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This paper proposes a novel prefabricated beam-to-column joint to increase the seismic performance and post-earthquake recoverability of steel frames, which use the shape memory alloy (SMA) bolts and replaceable steel ring dampers. The comparative analysis of the seismic behavior was conducted for three beam-to-column connection types using finite element models. The three connection types include those installed using internal SMA bolts, external SMA bolts, and external SMA bolts with novel ring dampers. In addition, the novel ring damper was analyzed separately. These analysis results indicate that the connection type installed using external SMA bolts is superior to that by internal SMA bolts for the seismic performance of beam-to-column joints. The beam-to-column joints have the best seismic performance among the three joints when equipped with the additional steel ring damper, which can be easily replaced. This ring damper can increase the energy dissipation by approximately 11% and effectively reduce the stress of SMA bolts, which can delay their failure. The increasing preload of SMA bolts and high-strength bolts has a certain positive effect on the improvement of the seismic performance. All of the three joints exhibit excellent self-centering characteristics, with residual displacements nearly at zero. The gap of replaceable ring dampers can keep the re-centering capacity and improve the energy dissipation of joints. However, the changes in the steel strength of dampers have little impact on the seismic performance. This study verifies the improvement of the replaceable ring dampers on the seismic performance and post-earthquake recoverability, providing a reference for the seismic design of resilient structures. Full article

(This article belongs to the Section Building Structures)

15 pages, 416 KB

Open AccessReview

Artificial Intelligence for the Early Detection of Patients with Cognitive Impairment: A Scoping Review

by María Moreno-Pineda, Víctor Ortiz-Mallasén and Águeda Cervera-Gasch

Healthcare 2026, 14(6), 768; https://doi.org/10.3390/healthcare14060768 (registering DOI) - 18 Mar 2026

Abstract

Background/Objectives: Cognitive impairment affects multiple brain functions, and its early detection is essential to prevent progression to dementia; artificial intelligence has shown considerable potential in this field. This scoping review aims to map the impact of artificial intelligence–based tools for the early detection of cognitive impairment by identifying the main technologies used, examining their effectiveness, and exploring their ethical implications. Methods: A scoping review was conducted between April and May 2025 following the PRISMA-ScR methodological framework; the review protocol was previously registered on the Open Science Framework. PubMed, Scopus, and Cochrane databases were searched using natural language and controlled vocabulary terms via Medical Subject Headings. The search was limited to articles published between 2020 and 2025, in English or Spanish, with free full-text access. Methodological quality was assessed using CASPe, JBI, and MMAT. Results: A total of 14 studies were included after the selection and critical appraisal process. The findings show that artificial intelligence–based tools such as deep-learning models applied to neuroimaging, speech and gait analysis, electronic health record analysis, and mobile health applications demonstrate promising accuracy in detecting early cognitive changes. These technologies enable the identification of subtle patterns that may be difficult to detect using conventional clinical assessments. Conclusions: AI-based tools can provide substantial support for clinical decision-making by effectively identifying subtle changes that are imperceptible to human intelligence. However, their use also raises ethical issues related to patient privacy and data security. Full article

(This article belongs to the Special Issue Novel Research and Care Strategies for Older Adults with Stroke and/or Dementia)

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13 pages, 630 KB

Open AccessArticle

The Impact of Age at First Mating on Lifetime Milk Yield in Alpine Goats: Balancing Early Gains and Lifetime Efficiency

by Ante Kasap, Danijel Mulc, Marija Špehar, Valentino Držaić, Zvonimir Prpić, Darko Jurković, Zdravko Barać and Boro Mioč

Agriculture 2026, 16(6), 687; https://doi.org/10.3390/agriculture16060687 (registering DOI) - 18 Mar 2026

Abstract

The longitudinal study investigated the impact of age at first mating (AFM) on milk yield (MY) across the productive lifespan of Alpine goats born between 2005 and 2018. Data from 740 animals across three herds and 3200 lactations were analyzed. The AFM of the studied population ranged from 7 to 23 months. The impact of AFM on MY was estimated using a linear mixed model, accounting for the fixed effects of parity, litter size, season, herd, and suckling and milking durations, with the individual goat included as a random effect to control for repeated measures. The impact of AFM on lifetime production was estimated by regressing total milk yield (TMY) and number of lactations (TNL) on AFM, while accounting for herd effect. The study revealed a notable shift in productivity patterns across the animal’s life. Every additional month of AFM significantly increased milk yield in the first lactation (13.28 kg; p < 0.001), but this influence vanished in subsequent parities (p > 0.05). These higher initial yields were insufficient to compensate for the losses caused by a shortened productive lifespan. Specifically, each month of mating delay resulted in a loss of ~0.08 TNL and 34 kg TMY, totaling ~1 lactation and ~400 kg of milk for a 12-month delay. Results suggest that earlier mating may improve lifetime productivity under intensive production systems. Full article

(This article belongs to the Section Farm Animal Production)

42 pages, 2170 KB

Open AccessArticle

A Tripartite Evolutionary Game Analysis for Developing Sustainable Rural Cold Chain Logistics

by Xiaohu Xing, Meiqi Zhang and Xinqiang Chen

Sustainability 2026, 18(6), 2989; https://doi.org/10.3390/su18062989 - 18 Mar 2026

Abstract

Achieving sustainability in rural cold chain logistics requires resolving inherent conflicts among participating agents. This paper develops an evolutionary game theory framework to examine the dynamic interactions between government regulators, cold chain enterprises, and agricultural producers. The model identifies three evolutionarily stable strategies (ESS) under different policy environments. Numerical simulations, using parameters calibrated from industry data and survey results, quantify the impact of key policy variables: (1) Subsidy intensity has a diminishing marginal effect on green technology adoption, with an optimal range between 12–18% of project cost; (2) Monitoring probability exhibits a threshold effect, needing to exceed 60% to deter non-compliance effectively; (3) Farmer organization reduces system stabilization time by approximately 30%. Our results challenge the conventional focus on single-policy solutions and instead demonstrate the necessity of integrated approaches that simultaneously address economic viability, operational efficiency, and community engagement. These insights offer evidence-based guidance for designing multi-stakeholder governance mechanisms in resource-constrained rural settings. Full article

(This article belongs to the Section Sustainable Agriculture)

39 pages, 12551 KB

Open AccessArticle

Spatiotemporal Modeling and Prediction of Urban Thermal Field Variation and Land Use Dynamics in Riyadh Using Machine Learning and Remote Sensing

by Md Tanvir Miah, Raiyan Raiyan, Ayad Khalid Almaimani and Khan Rubayet Rahaman

World 2026, 7(3), 49; https://doi.org/10.3390/world7030049 - 18 Mar 2026

Abstract

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

(This article belongs to the Special Issue Urban Planning and Regional Development for Sustainability)

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13 pages, 379 KB

Open AccessArticle

Reducing the Rate of Treatment Disruptions Through a Digital Structured Exercise and Mind–Body Program During Systemic Cancer Therapy: A Secondary Analysis of a Randomized Clinical Trial

by Karolina L. Bryl, Marco Santos Teles, Raymond E. Baser, Jun J. Mao and Bobby Daly

Cancers 2026, 18(6), 984; https://doi.org/10.3390/cancers18060984 - 18 Mar 2026

Abstract

Background/Objectives: Treatment disruptions and discontinuations during systemic cancer therapy are common and can compromise treatment delivery and outcomes. Structured exercise and mind–body interventions improve cancer-related symptoms, but their impact on treatment disruptions and discontinuations remains unclear. This secondary analysis of the IMPROVE trial evaluated whether participation in Integrative Medicine at Home (IM@Home), a digital multimodal mind–body and structured exercise program, was associated with differences in treatment discontinuation and related treatment disruption outcomes among patients undergoing systemic therapy. Methods: A total of 127 adults with solid tumors were randomized to IM@Home (n = 64) or enhanced usual care (EUC; n = 63) for 12 weeks. Treatment discontinuation, dose delays, dose reductions, and overall treatment disruptions were compared between arms using chi-square tests and regression models adjusted for cancer type and disease stage. Results: In unadjusted analyses, treatment discontinuation occurred less frequently in the IM@Home group compared with EUC (9.4% vs. 22.6%; p = 0.043), but this association was attenuated after adjustment for cancer type and disease stage (aOR 0.41, 95% CI 0.13–1.17; p = 0.105). The proportion of patients experiencing any treatment disruption, as well as rates of dose delays and dose reductions, did not differ significantly between groups (p = 0.16, p = 0.18, and p = 0.85, respectively). In contrast, IM@Home participants experienced fewer treatment disruption events per patient (adjusted RR 0.58, 95% CI 0.35–0.96; p = 0.036). Conclusions: These exploratory findings suggest that digital structured exercise and mind–body programs may help mitigate treatment interruptions during systemic cancer therapy and should be explored further in an adequately powered prospective trial to confirm these promising findings. Full article

(This article belongs to the Special Issue Implementation of Physical Activity Promotion in Cancer Care)

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24 pages, 3330 KB

Open AccessArticle

A Hybrid CNN-SVM for Oil Leakage Detection in Transformer Monitoring

by Wenbi Tan, Tzer Hwai Gilbert Thio, Fei Lu Siaw, Youdong Jia, Xinzhi Li, Jiazai Yang and Haijun Li

Processes 2026, 14(6), 970; https://doi.org/10.3390/pr14060970 - 18 Mar 2026

Abstract

Oil leakage in oil-immersed power transformers poses a significant threat to grid reliability, potentially causing severe electrical accidents and environmental pollution if not detected in time. Detecting oil leakage outdoors, however, remains challenging due to the impact of weather conditions such as fog, humidity, and rain, which obscure the leakage signs and complicate real-time detection. To address these challenges, we propose a solution that integrates infrared thermal imaging with a CNN-SVM hybrid architecture. The core of this approach lies in shifting from traditional Softmax-cross-entropy-based empirical risk minimization (ERM) to maximum-margin-based structural risk minimization (SRM). A fully fine-tuned MobileNetV3 transforms low-contrast, boundary-softened infrared thermal images—often affected by fog and moisture—into a more discriminative high-dimensional feature space, where positive and negative samples become linearly separable. This is followed by replacing Softmax with a linear SVM and using hinge loss to enforce a margin constraint, which maximizes the classification margin and improves robustness to input perturbations. Experimental results show that our proposed method outperforms all compared models, achieving an accuracy of 0.990, significantly higher than ResNet50_BCE (0.908), EfficientNetB0 (0.925), YOLOv11n-CLS (0.930), and ViT (0.929). In terms of F1-Score (0.989) and AUC (0.995), MobileNetV3-SVM also demonstrates excellent performance, ensuring outstanding classification capability. Additionally, the model achieves an inference latency of only 6.3 ms, demonstrating excellent real-time inference performance, highlighting its potential for transformer oil monitoring applications. This research contributes to SDG 6 by preventing industrial water pollution resulting from transformer oil runoff, thereby protecting vital water sources in remote environments. Full article

(This article belongs to the Special Issue Advances in Monitoring and Control for Safe and Secure Industrial Processes)

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35 pages, 2351 KB

Open AccessArticle

A Bilevel Optimization Model Based on Agency Theory in Relief Supply Chain Considering Authorization

by Xiaoli Wu and Xiulan Wang

Symmetry 2026, 18(3), 524; https://doi.org/10.3390/sym18030524 - 18 Mar 2026

Abstract

As a proactive response, reserving a certain amount of relief materials in advance is crucial for responding to potential disasters. Different from public tendering and bidding, this study proposes the purchasing mode of authorization, under which a nonprofit organization (NPO), as a buyer, wholly authorizes the procurement of relief materials to a professional agent. The relief material procurement system under the purchasing mode of authorization is regarded as a bilevel relief supply chain consisting of one buyer, one agent, and two suppliers with private information about the quality levels of relief materials. For the disclosure of private information, the quality-related procurement strategy is designed in the form of a menu based on the suppliers’ private information. A bilevel optimization model is developed based on agency theory to derive the optimal strategic decisions, and the impacts of the main influencing factors on the optimal procurement strategy and the buyer’s minimum expected cost are discussed via numerical analysis. Then, the study is extended by exploring supplier’s alternative cost functions and supply availability, as well as proposing future research directions. This paper presents an optimal quality-related procurement strategy, which provides rules for quickly responding to the changes in influencing factors during the material procurement process, as well as the minimum expected cost for the buyer to purchase relief materials, which serves as a threshold for screening a reliable retail enterprise as the agent. Finally, three managerial implications with practical significance, drawn from our findings, are presented to facilitate cooperation between NPO and large retail enterprises in order to achieve effective procurement of relief materials at the pre-disaster preparation stage. Full article

(This article belongs to the Section Mathematics)

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18 pages, 1857 KB

Open AccessArticle

Modeling the Effect of Treatments on Prostate Cancer-Specific Mortality and the Relevant Geographical Variation and Racial Disparities

by Wensheng Zhang, Christopher Williams, Guangdi Wang and Kun Zhang

Cancers 2026, 18(6), 983; https://doi.org/10.3390/cancers18060983 - 18 Mar 2026

Abstract

Background/Objectives: African American (Black) prostate cancer (PCa) patients have a higher risk of dying from the disease and are less likely to undergo radical treatment than European Americans (White). The disparities in PCa-specific mortality (PCSM) and mortality rate (PCSMR) vary geographically. This study investigated the impact of treatments on PCSM, PCSMR and the relevant disparities. Methods: Using the Cox PH model and other statistical methods, we analyzed two datasets extracted from the SEER and PLCO databases. The SEER dataset contains 650,754 White patients and 113,598 Black patients. The PLCO dataset included 7463 Whites and 495 Blacks, and supplemented the SEER data with information on PCa family history (pros_fh). Results: Analysis of SEER data showed that the relative mortality risk (RR) of patients undergoing surgery alone was significantly lower than that of patients receiving radiotherapy alone or a combination of surgery and radiotherapy. Black patients’ RR estimated by the model including treatment was substantially smaller than that estimated by the reduced model excluding treatment. The differences between Black and White in the three-nine-year PCSMR of patients with high-grade or non-localized cancer were significantly correlated with the differences in surgery alone rate (r < −0.65, p < 0.001). Regression-based mediation analysis indicated that treatment disparity had a significant direct effect on mortality disparity and did not mediate the effect of age disparity. Analysis of PLCO data showed that pros_fh had no significant effect on survival but confirmed the survival advantage of surgery over radiotherapy. Conclusions: The results of this study support the hypothesis that, for PCa patients in the United States, geographic variation in treatment disparities partially explains variation in mortality disparities. Full article

(This article belongs to the Section Cancer Epidemiology and Prevention)

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28 pages, 8650 KB

Open AccessArticle

Mesoscale Steady-State Dynamics Modeling and Parametric Analysis of the Viscoelastic Response of Asphalt-Bonded Calcareous Sand

by Linyu Xie, Bowen Pang, Peng Cao, Jianru Wang and Zhifei Tan

Materials 2026, 19(6), 1194; https://doi.org/10.3390/ma19061194 - 18 Mar 2026

Abstract

Due to the complex mesostructure of calcareous sand, accurately predicting the mechanical response of Asphalt-Bonded Calcareous Sand (ABCS) is extremely challenging. This study pioneers the development of a mesoscale model for ABCS that explicitly incorporates the Interfacial Transition Zone (ITZ) via a random particle algorithm. To overcome the efficiency bottlenecks of traditional time-domain integration, this study establishes a mesoscale framework coupling a random polygonal aggregate algorithm with direct Steady-State Dynamics (SSD) analysis. A major advantage of this framework is its capacity for large-scale parametric sensitivity analysis; herein, 920 independent mesoscale models were generated and rapidly solved across the broadband frequency domain. The framework was rigorously validated, demonstrating high predictive accuracy for both the baseline calibration and an independent 12% asphalt content mixture (baseline R² = 0.99, MAPE = 6.94%; independent validation R² = 0.96, MAPE = 9.73%). Notably, the SSD approach completes calculations (10⁻³ to 10³ Hz) for 10 massive 300 mm RVEs in just 6.5 min. Leveraging this high-throughput capability, the extensive parametric analysis reveals that variations in maximum aggregate size negligibly impact the dynamic modulus under a constant volume fraction. Conversely, an optimal Interfacial Transition Zone (ITZ) thickness of ~75 µm was identified, representing a physical equilibrium between interfacial reinforcement and bulk binder cohesion. Furthermore, an analytical RVE size criterion of 1.7–5.3 times the maximum aggregate size is proposed to satisfy a 5% engineering error tolerance, providing a highly efficient numerical tool for the virtual mix design of reef pavements. Full article

(This article belongs to the Special Issue Material Characterization, Design and Modeling of Asphalt Pavements)

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25 pages, 1126 KB

Open AccessArticle

Energy-Efficient Path Planning for AMR Using Modified A* Algorithm with Machine Learning Integration

by Mishell Cadena-Yanez, Danel Rico-Melgosa, Ekaitz Zulueta, Angela Bernardini and Jorge Rodriguez-Guerra

Robotics 2026, 15(3), 62; https://doi.org/10.3390/robotics15030062 - 18 Mar 2026

Abstract

Energy consumption optimisation has emerged as a critical need in Autonomous Mobile Robots (AMRs). Conventional A* implementations typically minimise path distance, neglecting energy-relevant factors such as directional changes and trajectory smoothness that significantly impact battery life and operational costs. This work proposes two energy-aware A* variants trained on empirical data from the KUKA KMP 1500 platform, where energy consumption is measured as battery SoC depletion: A*-RF, which integrates a Random Forest (RF) model directly into the cost function, and A*-MOD, which approximates the energy model through RF feature importance weights, achieving linear computational complexity O(nf). The RF model predicted energy consumption with an RMSE below 1.5% relative error, identifying travel distance and rotation angle as the dominant energy factors. Experimental validation across 42 path planning scenarios on a real industrial factory floor demonstrates that A*-MOD reduces energy consumption by up to 58.91% and improves operational autonomy by 2.21 times, with statistically significant improvements (p < 0.01) across all evaluated metrics. Full article

(This article belongs to the Section Sensors and Control in Robotics)

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27 pages, 2944 KB

Open AccessArticle

Presale Strategies for Fresh Agricultural Products Considering Option Ordering

by Zhong Zhao and Chunyu Dai

Systems 2026, 14(3), 322; https://doi.org/10.3390/systems14030322 - 18 Mar 2026

Abstract

Under traditional spot-sale strategies, the perishability and demand uncertainty of fresh agricultural products often result in market share erosion and profit losses for retailers. To address this challenge, this study constructs and compares decision models under different combinations of ordering modes and sales strategies. Specifically, for ordering modes, retailers can choose between wholesale ordering and option ordering as their ordering mode, while for sales strategies, they can select either presale or spot sale based on consumer presale preference. The study aims to identify the conditions for implementing presales, examine the impact mechanism of option ordering on presales, and analyze differences in market share and expected profit across various ordering–sales strategy combinations. The results reveal the following: (1) presales outperform spot sales in market share and expected profit only when consumer presale preference exceeds a critical threshold, which is higher under option ordering; (2) compared to wholesale ordering, option ordering reduces the incremental market share and profit gains from presales but allows retailers adopting presales to achieve higher expected profits; (3) once the critical threshold for presale implementation is met, the presale strategy under wholesale ordering facilitates faster market share capture, whereas the presale strategy under option ordering maximizes retailer profits. Furthermore, retailers can lower the threshold for implementing presales and expand their applicability by optimizing freshness-keeping efforts or adjusting option contract parameters. Full article

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15 pages, 1270 KB

Open AccessArticle

Effects of Long-Term Nitrogen Fertilization on Soil Respiration in Acidic Tea (Camellia sinensis L.) Plantation Soils

by Zhidan Wu, Yunni Chang, Xiangde Yang and Fuying Jiang

Horticulturae 2026, 12(3), 372; https://doi.org/10.3390/horticulturae12030372 - 18 Mar 2026

Abstract

Soil respiration (Rs) plays an important role in the carbon (C) dynamics of terrestrial ecosystems and is strongly regulated by nitrogen (N) inputs. While the impact of N fertilization on Rs has been widely documented in conventional farmland ecosystems, its patterns and influencing factors in perennial tea plantation systems are still poorly understood. In the study, we conducted a 15-year field experiment in a representative tea plantation to investigate the effects of different N rates (0, 112.5, 225, and 450 kg N ha⁻¹ yr⁻¹) on Rs. Compared to the control (N0), soil pH decreased significantly (p < 0.05) by 6.07%, 11.82%, and 16.12% under N112.5, N225, and N450, respectively. Concurrently, cation exchange capacity (CEC), ammonium (NH₄⁺-N), nitrate (NO₃⁻-N), and available phosphorus (AP) increased with increasing N rates, whereas available potassium (AK) decreased. Soil microbial biomass carbon (MBC) initially increased and then decreased with increasing N rates, while dissolved organic carbon (DOC) content increased consistently. The Rs rate exhibited a distinct seasonal pattern with a single peak in August. The annual mean Rs rates were 2.79, 3.15, 4.06, and 3.85 μmol·m⁻²·s⁻¹ for the N0, N112.5, N225, and N450 treatments, respectively. Soil temperature explained 55.41% to 61.08% of the variation in Rs rates across N treatments, and a composite model incorporating both soil temperature and moisture further improved the prediction of Rs dynamics. Cumulative soil CO₂ emissions (CCEs) over the study period ranged from 10,427 to 14,221 kg CO₂-C ha⁻¹ across treatments and were significantly negatively correlated with soil pH, and positively correlated with DOC, MBC, and NO₃⁻-N content. A non-linear relationship between N application rate and CCEs was observed, highlighting the complexity of optimizing N management for balancing productivity and climate mitigation in tea plantation systems. These findings provide a theoretical basis for developing rational N fertilization strategies and improving the predictive capacity of C cycle models in agroecosystems. Full article

(This article belongs to the Special Issue Sustainable Soil Management for Tea Plantations)

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27 pages, 1023 KB

Open AccessArticle

Historical Scarcity Within Rural Land Systems: How Early-Life Famine Exposure Impacts Compensatory Food Consumption Among Rural Chinese Residents

by Xiaotong Li, Zhenpeng Liu and Li Zhou

Land 2026, 15(3), 491; https://doi.org/10.3390/land15030491 - 18 Mar 2026

Abstract

Understanding the long-term impact of historical land system failures on rural elderly dietary habits is essential for enhancing rural well-being. Existing studies focus on physiological effects but often neglect the deep-seated psychological mechanisms and resource boundaries driving irrational late-life consumption. By integrating the Stimulus-Organism-Response (S-O-R) model and compensatory consumption theory, this study uses balanced panel data from the CLHLS and a Cohort-Difference-in-Differences framework to identify causal effects. The results show that: (1) Early-life famine exposure creates a rigid life-cycle consumption imprint. Adolescent exposure leads to significantly higher levels of compensatory food consumption in later life despite current improvements in material conditions. (2) Learned helplessness drives historical trauma into compensation. Mechanism analysis shows that individuals attempt to restore a sense of order and security by controlling micro-level food intake. (3) The behavioral impact of this trauma depends on resource boundary conditions. The compensatory drive is stronger in resource-scarce regions but weakens with individual economic self-reliance. Additionally, professional community counseling shows a reversal effect, surpassing informal family support which suffers from a “compliance paradox”. These results are robust after a series of validation tests. Our study supports shifting rural revitalization policies from material aid to professional psychological intervention. Full article

(This article belongs to the Special Issue Institutions in Governance of Land Use: Mitigating Boom and Bust (Second Edition))

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18 pages, 546 KB

Open AccessReview

A Review of Data-Model Hybrid-Driven Early Warning Research for Wideband Oscillation Risks in Power Systems

by Hong Fan and Mingze Sun

Appl. Sci. 2026, 16(6), 2918; https://doi.org/10.3390/app16062918 - 18 Mar 2026

Abstract

The problem of power system oscillation stability has become more and more prominent in the context of a high proportion of new energy sources and the gradual increase in power electronic devices. Broadband oscillations pose new challenges to the security and stability of power systems. In recent years, the frequency of power system oscillations around the world, especially those triggered by wind, solar, and power electronic devices such as flexible direct current (DC) transmission, has shown that the geographic and system scale of their impacts continue to expand. Failure to properly control these broadband oscillations can lead to serious consequences such as equipment damage, off-grid renewable energy generation systems, and large-scale blackouts. Two strategies, data-driven and model-driven, have been used for monitoring and controlling broadband oscillations, but each has its limitations. The data-driven approach relies on data quality, while the model-driven approach requires high accuracy of the system model. For this reason, hybrid data-model-driven strategies have emerged. They combine the advantages of both to improve the accuracy and robustness of system analysis. In this paper, we will discuss the principle of hybrid data-model driving and its application to broadband oscillations, classify different frequency oscillations, and introduce risk warning methods, and finally summarize future research challenges such as quantitative analysis, propagation mechanisms and suppression measures of broadband oscillations. Full article

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