Search Results (87)

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

Databases serve as critical repositories of digital evidence in criminal investigations, and the recoverability of deleted data is a key determinant of forensic success. Microsoft SQL Server, one of the most widely deployed relational database management systems, has been the subject of multiple forensic studies examining how deleted records persist in physical database files across different acquisition methods. A previous study established a reference baseline using SQL Server 2008 and 2017, demonstrating that the Database Shrink operation causes version-specific and method-specific behavior: under logical collection with Shrink applied in SQL Server 2017, unallocated deleted data becomes fully initialized, rendering recovery impossible—a pattern not observed in SQL Server 2008 or under physical collection in either version. With the release of SQL Server 2025, the most significant architectural update to the platform in a decade, it remained unknown whether these forensic behaviors persist in the latest version. This study replicates the experimental design of in a controlled SQL Server 2025 environment, applying the same deletion scenario (DELETE command without conditions), the same two acquisition methods (logical and physical collection), and the same Shrink condition. The results demonstrate that SQL Server 2025 does not reproduce the version-specific initialization behavior observed in SQL Server 2017: across all four experimental conditions, deleted data residue in unallocated page space remains recoverable, indicating a fundamental change in the interaction between the Shrink operation and the logical collection mechanism. This recoverability is a double-edged property: while it benefits forensic investigators by preserving deleted evidence, it simultaneously represents a data-sanitization risk from a security and privacy standpoint, as deleted records are not reliably erased. These findings provide updated forensic guidance for digital investigators operating in contemporary SQL Server environments. Specifically, the results inform acquisition-method selection in real-world investigations where a suspect may have deleted records and where only a logical backup (.bak) is available to investigators. Full article

Particularly in regions experiencing rapid industrial and healthcare development, the presence of pharmaceutical residues in wastewater is becoming an increasingly pressing environmental concern. In this study, an analytical method was developed to quantify lisinopril (LIS), ramipril (RAM), and enalapril (ENA) in wastewater while being both sensitive and inexpensive. To improve the precision and accuracy of the measurements, propranolol (PRO) was used as an internal standard. To achieve dual preconcentration and enhanced sensitivity, the method integrates filed amplified sample stacking (FASS) with solid-phase extraction (SPE) before capillary electrophoresis (CE) in a synergistic way. Important experimental factors such the composition of the background electrolyte (BGE), pH, injection settings, stacking efficiency, and selection of the SPE sorbent were meticulously calibrated. Under ideal circumstances, the SPE-CE-FASS method demonstrated remarkable linearity within the concentration range of 10–1000 ng L⁻¹ (R² > 0.999), an outstanding level of accuracy (intra- and inter-day RSD < 6%), and satisfactory recovery percents (90–97%) in real wastewater samples. This method offers an eco-friendly and cost-effective alternative to liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) by reducing waste, using less solvent, and providing enough sensitivity for trace-level analysis. Hence, it is very suitable for the regular monitoring of angiotensin converting enzyme (ACE) inhibitors in complex wastewater matrices. Full article

The escalating sophistication of ransomware requires defensive strategies that are both proactive against zero-day attacks and operationally efficient. Existing solutions often force a trade-off—sacrificing low false-positive rates for broad detection, or vice versa. This work introduces an integrated framework designed to transcend this limitation. Our dual-stage approach synergizes pre-encryption behavioral analysis with definitive post-encryption confirmation. The first stage employs a specialized artificial immune system (AIS) that monitors a curated set of 47 features, including API-call n-grams and file entropy dynamics, to identify malicious activity before file encryption begins. This pre-emptive analysis is complemented by an enhanced, cross-platform R-Locker mechanism, which uses Windows named pipes and symbolic links to deploy honeyfiles that trap ransomware during I/O operations, providing a high-fidelity trigger for automated containment. We subjected this framework to a rigorous evaluation against 3500 real-world ransomware samples and 12,000 benign applications. The results demonstrate a 98.2% detection rate with a 0.8% false-positive rate, achieving a mean response time of 1.3 s. A key finding is the framework’s efficiency on both Windows and Linux (the only platforms tested), with the AIS and R-Locker modules consuming a combined 101 MB of memory. While the system excels in real-time detection, we note that its current memory forensics capability for key recovery is incompatible with certain ransomware families due to architectural obfuscations. Our findings suggest that the integrated approach performs well under laboratory conditions; further real-world validation is required to confirm robustness in diverse environments. Full article

This study provides the first empirical analysis of how energy-sector corporate filing events transmit to financial markets, bridging a critical gap between corporate financial distress literature and commodity market dynamics. The analysis employs an event study methodology with Wilcoxon signed-rank tests and panel regression models to examine 51 U.S. energy firms that experienced financial distress (2015–2021) across the NYSE and NASDAQ. Post-announcement cumulative abnormal returns (CARs) show positive median values (WSR: 40.5 for NYSE in 10-day window, p < 0.10; 97.8 for NASDAQ in 10-day window, p < 0.05; 36.24 for NASDAQ in 5-day window, p < 0.10). Panel regression results show significant differences in post-announcement CARs relative to the event day for both indices (NYSE: 10-day window coefficient = 117.1, p < 0.05; NASDAQ: 10-day = 199.6, p < 0.01; 5-day = 150.8, p < 0.05), as well as in pre-announcement windows for NYSE (5-day coefficient = 93.5, p < 0.10; 10-day = 86.6, p < 0.10). The findings suggest that markets respond to energy-sector corporate distress events without broad-based disruption, likely due to early signals of financial distress, clarified expectations regarding recovery paths under Chapter 11 restructuring, and reduced information asymmetry through disclosures. Policymakers can leverage these insights to refine corporate filing frameworks for commodity-dependent sectors. Full article

Heritage buildings constitute a significant element of the United Kingdom’s (UK) built environment, with 460,000 listed buildings across England, Scotland, Wales and Northern Ireland. These assets present substantial challenges for national decarbonisation due to statutory constraints on fabric alteration and the need to consider whole-life carbon impacts. This study evaluates the impact of conservation-compatible retrofit strategies on the operational energy and carbon performance of Fitzroy House, a Grade II listed late-modern office building in Nottingham. Dynamic building simulation (IES Virtual Environment) was used to assess baseline performance and to develop two retrofit scenarios incorporating improvements to glazing, airtightness, roof insulation, and the introduction of mechanical ventilation with heat recovery (MVHR). Climate resilience was evaluated using future weather files for the 2080s. Results are derived from comparative scenario-based modelling rather than calibrated predictions of absolute performance. Within this framework, the proposed measures can reduce annual heating demand by up to 68%, cooling demand by 60%, and operational carbon emissions by approximately 41% (district heating) to 64% (natural gas), relative to the as-built baseline under the most advanced retrofit scenario. Performance remains broadly robust under future climate scenarios, although cooling loads increase modestly. The findings demonstrate that, while meaningful reductions in operational carbon are achievable, retrofit outcomes are fundamentally shaped by conservation constraints, which act as an interpretive framework defining the limits and possibilities of intervention. However, results should be interpreted as indicative of relative performance improvements rather than fully generalizable or predictive outcomes, and embodied carbon impacts are not included within the scope of this study. The research provides an evidence-based pathway for improving similar late-modern listed office buildings while highlighting the limits imposed by conservation requirements and existing building fabric. Full article

File fragment classification is crucial in digital forensics, aiding in the recovery and reconstruction of fragmented files, which serve as key evidence; while deep learning techniques have advanced in this area, challenges remain, particularly regarding the consideration of inter-file-type relationships and the granularity of classification. To overcome these challenges, we introduce a hierarchical classification approach that leverages an agglomerative hierarchical clustering algorithm combined with a dynamic adjustment mechanism, optimizing category distribution among leaf nodes. This structure is further enhanced by developing specific classifiers for each leaf node, tailored to its unique characteristics. Experimental results on the FFT-75 dataset show that our method achieves 76.3% accuracy in a 75-class scenario (512-byte blocks), surpassing the accuracy achieved with existing approaches. This method improves classification accuracy, addressing misclassification issues caused by excessive classification types. Full article

Seismic–volcanic monitoring networks often operate in remote areas over heterogeneous links (e.g., microwave radio and cellular). During event-driven seismic episodes, sustained multi-station waveform streams can stress both last-mile connectivity and data acquisition systems, yielding discontinuities in center-side archives even when stations keep recording locally. This paper presents the Python Recovery Tool (PRT), a lightweight command-line artifact that retrieves buffered waveform files after reconnection and rebuilds daily archives that can be ingested by the monitoring center without hardware upgrades. PRT detects archive gaps from daily (Julian day) file partitions and embedded timestamps, and reduces recovery traffic by selectively fetching only the files needed to backfill missing intervals. We evaluated PRT on five event-driven recovery cases using operational file-based evidence from station and center listings complemented with a simple bandwidth-based recovery-time model. Across the cases, PRT restored archive continuity while reducing download volume by 4.43–93.75% relative to naive bulk retrieval, with modeled catch-up times ranging from 0.79 to 207.59 min, depending on station-side packaging granularity and bottleneck link capacity. These results support a practical retrofit path to improve archive completeness under constrained links and heterogeneous deployments. Full article

Communities susceptible to disasters frequently endure severe socio-economic and psychological repercussions. Therefore, it is essential to thoroughly understand the various vulnerabilities encountered by different groups. Residents of New Orleans, Louisiana, faced significant hardships after Hurricane Katrina hit on 29 August 2005. A multitude of individuals lost their residences, while others, regrettably, lost family members. The Displaced New Orleans Residents Survey (DNORS) offered significant insights into individuals and households living in New Orleans immediately prior to Hurricane Katrina’s impact in August 2005. The survey interview was conducted from mid-2009 until mid-2010. This study utilizes DNORS public data files to evaluate socio-demographic characteristics pertinent to the analysis, including age, gender, race/ethnicity, marital status, household income, education level, employment status in 2005, and insurance coverage, alongside psychological measures such as mental health symptoms, posttraumatic stress, depression, and perceived stress. The research employs various regression techniques to identify the at-risk categories affected psychologically and physically by the hurricane. These findings may aid policymakers in developing targeted post-disaster recovery strategies, thereby promoting more resilient and sustainable communities. Full article

A large number of architectural drawings have historically existed in paper form or as non-editable raster images, which makes them difficult to directly support information reuse and digital management, while manual CAD reconstruction is time-consuming and inefficient. This paper proposes a hybrid deep learning and rule-based method for architectural drawing vectorization and CAD reconstruction, which automatically converts scanned raster images into editable CAD vector files while preserving geometric structure and scale consistency. The proposed method consists of four modules: axis grid and dimension detection, text recognition and scale recovery, architectural line topology reconstruction, and CAD geometric rectification and reconstruction. The method utilizes object detection and OCR technologies to extract key semantic information from the drawings. By extracting semantic information, the method constructs a line topology structure and applies architectural drawing constraints to parameterize and normalize geometric results, thereby achieving the recognition and vectorization of raster drawings. Experimental results and engineering case studies demonstrate that the proposed method can effectively extract typical architectural elements, and generate directly editable CAD vector drawings. The method achieves favorable geometric accuracy and topological consistency in architectural drawing digitization and automatic CAD reconstruction tasks, providing a technical solution for the automatic vectorization of existing architectural drawings. Full article

Background/Objectives: Disasters have a substantial impact on health systems and populations worldwide, with increasing frequency, mortality, and economic losses associated with natural hazards. The United Nations emphasises that disasters result from the interaction between hazards, exposure, and vulnerability, requiring integrated, people-centred health responses aligned with the 2030 Agenda. However, empirical evidence describing specific nursing interventions, particularly during response and recovery phases, is limited. This study aims to analyse the fundamental nursing care interventions provided to disaster victims in the Autonomous Region of Azores, Portugal. Methods: A qualitative multiple case study was conducted using documentary analysis of the nursing records from two disaster survivors with different clinical trajectories. Data were collected between August 2023 and May 2024 through complete transcription of nursing documentation contained in the clinical files. Data analysis followed Yin’s case study methodology and was theoretically supported by the Fundamentals of Care Framework. Results: The findings indicated a predominance of interventions addressing physiological needs during the acute phase, which progressively evolved to maintenance, psychosocial support, and adaptation needs during prolonged hospitalizations. Nursing care integrates advanced technical skills with relational and person-centred interventions, including emotional support, therapeutic communication, and promotion of patient autonomy. Conclusions: Nursing practice in disaster situations should be conceptualised as integrative, person-centred care grounded in international nursing frameworks. Strengthening disaster-specific nursing education, developing phase-adapted care protocols, and promoting multicentre longitudinal research appear to play a critical role for advancing nursing care models and informing health policies in disaster-prone regions. Full article

Artificial intelligence (AI) systems have become operational infrastructure whose value is increasingly dominated by trained models, behavioral configurations, and decision-making logic rather than by software binaries alone. As a result, ransomware threats against AI systems cannot be adequately addressed by conventional recovery strategies that assume service availability can be restored through file and code recovery. In AI environments, assets such as model parameters, training data, inference pipelines, and safety policies constitute primary attack targets, and their compromise can invalidate system behavior even when files are successfully restored. This study re-examines ransomware threats against AI systems from an asset-based protection perspective and demonstrates why traditional recovery assumptions structurally fail in AI-centric environments. Based on this analysis, we show that protection mechanisms limited to file integrity are insufficient and must be extended to include behavioral consistency and decision-making reliability. To address this gap, we propose a behavior-aware ransomware protection methodology, implemented as the Behavior-Aware Integrity Protection System (BIPS). BIPS augments existing ransomware response processes by redefining protection targets, establishing behavioral baselines, verifying post-recovery behavioral integrity, and supporting risk-based operational decisions. This work contributes by reframing ransomware threats against AI systems as an issue rooted in protection scope and recovery assumptions rather than isolated attack techniques, thereby extending ransomware response for AI systems toward a reliability- and risk-oriented protection framework. Full article

Background: Extramedullary spinal tumors represent a significant challenge for spine surgeons. Currently, various techniques exist to perform tumor resection safely while optimizing patient outcomes. Historically, the standard of care has been open surgery; however, in the last two decades, Minimally Invasive Spine Surgery (MISS) techniques have gained importance due to superior postoperative recovery. Literature on Unilateral Biportal Endoscopy (UBE) for tumor resection is currently limited. We propose that UBE has the potential to become a standard approach for these lesions due to its distinct advantages. Methods: We performed a retrospective review of 11 patients who underwent UBE resection of lower thoracic and lumbar spinal extramedullary tumors. We analyzed clinical files and intraoperative endoscopic videos to describe our surgical technique step by step. We also evaluated the advantages of this approach in terms of resection rate, operative time, operative blood loss, and hospital stay. A representative case is also presented. Results: Clinical resolution and significant symptomatic improvement were achieved in all cases, as evidenced by functional and pain scales. In terms of tumor resection, we obtained results comparable to other MISS techniques and open surgery, with a low complication rate. Conclusions: UBE represents a safe, effective evolution in MISS for spinal tumors. Future studies with larger cohorts are needed to validate these findings as a standard of care. Full article

Background and Objectives: Vaginal natural orifice transluminal endoscopic surgery, vNOTES, has become an increasingly preferred minimally invasive option for benign hysterectomy. General anesthesia is still the routine choice, yet regional methods such as combined spinal epidural anesthesia may support a smoother postoperative course. Although the use of vNOTES is expanding, comparative information on anesthetic approaches remains limited, and its unique physiologic setting requires dedicated evaluation. To compare combined spinal epidural anesthesia with general anesthesia for benign vNOTES hysterectomy, focusing on postoperative nausea and vomiting, recovery quality, and intraoperative physiologic safety. Materials and Methods: This retrospective cohort study was conducted in a single center and identified women who underwent benign vNOTES hysterectomy between March 2024 and August 2025 from electronic medical records. Participants received either combined spinal epidural anesthesia or general anesthesia according to routine clinical practice. All patients were managed within an enhanced recovery pathway that incorporated standardized analgesia and prophylaxis for postoperative nausea and vomiting. The primary outcome was the incidence of postoperative nausea and vomiting during the first day after surgery. Secondary outcomes included time to discharge from the recovery unit, pain scores at set postoperative intervals, early functional recovery, patient satisfaction and physiologic parameters extracted from intraoperative monitoring records. Analyses were performed according to the anesthesia group documented in the medical files. Results: One hundred forty patients met inclusion criteria and were included in the analysis. Combined spinal epidural anesthesia was linked to a lower incidence of postoperative nausea and vomiting, a shorter stay in the post-anesthesia care unit, and reduced pain scores in the first 24 h (adjusted odds ratio 0.32, ninety five percent confidence interval 0.15 to 0.68). Early ambulation and oral intake were reached sooner in the combined spinal epidural group, with higher overall satisfaction also noted. Adherence to ERAS elements was similar between groups, with no meaningful differences in early feeding, mobilization, analgesia protocols or PONV prophylaxis. During the procedure, combined spinal epidural anesthesia produced more episodes of hypotension and bradycardia, while general anesthesia was linked to higher airway pressures and lower oxygen saturation. Complication rates within the first month were low in both groups. Conclusions: In this observational cohort study, combined spinal epidural anesthesia was associated with lower postoperative nausea, earlier recovery milestones and greater patient comfort compared with general anesthesia. Hemodynamic instability occurred more often with neuraxial anesthesia but was transient and manageable. While these findings point to potential recovery benefits for some patients, the observational nature of the study and the modest scale of the differences necessitate a cautious interpretation. They should be considered exploratory rather than definitive. The choice of anesthesia should therefore be individualized, weighing potential recovery benefits against the risk of transient hemodynamic effects. Larger and more diverse studies are needed to better define patient selection and clarify the overall risk benefit balance. These findings should be interpreted cautiously and viewed as hypothesis-generating rather than definitive evidence supporting one anesthetic strategy over another. Full article

This study presents a novel framework for digital copyright management that integrates AI-enhanced perceptual hashing, blockchain technology, and digital watermarking to address critical challenges in content protection and verification. Traditional watermarking approaches typically employ content-independent metadata and rely on centralized authorities, introducing risks of tampering and operational inefficiencies. The proposed system utilizes a pre-trained convolutional neural network (CNN) to generate a robust, content-based perceptual hash value, which serves as an unforgeable watermark intrinsically linked to the image content. This hash is embedded as a QR code in the frequency domain and registered on a blockchain, ensuring tamper-proof timestamping and comprehensive traceability. The blockchain infrastructure further enables verification of multiple watermark sequences, thereby clarifying authorship attribution and modification history. Experimental results demonstrate high robustness against common image modifications, strong discriminative capabilities, and effective watermark recovery, supported by decentralized storage via the InterPlanetary File System (IPFS). The framework provides a transparent, secure, and efficient solution for digital rights management, with potential future enhancements including post-quantum cryptography integration. Full article