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Construction projects in deep underground engineering are associated with the recording of massive amounts of diversified signals during real time and continuous microseismic monitoring given the complexity and specificity of the construction environment. Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. In this method, a score map is used to label the onset time of micro-fracture waveforms to improve the picking accuracy. The proposed model can simultaneously handle the onset time picking and detection tasks of microseismic signals to achieve optimal performance. Based on the similarity of data structures, the output from the onset time picking section is imported into the detection section to classify different types of microseismic waveforms. The onset time picking and detection procedures can be seamlessly integrated, where the score map of the onset time can help improve the detection accuracy. The results show that this method has a good performance for the onset time picking and detection of microseismic waveforms that are polluted by noises of various types and intensities. A comparison of the proposed method with existing methods and applications in underground engineering projects helped demonstrate the excellent performance of this method. The proposed approach can accelerate the automatic processing of microseismic signals and has significant potential for the exploration of seismology and earthquake research. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for the region segmentation of images. The approach is based on the raster-scan paradigm and builds the segments incrementally. The pixels are processed in the raster-scan order, while the construction of the segments is based on a distance metric in regard to the already segmented pixels in the neighbourhood. The segmentation procedure operates in linear time according to the total number of pixels. The proposed method, named the RSM (raster-scan segmentation method), was tested on selected images from the popular benchmark datasets MS COCO and DIV2K. The experimental results indicate that our method successfully extracts regions with similar pixel values. Furthermore, a comparison with two of the well-known segmentation methods—Watershed and DBSCAN—demonstrates that the proposed approach is superior in regard to efficiency while yielding visually similar results. [ABSTRACT FROM AUTHOR]

In this paper, tartaric acid (C4H6O6) was used as a reductant to treat chromium (VI)-containing solution. Several independent experimental parameters, including reaction temperature, concentration of H2SO4, concentration of C4H6O6 and reaction time, on the reduction process were studied. The results showed that 100% of the Cr (VI) could be reduced by C4H6O6 in a strong acidic environment under a high reaction temperature. All of the experimental parameters showed positive effects on the reduction process and followed the order [H2SO4] > [C4H6O6] > reaction temperature > reaction time. A higher concentration of tartaric acid and higher reaction temperature could facilitate the reduction process and reduce reaction time. [ABSTRACT FROM AUTHOR]

Aflatoxin M1 (AFM1) in milk poses a significant threat to human health. This study examined the capacity of Bacillus licheniformis CotA laccase to oxidize AFM1. The optimal conditions for the CotA laccase-catalyzed AFM1 oxidation were observed at pH 8.0 and 70 °C, achieving an AFM1 oxidation rate of 86% in 30 min. The Km and Vmax values for CotA laccase with respect to AFM1 were 18.91 μg mL−1 and 9.968 μg min−1 mg−1, respectively. Computational analysis suggested that AFM1 interacted with CotA laccase via hydrogen bonding and van der Waals interactions. Moreover, the oxidation products of AFM1 mediated by CotA laccase were identified as the C3-hydroxy derivatives of AFM1 by HPLC-FLD and UPLC-TOF/MS. Toxicological assessment revealed that the hepatotoxicity of AFM1 was substantially reduced following oxidation by CotA laccase. The efficacy of CotA laccase in removing AFM1 in milk was further tested, and the result showed that the enzyme agent achieved an AFM1 removal rate of 83.5% in skim milk and 65.1% in whole milk. These findings suggested that CotA laccase was a novel AFM1 oxidase capable of eliminating AFM1 in milk. More effort is still needed to improve the AFM1 oxidase activity of CotA laccase in order to shorten the processing time when applying the enzyme in the milk industry. [ABSTRACT FROM AUTHOR]

In this study, a centrifugal feeding disc device is proposed. To investigate the influence of the process parameters on the discharging efficiency and the lifting of the discharging efficiency, the centrifugal feeding disc device was dynamically simulated based on the discrete element method (DEM), and the simulation results were experimentally verified. Based on the quadratic regression orthogonal test method, a significant lossless regression model of process parameters and discharging efficiency was established, and the response surface of the interaction of process parameters was obtained. The results indicated that the order of influence of the process parameters on the discharging speed of the centrifugal feeding disc was as follows: outer turntable speed > inner turntable speed > inner turntable tilt angle > conical turntable angle. The interaction of the conical turntable angle and the inner turntable tilt angle had the greatest influence on the centrifugal feed disc discharge efficiency. The response surface method (RSM) was used to optimize the process parameters, and the optimal combination of process parameters included an outer turntable speed of 135 r/min, an inner turntable speed of 64 r/min, an inner turntable tilt angle of 7°, and a conical angle of 15°. The discharged efficiency of the optimized centrifugal feeding disc device was increased by 31.9%. [ABSTRACT FROM AUTHOR]

Denial of Service (DoS) attacks remain a persistent threat to online systems, necessitating continual innovation in defense mechanisms. In this work, we present an improved algorithm for mitigating DoS attacks through the augmentation of client puzzle protocols. Building upon the foundation of hashcash trees, a recently proposed data structure combining hashcash and Merkle trees, we introduce a new version of the data structure that enhances resistance against parallel computation (a common tactic employed by attackers). By incorporating the labels of children and the next node in a breadth-first traversal into the hash function, we establish a sequential processing order that inhibits parallel node evaluation. The added dependency on the next node significantly elevates the complexity of constructing hashcash trees, introducing a linear number of synchronization points and fortifying resilience against potential attacks. Empirical evaluation demonstrates the efficacy of our approach, showcasing its ability to accurately control puzzle difficulty while bolstering system security against DoS threats. [ABSTRACT FROM AUTHOR]

It is known that artificial intelligence algorithms are based on calculations performed using various mathematical operations. In order for these calculation processes to be carried out correctly, some types of data cannot be fed directly into the algorithms. In other words, numerical data should be input to these algorithms, but not all data in datasets collected for artificial intelligence algorithms are always numerical. These data may not be quantitative but may be important for the study under consideration. That is, these data cannot be thrown away. In such a case, it is necessary to transfer categorical data to numeric type. In this research work, 14 encoding methods of transforming of categorical data were considered. At the same time, conclusions are given about the general conditions of using these methods. During the research, categorical data in the dataset that were collected in order to assess whether it is possible to give credit to customers will be transformed based on 14 methods. After applying each encoding method, experimental tests are conducted based on the classification algorithm, and they are evaluated. At the end of the study, the results of the experimental tests are discussed and research conclusions are presented. [ABSTRACT FROM AUTHOR]

This study describes our thorough experience of formal modeling and exhaustive verification of concurrent systems, particularly mutual exclusion algorithms. The experience focuses on Lycklama and Hadzilacos's (LH) mutual exclusion algorithm. LH rests on the reduced size of the shared state, contains a mechanism that tries to enforce an FCFS order to processes entering their critical section, and embodies Burns and Lamport's (BL) mutual exclusion algorithm. The modeling methodology is based on timed automata and the model checker of the popular Uppaal toolbox. The effectiveness of the modeling and analysis approach is first demonstrated by studying the BL's solution and retrieving all its properties, including, in general, its unbounded overtaking, which is the non-limited number of by-passes a process can suffer before accessing its critical section. Then, the LH algorithm is investigated in depth by showing it fulfills all the mutual exclusion properties when it operates with atomic memory. However, as this study demonstrates, LH is not free of deadlocks when used with non-atomic memory. Finally, a state-of-the-art mutual exclusion solution is proposed, which relies on a stripped-down LH version for processes, which is used as the arbitration unit in a tournament tree (TT) organization. This study documents that LH's TT-based algorithm satisfies all the mutual exclusion properties, with a linear overtaking, both using atomic and non-atomic memory. [ABSTRACT FROM AUTHOR]

The four-dimensional ellipsoid of an anisotropic hyper-structure corresponds to the four-dimensional sphere of an isotropic hyper-structure. In three dimensions, both theories for spherical and ellipsoidal harmonics have been developed by Laplace and Lamé, respectively. Nevertheless, in four dimensions, only the theory of hyper-spherical harmonics is hitherto known. This void in the literature is expected to be filled up by the present work. In fact, it is well known that the spectral decomposition of the Laplace equation in three-dimensional ellipsoidal geometry leads to the Lamé equation. This Lamé equation governs each one of the spectral functions corresponding to the three ellipsoidal coordinates, which, however, live in non-overlapping intervals. The analysis of the Lamé equation leads to four classes of Lamé functions, giving a total of 2n + 1 functions of degree n. In four dimensions, a much more elaborate procedure leads to similar results for the hyper-ellipsoidal structure. Actually, we demonstrate here that there are eight classes of the spectral hyper-Lamé equation and we provide a complete analysis for each one of them. The number of hyper-Lamé functions of degree n is (n + 1)2; that is, n2 more functions than the three-dimensional case. However, the main difficulty in the four-dimensional analysis concerns the evaluation of the three separation constants appearing during the separation process. One of them can be extracted from the corresponding theory of the hyper-sphero-conal system, but the other two constants are obtained via a much more complicated procedure than the three-dimensional case. In fact, the solution process exhibits specific nonlinearities of polynomial type, itemized for every class and every degree. An example of this procedure is demonstrated in detail in order to make the process clear. Finally, the hyper-ellipsoidal harmonics are given as the product of four identical hyper-Lamé functions, each one defined in its own domain, which are explicitly calculated and tabulated for every degree less than five. [ABSTRACT FROM AUTHOR]

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators—such as shelf access frequency and the total processing time of the RMFS—are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency. [ABSTRACT FROM AUTHOR]

We propose a new definition of the γ -convex stochastic processes (C SP) using center and radius (CR) order with the notion of interval valued functions (C. R I. V) . By utilizing this definition and Mean-Square Fractional Integrals, we generalize fractional Hermite–Hadamard–Mercer-type inclusions for generalized   C. R I. V versions of convex, tgs-convex, P-convex, exponential-type convex, Godunova–Levin convex, s-convex, Godunova–Levin s-convex, h-convex, n-polynomial convex, and fractional n-polynomial (C SP) . Also, our work uses interesting examples of   C. R I. V (C SP) with Python-programmed graphs to validate our findings using an extension of Mercer's inclusions with applications related to entropy and information theory. [ABSTRACT FROM AUTHOR]

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot's performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well. [ABSTRACT FROM AUTHOR]

Accurate detection of muskmelon fruit ripeness is crucial to ensure fruit quality, optimize picking time, and enhance economic benefits. This study proposes an improved lightweight YOLO-RFEW model based on YOLOv8n, aiming to address the challenges of low efficiency in muskmelon fruit ripeness detection and the complexity of deploying a target detection model to a muskmelon picking robot. Firstly, the RFAConv replaces the Conv in the backbone part of YOLOv8n, allowing the network to focus more on regions with significant contributions in feature extraction. Secondly, the feature extraction and fusion capability are enhanced by improving the C2f module into a C2f-FE module based on FasterNet and an Efficient Multi-Scale attention (EMA) mechanism within the lightweight model. Finally, Weighted Intersection over Union (WIoU) is optimized as the loss function to improve target frame prediction capability and enhance target detection accuracy. The experimental results demonstrate that the YOLO-RFEW model achieves high accuracy, with precision, recall, F1 score, and mean Average Precision (mAP) values of 93.16%, 83.22%, 87.91%, and 90.82%, respectively. Moreover, it maintains a lightweight design and high efficiency with a model size of 4.75 MB and an inference time of 1.5 ms. Additionally, in the two types of maturity tests (M-u and M-r), APs of 87.70% and 93.94% are obtained, respectively, by the YOLO-RFEW model. Compared to YOLOv8n, significant improvements in detection accuracy have been achieved while reducing both model size and computational complexity using the proposed approach for muskmelon picking robots' real-time detection requirements. Furthermore, when compared to lightweight models such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, YOLOv8s, and YOLOv8n, the YOLO-RFEW model demonstrates superior performance with only 28.55%, 22.42%, 24.50%, 40.56%, 22.12%, and 79.83% of their respective model sizes, respectively, while achieving the highest F1 score and mAP values among these seven models. The feasibility and effectiveness of our improved scheme are verified through comparisons between thermograms generated by YOLOv8n and YOLO-RFEW as well as detection images. In summary, the YOLO-RFEW model not only improves the accuracy rate of muskmelon ripeness detection but also successfully realizes the lightweight and efficient performance, which has important theoretical support and application value in the field of muskmelon picking robot development. [ABSTRACT FROM AUTHOR]

We consider two companies that are competing for orders. Let X 1 (n) denote the number of orders processed by the first company at time n, and let τ (k) be the first time that X 1 (n) < j or X 1 (n) = r , given that X 1 (0) = k . We assume that { X 1 (n) , n = 0 , 1 , ... } is a controlled discrete-time queueing system. Each company is using some control to increase its share of orders. The aim of the first company is to maximize the expected value of τ (k) , while its competitor tries to minimize this expected value. The optimal solution is obtained by making use of dynamic programming. Particular problems are solved explicitly. [ABSTRACT FROM AUTHOR]

Today, the Ising model is an archetype describing collective ordering processes. As such, it is widely known in physics and far beyond. Less known is the fact that the thesis defended by Ernst Ising 100 years ago (in 1924) contained not only the solution of what we call now the 'classical 1D Ising model' but also other problems. Some of these problems, as well as the method of their solution, are the subject of this note. In particular, we discuss the combinatorial method Ernst Ising used to calculate the partition function for a chain of elementary magnets. In the thermodynamic limit, this method leads to the result that the partition function is given by the roots of a certain polynomial. We explicitly show that 'Ising's roots' that arise within the combinatorial treatment are also recovered by the eigenvalues of the transfer matrix, a concept that was introduced much later. Moreover, we discuss the generalization of the two-state model to a three-state one presented in Ising's thesis, which is not included in his famous paper of 1925 (E. Ising, Z. Physik 31 (1925) 253). The latter model can be considered as a forerunner of the now-abundant models with many-component order parameters. [ABSTRACT FROM AUTHOR]

Addressing the issues of low harvesting efficiency and high losses in current pod pepper harvesters, this study presents the design of a bionic comb finger pod pepper picking device and an inclined double-spiral bell pepper harvester to improve the harvest rate and reduce loss rate. Through the utilization of discrete element simulation software EDEM, a discrete element model for pod peppers is established. Additionally, a simulation platform for ground drop loss during pod pepper picking is developed, enabling exploration of the movement trajectory and velocity changes of the pod pepper elements. The study also conducts an analysis on the impact of the speed of the picking rollers X1, the feeding speed of pod peppers X2, and the spacing between the two picking rollers X3 on ground drop losses. Based on the results of the single-factor test, the Box–Behnken response surface test was used to optimize the working parameters of the picking device, which resulted in the optimal combination of the working parameters of the picking device: the speed of the picking rollers was 680.41 rpm, the feeding speed of the pod peppers was 0.5 m/s, and the spacing between the two picking rollers was 12 mm, which resulted in the loss rate of pod peppers on the floor of the ground being 3.526%. [ABSTRACT FROM AUTHOR]

Filesystems are essential components in contemporary computer systems that organize and manage data. Their performance is crucial in various applications, from web servers to data storage systems. This paper helps to pick the suitable filesystem by comparing btrfs with ZFS by considering multiple situations and applications, ranging from sequential and random performance in the most common use cases to extreme use cases like high-performance computing (HPC). It showcases each option's benefits and drawbacks, considering different usage scenarios. The performance of btrfs and ZFS will be evaluated through rigorous testing. They will assess their capabilities in handling huge files, managing numerous small files, and the speed of data read and write across varied usage levels. The analysis indicates no definitive answer; the selection of the optimal filesystem is contingent upon individual data-access requirements. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problems of the complex growth state of dragon fruit and how the picking process is mostly manual, this study designed a picking and selecting integrated remote-operation-type dragon-fruit-picking device. Based on SOLIDWORKS 2020 software for the three-dimensional digital design and overall assembly of key components, the structure and working theory of the machine are introduced. By improving the high-recognition-rate dragon fruit target detection algorithm based on YOLOv5, better recognition and locating effects were achieved for targets with a small size and high density, as well as those in bright-light scenes. Serial communication, information acquisition, and the precise control of each picking action were realized by building the software and hardware platforms of the picking device control system. By analyzing the working principle of the mechanical system and the mechanics of the machine picking process, the critical factors affecting the net picking rate and damage rate of the dragon-fruit-picking device were confirmed. Based on the force and parameter analysis of the test results, it was confirmed that the machine had an optimal picking influence when the flexible claw closing speed was 0.029 m/s, the electric cylinder extending speed was 0.085 m/s, and the mechanical arm moving speed was 0.15 m/s. The net picking rate of the device reached 90.5%, and the damage rate reached 2.9%. The picking device can complete the picking of a single dragon fruit, as well as a plurality of fruits grown at a growing point, and integrates the integration of picking fruits, removing bad fruits, and sorting fruits, which can improve the efficiency of dragon fruit harvesting and replace manual work. [ABSTRACT FROM AUTHOR]

The following article proposes a reflection on the experience of Narrative Therapy of a group of cancer patients, non-homogeneous for sites and stages of disease, participating to a therapeutic writing path, in order to process the trauma reactive to cancer and to reflect on themselves. Taking inspiration from the assumption that "writing helps when people are faced with a stumble", facilitating the process of distancing from life-events, each writer establishes either context, in a more intimate and true way, or memories and emotions connected to it, in order to express them to the group and to themselves too, and to identify new adaptation styles. The therapeutic path lasted eight meetings, during which some themes were chosen to guide the written reflection, which was carried out at home, and then shared with the group. The therapeutic path is an opportunity to adjust the perspective with which the specialist accompanies patients during the adaptation process to the disease, moving from the "explanation" to "comprehension"; from symptom to "sense". The group narration is based on a relational perspective of co-construction of the meaning of experiences, highlighting the different roles and relevance of the factors involved. [ABSTRACT FROM AUTHOR]

To address the challenges of missed and disorderly picking in tea harvesters, this study focused on the leaf-collection pipeline and utilized Fluent simulation 19.0 software. A single-factor test identified key parameters affecting airflow velocity. An orthogonal test evaluated the main pipe taper, number of branch pipes, and branch pipe outlet diameter, with average outlet wind speed and wind speed non-uniformity as indicators. The optimal parameters were a main pipe taper of 25.5 mm, 10 branch pipes, and an inner diameter of 17.10 mm for the outlet, resulting in 10.73 m/s average wind speed and 8.24% non-uniformity. Validation tests showed errors under 1%. Further optimization on the internal structure's extension length led to 11.02 m/s average wind speed and 8.04% non-uniformity. Field experiments demonstrated a 3.40% stalk leakage rate and 90.36% bud leaf integrity rate; the optimized structure of the leaf-collecting pipeline significantly improved the uniformity of airflow and the picking efficiency. These findings offer valuable insights and practical benefits for enhancing the efficiency of tea harvesters. [ABSTRACT FROM AUTHOR]

This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. [ABSTRACT FROM AUTHOR]

The high standard of communication quality in optical access networks makes forward error correction (FEC) schemes, such as LDPC, an integral part of the system. However, pipeline conflict arising from data dependencies is a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles. It can solve some of the pipeline conflicts by flexibly reordering the processing order of inter-layer and intra-layer submatrices offline. In addition, the patch method, based on variable-to-check messages, allows for the delayed use of gains between layer iterations, which can further minimize the performance loss caused by the remaining pipeline conflicts. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has sensitivity improvements of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work. The optimized architecture improves the reliability of the decoder and can also make a contribution to efficient PON systems. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area. [ABSTRACT FROM AUTHOR]

Background: Despite the general impression that digital order-picking supportive technologies can manage a series of emerging challenges, there is still a very limited amount of research concerning the implementation and evaluation of such technologies in manual picker-to-goods order-picking systems. Therefore, this paper aims to evaluate the performance of three alternative picker-to-goods technologies (i.e., Pick-by-Radio Frequency (RF) Scanner, Pick-to-light, and Pick-by-vision) in terms of completion time and perceived workload. Methods: The Design of Experiments (DoE) methodology is adopted to investigate order-picking technologies in terms of completion time. More specifically, a full factorial design has been used (23 × 3 full factorial design) for the assessment of the aforementioned order-picking technologies via laboratory testing. Furthermore, for the comparative assessment of the reviewed order-picking technologies in terms of workload, the NASA Task Load Index (NASA-TLX) is embraced by system users. Results: The results reveal that the best picker-to-goods technology in terms of order-picking completion time and perceived workload under certain laboratory setup is light picking when combined with few items per order line and many order lines per order. Conclusion: The paper successfully identified the best picker-to-goods technology, however it is important to mention that the adoption of such order-picking technology implies certain managerial implications that include training programs for employees to ensure they are proficient in using such technologies, upfront costs for purchasing and implementing the order picking system, and adjustments to existing workflows. [ABSTRACT FROM AUTHOR]

In the context of increasingly competitive shipbuilding, the flexible multi-level picking system, composed of high-rise shelves, Automated Guided Vehicles (AGVs), and picking stations, has been of gradual interest because of its advantages in operation efficiency, system flexibility, and system robustness. Compared with other simple-level systems, the flexible multi-level picking system has a more complex coupling temporal relationship, which makes the scheduling optimization of shipbuilding automated collaborative order picking (SACOP) extremely difficult. In order to avoid the dilemma of finding a feasible and optimal collaborative scheduling scheme under the constraints of a complex temporal relationship, this paper proposed a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method, which takes the AGV scheduling scheme as the direct solution and modifies it to a feasible solution under the reasonably designed interaction strategy of stacker, AGV, and the interaction strategy of picking station, AGV. Furthermore, taking the minimum energy consumption and operation time as the optimization objectives, a multi-objective optimization mathematical model was established to describe MADPSO, and an improved NSGA-III algorithm was designed to solve the problem. Finally, several experiments were conducted in various scenarios and verified that using MADPSO can achieve a comprehensive optimization index improvement of 52.02–75.66% compared with traditional picking methods, which has a certain reference significance for shipyards. [ABSTRACT FROM AUTHOR]

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis. In synthetic data experiments and on real-world datasets, our model successfully captures all true targets while simultaneously proposing fewer computationally costly false positives. With this approach, we take an important step towards fully automating the detection of high-risk subregions, but we wish to emphasize the importance of prudent skepticism until it has been tested and proven on more diverse data. [ABSTRACT FROM AUTHOR]

Fast switching within static converters is a key to high-efficiency operation and a source of electromagnetic disturbances that can harm the proper functioning of the converters themselves or the electronic equipment placed in their neighborhood. To characterize disturbances, engineers are mainly focused on the spectral content since the higher the switching speed, the more important the high-frequency components are. In this article, a figure of merit (FOM) independent of switching speed is proposed. It allows us to compare switching patterns produced by a gate driver to each other, using as a reference the mathematical optimum of a Gaussian pattern as well as other elementary forms for which the FOM is known. A complete implementation methodology is presented to properly use this FOM that considers an adapted sampling frequency and filtering of signals before processing in order to correctly obtain information for the optimal adjustment of a driver. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem. [ABSTRACT FROM AUTHOR]

Variational auto-encoders (VAE) have been widely used in process modeling due to the ability of deep feature extraction and noise robustness. However, the construction of a supervised VAE model still faces huge challenges. The data generated by the existing supervised VAE models are unstable and uncontrollable due to random resampling in the latent subspace, meaning the performance of prediction is greatly weakened. In this paper, a new multi-layer conditional variational auto-encoder (M-CVAE) is constructed by injecting label information into the latent subspace to control the output data generated towards the direction of the actual value. Furthermore, the label information is also used as the input with process variables in order to strengthen the correlation between input and output. Finally, a neural network layer is embedded in the encoder of the model to achieve online quality prediction. The superiority and effectiveness of the proposed method are demonstrated by two real industrial process cases that are compared with other methods. [ABSTRACT FROM AUTHOR]

Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal. [ABSTRACT FROM AUTHOR]

A chatbot, or else a conversational agent (CA), is a technology that is used in order to imitate the process of a conversation between a human being and a software application for supporting specific services. The utilization of this technology has been increasing considerably over the past five years, particularly in education where CAs are mostly utilized as teaching assistants that provide educational content. This paper aims to contribute to the existing body of knowledge by systematically reviewing the support provided by chatbots both to educational institutions and their students, investigating their capabilities in further detail, and highlighting the various ways that this technology could and should be used in order to maximize its benefits. Emphasis is given to analyzing and synthesizing the emerging roles of CAs, usage recommendations and suggestions, student's desires, and challenges recorded in the literature. For this reason, a systematic literature review (SLR) was carried out using the PRISMA framework in order to minimize the common biases and limitations of SLRs. However, we must note that the SLR presented has specific limitations, namely using only Scopus as a search engine, utilizing a general search query, and selecting only journal articles published in English in the last five years. [ABSTRACT FROM AUTHOR]

Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard. [ABSTRACT FROM AUTHOR]

The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them. [ABSTRACT FROM AUTHOR]

The development of automated segmentation and quantitative characterization of microtextured regions (MTRs) from the complex heterogeneous microstructures is urgently needed, since MTRs have been proven to be the critical issue that dominates the dwell-fatigue performance of aerospace components. In addition, MTRs in Ti alloys have similarities to microstructures encountered in other materials, including minerals and biomaterials. Meanwhile, machine learning (ML) offers new opportunities. This paper addresses segmentation and quantitative characterization of MTRs, where an ML approach, the Gaussian mixture models (GMMs) coupled with density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms, was employed in order to process the orientation data acquired via EBSD in the Matlab environment. Pixels with orientation information acquired through electron backscatter diffraction (EBSD) are divided and colored into several "classes" (MTRs) within the defined c-axis misorientations (i.e., 25°, 20°, 15°, 10°, and 5°), the precision and efficacy of which are verified by the morphology and pole figure of the segmented MTR. An appropriate range of c-axis misorientations for MTR segmentation was derived, i.e., 15~20°. The contribution of this innovative technique is compared with previous studies. At the same time, the MTRs were statistically characterized in the global region. [ABSTRACT FROM AUTHOR]

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications. [ABSTRACT FROM AUTHOR]

For a 50G passive optical network (PON) low-density parity-check (LDPC) decoder, decoding performance and area efficiency must be balanced. This paper adopts a layered decoder method to improve the area efficiency of the decoder. By parallel processing of three submatrices and storage reuse of node information, optimizing the matrix partitioning and processing order of the 50G-PON standard, the throughput of 1235 bps was reached under 100 MHz circuit frequency in field-programmable gate array (FPGA) implementation, and 9.864 Gbps was achieved based on Taiwan semiconductor manufacturing company (TSMC) 65 nm synthesis with 800 MHz circuit frequency in an area of 2.61 mm 2 by proposing a mechanism of spare decision storage to avoid errors caused by quantization overflow of the decoder and using full verification to terminate decoding in advance to improve performance. Finally, at an input bit error rate (BER) of 2.3 × 10 − 2 (signal-to-noise ratio (SNR) of about 3.72 dB), the output BER was lower than 10 − 12 , and the throughput area rate (TAR) also increased by 2 to 4 times compared with other papers. In conclusion, an area-efficient LDPC decoder without sacrificing decoding performance is made. [ABSTRACT FROM AUTHOR]

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution focuses on the digitization and automation of supply chains resulting in a significant transformation of methods for goods production and delivery systems. To enable this, automated warehousing is demanding unprecedented vehicle-to-vehicle and vehicle-to-infrastructure communication rates and reliability. The 60 GHz frequency band can deliver multi-gigabit/second data rates to satisfy the increasing demands of network connectivity by smart warehouses. In this paper, we aim to investigate the network connectivity in the 60 GHz millimeter-wave band inside an automated warehouse. A key hindrance to robust and high-speed network connectivity, especially, at mmWave frequencies stems from numerous non-line-of-sight (nLOS) paths in the transmission medium due to various interacting objects such as metal shelves and storage boxes. The continual change in the warehouse storage configuration significantly affects the multipath reflected components and shadow fading effects, thus adding complexity to establishing stable, yet fast, network coverage. In this study, network connectivity in an automated warehouse is analyzed at 60 GHz using Network Simulator-3 (NS-3) channel simulations. We examine a simple warehouse model with several metallic shelves and storage materials of standard proportions. Our investigation indicates that the indoor warehouse network performance relies on the line-of-sight and nLOS propagation paths, the existence of reflective materials, and the autonomous material handling agents present around the access point (AP). In addition, we discuss the network performance under varied conditions including the AP height and storage materials on the warehouse shelves. We also analyze the network performance in each aisle of the warehouse in addition to its SINR heatmap to understand the 60 GHz network connectivity. [ABSTRACT FROM AUTHOR]

Permanent highway support in deep coal mines now depends on the anchor drilling robot's drill arm. The drilling arm's trajectory planning using the conventional RRT (rapid-expanding random tree) algorithm is inefficient and has crooked, rough paths. To improve the accuracy of path planning, we propose an improved RRT algorithm. Firstly, the kinematic model of the drill arm of the drill and anchor robot was established, and the improved DH solution parameters and the positive solution of the drill arm kinematics were solved. The end effector's attainable working space was calculated using the Monte Carlo approach. Additionally, to address the problem of the slow running speed of the RRT algorithm, an artificial potential field factor was introduced to construct virtual force fields at obstacle and target points and calculate the potential field map for the entire reachable workspace to improve the speed of the sampling points close to the target point. At the same time, the greedy approach and the three-time B-sample curve-fitting method were used simultaneously to remove unnecessary points and carry out smooth path processing in order to improve the quality of the drill arm trajectory. This was carried out in order to solve the issue of rough pathways generated by the RRT algorithm. Finally, 50 time-sampling comparison experiments were conducted on 2D and 3D maps. The experimental results showed that the improved RRT algorithm improved the average sampling speed by 20% and reduced the average path length by 14% compared with the RRT algorithm, which verified the feasibility and effectiveness of this improved RRT algorithm. The improved RRT algorithm generates more efficient and smoother paths, which can improve the intelligence of the support process by integrating and automating drilling and anchoring and providing reliable support for coal mine intelligence. [ABSTRACT FROM AUTHOR]

To meet the working performance demand of cotton pickers, a hydrostatic power shift composite drive system design is proposed. This study aims to enhance the driving function of the cotton picker in various working conditions and improve its adaptability by combining a hydrostatic speed control system with a mechanical power shift structure. To achieve this, a single variable pump + double variable motor closed circuit is adopted. By adjusting the pump and motor displacement in stages, the driving speed of the cotton picker can be optimized for different working conditions. Additionally, the power shift mechanism is employed to increase the speed range and improve the transmission efficiency, enabling higher speeds to be achieved. Firstly, the main components of the composite drive system were calculated and selected, and then AMESim software was used for modeling and simulation analysis, and the results are as follows: When the cotton picker starts and picking operation stage variable displacement pump + fixed displacement dual motor speed control, the highest driving speed is 8.5 km/h. During the field and road transport operation stage fixed displacement pump + variable displacement dual motor speed regulation, the highest speed of 14.5 km/h was achieved in the field. When transferring to the road, the instant mechanical power shift speed and, the highest speed on the road was up to 27.5 km/h. Finally, the field experiment and speed ratio analysis of the drive system was conducted, and the average error of the experimental speed measurement was 0.588%. The speed ratio matching was in line with the design expectation. The results show that the hydrostatic power shift composite drive system designed in this study has good driving adaptability and can effectively meet the functions of cotton picker field picking, transport operation and road transportation in transit, which provides theoretical support for the design of cotton picker chassis drive system. [ABSTRACT FROM AUTHOR]

Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial role in enabling efficient and reliable operations. Meanwhile, channel measurements and modeling in smart warehouse scenarios are essential for understanding and optimizing wireless communication performance. By accurately characterizing radio channels, communication systems can be better designed and deployed to meet unique challenges in smart warehouse scenarios. In this paper, we present an overview of smart warehouse scenarios and explore channel characteristics in smart warehouse scenarios. We conducted a measurement campaign for millimeter wave radio channels in smart warehouse scenarios. A vector network analyzer-based channel sounder was exploited to record channel characteristics at 28 GHz. Based on the measurements, large-scale channel parameters, including path loss, root-mean-square (RMS) delay spread, and Rician K factor were investigated. The unique channel characteristics in smart warehouse scenarios were explored. [ABSTRACT FROM AUTHOR]

This paper proposes a new metaheuristic algorithm called Particle Swarm-based picking time minimization (Pkt_PSO), ideated for picking time minimization in manual warehouses. As the name suggests, Pkt_PSO is inspired by Particle Swarm Optimization (PSO), and it is specifically designed to minimize the picking time in order case picking contexts. To assess the quality and the robustness of Pkt_PSO, it is compared to five alternative algorithms used as benchmarks. The comparisons are made in nine different scenarios obtained by changing the layout of the warehouse and the length of the picking list. The results of the analysis show that Pkt_PSO has a slower convergence rate and suffers less of early stagnation in local minima; this ensures a more extensive and accurate exploration of the solution space. In fact, the solutions provided by Pkt_PSO are always better (or at least comparable) to the ones found by the benchmarks, both in terms of quality (closeness to the overall best) and reliability (frequency with which the best solution is found). Clearly, as more solutions are explored, the computational time of Pkt_PSO is longer, but it remains compatible with the operational needs of most practical applications. [ABSTRACT FROM AUTHOR]

In the presented article, the initial phase is focused mainly on familiarization with the production focus of individual enterprises and their financial security, which provides a basis for the proposal of possible solutions to shortcomings. A more detailed study of production through simulation studies using Process Simulate software provides a more comprehensive view of production in the assessed enterprises, where the current course of production at workplaces is evaluated in detail, and suggestions are then offered to improve the revealed shortcomings. In the end, there is an assessment of the companies from the point of view of homogeneity, so that it is then possible to make an inter-industry comparison of the revealed errors and shortcomings, with the result of finding common recommendations for a wide industrial spectrum. [ABSTRACT FROM AUTHOR]

In response to the problem of robots needing to perform bin-picking in many industries, a small robot system was developed with a workpiece-position-detection system based on Field-Programmable Gate Array (FPGA) technology. The system integrated object detection and feature matching techniques, bolstered by optimization methods, to enhance its functionality on the FPGA platform. Furthermore, a two-wire control bus was designed specifically for reliable operation in environments characterized by strong interference, drawing inspiration from the Controller Area Network (CAN) bus. The experimental results demonstrated that the system's performance was comparable to that of the point-cloud-algorithm-based bin-picking system, while also exhibiting greater stability. Notably, the cost of the controller was significantly reduced, and the embedded hardware and software design ensured stability and adaptability for deployment in demanding settings, such as small-scale heavy industry factories. [ABSTRACT FROM AUTHOR]

Cellular metabolism (or energetics) and epigenetics are tightly coupled cellular processes. It is arguable that of all the described cancer hallmarks, dysregulated cellular energetics and epigenetics are the most tightly coregulated. Cellular metabolic states regulate and drive epigenetic changes while also being capable of influencing, if not driving, epigenetic reprogramming. Conversely, epigenetic changes can drive altered and compensatory metabolic states. Cancer cells meticulously modify and control each of these two linked cellular processes in order to maintain their tumorigenic potential and capacity. This review aims to explore the interplay between these two processes and discuss how each affects the other, driving and enhancing tumorigenic states in certain contexts. [ABSTRACT FROM AUTHOR]

Controlled fermentation processes have high potential for improving coffee quality. The effect of fermentation temperature on beverage quality was investigated with coffee cultivated at elevations between 1166 and 1928 m. A completely randomized design was carried out at five elevation ranges at 200 m intervals in five farms per elevation range, and two temperatures (15 °C and 30 °C), which were maintained in a temperature-controlled bioreactor. Each temperature-controlled fermentation batch had a spontaneous fermentation batch (control treatment). Microbial identification of LAB and yeast was performed using a Biolog™ MicroStation™ ID System, and cup quality tests were performed following the SCA protocol. Tests conducted at 15 °C showed higher microbial community activity on the substrates used, indicating greater transformation potential than those conducted at 30 °C or those of spontaneous fermentation. According to Wilcoxon and Kruskal–Wallis tests, temperature-controlled fermentation resulted in high-quality coffee for all elevation ranges, with coffee from higher elevations and processed at controlled temperatures of 15 °C receiving the highest cup scores compared to coffee that was subjected to 30 °C. These results suggest that controlled temperature can be used to design standardized fermentation processes in order to enhance coffee quality through differentiated sensory profiles. [ABSTRACT FROM AUTHOR]

Processing the ordered relationships between sequential items is a key element in many cognitive abilities that are important for survival. Specifically, order may play a crucial role in numerical processing. Here, we assessed the existence of a cognitive system designed to implicitly evaluate numerical order, by combining continuous flash suppression with a priming method in a numerical enumeration task. In two experiments and diverse statistical analysis, targets that required numerical enumeration were preceded by an invisibly ordered or non-ordered numerical prime sequence. The results of both experiments showed that enumeration for targets that appeared after an ordered prime was significantly faster, while the ratio of the prime sequences produced no significant effect. The findings suggest that numerical order is processed implicitly and affects a basic cognitive ability: enumeration of quantities. [ABSTRACT FROM AUTHOR]

Currently, microwave radiation is widely used in various chemical processes in order to intensify them and carry out processes within the framework of "green" chemistry approaches. In the last 10 years, there has been a significant increase in the number of scientific publications on the application of microwaves in catalytic reactions and synthesis of nanomaterials. It is known that heterogeneous catalysts obtained under microwave activation conditions have many advantages, such as improved catalytic characteristics and stability, and the synthesis of nanomaterials is accelerated several times compared to traditional methods used to produce catalysts. The present review article is to summarize the results of modern research on the use of microwave radiation for the synthesis of heterogeneous catalytic nanomaterials and discusses the prospects for research in the field of microwave-induced liquid-phase heterogeneous catalysis in hydrogenation. [ABSTRACT FROM AUTHOR]

The open shop scheduling problem (OSSP) is one of the standard scheduling problems. It consists of scheduling jobs associated with a finite set of tasks developed by different machines. In this case, each machine processes at most one operation at a time, and the job processing order on the machines does not matter. The goal is to determine the completion times of the operations processed on the machines to minimize the largest job completion time, called Cmax. This paper proves that each OSSP has associated a path algebra called Brauer configuration algebra whose representation theory (particularly its dimension and the dimension of its center) can be given using the corresponding Cmax value. It has also been proved that the dimension of the centers of Brauer configuration algebras associated with OSSPs with minimal Cmax are congruent modulo the number of machines. [ABSTRACT FROM AUTHOR]

Wheatgrass is a valuable source of nutrients and phytochemicals with therapeutic properties. However, its shorter life span makes it unavailable for use. So, storage-stable products must be developed through processing in order to enhance its availability. Drying is a very important part of the processing of wheatgrass. Thus, in this study, the effect of fluidized bed drying on the proximate, antioxidant, and functional properties of wheatgrass was investigated. The wheatgrass was dried in a fluidized bed drier at different temperatures (50, 55, 60, 65, 70 °C) using a constant air velocity of 1 m/s. With increasing temperature, the moisture content was reduced at a faster rate, and all drying processes took place during the falling rate period. Eight mathematical models under thin layer drying were fitted into the moisture data and were evaluated. The Page model was the most effective in explaining the drying kinetics of wheatgrass, followed by the Logarithmic model. The R2, chi-square, and root mean squared value for Page model was 0.995465–0.999292, 0.000136–0.0002, and 0.013215–0.015058, respectively. The range of effective moisture diffusivity was 1.23–2.81 × 10−10 m2/s, and the activation energy was 34.53 kJ/mol. There was no significant difference in the proximate composition of was seen at different temperatures. The total phenolic content (117.16 ± 0.41–128.53 ± 0.55 mgGAE/g), antioxidant activity (33.56 ± 0.08–37.48 ± 0.08% (DPPH), and FRAP (1.372 ± 0.001–1.617 ± 0.001 mgAAE/g) increased with the rise in temperature. A significant increase was observed in functional properties, except for the rehydration ratio, which decreased with rising temperature. The current study suggests that fluidized bed drying improves the nutritional retention of wheatgrass with good antioxidant activity and functional properties that can be used to make functional foods. [ABSTRACT FROM AUTHOR]