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As a kind of software system, the Event-Based Systems (EBS) respond to events rather than executing a predefined sequence of instructions. Events usually occur in real time, so it is crucial that they are processed in the correct order and within temporal constraints. The objective of this work is to propose an approach to check if events of EBS at runtime preserve the specification of temporal constraints. To form the approach by logic process, we have formalized the EBS model, through which, we have proved that the complexity of the checking algorithms is only polynomial. The approach has been implemented as a tool (VER) to check EBS at runtime automatically. The results of the proposed method are illustrated by checking a real-world Event Driven Architecture (EDA) application, an Intelligent transportation system. [ABSTRACT FROM AUTHOR]

Route planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard-like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy-saving routes. Although the traditional route planning algorithms such as the classical Q-learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi-AGV route planning approach to solving the AGV route planning problem in the chessboard-like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low-dimensional adjacency-reward matrix for route planning. This algorithm improves the efficiency of classical Q-learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi-AGV online collision-free route planning algorithm based on these considerations for dynamic route planning for multi-AGVs operating in a large-scale warehouse. The proposed algorithms are validated with several case studies. [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]

Production conditions are complex and varied for a number of reasons. Models for defect diagnosis may perform worse as a result of the distributional mismatch between test data and training data. In order to diagnose process faults, it is crucial to take into account the fact that data exhibits varied distribution characteristics under various conditions. In the case of multiple operating conditions, the cross‐domain problem caused by different data distributions can degrade the performance of deep learning‐based fault diagnosis models. To overcome this challenge, a joint alignment network preserving structural information (JANSI) method is proposed. To extract richer and fine‐grained domain‐invariant features, the structural information preservation is proposed, which combines domain labels, category labels, and data distribution structures. To increase intra‐class compactness and inter‐class separability, class centre alignment is proposed. The effectiveness of the method on the cross‐domain unsupervised fault diagnosis problem is verified through three case studies. [ABSTRACT FROM AUTHOR]

This paper studies the fire performance of steel warehouse with their main elements insulated with fire protection boards composed of gypsum‐based mortars with perlite using the heat transfer and thermomechanical analysis in finite element (FE) software. The primary aim is to identify the optimal thickness of the fire protections boards based on the assigned fire model in thermal analysis. Besides that, a parametric study is also conducted by considering various factors that are often overlooked or not explicitly mentioned in fire design guidelines, such as EN 1991‐1‐2. The findings facilitate the development of optimized fire design approaches, ensuring the structural integrity and safety of steel warehouses under fire conditions. [ABSTRACT FROM AUTHOR]

Objectives: The Veggie Van model is a mobile market model that is efficacious in increasing fruit and vegetable consumption for lower-income participants. The model is currently being evaluated for its effectiveness in a multi-state trial. Preliminary implementation data, collected through process measures surveys and implementation interviews, indicate that there are several barriers to implementation among partner organizations and implementation fidelity to the Veggie Van model was low. Consideration and planning for implementation ought to occur early and often throughout the research process order to ensure Veggie Van model effectiveness. This paper describes the step-by-step process for creating strategies to enhance implementation of Veggie Van model components. Methods: Implementation mapping is a systematic process to develop implementation strategies through engagement with key stakeholders. We conducted a series of interviews (n = 31 representatives) with partner organizations (n = 8) to identify facilitators and barriers to Veggie Van model implementation. We then applied interview findings to an Implementation Mapping process to develop theory and practice-driven strategies to be integrated into existing implementation tools and technical assistance. Results: We identified implementation outcomes (e.g., staff implement the Veggie Van model component of nutrition education with fidelity) and performance objectives (e.g., offer nutrition education, in the form of food lessons and/or food demonstrations, at least bi-weekly) to achieve them. We conducted a secondary qualitative analysis of the findings from implementation interviews with partner organizations to identify behavioral determinants (e.g., attitudinal beliefs, social support) which were combined with the performance objectives to generate change objectives (e.g., view the Veggie Van model as advantageous to an organization and communities served). To achieve the change objectives, we developed implementation strategies that would be integrated into existing Veggie Van training resources including an online toolkit, webinars and trainings, an annual mobile market conference, and technical assistance. Conclusion: The development of theory and practice-driven implementation strategies will enable us to improve our implementation tools, thereby improving fidelity to the Veggie Van model among organizations and increasing the likelihood of its effectiveness. Detailing the design of a multifaceted implementation strategy using Implementation Mapping also provides a model to design similar strategies for other community-based interventions. [ABSTRACT FROM AUTHOR]

The object of research is a distributed order processing system for a restaurant chain. The subject of the research is the analysis of the use of Redis for managing event queues in distributed systems. When implementing a distributed order processing system in a restaurant chain with a possible load of up to 20,000 users per day, the Redis system was used. Management of 9 distributed subsystems was organized through Redis. This solution showed an increase in the performance of the system under heavy load (from 50 transactions per second), but the response time of the system in some cases of its operation was longer than without using Redis. When working systems using Redis, it is necessary to take into account the amount of data with which Redis will work, since it does not exceed the amount of RAM, the absence of differentiation into users and groups, and the absence of a query language, which is replaced by a key-value scheme. This research is aimed at analyzing the operation of the system during trial operation under real load. We compared the operation of a configured system with Redis enabled and disabled. The main indicators for the analysis were the system response time and the maximum request execution time. The research was carried out for 2 weeks, the first week using the system settings with disabled Redis, the second – with enabled Redis. We selected 2 days with a similar load on the system to each other. Especially indicative are the results of comparing the durations of the longest queries, which show an almost constant value of the duration for the system in the mode of enabled Redis. The hypothesis of an increase in the system response time at low loads was confirmed, but this value not only leveled off at a load of 500 unique users but also became less at loads of 1000 unique users. [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]

This paper reports results of an experimental study on hydrodynamic processes when water is dropped from a height, as well as their influence on the movement of liquid, in order to optimize the processes of aviation firefighting. The main parameters of water dispersion were determined based on a series of experiments. It was established that the average diameter of drops when dropped from a height of 10 meters is 2.5 mm, with a maximum spraying distance of up to 15 meters. The size distribution of the droplets showed that 30 % of the droplets have a diameter of less than 2 mm, 50 % are between 2 and 5 mm, and 20 % are more than 5 mm. During the experiments, it was measured that the time for pouring water from the container is 3 seconds, and the speed of movement of water fractions when dropped from a height of 10 meters is 9.8 m/s. With an increase in the discharge height by 5 meters, the speed of water movement increased by 20 %. One of the key points of the study is the improvement of the Torricelli equation by introducing the coefficient K (0.85), which takes into account the viscosity and density of water, which improved the accuracy of calculations by 15 % compared to the standard equation. Python was used to process and interpret the video data, along with libraries such as OpenCV for image processing, NumPy for high-performance mathematical computations, and Matplotlib for data visualization. The resulting data open up new prospects for devising aerial firefighting strategies, providing a 20 % increase in coverage area, and a 25 % reduction in firefighting time. Torricelli’s equation was improved; a new procedure was devised for the experimental determination of water dispersion parameters, which has an important practical application in the field of fire safety. In the field of fire safety, the data obtained could be used to improve aviation firefighting strategies, which would allow for greater efficiency and safety. [ABSTRACT FROM AUTHOR]

In view of the higher demand and customer expectations on the speed and accuracy of e-commerce logistics service as well as the repetitive and time-consuming nature of manual order processing operation. This paper proposes a robotic process automation (RPA) model to liberate human resources over time-consuming, inefficient, non-value-added, and repetitive operational processes occupying workforces. This paper proposed an RPA model that integrates three functional RPA bots in (1) tracking order status, (2) capturing order data, and (3) verifying order data to increase the efficiency and accuracy of logistics operation and the order-handling process in small- and medium-sized enterprise (SME) logistics company. A case study was conducted on an SME logistics service providing customers with inbound and outbound operation services. The result demonstrated that the proposed model significantly improves logistics operations performance against a human approach concerning key indicators after implementation. Logistics companies could free up the workforce for value-creating activities in value-added services. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess the key factors that have the greatest impact on the inventory level, with a special emphasis on packaging products in the beverage industry in seven different countries. The research methodology includes: (1) Defining the pool of Key Inventory Factors, (2) Defining the final list of Key Inventory Factors, (3) Collecting data, (4) Analysis of the data (according to four different aspects) and recommendations. This study shows that these three factors: Holding costs, Information flow and Systems for order processing, inventory monitoring and inventory control (Systems for OPIMIC) have a significant correlation with the company's success. The paper offers additional analysis regarding the differences among them when it comes to their treatment in the Inventory Management Systems. [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]

We introduce a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array. The former, signatured and dispersed source array has much less constraints in the encoding with operational flexibility, allowing non‐uniform sampling and non‐patterned shooting both in the space and time dimension. The latter, modulated and dispersed source array allows straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We demonstrate our method by synthesizing the blended acquisition followed by deblended‐data‐reconstruction processing in order to discuss the virtues. The examples show that this method could make the blended‐acquisition encoding and operations indeed simple and robust; the same is true for the deblending processing. [ABSTRACT FROM AUTHOR]

In spatial count data analysis, modeling with a multilateral lattice structure presents some important challenges. They include both the model construction and the estimation of the model parameters, since the structure accommodates the left, right, top, bottom, and diagonal site effects. Thus, the multilateral spatial process unifies all the popular spatial subclasses that include the unilateral, Rook, Bishop, and Queen models and, hence, makes it suitable for a wide variety of applications. This paper introduces a first‐order multilateral integer‐valued spatial process, based on a binomial thinning mechanism and some innovation term, under both stationary and nonstationary conditions. The estimation of parameters is handled by the conditional maximum likelihood estimation (CML) approach. Simulation experiments are implemented to assess the consistency of the CML estimators in the stationary and nonstationary multilateral spatial model and its subclasses, based on different grid sizes and under both covariate and noncovariate designs. The proposed model, along with its subclasses are applied to real datasets. [ABSTRACT FROM AUTHOR]

We consider the (discrete) parabolic Anderson model ∂u(t,x)/∂t=Δu(t,x)+ξt(x)u(t,x)$\partial u(t,x)/\partial t=\Delta u(t,x) +\xi _t(x) u(t,x)$, t≥0$t\ge 0$, x∈Zd$x\in \mathbb {Z}^d$, where the ξ‐field is R$\mathbb {R}$‐valued and plays the role of a dynamic random environment, and Δ is the discrete Laplacian. We focus on the case in which ξ is given by a properly rescaled symmetric simple exclusion process under which it converges to an Ornstein–Uhlenbeck process. Scaling the Laplacian diffusively and restricting ourselves to a torus, we show that in dimension d=3$d=3$ upon considering a suitably renormalised version of the above equation, the sequence of solutions converges in law. As a by‐product of our main result we obtain precise asymptotics for the survival probability of a simple random walk that is killed at a scale dependent rate when meeting an exclusion particle. Our proof relies on the discrete theory of regularity structures of Erhard and Hairer and on novel sharp estimates of joint cumulants of arbitrary large order for the exclusion process. We think that the latter is of independent interest and may find applications elsewhere. [ABSTRACT FROM AUTHOR]

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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]

Agile development methods have become a standard in the software industry, including in large-scale projects. These methods share a set of underlying assumptions that distinguish them from more traditional plan-driven approaches. In this article, we adopt Alvesson and Sandberg's problematization approach to challenge three key assumptions that are prevalent in the large-scale agile literature: (1) agile and plan-driven methods are mutually exclusive; (2) self-managing and hierarchically organized teams are mutually exclusive; and (3) agile methods can scale through simple linear composition. Using a longitudinal case study of large-scale agile development, we describe a series of trigger events and episodes whereby the agile approach was tailored to address the needs of the large-scale development context, which was very much at odds with these fundamental assumptions. We develop a set of new underlying assumptions which suggest that agile and plan-driven practices are mutually enabling and necessary for coordination and scaling in large-scale agile projects. We develop nine propositions for large-scale agile projects based on these new alternative underlying assumptions. Finally, we summarize our theoretical contribution in a generic process model of continuously adjusting agile and plan-driven practices in order to accommodate process challenges in large-scale agile projects. [ABSTRACT FROM AUTHOR]

We present "E-shopaholic," an innovative e-commerce platform that integrates advanced automation techniques to optimize the online shopping experience. "E-shopaholic" exploits automation to address challenges of traditional e-commerce systems, particularly in order-picking. Leveraging the Internet of Things (IoT), "E-shopaholic" employs a NodeMCU ESP8266 module and a servo motor to simulate the picking and placing of products, aiming to contribute to the evolution of efficient e-commerce operations through automation. The platform, with its real-time automated order processing and updates, aims to enhance operational efficiency by lowering order processing times, increasing accuracy, and improving the overall order fetching process. The system design and implementation are discussed in detail, highlighting the potential of IoT and automation to transform e-commerce practices. "Eshopaholic" serves as a proof of concept for scalable and cost-effective solutions in the ecommerce sector, suggesting a path forward for research and development in automated systems. [ABSTRACT FROM AUTHOR]

Translating the sacred in a so-called divine language has been shown to be so difficult and challenging a task for translators of the Bible or the Qur'an. The human reproduction of the divine product(ion) has biblically and qur'anically been found to raise (in)soluble challenges and (in)surmountable hurdles in bicultural and cross-cultural communication and transfer in translation. One great challenge or big obstacle thereof is the (un)transferability of noncanonical ancillary oppositions from SL (QA) to TL (E) which is sought to be explored and investigated by the current study from a linguistic-translational perspective. Based on corpus data across several languages, noncanonical ancillary opposition has been shown to co-occur in preponderant syntactic frames loaned from fellow categories, such as coordination, subordination, transition, and so forth, to perform cross-categorial discourse functions in canonical, semicanonical, and noncanonical configurations, and to entertain a propensity and penchant for special multi-principled and rulegoverned sequences based on morphology, gender, agency, and so on. The specific objectives of this study are to compare and contrast SL and TL frequent frameworks and ordering sequences in the process and product of translation. It was found that a number of these frames and functions have gone untended and unrendered by the two Qur'an translators under scrutiny due to their incognizance of colligational forms and discursive functions as translation units of functional sentence perspective contributing to the semantic ends of intraversially sentential structures. It was also revealed that SL paradigmatic lexical choices were too semantically distinct and complex to lexicalize into TL. [ABSTRACT FROM AUTHOR]

The paper presents a Monte Carlo simulation model to estimate the average rate of energy expenditure (Kcal/min) and, consequently, fatigue allowance in manual order picking systems with high demand rates. The study is limited to picking a low number of low weight items per order in a compact warehouse system with traditional layout configuration of parallel racks with equal size storage cubicles. Replicates of 10,000 orders with random order sizes and random locations of items per order are used to estimate the average rate of energy expenditure for female pickers with various combinations of body mass and walking speeds. Design for the average and design for percentile are investigated to estimate a maintainable work standard to maximise productivity while maintaining energy expenditure within acceptable limits. Results illustrate the significant impacts of body mass and walking speed on the productivity and rate of energy expenditure, especially for the above average weight picker. Moreover, results reveal the difficulty of adopting recommendations on limitations on energy input and degrade in aerobic capacity with age and health conditions when setting productivity limits for pickers with a wide range of weights. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, the orders processed in B2C warehouses are characterised by heterogeneous and small volume. The traditional storage assignment strategies used in the picker-to-parts warehouses do not have advantage any more. In this case, the scattered storage strategy is a good alternative. In this paper, we study a new scattered storage strategy that allows the same product to be placed in multiple storage locations. The correlation between products which reflects how frequently any two products will be ordered together in the same order is considered. The problem is formulated as a 0-1 integer programming model to minimise the weighted sum of distances between the products, with weight being the elements of the correlation matrix. To solve large-scale problems, a GA and a basic PSO algorithm are developed. To improve solution quality, a new PSO algorithm based on the problem characteristic is designed and a hybrid algorithm combing it with GA is proposed. Experiments show that the solutions of these algorithms are close to the optimal solutions for the small-sale problems. For larger problems, the specially designed new PSO greatly improves solution quality as compared to the basic algorithms and the hybrid algorithm makes further improvement. [ABSTRACT FROM AUTHOR]

Context. A leading paradigm in planet formation is currently the streaming instability and pebble accretion scenario. Notably, dust must grow into sizes in a specific regime of Stokes numbers in order to make the processes in the scenario viable and sufficiently effective. The dust growth models currently in use do not implement some of the growth barriers suggested to be relevant in the literature. Aims. We investigate if the bouncing barrier, when effective, has an impact on the timescales and efficiencies of processes such as the streaming instability and pebble accretion as well as on the observational appearance of planet-forming disks. Methods. We implemented a formalism for the bouncing barrier into the publicly available dust growth model DustPy and ran a series of models to understand the impact. Results. We found that the bouncing barrier has a significant effect on the dust evolution in planet-forming disks. In many cases, it reduces the size of the typical or largest particles available in the disk; it produces a very narrow, almost monodisperse, size distribution; and it removes most μm-sized grains in the process, with an impact on scattered light images. It modifies the settling and therefore the effectiveness of and timescales for the streaming instability and for pebble accretion. An active bouncing barrier may well have observational consequences: It may reduce the strength of the signatures of small particles (e.g., the 10 μm silicate feature), and it may create additional shadowed regions visible in scattered light images. Conclusions. Modeling of planet formation that leans heavily on the streaming instability and on pebble accretion should take the bouncing barrier into account. The complete removal of small grains in our model is not consistent with observations. However, this could be resolved by incomplete vertical mixing or some level of erosion in collisions. [ABSTRACT FROM AUTHOR]

Chinese lunar landing mission Chang'E-4 reached the far side of the Moon in January 2019 and has been providing unprecedented Lunar Penetrating Radar data able to explore the lunar subsurface down to more than 40 m (with its more resolutive high frequency band). Data are periodically released to the scientific community in raw PDS4 format. Here we provide different versions of the radar dataset after editing (i.e. pre-processing), partial, and full processing in order to provide a complete ready-to-use dataset to end-users (data collected since 4th January 2019 until 27th March 2023) which can be directly exploited for analysis, interpretation, inversion, as well as integration with imagery or other information. In particular, we implemented an efficient and objective way to remove duplicated traces representing more than 90% of original data, as well as a processing flow able to retain all the original data information, while avoiding redundancies. The provided datasets can be implemented with future data releases and straightforwardly exploited for any future analysis. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the innovative concept of a smart warehouse is defined as a progressive development in logistics and operations management. Numerous advanced techniques are recommended for integration, including wearable devices, communication, loading/unloading, optimization, robotics, and automated transportation, among others. Currently, the growing demand for swift cargo distribution, cost‐effective labour utilization, and enhanced productivity stands as an urgent topic for both enterprises and scholars. This paper presents an EtherCAT‐based model of automated guided vehicle (AGV)‐manipulation introduced within the Unity platform for smart warehouse management. The working environment selected for this model is a cross‐docking warehouse with a chessboard layout. Initially, theoretical computations pertaining to mechanical design are established to provide accurate parameters. Subsequently, the structure of the TwinCAT Human–Machine Interface Server and the configuration within the Unity environment are outlined. Following that, the software architecture, encompassing data exchange and a set of motion commands, is meticulously explained. To validate the proposed approach, several laboratory tests are conducted under identical conditions. This research offers several contributions: firstly, it introduces the innovative use of real‐time technology to represent AGV systems; secondly, it elucidates the potential connection between virtual reality and the physical model; and thirdly, it employs fluid motion techniques to illustrate and visualize driving performance. These findings hold significant promise not only for the advancement of smart warehouse management but also for the broader landscape of digital trade. [ABSTRACT FROM AUTHOR]

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored. [ABSTRACT FROM AUTHOR]

Order picking is one of the most important activities in warehouse management. By optimising the picking process, warehouse operations can be managed efficiently in terms of both time and logistics costs. In this work we apply operations research techniques to support the order picking process of an Italian retail distribution company. The picking process poses several challenges from an optimisation point of view, and the related optimization problems are very complex. Therefore, we define an Adaptive Large Neighbourhood Search (ALNS) algorithm that heuristically solves the problem. The proposed metaheuristic is tested on a set of 101 real orders processed by the company within one day. The computational experiments show that the ALNS shows good performances in terms of effectiveness, compared to the optimal solution, as well as allows to implement a better organisation of the order picking process than the one currently adopted by the company. [ABSTRACT FROM AUTHOR]

Stocktaking refers to the examination or counting over of materials, supplies, goods or products inside a stockroom, store, warehouse. There are many challenges concerning this important business function, which are even more demanding inside an unstructured large warehouse. Huge variance in size, volume, shape of the stock-keeping units that are stored in bulk without packaging, leads to many emerging problems that necessitate significant research endeavors so as to develop solutions that can automate this process. Nowadays, there are many approaches related to computer vision-based stocktaking in unstructured warehouses with products stored in bulk, which employ several existing technologies namely Deep Learning, Optical Character Recognition, and 3D model retrieval. The paper at hand aims at presenting the i-Count Air concept which outlines the utilization of Unmanned Aerial Vehicles, endowed with cognitive hardware sensors, active vision and an innovative perception and stocktaking software, that will push the boundaries of robotic perception and help automate the inventory counting function in a Warehouse 5.0 environment. [ABSTRACT FROM AUTHOR]

Increasing online retail has resulted in increased automation in order picking systems, leading to new challenges and opportunities in task scheduling. The job-shop scheduling problem is an optimization problem essential in such systems, but existing JSP literature often overlooks workplace fatigue, which harms employees' well-being and costs U.S. employers up to €127 billion annually. In this work, we propose fatigue consideration in the job-shop scheduling problem in a cobotic order picking system to mitigate its negative effects. We present a new bi-objective mixed integer nonlinear programming problem formulation that considers worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model and scheduling results in a real operation. The mathematical model finds solutions that conventional single-objective optimisation cannot, allowing fractional fatigue distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. The experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements. It also shows a low correlation, meaning it is unfit for application. On the other hand, fatigue-conscious schedules show no clear benefit regarding measured and perceived fatigue. However, the scheduling model could also use heart rate measurements that do not show these inaccuracies. Our study highlights the need to further develop and validate the mathematical formulation and fatigue model and extend to other human factors and indirect fatigue effects. [ABSTRACT FROM AUTHOR]

In order to accompany the process of work design, a systemic analysis is useful, which reveals changes in elements of the work system. Physical and psychological situations at work that can lead to adverse effects should be avoided. In processes of change, it is essential to identify developments at an early stage so that appropriate measures can be taken into account. This is also valid in the context of work design. This article presents an instrument that addresses the analysis of work system elements in the context of change. Besides content, structure and intended process, findings from the first application are presented. These experiences within the research project WIRKsam show the potentials of the instrument and allow to discuss the limitations as well as the possibilities for further development. [ABSTRACT FROM AUTHOR]

Although optimization models can be used to plan the production process, in most cases static heuristics, such as earliest due date (E), longest processing time (L), and shortest processing time (S), are used because of their simplicity. This study aims to analyze the production cost of the static heuristics and to determine how this cost relates to the size of the production orders in the sawmilling industry. We set a planning problem with different orders and due dates and solved it using two cost-minimization models to compare their solutions. The first was a planning model (PL) where orders were split up into products demand by period, and the second, a planning scheduling (PS) where the sequence of processing orders based on static heuristics was assumed as known. In the latter, the minimum production cost for each static heuristic was found. In both models, the same resource constraints were assumed. The costs showed no significant changes based on order sizes. However, 0,5 % of orders were delayed using PS-E, and 17 % of orders were delayed using PL. PL was an efficient solution method when changing the orders' size and when looking for the best static heuristic to process the orders. However, PS-E showed the ability to reduce the backlog close to zero while the PL backlog ratio was 17 %. No penalties were applied to backlogs due to their subjective nature; however, when shortages occurred, the demand was unmet or backlogged with substantial costs. Thus, in case the proposed method is adopted using a conservative backlog cost, a sawmill producing under the cut-to-order environment that produces 300000 m³/year would reduce backlogged orders by 51000 m3. If the holding lumber cost is 2 $/m³, annual savings would be $408000. [ABSTRACT FROM AUTHOR]

The present study focuses on the rise of rollo as a catchall noun and as a pragmatic marker that developed in oral conversation over the last 50 years. In order to explain this process, an oral corpus of colloquial Spanish has been compiled and the data has been analyzed by means of the Behavioral Profile methodology. After detecting the changing dynamic relationships between meanings, an in-depth qualitative explanation of the diachronic route of abstract meanings is presented, taking into account the sociocultural factors that motivate them. The results reveal that rollo underwent significant semantic changes within the sociocultural Counterculture movement of the seventies. Its configuration as a vague element in the sociolect of young people made it possible to become pragmaticalized at the end of the nineties as an approximator, which turns out to be the most frequent use in current youth language. [ABSTRACT FROM AUTHOR]

Somatic Experiencing (SE)®, as developed by Peter A. Levine, will be presented as a basic neuropsychological concept that is suitable in particular for the treatment of trauma-related disorders and psychosomatic illnesses, as it works directly with the symptoms of trauma-related dysregulation in the autonomic nervous system (ANS), which can find expression in multiple levels of experience. Through various techniques and a mindful, targeted use of interventions – both activating and calming – the innate self-regulating abilities of the ANS are restored and strengthened, in order to help process and integrate what has been experienced. Basic neurological principles as well as SE intervention techniques will be described, and the interplay of these techniques will be illustrated in three case studies. [ABSTRACT FROM AUTHOR]

This review deals with the current research on wind-tree interaction, focusing on methods and technologies applied on tree monitoring. 101 studies were included and classified according to the different biomechanical approach used, dividing them according to methods and technologies applied. The studies were selected following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement. A great variety of sensors and testing protocols have been used in the last 30 years, and several new technologies have been proposed and tested especially in the last five years, considerably expanding the available systems for the study of wind-tree interaction. The most modern technologies are based on smart sensing technologies and on developing real-time monitoring and alarm systems, understanding and forecasting the interaction between windstorms and trees, then preventing wind damage to human beings. The works based on actual field observation of the tree's response to wind appears to provide the most reliable comprehension of the wind-tree interaction phenomena, especially when wind data monitoring is included. Main outcomes shows that the choice of specific methods and sensors are, in a qualitative form, strongly dependent to the purpose of the study. An important result is the total absence of any clear standard procedures provided for these applications. Comparative analyses involving multiple sensors simultaneously applied could be encouraged to shed light about requirements on measuring systems and consequent signal processing in order to define standardised procedures to be used for each given procedure. • Wind-tree interaction is progressively being more researched in the last 30 years. • Which monitoring systems and how they should be used for given aim is unclear. • No standardized protocols and lacks on wind monitoring requirements has been found. • Smart sensing technologies are becoming increasingly included in monitoring systems. [ABSTRACT FROM AUTHOR]

Objective: Analyze how, based on the identification of a fragile academic definition of the concept of populism, in the field of Latin America and especially in Colombia, an analytical-conceptual process is developed, regarding what are the definitions and applications of the concepts in political reality. Methods: The article proposes, through a methodological-experimental model, the possibility of deepening concepts that allow explaining and identifying populist actions or discourses in the Colombian context. Results: It was found that in many of the investigations an attempt is made to define the concept based on the different events in the political history of the countries under analysis, and above all, from the politicians in whom populism is suspected based on their speeches; Consequently, in the application of the experimental methodology of this research, authors such as Zanatta (2013) and Rosanvallon (2020) were taken, in which conceptual categories are identified such as: populism as a community axis, populism as centrality and security, populism as encompassing a large majority with a margin of small minorities that constitute the enemies, in turn some subcategories of political emotions emerged that guarantee these first ones, among them emotions of position, emotions of action and emotions of intellection, all six connected respectively. Conclusions: The development of concepts that contribute to the explanation of the populist phenomenon in electoral campaigns is key to understanding the edges of political culture and the new sociopolitical orders in electoral processes. [ABSTRACT FROM AUTHOR]

Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers' like SCIP capability. [ABSTRACT FROM AUTHOR]

Research in psychology has seen a rapid increase in the usage of experience sampling methods and daily diary methods. The data that result from using these methods are typically analyzed with a mixed-effects or a multilevel model because it allows testing hypotheses about the time course of the longitudinally assessed variable or the influence of time-varying predictors in a simple way. Here, we describe an extension of this model that does not only allow to include random effects for the mean structure but also for the residual variance, for the parameter of an autoregressive process of order 1 and/or the parameter of a moving average process of order 1. After we have introduced this extension, we show how to estimate the parameters with maximum likelihood. Because the likelihood function contains complex integrals, we suggest using adaptive Gauss-Hermite quadrature and Quasi-Monte Carlo integration to approximate it. We illustrate the models using a real data example and also report the results of a small simulation study in which the two integral approximation methods are compared. [ABSTRACT FROM AUTHOR]

Content and Language Integrated Learning, or CLIL, is an eclectic approach, which makes use of a foreign language to teach academic content in different areas. Within this context, those communicative activities of mediation proposed by the Common European Framework of Reference (CEFR) deserve a particular interest. This small-scale study employs a linguistic mediation activity involving information processing in order to compare the outcomes of two learner groups of German as a foreign language (DaF) at an A2-B1 level. One of the groups has received CLIL instruction, whereas the other one was taught following a mainstream approach (non-CLIL group). Results show that CLIL learners perform better at using reformulation strategies to present a mediated text. Furthermore, the data also show a positive effect regarding L1 use during mediation activities. [ABSTRACT FROM AUTHOR]