Search Results (156)

Strawberries are highly perishable fruits prone to postharvest losses due to defects, diseases, and uneven ripening. This study proposes a deep learning-based approach for automated quality assessment using the YOLOv8n object detection model. A custom dataset of 5663 annotated strawberry images was compiled, covering eight quality categories, including anthracnose, gray mold, powdery mildew, uneven ripening, and physical defects. Data augmentation techniques, such as rotation and Gaussian blur, were applied to enhance model generalization and robustness. The model was trained over 100 and 200 epochs, and its performance was evaluated using standard metrics: Precision, Recall, and mean Average Precision (mAP). The 200-epoch model achieved the best results, with a mAP50 of 0.79 and an inference time of 1 ms per image, demonstrating suitability for real-time applications. Classes with distinct visual features, such as anthracnose and gray mold, were accurately classified. In contrast, visually similar categories, such as ‘Good Quality’ and ‘Unripe’ strawberries, presented classification challenges. Full article

To address the challenges of low density, loose structure, high utilization costs, and inadequate molding effects of corn straw char under ambient temperature and pressure conditions, this study investigated the utilization of waste soybean powder (WSP) as a binder to produce biochar pellets via ultrasonic-assisted processing. A single-factor experiment was initially conducted to assess the effects of key variables. Subsequently, a Central Composite Rotatable Design (CCRD) was employed to evaluate the individual and interactive effects of these variables, in which pellet density and durability served as response indicators. Regression models for both responses were developed and validated using analysis of variance (ANOVA). The results indicated that, at a 0.05 significance level, the mixing ratio of corn straw char to WSP and molding pressure had highly significant effects on pellet density, while pelleting time had a significant effect and ultrasonic power had no significant influence. All four factors significantly affected pellet durability, and their interactions were further analyzed. The optimal conditions were a mixing ratio of 45%, pelleting time of 33 s, an ultrasonic power of 150 W, and a molding pressure of 5 MPa, yielding pellets with a density of 1140.41 kg/m³ and a durability of 98.54%. These results demonstrate that WSP is an effective binder for the ultrasonic-assisted fabrication of biochar pellets. Full article

This study explores the thermophysical properties and crystallization behavior of two industrial Mold Fluxes (MF1 and MF2) used in continuous steel casting. Viscosity, density, and surface tension were measured using the Rotating Bob Viscometry (RBV) and the Maximum Bubble Pressure (MBP) method, while crystallization dynamics were assessed via the Single Hot Thermocouple Technique (SHTT). Both fluxes showed temperature-dependent viscosity with distinct break temperatures influenced by chemical composition. MF1 had higher viscosity and activation energy (127.72 kJ mol⁻¹) than MF2 (112.11 kJ mol⁻¹) due to its higher Al₂O₃ content. Density and surface tension decreased linearly from 1523 to 1623 K, with values of 2642–2618 kg m⁻³ and 299–291 mN m⁻¹ for MF1, and 2708–2656 kg m⁻³ and 348–305 mN m⁻¹ for MF2. Crystallization analysis showed that MF1 required higher cooling rates (critical cooling rates: 21 K s⁻¹ vs. 18 K s⁻¹ for MF2) for glass formation, highlighting its greater glass-former content. Full article

To address the limitation of current upper limb rehabilitation exoskeletons—where pattern recognition-based assistance disrupts patients’ continuous motion—this study proposes a mechanomyography-based model for predicting shoulder and elbow joint angles. Small contact microphones were employed to collect mechanomyography signals, leveraging their ability to capture vibration signals above 8 Hz, making them ideal for mechanomyography acquisition. After extracting raw mechanomyography data, a bandpass filter (10–50 Hz) was applied to eliminate low- and high-frequency noise. To reduce computational overhead during model training, a Broad Learning System was adopted, which iteratively refines predictions by incrementally expanding nodes in the feature and enhancement layers rather than adding hidden layers. The Slime Mold Algorithm was further used to optimize hyperparameters of the Broad Learning System, enhancing prediction accuracy. Experimental results demonstrate that mechanomyography signals exhibit a typical central frequency range of 10–50 Hz, and the Slime Mold Algorithm-optimized Broad Learning System model achieved a minimum coefficient of determination (R²) of 0.978, effectively predicting arm joint angles. This approach shows promise for exoskeletons, combining high control accuracy, real-time joint angle prediction, and computational efficiency. Full article

In this study, a flame-retardant wood-polymer composite was produced using huntite-hydromagnesite mineral, recognized for its non- flammability properties. In this context, wood-polymer composites were produced with the co-rotating twin-screw extrusion technique, while polypropylene was applied as the composite matrix, medium density fiberboard waste and inorganic huntite-hydromagnesite mineral were used as the reinforcement material. The proportion of wood powder additives was changed to 10% and 20%, and the huntite and hydromagnesite ratio was changed to 30%, 40%, 50% and 60%. Maleic anhydride grafted polypropylene, i.e., MAPP, was applied as a binder at a rate of 3%. Polypropylene, wood fibers, mineral powders, and MAPP blended in the mixer were processed in the extruder and turned into granules. Structural, morphological, thermal, mechanical, and flame-retardant properties of the composites were analyzed using XRD, SEM, FTIR, TGA, tensile testing, and the UL-94 vertical flammability test. Test samples were prepared to evaluate the physical and mechanical properties with a compression molding machine. It was concluded that the composites gained significant flame retardancy with the addition of huntite hydromagnesite. The potential for using this material in various fields and its compliance with the principles of circular economy and the Sustainable Development Goals (SDG 12) were noted. Full article

Electric discharge machining (EDM) is widely employed for machining hard, conductive materials. Tool rotation has emerged as an effective strategy to enhance debris flushing and improve stability during deep-hole EDM drilling. This study proposes a machine learning-based approach to evaluate the influence of tool rotation and predict the unstable machining conditions in EDM of ultrafine grained tungsten carbide. A structured analytical workflow, combining Taguchi–Grey optimization, regression analysis, and classification models, was designed to capture discharge dynamics across macro- and micro-timescales. Classification models trained on raw and processed electrical signal features achieved over 88% accuracy and 90% recall. SHAP analysis revealed that the relationship between key discharge events such as sparks and short circuits varied significantly across stable and unstable machining phases, underscoring the importance of phase-specific modeling. While correlation analysis showed weak global associations, phase-dependent SHAP values revealed opposing feature effects, allowing the context-informed interpretation of model behavior. Phase segmentation revealed that, compared to 1000 RPM, short circuits were reduced by about 40% during stable machining at 8000–9000 RPM. Conversely, during unstable phases, spark effectiveness dropped by nearly 45%, and secondary discharges increased throughout this range. These insights support the design of adaptive control strategies that adjust the rotation rate in response to detected phase changes, aiming to sustain machining stability. The findings support the development of dynamic control frameworks to improve EDM performance, particularly for mold fabrication using tungsten carbide. Full article

This study investigated the effect of adding polydimethylsiloxane (PDMS) on the processing, surface and mechanical properties of linear low-density polyethylene (rLLDPE) recyclate generated as post-production waste in the rotational molding process. Polymer blends containing 0.1, 0.2, 0.4, 1.0 and 2.0 wt.% of polydimethylsiloxane were produced during twin-screw extrusion, followed by cold granulation. The addition of the modifier at the adopted concentration range lowered the water absorption of the recyclate and contributed to a slight increase in processing shrinkage; however, it did not significantly affect its processability (MFR~const). The modification carried out increased the hydrophobic character of the recyclate surface (the wetting angle for water was enhanced) and decreased the value of the dynamic friction coefficient. It also contributed to an improvement in surface gloss. The deterioration of point hardness and scratch hardness of the recyclate was noted with an increase in the PDMS content in the mixture. The addition of polydimethylsiloxane caused changes in the nature of resulting cracks (increased width and reduced longitudinal deformation), which led to surface smoothing and increased the sliding effects. There was no negative effect of PDMS addition on the mechanical properties (static tensile) of the recyclate. The impact strength of rLLDPE deteriorated slightly. The research conducted shows the high application potential of PDMS as a modifier of the surface properties of low-density polyethylene linear recyclate and of selected processing properties, which can contribute to the shortening of the production cycle, thus potentially increasing its attractiveness compared to the original raw materials. Full article

In this paper, we report a novel thermally conductive semi-aromatic heat-resistant PA5T-CO-10T/hexagonal boron nitride (PA5T-CO-10T/BN) composite, based on as-synthesized PA5T-CO-10T, which is a copolymer of poly (pentamethylene terephthalamide) (PA5T) and poly (decamethylene terephthalamide) (PA10T). We confirmed the structure of PA5T-CO-10T through a nuclear magnetic resonance carbon spectrometer (¹³C-NMR). The differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results indicate that PA5T-CO-10T demonstrates a processing window (greater than 90 °C) which is suitable for melt processing and injection molding. Moreover, the PA5T-CO-10T composites with different BN contents were tested by scanning electron microscopy (SEM), a thermal conductivity meter, a rotational rheometer and X-ray diffraction (XRD). The results indicate that as the content of h-BN increases, the thermal conductivity of the PA5T-CO-10T/BN composites is significantly enhanced. When the mass of h-BN reaches 30 wt%, the thermal conductivity of the composite material is 2.5 times that of the original matrix resin. Simultaneously, there is a notable upward trend observed in the storage modulus, loss modulus, complex viscosity and orientation degree of h-BN. This is attributed to the high thermal conductivity and the high orientation degree of h-BN, which ensure the continuous enhancement of the material’s thermal conductivity. Additionally, the introduction of h-BN enhances the degree of connection between the material’s molecular chains. PA5T-CO-10T/BN possesses excellent heat resistance and thermal conductivity, presenting significant application prospects in the fields of electronics, electrical appliances and automobiles. Full article

Pneumatic polishing tools are commonly used in traditional robot mold polishing systems, but they have problems with the stable control of mold surface roughness due to low precision and poor adaptability in polishing force adjustment. The integration of an adaptive hydraulic polishing (AHP) tool and robot system effectively solves the above problems, providing a robust solution for the high-precision polishing of various molds. This study systematically investigates the robotic polishing of NAK80 mold steel using an AHP-equipped robotic platform with 3M abrasive discs of progressively refined grit sizes (P180, P400, P800). Through single-factor experiments and response surface methodology, the effects of polishing force, rotational speed, and feeding speed on surface roughness were quantitatively analyzed. The relationship between surface roughness and the polishing parameters was derived to elucidate the roughness evolution before and after over-polishing. Orthogonal experiments combined with range analysis identified optimal parameter combinations for P180 (20 N polishing force, 5000 RPM rotational speed, and 5 mm·s⁻¹ feeding speed) and P400 abrasives (10 N polishing force, 4000 RPM rotational speed, and 5 mm·s⁻¹ feeding speed), achieving minimum surface roughness values of 0.08 µm and 0.044 µm, respectively. For P800 abrasives, a central composite design was used to develop a roughness prediction model with a ≤7.14% relative error, and the optimal parameters are a 20 N polishing force, a 5000 RPM rotational speed, and a 5 mm·s⁻¹ feeding speed. The sequential application of the optimized parameters across all the grit sizes can reduce the surface roughness from an initial 0.4 µm to a final 0.017 µm, representing a 95.75% improvement in the surface finish. Full article

Sorghum (Sorghum bicolor L.) is a globally important energy and food crop that is becoming increasingly integral to food security and the environment. However, its production is significantly hampered by various fungal phytopathogens that affect its yield and quality. This review aimed to provide a comprehensive overview of the major fungal phytopathogens affecting sorghum, their impact, current management strategies, and potential future directions. The major diseases covered include anthracnose, grain mold complex, charcoal rot, downy mildew, and rust, with an emphasis on their pathogenesis, symptomatology, and overall economic, social, and environmental impacts. From the initial use of fungicides to the shift to biocontrol, crop rotation, intercropping, and modern tactics of breeding resistant cultivars against mentioned diseases are discussed. In addition, this review explores the future of disease management, with a particular focus on the role of technology, including digital agriculture, predictive modeling, remote sensing, and IoT devices, in early warning, detection, and disease management. It also provide key policy recommendations to support farmers and advance research on disease management, thus emphasizing the need for increased investment in research, strengthening extension services, facilitating access to necessary inputs, and implementing effective regulatory policies. The review concluded that although fungal phytopathogens pose significant challenges, a combined effort of technology, research, innovative disease management, and effective policies can significantly mitigate these issues, enhance the resilience of sorghum production to facilitate global food security issues. Full article

The ability to control and manipulate biological fluids within microchannels is a fundamental challenge in biological diagnosis and pharmaceutical analyses, particularly when buffers with very high ionic strength are used. In this study, we investigate the numerical and experimental study of fluidic biochips driven by ac electrothermal flow for controlling and manipulating biological samples inside a microchannel, e.g., for fluid-driven and manipulation purposes such as concentrating and mixing. By appropriately switching the voltage on the electrode structures and inducing AC electrothermal forces within the channel, a fluidic network with pumping and manipulation capabilities can be achieved, enabling the control of fluid velocity/direction and also fluid rotation. By using finite element analysis, coupled physics of electrical, thermal, fluidic fields, and molecular diffusion transport were solved. AC electrothermal flow was studied for pumping and mixing applications, and the optimal model was extracted. The microfluidic chip was fabricated using two processes: electrode structure development on the chip and silicon mold fabrication in a cleanroom. PDMS was prepared as the microchannel material and bonded to the electrode structure. After implementing the chip holder and excitation circuit, a biological buffer with varying ionic strengths (0.2, 0.4, and 0.6 [S/m]) was prepared, mixed with fluorescent particles, and loaded into the microfluidic chip. Experimental results demonstrated the efficiency of the proposed chip for biological applications, showing that stronger flows were generated with increasing fluid conductivity and excitation voltage. The system behavior was characterized using an impedance analyzer. Frequency response analysis revealed that for a solution with an electrical conductivity of 0.6 [S/m], the fluid velocity remained almost constant within a frequency range of 100 kHz to 10 MHz. Overall, the experimental results showed good agreement with the simulation outcomes. Full article

To address the frequent cleaning requirements of casting molds in bridge tower construction, a wall-climbing cleaning robot based on negative pressure adsorption is designed to safely and efficiently replace manual labor for cleaning tasks. The primary focus of this paper is the establishment of a theoretical model for negative pressure adsorption, along with an analysis of potential adhesion hazards. Initially, the robot’s chassis was designed, followed by the development of a theoretical model for the rotational-flow suction unit that incorporates two critical parameters: the number of blades and their thickness. This model was validated through computational fluid dynamics (CFD) and experimental methods. The findings indicate that, with fewer blades, an increase in blade quantity significantly improves the distribution of nonlinear velocity in the z-plane, resulting in a substantial enhancement of suction force up to a certain limit. As the number of blades increases, the thickness of the blades primarily influences the volume of air within the rotating domain, thereby affecting the suction force; thinner blades are preferable. Moreover, this study reveals that square suction units provide greater suction force compared to circular ones, attributable to their superior negative pressure effect and larger adsorption area. The most critical adhesion risk identified is leakage at the edges of the suction unit. Full article

Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) of the head are a predominant cause of TBI or concussion. The conventional snowboarding helmet, which mitigates linear acceleration, is typically used in snow sports. However, the role of conventional snowboarding helmets in mitigating angular head kinematics is marginal or insignificant. In recent years, new anti-rotational technologies (e.g., MIPS, WaveCel) have been developed that seek to reduce angular kinematics (i.e., angular velocity, angular acceleration). However, investigations regarding the performance of snowboarding helmets in terms of the mitigation of head kinematics and brain responses are either extremely limited or not available. Toward this end, we have evaluated the performance of snowboarding helmets (conventional and anti-rotational technologies) against blunt impact. We also evaluated the performance of newly developed low-cost, silica-based anti-rotational pads by integrating them with conventional helmets. Helmets were mounted on a head surrogate–Hybrid III neck assembly. The head surrogate consisted of skin, skull, dura mater, and brain. The geometry of the head surrogate was based on the GHBMC head model. Substructures of the head surrogate was manufactured using additive manufacturing and/or molding. A linear impactor system was used to simulate/recreate snowfield hazards (e.g., tree stump, rock, pole) loading. Following the ASTM F2040 standard, an impact velocity of 4.6 ± 0.2 m/s was used. The head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and brain simulant pressures were measured in the head surrogate. Further, using the concurrent simulation, the brain simulant responses (i.e., pressure, von Mises stress, and maximum principal strain) were computed. The front and side orientations were considered. Our results showed that the helmets with anti-rotation technologies (i.e., MIPS, WaveCel) significantly reduced the angular kinematics and brain responses compared to the conventional helmet. Further, the performance of the silica pad-based anti-rotational helmet was comparable to the existing anti-rotational helmets. Lastly, the effect of a comfort liner on head kinematics was also investigated. The comfort liner further improved the performance of anti-rotational helmets. Overall, these results provide important data and novel insights regarding the performance of various snowboarding helmets. These data have utility in the design and development of futuristic snowboarding helmets and safety protocols. Full article

Multi-head 3D printers afford the ability to create composite structures with significant differences in properties compared to those created through traditional molding techniques. In addition to the usage of different viscoelastic polymeric materials, the selective spatial placement of the build materials enables the creation of layered and graded geometries to achieve specific mechanical and/or vibrational characteristics. This paper describes how the mechanical properties of the individual materials can be used to predict the damping and natural frequencies of a 3D-printed graded structure. Such structures can find usage in rotating machinery, beams, etc., where vibrational characteristics must be controlled. The simulation and experimental results are presented and two forms of the storage and loss modulus are considered: fixed and variable. For the latter condition, E′ and E″ are established as functions of temperature and frequency. Modal vibration testing of the graded samples shows a good match between the simulation and experimental trials, thereby supporting the proposed model as a useful tool for prescribing the structure of a printed part with tailored dynamic properties. Full article

In this study, using the Box-Behnken design (BBD) experimental method, a plunger-type three-roller pelletizer was employed to explore the optimal pelletizing parameters for biomass fuel pellets with Caragana korshinskii Kom. strip as the raw material. The moisture content of the raw material, the length-to-diameter ratio of the forming die, and the rotational speed of the ring mold were identified as the experimental factors. The relaxation density of the biomass fuel (BMF) pellets and the productivity of the pelletizer were set as the experimental indicators. The study aimed to uncover the influence patterns of these factors on the pelletizing outcomes and establish regression equations between various factors and indicators. The results revealed that when Caragana korshinskii Kom. strip was used as the raw material in this pelletizer, the optimal pelletizing parameters were as follows: a moisture content of 15.5%, a forming die length-to-diameter ratio of 5.3, and a ring mold rotational speed of 30 rpm. Under these conditions, the relaxation density, mechanical durability, and productivity reached 1.139 g/cm³, 96.21%, and 6.278 t/h, respectively. The energy consumption per ton of pellets did not exceed 41.3 kWh. The significance of this study is its potential to expand the utilization range of Caragana korshinskii Kom., reduce environmental pollution at the same time, and make a certain contribution to carbon peak and carbon neutrality. Full article