The Windows-APT Dataset 2025 represents a significant advancement in cybersecurity research, addressing critical gaps in the understanding of advanced persistent threat (APT) tactics against Windows systems. Existing datasets largely focus on network data, often overlooking the detailed tactics, techniques, and procedures (TTPs) used by sophisticated threat actors. To bridge this gap, we developed a comprehensive dataset of 36 APT-inspired scenarios derived from threat actor profiles documented in the MITRE ATT&CK framework. Scenario selection mirrors MITRE ATT&CK group entries reported as China-attributed; we do not assert attribution and focus strictly on reproducing reported TTPs for research. Leveraging the MITRE Caldera framework for adversary emulation, we generated extensive system and network event logs, collected via Wazuh, and systematically mapped them to the MITRE ATT&CK framework. This dataset provides a valuable asset for machine learning model training, intrusion detection system evaluation, and the enhancement of APT dynamics studies. By providing a detailed view of APT activities in Windows environments, it enables stronger threat detection, informs defensive strategies, and facilitates development of effective countermeasures against emerging cyber threats. The dataset package contains 19 CSV files (including 16 per-period logs, one combined log, and two supplementary CSVs for manifest and validation), along with configuration files to support exact replication.

An inverse reinforcement learning approach for truck route choice analysis with BeiDou navigation satellite system data in urban network

A novel traditional Chinese medicine compound alleviates broiler ascites syndrome by modulating the IL-6/STAT3/FOXO3a signaling pathway.

Optimization of municipal solid waste (MSW) collection routes in Hengyang City, china using an enhanced genetic algorithm based on Amap application programming interface (API).

Graph Neural Networks (GNNs) have made significant strides in the analysis and modeling of complex network data, particularly excelling in graph and node classification tasks. However, the "closed box" nature of GNNs impedes user understanding and trust, thereby restricting their broader application. This challenge has spurred a growing focus on demystifying GNNs to make their decision-making processes more transparent. Traditional methods for explaining GNNs often rely on selecting subgraphs and employing combinatorial optimization to generate understandable outputs. However, these methods are closely linked to the inherent complexity of GNNs, leading to higher explanation costs. To address this issue, we introduce a lower-complexity proxy model to explain GNNs. Our approach leverages knowledge distillation with inter-layer alignment, specifically targeting the challenge of over-smoothing and its detrimental impact on model explanation. Initially, we distill critical insights from complex GNN models into a more manageable proxy model. We then apply an inter-layer alignment-based distillation technique to ensure alignment between the proxy and the original model, facilitating the extraction of node or edge-level explanations within the proxy framework. We theoretically prove that the explanations derived from the proxy model are faithful to both the proxy and the original model. Additionally, we show that the upper bound of unfaithfulness between the proxy and the original model remains consistent when the distillation error is infinitesimal. This inter-layer alignment knowledge distillation technique enables the proxy model to retain the knowledge learning and topological representation capabilities of the original model to the greatest extent. Experimental evaluations on numerous real-world datasets confirm the effectiveness of our method, demonstrating robust performance.

Timely surgical treatment is critical for optimal breast cancer (BC) outcomes, yet delays in care persist. This study examined associations between social drivers of health (SDOH), race/ethnicity, and delayed BC surgery in New York City, stratified by surgery type. We conducted a retrospective analysis of BC cases diagnosed between 2007 and 2021 using INSIGHT Clinical Research Network data. Neighborhood-level SDOH, including household income, unemployment, percent of non-English speakers, and high school completion, were evaluated in relation to delayed surgery (>60 days from diagnosis). Multivariable logistic regression assessed associations between (1) SDOH and (2) race/ethnicity and delay of BC surgery, adjusting for confounders. Surgical delay occurred in 10.7% of lumpectomy and 25.3% of mastectomy patients. For lumpectomy, delays were more likely in neighborhoods with the highest non-English-speaking (OR = 1.37, 95%CI = 1.12-1.68) and higher unemployment quartiles (OR = 1.49, 95%CI = 1.22-1.82) and less likely in the highest income (OR = 0.69,95%CI = 0.56-0.84) and education quartiles (OR = 0.60, 95%CI = 0.49-0.74). Mastectomy showed similar patterns. Delays were more frequent among Non-Hispanic Black (lumpectomy OR = 2.23; mastectomy OR = 1.82) and Hispanic patients (OR = 1.64; OR = 1.39). This large, multi-institutional study reveals persistent disparities in timely surgical care. Nearly 15% experienced delays, with higher odds among Non-Hispanic Black, Hispanic, and socioeconomically disadvantaged patients, underscoring the need for equity-focused interventions.

Adult SMA REACH is a Research and Clinical Hub in the UK that established a collaborative clinical network for Spinal Muscular Atrophy (SMA) in 2020 across 19 clinical sites, patient advocacy groups, regulators, and industry. In recent years, the treatment landscape in the SMA setting has rapidly evolved with Nusinersen and Risdiplam receiving conditional approval via a Managed Access Agreement (MAA) in the UK. Here we describe the structure of a Real-World Data (RWD) collection study implemented to collect standardised outcome measures to inform on the natural history of the disease and the impact of novel treatments. The study also reports data to The National Institute for Health and Care Excellence (NICE) and NHS England (NHSE) for the purpose of the MAA's. The Adult SMA REACH database currently contains data from 466 patients and 2255 visits, with more than 8000 functional outcome measure assessments. Adult SMA REACH provides insights into how real-world data can be used to evaluate treatment outcomes in rare diseases, where conducting randomised controlled trials may be difficult. The registry also offers an infrastructure that supports collaborative research and reduces data silos. In this paper we describe the complexity of establishing such a study and clinical network including considerations for adapting this model to other disease areas. Further information on the Adult SMA REACH data collection study and clinical network can be found on the website (https://adultsmareach.co.uk/) and ClinicalTrials.gov (NCT06978985, https://clinicaltrials.gov/study/NCT06978985).

To address the limitation of harmonic monitoring on the low-voltage side of distribution networks, this paper proposes a multi-source harmonic estimation method based on variational mode decomposition. The method integrates short-term test data with long-term power data. First, dominant harmonic users are identified through a strategy that combines Fisher optimal segmentation and derivative dynamic time warping. Second, an electrical data transformation approach is designed by combining variational mode decomposition with Gramian angular fields, which maps the power signals of dominant harmonic users and low-voltage side harmonic signals into pseudo-color Gramian power images and grayscale Gramian harmonic images, respectively. Finally, an improved PSRGAN (pix2pix-super-resolution generative adversarial network) model is constructed to train and learn from these images, establishing the mapping relationship between power data and low-voltage side harmonic data of the distribution network, thereby enabling the migration and generation of long-term low-voltage side harmonic monitoring data. Simulation cases and field measurements validate the effectiveness and accuracy of the proposed method in multi-source harmonic scenarios. Moreover, the required data are easily accessible, demonstrating strong potential for engineering applications.

To assess the clinical effectiveness, safety, technical feasibility, and challenges of remote robotic surgery in general surgery, gynecology, orthopedics, and urology, focusing on issues like communication technology, ethics, and cost. Relevant reports on remote robotic surgery were retrieved from databases such as PubMed, EMbase, Web of Science, Cochrane Library, VIP, CNKI, and Wanfang (2001-2025). Literature quality was evaluated using tools from the Joanna Briggs Institute (JBI). A total of 26 articles were included (13 in Chinese, 13 in English) covering general surgery, gynecology, orthopedics, urology, and thyroid surgery. The results showed that the average operation time was slightly longer than that of traditional methods (about 10-30min), but the safety was good. Most studies had communications backup plans, such as backup networks or local physician take-overs, to deal with the risk of disruption. Tele-robotic surgery has a high success rate and accuracy with controllable network latency, showing the potential to expand access to medical resources, especially in remote areas. However, it still faces challenges such as network stability, equipment cost, operation accuracy, data privacy, and ethical issues. Most of the included studies reported successful cases, and there was a lack of in-depth analysis of failure cases or adverse outcomes of patients, which may have publication bias. Despite challenges, remote robotic surgery shows promise in overcoming geographical and resource limitations. The quality of current evidence is low, with serious methodological limitations and reporting bias. The establishment of international mandatory registration systems, standardized safety protocols, and transnational collaboration networks are urgent to promote this field from "proof of concept" to "clinical practice". But, with continuous technological advancements, it is expected to play an increasingly significant role in global healthcare.

Cancer registries are essential to monitor cancer incidence and survival to provide better quality cancer data for research. In Switzerland, the pediatric oncology units within pediatric hospitals actively report cancer cases, and the coding and registration team of the Childhood Cancer Registry (ChCR) enters data manually from medical files into the registry database. There are no automated data transfers or feedback loops between the pediatric oncology clinics and the ChCR. This ongoing process is time-consuming, inefficient, and a source of potential errors. SwissPedCancer aims to explore the options for automated data transfers from clinical data warehouses and feedback loops to make cancer registry processes more efficient. SwissPedCancer is a nested project within the national data stream initiative, the Swiss Pediatric Personalized Research Network (SwissPedHealth). Since September 2022, SwissPedHealth has developed and piloted structures to make routine clinical data from pediatric oncology clinics available for monitoring, benchmarking, and research in an interoperable, standardized, and quality-controlled way. SwissPedCancer expects to include approximately 2800 patients diagnosed with cancer before the age of 20 years between 2017 and 2023. The pediatric oncology clinics' data and the manually validated ChCR data will be delivered separately to a secure national computing network for health-related data (Biomedical Information Technology). We will compare these two data sources to assess completeness (case ascertainment), accuracy (validity), and timeliness of cancer registration in the ChCR. We will evaluate data on diagnosis, treatments, underlying genetic disease, remission, relapse, and late effects. SwissPedCancer will provide a framework for optimizing standardized and uniform data transfers between pediatric oncology clinics and the ChCR and for other registries within Switzerland. The project was funded in September 2022 and received ethics exemption in October 2023. Data extraction from participating hospitals and the ChCR is expected to commence in January 2026. Study results are anticipated to be available in summer 2026. SwissPedCancer aims to reduce manual workload while improving the completeness, accuracy, timeliness, and comparability of childhood cancer data in Switzerland. The project will contribute to a robust, interoperable, and sustainable national infrastructure supporting high-quality cancer registration, timely analyses, and evidence-based decision-making.

Population-Based Investigation of DMD Genotype and Neurodevelopmental Concerns in Duchenne Muscular Dystrophy.

In Kenya and many other low- and middle-income countries, prehospital care systems are underdeveloped or entirely non-existent, leaving emergency departments (EDs) as the primary point of care for medical emergencies. The aim of this cross-sectional observational study was to use a geographic information system (GIS) to comprehensively analyse access to public EDs in Kenya within 1-hour and 2-hour travel times. Using open-source GIS software, population, land cover, elevation and road network data were analysed to create maps of 1-hour and 2-hour travel time catchment areas around public EDs in Kenya. Travel time analysis was calculated using AccessMod with a combined walking and motorised transport model. Approximately 93.7% of Kenya's population has access to a public ED within 1 hour, and 98.2% within 2 hours. Of the 6.3% of the population lacking access to a public ED within 1 hour, many reside in rural areas with suboptimal road conditions. There was a significant difference in the proportions within 1-hour and 2-hour travel times across all counties (p<0.001). There was a weak association between the number of facilities in each county and the population proportion within 1 hour (ρ=0.237, p=0.109) and 2 hours (ρ=0.230, p=0.119). By mapping population distribution in Kenya against the availability of public EDs, geospatial analysis provides crucial insights into emergency care access gaps, guiding policymakers in identifying areas that require infrastructure investments or prehospital service enhancements.

Abstract Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention—induced modified treatment policies—which we show identify such causal effects in the presence of network interference. Building on recent developments for causal inference in networks, we provide flexible, semi-parametric efficient estimators of the statistical estimand. Numerical experiments demonstrate that an induced modified treatment policy can eliminate the causal, or identification, bias that results from network interference. We use the methodology developed to evaluate the effect of zero-emission vehicle uptake on air pollution in California, strengthening prior evidence.

This study aims to analyze the level and typology of Urban Sprawl in Padang City in 2024 using a Geographic Information System (GIS) approach. The continuous growth of the population, along with the expansion of economic and social activities within the community, has become a major factor driving the uncontrolled expansion of urban areas toward the city’s outskirts. This phenomenon is generally characterized by increasing population density, a growing number of new buildings, changes in road network patterns, and shifts in land use from non-urban areas to built-up areas. This study utilizes several types of data, including population data, building data, new building data, the location of the city center, and road network data for the year 2024, which are analyzed spatially. The analytical method is conducted through scoring and classification techniques to identify the spatial distribution patterns of urban development and to determine three typologies of Urban Sprawl, namely low, medium, and high levels.

This paper develops a pessimistic two-stage network data envelopment analysis (DEA) model that integrates interval-valued data and endogenous weight restrictions within a unified linear programming framework. The proposed approach explicitly captures internal network structures while addressing bounded data uncertainty through an interval-to-deterministic transformation that preserves linearity and avoids probabilistic assumptions. Robustness is interpreted in the pessimistic interval DEA sense, where efficiency is evaluated under worst-case realizations of observed bounds rather than through explicit uncertainty-set optimization. To mitigate weight degeneracy and enhance discrimination power, data-driven proportional weight restrictions are introduced; these endogenous bounds are constructed solely from observed data and regularize the multiplier space without relying on subjective preferences or tuning parameters, while maintaining scale invariance and the nonparametric nature of DEA. The model admits equivalent multiplier and envelopment formulations and enables meaningful decomposition of overall efficiency into stage-specific components. Fundamental theoretical properties—including feasibility, boundedness, monotonicity, efficiency decomposition, and special case consistency—are rigorously established. An empirical application to OECD macroeconomic data, accompanied by sensitivity evaluation, demonstrates the stability and discriminatory capability of the proposed framework under bounded variability. Computational analysis confirms that the model retains linear programming structure and exhibits linear growth in problem size with respect to the number of decision-making units, thereby preserving the scalability characteristics of classical two-stage network DEA formulations. The proposed framework provides a theoretically grounded and computationally tractable approach for network efficiency analysis under bounded interval uncertainty.

ABSTRACT This paper develops a framework to benchmark public water sector efficiency across Europe, addressing the lack of a relevant comparative assessment tool. Using dynamic network data envelopment analysis (DEA) and statistical analysis, it evaluates water provision, wastewater treatment and overall performance in 26 countries from 2013 to 2020. Results show that modern infrastructure, advanced treatment technologies and effective resource management are associated with higher efficiency, while structural and operational constraints hinder performance. Improving efficiency requires targeted infrastructure upgrades, enhanced resource recovery and broader sector restructuring. The paper provides the first comprehensive, comparative pan‑European water sector efficiency study and offers decision‑support insights for integrated, context‑sensitive governance.

The study analyzes agricultural labour productivity in the context of the economic dimension of sustainability and the idea of European Union (EU) cohesion. This idea constitutes a central principle of European integration. The basis for implementing the concept of cohesion in European agriculture is the convergence of labour productivity levels. Convergence in this area forms the foundation of economic sustainability and serves as a prerequisite for the social dimension of sustainability, while often also being an underlying factor in environmental sustainability. The analysis concerns the productivity of labour in farms by the economic size, both at the national and regional levels, based on Farm Accountancy Data Network (FADN) data for the years 2007–2022. The β and σ-convergence methods were used. The results indicate that processes of labour productivity convergence occur in EU agriculture. This phenomenon was manifested by a decline in the heterogeneity of labour productivity levels among agricultural holdings. The fastest reduction in regional diversity was observed among the group of the largest economically farms (GE6). However, the dispersion of labour productivity levels remains considerable, and the rate of convergence continues to be slow. The convergence of labour productivity in agriculture will not accelerate without widespread and comprehensive structural changes in the sector, extending beyond mere changes in land use patterns.

The aim of this study is to analyze scientific publications in the field of occupational health and safety in the agricultural sector using bibliometric methods to evaluate publication trends, prominent topics, and collaboration networks. The literature review and dataset consist of a total of 774 articles published between 1981 and 2025 from the Web of Science database. RStudio and VOSviewer software were used to map the bibliometric network data. The analysis evaluated annual publication numbers, the most cited studies, authors, institutions, journals, keywords, and collaboration networks. The findings indicate that research on the agricultural sector has increased in recent years, reaching a peak in publications and citations in 2022. The most productive author in the study was Risto H. Rautiainen, while the institution that published the most articles was the University of Nebraska Medical Center, based in the United States. The most productive and influential resource on the subject was the US-based Journal of Agromedicine. In their early studies, researchers focused on the keywords “farm injuries,” “accidents,” “wounds and injuries,” and “epidemiology.” Trend analyses revealed keywords such as agricultural machinery, tractors, child labour, mental health, stress, ergonomics, and accessibility as emerging trends.

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48-49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments.

Accurate estimation of network density is central to egocentric social network analysis, yet existing survey-based methods require researchers to balance accuracy against participant burden and systematic recall bias. Traditional approaches, such as fixed-list name generators, tend to overrepresent salient ties. Although the more recent random sampling method yields better accuracy, it relies on exhaustive free recall, which can be cognitively demanding and impractical for researchers. In this study, we introduce and evaluate an alternative approach—incremental recall—that structures alter nomination across relationship categories to improve coverage of differing tie strengths while reducing respondent burden. Using a large-scale Monte Carlo simulation encompassing over 9 million egocentric networks, we compare incremental recall against traditional fixed-list recall and random sampling across a wide range of network sizes, compositions, and recall bias assumptions. Results show that the incremental recall method consistently outperforms traditional fixed-list recall and performs comparably to or better than random sampling under unbiased and moderately biased recall conditions. Performance advantages persist even when respondents are unable to provide the full number of alters specified by design. We further validate these findings using empirical egocentric network data from 103 participants. Treating observed networks as proxy ground truths, empirical results closely mirror the simulation patterns, confirming the robustness of incremental recall under real-world reporting conditions. These findings demonstrate that incremental recall addresses a central practical challenge in egocentric social network research: balancing feasibility and accuracy in density estimation. The proposed method maintains strong performance while substantially reducing respondent burden and simplifying administration for applied studies. For researchers conducting large scale surveys where network density is one of several measures, incremental recall provides a practical and validated alternative to exhaustive recall that maintains robustness to realistic reporting biases.