These files include: (a) the SAS program to create the transformed GFT estimates and conduct the analysis, (b) the R program to plot the figures in the publication, and (c) csv files with the transformed GFT estimates (national and regional) created using the SAS program. Original GFT estimates and ILINet values are available online.

North America SSD Caching Market size was valued at USD 2.02 Billion in 2024 and is projected to reach USD 5.76 Billion by 2032, growing at a CAGR of 14% from 2026 to 2032.

Key Market Drivers:

Growth of Data Centers and Cloud Computing: The International Trade Administration of the U.S. Department of Commerce projects that the data center market in the United States will grow at a compound annual growth rate of 16.4% to reach $164.36 billion by 2026. The need for SSD caching technology is fueled by this enormous growth, as data centers look to minimize latency and maximize speed.

Initiatives for the Enterprise Digital Transformation: According to data from the U.S. Bureau of Economic Analysis, businesses’ spending in digital transformation rose by 22.3% in 2022, with a sizable amount going toward enhancing IT infrastructure.

Indonesia Big Data Analytics Software Market Analysis The Indonesia Big Data Analytics Software market is poised to witness substantial growth over the forecast period of 2025-2033, with a CAGR of 9.35%. In 2025, the market stood at a value of USD 43.15 million and is projected to reach a remarkable value by 2033. This growth is primarily driven by the increasing adoption of digital technologies, the proliferation of data-intensive applications, and the growing need for businesses to make data-driven decisions. Key trends shaping the market include the rising popularity of cloud-based big data analytics solutions, the emergence of advanced analytics techniques such as machine learning and artificial intelligence, and the growing awareness of data privacy and security concerns. Despite these positive factors, the market faces challenges such as the lack of skilled professionals in data analytics, the high cost of implementation, and the complexities associated with managing and integrating large volumes of data. Prominent players in the market include Teradata, SAS, SAP, Tableau Software, and IBM Corporation, among others. Market Size and Growth The Indonesia Big Data Analytics Software Market is projected to grow from USD 235.6 million in 2023 to USD 1,159.1 million by 2029, exhibiting a CAGR of 24.3% during the forecast period. This growth can be attributed to the increasing adoption of big data analytics solutions by organizations to enhance their decision-making, improve operational efficiency, and gain a competitive advantage. Recent developments include: June 2024: Indosat Ooredoo Hutchison (Indosat) and Google Cloud expanded their long-term alliance to accelerate Indosat’s transformation from telco to AI Native TechCo. The collaboration will combine Indosat’s vast network, operational, and customer datasets with Google Cloud’s unified AI stack to deliver exceptional experiences to over 100 million Indosat customers and generative AI (GenAI) solutions for businesses across Indonesia. These include geospatial analytics and predictive modeling, real-time conversation analysis, and back-office transformation. Indosat’s early adoption of an AI-ready data analytics platform exemplifies its forward-thinking approach., June 2024: Palo Alto Networks launched a new cloud facility in Indonesia, catering to the rising demand for local data residency compliance. The move empowers organizations in Indonesia with access to Palo Alto Networks' Cortex XDR advanced AI and analytics platform that offers a comprehensive security solution by unifying endpoint, network, and cloud data. With this new infrastructure, Indonesian customers can ensure data residency by housing their logs and analytics within the country.. Key drivers for this market are: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Potential restraints include: Higher Emphasis on the Use of Analytics Tools to Empower Decision Making, Rapid Increase in the Generation of Data Coupled with Availability of Several End User Specific Tools due to the Growth in the Local Landscape. Notable trends are: Small and Medium Enterprises to Hold Major Market Share.

In the present study, a novel technique has been developed for producing progesterone form II crystals by using shear-assisted sonocrystallization (SAS) method. Progesterone, a steroid hormone, has been recognized for more than 70 years as having two polymorphs, a stable form (form I) and a metastable form (form II). Previous attempts have failed to produce a single crystal of form II of progesterone without the presence of a cocrystal additive or template. The technique proposed in the current study is the first to report the growth of single crystals of progesterone form II. The produced crystals were characterized using X-ray diffraction, differential scanning calorimeter, and Fourier transform infrared spectroscopy. Single crystal X-ray diffraction was performed for comparing the hydrogen bond geometry of forms I and II. Solubility and dissolution rates were estimated, providing insight on the thermodynamics of both forms. Stability studies of both forms were conducted for 60 days, which confirmed the higher stability of progesterone form I. Comparing the crystal structure of form I and form II provides evidence for their relative stabilities. The SAS technique can be proposed as a novel strategy for polymorphic transformation of progesterone, which can increase the dissolution rate, enhance oral bioavailability, and decrease dose-related side effects.

The size of the Embedded Analytics Market was valued at USD 42.16 Billion in 2023 and is projected to reach USD 98.21 Billion by 2032, with an expected CAGR of 12.84% during the forecast period. The embedded analytics market has seen significant growth in recent years, driven by the increasing demand for data-driven decision-making across various industries. Embedded analytics integrates analytical capabilities directly into business applications, enabling users to access insights without switching between tools. This seamless integration helps organizations improve productivity, enhance user experience, and make informed decisions. Key factors fueling market expansion include the rising adoption of advanced technologies such as artificial intelligence, machine learning, and big data analytics. Industries such as healthcare, finance, retail, and manufacturing are leveraging embedded analytics to gain real-time insights, optimize operations, and enhance customer engagement. Moreover, the growing focus on self-service analytics and the need for predictive analytics to stay competitive are boosting market adoption. Cloud-based solutions are further accelerating the market, offering scalability, cost-efficiency, and ease of integration. However, challenges like data security concerns and the complexity of implementation may hinder growth to some extent. With increasing investments in digital transformation and the rising importance of data democratization, the embedded analytics market is poised for continued expansion in the coming years. Recent developments include: August 2022: SAS and SingleStore have announced a collaboration to help organizations remove barriers to data access, maximize performance and scalability, and uncover key data-driven insights. SAS Viya with SingleStore enables using SAS analytics and Al technology on data stored in SingleStore's cloud-native real-time database. The integration provides flexible, open access to curated data to help accelerate value for cloud, hybrid, and on-premises deployments.. Key drivers for this market are: Increasing demand for real-time data and insights Growing adoption of cloud-based applications Rise of artificial intelligence and machine learning Integration of embedded analytics with other business applications. Potential restraints include: Data security and privacy concerns Lack of skilled professionals Complexity of implementation High cost of ownership. Notable trends are: Increased adoption of artificial intelligence and machine learning Integration of embedded analytics with the Internet of Things (IoT) Development of new embedded analytics applications for specific industries.

We tested whether the probability of a visit was a function oftreatment (dietary N content as a continuous variable) using logistic regression in SAS (PROC GLIMMIX with a binomial distribution and logit link function, SAS 9.4). Day (fixed effect), site (random effect) and feeding station nested within site (random effect) were also included in the model. We then analysed the effect of treatment (dietary N content as a continuous variable) on visit length (min), each behaviour (% of total time) and GUD (count) separately using the generalized linear mixed model (GLMM) procedure in SAS (PROC GLIMMIX with lognormal distribution and identity link function, SAS 9.4). Day (1-4) was included in the models as a fixed effect, and site and feeding station (nested within site) were random effects.To analyse our VOCs data we looked at the odours of the diets using a canonical analysis of principal coordinates(CAP) analysis in the PERMANOVA+ add-on of PRIMER v6to determine whether the multivariate VOC data could differentiate the diets along a continuous (dietary nitrogencontent) gradient, similar to analyses of VOCs from other plant/food material. We applied a dispersion weighting followed by square root transformation to the VOC peak area values, then performed CAP analysis on the Bray-Curtis resemblance matrix of the transformed data. To tease apart the contributing VOCs we then applied the CAPanalysis using diet as a class variable. We also isolated the specific volatile signature of the highest quality diet usingthe Random Forests (RF). We analysed the data with RF, using a one treatment-versus-the rest approach with the VSURF package (version 1.0.3) in R (version 3.1.2; R Core Team, 2015). Before analysis, TQPA data were transformed using the centred log ratio method using CoDaPack v. 2.01.15.