Search Results (1,260)

The Kerper–Bowron Method (KB Method) is a patent-pending approach that revolutionizes service contract loss estimation and accounting by introducing a precise, contract-level approach to forecasting expected losses and cancellations. Building on a prior 2007 paper, this update presents the Earned Contract formula, aligning with Solvency II and modern accounting standards. By leveraging a probabilistic exposure base and Generalized Linear Models, the KB Method enhances accuracy in claims and cancel liabilities as well as other liability and asset estimates across global service contract markets. This methodology offers superior precision, automation, and compliance, redefining actuarial and financial practices for vehicle and other service contracts. Full article

Colistin sulfate (COL), a critical last-line antibiotic, poses a severe environmental threat due to its persistence and role in spreading mobile resistance genes. This study introduces a novel quaternary Ni-Co-Zn-Al layered double-hydroxide/activated carbon composite (Q-LDH/AC) for highly efficient COL remediation. The composite’s unique architecture, revealed through comprehensive characterization, enables an exceptional adsorption capacity of 952.52 mg·g⁻¹ under optimal conditions (pH 7, 55 °C), a value that significantly surpasses those reported for most previous adsorbents. The process was spontaneous and endothermic, with kinetics and isotherms best described by the pseudo-second-order and Langmuir–Freundlich models, respectively, indicating a complex mechanism dominated by chemisorption on both homogeneous and heterogeneous sites. A key innovative feature is the successful regeneration and reusability of the composite, which retained over 70% efficiency after five cycles, enhancing its potential for practical, cost-effective water treatment applications. The thermodynamic parameters (ΔG° = −8140.68 kJ/mol, ΔH° = +61.22 kJ/mol) indicate that the reaction is spontaneous and endothermic. The interaction mechanism of COL on Q-LDH/AC can be deduced by FT-IR including hydrogen bonding, π-π bonding, electrostatic interactions, and surface complexation. Beyond mere regeneration, this work demonstrates a pioneering circular economy strategy by repurposing the spent COL-laden adsorbent not as waste, but as a high-performance electrocatalyst. In direct urea fuel cell tests, this electrode achieved a superior and stable current density of 45.63 mA/cm² for Q-LDL/AC, substantially outperforming the pristine Q-LDH/AC/COL (206.63 mA/cm²) and highlighting how the captured pollutant enhances functionality. This dual-purpose approach successfully closes the loop, transforming the environmental liability of antibiotic-laden waste into a valuable resource for energy applications. With a production cost of 2.755 USD/g, this work presents not only a highly effective adsorbent but also a transformative, circular strategy that simultaneously addresses water pollution and energy recovery. These findings offer a promising dual-purpose solution for mitigating the environmental spread of antibiotic resistance through a sustainable cycle that enables efficient antibiotic removal from wastewater while simultaneously converting the captured pollutant into a useful energy resource. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly applied to drug safety evaluation, yet evidence is dispersed across lifecycle stages and tasks. This scoping review aimed to (1) map how AI supports safety- and treatment-related decision types across the drug lifecycle, and (2) examine evaluation strategies used to assess model reliability for clinical or regulatory use. Methods: Using Arksey and O’Malley’s framework, we searched a major database for studies published in the past decade that applied AI or machine learning to drug safety or medication-related decisions. After screening, we extracted data on lifecycle stage, decision type, AI methods, data sources, and evaluation strategies. A lifecycle–decision matrix was constructed to characterize application patterns. Results: AI applications were concentrated in real-world clinical care × patient-level safety prediction and post-marketing × safety surveillance, using EHRs, spontaneous reporting systems, and clinical text. Common methods included gradient boosting, deep neural networks, graph neural networks, and natural language processing models. This concentration reflects structural incentives favoring safety-oriented applications with readily available data and lower decision liability. Evidence for treatment optimization, regulatory decision modeling, and evidence synthesis was limited. Most studies used internal validation; external validation and real-world deployment were uncommon, indicating early methodological maturity and limited translational readiness. Conclusions: AI demonstrates strong potential to enhance drug safety—particularly in risk prediction and pharmacovigilance—but its use remains uneven across the lifecycle. By situating AI applications within explicit lifecycle stages and decision contexts, this review clarifies where progress has advanced, where translation has stalled, and why these gaps persist. Limited external validation and minimal real-world testing constrain clinical and regulatory adoption. These findings suggest that external validation and real-world testing may contribute to further advances in AI for drug safety. Full article

Methane emissions from abandoned coal mining operations represent a persistent environmental and safety challenge in post-mining regions undergoing urban redevelopment. As urban infrastructure expands over former underground workings, the uncontrolled migration of mine gas can compromise public safety, exacerbate greenhouse gas emissions, and undermine sustainable development goals. This study investigates the origin of methane emissions detected in an urban area of the municipality of Lupeni, Romania, following the commissioning of a new natural gas distribution pipeline installed within a historically mined perimeter. The emissions had not been previously reported and were unexpectedly discovered during technical inspections conducted after the gas network installation, highlighting the absence of historical data on gas presence in this area. This is the first documented case of an accidental discovery of methane emissions in an urban perimeter overlying historical coal mine workings in Romania, granting this study a pioneering status, both scientifically and in terms of urban risk management. The findings emphasize that administrative mine closure does not equate to risk closure, as latent methane emissions may reactivate during urban transformations (e.g., excavations, utility upgrades, drainage changes). To ensure a scientifically sound and sustainable risk assessment, an integrated diagnostic framework was applied, combining chronological field monitoring with chromatographic gas composition analysis. This methodology enabled precise attribution of the methane source to abandoned underground mine workings, excluding the public gas network as a contributor. Based on this diagnosis, a controlled drainage and methane recovery system was implemented, resulting in the elimination of detectable concentrations at all monitoring points by February 2025. The captured methane was redirected for local energy use, transforming an environmental liability into a usable resource. This intervention supports circular economy principles and aligns with EU climate and energy transition goals. The proposed methodological framework provides a replicable model for identifying and managing residual mine gas in post-industrial urban environments. Although emission fluxes were not quantified, concentration-based screening enabled risk diagnosis, prioritization, and targeted intervention. These findings are relevant to EU Regulation (2024/1785) on methane emission reduction, emphasizing the need to include post-mining methane (AMM) in urban planning and environmental monitoring strategies. Limitations of the study include the absence of baseline data and the inability to calculate total methane flux. However, the results support immediate and practical risk mitigation and highlight the need for future work focused on long-term monitoring and emission quantification. This case provides critical insights for other post-mining cities in Central and Eastern Europe facing similar challenges at the intersection of legacy coal infrastructure and modern urban development. This study is designed as a concentration-based diagnostic and risk-oriented case study and does not aim to quantify methane emission fluxes. Full article

Coastal regions concentrate livestock and fisheries activities that generate large volumes of organic residues, often managed inadequately and contributing to nutrient loading, soil degradation, and marine pollution. At the same time, these territories face increasing pressure to decarbonise energy systems and restore degraded soils under climate change. This article proposes an integrated conceptual framework for the valorisation of livestock and fisheries residues through hydrogen-centred energy recovery and biochar-based soil regeneration, with a focus on coastal regions of Colombia. The framework integrates biological and thermochemical conversion pathways, including anaerobic digestion, fermentation, gasification, and pyrolysis, within a unified system boundary that treats organic residues as secondary resources rather than environmental liabilities. Hydrogen is a transitional energy carrier enabling near-term decarbonisation within decentralised residue valorisation systems, while biochar is positioned as a key co-product enabling long-term carbon stabilisation and soil regeneration. By linking material and energy flows at the territorial scale and accounting for governance constraints and environmental vulnerabilities, the framework highlights the potential of decentralised residue valorisation systems. These systems can reduce coastal pollution, enhance soil resilience, and contribute to climate mitigation in fragile ecosystems. Full article

Orthopedic surgery is undergoing a transformation driven by artificial intelligence (AI), which is reshaping clinico-surgical decision-making. While the operative strategy and professional responsibility traditionally relied on the surgeon’s intuition and manual skills, advanced algorithms now provide predictive, analytical, and procedural decision supports. This paradigm shift is redefining the concept of human error as well as the relationship between technological tools and human decision-makers. As a result, the foundational elements of the healthcare liability framework are being affected. This paper offers a narrative discussion on selected applications of artificial intelligence in orthopedic surgical practice, including patient risk stratification, surgical indication and prosthesis positioning, with a particular focus on the liability implications for healthcare professionals who rely on these systems in terms of therapeutic decision-making. The aim is then to provide a comprehensive medico-legal perspective within the highly regulated and high-risk field of biomedicine, acknowledging and critically assessing the roles and responsibilities of all stakeholders involved—patients, healthcare professionals, innovative technologies, healthcare organizations, and facility management—while balancing innovation, evidence-based practice, and accountability in healthcare delivery. Full article

Background: From the perspective of product liability, this study explores how agricultural product e-commerce enterprises can enhance the quality of the agricultural product supply chain through quality incentive strategies. Methods: Based on a tripartite evolutionary game model, the strategic interactions among farmers, agricultural product e-commerce enterprises, and the government are analyzed. Results: The research finds that whether the system converges to the ideal equilibrium of “high-quality production—ex-ante quality cost-sharing—collaborative governance” depends on the combined effects of revenue distribution, liability costs, and external incentives or penalties. Among these, government-led collaborative governance plays a key guiding role in incentivizing enterprises and influencing farmers’ behaviors. The incentive measures implemented by e-commerce enterprises and government penalties can effectively curb farmers’ low-quality production behaviors. Conclusions: The study further reveals how factors such as ex-ante cost-sharing, liability allocation, and farmers’ conformity psychology affect the stability of agricultural product supply chain quality, thereby providing theoretical support for constructing a “policy-platform-farmer” collaborative governance framework. Full article

The Ovor Bayan molybdenum deposit in Mongolia is located within the western segment of the Nukhetdavaa–Erlianhot–Dongwuzhumuqin–Aershan Mo–Pb–Zn–W–Cu–Sn–Cr–Fe metallogenic belt in the Mongolia–Daxing’anling Metallogenic Province. This metallogenic belt lies in the Sino-Mongolian border region, where over ten large-sized deposits have been discovered on the Chinese side in the past two decades. However, the discovered deposits in Mongolia side are relatively small in scale, primarily medium to small-sized, with no large deposits identified to date. Therefore, strengthening research on typical deposits and summarizing metallogenic patterns in this area are the optimal approaches to achieving breakthroughs in prospecting. This study focuses on the Ovor Bayan deposit, a newly identified deposit of molybdenum mineralization within the western segment of the belt. We examine the deposit’s zircon U-Pb geochronology and Re-Os isotopic data. The SHRIMP zircon U–Pb dating of the ore-bearing granite indicates crystallization ages of 183.3 ± 1.9 Ma, which closely align with the mean Re-Os age of 183.1 ± 2.3 MaMa for the Ovor Bayan molybdenum deposit, suggesting an Early Jurassic magmatic event marked by Mo-dominated mineralization coinciding with the extensional tectonic setting following the southeastward subduction-collision of the Mongol–Okhotsk Plate. Regional data indicate that the Nukhetdavaa–Erlian–Dongwuqi–Aershan metallogenic belt experienced concentrated Mo-W mineralization between 240 and 131 Ma. The formation of Mo-dominated deposits, such as Ovor Bayan and Aryn nuur in the western segment of the belt, is at least 50 Ma earlier, which underscores the presence of a significant Mo metallogenic event during this critical post-collision to extensional mineralization period. The molybdenite sample exhibits Re contents ranging from (969.2–1209) × 10⁻⁶, suggesting a mantle-derived source for the molybdenum mineralization. Full article

Existing law provides no settled account of how deepfake sextortion should be characterised and regulated in England, Wales and Northern Ireland, creating uncertainty for charging, adjudication and platform compliance at the point when the Online Safety Act 2023 allocates duties to regulated services under Ofcom oversight. This article responds by analysing and synthesising the Online Safety Act 2023 with the Sexual Offences Act 2003 and residual harassment and communications offences, using doctrinal analysis and normative evaluation to identify points of alignment and misfit. It establishes criteria for identifying synthetic sexual coercion, including the elements that mark threat-stage conduct, the role of fabrication in the wrong, and the conditions under which epistemic harms should be treated as legally relevant within ordinary doctrine. It rejects three propositions: that intimate-image abuse is primarily a publication-based wrong; that an authentic image is a precondition for liability; and that content-led platform duties adequately address coercion before dissemination. This analysis specifies how courts and prosecutors should classify conduct and select offences, how services should operationalise risk assessment and mitigation for threat-stage harms, and which targeted reforms to offence design, platform duties and victim-facing procedures are required to secure predictable protection and effective redress. Full article

We examine whether gender influences formal tax compliance among self-employed taxpayers in Southern Albania—focusing on two observable behaviors: paying taxes on time and the amount of unpaid tax debt (arrears). The study does not examine tax evasion or tax avoidance, as these behaviors cannot be directly observed in the available data. Using administrative data on 500 taxpayers in Fier, Vlorë, Berat, Gjirokastër, and Sarandë (January 2022–March 2025), we estimate the likelihood of timely payment with logistic and probit models and study unpaid liabilities using linear regression. Female-led businesses are more likely to meet deadlines and hold lower unpaid debts than male-led firms. These differences persist across sectors after controlling for firm size, region, income, and time. A negative and significant Gender × Sector term indicates that sectoral composition does not offset women’s compliance advantage in these formal outcomes. The effect size is relatively large for an environment with imperfect monitoring, suggesting that moral norms, reputational concerns, and perceived control weigh more heavily where deterrence is limited. From a policy perspective, adding gender to compliance-risk models and tailoring taxpayer services may indirectly improve voluntary payments and reduce arrears by refining compliance-risk assessment and targeting. To our knowledge, this is the first study in Albania using official administrative microdata to analyze gendered formal tax behavior, addressing a clear empirical gap in Southeastern Europe and providing evidence relevant for discussions of fair and inclusive fiscal policy in an EU-harmonization context. While the findings are derived from Southern Albania, they offer indicative insights for comparable transition economies in Southeastern Europe, rather than direct generalization. Full article

Cross-border e-commerce, as a vital form of digital trade, is emerging as a new engine for corporate internationalization. This study employs China’s cross-border e-commerce pilot zones (established since 2015) as a quasi-natural experiment to investigate their causal effects on Chinese cities’ outward foreign direct investment (OFDI) and the underlying mechanisms. Distinct from previous trade-focused studies, this paper innovatively adopts a greenfield investment perspective. By integrating the Global Greenfield Investment Database (2010–2022) with the China City Statistical Yearbook, we constructed a greenfield OFDI dataset spanning the city–destination–target industry dimensions. Based on this dataset, this study employs a time-varying DID approach combined with PSM-DID, parallel trend tests, and placebo tests to empirically analyze how cross-border e-commerce development influences OFDI and its underlying mechanisms. The findings reveal that establishing cross-border e-commerce pilot zones boosts local outward investment by approximately 18.8%. A binary marginal decomposition analysis indicates that this effect primarily manifests through the extensive margin—significantly driving investment into new destination markets. Additionally, the mechanism operates by reducing information search costs and enhancing factor allocation efficiency. Furthermore, the outward investment promotion effect of cross-border e-commerce pilot zones is more pronounced in samples where the destination is a developed country, the target industry is high-tech, and the origin is eastern China. This study not only expands the dimensions for assessing the economic effects of cross-border e-commerce but also provides concrete empirical evidence for governments to optimize digital trade policy arrangements and for enterprises to leverage digital tools to overcome the “Liability of Foreignness” and achieve internationalization. Full article

Leishmaniasis remains a neglected tropical disease with treatment limitations driven by toxicity, cost, and emerging resistance. Trypanothione reductase (TryR) and glycerol-3-phosphate dehydrogenase (GPDH) are essential Leishmania enzymes supporting redox homeostasis and energy/redox-linked metabolism, providing a biologically grounded rationale for dual-target inhibition. We applied an integrated in silico workflow to prioritize candidate inhibitors using ADMET prediction (SwissADME/pkCSM), molecular docking (AutoDock Vina), and 100 ns molecular dynamics (MD) simulations; human GPDH was included as a negative control to probe potential off-target liability. ADMET screening identified 41 drug-like candidates, with most predicted to have high GI absorption and low toxicity flags across assessed endpoints (computational predictions interpreted cautiously). Docking highlighted two leading compounds. CID 6529858 showed the most favorable predicted binding to Leishmania GPDH (−8.9 kcal/mol) with a modest parasite-favored score difference versus human GPDH (−7.2 kcal/mol; Δ = −1.7 kcal/mol), while eupatorin (CID: 97214) displayed dual-target potential (TryR −7.5 kcal/mol; Leishmania GPDH −8.2 kcal/mol; human GPDH −7.2 kcal/mol; Δ = −1.0 kcal/mol). In MD, key complexes remained stable: CID 6529858 exhibited low GPDH backbone deviation (~0.25–0.40 nm), and eupatorin showed the most stable TryR trajectory (average RMSD ~0.45 nm), supported by generally low residue fluctuations across complexes. PCA further suggested ligand-associated restriction of large-scale motions (e.g., GPDH PC1 = 27.38%; TryR PC1 = 18.1%). Overall, these results nominate eupatorin as a promising dual-target lead and CID 6529858 as a strong GPDH-focused scaffold, warranting experimental enzyme inhibition, antiparasitic efficacy, and host–cell cytotoxicity testing to confirm potency and selectivity. Full article

The transcription factor Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) governs cellular redox homeostasis and serves as a primary defense mechanism against oxidative stress-driven cardiac remodeling. Beyond basal antioxidant effects, NRF2 coordinates a broad defensive network that preserves mitochondrial bioenergetics, maintains proteostasis, and inhibits regulated cell death pathways, including necroptosis and ferroptosis. Despite robust efficacy in preclinical models, translating these findings to the clinic remains challenging. This review examines the molecular structure of the NRF2-KEAP1 axis, synthesizing evidence regarding its efficacy in ischemia–reperfusion injury and diabetic cardiomyopathy, while assessing the mechanisms of pathway repression and the liabilities of indiscriminate activation. We further review different pharmacological strategies, contrasting the clinical limitations of electrophiles with the potential of protein–protein interaction inhibitors. Finally, we discuss innovations such as cardiac-targeted delivery and biomarker-guided stratification, critically assessing whether these approaches can overcome safety barriers and emphasizing that rigorous validation is essential for clinical viability. Full article

Colombia generates large volumes of lignocellulosic residues from agriculture, forestry, and agro-industrial activities. Much of this material is landfilled, openly burned, or left to decompose. These practices drive greenhouse-gas emissions (methane and CO₂), particulate air pollution, water contamination, and pest proliferation. Therefore, this study focuses on the design, simulation, exergetic and economic analysis of lignocellulosic biorefinery schemes in Colombia using corn stover (CS) as feedstock. This approach thus turns an environmental liability into valuable resources. Mass and energy balances obtained from Aspen Plus V10^® were used to calculate exergy efficiency. Economic indicators were provided by the Aspen Process Economic Analyzer (APEA) V10^® software. The first scenario (SCE01) included xylitol, lignin, carbon dioxide, biogas, and biofertilizer production along with in situ ethanol co-production; for scenario 2 (SCE02), a cogeneration (CHP) stage using biogas and biofertilizer as fuel was added; in scenario 3 (SCE03), the ethanol production of scenarios 1 and 2 was replaced by glutamic acid production. The exergy efficiency results were as follows: SCE01 (60.1%), SCE02 (36.8%), SCE03 (37.5%). The largest exergy losses were found in the CHP system. In terms of economic viability, all scenarios showed favorable economic parameters. SCE03 showed better results with an Internal Rate of Return (IRR) of 28.01% and a Net Present Value (NPV) of USD 985.1 M compared to SCE01 (27.48%; USD 769.1 M) and SCE02 (27.13%; USD 643.1 M). In light of these results, the SCE03 approach represents the most attractive investment opportunity, with the potential to integrate the social and environmental pillars of sustainability by fostering rural economic development and CO₂ capture. Optimization strategies can be readily adopted to enhance the overall efficiency of the proposed model, enabling it to serve as a benchmark for scaling and comparing alternative lignocellulosic waste valorization pathways at a national level. Full article

Effective clash management is critical to the success of complex construction projects, yet BIM coordinators face severe information overload when modern detection tools generate thousands or even millions of collision reports, making interdisciplinary coordination increasingly difficult. This article presents a conceptual framework for using AI for collision triage in a Building Information Modeling (BIM) environment. Previous approaches have focused mainly on collision detection itself and simple, rule-based prioritization, rarely exploiting the potential of Artificial Intelligence (AI) methods for post-processing of results, which constitutes the main innovation of this work. The proposed framework describes a modular system in which collision detection results and data from BIM models, schedules (4D), and cost estimates (5D) are processed by a set of AI components, offering adaptive, data-driven decision support unlike static rule-based methods. These include: a classifier that filters out irrelevant collisions (noise), algorithms that group recurring collisions into single design problems, a model that assesses the significance of collisions by determining a composite ‘AI Triage Score’ indicator, and a module that assigns responsibility to the appropriate trades and process participants. The framework leverages supervised machine learning methods (gradient boosting algorithms, selected for their effectiveness with tabular data) for noise filtering, density-based clustering (HDBSCAN, chosen for its ability to detect clusters of varying densities without predefined cluster count) for clash aggregation, and multi-criteria scoring models for priority assessment. The article also discusses a potential way to integrate the framework into the existing BIM workflow and possible scenarios for its validation based on case studies and expert evaluation. The proposed conceptual framework represents a step towards moving from manual, intuitive collision triage to a data- and AI-based approach, which can contribute to increased coordination efficiency, reduced risk of errors, and better use of design resources. As a conceptual study, the framework provides a foundation for future empirical validation and its limitations include dependency on historical training data availability and the need for calibration to project-specific contexts. Full article