Search Results (184)

The human circadian rhythm is connected to the body’s endogenous clock and can influence people’s natural sleeping habits as well as a variety of other biological functions. According to research, various electric light sources in interior locations can disrupt the human circadian rhythm. Many psychological studies, on the other hand, reveal that different colors can have varied connections with and a variety of effects on people’s emotions. In this study, the effects of light source attributes and interior space paint color on human circadian rhythm were studied using 24 distinct computer simulations. Simulations were performed using the ALFA plugin for Rhinoceros 6 on an unfurnished bedroom 3D model at night. Results suggest that cooler hues, such as blue, appear to have an unfavorable effect on human circadian rhythm at night, especially when utilized in spaces that are used in the evening, which contradicts what psychologists and interior designers advocate in terms of the soothing mood and nature of the color. Furthermore, the effects of Correlated Color Temperature (CCT) and the intensity of a light source might be significant in minimizing melanopic lux to prevent melatonin suppression at night. These insights are significant for interior designers, architects, and lighting professionals aiming to create healthier living environments by carefully selecting lighting and color schemes that support circadian health. Incorporating these considerations into design practices can help mitigate adverse effects on sleep and overall well-being, ultimately contributing to improved occupant comfort and health. Full article

The assembly optimization design of satellite components is a crucial element in the overall design of satellites. In this paper, a novel three-dimensional assembly optimization design problem (3D-AODP) for multi-module micro–nano satellite components is proposed according to the engineering requirements, aiming at optimizing the satellite mass characteristics, and taking into account constraints such as space interference, space occupation and special location. Multi-module micro–nano satellites are a new type of satellite configuration based on the assembly of multiple U-shaped cube units. The 3D-AODP of its components is a challenging two-layer composite optimization task involving discrete variable optimization of component allocation and continuous variable optimization of component layout, which interact with each other. To solve the problem, a hybrid assembly optimization method based on tabu search (TS) and multi-objective differential evolutionary (MODE) algorithms is proposed, in which the assignment problem of the components is converted into a domain search problem by the TS algorithm. The space interference constraints and space occupancy constraints of the components are considered, and an assignment scheme with the minimum mass difference is obtained. On this basis, a bi-objective differential evolutionary algorithm is used to develop the layout optimization problem for the components, which takes into account the spatial non-interference constraints and special location constraints of the components, and obtains the Pareto solution set of the assembly scheme under the optimal mass characteristics (moment of inertia and product of inertia). Finally, the feasibility and effectiveness of the proposed method is demonstrated by an engineering case. Full article

The increasing threat of quantum computing to traditional cryptographic systems has prompted intense research into post-quantum schemes. Despite SNOVA’s potential for lightweight and secure digital signatures, its performance on embedded devices (e.g., ARMv8 platforms) remains underexplored. This research addresses this gap by presenting the optimal SNOVA implementations on embedded devices. This paper presents a performance-optimized implementation of the SNOVA post-quantum digital signature scheme on ARMv8 processors. SNOVA is a multivariate cryptographic algorithm under consideration in the NIST’s additional signature standardization. Our work targets the performance bottlenecks in the SNOVA scheme. Specifically, we employ matrix arithmetic over ${\mathbb{GF}}_{16}$ and AES-CTR-based pseudorandom number generation by exploiting the NEON SIMD extension and tailoring the computations to the matrix rank. At a low level, we develop rank-specific SIMD kernels for addition and multiplication. Rank 4 matrices (i.e., 16 bytes) are handled using fully vectorized instructions that align with 128-bit-wise registers, while rank 2 matrices (i.e., 4 bytes) are processed in batches of four to ensure full SIMD occupancy. At the high level, core routines such as key generation and signature evaluation are structurally refactored to provide aligned memory layouts for batched execution. This joint optimization across algorithmic layers reduces the overhead and enables seamless hardware acceleration. The resulting implementation supports 12 SNOVA parameter sets and demonstrates substantial efficiency improvements compared to the reference baseline. These results highlight that fine-grained SIMD adaptation is essential for the efficient deployment of multivariate cryptography on modern embedded platforms. Full article

Lecture halls are characterized by large spatial dimensions, deep floor plans, and high occupant densities. Lectures are typically conducted using multimedia and blackboard-based teaching, placing higher demands on the indoor light and thermal environment compared to standard classrooms. This study aims to simulate the interrelationships between multiple building envelope parameters and building performance, in order to improve visual and thermal comfort while reducing energy consumption in cold-region lecture halls. Based on seven key envelope parameters—including openable window area ratio, west-facing window-to-wall ratio, exterior insulation thickness, shading element spacing, angle and width, and window glass type—a multi-objective optimization framework was established. The optimization process targeted three key performance indicators—useful daylight illuminance (UDI), energy use intensity (EUI), and thermal comfort percentage (TCP)—in the context of a stepped classroom. The results show that increasing the thickness of exterior insulation and reducing the width of shading components contribute positively to photothermal comfort without compromising thermal and visual performance. Compared with the baseline design, optimized schemes that incorporate appropriate west-facing window-to-wall ratios, openable window areas, insulation thicknesses, and external shading designs can reduce annual energy consumption by up to 10.82%, and increase UDI and TCP by 12.79% and 36.41%, respectively. These improvements are also found to be economically viable. Full article

The multi-agile Earth observation satellite scheduling problem is challenging because of its large solution space and substantial task volume. This study generates observation schemes for static tasks over an execution period. To balance solution quality and computational efficiency, a deep reinforcement learning (DRL)-based algorithmic framework is proposed. A Markov decision process (MDP) is formulated as the foundational model for the DRL architecture. To mitigate problem complexity, the action space is decomposed into two interdependent decision layers: task sequencing and resource allocation. Given the resource occupation constraints during action execution, a novel reward function is designed by integrating resource occupation utility into the immediate reward mechanism. Corresponding to these dual decision layers, a Two-Phase Hybrid Optimization (TPHO) framework is developed. The task sequencing subproblem is addressed through an encoder–decoder architecture based on sequence-to-sequence learning. To preserve resource diversity throughout the scheduling horizon, a maximum residual capacity (MRC) heuristic is introduced. A comprehensive experimental suite is constructed, incorporating multi-satellite scheduling scenarios with capacity and temporal constraints. The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS. Full article

With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. Full article

Food security is a fundamental issue that has long been of great concern, and cultivated land resources are the core elements of food security. In recent years, the problem of “non-agriculturalization” and “non-grain” conversion of cultivated land has become prominent. The need for further strict control of cultivated land use has gained significant attention from the government and academia. Recently, it has been proposed in China that all forms of cultivated land occupation should be integrated into the management policy for balancing cultivated land occupation and reclamation. In this study, the concept of provincial-level land-type classification, along with agricultural land potential productivity evaluation, is adopted to determine the optimal scheme for balancing cultivated land occupation and reclamation. Thus, an analysis of the optimization scheme for implementing the cultivated land occupation and reclamation balance policy in Heilongjiang, along with a macro-level layout of this balance scheme, is carried out at the provincial level. The results show that the land-type classification system constructed from five dimensions—climatic conditions, geomorphic conditions, geological conditions, edaphic conditions, and hydrologic conditions—as well as the agricultural land potential productivity evaluation system constructed based on land types, can effectively identify the potential cultivated land utilization space in Heilongjiang Province. Based on the zoning of land suitable for farming, the cultivated land in unsuitable farming areas in Heilongjiang should be transferred out (403.01 km²) and, according to the principle of the balancing cultivated land occupation and reclamation policy, the non-cultivated land in highly and moderately suitable farming areas should be transferred in (249.80 km² and 163.39 km², respectively) to achieve balance. The results can provide reference for the implementation of the cultivated land occupation and reclamation policy at the provincial level, as well as for promoting the implementation of the strategy of “storing grain in the land”. Full article

In order to reduce space occupation and improve reliability, the modularization and integration of micro switches and their components are a necessary path for development. In this paper, a scheme for an alloy metal inertial micro switch using 3D printing technology is proposed for an integrated design. The switch realizes the turn-on function by causing the deformable electrodes to undergo plastic deformation and make close contact with the outer sleeve under the columnar block extrusion. The influence of electrode structure parameters on electrode contact performance was studied by the orthogonal experimental method. And the best parameter combination scheme for the electrode was determined. The aluminum alloy switch and titanium alloy switch were processed by SLM (selective laser melting) technology. The plastic deformation of the 3D-printed titanium alloy electrode occurred later than that of the 3D-printed aluminum alloy electrode under the same impact. The aluminum alloy electrode underwent plastic deformation and realized stable contact with a response time of 5 µs when the impact load was applied with an amplitude of 627 N and a pulse width of 2.7 ms (simulating high acceleration), which meets the application requirement of the response time being no more than 20 µs. The feasibility of 3D printing technology in high-precision and complex-structure micro switch manufacturing was verified. The research in this paper will provide guidance and reference for engineering applications. Full article

This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed event-triggered mechanism is innovatively developed to eliminate dependency on global information while rigorously excluding the Zeno phenomenon through nonperiodic threshold verification. The proposed mechanism enables neighboring UAVs to exchange information and update control signals exclusively at triggering instants, significantly reducing communication burdens and energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel event-triggered neural network (NN) approximation scheme is established where weight updating occurs only during event triggers, effectively decreasing computational resource occupation. Simultaneously, an adaptive robust compensation mechanism is constructed to counteract composite disturbances induced by actuator failures and approximation residuals. Based on the Lyapunov stability analysis, we theoretically prove that all closed-loop signals remain uniformly ultimately bounded while achieving prescribed bipartite containment objectives, where follower UAVs ultimately converge to the dynamic convex hull formed by multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted to validate the effectiveness of the theoretical results. Comparative simulation results show that the proposed event-triggered control scheme reduces the utilization of resources by $95\%$ and $67\%$ compared with the traditional time-triggered and static-triggered mechanisms, respectively. Full article

The Transformer-based target detection model, DETR, has powerful feature extraction and recognition capabilities, but its high computational and storage requirements limit its deployment on resource-constrained devices. To solve this problem, we first replace the ResNet-50 backbone network in DETR with Swin-T, which realizes the unification of the backbone network with the Transformer encoder and decoder under the same Transformer processing paradigm. On this basis, we propose a quantized inference scheme based entirely on integers, which effectively serves as a data compression method for reducing memory occupation and computational complexity. Unlike previous approaches that only quantize the linear layer of DETR, we further apply integer approximation to all non-linear operational layers (e.g., Sigmoid, Softmax, LayerNorm, GELU), thus realizing the execution of the entire inference process in the integer domain. Experimental results show that our method reduces the computation and storage to 6.3% and 25% of the original model, respectively, while the average accuracy decreases by only 1.1%, which validates the effectiveness of the method as an efficient and hardware-friendly solution for target detection. Full article

Artificial intelligence in the mining industry is key to improving safety, optimizing resources, and ensuring sustainable operations in complex environments. The main objective of this research is to develop an autonomous mobile station equipped with artificial vision and artificial intelligence to identify and track equipment, people, and animals in critical areas of mining operations, issuing real-time alerts to reduce occupational risks and improve operational control. The research is applied with an experimental approach, designed to validate the effectiveness of the proposed system in real open-pit mining environments. The proposed methodology consisted of five stages: (i) Selection of data collection equipment, (ii) Definition of the positioning scheme, (iii) Incorporation of the communication system, (iv) Data processing and transformation, and (v) Equipment identification and tracking. The results showed an average accuracy of 98% in the validation and 95% in the test, achieving perfect performance (100%) in key categories such as excavators and drills, highlighting the potential of this technology to transform mining towards safer and more efficient standards. Full article

A significant amount of existing building stock needs renovation to reach nearly zero energy building (nZEB) status with minimal intervention. This paper aims to research and form a basis for future studies to build upon regarding the additional renewable energy requirements for existing buildings under future climate change. The objectives are to investigate the effect of the changing energy requirements in different current and future regional climates, the change in the number of occupants, and the required additional renewable energy. The case building is modeled on an apartment scheme built in different climatic regions. The method is the Transient System Simulation Tool (TRNSYS) building energy simulation to evaluate both the contemporary and changing weather conditions for 2050 according to three Intergovernmental Panel on Climate Change (IPCC) scenarios for 2.6, 4.5, and 8.5 degrees of temperature increase. The integration of required renewable energy and occupancy size with climate change scenarios for various climates fills a gap in the existing research. The results show that while each climatic region responds differently to climate change scenarios, climates that currently have more cooling demands are impacted the most adversely. However, there is no need to change the amount of additional renewable energy installed for 2050. Full article

Extremely large-scale multi-input and multi-output (XL-MIMO) communication, compared to conventional massive multi-input multi-output communication, can support more users and higher data throughput, thereby significantly improving its spectral efficiency and spatial multiplexing capabilities. This paper investigates the optimization of resource allocation for device-to-device (D2D) multicast communication in XL-MIMO cellular networks. The “many-to-many” sharing model permits one subcarrier to be shared among multiple D2D groups (DGs) and each DG to reuse multiple subcarriers. The objective is to maximize the total multicast data rate of DGs while meeting the data rate requirements of cellular users. This optimization problem is formulated as a 0–1 mixed-integer nonlinear programming problem, with the challenge lying in the fact that adjusting the subcarriers and the power of the user equipment alters the network’s carrier occupation and interference relationships, thereby increasing computational complexity. To address this challenge, a phased strategy is proposed. Initially, subcarrier allocation and coarse power allocation are conducted for cellular users. Subsequently, an adversarial multi-player multi-armed bandit framework is employed, treating DGs as players and subcarrier and power combinations as arms, to maximize the total multicast data rate. An improved Exp3 algorithm is utilized for selecting the optimal combination of arms. Finally, precise power allocation for cellular users is conducted based on the allocation results of the DGs. A comparative analysis of various simulations confirms the superiority of our algorithm over the established heuristic subcarrier assignment and proposed power allocation (HSAPP) and the channel allocation scheme using full information of device locations (CAFIL) approaches. Full article

Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind zones, especially in elongated vehicles. To mitigate these challenges, we propose a distributed Time-of-Flight (ToF) sensor system with a flexible hardware–software architecture designed for multi-sensor synchronous triggering and fusion. We formalize the sensor triggering, interference mitigation scheme, data aggregation and fusion procedures and highlight challenges in achieving accurate global registration with current state-of-the-art methods. The resulting surround view visual information is then applied to Spiking Neural Network (SNN)-based object detection and probabilistic occupancy grid mapping (OGM) for enhanced environmental awareness. The proposed system is demonstrated on a test vehicle, achieving coverage of blind zones in a range of 0.5–6 m with a scalable and reconfigurable sensor mounting setup. Using seven ToF sensors, we can achieve a 10 Hz synchronized frame rate, with a 360° point cloud registration and fusion latency below 40 ms. We collected real-world driving data to evaluate the system, achieving 65% mean Average Precision (mAP) in object detection with our SNN. Overall, this work presents a replacement or addition to LiDAR in future high-level automation tasks, offering improved coverage and system integration. Full article

Background/Objectives: There is a paucity of research that explores the effectiveness of the Sequential Oral Sensory (SOS) Approach to Feeding. The purpose of this feasibility study was to evaluate the necessary components for the implementation of a treatment effectiveness study on the Sequential Oral Sensory (SOS) Approach to Feeding. The primary aims were to develop a fidelity measure, determine the feasibility of video coding, create an observational coding scheme, and determine if the outcome measures were sensitive to change. Methods: Over a 4-year period, data were collected from twelve participants aged 4 to 8 years with developmental disorders, with the assistance of four occupational therapy doctoral students. A fidelity measure was created, and inter-rater reliability was established among the four coders. Videotapes were collected at home and in the clinic. A behavioral coding system, consistent with the SOS Steps to Eating hierarchy, was developed for scoring feeding behaviors. Results: The preliminary inter-rater reliability was reported, and the coding results were represented graphically. Two additional outcome measures were piloted—a visual analog scale (VAS) and the Parenting Stress Index (PSI). The VAS was sensitive to changes in each parent’s ability to support their child, as well as in each client’s progress. The PSI also showed sensitivity to changes in the decline of parent-reported stress and child stress indices. Conclusions: Findings demonstrate fidelity to the SOS Approach, as well as sensitive outcomes, using behavioral coding and parent-reported measures. These evidence-based tools and procedures offer researchers and clinicians objective and meaningful feeding outcomes. Full article