Search Results (202)

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

Repairing veneer defects is the key to ensuring the quality of plywood. In order to improve the maintenance quality and material utilization efficiency during the maintenance process, this paper proposes a bidirectional maintenance method based on gear rack transmission and its related equipment. Based on the working principle, a geometric relationship model was established, which combines the structural parameters of the mold, punch, and gear system. Simultaneously, it solves the problem of motion attitude analysis of conjugate tooth profiles under non-standard meshing conditions, aiming to establish a constraint relationship between stamping motion and structural design parameters. On this basis, a constrained optimization model was developed by integrating multi-objective optimization theory to maximize maintenance efficiency. The NSGA-III algorithm is used to solve the model and obtain the Pareto front solution set. Subsequently, three optimal parameter configurations were selected for simulation analysis and experimental platform construction. The simulation and experimental results indicate that the veneer repair time ranges from 0.6 to 1.8 seconds, depending on the stamping speed. A reduction of 28 mm in die height decreases the repair time by approximately 0.1 seconds, resulting in an efficiency improvement of about 14%. The experimental results confirm the effectiveness of the proposed method in repairing veneer defects. Vibration measurements further verify the system’s stable operation under parametric modeling and optimization design. The main vibration response occurs during the meshing and disengagement phases between the gear and rack. Full article

This study investigates the fire dynamics and structural response of steel-framed warehouse racking systems under various fire scenarios, emphasizing the critical importance of fire safety measures in mitigating structural damage. Through advanced computational simulations (Fire Dynamics Simulator) and thermomechanical analysis, this research reveals that fire intensity and progression are highly influenced by the ignition point and the stored material types, with maximum recorded temperatures reaching 720 °C and 970 °C in different scenarios. The results highlight the localization of significant strain and drift ratios in structural elements near the ignition zone, underscoring their vulnerability. This study demonstrates the rapid loss of load-bearing capacity in steel elements at elevated temperatures, leading to severe deformations and increased collapse risks. Key findings emphasize the necessity of strategically positioned sprinkler systems and the integration of passive fire protection measures, such as fire-resistant coatings, to enhance structural resilience. Performance-based fire design approaches, aligning with FEMA-356 criteria, offer realistic frameworks for improving the fire safety of warehouse structures. Full article

Integrating ejectors into CO₂ transcritical refrigeration systems to reduce energy consumption has been performed successfully throughout the industry in recent years. The objective of the present work is to investigate the effect of indoor and outdoor operating conditions on the energy efficiency of ejector expansion supermarket refrigeration plants. The analysis uses the measured energy consumptions and loads for two supermarket refrigeration plants operating in two cities in the Republic of Moldova (Chisinau and Balti). A model for the prediction of the plant’s annual energy consumption and the loads of the refrigeration and freezing compressors is developed using experimental results. Although there are theoretical and experimental analyses of the investigated systems in the specialized literature, no studies were found in the specialized literature regarding energy consumption increase due to pressure losses through the pipe route in transcritical CO₂ refrigeration installations with an ejector for supermarkets. The results indicate that refrigeration compressors have a greater increase in energy consumption than freezing compressors with increases in the outdoor temperature. The study shows that the additional drop in evaporating pressure at the compressor rack due to incorrect sizing of the pipe route leads to higher energy consumption compared to what the same plant would consume if the pipe route were correctly sized and executed. For every one-degree increase in temperature loss due to additional pressure drop through the pipeline, the entire plant consumes around 1.5% more energy. Knowledge of these performance data of real systems provides designers and manufacturers with clues to understand the importance of the correct design of the pipe route to obtain maximum energy efficiency. Full article

The profile modular system offers variability, flexibility, ease of assembly, and corrosion resistance as well as non-time-consuming assembly while meeting the required conditions of the customer. It has a broad spectrum of usability. This article compares the results of a stress analysis solution for two variants of a rack structure, namely the original variant made by welding Jäkl profiles and the newly proposed design variant created with aluminum Bosch profiles. The finite element method (FEM) is used in computational analyses. FEM models are created using shell elements. Particular attention is given to the use of shell elements in the FEM and their suitability for finite element analyses of the selected structures. Finally, the advantages and disadvantages of both approaches are evaluated, including a safety assessment and an economic comparison of the variants. Full article

Cleaning operations in narrow pipelines are often hindered by limited maneuverability and low efficiency, necessitating the development of a high-performance and highly adaptable robotic solution. To address this challenge, this study proposes a pipeline-cleaning robot specifically designed for the heat-exchange tubes of industrial heat exchangers. The robot features a dual-wheel cross-drive configuration to enhance motion stability and integrates a gear–rack-based alignment mechanism with a cam-based propulsion system to enable autonomous deployment and cleaning via a flexible arm. The robot adopts a modular architecture with a separated body and cleaning arm, allowing for rapid assembly and maintenance through bolted connections. A vision-guided control system is implemented to support accurate positioning and task scheduling within the primary pipeline. Experimental results demonstrate that the robot can stably execute automatic navigation and sub-pipe cleaning, achieving pipe-switching times of less than 30 s. The system operates reliably and significantly improves cleaning efficiency. The proposed robotic system exhibits strong adaptability and generalizability, offering an effective solution for automated cleaning in confined pipeline environments. Full article

The Dujiangyan–Siguniangshan mountain rack railway project is China’s first mountain rail transit. Most of its lines are located in mountainous areas and close to natural ecological protection areas, which have strict restrictions on the vibration and noise of train operation. At the same time, the vibration of mountain rack railway trains is also an important factor affecting the safety and riding comfort of trains. However, due to the multi-source vibration of gear teeth, wheels, rails, and suspensions, it is difficult to clearly define the vibration characteristics and vibration transmission path of the train, which has a serious impact on its vibration noise suppression and optimization. To this end, this study proposed a set of evaluation methods for the vibration characteristics and transfer paths of mountain rack trains based on a combination of dynamics and operational transfer path analysis (OTPA). Considering the interaction between the dynamic behaviors of the primary and secondary suspensions, the gear tooth contact behavior, the wheel–rail contact behavior and the dynamic behaviors of the track system, a dynamic model of a mountain rack train based on the finite element method was established, and the effectiveness of the model was verified through field experiments. On this basis, the OTPA method was used to establish a vibration transfer path model between the secondary suspension and the center of mass of the car body, and it was used to analyze the vibration mechanism and transfer path of the train body at the rated speed (20 km/h) and the limited speed (30 km/h). This study is of great significance for suppressing the vibration noise of mountain rack trains, reducing the impact on the ecological environment and improving ride comfort. Full article

Optimizing airflow organization is essential for ensuring the energy-efficient and secure operation of data centers. To address common airflow distribution issues in air-cooled systems, such as uneven air supply and cooling capacity imbalance, this study investigates a bidirectional airflow data center room located in a hot-summer and warm-winter region. A computational fluid dynamics (CFD) model was developed based on field-measured data to analyze the airflow distribution characteristics and evaluate the existing thermal conditions. Three optimization strategies were systematically examined: (1) Installation of rack blanking panels, (2) cold aisle containment with varying degrees of closure, and (3) combined implementations of these measures. Performance evaluation was conducted using three thermal metrics: the Return Temperature Index (RTI), Supply Heat Index (SHI), and Rack Cooling Index (RCIHI). The results demonstrate that among individual optimization strategies, rack blanking panels achieved the most significant improvement, reducing SHI by 42.61% while effectively eliminating local hotspots. For combined optimization strategies, the rack blanking panels and fully contained cold aisle containment showed optimal performance, improving cooling utilization efficiency by 88.26%. The optimal retrofit solution for this data center is the rack blanking panels with fully contained cold aisle containment. When considering budget constraints, the secondary option would be rack blanking panels with cold aisle top-only containment. These findings provide practical guidance for energy efficiency improvements in similar data center environments. Full article

This paper presents an experimental and numerical investigation of the axial compression behavior of perforated cold-formed steel upright profiles commonly used in pallet racking systems. The primary objective is to examine how slenderness influences the failure modes and load-bearing capacity of these structural elements. Three column lengths, representative of typical vertical spacing in industrial rack systems, were tested under pin-ended boundary conditions. All specimens were fabricated from 2 mm thick S355 steel sheets, incorporating web perforations and a central longitudinal stiffener. Experimental results highlighted three distinct failure mechanisms dependent on slenderness: local buckling for short columns (SS-340), combined distortional–flexural buckling for medium-length columns (MS-990), and global flexural buckling for slender columns (TS-1990). Finite Element Method (FEM) models developed using ANSYS Workbench 2021 R1 software accurately replicated the observed deformation patterns, stress concentrations, and load–displacement curves, with numerical results differing by less than 5% from experimental peak loads. Analytical evaluations performed using the Effective Width Method (EWM) and Direct Strength Method (DSM), following EN 1993-1-3 and AISI S100 specifications, indicated that EWM tends to underestimate the ultimate strength by up to 15%, whereas DSM provided results within 2–7% of experimental values, especially when the entire net cross-sectional area was considered fully effective. The originality of the study is the comprehensive evaluation of full-scale, perforated, stiffened cold-formed steel uprights, supported by robust experimental validation and detailed comparative analyses between FEM, EWM, and DSM methodologies. Findings demonstrate that DSM can be reliably applied to perforated sections with moderate slenderness and adequate web stiffening, without requiring further local reduction in the net cross-sectional area. Full article

Real-time estimation of the road surface friction coefficient is crucial for vehicle dynamics control. Under large steering angles, the accuracy of existing road surface friction coefficient estimation methods is unsatisfactory due to the nonlinear characteristics of the tire. This paper proposes a segmented estimation method for the road adhesion coefficient, which considers different steering angle ranges and utilizes multimodal vehicle dynamics fusion. The method is designed to accurately estimate the road adhesion coefficient across the full steering angle range of the steer-by-wire system. When the front wheel angle is small (less than 2.8°), an improved Unscented Kalman Filter (AUKF) algorithm is used to estimate the road surface friction coefficient. When the front wheel angle is large (greater than 3.2°), a rack force expansion state observer is constructed using the dynamics model of the steer-by-wire actuator to estimate the rack force. Based on the principle that the rack force varies with different road surface friction coefficients for the same steering angle, the rack force is used to distinguish the road surface friction coefficient. When the front wheel angle is between the two ranges, the average value of both methods is taken as the final estimate. The method is verified through Matlab/Simulink and CarSim co-simulation, as well as hardware-in-the-loop experiments of the steer-by-wire system. Simulation results show that the relative error of road surface friction coefficient estimation is less than 10% under different steering angles. The segmented combination estimation strategy proposed in this paper reduces the impact of tire nonlinearities on the estimation result and achieves high-precision road surface friction coefficient estimation over the entire steering angle range of the steer-by-wire system, which is of significant importance for vehicle dynamics control. Full article

Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity. Full article

Aging spirits in wooden casks is a traditional and mandatory process for the production of certain products, such as whisky. The physicochemical and sensory changes that occur during aging are shaped by the characteristics of the barrels and the aging method used. In this paper, we examined the behavior of the same malt spirit when aged using two different Sherry Casks^® methods. The first one was static aging, with the distillate remaining still in the cask, and the second one was a dynamic system, characterized by the regular racking of the spirit between casks at different aging stages (Criaderas and Solera). For 36 months, the aging spirits were sampled and analyzed to determine any changes in acidity, volatile, and phenolic compound content that might indicate changes in their chemical profile. The spirits were also subjected to sensory evaluations. The analysis revealed a significant evolution of the distillate in either system, although with different chemical profiles. Multiple Linear Regression Models (MLR and PLS) were successfully used to estimate the age of the distillates at a high level of confidence. Although, after the first racking operation, the distillates in the dynamic system had an average age greater than the theoretical one, these differences tended to fade away as the system gradually stabilized. Full article

A clustered distributed storage system (DSS), also called a rack-aware storage system, is a distributed storage system in which the nodes are grouped into several clusters. The communication between two clusters may be restricted by their connectivity; that is to say, the communication cost between nodes differs depending on their location. As such, when repairing a failed node, downloading data from nodes that are in the same cluster is much cheaper and more efficient than downloading data from nodes in another cluster. In this article, we consider a scenario in which the failed nodes only download data from nodes in the same cluster, which is an extreme and important case that leverages the fact that the intra-cluster bandwidth is much cheaper than the cross-cluster repair bandwidth. Also, we study the problem of repairing multiple failures in this article, which allows for collaboration within the same cluster, i.e., failed nodes in the same cluster can exchange data with each other. We derive the trade-off between the storage and repair bandwidth for the clustered DSSs and provide explicit code constructions achieving two extreme points in the trade-off, namely the minimum storage clustered collaborative repair (MSCCR) point and the minimum bandwidth clustered collaborative repair (MBCCR) point, respectively. Full article

Endocrine-disrupting chemicals (EDCs) are natural or synthetic substances that are able to interfere with hormonal systems and alter their physiological signaling. EDCs have been recognized as a public health issue due to their widespread use, environmental persistence and the potential levels of long-term exposure with implications in multiple pathological conditions. Their reported adverse effects pose critical concerns about their use, warranting their strict regulation. This is the case of bisphenol A (BPA), a well-known EDC whose tolerable daily intake (TDI) was re-evaluated in 2023 by the European Food Safety Authority (EFSA), and the immune system has been identified as the most sensitive to BPA exposure. Increasing scientific evidence indicates that EDCs can interfere with several hormone receptors, pathways and interacting proteins, resulting in a complex, cell context-dependent response that may differ among tissues. In this regard, the neuronal and immune systems are important targets of hormonal signaling and are now emerging as critical players in endocrine disruption. Here, we use BPA and its analogs as proof-of-concept EDCs to address their detrimental effects on the immune and nervous systems and to highlight complex interrelationships within the immune–neuroendocrine network (INEN). Finally, we propose that Receptor for Activated C Kinase 1 (RACK1), an important target for EDCs and a valuable screening tool, could serve as a central hub in our toxicology model to explain bisphenol-mediated adverse effects on the INEN. Full article

With the rapid development of IoT technology, sensors are widely used for monitoring environmental parameters. The data collected by sensors needs to be stored, and distributed storage systems provide an excellent platform to handle this vast amount of data. To enhance data reliability and reduce storage costs, erasure coding technology can be employed within distributed storage systems. However, the process of recovering lost or damaged data inevitably generates significant cross-rack traffic. In recent years, various batch recovery methods have been designed to improve data recovery speed and reduce cross-rack recovery traffic, but they have limitations in different aspects, such as less consideration for heterogeneous cross-rack bandwidth and insufficient handling of recovery link scheduling. This paper proposes the HBRepair recovery framework, aimed at improving data recovery speed by balancing the recovery load and reducing cross-rack recovery traffic. Firstly, HBRepair strategically selects helper blocks and nodes for storing recovery blocks to find the optimal batch recovery plan. Then, it selectively and rationally schedules recovery links to saturate idle bandwidth resources and avoid network congestion. Experimental results show that by optimizing the use of cross-rack bandwidth, HBRepair can reduce cross-rack recovery time by up to 26.74%, effectively addressing the shortcomings of existing methods. Full article