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The functional nature of cognitive systems is outlined as a general conceptual model where typical notions of cognition are analyzed apart from the physical realization (biological or artificial) of such systems. The notion of function, one of the main logical bases of mathematics, logic, linguistics, physics, and computer science, is shown to be a unifying concept in analyzing cognition components: learning, meaning, comprehension, language, knowledge, and consciousness are related to increasing levels in the functional organization of cognition. Full article

Land degradation issues and declining fertility are driving the need for agroecological practices. This research analysed the determinants of acceptance and actual use of five main agroecological practices (contour farming techniques, organic fertiliser, crop association, improved seeds and integrated crop management practices) by farmers in Mali. The extended Unified Theory of Acceptance and Use of Technology (UTAUT) was used to develop the conceptual model. Data were collected from 505 randomly selected farming households in the cotton and cereal production zones in Mali. Partial Least Square–Structural Equation Modelling (PLS-SEM) was used to estimate technology acceptance and use. The findings revealed that behavioural intention is significantly and positively influenced by the expected performance and social influence. The expected effort is a key influential factor of the behavioural intention to adopt organic fertiliser. Experience has a mediating effect on the relationship between social influence and behavioural intention to adopt improved seeds adapted to the agroecological conditions. The actual use behaviour is directly and positively affected by the behavioural intention, facilitating conditions and expected net benefit. These findings align with the UTAUT model, have useful implications for both farmers and decision-makers and offer directions for technical approaches to agroecological practices’ development. Full article

This study proposes a seismic performance evaluation method for structures using the base shear index to calculate the collapse probability. After non-proportional damping was applied to the three-dimensional bar system model, the structural dynamic response was computed through large-scale finite element analysis. A three-dimensional matrix element for calculating viscous dampers was established in this study. The viscous unified elastoplastic (VUEL) damper element program was compiled using the Fortran language into the ABAQUS 6.14 software. An incremental dynamic analysis (IDA) routine was developed using Python 3.0 within the environment of ABAQUS. The uncontrolled structure was designed using the forced decoupling response spectrum method (FD-RSM), while the damped structure was designed using the complex modal response spectrum method (CM-RSM). Seismic fragility analysis was conducted on both uncontrolled and damped structures using the recommended far-field and near-field earthquake records from ATC-63 FEMAP-695. The shear-based fragility index and collapse probability were investigated to comprehensively assess the seismic performance of the uncontrolled and damped structures. The analysis results indicated that the ratios of the limit performance states for moderate damage (IO), severe damage (LS), and complete damage (CP) in the structure were 1:1.6:2.6. Compared with the various limit performance states of the uncontrolled structures, the increments in the moderate, severe, and complete damage limit performance states of the damped structures were 12.79%, 14.86%, and 16.97%, respectively. Full article

Based on the minimum conduction band edge caused by the minimum channel potential resulting from the quasi-3D scaling theory and the 3D density of state (DOS) accompanied by the Fermi–Dirac distribution function on the source and drain sides, a unified semiconductor-device-physics-based ballistic model is developed for the threshold voltage of modern multiple-gate (MG) transistors, including FinFET, Ω-gate MOSFET, and nanosheet (NS) MOSFET. It is shown that the thin silicon, thin gate oxide, and high work function will alleviate ballistic effects and resist threshold voltage degradation. In addition, as the device dimension is further reduced to give rise to the 2D/1D DOS, the lowest conduction band edge is increased to resist threshold voltage degradation. The nanosheet MOSFET exhibits the largest threshold voltage among the three transistors due to the smallest minimum conduction band edge caused by the quasi-3D minimum channel potential. When the n-type MOSFET (N-FET) is compared to the P-type MOSFET (P-FET), the P-FET shows more threshold voltage because the hole has a more effective mass than the electron. Full article

How to effectively extract spectral and spatial information and apply it to hyperspectral image classification (HSIC) has been a hot research topic. In recent years, the transformer-based HSIC models have attracted much interest due to their advantages in long-distance modeling of spatial and spectral features in hyperspectral images (HSIs). However, the transformer-based method suffers from high computational complexity, especially in HSIC tasks that require processing large amounts of data. In addition, the spatial variability inherent in HSIs limits the performance improvement of HSIC. To handle these challenges, a novel Spectral–Spatial Unified Mamba (SSUM) model is proposed, which introduces the State Space Model (SSM) into HSIC tasks to reduce computational complexity and improve model performance. The SSUM model is composed of two branches, i.e., the Spectral Mamba branch and the Spatial Mamba branch, designed to extract the features of HSIs from both spectral and spatial perspectives. Specifically, in the Spectral Mamba branch, a nearest-neighbor spectrum fusion (NSF) strategy is proposed to alleviate the interference caused by the spatial variability (i.e., same object having different spectra). In addition, a novel sub-spectrum scanning (SS) mechanism is proposed, which scans along the sub-spectrum dimension to enhance the model’s perception of subtle spectral details. In the Spatial Mamba branch, a Spatial Mamba (SM) module is designed by combining a 2D Selective Scan Module (SS2D) and Spatial Attention (SA) into a unified network to sufficiently extract the spatial features of HSIs. Finally, the classification results are derived by uniting the output feature of the Spectral Mamba and Spatial Mamba branch, thus improving the comprehensive performance of HSIC. The ablation studies verify the effectiveness of the proposed NSF, SS, and SM. Comparison experiments on four public HSI datasets show the superior of the proposed SSUM. Full article

Identifying priority conservation areas (PCAs) for national parks is critical for improving the cost-effectiveness and viability of conservation efforts, given the multiplicity of conservation values, the complexity of human activities, and the limited financial resources available. Assessing conservation costs is central to systematic conservation planning (SCP). To compensate for the limitations of the alternative cost method in small-scale case studies and accurately reflect the cost differences due to specific land use, tenure, and management strategies, conservation costs are quantified and spatialized in this study using monetization methods. Taking Shennongjia National Park (SNP) as an example, we considered the core conservation values of species, ecosystems, and geological heritage, using the Zonation 5 model to identify PCAs under three different targets: 17%, 30%, and 50%. The results indicated that, as the conservation targets increased, PCAs expanded from the central and southern high-altitude areas to the northwest and northeast. Conservation gaps are primarily concentrated in the western part of Songluo and the northern parts of Hongping and Songba. Conservation costs exhibit clear spatial heterogeneity, increasing gradually from the central high mountains towards the surrounding areas. Among these, ecological compensation cost was the primary factor driving the sharp increase in total costs, while opportunity cost remained consistently low with minimal fluctuations. Compared to the alternative method, our study clarified the spatial distribution and types of costs in the process of national park construction, providing a quantitative basis and scientific guidance for future fiscal investment directions, methods, and responsible entities. At the administrative division level, we revealed the main cost challenges faced by townships in balancing resource conservation with community development, leading to more targeted, timely, and actionable community governance strategies. These findings further illustrate the significant advantages of using monetary costs in optimizing the boundaries of individual national parks and enhancing funding allocation efficiency, while promoting effective unified management of natural resource assets within spatial planning. Full article

Invasive alien species often undergo shifts in their ecological niches when they establish themselves in environments that differ from their native habitats. Leptocybe invasa Fisher et LaSalle (Hymenoptera: Eulophidae), specifically, has caused huge economic losses to Eucalyptus trees in Australia. The global spread of eucalyptus cultivation has allowed L. invasa to threaten plantations beyond its native habitat. It is, therefore, urgent to implement effective control measures to mitigate the impact of this pest. The optimized MaxEnt model was used to predict the potential global distribution of L. invasa based on occurrence data and environmental variables. The centroid shift, overlap, unfilling, and expansion (COUE) framework was employed to evaluate niche dynamics during the global invasion process by comparing the ecological niches of L. invasa in both native regions and regions affected by invasions (hereafter referred to as “invaded”). The results indicated that the distribution of L. invasa is primarily influenced by temperature, precipitation, and the human influence index variables. Its ecological niche was shown to have considerably expanded from native to invaded regions. Under future climate scenarios, the potential geographical distribution of L. invasa is projected to be concentrated primarily in East Asia, Southeast Asia, Western Europe, and Southern Oceania. In the future, the potentially suitable areas for the establishment of L. invasa are expected to further expand. This study provides a unified framework for exploring the niche dynamics of invasive alien species globally. Emphasizing early warning and control in uninvaded areas is crucial for minimizing L. invasa ecological and economic threats. Full article

This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, the system uses existing road infrastructure to transmit live data on vehicle and pedestrian positions, speeds, and queues. AI agents, employing Deep Reinforcement Learning (DRL), process this data to manage traffic flows dynamically, applying anti-bottleneck and rerouting techniques to balance pedestrian and vehicle waiting times. A centralized global agent coordinates the local agents controlling each intersection, enabling indirect communication and data sharing to train a unified DRL model. This model makes real-time adjustments to traffic light phases, utilizing a queue/request/response system for adaptive intersection management. Tested using simulations and real-world trials involving standard and rerouting scenarios, the approach demonstrates significantly better performance in regard to the rerouting configuration, reducing congestion and enhancing traffic flow and pedestrian safety. Scalable and adaptable to various intersection types, including four-way, T-intersections, and roundabouts, the system’s efficacy is validated using the SUMO urban mobility simulator, resulting in notable reductions to travel and waiting times for both vehicles and pedestrians. Full article

This study presents a unified analytical model for multi-tower self-anchored suspension bridges integrating tower–girder connections (TGCs) and cable–girder connections (CGCs) within the framework of deflection theory. The connections are modeled as horizontal springs, and governing equations are derived based on force equilibrium and compatibility conditions. A comparison with a nonlinear finite element analysis under various live load scenarios confirms the accuracy of the proposed model. A parametric analysis reveals that increasing the CGC stiffness reduces girder deflection, decreasing the maximum vertical deflection by nearly 42.3% when the stiffness is increased from 0 to infinity and moving the maximum displacement from the mid-span section to the mid-tower section. Additionally, CGCs modify the load distribution between the main cable and the girder, limiting the longitudinal displacement of the tower in which the mid-tower displacement is reduced by 45.50%. Tower–girder connections improve the anchoring of the side cable to the tower. When connection stiffness is low, side- and middle-tower stiffness significantly reduce girder deflection, though this effect decreases with increasing stiffness. Enhancing mid-tower stiffness similarly reduces its longitudinal displacement regardless of the tower–girder connection. In longitudinal floating systems, mid-tower displacement rises with increasing side-tower stiffness. Establishing a unified analysis model reveals the key parameters in the structural analysis of suspension bridges, enabling an easier and faster analysis of multi-tower self-anchored suspension bridges. Full article

The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.0. The results highlighted the significant role of performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk in shaping user attitudes and trust, which in turn influence behavioral intention. The findings suggested that attitudes fully mediate the effects of performance expectancy and effort expectancy on behavioral intention, while social influence, facilitating conditions, and perceived risk exhibit partial mediation. Moderation analysis revealed significant effects of generation on the relationship between attitude, trust, and behavioral intention. Additionally, device type moderated the effect of trust on behavioral intention, showing a different influence between radar sensors and wearable devices. These findings provide theoretical contributions by extending the unified theory of acceptance and use of technology (UTAUT) model and offering practical implications for manufacturers and policymakers to tailor strategies that foster positive attitudes, enhance trust, and address generational and device-specific differences in healthcare technology adoption. Full article

The body structures and motion stability of worm-like and snake-like robots have garnered significant research interest. Recently, innovative serial–parallel hybrid segmented robots have emerged as a fundamental platform for a wide range of motion modes. To address the hyper-redundancy characteristics of these hybrid structures, we propose a novel caterpillar-inspired Stable Segment Update (SSU) gait generation approach, establishing a unified framework for multi-segment robot gait generation. Drawing inspiration from the locomotion of natural caterpillars, the segments are modeled as rigid bodies with six degrees of freedom (DOF). The SSU gait generation method is specifically designed to parameterize caterpillar-like gaits. An inverse kinematics solution is derived by analyzing the forward kinematics and identifying the minimum lifting segment, framing the problem as a single-segment end-effector tracking task. Three distinct parameter sets are introduced within the SSU method to account for the stability of robot motion. These parameters, represented as discrete hump waves, are intended to improve motion efficiency during locomotion. Furthermore, the trajectories for each swinging segment are determined through kinematic analysis. Experimental results validate the effectiveness of the proposed SSU multi-gait generation method, demonstrating the successful traversal of gaps and rough terrain. Full article

The success of reforestation and restoration projects depends on several factors, with proper seedling management and the selection of an appropriate planting area being crucial. In Peru, the populations of Retrophyllum rospigliosii (Pilg.) C.N.Page (Ulcumano) have been decreasing due to intensive logging of one of the most valuable woods in South America’s tropical forests. There are few studies that unify the production of plants through seeds in nurseries and the identification of suitable areas to place the plants produced. Our study has two components. The first aimed to optimize the plant production process through an experiment that evaluated the effects of three doses of controlled-release fertilizer (CRF) (4.2, 8.4, and 12.6 g/L) and two container sizes (115 and 180 cc) on the morphological quality of seedlings in the nursery. The second component involved identifying potential reforestation areas using ecological niche modeling, based on climatic and edaphic variables. The results indicated that the 4.2 g/L CRF treatment for both container sizes had a significant positive effect on seedling growth. The average germination rate was 85% at 120 days. At six months after seedling transplantation, treatments of 4.2 g/L CRF in 115 cc and 180 cc containers were shown to have the best positive effect on morphological variables of seedlings, with a root collar diameter of 3.76 mm and a height of 13.25 cm. Regarding the potential niche models, an area of 6321.97 km² with ideal conditions for reforestation with R. rospigliosii was estimated, with the departments of Huánuco, Pasco, Junín, and Cusco showing the highest potential. Based on this, it is estimated that over three million plants are needed for large-scale reforestation projects. Integrating silvicultural studies with niche models is a valuable tool for supporting reforestation and ecosystem restoration projects. Full article

Multi-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity. However, most industrial image anomaly detection methods are developed for one-class anomaly detection, which typically suffer significant performance drops in multi-class scenarios. Research specifically targeting multi-class anomaly detection remains relatively limited. In this work, we propose a powerful unified normalizing flow for multi-class anomaly detection, which we call UniFlow. A multi-cognitive visual adapter (Mona) is employed in our method as the feature adaptation layer to adapt image features for both the multi-class anomaly detection task and the normalizing flow model, facilitating the learning of general knowledge of normal images across multiple categories. We adopt multi-cognitive convolutional networks with high capacity to construct the coupling layers within the normalizing flow model for more effective multi-class distribution modeling. In addition, we employ a multi-scale feature fusion module to aggregate features from various levels, thereby obtaining fused features with enhanced expressive capabilities. UniFlow achieves a class-average image-level AUROC of 99.1% and a class-average pixel-level AUROC of 98.0% on MVTec AD, outperforming the SOTA multi-class anomaly detection methods. Extensive experiments on three benchmark datasets, MVTec AD, VisA, and BTAD, demonstrate the efficacy and superiority of our unified normalizing flow in multi-class anomaly detection. Full article

Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes. Full article

The current study highlights the potential of multimodal large language models (MLLMs) to transform higher education by identifying key factors influencing their acceptance and effectiveness. Aligning technology features with educational needs can enhance student engagement and learning outcomes. The study examined the role of MLLMs in enhancing performance benefits among higher education students, using the task–technology fit (T-TF) theory and the artificial intelligence device use acceptance (AIDUA) model. A structured questionnaire was used to assess the perceptions of 550 Saudi university students from various academic disciplines. The data were analyzed via structural equation modeling (SEM) using SmartPLS 3.0. The findings revealed that social influence negatively affected effort expectancy regarding MLLMs and that hedonic motivation was also negatively related to effort expectancy. The findings revealed that social influence and hedonic motivation negatively affected effort expectancy for MLLMs. Effort expectancy was also negatively associated with T-TF in the learning context. In contrast, task and technology characteristics significantly influenced T-TF, which positively impacted both performance benefits and the willingness to accept the use of MLLMs. A strong relationship was found between adoption willingness and improved performance benefits. The findings empower educators to strategically enhance MLLMs adoption strategically, driving transformative learning outcomes. Full article