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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]

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]

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 study analytically develops a new recovery planning optimisation model for managing the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a mathematical model for the ideal plan is designed and then extended to generate a recovery plan in a finite planning horizon that maximises total profit. Recovery plans are generated considering two scenarios, namely the dynamic and uncertain situations. For the dynamic situation, a realistic system with timedependent and dynamic demand, supply, and warehouse capacity for investigating the impacts of the COVID-19 outbreak is developed using several measures, such as collaborating with emergency suppliers, increasing warehouse capacity, and considering back-orders and lost sales to form recovery strategies. For the uncertain situation, demand, supply, and warehouse capacities are considered as uncertain variables. Further, an innovative solution approach using an adapted differential evolution technique, which is capable of (i) generating long-term recovery plans and (ii) solving both small- and large-scale problems, is developed. The results are illustrated using numerical analyses and simulation experiments. A sensitivity analysis is also conducted. In practice, the proposed optimisation model will assist the decision-makers of online business operations facing the COVID-19 outbreak to decide the optimal recovery plans. [ABSTRACT FROM AUTHOR]

Market trends such as globalisation, increasing customer expectations, expensive industrial land and high labour costs cause a need for efficient order picking systems in practice. However, managers often do not implement findings from academic research on order picking planning into practice because researchers hardly account for practical factors (e.g. high-level storage, human factors, pick vehicle properties) or make unrealistic assumptions in their solution algorithms. A state-of-the-art review of the scientific literature on order picking planning (1) identifies and classifies highly influential practical factors, (2) shows the impact of these practical factors on order picking performance, and (3) illustrates how existing order picking planning models should be elaborated to account for practical factors. This study contributes to close the gap between research and practice by guiding future researchers to further increase the practical applicability of their research results. [ABSTRACT FROM AUTHOR]

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

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

The autocovariance and cross‐covariance functions naturally appear in many time series procedures (e.g. autoregression or prediction). Under assumptions, empirical versions of the autocovariance and cross‐covariance are asymptotically normal with covariance structure depending on the second‐ and fourth‐order spectra. Under non‐restrictive assumptions, we derive a bound for the Wasserstein distance of the finite‐sample distribution of the estimator of the autocovariance and cross‐covariance to the Gaussian limit. An error of approximation to the second‐order moments of the estimator and an m‐dependent approximation are the key ingredients to obtain the bound. As a worked example, we discuss how to compute the bound for causal autoregressive processes of order 1 with different distributions for the innovations. To assess our result, we compare our bound to Wasserstein distances obtained via simulation. [ABSTRACT FROM AUTHOR]

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

We develop two models for the temporal evolution of extreme events of multivariate k$$ k $$th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data. [ABSTRACT FROM AUTHOR]

Racist hate speech on UK university campuses remains an increasing concern. This article examines the widespread problem of racist hate speech through a multilayered theoretical approach; and suggests that a fuller appreciation of the problem can only be gained through a race-centred analysis. Drawing on Fraser's work on the problematics of misrecognition and identity-reification, the article extends Levine-Rasky's theoreticisation of power dynamics in the intersectionality between whiteness and middle-classness to intersections of whiteness with both middle-classness and working-classness as a contributing factor to mobilisation of lad cultures which is termed as white-laddism. The article advances the understanding of racist hate speech through a conjoined analysis of these concepts through a Critical Race Theory (CRT) lens. The conjoined analysis is informed by the Deleuzian 'rhizomatic' approach in order to examine the process by which multiple, diverse and non-hierarchical lines of connection between the concepts shape, dislocate, modify each other in variable ways. The detail behind the bigger picture helps further our understanding of the inadequate nature of equality policies and strategies that often look at issues in isolation, and thus fail to address the inequality and injustice being perpetuated on campuses. [ABSTRACT FROM AUTHOR]

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

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

To probe the processing of gaze-dependent positional information and gaze-independent order information when matching strings of characters, we compared effects of visual similarity (hypothesized to affect gaze-centered position coding) with the effects of character transpositions (hypothesized to affect the processing of gaze-independent order information). In Experiment 1, we obtained empirical measures of visual similarity for pairs of characters, separately for uppercase consonants and keyboard symbols. These similarity values were then used in Experiment 2 to create pairs of four-character stimuli (four letters or four symbols) that could differ by substituting one character with a different character from the same category that was visually similar (e.g., FJDK–FJBK) or dissimilar (e.g., FJVK–FJBK). We also compared the effects of transposing two characters (e.g., FBJK–FJBK) with substituting two characters (e.g., FHSK–FJBK). "Different" responses were harder to make in the single substitution condition when the substituted character was visually similar, and this effect was not conditioned by character type. On the other hand, transposition costs (i.e., greater difficulty in detecting a difference with transpositions compared with double substitutions) were greater for letters compared with symbols. We conclude that visual similarity mainly affects the generic gaze-dependent processing of complex visual features, and that the encoding of letter order involves a mechanism that is specific to reading. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has become a widely used technique in 3D printing, but it has proven to be a very costly process, even when optimizing parameters in existing models. Due to the characteristics of AM, and in order to optimize its process, a new approach is introduced to the problem: the discretization of each layer to be printed. This involves establishing an order relation based on the sequence in which the layers should be printed. The valid orders for the execution of the process, referred to as compatible with the order relation, will be characterized. Additionally, algorithms will be provided to obtain new compatible orders from others that were already compatible, and strategies will be presented to optimally and efficiently reorder non-compatible orders, converting them into compatible ones. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results. Design/methodology/approach: This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance. Findings: The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants' experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs. Originality/value: Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM. [ABSTRACT FROM AUTHOR]

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

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

Human creativity is a powerful cognitive ability underlying all uniquely human cultural and scientific advancement. However, the neuronal basis of this creative ability is unknown. Here, I propose that slow, spontaneous fluctuations in neuronal activity, also known as "resting state" fluctuations, constitute a universal mechanism underlying all facets of human creativity. Support for this hypothesis is derived from experiments that directly link spontaneous fluctuations and verbal creativity. Recent experimental and modeling advances in our understanding of the spontaneous fluctuations offer an explanation for the diversity and innovative nature of creativity, which is derived from a unique integration of random, neuronal noise on the one hand with individually specified, deterministic information acquired through learning, expertise training, and hereditary traits. This integration between stochasticity and order leads to a process that offers, on the one hand, original, unexpected outcomes but, on the other hand, endows these outcomes with knowledge-based meaning and significance. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has changed people's shopping habits, causing them to turn to online shopping sites, especially on the internet. In the face of increasing order rates, it has become inevitable for manufacturers and suppliers to produce innovative solutions for collecting orders and delivering them on time. Companies/suppliers who had to use a limited number of manpower due to the pandemic have tried to solve this problem with the use of machinery. In this study, a new low-cost Order Picking and Distribution System application developed for the stages of order picking and distribution in the supply chain is described. This stage of the supply chain is the most problematic, time-consuming and costly. In general, more human resources are used in the proposed solutions. With this application, it is aimed to create a more efficient system in terms of time, manpower, warehouse space and cost. For this purpose, a prototype Order Picking and Distribution System application was designed, manufactured and implemented in real time. [ABSTRACT FROM AUTHOR]

Monte Carlo event generators for hard hadronic collisions depend on the evolution of parton showers backwards from a high-scale subprocess to the hadronization scale. The evolution is treated as a branching process with a sequence of resolvable parton emissions. The criterion of resolvability involves cutoffs that determine the no-emission probability (NEP) for a given range of the evolution scale. Existing event generators neglect cutoff-dependent terms in the NEP that, although formally power-suppressed, can have significant phenomenological effects. We compute such terms and study their consequences. One important result is that it is not possible for the backward shower to faithfully reproduce the cutoff-independent parton distribution functions (PDFs) used to generate it. We show that the computed NEP corrections mitigate but do not eliminate this problem. An alternative approach is to use cutoff-dependent PDFs that are consistent with the uncorrected NEP. Then one must apply cutoff-dependent corrections to hard subprocess matrix elements. We compute those corrections to the first nontrivial order for the Drell-Yan process and for Higgs production by gluon fusion. [ABSTRACT FROM AUTHOR]

3D point cloud has irregularity and disorder, which pose challenges for point cloud analysis. In the past, the projection or point cloud voxelization methods often used were insufficient in accuracy and speed. In recent years, the methods using Transformer in the NLP field or ResNet in the deep learning field have shown promising results. This article expands these ideas and introduces a novel approach. This paper designs a model AaDR-PointCloud that combines self-attention blocks and deep residual point blocks and operates iteratively to extract point cloud information. The self-attention blocks used in the model are particularly suitable for point cloud processing because of their order independence. The deep residual point blocks used provide the expression of depth features. The model performs point cloud classification and segmentation tests on two shape classification datasets and an object part segmentation dataset, achieving higher accuracy on these benchmarks. [ABSTRACT FROM AUTHOR]

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer. [ABSTRACT FROM AUTHOR]

Conical pick cutters are by far the most common type of rock cutting tools used on variety of excavation equipment in mining and civil applications. Prediction of the forces acting on pick cutters when cutting rock can be an important input towards cutterhead design and performance estimation for such equipment. The cutting forces are a function of the rock strength and pick tip geometry, which is impacted by the bit wear. To develop a prediction model, a database of mean cutting force (MCF) measured in full-scale testing has been compiled and subsequently analyzed using regression methods to find the empirical equations linking MCF to the bit, cutting geometry and rock properties. Full-scale cutting tests are known to offer precise measurement of the cutting forces for a given bit geometry, cutting geometry (spacing between the cuts and depth of penetration), and combined rock mechanic characteristics of the sample; consequently, the information can be used to develop models of prediction of cutting forces. Linear and log-linear regression and tree-based machine learning models (random forest, decision tree, and extreme gradient boost) were used for the analysis of the experimental data. The results demonstrated that while using input parameters including uniaxial compressive strength (UCS), spacing, Brazilian tensile strength (BTS), penetration, and pick cutter's tip radius and tip angle, models can offer a reasonable prediction of the cutting forces. Among the models that have been examined, regression tree models, especially extreme gradient boost shows the highest coefficient of determination (R2), and the lowest mean absolute error (MAE). Highlights: The key input parameters that affect MCF are recognized, and their influence on MCF is thoroughly examined. The most important parameters for predicting cutting forces are UCS, tip angle, penetration, and BTS. Tree-based machine learning models demonstrate superior performance in predicting MCF. [ABSTRACT FROM AUTHOR]

In the paper, we solved the problems regarding the improvement of the production process of allocating material to the inlet of a production hall using the Plant Simulation software in a selected company. The solved problematics are high costs for the shipping company, elimination of the necessity to transfer the semi-finished product from the external warehouse to the production hall and non-effective layout. We deal with the characteristics of the current state of the production process in the production company and create its model in Plant Simulation. Thereafter we propose solutions that can make that process more efficient. The software enables the comparison of complex production alternatives. [ABSTRACT FROM AUTHOR]

This paper establishes an Edgeworth expansion for the t-statistic of the Whittle Maximum Likelihood Estimator (WMLE) of a linear regression model whose residual component is stationary, Gaussian, and strongly dependent time series. Under the widely used set of assumptions and two more mild additional conditions on the spectral density function and the parametric values, an Edgeworh expansion of the t-statistic of arbitrarily large order of the process is proved to have an error of o (n 1 − s / 2) where s is a positive integer. [ABSTRACT FROM AUTHOR]

The flexibility and programmability of SDN enable dynamic and automated network configuration and traffic routing. However, this also provides more avenues for malicious code propagation, leading to serious risks such as service disruptions and privacy breaches. To address this problem, we first designed three modules to suppress malicious code propagation: the abnormal traffic detection module, the malicious code analysis module, and the abnormal traffic tracing module. Then, the sharing mechanism is introduced. In order to analyze the process of malicious code propagation more clearly, based on the above strategy, this paper introduces the warning node into the classical SIR model, which can be exploited for studying how to control malicious code propagation to prevent large-scale outbreaks. The propagation threshold and equilibrium point of the proposed model are obtained through calculations. By constructing a Lyapunov function, the equilibrium point is proven stable. Finally, numerical simulation results indicate that when the detection rate reaches 90%, approximately 86.3% fewer nodes are infected at the peak point. Through comparative analysis, our system demonstrates optimal performance, validating the effectiveness of the analytical results. [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]

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]

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]

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]

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]

Existing research on the contestation of the liberal order predominantly focuses on the corrosive effects of exogenous spoilers or the peculiarities of anti-liberal politics in domestic contexts. Yet, we know little about how the internal evolution of orders affects their long-term viability. As a first step towards developing an endogenous theory of international order transformation, this article draws inspiration from norm translation theory and develops the concept of the consolidation dilemma as a trade-off between rigidity and resilience in the evolution of international normative orders. This article argues that the process of order consolidation can paradoxically result in the decline of its overall resilience by undermining the constructive ambiguity needed to maintain a broader appeal. Applying this framework to the illustrative cases of Poland and Hungary, the article shows that challenges to the European liberal order did not materialise in the 1990s because its potential resisters were included within the thin liberal order of the time. Over time, however, Europe's liberal order underwent an endogenous transformation from a thin to a thick order with more clarity, institutionalisation, and value enforcement. This internal transformation narrowed down its support base and compromised its overall resilience to withstand endogenous and exogenous shocks. [ABSTRACT FROM AUTHOR]