Search Results (1,938)

Matrix converters (MC) offer a compact, bidirectional solution for power conversion; however, achieving precise reactive power control at the input terminals remains challenging under varying operating conditions. This paper presents an enhanced Singular Value Decomposition modulation technique (e-SVD) as a solution tailored to optimize reactive power management on the MC input side, enabling both active and reactive power control regardless of the power factor. The proposed method achieves input reactive power control based on a reactive power gain, a quantity derived from the apparent output power and defined by a mathematical expression involving electrical parameters and control variables. Experimental tests carried out on a low-power MC prototype to validate the proposal show that the measured reactive power gain closely aligns with theoretical predictions from the mathematical expressions. Overall, the proposed e-SVD modulation technique lays the foundation for more reliable reactive power regulation in applications such as microgrids and distributed generation systems, contributing to the development of smarter and more resilient energy infrastructures. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

The current research trends of production engineering are based on optimizing the machining process concerning human and environmental factors. High-performance materials, such as hardened steels, nickel-based alloys, fiber-reinforced polymer (FRP) composites, and titanium alloys, are classified as hard-to-cut due to their ability to maintain strength at high operating temperatures. Due to these characteristics, such materials are employed in applications such as aerospace, marine, energy generation, and structural. The purpose of this article is to investigate the machinability of these alloys under various cutting conditions. The purpose of this article is to compare cryogenic cooling and cryogenic processing from the perspective of machinability and sustainability in the manufacturing process. Compared to conventional machining, hybrid techniques, which mix cryogenic and minimal quantity lubricant, led to significantly reduced cutting forces of 40–50%, cutting temperatures and surface finishes by approximately 20–30% and more than 40%, respectively. A carbon footprint is determined by several factors including power consumption, energy requirements, and carbon dioxide emissions. As a result of the cryogenic technology, the energy consumption, power consumption, and CO₂ emissions were reduced by 40%, 28%, and 35%. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

The spatial distribution of ephemeral and perennial dryland plant species is increasingly modified and restricted by ever-changing climates and development expansion. At the interface of biodiversity conservation and developmental planning in desert landscapes is the growing need for adaptable tools in identifying and monitoring these ecologically fragile plant assemblages, habitats, and, often, heritage sites. This study evaluates usage of Sentinel-2 time series composite imagery to discriminate vegetation assemblages in a hyper-arid landscape. Spatial predictor spaces were compared to classify different vegetation communities: spectral components (PCs), vegetation indices (VIs), and their combination. Further, the uncertainty in discriminating field-verified vegetation assemblages is assessed using Shannon entropy and intensity analysis. Lastly, the intensity analysis helped to decipher and quantify class transitions between maps from different spatial predictors. We mapped plant assemblages in 2022 from combined PCs and VIs at an overall accuracy of 82.71% (95% CI: 81.08, 84.28). A high overall accuracy did not directly translate to high class prediction probabilities. Prediction by spectral components, with comparably lower accuracy (80.32, 95% CI: 78.60, 81.96), showed lower class uncertainty. Class disagreement or transition between classification models was mainly contributed by class exchange (a component of spatial allocation) and less so from quantity disagreement. Different artefacts of vegetation classes are associated with the predictor space—spectral components versus vegetation indices. This study contributes insights into using feature extraction (VIs) versus feature selection (PCs) for pixel-based classification of plant assemblages. Emphasising the ecologically sensitive vegetation in desert landscapes, the study contributes uncertainty considerations in translating optical satellite imagery to vegetation maps of arid landscapes. These are perceived to inform and support vegetation map creation and interpretation for operational management and conservation of plant biodiversity and habitats in such landscapes. Full article

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article

Societies are aiming to have a higher ecological consciousness in wastewater treatment operations and achieve a more sustainable future. With this said, global demands for larger quantities of resources and the consequent waste generated will inevitably lead to the exhaustion of current municipal wastewater treatment works. The utilization of biosolids (particularly microbial proteins) from wastewater treatment operations could generate a sustainable bio-adhesive for the wood industry, reduce carbon footprint, mitigate health concerns related to the use of carcinogenic components, and support a more circular economic option for wastewater treatment. A techno-economic analysis for three 10 MGD wastewater treatment operations producing roughly 11,300 dry pounds of biosolids per day, in conjunction with co-feedstock defatted soy flour protein at varying ratios (i.e., 0%, 15%, and 50% wet weight), was conducted. Aspen Capital Cost Estimator V12 was used to design and estimate installed equipment additions for wastewater treatment plant integration into an urban biorefinery process. Due to the mechanical attributes and market competition, the chosen selling prices of each adhesive per pound were set for analysis as USD 0.75 for Plant Option P1, USD 0.85 for Plant Option P2, and USD 1.00 for Plant Option P3. Over a 20-year life, each plant option demonstrated economic viability with high NPVs of USD 107.9M, USD 178.7M, and USD 502.2M and internal rates of return (IRRs) of 24.0%, 29.0%, and 44.2% respectively. The options examined have low production costs of USD 0.14 and USD 0.19 per pound, minimum selling prices of USD 0.42–USD 0.51 per pound, resulting in between 2- and 4-year payback periods. Sensitivity analysis shows the effects biosolid production fluctuations, raw material market price, and adhesive selling price have on economics. The results proved profitable even with large variations in the feedstock and raw material prices, requiring low market selling prices to reach the hurdle rate of examination. This technology is economically enticing, and the positive environmental impact of waste utilization encourages further development and analysis of the bio-adhesive process. Full article

To address the modeling complexity and multi-objective collaborative optimization challenges in multi-depot and multiple unmanned aerial vehicle (UAV) delivery task planning, this paper proposes a bi-layer planning framework, which comprehensively considers resource constraints, multi-depot coordination, and the coupling characteristics of path execution. The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. The upper layer tackles the task assignment problem by formulating a multi-objective optimization model aimed at minimizing economic costs, delivery delays, and the number of UAVs deployed. The Enhanced Non-Dominated Sorting Genetic Algorithm II (ENSGA-II) is developed, incorporating heuristic initialization, goal-oriented search operators, an adaptive mutation mechanism, and a staged evolution control strategy to improve solution feasibility and distribution quality. The main contributions are threefold: (1) a novel ENSGA-II design for efficient and well-distributed task allocation; (2) an improved PSO-based path planner with chaotic initialization and adaptive parameters; and (3) comprehensive validation demonstrating substantial gains over baseline methods. The lower layer addresses the path planning problem by establishing a multi-objective model that considers path length, flight risk, and altitude variation. An improved particle swarm optimization (PSO) algorithm is proposed by integrating chaotic initialization, linearly adjusted acceleration coefficients and maximum velocity, a stochastic disturbance-based position update mechanism, and an adaptively tuned inertia weight to enhance algorithmic performance and path generation quality. Simulation results under typical task scenarios demonstrate that the proposed model achieves an average reduction of 47.8% in economic costs and 71.4% in UAV deployment quantity while significantly reducing delivery window violations. The framework exhibits excellent capability in multi-objective collaborative optimization. The ENSGA-II algorithm outperforms baseline algorithms significantly across performance metrics, achieving a hypervolume (HV) value of 1.0771 (improving by 72.35% to 109.82%) and an average inverted generational distance (IGD) of 0.0295, markedly better than those of comparison algorithms (ranging from 0.0893 to 0.2714). The algorithm also demonstrates overwhelming superiority in the C-metric, indicating outstanding global optimization capability in terms of distribution, convergence, and the diversity of the solution set. Moreover, the proposed framework and algorithm are both effective and feasible, offering a novel approach to low-altitude urban logistics delivery problems. Full article

A three-year optimization study was conducted at a mechanical biological treatment plant with the aim of enhancing organic fractions recovery from mechanically separated fine fractions (MSFF) of residual waste using a screw press. The study aimed to optimize key operating parameters for the employed screw press, such as pressure, liquid-to-MSFF, feeding quantity per hour, and press basket mesh size, to enhance volatile solids and biogas recovery in the generated press water for anaerobic digestion. Experiments were performed at the full-scale facility to evaluate the efficiency of screw press extraction with other pretreatment methods, like press extrusion, wet pulping, and hydrothermal treatment. The results indicated that hydrolysis of the organic fractions in MSFF was the most important factor for improving organic extraction from the MSFF to press water for fermentation. Optimal hydrolysis efficiency was achieved with a digestate and process water-to-MSFF of approximately 1000 L/ton, with a feeding rate between 8.8 and 14 tons per hour. Increasing pressure from 2.5 to 4.0 bar had minimal impact on press water properties or biogas production, regardless of the press basket size. The highest volatile solids (29%) and biogas (50%) recovery occurred at 4.0 bar pressure with a 1000 L/ton liquid-to-MSFF. Further improvements could be achieved with longer mixing times before pressing. These findings demonstrate the technical feasibility of the pressing system for preparing an appropriate substrate for the fermentation process, underscoring the potential for optimizing the system. However, further research is required to assess the cost–benefit balance. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

Four machine learning models (Decision Tree Regressor, Linear Regression, XGBoost Regression, K-Neighbors Regressor) were developed to predict the outcomes of a composting process based on key input parameters, including Ambient Temperature, mixture composition, and initial feedstock volume. The models were trained on data from 88 composting batches, monitoring temperature evolution, and compost yield. Performance evaluation demonstrated high accuracy in predicting compost maturity, process duration, and final product quantity. These predictive models could optimize composting operations by enabling real-time adjustments, improving efficiency, and enhancing resource management in sustainable waste processing. Full article

Remanufacturing in reverse logistics can not only support sustainable development but also provide a tractable way to achieve carbon neutrality. This study evaluates whether an original equipment manufacturer (OEM) should remanufacture outsource or authorize this reverse channel activity in the presence of government subsidies. Additionally, the model considers the equilibrium acquisition quantities, collection rates, prices, and effects of government subsidy under three reverse channel options: centralizing remanufacturing, outsourcing remanufacturing, and authorization remanufacturing. The analysis indicates that (i) a centralized approach with manufacturing and remanufacturing operations under a fixed government subsidy is always in the interest of the supply chain; (ii) that for the profit-maximizing third-party remanufacturer (3PR), the differentials in variable collection costs drive the strategy choice, and that a higher fixed scaling parameter of the collection cost favors outsourcing; and (iii) when the government aspires to reduce environmental effects and subsidy payments, the OEM and government have different reverse channel choice preferences. Surprisingly, profitability and environmental goals align under a high consumer acceptance of the remanufactured product. This paper extends the understanding of the remanufacturing strategy of an OEM and provides new insights on which reverse channel is optimal. Full article

With the increasing penetration of renewables, the importance of electrical energy storage (EES) for power supply stabilization is growing. The intermittency of renewable energy sources remains the main issue limiting their rapid integration; however, the development of high-capacity batteries capable of storing large quantities of energy offers a way to address this challenge. This article presents and describes dedicated algorithms for controlling the EES system to enable the provision of individual system services. Five services are planned for implementation: RES power stabilization; voltage regulation using active and reactive power; reactive power compensation; power stabilization of unstable loads; and power reduction on demand. The aim of this paper is to develop new, dedicated energy storage control algorithms for delivering these specific services. Additionally, the voltage regulation algorithm includes two operating modes: short-term regulation (voltage fluctuation stabilization) and long-term regulation (triggered by an operator signal). Full article

Neonatal calves possess an immature and naïve immune system and are reliant on the intake of maternal colostrum for the passive transfer of immunoglobulins. Maternal antibodies delivered to the calf via colostrum, are crucial to prevent calfhood diseases and death. Failure of transfer of passive immunity (FTPI) is a condition in which calves do not acquire enough maternal antibodies, mostly in the form of IgG, due to inadequate colostrum quality or delayed colostrum feeding. The diagnosis and risk factors for FTPI have been widely studied in dairy cattle; however, in beef calves, the research interest in the topic is relatively recent, and the most adequate diagnostic and preventative methods are still in development, making it difficult to define recommendations for the assessment and prevention of FTPI in cow–calf operations. The objective of this scoping review is to identify the published literature on best practices for colostrum management and transfer of passive immunity (TPI) in neonatal beef calves. The literature was searched using three electronic databases (CAB Direct, Scopus, and PubMed) for publications from 2003 to 2025. The search process was performed during the period from May to July 2023, and was repeated in January 2025. All screening processes were performed using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). A total of 800 studies were initially identified through database searches. After removing duplicates, 346 studies were screened based on their titles and abstracts, leading to the exclusion of 260 studies. The remaining 86 studies underwent full-text screening, and 58 studies were considered eligible for data extraction. Hand-searching the references from published review papers on the subject yielded an additional five studies, bringing the total to 63 included articles. The prevalence of FTPI has been estimated to be between 5.8% and 34.5% in beef calves. Factors studied related to colostrum management include quality and quantity of colostrum intake, the timing and method of colostrum feeding, and the microbial content of the colostrum. Studies on risk factors related to the calf include the topics calf sex, twin status, calf vigor, weight, month of birth, cortisol and epinephrine concentrations, and the administration of nonsteroidal anti-inflammatory drugs to calves after difficult calving. The dam-related risk factors studied include dam body condition score and udder conformation, breed, parity, genetics, prepartum vaccinations and nutrition, calving area and difficulty, and the administration of nonsteroidal anti-inflammatory drugs at C-section. Most importantly for beef systems, calves with low vigor and a weak suckling reflex are at high risk for FTPI; therefore, these calves should be given extra attention to ensure an adequate consumption of colostrum. While serum IgG levels of < 8 g/L or < 10 g/L have been suggested as cutoffs for the diagnosis of FTPI, 16 g/L and 24 g/L have emerged as cutoffs for adequate and optimal serum IgG levels in beef calves. Several field-ready diagnostics have been compared in various studies to the reference standards for measuring indicators of TPI in beef calves, where results often differ between models or manufacturers. Therefore, care must be taken when interpreting these results. Full article

We report a new concept of a pressure sensor fully made from polydimethylsiloxane with a solid core and porous cladding that operates through (frustrated) total internal reflection. A flexible and sensitive rectangular cross-section waveguide was fabricated via the casting and molding method. The waveguide’s optical losses can be temperature-controlled during the fabrication process by controlling the quantity of microbubbles incorporated (2% approximately for samples made at 70 °C). By controlling the precuring temperature, the microbubbles are incorporated into the waveguides during the simple and cost-effective fabrication process through the casting and molding method. For these samples, we measured good optical loss tradeoff of the order of 1.85 dB/cm, which means that it is possible to fabricate a solid-core/clad waveguide with porous cladding able to guide light properly. We demonstrated the microbubble concentration control in the waveguide, and we measured an average diameter of 239 ± 16 µm. A sensitivity to pressure of 0.1035 dB/kPa optical power loss was measured. The results show that in a biomedical dynamic pressure range (0 to 13.3 kPa), this new device indicates the critical pressure threshold level, which constitutes a crucial asset for potential applications such as pressure injury prevention. Full article