Search Results (1,193)

The study of dialects leads very naturally to the study of their geographic distribution and the nature of the distribution, e.g., by examining whether the distribution is based simply on geographic distance or on relatively distinct dialect regions. Dialectal dynamics poses the further question of why the distribution takes the form it does. Does variation arise through migration, i.e., due to the relative lack of communication among people who live far from one another? Sociolinguists have shown convincingly that variation is often employed to indicate identification with others, leading to the adoption of speech habits and changes in the distribution of variation. Purely linguistic processes may push some varieties toward change while others are more resistant, and contact with other languages and dialects, including particularly standard languages, almost inevitably results in changes. This volume examines studies in the area of dialectal dynamics, including studies focused on methods that promise to illuminate this complex field. Full article

This study explores tourist motivations through a mixed-methods approach, combining qualitative coding of open-ended responses with quantitative network analysis. By examining why vacationing is important, we identified eight motivation categories including Physical & Mental Renewal, Social Bonding, and Novelty & Adventure. Network analysis revealed significant co-occurrence patterns between motivations, challenging traditional push–pull frameworks by demonstrating that travelers simultaneously hold multiple, sometimes paradoxical desires. Demographic comparisons showed that women emphasize relaxation and rejuvenation, while men prioritize novelty and exploration. Age-related differences revealed younger travelers seek adventure and personal growth, while middle-aged participants valued family time and relaxation. Our findings demonstrate how tourist motivations function as interconnected constellations rather than isolated factors. By highlighting tensions such as comfort versus sustainability, digital detox versus connectivity, and novelty versus familiarity, the study illustrates how motivational paradoxes can inform destination management strategies. These results offer practical guidance for DMOs, particularly in contexts of overtourism where repositioning is needed, and for new destinations seeking to differentiate themselves in a competitive global market. Framing motivations within these broader transformations—post-pandemic regeneration, sustainability debates, and digital lifestyle shifts—enhances the relevance of our contribution to both scholarship and practice. Full article

This paper investigates the mechanical behavior of PBL shear connectors in UHPC during the elastic stage, utilizing push-out experiments and numerical simulation. This study simplifies the mechanical behavior of PBL shear connectors in UHPC under normal service conditions as a plane strain problem for the UHPC dowel and a Winkler’s Elastic foundation beam theory for the transverse reinforcement. The UHPC dowel is a thick-walled cylindrical shell subjected to non-axisymmetric loads inside and outside simultaneously in the plane-strain state. The stress solution is derived by assuming the contact stress distribution function and using the Airy stress function. The displacement solution is subsequently determined from the stresses by differentiating between elastic and rigid body displacements. By modeling the transverse reinforcement as an infinitely long elastic foundation beam, its displacement solution and stress solution are obtained. We obtain the load–slip curve calculation method by superimposing the displacement of UHPC with the transverse reinforcement in the direction of shear action. The proposed analytical solutions for stress and slip, as well as the method for calculating load–slip, are shown to be reliable by comparing them to the numerical simulation analysis results. Full article

This paper pioneers the integration of hyperspectral imaging and robotics for the automated analysis of cultural heritage, representing a measurable advancement over existing manually operated systems. For the first time in the cultural heritage domain, a compact push-broom hyperspectral camera working in the VNIR range has been successfully mounted on a robotic arm, enabling precise and repeatable acquisition trajectories without the need for manual intervention. Unlike traditional approaches that rely on fixed paths or manual repositioning, the proposed approach allows dynamic and programmable imaging of both planar and volumetric objects, greatly improving adaptability to complex geometries. The integrated system achieves spectral reliability comparable to established manual methods, while offering superior flexibility and scalability. Current limitations, particularly regarding the illumination setup, are discussed alongside planned optimisation strategies. Full article

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

With the surge in global maritime trade of new energy vehicles (NEVs), the roll-on/roll-off (Ro-Ro) shipping market faces a severe supply–demand imbalance, pushing shipping rates to persistently high levels. To tackle this challenge, NEV manufacturers and other automakers have begun establishing their own Ro-Ro fleets, creating an urgent need for optimized operational scheduling of these proprietary fleets. Against this context, this study focuses on optimizing the operational scheduling of automakers’ self-owned Ro-Ro fleets. Under the premise of deterministic automobile export transportation demands, a mixed-integer programming model is developed to minimize total fleet operational costs, with decision variables covering vessel port call sequence/selection, port loading and unloading quantities, and voyage speeds. A genetic algorithm is designed to solve the model, and the effectiveness of the proposed approach is validated through a real-world case study. The results demonstrate that the optimization method generates clear, actionable scheduling schemes for self-owned Ro-Ro fleets, effectively helping automakers refine their maritime logistics strategies for proprietary fleets. This study contributes to the field by focusing on automaker-owned Ro-Ro fleets and filling the research gap in cargo-owner-centric scheduling, providing a practical tool for automakers’ overseas logistics operations. Full article

The emigration of highly skilled individuals has become a critical concern for many countries amid increasing global labor mobility. This study employs the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) method within a fuzzy multi-criteria decision-making (FMCDM) framework to identify and prioritize the key drivers of skilled migration. Drawing on opinions from sixteen Turkish emigrants currently residing abroad, the study captures firsthand perspectives on the structural factors influencing their migration decisions. The results indicate that the most influential factors are workplace conditions, living standards, and academic standards. These findings underscore the multifaceted nature of brain drain and highlight the necessity for comprehensive policy approaches that address both push and pull dynamics. By systematically ranking these determinants, the study contributes to the growing body of evidence-based research on international human capital flows. Full article

Under cyclic loading, fatigue limits Δσ_FL and fatigue crack growth (FCG) thresholds ΔΚ_th are usually examined using the S-N (or ε-N) and FCG da/dN-ΔK approaches, respectively. Historically, these two approaches are treated as a separate domain. This separation was due to the recognition that the nonuniform local stress field ahead of a crack differs significantly from the uniform stress field in a smooth specimen under axial fatigue loading. At present, there are no reliable approaches to analyzing these two regions in a unified way. In this paper, we first attempt to relate the experimental results of a cracked sample in the near-threshold region to the S-N fatigue limit of a smooth pull-push specimen. Then establish analytically the local stress intensity factor range ΔK at the process/damage zone ahead of the crack utilizing the local stress equal to Δσ_FL in a smooth specimen. Doing such an analysis, we can account the variations between the applied and the local stress ratios R (=min stress/max stress) for both cracked and smooth samples. The proposed relationship between ΔK_th and Δσ_FL would enable the development of a unified framework for fatigue analysis methods to predict damage evolution under low-stress in-service loading conditions. Full article

Quantum technology is significantly transforming businesses, organisations, and information systems. It will have a significant impact on accounting and finance, particularly in the context of cybersecurity. It presents both opportunities and risks in maintaining confidentiality and protecting financial data. This study aims to demonstrate the application of quantum technologies in accounting cybersecurity, utilising quantum algorithms and QKD to overcome the limitations of classical computing. The literature review emphasises the vulnerabilities of current accounting cybersecurity to quantum attacks and highlights the necessity for quantum-resistant cryptographic mechanisms. It discusses the risks related to traditional encryption methods within the context of quantum capabilities. This research enhances understanding of how quantum computing can revolutionise accounting cybersecurity by advancing quantum-resistant algorithms and implementing QKD in accounting systems. This study employs the PSALSAR systematic review methodology to ensure thoroughness and rigour. The analysis shows that quantum computing pushes encryption techniques beyond classical limits. Using quantum technologies in accounting reduces data breaches and unauthorised access. This study concludes that quantum-resistant algorithms and quantum key distribution (QKD) are crucial for securing the future of accounting and finance systems. Full article

There has been a large amount of research applying deep learning to the medical field. However, obtaining sufficient training data is challenging in the medical domain because annotation requires specialized knowledge and significant effort. This is especially true for segmentation tasks, where preparing fully annotated data for every pixel within an image is difficult. To address this, we propose methods to extract useful features for segmentation using two types of U-net-based networks and partially supervised learning with incomplete annotated data. This research specifically focuses on the segmentation of diffuse lung disease opacities in chest CT images. In our dataset, each image is partially annotated with a single type of lung opacity. To tackle this, we designed two distinct U-net architectures: a multi-head U-net, which utilizes a shared encoder and separated decoders for each opacity type, and a multi-channel U-net, which shares the encoder and decoder layers for more efficient feature learning. Furthermore, we integrated partially supervised learning with these networks. This involves employing distinct loss functions to both bring annotated regions (ground truth) and segmented regions (predictions) closer, and to push them apart, thereby suppressing erroneous predictions. In our experiments, we trained the models on partially annotated data and subsequently tested them on fully annotated data to compare the segmentation performance of each method. The results show that the multi-channel model applying partially supervised learning achieved the best performance while also reducing the number of weight parameters. Full article

The development of electromechanical linear actuators (EMLAs) aims at compactness, energy efficiency, and high reliability. Conventional design methods often rely on costly prototypes and individual considerations of mechanics, electromagnetics, and control dynamics. This leads to long development cycles, inadequate treatment of nonlinear effects, and suboptimal performance. To address these challenges, our paper introduces a novel hybrid design methodology, integrating Analytical Modeling, Finite Element Analysis (FEA), Genetic Algorithms (GAs), and targeted experiments. Analytical Modeling provides rapid sizing, FEA combined with a GA refines geometry, and targeted experiments quantify nonlinear effects (friction, wear, thermal variability, and dynamic resonances). Unlike conventional methods, the integration is performed within iterative loops, using empirical data to refine simulation assumptions. As a result, development time is reduced by 30% and nonlinear effects are precisely addressed. The method is demonstrated on an automotive-grade EMLA. Its design is based on a claw-pole Permanent Magnet Stepper Motor, a trapezoidal lead screw, and an open-loop control with Hall effect end-position detection. After applying the method, the EMLA delivers more than 40 N of push force and achieves 600,000 actuations under the required conditions, making it suitable for various applications. Full article

Production flow control is a key area affecting the productivity of production systems. The use of an appropriate control method ensures that customer requirements are met while maintaining an acceptable level of production costs. In many cases, the choice of control method does not allow for significant improvements in production processes, as the known guidelines are not very detailed. This article presents research on the impact of factors related to products, production processes, and customer orders on, for example, the number of technological operations, the number of production stations, product demand (product, process, and order conditions—PPOC), and the effectiveness of production flow control methods. This research was conducted for selected product families (water and gas fittings) for which various production flow control solutions were developed. The most popular control methods were used: push–schedule, supermarket-type pull, sequential pull, mixed pull, and drum-buffer-rope. The criteria for evaluation were in-process stocks and lead time of materials in the production process. As a result of the ranking, relationships were identified by indicating how the values of PPOC factors affect the effectiveness of a given production flow control method. The results of this research can serve as guidelines for companies in selecting the most appropriate method of controlling production processes. Full article

The global push toward digital transformation in education presents both opportunities and challenges for achieving sustainability goals. While digital technologies promise enhanced learning experiences and reduced environmental impacts, their implementation often overlooks complex trade-offs between pedagogical effectiveness, resource efficiency, and social equity. This study examines these critical intersections through a comprehensive investigation of geography education in Chinese secondary schools, comparing traditional, fully digital, and hybrid models across diverse urban, suburban, and rural contexts. Through a mixed-methods comparative design involving 262 participants (pilot) and 810 students (main study) and analysis of 17 geography textbooks, we assessed the environmental impacts, learning outcomes, economic viability, and social equity dimensions of each approach. Our findings reveal that thoughtfully designed hybrid models—which strategically combine digital tools for high-impact activities with traditional methods for local engagement—achieve optimal sustainability performance. These hybrid approaches reduced carbon emissions by 72.7% compared to traditional methods while improving learning outcomes and maintaining cost parity over five-year periods. Importantly, hybrid models demonstrated superior adaptability across different socioeconomic contexts, addressing equity concerns that purely digital approaches often exacerbate in resource-limited settings. This research challenges the prevailing technology-first narratives in educational reform, demonstrating that sustainable education transformation requires nuanced, context-sensitive integration strategies rather than wholesale digital transformation. The empirical evidence from this research provides robust support for achieving the United Nations Sustainable Development Goals through educational innovation. The hybrid model’s 73% carbon reduction and simultaneous improvement of learning outcomes by 35% directly support SDG 4 (Quality Education) and SDG 13 (Climate Action). The 78% reduction in paper consumption advances SDG 12 (Responsible Consumption and Production), while successful implementation across urban, suburban, and rural contexts addresses SDG 10 (Reduced Inequalities). These findings demonstrate that sustainable educational transformation can effectively balance technological innovation with environmental stewardship and social equity. Full article

Airport operations significantly contribute to air pollution in their vicinity through various sources, including aircraft activities—particularly taxiing and take-off—as well as ground support equipment, service vehicles, and maintenance work. Since emissions from aircraft engines represent the primary pollution source at airports, it is essential to reduce emissions at every phase of the LTO (landing and take-off) cycle to improve local air quality and promote environmental sustainability. Given the research gap in emission analysis, a comprehensive LCA framework for airport pushback and taxi operations is proposed, integrating tow truck propulsion, a taxiing strategy, and fleet management. Given the complexity of the issue, the authors first decided to investigate emissions from taxiing operations using tow trucks with different powertrains. The analyses performed were considered preliminary and a starting point for exploring emissions during taxiing operations at airports. Typically, aircraft are pushed back from the apron and then taxi under their own power using both engines at approximately 7% of maximum thrust. To substantially reduce exhaust emissions, external towing vehicles can be employed to move aircrafts from the apron to the runway. This study evaluates the potential for emission reductions in CO₂ and other harmful compounds such as CO, HC, NO_x, and PM by using electric towing vehicles (ETVs). It also compares emissions from different taxiing methods: full-engine taxiing, single-engine taxiing, ETV-assisted taxiing, and taxiing using diesel and petrol-powered tow vehicles. The analysis was conducted for Warsaw and Poznań airports. Three aircraft types—the most commonly operating at these airports—were selected to assess emissions under various taxiing scenarios. The results show that using electric towing vehicles can reduce CO and NO_x emissions to nearly zero compared to other methods. Interestingly, CO emissions from full-engine taxiing were lower than those from petrol-powered towing, although the Embraer 195 showed the highest CO emissions among the selected aircrafts. HC emissions were lowest for the A321neo and also relatively low for the diesel towing vehicle. The use of electric tow trucks significantly reduces CO₂ emissions: only 2.8–4.4 kg compared to 380–450 kg when taxiing with engines. This research highlights the critical role of sustainable ground operations in reducing harmful emissions and underscores the importance of integrating sustainability into airport taxiing practices. Full article

Modern power systems are facing growing complexity and uncertainty due to electrification, large-scale renewable integration, and evolving consumption behaviors. These changes have pushed traditional numerical optimization methods to their practical limits in terms of scalability and real-time applicability. In response, deep learning approaches that offer fast inference and robustness to uncertainty are gaining significant attention. This paper presents, to the best of our knowledge, the first systematic review from a functional perspective of deep learning research supporting power system decision making. Taking a functional perspective, we classify neural networks into three core roles: learning to predict, learning to surrogate, and learning to optimize. In the first role, neural networks forecast exogenous uncertainties serving as the instance input to operational optimization problems. In the second role, they approximate complex physical constraints, enabling the efficient formulation of problems that would otherwise be analytically intractable. In the third role, neural networks act as learning-based optimizers that either replace or augment conventional solvers. The core purpose of this paper is to emphasize that neural networks should not simply be regarded as generic data-driven tools, but rather as models serving distinct functional roles—each with its own objectives and considerations. In this regard, we introduce diverse approaches aligned with these roles, offering conceptual foundations for principled application in practice. Such functional insights will ultimately guide the design of modular and hybrid architectures that integrate these roles, which in turn may provide the basis for developing domain-specific foundation models for power system operations. Full article