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Supplementary workbook for the manuscript "Operationalizing Pakistan's National Integrated Energy Plan 2027–2060: An AHP-LEAP Decision-Support Framework for Multi-Criteria Energy Policy Evaluation." Contains 9 sheets reproducing the illustrative calculations in Sections 5 and Appendix B.7 of the manuscript, plus the full criteria hierarchy, evidence-stream mapping, and data dictionary. All values are HYPOTHETICAL and are included solely to demonstrate methodological reproducibility. They are not empirical findings about Pakistan's energy policy. Sheets: README, AHP_Pairwise_Matrix, Priority_Vector, Normalized_Scores, Integrated_Scores, Two_Level_Aggregation, Criteria_Hierarchy, Evidence_Streams, Data_Dictionary. The workbook is a methodological reference and reproducibility aid, not a source of primary empirical data.

The data used in this study were obtained from the India NREL Wind Speed Dataset provided through the National Renewable Energy Laboratory (NREL). The dataset was developed to provide meteorological conditions for applications of wind energy. It has a temporal resolution of 5 min and a spatial resolution of 3 km, with the year 2014. The India Wind Dataset contains simulated wind speed, wind direction, temperature, and atmospheric pressure parameters at multiple heights above ground level, including 40, 80, 100, and 120 m where available. For the current study, the extracted dataset contains the variables height, air pressure, wind direction, temperature, and wind speed. The wind-speed variable, expressed in m/s, is used as the primary, while height, atmospheric pressure, wind direction, and temperature are considered explanatory meteorological variables. The original dataset contains 315,362 wind-speed observations. The dataset is particularly suitable for this study because its 5-min temporal resolution provides a high-resolution representation of wind-speed variability. The availability of meteorological variables together with wind speed also enables the investigation of data-driven and signal-decomposition-based approaches for wind-speed prediction.

Background: Motorcycle designs require different riding postures that may influence lumbar spinal alignment and trunk muscle performance. Evidence comparing these outcomes across motorcycle types remains limited. Objective: To compare lumbar lordosis and transversus abdominis performance among sports, cruiser, and commuter motorcycle riders and examine their associations with motorcycle type and riding time. Methods: This cross-sectional observational study included 75 male riders aged 20–28 years, with 25 participants in each motorcycle group. Lumbar lordosis was measured using the Flexicurve method, and transversus abdominis performance was assessed indirectly using a Pressure Biofeedback Unit (PBU). Between-group differences were examined using one-way analysis of variance and Bonferroni post hoc tests. Regression analysis evaluated the combined contribution of motorcycle type and average riding time. Results: Lumbar lordosis differed significantly among groups (F = 29.881, p < 0.001, η² = 0.454), with the highest mean in sports riders (44.44 ± 8.61°), followed by commuter (38.56 ± 11.11°) and cruiser riders (25.75 ± 5.62°). Significant pairwise differences occurred between cruiser and sports riders and between cruiser and commuter riders. PBU values also differed overall (F = 5.237, p = 0.008), with a significant pairwise difference only between cruiser and commuter riders. Motorcycle type and riding time explained 52.4% of the variance in lumbar lordosis, compared with 13.9% in PBU values; the latter model was not statistically significant. Conclusion: Motorcycle type and riding time were associated with lumbar alignment, while associations with PBU-measured muscle performance were less consistent. These findings highlight the relevance of motorcycle ergonomics to rider spinal posture.

This dataset contains time-stamped environmental and operational data collected during an indoor air-quality monitoring and purification experiment carried out on the production floor of a garment factory near Dhaka, Bangladesh. The experiment was set up to reflect the kind of real-world, crowded conditions commonly found in active garment facilities - a large number of workers were present and working throughout the shared indoor space, naturally driving up particulate exposure levels and creating the fluctuating environmental conditions typical of such settings. To capture this, a smart mobile industrial air purification robot was deployed directly on the factory floor. The robot was equipped with a PMS7003 particulate matter sensor, a DHT22 temperature and humidity sensor, and a compact HEPA-based onboard purification system, allowing it to continuously monitor the surrounding air as it navigated through the workspace. Rather than operating on a fixed schedule, the robot was programmed with sensor-based decision logic so that it could respond dynamically to degrading air-quality conditions - meaning the dataset captures not just environmental variation, but also how the robot actually behaved in response to it. The variables recorded include timestamp, PM2.5 concentration, AQI value, AQI category, temperature, relative humidity, and bot movement status. This dataset is intended to support research in areas such as indoor air quality in garment factory environments, smart air-purification robotics, occupational environmental monitoring, embedded sensing systems, and mobile pollution-response technologies in dense industrial workplaces.

Higher education environments are becoming increasingly diverse due to the students' diversity, and Socio-Cultural Dynamics (SCD) continue to shape classroom teaching. Ideological and Political Education (IPE) is a comprehensive educational concept with unique Chinese characteristics, and its value orientation has a unique background in modernization. This investigation intends to analyze the association between SCD, IPE, technological integration, student engagement, and academic performance. To examine these associations, a quantitative, cross-sectional survey design was adopted. Using purposive sampling, 495 structured questionnaire responses were obtained from undergraduate college students in China. The obtained responses were analyzed in SPSS and SmartPLS. Partial Least Squares Structural Equation Modeling (PLS-SEM) reveals that SCD is positively associated with technological integration (β = 0.336) and student engagement (β = 0.227). Technological integration is positively related to the IPE (β = 0.450), which in turn is associated with student engagement (β = 0.379). Student engagement (β = 0.367) and technological integration (β = 0.206) are positively connected with academic performance. Moreover, student engagement partially mediates (β = 0.139) the association between IPE and academic performance. This examination contributes to existing literature by integrating IPE, SCD, technological integration, and academic performance into a single conceptual framework, with a mediating role of student engagement, and by demonstrating the connections between them. These outcomes provide a practical perspective for higher education institutions and education policymakers to build inclusive learning environments by bridging social and cultural differences among students and incorporating technologies into education. This is associated with an environment that supports student engagement and is linked to their academic performance.

Dataset and R code for the article: The Associations Between Negative Experiences and Problematic Social Media Use Among Adolescents: A Cross-Lagged Panel Network Analysis

The supplement materials for “Optimizing Embryo Transfer Management in Cows Ultrasound-Based Pre-Transfer Screening and Management of Non-Eligible Recipients”

This dataset contains the experimental results used in the study evaluating AI-generated test cases against human-written test cases. The dataset includes testing quality metrics, productivity metrics, user story evaluations, and survey responses collected from software quality assurance professionals.

Ayurveda is the traditional system of medicine of India, utilizing medicinal plants for healthcare and wellness. Bioactive components present in leaves of medicinal plants possess therapeutic properties and therefore it can be used as the major ingredient in many ayurvedic formulations. Accurate identification and authentication of medicinal leaves are essential for ensuring the quality and appropriate use of herbal materials. Closely related plant species, particularly those belonging to the same family or genus, may exhibit considerable similarities in leaf morphology, making their identification difficult. Such similarities can also increase the possibility of accidental substitution or adulteration, which may affect the quality and intended use of ayurvedic preparations. Therefore, reliable identification methods are important for supporting the authentication of medicinal plants. To ensure proper identification and to prevent adulteration, we are introducing this dataset that consists of the original medicinal leaf Wrightia Tinctoria (Danthapala) along with its probable adulterant leaves such as Holarrhena Pubescens (Kudagapala), Tabernaemontana Alternifolia (Kuruttupala), Tabernaemontana Divaricata (Nandyarvattam), and young Wrightia Tinctoria (Danthapala) leaves. All these plant species are from the same family, Apocynaceae. The dataset consists of two folders: the imbalanced and balanced dataset. The imbalanced dataset consists of high-resolution 310 raw images with five classes, and each class contains 30-90 images. Balanced dataset was constructed by applying augmentations to the existing images. This dataset consists of five classes and each class consists of 90 cleaned images, resulting in a total of 450 images. This dataset has been collected from Vaidyaratnam Ayurveda Research Institute (VARI), Thrissur, Kerala, India.

This dataset contains the supplementary material for the manuscript entitled “Heterologous expression and functional characterization of canine lactoferrin in Komagataella phaffii”. It includes supplementary tables (Tables S1–S4) and a supplementary figure (Figure S1). The tables present organ indices, serum biochemical parameters, serum and intestinal immune parameters, antioxidant indices, and intestinal cytokine and secretory immunoglobulin A levels of mice treated with recombinant canine lactoferrin or bovine lactoferrin. Figure S1 shows histological analysis of the ileum. These data support the main findings on the safety and intestinal effects of rcLF in juvenile mice.