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In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers. [ABSTRACT FROM AUTHOR]

The interplay between densification and positional ordering during the process of crystal nucleation is a greatly investigated topic. Even for the simplest colloidal model—hard spheres—there has been much debate regarding the potential foreshadowing of nucleation by significant fluctuations in either local density or local structure. Considering anisotropic particles instead of spheres adds a third degree of freedom to the self-organization process of crystal nucleation: orientational ordering. Here, we investigate the crystal nucleation of hard triangular prisms. Using Monte Carlo simulations, we first carefully determine the crystal–fluid coexistence values and calculate the nucleation barriers for two degrees of supersaturation. Next, we use brute force simulations to obtain a large set of spontaneous nucleation events. By studying the time evolution of the local density, positional ordering, and orientational ordering in the region in which the nucleus first arises, we demonstrate that all local order parameters increase simultaneously from the very start of the nucleation process. We thus conclude that we observe no precursor for the crystal nucleation of hard triangular prisms. [ABSTRACT FROM AUTHOR]

Colloidal and nanoparticle self-assembly enables the creation of ordered structures with a variety of electronic and photonic functionalities. The outcomes of the self-assembly processes used to synthesize such structures, however, strongly depend on the uniformity of the individual nanoparticles. Here, we explore the simplest form of particle size dispersity—bidispersity—and its impact on the self-assembly process. We investigate the robustness of self-assembling bcc-type crystals via isotropic interaction potentials in binary systems with increasingly disparate particle sizes by determining their terminal size ratio—the most extreme size ratio at which a mixed binary bcc crystal forms. Our findings show that two-well pair potentials produce bcc crystals that are more robust with respect to particle size ratio than one-well pair potentials. This suggests that an improved self-assembly process is accomplished with a second attractive length scale encoded in the particle–particle interaction, which stabilizes the second-nearest neighbor shell. In addition, we document qualitative differences in the process of ordering and disordering: in bidisperse systems of particles interacting via one-well potentials, we observe a breakdown of order prior to demixing, while in systems interacting via two-well potentials, demixing occurs first and bcc continues to form in parts of the droplet down to low size ratios. [ABSTRACT FROM AUTHOR]

Order picking in warehouses is a labour- and time-intensive logistical process that significantly impacts the efficiency of supply chains. Although technical progress facilitates the automation of specific order picking tasks, human workers remain the primary actors of order picking. Owing to high operating costs associated with manual order picking, its design and management have been increasingly researched for decades. Because manual order picking systems are socio-technical systems, human factors and workers' interaction with technology are essential for operational success. As innovative technologies become increasingly utilised, such as augmented reality or exoskeletons, warehouse managers need to consider the effects of supportive and substitutive technologies on operational outcomes. However, the potentials and obstacles of using technologies in manual order picking require further investigations. Therefore, this study analyses literature content on supportive and substitutive technologies in manual warehouse order picking and investigates the existing state of research in this field. Text mining is employed to enhance the insights regarding the content analysis. Additionally, future research opportunities on the integration of supportive and substitutive technologies are proposed for manual order picking improvement and development of sustainable and human-centered logistics systems, according to the Industry 5.0 vision. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed. [ABSTRACT FROM AUTHOR]

Picker to parts order picking (OP) systems are one of the most labour-intensive warehouse operations, accounting for 55% of warehouse expenses in the e-commerce industry. For this reason, the e-commerce industry has invested in fully automated and robotised warehouses. However, these warehouse solutions are very expensive. Furthermore, with the advent of Industry 5.0 with a human-centric focus, the OP system's automation should be designed and implemented in ways to improve the working conditions of the order pickers rather than replacing them completely. A pick and transport robot (PTR) is a solution for OP systems which is compatible with Industry 5.0. An example of such a solution is Grab™ by SOLWR. Previous studies have barely investigated the human factor effects of using PTRs in OP systems while optimising the performance of the systems. This research develops a mathematical model to optimise the performance of a picker to parts OP system using PTRs in terms of productivity, quality, and the well-being of the order pickers. The developed model is tested by using data from a case company. The results of this study support managerial decisions by achieving a better knowledge of how to set up such PTR solutions. [ABSTRACT FROM AUTHOR]

Robotisation is increasing in warehouse operations, but human employment continues to be relevant. Traditionally manual activities, such as order picking, are being re-designed into collaborative human–robot tasks. This trend exemplifies the transition towards a human-centric Industry 5.0, focusing on synergy instead of seeking human replacement. However, human workers are increasingly hard to recruit and retain. We contribute to the underrepresented literature on human factors within the domain of operations and production management research and investigate the deployment of robotic technologies alongside human workers in a sustainable way. With a unique real-effort experiment, we investigate how the manipulation of picker's experienced levels of autonomy affects their job satisfaction and core self-evaluations, two key behavioural outcomes that determine employee turnover intentions. We establish that the introduction of human–robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (i) gaining control (the human leading the robot) and (ii) ceding control (the human following the robot). This positive effect is larger when the human is following the robot. We additionally find that following the robot positively affects pickers' self-esteem and that self-efficacy related to human–robot interaction benefits from the introduction of collaborative robotics, regardless of the setup dynamics. [ABSTRACT FROM AUTHOR]

Order picking is the process of retrieving ordered products from storage locations in warehouses. In picker-to-parts order picking systems, two or more customer orders may be grouped and assigned to a single picker. Then routing decisions regarding the visiting sequence of items during a picking tour must be made. Won and Olafsson (2005) found that solving the integrated problem of batching and routing enables warehouse managers to organize order picking operations more efficiently compared with solving the two problems separately and sequentially. We therefore investigate the mathematical programming formulation of this integrated problem. We present several improved formulations of the problem based on the findings of Valle, Beasley, and Salles da Cunha (2017), that can significantly improve computational results. More specifically, we reconstruct the connectivity constraints and generate new cutting planes in our branch-and-cut framework. We also discuss some problem properties by studying the structure of a graphical representation of the warehouse, and we present two types of additional constraints. We also consider the no-reversal case of this problem. We present efficient formulations by building different auxiliary graphs. Finally, we present computational results for publicly available test problems for single-block and multiple-block warehouse configurations. [ABSTRACT FROM AUTHOR]

An accurate and efficient method for picking the first arrival of microseismic signals is crucial for processing microseismic monitoring data. However, the weak magnitude and low signal-to-noise ratio (SNR) of these signals make picking arrivals challenging. Recent advancements in deep learning-based methods for picking the first arrivals of microseismic signals have effectively addressed the inefficiencies and inaccuracies of traditional methods. Nevertheless, these methods often require many training samples, and the substantial size and labelling effort significantly hinder the development of deep learning-based first-arrival picking methods. This study introduces Semi-Picking: a semi-supervised method for picking the first arrival of microseismic signals, utilizing the TransUGA network and SimMatch. This approach automatically labels microseismic signals following sample augmentation by establishing a semi-supervised learning framework, significantly reducing the time required for sample labelling. Initially, the TransUNet model is enhanced by incorporating the Self-Supervised Predictive Convolutional Attention Block (SSPCAB) module to create a Deep-TransUNet architecture, which more effectively separates signal from noise in microseismic signals with low SNR and improves the accuracy of first-arrival picking. Subsequently, the data sets for this study are compiled from microseismic traces collected from field monitoring records. Finite-difference forward modelling is applied to the microseismic data to train the network, and hyperparameter tuning is performed to optimize the UGATIT and Deep-TransUNet architecture. The outcomes of the arrival-picking experiments, conducted under conditions of low SNR using both synthetic and real microseismic records, demonstrated that Semi-Picking offers robust resistance to incorrect labels. This resilience stems from the synergistic use of the semi-supervised learning framework and self-attention mechanisms. The proposed method demonstrates superiority over the TransUNet, the SSPCAB-TransUNet, the UNet++ and the traditional short-term average/long-term average method, respectively, with the picking error rate of the Semi-Picking Net being less than 0.1 s. The proposed method outperforms the commonly used deep learning-based approaches for picking the first arrivals of microseismic signals, exhibiting superior performance. [ABSTRACT FROM AUTHOR]

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity. [ABSTRACT FROM AUTHOR]

Purpose: Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots). Design/methodology/approach: Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance. Findings: We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders. Originality/value: Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology. [ABSTRACT FROM AUTHOR]

Maturation of human mitochondrial tRNA is essential for cellular energy production, yet the underlying mechanisms remain only partially understood. Here, we present several cryo-EM structures of the mitochondrial RNase Z complex (ELAC2/SDR5C1/TRMT10C) bound to different maturation states of mitochondrial tRNAHis, showing the molecular basis for tRNA-substrate selection and catalysis. Our structural insights provide a molecular rationale for the 5′-to-3′ tRNA processing order in mitochondria, the 3′-CCA antideterminant effect, and the basis for sequence-independent recognition of mitochondrial tRNA substrates. Furthermore, our study links mutations in ELAC2 to clinically relevant mitochondrial diseases, offering a deeper understanding of the molecular defects contributing to these conditions. Synopsis: The human mitochondrial RNase Z complex is essential for the production of mitochondrial tRNAs. This study reveals its complete structural framework, detailing its role in tRNA maturation and substrate recognition, and provides insights into mitochondrial disease mechanisms linked to mutations in its catalytic subunit ELAC2. Cryo-EM shows the full-length human mitochondrial RNase Z complex in various maturation states of mitochondrial tRNA. The structure explains substrate recognition and catalysis, the 5′-to-3′ processing order, and the 3′-CCA antideterminant effect that prevents unnecessary RNA processing. The structural findings provide a foundation to interpret clinical mutations in the ELAC2 gene in the context of mitochondrial pathologies and reveal that substrate recognition, cleavage and 3'-CCA antidetermination depend on tightly interconnected mechanisms. Cryo-EM reveals the molecular basis for substrate recognition and the 5′-to-3′ processing order of mitochondrial tRNAs. [ABSTRACT FROM AUTHOR]

In order to solve the problem of narrow band coverage of traditional infrared gratings, a new type of diffraction grating is invented and named as angle-gradient echelle grating. The blaze angle of the grating gradually changes along the direction of the grating grooves, which can be regarded as etching countless sub-gratings with different blaze angles on a grating, breaking the limitation of the number of blaze angles of traditional gratings. In addition, in order to solve the processing problem of angle-gradient echelle grating, a new processing method, shaped vibration cutting (SVC) with a time-varying trajectory is developed. By exploring the influence of different processing parameters on the grating processing results, a suitable processing plan was proposed. Eventually, angle-gradient echelle grating with blaze angles varying from 30° to 55° and surface roughness of as low as 20 nm was fabricated on metallic glass, a material that is difficult to machine. • A blazed grating with a gradient blaze angle is proposed. • The proposed grating has high diffraction efficiency in the infrared band. • A variable vibration trajectory processing method is proposed. • The feasibility of the processing method is verified on an aluminum substrate. • This method can achieve high-quality structural processing on metallic glass. [ABSTRACT FROM AUTHOR]

Forecasting future observations is one of the main goals of time series modeling. Point forecasts are the most common type of prediction, but interval predictions are more informative, usually obtained as prediction intervals (PIs). The conventional Box-Jenkins PIs perform well when the underlying distributional assumptions hold. To circumvent such adherence, resampling methods such as residual based Bootstrap were one of the approaches for constructing PIs, but these are only suitable when the order of the process is known. Sieve Bootstrap method has the advantage of requiring no such prior knowledge. In this work, two new Sieve Bootstrap methods have been proposed. The performance of the proposed methods for constructing PIs have been compared with the existing Bootstrap methods. One of the proposed methods, namely, predictive residual based rescaled Sieve Bootstrap (PRSB) has been found to outperform the other proposed predictive residual based Sieve Bootstrap (PSB) and also the existing methods considered. PSB method has been found to be superior to Box-Jenkins and existing Sieve Bootstrap methods. The proposed methods are not only an improvement over the existing predictive residual based Bootstrap methods in AR(p) set up but also have been extended to be applicable in ARMA(p, q) situations as well. [ABSTRACT FROM AUTHOR]

Due to population mixing and genetic recombination, each chromosome in the genome can be regarded as a chimera of ancestral chromosomes from different genetic backgrounds. Local Ancestry Inference (LAI) is an important part of genetic analysis, which infers the ancestral origin of each chromosome segment in the genome by using genetic marker information. In order to simplify the process of LAI design, most of the existing methods use fixed-size windows and ignore the real recombination breakpoints in the individual genome to be tested. In this study, we design a strategy based on k-mers to find recombination breakpoints, and combine Hidden Markov models (HMMs) to infer local ancestry based on splitting of variable-length recombination windows in each haploid. Simulation results show that the accuracy of Wadmix is similar to that of other mainstream tools including RFMix. [ABSTRACT FROM AUTHOR]

Cryo‐electron tomography (cryo‐ET) and subtomogram averaging (STA) are becoming the preferred methodologies for investigating subcellular and macromolecular structures in native or near‐native environments. Although cryo‐ET is amenable to a wide range of biological problems, these problems often have data‐processing requirements that need to be individually optimized, precluding the notion of a one‐size‐fits‐all processing pipeline. Cryo‐ET data processing is also becoming progressively more complex due to the increasing number of packages for each processing step. Though each package has its strengths and weaknesses, independent development and different data formats make them difficult to interface with one another. TOMOMAN (TOMOgram MANager) is an extensible package for streamlining the interoperability of packages, enabling users to develop project‐specific processing workflows. TOMOMAN does this by maintaining an internal metadata format and wrapping external packages to manage and perform preprocessing, from raw tilt‐series data to reconstructed tomograms. TOMOMAN can also export these metadata between various STA packages. TOMOMAN includes tools for archiving projects to data repositories, allowing subsequent users to download TOMOMAN projects and directly resume processing. By tracking essential metadata, TOMOMAN streamlines data sharing, which improves the reproducibility of published results, reduces computational costs by minimizing reprocessing, and enables the distribution of cryo‐ET projects between multiple groups and institutions. TOMOMAN provides a way for users to test different software packages in order to develop processing workflows that meet the specific needs of their biological questions and to distribute their results to the broader scientific community. [ABSTRACT FROM AUTHOR]

The removal of inclusion particles is an important function of tundish metallurgy. During ladle change, the liquid level fluctuation around the ladle shroud is severe, which may affect the effectiveness of inclusion removal. In the present study, the fluid flow characteristics and inclusion‐removal behavior in the tundish during ladle change are investigated by numerical simulation method. And the effectiveness of flow control devices on the flow characteristics of molten steel is evaluated. It is found that the escape rate of inclusion particles during ladle change is ordered as emptying process < steady process < filling process. The proper combination of weir, dam, and turbulence inhibitor can optimize the conditions for the floating and removal of inclusion particles to the greatest extent. In the tundish with weir and dam, the escape rates of inclusion particles with sizes of 10, 50, and 100 μm throughout the entire ladle change period are respectively reduced by 9.8%, 1.6%, and 1.4%, compared to not using flow control devices. With a combination of weir, dam, and turbulence inhibitor, the escape rates of the three types of inclusion particles are reduced by 24.7%, 9.9%, and 1.2%, respectively. [ABSTRACT FROM AUTHOR]

This article studies a scheduling problem of a two-machine flowshop , considering outsourcing lead-time, where outsourcing a job incurs outsourcing cost as well as outsourcing lead-time. Each job has an option of being outsourced, i.e. the first operation of the outsourced job is handled by an external subcontractor while the second operation must be processed in-house after its delivery. The objective is to find a schedule for minimizing the weighted sum of the outsourcing costs and the total completion time. For the problem, the subset of jobs to be outsourced and the processing order of jobs are interwoven, implying that decisions have to be made simultaneously. This study examines the properties of the optimal solution and proposes various heuristics based on iterative local search and the biased random-key genetic algorithm. Extensive computational experiments are carried out in various settings. [ABSTRACT FROM AUTHOR]

The concluding chapter of Karl Marx's Capital (volume 1) has received remarkably little scholarly commentary. This is especially surprising as Marx addresses there "the modern theory of colonization" developed by Edward Gibbon Wakefield. But rather than systematically pursue the issue of colonialism, Marx investigates Wakefield's theory in order to illuminate the processes of primitive accumulation (or "originary dispossession") that gave birth to capitalism in Europe. This article suggests directions in which Marx ought to have gone in order to analyze the globalization of capitalism outside of Europe by means of colonialism. In so doing, it proposes the need to recenter bondage, slavery, colonialism, and racism as constitutive elements of capitalism as a global system. JEL Classification: B51, N3, P1 [ABSTRACT FROM AUTHOR]

Local asymptotic normality (LAN) and uniform local asymptotic normality (ULAN) conditions are obtained for a linear functional autoregressive process of order p, p≥1, using a martingale central limit theorem. As a consequence of the LAN property, asymptotically efficient tests for determining the order are constructed. An example is discussed as a special cases. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of k-order (3 ≤ k ≤ 5) backward differentiation formulas (BDF-k) for the molecular beam epitaxial (MBE) model without slope selection. Convex splitting technique along with k-th order Douglas-Dupont regularization term τ n k (− Δ) k D _ k ϕ n ( D _ k represents a truncated BDF-k formula) is added to the numerical schemes to ensure unconditional energy stability. The stabilized convex splitting BDF-k (3 ≤ k ≤ 5) methods are unique solvable unconditionally. Then the modified discrete energy dissipation laws are established by using the discrete gradient structures of BDF-k (3 ≤ k ≤ 5) formulas and processing k-th order explicit extrapolations of the concave term. In addition, based on the discrete energy technique, the L 2 norm stability and convergence of the stabilized BDF-k (3 ≤ k ≤ 5) schemes are obtained by means of the discrete orthogonal convolution kernels and the convolution type Young inequalities. Numerical results are carried out to verify our theory and illustrate the validity of the proposed schemes. [ABSTRACT FROM AUTHOR]

The number of robotic compact storage and retrieval systems (RCS/RS) is one of the fastest growing compared to other storage systems. Over a thousand systems with a large spectrum of design and order speed are already in operation. Nonetheless, there is hardly any information on throughput or optimal system design. This paper presents an analytical approach for the performance calculation of an RCS/RS, which operates with several robots serving one I/O shaft. One robot's cycle time is calculated by assuming a uniform distribution of container stacks and a probabilistic storage height. Based on this, the interaction of the robots at the I/O shaft is considered using an open queueing model with limited capacity. After validating the analytical approach using a discrete event simulation model of an RCS/RS, an extensive parameter variation is done. The easy and fast solvability with standard calculation programs and applicability are just two benefits. [ABSTRACT FROM AUTHOR]

Delivery times represent a key factor influencing the competitive advantage, as manufacturing companies strive for timely and reliable deliveries. As companies face multiple challenges involved with meeting established delivery dates, research on the accurate estimation of delivery dates has been source of interest for decades. In recent years, the use of machine learning techniques in the field of production planning and control has unlocked new opportunities, in both academia and industry practice. In fact, with the increased availability of data across various levels of manufacturing companies, machine learning techniques offer the opportunity to gain valuable and accurate insights about production processes. However, machine learning-based approaches for the prediction of delivery dates have not received sufficient attention. Thus, this study aims to investigate the ability of machine learning to predict delivery dates early in the ordering process, and what type of information is required to obtain accurate predictions. Based on the data provided by two separate manufacturing companies, this paper presents a machine learning-based approach for predicting delivery times as soon as a request for an offer is received considering the desired customer delivery date as a feature. [ABSTRACT FROM AUTHOR]

The order scheduling and rack sequencing problem deals with the order picking process in robotic mobile fulfillment systems: automated guided vehicles lift and transport movable storage racks to picking stations to supply items requested by customer orders which are put together in cardboard boxes on a workbench of limited capacity. To efficiently operate the station, the sequence in which racks visit the station one after another and the intervals at which customer orders are scheduled must be coordinated. We present a dynamic programming algorithm for the order scheduling and rack sequencing problem at a single picking station minimizing the number of rack visits. Despite monolithic mixed integer linear programming formulations, our approach appears to be the first combinatorial solution method for the problem in literature. A computational study demonstrates the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Numerical simulation is an important tool in the development process of vehicles in order to predict the passive vehicle safety extensively. Based on simulation results, physical tests are carried out to evaluate critical points. The validity of numerical simulations is analysed by objective rating methods. Predominantly CORA (Correlation and Analysis) is used to compare the similarity of response time histories between simulation and physical test. In pedestrian protection, the peaks of the response time histories measured during the test are the injury criteria values. Additionally, the risk of injury is determined by these peak values. However, response time histories are found to be rated inadequately by CORA because this method is not emphasising on peak values. We investigate Flexible Pedestrian Legform Impactor (FlexPLI) and advanced Pedestrian Legform Impactor (aPLI) data with different CORA parameter settings in order to achieve adequate ratings. Thereby, we try to focus on the peak values of the curves while keeping the parameter variation as objective as possible. Further, we show a heuristic approach by adding peak value deviation as an additional element to the CORA metric – so called CORA+. [ABSTRACT FROM AUTHOR]

Missing observations and unevenly spaced data are problems common to different disciplines in the context of time series analysis. This paper introduces a new approach to deal with both issues, by considering an irregularly spaced autoregressive moving average process of order (1,1) that is stationary (and therefore homoscedastic) and invertible allowing temporal variations in its coefficients. We test our model in the analysis of greenhouse time series by comparing it with a standard benchmark in the literature. As a result, our methodology leads to a huge advantage in the computational time with respect to the competitor. [ABSTRACT FROM AUTHOR]

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

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

As a rapidly growing interdisciplinary literature has argued, rankings have become a key means of valuation in contemporary society. However, the majority of the work focuses on rankings in only a few areas, and even if spatial aspects play a role in the interdisciplinary literature, the number of explicitly geographical works on rankings is surprisingly low. Against this backdrop, in this paper I aim firstly to flesh out a pragmatism‐inspired geographical perspective on rankings. Secondly, using the example of wine rankings, I will ask the question as to how the growing importance of rankings has changed valuation schemes. The wine industry is particularly well suited to this, as nominal classifications in the form of designations of origin have historically played a central role here. A pragmatist, geographical perspective on ordinal ordering processes illustrates that rankings evoke both economic and geographical realities; they do this not only through the ranking processes as such, but also through observation of and engagement with rankings by different actors. In the case of wine, local/regional specificity is an inherent part of the world of rankings—be it through the fact that ranking processes build on (embodied) geographically contextualised knowledge of wine judges, through the possibilities of using (or not using) (different) rankings for different markets by wine producers, or through the balance between the marketing of wine through rankings and the suitability of those very wines for the specific markets in which they are to be sold. This is perhaps the biggest difference compared with other fields in which the importance of rankings has increased considerably—in particular, higher education. It helps to explain why the historically significant valuation scheme of geographical origin has not lost any of its significance, despite the increase in the importance of rankings in the world of wine. [ABSTRACT FROM AUTHOR]

The concept of sovereignty, like the concept of power, has been one of the key concepts that politicians, statesmen and thinkers have thought about, tried to define, conceptualize and draw the general framework since the emergence of the state. The concept of sovereignty, which has developed in connection with the change and development process of the state over time, has become a more prominent and considered concept, especially after the new nation-state-centered system that emerged after the Westphalia international system. This concept, which preoccupies thinkers, politicians and statesmen, has become questionable and debatable in the modern age, especially as world politics has changed following developments in the field of communication and processes such as Globalization. In order to overcome this process, the concept of Sovereignty has also changed, and in order to meet the conditions of the new era, a new concept emerged, called Cyber Sovereignty. [ABSTRACT FROM AUTHOR]

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

We study a social dilemma in a single-queue system in which human servers have discretion over the effort with which to process orders that arrive stochastically. We show theoretically that the efficient outcome in the form of high effort can be sustained in the subgame-perfect equilibrium if the interactions are long term (even when each server has a short-term incentive to free-ride and provide low effort). In addition, we show that queue visibility plays an important role in the type of strategies that can sustain a high-effort equilibrium. In particular, we show that limiting feedback about the current state of the queue may be beneficial if the expected duration of interaction is long. We conduct two controlled laboratory experiments to test the theoretical predictions and find that effort increases with the expected duration of an interaction. We also find that visibility has a strong impact on the strategies that human subjects use to provide effort in a dynamic setting. We discuss implications for managers and firms that are trying to improve service systems. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.00603. [ABSTRACT FROM AUTHOR]

Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

An unsupervised computational protocol is proposed with the aim of obtaining accurate structures of large molecules in the gas phase at the cost of standard density functional theory (DFT) computations. The whole workflow is fully automated and provides optimized equilibrium geometries and ground state rotational constants to be directly compared with experiments. The results for a panel of molecules of biological or medicinal interest show that the accuracy of the results delivered by the new tool at the cost of a single DFT geometry optimization is close to that delivered by state-of-the-art composite wavefunction methods for small semi-rigid molecules. [ABSTRACT FROM AUTHOR]

Third-party logistics (3PL) services are essential in many supply chains and aim at providing flexible and cost-effective transport and warehouse solutions. This study focuses on Warehouse Service Providers (WSPs) with shared, multi-client warehouses. We investigate how the main internal resources should be shared among clients. We focus on labor (order pickers and sorters) and pick storage space (zones) as the main internal resources in the order picking process in such a warehouse. We evaluate different possible resource-sharing configurations and develop a model to find the configuration that can satisfy set service-level agreements at minimum cost. The cost consists of labor and space cost, whereas the service-level requirements are based on the mean and tail of the order-throughput-time distribution. The results show that whether to share the resources or not highly depends on the clients' characteristics, such as the order demand rate. Generally, for low-demand clients, sharing all the resources is optimal, whereas for high-demand clients, dedicating all resources is preferred. In addition, the decision to share resources depends on the warehouse's characteristics, similarities between clients' product and order picking characteristics. The findings of this article provide valuable insights for WSPs to minimize cost, while meeting the service-level requirements of clients by optimally selecting the warehouse configuration. This may, in turn, benefit their clients by offering better pricing policies to them, which can also be a compelling selling point, ultimately contributing to business growth and attracting more clients. [ABSTRACT FROM AUTHOR]

Participants in open collaboration communities coproduce knowledge despite minimal explicit communication to coordinate the efforts. Studying how participants coordinate around the knowledge artifact and its impacts are critical for understanding the open knowledge production model. This study builds on the theory of stigmergy, wherein actions performed by a participant leave traces on a knowledge artifact and stimulate succeeding actions. We find that stigmergy involves two intertwined processes: collective modification and collective excitation. We propose a new measure of stigmergy based on the spatial and temporal clustering of contributions. By analyzing thousands of Wikipedia articles, we find that the degree of stigmergy is positively associated with community members' participation and the quality of the knowledge produced. This study contributes to the understanding of open collaboration by characterizing the spatial-temporal clustering of contributions and providing new insights into the relationship between stigmergy and knowledge production outcomes. These findings can help practitioners increase user engagement in knowledge production processes in order to create more sustainable open collaboration communities. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) materials and their heterostructures have been widely explored for high-performance energy conversion applications. The Thomson effect—a higher order transport process—plays an important role in thermoelectric devices, yet its effect on the performance of thermionic devices remains unknown thus far. Here, we investigate the performance of thermionic refrigeration in vertically stacked heterostructure (VHS) and laterally stitched heterointerface (LHS) composed of a graphene and a 2D semiconductor (i.e., MoS 2 and WSe 2) in the presence of the Thomson effect. Using a temperature-dependent Seebeck coefficient, we derived the analytical expressions of the cooling efficiency and the effective Z T. We shall show that the Thomson effect improves the coefficient of performance (COP) by up to 20%, particularly, in the case where the temperature difference between the cold and the hot electrodes is large. However, the Carnot efficiency decreases with the temperature difference. The overall COP is reduced by the Thomson effect. We calculate the COP in graphene/MoS 2 /graphene and graphene/WSe 2 /graphene VHS and LHS devices. We show that the LHS composed of WSe 2 significantly outperforms the VHS and MoS 2 counterpart. These findings provide an understanding of thermionic processes in the higher-order transport regime and shall offer insights into the design of novel 2D material heterostructure thermionic energy converters. [ABSTRACT FROM AUTHOR]

In sequential auctions, all the sub-orders from a buyer need to be sorted and consolidated within a short time window for shipping. Buyer demands and sub-order arrival times are uncertain. The current auction order fulfillment is facing several challenges. Based on a re-engineered Industrial Internet-of-Things (IIoT)-enabled automation system, this paper introduces an order batching approach with forecasting and postponement. Such an approach generates batches considering time interval and buyer completion rate to minimise the total processing time of the auction orders and system response time. The buyer completion rate refers to the ratio of current cumulative and predicted purchase quantity. We use the forecasting method proposed by Kong et al. (2021) to estimate the purchasing quantity. Through a series of computational experiments using real-life data, the proposed order batching method achieves a shorter order processing time and system response time. Results show that the number of auction buyers poses no effect on the performance of the proposed approach. Key parameters of order postponement rule influence on performance. [ABSTRACT FROM AUTHOR]

This note presents insights on the Jordan structure of a matrix which are derived from an extension of the I(1) and I(2) conditions in Johansen (1996). It is first observed that these conditions not only characterize, as it is well known, the size (1 or 2) of the largest Jordan block in the Jordan form of the companion matrix but more generally the geometric multiplicities, the algebraic multiplicities and the whole Jordan structure for eigenvalues of index 1 or 2. In the context of the Granger representation theorem, this means that the Johansen rank conditions do more than determine the order of integration of the process. It is then shown that an extension of these conditions leads to the characterization of the Jordan structure of any matrix. [ABSTRACT FROM AUTHOR]

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Positional reasoning is the process of ordering an unsorted set of parts into a consistent structure. To address this problem, we present Positional Diffusion , a plug-and-play graph formulation with Diffusion Probabilistic Models. Using a diffusion process, we add Gaussian noise to the set elements' position and map them to a random position in a continuous space. Positional Diffusion learns to reverse the noising process and recover the original positions through an Attention-based Graph Neural Network. To evaluate our method, we conduct extensive experiments on three different tasks and seven datasets, comparing our approach against the state-of-the-art methods for visual puzzle-solving, sentence ordering, and room arrangement, demonstrating that our method outperforms long-lasting research on puzzle solving with up to + 17 % compared to the second-best deep learning method, and performs on par against the state-of-the-art methods on sentence ordering and room rearrangement. Our work highlights the suitability of diffusion models for ordering problems and proposes a novel formulation and method for solving various ordering tasks. We release our code at https://github.com/IIT-PAVIS/Positional_Diffusion. • The article presents a novel method for Ordering Elements of a Set in 1D and 2D space. • We propose a task-agnostic method, Positional Diffusion for different ordering tasks • Our approach combines Graph Neural Networks with Diffusion Probabilistic Models. • Without any task-specific modes, our method can outperform task-specific approaches. • We test our approach on Sentence ordering, Visual Puzzles, and Furniture Arrangement. [ABSTRACT FROM AUTHOR]

The smart mobile Internet of Things (IoT) is a novel paradigm that is rapidly gaining ground in the field of modern wireless telecommunications. Picking operations are the key link in an entire warehousing operation, and congestion problems significantly contribute to managers' decisions regarding the warehousing layout, the storage strategy, and the number of pickers. In this paper, the discrete Markov method is used to study the problem of picking congestion in a narrow-aisle double picking system with a traditional layout, an S-type picking path and a classified storage strategy. First, congestion models are constructed when the picker picks one item with a picking speed that is equal to the walking speed and when the picker picks multiple items on one picking surface under a picking speed that is very different from the walking speed. A sensitivity analysis involving parameter changes was carried out. Second, the paper compares and analyzes situations in which the picking speed is equal to the walking speed and in which there is a large difference between the picking speed and the walking speed and determines the impact of the picking speed, the number of goods picked on a picking surface, the differentiated classified storage strategy, and the number of picking surfaces on the picking congestion rate. The effectiveness of the mathematical model was verified by a MATLAB simulation, and the exploration and discovery for subsequent research were performed. [ABSTRACT FROM AUTHOR]

This paper deals with some probabilistic and statistical properties of periodic generalized Poisson integer-valued autoregressive processes of order p, PG P INAR (p). Necessary and sufficient conditions for the periodic stationarity, both in mean and second order, are established. The closed-forms of the mean and the second moment are, under these conditions, obtained. Moreover, the Wold–Cramér expression of the underlying second-order periodically stationary process is then established. The autocovariance structure is studied, while providing the closed-form of the periodic autocorrelation function. The Yule–Walker (YW), the two-stage conditional least squares (CLS) and the conditional maximum likelihood (CML) estimation methods of the underlying parameters are obtained. An intensive simulation study and an application on a real count data consisting of the daily number of daytime road accidents in Schiphol area, in Netherlands are provided. [ABSTRACT FROM AUTHOR]

New families of composition methods with processing of order 4 and 6 are presented and analyzed. They are specifically designed to be used for the numerical integration of differential equations whose vector field is separated into three or more parts which are explicitly solvable. The new schemes are shown to be more efficient than previous state-of-the-art splitting methods. [ABSTRACT FROM AUTHOR]

Ethylene glycol (EG) is a valuable commodity organic intermediate that is produced using the catalyzed gas-phase hydrogenation process of dimethyl oxalate (DMO) from syngas. The reactor process is challenging to control because of its nonlinearity and multivariable condition. Thus, this study proposes the application of Neural Wiener model predictive control (NWMPC) for DMO hydrogenation reactor control. The application of empirical-based MPC, such as NWMPC, is still new in DMO hydrogenation reactor control. In order to simulate the process, the DMO hydrogenation reactor is modeled using Aspen Plus and Aspen Dynamic software. The Neural Wiener (NW) model is developed based on state space and neural network modeling using a Linear-Nonlinear (L-N) identification approach. A validation test is also performed to verify the accuracy of the NW model. Based on the test, the model accuracy is acceptable with the coefficient of determination (R2) of 0.965 for EG output mole fraction (first output) and R2 of 0.936 for product temperature (second output). The NWMPC capability is evaluated with a PID controller to handle a setpoint change in EG output mole fraction and reject disturbance in the feed stream flow rate. The control performance results have demonstrated the superior ability of the NWMPC to handle such scenarios better than PID in terms of controller action speed and profile. [ABSTRACT FROM AUTHOR]

Order batching facilitates order picking by merging orders into single vehicle trips. Filling orders with a total volume larger than a vehicle's bin capacity, however, requires binning into multiple suborders, a procedure that influences batching performance by altering both the number of suborders and the routes for each suborder. This paper introduces the binning and batching problem (BBP) in an order picking system with pick support vehicles. To minimize the weighted sum of the number of bins and the total travel distance, we propose a binning and batching model (BBM) based on a mixed-integer programming (MIP) and an MIP-based heuristic for large-scale BBPs. Our heuristic obtains near-optimal solutions by the tight lower bound in the problems. A comparison of the heuristic and lower bound shows optimal gaps between 1.38 and 9.21% in a parallel-aisle system for 250–1000 orders. We demonstrate that the heuristic achieves the shortest travel distance for a large number of orders when the number of bins varies within a reasonable range. [ABSTRACT FROM AUTHOR]