Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.For this purpose,this paper introduces an intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization.First,linear cutting tests and numerical simulations are used to investigate the physical rules between different cutting parameters(penetration,cutter spacing,etc.)and rock compressive strength.Second,a dual-driven mapping of rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking is established with high accuracy by combining rock-breaking rules and deep neural networks(DNNs).The decision-making method is established by dual-driven mapping,using the effective rock-breaking capacity and the rated value of mechanical parameters as constraints and the total excavation cost as the optimization objective.The best operational parameters can be obtained by searching for the revolutions per minute and penetration that correspond to the extremum of the constrained objective function.The practicability and effectiveness of the developed decision-making model is verified in the SecondWater Source Channel of Hangzhou,China,resulting in the average penetration rate increasing by 11.3%and the total cost decreasing by 10%.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA.

Artificial intelligence promotes shared decision-making through recommending tests to febrile pediatric outpatients

BACKGROUND:To promote the shared decision-making(SDM)between patients and doctors in pediatric outpatient departments,this study was designed to validate artificial intelligence(AI)-initiated medical tests for children with fever.METHODS:We designed an AI model,named Xiaoyi,to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic.We calculated the sensitivity,specificity,and F1 score to evaluate the efficacy of Xiaoyi’s recommendations.The patients were divided into the rejection and acceptance groups.Then we analyzed the rejected examination items in order to obtain the corresponding reasons.RESULTS:We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics.The recommended examinations given by Xiaoyi for 10,636(89.6%)patients were qualified.The average F1 score reached 0.94.A total of 58.4%of the patients accepted Xiaoyi’s suggestions(acceptance group),and 41.6%refused(rejection group).Imaging examinations were rejected by most patients(46.7%).The tests being time-consuming were rejected by 2,133 patients(43.2%),including rejecting pathogen studies in 1,347 patients(68.5%)and image studies in 732 patients(31.8%).The difficulty of sampling was the main reason for rejecting routine tests(41.9%).CONCLUSION:Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients,and is worth promoting in facilitating SDM.

Decision-Making Models Based on Meta-Reinforcement Learning for Intelligent Vehicles at Urban Intersections

Behavioral decision-making at urban intersections is one of the primary difficulties currently impeding the development of intelligent vehicle technology.The problem is that existing decision-making algorithms cannot effectively deal with complex random scenarios at urban intersections.To deal with this,a deep deterministic policy gradient(DDPG)decision-making algorithm(T-DDPG)based on a time-series Markov decision process(T-MDP)was developed,where the state was extended to collect observations from several consecutive frames.Experiments found that T-DDPG performed better in terms of convergence and generalizability in complex intersection scenarios than a traditional DDPG algorithm.Furthermore,model-agnostic meta-learning(MAML)was incorporated into the T-DDPG algorithm to improve the training method,leading to a decision algorithm(T-MAML-DDPG)based on a secondary gradient.Simulation experiments of intersection scenarios were carried out on the Gym-Carla platform to verify and compare the decision models.The results showed that T-MAML-DDPG was able to easily deal with the random states of complex intersection scenarios,which could improve traffic safety and efficiency.The above decision-making models based on meta-reinforcement learning are significant for enhancing the decision-making ability of intelligent vehicles at urban intersections.

Research on an Intelligent Maintenance Decision-making Support System

A new synthetic model of maintenance decision-making, which is made by anartificial neural network (ANN) , expert system (ES) and emulation technology, is put forward. Bymeans of this model all kinds of maintenance resources with low cost can be effectively harmonized;accordingly, the reliability, maintenance efficiency and quality of equipment can be improved, soservice life of equipments is enhanced.

An Intelligent Algorithm for Solving Weapon-Target Assignment Problem:DDPG-DNPE Algorithm

Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decisionmaking,such as large computational amount,slow solution speed,and low calculation accuracy,combined with deep reinforcement learning theory,an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed,which uses a double noise mechanism to expand the search range of the action,and introduces a priority experience playback mechanism to effectively achieve data utilization.Finally,the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield.The results of the experiment show that,under the framework of the deep neural network for intelligent weapon-target assignment proposed in this paper,compared to the traditional RELU algorithm,the agent trained with reinforcement learning algorithms,such asDeepDeterministic Policy Gradient algorithm,Asynchronous Advantage Actor-Critic algorithm,Deep Q Network algorithm performs better.It shows that the use of deep reinforcement learning algorithms to solve the weapon-target assignment problem in the field of air defense operations is scientific.In contrast to other reinforcement learning algorithms,the agent trained by the improved Deep Deterministic Policy Gradient algorithm has a higher win rate and reward in confrontation,and the use of weapon resources is more efficient.It shows that the model and algorithm have certain superiority and rationality.The results of this paper provide new ideas for solving the problemof weapon-target assignment in air defense combat command decisions.

Research on Big Data and Artificial Intelligence Aided Decision-Making Mechanism with the Applications on Video Website Homemade Program Innovation

In this paper, we conduct research on the big data and the artificial intelligence aided decision-making mechanism with the applications on video website homemade program innovation. Make homemade video shows new media platform site content production with new possible, as also make the traditional media found in Internet age, the breakthrough point of the times. Site homemade video program, which is beneficial to reduce copyright purchase demand, reduce the cost, avoid the homogeneity competition, rich advertising marketing at the same time, improve the profit pattern, the organic combination of content production and operation, complete the strategic transformation. On the basis of these advantages, once the site of homemade video program to form a brand and a higher brand influence. Our later research provides the literature survey for the related issues.

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry.AI,encompassing machine learning algorithms,deep learning,and data analytics,offers unprecedented opportunities to streamline and enhance various stages of drug development.This opinion review delved into the current landscape of AI-driven approaches,discussing their utilization in target identification,lead optimization,and predictive modeling of pharmacokinetics and toxicity.We aimed to scrutinize the integration of large-scale omics data,electronic health records,and chemical informatics,highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies.Despite the considerable potential of AI,the review also addressed inherent challenges,including data privacy concerns,interpretability of AI models,and the need for robust validation in realworld clinical settings.Additionally,we explored ethical considerations surrounding AI-driven decision-making in drug development.This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends,presenting critical insights and addressing potential hurdles.In conclusion,this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Building a Business and Strategic Intelligence Policy as a Strategy for Promoting Congolese Business Progress and Healthy Economic Development in Eastern DRC

The aim of this study was to verify the existence of business and strategic intelligence policies at the level of Congolese companies and at the state level, likely to foster progress and healthy development in the east of the DRC. The study was based on a mixed perspective consisting of objective analysis of quantitative data and interpretative analysis of qualitative data. The results showed that business and strategic intelligence policies have not been established at either company or state level, as this is an area of activity that is not known to the players in companies and public departments, and there are no units or offices in their organizational structures responsible for managing strategic information for competitiveness on the international market. In addition, there is a real need to establish strategic information management units within companies, upstream, and to set up a national strategic information management department or agency to help local companies compete in the marketplace, downstream. This reflects the importance and timeliness of building business and strategic intelligence policies to ensure economic progress and development in the eastern DRC. Business and strategic intelligence provides companies with an appropriate tool for researching, collecting, processing and disseminating information useful for decision-making among stakeholders, in order to cope with a crisis or competitive situation. The study suggests a number of key recommendations based on its findings. To the government, it is recommended to establish the national policy of business and strategic intelligence by setting up a national agency of strategic intelligence in favor of local companies;and to companies to establish business intelligence units in their organizational structures in favor of stakeholders to foster advantageous decision-making in the competitive market and achieve progress. Finally, the study suggests that studies be carried out to fully understand the opportunities and impact of business and strategic intelligence in African countries, particularly in the DRC.

The Study of Urban Planning Decision-Making in the Background of Rapid Economic Development —— A Case Study of Shanwei City

Urban planning decision-making is an activity for the decision-makers to collect information, judge function of cities, select project and make policy which is ainaed at the problem of urban planning that have taken place, is taking place and will take place. Using historical review and comparative analysis, the articles summarizes planning decision-makings of Shanwei city and it＇s problem, and makes some suggestions to urban planning decision-making system of Shanwei city in the new period.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.

A review of intelligent ore sorting technology and equipment development

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.

Rapid软件、CT血管造影和数字减影血管造影对侧支循环评估价值对比

目的探讨Rapid软件、CT血管造影(CTA)和数字减影血管造影(DSA)对侧支循环评估的一致性。方法选择2020年1月至2022年4月重庆医科大学附属永川医院收治的单侧大脑中动脉狭窄的患者75例,患者发病后接受CTA、CTP和DSA检查,由Rapid软件自动计算评估侧支循环,CTA和DSA侧支循环血流评分分别由2名医师双盲评估,以DSA评分为金标准,判断CTA及Rapid软件评估的准确性。应用Kappa检验评估DSA、CTA与Rapid软件血流评分的一致性。结果Rapid软件与DSA、CTA血流评分一致性好(r_(s)=0.729,κ=0.516;r_(s)=0.744,κ=0.543),CTA与DSA血流评分一致性好(κ=0.768,r_(s)=0.857)。Rapid软件和CTA判定侧支循环良好的灵敏度和特异度分别为83.33%、60.87%和100.00%、76.81%;Rapid软件和CTA判定侧支循环中等的灵敏度和特异度分别为46.67%、100.00%和86.67%、100.00%;Rapid软件和CTA判定侧支循环较差的灵敏度和特异度分别为66.67%、70.67%和62.50%、96.08%。结论CTA具有较高的灵敏度与特异度,且与DSA有很高的相关性。Rapid软件的灵敏度与特异度较低,但与DSA有较好的一致性,因此也可以作为评估侧支循环的检查手段。

The pioneer of intelligent construction—An overview of the development of intelligent compaction

As the pioneer in the intelligent construction technologies(ICT)of transportation infrastructure,intelligent compaction(IC)has been applied in the infrastructure construction of various countries.It is currently the technology that best reflects the intelligence of engineering construction.This article overviews the latest developments and trends in IC.Firstly,the basic meaning of ICT is defined based on the essential characteristics of intelligent construction of transportation infrastructure,“perception,analysis,decision-making,execution”(PADE).The concept of intelligent compaction technology classification is also introduced.The PADE requirements that intelligent compaction should meet are proposed.Secondly,according to the sequence of“perception,analysis,decision-making,execution,”the workflow and key technologies of intelligent compaction are analyzed,and the mechanism of using the response of the roller to solve the modulus is given and verified.On this basis,The IC feasibility test methods,including compaction degree,compaction stability,and compaction uniformity,are briefly described.The implementation scheme of feedback control is given.Then,the use of artificial neural networks(ANN),hybrid expert systems,and reinforcement learning in intelligent compaction are briefly introduced.Finally,several extended applications of intelligent compaction are expounded,including the development ideas of intelligent road rollers and the role of intelligent compaction in virtual construction,the layer-specific mechanical parameters of fillers,etc.

Use of Research Evidence in Policy and Decision-Making: Views, Attitudes and Practices of Health Policy Makers in Lusaka Province of Zambia

Background: The integration of relevant high-quality research evidence into the health decision and policy formulation process is a key strategy for improving health systems especially in developing countries such as Zambia. However, the lack of capacity to understand and value research evidence by policy and decision makers makes it difficult for them to find and use research evidence in a timely manner even when motivated to do so. This study aimed to establish the views, attitudes and practices of policy makers on the use of research evidence in policy and decision-making process in Zambia. Methodology: This descriptive cross-sectional study was conducted in Lusaka, Zambia among selected public health decision and policy making institutions. A purposive sample of 21 consenting policy makers who were working in different positions in the Ministry of Health Headquarters, Provincial and District Health Offices, Health Professions Regulatory Bodies, United Nations Agencies, International Non-Governmental Organizations and University Deans from the University of Zambia participated in the study. A self-administered questionnaire was used to collect data. The IBM? SPSS? Statistics for Windows Version 20.0 was used for data analysis. Results: The concept of Evidence Informed Health Policy was not well understood such that only less than half (47.5%) of the participants reported having heard specifically about Evidence Informed Health Policy meanwhile almost two thirds (61.9%) reported that they used research evidence in decision making and policy formulation. Similar discrepancy was expressed in the understanding of and use of rapid response mechanisms such that although (47.6%) of the participants reported having heard about it, (57%) had never used rapid response mechanisms for deci-sion-making. With regard to the sources of information, about half (52.3) of the participants reported scholarly articles as their main source of evidence. Con-clusion and Recommendations: There is need for more sensitization and ca-pacity building among the decision and policy makers on the importance of using research evidence in decision and policy making process as incorporation of relevant high-quality research evidence into the health policy making pro-cess is a key strategy for improving health systems.

Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins

This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties.