Search Results (2,839)

This paper examines the long-run relationship between subjective well-being and digital transformation in the six Gulf Cooperation Council (GCC) countries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2011–2025 using a balanced country-year panel dataset. Subjective well-being is measured by the national average Cantril Ladder score from the Gallup World Poll as reported in the World Happiness Report. Explanatory variables include a binary AI Readiness Period Indicator (AI) distinguishing the pre-AI-readiness phase (2011–2018, AI = 0) from the post-AI-readiness phase (2019–2025, AI = 1), anchored by the Oxford Insights Government AI Readiness Index, Internet penetration from the International Telecommunication Union (ITU), and real GDP per capita. After accounting for cross-sectional dependence and non-stationarity, the analysis employs a panel autoregressive distributed lag (ARDL) framework estimated via the Pooled Mean Group (PMG) approach. The results indicate the existence of a stable long-run cointegrating relationship among the variables. The baseline PMG estimates suggest positive long-run associations between GDP per capita and the AI Readiness Period Indicator with subjective well-being, and a negative association between Internet penetration and well-being in a high-connectivity regional context. Short-run effects are generally weak, while the error-correction term confirms adjustment toward the long-run equilibrium. Robustness checks based on alternative estimators confirm the positive long-run effect of income, while the estimated effects of the AI Readiness Period Indicator and Internet penetration show sensitivity in sign and significance across specifications and should therefore be interpreted as indicative rather than definitive. Overall, the findings suggest that digital transformation is not a homogeneous driver of subjective well-being. Instead, the AI Readiness Period Indicator and Internet penetration operate through distinct mechanisms, with potentially different welfare implications in highly connected rentier-state economies. Full article

Narrowband Internet of Things (NB-IoT) is a 3GPP-standardised low-power wide-area network (LPWAN) technology designed for massive machine-type communications in challenging propagation environments. Despite its growing deployment, empirical channel data for Thailand’s diverse terrain—urban dense, urban outdoor, suburban, rural, and forest/mountain—remains limited in the open literature. This paper presents a composite channel characterisation study encompassing sixteen measurement sites across five environment classes in central and western Thailand. A composite channel model combining log-distance path loss, log-normal shadowing, and Nakagami-m fast fading is applied across all sites, yielding 8000 reference signal received power (RSRP) samples. Path loss exponents range from n = 2.2 (rural) to n = 4.0 (forest/mountain), back-calculated Nakagami-m parameters from m = 0.44 to m = 3.51, and shadowing standard deviations from σ_sh = 4.16 to 8.38 dB; ECL distributions are derived for all five environment classes. The back-calculated Nakagami-m parameters reveal a coherence gradient from sub-Rayleigh forest terrain (m < 1) through urban Rayleigh (m = 1.00) to near-Rician rural conditions (m > 2)—a fading hierarchy not previously reported for NB-IoT in Thailand. Results confirm that the composite channel model accurately characterises RSRP distributions and provides actionable network planning parameters for NB-IoT deployment in varied Thai terrain. Full article

Mobile robots used in edge-assisted Industrial Internet-of-Things (IIoT) settings generate coupled motion, LiDAR, edge-compute, and network telemetry. Public datasets that place these streams in one tabular format, with scenario labels suitable for machine-learning experiments, are still limited. This data descriptor presents a pilot-real-calibrated indoor robotic IoT benchmark dataset with 120,000 records sampled at 2 Hz across nominal navigation and nine anomaly scenarios. The benchmark rows are generated from physically constrained simulation rules and are explicitly labeled as synthetic benchmark data. Real pilot evidence is included separately: ROS Noetic runs on a TurtleBot3 Burger, successful LD08 LiDAR bringup after resolving a driver mismatch, and NVIDIA Jetson Nano tegrastats logs under normal-navigation workloads. The calibrated file aligns normal-navigation LiDAR and edge-compute distributions with these pilot measurements while keeping the multi-scenario structure needed for controlled anomaly-detection experiments. The package includes CSV files, metadata, a data dictionary, validation reports, baseline scripts, ROS collection utilities, and a plan for future fully physical data collection. The complete dataset is openly available on Zenodo. Full article

Radio frequency (RF) energy harvesting offers a potential power source for low-power Internet of Things and wireless sensing nodes, but compact rectifiers must balance impedance matching, multiband response, and load-driving capability. This work presents a compact SMS7621 Schottky-diode RF rectifier for RF-powered wireless sensing applications. An 11-segment microstrip distributed-parameter collaborative optimization strategy is used to tune impedance transformation in a 3.48 cm × 1.98 cm single-layer layout while compensating for diode nonlinear impedance variation and package parasitics. Simulations show more than 40% RF-to-DC conversion efficiency from 1.90 to 2.35 GHz, with additional efficiency peaks of 40.55% at 4.45 GHz and 38.45% at 7.15 GHz. Measurements verify the 2.45 GHz output performance under controlled high-input-power excitation: with a 300 Ω load and 25 dBm input, the rectifier delivers a maximum DC voltage of 5.42 V. At 15 dBm input, the measured peak efficiency reaches 46.05% at 2 GHz and remains 35.69% at 4 GHz. These results indicate a compact rectifier front end with multiband harvesting potential and 5 V-class load-driving capability under dedicated RF powering conditions. Full article

This study aimed to validate the Meta AI Literacy Scale (MAILS) in Ecuadorian high school students and examine latent profiles of Artificial Intelligence (AI) literacy. An instrumental, cross-sectional study was conducted with a sample of 971 students aged 14 to 19 years. Confirmatory factor analysis (CFA) supported a nine-factor correlated model with satisfactory fit indices (CFI = 0.953, TLI = 0.946, RMSEA = 0.053, SRMR = 0.058), high internal consistency (total α = 0.973; ω = 0.974), and scalar measurement invariance by sex and type of educational centre. Latent profile analysis (LPA) identified four differentiated profiles organised along a quantitative continuum of competence. Overall, the findings provide empirical evidence regarding the validity and utility of the MAILS in a sample of Ecuadorian high school students, providing a basis for future validations in other Spanish-speaking and Latin American contexts, and suggest that intentional academic use of the internet is associated with higher levels of AI literacy, although with small effect sizes. The validated instrument constitutes a resource for educational researchers and practitioners seeking to characterise AI literacy in secondary school populations and to inform evidence-based curricular interventions. Full article

Background: Internet Gaming Disorder (IGD) is a growing mental health concern among university students, often linked to impulsivity and low resilience. However, traditional regression-based research often fails to evaluate latent measurement structures or simultaneously test indirect pathways within a single analytic framework. This study applied structural equation modeling (SEM) to examine the direct and indirect associations between impulsivity, resilience, and IGD symptom severity among Saudi university students, evaluating resilience as a potential mediator and testing measurement equivalence across sex. Methods: Data from 207 students (58.9% male) were analyzed using the lavaan package in R. Confirmatory factor analysis (CFA) evaluated the measurement model for the IGDS9-SF, CD-RISC-10, and S-UPPS-P. Structural paths were tested with indirect effects estimated via bootstrapping (5000 resamples), alongside multi-group invariance testing to examine equivalence across sex. Results: The measurement model demonstrated acceptable fit (CFI = 0.93, TLI = 0.91, RMSEA = 0.058 [90% CI: 0.044, 0.069], SRMR = 0.063). Significant direct associations were observed: impulsivity with IGD severity (β = 0.38, p < 0.001) and resilience with IGD severity (β = −0.26, p < 0.001). The bootstrapped indirect association of impulsivity with IGD through resilience was statistically significant (β = 0.083, 95% CI [0.031, 0.149]), consistent with partial mediation in this cross-sectional sample. Factor loadings were equivalent across sex (metric invariance supported); the impulsivity–IGD association was stronger in males (β = 0.46) than females (β = 0.27; χ²(1) = 4.65, p = 0.031), though this comparison should be treated as preliminary. Conclusions: Impulsivity was associated with IGD both directly and indirectly through lower resilience. These SEM-based findings are consistent with resilience as a partial mediator of the impulsivity–IGD association, suggesting potential value in multi-target mental health interventions in university nursing and counseling settings. Longitudinal research is needed to establish causal ordering. Full article

Background: Equitable engagement with technology-mediated learning, including artificial intelligence (AI)-supported pedagogies envisioned in current education policy, depends on material preconditions—school and home connectivity, device access—that are unevenly distributed. Empirical baselines for these preconditions in Central European secondary education remain sparse. This study examines digital infrastructure conditions among Slovak secondary-school students and tests whether connectivity and device access predict curriculum exposure to the smart-city concept, used here as one observable indicator of access to technology-mediated curricular content rather than as a direct measure of AI literacy. Methods: A cross-sectional survey collected data from N = 419 Slovak secondary-school students recruited through the Ministry of Education of the Slovak Republic, regional school authorities, and cooperating secondary schools. Self-rated school internet quality, home internet quality, and total household connected devices were analysed individually and combined into a standardised composite digital readiness index. Curriculum exposure to the smart-city concept (binary: any exposure vs. none/unsure) served as the outcome. Logistic regression was applied in unadjusted and gender-adjusted models (valid n = 383); component-level models tested which infrastructure dimension carried the association. Results: School internet quality was rated low (1–2 on a five-point scale) by 47.6% of students (mean = 2.59), whereas home internet quality was rated high (4–5) by 69.3% (mean = 3.89), indicating a substantial school–home connectivity gap. Only 35.8% of students (148/413) reported any curriculum exposure to the smart-city concept. School internet quality was the principal predictor: each one-standard-deviation (SD) increase corresponded to an odds ratio of 1.564 (95% CI: 1.261–1.939, p < 0.001), and in component-level models, neither home internet (OR = 1.038, p = 0.74) nor household device count (OR = 0.977, p = 0.83) carried independent predictive value. The composite index (OR = 1.337, 95% CI: 1.081–1.654, p = 0.0075 unadjusted; OR = 1.328, p = 0.0095 gender-adjusted) was essentially a noisier reflection of the school-connectivity signal. Male students were significantly more likely to report exposure than female students (OR = 1.794, 95% CI: 1.166–2.762, p = 0.0079). A readiness × gender interaction approached significance (OR = 0.641, p = 0.064), tentatively suggesting a steeper connectivity gradient for female students—an exploratory finding warranting replication. Model discrimination was modest (area under the curve, AUC = 0.621); the association, not predictive performance, is the quantity of interest. Conclusions: School-level connectivity—not home infrastructure or device count—is the infrastructure dimension associated with curriculum exposure to technology-mediated content. These findings indicate that school connectivity should be treated as a precondition for equitable participation in technology-mediated and AI-supported learning, and that pedagogical designs should function under infrastructural constraints. Limitations include reliance on self-rated measures, school-mediated convenience sampling, an observational cross-sectional design, and the indirect mapping between smart-city curriculum exposure and AI literacy proper. Full article

Air pollution in Almaty, Kazakhstan, poses a critical public health challenge intensified by the city’s basin topography and seasonal thermal inversions that trap anthropogenic emissions. The sparse stationary network (~5 stations for ~2 million inhabitants) lacks the spatial and temporal resolution needed to capture intra-urban variability. We present the design, deployment, and field evaluation of a low-cost distributed Internet of Things (IoT) network of six custom nodes—Winsen ZPHS01B multi-parameter modules with Raspberry Pi Zero 2 W edge units, at an estimated principal-component cost of ~US$100 per node—operated during a winter-spring campaign (February–April 2025) and yielding over 70,000 measurements of PM_2.5, PM₁₀, CO₂, temperature, and relative humidity. The system’s novelty lies in three integrated engineering features: an Active Airflow Stabilization enclosure that decouples sampling from external wind, context-aware adaptive edge filtering that reduces transmitted data volume by ~40%, and a secure Edge-DMZ-Core telemetry pipeline. Node readings were cross-validated against a Qingping Air Monitor Pro with documented traceability to FEM-grade reference analyzers (R² = 0.89–0.95), and city-scale consistency was confirmed against the national monitoring dashboard; the network is therefore characterized as providing internally consistent low-cost observations rather than reference-equivalent concentrations. Daily mean PM_2.5 exceeded the WHO 24 h guideline (15 µg/m³) on 84% of monitored days, with February concentrations (54.4 µg/m³) significantly above March (21.9 µg/m³; p < 0.001). A high PM_2.5/PM₁₀ ratio (~0.96), measured at the physically consistent nodes, together with higher weekend concentrations, points to coal-based residential heating as the most likely dominant source. A coupled WRF-SILAM framework is configured for future model-observation integration. The system offers a reproducible, scalable, and cost-effective template for ambient particulate monitoring in resource-constrained cities with complex terrain. Full article

Background/Objectives: Nutrition during childhood and adolescence is a key determinant of long-term health, influencing metabolic homeostasis, neurocognitive development, and immune system maturation. Globalization and technological advances have reshaped food production and consumption, increasing the availability of ultra-processed foods (UPF) of low nutritional quality. This study aimed to investigate the relationship between parental factors, namely food literacy, perceived barriers and enablers, dietary attitudes, and healthy eating behaviors, and the consumption of discretionary UPF among children and adolescents living in 5 Mediterranean countries. Methods: This cross-sectional study was based on a survey completed by 2011 parents of children and adolescents aged 6–17 years from 5 Mediterranean countries, who reported on their children’s dietary and lifestyle habits. Adherence to the Mediterranean diet was assessed through the KIDMED index. Parental food literacy was measured using the Short Food Literacy Questionnaire (SFLQ). Perceived barriers and enablers were assessed based on the Theory of Planned Behavior, and parents’ attitudes toward their child’s diet were evaluated using the Healthy-Eating Attitudes Questionnaire (HEAQ). Finally, the Theory of Internet Use Related to Health (TIUH) questionnaire was used to assess parents’ tendencies related to health information use online. Results: Higher perceived barriers and enablers were significantly associated with lower discretionary UPF consumption across all models. Parental food literacy (SFLQ) showed a positive association with discretionary UPF consumption, remaining significant in the fully adjusted model, although with reduced magnitude. Healthy-eating attitudes (HEAQ) were initially positively associated with discretionary UPF intake but lost statistical significance after full adjustment. Regarding health-related internet use (TIUH), the Health Information dimension showed a strong positive association with discretionary UPF consumption, while other dimensions (Consciousness and Beliefs) showed inconsistent and non-significant associations in the fully adjusted model. Conclusions: Children’s consumption of discretionary UPF is shaped by several interrelated factors, such as family environment, eating patterns, and parents’ perceptions, rather than solely by knowledge or attitudes. Full article

Energy-intelligent building control is widely advocated as a key strategy for reducing building energy use and associated carbon emissions. However, empirical evidence from real buildings remains relatively scarce and is often affected by methodological limitations. This study reports a 92-day field experiment on the 14th floor of an office building in Shenzhen, China, where an Internet of Things (IoT) and edge-computing-based intelligent building environmental control system was deployed to manage lighting and HVAC. An A-B-A experimental design was implemented in summer 2022, comprising two intelligent-control phases (A₁ and A₂, July and September) and one intermediate manual-control phase (B, August) during which the intelligent algorithms were disabled without notifying occupants. Aggregated monthly runtimes show average energy-saving potential ratios of approximately 25.98% for lighting and 19.50% for HVAC when the intelligent system is active. As this study was conducted on a single office floor in one building, the results should be interpreted as proof-of-concept field evidence for this specific operational and climatic context rather than as directly generalizable performance estimates for all office buildings. The study further highlights methodological considerations for future field evaluations, including the influence of outdoor climate, occupancy patterns and experimental design choices. The findings provide a real-world empirical case of the energy-saving potential performance of intelligent environmental control in office buildings and contribute to narrowing the gap between simulated and measured savings. Full article

Dataset quality significantly affects the effectiveness of a machine learning (ML) model in an intrusion detection system (IDS) for cyber-physical industrial control systems (CPS/ICS) and Industrial Internet of Things (IIoT). Existing surveys compare datasets qualitatively or along limited dimensions, whereas this review introduces quantitative documentation and decision-aid scoring across 23 ICS/OT/IIoT datasets. These datasets are analyzed along seven measurable axes, with their attacks mapped to MITRE ATT&CK for ICS tactics. Quantitatively, 14 of the 23 datasets (60.9%) are built on physical testbeds, and 22 of the 23 map to MITRE ATT&CK for ICS, spanning 11 of the 12 tactics. We introduce a checklist for documentation completeness (0–7) and a decision-aid rubric (0–15) covering realism, attack diversity, class imbalance, documentation, and reproducibility. Protocol coverage across these datasets is skewed toward Modbus (13 of 23 datasets, 57%), while many other protocols (such as Profinet and OPC UA) are underrepresented relative to their industry deployment. The available datasets show structural gaps in capturing multi-stage adversary behavior. In practice, dataset selection should pair a realism-anchored dataset with a high-reproducibility one, and account for protocol diversity and APT representation. Full article

Background/Objectives: Adolescent mental health problems have increased in recent years, with growing concern about the impact of digital behaviors such as problematic video game use and gambling. Internet Gaming Disorder (IGD) and Problem Gambling Symptoms may share psychological risk markers, but evidence in Spanish adolescents is limited. This study aimed to examine the relationship between IGD, problem gambling symptoms, and mental health, and to identify sociodemographic, psychological, and behavioral factors associated, including microtransactions and loot boxes. Methods: A cross-sectional study was conducted with secondary education students from Extremadura (Spain). The final sample included 343 participants. Measures included an ad hoc questionnaire on video game use, the IGDS9-SF, SOGS-RA, and the Strengths and Difficulties Questionnaire (SDQ). Descriptive analyses, Spearman correlations, and multivariable regression (Poisson and negative binomial) were performed. Results: IGD and gambling were positively correlated (Spearman’s ρ = 0.386, p < 0.001) and associated with higher mental health difficulty scores (IGD: ρ = 0.299, p < 0.001; gambling: ρ = 0.214, p < 0.001). Male gender was associated with both outcomes (IGD: incidence rate ratio [IRR] = 1.21 [95% Confidence Interval: 1.13–1.30]; gambling: IRR = 2.90 [1.85–4.60]). Microtransactions were associated with both behaviors (IGD: IRR = 1.17 [1.09–1.25]; gambling: IRR = 1.74 [1.19–2.54]), while loot box use was related only to IGD (IRR = 1.13 [1.05–1.21]). Total SDQ score was positively associated with both IGD (IRR = 1.02 [1.02–1.03]) and gambling (IRR = 1.10 [1.06–1.13]). Younger age was associated with higher IGD scores (IRR = 0.97 [0.96–0.99]). Conclusions: There are similarities in the associations among the examined factors and increased scores of IGD and gambling in adolescents, particularly male gender, higher mental health difficulties scores, and involvement in monetized gaming systems. School-based, family, and public health prevention strategies may benefit from addressing the importance of psychological well-being and increase awareness of the potential risks associated with digital gaming practices. Full article

Human kinematic analysis is an increasingly important tool in biomechanics, human performance, and wearable sensing research. Many emerging sensing modalities utilize custom sensors requiring accurate temporal alignment with ground-truth biomechanical movement data. Optical motion capture systems provide high-fidelity kinematic measurements but operate as closed, self-contained systems, making time synchronization with external sensor data non-trivial, particularly in wireless and mobile contexts. This work presents a wireless analog interface system built using commercially available components that enables alignment between analog sensor data (e.g., from custom wearables and Internet-of-Things devices) and a commercial motion capture system. The proposed architecture consists of a wearable data acquisition node and a receiver node interfaced directly with an optical motion capture system, allowing synchronized recording of analog sensor signals alongside kinematic data. Notably, the system reconstructs signals into the commercial hardware interface rather than relying on triggers or sync outputs, resulting in a single data file containing kinematics and sensor readings. Benchtop testing demonstrated a mean end-to-end frame delay of ~6 ms, with 95% of the sample exhibiting delay within 15 ms. Accounting for the typical offset, this leaves a standard deviation of 4 ms, within one motion capture frame of the true timestamp (at 100 Hz). Voltage reconstruction accuracy was within 30 mV across the tested conditions, with gain compression below 2.7%. Adjacent channel crosstalk remained below −83 dB across all test conditions. The use of commercial off-the-shelf components supports replication and adaptation by other research groups and integration with different optical motion capture systems. Full article

This study investigated Chinese EFL students’ behavioral intentions to learn English using AI-based chatbots. A total of 1052 questionnaire responses were collected from Chinese students. Structural equation modeling (SEM) was employed to assess the measurement model and test the proposed relationships. The results showed that facilitating conditions, social influence, performance expectancy, and effort expectancy were salient factors associated with students’ intentions to use AI chatbots. These UTAUT factors were also significantly related to information quality and AI trust. Information quality was positively associated with both AI trust and intention to use, while AI trust was directly associated with behavioral intention. In addition, information quality and AI trust mediated the relationships between the UTAUT factors and behavioral intention. Moderation analysis indicated that technological consciousness positively moderated the relationship between information quality and behavioral intention, but did not moderate the relationship between AI trust and behavioral intention. Internet experience also strengthened the positive relationships between information quality, AI trust, and behavioral intention. Finally, theoretical and practical implications are discussed, and limitations are highlighted. Full article

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment. Full article