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This research introduces a comprehensive framework for integrating Artificial Intelligence of Things (AIoT) into modern supply chain management. We examine five AIoT-enabled applications: dynamic fleet routing, supplier risk monitoring, autonomous inventory replenishment, Just-in-Time (JIT) delivery optimization, and warehouse robotics coordination. Synthetic datasets and machine learning models—ranging from regression to classification and reinforcement learning—simulate predictive scenarios for each domain. Our findings underscore measurable improvements in delivery efficiency, risk mitigation, and sustainability. This study demonstrates how AIoT facilitates real-time decision-making, enhances resilience, and fosters eco-conscious operations in complex supply chain ecosystems.

This dataset represents raw-data depicted in kinome analyses performed in the study (Figure 2). Kinases marked in blue represent deregulated kinases over the threshold of Mean Final Score >1.3. Mean kinase statistic refers to change in kinase activity in log2-space. SD kinase statistic refers to SD of kinase activity. For further information, please refer to the material and methods section of the study: DOI: 10.1128/jvi.00424-24.

Supplementary data for manuscript Lu et al "Effect of wildfires on soil nutrients, boron and lithium isotopes".

The dataset comprises individual-level responses collected through a structured survey instrument administered between August and December 2024. The data encompass four core constructs: Disorganization (DIS), Fear of Failure (FOF), Academic Procrastination (APC), and Academic Performance (APR), each measured through multiple Likert-scale items on a 5-point scale (1 = strongly disagree; 5 = strongly agree). The dataset includes responses from 2,111 higher education students spanning diploma, bachelor’s, master’s, and doctoral programs across East and Central Java. Each row in the dataset represents a single respondent, and each column corresponds to a specific survey item derived from previously validated psychometric instruments. The structure of the dataset is as follows: DIS1–DIS5: Indicators measuring students’ disorganized academic behavior, such as difficulty structuring study time and planning tasks. FOF1–FOF5: Indicators reflecting fear-based emotional responses to failure, including anxiety about evaluation and concern for others’ perceptions. APC1–APC5: Indicators capturing behavioral tendencies toward academic procrastination. APR1–APR4: Indicators of perceived academic competence and performance. The data have been cleaned and prepared for advanced statistical analysis, including mediation and moderation modeling using Structural Equation Modeling (SEM) techniques. This dataset is instrumental for understanding cognitive-behavioral mechanisms in higher education, particularly in the context of developing countries like Indonesia, and aligns with the broader agenda of educational quality improvement and student success.

We develop a dynamic sampling scale-up method for AGB estimation by integrating multi-source data from Sentinel-2 MSI, Unmanned Aerial Vehicle (UAV), and ground observations. Initially, UAV and Sentinel-2 multispectral images are separately combined with ground-measured data for feature interaction analysis and factors selection. Subsequently, eight machine-learning models are constructed and optimized for AGB estimation at different scales. The dynamic sampling scale-up algorithm based on UAV and ground data enhances AGB estimation accuracy based on the optimal models.

List of studies included in the systematic review of ICI-Induced LPP

The dataset was used to compare non-reduced Ks-values of soil core samples (Ksc) against borehole permeameter K-values (Ksf) on the one hand, and RoGeR-reduced K-values (Ksm) against borehole permeameter K-values (Ksf) on the other. Differences between Ksm and Ksf were explained by multiple linear regression using soil parameters and principal component analysis.

Transcriptional gene analysis from tumor infiltrating effector memory CD8 T cells in a DK210 (EGFR) study with B16F10 tumor bearing mice

The dataset consists of synthetic 7-DoF robotic joint trajectories generated using cubic polynomial baseline paths with added sinusoidal variations and Gaussian noise. Each trajectory is smoothed via cubic spline interpolation and concatenated across five configurations, resulting in time points and joint angles q_road ∈R^(250×7) . The perturbations emulate real-world motion variability for training PITNet.

EPMA and LA-ICP-MS geochemical data of the ferromanganese nodule from Caiwei Guyot and global geochemical data