Biological data were analysed with all 6 methods, the mean period value is reported in the table (standard deviation in brackets). The expected period is 24 h as the clock is entrained by a 24 h light:dark cycle. 1) The data were collected in two different conditions: LD and SD, monitoring 5 output genes in each of them. 2) (All) represents aggregated results from all data sets. 3) NoCAT3 represents aggregated results from all data sets except the CAT3 marker. +) The cases for which mean period is not statistically different from the 24 h are marked with +.

ABSTRACT This article presents potential successes and constraints presented in lessons based on written words problems of mathematics impacting on the learning process of students in the eighth year of Portuguese classes in an elementary school. Those problems have been proposed by future teachers during a supervised internship at the University of Lisbon. The data emerged from strata of interaction/intervention of a teacher-coach with three interns regarding the actions of their lessons based on written words problems of Mathematics. Successes have been identified such as the association of geometric figures to their algebraic expressions and the conduction of explanations by direct questions on the subject, as well as constraints as confusing mathematical concepts, written commands with no meaning for students, terms without proper contextualization to the mathematical context. The research has been supported by authors and researchers in the field of problem solving, the understanding of statements of math problems and the training in/of teaching practice.

This dataset is about books. It has 49 rows and is filtered where the book subjects is Management-Mathematical models. It features 9 columns including author, publication date, language, and book publisher.

This dataset contains student flexibility scores across four dimensions: Using multiple strategies, Identifying appropriate strategies from among self-generated strategies, Identifying appropriate strategies from among provided strategies, and Using appropriate strategies, as well as SRL scores across five dimensions: Value, Expectancy, Affect, Cognitive and Metacognitive Strategies, and Resource Management. The data were collected from a sample of 272 secondary students.

Please read the README files for instructions.. Visit https://dataone.org/datasets/sha256%3A03780b224e997fcbca9665602bcdc4589ae19ae9c6ccc94589ae344bdc810c31 for complete metadata about this dataset.

This dataset includes the results of a survey of research data management (RDM) practices distributed to researchers at the University of Victoria in 2017. In order to become better prepared to support the research data management (RDM) needs of their community, in 2017-2018 the University of Victoria Libraries conducted a mixed-methods study of the RDM practices of all ranks of faculty members, as well as postdoctoral fellows and graduate students, representing all major faculties across campus. As part of this study, UVic Libraries joined the Canadian RDM Survey Consortium and distributed a modified version of their common survey instrument to researchers at the University of Victoria to gather information about our respective researcher communities and to generate a richer understanding of our users’ RDM practices and attitudes. This public dataset has formatted to align with the national survey.

The global mathematics software market size was valued at USD XXX million in 2025 and is projected to grow from USD XXX million in 2026 to USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The increasing adoption of mathematics software in various industries and the rising demand for advanced data analytics and optimization techniques are the key factors driving the market growth. The market is segmented based on type into free software and commercial software. The commercial software segment is expected to hold a larger market share during the forecast period due to the growing adoption of paid software solutions by businesses and organizations. Based on application, the market is segmented into school, engineering construction, academic and research institutes, and others. The school segment is expected to grow at a significant rate during the forecast period due to the increasing need for interactive and engaging learning tools in educational institutions. Major companies operating in the market include Wolfram Research, The MathWorks, Saltire Software, Maplesoft, PTC, GAMS Development Corporation, Gurobi Optimization, Civilized Software, Signalysis, and others. Concentration Areas: The mathematics software market is concentrated in a few key areas, including:

Academic and research institutions: These institutions use mathematics software for teaching, research, and development. Engineering and construction: Engineers and construction professionals use mathematics software for design, analysis, and simulation. Financial services: Financial professionals use mathematics software for risk management, trading, and portfolio optimization. Manufacturing: Manufacturers use mathematics software for product design, process optimization, and quality control.

Characteristics of Innovation: The mathematics software market is characterized by a high level of innovation. Software developers are constantly releasing new products and features that improve the performance, usability, and functionality of their software. Key characteristics of innovation in mathematics software include:

User-friendliness: Mathematics software is becoming increasingly user-friendly, with intuitive interfaces and easy-to-use features. Increased automation: Mathematics software is automating more and more tasks, freeing up users to focus on more complex problems. Integration with other software: Mathematics software is becoming increasingly integrated with other software, such as CAD/CAM software and data analysis software. Cloud-based deployment: Mathematics software is increasingly being deployed in the cloud, which provides users with access to the software from anywhere, at any time.

Impact of Regulations: The mathematics software market is subject to a number of regulations, including:

Export controls: Some mathematics software products are subject to export controls, which restrict their sale to certain countries. Data protection laws: Mathematics software that collects and processes personal data is subject to data protection laws, such as the General Data Protection Regulation (GDPR).

Product Substitutes: There are a number of substitutes for mathematics software, including:

Spreadsheet software: Spreadsheet software can be used for basic mathematical calculations and data analysis. Programming languages: Programming languages can be used to develop custom mathematical software solutions. Online calculators: Online calculators can be used for simple mathematical calculations. Specialized software: There are a number of specialized software products that are designed for specific mathematical applications, such as CAD/CAM software and data analysis software.

End User Concentration: The end user market for mathematics software is concentrated in a few key industries, including:

Education: Mathematics software is used in schools, colleges, and universities for teaching and research. Engineering: Mathematics software is used in engineering firms for design, analysis, and simulation. Finance: Mathematics software is used in financial institutions for risk management, trading, and portfolio optimization. Manufacturing: Mathematics software is used in manufacturing firms for product design, process optimization, and quality control.

Level of M&A: The level of M&A in the mathematics software market is relatively low. However, there have been a number of notable acquisitions in recent years, including:

The MathWorks acquisition of Simulink: This acquisition strengthened The MathWorks' position in the simulation software market. Maplesoft acquisition of Virtual Laboratories: This acquisition expanded Maplesoft's product portfolio to include virtual reality and augmented reality software. PTC acquisition of Onshape: This acquisition gave PTC a strong presence in the cloud-based CAD software market.

Clustering performance on protein superfamily data sets.

Supplementary Material, Codes, and Data for "Order Book Queue Hawkes Markovian Modeling"

This study identified subgroups of bladder pain syndrome/interstitial cystitis (BPS/IC) patients and potential treatment targets by combining validated questionnaires and patient diaries with discrete mathematical techniques. Hierarchical clustering of questionnaire data revealed three distinct patient groups. Analysis of patient diaries, employing natural language processing—a form of discrete data analysis—found keywords capturing emotional and psychological experiences, complementing the questionnaire results. Integration of questionnaire and diary data visualized the relationships between symptoms and treatment targets through a network graph. This personalized approach, akin to solving the traveling salesman problem in discrete mathematics, was validated through case studies, demonstrating its utility in guiding targeted interventions. The study emphasizes the significant potential of discrete mathematics-based data integration and visualization for personalized management of this complex condition.

The data provide a Fuzzy-TOPSIS algorithm-based multi-criteria decision making platform based on an n-dimensional space of optimal integrated systems developed by applying multi-objective genetic algorithm on combined mathematical models.

Algorithm 1 Level- Spanning Graph Generation.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

The code in this replication package contains the data and code used for the implementation and analysis of the case study presented in the paper "Catastrophe risk in a stochastic multi-population mortality model". The data sets used in this paper are publicly available from the Human Mortality Database and Eurostat. The code is written in R and can be accessed and downloaded for further reference and replication of the obtained results.

Context

This data set is a sample for a competition being conducted at IIT - Kanpur. Participants who have registered for this are requested to use this documentation for understanding the sample data set. It consists of 2 .csv file. One is used for training and the other for testing. Due to an increased response for this competition, the deadline for submitting the 2nd report is extended to 1st May 2019. Please Find updated information for stage 2 below the dataset description.

PLEASE NOTE: Keggle may not be updated frequently with information about the competition as it is not the official website for the same. Keep checking your registered mail for updates on the dataset and competition.

Content

The data set is gathered from a residential hostel room with 3 occupants. The residential hostel room has an AC unit for cooling purposes, a water heater and a washing machine. The Idea is to use the data set to estimate the load ahead in time and optimally schedule it to math comfort and cost requirements due to time varying cost. Hence the data set is created using real-time data from the room for a period of 1 year. However, only 1 month wort of data is currently uploaded. The rest of the data will be uploaded post screening process on 10 April 2019. The data is sampled at a rate of sample/hour and logs power readings from the 3 major equipment listed above. The attributes of the data set are described below:

1.) Date, Month and Year: Used to represent the date, month and year of the data . 2.) Day - This represents the the day of the week with 0 being Monday and 6 being Sunday 3.) Occupancy - Represents the occupancy state of the room 4.) No. Of Occupants - Represents the number of occupants in the room based on student punch card entry 5.) Hour - Represents the hour of the day starting from 0Hrs to 23Hrs. 6.) Temp and Humidity - Are the outdoor temperature and humidity readings 7.) Water Heater, AC and Washing Machine - Represent the status of the appliance under consideration. (1 = ON, 0 = OFF) 8.) Total Power - Total Power consumed by the occupants of the room 9.) PWT, PAC and PWM - Power consumed by Water Heater, AC and Washing Machine respectively.

NOTE: The washing machine data is currently not significant for the month of MAY. Hence, candidates are allowed to neglect the attributes related to the washing machine. However, once the complete data set is made available, this attribute becomes significant as well.

For Stage 2 of this competition, the students are needed to perform load scheduling on the test dataset for the 1st of June 2018. The time varying cost starting at 0:00 Hrs to 23:00 Hrs is given as a list shown below: Tariff = [10,10,10,10,10,10,10,10,12,15,16,21,23,25,25,24,22,17,16,14,11,10,10,10]

The tariff is in paisa. In addition, it must be noted that the consumer of this residential plot require their load to be scheduled only between 7:00 Hrs and 21:00 hrs. Loads other that those mentioned in this range are to be neglected for scheduling.

Software has recently been developed to calculate space/time distributions of electrokinetic potentials resulting from histories of underground conditions (pressure, temperature, vapor saturation, concentration of dissolved species, flow rate, etc.) computed by unsteady multidimensional geothermal reservoir simulations (Ishido and Pritchett, 1999). The iEKP postprocessori can be applied to ihistory-matchingi of self-potential (SP) data since SP changes are caused principally by electrokinetic effects, particularly in the early stages of field exploitation. To assess the feasibility of using repeat SP surveys for reservoir monitoring, we carried out numerical simulations based on a mathematical model of a moderate-temperature two-phase reservoir. The results confirm the electrokinetic origin of production-induced changes in SP proposed by Ishido et al. (1989), and show that SP will respond to field-wide flow rate changes rapidly (within a month or so). This suggests that frequently-repeated SP surveys and/or continuous SP monitoring at times of flow rate changes will provide useful supplementary data for reservoir management.

This dataset has been prepared by Dr. Shariful Alam, Associate Professor in the Department of Mathematics, IIEST, Shibpur. Dr. Alam’s research focuses on multi-criteria decision making in uncertain or imprecise environments, fuzzy mathematics and optimization, ecological modeling, mathematical biology, and supply chain management under uncertainty. The dataset comprises [briefly describe what your data includes: decision-matrices, fuzzy/neutrosophic membership values, simulation outputs, ranking of alternatives, etc.] generated using [models/methods, e.g., extended COPRAS, generalized spherical fuzzy/neutrosophic frameworks, etc.]. These data were collected/computed in order to support experiments, reproduce results, validate methodologies, and enable comparative studies in MCDM, fuzzy logic, and optimization under uncertainty. Formats provided include [Excel / CSV / MATLAB / other], and code (if any) along with usage notes are included to facilitate reuse by researchers in mathematics, computer science, decision sciences, operations research, and related fields

The Global Tech-Enabled Learning Disabilities Tools Market Size Was Worth USD 15.1 Billion in 2023 and Is Expected To Reach USD 30.1 Billion by 2032, CAGR of 7.5%.

Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.

Abstract

The Snapshot for School Management Effectiveness (SSME) lets school, district, provincial, or national administrators and policymakers learn what is going on in their schools and classrooms and understand how to make their schools more effective. Management data include: pedagogical approach; time on task; interactions among students, teachers, administrators, district officials, and parents; record keeping; discipline; school infrastructure; pedagogical materials; and safety. SSME data are collected via direct classroom and school observation; student assessments; and interviews with parents, teachers, principals, and parents.

EdData II developed the SSME methodology and has applied it in six countries and six languages. It has been adopted and used by other implementing partners in eight other countries and six other languages. SSME has been used in conjunction with the Early Grade Reading Assessment (EGRA) and Early Grade Math Assessment (EGMA) to produce a broader picture of school-related factors that may affect student performance in reading and mathematics.

Geographic coverage

6 provinces, 3 regions. Representative within regions/provinces independent sampling.

Analysis unit

Student, teacher, school, school committee, village, Village Education Development Committee, Principals

Universe

Six provinces, three regions in country.

Kind of data

Sample survey data [ssd]

Sampling procedure

SSME is a field-tested instrument that can be used to determine whether schools in a country, region, or pilot project area are following "best practice" in terms of overall management and pedagogical management, as well as governance. The data can be used to feed deliberations as to improvement strategies or can be used to track improvements due to project and policy interventions. The pilot experiences confirm that relatively small samples that are based on intense visits and gathering can yield very telling data. Visits of one day each to between 40 and 70 schools, for example, are sufficient to characterize schools with sufficient specificity as to lead to actionable knowledge. Thus, it is possible to lower the cost and time-to-completion of data-gathering processes while maintaining sufficient (and specifiable) rigor.

Mode of data collection

Other [oth]

Research instrument

Case studies through interviews to pedagogical advisors, students, teachers, principals, village education development committees.