Execution Modes Research Articles

Foundation models assist in human–robot collaboration assembly

Human–robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model.

Advanced Security Auditing Methods for Solidity-Based Smart Contracts

The development of smart contracts remains in its early stages, with significant differences in underlying programming languages and application platforms resulting in a lack of standardization. This lack of standardization increases the susceptibility to vulnerabilities and associated financial losses. To address security vulnerabilities in smart contracts on the Ethereum blockchain platform, this paper proposes a security audit method based on formal verification. The method integrates an input module, static analysis module, formal verification module, analog execution module, and report and recommendation module, which can accurately discover the security vulnerabilities and logical flaws of smart contracts through formal verification and other analysis techniques, thus realizing correctness detection. During the experiment, the method detects 8 types of common vulnerabilities in 148 smart contracts and marks 21 smart contracts with vulnerabilities. After manual review and analysis, it is found that 17 of these 21 marked smart contracts do have security vulnerabilities. The experimental results show that the proposed method can accurately detect security vulnerabilities and logic flaws in smart contracts through formal verification and other analysis techniques before smart contracts are deployed, thus significantly improving the security of smart contracts and reducing the economic losses that may be caused by code defects.

Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles

The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy.

Monitoring model for enhancing adaptability in human–robot collaborative mold assembly

ABSTRACT Molds are assembled manually due to their inherent characteristics of low-volume and high-variety production. Given the ergonomic risks caused by heavy-handling and repetitive tasks and diverse requirements in mold assembly, collaborative robots offer adaptability and ease of reconfiguration, making them potential solutions to these challenges. This study introduces a monitoring model for human–robot collaborative mold assembly using two cobots. This model encompasses manual progress monitoring for cobot execution and position-sharing modules. Manual task actions are detected using the You-Only-Look-Once-v8 Nano model. Detected actions are subsequently classified into different states. These identified states are relayed to the cobots, enabling early execution and controlling the cobots’ entry into the assembly area. This study proposes a position-sharing approach to prevent collisions by receiving coordinates via Modbus between the two cobots. Most existing research has developed separate models for action and part recognition, excluding the utilization of recognition results to enable early execution. This study contributes to a novel model for monitoring manual progress to enable early execution of subsequent tasks and facilitate communication through position sharing during human–robot collaborative mold assembly tasks. The results show that assembly time and the risk of collisions between cobots can be reduced in human–robot collaborative mold assembly tasks.

Uncertainty-based quantization method for stable training of binary neural networks

Binary neural networks (BNNs) have gained attention due to their computational efficiency. However, training BNNs has proven to be challenging. Existing algorithms either fail to produce stable and high-quality results or are overly complex for practical use. In this paper, we introduce a novel quantizer called UBQ (Uncertainty-based quantizer) for BNNs, which combines the advantages of existing methods, resulting in stable training and high-quality BNNs even with a low number of trainable parameters. We also propose a training method involving gradual network freezing and batch normalization replacement, facilitating a smooth transition from training mode to execution mode for BNNs. To evaluate UBQ, we conducted experiments on the MNIST and CIFAR-10 datasets and compared our method to existing algorithms. The results demonstrate that UBQ outperforms previous methods for smaller networks and achieves comparable results for larger networks.

Software installation threat detection based on attention mechanism and improved convolutional neural network in IOT platform

With of the Internet of Things (IoT) developing and the network technique progressing, malware attacks continue to occur, seriously endangering the information and property security of Internet of Things device users. To ensure the security of the Internet of Things platform and improve the efficiency of malware and vulnerability detection, a software installation threat detection model based on attention mechanism and improved convolutional neural network is constructed. Firstly, the enhanced dynamic symbolic execution module and forward program slicing algorithm are used to extract dynamic features, and then the improved convolutional neural network is utilized to classify malware. In the existing software of IoT devices, the inlining correlation function is studied using the inlining strategy, and the weight between the target pixel and the global pixel is calculated using the attention mechanism, through which the logic and correlation between the triples are correlated. Then, deep residual network is used to detect software vulnerabilities. This enables threat detection before and after software installation. In comparison with the current popular vulnerability detection model experiments, the accuracy, recall rate, accuracy rate and running time of the constructed model in the process of vulnerability detection are 0.975, 0.970, 0.968 and 0.02 s, respectively. Compared with other models, the research design model has better performance. This shows that this built model can effectively detect software installation threats, and has high detection accuracy and operation efficiency, which can provide strong support for the Internet of Things platform’s security protection.

A Constraint Programming Approach for Discrete Time–Cost Tradeoff Problems in a Time-Constrained Activity Network

The discrete time-cost tradeoff problem (DTCTP) is a well-researched topic in the field of operations research. The majority of existing DTCTP models are based on traditional activity networks, which permit the execution of an activity as soon as all its predecessors have been completed. This assumption is reasonable, but it is important to note that there are always exceptions. The main work of this study was threefold. Firstly, we expanded the analysis of the DTCTP to encompass time-constrained activity networks (DTCTPTC), which encompassed three different types of time constraints. The first constraint was the time-window constraint, which limited the time interval during which an activity could be executed. The second constraint was the time-schedule constraint, which specified the times at which an activity could begin execution. The third constraint was the time-switch constraint, which required project activities to start at specific times and remain inactive during designated time periods. Secondly, a constraint programming (CP) model was developed for the purpose of solving the DTCTPTC. The model employed interval variables to define the activity and its potential time constraints, while CP expressions were utilized to ensure the feasibility of the solution. The objective was to identify the optimal execution mode for each activity, the optimal start times for time-scheduled activities, and the optimal work/rest patterns for time-switch activities, with the aim of minimizing the total cost of the project. Finally, the efficacy of the proposed CP model was validated through two case studies based on two illustrative projects of varying sizes. The outcomes were then compared against existing algorithms. The results demonstrated that time constraints were important factors affecting schedule optimization, and the proposed CP model had the ability to solve large-scale DTCTPTC.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

THE CONCEPT AND CHARACTERISTICS OF SAFETY MEASURES

This article deals with an important category of criminal sanctions: safeguards. The fundamental institution of criminal law, criminal sanctions, are nothing more than the effect of criminal liability, this in turn being the legal consequence of committing the crime. Within the framework of criminal sanctions, an important category is safety measures. The defense of society against crimes could not be effectively ensured if only the application of punishments, however severe they may be, both in duration and in the mode of execution, were used for this purpose. The crime must be examined in relation to the man who committed it, and not isolated as an abstract notion, without any connection to social life, and the criminal treatment to be applied must take into account everything that characterizes the person of the perpetrator as described it shows in the present and how it is shaping up in the future. When an act provided for by the criminal law is committed, the investigations carried out in the respective case sometimes reveal, in addition to the gravity of the respective act, some situations, some realities that reveal a danger for the future in terms of the commission of other crimes. In order to solve these "dangerous situations" safety measures were found as a solution.

A matheuristic-oriented iterated greedy algorithm for multi-mode resource-constrained project scheduling problem under uncertainty

Multi-mode resource-constrained project scheduling problem is one of the most important branches of combinatorial optimization problems. The aim is to find a feasible solution consisting of activity start time and execution mode so that the project makespan is minimized. However, there are a lot of uncertain events, that bring great challenges to project scheduling. We first propose a mixed integer linear programming model where the activity duration is uncertain. Then two uncertain parameters are introduced to describe the disturbance degree of uncertain activity duration and the allowable violation degree of constraints respectively, and the MILP model with uncertain activity duration is converted to its deterministic robust counterpart model. A matheuristic local optimization approach is proposed to balance computational time with the optimization of the computational result. A matheuristic-oriented iterated greedy algorithm that combines a matheuristic local optimization approach and an iterated greedy algorithm is proposed to solve this problem in this paper. Experimental results indicate that the robust counterpart model can obtain robust optimal solutions for small-scale instances in accepted computational time. Meanwhile, the proposed matheuristic-oriented iterated greedy algorithm can obtain robust near-optimal solutions for large-scale instances and is superior to other compared algorithms.

A domain-independent agent architecture for adaptive operation in evolving open worlds

1Angry Birds Competition. Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA, a framework for designing model-based agents operating in mixed discrete-continuous worlds that can autonomously detect when the environment has evolved from its canonical setup, understand how it has evolved, and adapt the agents' models to perform effectively. HYDRA is based upon PDDL+, a rich modeling language for planning in mixed, discrete-continuous environments. It augments the planning module with visual reasoning, task selection, and action execution modules for closed-loop interaction with complex environments. HYDRA implements a novel meta-reasoning process that enables the agent to monitor its own behavior from a variety of aspects. The process employs a diverse set of computational methods to maintain expectations about the agent's own behavior in an environment. Divergences from those expectations are useful in detecting when the environment has evolved and identifying opportunities to adapt the underlying models. HYDRA builds upon ideas from diagnosis and repair and uses a heuristics-guided search over model changes such that they become competent in novel conditions. The HYDRA framework has been used to implement novelty-aware agents for three diverse domains - CartPole++ (a higher dimension variant of a classic control problem), Science Birds (an IJCAI competition problem1), and PogoStick (a specific problem domain in Minecraft). We report empirical observations from these domains to demonstrate the efficacy of various components in the novelty meta-reasoning process.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Cognitive brain activity before and after surgery in meningioma patients.

Neuropsychological studies have demonstrated that meningioma patients frequently exhibit cognitive deficits before surgery and show only limited improvement after surgery. Combining neuropsychological with functional imaging measurements can shed more light on the impact of surgery on cognitive brain function. We aimed to evaluate whether surgery affects cognitive brain activity in such a manner that it may mask possible changes in cognitive functioning measured by neuropsychological tests. Twenty-three meningioma patients participated in a fMRI measurement using a verbal working memory task as well as three neuropsychological tests focused on working memory, just before and 3 months after surgery. A region of interest based fMRI analysis was used to examine cognitive brain activity at these timepoints within the central executive network and default mode network. Neuropsychological assessment showed impaired cognitive functioning before as well as 3 months after surgery. Neuropsychological test scores, in-scanner task performance as well as brain activity within the central executive and default mode network were not significantly different between both timepoints. Our results indicate that surgery does not significantly affect cognitive brain activity in meningioma patients the first few months after surgery. Therefore, the lack of cognitive improvement after surgery is not likely the result of compensatory processes in the brain. Cognitive deficits that are already present before surgery appear to be persistent after surgery and a considerable recovery period. Our study shows potential leads that comprehensive cognitive evaluation can be of added value so that cognitive functioning may become a more prominent factor in clinical decision making.

Altered static and dynamic cerebellar-cerebral functional connectivity in acute pontine infarction.

This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.

Simulation-based hybrid genetic algorithms for the stochastic multi-mode resource-constrained project scheduling problem with minimized financial risk

Projects face uncertainties during implementation, and these uncertainties often lead to risk factors that disrupt successful project execution and cause a loss of profit. The stochastic project scheduling technique has been an effective avenue for reducing the risks in project management. Nevertheless, previous studies often neglected resource limits for minimizing the financial risks of projects in stochastic project scheduling problems. This study aims to optimize the conditional net present value at risk (CNPVaR), which measures a project’s expected loss of net present value (NPV) in a stochastic multi-mode resource-constrained project scheduling problem (SMRCPSP). Accordingly, considering the stochastic activity duration and cash flow, we construct a scenario-based optimization model for the SMRCPSP, where the solution (strategy) of the SMRCPSP is represented by a policy that includes an activity list and execution mode list; a scheduling policy is then employed to map the start times of activities at each decision time based on the solution. Three simulation-based hybrid genetic algorithms embedded with different iteration alternatives according to activity-based (AB) or resource-based (RB) scheduling policies are used. Furthermore, computational experiments are conducted to evaluate the proposed procedures through 480 instances. With respect to the CNPVaR, expected NPV, and algorithm stability metrics, the results of the experiments demonstrate that the genetic algorithm (GA) with local search based on the RB scheduling policy performed best compared with two other hybrids of the GA, the existing vibration damping optimization (VDO) method and a pure GA for large-sized projects. However, the VDO outperforms the other approaches according to the AB scheduling policy for small-scale projects. Additionally, a case study is conducted; the findings demonstrate that the risk attitude (confidence level) of project managers and the discount factor have impacts on the CNPVaR and expected NPV, but the CNPVaR is insensitive to the number of simulations.

Study on intelligent assembly process planning and execution system based on digital twin

PurposeThe purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.Design/methodology/approachBased on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.FindingsDigital twin technology enhances the quality and efficiency of assembly process planning and execution system.Originality/valueIt provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.

Neurobiological correlates of religious coping among older adults with and without mood disorders: An exploratory study

In this study, 32 older adults with and without mood disorders completed resting-state functional Magnetic Resonance Imaging and measures of demographics, spirituality/religion, positive and negative religious coping, and depression. Group Independent Component Analysis identified and selected three a priori resting state networks [cingulo-opercular salience (cSN), central executive (CEN) and Default Mode Networks (DMN)] within the Triple Network Mode. We investigated associations of religious coping with within- and between-network connectivity, controlling for age. Insular connectivity within the cSN was associated with negative religious coping. Religious coping was associated with anti-correlation between the DMN and CEN even when controlling for depression.

Three-dimensional Geospatial Interlinking with JedAI-spatial

Geospatial data constitutes a considerable part of Semantic Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling, which determines the approach that reduces the search space, (ii) Budget-awareness, which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode, which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial’s architecture and capabilities and perform thorough experiments to provide interesting insights about the relative performance of its algorithms.

Abstract 863: Scratch: A highly modular pipeline for single-cell cancer research

Abstract Background: Single-cell RNA sequencing (scRNA-seq) is a robust approach to facilitate cancer research, revealing insights into tumor heterogeneity, microenvironment, and treatment response. However, scRNA-seq results frequently encounter reproducibility challenges due to i) high data complexity, intensified by human intervention, and ii) insufficient methods standardization, leading to inconsistent findings. Methods: Here, we introduce SCRATCH (Single-Cell RnA-seq Toolkit and Pipeline for Cancer researcH), a Nextflow-based pipeline designed to improve reproducibility through a layered and modular architecture. SCRATCH follows FAIR principles and guidelines provided by the nf-core community. Result/Discussion: The pipeline provides three execution modes: end-to-end, iterative, and custom. In the end-to-end mode, the pipeline processes data from raw input to downstream analyses automatically. This mode employs ranking- and aggregation-based approaches. For instance, the ranking approach leverages benchmark metrics to select the most suitable method in distinct steps, e.g., batch correction. Therefore, ensuring a consistent and data-driven selection. On another hand, the aggregation approach uses multiple predictions to increase confidence levels, such as on CNV inference and malignant cell identification. These strategies minimize human intervention, ideal for beginner users, enabling rapid access to preliminary results and biological insights. Alternatively, the iterative mode allows intermediate users to define workflow breakpoints in a layered-based fashion. Users can pause, review results, and adjust decisions at stages (e.g., TME annotation), facilitating a "semi-supervised" approach for a more tailored analysis while retaining the SCRATCH framework. Thirdly, the custom mode enables precise executions based on modules for similar tasks (e.g., trajectory analysis and cell-cell communication), allowing experienced users to bypass the SCRATCH workflow and use it as an on-demand toolkit. This mode leverages pipeline parallelism for efficient processing, perfect for ongoing single-cell projects. SCRATCH produces HTML reports to ensure traceability and reproducibility. Conclusion: SCRATCH, an evolving project, comprises 05 subworkflows, 18 modules, and 25 tools. We envisage SCRATCH as an open-source tool and invite developers to leverage its modules for their pipelines. For more information, please visit https://break-through-cancer.github.io/btc-scrna-training. Citation Format: Andre F. Fonseca, Guangchun Han, Marcel Ribeiro-Dantas, Enyu Dai, Diljot Grewal, Matthew Zatzman, Eliyahu Havasov, Andrew McPherson, Break Through Cancer, Data Science TeamLab, Michael N. Noble, Rameen Beroukhim, Rachel Karchin, Sohrab P. Shah, Linghua Wang. Scratch: A highly modular pipeline for single-cell cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 863.