Search Results (219)

This study addresses the challenge of anticipatory resource allocation in field-deployed communication networks under dynamic unmanned aerial vehicle jamming. In such scenarios, energy supply is severely constrained. It cannot be replenished in real time, necessitating a one-time resource pre-allocation that must remain effective throughout the mission. To overcome these limitations, we propose a novel optimization framework consisting of four integrated components: (1) independent threat assessment via trajectory-coverage spatial mapping using digital elevation models and ray-tracing algorithms, (2) evidence-theoretic fusion of heterogeneous information sources—including objective intelligence data and subjective expert knowledge, (3) jamming distribution modeling through dedicated probability transformation algorithms for fixed-interval and continuous random jamming modes, and (4) decoupled resource-confidence optimization solved via convex programming. By employing evidence discount factors and Dempster’s combination rule, the framework quantifies reliability disparities. It integrates multiple heterogeneous sources and uses theoretically derived, forward-computable models—combining Binomial distributions, piecewise cubic Hermite interpolation, and uniform distribution assumptions—to efficiently convert threat basic probability assignments into jamming duration probability density functions. Extensive Monte Carlo simulations demonstrate significant improvement in mission assurance metrics, with consistent performance under diverse uncertainties. The approach is also validated in cross-domain applications using Bohai rescue data, confirming its utility in resource-limited, highly uncertain environments. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

Occupational safety and health appears in many higher education programs. Universities rarely state what students must be able to do in situations involving occupational risk. This study analyses how occupational safety and health is defined and assessed in research on curriculum design, competence frameworks, and educational evaluation. The analysis used competence mapping, alignment checks, and cross-level tracing to examine learning outcomes, teaching activities, and assessment formats. The results suggest that safety is treated as knowledge of hazards and rules. Assessment depends on recall and procedural compliance. Judgment, decisions under real conditions, and responsibility allocation are not evaluated. A framework is derived that defines safety through three forms of performance, risk interpretation, action selection, and decision justification, organized across non-equivalent levels of evidence. Learning outcomes, learning activities, and assessment are connected to these performance requirements. Progression can be defined and checked across program stages. Occupational safety and health become a form of transversal academic competence when it is defined through evidence from performed tasks instead of topic coverage or regulatory content. Full article

Embodied-carbon accounting is increasingly required at the early design stage to guide material and construction choices during design iterations. However, many life-cycle assessment (LCA) workflows and centralized building information modeling (BIM)–LCA plugins still rely on fragmented data, non-transparent mapping rules, and limited cross-project reuse, which slows rapid iteration. This study develops an open and traceable embodied-carbon assessment workflow driven by BIM object geometry and semantic attributes and demonstrates it through a single case study, enabling automated accounting for the A1–A3 stages from model input to result reporting. The framework is implemented as a Revit add-in prototype connected to an open-data platform. It uses assemblies as standardized assessment units, applies configurable rule-based mapping, and performs unit normalization to link model quantities with carbon factors. A single three-story brick–concrete residential building in Wuhan with an LoD 300 model is used as the sole validation case to demonstrate workflow feasibility, report coverage, and time metrics. The case yields an A1–A3 embodied-carbon intensity of approximately 333 kgCO₂ e/m², dominated by the structural system. Rule mapping achieves 82% coverage within the defined accounting scope. Compared with manual workflows (290–380 min), first-time accounting is reduced to 83–98 min and further to within 30 min when assemblies and rules are reused. Contribution decomposition shows a concentrated pattern and supports traceability from assemblies to material types. Overall, within the tested scope, the Revit-based prototype provides efficient and verifiable embodied-carbon feedback for early-stage design. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

Background: European population-based cancer registries (PBCRs) provide the foundation for monitoring skin cancer, yet registration practices and coverage vary, particularly for non-melanoma skin cancer (NMSC). Methods: We conducted a narrative review combining descriptive analyses of European Cancer Information System (ECIS) outputs with evidence from the European Network of Cancer Registries (ENCR) Working Group on NMSC and from national reports. A targeted PubMed search (2015–2025) assessed scientific usage of European registry data. Results: Nearly 200 PBCRs operate across about 40 European countries, with heterogeneous structures and timeliness. The ECIS estimated 101,500 incident cutaneous melanomas (CM) in the European Union in 2022. Long-term data from Nordic countries show a tenfold increase in CM incidence over the last six decades, with recent plateauing in younger cohorts. NMSC registration remains inconsistent: some countries record both cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC), others record cSCC only, and several omit NMSC entirely. Consequently, Europe-wide NMSC figures are not available from the ECIS. Global estimates exclude BCC and understate the true burden, which is likely between 1 and 1.6 million incident cases annually in Europe. The PubMed search identified 538 European registry-based publications on skin cancer (2015–2025). Conclusions: Melanoma registration in Europe is robust, but NMSC remains under-registered. Priorities include harmonized definitions and counting rules, better integration of outpatient and pathology data, streamlined EU-level reporting, digital/AI-enabled case ascertainment, and sentinel regions to generate reliable NMSC estimates. Full article

Background/Objectives: Cephalosporins represent one of the most important classes of β-lactam antibiotics, widely used in clinical practice due to their broad-spectrum activity and favorable safety profile. As generations evolved, structural modifications were introduced to expand antimicrobial coverage and overcome β-lactamase resistance. This study aimed to analyze the drug-like properties of cephalosporins across different generations using molecular descriptors to identify structural and pharmacokinetic patterns influencing bioavailability and oral administration profiles. Methods: Thirty-eight cephalosporins representative of different generations were selected. Molecular data were obtained from PubChem, and SMILES were extracted and validated. Molecular descriptors (including MW, logP, TPSA, HBA, HBD, rotatable bonds, and global complexity indices) were calculated using the SwissADME and ChemDes platforms. Statistical analysis included ANOVA followed by post hoc tests, and principal component analysis (PCA). Results: A progressive increase in molecular weight, polarity, and TPSA was observed across generations, with fourth-generation cephalosporins showing significantly higher values compared to first-generation compounds (p < 0.0001). LogP decreased significantly in fourth-generation agents (p < 0.0001), reflecting increased polarity. PCA revealed that most compounds from generations 1–2 cluster in regions consistent with Lipinski’s and Veber’s rules, whereas fourth- and fifth generation - cephalosporins deviated substantially, prioritizing antimicrobial efficacy over oral bioavailability. Recurrent structural modifications such as oximes, tetrazoles, and aminothiazoles were identified, with increasing frequency in modern generations. Conclusions: The evolution of cephalosporins reflects a strategic shift toward enhanced antimicrobial potency and β-lactamase stability at the expense of oral bioavailability. Understanding these structural transitions provides valuable insights for rational drug design, aiming to balance antimicrobial effectiveness with favorable pharmacokinetic profiles essential for therapeutic success. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

As an essential blue carbon ecosystem, mangroves play a vital role in coastal protection, biodiversity conservation, and climate regulation. However, their complex and variable growth environments pose challenges for precise monitoring. Hainan Island represents a region within China where mangrove forests are the most concentrated and diverse in type. In recent years, ecological restoration efforts have led to the recovery of their coverage areas. This study analyzed the spatial distribution, canopy height, and aboveground carbon storage variations in Hainan mangrove forests. Deep-learning and multiple machine-learning algorithms were used to integrate multitemporal Sentinel-2 remote sensing imagery from 2019 to 2023 with unmanned aerial vehicle observations and field survey data. Multi-rule image fusion and deep-learning techniques effectively enhanced mangrove identification accuracy. The mangrove classification achieved an overall accuracy exceeding 90%. The mangrove area in Hainan increased from 3948.83 ha in 2019 to 4304.29 ha in 2023. Gradient-boosted decision tree (GBDT) models estimated average canopy height with a high coefficient of determination (R² = 0.89), and Random Forest (RF) models yielded the best estimations of total above-ground carbon stock with strong agreement to field observations. Integrating multisource remote sensing data with artificial intelligence algorithms enabled high-precision dynamic monitoring of mangrove distribution, structure, and carbon storage to provide scientific support for the assessment, management, and carbon sink accounting of Hainan mangrove ecosystems. Full article

To address the problem of regional coverage path planning for unmanned aerial vehicle swarms (UAVs), this study proposes an algorithm based on objective programming partitions (OPP) and rule-based spanning tree coverage (RSTC). Aiming at the shortcomings of the traditional Divide Areas based on Robots Initial Positions combined with Spanning Tree Coverage (DARP-STC) algorithm in two core stages, that is, region partitions and spanning tree generation, the proposed algorithm conducts a targeted design and optimization, respectively. In the region partition stage, an objective programming and 0–1 integer programming model are adopted to realize the balanced allocation of UAVs’ task regions. In the spanning tree generation stage, a rule is designed to construct a spanning tree of coverage paths and is proven to achieve the minimum number of turns for the UAV under certain conditions. Both simulations and physical experiments demonstrate that the proposed algorithm can not only significantly reduce the number of turns of UAVs but also enhance the efficiency and coverage degree of tasks for UAV swarms. Full article

To address the structural differences between closed-track and open-road testing in terms of scenario coverage, risk controllability, and validation consistency, this study proposes a scenario-driven combined testing method for automated driving systems. The proposed approach constructs a multi-dimensional scenario space based on functional decomposition and jointly quantifies scenario complexity and hazard level from the perspectives of information heterogeneity and interaction-induced risks. Based on these two-dimensional scenario attributes, a fuzzy inference mechanism is developed to dynamically allocate validation resources across different testing environments. To further improve rule-base generalization and mapping stability, an enhanced genetic algorithm integrating simulated annealing and K-means clustering is introduced to optimize the rule structures in an evolutionary manner. Experimental results demonstrate that, compared with traditional testing methods and single-mechanism optimization strategies, the proposed approach achieves a more consistent and interpretable mapping between scenarios and testing proportions in high-complexity urban traffic scenarios. While ensuring test adequacy, the testing economy is significantly improved, with an overall average improvement exceeding 20%. In addition, stable resource allocation performance is observed across multiple scenarios with different levels of complexity and risk, confirming the scalability and applicability of the proposed method for multi-scenario automated driving system testing. Full article

A growing body of literature recognises media narratives’ influence in shaping public perceptions of leadership and governance. Studies suggest that women presidential aspirants are often framed within symbolic constraints, where they are perceived as capable leaders in supportive roles but not as legitimate rulers. This study systematically reviews news coverage of Malawi’s first female president, Joyce Banda, examining how the media differentiates women’s ability to “lead” and their perceived inability to “rule”. Specifically, the study seeks to answer two key questions: How does the media in Malawi frame women’s political leadership in terms of governance and executive power? And what recurring gendered narratives emerge in media portrayals of women seeking the presidency? This investigation employs a content analysis of Malawi24, utilising Framing Theory and Feminist Theory to examine the dominant themes in political reporting. Analysis showed that media coverage reinforces a symbolic barrier to power, portraying women as leaders within limits while positioning men as natural rulers. Various perspectives on women’s legitimacy in executive positions were expressed, with narratives frequently questioning their authority and decision-making capabilities. The findings of this study suggest that gendered media framing constrains women’s political ambitions by reinforcing patriarchal expectations of leadership. Addressing this bias requires greater media accountability and equitable portrayals of women in executive political roles. Full article

Conventional PMSs often rely on static age-based assumptions, which can fail to capture nonlinear, state-dependent deterioration and improvement-like responses observed in long-term monitoring data. This study addresses these limitations by proposing a reaction-oriented analytical framework using eight years of Korea Highway PMS data (2015–2022). We construct a Δ–State Vector by combining the previous-year condition grade with noise-filtered annual changes in the International Roughness Index (IRI) and Rut Depth (RD). Measurement noise is separated from structural signals via MAD-based noise bands (ΔIRI: ±0.089 m/km; ΔRD: ±0.993 mm), with a global MAD floor (minimum-threshold constraint) to avoid degenerate zero-band cases under sparse or near-constant transitions. The resulting vectors are embedded into a low-dimensional Reaction Space using UMAP and clustered with HDBSCAN. To validate interpretability, a rule-based Trend × Mode Reaction Signature taxonomy is used to assess the semantic consistency of unsupervised clusters. Five dominant reaction regimes are identified, showing strong agreement with signature-based labels (weighted purity = 0.927; coverage for purity ≥ 0.60 = 0.911). Overall, the results indicate that deterioration dynamics are governed by lane–segment heterogeneity and prior-state dependence rather than chronological age, providing a reproducible foundation for future event-sensitive, dynamic age reset frameworks. Full article

Smart cities increasingly reuse existing Wi-Fi infrastructure to sense crowding, but many smart-campus tools still fail to support routine, day-to-day decisions. A short-horizon field feasibility study was conducted to prototype a low-maintenance, prefix-based early-warning rule that turns anonymized campus Wi-Fi access-point counts into an interpretable lunchtime crowd signal. Daily 7-min access-point profiles from five university canteens (11:00–14:00) were aggregated, winsorized, smoothed, and row-z-scored, then clustered into demand-shape typologies using k-means++. Two typologies were obtained (Early Peak and Late Shift), and a cosine-similarity atlas was frozen. At 11:28, the five-bin occupancy prefix was compared to typology centroids, and an Early Peak badge was issued when similarity to the Early Peak centroid exceeded a preset threshold. On held-out days, the Early Peak typology could be identified at 11:28 with coverage of 0.73 and agreement of 0.86 relative to end-of-day labels. In 20 matched canteen-weekday pairs, badge days were associated with a Hodges–Lehmann median reduction of 0.193 standard-deviation units in peak crowding (≈9% lower). Given the short (3-week) horizon and limited hold-out window, results are presented as feasibility evidence and motivate a larger controlled evaluation. Simple, interpretable rules built on existing Wi-Fi telemetry were shown to be deployable as a feasibility-level decision aid on a smart campus, while broader smart-city transferability should be validated through longer-horizon controlled evaluations. Full article