force and absolute change in force on Gamma power before peak force (where it showed an increase) and on Beta power after the Value feedback (where it showed a correlation with Value).

(iii) DBS effects on behaviour:

Scripts: ‘GetEvents_Stim’: Imports the events file from PsychoPy using ‘ExtractData’ and saves it as a mat-file. ‘GetForce_Stim’: Computes several variables reflecting force production and adaptation using several functions: ‘Forceparameters’ computes measures of force production and the time of peak force, and allows illustrating single trials, ‘Forces_within’ computes mean and standard error of mean (SEM) of single subject force traces, ‘Forces_across’ computes mean and SEM of group force traces. The results are saved as a mat-file for subject-averaged data and a csv-file for single subject data. ‘GetToS’ loads a file with the stimulation trace during the task, calls the function ‘ToS_DownsampleBinaryRemoveRamp’ (which downsamples the data to 1000Hz, makes stimulation binary (1 for ON, 0 for OFF) and removes the ramping so that only stimulation at effective intensities counts as stimulation) and loads the relevant behavioural data (change in force and absolute change in force). It then calls the functions ‘ToS_WindowedStim’ (which computes for each trial whether or not stimulation was given in any 100 ms moving windows for cue- and movement aligned data) and ‘ToS_Windowed_nexttrial’ (which computes change in force and absolute change in force for windows in which stimulation was applied vs. was not applied).The results are saved in a mat-file. ‘Plot_ToS’ loads this data, plots effects of stimulation on absolute change in force and change in force and provides statistics using cluster-based permutation tests (‘PermTests_ToS’). It also saves single trial behavioural data with a column stating whether DBS was applied in the critical time windows (which is used for the DBS effects on local field potentials analysis).

(iv) DBS effects on local field potentials:

Scripts: ‘GetLFP_FirstLevel_Stim.m’: Loads data and applies preprocessing, time-frequency analysis and re-aligning of data using FieldTrip analogously to the script described under (ii) except that it also detrends and demeans the data, applies a low-pass filter at 100 Hz and excludes noisy data points, which are then interpolated. The epoched spectra aligned to feedback and movement are saved.

‘GetLFP_FirstLevel_Stim_TrigOnset.m’: Same as above, but aligned to onset of stimulation bursts.

‘GetLFP_SecondLevel_Stim.m’: Loads data from the previous analysis, loads single trial data with info whether DBS was applied at critical time windows and plots these beta traces together with beta power off stimulation for time windows of interest. Cluster-based permutation tests are applied using the function ‘PermTests_ToS’. Grand-average spectra and beta power irrespective of stimulation-timing are also plotted.

‘GetLFP_SecondLevel_TrigOnset.m’: Loads data from the previous _TrigOnset analysis and plots the group average.

(v) Downloaded scripts:

The following scripts were downloaded from mathworks.com: ‘computeCohen_d’ (measure of effect size), ‘jblill’ (filling significant clusters from permutation tests), ‘shadedErrorBar’ (illustrating mean and SEM).

(vi) Testing example data:

Two example datasets are provided (termed Kont01 & Kont02), which allow testing the behavioural force analysis. To do this the script GetForce_HC.m should be opened. DirName and EventPath should be adjusted for the actual path. Line 24: Should be changed to ‘for Subj=1:2’ Lines 103-108 should be commented, i.e. not used. Setting plotforce to 1 (line 12) plots the single subject and group average force spectra. Setting check to 1 (line 11) plots single trial force data. For this only use subject 1 or 2, not both (i.e. in line 24 use ‘for Subj=1’ or ‘for Subj=2’).

What's in this dataset

We hope that this dataset will prove useful to answer questions pertaining to "What do we know about non-pharmaceutical interventions?". This is a machine-readable dataset related to socioeconomic factors that may affect the spread and/or consequences of epidemiological outbreaks of the novel coronavirus (COVID-19). This is combined with timeseries of the infections and deaths from 1/22 to now and the foot traffic at points of interest of different types, aggregated at the county level. By combining these, we want to measure whether NPIs work differently in different counties, and whether their effects can be predicted by county-specific traits. This dataset is envisioned to serve the data science, machine learning, and epidemiological modeling communities.

We collected the data set from a variety of sources. In the interest of not cluttering this dataset, we only included the data after it has been processed into a machine readable format. Please see the raw data with the full acknowledgements to our sources at our Github page. https://github.com/JieYingWu/COVID-19_US_County-level_Summaries

ArXiv report on dataset: http://arxiv.org/abs/2004.00756

Acknowledgements

Thank you to all our sources, especially the JHU CSSE COVID-19 Dashboard for making their data public and SafeGraph, for providing researchers their data for COVID-19 related work.

Inspiration

Using this dataset, we hope to promote better understanding of how diseases spread differently in different communities, as well as how policies to limit a disease's spread will impact different communities. We hope that this can inform policy makers to enact interventions that are effective for each county.

Citation

If you find this dataset useful, please consider citing our paper: latex @article{killeenCountylevelDatasetInforming2020, title = {A {{County}}-Level {{Dataset}} for {{Informing}} the {{United States}}' {{Response}} to {{COVID}}-19}, author = {Killeen, Benjamin D. and Wu, Jie Ying and Shah, Kinjal and Zapaishchykova, Anna and Nikutta, Philipp and Tamhane, Aniruddha and Chakraborty, Shreya and Wei, Jinchi and Gao, Tiger and Thies, Mareike and Unberath, Mathias}, year = {2020}, month = apr, }

Insider Threat Case Management for Agencies Market Outlook

According to our latest research, the global Insider Threat Case Management for Agencies market size reached USD 2.37 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected from 2025 to 2033. The market is expected to attain a value of USD 6.74 billion by 2033. This growth is primarily driven by the escalating frequency and sophistication of insider threats targeting governmental, defense, intelligence, and public sector organizations worldwide. As agencies increasingly prioritize cybersecurity and compliance, the demand for advanced case management solutions to detect, investigate, and mitigate insider risks is accelerating at an unprecedented pace.

One of the primary growth factors fueling the Insider Threat Case Management for Agencies market is the rapid digital transformation and adoption of cloud-based infrastructure across public sector entities. As agencies embrace digital workflows, remote operations, and interconnected systems, their exposure to insider threats grows exponentially. Malicious insiders, negligent employees, and third-party contractors now have more access points than ever, increasing the risk of data breaches, intellectual property theft, and unauthorized disclosures. To counter these risks, agencies are investing in sophisticated insider threat case management solutions that provide real-time monitoring, behavioral analytics, and automated response capabilities, ensuring both proactive detection and swift incident resolution.

Another significant driver is the evolving regulatory landscape governing data security and privacy within government and public sector organizations. With stringent compliance mandates such as FISMA, GDPR, and NIST frameworks, agencies are compelled to implement comprehensive insider threat management programs that document, audit, and report on all suspicious activities. Failure to comply can result in severe financial penalties and reputational damage. Consequently, agencies are increasingly adopting end-to-end case management platforms that streamline investigations, maintain digital evidence, and facilitate regulatory reporting, thereby reducing compliance risks and reinforcing public trust.

In addition to regulatory pressures, the growing sophistication of insider attack vectors is prompting agencies to rethink their security strategies. Modern insider threats often leverage advanced techniques such as social engineering, credential theft, and lateral movement within agency networks. Traditional security tools are often inadequate to identify these subtle, multi-stage attacks. As a result, agencies are turning to integrated case management solutions that leverage artificial intelligence, machine learning, and user behavior analytics to detect anomalies, correlate disparate data sources, and prioritize high-risk cases for investigation. This technological evolution is reshaping the market and driving continuous innovation among solution providers.

Regionally, North America continues to dominate the Insider Threat Case Management for Agencies market, accounting for the largest revenue share in 2024. This can be attributed to the high concentration of federal agencies, defense organizations, and public sector entities in the United States and Canada, coupled with substantial investments in cybersecurity infrastructure. However, the Asia Pacific region is anticipated to exhibit the fastest CAGR over the forecast period, fueled by expanding digital government initiatives, increasing cyber incidents, and growing awareness of insider threat risks among regional agencies. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, driven by regulatory reforms and heightened focus on public sector cyber resilience.

Component Analysis

The Insider Threat Case Management for Agencies market is segmented by component into Software and Services, each playing a crucial role in the overall adoption and effectiveness of insider threat management programs. Software solutions form the backbone of insider threat detection and case management, offering agencies a suite of tools for monitoring user activity, analyzing behavioral patterns, and automating the investigation process. These platforms are increasingly leveraging artificial intelligence and machine learning to enhance anomaly detection,

Incident Triage AI Market Outlook

According to our latest research, the global Incident Triage AI market size reached USD 2.4 billion in 2024, driven by the escalating complexity and frequency of cyber threats worldwide. The market is expected to grow at a robust CAGR of 18.7% from 2025 to 2033, propelling the market value to an estimated USD 12.3 billion by 2033. This remarkable growth is primarily attributed to the increasing adoption of artificial intelligence for automating and accelerating incident response, minimizing human error, and enhancing the overall efficiency of security operations across diverse industries.

A key growth factor for the Incident Triage AI market is the exponential rise in sophisticated cyberattacks targeting organizations of all sizes and sectors. With the digital transformation of business processes, there has been a surge in the volume and complexity of security incidents, making manual triage not only labor-intensive but also error-prone and slow. Incident Triage AI leverages machine learning and advanced analytics to rapidly assess, prioritize, and respond to security alerts, significantly reducing response times and improving threat detection accuracy. As organizations seek to protect sensitive data and maintain business continuity, the deployment of AI-driven incident triage solutions is becoming a strategic imperative, especially in sectors such as BFSI, healthcare, and government, where the stakes for data breaches are particularly high.

Another significant driver is the growing shortage of skilled cybersecurity professionals, which has created a pressing need for automation in security operations centers (SOCs). Incident Triage AI platforms are designed to augment human analysts by automating routine and repetitive tasks, allowing security teams to focus on more complex investigations and strategic decision-making. This not only enhances the productivity of existing teams but also helps organizations manage increasing alert volumes without expanding their workforce proportionally. Furthermore, the integration of AI with existing security infrastructure enables organizations to adapt to evolving threat landscapes and regulatory requirements more efficiently, further fueling market growth.

The proliferation of cloud computing and remote work environments has also contributed to the expansion of the Incident Triage AI market. As organizations migrate critical workloads and data to the cloud, the attack surface expands, necessitating advanced security solutions capable of real-time threat detection and response across hybrid and multi-cloud environments. Incident Triage AI solutions offer the scalability, flexibility, and speed needed to secure dynamic digital ecosystems, making them increasingly attractive to enterprises seeking to ensure robust cybersecurity postures in the face of evolving risks. The integration of AI with cloud-based security platforms is expected to further accelerate the adoption of Incident Triage AI over the forecast period.

From a regional perspective, North America currently dominates the global Incident Triage AI market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The high concentration of technology-driven enterprises, advanced cybersecurity infrastructure, and early adoption of AI-powered solutions in the United States and Canada are key contributors to this leadership. Meanwhile, the Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by rapid digitalization, increasing cyber threats, and significant investments in AI and cybersecurity by governments and private organizations. Europe continues to show strong demand, particularly in regulated sectors such as BFSI and healthcare, where compliance with stringent data protection regulations is a critical priority.

In the realm of cybersecurity, Contract Tracing for Security Incidents has emerged as a pivotal strategy to enhance the effectiveness of incident response. This approach involves meticulously tracking and documenting the sequence of events leading up to and following a security breach, enabling organizations to identify vulnerabilities and prevent future incidents. By leveraging AI and machine learning, contract tracing can automate the correlation of disparate data points, providing security teams with comprehensive insights int

It is well-known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene expression data by an appropriate ordering of genes. These substructures include stochastic proportionality of expression signals in a large percentage of all gene pairs, negative correlations hidden in ordered gene triples, and a long sequence of weakly dependent random variables associated with ordered pairs of genes. The reported striking regularities are of general biological interest and they also have far-reaching implications for theory and practice of statistical methods of microarray data analysis. We illustrate the latter point with a method for testing differential expression of non-overlapping gene pairs. While designed for testing a different null hypothesis, this method provides an order of magnitude more accurate control of type 1 error rate compared to conventional methods of individual gene expre ssion profiling. In addition, this method is robust to the technical noise. Quantitative inference of the correlation structure has the potential to extend the analysis of microarray data far beyond currently practiced methods.

Following Barker, Pople and Gubbins & Gray, the u-expansion of the perturbation theory, used for developing equations of state for fluids, requires sets of J-, K-, L-, M-, and N-integrals as a function of rho* and T*. These integrals are calculated here from pair and triplet correlation functions, which were derived in a previous communication, using particle configurations from extensive Monte-Carlo simulations of a Lennard-Jones fluid. The pair and triplet correlation functions are based on 27615 state points covering a rho*-T* space from 0.002-1.41 and 0.45-25 in reduced variables, respectively, which is also the range of the calculated integrals. Quadruplet correlation functions, required by the M- and N-integrals, were calculated using the trans-superposition approximation, using pair and triplet correlation functions. Here the unfitted raw data of 597 J-, 90 K-, 256 L-, 4M-, and 4N-integrals are reported. The number of available values at different rho*-T* state points are 27615 for the J-integrals, and in the range of 6999-7053, 6789-7055, 6440-6587, 6544-6751 for the K-, L-, M-, andN-integrals, respectively.

Data Exfiltration Detection AI Market Outlook

According to our latest research, the global Data Exfiltration Detection AI market size reached USD 1.57 billion in 2024, reflecting the rapid adoption of advanced AI-driven cybersecurity solutions. The market is projected to expand at a robust CAGR of 21.4% from 2025 to 2033, reaching a forecasted value of USD 11.45 billion by 2033. This remarkable growth is primarily fueled by the escalating sophistication of cyber threats, the proliferation of digital transformation initiatives, and a heightened focus on data privacy and regulatory compliance across sectors worldwide.

The primary growth factor driving the Data Exfiltration Detection AI market is the increasing frequency and complexity of cyberattacks targeting sensitive organizational data. As businesses continue to digitize operations and store vast amounts of confidential information electronically, the risk of unauthorized data transfer and theft has surged significantly. AI-powered detection solutions are being embraced for their ability to intelligently monitor network traffic, identify anomalous behavior, and provide real-time alerts, thus enabling organizations to respond proactively to exfiltration attempts. The integration of machine learning algorithms further enhances the accuracy of threat detection, reducing false positives and ensuring that security teams can focus on genuine incidents. As a result, enterprises across industries are investing heavily in AI-based exfiltration detection tools to safeguard proprietary data and maintain stakeholder trust.

Another critical driver is the evolving regulatory landscape, which mandates stringent data protection and breach notification requirements. Legislation such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and various sector-specific data security standards have compelled organizations to adopt advanced monitoring technologies capable of detecting and preventing data leakage. AI-based detection solutions offer the scalability and adaptability required to comply with these regulations, providing automated reporting and forensic analysis capabilities. Moreover, the growing adoption of cloud computing and remote work models has expanded the attack surface, making traditional security approaches insufficient. AI-driven exfiltration detection systems are uniquely positioned to address these challenges by offering continuous, adaptive protection across diverse IT environments.

The surge in high-profile data breaches and the increasing sophistication of insider threats are also propelling the demand for Data Exfiltration Detection AI solutions. Organizations are recognizing that not all threats originate from external actors; malicious insiders, compromised credentials, and inadvertent data transfers pose significant risks. AI-powered systems excel at monitoring user behavior, detecting deviations from normal activity, and correlating multiple data points to identify potential exfiltration attempts. This comprehensive approach to threat detection is particularly valuable in industries such as finance, healthcare, and government, where the consequences of data breaches can be severe. As a result, the market is witnessing robust investment from both public and private sector entities seeking to fortify their cybersecurity posture.

Regionally, North America continues to dominate the Data Exfiltration Detection AI market, accounting for the largest share in 2024, driven by the presence of leading technology vendors, high cybersecurity awareness, and significant regulatory enforcement. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing investments in cybersecurity infrastructure, and the growing incidence of cyberattacks targeting enterprises and critical infrastructure. Europe also represents a substantial market, underpinned by strong regulatory frameworks and widespread adoption of advanced security solutions across industries. The Middle East & Africa and Latin America are experiencing steady growth, supported by rising awareness of cyber risks and government-led digital transformation initiatives.

Component Analysis

The Data Exfiltration Detection AI market is segmented by component into software, hardware, and services. The software segment currently holds the largest market share, accounting for over half of global revenues in 2024. This dominance is attributed to the c

Identity Graph Enrichment for Banking Market Outlook

According to our latest research, the global Identity Graph Enrichment for Banking market size reached USD 1.37 billion in 2024, demonstrating a robust expansion driven by the sector’s digital transformation. The market is expected to grow at a CAGR of 16.2% from 2025 to 2033, with the forecasted market size projected to reach USD 5.13 billion by 2033. This growth is primarily fueled by increasing regulatory demands, the urgent need for advanced fraud management, and the rapid adoption of digital banking services worldwide. As per our latest research, the market is witnessing a paradigm shift as financial institutions prioritize secure, seamless, and hyper-personalized customer experiences.

One of the primary growth drivers for the Identity Graph Enrichment for Banking market is the escalating sophistication of financial fraud and cyber threats. Banks and financial institutions are under increasing pressure to safeguard customer data and ensure transaction integrity across multiple digital touchpoints. Identity graph enrichment enables these organizations to aggregate and correlate disparate identity signals from various sources, providing a comprehensive and unified view of customer identities. This holistic approach is crucial for detecting anomalies, preventing account takeovers, and mitigating fraud risks in real time. As regulatory bodies worldwide tighten compliance requirements, the adoption of advanced identity graph solutions has become not just a competitive advantage but a necessity for maintaining trust and operational resilience in the digital age.

Another significant factor propelling market growth is the surge in digital onboarding and remote banking services. With consumers demanding frictionless, omnichannel experiences, banks are leveraging identity graph enrichment to streamline customer onboarding, enhance KYC (Know Your Customer) processes, and personalize service delivery. By integrating diverse data points—such as device fingerprints, behavioral patterns, transaction histories, and social signals—banks can accurately verify identities and reduce onboarding times while minimizing the risk of synthetic identity fraud. This capability is especially critical in emerging markets, where digital-first banking models are gaining traction and customer acquisition is a key business imperative.

The market is also benefiting from the growing emphasis on personalized marketing and customer engagement. As banking becomes increasingly data-driven, institutions are utilizing identity graph enrichment to gain deep insights into customer preferences, life stages, and financial behaviors. This enables banks to deliver targeted product recommendations, tailored communications, and contextual offers, thereby improving customer satisfaction and loyalty. Moreover, the integration of artificial intelligence and machine learning with identity graph solutions is unlocking new possibilities for predictive analytics, cross-selling, and proactive risk management. These advancements are positioning identity graph enrichment as a strategic enabler of digital transformation and revenue growth in the banking sector.

From a regional perspective, North America currently dominates the Identity Graph Enrichment for Banking market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership can be attributed to its advanced digital banking infrastructure, stringent regulatory frameworks, and high adoption of innovative technologies. However, Asia Pacific is expected to witness the fastest growth during the forecast period, fueled by the rapid expansion of digital banking, increasing fintech investments, and a burgeoning middle-class population. Meanwhile, Europe continues to see strong uptake driven by GDPR compliance and open banking initiatives, while Latin America and the Middle East & Africa are emerging as promising markets due to their digitalization efforts and financial inclusion programs.

Component Analysis

The Identity Graph Enrichment for Banking market is broadly segmented by component into Solutions and Services. Solutions form the backbone of the market, encompassing the software platforms and tools that enable banks to build, enrich, and manage identity graphs. These solutions are designed to integrate seamlessly with existing banking infrastructure, aggregating data from a multitu

Fault Correlation with Graph AI Market Outlook

According to our latest research, the global Fault Correlation with Graph AI market size reached USD 1.38 billion in 2024, reflecting robust adoption across multiple industries. The market is expected to expand at a CAGR of 23.1% from 2025 to 2033, reaching an estimated USD 10.08 billion by the end of the forecast period. This remarkable growth is driven by the increasing complexity of IT environments and the critical need for advanced, AI-powered fault detection and correlation solutions that can proactively mitigate risks and improve operational efficiency.

The primary growth factor for the Fault Correlation with Graph AI market is the exponential rise in data complexity and volume across enterprise IT infrastructures. As organizations embrace digital transformation, the proliferation of devices, applications, and services has made network management and fault detection increasingly challenging. Traditional monitoring tools often fail to provide real-time, accurate insights due to their linear and siloed approach. In contrast, Graph AI leverages graph-based algorithms to analyze complex relationships between diverse data points, enabling rapid root cause analysis and automated fault correlation. This capability is particularly vital for industries with mission-critical operations, such as telecommunications, BFSI, and healthcare, where downtime or faults can lead to significant financial and reputational losses.

Another significant driver is the growing adoption of cloud computing and hybrid IT environments, which introduce new layers of complexity in managing distributed systems. Cloud-native architectures, microservices, and containerization have increased the number of interdependencies within IT ecosystems, making it difficult to trace faults using conventional methods. Fault Correlation with Graph AI provides a holistic view of these complex infrastructures, allowing organizations to pinpoint anomalies and performance bottlenecks with greater precision. Furthermore, the integration of AI and machine learning enhances the system's ability to learn from historical incidents, thereby continuously improving its fault detection and correlation accuracy over time. This adaptability is crucial for maintaining service reliability and regulatory compliance in dynamic business environments.

The surge in cyberattacks and compliance mandates is also fueling demand for Fault Correlation with Graph AI solutions. As cyber threats become more sophisticated and regulations more stringent, organizations are under immense pressure to ensure the integrity and availability of their IT systems. Graph AI's advanced analytics can detect subtle patterns indicative of security breaches or policy violations, enabling proactive mitigation before issues escalate. This is particularly important for sectors like BFSI and healthcare, where data breaches can have severe legal and financial repercussions. The ability to automate compliance monitoring and incident response further reduces operational overhead and enhances overall security posture.

From a regional perspective, North America currently dominates the Fault Correlation with Graph AI market, accounting for the largest revenue share in 2024. This leadership can be attributed to the region's advanced IT infrastructure, high adoption rate of AI-driven technologies, and significant investments in digital transformation initiatives. However, the Asia Pacific region is poised for the fastest growth during the forecast period, driven by rapid industrialization, expanding telecommunication networks, and increasing focus on smart manufacturing. Europe also represents a substantial market, supported by stringent data protection regulations and a strong emphasis on operational resilience across industries. Latin America and the Middle East & Africa are gradually catching up, with growing investments in IT modernization and automation projects.

Component Analysis

The Fault Correlation with Graph AI market by component comprises Software, Hardware, and Services. Software forms the backbone of this market, representing the largest share in 2024 due to the critical role of advanced analytics engines, machine learning algorithms, and graph databases in processing and correlating vast volumes of fault data. Modern software platforms offer user-friendly interfaces, seamless integration with existing IT systems, and scalable a

The aim of this study was to investigate the effect of zinc sulphate on the activities of different enzymes and metabolites of Pholiota adiposa. In the experiment, we used the conventional enzyme activity assay to determine the changes of six indicators, including protein content, laccase activity, cellulase activity, amylase activity and polyphenol oxidase activity, under different concentrations of zinc sulphate treatment. The results showed that the activities of amylase, laccase, cellulase and peroxidase were Zn2+(200)>Zn2+(0)>Zn2+(400)>Zn2+(800).The activities of catalase and superoxide dismutase were Zn2+(200)>Zn2+(400)>Zn2+(800), and zinc sulfate could significantly affect the activity of polylipic squamase in a dose-dependent manner. Further correlation analysis showed that all six enzyme activities were significantly correlated with each other (P

Abstract–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists in sequential data. It has been well demonstrated in the literature that control charts designed for independent data are unstable for monitoring serially correlated data. Thus, it is important to develop control charts specifically for monitoring serially correlated data. To this end, there is some existing discussion in the SPC literature. Most existing methods are based on parametric time series modeling and residual monitoring, where the data are often assumed to be normally distributed. In applications, however, the assumed parametric time series model with a given order and the normality assumption are often invalid, resulting in unstable process monitoring. Although there is some nice discussion on robust design of such residual monitoring control charts, the suggested designs can only handle certain special cases well. In this article, we try to make another effort by proposing a novel control chart that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept. Our proposed chart uses observations within the spring length of the current time point and ignores all history data that are beyond the spring length. It does not require any parametric time series model and/or a parametric process distribution. It only requires the assumption that process observation at a given time point is associated with nearby observations and independent of observations that are far away in observation times, which should be reasonable for many applications. Numerical studies show that it performs well in different cases.

IntroductionA novel indicator of inflammation is the systemic immune-inflammation index (SII), and liver dysfunction is linked to the advancement of inflammation. In light of this, this study aims to look into any potential connections between SII and markers of liver injury.MethodsA cross-sectional study was conducted using the National Health and Nutrition Examination (NHANES) dataset for 2017–2020. The linear relationship between SII and markers of liver injury was examined using multiple linear regression models. Examining threshold effects and fitted smoothed curves were utilized to describe nonlinear connections.ResultsA total of 8213 adults aged 18–80 years participated in this population-based study. In the fully adjusted model, SII maintained a negative association with ALT(β = -0.003, 95%CI:-0.005, -0.002, P