Forecasting the maintenance needs of machinery is gaining traction in diverse sectors, aiming to reduce downtime and expenses, and improving overall operational efficiency in contrast to traditional, reactive maintenance. State-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques underpin predictive maintenance (PdM) methods, which heavily rely on data to construct analytical models capable of recognizing patterns indicative of malfunctions or deterioration in monitored machinery. Therefore, a dataset which is both representative and authentic to the phenomena being studied is vital for the creation, training, and verification of predictive maintenance techniques. A novel dataset, sourced from real-world home appliance data, specifically refrigerators and washing machines, is introduced in this paper for the purpose of developing and rigorously testing PdM algorithms. Measurements of electrical current and vibration were taken on assorted home appliances at a repair center, with data captured at low (1 Hz) and high (2048 Hz) sampling frequencies. Normal and malfunction types are used to filter and tag the dataset samples. The dataset of extracted features, which corresponds to the gathered work cycles, is also provided. Home appliance predictive maintenance and outlier analysis techniques can be significantly improved through the use of this dataset for AI system development. Smart-grid and smart-home applications can capitalize on this dataset to forecast consumption patterns for various home appliances.

The current dataset was used to examine the relationship between student attitude toward mathematics word problems (MWTs) and their performance, as mediated by the active learning heuristic problem-solving (ALHPS) method. Specifically, the data details the relationship between student performance and their mindset concerning linear programming (LP) word problems (ATLPWTs). A total of 608 Grade 11 students, sourced from eight secondary schools (comprising both public and private schools), participated in the collection of four distinct types of data. Mukono and Mbale districts in Central and Eastern Uganda, respectively, provided the participants. Using a quasi-experimental non-equivalent group design, a mixed methods approach was undertaken. Standardized LP achievement tests (LPATs) for pre- and post-test evaluations, the attitude towards mathematics inventory-short form (ATMI-SF), a standardized active learning heuristic problem-solving tool, and an observation scale, formed part of the data collection tools. Data collection spanned the period from October 2020 to February 2021. After thorough mathematical validation, pilot testing, and assessment, all four tools were judged to be suitable and reliable for measuring student performance and attitude toward LP word tasks related to LP words. In order to fulfill the objectives of the study, eight complete classes from the sampled schools were chosen using a cluster random sampling technique. Through a random process determined by a coin toss, four were assigned to the comparison group. The remaining four were then randomly assigned to the treatment group. All teachers selected for the treatment group received instruction in implementing the ALHPS approach before the start of the intervention phase. In tandem, the raw scores for pre-test and post-test, along with the participants' demographic information—identification numbers, age, gender, school status, and school location—were presented, marking the results before and after the intervention. The LPMWPs test items were administered to the students to comprehensively analyze and ascertain their proficiency in problem-solving (PS), graphing (G), and Newman error analysis strategies. Rodent bioassays Students' pre-test and post-test scores were established through the application of mathematical problem-solving strategies to the optimization of linear programming problems. In accordance with the study's aim and outlined goals, the data underwent analysis. This data complements other datasets and empirical results regarding the mathematization of mathematical word problems, problem-solving techniques, graphing, and error analysis prompts. reuse of medicines ALHPS strategies' effectiveness in cultivating students' conceptual understanding, procedural fluency, and reasoning is explored through the analysis of this data, encompassing secondary and post-secondary learners. The supplementary data files contain LPMWPs test items, which can be used as a springboard for applying mathematics to real-world scenarios that extend beyond the obligatory academic level. Data is being implemented to cultivate, sustain, and fortify secondary school students' problem-solving and critical thinking skills, with the overall objective of refining both instruction and assessment methods, extending beyond secondary education.

Science of the Total Environment's publication of the research paper 'Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data' is related to this data set. The case study, fundamental to demonstrating and validating the proposed risk assessment framework, has its necessary information included in this document for reproduction. Indicators for assessing hydraulic hazards and bridge vulnerability are integrated into a simple and operationally flexible protocol of the latter, used to interpret consequences of bridge damage on the serviceability of the transport network and the affected socio-economic environment. The data set encompasses (i) the inventory of the 117 bridges in Karditsa Prefecture, Greece, impacted by the 2020 Mediterranean Hurricane (Medicane) Ianos; (ii) risk assessment findings, including a geospatial analysis of the hazard, vulnerability, bridge damage, and impact on transportation; and (iii) a thorough damage inspection record collected soon after the storm, focusing on a representative sample of 16 bridges (reflecting damage from minor to complete failure), enabling validation of the presented methodological approach. To improve understanding of the observed damage patterns on the bridges, photographs of the inspected bridges are included in the dataset. This comprehensive analysis of riverine bridge behavior during severe flooding provides a benchmark for evaluating flood hazard and risk mapping tools. The intended audience includes engineers, asset managers, network operators, and decision-makers involved in climate-resilient road infrastructure.

Analysis of RNA sequencing data from Arabidopsis seeds, both dry and 6 hours imbibed, was performed to evaluate the RNA-level response of wild-type and glucosinolate (GSL)-deficient genotypes to nitrogenous compounds such as potassium nitrate (10 mM) and potassium thiocyanate (8 M). The transcriptomic study employed genotypes including a cyp79B2 cyp79B3 double mutant lacking Indole GSL, a myb28 myb29 double mutant deficient in aliphatic GSL, a quadruple mutant cyp79B2 cyp79B3 myb28 myb29 (qko) deficient in total seed GSL, and a wild-type (WT) reference in the Col-0 genetic background. The NucleoSpin RNA Plant and Fungi kit was chosen for the extraction of total ARN from plant and fungal samples. The library construction and sequencing process, employing DNBseq technology, was performed at Beijing Genomics Institute. Salmon's quasi-mapping alignment was used for the mapping analysis of reads, previously quality-checked using FastQC. Differential gene expression in mutant seeds, as contrasted with wild-type seeds, was evaluated via the DESeq2 algorithms. Mutants qko, cyp79B2/B3, and myb28/29, when compared, resulted in the identification of 30220, 36885, and 23807 differentially expressed genes (DEGs), respectively. MultiQC compiled the mapping rate results into a unified report. The graphical data was subsequently illustrated using Venn diagrams and volcano plots. The Sequence Read Archive (SRA), maintained by the National Center for Biotechnology Information (NCBI), hosts 45 sample FASTQ raw data and count files, identified by GSE221567. These files are publicly accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221567.

Cognitive prioritization, a consequence of the relevance of affective information, is determined by both the attentional burden of the relevant task and socio-emotional capacities. Electroencephalographic (EEG) signals from this dataset concern implicit emotional speech perception, categorized by low, intermediate, and high attentional demands. Likewise, data on demographics and behaviors are made available. Processing affective prosodies can be affected by the prominent features of social-emotional reciprocity and verbal communication often found in individuals with Autism Spectrum Disorder (ASD). To ensure data integrity, 62 children and their parents or legal guardians participated in data collection, including 31 children with high autistic characteristics (xage=96 years old, age=15), previously diagnosed with ASD by a medical professional, and 31 neurotypical children (xage=102, age=12). Every child receives an assessment of the extent of their autistic behaviors using the parent-reported Autism Spectrum Rating Scales (ASRS). Children participated in an experiment involving the presentation of irrelevant emotional vocal tones (anger, disgust, fear, happiness, neutrality, and sadness) while simultaneously engaged in three visual tasks: observing pictures without a specific focus (low cognitive load), tracking a single object amongst four objects (medium cognitive load), and tracking a single object among eight objects (high cognitive load). The dataset contains the EEG results from all three tasks, as well as the motion tracking (behavioral) data obtained through the MOT protocols. To compute the tracking capacity during the Movement Observation Task (MOT), a standardized index of attentional abilities was used, with adjustments for any guessing. Before the EEG recording, children completed the Edinburgh Handedness Inventory, and their resting-state EEG activity was then measured for two minutes with their eyes open. These provided data sets are also included. DNA Damage inhibitor The current dataset provides the basis for exploring the electrophysiological connections between implicit emotional and speech perceptions, their modulation by attentional load, and their correlation with autistic traits.