Search Results (202)

Within the framework of a discrete-time chronon model, we consider a dual description of physical time. In this description, macroscopic time is a continuous parameter, while a microscopic integer chronon index labels elementary updates of the system. On this basis, a hierarchy of temporal layers ${\mathrm{Ch}}_N$ (Chronon) is introduced. The simple layers $\mathrm{Ch}2$, $\mathrm{Ch}3$ and $\mathrm{Ch}4$ are analysed, and it is shown that they naturally support $U\left(1\right)$ (Unitary group), $SU\left(3\right)$ (Special Unitary group) and a pair-locked $SU\left(2\right)$ (Special Unitary group) symmetry, respectively. Special attention is paid to the twelve-slot layer $\mathrm{Ch}12$. This layer is the minimal one which simultaneously separates partitions into four triads and three quartets. For $\mathrm{Ch}12$, we demonstrate that the intersection of the corresponding commutants in ${\mathbb{C}}^3\otimes {\mathbb{C}}^4$ reproduces the Standard Model gauge algebra $SU{\left(3\right)}_C\times SU{\left(2\right)}_L\times U{\left(1\right)}_Y$ and the pattern of hypercharges and anomaly cancellation. The appearance of three fermion generations is interpreted in terms of three inequivalent embeddings of a triad into the dodecad which preserve the quartet structure. Possible connections of the chronon dynamics with the hierarchy of masses (via Floquet-type quasi-energies), with dark sectors associated with misaligned layers, and with a temporal interpretation of entanglement are briefly discussed on a qualitative level. These questions are formulated as open problems for further study. Full article

Large field-of-view (FOV) infrared imaging, widely utilized in applications including target detection and remote sensing, generates massive datasets that pose significant challenges for transmission and storage. To address this issue, we propose an efficient lossless compression method for large FOV infrared video. Our approach employs a hybrid prediction strategy within the transform domain. The video frames are first decomposed into low- and high-frequency components via the discrete wavelet transform. For the low-frequency subbands, an improved low-latency Multi-view High-Efficiency Video Coding (MV-HEVC) encoder is adopted, where the background reference frames are treated as one view to enable more accurate inter-frame prediction. For high-frequency components, pixel-wise clustered edge prediction is applied. Furthermore, the prediction residuals are reduced by optimal direction prediction, according to the principle of minimizing residual energy. Experimental results demonstrate that our method significantly outperforms mainstream video compression techniques. While maintaining compression performance comparable to MV-HEVC, the proposed method exhibits a 19.3-fold improvement in computational efficiency. Full article

The growing demand for electric vehicles, renewable energy systems, and portable electronics has led to the widespread adoption of power conversion systems. Although advanced structures like the superjunction MOSFET have prolonged the viability of silicon in power applications, maintaining its dominance through cost efficiency, Si-based technology is ultimately constrained by its intrinsic limitations in critical electric fields. To address these constraints, research into wide bandgap semiconductors aims to minimize system footprint while maximizing efficiency. This study reviews the semiconductor landscape, demonstrating why Gallium Nitride (GaN) has emerged as the most promising technology for next-generation power applications. With a critical electric field of $3.75\mathrm{MV}/\mathrm{cm}$ ($12.5\times$ higher than Si), GaN facilitates power devices with lower conduction loss and higher frequency capability when compared to their Si counterpart. Furthermore, this paper surveys the GaN ecosystem, ranging from device modeling and packaging to monolithic ICs and switching converter implementations based on discrete transistors. While existing literature primarily focuses on discrete devices, this work addresses the critical gap regarding GaN monolithic integration. It synthesizes key challenges and achievements in the design of GaN integrated circuits, providing a comprehensive review that spans semiconductor technology, monolithic circuit architectures, and system-level applications. Reported data demonstrate monolithic stages reaching $30\mathrm{m}\Omega$ and $25\mathrm{MHz}$, exceeding Si performance limits. Additionally, the study reports on high-density hybrid implementations, such as a space-grade POL converter achieving $123.3\mathrm{kW}/\mathrm{L}$ with $90.9\%$ efficiency. Full article

In freeform optical metrology, wavefront fitting over non-circular apertures is hindered by the loss of Zernike polynomial orthogonality and severe sampling grid distortion inherent in standard conformal mappings. To address the resulting numerical instability and fitting bias, we propose a unified framework curve-shortening flow (CSF)-guided progressive quasi-conformal mapping (CSF-QCM), which integrates geometric boundary evolution with topology-aware parameterization. CSF-QCM first smooths complex boundaries via curve-shortening flow, then solves a sparse Laplacian system for harmonic interior coordinates, thereby establishing a stable diffeomorphism between physical and canonical domains. For doubly connected apertures, it preserves topology by computing the conformal modulus via Dirichlet energy minimization and simultaneously mapping both boundaries. Benchmarked against state-of-the-art methods (e.g., Fornberg, Schwarz–Christoffel, and Ricci flow) on representative irregular apertures, CSF-QCM suppresses area distortion and restores discrete orthogonality of the Zernike basis, reducing the Gram matrix condition number from >900 to <8. This enables high-precision reconstruction with RMS residuals as low as $3\times {10}^{-3}\lambda$ and up to 92% lower fitting errors than baselines. The framework provides a unified, computationally efficient, and numerically stable solution for wavefront reconstruction in complex off-axis and freeform optical systems. Full article

Currently, wireless sensor networks (WSNs) have been mutually applied to environmental monitoring and industrial control due to their low-cost and low-energy sensor nodes. However, WSNs are composed of a large number of energy-limited sensor nodes, which requires balancing the relationship among energy consumption, transmission delay, and network lifetime simultaneously to avoid the formation of energy holes. In nature, gregarious herbivores, such as the white-bearded wildebeest on the African savanna, employ a “fast-transit and selective-dwell” strategy when searching for water; they cross low-value regions quickly and prolong their stay in nutrient-rich pastures, thereby minimizing energy cost while maximizing nutrient gain. Ants, meanwhile, dynamically evaluate the “energy-to-reward” ratio of a path through pheromone concentration and its evaporation rate, achieving globally optimal foraging. Inspired by these two complementary biological mechanisms, our study proposes a novel ACO-conceptualized optimization model formulated via mixedinteger linear programming (MILP). By mapping the pheromone intensity and evaporation rate into the MILP energy constraints and cost functions, the model integrates discrete decision-making (path selection) and continuous variables (dwell time) by dynamic path planning and energy optimization of mobile sink, constituting multi-objective optimization. Firstly, we can achieve flexible trade-offs between multiple objectives such as data transmission delay and energy consumption balance through adjustable weight coefficients of the MILP model. Secondly, the method transforms complex path planning and scheduling problems into deterministic optimization models with theoretical global optimality guarantees. Finally, experimental results show that the model can effectively optimize network performance, significantly improve energy efficiency, while ensuring real-time performance and extended network lifetime. Full article

This paper proposes a hybrid optimization–control framework that combines the Particle Swarm Optimization (PSO) algorithm, the Alternating Direction Method of Multipliers (ADMM), and a Linear–Quadratic Regulator (LQR) for energy-efficient and reliable operation of parallel pump systems. The PSO layer performs global exploration over mixed discrete–continuous design variables, while the ADMM layer coordinates distributed flows under head and reliability constraints, yielding hydraulically feasible operating points. The inner LQR controller achieves optimal speed tracking with guaranteed asymptotic stability and improved robustness against nonlinear load disturbances. The overall PSO–ADMM–LQR co-design minimizes a composite objective that accounts for steady-state efficiency, transient performance, and control effort. Simulation results on benchmark multi-pump systems demonstrate that the proposed framework outperforms conventional PSO- and PID-based methods in terms of energy savings, dynamic response, and robustness. The method exhibits low computational complexity, scalability to large systems, and practical suitability for real-time implementation in smart water distribution and industrial pumping applications. Full article

Low-disturbance liquid manure injection is increasingly important for sustainable soil management because it reduces residue burial, minimizes surface disruption, and lowers energy demand during application. However, the performance of low-disturbance shanks has not been systematically optimized, and their interaction with soil remains poorly quantified. This study developed an integrated discrete element method (DEM)–experimental framework to evaluate and optimize the performance of a purpose-built injector shank featuring a 45° rake angle, 25 mm thickness, and 110 mm width. The framework aimed to identify operating conditions that balance soil disturbance and energy efficiency. A DEM soil model was constructed using mechanical properties obtained from laboratory characterization tests and validated against soil bin experiments measuring draft force and soil rupture area across five working depths (100–250 mm) and three travel speeds (350–450 mm/s). The calibrated model showed strong agreement with experimental observations, yielding mean absolute relative errors of 1.7% for draft force and 6.2% for rupture area. Following validation, a multi-objective optimization was performed to minimize draft force while maximizing soil rupture, two key indicators of energy demand and injection effectiveness. Optimization results identified the most favorable operating parameters at a forward speed of 450 mm/s and an injection depth of 150 mm, achieving a desirability score of 0.884. The integrated DEM–experimental framework demonstrated reliable predictive capability and enables virtual testing of soil–tool interactions prior to field implementation. This study provides a scientifically grounded approach for improving injector shank operation and supports sustainable manure management by identifying settings that achieve adequate soil disruption while reducing energy consumption. Full article

The kinematic and dynamic characteristics of the grinding media during the wet grinding process are investigated using a coupled Discrete Element Method (DEM)–Computational Fluid Dynamics (CFD) approach. Firstly, a coupled DEM-CFD model of the vertical spiral agitator mill is established and validated with experimental torque measurements. Subsequently, a velocity analysis model is established using the vector decomposition method. The cylinder is then divided into multiple regions along its radial and axial directions. The effects of spiral agitator rotational speed, diameter, pitch, and media filling level are investigated with respect to the circumferential velocity, axial velocity, collision frequency, effective energy between media, and energy loss of the grinding media. The average effective energy between media is an innovative metric for evaluating the grinding effect. The results indicate that the peripheral region of the spiral agitator demonstrates superior kinematic and dynamic performance. The rotational speed of the spiral agitator exerts a highly significant influence on the kinematic and dynamic characteristics of the media. With a maximum rise of 0.2 m/s in circumferential velocity and a 16.7 J gain in total energy. The media filling level demonstrates a negligible influence on media kinematics, while it profoundly affects dynamic properties, evidenced by a substantial increase of 83.09 J in the total media–media energy. As the diameter increases, the peak media circumferential velocity shifts outward, and the total media–media energy rises by 5.4 J. The spiral agitator pitch has a minimal impact on both the kinematic and dynamic characteristics of the media. Full article

This paper presents a computationally efficient two-stage analytical framework for predicting daylight performance in buildings. It is designed to support real-time applications in smart lighting and intelligent building management systems. This approach combines a facade lighting model—driven by solar geometry and atmospheric transmittance—with an interior light distribution module that represents the window as a discretized light source. This formulation provides a lightweight alternative to computationally intensive ray tracing methods. It allows rapid estimation of spatial lighting patterns with minimal input data. The framework is validated using a one-year measurement campaign with class A photometric sensors in three facade orientations. The facade module achieved an average relative error below 15%, while the interior lighting model yielded an RMSE of 83 lx (≈10% error). The integrated system demonstrated an overall average deviation of 18.6% under different sky and season conditions. Owing to its low computational complexity and physically transparent formulation, the proposed method is suitable for deployment in smart building platforms, including daylight-responsive lighting control, embedded energy management systems, and digital twins requiring fast and continuous simulation of daylight availability. Full article

This study integrates Discrete Element Method (DEM) simulations, soil bin experiments, and multi-objective optimization to develop an energy-efficient manure injector shank. Eighteen geometries were first screened using DEM, reducing the set to six designs (S_1–S_6) based on draft force–rupture area performance. The selected designs, varying in rake angle (30°, 45°, 60°), thickness (25 and 30 mm), and width (102, 110, and 118 mm), were tested in a soil bin to measure draft, trench width, spoil cross-sectional area, and soil rupture. Statistical analysis revealed significant differences among designs (p < 0.05), confirming that rake angle, width, and thickness have a strong influence on the soil–tool interaction. A multi-objective optimization framework was then used to minimize draft, trench width, and spoil area while maximizing rupture, with performance quantified through overall desirability values (0–1). Shank S_3 (45° rake, 25 mm thickness, 110 mm width) achieved the highest desirability (0.6676), representing the best trade-off between energy efficiency, minimal surface disturbance, and effective subsurface loosening. This integrated DEM–experimental–optimization approach demonstrates a reliable, data-driven workflow for implement design, reducing reliance on extensive field trials. However, future studies should validate the performance of S_3 and other candidate designs under diverse soil types, moisture levels, and operating conditions to confirm their agronomic and environmental benefits. Full article

The deposition of particulate matter on rough pipe surfaces is critical in fields such as energy, chemical engineering, and air pollution control. This study employs a combined approach utilizing the Renormalized Group (RNG) k-ɛ model and the discrete phase model (DPM). The particle deposition characteristics in circular bent pipe channels with different dimple positions were investigated. To improve simulation fidelity, a model for particle-wall rebound was developed using user-defined function (UDF). The results indicate that the dimple structure influences the deposition location of particles. Particle deposition is minimal on the lower surface and leeward side of the dimple structure. For operating conditions where St ≤ 0.27, θ = 15° yields the optimal effect on enhancing the particle deposition rate, achieving a maximum increase of 18.2%. For conditions where St ≥ 0.461, the optimal angle is θ = 30°, resulting in a maximum deposition rate increase of 14.126%. The deposition rate of dimple structures varies depending on their installation location. In this study, the deposition rate was lowest at θ = 65°. The dimple structure can serve as a sacrificial element, providing protection for the rest of the bent pipe. In the future, channels incorporating this structure can be applied to removal or air purification equipment. Full article

The efficiency of intelligent urban mobility increasingly depends on adaptive mathematical models that can optimize multimodal transportation resources under stochastic and heterogeneous conditions. This study proposes a Markovian stochastic modeling and metaheuristic optimization framework for the adaptive management of bus lane capacity in mixed connected traffic environments. The heterogeneous vehicle arrivals are modeled using a Markov Arrival Process (MAP) to capture correlated and busty flow characteristics, while the system-level optimization aims to minimize total fuel consumption through discrete lane capacity allocation. To support real-time adaptation, a Hidden Markov Model (HMM) is integrated for queue-length estimation under partial observability. The resulting nonlinear and nonconvex optimization problem is solved using Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), ensuring robustness and convergence across diverse traffic scenarios. Numerical experiments demonstrate that the proposed stochastic–adaptive framework can reduce fuel consumption and vehicle delay by up to 68% and 65%, respectively, under high saturation and connected-vehicle penetration. The findings verify the effectiveness of coupling stochastic modeling with adaptive control, providing a transferable methodology for energy-efficient and data-driven lane management in smart and sustainable cities. Full article

Grain breakage serves as a primary causative factor for microbial infestation and oxidative deterioration, significantly diminishing product value and resulting in substantial grain waste and economic losses. The grain discharging process represents the most extensively involved and primary breakage-inducing stage throughout harvest handling and processing operations. However, impact and impact-induced breakage behavior during grain discharge are still poorly understood. To elucidate the impact-induced breakage behavior during grain discharge, this study first employed the discrete element method (DEM) to numerically simulate the discharging process, thereby quantifying the variation patterns of grain kinematic characteristics (e.g., velocity and attitude). Building upon the simulated kinematic data, a dedicated impact testing platform was constructed to investigate single-grain breakage. This enabled the determination of critical unit mass impact energy (along 90°: 106.4 J kg⁻¹; along 0°: 57.28 J kg⁻¹) and critical breakage velocity (along 90°: 14.59 m s⁻¹; along 0°: 10.70 m s⁻¹) under two extreme impact attitude conditions. By integrating the DEM-derived kinematics with the experimentally obtained breakage thresholds, a breakage probability zoning diagram for both large-scale and small-scale discharge processes was developed. Finally, leveraging this comprehensive understanding of the flow and breakage mechanics, theoretical models were successfully established to predict key engineering design parameters, including mass flow rate, impact force, and impact pressure. All models were validated and demonstrated excellent predictive capabilities. The research result is of guiding significance for the design of relevant parameters of discharge systems to minimize grain breakage loss to the greatest extent possible. Full article

Intelligent connected vehicles (ICVs) face challenges in handling intensive onboard computational tasks due to limited computing capacity. Vehicular edge computing networks (VECNs) offer a promising solution by enabling ICVs to offload tasks to mobile edge computing (MEC), alleviating computational load. As transportation systems are dynamic, vehicular tasks and MEC capacities vary over time, making efficient task offloading and resource allocation crucial. We explored a vehicle–road collaborative edge computing network and formulated the task offloading scheduling and resource allocation problem to minimize the sum of time and energy costs. To address the mixed nature of discrete and continuous decision variables and reduce computational complexity, we propose a hybrid hierarchical deep reinforcement learning (HHDRL) algorithm, structured in two layers. The upper layer of HHDRL enhances the double deep Q-network (DDQN) with a self-attention mechanism to improve feature correlation learning and generates discrete actions (communication decisions), while the lower layer employs deep deterministic policy gradient (DDPG) to produce continuous actions (power control, task offloading, and resource allocation decision). This hybrid design enables efficient decomposition of complex action spaces and improves adaptability in dynamic environments. Results from numerical simulations reveal that HHDRL achieves a significant reduction in total computational cost relative to current benchmark algorithms. Furthermore, the robustness of HHDRL to varying environmental conditions was confirmed by uniformly designing random numbers within a specified range for certain simulation parameters. Full article

The study examines the influence of key structural and technological parameters of a disc crusher with impact plates—the distance between liners, installation angle, and linear movement speed—on the crushing process of maize, wheat, and barley grains. Numerical modeling using the Discrete Element Method (DEM) in Simcenter STAR-CCM+ revealed patterns of variation in breaking force during impact cutting. An integral efficiency criterion was proposed to minimize the breaking force while maximizing productivity and reducing energy consumption. Rational process parameters were determined for each crop, considering their physico-mechanical properties: liner distance l = 1.68–1.79 mm, installation angle β = 21.8–25.3°, particle velocity V = 4.72–5.86 m/s, disc speed n = 1503–1865 rpm, and clearance δ = 0.68–0.79 mm. Experimental studies yielded models describing specific energy consumption, dust-like fraction, and crushing degree depending on the liner angle, number, and rotation speed. Optimization showed that energy consumption was lowest for wheat (3.63 kWh/t) and highest for barley (6.76 kWh/t). The dust fraction was greatest for maize (5.13%) and lowest for barley (1.34%). Optimal grinding regimes were found at n = 1500–1764 rpm, β = 15.9–17.7°, and z = 9 plates. The results confirm the efficiency of adapting crusher parameters to grain properties. Full article