Predicting the upkeep demands of machines is generating considerable interest within numerous industrial sectors, leading to a decrease in equipment downtime, reduced expenditures, and enhanced efficiency, compared to conventional maintenance models. Sophisticated Internet of Things (IoT) and Artificial Intelligence (AI) systems are crucial components in predictive maintenance (PdM) methodologies, which necessitates data-rich analytical models to pinpoint patterns representative of malfunction or deterioration in monitored machines. Thus, a data set that is truly representative of the field and is realistic in its depiction is essential for developing, training, and assessing PdM strategies. This paper details a new dataset, constructed from practical data gathered from domestic appliances, such as refrigerators and washing machines, which is suitable for the development and validation of PdM algorithms. Readings of electrical current and vibration, gathered from various home appliances at a repair center, encompassed low (1 Hz) and high (2048 Hz) sampling frequencies. After filtering, dataset samples are labeled with categories of normal and malfunction. A dataset of extracted features, aligning with the gathered working cycles, is likewise accessible. This dataset has the potential to advance research and development in AI systems, particularly for predicting maintenance needs and identifying anomalies in home appliances. For predicting the consumption patterns of home appliances in smart-grid or smart-home applications, this dataset is also applicable.
The provided data were leveraged to investigate the connection between student attitudes toward mathematics word problems (MWTs) and their performance, mediated by the active learning heuristic problem-solving (ALHPS) approach. The data specifically examines the connection between student performance and their stance on linear programming (LP) word problems (ATLPWTs). From eight secondary schools (public and private), a cohort of 608 Grade 11 students was sampled for the collection of four types of data. Representing both Central Uganda's Mukono District and Eastern Uganda's Mbale District, the study participants were gathered. The chosen research methodology comprised a mixed methods approach, employing a quasi-experimental design with non-equivalent groups. Utilizing standardized LP achievement tests (LPATs) for pre-test and post-test evaluations, the attitude towards mathematics inventory-short form (ATMI-SF), a standardized active learning heuristic problem-solving instrument, and an observation scale, constituted the data collection. Data collection efforts were focused on the time frame between October 2020 and February 2021, inclusive. All four tools, having undergone validation by mathematical experts, pilot testing, and a rigorous assessment, were determined to be reliable and appropriate instruments for evaluating students' performance and attitude toward LP word tasks. Eight classes from the selected schools, each complete, were picked utilizing the cluster random sampling method, in line with the objectives of the research. After a coin flip, four were arbitrarily selected for the comparison group, and the remaining four subjects were randomly assigned to the treatment group. Before the intervention began, the teachers in the treatment group were trained on the correct procedures of applying the ALHPS method. The intervention's impact was assessed by presenting the pre-test and post-test raw scores together with the participants' demographic data (identification numbers, age, gender, school status, and school location), gathered before and after the intervention. To determine student proficiency in problem-solving (PS), graphing (G), and Newman error analysis strategies, the LPMWPs test items were given to the students for assessment. biogenic nanoparticles Assessment of pre-test and post-test results focused on students' ability to convert word problems into optimization models using linear programming methodologies. The data was analyzed, aligning with the study's declared intent and set objectives. This dataset serves to improve other data sets and empirical studies pertaining to the mathematization of mathematical word problems, problem-solving approaches, graphical representation, and error analysis. RIPA radio immunoprecipitation assay This data may reveal a pattern regarding the relationship between ALHPS strategies and secondary and post-secondary learners' conceptual understanding, procedural fluency, and reasoning. The LPMWPs test items, contained in the supplementary data files, offer a basis for applying mathematical skills in realistic settings, exceeding the requirements of the mandatory curriculum. To cultivate and fortify students' problem-solving and critical thinking skills, the data will be used, aiming to improve instruction and assessment techniques in secondary schools and beyond.
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. With a simple and operationally flexible protocol, the latter integrates indicators for the assessment of hydraulic hazards and bridge vulnerability, interpreting how bridge damage impacts the serviceability of the transport network and the affected socio-economic environment. Included in this dataset are (i) details about the inventory of the 117 bridges within Karditsa Prefecture, Greece, affected by the 2020 Mediterranean Hurricane (Medicane) Ianos; (ii) risk assessment analysis outcomes mapping the geospatial distribution of hazard, vulnerability, bridge damage, and the ensuing effects on the transportation network; and (iii) a comprehensive damage inspection record of a sample of 16 bridges, representing diverse damage levels from minor to total collapse, critically used for the validation of the suggested framework. Photos of the inspected bridges, incorporated into the dataset, aid in comprehending the observed damage patterns of the bridges. This report delves into the behavior of riverine bridges under severe flood conditions, forming a crucial benchmark for comparing and validating flood hazard and risk mapping tools. It is geared towards engineers, asset managers, network operators, and stakeholders involved in the road sector's climate change adaptation measures.
RNA sequencing data were acquired from Arabidopsis seeds that were either dry or imbibed for six hours. These data were then used to characterize the RNA-level responses of wild-type and glucosinolate-deficient genotypes to nitrogenous compounds such as potassium nitrate (10 mM) and potassium thiocyanate (8 M). In a transcriptomic study, the following genotypes were used: a cyp79B2 cyp79B3 double mutant deficient in Indole GSL; a myb28 myb29 double mutant deficient in aliphatic GSL; the cyp79B2 cyp79B3 myb28 myb29 quadruple mutant deficient in all seed GSL types; and a wild-type reference in a Col-0 genetic background. The NucleoSpin RNA Plant and Fungi kit facilitated the extraction of total ARN. At Beijing Genomics Institute, DNBseq technology was used for library construction and sequencing. FastQC examined the quality of reads, and the mapping analysis employed a quasi-mapping alignment algorithm from Salmon. Analysis of gene expression changes in mutant seeds, in relation to wild-type seeds, was carried out using the DESeq2 algorithms. A comparative analysis of the qko, cyp79B2/B3, and myb28/29 mutants highlighted 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. NCBI's Sequence Read Archive (SRA) contains the FASTQ raw data and count files from 45 samples, available under accession number GSE221567. Information can be found 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. The dataset features electroencephalographic (EEG) signals of implicit emotional speech perception, corresponding to low, intermediate, and high levels of attentional engagement. Additional information regarding demographics and behaviors is given. Autism Spectrum Disorder (ASD) is frequently marked by unique patterns of social-emotional reciprocity and verbal communication, factors that could potentially affect the processing of affective prosodies. 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). A parent-reported assessment of the range of autistic behaviors in each child is provided via the Autism Spectrum Rating Scales (ASRS). Affective vocalizations, devoid of task relevance (anger, disgust, fear, happiness, neutrality, and sadness), were played to children during an experiment, while they concurrently performed three visual tasks: observing static images (minimal attentional demand), the tracking of a single target within a set of four moving objects (moderate attentional demand), and tracking a single target within a set of eight moving objects (high attentional demand). Included in the dataset are the EEG readings taken throughout all three tasks, as well as the tracking data (behavioral) acquired under the MOT conditions. A standardized index of attentional abilities, calculated during the Movement Observation Task (MOT), was used to compute the tracking capacity, taking into account potential guessing. Prior to the experiment, children completed the Edinburgh Handedness Inventory, followed by a two-minute resting-state EEG recording with their eyes open. Data concerning this topic are also present. AZD4573 research buy This dataset offers the potential to explore how attentional load and autistic traits modify the electrophysiological responses to implicit emotional and speech perceptions.