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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]

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]

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

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

This work focuses on the modeling of the features of a real-world pharmaceutical production problem, where several limited resources must be gathered and scheduled in order to mix and process active substances. The problem can be modelled as a generalization of the job-shop scheduling problem, in which each job corresponds to an order. Besides the standard precedence and capacity constraints of a classical job-shop scheduling problem, release, no-wait and time interval production constraints are added. Furthermore, the processing of each job task must be assisted by an appropriate number of operators and performed by a specific machine, out of a set of feasible machines, that is temporarily installed in a compatible clean room. An innovative generalized disjunctive graph that models all the constraints and the resource conflicts is presented. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

This article designs a conceptual model for the procurement and distribution processes in nanostores in the southwest of Barran-quilla, Colombia considering 256 nanostores. The data collection involved identifying the nanostores through a visit to the area and creating a data collection instrument with specific questions covering various variables. As a result of the model, the procurement process comprises stages such as ordering, order processing, preparation and dispatch, distribution, payment, return, and after-sales. The findings show that nanostores primarily use indirect distribution channels, establishing relationships with suppliers and placing orders directly or through sales agents. Based on the research findings, the article suggests implementing digital marketing platforms to facilitate order placement and delivery for nanostore owners, reducing their transportation costs and improving the condition of products received. This study contributes to understanding procurement and distribution dynamics in nanostores, providing valuable insights for nanostore owners, policymakers, and researchers, improving the operations and competitiveness of nanostores, benefiting both the local economy and the broader retail sector. [ABSTRACT FROM AUTHOR]

Organisms experience environmental conditions over contiguous geographic space. However, empirical studies tend to characterize this environment in a way that is discrete and often subjective. Owing to practical limitations, the spatial grain (resolution) at which we record environmental conditions often fails to capture the true environmental variation experienced by a species, therefore hindering estimation of the ecological niches of species. Despite the general awareness of the importance of spatial scale in niche studies, the impact of variable spatial grains in biodiversity and environmental data on ecological and evolutionary inference remains insufficiently appreciated. We illustrate the key drivers and consequences of the grain size-dependence of environmental niche estimates and provide general recommendations on how to address the issue of grain size-dependence in empirical studies. Species environmental niches are central to ecology, evolution, and global change research, but their characterization and interpretation depend on the spatial scale (specifically, the spatial grain) of their measurement. We find that the spatial grain of niche measurement is usually uninformed by ecological processes and varies by orders of magnitude. We illustrate the consequences of this variation for the volume, position, and shape of niche estimates, and discuss how it interacts with geographic range size, habitat specialization, and environmental heterogeneity. Spatial grain significantly affects the study of niche breadth, environmental suitability, niche evolution, niche tracking, and climate change effects. These and other fields will benefit from a more mechanism-informed choice of spatial grain and cross-grain evaluations that integrate different data sources. [ABSTRACT FROM AUTHOR]

Polycentric organizations allow highly functional, or "group-beneficial," outcomes to emerge from the myopic behavior of rule-guided individuals. How does polycentricity achieve this feat? Drawing on multilevel selection theory, I argue that polycentric orders support successful outcomes by defining group boundaries and reducing within-group fitness variance relative to between-group variance. The Price equation suggests that, by doing so, polycentric orders facilitate a process of collectively beneficial adaptation, including the capacity to evolve mechanisms for monitoring and punishing rule violators. [ABSTRACT FROM AUTHOR]

In order to facilitate the processing of vast amounts of data that emerges in the study of fuzzy social networks, scholars have developed various procedures for reducing these networks. Some procedures use regular and structural fuzzy relations to reduce such networks. In this article, we generalize the notions of regular and structural fuzzy relations to obtain even better reductions of fuzzy networks. More precisely, for a fuzzy social network given by the set of social entities and the family of fuzzy relations between them, we define μ -approximate regular fuzzy relations, where μ is the degree taken from the underlying set of truth values, which is a complete Heyting algebra. Using these specific fuzzy relations, we show that it is possible to reduce a fuzzy social network in some cases when previously developed algorithms fail to reduce it. We investigate the properties of μ -approximate regular fuzzy relations. We show that the blockmodel of a fuzzy social network, which is its reduced fuzzy social network built via μ -approximate regular fuzzy preorder, retains specific structure-preserving properties. We give a method for calculating the greatest μ -approximate regular fuzzy relation on a given fuzzy social network. For fuzzy social networks defined over the real-unit interval [ 0 , 1 ] , we give a procedure that determines all subintervals of [ 0 , 1 ] that share the greatest μ -approximate regular fuzzy relation. Analogous results are provided for μ -approximate structural fuzzy relations. [ABSTRACT FROM AUTHOR]

• Neural maps convert complex environments into neural space in the brain. • Maps form by molecular guidance, competition, and activity-dependent plasticity. • Maps exhibit a nested structure that allows multiplexing of information. • Fine-scale heterogeneity enhances the computational capacity of the neural circuit. Neural maps are found ubiquitously in the brain, where they encode a wide range of behaviourally relevant features into neural space. Developmental studies have shown that animals devote a great deal of resources to establish consistently patterned organization in neural circuits throughout the nervous system, but what purposes maps serve beneath their often intricate appearance and composition is a topic of active debate and exploration. In this article, we review the general mechanisms of map formation, with a focus on the visual system, and then survey notable organizational properties of neural maps: the multiplexing of feature representations through a nested architecture, the interspersing of fine-scale heterogeneity within a globally smooth organization, and the complex integration at the microcircuit level that enables a high dimensionality of information encoding. Finally, we discuss the roles of maps in cortical functions, including input segregation, feature extraction and routing of circuit outputs for higher order processing, as well as the evolutionary basis for the properties we observe in neural maps. [ABSTRACT FROM AUTHOR]

• The performance of batch picking (BP) and batch-synchronized zone picking (BSZP) are compared with learning effects. • The heuristic methods, i.e. genetic algorithms, are proposed to solve BP and BSZP strategies. • With learning effects, although BSZP needs to travel longer than BP , it needs a shorter search time than BP. • If pickers' working days are long enough, the search time of BP becomes closer to BSZP , leading BP to outperform BSZP. • Which strategy outperforms mainly depends on learning rates, storage assignments, and pickers' working days. The performance of two common picking strategies, batch picking (BP) and batch-synchronized zone picking (BSZP), are compared by considering pickers' learning effects in online-to-offline (O2O) groceries. As we know from previous research, one major advantage of BSZP is that pickers can be more familiar with the item locations because each picker works in a relatively smaller picking area. However, few papers have formulated a quantitative method to verify this advantage. To address this, we use pickers' learning effects to quantify their familiarity with the item locations, and then compare the picking efficiency in BP and BSZP with learning effects. The optimization models of BP and BSZP are formulated to minimize the total service time, and heuristic methods are proposed to solve these models. The case study is implemented under three order types and three storage assignment policies, i.e. Random, ABC-1, and ABC-2. The results of the study indicate that with learning effects, although BSZP needs a longer travel time than BP , it requires a shorter search time. Which strategy, BP or BSZP , is more efficient mainly depends on the learning rates, storage assignment policies, and pickers' working days. When the storage assignments are Random and ABC-1 , a smaller learning rate can lead BSZP to perform better than BP. When the storage assignment is ABC-2, BSZP performs better than BP. When pickers' working days are long enough, the learning effect is weakened, and BP outperforms. Awareness of the learning effects is required to choose a better picking strategy, evaluate more accurate picking efficiency and hire an appropriate number of pickers. [ABSTRACT FROM AUTHOR]

What is the appropriate role of the state in economic policy-making? This paper shows that Friedrich Hayek, who is often considered a proponent of laissez-faire liberalism, offers three different answers to this problem. First, Hayek argues that the state should provide a legal framework for competitive markets. Second, he proposes to employ the rule of law criteria – generality, equality, and certainty – to distinguish permissible from non-permissible state interventions. Third, he downplays the importance of deliberate legislation and moves closer to the Misesian idea of a minimal state. The paper considers these answers in light of Hayek's analysis of the knowledge problem. We suggest that a Hayekian approach to economic policy-making should focus on improving the framework of general rules that guide individual behavior, thereby enabling spontaneous ordering processes and reducing the epistemological burden placed on policy-makers. [ABSTRACT FROM AUTHOR]

We propose a model for image classification by attentional search. Analogous to how humans scan an image by a sequence of saccades, in this model, an attentional window of size much smaller than the target size scans the target by a sequence of "saccades", integrates the information acquired, and makes a classification decision. In order to process a sequence of attended image segments, the network must have memory, which is incorporated through 3 kinds of recurrent elements in the network architecture: Elman connections, Jordan connections, and Flip-flop neurons. The architecture of the model is designed as three separate channels labeled as – classifier network, eye-position network, and saccade network. Multiple attentional windows with different resolutions and a common center are given as input to the classifier network and the saccade network. The heat-map representation of the location of the attentional windows is given as input to the eye-position network. The saccade network predicts the next jump of the attention windows with the help of reward signals received by the classifier network. The output features of all the three channels are concatenated, before finally terminating in two output layers representing class prediction and next saccade prediction. The model is trained using deep Q-learning algorithm. Attentional search model is evaluated on MNIST handwritten digit, Kannada MNIST, Medical-MNIST, OCTMNIST, and QuickDraw datasets. Translated and Cluttered Translated versions of each dataset are generated to perform the task of classification based on local target search. Original datasets are used to show the task of classification based on search with global target integration. We also evaluate the saccade performance on Extended Yale Face B database. In various problem cases, the model exhibits comparable or superior performance to a state-of-the-art recurrent attention model. Demo code is available in this link. [ABSTRACT FROM AUTHOR]

This paper is a continuation of our previous paper (Liu and Pagès, 2020). In this paper, we establish the monotone convex order (see further (1.1)) between two R -valued McKean–Vlasov processes X = (X t) t ∈ [ 0 , T ] and Y = (Y t) t ∈ [ 0 , T ] defined on a filtered probability space (Ω , F , (F t) t ≥ 0 , P) by d X t = b (t , X t , μ t) d t + σ (t , X t , μ t) d B t , X 0 ∈ L p (P) with p ≥ 2 , d Y t = β (t , Y t , ν t) d t + θ (t , Y t , ν t) d B t , Y 0 ∈ L p (P) , where ∀ t ∈ [ 0 , T ] , μ t = P ∘ X t − 1 , ν t = P ∘ Y t − 1. If we make the convexity and monotony assumption (only) on b and | σ | and if b ≤ β and | σ | ≤ | θ | , then the monotone convex order for the initial random variable X 0 ⪯ mcv Y 0 can be propagated to the whole path of processes X and Y. That is, if we consider a non-decreasing convex functional F defined on the path space with polynomial growth, we have E F (X) ≤ E F (Y) ; for a non-decreasing convex functional G defined on the product space involving the path space and its marginal distribution space, we have E G (X , (μ t) t ∈ [ 0 , T ] ) ≤ E G (Y , (ν t) t ∈ [ 0 , T ] ) under appropriate conditions. The symmetric setting is also valid, that is, if Y 0 ⪯ mcv X 0 and | θ | ≤ | σ | , then E F (Y) ≤ E F (X) and E G (Y , (ν t) t ∈ [ 0 , T ] ) ≤ E G (X , (μ t) t ∈ [ 0 , T ] ). The proof is based on several forward and backward dynamic programming principles and the convergence of the truncated Euler scheme of the McKean–Vlasov equation. [ABSTRACT FROM AUTHOR]

Dual-layer displays (DLDs) can achieve high dynamic range by reducing light leakage via bonding two LC panels. However, the physical distance between the two LC panels introduces a parallax error when the display is viewed off-axis. This paper proposes a convolutional neural network-based image splitting algorithm for improving the display quality of a DLD. In order to improve processing speed, the network structure is designed relatively simply. The training dataset is established mainly based on TV signals. Different from the common image evaluation metrics based on PSNR and SSIM, the paper proposes to apply learned perceptual image patch similarity (LPIPS) to define the loss function for DLDs. The simulation and experiment results show that compared with the existing method, the proposed method has better performance in reducing parallax error and improving image details. The processing time can be shortened by 2.7 times. The proposed CNN-based method can greatly reduce the processing time while maintaining high display quality, which presents high practicability. [ABSTRACT FROM AUTHOR]

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use. [Display omitted] • Comparison of msDial, mzMine and Progenesis Qi. • Targeted and untargeted analysis of peak picking data. • Progenesis Qi shows no superiority over msDial or mzMine. • Peak width a new quality parameter proposed. • Manual classification of features shows 62 % true positive rate in msDial. [ABSTRACT FROM AUTHOR]

In order to process speech, most state-of-the-art experimental methods employ convolutional neural networks (CNNs), which operate on a continuous, 1-dimensional (1-D) time stream. In an audio signal, the mel-spectrogram facilitates the representation of attributes of the utterances' in the frequency domain (which corresponds to the speech spectrum). Moreover, for a time-series speaker signal, CNNs are superior to machine or transfer learning models in capturing characteristics from long-form talks. This paper introduces a jump-connected 1-D CNN that employs a combined loss function for speaker recognition. The suggested model uses a 1-D convolutional layer combined with jump connections to extract speaker-specific characteristics; this reduces time-based and frequency-based variability for faster computing. A combined softmax loss, stable L2-norm, and smooth L1-norm loss function guide the proposed compact convolutional neural networks (CCNN) to identify the correct spokesman with improved efficacy. We evaluated the proposed framework using various standard and real-time audio datasets. The experimental findings demonstrate that the proposed CCNN outperforms existing approaches by reducing the equal error rate by 9.02 %. Also, our recommended voiceprint identification model achieves an impressive average speaker recognition rate of 98.76 %. Simultaneously, the reliability of the 1-D CCNN is tested under various conditions. Other fields of study, like language modelling, could employ this approach after some fine-tuning. Relevance of the work: Speaker recognition is an area of interest in which machine learning (ML) and deep learning (DL) schemes, when combined, have the potential to make history in the areas of forensic sciences, automation, and authentication. Using a modest CNN can enhance the identification and verification process by ignoring many issues such as false positives, background noise, and so on. Expanding this process would facilitate raga identification and disease treatment therapies. [ABSTRACT FROM AUTHOR]

A relatively new goods-to-person order picking system (robotic compact storage and retrieval system, RCS/RS) has been implemented within order fulfillment centers. In such a system, robots move in x - and y -dimensions on top of a grid-based storage area to retrieve bins from stacks by "digging" in z -dimension. To transport a requested bin to a port, robots (or a robot) remove(s) all blocking bins above the requested bin and place(s) them around neighboring stacks. Defining the throughput of an RCS/RS as the number of bins processed per unit time, we analyze the time required for bins to be removed from stacks, transported to ports, returned to stacks, and placed in stacks. In making a case for considering skewness among activity levels for bins, we develop closed-form expressions to calculate the expected operation time for the movement of bins, validate the closed-form expressions using simulation, and obtain the system configuration that maximizes throughput performance (or minimizes operation time for bins) based on current practice. Then, with an objective of having an optimal RCS/RS design based on activity levels of bins, we show the significant impact on throughput when different skewness values for activity levels of the bins are incorporated into the design. [ABSTRACT FROM AUTHOR]

• NEON plant diversity data are not ready for most ecological analysis out of the box. • neonPlantEcology is an R package for accessing and formatting these data. • It aggregates to 5 spatial and 3 temporal scales aligned with the sampling design. • It provides functions for a workflow from raw data to analysis-ready data. The National Ecological Observatory Network (NEON) is a continental-scale endeavor of ecological data collection for 30 years. We created a software package, neonPlantEcology that automatically arranges the raw data from the plant presence and percent cover (DP1.10058.001) data product from NEON into tables familiar to plant ecologists. Because of the broad scale of the observatory, it is necessary to tailor the data collection to the idiosyncrasies of each of 47 different ecosystems. Furthermore, data collection practices are occasionally modified for various reasons. These complexities, along with the volume and multiscalar nature of the data, need to be understood and accounted for in order to correctly process the data. This is particularly true for the plant diversity data product. We present three case studies using the package, centered around the three primary functions of neonPlantEcology. By automating the process of preparing NEON's plant diversity data, neonPlantEcology makes it more accessible to a wide range of users. [ABSTRACT FROM AUTHOR]

Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies. • 94 studies on the functional brain development of visual processing from infancy to adulthood were reviewed. • No clear consensus supporting either the interactive specialization or maturational frameworks of development. • Implications and suggestions for future research in the field of developmental neuroimaging are discussed. [ABSTRACT FROM AUTHOR]

This paper is concerned with consensus for fractional-order multi-agent systems by observer-type output feedback with positive constraint depicted by general linear model. First, an observer-based protocol is presented and the sufficient conditions for the consensus for fractional-order multi-agent systems with positive constraint are derived. Then, the simplified conditions without utilizing the detailed topology of the graph are given. In order to simplify the process of the solution of the feedback gain matrix and the observer gain matrix, the linear programming approach is used. When the system matrix is strictly Metzler, semidefinite programming approach is adopted. Finally, some illustration examples are presented as the verification of the obtained methods. [ABSTRACT FROM AUTHOR]

Over the past decade, increased use of social media has led to an increase in hate content. To address this issue new solutions must be implemented to filter out this kind of inappropriate content. Because manual filtering is difficult, several studies have been conducted in order to automate the process. This paper introduces a method based on a combination of Glove and FastText word embedding as input features and a BiGRU model to identify hate speech from social media websites. The obtained results show that our proposed model (BiGRU Glove FT) is effective in detecting inappropriate content. This model detect hate speech on OLID dataset, using an effective learning process that classifies the text into offensive and not offensive language. The performance of the system attained 84%, 87%, 93%, 90% accuracy, precision, recall, and f1-score respectively. [ABSTRACT FROM AUTHOR]

It is important from a techno-economic perspective to understand the challenges associated with developing scalable processes to extract functional proteins from rapeseed cake for applications as functional food ingredients. One of these challenges is to develop a better understanding of the link between upstream mechanical processing of the rapeseed cake and the yield of extracted protein. An initial trial with cold pressed rapeseed cake (CPR) indicated that wet milling had no impact on protein yield. Measurement of protein yields with the Bradford assay indicated that 14% would be recovered. The total protein recovered (5.4% by Kjeldahl analysis) contained a glucosinolate and phytate content that was similar to the rapeseed cake at the start of the process. The fate of the protein extraction was followed during a pilot scale trial with hot-pressed rapeseed cake (HPR), using only stirring followed by sieving, revealing an increase in protein yields, as determined by the Bradford assay, from 8.0% at the start to 22.6% at the end of processing In addition, only 16% of HPR was solubilized. A similar pilot scale trial with HPR using wet milling and stirring followed by decanting also showed an increase in protein concentration from 15.3% at the start to 23.7% at the end of processing, In addition, 38% of HPR was solubilized. This study provides an insight into what the effect of upstream mechanical processing (wet milling) has in releasing protein from rapeseed cake in order to improve process efficiency. This could have important consequences for upscaling and commercial extraction of rapeseed cake protein. [ABSTRACT FROM AUTHOR]

• Market redevelopment in Ghanaian cities is implemented through six phases. • The governance of market redevelopment in African cities is highly political. • Citizen resistance of urban redevelopment emanates from a politicized urban governance. • Analysis of the nexus between urban regeneration, politics and governance in African cities produces new insights and concepts. Recent studies have argued that most state-led urban regeneration projects around the globe are implemented through the four-stage process of scoping, planning, financing and implementation. This article examines the processes of market infrastructural redevelopment in Ghanaian cities and the politics that characterize each phase of the process. Through case studies of market projects in Cape Coast and Kumasi, I argue that, unlike other urban regeneration projects, the redevelopment process of market infrastructure in African cities is better conceptualized through the six stages of scoping, financing, planning, relocation, construction and allocation. I demonstrate that state-led redevelopment of market infrastructure reflect a highly politicized nature of existing practices of urban governance in Ghana and Africa more broadly. In response, there is increasing activism among market traders to intervene, challenge and change the predominant forms of urban governance. I conclude that we must pay more attention to the politics embedded in the governance of urban (re)development processes in order to understand the contemporary dynamics of citizen resistance and activism in African cities. It is through such an investigation that new insights, meanings and concepts emerge in the fields of urban politics and urban governance in Africa. [ABSTRACT FROM AUTHOR]

• Investigates a joint optimization problem of order picking and delivery. • Includes due dates, multi-picking stations, and multi-regions in the problem. • Develops a MIP model, a relaxed model for the lower bound, and a heuristic procedure. • Reduces the total costs significantly compared with benchmark procedures. • Provides guidance and managerial implications to warehouse logistics management. E-commerce platforms, driven by consumer demand for same-day or even half-day delivery, face challenges such as high order volumes with varying due dates, multiple picking stations, and multiple delivery regions. These challenges may lead to order tardiness and decreased efficiency and responsiveness of warehouse logistics. This study jointly optimizes order picking and delivery in ergonomic picking systems by addressing order assignment, sequencing, picking, and delivery operations while considering due dates and minimizing order tardiness and delivery costs. A mixed integer programming (MIP) model is formulated, and an improved knowledge-guided fruit fly optimization algorithm with the shortest waiting time rule (IKGFOA-SWT) as a heuristic solution procedure is developed to solve the problem. The IKGFOA-SWT comprises three modules including an order assignment and sequencing module using IKGFOA, an order picking module employing the SWT rule, and a batch delivery module balancing the delivery and tardiness costs. Computational experiments are conducted to validate the MIP model and evaluate the performance of IKGFOA-SWT. The benchmark experiment underscores the importance of integrating order picking and delivery operations within a unified scheduling framework rather than treating them as independent optimization tasks. The small-scale experiment verifies the effectiveness of IKGFOA-SWT in finding optimal solutions. The large-scale experiment demonstrates the superiority of IKGFOA-SWT in generating high-quality solutions. This study provides valuable managerial insights and practical tools for the optimization of warehouse logistics operations, and contributes to the new norms of supply chain sustainability and resilience. [ABSTRACT FROM AUTHOR]

Residual moveout (RMO) correction is significant for optimizing the common image gathers (CIGs) and migration stacked sections, which is a vital step in seismic data processing. The conventional RMO correction methods flatten the migrated gathers by inverse normal moveout and then pick up the velocity parameters, which require high labor of manual picking with low efficiency. The existing high-order RMO correction methods by scanning technology need a bispectral picking process. To address these issues, we propose a novel high-order RMO correction method based on a crosscorrelation-based objective function with global optimization in local time windows. The selected time windows covering the reflected events in the stacked section are used for parameter estimation. Then the very fast simulated annealing (VFSA) algorithm is used to simultaneously invert the two effective parameters (migration velocity and anellipticity anisotropic parameter) with the local CIGs in the time window, which has relatively high computational efficiency. The Hess model example test results show that the proposed method can effectively deal with the RMO problem in the case of anisotropic media, and the developed high-order RMO correction based on anisotropic assumption significantly improved the imaging performance compared to the conventional isotropic RMO correction methods, especially for steep structures. The application to a real 3D land data set further confirms the effectiveness and practicability of the proposed method. • We have developed a novel high-order residual moveout correction method. • The new scheme estimates the two effective parameters simultaneously. • The method can efficiently update the velocity and anisotropic parameter models. • The technique improves both the migrated gathers and stacked images. [ABSTRACT FROM AUTHOR]

• Adaptive Zone-fusion Network for waxberry semantic segmentation was proposed. • We have captured and provided a comprehensive and complete dataset. • Improve segmentation accuracy and reduce the impact of various interferences. • Our research promotes the advancement of automated fruit harvesting technology. In China, waxberries are a specialty fruit that require large harvesting efforts each season. To ameliorate these issues, automated fruit-picking equipment has been widely developed within the last decade. However, most image analysis research used for waxberry segmentation contains issues such as incomplete datasets, insufficient segmentation accuracy, and susceptibility to interference. Here, we developed the Adaptive Zone-fusion Network (AZNet) that enables automated fruit harvesting and is designed to precisely segmentate waxberries in an orchard environment. Initially, we collected a diverse range of images to train and evaluate a better model, and found that AZNet demonstrated exceptional performance and achieved a mean Accuracy (mAcc) of 99.83%, mean Intersection over Union (mIoU) of 96.57%, and mean Dice coefficient (mDice) of 98.20%. To further validate AZNet's effectiveness, we compared several state-of-the-art (SOTA) models, including DeepLab V3, SegNet, PSPNet, U-Net, RefineNet, FCN, DANet, and FastSCNN. Along with these comparisons, we also performed an ablation study to substantiate the individual impact of each module within AZNet. Through these experiments, we found that AZNet exhibits the capability to achieve accurate semantic segmentation of waxberries in complex orchard environments, and is suitable for automated fruit harvesting devices. Ultimately, AZNet improves upon previous models by constructing information fusion modules through spatial attention, channel attention, Atrous Spatial Pyramid Pooling (ASPP), and graph convolutional methods, which can be utilized in the future to enhance other fruit-picking software to reduce fruit loss and the investment of human resources. [ABSTRACT FROM AUTHOR]

Non-targeted and suspect screening analysis using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) holds great promise to comprehensively characterize complex chemical mixtures. Data preprocessing is a crucial part of the process, however, some limitations are observed: (i) peak-picking and feature extraction might be incomplete, especially for low abundant compounds, and (ii) limited reproducibility has been observed between laboratories and software for detected features and their relative quantification. We first conducted a critical review of existing solutions that could improve the reproducibility of preprocessing for LC-HRMS. Solutions include providing repositories and reporting guidelines, open and modular processing workflows, public benchmark datasets, tools to optimize the data preprocessing and to filter out false positive detections. We then propose harmonized quality assurance/quality control guidelines that would allow to assess the sensitivity of feature detection, reproducibility, integration accuracy, precision, accuracy, and consistency of data preprocessing for human biomonitoring, food and environmental communities. • Preprocessing of raw data from SS and NTA by (LC-HRMS) is affected by reproducibility and incomplete peak peaking. • Optimization tools and guidelines were developed to improve SSA/NTA LC-HRMS data preprocessing. • QA/QC provisions for SSA/NTS LC-HRMS data preprocessing are proposed to assess performance of preprocessing. [ABSTRACT FROM AUTHOR]

Cross-domain recommendation (CDR) leverages knowledge from the source domain to make recommendations for the cold-start users in the target domain. On account of fully utilizing information, various relationships among users and items are taken into account, i.e., the interaction relationship between users and their corresponding items; the relationship among users or items; and the indirect relationship between the user and items related to other users. In order to process these relationships, we propose a novel framework named Memory Pool Variational AutoEncoder (MPVAE). The main advantages of the MPVAE model lie in three aspects: (1) it generates the embedding representations that incorporate more information by a memory pool mechanism in the source and target domains; (2) it involves the relationship among users or items efficiently by the similarity measurement, further, the indirect relationship can be explicitly described, which makes full use of information in the source domain; and (3) it leverages the superiority of the probability model from the perspective of the VAE structure, which ensures generation and robustness. Comprehensive experiments on three real datasets show that the proposed model achieves remarkable superiority over several competitive CDR models. [Display omitted] • Propose MPVAE to generate embeddings that incorporate more information. • MPVAE involves various relationships among data in the source domain. • MPVAE ensures generation and robustness by the VAE structure. • Conduct extensive experiments on three datasets. [ABSTRACT FROM AUTHOR]

• Acceptability of copredication sentences can change with the sense order. • Acceptability is driven by context-sensitive expectations in the mind's model. • Copredication and adjective ordering effects driven by similar expectations. • Various expectation types: coherence, information structure, and structural primes. • Ordering effects can be accounted for by the predictive processing framework. Copredication is the phenomenon that two or more predicates can jointly apply to the same argument. In "The book is heavy and informative" the word book seems to refer to both a concrete physical object that can be heavy and an abstract content that can be informative. It has been observed that if the concrete sense of the nominal is triggered first, the copredication statement often sounds better compared to when the abstract sense is triggered first. However, the cognitive underpinnings of this effect are not well understood. In this theoretically oriented paper, we propose a predictive processing model of order effects aimed at advancing our understanding of that phenomenon. We also connect the debate regarding ordering preferences with an existing strand of research on ordering preferences in multi-adjective strings and the information structure of sentences. [ABSTRACT FROM AUTHOR]

• Predicting a metal-organic frameworks deliverable capacity using Gaussian processes. • Locating active subspaces enables optimal dimension reduction of pore properties. • Applied to a practical exploration example comparing methane and oxygen storage. • Found top-performing oxygen structures irrelevant to the gas in the training data. • Presented an efficient method to predict and search for promising frameworks. To date over 80,000 metal-organic framework (MOF) structures have been synthesised and only ca. 3% of these have had their adsorption capabilities measured for storing oxygen alone. As such, in order to aid the process of producing top-performing MOFs for storing various gases, accurate methods to predict the deliverable capacity of MOFs that have their synthesis method already known is increasingly important. For this purpose, this paper develops a reduced order model (ROM) that can predict the deliverable capacity of synthesised MOFs irrespective to the storage gas across similar gases. The ROM is constructed by identifying the active subspaces through a Sobol' index-based global sensitivity analysis (GSA). The resulting Gaussian process (GP) regression model efficiently predicts the deliverable capacity given a MOFs pore properties with this reduced dimensional space. This approach was applied to a practical MOF exploration example by training a ROM with 2745 MOFs storing methane at 30 bar. The ROM was robustly tested and analysed before using it to predict the deliverable capacity of 82,221 synthesised MOFs storing oxygen at 30 bar. To ensure validity in the exploration example, the predictions produced from the methane trained ROM were compared to a separate ROM trained using the same MOFs but storing oxygen gas. The methane trained ROM was found to be in agreement with the oxygen trained ROM, and was shown to be a viable tool to identify the top-performing MOF structures for oxygen storage. [ABSTRACT FROM AUTHOR]

• The dynamics of cross-diffusion leads to a high complexity on implicit finite difference schemes implementations. • Two stable operator splitting schemes are proposed to lower the complexity. • A stable factorization of the system matrix is derived to fasten the computations. • We achieve competitive running times (similar to explicit approaches) for these implicit implementations. The dynamics of cross-diffusion models leads to a high computational complexity for implicit difference schemes, turning them unsuitable for tasks that require results in real-time. We propose the use of two operator splitting schemes for nonlinear cross-diffusion processes in order to lower the computational load, and establish their stability properties using discrete L 2 energy methods. Furthermore, by attaining a stable factorization of the system matrix as a forward-backward pass, corresponding to the Thomas algorithm for self-diffusion processes, we show that the use of implicit cross-diffusion can be competitive in terms of execution time, widening the range of viable cross-diffusion coefficients for on-the-fly applications. [ABSTRACT FROM AUTHOR]

Here we present a method for measurement of the rate of solvent absorption by perfluorosulfonic acid (PFSA) membranes using a force tensiometer. The method presented here can be used as a tool to understand solvent absorption and should provide a rationale for designing catalyst inks for direct coating processes since it is necessary to understand how the absorption rate – on a time scale of seconds to minutes – compares to the time scales of the coating process in order to minimize membrane swelling. This method allows for rapid screening of the absorption and swelling behavior of different solvents and mixtures. Using this method, the absorption of water/1-propanol mixtures were measured for three thicknesses of Nafion PFSA membrane – Nafion 1135, Nafion 115, and Nafion 117. We find that the absorption rate is dependent on the ratio of the solvents as well as the thickness of the membrane. The analysis indicates that the highest absorption rate occurs when the mass percentage of 1-propanol in the mixture is 50%, whereas the lowest rates are for pure water and 1-propanol. In addition, membrane distortion also occurs most quickly for the 50% 1-propanol mixture. Our results suggest that catalyst inks that are highly rich (≥90%) in water or 1-propanol are likely best for direct coating as such formulations will minimize absorption and swelling. [Display omitted] • Solvent absorption studied formulate catalyst inks for direct coating on membrane. • Force tensiometer method developed to measure solvent uptake in Nafion membranes. • Pure water and 1-propanol absorbed slower than water/1-propanol mixtures. • Mixtures of ≥90% water likely best to minimize swelling during coating. [ABSTRACT FROM AUTHOR]

Agriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use cases. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances. [ABSTRACT FROM AUTHOR]

The purpose of the present paper is to investigate a class of spherical functional autoregressive processes in order to introduce and study LASSO (Least Absolute Shrinkage and Selection Operator) type estimators for the corresponding autoregressive kernels, defined in the harmonic domain by means of their spectral decompositions. Some crucial properties for these estimators are proved, in particular, consistency and oracle inequalities. [ABSTRACT FROM AUTHOR]

For the first time, the effect of calcination process on characteristics and catalytic performances of Pd supported on different MOFs (MIL-101(Cr), NH 2 -MIL-101(Cr), and HKUST-1) was evaluated. Besides, the various orders of calcination process and reduction one on Pd/MOF and Pd/calcinated-MOF were studied, and their performances in CO oxidation reaction were presented to find the effect of H 2 and O 2. Results showed that the effect of calcination and reduction processes on the catalytic activities and characteristics strongly depends on the nature of MOF. Among MIL-based catalysts, the catalyst with no calcination treatment showed the best activity. Among MNH 2 -based catalysts, high activity was obtained for Pd/MNH 2 , Pd/MNH 2 -C, and Pd/MNH 2 (RC) samples and Pd/calcinated-MNH 2 was the best. Catalytic activities of HKUST-1-based catalysts decreased with calcination due to high changes in their structures. The results are useful for predicting the performance of MOFs in oxidation processes, especially reactions in which high oxygen concentrations are involved. [Display omitted] • Calcination was carried out on MOF, Pd/MOF, and Pd/MOF-C. • Various orders of calcination process and reduction one were investigated. • The performances of the samples were investigated in CO oxidation reaction. • Calcination affects the characteristics and catalytic activities of the samples. • Among the studied catalysts, Pd/MNH 2 -C shows the highest activity in the reaction. [ABSTRACT FROM AUTHOR]

With the increasing importance of various countries' initiatives on reducing carbon emissions (e.g., carbon border adjustment mechanism, CBAM), intelligent order picking systems have assisted electronic retailers in realizing a green supply chain. Previous studies on picker routing operations focused on adopting offline static customer order information to carry out operational decisions. However, in practice, the customer order information is updated dynamically; and high-efficiency warehouse layouts have been increasingly concerned. Therefore, this study creates a mixed-integer programming model for minimizing both the total carbon footprint and the total penalty cost for delayed orders in the joint order batching and picker routing problem with dynamic arriving orders and shipping time constraints in a high-efficiency fishbone warehouse layout. To solve this complex problem, a particle swarm optimization algorithm that integrates the swarm's previous global and local worst experiences and a migration mechanism is further proposed to increase solution quality and computing efficiency. In addition, a three-dimensional space for the warehouse floorplan and shipping time constraints is designed to divide orders into batches to address the constraints. Theoretical analysis of this algorithm is conducted, and experimental analysis shows that this algorithm finds superior solutions than current practical strategies, to offer a valuable reference for carbon emission reduction in greening warehousing. • Minimize carbon footprint and penalty for jointly order batching and picker routing. • This joint problem considers dynamic arriving orders and shipping time constraints. • A PSO method with global worst experiences is proposed and analyzed theoretically. • Batches are based on a 3D space for warehouse floorplan & shipping time constraints. • This method obtains solutions with less carbon footprint than current practice. [ABSTRACT FROM AUTHOR]

Warehouse operations cover activities that must be carried out systematically. Warehouse management systems (WMS) are needed for successful warehouse management. With digitalization, alternative software programs for WMS have increased. In this study, the selection of the WMS software program for the general warehouse type is discussed. The main motivation of the research is to develop a suitable alternative selection methodology based on criteria for warehouse managers. The research is carried out in several stages. In the first stage, the software selection criteria are determined based on the literature review. In the second stage, the criteria are weighted with the criteria importance through the intercriteria correlation (CRITIC) method based on single-valued neutrosophic sets (SVNSs). In the third stage, alternatives for WMS software are ranked with the technique for order preference by similarity to an ideal solution (TOPSIS) based on SVNSs. Three decision-makers, four alternatives, and eight criteria are used in the study. The empirical research is conducted on a warehouse providing general storage services in Turkey. In addition, the sensitivity analyses are performed and the results are compared. According to the research findings, the most valuable criterion in WMS software selection is determined as "ease of use". As a result of the research, suggestions are developed for WMS software manufacturers, warehouse managers, and researchers. The novelty of this research is that WMS selection is applied for the first time using multi-criteria decision-making methods. • The problem of software selection for warehouse management is addressed. • A practical evaluation framework is offered for manufacturers and warehouse managers. • WMS selection criteria are determined for the general warehouse type. • The hybrid SVNS-CRITIC-TOPSIS model is innovatively used in software selection. • Ease of use is the most valuable criterion in WMS software selection. [ABSTRACT FROM AUTHOR]

We construct the effective Hamiltonian for hadronic parity violation in strangeness-nonchanging (Δ S = 0) processes in next-to-leading order (NLO) in QCD, for all isosectors, and at a renormalization scale of 2 GeV , thus extending our earlier leading-order (LO) analysis [1,2]. Hadronic parity violation, studied in the context of the low-energy interactions of nucleons and nuclei, exposes the complex interplay of weak and strong interactions in these systems, and thus supports our extension to NLO. Here we exploit the flavor-blind nature of QCD interactions to construct the needed anomalous dimension matrices from those computed in flavor physics, which we then use to refine our effective Hamiltonian and finally our predicted parity-violating meson-nucleon coupling constants, to find improved agreement with few-body experiments. [ABSTRACT FROM AUTHOR]

Silica scaling has become a common problem in membrane systems due to the ubiquity of silica in the natural environment. In this work, a series of N -[3-(dimethylamino)propyl]methacrylamide-grafted hydroxyethyl cellulose (DHEC) with different grafting ratios was designed and used as a silica scaling inhibitor. The performances and mechanisms of DHEC in the inhibition of silica scaling were evaluated in detail by static and reverse osmosis (RO) tests. The maximum inhibition efficiency of DHEC with the grafting ratio of 97% reached as high as 77.20% ± 0.26% in the static test. The permeate flux in the presence of this DHEC gradually decreased and was eventually maintained at 89.12% after 10 h of RO operation. The efficient mitigation of silica scaling was ascribed to the synergistic effect of the grafted copolymer, specifically, the hydrophilic cellulose backbone of DHEC exerted a dispersion effect and the cationic grafted chains acted as aggregators to inhibit silica scaling in the solution and at the membrane surface. This synergistic effect cannot delay the induction time of silica polymerization but can change the dominant mode of polymerization in the solution from the first-order to the second-order reaction as confirmed by the kinetic study. Thus, DHEC is an efficient and environmentally friendly inhibitor with high application potential for membrane fouling control in RO operations. [Display omitted] • A novel hydroxyethyl cellulose-based inhibitor (DHEC) was prepared simply. • DHEC can hinder silica polymerization in both bulk solution and RO membrane surface. • DHEC with suitable grafting ratio and appropriate dose owns good inhibition behavior. • DHEC can't delay induction time but inhibit silica polymerization by synergistic effect. • DHEC can change the polymerization mode from first-to second- order kinetic process. [ABSTRACT FROM AUTHOR]

In present work we study the production of ground and excited charmonium states in e − e + → γ χ c J (n P) (J = 0 , 1) , and e − e + → γ η c (n S) , through leading order (LO) diagrams, which proceed through exchange of a virtual photon that couples to γ and η c / χ c J through the triangular quark loop diagram, in the framework of 4 × 4 Bethe-Salpeter equation (BSE), at center of mass energies s = 10.6 GeV (Belle energy), and 4.6 GeV (BESIII energy). The amplitude simplifies to a general form required by Lorentz-covariance, in terms of its form factors. The cross sections for these processes with leading order diagrams alone at s = 10.6 GeV provide a sizable contribution, which might be mainly due to the BSE being a fully relativistic approach that incorporates the relativistic effect of quark spins and can also describe internal motion of constituent quarks within the hadron in a relativistically covariant manner. Our results are compared with recent Belle data at 10.6 GeV, and BESIII data at 4.6 GeV, as well as other models. Plots of cross sections versus the center-of mass energy, s reveal mild fluctuations in their behaviour for all the three processes in the low energy (s = 4 – 6 GeV) region, and are analyzed in terms of the form factors. [ABSTRACT FROM AUTHOR]

Many factors influence rock breakage during cutting processes, including cutting parameters (attack angle, cutting depth and cutting spacing), rock properties and stress conditions. The influence of confining stress on the linear cutting process is currently not well understood. In this study, a 2D discrete element numerical model is developed utilizing DEM software to simulate the linear cutting process of rock using a conical pick. The model accounts for different levels of confining stress to investigate their effect on the cutting process. The numerical model was first validated to ensure its rationality. Subsequently, the rock breakage process under different confining stresses was analysed by observing variations in cutter force, the development of crack extension, particle displacement, velocity variations, and alterations in internal stress. According to the results, the presence of confining stress facilitated the propagation and expansion of cracks within the original rock, particularly along the region where the free surface was present. Subsequently, during the cutting process under different stress conditions, it was observed that as the confining stress increased, the length of radial crack expansion decreased gradually along the tip of the cutter. Under higher stress conditions, a uniform distribution of mostly tensile cracks appeared inside the rock during the cutting process. Additionally, the regression analyses show that average cutting force and specific energy for rock breakage both exhibit an initial increase, followed by a decrease, with increasing confining stress. This indicates that the rock breakage efficiency was high when the confining stress was low and that a high confining stress can instead improve rock cutting. Through these analyses, insights were gained into the mechanisms underlying rock breakage during the linear cutting process using a conical pick under different levels of confining stress. Ultimately, these findings provide valuable knowledge for predicting and optimizing rock cutting processes under different stress conditions. [ABSTRACT FROM AUTHOR]