Smallholder farmers in Ethiopia face significant postharvest losses of perishable crops like onions. Solar drying offers a promising solution, yet adoption remains limited. This study explores eco-friendly solar drying methods to extend the shelf life of onions. It focuses on the design, manufacture, and performance evaluation of a box-type direct solar dryer for drying onions. The dryer’s performance was assessed in terms of efficiency, drying rate, and uniformity of temperature distribution. Results showed a drying efficiency of 20%, compared to 16.4% for open-sun drying, with a mass flow rate of 2.84 kg/hr. The dryer reduced onion moisture content from 87% to 7.5% in 77 hours, achieving a drying rate of 0.024 kg/hr, twice that of open-sun drying (0.0124 kg/hr). The drying chamber maintained a temperature of 3–5 °C above ambient, ensuring consistent drying conditions, with an average heat energy transfer of 7265 J. Overall, the solar dryer dried onions 21% faster than open-sun methods, making them safer for storage and transport. Drying data were fitted to seven mathematical models, with the Page model providing the best fit based on maximum coefficient of determination (R2), minimum reduced chi-square (χ2), and root mean square error (RMSE). These findings highlight the potential of direct solar drying to improve postharvest onion preservation.

Multi-attribute decision-making in manufacturing is often hindered by nonlinear process-response relationships and the inadequate integration of weighted performance criteria. To overcome these limitations, this study proposes the Response Surface Methodology-based Multi-Attribute Genetic Algorithm (RAGA), a novel optimisation methodology developed for single-weld-bead deposition of Al4043 alloy using gas metal arc-based processes. Unlike conventional techniques such as Artificial neural network (ANN)-Genetic algorithm (GA), n-ary Technique for order of preference by similarity to ideal solution (TOPSIS), and Multi objective GA (MOGA), RAGA offers three key advancements: (i) it uses analytically derived weights from the Analytic Hierarchy Process into the genetic algorithm, ensuring criteria relevance is explicitly considered; (ii) it integrates response surface models to efficiently handle non-linearities in process-response mapping; and (iii) it introduces user-defined optimisation intentions (maximisation/minimisation) to tailor objectives to optimisation problems. Comparative assessments reveal that RAGA achieves up to 20% higher optimisation accuracy than other methods, showcasing both precision and flexibility. Case studies in welding, cladding, and additive manufacturing confirm RAGA’s adaptability across materials and processes. Sensitivity analysis identifies current and travel speed as dominant input variables influencing bead geometry and hardness. These findings position RAGA as a robust, scalable tool for solving complex multi-attribute optimisation challenges in advanced manufacturing.

This article evaluates the influence of different fabrication techniques, extrusion (ET), cutting (CT), and deep rolling techniques (DRT) on the microstructural and mechanical behavior of medium carbon steel screws for fastening applications. It involves mechanical, microstructural, and compositional analyses of an identical medium-carbon steel composed of chromium (Cr), vanadium (V), niobium (Nb), silicon (Si), manganese (Mn), and carbon (C). It also considers the effects of fabrication method on the fatigue performance, hardness, and fracture characteristics of the screws. The analytical studies considered constitutive equations; whereas, the numerical approaches used finite element analyses (FEA) to corroborate the fatigue stress distributions in the threads of the screws manufactured by ET, CT and DRT. The results showed that the microstructures had area fractions of ∼0.03, ∼0.20 and ∼0.23 for DRT, CT, and ET, respectively, owing to the respective phase structures high in carbon in the steels. The DRT significantly reduced martensite area fraction and refined grain structure, leading to improved ductility and fatigue performance. Mechanical testing showed that DRT screws had the highest fatigue limit (109 MPa), while CT screws had the greatest surface hardness (∼467 HV). The FEA showed a DRT sample with improved fracture toughness (∼300 MPa√mm) and a slower rate of crack growth. The stress distributions and crack growth under loading in the FEA analyses corroborate the experimental trends. The results suggest that the DRT is particularly beneficial for screw components subjected to cyclic stresses and fatigue-critical applications.

The objective of this study was to compare the hardness, tensile strength, and fracture surface analysis of LM4, LM4+ (1, 2, and 3 wt.%) TiB2, LM4+ (1, 2, and 3 wt.%) Si3N4, and LM4+ (1, 1.5, and 2 wt.%) TiB2+ (1, 1.5, and 2 wt.%) Si3N4 hybrid composites. Composites were cast using a two-stage stir casting method, and as-cast samples were subjected to precipitation hardening treatment, including single-stage and multi-stage solutionizing, followed by artificial aging at 100 and 200 °C. When the as-cast and precipitation heat-treated samples were compared, the precipitation heat-treated samples of both the alloy and composites had greater hardness and tensile strength values than those of the as-cast samples. Scanning electron microscopy images were used to analyze and discuss the failure mechanisms related to the tensile strength of the samples. Crack propagation was also studied to understand failure mechanisms better. Because of the presence of coarser dimples, LM4 exhibited ductile failure, whereas monolithic and hybrid composites failed in a mixed manner, with both ductile and brittle failures prevailing equally. The precipitation heat-treated samples (both LM4 and composites) had a mixed failure mode, with dominant brittle failure.

Ground source heat pump (GSHP) systems typically use vertical borehole heat exchangers (BHEs) as heat sources or sinks. The performance of GSHP systems is highly dependent on the ground temperature surrounding the BHEs, which varies with several parameters during operation. Accurate prediction of this temperature is crucial for optimizing the design and sizing of the BHEs.This study proposes an approach using artificial neural networks (ANNs) to model temperature variations around the borehole based on six operational parameters: time, ground thermal diffusivity, porosity, groundwater speed, heat extraction rate, and distance from the borehole. Various ANN configurations were explored to derive an explicit mathematical equation from the relationships stored within the trained ANN model. The Levenberg–Marquardt algorithm was identified as the most suitable for training, achieving a maximum accuracy of R2 = 0.9997 with ten neurons and the Tansig transfer function in both layers. Simpler configurations, such as an ANN with two neurons (R2 = 0.9783) and one neuron (R2 = 0.9437), were also evaluated to provide more practical equations with reduced complexity.The results demonstrate that careful selection of ANN structure is essential for balancing accuracy and usability. The derived mathematical equations can assist system designers in predicting ground temperature changes for different operational conditions, thus optimizing GSHP system performance. Future work will validate the model with real-world GSHP data to further enhance its practical applicability.

Brake failure is a critical safety issue in the automotive industry, often resulting from mechanical wear, hydraulic malfunctions, or electronic failure. Common causes include brake fluid leakage, worn-out brake pads, and damaged brake lines, exacerbated by poor maintenance and ignored warning signs, such as squealing. Effective fault detection is essential to ensure road safety. This study is motivated by the critical need to enhance the accuracy of brake fault detection and classification, which is a key factor in improving road safety and reducing the risk of accidents caused by brake system failures. This paper presents an automated approach for detecting hydraulic brake faults by analyzing five types of brake sound anomalies, Type I to Type V, covering various disk pad wear scenarios. These time-domain sound signals are converted to frequency-domain image representation using a mel spectrogram, and a custom 14 layer Deep Sequential Convolutional Neural Network (CNN) model classifies these images. The model achieved 97.7% accuracy in binary classification and 97.8% in 5-class classification, with perfect results at 20 epochs during hold-out cross-validation. This study significantly enhances the safety of hydraulic brake systems by automating fault detection and classification using a deep sequential CNN framework.

In the current study, the mechanical and fracture characterization of different particulate-reinforced epoxy composites is carried out. Seashell particles, pistachio shell particles, and nanoclay are used as reinforcements for epoxy with varying wt.%. The composite specimens are prepared using a general casting technique. The tensile and flexural tests are conducted as per the ASTM standard. Adding 2 and 4 wt.% seashell particles and nanoclay improves epoxy resin’s tensile and flexural strengths by 6 to 16% and 5 to 13%, respectively. However, adding 2 wt.% pistachio shell particles improves the properties, and 4 wt.% pistachio shell particles decreases the properties. SEM analysis is carried out on the fracture surface to understand the failure mechanisms and the influence of different reinforcements on the fracture behavior of the composites. The current study’s findings offer insights into the suitability of each reinforcement type, bridging the knowledge gap regarding their individual effects on epoxy.

Multi-object tracking involves detecting and linking individuals over time while assigning unique IDs. However, traditional motion models often assume linear trajectories and require continuous observations, making them vulnerable to occlusions and non-linear motion. Additionally, tracking individuals with similar appearances remains a challenge. The OC-SORT tracker recently introduced an improved Kalman Filter (KF)-based motion model, enhancing performance but increasing fragmentations and ID switches, which negatively affect track quality and IDF1 scores. To address these issues, we re-evaluate and enhance OC-SORT across multiple components, including detection, data association, and post-processing. We incorporate an appearance-free linking model to merge short tracklets into complete trajectories. To recover missing detections and refine localization, we apply Gaussian smoothed interpolation during post-processing. The resulting tracker, named OCAFI (Observation-Centric Appearance-Free Interpolated tracker), effectively combines motion, spatiotemporal cues, an improved KF, and robust post-processing. These enhancements significantly improve HOTA, IDF1, and overall track quality on benchmark MOT datasets. OCAFI achieves a MOTA of 80.5% on MOT17, 77.8% on MOT20, and 92.1% on DanceTrack, demonstrating its superior accuracy and robustness in challenging multi-object tracking scenarios.

The present study evaluates the effectiveness of dry and cryogenic machining environments while face milling carbon steel alloy E350, considering the surface integrity (SI) characteristics. The tests are performed using CVD-coated carbide inserts at three levels of cutting speed (150, 225, and 300 m/min), feed (0.05, 0.1, and 0.15 mm/tooth), and depth of cut (0.5, 1.0, and 1.5 mm). The primary SI features, roughness and microhardness, are evaluated. Furthermore, multi-criteria decision-making (MCDM) methods are employed to determine the best conditions for milling E350 alloy. Milling under cryogenic conditions reduced the roughness by 17% while increasing the microhardness by 14%, compared to dry milling conditions. The cutting speed significantly impacted the surface microhardness (92.2% – dry, 95.3% – cryogenic), and the feed had a greater impact on surface roughness (53.3% – dry, 67.5% – cryogenic). Furthermore, X-ray diffraction (XRD) pattern comparison provided more evidence for different levels of work hardening in milled surfaces compared to the polished as-cast material surface. Evaluation based on Distance from Average Solution (EDAS) and Grey Relational Analysis (GRA) methods showed identical outcomes, with optimal milling conditions for improved SI pegged at a cutting speed of 150 m/min, depth of cut of 1.5 mm, and feed of 0.05 mm/teeth.

Tedecha Island, Ethiopia, faces unique energy challenges due to its isolation and reliance on traditional energy sources. This research proposes a sustainable hybrid power system for the island’s 2,500 residents, integrating solar, wind, and pumped hydro storage (PHS). Wind data, collected over a year, informed the system design. The PHS utilizes Lake Ziway and a nearby crater pond, with a GIS-LiDAR based 3D model guiding its development. Solar energy potential was assessed using PVGIS 5.2 and local data. Homer Pro optimized hybrid configurations, including a 245 kWh PHS component, resulting in a cost-effective wind-solar-PHS microgrid with a COE of $0.130-$0.162/kWh. Crucially, the system design aligns with the Water-Food-Energy Nexus (W-F-E-N) approach. Analysis of upper reservoir water levels (0-100%) demonstrated robust operation within a 0-75% range, maintaining energy supply. This study showcases the viability of an environmentally friendly hybrid system for Tedecha Island, leveraging existing natural reservoirs to minimize costs and environmental impact, providing a valuable model for similar off-grid communities.