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This paper introduces the three‐dimensional case picking problem (3D‐CPP) and proposes a multistart biased‐randomized algorithm (BRA) to solve it. The 3D‐CPP combines two important topics in modern warehouse logistics: the pallet loading problem and the routing of pickers in manual warehouses. The proposed optimization procedure aims at minimizing the overall distance traveled by the pickers, and is achieved by combining a routing problem (i.e., the order in which picking positions are visited) with a loading problem (i.e., the way in which cases are placed onto the pallet). We also consider additional constraints regarding the weight, vertical support, and strength of the cases. In order to solve this problem, we first propose a constructive heuristic which combines routing and packing procedures. This initial heuristic is then extended into a multistart BRA by employing a skewed probability distribution to introduce a certain degree of randomness during the solution‐construction process. A series of computational experiments allow us to assess the quality of the proposed approach, through a comparison with other algorithms as well as using real‐life data provided by an industrial partner. [ABSTRACT FROM AUTHOR]

Summary: The sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all orders and corresponding recycling or return tasks. This paper conducts experiments on global path planning for multiple AGVs based on the A* algorithm, both without considering collision and deadlock situations, and considering collision and deadlock situations. The results show that without considering collision and deadlock situations, the method of matching fixed storage locations and picking stations can improve overall efficiency, while considering collision and deadlock problems will result in reduced efficiency. In the future, the model will be further optimized taking into account actual conditions to improve overall efficiency. [ABSTRACT FROM AUTHOR]

Estimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring. [ABSTRACT FROM AUTHOR]

Summary: In order to achieve efficiency extremum of power conversion independent of parameter values and operational mode, an adaptive dual‐controller on‐line efficiency optimization method is presented in this paper. Firstly, based on tri‐modal analysis for four‐switch buck‐boost (FSBB) converter, efficiency extremum analysis is given and existed for such this converter. Then, adaptive dual controller on‐line efficiency seeking algorithm is shown to adaptive two variables for efficiency optimization and output voltage regulation. Furthermore, perturb and observe detector is described to realize control variables decoupling of dual‐controller and to prevent the instability of the whole system during the algorithm tuning process and to improve the algorithm speed of the dual controller. The proposed method is model independent and does not depend on the circuit parameter values. The detail implement of algorithm and performance analysis is given, and the experimental platform and experimental results are put forward to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new approach based on the excess components of a frame for recognizing norm retrieval frames (NRFs) in finite‐dimensional Hilbert spaces. This method leads to a complete characterization of NRFs in ℝ2$$ {\mathrm{\mathbb{R}}}^2 $$ and ℝ3$$ {\mathrm{\mathbb{R}}}^3 $$ and then to 1‐excess NRFs in ℝn$$ {\mathrm{\mathbb{R}}}^n $$. This approach is not only more effective in higher dimensions for detecting NRF than the previous approaches but is also applicable to constructing NRF from a given Riesz basis. In addition, we establish a relationship between the norm retrievability of a frame and its duals. We also show that, unlike the phase retrieval property, the set of all norm retrievable dual frames is generally not dense in the set of all dual frames. Then, we present numerical results to illustrate the potentiality of the NRF approach in signal recovery with respect to the classical framework in frame theory, and finally, we raise some perspectives and problems concerning NRF. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem of networked Euler–Lagrange (EL) systems with actuator faults and unknown model parameters. In order to solve this issue, some event‐triggered fault‐tolerant (ETFT) optimal coordination algorithms with the mechanism of intermittent communication and intermittent controller update are designed for the first time to compensate for the impact of physical faults on the system, and reduce communication costs and control resource consumption. For practical applications, it is difficult for the system to have a secure environment if there exist malicious attacks blocking network channels among agents. Therefore, the resilient ETFT optimal coordination with intermittent communication subject to asynchronous denial‐of‐service (DoS) attacks is considered. Especially, auxiliary systems with adaptive gains are introduced in the algorithms due to the unavailability of the EL model parameters, which also enable virtual data exchange between agents and protect actual state information. Note that the algorithms are fully distributed as the global topology information is not required. Finally, the feasibility of the proposed algorithms is verified through numerical simulation of examples. [ABSTRACT FROM AUTHOR]

A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning‐based multi‐view stereo (MVS) algorithms have emerged, replacing conventional hand‐crafted features with convolutional neural network‐encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state‐of‐the‐art architectures are not designed for an indoor environment with abundant weakly textured or textureless objects. This paper proposes AttentionSPP‐PatchmatchNet, a deep learning‐based MVS algorithm designed for indoor 3D reconstruction. The algorithm integrates multi‐scale feature sampling to produce global‐context‐aware feature maps and recalibrates the weight of essential features to tackle challenges posed by indoor environments. A new dataset designed exclusively for indoor environments is presented to verify the performance of the proposed network. Experimental results show that AttentionSPP‐PatchmatchNet outperforms state‐of‐the‐art algorithms with relative 132.87% and 163.55% improvements at the 10 and 2 mm threshold, respectively, making it suitable for accurate and complete indoor 3D reconstruction. [ABSTRACT FROM AUTHOR]

Summary: IOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network. [ABSTRACT FROM AUTHOR]

Summary: Convolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field‐programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co‐optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware‐friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row‐wise input‐stationary dataflow, which is tightly coupled with the algorithm. A row‐wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar‐vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co‐optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co‐optimized method implements a 10.9×$$ \times $$ speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss. [ABSTRACT FROM AUTHOR]