Search Results (10,753)

Artificial intelligence (AI), business intelligence (BI) and analytics are increasingly used in hotel accounting, financial reporting and performance management. Existing research, however, has concentrated mainly on prediction, automation and operational optimisation. Their integration with the critical interpretation of accounting information and human judgement remains less clearly developed. This study provides a structured review and bibliometric mapping of research on AI-supported accounting and financial reporting in the hospitality industry. It adopts a structured bibliometric review combining science mapping with qualitative content-oriented interpretation. The review comprises three complementary search streams addressing: (i) AI, BI and analytics in hotel accounting; (ii) AI-supported reporting, dashboards and decision-support systems; and (iii) the broader contextual literature on human judgement and the interpretation of accounting information. The results identify machine learning, revenue management, forecasting and dynamic pricing as the most prominent and structurally influential areas within the retrieved literature. Research on reporting interpretation, dashboard-based judgement, anomaly detection and human validation is more fragmented and remains weakly integrated across the mapped literature. The principal research gap therefore concerns the limited connection between AI-supported technologies, reporting outputs and human interpretive judgement, rather than the absence of relevant research in these areas. The study proposes a human-centred perspective on AI in hotel accounting and identifies future research directions in which AI supports critical interpretation, professional judgement and responsible managerial decision-making. Full article

Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen and a leading cause of severe healthcare-associated infections, particularly ventilator-associated pneumonia (VAP) and bloodstream infections (BSIs) in critically ill patients. Its remarkable capacity for both intrinsic and acquired resistance leads to complex phenotypic profiles, culminating in difficult-to-treat resistant P. aeruginosa (DTR-PA). Managing DTR-PA presents a daunting clinical challenge characterised by a narrow therapeutic armamentarium, treatment delays, and high attributable mortality. To bridge the gap between complex molecular microbiology and bedside decision-making, this narrative review employs a pragmatic, clinical vignette-based approach. Through six representative fictional scenarios of pneumonia and BSI, we systematically dissect the underlying mechanisms of resistance and their direct therapeutic implications. The vignettes explore combinations of intrinsic adaptations—such as Pseudomonas-derived cephalosporinase (PDC/AmpC) hyperexpression, OprD porin loss, and efflux pump upregulation—as well as the acquisition of serine- (e.g., GES) and metallo-β-lactamases (e.g., VIM, NDM, IMP). Grounded in the updated 2026 Infectious Diseases Society of America (IDSA) guidelines, we evaluate the optimal deployment of newer β-lactam/β-lactamase inhibitor combinations. We highlight the specific preference for ceftolozane–tazobactam in pneumonia, the roles of ceftazidime–avibactam and imipenem–relebactam, and the critical use of cefiderocol for metallo-β-lactamase producers and highly resistant phenotypes. Furthermore, the review addresses pressing clinical controversies: the superiority of targeted monotherapy over historical, toxic combination regimens; the risks of unconditionally applying “shorter-is-better” duration paradigms to DTR-PA; and the alarming frequency of treatment-emergent cross-resistance among novel agents. We also clarify common clinical misconceptions, such as the limited utility of meropenem–vaborbactam and aztreonam–avibactam against specific pseudomonal mechanisms. Ultimately, effective DTR-PA management precludes class-based empirical assumptions. By providing a phenotype-driven bedside aide-mémoire, this review reinforces that rapid recognition, direct agent-specific antimicrobial susceptibility testing (AST), pharmacokinetic/pharmacodynamic (PK/PD)-optimised dosing, and prompt source control remain the absolute cornerstones of survival for patients afflicted by these formidable infections. Full article

Wireless sensor networks (WSNs) in critical environments need to conserve energy and deliver fresh information despite possible cyberattacks. Meeting these requirements remains a challenge. Classical protocols such as LEACH optimise energy but neglect security and freshness, while recent reinforcement-learning and fuzzy-trust methods address security at the expense of energy balance or data freshness. This paper proposes SecureFresh-WSN, a unified clustering and routing protocol that jointly balances the optimisation of energy consumption, cyberattack resilience, and Age-of-Information (AoI) freshness in terrestrial WSNs. The framework integrates (1) K-Means spatial clustering for stable topology formation; (2) a temporal anomaly-detection mechanism with per-node risk memory over a six-round sliding window; (3) a multi-criteria, trust-aware cluster-head (CH) selection mechanism that penalises suspicious, attacked, and energy-depleted nodes; and (4) confidence-weighted routing that prioritises fresh data delivery while reducing exposure to compromised forwarding nodes. MATLAB R2025a simulations were conducted for the network sizes of 100, 500, and 1000 nodes. Each of the four independent attack types (Denial of Service, False Data Injection, Selective Forwarding, Sinkhole) was injected independently with the probability of 0.015 per node per round, which led to an overall attack probability of approximately 5.87% per node per round. Over 20 independent random seeds, SecureFresh-WSN decreased the mean network Age-of-Information (AoI) by 66.4% compared to the LEACH protocol, increased the network lifetime (LND) by 28.4%, improved the packet delivery ratio by 11.3%, and reduced false suspicion of selected CHs by 41.7% in the case of 100 nodes, with similar trends observed for larger network sizes. Energy consumption was reduced in most cases while maintaining a good level of performance. Full article

Maritime unmanned aerial vehicles (UAVs) are increasingly transforming ocean, coastal, port, and offshore operations through rapid sensing, intelligent communication, autonomous perception, and cooperative decision support. This trend-oriented review analyses 471 peer-reviewed journal articles published between 2020 and March 2026, supported by selected foundational pre-2020 studies, to examine the evolution, technological structure, methodological maturity, and deployment readiness of maritime UAV systems. The bibliometric analysis shows rapid publication growth, from 22 papers in 2020 to 160 in 2025, and identifies five interconnected research clusters centred on maritime communications and networking; artificial intelligence (AI)-enabled perception, surveillance, and search and rescue; navigation and cooperative operations; distributed and federated intelligence; and multi-agent autonomous systems. The literature further shows a transition from task-specific aerial monitoring toward integrated maritime cyber–physical systems combining UAVs with vessels, unmanned surface vehicles (USVs), unmanned underwater vehicles (UUVs), edge computing, next-generation communication networks, digital twins, and AI-enabled decision support. However, evidence maturity remains uneven: many AI, communication, optimisation, and cooperative-autonomy methods are still evaluated predominantly using datasets or simulation, while fewer studies provide integrated system-level evidence under representative maritime conditions. The review therefore advocates a layered validation strategy combining high-fidelity scenario-based simulation, hardware-in-the-loop testing, controlled experiments, and representative maritime trials, with traceability between simulated and observed system behaviour. Deployment readiness additionally depends on environmental robustness, interoperability, cybersecurity, energy feasibility, human oversight, maintainability, and regulatory assurance. The review therefore proposes an evidence-gated roadmap to progress maritime UAV technologies from promising algorithms and prototypes to reliable, operationally deployable smart-ocean systems. Full article

Pneumatic separation is essential in paddy processing, but poor pre-segregation of rice husk–brown rice mixtures causes rice husk retention and brown rice loss. A stepped feeding chute was proposed in this study to enhance the gravity-driven segregation of rice husk–brown rice mixtures before pneumatic separation. The discrete element method was used to simulate particle motion under different step heights and analyze the effects of step height on particle bed porosity, vertical displacement difference, and motion trajectories. Results showed that segregation was governed by the coupled effects of density and surface-roughness differences: brown rice tended to percolate downward, whereas rice husks tended to interlock and remain in the upper layer. The degree of separation first increased and then decreased with increasing step height, owing to the combined influence of porosity-induced changes in vertical displacement difference and impact-induced trajectory fluctuations that promoted remixing. An optimal step height of 13 mm, approximately 1.86 times the brown rice kernel length, reduced rice husk retention by more than 30% and brown rice loss by more than 40%. This design provides an effective approach to improving pneumatic separation. It may also guide the design of segregation-enhanced conveying systems for heterogeneous particulate food materials. Full article

Decarbonising urban passenger transport is central to China’s carbon-peaking commitments and to sustainable cities, yet through which channel the built environment shapes travel carbon remains contested. This study applies a geocomputational framework—from trajectory-based emission accounting to counterfactual optimisation—to morning- and evening-peak taxi commuting carbon across 1494 grid cells and 29 built-environment indicators in central Shanghai. Because the taxi fleet was predominantly new-energy (electric), the outcomes are internal-combustion-equivalent carbon; taxi travel is a self-selected minority of trips, so the associations show where taxi commuting occurs, not a mode-independent effect. Higher density is associated with higher total commuting emissions predominantly through more trips, not higher per-trip carbon. Distance to the second-nearest centre and public-service point-of-interest density are the leading predictors, and empty cruising—about half of fleet carbon—co-locates with commuting demand. The framework diagnoses a definitional-coupling trap in accessibility-based optimisation, where an objective built from the decision variables mechanically registers a trade-off, and supplies a construction audit. Associations are predictive, not causal. For low-carbon urban planning, the results point to trip generation and deadheading, rather than per-trip intensity, as the channels to act on; we recommend treating essential-service accessibility as a minimum requirement rather than a tradable objective. Full article

Based on actual operational data from a methanol production enterprise in China, this study conducts a cradle-to-gate life-cycle carbon footprint assessment of bio-e-methanol. The carbon emission contributions of each process stage are systematically quantified and further extended to China’s seven regional power grids to evaluate their capability to meet China’s Grade A/B/C standards and the EU RED II carbon intensity thresholds under current power mixes. The results show that under the baseline scenario using the Chinese public power grid and coal-derived steam, electricity consumption is the largest contributor (76.6%) of the carbon footprint of bio-e-methanol, followed by steam (22.6%). The combined strategy of “ low-carbon electricity + biomass-derived steam” is critical for deep decarbonisation. Regional analysis reveals that the Southwest China grid, with its uniquely high low-carbon electricity share of 71%, can meet the Grade C standard without any adjustment, and the Grade A, B and EU RED II standards with only minor adjustments to its power mix. All other regions require varying degrees of low-carbon electricity penetration increases. Residual power composition is the dominant factor determining compliance requirements for carbon threshold, far outweighing regional differences in current low-carbon electricity shares. This study provides a quantitative basis for methanol producers in different regions of China to optimise their power structures and devise differentiated low-carbon transition pathways. Full article

Peritoneal dialysis (PD)-associated peritonitis remains a major cause of treatment failure, hospitalisation, catheter loss and transition to haemodialysis despite reductions in incidence over the decades. This narrative review examines the global evolution of microbiological epidemiology and pharmacological management of PD-associated peritonitis, looking at global trends in microbiological epidemiology and exploring how treatment recommendations have evolved in response to changing pathogen distribution, antibiotic resistance patterns and associated clinical outcomes. Although Gram-positive organisms remain the predominant pathogens in most PD programmes, recent evidence demonstrates marked geographical and centre-level heterogeneity with rising clinical importance of Gram-negative, polymicrobial, fungal and culture-negative infections in certain regions. Antibiotic resistance has emerged as an additional challenge, including methicillin-resistant staphylococci, vancomycin-resistant enterococci, extended-spectrum β-lactamase-producing Enterobacterales and multidrug-resistant non-fermenting Gram-negative organisms. These changes challenge conventional empirical regimens and emphasise the importance of programme-specific microbiological surveillance. In parallel, pharmacological management has evolved considerably from relatively standardised empirical antibiotic combinations towards an individualised approach incorporating intraperitoneal administration, local susceptibility profiles, organism-directed therapy, pharmacokinetic optimisation, therapeutic drug monitoring and antifungal prophylaxis. However, substantial evidence gaps remain, particularly regarding antibiotic resistance patterns globally, optimal dosing in automated PD, intraperitoneal stability and clinical efficacy of newer antimicrobial agents. Full article

The Artificial Electric Field Algorithm (AEFA) suffers from premature convergence and local minima entrapment, limiting its effectiveness in complex optimisation scenarios. To address these limitations, we propose the Lévy-Flight and Chaos-based Artificial Electric Field Algorithm (LCAEFA), which synergistically combines Lévy flight distribution for enhanced global exploration and chaotic dynamics for improved search diversity. The Lévy flight mechanism enables particles to perform strategic long-distance jumps guided by power-law distributions, while ten distinct chaotic maps introduce controlled perturbations that prevent stagnation in local optima. This dual enhancement creates an optimal balance between exploration and exploitation phases throughout the optimisation process. LCAEFA is rigorously evaluated on six benchmark functions spanning unimodal, multimodal, and fixed-dimensional categories, demonstrating superior convergence rates and solution quality compared to the original AEFA. Furthermore, we validate LCAEFA’s practical applicability by employing it as a trainer for Multilayer Perceptron (MLP) neural networks across five MLP training benchmarks: two real-world medical classification datasets (breast cancer, heart disease), one synthetic classification benchmark (XOR), and two synthetic function approximation tasks (sigmoid, cosine). Comparative analysis against ten state-of-the-art heuristic algorithms reveals that LCAEFA achieves up to 100% classification accuracy on the XOR benchmark and 88% on the breast cancer dataset. Statistical validation through Wilcoxon signed-rank tests confirms the significance of performance improvements. The integration of Lévy flight and chaotic dynamics successfully transforms AEFA into a robust optimiser capable of handling diverse optimisation challenges with enhanced convergence characteristics and superior solution quality. Full article

Decarbonisation is crucial for mitigating climate change, with ground source heat pumps (GSHPs) playing a key role in reducing reliance on gas for space heating. This study investigates the potential of an open-loop GSHP system in the Carboniferous Millstone Grit of Ilkley, West Yorkshire, using Ilkley Lido and surrounding sports facilities as an example heating demand. The feasibility of systems installed at depths less than 150 m is examined, considering both shallow and deeper geological conditions. Available data on subsurface geology, hydrogeology, and geothermal gradients are utilised to characterise the formations and target depths, with cross-sectional models developed to assess the potential. The Marchup Grit aquifer is identified as the primary target due to its relatively shallow depth (~90 m), expected subsurface temperature (~14.5 °C), and moderate transmissivity. Additional geothermal potential is also considered in the Warley Wise Grit and Pendleside Limestone. The study contrasts borehole and field data with literature findings, including measurements at outcrop level of the Marchup Grit. To assess the feasibility of an open-loop GSHP system, a doublet configuration is simulated, matching the estimated heat demand of the facilities. The results demonstrate that an open-loop doublet system is conditionally feasible within the Marchup Grit aquifer; however, long-term operational performance remains sensitive to thermal feedback (estimated at ~22 years analytically for a 300 m well spacing and 8–10 years numerically under a 130 m minimum spacing constraint at peak abstraction rates). By reducing abstraction to 70% of the peak heating demand, long-term sustainability can be improved. Overall, while the system exhibits preliminary potential, commercial implementation remains subject to confirmatory site-specific borehole drilling, hydrochemical sampling, and long-duration pumping tests to validate reservoir capacity and optimise system longevity. Full article

The construction sector faces environmental pressures from material use, waste, energy and carbon emissions. Building Information Modelling (BIM) and Design for Manufacture and Assembly (DfMA) can support environmental improvement through more integrated design, production and assembly. However, environmental benefits are often attributed to BIM-enabled DfMA without direct assessment, and it remains unclear which outcomes have been demonstrated and which mechanisms are associated with them. This study addresses this gap through a systematic literature review of 43 studies published between 2018 and 2026. Using an evidence-based management lens, the review examined BIM–DfMA integration, the environmental outcomes and mechanisms reported, and their evidential basis. Material efficiency and waste reduction were the most frequently reported outcomes and had the largest directly assessed evidence base, whereas carbon reduction, energy efficiency and circularity were less frequently assessed. Design optimisation, reduced rework, standardisation and reduced component variation were the most frequently identified mechanisms. The review proposes a conceptual framework showing that BIM–DfMA adoption alone does not establish environmental improvement and that environmental performance needs to be considered in relation to environmental criteria embedded in decisions, the material and construction system, and the lifecycle stages assessed. BIM–DfMA workflows should therefore include measurable environmental criteria, defined baselines and appropriate assessment boundaries to support consistent evaluation of environmental claims. Full article

This study investigated the dry sliding wear behaviour of Al₂O₃ particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al₂O₃ particle dispersion, minimal agglomeration, and strong interfacial bonding between the Al6061 and Al₂O₃ phases. With an increase in Al₂O₃ content, density and hardness increased by 1.3% and 33%, respectively. The Al6061–6 wt.% Al₂O₃ composite exhibited 40% higher wear resistance than the base alloy in dry-sliding conditions, with sliding distance varying between 0 and 10,000 m and load varying between 0 and 50 N. At lower loads and shorter sliding distances, abrasive wear dominated; as load and sliding distance increased, the dominant wear mechanism shifted to delamination and adhesion wear. Moreover, tribological testing at 6 wt.% reinforcement showed a 40% improvement in dry sliding wear resistance, with applied normal load and sliding distance varying between 10 and 50 N, and 1000 and 10,000 m, respectively. The specific wear rate was subsequently modelled and predicted using K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANNs), Random Forests (RFs), and Gradient Boosting Machines (GBMs). Among these, the RF model achieved the highest accuracy (R² = 0.946). Based on feature importance analysis, applied normal load and sliding distance are the most influential factors in wear. As a result, in dry-sliding conditions, Al6061-Al₂O₃ MMCs show a stable wear response, thereby improving dataset homogeneity and model performance. Overall, this study used experimental insights and predictive analytics to predict MMC wear behaviour. By employing ML models, composite design and wear can be optimised. Additionally, feature importance analysis showed that applied normal load and sliding distance best predicted wear rate. Full article

Climate change significantly threatens maize (Zea mays L.) production through rising temperatures, erratic precipitation, and extreme weather events, thereby jeopardising global food security. This review synthesises current knowledge on the impacts of climate change, abiotic stresses, crop simulation models, and adaptation strategies for maize. A bibliometric analysis of 485 articles published between 2015 and 2025 and indexed in the Web of Science was conducted using VOSviewer. The results showed a significant increase in publications (Mann–Kendall test, p = 0.0001; R² = 0.8207), with dominant research themes shifting toward climate-smart agriculture and sustainable intensification. Heat stress, drought, and nutrient deficiencies were identified as the primary abiotic constraints. DSSAT-CERES-Maize and APSIM emerged as the most widely used models for assessing climate impacts and the effectiveness of adaptation strategies. Effective adaptation measures include the use of resilient varieties, optimised water management, conservation agriculture, and diversified cropping systems. Future research should focus on integrated adaptation approaches, improved model validation, and long-term field studies to enhance maize resilience. This study provides a comprehensive overview of the research landscape and highlights critical pathways for ensuring sustainable maize production under changing climate scenarios. Full article

At present, the PCI ratio of a blast furnace is still regulated mainly by operator experience, because BF production data are high-dimensional, non-linear and strongly coupled. In this paper, for the key role of blast furnace (BF) pulverised coal injection (PCI) technology in the steel industry, grey correlation analysis (GCA) is introduced for the first time into the analysis of BF production data, and a method for determining the reasonable PCI rate of BF based on GCA and petrographic analysis is proposed. By collecting BF production data and applying GCA, the key factors affecting the PCI ratio of the BF and their correlation degrees were determined, and then the optimal PCI ratio interval of 175–181 kg/t was screened. In addition, petrographic analysis of BF dust at different PCI ratio stages was carried out to calculate the utilisation rate of PC, and the reasonableness of the optimal PCI ratio was verified by combining the comparison of key indexes and the analysis of combustibility of injection coal. With the determined optimal PCI ratio applied, the PCI ratio of the studied BF was increased from 167.0 kg/t to 180.75 kg/t, the coke ratio was reduced by 72.75 kg/t, and the utilisation rate of PC was increased from 79.44–89.03% to 92.71–96.86%. The results showed that this method can effectively optimise the PCI ratio, improve the utilisation rate of pulverised coal, reduce the production cost, and provide a scientific basis for the regulation of PCI ratio for steel enterprises. Full article

The present study investigates the shear behaviour of screw-glued and mechanical flange-to-web connections intended for cross-laminated timber (CLT) ribbed panels, with the aim of improving composite behaviour and enhancing the structural performance of long-span timber floor and roof systems. Fifteen CLT-glue laminated timber (GLT) specimens were divided into five groups. One group incorporated SHARP501200 (SHARP) serrated steel plates, while four groups used screw-glued connections manufactured with LOCTITE HB (HB) or LOCTITE HB XE (HB XE) polyurethane (PUR) adhesive used in CLT manufacturing. For the adhesive groups, the screws were installed either from the CLT-flange side or from the GLT-web side to evaluate the influence of the assembly configuration. The specimens were subjected to monotonic quasi-static loading, and the resulting load–slip and load–displacement relationships were evaluated in terms of peak resistance, initial stiffness, post-peak response, and failure mode. The mechanically connected specimens achieved an average maximum load of 39.57 kN, whereas the screw-glued groups ranged from 141.20 kN to 198.90 kN. Installing the screws from the GLT-web side increased the average load-bearing capacity. The screw-glued connections exhibited high initial stiffness and predominantly timber-related failure, while the mechanical connection showed lower resistance and a more gradual post-peak response. Nonlinear finite element models were developed in Verisim4D (V2026), in which the CLT–GLT interfaces were represented by cohesive-zone contact elements governed by a tri-linear shear stress–slip law, consistent with the shear-dominated (Mode II) failure mode observed experimentally for both joint types. The results obtained provide design-relevant parameters and modelling strategies for optimising CLT ribbed panel joints in ultimate and serviceability states, thereby contributing to the development of efficient and environmentally sustainable multi-storey timber structures. Full article