Search Results (83)

The main goal of a garlic planter is to plant garlic cloves individually with even spacing, requiring minimization of missed or multiple plantings. This study adopted an air-suction metering device to increase planting speed. To handle irregularly shaped garlic cloves effectively, soft silicone suction holes were fabricated and attached to the metering device. Performance was assessed by varying suction hole diameters (8, 11, 14, and 17 mm) and material hardness levels (1.4, 16.7, and 27.2 HA Shore A) at multiple-metering rates. The optimal metering rate of 98.2% was achieved with a 14 mm suction hole diameter and 16.7 HA hardness. This success was attributed to the soft suction hole effectively conforming to the garlic clove surface. The findings revealed a critical limitation: when metering rates exceeded 90%, multiple-metering rates increased linearly, indicating inherent constraints of air-suction techniques for single-seed metering. These results provide valuable insights into design parameters needed to improve reliability, operational efficiency, and mechanization performance of garlic planters. The metering device type is expected to be applicable to autonomous or unmanned agricultural machines, advancing agricultural mechanization capabilities. Full article

Geographic routing is an appealing method for wireless sensor networks, as it routes data packets solely based on nodes’ location information rather than global network topology. A fundamental requirement for geographic routing is that source nodes must know the locations of sink nodes to deliver their data. To efficiently provide sink location information, quorum-based sink location service schemes have been introduced, using crossing points between sink location announcement (SLA) and sink location query (SLQ) quorums. However, existing quorum-based schemes typically construct quorums along fixed paths, causing rapid energy depletion of particular sensor nodes and resulting in shorter network lifetimes, especially in irregular sensor fields. To overcome this limitation, we propose an energy-efficient quorum-based sink location service scheme that extends network lifetime by reducing and balancing sensor nodes’ energy consumption. Specifically, our scheme constructs a quadrilateral-shaped SLA quorum using four randomly selected points, and a line-shaped SLQ quorum defined by two randomly chosen points located inside and outside the SLA quorum, respectively. We also address key issues of the proposed scheme, including network holes, irregular boundaries, multiple sources and sinks, and Base Zone sizing, and present methods to handle them. Simulation results demonstrate that the proposed scheme outperforms existing approaches, achieving approximately 29% lower total energy consumption and 27% higher energy balancing across sensor nodes on average. Full article

Artisanal fisheries in southern Chile rely heavily on the Patagonian red octopus (Enteroctopus megalocyathus) as a valuable resource, contributing significantly to local economies. This octopus species accounts for 25–40% of Chilean octopus landings. It is a merobenthic species, characterized by a semelparous life cycle and a long brooding period, and it is distributed along the Pacific and Atlantic coasts of the southern tip of South America, inhabiting holes and crevices in rocky substrates. However, this fishery faces critical challenges to both its ecological sustainability and the food safety of octopus products. The primary fishing method, using hooks, poses a risk to reproductive capacity as it can capture brooding females. Food safety concerns arise from microbial contamination during pre- and post-harvest handling, bioaccumulation of toxins from algal blooms, and the presence of heavy metals in the marine environment. While evisceration effectively reduces the risk of consuming toxins and heavy metals, inadequate hygiene practices and insufficient ice usage throughout the production chain represent significant food safety risks. Chilean fishing Law No. 18892/1989 defines artisanal fishing and establishes territorial use rights in fisheries (TURFs) to promote sustainable extraction of benthic resources. Integrating training programs on post-harvest handling, hygiene practices, and food safety measures into the TURFs framework, along with targeted investments in infrastructure and technical assistance, is crucial to ensure the long-term sustainability of the E. megalocyathus fishery, protect consumer health, and maintain the economic viability and environmental sustainability of this vital resource for local communities. Full article

This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force and torque signals at a 10 kHz sampling rate during the drilling process. These signals are employed for real-time process monitoring, focusing on material change detection and anomaly identification, where anomalies are defined as holes that fail to meet predefined quality criteria. An innovative approach based on unsupervised learning is proposed to enable automatic material change identification, signal segmentation, feature extraction, and hole quality assessment. Specifically, a semi-supervised approach based on a Gaussian Mixture Model (GMM) and 1D Convolutional AutoEncoder (1D-CAE) is employed to detect deviations from normal drilling conditions. The proposed method is benchmarked against state-of-the-art supervised techniques, including logistic regression (LR) and Support Vector Machines (SVMs). Results show that these traditional models struggle with class imbalance, leading to overfitting and limited generalisation, as reflected by the F1 scores of 0.78 and 0.75 for LR and SVM, respectively. In contrast, the proposed semi-supervised approach improves anomaly detection, achieving an F1 score of 0.87 by more effectively identifying poor-quality holes. This study demonstrates the potential of deep learning-based semi-supervised methods for intelligent process monitoring, enabling adaptive control in the drilling process of hybrid stacks and detecting anomalous holes. While the proposed approach effectively handles small and imbalanced datasets, further research into the application of generative AI could enhance performance, aiming for F1 scores above 0.90, thereby supporting adaptation in real industrial environments with high performance. Full article

Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning (ML) model in classifying optical coherence tomography (OCT) images of VMI disorders. Materials and Methods: A balanced dataset of OCT images across five classes—normal, epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitreomacular traction (VMT)—was used. The expert-designed model combined ResNet-50 and EfficientNet-B0 architectures with Monte Carlo cross-validation. The AutoML model was created on Google Vertex AI, which handled data processing, model selection, and hyperparameter tuning automatically. Performance was evaluated using average precision, precision, and recall metrics. Results: The expert-designed model achieved an overall balanced accuracy of 95.97% and a Matthews Correlation Coefficient (MCC) of 94.65%. Both models attained 100% precision and recall for normal cases. For FTMH, the expert model reached perfect precision and recall, while the AutoML model scored 97.8% average precision, and 97.4% recall. In VMT detection, the AutoML model showed 99.5% average precision with a slightly lower recall of 94.7% compared to the expert model’s 95%. For ERM, the expert model achieved 95% recall, while the AutoML model had higher precision at 93.9% but a lower recall of 79.5%. In LMH classification, the expert model exhibited 95% precision, compared to 72.3% for the AutoML model, with similar recall for both (88% and 87.2%, respectively). Conclusions: While the AutoML model demonstrated strong performance, the expert-designed model achieved superior accuracy across certain classes. AutoML platforms, although accessible to healthcare professionals, may require further advancements to match the performance of expert-designed models in clinical applications. Full article

Gallium nitride (GaN), an advanced piezoelectric semiconductor, shows strong potential for ultraviolet (UV) applications due to its prominent thermoelectric, photoelectric, and mechanoelectrical coupling effects, all of which are critical to device performance. This paper focuses on one-dimensional GaN nanowires and introduces a nonlinear theoretical model to describe pyroelectric and photoelectron effects under UV excitation. The model accounts for both photothermal and photoconductive effects. Using the perturbation method, we derive an approximate analytical solution for the internal physical field of the nanowire under UV light irradiation, which aligns well with the results from nonlinear numerical simulations. Compared to a light intensity of 2 W/m², a light intensity of 6 W/m² leads to a 45% increase in electron concentration, a 235% rise in hole concentration, a 146% increase in potential, and a 274% increase in polarization charge concentration. The pyro-phototronic effect enables UV light to modulate the electrical transport characteristics of a Schottky junction. This study addresses the limitations of linearized models for handling large disturbances, providing a comprehensive theoretical and computational framework for advancing GaN micro- and nanoscale devices and enabling effective, non-contact control. Full article

This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. Specifically, for the aforementioned complex deployment regions, a region decomposition approach based on convex partitioning is proposed. This approach allows for the decomposition of complex regions into multiple non-overlapping convex subregions. Moreover, for convex deployment regions or subregions, we propose a coordinate transformation approach to eliminate the constraints introduced by the shape of the convex region. By combining the above approaches, we introduce a novel MOPSO based on decomposition and transformation, named MOPSO-DT. This algorithm aims to optimize MFRN deployment in these challenging environments. Experimental results demonstrate the superiority of the MOPSO-DT algorithm over two existing algorithms across a variety of deployment cases, highlighting its enhanced efficiency, effectiveness, and stability. These findings indicate that the proposed algorithm is well suited for optimizing MFRN deployment in complex, irregular regions, offering significant improvements in performance compared to conventional methods. Full article

The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements. Full article

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. Full article

Nipah virus (NiV) is known to be a highly pathogenic zoonotic virus, which is included in the World Health Organization Research & Development Blueprint list of priority diseases with up to 70% mortality rate. Due to its high pathogenicity and outbreak potency, a therapeutic countermeasure against NiV is urgently needed. As NiV needs to be handled within a Biological Safety Level (BSL) 4 facility, we had developed a safe drug screening platform utilizing a baculovirus expression vector system (BEVS) based on a NiV-induced syncytium formation that could be handled within a BSL-1 facility. To reconstruct the NiV-induced syncytium formation in BEVS, two baculoviruses were generated to express recombinant proteins that are responsible for inducing the syncytium formation, including one baculovirus exhibiting co-expressed NiV fusion protein (NiV-F) and NiV attachment glycoprotein (NiV-G) and another exhibiting human EphrinB2 protein. Interestingly, syncytium formation was observed in infected insect cells when the medium was modified to have a lower pH level and supplemented with cholesterol. Fusion inhibitory properties of several compounds, such as phytochemicals and a polysulfonated naphthylamine compound, were evaluated using this platform. Among these compounds, suramin showed the highest fusion inhibitory activity against NiV-induced syncytium in the baculovirus expression system. Moreover, our in silico results provide a molecular-level glimpse of suramin’s interaction with NiV-G’s central hole and EphrinB2’s G-H loop, which could be the possible reason for its fusion inhibitory activity. Full article

Prefabricated wood construction relies heavily on efficient material handling, yet rigging system design for floor panels remains understudied. This study introduces a novel rigging device that attaches to prefabricated wood I-joist floor panels using self-tapping screws, avoiding potential damage caused by predrilled holes in the sheathing panels and framing members. To establish allowable lifting capacities and optimal installation practices, comprehensive withdrawal tests were conducted on 114-floor panel specimens. Factors influencing withdrawal capacity, such as anchor plate placements, flange materials and width, screw type and quantity, and sheathing panel thickness, were systematically evaluated. Results indicate that withdrawal capacity does not scale linearly with screw quantity and that anchor plates with eight screws centered on the flange enhance performance by up to 20% compared to four-screw configurations. Unexpectedly, thinner sheathing panels yielded higher capacities, potentially due to increased screw penetration depth in the joist flange. In addition, anchor plate orientation, flange width, and flange materials also impact capacity. These findings provide essential data for designing reliable and efficient rigging systems in prefabricated wood construction. Full article

Laser drilling is widely used for fabricating holes in the semiconductor industry due to high throughput and a small heat-affected zone. However, it produces varying depths owing to uncertain external conditions and requires live control at the rate of a few tens of kHz to handle the fast material removal rate. Optical coherent tomography is capable of in situ acquiring a raw interferogram at a high rate (>80 kHz), but the depth extraction is slow due to the involved heavy Fast Fourier Transform (FFT). To address this, an efficient depth-tracking algorithm is proposed to save the FFT. It searches the depth in the raw interferogram locally with a known last depth given the two truths that only one depth exists and the adjacent depths do not change significantly. The proposed algorithm was proven to expedite the measuring rate six times with sub-pixel tracking precision. To further secure the rate against the interrupting of the system, the tracking process is parallelly implemented in a field-programmable gate array. The closed-loop control tests were conducted on probe cards with depth variations introduced by offsetting laser focus. The proposed method maintained a uniform depth, with variations reduced by 80% compared to traditional methods. Full article

The low delamination tendency and high damage tolerance of three-dimensional (3D) braided composites highlight their significant potential in handling defects. To enhance the engineering potential of three-dimensional four-directional (3D4d) braided composites and assess the failure mode of hole defects, this study introduces a series of 3D4d braided composites with prefabricated holes, studying their compressive properties and failure mechanisms through experimental and finite element methods. Digital image correlation (DIC) was used to monitor the compressive strain on the surface of materials. Scanning acoustic microscope (SAM) and scanning electron microscopy (SEM) were used to characterize the longitudinal compression failure mode inside the material. A macroscopic model is established, and the porous materials are predicted by using the general braided composite material prediction theory. While reducing the forecast cost, the error is also controlled within 21%. The analysis of failure mechanisms elucidates the damage extension mode, and the porous damage tolerance ability aligns closely with the bearing mode of braided material structure. Different braiding angles will lead to different bearing modes of materials. Under longitudinal compression, the average strength loss of 15° specimens is 38.21%, and that of 30° specimens is 8.1%. The larger the braided angle, the stronger the porous damage tolerance. Different types of prefabricated holes will also affect their mechanical properties and damage tolerance. Full article

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas–liquid two-phase flow drift-flux model and heat transfer model. The key features include handling the heat transfer between the formation and the wellbore, addressing the slip relationship between the gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite difference method, and the highly nonlinear system is solved using the Newton–Raphson method. The numerical simulation, validation, and comparison with existing models demonstrate the enhanced accuracy of this model. In our tests, the model achieved a high accuracy in calculating the bottom-hole pressure and temperature, with mean relative errors (MREs) significantly lower than those of other models. For example, the MREs for the bottom-hole pressure and temperature of the Rongxi area well in Xiongan, calculated by this model, are 1.491% and 1.323%, respectively. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geothermal energy development. Full article

The use of biomass as a source of energy has been identified to be energy intensive, involving high handling costs. However, pelletization reduces the bulk density of biomass, thereby reducing the handling costs and enhancing ease of use. This study modified and evaluated an existing hand-operated fish feed pelleting machine. The parts of the machine that were redesigned were the hopper and the power transmission unit. Corncob was used to evaluate the modified machine using the die hole diameter (5, 6 and 7 mm) and the binder quantity (0, 2.5 and 5 wt%) as factors. The average results obtained for machine efficiency, throughput, pellet length and bulk density were 58.83%, 4.24 kg/h, 15.51 mm and 0.160 g/cm³, respectively. The die hole diameter had a significant effect on the pellet length only. The binder quantity had a significant effect on machine efficiency, throughput and pellet length. Machine efficiency and throughput decreased as the quantity of binder increased, and the pellet length increased with the increasing quantity of binder. Full article