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Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem's criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively. [ABSTRACT FROM AUTHOR]

The development of e-commerce faces many problems, among which the real-time information exchange between customers and websites is the most urgent. To study the application of software robots in the real-time processing of e-commerce orders. According to the working mechanism of ALICE, JAVAPROGRAMD, AIML technology, database technology, knowledge base principle, Rule of inference, and reasoning strategies are used to design the structure, workflow, knowledge processing flow, and Inference engine supporting human-computer automatic negotiation of the software robot. Establishing a rule base is the acquisition, induction, and organization of knowledge related to order processing. It requires learning and analyzing the problems and related materials to be solved, simulating the actual process of order processing, and extracting useful processing rules and processes from it. The rules in the system are not organized in a simple list form, but have a certain hierarchical structure. This way, when using these rules for reasoning, the hierarchy is clear and it is easy to modify and add rules. The hierarchical structure of the rules referred to here is to simulate the thinking activities of the human brain in the process of order processing, and organize them in a certain way. Based on the basic rules of order processing, the process of order processing, the rational performance of the order and the possibility of necessary events, so as to realize the structure of the reasoning machine. Expands the application of Chatbot ALICE, and provides a new tool for real-time online negotiation and negotiation of e-commerce order processing. The core part of the e-commerce order processing software robot is the human-machine automatic interaction module. Database module, knowledge base module and rule base module provide the necessary data, knowledge and rule basis for its realization, while the implementation of reasoning machine provides the possibility for the intelligence and dynamic interaction of human-machine automatic interaction module. [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]

Predictive coding accounts of perception state that the brain generates perceptual predictions in the service of processing incoming sensory data. These predictions are hypothesized to be afforded by the brain's ability to internalize useful patterns, that is, statistical regularities, from the environment. We have previously argued that the N300 ERP component serves as an index of the brain's use of representations of (real-world) statistical regularities. However, we do not yet know whether overt attention is necessary in order for this process to engage. We addressed this question by presenting stimuli of either high or low real-world statistical regularity in terms of their representativeness (good/bad exemplars of natural scene categories) to participants who either fully attended the stimuli or were distracted by another task (attended/distracted conditions). Replicating past work, N300 responses were larger to bad than to good scene exemplars, and furthermore, we demonstrate minimal impacts of distraction on N300 effects. Thus, it seems that overtly focused attention is not required to maintain the brain's sensitivity to real-world statistical regularity. Furthermore, in an exploratory analysis, we showed that providing additional, artificial regularities, formed by altering the proportions of good and bad exemplars within blocks, further enhanced the N300 effect in both attended and distracted conditions, shedding light on the relationship between statistical regularities learned in the real world and those learned within the context of an experiment. [ABSTRACT FROM AUTHOR]

In this article, we improve the behavior of nonparametric Shewhart-type control charts, which employ order statistics and multiple runs-type rules. The proposed class of monitoring schemes includes some existing control charts. In addition, new distribution-free monitoring schemes that pertain to the class, are set up and examined extensively. Explicit expressions for determining the variability and the mean of the run length distribution for the enhanced control charts are additionally delivered. As an example, a real-life managerial application is considered, where the proposed framework is implemented in order to enhance the provided services of a company under a supply chain management environment. Based on numerical comparisons, we draw the conclusion that the new charts outperform their competitors in identifying potential changes in the fundamental distribution in almost all cases considered. [ABSTRACT FROM AUTHOR]

Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanismunderpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, ourmodel reproduces the the experimental finding that as themaintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM. [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]

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet [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]

In the traditional picking and sorting process of fragrant pears, the quality control process is not perfect, and the identification and extraction methods are not mature enough. By combining the Internet of Things (IoT) and image detection technologies, the image data of pear planting area in the orchard was obtained, and the image of pear was preprocessed, denoised, and feature extracted by the visual background extraction algorithm. In order to verify the actual effect of the optimized identification and extraction method for fragrant pears using IoT and image detection technologies, a comparative experiment was conducted. The results show that compared with the traditional pear recognition and extraction method, the optimized pear recognition and extraction method has a better predictive effectiveness in the process of pear image processing, and the recall and accuracy rates are improved by approximately 8% and 12.7%, respectively. This verifies the reliability and efficiency of the identification extraction method for fragrant pear based on IoT and image detection technologies. In this paper, the traditional pear identification and extraction methods are improved by IoT technology and UAV (unmanned aerial vehicle), which improves the picking efficiency and sorting quality of pear picking robots. [ABSTRACT FROM AUTHOR]

In order processing, consecutive sequences (e.g., 1‐2‐3) are generally processed faster than nonconsecutive sequences (e.g., 1‐3‐5) (also referred to as the reverse distance effect). A common explanation for this effect is that order processing operates via a memory‐based associative mechanism whereby consecutive sequences are processed faster because they are more familiar and thus more easily retrieved from memory. Conflicting with this proposal, however, is the finding that this effect is often absent. A possible explanation for these absences is that familiarity may vary both within and across sequence types; therefore, not all consecutive sequences are necessarily more familiar than all nonconsecutive sequences. Accordingly, under this familiarity perspective, familiar sequences should always be processed faster than unfamiliar sequences, but consecutive sequences may not always be processed faster than nonconsecutive sequences. To test this hypothesis in an adult population, we used a comparative judgment approach to measure familiarity at the individual sequence level. Using this measure, we found that although not all participants showed a reverse distance effect, all participants displayed a familiarity effect. Notably, this familiarity effect appeared stronger than the reverse distance effect at both the group and individual level; thus, suggesting the reverse distance effect may be better conceptualized as a specific instance of a more general familiarity effect. [ABSTRACT FROM AUTHOR]

Theoretical background: The literature analysis focuses on the importance of spatial design of warehouses, especially the retrieval zone. Research shows that warehouse layout has a key impact on operational efficiency, especially in manual order picking. The authors discuss various approaches that are mainly aimed at minimizing the average distance traveled by warehouse employees when picking orders. Other studies also include warehouse workers’ movements and operations planning. The authors note that existing research focuses mainly on operational efficiency, ignoring social and environmental aspects. Hence, there is a need to develop a methodology that will take into account these additional aspects of designing a sustainable warehouse. Purpose of the article: The article in the context of scientific research aims to present the methodology of designing a spatial warehouse system in terms of sustainable development. In a practical context, a tool for assessing the effectiveness of sustainable design was developed. Research methods: A case study was chosen as the research method. Observation and interviews with employees were used. These methods allowed for a better understanding of the analyzed issue and occurring phenomena. In addition, the methods used allowed to verify the developed method of spatial design of the warehouse in terms of sustainable development. Main findings: By achieving scientific and practical goals, the article can contribute to promoting sustainable design in business and architectural practice and contribute to building a more sustainable and effective work environment. [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]

Rapid, high-precision pickup of microseismic P- and S-waves is an important basis for microseismic monitoring and early warning. However, it is difficult to provide fast and highly accurate pickup of micro-seismic P- and S-waves arrival-time. To address this, the study proposes a lightweight and high-precision micro-seismic P- and S-waves arrival times picking model, lightweight adversarial U-shaped network (LAU-Net), based on the framework of the generative adversarial network, and successfully deployed in low-power devices. The pickup network constructs a lightweight feature extraction layer (GHRA) that focuses on extracting pertinent feature information, reducing model complexity and computation, and speeding up pickup. We propose a new adversarial learning strategy called application-aware loss function. By introducing the distribution difference between the predicted results and the artificial labels during the training process, we improve the training stability and further improve the pickup accuracy while ensuring the pickup speed. Finally, 8986 and 473 sets of micro-seismic events are used as training and testing sets to train and test the LAU-Net model, and compared with the STA/LTA algorithm, CNNDET+CGANet algorithm, and UNet++ algorithm, the speed of each pickup is faster than that of the other algorithms by 11.59ms, 15.19ms, and 7.79ms, respectively. The accuracy of the P-wave pickup is improved by 0.221, 0.01, and 0.029, respectively, and the S-wave pickup accuracy is improved by 0.233, 0.135, and 0.102, respectively. It is further applied in the actual project of the Shengli oilfield in Sichuan. The LAU-Net model can meet the needs of practical micro-seismic monitoring and early warning and provides a new way of thinking for accurate and fast on-time picking of micro-seismic P- and S-waves. [ABSTRACT FROM AUTHOR]

Robot technology is considered one of the most promising technologies to achieve intelligent production, with picking robots being the most common type. Picking robots are highly integrated mechatronic systems, which autonomously complete tasks including picking, carrying, and sorting. The application of picking robots enhances the efficiency of production across various environments. In this paper, a classification-design-optimization-application integrated framework of picking robots is addressed, contributing to theoretical research and application of picking robots. Classification of picking robot is established and analyzed considering the differences of overall form and end-effector to guide the development of research strategies and approaches of picking robot. Design of picking robot is described from different aspects of the target, structure, monitoring, and control design. Additionally, the commonly used optimization methods for picking robots, including structural parameters, kinematics and dynamics, and energy consumption, are discussed. Finally, the application of picking robots under different environments is expounded, and the challenges and prospects of picking robots are highlighted. This study could present theoretical support and application measures for picking robot study and technology development. [ABSTRACT FROM AUTHOR]

In order to handle second order lead processes with time delay, this paper provides a unique dominant pole placement based filtered PID controller design approach. This method does not require any finite term approximation like Pade to obtain the quasi-polynomial characteristic polynomial, arising due to the presence of the time delay term. The continuous time second order plus time delay systems with zero (SOPTDZ) are discretized using a pole-zero matching method with specified sampling time, where the transcendental exponential delay terms are converted into a finite number of poles. The pole-zero matching discretization approach with a predetermined sampling period is also used to discretize the continuous time filtered PID controller. As a result, it is not necessary to use any approximate discretization technique, such as Euler or Tustin, to derive the corresponding discrete time PID controller from its continuous time counterpart. The analytical expressions for discrete time dominant pole placement based filtered PID controllers are obtained using the coefficient matching approach, while two distinct kinds of non-dominant poles, namely all real and all complex conjugate, have been taken into consideration. The stabilizable region in the controller and design parameter space for the chosen class of linear second order time delay systems with lead is numerically approximated using the particle swarm optimization (PSO) based random search technique. The efficacy of the proposed method has been validated on a class of SOPTDZ systems including stable, integrating, unstable processes with minimum as well as non-minimum phase zeros. [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]

This paper discusses a case study of a zone-picking system in a distribution centre. In particular, we designed a basic simulation model for analysing the system using Simio and connected the model to the MySQL database using the existing and new steps in Simio. A ranking and selection problem was subsequently formulated to determine the capacity of the system that maximises the expected throughput while satisfying the constraints on: (1) the expected maximum utilisation, (2) expected time-averaged number of boxes in the main conveyor, and (3) expected flow time. To solve the problem under a limited simulation budget, we implemented an optimal computing budget allocation procedure, which may work in the presence of stochastic constraints. The experimental results demonstrate that our approach can more efficiently and effectively determine the capacity of the system than the equal allocation scheme. [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]

The human mitochondrial genome is transcribed into two RNAs, containing mRNAs, rRNAs and tRNAs, all dedicated to produce essential proteins of the respiratory chain. The precise excision of tRNAs by the mitochondrial endoribonucleases (mt-RNase), P and Z, releases all RNA species from the two RNA transcripts. The tRNAs then undergo 3′-CCA addition. In metazoan mitochondria, RNase P is a multi-enzyme assembly that comprises the endoribonuclease PRORP and a tRNA methyltransferase subcomplex. The requirement for this tRNA methyltransferase subcomplex for mt-RNase P cleavage activity, as well as the mechanisms of pre-tRNA 3′-cleavage and 3′-CCA addition, are still poorly understood. Here, we report cryo-EM structures that visualise four steps of mitochondrial tRNA maturation: 5′ and 3′ tRNA-end processing, methylation and 3′-CCA addition, and explain the defined sequential order of the tRNA processing steps. The methyltransferase subcomplex recognises the pre-tRNA in a distinct mode that can support tRNA-end processing and 3′-CCA addition, likely resulting from an evolutionary adaptation of mitochondrial tRNA maturation complexes to the structurally-fragile mitochondrial tRNAs. This subcomplex can also ensure a tRNA-folding quality-control checkpoint before the sequential docking of the maturation enzymes. Altogether, our study provides detailed molecular insight into RNA-transcript processing and tRNA maturation in human mitochondria. Mitochondrial tRNAs are less structurally stable than nuclear tRNAs, and their maturation pathway is unique. Here, the authors reveal how human mitochondrial precursor tRNAs are recognised, processed, methylated and prepared for full functionality in mitochondrial translation. [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]

The most labor-intensive and time-consuming part of warehouse operations is order picking. This paper proposes a correlation-based storage location assignment (CBSLA) approach to minimize the travel distance of the picker in a picker-to-parts warehouse. At first, the proposed CBSLA approach forms some groups of stock-keeping units (SKUs) for different warehouse aisles. Then these groups of SKUs are assigned to the storage locations considering both the correlations between SKUs in a group and the correlation between groups of SKUs for efficient order picking. The effectiveness of the proposed method is measured for various warehouse configurations using simulation and compared with other well-known storage allocation methods. [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]

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]

Copyright of Direito e Práxis is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explored sentence processing in two typologically distinct languages: Korean, a verb-final language, and Tongan, a verb-initial language. The first experiment revealed that in Korean, sentences arranged in the scrambled OSV (Object, Subject, Verb) order were processed more slowly than those in the canonical SOV order, highlighting a scrambling effect. It also found that sentences with subject topicalization in the SOV order were processed as swiftly as those in the canonical form, whereas sentences with object topicalization in the OSV order were processed with speeds and accuracy comparable to scrambled sentences. However, since topicalization and scrambling in Korean use the same OSV order, independently distinguishing the effects of topicalization is challenging. In contrast, Tongan allows for a clear separation of word orders for topicalization and scrambling, facilitating an independent evaluation of topicalization effects. The second experiment, employing a maze task, confirmed that Tongan’s canonical VSO order was processed more efficiently than the VOS scrambled order, thereby verifying a scrambling effect. The third experiment investigated the effects of both scrambling and topicalization in Tongan, finding that the canonical VSO order was processed most efficiently in terms of speed and accuracy, unlike the VOS scrambled and SVO topicalized orders. Notably, the OVS object-topicalized order was processed as efficiently as the VSO canonical order, while the SVO subject-topicalized order was slower than VSO but faster than VOS. By independently assessing the effects of topicalization apart from scrambling, this study demonstrates that both subject and object topicalization in Tongan facilitate sentence processing, contradicting the predictions based on movement-based anticipation. [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]

Establishing warehouses is necessary in societies where commercial activities have grown to the extent that efficient storage becomes a prerequisite for seamless exchange. Variable holding cost is a pivotal factor influencing warehouse costs, as is the dynamic nature of holding expenses, which tend to increase over time. This study focuses on an inventory model designed for goods subject to degradation, stored across two distinct warehouses (rented and owned) within a partial backlogging and inflationary economic environment. The cost associated with holding these goods is linearly dependent on time. The demand pattern in the inventory model for goods follows an exponential trajectory over time. The primary objective of the proposed model is to forecast the optimal quantity and the corresponding timeframe, thereby minimizing the overall cost. Rigorous validation of the model's outcomes is undertaken through sensitivity analysis utilizing MATLAB R2017b software, ensuring the robustness and reliability of the findings. [ABSTRACT FROM AUTHOR]

A queueing system with the discipline of flexible limited sharing of the server is considered. This discipline assumes the admission, for a simultaneous service, of only a finite number of orders, as well as the use of a reduced service rate when the bandwidth required by the admitted orders is less than the total bandwidth of the server. The orders arrive following a batch-marked Markov arrival process, which is a generalization of the well-known MAP (Markov arrival process) to the cases of heterogeneous orders and batch arrivals. The orders of different types have different preemptive priorities. The possibility of an increase or a decrease in order priority during the service is suggested to be an effective mechanism to prevent long processing orders from being pushed out of service by justarrived higher-priority orders. Under a fixed priority scheme and a mechanism of dynamic change of the priorities, the stationary analysis of this queueing system is implemented by considering a suitable multidimensional continuous-time Markov chain with a generator that has an upper Hessenberg structure. The possibility of the optimal restriction on the number of simultaneously serviced orders is numerically demonstrated. [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]

In e-commerce logistics, it is critical to enhance the efficiency of the order-picking system. Motivated by applications of automatic logistics, we consider the mobile robot based order batching problem. In this problem, mobile robots carry shelves to the picking station for order picking and then return them. The objective is to reduce shelf movements while minimizing the number of delayed orders. We introduce a hyper-heuristic method based on deep reinforcement learning to optimize the order batching strategy in the system. The proposed method adaptively selects the order batching strategy, significantly improving the sequential decision-making process in order picking. Through extensive tests, we demonstrate the superiority of the proposed method over several existing heuristic methods in a range of test scenarios. The results show that the proposed method outperforms other existing heuristic methods in a range of test scenarios, offering more stable and effective solutions. This study is a pioneer in the application of deep reinforcement learning to the mobile robot based order batching problem, offering a novel perspective and methodology to overcome the challenges of sequential decision-making optimization in order picking systems. [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]

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]

The article presents a mathematical model and algorithm for optimizing the picking process in a warehouse. The algorithm aims to minimize transportation costs and maximize efficiency by determining which shelves should be transported to the picking station. It uses a hybrid intelligent approach, combining a heuristic algorithm and a genetic algorithm. The algorithm is validated through simulations and found to outperform other algorithms. The article concludes that the proposed algorithm improves picking efficiency and can be applied in various contexts. [Extracted from the article]

Most bats hunt insects on the wing at night using echolocation as their primary sensory modality, but nevertheless maintain complex eye anatomy and functional vision. This raises the question of how and when insectivorous bats use vision during their largely nocturnal lifestyle. Here, we test the hypothesis that the small insectivorous bat, Myotis daubentonii, relies less on echolocation, or dispenses with it entirely, as visual cues become available during challenging acoustic noise conditions. We trained five wild-caught bats to land on a spherical target in both silence and when exposed to broad-band noise to decrease echo detectability, while light conditions were manipulated in both spectrum and intensity. We show that during noise exposure, the bats were almost three times more likely to use multiple attempts to solve the task compared to in silent controls. Furthermore, the bats exhibited a Lombard response of 0.18 dB/dBnoise and decreased call intervals earlier in their flight during masking noise exposures compared to in silent controls. Importantly, however, these adjustments in movement and echolocation behaviour did not differ between light and dark control treatments showing that small insectivorous bats maintain the same echolocation behaviour when provided with visual cues under challenging conditions for echolocation. We therefore conclude that bat echolocation is a hard-wired sensory system with stereotyped compensation strategies to both target range and masking noise (i.e. Lombard response) irrespective of light conditions. In contrast, the adjustments of call intervals and movement strategies during noise exposure varied substantially between individuals indicating a degree of flexibility that likely requires higher order processing and perhaps vocal learning. [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]

This study aimed to enhance the efficiency of the order picking process in Carclasse's parts warehouse in Braga to achieve this objective, a comprehensive analysis of all warehouse processes was conduct. The initial analysis revealed that approximately 59% of the references in stock were considered obsolete, as they had had zero turnover in the last 5 years. Additionally, a high error rate was identify in the order picking process, with around 6% of references being pick incorrectly. These errors led to discrepancies between the physical stock levels and the system records since some order picking errors went undetected. Moreover, queues were observed among internal customers when requesting material retrieval, which hindered workflow. Based on these findings, improvements were proposed to address the identified issues. One of the key proposals was the implementation of a Barcode scanning system to increase the digitalization of warehouse processes and rectify order picking and stock errors. Additionally, to reduce internal customers' waiting time, it was suggested to shift the responsibility of part identification for collection to mechanics and increase from 2 to 4 the number of identification stations. It is crucial to highlight that the implementation of the Barcode scanning system can not only reduce order-picking errors but also contributed to the overall optimization of Carclasse's parts warehouse. With the digitalization of processes, it is expected an enhancement in traceability and inventory accuracy, avoiding discrepancies between physical stock and system records. This can resulted in greater control and reliability of inventory data, providing a solid foundation for operational and strategic decision-making. [ABSTRACT FROM AUTHOR]

With increasing customer expectations in close shopping possibilities and shorter lead time in e-commerce, the home furnisher companies have a higher interest in automated order picking systems. Although technologies for automated order picking processes are developing rapidly, picking of the unergonomically large and heavy flatpacks is mainly conducted manually. This is due to the flexibility of manual processes, but also due to the long planning and evaluation time as the overall system is more complex with automation. This paper presents the conceptual architecture of a Reference Model to support the planning and evaluation of automated order picking systems of heavy flatpacks for retail and wholesale fulfilment units. The aim of the Reference Model is to provide a systematic approach in the project management, mainly focusing on the planning and evaluation process of automated order picking systems by supporting the decision-making and enabling the earlier integration of simulation in the planning phase. [ABSTRACT FROM AUTHOR]

We report the results of one visual-world eye-tracking experiment and two referent selection tasks in which we investigated the effects of information structure in the form of prosody and word order manipulation on the processing of subject pronouns er and der in German. Factors such as subject-hood, focus, and topicality, as well as order of mention have been linked to an increased probability of certain referents being selected as the pronoun's antecedent and described as increasing this referent's prominence, salience, or accessibility. The goal of this study was to find out whether pronoun processing is primarily guided by linguistic factors (e.g., grammatical role) or nonlinguistic factors (e.g., first-mention), and whether pronoun interpretation can be described in terms of referents' "prominence" / "accessibility" / "salience." The results showed an overall subject preference for er, whereas der was affected by the object role and focus marking. While focus increases the attentional load and enhances memory representation for the focused referent making the focused referent more available, ultimately it did not affect the final interpretation of er, suggesting that "prominence" or the related concepts do not explain referent selection preferences. Overall, the results suggest a primacy of linguistic factors in determining pronoun resolution. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There has been a growing interest in halal-related ontology research in recent years, as ontology has gained recognition in the halal industry. This paper discusses the development of a flavouring ontology that will assist halal management auditors in predicting the halal status of flavours in order to process food producers’ applications for halal certification. The development of a flavouring ontology is based on multiple references, because the auditors of halal management divisions must consult a variety of sources independently in order to determine the halal status of flavourings. The process includes 1) determining the ontology goal and scope, 2) building ontologies, and 3) evaluating the ontologies. The researcher used Protégé to design the ontologies, and Phyton was used to develop a prototype based on flavouring ontology. The developed ontology consists of four classes, nine sub-classes, and 11 relationships. The evaluation of the ontology using the prototype revealed that the majority of experts were satisfied with the information generated by the ontology in the prototype, particularly in relation to synonyms and the hierarchical structure of a flavour. However, the experts suggest improvements in terms of flavour metadata, especially on raw materials and natural occurrence data, so that the flavour information retrieved is comprehensive and accurate. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The International Journal of Information Technology & Decision Making has published its fourth issue, featuring 10 original papers from distinguished authors in eight countries. The papers cover a range of topics, including deep learning for malaria detection, social banking, multi-attribute decision-making, video event detection, ERP system evaluation, COVID-19 vaccine analysis, order picking systems, big data analytics in central banking, and estimation of missing entries in preference matrices. The articles provide valuable insights and innovative solutions in the field of information technology and decision making. [Extracted from the article]

Determining if a sequence of numbers is ordered or not is one of the fundamental aspects of numerical processing linked to concurrent and future arithmetic skills. While some studies have explored the neural underpinnings of order processing using functional magnetic resonance imaging, our understanding of electrophysiological correlates is comparatively limited. To address this gap, we used a three-item symbolic numerical order verification task (with Arabic numerals from 1 to 9) to study event-related potentials (ERPs) in 73 adult participants in an exploratory approach. We presented three-item sequences and manipulated their order (ordered vs. unordered) as well as their inter-item numerical distance (one vs. two). Participants had to determine if a presented sequence was ordered or not. They also completed a speeded arithmetic fluency test, which measured their arithmetic skills. Our results revealed a significant mean amplitude difference in the grand average ERP waveform between ordered and unordered sequences in a time window of 500–750 ms at left anterior-frontal, left parietal, and central electrodes. We also identified distance-related amplitude differences for both ordered and unordered sequences. While unordered sequences showed an effect in the time window of 500–750 ms at electrode clusters around anterior-frontal and right-frontal regions, ordered sequences differed in an earlier time window (190–275 ms) in frontal and right parieto-occipital regions. Only the mean amplitude difference between ordered and unordered sequences showed an association with arithmetic fluency at the left anterior-frontal electrode. While the earlier time window for ordered sequences is consistent with a more automated and efficient processing of ordered sequential items, distance-related differences in unordered sequences occur later in time. [ABSTRACT FROM AUTHOR]

Summary: Background: The precise estimation of cases with significant fibrosis (SF) is an unmet goal in non‐alcoholic fatty liver disease (NAFLD/MASLD). Aims: We evaluated the performance of machine learning (ML) and non‐patented scores for ruling out SF among NAFLD/MASLD patients. Methods: Twenty‐one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically‐proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological‐SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1‐score as model‐selection criteria). Results: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological‐SF were included in the study. Patients with SFvs.no‐SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p < 0.001, each). ML models showed 7%–12% better discrimination than FIB‐4 to detect SF. Optimised random forest (RF) yielded best NPV/F1 in overall set (0.947/0.754), test set (0.798/0.588) and validation set (0.852/0.559), as compared to FIB4 in overall set (0.744/0.499), test set (0.722/0.456), and validation set (0.806/0.507). Compared to FIB‐4, RF could pick 10 times more patients with SF, reduce unnecessary referrals by 28%, and prevent missed referrals by 78%. Age, AST, ALT fasting plasma glucose, and platelet count were top features determining the SF. Sequential use of SAFE < 140 and FIB4 < 1.2 (when SAFE > 140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set). Conclusions: ML with clinical, anthropometric data and simple blood investigations perform better than FIB‐4 for ruling out SF in biopsy‐proven Asian NAFLD/MASLD patients. [ABSTRACT FROM AUTHOR]