Search Results (7,193)

The addition of sea buckthorn(Hippophae rhamnoides L.) fruits as well as their extracted juice or, even more interestingly, related by-products into chocolate results in manufacturing an innovative functional food rich in bioactive substances. Thirteen treatments derived from the factorial combination of three types of H. rhamnoides materials (total fruit powder; fruit by-product powder; and fruit juice) and four concentrations (10%, 15%, 20% and 25%), plus an untreated control, were compared in terms of texture, quality, colour, antioxidant, mineral and sensorial properties of white chocolate. The untreated control showed the highest values of most of the texture parameters, as well as of pH, dry matter, soluble solids and colour component ‘L’. The colour component ‘b’ was best influenced by the 10% by-product addition to chocolate, whereas mineral substances, ash and colour component ‘a’ augmented with the increasing concentration of added H. rhamnoides materials. Compared to the untreated control, protein and fat contents in chocolate decreased with the rising added concentration of sea buckthorn fruit juice but showed the opposite trend under the integration of the whole fruit and its by-products. The antioxidant compounds and activity increased from the untreated chocolate to the highest concentration of added sea buckthorn materials. The juice addition to the chocolate best affected vitamin C, total carotenoids, β-carotene and lycopene, whereas the whole fruit integration led to the top levels of flavonoids, polyphenols and antioxidant activity. Potassium and zinc contents decreased from the untreated control to the highest H. rhamnoides material addition, whereas opposite trends were shown by calcium, magnesium, sodium and phosphorus. The integration of H. rhamnoides fruit materials into chocolate presents a valuable strategy to produce innovative health beneficial functional food. Full article

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

In advanced manufacturing, nanotechnology, and aerospace fields, the demand for precision is increasing. Driven by this demand, multi-degree-of-freedom grating encoders have become particularly crucial in high-precision displacement and angle measurement. Over the years, these encoders have evolved from one-dimensional systems to complex multi-degree-of-freedom measurement solutions that can achieve real-time synchronization. There can also be high-resolution feedback. Its structure is relatively compact, the signal output is also very stable, and the integration degree is high. This gives it a significant advantage in complex measurement tasks. Recently, there have been new developments. The functions of grating encoders in terms of principle, system architecture, error modeling, and signal processing strategies have all been expanded. For instance, accuracy can be improved by integrating multiple reading-heads, while innovative strategies such as error decoupling and robustness enhancement have further advanced system performance. This article will focus on the development of two-dimensional, three-dimensional and multi-degree-of-freedom grating encoders, exploring how the measurement degrees of freedom have evolved, and emphasizing key developments in spatial decoupling, error compensation and system integration. At the same time, it will also discuss some challenges, such as error coupling, system stability and intelligent algorithms for integrating real-time error correction. The future of grating encoders holds great potential. Their applications in precision control, semiconductor calibration, calibration systems, and next-generation intelligent manufacturing technologies can bring promising progress to both industrial and scientific fields. Full article

Addressing the challenge of efficient gas control in high-gas coal mines with ultra-long panels, this study focuses on the No. 8 coal seam in the Baode Mine. A multi-parameter integrated methodology was developed to establish a hierarchical classification system of Gas Geological Units (GGUs), aiming to identify regions suitable for large-scale gas extraction. The results indicate that the overall structure of the No. 8 coal seam is a simple monocline. Both gas content (ranging from 2.0 to 7.0 m³/t) and gas pressure (ranging from 0.2 to 0.65 MPa) generally increase with burial depth. However, local anomalies in these parameters, caused by geological structures and hydrogeological conditions, significantly limit the effectiveness of large-scale drainage using ultra-long boreholes. Based on key criteria, the seam was classified into three Grade I and ten Grade II GGUs, distinguishing anomalous zones from homogeneous units. Among the Grade II units, eight (II-i to II-viii) were identified as anomalous zones with distinct geological constraints, while two (II-ix and II-x) exhibited homogeneous gas geological parameters. Practical implementation of large-scale gas extraction strategies—including underground ultra-long boreholes and a U-shaped surface well—within the homogeneous Unit II-x demonstrated significantly improved gas drainage performance, characterized by higher methane concentration, greater flow rate, enhanced temporal stability, and more favorable decay characteristics compared to conventional boreholes. These findings confirm the critical role of GGU delineation in guiding efficient regional gas control and ensuring safe production in similar high-gas coal mines. Full article

Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

To address the stability control challenges of narrow coal pillar roadways along goaf-sides affected by thick coal seam secondary mining, this study investigates the 51507 track gateway in Liuyuanzi Coal Mine through theoretical analysis, numerical simulation, and field testing. The research focuses on stress evolution and energy distribution characteristics during secondary mining extraction. Key findings include the following: (1) Under the superimposed influence of goaf-side abutment pressure and secondary mining front abutment pressure, roadway surrounding rock exhibits regional asymmetric characteristics in energy dissipation. (2) Within 10 m ahead of the secondary mining face, the coal pillar experiences intense energy dissipation and plastic zone penetration, leading to bearing structure failure. (3) The energy mechanism reveals that asymmetric dissipative energy distribution drives plastic zone expansion. Accordingly, an integrated control strategy combining differentiated support (bolts/cables + tension-type opposite anchor cables + hydraulic props) with coal pillar grouting modification was developed. Field implementation demonstrated effective control of surrounding rock deformation within 200 mm. This study provides theoretical foundations and technical references for roadway stability control under similar mining conditions. Full article

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies. Full article

With the proliferation of multimedia data, controllable summarization generation has become a key focus in Artificial Intelligence Content Generation. However, many traditional methods lack precise control over output length, often resulting in summaries that are either too verbose or too brief, thus failing to meet diverse user needs. In this paper, we propose a length-customizable approach for multimodal image-text summarization. Our method integrates combinatorial optimization with deep learning to address the length-control challenge. Specifically, we formulate the summarization task as a knapsack optimization problem, enhanced by a greedy algorithm to strictly adhere to user-defined length constraints. Additionally, we introduce a multimodal attention mechanism to ensure balanced and coherent integration of textual and visual information. To further enhance semantic alignment, we employ a cross-modal matching strategy for image selection based on pre-trained vision-language models. Experimental evaluations on the MSMO dataset and validate against baselines like LEAD-3, Seq2Seq, Attention, and Transformer that our method achieves a ROUGE-1 score of 40.52, ROUGE-2 of 16.07, and ROUGE-L of 35.15, outperforming existing length-controllable baselines. Moreover, our approach attains the lowest length variance, confirming its precise adherence to target summary lengths. These results validate the effectiveness of our method in generating high-quality, length-constrained multimodal summaries. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

In modern power systems, distributed generation (DG) clusters such as wind and solar resources are increasingly being integrated into active distribution networks through DG cluster control, which enhances the economic efficiency and adaptability of the DGs. However, cyber attacks on cyber–physical systems (CPS) may disable control links within the DG cluster, leading to the loss of control over slave DGs and resulting in power deficits, thereby threatening system stability. Existing CPS security assessment methods have limited capacity to capture cross-domain propagation effects caused by cyber attacks and lack a comprehensive evaluation framework from the attacker’s perspective. This paper establishes a CPS system model and control–communication framework and then analyzes the cyber–physical interaction characteristics under DG cluster control. A logical model of cyber attack strategies targeting DG cluster inverters is proposed. Based on the control topology and master–slave logic, a probabilistic failure model for DG cluster control is developed. By considering power deficits at cluster point of common coupling (PCC) and results in internal network of the DG cluster, a physical consequence quantification method is introduced. Finally, a cyber risk assessment method is proposed for DG cluster control under cyber attacks. Simulation results validate the effectiveness of the proposed method. Full article

Cordyceps militaris (C. militaris) is a medicinal fungus renowned for its diverse therapeutic properties, largely attributed to bioactive compounds such as cordycepin, polysaccharides, adenosine, D-mannitol, carotenoids, and ergosterol. However, the production and composition of these metabolites are highly influenced by cultivation conditions, highlighting the need for systematic optimization strategies. This review synthesizes current findings on how nutritional factors—including carbon and nitrogen sources, their ratios, and trace elements—and environmental parameters such as oxygen availability, pH, temperature, and light regulate C. militaris metabolite biosynthesis. The impacts of solid-state fermentation (using grains, insects, and agro-industrial residues) and liquid state fermentation (submerged and surface cultures) are compared, with attention to their roles in mycelial growth, fruiting body formation, and secondary metabolite production. Special emphasis is placed on mixed grain–insect substrates and light regulation, which have emerged as promising methods to enhance cordycepin accumulation. Beyond summarizing advances, this review also identifies key knowledge gaps that must be addressed: (i) the incomplete understanding of metabolite regulatory networks, (ii) the absence of standardized cultivation protocols, and (iii) unresolved challenges in scale-up, including oxygen transfer, foam control, and downstream processing. We propose that future research should integrate multi-omics approaches with bioprocess engineering to overcome these limitations. Collectively, this review highlights both current progress and remaining challenges, providing a roadmap for advancing the sustainable, scalable, and application-driven production of bioactive compounds from C. militaris. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article