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Background: Infertility is a common reproductive health disorder that affects roughly 10–15% of couples during their reproductive period. Hysterosalpingography (HSG) is a widely utilized imaging modality for assessing uterine cavity morphology and fallopian tube patency and continues to play a central role in infertility investigations. Nevertheless, the interpretation of HSG findings may vary according to the experience and expertise of the evaluator, potentially leading to inconsistencies in clinical decision-making. Although artificial intelligence (AI) has demonstrated considerable potential in medical image analysis across various specialties, evidence regarding its application in the interpretation of HSG examinations remains scarce. Therefore, this study aimed to evaluate the level of agreement among radiologists, gynecologists, and an AI-based system in the assessment of identical HSG images. Methods: In this retrospective study, a total of 1443 HSG images obtained from 414 women who underwent hysterosalpingography as part of an infertility evaluation between January 2021 and January 2025 were reviewed. Cases with incomplete clinical records or suboptimal image quality were excluded from the analysis. All examinations were independently assessed by an experienced radiologist, a gynecologist specializing in infertility management, and a multimodal artificial intelligence system based on ChatGPT-5, with each evaluator blinded to the assessments of the others and to the patients’ clinical information. Image interpretation included the evaluation of contrast distribution, peritoneal spill, uterine cavity findings, tubal patency, and overall HSG impression, which were categorized according to predefined diagnostic criteria. The primary outcome was the degree of interobserver agreement among the evaluators. Agreement analyses were performed using Cohen’s kappa (κ) and Gwet’s AC1 coefficients. Analyses were conducted using IBM SPSS Statistics (version 30.0; IBM Corp., Armonk, NY, USA) and R statistical software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at p < 0.05 (two-sided). Results: A total of 1443 HSG images obtained from 414 women were included in the final analysis. The mean age of the study population was 30.97 ± 5.59 years, and primary infertility accounted for 87.9% of cases. The average number of images acquired per examination was 3.49 ± 1.05. According to Cohen’s kappa analysis, the highest levels of agreement were observed for the assessment of image artifacts and contrast medium distribution. Agreement between the AI system and the radiologist was particularly strong for contrast medium distribution (κ = 0.757). For the overall interpretation of HSG findings, AI demonstrated substantial agreement with the radiologist (κ = 0.637), exceeding the level of agreement observed between the radiologist and the gynecologist (κ = 0.363). In contrast, concordance involving AI was lower for the evaluation of uterine abnormalities, intrauterine filling defects, and tubal patency. When agreement was reassessed using Gwet’s AC1 statistic, concordance coefficients were consistently higher than the corresponding kappa values across all evaluator pairs. Near-perfect agreement between AI and the radiologist was identified for contrast medium distribution (AC1 = 0.954), peritoneal spill (AC1 = 0.893), and patterns of peritoneal contrast passage (AC1 = 0.841). Procedures performed under local anesthesia yielded a significantly greater number of images than those conducted under general anesthesia (3.86 ± 0.86 vs. 3.08 ± 1.10, p < 0.001). No significant associations were detected between abnormal HSG findings and either infertility type or anesthetic technique. In multivariable analysis, the use of general anesthesia was independently associated with a lower image count, whereas the presence of tubal pathology emerged as an independent predictor of acquiring a greater number of images during the examination. Conclusions: Our findings indicate that AI-assisted interpretation of HSG images has the potential to complement expert assessment, showing substantial concordance in several key diagnostic domains. While the technology appears promising as a decision-support tool in infertility evaluation, further research and refinement are warranted, particularly regarding the assessment of tubal and uterine pathologies. Full article

With the rapid development of artificial intelligence, AI-driven algorithmic goal setting has become an important mechanism of digital platform management. In the livestream e-commerce industry, platforms increasingly use algorithmic systems to assign tasks, monitor performance, and regulate streamers’ work. Although this intensive and dynamic form of algorithmic management can improve operational efficiency, it may also be associated with potential ethical risks. Drawing on Conservation of Resources Theory, Emotional Labor Theory, and Sociotechnical Systems Theory, this study examines whether AI-driven algorithmic goal setting is associated with streamers’ unethical selling behavior through emotional dissonance and whether AI transparency moderates this relationship. Using a three-wave time-lagged survey design, data were collected from 427 livestream e-commerce streamers in China. SPSS-based hierarchical regression analysis and bootstrapping were employed to test the proposed moderated mediation model. The results showed that AI-driven algorithmic goal setting was significantly and positively associated with streamers’ unethical selling behavior and that emotional dissonance partially mediated this relationship. Furthermore, the positive relationship between AI-driven algorithmic goal setting and emotional dissonance, as well as the corresponding indirect relationship with unethical selling behavior, was weaker at higher levels of AI transparency. These findings identify emotional dissonance as an important psychological mechanism linking algorithmic performance demands with unethical selling behavior and indicate the conditional buffering role of AI transparency. This study extends algorithmic management research to the livestream e-commerce context and provides practical implications for enhancing algorithmic transparency and reducing ethical risks in platform governance. Full article

The maturity method (ASTM C1074) predicts in situ concrete strength from a recorded temperature history but assumes a constant apparent activation energy, contradicting the experimental evidence that the activation energy falls as hydration shifts from kinetics control to diffusion control—an effect that differs between binder chemistries when supplementary cementitious materials (SCMs) are used. This study develops a physics-based maturity model in which the apparent activation energy varies linearly with temperature, Q(T) = Q₀ + β_Q(T − T_ref), coupling a variable-energy Arrhenius equivalent age to a hyperbolic strength–maturity relationship. The model was calibrated on 196 mean-strength observations (588 cube tests) from seven self-compacting concrete mixtures cured isothermally at 10, 20, 35 and 50 °C and tested at seven ages (1–90 days). All four SCM systems (fly ash, GGBS, silica fume and rice husk ash) returned a negative coefficient (−210 to −974), enclosing the temperature sensitivity implied by independent calorimetric measurements on Portland cement paste (≈−580 J/(mol·K)), whereas the ordinary Portland cement control returned a positive point estimate (+101) that is not statistically distinguishable from zero. The model achieved R² = 0.929 (RMSE = 4.74 MPa), outperforming the constant-energy ASTM C1074 baseline in both accuracy and the Akaike Information Criterion while eliminating its systematic bias at the temperature extremes. Five-fold cross-validation confirms the out-of-sample accuracy (R² = 0.901, RMSE = 5.59 MPa), and bootstrap analysis shows the negative coefficients of the fly ash, GGBS and rice husk ash systems to be statistically significant. External validation on 120 independent literature observations gave R² = 0.881. Full article

Personalized recommendation has become an essential component of intelligent information systems and electronic multimedia platforms. However, cold-start items with limited user–item interactions remain difficult to model, especially when collaborative signals are sparse and heterogeneous side information is underutilized. To address this problem, this paper proposes Heterogeneous Graph Contrastive Recommendation (HGCRec), a heterogeneous graph contrastive learning framework for cold-start multi-modal recommendation. HGCRec constructs a heterogeneous structural graph containing users, items, categories, brands, and semantic entities, while visual and textual information is incorporated as item-side feature views. A relation-aware heterogeneous graph encoder captures relation-specific structural semantics, and a cross-modal graph contrastive learning objective coordinates structural, visual, and textual item representations. An interaction-sparsity-aware weighting strategy allocates stronger contrastive supervision to items with fewer training interactions. Furthermore, an item-specific adaptive fusion module integrates collaborative, structural, visual, and textual representations according to interaction sparsity and learned multi-source representation states. Experiments are conducted on three Amazon multi-modal recommendation datasets, including Baby, Sports, and Clothing. The results show that HGCRec consistently outperforms representative graph-based and multi-modal recommendation baselines. Compared with the strongest baseline, Freezing and Denoising Graph Structures for Multimodal Recommendation (FREEDOM), HGCRec improves Recall@20 by 9.65%, 10.44%, and 12.00% on Baby, Sports, and Clothing, respectively, and improves NDCG@20 by 11.14%, 11.35%, and 13.10%. Sparsity-aware analysis further shows larger relative improvements for items with extremely limited training interactions, demonstrating the effectiveness of HGCRec under the evaluated interaction-sparse and few-shot settings. Full article

Blockchain-based peer-to-peer (P2P) energy trading enables prosumers to sell surplus electricity directly to one another without a central intermediary, but its open ledger reveals each participant’s bid price to every blockchain node, including the distribution system operator (DSO) that settles the trades. Prior work has addressed this concern by using inner product encryption (IPE) to encode each bid as a vector and perform matching directly over ciphertexts, yet the resulting pairing-based comparison and heavy on-chain heap restructuring still incur substantial gas costs. To resolve this issue, this paper proposes two orthogonal optimization methods. First, we replace the pairing-based IPE of the prior baseline with a matrix-based IPE, which substitutes pairing operations with matrix multiplication and a trace evaluation. This allows compact ternary encoding of integers and enables ciphertext entries to be packed into narrower Solidity integer types. Second, we introduce two heap-management policies: (i) the index-based heap, which exchanges integer indices instead of full ciphertexts during swaps, and (ii) the root-retention heap, which eliminates redundant heap restructuring under residual rebidding. Our performance analysis shows that the proposed optimization strategies substantially reduce the total gas consumption of the energy trading system compared with the pairing-based baseline, and the heap-management policies deliver consistent savings regardless of the underlying cryptographic primitive. Full article

Handwriting is a fundamental fine motor skill in early childhood development, yet between 10% and 30% of school-age children experience significant difficulties in its acquisition. Existing automated assessment approaches predominantly classify whether the correct character was produced, rather than evaluating the morphological quality of the stroke itself—the level at which handwriting difficulties are believed to manifest. This article addresses this gap by presenting the design, implementation, and technical evaluation of an interactive social robot shaped like a capybara, developed to support Spanish-language handwriting learning in preschool children through stroke-level, rather than character-level, assessment. The system integrates a Raspberry Pi 5, a 15.6-inch touchscreen, and a stroke morphological comparison algorithm implemented with the Open Source Computer Vision Library. The evaluation engine performs preprocessing, region-of-interest masking, and pixel-level coverage analysis based on the standard recall formulation, translated into 1-to-5-star multimodal feedback. A controlled technical evaluation of 360 trials, conducted by four trained adult evaluators, yielded an overall recognition rate of 82.78% (95% CI: 78.54–86.33%) and a mean response time of 0.68 s, well below the threshold identified in the literature as critical for sustaining engagement in preschool children. Recognition was statistically equivalent across character categories (p = 0.547) but differed markedly across stroke-quality levels (p < 0.001), evidencing the formative sensitivity of the algorithm. Exploratory observations in two Peruvian preschools indicated operational stability and children’s spontaneous engagement with the system. These results position the prototype as a technically validated, replicable foundation—based on general-purpose embedded hardware—for future pedagogically oriented research on child–robot interaction in handwriting instruction. Full article

In response to the increasingly complex secondary system of intelligent substations and the limitations of traditional evaluation methods such as data silos and single indicators, it is urgent to build a real-time high-precision status evaluation system. This article proposes a state evaluation method for the secondary system of intelligent substations based on digital twins and the combination weighting method. Firstly, a four-layer digital twin architecture consisting of physical entities, data interaction, virtual twins, and service applications is constructed to achieve precise mapping and real-time interaction of multi-source heterogeneous data in the secondary system. A comprehensive evaluation index system covering the operation status and information quality of key equipment in the secondary system of intelligent substations is built from multiple dimensions such as health status, information transmission, and environmental conditions. Next, a combined weighting evaluation model is established that integrates the subjective weights of the Analytic Hierarchy Process and the objective weights of the coefficient of variation method, and the principle of minimizing the sum of squares of subjective and objective deviations is introduced to achieve adaptive optimization of weight configuration. Finally, simulation analysis is conducted on the digital twin platform of the 220 kV intelligent substation, and the effectiveness and robustness of the proposed model are verified through multi-state evaluation, typical fault case verification, and ablation and sensitivity analysis. The results show that compared with the mainstream AHP entropy weight method, this method has a maximum deviation of 15.3% in evaluation indicators, a state evaluation score of 97.3164, a full process calculation time of about 2.6138 s, and communication delay that meets the requirements of the IEC 61850 standard. It can effectively improve the real-time accuracy of secondary system state perception and provide technical support for on-site operation and maintenance of intelligent substations. Full article

In this work, we study the joint performance of the quality and reliability in terms of quality–reliability (JQR) performance of an intelligent reflecting surface (IRS)-assisted wireless communication system under severe multipath fading and shadowing. The wireless channel model is given by the Inverse Power Lomax (IPL) fading model, representing a heavy-tailed fading channel, which can describe the hyper-Rayleigh fading and is verified using two different experimentally obtained measurement scenarios, namely, the LTE-case for high-frequency, long-range cellular communications and the device-to-device (D2D) case for lower-frequency short-range communications. For the considered channel model and communication scheme, analytical expressions for the outage probability (a metric related to the reliability) and the average bit error rate for both coherent and non-coherent modulation schemes (metrics associated with the quality of the communication system) are provided. By combining the aforementioned expressions, a unified JQR curve, together with its asymptotic forms in the high signal-to-noise ratio regime and asymptotically large number of IRS elements, is derived. It is proved analytically that the use of IRS with infinite elements can remove fading, while for a finite number of IRS elements, a closed-form signal-to-noise ratio (SNR) penalty factor is presented. The numerical analysis demonstrates that coherent modulations outperform non-coherent ones, higher-order quadrature amplitude modulation (QAM) systems are highly sensitive to the multipath fading, and the LTE-case exhibits better performance compared to the D2D-case for equal settings. Moreover, the joint quality–reliability approach highlights the existence of regions where quality is more preferable than reliability, allowing the allocation of resources based on these regions. All expressions have been verified using Monte Carlo simulations with excellent agreement. Full article

Previous analyses of substance-involved craniofacial trauma have often combined alcohol and drug/medication involvement. We conducted a cross-sectional analysis comparing alcohol-only and drug/medication-only facial injury encounters in the public Consumer Product Safety Commission National Electronic Injury Surveillance System (NEISS), 2019–2023. Primary facial injuries (Body_Part = 76) were classified using the released Alcohol_Involved and Drug_Involved fields. Survey-weighted estimates and Taylor-linearized 95% confidence intervals incorporated the released PSU, stratum, and weight variables; design-based logistic regression evaluated code-4 hospitalization. Among 146,857 observed facial injury encounters, 4117 were alcohol-only and 2302 were drug/medication-only. Weighted annual alcohol-only proportions were stable (trend odds ratio [OR] per year, 0.99; 95% CI, 0.96–1.03; p = 0.716), whereas drug/medication-only proportions increased (OR, 1.15; 95% CI, 1.04–1.27; p = 0.009). Compared with alcohol-only encounters, drug/medication-only encounters involved older patients (weighted mean age, 63.1 vs. 47.6 years) and a lower weighted male proportion (55.6% vs. 67.5%). Weighted code-4 hospitalization was 23.1% versus 11.8%; the adjusted OR was 1.91 (95% CI, 1.45–2.53; p < 0.001). Adult-only and alternative-period sensitivity analyses supported the main conclusions. Separate classification of alcohol and drug/medication involvement revealed distinct demographic, temporal, and disposition patterns among NEISS-captured product-associated ED encounters. Full article

Connected, automated, shared, and electric (CASE) vehicle concepts are considered a promising approach for improving the sustainability of urban mobility by increasing vehicle utilization and reducing fleet size. While the energy consumption of conventional battery electric vehicles has been investigated extensively, the influence of fleet size, speed limits, and ambient temperature on the energy demand of CASE vehicle fleets has received little attention. This study presents a simulative consumption analysis of an all-electric CASE vehicle fleet based on the EDAG CityBot concept. A validated microscopic traffic simulation of the city center of Darmstadt, Germany, is coupled with a backward-facing powertrain model and detailed secondary consumer models to determine the total fleet energy consumption under varying operating conditions. The analysis considers fleet sizes between 20% and 100% of a reference fleet, together with a 17% fleet size scenario, which allows for the fulfillment of the urban mobility demand according to the vehicle system provider. Besides fleet size, three urban speed limit scenarios and five ambient temperature scenarios are evaluated. Among the investigated fleet size scenarios, the lowest mean fleet energy demand is observed at a fleet size of 20%, resulting from the opposing effects of increasing driving energy consumption and decreasing secondary consumer energy consumption. However, the difference between the 20% and 17% scenarios is not statistically significant. Furthermore, the study demonstrates that secondary consumers, particularly automated driving hardware and heating, ventilation and air conditioning systems, represent a major contribution to the total energy consumption of CASE vehicles and must therefore be considered in fleet-level energy analyses. Although an individual CASE vehicle exhibits higher average energy consumption than a conventional battery-electric vehicle, primarily due to its greater average weight and rolling resistance, an increase in utilization of more than 16% would be sufficient to offset this disadvantage. Full article

This paper aims to address the challenge of identifying early-stage short-circuit faults in lithium-ion battery modules through conventional voltage and current signals. The perturbation injection method is employed to inject low-amplitude AC disturbances into battery circuits under constant-current charging cases. By synchronously capturing voltage and current responses and performing frequency-domain analysis, the system achieves real-time impedance spectrum acquisition. A comparative analysis is given based on linear frequency-modulated signals and multiple sine wave signals. The results demonstrate superior performance in wide-band impedance measurement regarding spectral distribution and measurement accuracy. A MATLAB/Simulink-based impedance measurement model including individual cells and battery modules is developed. Simulation results show that within the 1~1000 Hz frequency range, the maximum impedance amplitude errors are 0.0129 Ω for individual cells and 0.077 Ω for battery modules. Further experiments utilizing parallel short-circuit resistors with varying values verify that increased short-circuit severity can lead to reduced impedance amplitude and expanded phase angle. It also validates that module impedance characteristics are closely correlated to the number and spatial distribution of short-circuited cells. These results validate the feasibility and effectiveness of the proposed method in detecting short-circuit faults in battery modules. Full article

Migrant resettlement communities in large-scale development regions face persistent pressures from environmental change, livelihood disruption, and social restructuring. Evaluating resilience in such communities requires a framework that captures both external stressors and internal adaptive capacity. This study asks whether differences in resilience between resettlement communities are driven more by differences in external pressure exposure or by differences in internal development conditions. To address this question it applies a Pressure–State–Response (PSR) framework to assess community resilience in the Three Gorges Reservoir Area (TGRA) of China. A four-level indicator system of 29 variables was developed across pressure, state, and response dimensions. Weights were determined by combining the Analytic Hierarchy Process with the entropy method; standardisation and entropy weights were computed across all 25 surveyed communities. Survey data statistical records, and remote sensing information were collected from 25 migrant resettlement communities. Three of these communities—CT, SQ, and GGB—were then selected by purposive maximum-variation sampling for detailed comparison. Results show that overall resilience ranked CT (0.646) > GGB (0.600) > SQ (0.483), with state resilience contributing the largest share to composite scores in all three communities. Obstacle-factor analysis revealed distinct constraint profiles: CT was primarily limited by ecological pressure, SQ by weak livelihood development, and GGB by limited industrial diversity and incomplete social integration. These findings indicate that resilience in resettlement communities depends more on internal development quality than on exposure levels alone; a Monte Carlo analysis confirmed that this ordering held in 99.9% of simulated draws. Differentiated governance strategies are needed rather than uniform approaches. The PSR-based framework and obstacle-factor diagnosis provide a practical tool for assessing and improving resilience in communities shaped by large-scale infrastructure development. Full article

The increasing penetration of renewable energy has made the transient stability of new power systems a critical concern. Grid-forming (GFM) energy storage can provide voltage and frequency support for renewable energy stations. However, existing studies on the transient stability of GFM converters predominantly consider only the synchronization of a GFM converter with an infinite bus and do not fully account for the effects of renewable-energy injection and LVRT control in remote-grid-connected renewable energy stations. To fill this gap, this paper establishes a transient power-angle stability analysis model for a GFM energy storage system in renewable energy stations connected to a remote grid. Based on the equivalent swing equation and the equal-area criterion, the transient instability mechanisms under different renewable energy source LVRT depths are investigated. The results demonstrate that increasing renewable energy output reduces the transient stability margin of the GFM converter. Furthermore, the system exhibits two distinct transient response modes depending on the renewable energy source LVRT depth: under shallow LVRT depth, the GFM converter accelerates first and then decelerates, whereas under deep LVRT depth, it decelerates first and then exhibits a swing-back oscillation. These findings, validated through time-domain simulations, provide a theoretical basis for understanding the effects of renewable energy output, LVRT control, virtual inertia, and virtual damping on the transient stability of GFM-integrated renewable energy systems. Full article

This study presents and validates a modular Internet of Farming (IoF) sensing architecture designed for high-fidelity microclimate monitoring to address the spatial biases in reference evapotranspiration (ET₀) estimation for precision viticulture. Deployed in a Mediterranean vineyard in Antalya, Türkiye, the system demonstrated high operational robustness with a 98.6% data completion rate over an annual cycle. Comparative analysis between field-level IoF data and regional meteorological station (TSMS) records revealed a systematic overestimation of ET₀ by regional networks (MBE = −0.695 mm/day, MAPE = 10.56%). Sensitivity analysis identified discrepancies in wind speed (36.6%), net radiation (31%), and relative humidity (30%) as the primary physical drivers of this spatial bias, with air temperature serving as a seasonal index for their integrated intensification rather than as a direct dominant contributor (~2.4%). Results indicate that reliance on regional data would lead to an irrigation surplus of approximately 97.5 mm (11.4%) during the growing season. The findings underscore the critical role of localized sensing layers in resolving microclimatic heterogeneities that regional grids fail to capture within the specific site and year examined. While these results motivate further investigation into IoF-based architectures as a potentially transferable framework for ET₀ bias correction, broader generalisation to other crops, climate regimes, and management systems remains a promising avenue for future multi-site, multi-year validation rather than an established conclusion of the present study. Full article

The performance of physical workers is significantly influenced by the extent and quality of their motions. However, accurately measuring and assessing these motions remains a challenge due to the limitations in conventional instrumentation; wearable sensors require calibration and restrict mobility, while marker-based motion capture systems are costly and impractical for field deployment. Recent advancements have enabled in situ video analysis for the real-time observation of worker behaviors. To address these measurement constraints, this paper introduces a novel framework for tracking and quantifying upper and lower limb motions, issuing alerts when critical thresholds are reached. Using joint position data from posture estimation, the framework employs Hotelling’s $T^2$ statistic to quantify and monitor motion amounts. A significant positive correlation was noted between motion warnings and the overall NASA Task Load Index (TLX) workload rating (r = 0.218, p < 0.005). A supervised Random Forest model trained on the collected motion data was benchmarked across multiple datasets, including the in-house assembly dataset, G-AI-HMS, UCF Sports Action, UCF50, and PE-USGC. The proposed framework identified motion anomaly patterns with a maximum accuracy of 94% on the in-house assembly dataset, while performance varied across the external benchmark datasets. Full article