Optical conveyor belt of moving standing wave on DC waveguide for accurate particle releasing control

For the Circular Restricted 3-Body Problem (CR3BP), the topologies present within a Poincaré map enable the extraction of useful information regarding periodic, quasi-periodic, and chaotic trajectory behavior. Aside from the prominent topologies that follow distinct concentric patterns around fixed points, indicative of the periodic and quasi-periodic motion that is often the central focus of CR3BP research, there are also many “dusty” regions on the Poincaré map that appear random without an apparent structure and are indicative of chaotic motion. This paper, for the first time in literature, identifies dynamical structures associated with chaotic transport residing in the “dusty” region of a Poincaré map for the Earth-Moon system employing a novel methodology called conditional behavior mapping. Using a Jacobi constant of 3.175 to allow access to the Moon via L 1 but preventing system exit via L 2 , 8 distinct deterministic pathways–a series of dynamical conveyor belts –for chaotic transport to the Moon are mapped traversing these dynamical structures. In addition, this paper identifies sub-structures present within the dynamical structures that create a definable pattern distinguishing whether a chaotic transfer will result in lunar collision or a circumlunar trajectory, and shows the existence of at least one unstable translunar periodic orbit associated with these structures. This paper advances ongoing multi-body astrodynamics research by allowing for the mapping of chaotic transport to the Moon. • Dynamical structures associated with chaotic transport reside in the “dusty” region of a Poincaré map. • Sub-structures exist indicating if chaotic transfers result in lunar collision or circumlunar trajectories. • At least one unstable translunar periodic orbit can be mapped to the dynamical structures for chaotic transport.

Metal electrodeposition is a widely used materials synthesis technique; however, industrial applications often require the optimization of complex precursor formulations and electrodeposition parameters, which is typically performed in a slow, inefficient, and empirical manner. Self-driving laboratories (SDLs) that combine automated electrodeposition with machine-learning-guided decision-making could accelerate discovery in these complex, high-dimensional parameter spaces, but they face practical challenges. Notably, automated electrodeposition platforms need a continuous supply of pristine electrode surfaces, preservation and logging of electrodeposited samples, and the ability to easily incorporate nonelectrochemical downstream characterization. To overcome these challenges, we developed a modular, affordable, and accessible automated electrochemistry platform that uses a roll-to-roll design to enable continuous electrodeposition experiments. The roll-to-roll architecture enables easy integration with downstream characterization techniques in a conveyor belt fashion, and electrodeposited samples are stored by rolling up the spent electrode material. We performed 300 identical Cu electrodeposition experiments over 35 h, demonstrating high repeatability and continuous 24/7 experimentation. We demonstrate the use of our platform for an autonomous campaign, employing a vision-guided Bayesian optimization (BO) workflow to tune electrodeposition parameters and additive concentrations to achieve a visible-light-absorbing Cu deposit. An interpretable machine-learning approach was used to identify benzotriazole as a key additive for controlling the brightness of deposited Cu films. The roll-to-roll design enabled the preservation of samples and offline analysis with scanning electron microscopy, providing insight into the impact of benzotriazole on the morphology of the deposited films.

The increasing demand for G0 seed potatoes in Indonesia, reaching approximately 143,740 tons in 2021 while only 8.6% of the demand could be supplied, highlights the need for more efficient production and monitoring systems at the early stage of the potato seed supply chain. Current manual sorting and counting processes are labor-intensive, time-consuming, and prone to human error, creating a need for an automated and reliable monitoring solution. This study develops and implements an Artificial Intelligence of Things (AIoT) system based on the YOLOv11n object detection algorithm for real-time detection, size classification, and counting of G0 seed potatoes. The proposed system integrates a conveyor belt, a Logitech C922 Pro USB webcam for image acquisition, and a laptop as the edge computing unit running the YOLOv11n model. Detection results are transmitted through the MQTT protocol to a Node-RED dashboard for real-time remote monitoring. Unlike conventional approaches, the system combines a lightweight YOLOv11n model with MQTT communication to support simultaneous multi-category size classification and synchronized dashboard visualization. Detected potatoes are classified into three size categories (small, medium, and large) based on calibrated bounding-box pixel areas validated with potato farmers. The model was trained and evaluated using four epoch configurations (25, 50, 75, and 100 epochs) with Precision, Recall, F1-score, mAP@0.5, and mAP@0.5–0.95 as evaluation metrics. The 100-epoch model achieved the best performance, with precision approaching 1.00, recall of approximately 0.98, mAP@0.5 of 0.986, and mAP@0.5–0.95 of 0.96. Validation confirmed that calibrated geometric measurements matched the physical potato dimensions, while dashboard data were fully consistent with edge-computing outputs. These findings demonstrate that the proposed YOLOv11n-based AIoT system provides accurate, reliable, and real-time monitoring of G0 potato production, offering a practical solution to improve operational efficiency and data accuracy in Indonesia's potato seed supply chain.

Radioactive technetium-99 (99Tc) is present in nuclear and medical wastes. Its immobilization by magnetite (FeIIFeIII2O4) has been studied in the last decades, showing that magnetite reduces pertechnetate (TcVIIO4-) to TcIV, which is either incorporated into the magnetite structure or forms TcIV-TcIV-dimers attached to the magnetite surface. The distribution between both phases and the incorporation mechanism remain, however, unclear. Therefore, we investigated the molecular environment of Tc after contacting TcVII with synthesized nanoparticulate magnetite as a function of pH (2-13) and time (up to 7 weeks). X-ray absorption spectroscopy was combined with density functional theory (DFT) simulations to decipher the mechanism of TcIV incorporation. We observed that the sorption of TcIV-TcIV-dimers initially occurs at pH 5 and pH 7, while TcIV incorporation in magnetite prevails at longer times and at pH 10. We suggest that TcIV-TcIV-dimer sorption on magnetite is due to (surficial) maghemitization of the magnetite nanoparticles, whereas TcIV incorporation is due to the electron transfer from sorbed Fe2+ through magnetite and subsequent release of FeII in solution (redox conveyor belt model), "burying" TcIV into the magnetite structure. DFT calculations indicate that TcIV incorporates in magnetite by an exchange of two FeII atoms for one TcIV, keeping the charge balanced by creating a vacancy.

Function diagrams focus on physiological concepts rather than associated structures and can serve as elaboration tools and mnemonic aids. A function diagram prototype of the gastrointestinal system was recently described (Wilson TE, Barrett KE. Adv Physiol Educ 45: 264-268, 2021). In this article, a functional diagram of the glomerulus, nephron, urinary system, and bladder is proposed. Colloquially named "Nessie the Nephron" to loosely capture the looping structure and elusive understanding of renal blood flow, glomerular filtration, and epithelial transport for the student, not to mention the nearly mythical countercurrent multiplier. "Navigating the Lochs" references Nessie's possible habitat and more importantly the movement and storage of the modified ultrafiltrate from the nephron. The primary analogies that form the structure of this function diagram are Rain Barrel, Soaker Hose, and Hose Nozzle (afferent and efferent blood flow and filtration pressure); Water Purification System (glomerular filtration barrier forming multiple step filtration process involving the capillary, basement membrane, and podocytes); Mixed Recycling Machine (proximal tubule individual, co-, and bulk transport); Desiccator and Briner (thin descending limb removal of water and thin/thick ascending limb removal of salt); Conveyor Belt Picker (distal nephron selective ion transport); and Concentrator (collecting duct water and urea transport). The primary analogies that elaborate Navigating the Lochs are Aqueduct and Cistern System (fluid movement and collection); and Pressure Gauge, Syringe Bulb, and Two-Valve Plumbing (bladder storage and micturition). Complementing these analogies is the rich potential for inclusion of clinical and comparative applications and examples to link previous knowledge and strengthen memories for future retrieval.NEW & NOTEWORTHY Function diagrams put the focus on physiology and physiological concepts rather than the associated anatomy and can serve as elaboration tools and mnemonic aids. The function diagram of the nephron can provide analogies for glomerular filtration, bulk and selective epithelial transport, and overall ability to create concentrated or dilute urine. The function diagram of the urinary system and bladder can provide analogies for moving and storing urine and finally micturition.

Foreign object detection method for coal mine conveyor belts based on dynamic snake convolution and spatial-channel synergistic attention

Kinetic heterogeneity and combustion risk in coal mine conveyor belts: A pyrolysis-driven comparative analysis of PVG and PVC flame-retardant systems

The rising growth of agricultural waste poses significant challenges to environmental sustainability, community health, and commercial stability. However, the development of multifunctional bio-based additives that simultaneously provide reinforcement and antioxidant performance in natural rubber (NR) composites remains limited. This study examines the potential of an aluminum (lignin/silica/fatty acid) hybrid (Al(LSF)), derived from rice straw black liquor, as a dual-functional additive with reinforcing and antioxidant properties in natural rubber (NR) composites. The Al(LSF) hybrid was analyzed by XRF, SEM-EDX, TEM, particle size analysis, and zeta potential measurements. Morphology, curing behavior, mechanical properties, and thermo-oxidative aging resistance of Al(LSF) in NR matrices were also investigated. Results showed that Al(LSF) exhibited nanoscale characteristics enabling uniform dispersion in the NR matrix. The incorporation of Al(LSF) (1–4 phr) improved curing properties, as evident by the decreased optimum curing time by approximately 18.3% as compared to TMQ/NR composites. Tensile strength of NR composites increased by approximately 45.4%, and elongation at break reduced by approximately 26.4% at 1 phr loading of Al(LSF) hybrid. The improvement in thermo-oxidative aging resistance of NR composites with Al(LSF) could be observed from th increased aging coefficient from 0.43 to 0.55 (approximately 27.9%). NR composites reinforced with Al(LSF) displayed better mechanical strength and aging properties than those filled with conventional fillers such as silica and sodium bentonite, depending on hybrid loading. Al(LSF)/NR composites can be explored for applications requiring improved long-term thermo-oxidative aging resistance and mechanical strength, such as tires, conveyor belts, gaskets, shoe soles, and other rubber products used outdoors exposed to extreme environmental conditions. Therefore, Al(LSF) hybrid could be considered as a potential multifunctional bio-additive to enhance mechanical strength and thermo-oxidative stability of NR while valorizing agricultural waste.

ABSTRACT To study the gas explosion propagation trends of a Y-type ventilation coal face with a sudden change in the cross-sectional size of the roadway, numerical simulation (Fluent) is used to characterize the N2105 working face of the Yuwu Coal Mine. The result shows that there is a critical size for the section in front of the abrupt cross-section. When the cross-section exceeds this critical size, the overpressure peak decreases with the increase in the abrupt cross-section degree. Quantitatively, in the return airway, the overpressure attenuation rate between 15 m and 25 m is approximately 36% for sudden expansion roadways, with a time interval of about 12.8 ms. In the working face, the overpressure attenuation rate follows a cubic function (R2 = 0.902) before the abrupt section and a power-law function (R2 = 0.904) after it. In the conveyor belt and working face, a secondary overpressure peak appears in front of the sudden shrinkage section, which is proportional to the shrinkage degree. The overpressure curve in the front of the abrupt cross section is consistent and stable, and the overpressure behind the abrupt cross section oscillates. When the cross section of the roadway changes abruptly, the partial pressure at the bifurcation can inhibit the conveyor belt and promote the working face. Based on the results of quantitative simulations, the following design guidelines for Y-type ventilation coal mining faces are proposed: (1) The sudden expansion ratio of the cross-sectional area between the return airway and the conveyor belt should not exceed 1.2, because the overpressure attenuation rate tends to saturate as the cross-sectional area is further expanded; (2) Within 25 m of the explosion source, the conveyor belt should avoid sudden constrictions with an area ratio below 0.8, as these can generate intense secondary overpressure peaks, exacerbating the hazards of the explosion; (3) Due to the extreme length of the working face, the pressure relief effect following a sudden expansion of the cross-section is delayed; therefore, it is recommended to install explosion-proof devices within 50 m downstream of the expansion section.

Machine Learning (ML) and Artificial Intelligence (AI) are important tools for modelling drying processes to reduce moisture and preserve food products. This study investigated the drying performance of an industrial infrared conveyor belt drying system on onion slices under different drying conditions. The effects of drying temperature, infrared intensity, and airflow rates were evaluated. The results demonstrated that increasing IR power and air temperature significantly reduced drying time by 44.23%. Effective moisture diffusivity increased from 0.238 × 10⁻¹⁰ to 0.457 × 10⁻¹⁰ m²/s, indicating enhanced internal moisture transport at elevated thermal inputs. The lowest Sect. (10.72 kWh/kg) was achieved at 600W, 65°C, and 0.3m/s, while the highest (22.26 kWh/kg) occurred at low temperature and high airflow conditions. Thermal efficiency improved with increasing temperature and radiation intensity, reaching a maximum of 21.92%. However, the Artificial Neural Network model exhibited excellent predictive capability with a correlation coefficient (R) of 0.999, accurately estimating key drying parameters. Self-Organizing Map (SOM) analysis identified distinct operational clusters, revealing that higher air temperature and IR power reduced drying time and energy consumption, whereas increased airflow increased energy usage. Therefore, the study demonstrates that integrating AI and statistical tools provides a robust framework for optimizing industrial drying systems, enabling reduced energy consumption and improved process efficiency.

LIBS–ML framework for real time feedstock characterizationduring continuous conveyor transport Heterogeneous waste derived feedstocks(e.g., waste coal, biomass and blends) introduce rapid variabilityin heating value and ash chemistry that affect gasifier operation,yet conventional laboratory characterization techniques are too slowto support proactive control. To address this gap, this study reportson an online, in situ, dynamic characterization framework that couple’slaser-induced breakdown spectroscopy (LIBS) with leakage safe machinelearning (ML) regression to deliver real time, decision quality predictionsof gasifier relevant properties. A controlled sample matrix spanningtwo different waste coals, two different biomasses, and engineeredblends under two particle size conditions were constructed and benchmarkedusing standardized laboratory analyses for proximate/ultimate propertiesand ash composition. LIBS spectra were acquired dynamically as materialflowed on a conveyor belt, using high energy 1064 nm laser ablationand shot averaging to improve repeatability and precision. Supervisedregression models (multi layer perceptron (MLP) /artificial neuralnetwork (ANN), random forest (RF), and support vector regression (SVR))and an optimized weighted ensemble were trained on emission line featuresets using nested cross validation with Bayesian hyperparameter tuningand validated against an independent hold out set. The proposed LIBS–MLworkflow achieves near laboratory predictive fidelity across parametrictargets (including higher heating value (HHV), ash content, fixedcarbon, sulfur, major ash forming oxides, and initial deformationtemperature (IDT)), with the weighted ensemble providing a robustdefault predictor under dynamic measurement conditions. These resultsdemonstrate a practical pathway for real time feedstock characterizationthat can enable feedforward adjustments and more resilient gasifieroperation for variable quality waste derived fuels.

Traditional conveyor belt object detection methods often lack robustness and adaptability under challenging conditions such as low-light and low-resolution environments. This study proposes an improved detection method specifically designed for conveyor belt environments, built upon the YOLOv11 object detection framework. A custom dataset was created to support foreign object detection on factory conveyor belts. To overcome the low resolution of image recognition, the Enhanced Super- Resolution Generative Adversarial Network (ESRGAN) was employed to improve the input image clarity. Additionally, to enhance the performance under low-illumination conditions, several architectural improvements were embedded in the YOLOv11 framework, leading to the proposed Conveyor Belt Foreign Object Detection (YOLOv11-CBFD) algorithm. These enhancements included an optimized upsampling module, integrated attention mechanisms, a modified convolution module, an improved loss function, and a modified convolution module. Experimental results demonstrated that the proposed YOLOv11-CBFD algorithm significantly enhanced the accuracy of foreign object recognition. Based on a dataset collected from a factory conveyor belt, YOLOv11-CBFD achieved an accuracy of 86.1%, a recall of 86.7%, an [Formula: see text] of 89.1%, and a model size of only 2.17 M parameters. Compared to the original YOLOv11n model, the proposed method reduced the parameter count by 16.2% while demonstrating no significant degradation in recognition capabilities. In terms of computational efficiency, the optimized architecture demonstrated a 12.4% increase in the number of frames per second when deployed on a Jetson Orin NX-embedded AI computer. Field experiments conducted in industrial inspection scenarios validated the practical effectiveness of the system, demonstrating continuous operation over 48 h under real-time constraints (average latency <33 ms/frame), while consistently maintaining an accuracy of 86.1% across multiple deployment cycles. The experimental results highlight the ability of the model to effectively balance computational efficiency and detection performance on embedded AI platforms.

Blockchain is a distributed database technology that enables immutable, verifiable data recording, properties that are useful for failure analysis processes requiring high data integrity and traceability. In conveyor belt failure analysis, there is a growing need for reliable management of experimentally obtained data, especially for long-term monitoring of operating and failure states. The presented article focuses on customizing the blockchain architecture to support recording and validating experimental data used in the failure analysis of rubber-textile conveyor belts in pipe conveyors. The proposed methodology integrates a private blockchain system as a layer for storing and validating raw measured data obtained during experiments. The system meets technical accuracy requirements and is defined as a private blockchain with a permissioned system, which uses the Proof of Authority consensus algorithm and is characterized by centrally managed administration. The prototype of the “LogBlock” application demonstrates the storage and validation of data in the form of plain text and compressed (.zip) files, providing robust protection against unauthorized data modifications, auditability, and resistance to unauthorized interference, while being adapted to the specific requirements of the analyzed technical system. Experimental results indicate the feasibility of the proposed blockchain system in storing, validating, and managing raw measurement data, processed data, metadata, and related source files throughout the failure analysis process. The achieved results confirm the system’s ability to identify unauthorized data modifications and ensure their traceability after entering the system. The implemented solution confirms the suitability of using blockchain as a support tool for technically oriented failure analysis applications of conveyor systems.

ABSTRACT This study investigates the influence of die design parameters—specifically the flow‐blocking dam gap (2–8 mm), sizing section length (10–25 mm), and sizing section thickness (4–10 mm) of a wide‐width orientation die—on the microstructure and mechanical properties of short basalt fiber‐reinforced rubber composites for conveyor belt covers. A combined approach of finite element simulation (ANSYS) and experimental validation was employed to analyze the flow behavior (pressure, velocity, and shear rate fields) and its correlation with composite performance. The results demonstrate that optimal mechanical properties—including crosslinking density, tensile strength, tear strength, and wear resistance—are achieved with a flow‐blocking dam gap of 2 mm, a sizing section length of 15 mm, and a sizing section thickness of 8 mm. Mechanistically, these optimized die parameters generate a specific shear flow field that minimizes flow stagnation and aligns the short basalt fibers radially along the stress direction. This enhanced radial orientation, confirmed via SEM, significantly improves fiber–matrix adhesion and load transfer efficiency, thereby maximizing composite performance. This work provides practical guidelines for tailoring die geometry to achieve desired fiber alignment and property enhancement in short‐fiber‐reinforced rubber composites.

Abstract Ore image segmentation plays a vital role in mineral processing, directly affecting the accuracy of crushing quality assessment and particle size analysis. However, the accuracy of traditional segmentation techniques is severely compromised by three inherent challenges in ore images: the wide size range of particles, significant color variations within individual ores, and indistinct boundaries between agglomerated particles, leading to imprecise segmentation results. To overcome these challenges, this study introduces LAES-UNet, an integrated network based on EfficientSAM, which combines a pre-trained EfficientSAM encoder with a multi-level decoder. The proposed architecture incorporates three tailored modules: the Local-Global Hierarchical Interaction (LGHI) for multi-scale feature enhancement, the Adaptive Spatial Feature Refinement (ASFR) for adaptive weighting across color-variable regions, and the Edge Focusing Module (EFM) for explicit edge and fine-detail perception. Experiments were conducted on a self-built conveyor belt ore image dataset containing 148 manually annotated images, which were cropped into 296 non-overlapping samples, as well as on a public mineral image benchmark. The results show that LAES-UNet achieves the best overall segmentation performance among the compared methods, with up to 2.8% higher IoU and consistently improved boundary delineation. Furthermore, a kernel density estimation (KDE)-based particle size distribution fitting method verifies the practical value of the segmentation results in quantitative ore particle size analysis. Overall, LAES-UNet delivers a generalizable solution for automated, high-precision particle size measurement within intelligent mineral processing systems.

Effects of thermal-oxidative aging on flame-retardant mining conveyor belt: Pyrolysis characteristic and combustion behavior

Open vortex beams as optical conveyor belts for programmable particle transport and sorting

In response to the lack of machines for cutting dried chili peppers into segments and the problems of low efficiency and high damage rate of existing segmenting machines, a rotary knife-style dried chili pepper segmenting machine was designed. The working mechanism of the rotary knife in coordination with the drum for cutting was explained, and structural parameters were determined based on the motion characteristics of the rotary knife and practical requirements. Three factors affecting chili pepper segmenting efficiency were identified, and the chili pepper segmenting qualification rate and damage rate were used as experimental indicators. Experiments and data processing were conducted using software, a regression model between the experimental indicators and influencing factors was established, and the optimal parameter combination was determined. The experimental results showed that when the rotary knife speed was 51 r/min, the conveyor belt speed was 0.6 m/s, and the drum speed was 51 r/min, the device damage rate was 1.42%, and the cleaning rate was 98.21%, meeting industry standards. This study can provide a reference for the research of rotary knife-style dried chili pepper segmenting machine technology and equipment.

Moisture in run-of-mine coal significantly reduces its calorific value, increases transportation cost, and causes operational difficulties in furnace systems. This paper presents the design, fabrication, and experimental evaluation of a laboratory-scale Coal moisture remover that employs forced convective thermal drying. The system integrates four core subassemblies: a motorised conveyor belt for continuous coal feeding, nichrome/FeCrAl alloy electric resistance heating coils as the heat source, a suction fan running at 2900 RPM to drive heated airflow through the coal bed, and three temperature sensors positioned at the inlet duct, drying zone, and outlet to monitor the real-time thermal profile. The prototype was designed with low-cost, locally procurable components and standard workshop fabrication processes, keeping total outlay well below commercially available industrial dryers. Experimental trials conducted at varying coil power settings and belt feed rates indicate an expected moisture reduction of 50% to 70% relative to initial moisture content, with a corresponding improvement of 10%– 20% in effective calorific value. This work demonstrates a practical, affordable, and fully instrumented drying platform suitable for laboratory research, small industrial units, and educational settings.