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Intergenerational contact is a key component of the informal support systems that contribute to the wellbeing of older adults. As societies age and migration patterns diversify family structures, understanding how contact is sustained across generations becomes increasingly relevant for health equity and the sustainability of care systems. In this study, we conceptualise sustainability not in environmental terms but as social and health-system sustainability—that is, the long-term ability of families and care systems to maintain intergenerational ties, ensure equitable access to support, and remain resilient under demographic and social pressures. Drawing on theories of intergenerational solidarity and social capital, this study situates contact as both a resource for individual wellbeing and a pillar of care sustainability in diverse societies. We examine the frequency of contact between parents and adult children among adults aged 50 and above, comparing migrant and non-migrant populations across 25 European countries. Using data from Waves 7, 8, and the COVID-19 wave of the Survey of Health, Ageing and Retirement in Europe (SHARE), we assess both in-person and digital forms of contact before and during the pandemic. Results show that older migrants have less frequent contact with parents but more frequent contact with children than do non-migrants, with similar patterns attested across repeated cross-sections (2017, 2019, 2021). The strong contact observed in each cross-section, facilitated by digital tools, implies resilient family ties under public-health stress. However, resilience is uneven: weaker contact with parents among migrant populations reflects structural barriers such as visa restrictions, caregiving responsibilities, discrimination, language barriers, and unequal digital access. Moreover, differences in access and proficiency with digital tools suggest that digital contact did not compensate equally across groups. These findings underscore the importance of sustainable and inclusive strategies in ageing and health policy. Specifically, targeted digital literacy programmes for older migrants, policies supporting transnational caregiving, affordable internet access, mobility solutions, and anti-discrimination measures in family visitation are crucial to reducing inequities. Full article

(This article belongs to the Special Issue Sustainable Innovations in Health Economics and Health Management: Opportunities and Challenges)

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21 pages, 388 KB

Open AccessArticle

PhishGraph: A Disk-Aware Approximate Nearest Neighbor Index for Billion-Scale Semantic URL Search

by Dimitrios Karapiperis, Georgios Feretzakis and Sarandis Mitropoulos

Electronics 2025, 14(21), 4331; https://doi.org/10.3390/electronics14214331 (registering DOI) - 5 Nov 2025

Abstract

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional in-memory ANN indexes are prohibitively expensive at this scale, while existing disk-based solutions fail to address the unique challenges of the cybersecurity domain: the high velocity of streaming data, the complexity of hybrid queries involving rich metadata, and the highly skewed, adversarial nature of query workloads. PhishGraph addresses these challenges through a synergistic architecture built upon the foundational principles of DiskANN. Its core is a Vamana proximity graph optimized for SSD residency, but it extends this with three key innovations: a Hybrid Fusion Distance metric that natively integrates structured attributes into the graph’s topology for efficient constrained search; a dual-mode update mechanism that combines high-throughput batch consolidation with low-latency in-place updates for streaming data; and an adaptive maintenance policy that monitors query patterns and dynamically reconfigures graph hotspots to mitigate performance degradation from skewed workloads. Our comprehensive experimental evaluation on a billion-point dataset demonstrates that PhishGraph’s adaptive, hybrid design significantly outperforms strong baselines, offering a robust, scalable, and efficient solution for modern threat intelligence. Full article

(This article belongs to the Special Issue Advanced Research in Technology and Information Systems, 2nd Edition)

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17 pages, 622 KB

Open AccessArticle

Machine Learning-Assisted Systematic Review: A Case Study in Learning Analytics

by Zhihong Xu, Xiting Zhuang and Shuai Ma

Educ. Sci. 2025, 15(11), 1488; https://doi.org/10.3390/educsci15111488 (registering DOI) - 5 Nov 2025

Abstract

Traditional systematic reviews, despite their high-quality evidence, are labor-intensive and error-prone, especially during the abstract screening phase. This paper investigates the application of machine learning-assisted systematic reviewing in the context of Learning Analytics (LA) in higher education. This study evaluates two approaches—ASReview, an active traditional machine learning tool, and GPT-4o, a large language model—to automate this process. By comparing key performance metrics such as sensitivity, specificity, accuracy, precision, and F1-score, we assess the effectiveness of these tools against traditional manual methods. Our findings demonstrate the potential of machine learning to enhance the efficiency and accuracy of systematic reviews in learning analytics. Full article

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20 pages, 14465 KB

Open AccessArticle

Population Flow-Based Evaluation and Zoning Optimization for Rural Settlement Utilization Efficiency in Metropolitan Suburbs

by Jiancong Xiang, Ruitian Zhang, Dun Wang and Xuesong Kong

Land 2025, 14(11), 2194; https://doi.org/10.3390/land14112194 (registering DOI) - 5 Nov 2025

Abstract

To address the utilization challenges of rural settlements in metropolitan suburbs, this study takes the suburban areas of Wuhan as its research object. Based on mobile signaling data and multi-source geographic data, it evaluates their utilization efficiency and construction suitability from both dynamic and static dimensions, and proposes zoning optimization strategies. The study constructed a population mobility network, revealing that rural population flow in Wuhan radiates primarily from the main urban core, with net inflow or balance prevalent in near-suburban areas and net outflow dominant in distant suburbs. The results indicate that only 11.45% of villages achieve medium-to-high utilization efficiency, while 94.50% of the area is classified as highly suitable for development. Based on the “Efficiency-Potential” matrix, villages are categorized into five types, including key development type, gradual optimization type, potential activation type, steady-state improvement type and priority exit type. This study contributes to our understanding of the relationship between rural population and land, and provides support for the optimization of suburban settlements and rural revitalization. Full article

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9 pages, 1052 KB

Open AccessArticle

Advanced Particle Classification in Space Calorimetry Using Transformer-Based and Gradient Boosting Models

by Maria Bossa, Federica Cuna and Fabio Gargano

Particles 2025, 8(4), 87; https://doi.org/10.3390/particles8040087 (registering DOI) - 5 Nov 2025

Abstract

Artificial intelligence has made remarkable progress across numerous domains, including physics, where machine learning and deep learning methods have become increasingly common. In high-energy physics, these approaches have significantly advanced tasks like event reconstruction, pattern recognition, and large-scale data analysis. The present study explores the application of machine learning techniques to the classification of electromagnetic and hadronic showers in space calorimeter experiments. Leveraging Monte Carlo simulations and a dedicated feature engineering, the findings demonstrate the strong potential of AI to improve classification performance in space calorimetry. Full article

(This article belongs to the Special Issue Advances in Space AstroParticle Physics: Frontier Technologies for Particle Measurements in Space, 2025 Edition)

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16 pages, 23598 KB

Open AccessArticle

A Systems Approach to Validating Large Language Model Information Extraction: The Learnability Framework Applied to Historical Legal Texts

by Ali Çetinkaya

Information 2025, 16(11), 960; https://doi.org/10.3390/info16110960 (registering DOI) - 5 Nov 2025

Abstract

This paper introduces a learnability framework for validating large language model (LLM) information extraction without ground-truth annotations. Applied to 20,809 Ottoman legal texts, the framework achieves a Learnability Score of 0.891 through multi-classifier consensus, with external validation confirming substantial agreement across five diverse [...] Read more.

κ

= 0.785) and human experts (

κ

= 0.786). The approach treats internal consistency as a measurable systemic property, where heterogeneous machine learning models independently rediscover LLM-assigned patterns. Confusion analysis reveals errors concentrate at jurisprudentially meaningful boundaries (e.g., commercial-inheritance: 20.4% of disagreements), demonstrating semantic coherence rather than arbitrary noise. The framework offers practical validation for historical and specialized corpora where traditional annotation is infeasible, processing documents at USD 0.01 each with parallelizable throughput. Validated annotations enable knowledge graph construction with 20,809 document nodes, 7 category nodes, and confusion-weighted semantic proximity edges. This systems-based methodology advances reproducible computational research in domains lacking established benchmarks. Full article

(This article belongs to the Special Issue Applications of Information Extraction, Knowledge Graphs, and Large Language Models)

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10 pages, 1380 KB

Open AccessArticle

TUS-EPIC: Thoracic Ultrasonography for Exclusion of Iatrogenic Pneumothorax in Post Transbronchial Lung Cryobiopsy—A Safe Alternative to Chest X-Ray

by Ismael Matus, Sameer Akhtar and Vamsi Matta

J. Respir. 2025, 5(4), 18; https://doi.org/10.3390/jor5040018 (registering DOI) - 5 Nov 2025

Abstract

Background: The incidence of iatrogenic pneumothorax (IPTX) following transbronchial lung cryobiopsy (TBLCB) ranges from 1.4% to 20.2%. While chest X-ray (CXR) is the standard imaging modality to exclude IPTX, thoracic ultrasound (TUS) has demonstrated superior accuracy in detecting pneumothorax across various contexts. This study evaluates TUS as a reliable alternative to routine CXR for ruling out IPTX after TBLCB. Methods: A retrospective observational study included 51 patients undergoing ambulatory TBLCB. Pre- and post-TBLCB TUS were performed. CXR was reserved for cases where TUS findings were inconclusive (absence of sliding lung [SL] and seashore sign [SS] in any lung zones) or if patients exhibited symptoms or signs of IPTX. Results: TUS findings were concordant in 44 (86.1%) patients, of whom 42 (95.5%) did not require CXR. Two patients (4.5%) with symptomatic IPTX were identified and managed. Among the seven patients (13.7%) requiring CXR due to inconclusive TUS or symptoms, five (71.4%) were negative for IPTX, and two (28.6%) had asymptomatic IPTX. Conclusion: Our TUS protocol effectively ruled out clinically significant IPTX, eliminating routine CXR in 95.5% of patients. TUS is a safe alternative to CXR post-TBLCB, with CXR reserved for inconclusive TUS findings or symptomatic cases. Full article

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14 pages, 478 KB

Open AccessFeature PaperArticle

Protective or Pressuring? Multi-Group Structural Path Analysis of Family–School Support and Mental Health Among Postgraduates

by Ying Zhou, Jinbo Hou, Chenling Liu, Chunyan Zhou, Jingjing Song and Lin Li

Eur. J. Investig. Health Psychol. Educ. 2025, 15(11), 227; https://doi.org/10.3390/ejihpe15110227 (registering DOI) - 5 Nov 2025

Abstract

(1) Background: With the continuous expansion of graduate education, the mental health of postgraduates has become a growing concern for both academia and society. Understanding how family and institutional resources influence psychological outcomes is critical for developing effective support strategies; (2) Methods: A cross-sectional survey was conducted among 3998 postgraduate students across China, including 3393 master’s students (51.78% female, M = 24.21, SD = 1.521) and 605 doctoral students (37.19% female, M = 27.77, SD = 2.841). Multi-group structural equation modeling was employed to examine how family functioning and supervisor–postgraduate relationships influenced mental health, research self-efficacy, and suicidal tendencies; (3) Results: The findings showed that although most structural relationships were consistent across groups, two critical pathways were nonsignificant at the doctoral stage, providing evidence of partial structural invariance; (4) Conclusions: The study suggests that while family and school support generally play a protective role, their influence varies across educational stages. Tailoring psychological interventions to the distinct needs of master’s and doctoral students is essential, offering both theoretical insights into the dual role of contextual resources and practical guidance for targeted mental health support in graduate education. Full article

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11 pages, 248 KB

Open AccessArticle

Caregiver–Child Discordance on the DSM-5 Cross-Cutting Symptom Measure Among Youth in Outpatient Psychiatry

by Michèle Preyde, Andre Watkis and Shrenik Parekh

Psychiatry Int. 2025, 6(4), 137; https://doi.org/10.3390/psychiatryint6040137 (registering DOI) - 5 Nov 2025

Abstract

Psychiatric illness during childhood and adolescence is a growing concern, placing increased pressure on psychiatric services. Reliance on an evidence-based assessment tool may facilitate the identification and management of symptoms and may facilitate accountability. The purposes for this study were to characterize the psychiatric symptoms of a sample of pediatric patients accessing outpatient psychiatry using the DSM Level 1 Cross-Cutting Measure (CCSM), compare patient and caregiver ratings (CCSM), and explore patients’ acceptability of using the CCSM. The sample consisted of 51 patients (mean age 14 years) and 46 caregivers (mean age 43 years). Patient and caregiver ratings suggested problems with sleep, inattention, depression, irritability/anger, and anxiety. The most common discordance concerned suicide ideation. Most patients (34 of 38) reported that the assessment tool was easy to complete. The CCSM may be a useful, evidence-based, standardized, transdiagnostic assessment tool aligned with the DSM-5 that can be used in a variety of mental health settings to identify symptoms, inform treatment planning, and track progress. Full article

4 pages, 156 KB

Open AccessEditorial

Research Progress in Groundwater Contamination and Treatment

by Eleni Gianni and Panagiotis Papazotos

Environments 2025, 12(11), 419; https://doi.org/10.3390/environments12110419 (registering DOI) - 5 Nov 2025

Abstract

Groundwater constitutes approximately 99% of the total freshwater volume circulating on Earth [...] Full article

(This article belongs to the Special Issue Research Progress in Groundwater Contamination and Treatment)

21 pages, 3645 KB

Open AccessArticle

Dual Role of Diallyl Disulfide (DADS) on Invasive Potential and β-Catenin Dynamics in HER2-Positive Breast Cancer Cells

by Marcello Dell’Aira, Silvia Grassilli, Marina Pierantoni, Valeria Bertagnolo and Federica Brugnoli

Cancers 2025, 17(21), 3572; https://doi.org/10.3390/cancers17213572 (registering DOI) - 5 Nov 2025

Abstract

Background/Objectives: Natural compounds are being increasingly explored as potential adjuvants to conventional drugs in oncological treatments. Regarding breast tumors, several studies indicate that garlic (Allium sativum) may protect against onset, counteracts aggressiveness, and prevents malignant progression of cells from non-invasive tumors. It has been widely demonstrated that garlic derivatives induce apoptosis and reduce invasive potential in ER-positive and triple-negative breast tumor cells. However, the current literature lacks studies investigating their effects on HER2-positive (HER2+) breast cancers. This study therefore aimed to explore the effects of a garlic extract and diallyl disulfide (DADS), one of its most bioactive organosulfur compounds, on HER2+ phenotype breast tumor cells. Methods: The effects of a garlic extract and diallyl disulfide (DADS) were investigated on MDA-MB-453 and SKBR3 breast tumor cell lines. Cell growth, invasive potential, and Akt-related signaling were assessed after 4–72 h of garlic derivatives administration. The intracellular localization of β-catenin was examined with immunofluorescent confocal microscopy. Results: A dual role of DADS, dependent on the duration of treatment, was revealed. Acute administration induced a significant decrease in invasive potential, while prolonged treatment promoted HER2+ cell invasiveness. These effects were directly correlated with the activation of Akt and the nuclear accumulation of β-catenin, known to induce expression of genes associated with tumor malignancy. Conclusions: Although further investigations are needed to establish the exact mechanism and to assess the in vivo reproducibility of these phenomena, our results highlight the heterogeneous response to natural compounds of complex diseases like cancer. Full article

(This article belongs to the Special Issue Cancer Cell Motility (2nd Edition))

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20 pages, 31876 KB

Open AccessArticle

PhixCam: A Tool to Georeference Images Captured by Visible Cameras with Applications for Volcano Monitoring

by Alvaro Aravena, Gabriela Pedreros, Francisco Bucchi, Miguel Gutiérrez-Riquelme and Raffaello Cioni

Remote Sens. 2025, 17(21), 3643; https://doi.org/10.3390/rs17213643 (registering DOI) - 5 Nov 2025

Abstract

Visible cameras are widely adopted low-cost instruments for volcano monitoring. Images can be used to characterize volcanic activity of variable intensity and style and to estimate key eruption source parameters that are essential for assessing volcanic hazards. Nevertheless, the analysis of images from visible cameras is subject to significant sources of uncertainty and operational limitations. In addition to visibility issues caused by meteorological phenomena and variable illumination, assigning the pixel position of an object of interest (e.g., volcanic plumes, ballistic projectiles) to a specific geographic location and elevation is not straightforward, introducing substantial uncertainty in the estimation of eruption parameters. We present PhixCam, a Python tool that allows the user to georeference in the 3D space the visual field of surveillance cameras from minimal input data: a DEM, the camera position, and a reference image where the framed relief can be outlined. The software includes functions to construct conversion matrices that can be adopted to translate the position of pixels into elevation above sea level when different emission directions of volcanic products are considered, thereby allowing users to assess the confidence of the results. This code was tested on a series of cameras of the Chilean Volcanic Surveillance Network, showing its operative potential in volcanic observatories, and on historical pictures, allowing us to estimate data of interest in volcanology for poorly monitored volcanic events. Full article

(This article belongs to the Section Earth Observation for Emergency Management)

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20 pages, 4864 KB

Open AccessArticle

A Multi-Objective Optimization Method for Cylindrical Surface Ultrasonic Array Parameters Based on BPNN and NSGA-II

by Xin Zeng, Xueshen Cao, Jiaheng Zhao, Yuyu Dai, Chao Li and Hao Chen

Sensors 2025, 25(21), 6762; https://doi.org/10.3390/s25216762 (registering DOI) - 5 Nov 2025

Abstract

Key detection performance metrics, particularly resolution, are largely determined by the design parameters of ultrasonic arrays. The structural design of the transducer strongly influences critical indicators, including side lobe levels, beam directivity, and focal spot size. To improve parameter selection, this study proposes a multi-objective optimization strategy specifically tailored for cylindrical surface ultrasonic transducers. The geometric parameters of the array and the variables influencing resolution performance are mapped in a nonlinear manner. The NSGA-II algorithm is employed to perform extremum seeking optimization on a trained BPNN, generating a Pareto-optimal solution set by specifying main-lobe width, side-lobe intensity, and sound-pressure uniformity as optimization objectives. For validation, the geometric configurations derived from this solution set are applied in acoustic field simulations. Simulation results demonstrate that the dynamic aperture exhibits clear regularity when the array settings meet millimeter-level resolution requirements. These findings support real-world engineering applications and provide valuable insights for enhancing the geometric design of cylindrical ultrasonic arrays. Full article

(This article belongs to the Special Issue Ultrasonic Sensors and Ultrasonic Signal Processing)

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22 pages, 2423 KB

Open AccessArticle

Benefit Allocation Strategies for Electric–Hydrogen Coupled Virtual Power Plants with Risk–Reward Tradeoffs

by Qixing Liu, Yuzhu Zhao, Wenzu Wu, Zhe Zhai, Mengshu Shi and Yuanji Cai

Sustainability 2025, 17(21), 9861; https://doi.org/10.3390/su17219861 (registering DOI) - 5 Nov 2025

Abstract

Driven by carbon neutrality goals, electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources, emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify, heterogeneous resources generate diversified market values. However, inadequate benefit allocation mechanisms risk reducing participation incentives, destabilizing cooperation, and impairing operational efficiency. To address this, benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities, risk tolerance, and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities, while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares, increasing operator revenue by 0.69%, demand-side gains by 3.56%, and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations. Full article

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7 pages, 450 KB

Open AccessEditorial

Editorial of Maximum Power Point Tracking Methods for PV Systems in Micro-Grids

by Yuanyuan Liu and Jun-Ho Huh

Energies 2025, 18(21), 5830; https://doi.org/10.3390/en18215830 (registering DOI) - 5 Nov 2025

Abstract

In recent years, renewable energy has become one of the most critical pillars for addressing the dual challenges of global energy shortages and environmental degradation [...] Full article

(This article belongs to the Special Issue Overview of Maximum Power Point Tracking Methods for PV System in Micro-Grid)

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