Search Results (4,606)

The study analyzes agricultural labour productivity in the context of the economic dimension of sustainability and the idea of European Union (EU) cohesion. This idea constitutes a central principle of European integration. The basis for implementing the concept of cohesion in European agriculture is the convergence of labour productivity levels. Convergence in this area forms the foundation of economic sustainability and serves as a prerequisite for the social dimension of sustainability, while often also being an underlying factor in environmental sustainability. The analysis concerns the productivity of labour in farms by the economic size, both at the national and regional levels, based on Farm Accountancy Data Network (FADN) data for the years 2007–2022. The β and σ-convergence methods were used. The results indicate that processes of labour productivity convergence occur in EU agriculture. This phenomenon was manifested by a decline in the heterogeneity of labour productivity levels among agricultural holdings. The fastest reduction in regional diversity was observed among the group of the largest economically farms (GE6). However, the dispersion of labour productivity levels remains considerable, and the rate of convergence continues to be slow. The convergence of labour productivity in agriculture will not accelerate without widespread and comprehensive structural changes in the sector, extending beyond mere changes in land use patterns. Full article

Operable windows are critical for indoor environmental quality (IEQ) and occupant agency, yet their usability is increasingly compromised by conflicts between regulatory compliance and building performance. This study investigates the gap between geometrically compliant provisions and effectively operable windows through a comparative policy analysis of mandatory codes (Level 1), green rating systems (Level 2), and regenerative frameworks (Level 3). The findings identify a structural discrepancy termed the Geometric Trap: while minimum opening areas are legally required, mechanical ventilation often substitutes for natural access. In the United States, Japan, and Republic of Korea, explicit waivers permit full substitution, while in the United Kingdom, conditional constraints such as environmental noise limit practical operability. Germany, by contrast, maintains operable windows as an independent mandate, restricting substitution to defined environmental conditions. Although emerging green rating systems increasingly recognize resilience and adaptive comfort, operability remains optional. Regenerative standards, however, treat it as a prerequisite for occupant health. This study proposes a shift from static geometric compliance toward an Effective Opening Area framework that evaluates actual accessibility and usability, advancing a performance-based and occupant-centered regulatory perspective. Full article

Safety production constitutes a core pillar of operational management for power grid enterprises. Assessing the safety production risks of target entities in mergers and acquisitions (M&A) is a prerequisite for strengthening safety governance, and it holds significant value for elevating the safety levels of power grids, equipment, and personnel. To address the issues of inconsistent assessment dimensions and over-reliance on empirical judgment in safety production risk evaluation during power grid M&A activities, this paper proposes an assessment model that integrates due diligence information with hybrid multi-attribute decision-making (MADM). By systematically identifying safety production risk factors throughout the M&A process, an indicator system encompassing four dimensions—physical constraints, management systems, historical performance, and dynamic adaptability—is established. A game-theoretic approach is adopted to combine the Level-Based Weight Assessment (LBWA) method and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method for subjective–objective integrated weighting. Additionally, grey relational analysis (GRA) is introduced to refine the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) algorithm, enabling quantitative evaluation of risk levels. Case analysis results demonstrate that the proposed model can effectively distinguish risk discrepancies across different M&A scenarios with rational weight allocation for key indicators. Compared with traditional methods, it maintains ranking consistency while exhibiting higher discrimination efficiency, thus providing a scientific and effective risk assessment tool for power grid enterprises’ M&A decision-making. Full article

As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m. Full article

This study examines religion as a potent pre-socialisation factor in modern military socialisation, exploring how sacred roots and transcendent anchors influence the formation of military identity. By synthesising Ecological Systems Theory, the Religion–Military Model, and an Integrative Model, the analysis frames religiosity as a multidimensional construct that shapes integration across macro (societal), meso (organisational), and micro (individual) levels. The research reveals the dualistic nature of religious influence. On the one hand, religious pre-socialisation instils a habitus defined by normative commitment, sacrificial ethics, and ritual familiarity. These elements facilitate Person–Organisation fit and act as catalysts for identity fusion, where personal agency is united with the group’s strength. On the other hand, the study identifies a critical theological and psychological vulnerability: moral injury. When absolute religious commandments—such as the sanctity of life—collide with the lethal demands of combat, an irresolvable normative conflict arises, mirroring historical tensions between the Christian conscience and the sacramentum. By identifying strategic intervention points for chaplaincy and leadership, the study demonstrates that integrating the religious dimension is not only an ethical duty but a prerequisite for maintaining triadic equilibrium, resilience, and institutional stability. Full article

In public safety scenarios, the precise detection and positioning of prohibited weapons such as firearms and knives along with the involved personnel are the core pre-requisite technologies for violent risk warning and emergency response. However, in security surveillance scenarios, there are common problems such as object occlusion, difficulty in capturing small-sized weapons, and complex background interference, which lead to the shortcomings of existing general object detection models in the tasks of detecting and locating security-related objects, including poor adaptability, low detection accuracy, and insufficient robustness in complex scenarios. Therefore, this paper proposes a threat object detection framework for security scenarios (PDGV-DETR) based on adaptive dynamic convolution and cross-scale semantic fusion, specifically optimized for the detection and positioning tasks of weapons and personnel objects in static security surveillance images. This research focuses on category recognition at the object level and pixel-level spatial positioning, and does not involve the classification and identification of violent behaviors based on temporal information. There are clear technical boundaries and scene limitations between the two. This framework is optimized through three core modules: designing a dynamic hierarchical channel interaction convolution module to reduce computational complexity while enhancing the ability to detect occluded and incomplete objects; constructing an improved bidirectional hybrid feature pyramid network, combining the cross-scale fusion module to strengthen multi-scale feature expression, and adapting to the simultaneous detection requirements of small weapon objects and large personnel objects; and introducing a global semantic weaving and elastic feature alignment network to solve the problem of low discrimination between objects and complex backgrounds. Under the same experimental configuration, the proposed model is verified against current mainstream models on typical datasets: on a dataset of 2421 conflict scene personnel violent images, the peak average precision mAP50 of PDGV-DETR reached 85.9%. Through statistical verification, compared with the baseline model RT-DETR with an average value ± standard deviation of 0.840 ± 0.007, the average value ± standard deviation of PDGV-DETR reached 0.858 ± 0.004, demonstrating statistically significant performance improvement, with a p-value less than 0.01. This model can accurately complete the task of locating the object area of personnel, and compared with the deformable DETR, the accuracy improvement rate reached 15.1%.; on the weapon-specific dataset OD-WeaponDetection, the mAP for gun and knife detection reached 93.0%, improving by 2.2% compared to RT-DETR. Compared to the performance fluctuations of other general object detection models in complex security scenarios, PDGV-DETR not only has better detection and positioning accuracy for security-related objects, but also significantly improves the generalization and stability of the model. The results show that PDGV-DETR effectively balances the accuracy of positioning, detection, and computational efficiency, accurately completing end-to-end detection and positioning of weapon and personnel objects in static security surveillance images, demonstrating highly competitive performance in the detection and positioning of security-related objects in security scenes, providing core object-level pre-processing technology support for scenarios such as public area monitoring, intelligent video monitoring, and early warning of violent risks, and providing basic data for subsequent violent behavior recognition based on temporal data. Full article

Soybean lodging severely impairs yield and quality, and its precise grading is a key prerequisite for intelligent agricultural management and loss assessment in agricultural insurance. Most existing studies have focused primarily on soybean lodging identification and lodging resistance evaluation, whereas methods for the precise differentiation of lodging grades remain to be refined. This study presents an improved AlexNet model integrated with a Local Feature Aggregation (LFA) attention mechanism and a dynamic optimization strategy for the accurate grading of soybean lodging. RGB imagery of soybean canopies during the grain-filling to early maturity stages was acquired via a multispectral unmanned aerial vehicle (UAV). A dynamic Dropout strategy was adopted to enhance model stability and mitigate overfitting, and the Particle Swarm Optimization (PSO) algorithm was employed to intelligently optimize key hyperparameters of the model. The results demonstrate that the optimized model achieved an overall accuracy of 94.23% on the test set, with an average loss of 0.0682 and an inference speed of 0.422 s/step. In independent field validation, the grading accuracies for the five lodging grades were 90.12%, 86.35%, 89.47%, 88.93%, and 92.76%, respectively, with a mean accuracy of 89.53%. The proposed model enables the rapid and precise grading of soybean lodging under field conditions, thereby providing effective technical support for intelligent field management and disaster loss assessment in soybean production. Full article

Intelligent vehicles represent a frontier in technological innovation. Effectively identifying their key core technologies and primary competitors is a crucial prerequisite for overcoming industrial technological bottlenecks, playing a pivotal role in promoting sustainable industrial development and enhancing global market competitiveness. This study is based on 46,373 authorized invention patents in the field of intelligent vehicles from 1950 to 2024 and based on four core characteristics of key core technologies: technological centrality, technological value, economic value, and competitive monopoly. Combining the entropy weight method and gray correlation analysis method, it effectively identifies 15 key core technologies in the field of intelligent vehicles, including G05D1, B60W30, G08G1, etc. These technologies cover four core domains: autonomous driving and vehicle control, intelligent transportation and vehicle–road coordination, onboard computing and data processing, and powertrain system integration and optimization. Building on this foundation, the study analyzes the technological competitive landscape from both national and corporate perspectives. The results show that the United States and Japan, with their profound technological accumulation, demonstrate strong competitive strength. China leads globally with 25.56% of worldwide patents, exhibiting rapid growth in R&D scale. However, the technological influence of key core technology patents held by major Chinese enterprises still lags significantly behind that of the United States and Japan, indicating room for improvement in R&D quality. By precisely identifying core R&D directions for intelligent vehicles, this study provides strategic guidance and practical references for optimizing green innovation resource allocation within the industry. It aims to overcome key technological bottlenecks in low-carbon intelligent vehicles, thereby achieving breakthroughs in key core technologies and enabling high-quality, sustainable industrial development. Full article

Aligning system design with evolving user expectations remains a critical challenge in contemporary digital markets. While online reviews offer vast potential for informing product development, transforming unstructured feedback into actionable system intelligence requires rigorous analytical frameworks. This study proposes a unified framework that synthesizes topic-related text analysis, sentiment analysis, and time-series trends to model user requirements as indicators of multidimensional system value. Based on this framework, we introduce the Product Online User Perception Score to quantify user perception of product attributes through the integration of attention, discussion richness, and sentiment. Crucially, a User Requirement Value model is developed to assess the strategic priority of requirements. The model applies a discussion richness dimension to filter superficial noise and employs a reverse valuation mechanism to identify systematic gaps between high attention and low satisfaction. Comparative evidence from the Chinese automotive market highlights the evolution of user needs during the transition from fuel-powered to new energy vehicles. While manufacturers prioritize enterprise-centric intelligent features, user dissatisfaction is systematically concentrated on basic ergonomic deficits, revealing that foundational operational value remains a prerequisite for overall system success. This study shifts the analytical paradigm from descriptive monitoring to diagnostic system valuation, providing a measurable and diagnostic instrument for supporting evidence-based product iteration. Full article

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality. Full article

Understanding hydrological, agricultural, and environmental processes in soils relies on accurately measuring volumetric water content (θ), matric potential (h), and hydraulic conductivity (K). These parameters are fundamental for quantifying plant-available water, optimizing irrigation scheduling in precision agriculture, modeling watershed responses, and studying the impacts of climate change in complex ecosystems. Among these parameters, θ is truly indispensable, as it represents the primary indicator of the water status of soils and a prerequisite for interpreting the other hydraulic variables. In recent years, capacitive sensors have become one of the most widely adopted technologies for θ estimation, owing to their favorable balance between accuracy, robustness, and affordability. These sensors infer soil moisture by measuring dielectric permittivity of soils, which is strongly governed by water content, making them particularly suitable for distributed monitoring and IoT-based environmental applications. The present study aimed to develop a low-cost capacitive sensor for θ estimation. This sensor can be made using 3D printing technology combined with conductive, nickel-based paint, which (once applied on the 3D-printed guides) forms the capacitive electrode. The capacitive component operates at an operational frequency of 60 MHz. The system was subjected to a rigorous testing protocol, including calibration and validation phases under laboratory conditions using three soils of different textures. Its performance was specifically compared with the time-domain reflectometry (TDR) technique, which is widely recognized in Soil Physics and Soil Hydrology as the reference method for θ estimation due to its reliability and accuracy. These tests confirmed the effective performance of the proposed sensor, which overall exhibited good reliability within the selected validation range, corresponding to a θ range of 0 to 0.40 cm³/cm³. Full article

Social cohesion is a core dimension of social sustainability and a prerequisite for inclusive, resilient cities. Rapid rural-to-urban migration often exceeds the capacity of cities to accommodate newcomers, leading many immigrants to settle in informal neighborhoods. These areas, typically composed of diverse ethnic groups with distinct cultural, linguistic, and social backgrounds, frequently face challenges in building social cohesion. This study examines how physical elements of public spaces influence social cohesion in multiethnic informal settlements, using the Hesar Imam Khomeini neighborhood in Hamadan, Iran, as a case study. Hesar, with its rural origins and recent influx of Lor, Kurdish, Turkish, and Fars migrants, provides a unique setting to explore the relationship between the built environment and interethnic relations. A conceptual model was developed based on existing literature, and data were collected through a questionnaire survey using a Likert scale. Partial least squares structural equation modeling (PLS-SEM) was applied to test the hypothesized relationships. The findings demonstrate that physical factors shape social cohesion through a three-stage mechanism: they first foster social interactions among residents, which then contribute to the development of social capital, and ultimately lead to greater social cohesion and integration. These results highlight how inclusive public-space design can support community-based informal-settlement upgrading and sustainable urban development, by strengthening social sustainability outcomes such as cohesion, integration, and resilience. Full article

Accurate prediction of slope displacement is an important prerequisite for building an effective geological hazard early warning system for disaster prevention and reduction. However, the inherent nonlinearity and time-varying characteristics of slope displacement evolution greatly affect the prediction accuracy. To improve the slope displacement prediction accuracy, a multi-modal data-driven Bayesian-optimized Convolutional Neural Network and Long Short-Term Memory (Bayes-CNN-LSTM) model was constructed. The performance of the model was evaluated using multi-modal monitoring data from the GuShan mine slope. Experimental results showed that the Bayes-CNN-LSTM model achieved an average coefficient of determination (R²) of 0.971, with a mean absolute error (MAE) of 0.444 mm and a root mean square error (RMSE) of 0.618 mm. Compared with the CNN-LSTM, LSTM, CNN, SVM, TCN, and Transformer models, the MAE of the constructed model was decreased by 25.1%, 31.3%, 32.3%, 24.1%, 24.7%, and 17.7%, respectively, and the RMSE decreased by 20.1%, 26.9%, 29.5%, 18.0%, 20.7%, and 12.4%, respectively. Furthermore, the proper integration of multi-modal data can effectively improve the prediction accuracy when extrapolating slope displacement. Based on rainfall and earth pressure data, the average MAE and RMSE of extrapolation (24-h) prediction using the constructed model were decreased by 30.2% and 24.6%, respectively. The model effectively improves the accuracy of slope displacement prediction and enhances the practicality of the slope safety monitoring system, providing valuable reference for slope safety monitoring. Full article

Planned cutting is a core technique for intelligent coal mining, relying on high-precision geological models of fully mechanized mining faces to plan the cutting trajectory of mining equipment, with model accuracy as a prerequisite for intelligent mining. To address the limitations of traditional interpolation methods in dynamic model updating and the technical gap between geological information and equipment control parameters, this study proposes a coal mining machine cutting path control method based on dynamic geological models. An improved smooth discrete interpolation method is developed to realize dynamic updating of the geological model, effectively improving the accuracy of local geological models and ensuring safe mining operations. Meanwhile, a method for converting geological information into coal mining equipment control parameters is proposed, breaking the technical barrier between geological data and production control information and laying a foundation for unmanned and intelligent mining. Field tests conducted in a shaft coal mine in Shaanxi demonstrate that the method achieves precise control of the coal mining machine’s trajectory: during a 7-day trial, the working face advanced 56 m and mined 51,000 tons of coal with minimal human intervention. Comparative analysis shows that the error between the planned cutting based on the dynamic geological model and manual cutting is within 10 cm, and the drum height curve is smoother, reducing frequent adjustments and facilitating equipment protection. Dynamic model updating ensures high accuracy, with an average absolute error of 0.029 m at 5 m from the update point and 0.101 m at 10 m, meeting the requirements for automated cutting. The successful application of this method verifies its feasibility in actual mining processes, providing a new technical approach for achieving unmanned and intelligent coal mining. Full article

Time-resolved (TR) resonance Raman (RR) spectroscopy with continuous-wave excitation is a fundamental technique that has contributed substantially to the understanding of the structure and dynamics of retinal proteins. However, the underlying principles were developed about fifty years ago for instrumentation that is hardly in use anymore. Thus, the adaptation of the technique to the current state-of-the-art equipment is needed to satisfy the increasing demand for the spectroscopic characterization of novel retinal proteins. In this work, we focus on pump–probe TR RR experiments with a confocal spectrometer using a rotating cell. We define the parameters ensuring fresh-sample condition and the photochemical innocence of the probe beam as a prerequisite for studying retinal proteins that undergo a cyclic photoinduced reaction sequence. For the measurements of intermediate states and reaction kinetics, pump–probe experiments are required in which the two laser beams hit the flowing sample with a defined but variable delay time. An appropriate set-up for such two-beam experiments with a confocal spectrometer is proposed and tested in TR experiments of bacteriorhodopsin. The comparison with the results obtained with classical slit spectrometers using a 90-degree scattering illustrates the advantages and disadvantages of the confocal arrangement. It is shown that modern confocal spectrometers substantially decrease the spectra acquisition time but require a more demanding optical set-up. Furthermore, the extent of photoconversion by the pump beam is lower than for the 90-degree-scattering arrangement, which reduces the accuracy of kinetic measurements. Full article