Search Results (195)

The construction industry is moving towards the era of industry 4.0; 5.0 with Building Information Modelling (BIM) as the tool gaining significant traction owing to its inherent advantages such as enhancing construction design, process and data management. However, the integration of BIM presents risks that are often overlooked in project implementation. This study aims to develop a novel amalgamated dimensional factor (Techno-organizational Aspect) that is set out to identify and align appropriate management strategies to these risks. Firstly, it encompasses an in-depth analysis of BIM and risk management, through an integrative review approach. The study utilizes an exploratory-based review centered around journal articles and conference papers sourced from Scopus and Google Scholar. Then processed using NVivo 12 Pro software to categorise risks through thematic analysis, resulting in a comprehensive Risk Breakdown Structure (RBS). Then qualitative content analysis was employed to identify and develop management strategies. Further data collection via online survey was crucial for closing the research gap identified. The analysis by mixed method research enabled to determine the risk severity via the quantitative approach using SPSS (version 29), while the qualitative approach linked management strategies to the risk factors. The findings accentuate the crucial linkages of key strategies such as version control system that controls BIM data repository transactions to mitigate challenges controlling transactions in multi-model collaborative environment. The study extends into underexplored amalgamated domains (techno-organisational spectrum). Therefore, a significant contribution to bridging the existing research gap in understanding the intricate relationship between BIM implementation risks and effective management strategies. Full article

Generative Artificial Intelligence (GenAI)-assisted Virtual Reality (VR) heritage serious game design constitutes a complex adaptive socio-technical system in which natural language prompts act as control levers shaping designers’ cognition and action. However, the systemic effects of prompt type on agency construction, decision boundaries, and process strategy remain unclear. Treating the design setting as adaptive, we captured real-time interactions by collecting think-aloud data from 48 novice designers. Nine prompt categories were extracted and their cognitive effects were systematically analyzed through the Repertory Grid Technique (RGT), principal component analysis (PCA), and Ward clustering. These analyses revealed three perception profiles: tool-based, collaborative, and mentor-like. Strategy coding of 321 prompt-aligned utterances showed cluster-specific differences in path length, first moves, looping, and branching. Tool-based prompts reinforced boundary control through short linear refinements; collaborative prompts sustained moderate iterative enquiry cycles; mentor-like prompts triggered divergent exploration via self-loops and frequent jumps. We therefore propose a stage-adaptive framework that deploys mentor-like prompts for ideation, collaborative prompts for mid-phase iteration, and tool-based prompts for final verification. This approach balances creativity with procedural efficiency and offers a reusable blueprint for integrating prompt-driven agency modelling into GenAI design workflows. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

Background: The current study explores the relationship between children’s risk willingness and their motor behavior in a virtual playground setting and its association with perceived gross motor competence. Methods: A total of 96 children aged seven to ten participated. They were categorized into high-risk-willingness (HRW) and low-risk-willingness (LRW) groups based on their exploratory behavior and engagement with riskier zones and tasks in the playground. Using whole-body motion capture and virtual reality data, the children’s motor behavior and variability were analyzed alongside self-reported perceived gross motor competence. Results: The results indicated that HRW children displayed significantly greater movement variability, including higher joint movement variability and increased exploration of challenging areas compared to LRW children. HRW children also covered greater distances, moved faster, and exhibited more frequent acceleration changes. Conclusions: These findings suggest that higher risk willingness is associated with greater adaptability and flexibility in motor behavior, aligned with the concept of freeing degrees of freedom. In contrast, no significant differences were found in perceived gross motor competence ratings between HRW and LRW groups. This indicates that perceived motor competence may not directly influence children’s willingness to take risks or their motor behavior in exploratory play. These findings emphasize the importance of studying dynamic interactions between risk-taking, motor behavior, and self-perception to understand the development of adaptive motor skills through risky play. Full article

Jazz music has long been a subject of interest in the field of generative music. Traditional jazz chord progressions follow established patterns that contribute to the genre’s distinct sound. However, the demand for more innovative and diverse harmonic structures has led to the exploration of alternative approaches in music generation. This paper addresses the challenge of generating novel and engaging jazz chord sequences that go beyond traditional chord progressions. It proposes an unconventional statistical approach, leveraging a corpus of 1382 jazz standards, which includes key information, song structure, and chord sequences by section. The proposed method generates chord sequences based on statistical patterns extracted from the corpus, considering a tonal context while introducing a degree of unpredictability that enhances the results with elements of surprise and interest. The goal is to move beyond conventional and well-known jazz chord progressions, exploring new and inspiring harmonic possibilities. The evaluation of the generated dataset, which matches the size of the learning corpus, demonstrates a strong statistical alignment between distributions across multiple analysis parameters while also revealing opportunities for further exploration of novel harmonic pathways. Full article

This paper presents a three-degrees-of-freedom origami parallel robot that is free from parasitic motion. This robot is designed to achieve one translational and two rotational motions within its workspace, enabling precise orientation about a fixed point—a capability unattainable for parallel robots with parasitic motion. The elimination of parasitic motion is critical, allowing the use of this device in applications requiring high precision. The robot’s key kinematic features include a parasitic motion-free workspace, large orientational capability, compactness, decoupled motion, simplicity in manufacturing and control, mechanically pivoted rotation of the moving platform, and scalability. These characteristics make the robot particularly well-suited for micromanipulation tasks in both manufacturing and medical applications. In manufacturing, it can enable high-precision operations such as micro-assembly, optical fiber alignment, and semiconductor packaging. In medicine, it can support delicate procedures such as microsurgery and cell injection, where sub-micron accuracy, high stability, and precise motion decoupling are critical requirements. The use of nearly identical limbs simplifies the architecture, facilitating easier design, manufacture, and control. The kinematics of the robot is analyzed using reciprocal screw theory for an analytic constraint-embedded Jacobian. To further enhance operational accuracy and robustness, particularly in the presence of uncertainties or disturbances, a deep neural network (DNN)-based state estimation method is integrated, providing accurate forward kinematic predictions. The construction of the robot utilizes origami-inspired limbs and joints, enhancing miniaturization, manufacturing simplicity, and foldability. Although capable of being scaled up or further miniaturized, its current size is 66 mm × 68 mm × 100 mm. The robot’s moving platform is theoretically and experimentally proven to be free of parasitic motion and possesses a large orientation capability. Its unique features are demonstrated, and its potential for high-precision applications is thoroughly discussed. Full article

The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol—a leaderless, feeless DAG designed for the IoT—with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT. Full article

This study aims to develop a strategic framework for biophilic residential design (BRD) tailored to the diverse health profiles of seniors. To achieve this, a nationwide survey of 424 seniors in South Korea was conducted to assess their health-related quality of life (HRQoL) and preferences for BRD elements. Through principal component and cluster analyses, three HRQoL dimensions—social-economic, mental-sensory, and physical QoL—were extracted, and four distinct senior clusters were identified: Optimal Health, Physically Declining, Overall Low Health, and Socially Vulnerable. Statistically significant differences in BRD preferences were found across clusters for 11 out of 28 BRD elements (p < 0.05), particularly in categories related to sensory-based physiological stability, cognitive stimulation, and external-social connectivity. Notably, the Physically Declining Group expressed a strong preference for restorative and stable features (e.g., natural colors and ventilation systems), while the Socially Vulnerable Group prioritized elements promoting external interaction and social engagement (e.g., balconies, indoor gardens, and walkways). Based on these results, BRD elements were reclassified by function and mapped to the spatial needs of each cluster, leading to a strategic design matrix that supports adaptive and user-centered residential planning. This HRQoL-driven framework contributes a novel link between multidimensional health diagnostics and biophilic design application, moving beyond generalized aging-in-place models. The findings offer practical insights by linking BRD strategies to distinct health profiles. For practitioners, the matrix can inform spatial layouts and design priorities. For policymakers, it provides a basis for developing differentiated housing standards aligned with seniors’ health conditions. Full article

Under large azimuth misalignment conditions, the initial alignment of strapdown inertial navigation systems (SINS) is challenged by the nonlinear characteristics of the error model. Traditional particle filter (PF) algorithms suffer from the inappropriate selection of importance density functions and severe particle degeneration, which limit their applicability in high-precision navigation. To address these limitations, this paper proposes an adaptive mixed-order spherical simplex-radial cubature particle filter (MLG-AMSSRCPF) algorithm based on matrix Lie group representation. In this approach, attitude errors are represented on the matrix Lie group SO(3), while velocity errors and inertial sensor biases are retained in Euclidean space. Efficient bidirectional conversion between Euclidean and manifold spaces is achieved through exponential and logarithmic maps, enabling accurate attitude estimation without the need for Jacobian matrices. A hybrid-order cubature transformation is introduced to reduce model linearization errors, thereby enhancing the estimation accuracy. To improve the algorithm’s adaptability in dynamic noise environments, an adaptive noise covariance update mechanism is integrated. Meanwhile, the particle similarity is evaluated using Euclidean distance, allowing the dynamic adjustment of particle numbers to balance the filtering accuracy and computational load. Furthermore, a multivariate Huber loss function is employed to adaptively adjust particle weights, effectively suppressing the influence of outliers and significantly improving the robustness of the filter. Simulation and the experimental results validate the superior performance of the proposed algorithm under moving-base alignment conditions. Compared with the conventional cubature particle filter (CPF), the heading accuracy of the MLG-AMSSRCPF algorithm was improved by 31.29% under measurement outlier interference and by 39.79% under system noise mutation scenarios. In comparison with the unscented Kalman filter (UKF), it yields improvements of 58.51% and 58.82%, respectively. These results demonstrate that the proposed method substantially enhances the filtering accuracy, robustness, and computational efficiency of SINS, confirming its practical value for initial alignment in high-noise, complex dynamic, and nonlinear navigation systems. Full article

Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional supervised learning approaches, our model employs unsupervised learning to classify local motion direction detection neurons and group those with similar directional preferences to form macroscopic motion direction detection neurons. The activation of these neurons is proportional to the received input, and the neuron with the highest activation determines the macroscopic motion direction of the object. The proposed system consists of two layers: a local motion direction detection layer and an unsupervised global motion direction detection layer. For local motion detection, we adopt the Local Motion Detection Neuron (LMDN) model proposed in our previous work, which detects motion in eight different directions. The outputs of these neurons serve as inputs to the global motion direction detection layer, which employs a Gaussian Mixture Model (GMM) for unsupervised clustering. GMM, a probabilistic clustering method, effectively classifies local motion detection neurons according to their preferred directions, aligning with biological principles of sensory adaptation and probabilistic neural processing. Through repeated exposure to motion stimuli, our model self-organizes to detect macroscopic motion direction without the need for labeled data. Experimental results demonstrate that the GMM-based global motion detection layer successfully classifies motion direction signals, forming structured motion representations akin to biological visual systems. Furthermore, the system achieves motion direction detection accuracy comparable to previous supervised models while offering a more biologically plausible mechanism. This work highlights the potential of unsupervised learning in artificial vision and contributes to the development of adaptive motion perception models inspired by neural computation. Full article

Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a learning-based model incorporating two key components: the Hierarchical Motion Field Alignment module, which ensures accurate estimation of objects of varying sizes while maintaining manageable computational complexity, and the Correlation Self-Attention module, which effectively handles large displacements, making the model suitable for scenarios with fast-moving objects. Additionally, we introduced a Multi-Scale Correlation Search layer to enhance the four-dimensional cost volume, enabling the model to address various types of motion. Experimental results demonstrate that our model achieves superior generalization performance and significantly improves the estimation of small, fast-moving objects. Full article

The specialization of public prosecution offices has been a growing international trend, particularly in addressing complex forms of crime such as corruption, economic crime, and organized crime. Many countries have established specialized prosecution bodies to enhance the efficiency and effectiveness of law enforcement in these areas. However, Slovakia has recently taken a different approach by abolishing its Office of the Special Prosecution, a decision that contrasts sharply with prevailing global tendencies. This paper explores the reasons behind this shift, analyzing the political and legal arguments presented by both proponents and opponents of the abolition. The paper examines whether this move aligns with the rule of law and international legal obligations and considers its potential consequences for the effectiveness of criminal justice in Slovakia. While the paper is based on legal principles and comparative methods, it acknowledges the inherently political nature of decisions concerning the structure of prosecution services. Full article

To achieve the dual carbon goals, countries are transforming their energy structures, with green electricity trading playing a pivotal role in this transition. This paper first analyzes the mechanisms and current state of green electricity trading. A bibliometric analysis was conducted using the keywords “green power” and “green electricity” on 2427 articles from the Web of Science core database (1984–2024). CiteSpace software 6.3 R1 was used to analyze publication volumes, contributing countries, and co-citation patterns of cited references, highlighting foundational research in this field. A deeper analysis of recent five-year trends reveals a focus on renewable energy, low-carbon policies, and the relationship between the green electricity economy and environmental development. This study finds that green electricity trading has become a growing theoretical research hotspot. Practically, China’s green electricity trading has made significant progress but still encounters challenges, such as insufficient operational mechanisms, technical barriers to grid integration, and obstacles in international green certificate trading. Based on the findings, targeted recommendations include enhancing market synergies, refining tariff mechanisms, and streamlining the trading process to support the sustainable growth of the green electricity market. This study highlights that green electricity trading is an emerging research focus, though its supporting infrastructure remains underdeveloped. Moving forward, enhanced policy support and increased R&D investment in renewable energy are urgently needed, particularly for advancing grid integration technologies for distributed energy. Additionally, aligning green electricity policies with broader low-carbon policies is essential. Furthermore, attention should be paid to the coordination between green electricity trading, economic development, and environmental protection. Full article

One of the key reasons why the road capacity of urban roads in China often fails to meet the designed capacity is the mixture of heavy vehicles (slow-moving) and light vehicles (fast-moving). This paper presents a two-lane cellular automaton model suitable for simulating urban road traffic that includes intersections, based on the NaSch model. The model comprehensively takes into account multiple key factors, such as vehicle safety distance, speed differences between adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time, enabling it to more realistically reproduce the complex characteristics of mixed traffic flows on urban roads. The paper investigates and analyzes the influence of traffic flow density and the proportion of heavy vehicles on the moving bottleneck effect in urban roads from several aspects, including space–time evolution diagrams, traffic flow, average speed, and lane-changing rates. The results indicate that the model established in this paper successfully simulates the characteristics of mixed traffic flows on urban roads, and the simulation results align with actual traffic conditions, achieving the expected simulation effects. Before the traffic volume reaches saturation, the higher the proportion of heavy vehicles on urban roads, the stronger the moving bottleneck effect. This confirms the necessity of conducting research on the phenomenon of moving bottlenecks and the mechanisms of traffic impacts in urban roads, providing a scientific basis for formulating effective traffic dispersion measures and alleviating traffic congestion in the future. This research possesses significant practical meaning and value. Full article

The long-edge characteristics of narrow elongated suction holes in air suction seed metering devices guide the alignment of multiple seeds during multiple-seed adsorption. This feature offers advantages in applications requiring seed singulation. However, research on the application of narrow elongated suction holes in air suction seed metering devices is still limited. To explore the applicability of such suction holes in seeding operations, we conducted single-factor experiments and Box–Behnken experiments. The single-factor experiments, based on computational fluid dynamics (CFD) simulations, analyzed the effects of suction hole width, length, vacuum pressure, and particle diameter on the suction force acting on a single spherical particle as it moved from the suction hole center to the outer region near the seed metering disk wall. Additionally, the Box–Behnken experiments were conducted using a dynamic–static combined adsorption measurement test platform, establishing a regression equation with suction hole width, particle diameter, and vacuum pressure as the experimental factors and the critical suction hole length for dual-particle adsorption as the response variable. The single-factor experiments indicated that suction hole width, length, vacuum pressure, and particle diameter significantly influenced the near-wall adsorption capacity of the suction hole. Analysis of the dual-particle adsorption experiments revealed that when the narrow elongated suction hole was in a vertical position, the lower particle in the adsorbed pair was more likely to detach. The critical adsorption characteristics of the narrow elongated suction hole enable dual-particle deduplication while ensuring continuous single-particle capture, thereby facilitating precision seeding. Full article