Search Results (4,908)

This study explored the associations among business intelligence (BI) capabilities and innovative performance (IP) in four- and five-star luxury hotels, while also examining the moderating key role of absorptive capacity (ACAP). Based on the Resource-Based View (RBV), the study conceptualised BI as a multidimensional construct comprising six key capabilities. Data were collected from a sample of 470 hotel managers, and the model was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results revealed that four BI dimensions (analytical decision-making culture, use of information in business processes, information access quality, and information content quality) have a significant positive association with IP. On the contrary, analytical capability and data integration did not exhibit a direct significant association with IP. The moderation analysis offered further insights, illustrating that ACAP can selectively strengthen the association between information content quality and IP, as well as between data integration and IP. These findings highlighted that, in the luxury hotel context, the value of BI depends not only on technological infrastructure but also on the firm’s ability to transform high-quality, integrated data into actionable knowledge. The study contributed to the literature by indicating the moderating role of absorptive capacity in the BI–IP relationship and by providing nuanced insights into how distinctive BI capabilities can drive innovation in a service-intensive setting. From a practical perspective, the results suggested that hotel managers should prioritise promoting a data-driven culture, improving data quality, and designing organisational learning capabilities to leverage BI for IP fully. Full article

Aberrant RNA splicing and metabolic reprogramming are defining hallmarks of cancer that were historically studied as parallel processes. Increasing evidence now reveals extensive crosstalk between these pathways, whereby RNA splicing reshapes metabolic circuits, and metabolic states reciprocally influence splice-site selection and spliceosome activity. In this review, we synthesize recent mechanistic insights into how splicing programs regulate metabolic adaptation across diverse cancer contexts. We discuss recurrent oncogenic mutations in spliceosomal components and dysregulation of RNA-binding proteins (RBPs) that drive alternative splicing events in key metabolic regulators, which promote metabolic plasticity required for tumor growth. We further examine how metabolites and nutrient-sensing pathways directly modulate splicing factor activity, spliceosome dynamics, and RNA processing. We also summarize a new mechanism of mitochondrial quality control mediated by retrograde signals from mitochondria to the spliceosome to enhance mitophagy of dysfunctional mitochondria. Full article

Building inclusive, high-density cities requires understanding vulnerable groups’ public space usage. While green exposure significantly impacts urban health, existing research frequently overlooks females’ specific needs regarding streetscape visual quality, green space structures, and daily travel experiences. To address this, the study investigates spatial disparities in Suzhou’s historic district. Utilizing multi-source data and mixed modeling strategies, including Partial Least Squares and Ordinary Least Squares (PLS-OLS) and eXtreme Gradient Boosting (XGBoost), the research analyzes how streetscape perceptions and green space characteristics affect female life satisfaction and expressed sentiment. Results indicate three main findings. (1) Streetscape visual features fundamentally drive subjective evaluations. Safe significantly enhances well-being, whereas boring and lively negatively impact life satisfaction, reflecting females’ acute sensitivity to environmental oppressiveness during daily travel. (2) Park diversity elevates expressed sentiment, while patch density positively influences life satisfaction, demonstrating the vital value of fragmented greenery for daily public space usage. (3) Boring precipitously diminishes life satisfaction after surpassing a specific threshold, while park diversity elevates expressed sentiment only after crossing a critical interval. The study establishes an integrated analytical framework linking visual perception, green space structure, emotional response, and satisfaction. These findings provide targeted strategies for enhancing inclusive urban design and optimizing green space allocation to improve streetscape safety and alleviate visual oppressiveness, thereby advancing gender social justice for vulnerable groups in historic districts. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Tropical coastal waters are increasingly recognized as critical reservoirs for virulent, antibiotic-resistant enteric pathogens, yet seasonal dynamics governing their spatial distribution remain poorly characterized. We hypothesized that hydrological shifts and anthropogenic nutrient enrichment drive the seasonal distribution, virulence profiles, and antimicrobial resistance (AMR) of Escherichia coli, Salmonella spp., and Shigella spp. in the Jaffna Peninsula, Sri Lanka. Across 25 coastal sites during dry and transitional seasons, we integrated physicochemical water quality assessment, culture-based enumeration, PCR-based virulence gene profiling, Minimum Inhibitory Concentration (MIC) assays, GIS mapping, and statistical analyses. Key water quality parameters, including ammonium, nitrite, and total phosphorus, showed significant seasonal variation (p < 0.05), reflecting distinct hydrological regimes across seasons. A total of 220 E. coli, 200 Salmonella spp., and 100 Shigella spp. isolates were examined for virulence gene profiles and antibiotic tolerance. E. coli was detected at 80–88% of sites, Salmonella spp. at 72–88%, and Shigella spp. at 32–48%. Among E. coli isolates, stx1 was detected at 20–28% of sites and eae at 16% across both seasons. The stn gene was detected in Salmonella spp. at 12–28% of sites seasonally. Virulence profiling confirmed STEC harbouring stx1, stx2, and eae; Salmonella spp. carried stn; and Shigella spp. possessed invasion-associated genes. Trimethoprim–sulfamethoxazole resistance was recorded in 63.2% of E. coli, 33.0% of Salmonella spp., and 31.0% of Shigella spp. isolates at the lowest tested concentration of 4 µg/mL., while ciprofloxacin and piperacillin–tazobactam retained greater efficacy. Correlation analyses revealed significant associations among faecal contamination, nutrient enrichment, and virulence gene prevalence, implicating untreated sewage discharge and eutrophication as likely ecological factors associated with pathogen occurrence. These findings designate the Jaffna coastal zone as a significant reservoir of virulent AMR enteric pathogens, underscoring the urgent need for integrated One Health surveillance and seasonally adaptive coastal water quality management. Full article

The growing complexity of software-defined automotive systems generates massive heterogeneous sensor and ECU data during real and virtual validation, and conventional rule-based analysis of such multivariate time series struggles under dynamic operating conditions, noise, and diverse fault scenarios. Deep learning-based anomaly detection has shown promising performance, yet existing approaches remain limited by static thresholds, insufficient robustness, and reduced interpretability. This study proposes an adaptive framework for intelligent fault detection in test recordings of automotive software systems (ASSs), integrating deep denoising autoencoders (DAEs), adaptive Gaussian thresholding, and explainable artificial intelligence (XAI) techniques. Four DAE architectures (ANN-, RNN-, GRU-, and LSTM-DAE) are systematically evaluated under different noise levels, system versions, and fault conditions, with detection thresholds that adapt dynamically to the statistical behavior of the reconstructed signals, thereby reducing false alarms under varying operating conditions. The framework was evaluated using real-world test recordings from IAV and Hardware-in-the-Loop (HIL)-based digital test drives, where ANN-DAE achieved the most robust detection performance, with F1-scores of 93.91% and 96.39% on the real and virtual test-drive data, respectively. Furthermore, the integration of XAI improved the transparency of anomaly interpretation at the signal level. Overall, the proposed framework shows strong potential for intelligent anomaly detection and quality assurance in safety-critical automotive systems. Full article

The mean stand diameter at breast height (DBH) is a key indicator of stand structure and productivity and is widely used in forest resource inventory and management planning. When using regional inventory data, nonlinear interactions between plot-level conditions and predictor variables can undermine the stability of traditional empirical equations across varying site qualities and stand densities. To improve the accuracy and robustness of inventory-scale predictions of mean stand DBH, this study utilized data from 854 forest plots and employed stand age, site class index (SCI), and stand density index (SDI) as independent variables. The predictive performance of traditional growth equations, machine learning models (Random Forest, XGBoost, LightGBM, and support vector machine), and deep learning models (MLP and CNN, ResNet, RNN) was systematically compared, and ensemble learning strategies were further applied to optimize model performance. The results indicated that the Weibull model based solely on stand age achieved the best fit (R² = 0.669). Incorporating SCI and SDI greatly improved model explanatory capability with R² rising to 0.838. XGBoost and CNN further improved predictive performance (R² = 0.852 and 0.861, respectively), while the ensemble model exhibited the highest goodness-of-fit (R² = 0.893), outperforming all individual models. Compared with linear regression, machine learning models demonstrated superior predictive capability. A feature importance analysis indicated that stand age, site quality and stand density together drive mean stand DBH prediction, among which stand age and stand structural characteristics are the dominant influencing factors, whereas SCI and SDI have comparatively weaker effects. Overall, the ensemble model substantially enhanced the prediction accuracy of mean DBH in Pinus yunnanensis stands, thereby providing for precision forest management and ecological function assessment. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

Against the backdrop of the transition of new-type urbanization towards high-quality development, the triple contradictions of population agglomeration, land constraints, and housing supply-demand imbalance have become increasingly prominent. The conventional binary framework of human–land relations can no longer meet the requirements of coordinated development within human settlement systems, creating an urgent need to examine the multi-system interactions among population, land, and housing in order to resolve spatial mismatch. Taking Guangzhou as a case study, this research integrates 2020 population census data, land-use data from the European Space Agency (ESA), housing-price data from the Anjuke platform, and multi-source data on related influencing factors, and conducts a systematic empirical analysis by combining coupling coordination analysis, a relative development model, and the geographical detector. The findings reveal that the coupling coordination level of population, land and housing in Guangzhou exhibits a concentric, ring-shaped distribution pattern with central agglomeration and peripheral decline. The relative development among the three systems can be classified into matching types including the core-differentiated type, the peripheral-imbalanced type, and the surrounding-equilibrium type. With respect to influencing factors, all pairwise interactions are of the bi-factor enhancement type, and the driving mechanism displays a three-stage dynamic evolution. This study enriches research on human–land relations, provides precise guidance for optimizing spatial allocation and alleviating housing mismatch conflicts in Guangzhou, and offers transferable practical experience for comparable cities in China seeking to advance the high-quality development of new-type urbanization. Full article

Background. First-mile walking to mass rapid transit (MRT) has two methodological problems. Composite walkability scores blur which features drive walking. And because walking to transit is itself transport physical activity (PA), linking it to total PA is circular. Both issues are sharper in tropical Asian cities. Methods. We surveyed 378 adults within a 1 km network distance of 20 stations on Bangkok’s Pink Line MRT. Walkability was measured with NEWS-A (aggregate and eight subscales); PA with the GPAQ. Binary logistic regression with station-cluster-robust standard errors tested which NEWS-A subscales predict first-mile walking and whether walkers meet the WHO PA guideline (≥150 min/week MVPA). A tautology sensitivity test removed transport PA from the outcome. Results. Walkers were 71.7% of the sample. Disaggregating NEWS-A improved fit; two subscales were the dominant predictors: pedestrian infrastructure and traffic safety. Walkers were 30.6 percentage points more likely to meet the overall PA guideline; with transport PA removed, the gap was 17.5 points and still significant. The pedestrian infrastructure effect was strongest 201–1000 m from a station, not at the immediate frontage. Conclusions. Perceived pedestrian infrastructure quality and perceived traffic safety drive first-mile walking in suburban Bangkok. The walking–PA link is not entirely a measurement artefact. The 201–1000 m ring is a plausible priority for pedestrian investment. Full article

The integration of the digital and real economies is a pivotal engine driving the development of new, quality productive forces. Tourism ecological governance is the concrete manifestation of the green dimension of new-quality productive forces in the cultural and tourism sector, as well as being a path for converting ecological value to drive regional sustainable development. The relationship and mechanisms between digital–real integration and tourism ecological governance are critical issues requiring urgent breakthroughs. However, existing research primarily explores the economic factors influencing tourism ecology and has yet to systematically reveal the intrinsic mechanisms through which digital–real integration affects tourism ecological efficiency from the perspective of typical ecological functional zones. Based on data from 78 counties (municipalities, districts) in China’s Taihang Mountains from 2011 to 2023, this study examines the impact of digital–real integration on tourism ecological efficiency and its operational pathways. The findings are as follows: Firstly, from a temporal evolution perspective, tourism ecological efficiency in the Taihang Mountains underwent a phase of dynamic adjustment and gradual improvement between 2011 and 2023, while the level of digital–real integration experienced a phase of general enhancement and phased advancement. From a spatial evolution perspective, neighboring sub-regions within the Taihang Mountains exhibit positive spatial correlations in terms of both digital–real integration and tourism ecological efficiency. From the perspective of spatiotemporal transfer characteristics, changes in tourism ecological efficiency and the level of integration of the digital and real economies in the Taihang Mountains are influenced by neighboring regions. The development processes of tourism ecology and digital–real integration exhibit a relatively stable and gradually improving pattern, driving the agglomeration of regions toward higher levels. Secondly, digital–real integration has a positive impact on improving tourism ecological efficiency by releasing ecological pressure, promoting industrial synergy agglomeration, and driving green innovation development. Heterogeneity analysis reveals that the positive effect of this integration on tourism ecological efficiency is more pronounced in national e-commerce demonstration cities. Digital–real integration has had a positive impact on improving tourism ecological efficiency in the Southern and Western Taihang Mountain regions, while its impact on the Eastern Taihang Mountain region was not statistically significant. This study incorporates digital–real integration with tourism ecological efficiency, as well as environmental, structural, and capacity factors, into a unified analytical framework, providing theoretical references and practical insights for exploring pathways of digital transformation and innovative tourism ecological governance in ecologically sensitive functional zones. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional–integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions. Full article

Plant growth-promoting rhizobacteria (PGPR) foster sustainable and environmentally friendly agriculture by promoting plant growth and development. PGPR colonize the root rhizosphere, rhizoplane and root tissues, where they drive organic matter turnover and nutrient cycling, thereby increasing the (phyto)availability of essential macro- (P, N, K, S, Ca, Mg) and micronutrients (Fe, Zn, Mn, Mo, Co, Ni, Cu, B). This process relies on various mechanisms, including acid secretion (rhizospheric acidification and metal chelation), siderophore production (binding Fe, Zn, and other metals) and hydrolytic enzyme-mediated catalysis (phosphatases, phytases). Some of these microorganisms can also modulate the phytohormonal balance, reshaping root architecture and enhancing nutrient uptake, and even can alleviate abiotic stress or serve as biocontrol agents, contributing to pathogen resistance. Even though plant cultivation practices relying solely on synthetic fertilizers rapidly increase crop yield and productivity, they eventually result in crops poor in essential micronutrients and trace elements. This may contribute to micronutrient malnutrition in the human population. On the contrary, PGPR enhance both crop yield and nutritional quality. Therefore, in utilization with other nutrient sources, PGPR provide a promising and scalable approach towards advancing environmentally sustainable agriculture systems. Full article

Understanding the coordinated development of production, living, and ecological (P–L–E) functions is critical for sustainable watershed governance in rapidly transforming regions. Using the Chaohu Lake Basin, China, as a case study, this study developed a process–pattern–potential–driver framework for watershed-scale P–L–E coordination analysis from 2000 to 2020. Unlike previous studies that mainly assess coordination levels or map spatial patterns, this framework further identifies subsystem constraints, quantifies coordinated development potential, and determines key factors driving spatial differences. The results show that production and ecological functions remained weakly coordinated, indicating persistent tension between economic growth and ecological protection. In contrast, the relationships between production and living functions and between living and ecological functions improved from strong imbalance to moderate coordination. Spatially, higher coordination levels were concentrated in the southwestern basin. Decoupling analysis further reveals that production activities, especially the energy-intensive secondary industry, were the main constraint on ecological function. In addition, 88.2% of the basin showed an increasing trend in coordinated development potential. Land-use patterns, socioeconomic conditions, and eco-environmental quality were identified as direct drivers, whereas climate change mainly acted indirectly. By linking diagnostic results with spatially differentiated management needs, this study provides a basis for more targeted watershed governance. Full article

Daqu is a key starter used in Baijiu production, and its microbial composition and associated metabolic functions play critical roles in fermentation performance and flavor development. This work aimed to reveal how Daqu-making temperature regulates microbial community divergence and subsequent metabolite formation via multi-omics analysis so as to provide theoretical guidance for Daqu quality control. In this study, physicochemical analysis, metagenomic sequencing, and metabolomic profiling were combined to investigate the microbial community structure, functional differentiation, and metabolite characteristics of nine Daqu samples collected from six major Baijiu-producing regions in China. The temperature during Daqu preparation was found to be a primary factor driving microbial community assembly and functional specialization. Medium-temperature Daqu exhibited higher saccharifying activity (up to 867 U) and greater microbial diversity with the enrichment of amino acid metabolism-related pathways, indicating enhanced protein degradation and amino acid utilization for the formation of flavor precursors. In contrast, high-temperature Daqu showed stronger capacities for carbohydrate degradation and conversion, particularly in starch and sucrose metabolism, which were closely associated with the enrichment of thermotolerant fungi and bacteria. LEfSe analysis identified 47 distinct microbial biomarkers (LDA score > 3.0), which could differentiate between medium- and high-temperature Daqu. Redundancy analysis indicated that environmental factors (moisture and acidity) together with functional properties (fermentation, esterification, liquefaction, and saccharification) act as key drivers of microbial functional patterns. Metabolomic analysis further revealed that medium-temperature Daqu had higher abundances of esters and fatty acids, whereas high-temperature Daqu had higher proportions of alcohols and ketones. Taken together, these results provide a multi-omics perspective on temperature-driven microbial functional differentiation in Daqu and offer a scientific basis for quality-oriented regulation and process optimization in Baijiu production. Full article