The increasing interest in anticipating machine maintenance needs spans a broad range of industries, leading to decreased downtime, reduced costs, and improved operational efficiency when contrasted with conventional maintenance techniques. Analytical models for predictive maintenance (PdM), built upon advanced Internet of Things (IoT) and Artificial Intelligence (AI), heavily depend on data to identify patterns associated with malfunction or degradation in the monitored machines. 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. We introduce a new dataset, derived from real-world usage patterns of home appliances, including refrigerators and washing machines, for training and testing the effectiveness of PdM algorithms. A repair center's data on various home appliances included readings of electrical current and vibration, obtained via low (1 Hz) and high (2048 Hz) sampling frequencies. After filtering, dataset samples are labeled with categories of normal and malfunction. The collected working cycles' corresponding extracted feature dataset is also supplied. AI system development for predictive maintenance and outlier analysis in home appliances can find crucial support from the information provided in this dataset. In the realm of smart-grid and smart-home applications, this dataset allows for the prediction of consumption patterns related to home appliances.

The current data were scrutinized to ascertain the correlation between students' attitudes toward mathematics word problems (MWTs) and their performance, with the active learning heuristic problem-solving (ALHPS) approach hypothesized as a mediating factor. The data's focus is on the correlation between students' academic success and their outlook on linear programming (LP) word problem-solving (ATLPWTs). Eight secondary schools (comprising both public and private institutions) yielded a sample of 608 Grade 11 students, who provided data across four categories. 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. The data collection tools encompassed standardized LP achievement tests (LPATs) for pre- and post-test, the attitude towards mathematics inventory-short form (ATMI-SF), a standardized active learning heuristic problem-solving apparatus, and an observation instrument. Data collection efforts were focused on the time frame between October 2020 and February 2021, inclusive. Mathematics experts validated, pilot-tested, and deemed reliable and suitable for assessing student performance and attitude toward LP word tasks all four tools. To meet the aims of the research, the cluster random sampling approach was utilized to choose eight whole classes from the schools that were part of the sample. From amongst these, four were randomly selected via a coin flip and placed in the comparison group, leaving the remaining four to be randomly assigned to the treatment group. All teachers within the treatment group undertook training in utilizing the ALHPS method's application prior to the intervention. The pre-test and post-test raw scores, along with the participants' demographic data (identification numbers, age, gender, school status, and school location), were presented in a combined format, reflecting results before and after the intervention. For the purpose of exploring and evaluating students' problem-solving (PS), graphing (G), and Newman error analysis strategies, the students were administered the LPMWPs test items. virologic suppression Students' percentage scores in the pre-test and post-test were evaluated by assessing their ability to convert word problems into optimization problems using linear programming techniques. The stated aims and objectives of the study served as the framework for analyzing the data. This data set is a valuable addition to existing data and empirical findings on the mathematical transformation of word problems, problem-solving strategies, graphing, and error identification. see more This data may demonstrate the extent to which ALHPS strategies enhance learners' conceptual understanding, procedural fluency, and reasoning abilities in secondary schools and beyond. The supplementary data files' LPMWPs test items can serve as a foundation for applying mathematics to real-world situations exceeding the required curriculum. This data is designed to improve instruction and assessment, particularly in secondary schools and beyond, through the development, support, and strengthening of students' problem-solving and critical thinking abilities.

The research paper 'Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data,' published in Science of the Total Environment, is associated with this dataset. The case study utilized in demonstrating and validating the proposed risk assessment framework is fully documented here, enabling its reproduction with the relevant data. The latter's protocol, both simple and operationally flexible, assesses hydraulic hazards and bridge vulnerability, interpreting consequences of bridge damage on the transport network's serviceability and the affected socio-economic environment. Data pertaining to the 117 bridges of the Karditsa Prefecture, Central Greece, which sustained damage from the 2020 Mediterranean Hurricane (Medicane) Ianos, encompasses (i) inventory information; (ii) risk analysis results, including the spatial distribution of the hazard, vulnerability, and bridge damage, along with their effects on the local transportation infrastructure; and (iii) a thorough damage assessment record, compiled after the Medicane, of a 16-bridge sample with varying degrees of damage (from minimal to complete failure), used to validate the suggested methodological approach. The dataset's value is increased by the addition of photos of the inspected bridges, which provide visual context for the observed bridge damage patterns. The document details the response of riverine bridges to severe flood events, establishing a reference point for validating and comparing flood hazard and risk mapping tools. This resource is intended for engineers, asset managers, network operators, and decision-makers in the road sector working toward climate adaptation.

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). Transcriptomic analysis used four genotypes: a cyp79B2 cyp79B3 double mutant, which lacks Indole GSL; a myb28 myb29 double mutant, deficient in aliphatic GSL; a quadruple mutant, cyp79B2 cyp79B3 myb28 myb29, deficient in all GSL; and the Col-0 wild-type reference strain. To extract total ARN, the NucleoSpin RNA Plant and Fungi kit was applied to the plant and fungal samples. DNBseq technology facilitated library construction and sequencing procedures at the Beijing Genomics Institute. A quasi-mapping alignment from Salmon was utilized for mapping analysis, after FastQC ensured the quality of the reads. Analysis of gene expression changes in mutant seeds, in relation to wild-type seeds, was carried out using the DESeq2 algorithms. The study of gene expression in the qko, cyp79B2/B3, and myb28/29 mutants, through comparison, revealed 30220, 36885, and 23807 differently expressed genes (DEGs), respectively. The mapping rate results, consolidated into a single report using MultiQC, were visualized using Venn diagrams and volcano plots to display the graphical results. Within the National Center for Biotechnology Information's (NCBI) repository, the Sequence Read Archive (SRA), 45 samples' FASTQ raw data and count files are available. These files are indexed under GSE221567, accessible at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE221567.

Prioritization of cognitive processes, in response to the significance of affective information, is guided by the interplay between the attentional needs of the associated task and socio-emotional aptitude. The dataset furnishes electroencephalographic (EEG) signals linked to implicit emotional speech perception, under conditions of low, intermediate, and high attentional engagement. 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). For data collection, 62 children and their parents or guardians were involved, encompassing 31 children exhibiting prominent autistic characteristics (xage=96, age=15), previously diagnosed with ASD by a medical professional, and 31 neurotypical children (xage=102, age=12). The Autism Spectrum Rating Scales (ASRS), a parent-reported instrument, is used to evaluate the extent of autistic behaviors displayed by each child. 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 incorporates the EEG recordings from all three tasks, along with the movement tracking (behavioral) information obtained from the MOT procedures. As a standardized index of attentional abilities, the tracking capacity was determined during the Movement Observation Task (MOT), accounting for any influence of guessing. Children were given the Edinburgh Handedness Inventory in advance, and their resting-state EEG activity was recorded for two minutes while their eyes were open. The data, as mentioned, are also available. biologicals in asthma therapy An investigation of the electrophysiological connections between implicit emotional and speech perceptions, along with the impact of attentional load and autistic traits, can be conducted using the available dataset.