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Multiobjective Flexible Job Shop Scheduling Problem (MO-FJSP) is a scheduling problem used in manufacturing sectors to use energy efficiently and thriftily. The scheduling problem aims to increase productivity and reduce energy consumption via a mathematical model. With this paper, an effective genetic algorithm is proposed for MO-FJSP based on maximum completion time, total machine load, and bottleneck machine load. The solution method utilizes a hybrid multiobjective genetic algorithm. A combination of global selection and fast selection is used for initialization and obtaining a uniformly distributed initial population. The cross-variance operator is adaptively improved to enhance the searching in the population. Following that an elite retention mechanism is designed to address the possible limitations of the elite strategy in maintaining population diversity. As a result, an improved harmonic search algorithm is introduced to improve the quality of individuals in the elite pool. The proposed hybrid method is implemented in MATLAB R2018a. Tests were conducted using the benchmark Kacem test set, the BR data data set, and with the actual production cases. The algorithm succeeded in achieving 13 nondominated solutions in the initial 20 runs. Moreover, the method obtains the optimal value criterion for the solution accuracy factor. As a whole, results of the evaluation testify that the proposed method can be used to solve the MO-FJSP with high accuracy and fast convergence. The method also provides feasible and effective scheduling solutions for the decision-makers in actual production. Based on the promising results obtained, it is deduced that the method has a wide applicability range particularly in manufacturing sector. [ABSTRACT FROM AUTHOR]

In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are "black box" algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limiting their practical use. In this work, we propose an alternative approach: using machine learning to generate optimal, comprehensible strategies which can be understood and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain near-optimal results. [ABSTRACT FROM AUTHOR]

Motivation Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides. Results We present ToxIBTL, a novel deep learning framework by utilizing the information bottleneck principle and transfer learning to predict the toxicity of peptides as well as proteins. Specifically, we use evolutionary information and physicochemical properties of peptide sequences and integrate the information bottleneck principle into a feature representation learning scheme, by which relevant information is retained and the redundant information is minimized in the obtained features. Moreover, transfer learning is introduced to transfer the common knowledge contained in proteins to peptides, which aims to improve the feature representation capability. Extensive experimental results demonstrate that ToxIBTL not only achieves a higher prediction performance than state-of-the-art methods on the peptide dataset, but also has a competitive performance on the protein dataset. Furthermore, a user-friendly online web server is established as the implementation of the proposed ToxIBTL. Availability and implementation The proposed ToxIBTL and data can be freely accessible at http://server.wei-group.net/ToxIBTL. Our source code is available at https://github.com/WLYLab/ToxIBTL. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this paper, we consider a queueing-inventory system under continuous review with a random order size policy and lost sales. If the inventory is depleted after the service of a customer, a replenishment order is instantaneously triggered. The replenishment order size may be randomized according to a discrete probability distribution. Customers arrive in the system according to a Poisson process and require service from a server. The server takes multiple vacations once the inventory is depleted. The service time, the lead time, and the vacation time are all assumed to be distributed exponentially. We derive the stationary joint distribution of the queue length, the on-hand inventory level, and the status of the server in explicit product form. Furthermore, the conditional distributions of the on-hand inventory level when the server is off due to a vacation or depleted inventory, and when the server is on and working, are derived. Then, we calculate some of the system performance measures. The effect of the server's vacation on the performance measures is investigated analytically. Finally, some numerical results are presented. The simulation study of the model in the context of more general arrival processes and service time distributions is presented. [ABSTRACT FROM AUTHOR]

Pavlovian fear conditioning is widely used to study mechanisms of fear learning, but high-throughput studies are hampered by the labor-intensive nature of examining participants in the lab. To circumvent this bottle-neck, fear conditioning tasks have been developed for remote delivery. Previous studies have examined remotely delivered fear conditioning protocols using expectancy and affective ratings. Here we replicate and extend these findings using an internet-delivered version of the Screaming Lady paradigm, evaluating the effects on negative affective ratings and response time to an auditory probe during stimulus presentation. In a sample of 80 adults, we observed clear evidence of both fear acquisition and extinction using affective ratings. Response times were faster when probed early, but not later, during presentation of stimuli paired with an aversive scream. The response time findings are at odds with previous lab-based studies showing slower as opposed to faster responses to threat-predicting cues. The findings underscore the feasibility of employing remotely delivered fear conditioning paradigms with affective ratings as outcome. Findings further highlight the need for research examining optimal parameters for concurrent response time measures or alternate non-verbal indicators of conditioned responses in Pavlovian conditioning protocols. [ABSTRACT FROM AUTHOR]

A well-balanced manufacturing system is key for ensuring a smooth production flow and the most efficient use of manpower and equipment This study aims to improve the cellular manufacturing system's assembly line balance and achieve optimal manpower utilisation at the bottleneck process. The methodology for evaluating the equipment and manpower performance for the manufacturing system was WITNESS simulation modelling and Maynard Operational Sequence Technique. Based on the simulation results, the test process was the bottleneck and the existing production was struggling to meet the customer demand due to machine shortages. Overproduction waste and a high work in progress inventory were discovered due to an imbalance in the Die Clip Bond and Trim and Mark processes causing overproduction wastes and very high work in progress inventory. A more balanced production line was observed after changes to the Die Clip Bond, Test and Mark and Test machine configurations. Inventory wastes at the Die Clip Bond and Trim and Mark were reduced by 52% and 73%, respectively. The test area's manpower was kept at two, but two machines were added to increase the machine capacity and meet expected increases in customer demand. The top three major contributors to the high manpower utilisation of 98% during stagger break were identified and proposed to be the focus for productivity improvement projects. Overall, simulation and MOST work study technique have proven to be effective tools for evaluating the performance of cellular manufacturing system. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries are overreliant on cobalt containing cathodes. Current projections estimate that hundreds of millions of electric vehicles (EVs) will be on the road by 2050, and this ever‐growing demand threatens to deplete global cobalt reserves at an alarming rate. Moreover, cobalt supply chain issues have significantly increased cobalt prices throughout the last decade. As such, energy storage research and development need to reduce the reliance on cobalt to meet ever‐growing demand for lithium‐ion batteries. The present review summarizes the science and technology gaps and potential of numerous cobalt‐free Li‐ion cathodes including layered, spinel, olivine, and disordered rock‐salt systems. Despite the promising performance of these Co‐free cathodes, scale‐up and manufacturing bottlenecks associated with these materials must also be addressed to enable widespread adoption in commercial batteries. Overall, this review broadly highlights the enormous promise of "zero‐cobalt" Li‐ion batteries to enable sustainable production of EVs in the coming decades. [ABSTRACT FROM AUTHOR]

With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label learning, multiple-instance learning, and active learning. Here, we focus on multiple-instance multiple-label learning with missing bag labels. Little research has been done for this challenging yet potentially powerful variant of incomplete supervision learning. We introduce a novel discriminative probabilistic model for missing labels in multiple-instance multiple-label learning. To address inference challenges, we introduce an efficient implementation of the EM algorithm for the model. Additionally, we consider an alternative inference approach that relies on maximizing the label-wise marginal likelihood of the proposed model instead of the joint likelihood. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach. In particular, comparison to state-of-the-art methods shows that our approach introduces a significantly smaller decrease in performance when the proportion of missing labels is increased. [ABSTRACT FROM AUTHOR]

In this article, an analytical method is introduced to evaluate the flow time of an assembly process with collaborative robots. In such a process, an operator and a collaborative robot can independently carry out preparation tasks first, then they work jointly to finish the assembly operations. To study the productivity performance of such systems, a stochastic process model is developed, where the joint work is modeled as an assembly merge process, and the task times of all operation steps are described by phase-type distributions. Closed-form solutions of system performance, such as flow time expectation and variability, as well as service rate, are derived analytically. The system properties of monotonicity, work allocation, and bottleneck identification are investigated. In addition, a case study is introduced to evaluate the performance of a front panel assembly process in automotive manufacturing. [ABSTRACT FROM AUTHOR]

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remain a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation, and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction and cross-disciplinary scientific communication. [ABSTRACT FROM AUTHOR]

The bottleneck between the processor and memory is the most significant barrier to the ongoing development of efficient processing systems. Therefore, a research effort begun to shift from processor-centric architectures to memory-centric architectures. Various in-memory processor architectures have been proposed to break this barrier to pave the way for ever-demanding memory-bound applications. Associative in-memory processing is a successful candidate for truly in-memory computing, in which processor and memory are combined in the same location to eliminate the expensive data access costs. The architecture exhibits an unmatched advantage for data-intensive applications due to its memory-centric design principles. On the other hand, this advantage can be revealed fully by an efficient design methodology. This study puts further progressive effort by proposing a hardware/software design methodology for associative in-memory processors. The methodology aims to decrease energy consumption and area requirement of the processor architecture specifically programmed to perform a given task. According to the evaluation of nine different benchmarks, such as fast Fourier transform and multiply-accumulate, the proposed design flow accomplishes an average ∼7% reduction in memory area and ∼18% savings in total energy consumption. • A hardware/software co-design flow specific to associative in-memory processors. • Programming language, scheduler, and power optimizer for associative processors. • An improved depth-first search (DFS)-based scheduler for energy and area optimization. • A multi − V DD design technique by multi-banking and dynamic voltage scaling. [ABSTRACT FROM AUTHOR]

Allogeneic cell therapy products, such as therapeutic cells derived from pluripotent stem cells (PSCs), have amazing potential to treat a wide variety of diseases and vast numbers of patients globally. However, there are various challenges related to manufacturing PSCs in single-use bioreactors, particularly at larger volumetric scales. This manuscript addresses these challenges and presents potential solutions to alleviate the anticipated bottlenecks for commercial-scale manufacturing of high-quality therapeutic cells derived from PSCs. [ABSTRACT FROM AUTHOR]

Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography. [ABSTRACT FROM AUTHOR]

Medical images from multicentres often suffer from the domain shift problem, which makes the deep learning models trained on one domain usually fail to generalize well to another. One of the potential solutions for the problem is the generative adversarial network (GAN), which has the capacity to translate images between different domains. Nevertheless, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practicality on domain adaptation tasks. In this regard, a novel GAN (namely IB-GAN) is proposed to preserve image-objects during cross-domain I2I adaptation. Specifically, we integrate the information bottleneck constraint into the typical cycle-consistency-based GAN to discard the superfluous information (e.g., domain information) and maintain the consistency of disentangled content features for image-object preservation. The proposed IB-GAN is evaluated on three tasks—polyp segmentation using colonoscopic images, the segmentation of optic disc and cup in fundus images and the whole heart segmentation using multi-modal volumes. We show that the proposed IB-GAN can generate realistic translated images and remarkably boost the generalization of widely used segmentation networks (e.g., U-Net). [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the optimal integrated planning of medium- and low-voltage distribution systems, considering the location and sizing of distributed generation. The integrated problem is formulated as a mixed-integer nonlinear model and, to solve it, two well-known optimization algorithms (simulated annealing and iterated local search) are used. The intensification and diversification processes are usually the bottleneck of metaheuristic techniques for solving complex problems. To overcome such complexity, a new neighborhood search method based on the Zbus matrix (NSZM) is proposed, to explore the solution space more efficiently and effectively for both algorithms. The proposed methodology is validated and tested on a real distribution system taken from the literature. The results obtained are better than those reported in the literature. To verify the efficiency of the new NSZM method, the Wilcoxon signed ranks test is used to measure the performance behavior of the NSZM method in the two optimization algorithms used. The numerical results demonstrate that the NSZM method enhances both algorithms equally. [ABSTRACT FROM AUTHOR]

Parts or products can be considered as the carrier of energy consumption during manufacturing since they are the final output of workshops. The concept of "embodied energy" is presented as a feasible indicator to characterize the energy consumption of a part or a product. Previous work mainly discussed methods and technologies to apply the embodied energy of parts for selecting materials and processes in product design; the role that embodied energy of parts can play in production optimization was seldom investigated. In addition, different from machining workshops, which has been widely discussed, there is less research on energy saving in the sheet metal forming workshop. In this paper, modeling and application of embodied energy for energy saving in the sheet metal forming workshop is presented. The embodied energy of parts during the manufacturing phase (EEPM) is modeled. The EEPM is evaluated by discrete event simulation to identify energy distribution, energy-intensive processes, and bottlenecks (e.g., processes with feed blocking, machines with low utilization, and others) during production. Energy-oriented scheduling in the workshop is implemented, and a two-stage approach based on ranks of EEPM that can quickly select more efficient production solutions is proposed. A case in a stamping workshop of a partner company validates the proposed methods. This study provides a practical approach to seek a trade-off between energy saving and high production efficiency for energy-intensive discrete workshops. [ABSTRACT FROM AUTHOR]

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data. [ABSTRACT FROM AUTHOR]

Recent studies have advocated using the total dissipation rate under topology optimization to realize material designs involving the flow of fluids through porous media. However, these studies decided how to pose the design problem, such as maximizing the total dissipation rate for some situations while minimizing for others, by solving one-dimensional problems and justifying their choices using numerical experiments. This approach lacks rigor—a bottleneck for further scientific advancements to computational material design. This paper provides the missing theoretical justification. We identify four classes of boundary value problems using the adjoint state method and analytically calculate the sensitivity of the total dissipation rate to the permeability field. For two of those classes in which the flow of fluids is pressure-driven, the sensitivity is positive—the total dissipation rate increases if the medium's permeability increases. While for the other two classes, in which the flow is velocity-driven, the trend is the opposite. These sensitivities provide rigorous answers to the central question: how to pose a material design problem for flow through porous media applications. The impact of our work is multifold. First, this study further elevates the role of the dissipation rate in posing well-posed material design problems using topology optimization. Second, besides the theoretical significance, the results benefit computational scientists and practitioners to realize optimal designs. Third, given their simplicity yet far-reaching impact, both the approach and results possess immense pedagogical value. [ABSTRACT FROM AUTHOR]

Since the discovery of penicillin, the development and use of antibiotics have promoted safe and effective control of bacterial infections. However, the number of antibiotic-resistance cases has been ever increasing over time. Thus, the drug discovery process demands fast, efficient and cost-effective alternative approaches for developing lead candidates with outstanding performance. Computational approaches are appealing techniques to develop lead candidates in an in silico fashion. In this review, we provide an overview of the implementation of current in silico state-of-the-art techniques, including machine learning (ML) and deep learning (DL), in drug discovery. We also discuss the development of quantum computing and its potential benefits for antibiotics research and current bottlenecks that limit computational drug discovery advancement. [ABSTRACT FROM AUTHOR]

This paper describes a new robot specifically developed for topiary trimming and evaluates its performance through a novel evaluation method. Experiments were carried out in a real garden letting the robot trim spherical-, cylindrical- and cuboid-shaped boxwood topiaries. The robot's performance was evaluated using a quantitative evaluation method, which made accurate 3D point clouds of the bush before and after trimming using photogrammetry. For each point in these point clouds, the Euclidean distance to the ground-truth shape was calculated and classified as correctly trimmed (CT) when the distance was within 2 cm of the ground-truth shape. Using this information the percentage of CT points before and after trimming were compared. Results show a reasonable robot accuracy when trimming spherical topiaries, reporting a 24 percentage point increase of CT points, going from 27% (before) to 51% (after) trimming of the bush surface. Cylinders and cuboids had a lower performance, which reported an increase of CT between −0.2 and 6 percentage point. The performance of the robot was also qualitatively assessed by human observers by scoring topiaries. The highest score of a single bush is a 3.1 out of 5. These results, combined with the quantitative evaluation show that the robot can trim autonomously, but it does not reach the required accuracy for practical application. Despite this, the evaluation method was able to reveal main bottlenecks of the robot in real-world experiments. Together with the robot description, this paper can be used as a guideline for the development and evaluation of gardening robots. • A complete description of an autonomous outdoor trimming robot. • Evaluation of trimming robot in real-world conditions using 3D reconstructions. • Correctly trimmed points for spherical topiaries increased by 24 percentage point. • The evaluation method is suitable to identify bottlenecks of autonomous systems. [ABSTRACT FROM AUTHOR]

The aim of the paper is to present a case study and to point out the possibilities of using computer simulation for the purpose of increasing the efficiency and efficiency of custom production of a company. The use of simulation modeling as a scientific method in research and in practice brings benefits such as financial, time, material and energy savings, as well as streamlining activities in real practice. The development of advanced simulation systems has opened up new possibilities and significantly supported the trend of streamlining production activities, thus reducing costs and improving business performance. Simulation, however, is not a tool for obtaining an optimal solution, but rather a tool that allows you to test different decision outputs on a simulation model. Such a simulation model makes it possible to carry out various experiments to evaluate, analyse and determine solution parameters that can then be used in a real system. Risk factors can be investigated and determined beforehand by 'replacing' the running simulation model while monitoring system performance and behaviour, then, after applying the required changes, the future behaviour of the system is examined for any potential problems and obstacles. Can be removed in advance. The goal is to analyse the material flow in the production process and then create a simulation to determine the length of production and identify bottlenecks in the production process. In order to get a better idea of the production process, a simulation model was developed in the selected software tool as a custom production project under the conditions of a particular company. The incentive to start production is given to the customer, where every order placed is immediately sent to the customer. The highest frequency order in the enterprise's production program is used to create the material flow. [ABSTRACT FROM AUTHOR]

As the unique power entrance of high-speed trains, the pantograph–catenary system (PCS) plays the critical role of power transmission for high-speed trains. The pantograph achieves the electrical power based on the sliding electrical contact between the pantograph strip and the high-voltage contact line. However, when the environmental temperature is relatively low especially during the winter, the contact line is covered by ice or snow easily. The ice or snow adhering on the surface of contact line may lead to poor electrical contact between the pantograph strip and the contact line, which may trigger the pantograph–catenary arc appearing. Accompanying with mechanic shock and friction heat, the off-line arc may deteriorate the quality of power transmission and meanwhile erode the contacting surface of the pantograph strip and the contact line to some extent. In order to solve this technique bottleneck, a novel rotatable circular-shape pantograph strip is proposed to improve the overall performance of the traditional pantograph strip. Compared with the traditional strip, the circular pantograph strip owns many advantages, such as less friction, better thermal dissipation, and better shock resistance. The thermal dissipation performance of both the traditional and circular pantograph strip is evaluated in this article, with the consideration of suffering off-line arc under the icing conditions. [ABSTRACT FROM AUTHOR]

The Backward Stochastic Differential Equation (BSDE) is an important tool for pricing and hedging. Highly accurate pricing for low computation time becomes interesting for minimizing monetary loss. Therefore, we explore the opportunity of parallelizing high-order multistep schemes in option pricing. In the multistep scheme the computations at each space grid point are independent and this fact motivates us to select massively parallel GPU computing using CUDA. In our investigations we identify performance bottlenecks and apply appropriate optimization techniques to reduce the computation time in a uniform space domain. Runtime experiments manifest optimistic speedups for the parallel implementation on a single GPU, NVIDIA GeForce 1070 Ti. [ABSTRACT FROM AUTHOR]

Nanotechnology is the most interesting and advance area of research field in the current date. Nanoparticles are defined as a substance whose particle size varies between 1-100nm. This review focus on the potential of gold nanoparticles to be used as an alternative to available neuromedicines due to their various unique properties. This paper also deals with advantages of gold nanoparticles over available neuromedicines along with various bottlenecks in the translation of gold nanoparticles into effective neuromedicines and the future perspective or scope of gold nanoparticles to be used as neuromedicines. [ABSTRACT FROM AUTHOR]

Logistics is a key aspect of cross-border e-commerce (CBEC). If any link of logistics fails, the outcome of CBEC may fall shortage of expectation. Logistics risks are now major bottlenecks of CBEC development. Therefore, it is important to study CBEC logistics risks. This paper divides CBEC logistics risks into environmental risks, management risks, operation risks, and clearance risks and establishes a Bayesian model to warn CBEC logistics risks. Then, GeNIe software was employed to simulate the model, and an empirical analysis was conducted through forward prediction and reverse diagnosis. The results help enterprises to intuitively identify risk reducers, formulate effective risk control schemes, and thereby reduce CBEC logistics risks. [ABSTRACT FROM AUTHOR]

The development of connected and automated vehicle (CAV) techniques brings an upcoming revolution to traffic management. The control of CAVs in potential conflict areas such as on-ramps and intersections will be complex to traffic management when considering their deployment. There is still a lack of a general framework for dispatching CAVs in these bottlenecks, which is expected to ensure safety, traffic efficiency, and energy consumption in real time. This study aimed to fill the technique gap, and a comprehensive cooperative intelligent driving framework is put forward to study the problem, which can be used in both on-ramp and intersection scenarios. Based on a multi-objective evolutionary algorithm, CAVs are denoted as a sequence to be searched in solution space, while a multitask learning neural network with adaptive loss function is implemented for optimization target feedback to surrogate the simulation test procedure. The simulation results show that the proposed framework can get satisfying performance with low time and energy consumption. It can reduce time consumption by up to 16.51% for the on-ramp scenario and 9.8% for the intersection scenario, while reducing energy consumption by up to 16.39% and 11.39% for the two scenarios. Meanwhile, an analysis of computation time is carried out, illuminating the flexibility and controllability of the new strategy. [ABSTRACT FROM AUTHOR]

Distributed machine learning has attracted much attention in the last decade with the widespread use of the Internet of Things. As a generative model, Generative Adversarial Network (GAN) has excellent empirical performance. However, the distributed storage of data and the fact that data cannot be shared for privacy reasons in a federated learning setting bring new challenges to training GAN. To address this issue, we propose private FL-GAN, a differentially private GAN based on federated learning. By strategically combining the Lipschitz condition with differential privacy sensitivity, our model can generate high-quality synthetic data without sacrificing the training data's privacy. When communication between clients becomes the main bottleneck for federated learning, we propose to use a serialized model-training paradigm, which significantly reduces communication costs. Considering the distributed data is often non-IID in reality, which poses challenges to modeling, we further propose universal private FL-GAN to approach this problem. We not only theoretically prove that our algorithms can provide strict privacy guarantees with differential privacy, but also experimentally demonstrate that our models can generate satisfactory data while protecting the privacy of the training data, even if the data is non-IID. [ABSTRACT FROM AUTHOR]

We propose a general scheme to compute tree-based data structures on arbitrary networks. This scheme is self-stabilizing, silent, and despite its generality, also efficient. It is written in the locally shared memory model with composite atomicity assuming the distributed unfair daemon, the weakest scheduling assumption of the model. Its stabilization time is in at most 4 n maxCC rounds, where n maxCC is the maximum number of processes in any connected component of the network. We illustrate the versatility and efficiency of our approach by proposing several instantiations solving classical spanning tree problems such as DFS, BFS, shortest-path, or unconstrained spanning tree/forest constructions, as well as other fundamental problems like leader election or finding maximum-bottleneck-bandwidth paths. We also exhibit polynomial upper bounds on its stabilization time in steps and process moves, holding for a large class of instantiations. In several cases, the polynomial step and move complexities we obtain for those instantiations match the best known complexities of existing algorithms, despite the latter being dedicated to particular problems. Furthermore, a significant set of instantiations of our scheme requires only bounded memory space per process. This set includes, but is not limited to, DFS, BFS, and shortest-path spanning tree constructions. [ABSTRACT FROM AUTHOR]

Near-memory processing (NMP) is a prospective paradigm enabling memory-centric computing. By moving the compute capability next to the main memory (DRAM modules), it can fundamentally address the CPU-memory bandwidth bottleneck and thus effectively improve the performance of memory-constrained workloads. Using the personalized recommendation system as a driving example, we developed a scalable, practical DIMM-based NMP solution tailor-designed for accelerating the inference serving. Our solution is demonstrated on a versatile FPGA-enabled NMP platform called AxDIMM that allows rapid prototyping and evaluation of NMP's performance potential on real hardware under a realistic system setting using industry-representative recommendation framework. We experimentally validated the performance of a two-ranked AxDIMM prototype, which achieves up to 1.89× speedup in latency and 31.6% memory energy saving for embedding operations. For end-to-end recommendation inference serving, AxDIMM improves the throughput up to 1.5× and latency-bounded throughput up to 1.77×, respectively. [ABSTRACT FROM AUTHOR]

Today, the competitiveness of companies is directly dependent on the quality and price of their products. This is because consumers have more options to buy a product than in previous years. In this study, two different mathematical models are developed to determine the best product mix according to different constraints for a fruit nectar producer. 4 main and 9 sub-quality criteria, whose importance levels were determined by the analytical hierarchy process (AHP) method, were used as inputs in two mathematical models that found the optimum product mix. These models, created for optimum product mix, consider not only the integrated quality criteria but also different constraints such as capacity, labor, and raw material. Models aiming at breakeven and maximum profit have been run for different scenarios such as consumer demands, machine maintenance, and the increase in bottlenecks. The results have shown that the quality criteria change the production efficiency and sales quantities, and how much the existing production system can adapt if consumer demands change. [ABSTRACT FROM AUTHOR]

This article presents our work toward correct and efficient automatic differentiation of OpenMP parallel worksharing loops in forward and reverse mode. Automatic differentiation is a method to obtain gradients of numerical programs, which are crucial in optimization, uncertainty quantification, and machine learning. The computational cost to compute gradients is a common bottleneck in practice. For applications that are parallelized for multicore CPUs or GPUs using OpenMP, one also wishes to compute the gradients in parallel. We propose a framework to reason about the correctness of the generated derivative code, from which we justify our OpenMP extension to the differentiation model. We implement this model in the automatic differentiation tool Tapenade and present test cases that are differentiated following our extended differentiation procedure. Performance of the generated derivative programs in forward and reverse mode is better than sequential, although our reverse mode often scales worse than the input programs. [ABSTRACT FROM AUTHOR]

With the ever-increasing service requirements of wireless networks, the spectrum resources of the cellular architecture are limited, and the backhaul capacity will become a system bottleneck. In order to alleviate this bottleneck, this paper considered a special heterogeneous cellular network, combined with the deployment of cache nodes, user location distribution, and considers user preferences for content requests and the limited storage space of cache nodes, modeling and analysis of the joint optimization problem of content storage and user association. And it modeled the objective function as the minimization of request delay. It simply proves that the problem is NP-hard, and designed a content placement strategy based on improved Kuhn-Munkres. Finally, the performance of this algorithm was compared with other benchmark schemes through experi-ments. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ [ABSTRACT FROM AUTHOR]

This article reports on a study investigating the link between education and work. Instead of looking at the labour outcomes of graduates, the study examined the qualifications held by workers in technician- and professional-level jobs from three types of occupational fields: regulated, applied, and general. The approach shifts the focus away from the supply of qualifications to the way qualifications are used in the workplace. The findings show evidence of disparities between qualifications and work levels and of the presence of obstacles preventing workers with additional qualifications from securing access to better jobs. Overall, the findings show that the structure of the labour market shapes how workers and employers make use of qualifications. They highlight some of the limitations of human capital theory in explaining the links between education and the labour market and call for a reframing of the purpose of postsecondary qualifications. [ABSTRACT FROM AUTHOR]

This paper aims to identify and evaluate the bottlenecks and wastes in the loading, unloading, and in-transit processes of iron ore railcars using simulation. A field survey was conducted in one of the largest steel factories and a mine, and each process time was extracted using the work-study and time-study methods. The data for more than 34 thousand railcars were collected from the database of the steel company, Iran Rail Transport company, and a private rail transport company. Seven scenarios have been developed according to (a) operations management, (b) equipment procurement, and (c) infrastructure development. It was found that utilization of the automated loading system would improve performance by 20%. This causes a 24% increase in railcars' turnover, which means obtaining US$10,000 additional income per railcar. It was expected, it would improve performance. Conversely, adding a railcar tippler and developing the tracks reduces performance. [ABSTRACT FROM AUTHOR]

• A continuous division method is developed for the dynamic boundary of single congested region. • A congested area range estimation method is applied to determine the boundary change trend. • A three-dimensional MFD surface model is proposed considering the scope of the sub-region. • A sliding mode control method is integrated for the perimeter flow of dynamic changing congested sub-region. • The proposed control method can improve the efficiency of road network compared to PI and PIDB. The urban traffic system is a complex dynamic system, and its state changes with the real-time traffic demand; correspondingly, the range of congested areas also continuously transforms spatiotemporally. Affected by the traffic congestion range, the dynamics of a road network and the macroscopic control model that describes the road network also change. The existing sub-region division methods divide sub-regions based on cluster algorithms, and their results cannot be directly used to establish a dynamic congested area model with varying ranges or to implement perimeter control because they fail to consider the continuity of congested area transfer. To achieve control of the dynamic entrance boundary of a continuously changing congested range, this paper carries out three related analyses. First, depending on the section data of the kernel congested area, a continuous division method for generating sub-regions is proposed on the basis of similarity theory to determine the dynamic boundary of the congested area. Then, after the boundary intersections of bottlenecks are categorized as output, input, or passing types, a congested area range estimation model is established using the density wave transfer speed to estimate the boundary range change trend caused by the influence of traffic congestion diffusion or dissipation. A three-dimensional macroscopic fundamental diagram (MFD) surface model with an independent section length, traffic flow density, and road network trip completion rate of the congested area is then established according to the changes in the congested area. On the basis of the three-dimensional MFD surface model, a sliding mode control with dynamic boundary (SMCDB) method is proposed to determine the entrance of the congested area. With a regional road network in Hefei taken as an example, numerical experiments are carried out, and the simulation results are compared with those of PI control with and without boundary changes, showing that the SMCDB method can track congested boundaries and adjust boundary flows according to the traffic state, protect the centre network from over-saturation, effectively improve the trip completion flow, and decrease the network travel delay. [ABSTRACT FROM AUTHOR]

• Propose an MFD multiple-bottleneck bathtub model • Derive the no-toll commute equilibrium with the composite model • Establish the system optimum supported by cordon pricing This paper constructs a dynamic model for a periphery-downtown urban network combining Vickrey's theory with Macroscopic Fundamental Diagram (MFD) to capture interactions between the peripheral traffic and the downtown, and to examine how travelers on different corridors compete for the downtown road resources in the context of dynamic user equilibrium. In no-toll equilibrium if existing, when there exists no congestion delay in the downtown area, the departures for each corridor are regulated only by its bottleneck's capacity on the periphery of the downtown area, as occurred in Vickrey's bottleneck model, whereas when the downtown accumulation goes beyond the critical value, the departures for each corridor are constrained not only by its bottleneck's capacity but also by the downtown congestion level, regulated by all corridors' departures in turn. We show that under specific assumptions, at the system optimum, the entrances at the boundary of the downtown area should be running at their full capacities before the onset of the downtown congestion, and the downtown network should be operating upon the onset of the downtown congestion at the critical accumulation with maximum production and highest traveling speed. Two optimal time-varying cordon pricing schemes (entrance-independent and entrance-dependent respectively) are developed to minimize the total social cost, which includes the queuing time costs concerning all corridors, the moving time cost in the interior of the downtown area, and schedule delay cost. What's more, the two time-varying cordon tolls to support the system optimum are analyzed from practical views. In the end, analytical results are illustrated and verified with numerical experiments. [ABSTRACT FROM AUTHOR]

Quantum annealing simulation attracts much attention recently for solving combinatorial optimization problems. FPGA acceleration is a promising way to reduce the huge processing time in quantum annealing simulations. However, the performance of FPGA accelerators is often restricted by the small external memory bandwidth. To solve this problem, we propose a data-transfer-bottleneck-less FPGA-based accelerator for quantum annealing simulation. The proposed architecture is implemented on an FPGA and achieved up to 179 times speed-up compared to single-core CPU implementation. The proposed accelerator is two times faster compared to previous FPGA accelerators, and process up to 262,144 spins, which is not possible in any existing FPGA accelerators due to limited external memory capacity. [ABSTRACT FROM AUTHOR]

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification: Given a road network R, a trajectory database T, find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF, with an approximation ratio of 1-1/e. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods. [ABSTRACT FROM AUTHOR]

(1) Background—Natural resource assets are of great significance to China's green development, but the absence of property system and value assessment is the bottleneck to restrict the value realization of natural re-source assets. (2) Methods—From the perspective of Marx's labor theory of value, the new era connotation of Two mountains theory was interpreted and theoretical analysis to the realization path of the value of natural resource assets was conducted. The data of this article is mainly derived from the survey of the Forestry Bureau and Forest Eco-Bank of Shunchang County, Fujian from July to August 2019. Case analysis and qualitative research methods are used to explore the operation mode of the forest eco-bank and the realization path and problems of the value of natural resource assets in the operation of the forest eco-bank. (3) Results—From the perspective of Marx's labor theory of value, the forest eco-bank spent a lot of socially necessary labor time in the process of forestland resource storage, capital empowerment and market operation, and it has proved that forestry resources are rich in economic value. According to Man's thesis of "the usefulness of objects", forestry resources have existential value. By defining the rights of forest resource property owners, the forest eco-bank transforms the existence value of forest resources into the use value of forestry assets. Under the catalysis of the market mechanism, the transformation of the use value of forest resources into the value of production factors is introduced into the ecological industry, which produce ecological products that meet market needs, and realize the exchange value of forestry resources with the transaction of forest ecological products as the cattier. Price is the manifestation of value. According to the "beneficiary payment" principle, consumers need to pay a certain fee when consuming forest ecological products. This part of the cost in the form of currency is the main way to realize the value of natural resources. For this reason, it is necessary to perfect the ecological product trading market mechanism and realize the value of ecological products. (4) Conclusions and Discussions—Forest Eco-Bank confirm and register forestry resources, and collect and store the fragmented forest land in the hands of forestry organizations or forest farmers through forest rights redemption, forest rights share cooperation, forest land lease and forest land trusteeship. The Asset Evaluation Center calculates the value of forestry, grafts financial derivatives to promote the proliferation of natural capital, relies on professional institutions to introduce ecological industries, and uses ecological products as the carrier to realize its economic value. The practice of the Forest Eco-Bank shows; the sustainable realization of the value of natural resource assets is based on ecological resources. Through precise investigations, and under the premise of clarifying the property rights of ecological resources, economic method is used to account for its value. Realizing value through the monetization of ecological products through direct or indirect use of ownership transactions, ecological service transactions, industrialization, etc, it is the transformation process of natural resources "existence value-use value-element value-exchange value". Forest Eco-Bank is a material platform that implements Two mountains theory and promotes the realization of the value of natural resource assets. How-ever, in the process of value realization, it still faces the boundary of natural resource property rights, the un-sound transaction system of natural resources and ecological products, and the ecological industry difficulties in importing and other bottlenecks. Therefore, it is recommended: First, market-oriented ecological element property rights system should be established. Second, market operators can be introduced to develop ecological industries. Finally, joint state-private ownership ecological product trading system is required. [ABSTRACT FROM AUTHOR]

Weighted multi-view clustering (MVC) aims to combine the complementary information of multi-view data (such as image data with different types of features) in a weighted manner to obtain a consistent clustering result. However, when the cluster-wise weights across views are vastly different, most existing weighted MVC methods may fail to fully utilize the complementary information, because they are based on view-wise weight learning and can not learn the fine-grained cluster-wise weights. Additionally, extra parameters are needed for most of them to control the weight distribution sparsity or smoothness, which are hard to tune without prior knowledge. To address these issues, in this paper we propose a novel and effective Cluster-weighted mUlti-view infoRmation bottlEneck (CURE) clustering algorithm, which can automatically learn the cluster-wise weights to discover the discriminative clusters across multiple views and thus can enhance the clustering performance by properly exploiting the cluster-level complementary information. To learn the cluster-wise weights, we design a new weight learning scheme by exploring the relation between the mutual information of the joint distribution of a specific cluster (containing a group of data samples) and the weight of this cluster. Finally, a novel draw-and-merge method is presented to solve the optimization problem. Experimental results on various multi-view datasets show the superiority and effectiveness of our cluster-wise weighted CURE over several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Nanotechnology is the most interesting and advance area of research field in the current date. Nanoparticles are defined as a substance whose particle size varies between 1-100nm. This review focus on the potential of gold nanoparticles to be used as an alternative to available neuromedicines due to their various unique properties. This paper also deals with advantages of gold nanoparticles over available neuromedicines along with various bottlenecks in the translation of gold nanoparticles into effective neuromedicines and the future perspective or scope of gold nanoparticles to be used as neuromedicines. [ABSTRACT FROM AUTHOR]

Recent temporal action detection models have focused on end-to-end trainable approaches to utilize the representational power of backbone networks. Despite the advantages of end-to-end trainable methods, these models still employ a small spatial resolution (e.g., 96 × 96) due to the inefficient trade-off between computational cost and spatial resolution. In this study, we argue that a simple pooling method (e.g., adaptive average pooling) acts as a bottleneck at the spatial aggregation part, restricting representational power. To address this issue, we propose a temporal-wise spatial attentive pooling (TSAP), which alleviates the bottleneck between the backbone and the detection head using a temporal-wise attention mechanism. Our approach mitigates the inefficient trade-off between spatial resolution and computational cost, thereby enhancing spatial scalability in temporal action detection. Moreover, TSAP is adaptable to previous end-to-end approaches by simply replacing the spatial pooling part. Our experiments demonstrated the essential role of spatial aggregation, and consistent improvements are observed by incorporating TSAP into previous end-to-end methods. [ABSTRACT FROM AUTHOR]

Zero-Shot Learning (ZSL) has rapidly advanced in recent years. Towards overcoming the annotation bottleneck in the Dynamic Hand Gesture Recognition (DHGR), we explore the idea of Zero-Shot Dynamic Hand Gesture Recognition (ZS-DHGR) with no annotated visual examples, by leveraging their textual descriptions. In this way, we propose a multi-modal Zero-Shot Dynamic Hand Gesture Recognition (ZS-DHGR) model harnessing from the complementary capabilities of deep features fused with the skeleton-based ones. A Transformer-based model along with a C3D model is used for hand detection and deep features extraction, respectively. To make a trade-off between the dimensionality of the skeleton-based and deep features, we use an Auto-Encoder (AE) on top of the Long Short Term Memory (LSTM) network. Finally, a semantic space is used to map the visual features to the lingual embedding of the class labels, achieved via the Bidirectional Encoder Representations from Transformers (BERT) model. Results on four large-scale datasets, RKS-PERSIANSIGN, First-Person, ASLVID, and isoGD, show the superiority of the proposed model compared to state-of-the-art alternatives in ZS-DHGR. The proposed model obtains an accuracy of 74.6% , 67.2% , 68.8% , 60.2% on the RKS-PERSIANSIGN, First-Person, ASLVID, and isoGD datasets, respectively. [Display omitted] • We propose a Zero-Shot Dynamic Hand Gesture Recognition model with no annotated data. • Our model uses the complementary capabilities of the fused features. • A Transformer-based model along with a C3D model is used in the model. • A semantic space is used to map the visual features to the lingual embedding. • Results on four datasets show the superiority of the proposed model compared to SOTA. [ABSTRACT FROM AUTHOR]

Advanced machine learning algorithms require large datasets, along with good-quality labels to reach state-of-the-art performance. Although measurements themselves can often be easily available, the labelling process is usually a bottleneck. To address this, active learning approaches exploit the fact that different samples provide varying levels of information to the algorithm. However, these approaches often rely on several unrealistic assumptions — an oracle is assumed to provide error-free labels, all at the same cost and effort. We propose novel active learning-based methods for classification of time series measurements, typically obtained from sensors continuously measuring highly fluctuating environmental conditions including electricity consumption, and demonstrate their effectiveness for home energy management applications, where data labelling is a challenge. A new acquisition function is proposed, which accounts for both model and labelling uncertainty and class balancing. A stopping criterion is designed to stop the active learning process after an optimal point is achieved, to reduce labelling effort. We assess the effect of labelling errors on classification performance and propose two ways of mitigating their effects: (i) a re-labelling mechanism based on similarity of provided labels; (ii) a revised loss function based on confidence levels provided by experts. We validate our contributions for energy disaggregation task in a real-world scenario with three application domain experts. Our results show that the proposed methodology significantly improves performance of algorithms transferred to unseen domains with reduced number of labelled samples — from 61% reduction for dishwasher to 93% reduction for kettle. • Continuous human-in-the-loop active learning for challenging time-series data. • Acquisition function and stopping criterion to reduce labelling effort. • Labelling effort reduced by up to 89%. • Detecting and re-labelling wrongly labelled samples improves performance. • Embedding expert confidence level into loss function mitigates effect of wrong labels. [ABSTRACT FROM AUTHOR]

As the phenomenon of knowledge overload becomes more and more obvious, the automatic summarization technology still needs to break through the bottleneck in order to improve the application value and expand the scope of the application. Traditional training paradigms for extractive summarization systems suffer from the inconsistency in training and evaluation. In this paper, we propose an innovative and interpretable contrastive learning based framework for extractive summarization called DCDSum , which comprises a D iverse Oracle evaluator, a C ontrastive learning extractor, and a D ynamic Top-k selector. Different from previous models that consider the extractive summarization task as a sequence labeling problem, our contrastive learning extractor treats it as a sentence reranking problem and introduces contrastive loss to achieve it, which can bridge the gap between objective function and evaluation metrics. The experimental results demonstrate the outstanding performance of our approach on the CNN/DailyMail, XSum, and PubMed datasets, achieving highly competitive results. In particular, our method achieves ROUGE-1 of 44.65, ROUGE-2 of 21.32, and ROUGE-L of 40.87 on the CNN/DailyMail dataset. The outcomes across various evaluation metrics substantiate that the Diverse Oracle extraction algorithm adeptly captures a broader array of sentences with reduced redundancy, consequently enhancing the interpretability of the DCDSum framework. [ABSTRACT FROM AUTHOR]

A control scheme for the multi-gated perimeter traffic flow control problem of monocentric cities is presented. The proposed scheme determines feasible and optimally distributed input flows for various gates located at the periphery of a protected network area. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the basic state-space model is augmented with additional state variables to account for vehicle queues at store-and-forward origin links at the periphery. The multi-gated perimeter flow control problem is formulated as a convex optimisation problem with finite horizon, and constrained control and state variables. This scheme aims to equalise the relative queues at origin links and to maintain the vehicle accumulation in the protected network around a desired set point, while the system's throughput is maximised. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at origin/entrance links. Furthermore, practical flow allocation policies for single-region perimeter control strategies without explicitly considering entrance link dynamics are presented. These policies allocate a global perimeter-ordered flow to a number of candidate gates at the periphery of a protected network area by taking into account the different geometric characteristics of origin links. The proposed flow allocation policies are then benchmarked against the multi-gated perimeter flow control. A meticulous study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. The results showed that the proposed approach is able to manage excessive queues outside of the protected network area and to optimally distribute the input flows, which confirms its efficiency and equity properties. Similar policies are expected to be utilised for dynamic routing and road pricing. • Fair allocation of perimeter control flows by considering entrance link dynamics. • Practical flow allocation policies for single-region perimeter control strategies. • Efficiency and equity analysis for a protected network area of San Francisco, CA. [ABSTRACT FROM AUTHOR]

State-space modeling is an emerging approach to age structured fisheries stock assessment that can accommodate multiple sources of variability in processes like recruitment, abundance, and selectivity. By maximizing the marginal likelihood by treating yearly deviations as random effects and then integrating them from the likelihood, these models can estimate multiple process variances. Several fisheries software packages have been developed that use a state-space framework with marginal likelihood, which has increased their popularity and usage across the U.S. Atlantic coast, Canada, and Europe. However, robust testing is still needed to gauge the applicability of these models and understand how they perform under a range of realistic variability in the process or observation error. Using an assessment model fit to Gulf of Maine Haddock as a baseline, we used a simulation-estimation procedure to test if state-space stock assessment models could produce approximately unbiased and precise estimates over a range of process variances that extended from zero to well above the levels estimated in the Gulf of Maine Haddock assessment, or when observations were noisier (i.e., more variable around their true value) than had been assumed in the assessment. We fit alternative estimation models that differed in which processes errors were included (of recruitment, expected survival, and fishery selectivity). State-space models which specify random effects in all three processes produced approximately unbiased and precise estimates of biomass and exploitation for most operating models and therefore are recommended except when variability in expected survival is absent (or very low), in which case the model is unlikely to converge. A conventional statistical-catch-at-age model with recruitment estimated as a fixed effect for each year, deterministic expected survival, and constant selectivity produced estimates that were comparable to the best state-space model, but do not provide internal estimates of process variances and did not perform well when recruitment was highly variable. This work will facilitate the use of state-space stock assessment models and choosing the parameterization that will produce the most accurate output to inform future predictions and management. [ABSTRACT FROM AUTHOR]

Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations. [ABSTRACT FROM AUTHOR]