Search Results (184)

Minor cereals are an important part of the Chinese grain industry, accounting for about 8 percent of the country’s total grain-growing area. Minor cereals include millet, buckwheat, Panicum miliaceum, and other similar grains. Influenced by topographical and climatic factors, the distribution of minor cereals in China is mainly concentrated in the plateau and hilly areas north of the Yangtze River. In addition, there are large concentrations of minor cereals in Inner Mongolia, Heilongjiang, and other areas with flatter terrain. However, the level of mechanized harvesting in these areas is still low, and there is little research on the whole process of mechanized harvesting of minor cereals. This paper aims to discuss the current status of the minor cereal industry and its mechanization level, with particular attention to the challenges encountered in research related to the mechanized harvesting of minor cereals, including limited availability of suitable machinery, high losses, and low efficiency. The article provides a comprehensive overview of the key technologies that must be advanced to achieve mechanized harvesting throughout the process, such as header design, threshing, cleaning, and intelligent modular systems. It also summarizes current research progress on advanced equipment for mechanized harvesting of minor cereals. In addition, the article puts forward suggestions to promote the development of mechanized harvesting of minor cereals, focusing on aspects such as crop variety optimization, equipment modularization, and intelligentization technology, aiming to provide a reference for the further development and research of mechanized harvesting technology for minor cereals in China. Full article

Conventional cast-in-place counterfort retaining walls, while widely used to support the fill body in geotechnical engineering cases, suffer from extended construction cycles and environmental impacts that constrain their usage more widely. In this study, in order to overcome these limitations, the performance of two types of innovative prefabricated counterfort retaining wall system—a monolithic design and a modular design—was investigated through physical modeling. The results reveal that failure mechanisms are fundamentally governed by the distribution of stress at the connection interfaces. The monolithic system, with fewer connections, concentrates stress and is more vulnerable to cracking at the primary joints. In contrast, the modular system disperses loads across numerous connections, reducing localized stress. Critically, this analysis identified a construction-dependent failure mode: incomplete contact between the foundation and the base slab induces severe bending moments that can lead to catastrophic failure. Furthermore, this study shows that complex stress states due to backfill failure can induce detrimental tensile forces on the wall structure. To address this, a composite soil material–wall structure system incorporating geogrid reinforcement was developed. This system significantly enhances the backfill’s bearing capacity and mitigates adverse loading. Based on the comprehensive analysis of settlement and structural performance, the optimal configuration involves concentrating geogrid layers in the upper third of section of the backfill, with sparser distribution below. Full article

We develop a modular, wireless, solar- and battery-powered system for detecting chlorpyrifos (Lorsban^TM 2.5% DP) in water using electrochemical impedance spectroscopy (EIS). The system integrates a Raspberry Pi Zero 2W for data processing, Python-based software (version 3.12.2), and a solar charge manager to power all components via a lithium-ion battery and solar panel. A commercial EmStat Pico Module and an amperometric biosensor with acetylcholinesterase (AChE) detect chlorpyrifos. Nine water samples with varying concentrations were tested using a 20 Hz–200 kHz frequency sweep and 15 mV excitation. Bode plots and statistical analyses confirmed statistically significant impedance variation as a function of chlorpyrifos concentration, validating the system as a portable, sensitive, and effective tool for environmental monitoring. Full article

This study consisted of the injection molding simulation of polycaprolactone (PCL)-based nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) for biomedical implant manufacturing. The simulation was additionally supported by experimental validation. The influence of varying MWCNT concentrations (0.5%, 5%, and 10% by weight) on key injection molding parameters, i.e., melt flow behavior, pressure distribution, temperature profiles, and fiber orientation, was analyzed with SolidWorks Plastics software. The results proved the low CNT content (0.5 wt.%) to be endowed with stable filling times, complete mold cavity filling, and minimal frozen regions. Thus, this formulation produced defect-free modular filament sticks suitable for subsequent 3D printing. In contrast, higher CNT loadings (particularly 10 wt.%) led to longer fill times, incomplete cavity filling, and early solidification due to increased melt viscosity and thermal conductivity. Experimental molding trials with the 0.5 wt.% CNT composites confirmed the simulation findings. Following minor adjustments to processing parameters, high-quality, defect-free sticks were produced. Overall, the PCL/MWCNT composites with 0.5 wt.% nanotube content exhibited optimal injection molding performance and functional properties, supporting their application in modular, patient-specific biomedical 3D printing. Full article

Membrane-based CO₂ capture has emerged as a promising technology for industrial decarbonization, offering advantages in energy efficiency, modularity, and environmental performance. This review presents a comprehensive assessment of membrane processes applied across major emission-intensive sectors, including power generation, cement, steelmaking, and biogas upgrading. Drawing from pilot-scale demonstrations and simulation-based studies, we evaluate how flue gas characteristics, such as CO₂ concentration, pressure, temperature, and impurity composition, govern membrane selection, process design, and operational feasibility. Case studies highlight the technical viability of membrane systems under a wide range of industrial conditions, from low-CO₂ NGCC flue gas to high-pressure syngas and CO₂-rich cement emissions. Despite these advances, this review discusses the key remaining challenges for the commercialization of membrane-based CO₂ capture and includes perspectives on process design and techno-economic evaluation. The insights compiled in this review are intended to support the design of application-specific membrane systems and guide future efforts toward scalable and economically viable CO₂ capture across industrial sectors. Full article

Salivary $\alpha$-amylase (sAA) is a widely recognized biomarker for stress and autonomic nervous system activity. However, conventional enzymatic assays used to quantify sAA are limited by time-consuming, lab-based protocols. In this study, we present a portable, AI-driven point-of-care system for automated sAA classification via colorimetric image analysis. The system integrates SCHEDA, a custom-designed imaging device providing and ensuring standardized illumination, with a deep learning pipeline optimized for mobile deployment. Two classification strategies were compared: (1) a modular YOLOv4-CNN architecture and (2) a unified YOLOv8 segmentation-classification model. The models were trained on a dataset of 1024 images representing an eight-class classification problem corresponding to distinct sAA concentrations. The results show that red-channel input significantly enhances YOLOv4-CNN performance, achieving 93.5% accuracy compared to 88% with full RGB images. The YOLOv8 model further outperformed both approaches, reaching 96.5% accuracy while simplifying the pipeline and enabling real-time, on-device inference. The system was deployed and validated on a smartphone, demonstrating consistent results in live tests. This work highlights a robust, low-cost platform capable of delivering fast, reliable, and scalable salivary diagnostics for mobile health applications. Full article

China’s accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. The accurate quantification of PM_2.5 distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM_2.5 concentration fluctuations with moderate accuracy, their dependence relies heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM_2.5 concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model’s capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM_2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 μg/m³, and a mean absolute error (MAE) of 3.23 μg/m³. Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting. Full article

Aiming at the defects of the low mass transfer efficiency and large floor space of the traditional ozone process, a cross-tube ozone catalytic oxidation pilot plant was designed and developed. By implementing lateral aeration and a modular series configuration, the gas–liquid mass transfer pathways were optimized, achieving a hydraulic retention time of 25 min and maintaining an ozone dosage of 43 mg/L, which significantly improved the ozone utilization efficiency. During the pilot operation in an industrial park in Suzhou, Anhui Province, the average COD removal efficiency of the device for the actual biochemical tail water (COD 82.5~29.7 mg/L) reached 35.47%, and the effluent concentration was stably lower than 50 mg/L, which meets the stricter discharge standard. The intermediate products in the system were also analyzed by liquid chromatography–mass spectrometry (LC-MS), and the key pollutants were selected for degradation path analysis. Compared to the original tower process in the park, the ozone dosage was reduced by 46%, the reaction residence time was reduced by 60%, and the cost of water treatment was reduced to 0.067 USD, which is both economical and applicable to engineering. This process provides an efficient and low-cost solution for the deep treatment of wastewater in industrial parks, and has a broad engineering application prospect. Full article

Livestock farming has been identified as a significant contributor to atmospheric pollution, underscoring the necessity for the design and management of housing systems to adopt mitigation strategies. In the context of civil engineering, green wall systems are proving to be effective solutions for air filtration and purification. Nevertheless, research related to their application in livestock buildings is limited. This study focuses on the design, implementation, and performance evaluation of a modular, mobile green wall system that has been specifically developed to test PM2.5 concentrations’ reduction in naturally ventilated, free-stall dairy barns in the Mediterranean region. To this end, PM2.5 concentrations and climatic parameters have been measured before and after the application of the green wall system. Based on one-way analysis of variance, PM2.5 concentrations after the application were significantly lower (p < 0.001) than those before the mitigation strategy. The results of this study showed that the overall efficacy of the green wall reached 44%. The implementation of green wall systems offers a promising strategy to improve air quality in livestock facilities and to design aesthetically pleasing barns with a positive impact on the surrounding landscape. Full article

Non-Newtonian mud and debris flows include a wide range of physical processes depending on the setting, concentration, and soil properties. Numerical modelers have developed a variety of non-Newtonian algorithms to simulate this range of physical processes. However, the assumptions and limitations in any given model or software package can be difficult to replicate. This diversity in the physical processes and algorithmic approach to non-Newtonian numerical modeling makes a modular computation library approach advantageous. A computational library consolidates the algorithms for each process. This work presents a flexible numerical library framework (DebrisLib) that has a diverse range of software implemented to simulate geophysical flows using steady flow, kinematic wave, diffusion wave, and shallow-water models with finite difference, finite element, and finite volume computational schemes. DebrisLib includes a variety of non-Newtonian closures that predict a range of geophysical flow conditions and modular code designed to operate with any Newtonian parent-code architecture. This paper presents the DebriLib algorithms and framework and laboratory validation simulation. The simulations demonstrate the utility of the algorithms and the value of the library architecture by calling it from different modeling frameworks developed by the US Army Corps of Engineers (USACE). We present results with the one-dimensional (1D) and two-dimensional (2D) Hydrologic Engineering Center River Analysis System (HEC-RAS) and the 2D Adaptive Hydraulics (AdH) numerical models, each calling the same library. Full article

This study introduces a farm-to-fork quantitative risk assessment (QRA) model for invasive listeriosis from ready-to-eat diced cantaloupe. The modular model comprises seven stages—preharvest (soil and irrigation contamination), harvest (cross-contamination and survival), pre-processing (brushing), processing (flume tank washing, dicing and equipment cross-contamination), lot testing, cold-chain transport and retail growth, and consumer storage/handling. Each stage employs stochastic functions to simulate microbial prevalence and concentration changes (growth, inactivation, removal, partitioning, cross-contamination) using published data. In a reference scenario—good agricultural practices (soil barriers, no preharvest irrigation), hygienic processing and proper cold storage—the model predicts low lot- and pack-level contamination, with few packs >10 CFU/g and most servings below detection; the mean risk per serving is very low. “What-if” analyses highlight critical control points: the absence of soil barriers with preharvest irrigation can increase the risk by 10,000-fold; flume tank water contamination has a greater impact than harvest-stage cross-contamination; and poor consumer storage can raise the risk by up to 500-fold. This flexible QRA framework enables regulators and industry to evaluate and optimize interventions—from improved agricultural measures to targeted sampling plans and consumer guidance—to mitigate listeriosis risk from RTE diced cantaloupe. Full article

The management of municipal solid waste remains a critical environmental and energy challenge across the European Union (EU), where a significant portion of waste still ends up in landfills, generating landfill gas (LFG) rich in methane and harmful impurities. In Latvia, despite national strategies to enhance circularity, untreated LFG is underutilized due to inadequate purification infrastructure, particularly in meeting biomethane standards. This study addressed this gap by proposing and evaluating an innovative, multistep LFG purification system tailored to Latvian conditions, with the aim of enabling the broader use of LFG for energy cogeneration and potentially biomethane injection. The research objective was to design, describe, and preliminarily assess a pilot-scale LFG purification prototype suitable for deployment at Latvia’s largest landfill facility—Landfill A. The methodological approach combined chemical composition analysis of LFG, technical site assessments, and engineering modelling of a five-step purification system, including desulfurization, cooling and moisture removal, siloxane filtration, pumping stabilization, and activated carbon treatment. The system was designed for a nominal gas flow rate of 1500 m³/h and developed with modular scalability in mind. The results showed that raw LFG from Landfill A contains high concentrations of hydrogen sulfide, siloxanes, and volatile organic compounds (VOCs), far exceeding permissible thresholds for biomethane applications. The designed prototype demonstrated the technical feasibility of reducing hydrogen sulfide (H₂S) concentrations to <7 mg/m³ and siloxanes to ≤0.3 mg/m³, thus aligning the purified gas with EU biomethane quality requirements. Infrastructure assessments confirmed that existing electricity, water, and sewage capacities at Landfill A are sufficient to support the system’s operation. The implications of this research suggest that properly engineered LFG purification systems can transform landfills from passive waste sinks into active energy resources, aligning with the EU Green Deal goals and enhancing local energy resilience. It is recommended that further validation be carried out through long-term pilot operation, economic analysis of gas recovery profitability, and adaptation of the system for integration with national gas grids. The prototype provides a transferable model for other Baltic and Eastern European contexts, where LFG remains an underexploited asset for sustainable energy transitions. Full article

Background and Objectives: Robotic surgery represents one of the most significant innovations in the field of surgery, offering new opportunities for the treatment of complex pathologies that require greater accuracy and precision. It is a technology that has become widely used in general, urologic, gynecologic, and cardio-thoracic surgery, but has a limited evidence in the head and neck region. This review explores the use of robotic surgery in orbital pathology, focusing on its applications, benefits, and limitations. Materials and Methods: A cross-sectional search method was performed in multiple databases to answer the following question: “What are the applications of robotic surgery in the management of orbital pathologies?” Studies were carefully reviewed by two simultaneous researchers, and, in case of disagreement, a third researcher was engaged. Care was taken to identify the surgical hardware (robotic station) used to perform the surgical procedure. Results: Out of 491 records, eight studies met the inclusion criteria. These included cadaveric, preclinical, in vitro, and early clinical investigations assessing robotic approaches for fronto-orbital advancement, tumor resection, orbital decompression, and other surgical procedures such as lacrimal gland dissection and biopsy, medial and lateral orbital wall dissections, enucleation, and lid-sparing orbital exenteration. The robotic systems evaluated included the Da Vinci Xi, Da Vinci SP, Medineering Robotic Endoscope Guiding System, and a modular multi-arm concentric tube robot, each with specific advantages and limitations. Conclusions: Robotic surgery provides significant advantages for orbital pathologies such as improved precision, visualization, and tissue preservation, with reduced complications and faster recovery, although some limitations still exist. Future advancements, such as smaller instruments and AI integration, promise to improve outcomes, making robotic surgery more effective in treating orbital conditions. Full article

As global mineral resources at shallow depths continue to deplete, thermal hazards have emerged as a critical challenge in deep mining operations. Conventional localized cooling systems suffer from a fundamental inefficiency where their cooling capacity is rapidly dissipated by the main ventilation airstream. This study introduces the innovative concept of a “microclimatic circulation zone” implemented through a convection–radiation cooling system. The design incorporates a synergistic arrangement of dual fans and flow-guiding baffles that creates a semi-enclosed air circulation field surrounding the modular convection–radiation cooling apparatus, effectively preventing cooling capacity loss to the primary ventilation flow. The research develops comprehensive theoretical models characterizing both internal and external heat transfer mechanisms of the modular convection–radiation cooling system. Using Fluent computational fluid dynamics software, we constructed an integrated heat–moisture–flow coupled numerical model that identified optimal operating parameters: refrigerant velocity of 0.2 m/s, inlet airflow velocity of 0.45 m/s, and outlet aperture height of 70 mm. Performance evaluation conducted at a mining operation in Yunnan Province utilized the Wet Bulb Globe Temperature (WBGT) index as the assessment criterion. Results demonstrate that the enhanced microclimatic circulation system exhibits superior cooling retention capabilities, with a 19.83% increase in refrigeration power and merely 3% cooling capacity dissipation at a 7 m distance, compared to 19.23% in the conventional system. Thermal field analysis confirms that the improved configuration successfully establishes a stable microclimatic circulation zone with significantly more concentrated low-temperature regions. This effectively addresses the principal limitation of conventional systems where conditioned air is readily dispersed by the main ventilation current. The approach presented offers a novel technological pathway for localized thermal environment management in deep mining operations affected by heat stress conditions. Full article

The article describes an Arduino-based, portable, remotely usable weather monitoring station capable of measuring temperature, relative humidity, pressure, and carbon monoxide (CO) concentration and transmitting the collected data to the Cloud through the mobile telephony network. The main modules of the station are as follows: a DHT11 sensor for temperature and relative humidity sensing, a BMP180 sensor for pressure monitoring (with temperature compensation), a MQ7 sensor for the monitoring of the CO concentration, an Arduino Uno board, a GSM SIM900 module, and a buzzer, which is activated when the temperature exceeds 35 °C. The station operates as follows: the Arduino Uno board gathers the data collected by the sensors and, by means of the GSM SIM900 module, it transmits the data to the Cloud by using the mobile telephony network as well as the ThingSpeak software which is an open-code IoT application that, among others, enables saving and recovering of sensing and monitoring data. The main novelty of this work is the combined use of the GSM network and the Cloud which enhances the portability and usability of the proposed system and enables remote collection of data in a straightforward way. Additional merits of the system are the easiness and the low cost of its development (owing to the easily available, low-cost hardware combined with an open-code software) as well as its modularity and scalability which allows its customization depending on specific application it is intended for. The system could be used for real-time, remote monitoring of essential environmental parameters in spaces such as farms, warehouses, rooms etc. Full article