Using multi-criteria decision-making and machine learning for football player selection and performance prediction:a systematic review

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.

Application of Interval Multi-attribute Decision-Making Method to Aeroengine Performance Ranking

In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine, The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance stares of aeroengins.

Monitoring and Measuring Event Decision-Making Process Using Key Performance Indicators： Evidence From Italian Cruise Terminals

This paper investigates the control systems and coordination mechanisms applied in the relationships between cruise line companies and terminal concessionaires in the cruise events management when the ships are stopping on the quay. Using a case study methodology and focusing on the Italian context, this exploratory study, through a qualitative approach, interviewed two main cruise terminal concessionaires and indirectly observed events planned by cruise companies in sea ports selected for outlining the role of control systems, coordination, and knowledge sharing between the players in cruise events decision-making processes. The first results show that the cruise event management processes on ship berthing and on terminal are separated. The cruise companies don＇t involve terminal concessionaires in the event organization considering them only service suppliers Also, the concessionaires conceive the events planned in the cruise infrastructures as a business unit to optimize the space use, without any forms of coordination or control with cruise companies. Knowledge sharing, coordination, and control among the two players could support the planning, and management of ＂new cruise events＂ in which the visitors may be led from the infrastructure to the ships stopped on the quay. This study offers a set of key performance indicators to support the management, control, and coordination of each inter-organizational relationship identified. Managerial implications and suggestions for improving cruise events management have been provided.

Performance of Children and Adolescents from a School of the City of Sogamoso on a Decision-Making Test

The aim of this study was to describe the performance on a decision-making task of children and adolescents (aged 7 - 17 years) from a school in the city of Sogamoso. This non-experimental descriptive, cross-sectional study was conducted by means of a non probability sampling of type cases. The sample consisted of 325 subjects, to whom the Hungry Donkey Task in its version adapted for Colombia was applied. Through descriptive statistics, the test performance trend was established, finding that participants aged 7 to 11 years had better performance than adolescents 12 to 14 years old, which partially supports the hypothesis of decision-making development in J shape. Subjects aged 15 to 17 years had the most disadvantageous performance, which may indicate immaturity and slow development of prefrontal areas and the influence of the trend towards risk-taking, characteristic of this age range.

Effects of thinning on the understory light environment of different stands and the photosynthetic performance and growth of the reforestation species Phoebe bournei

Light levels determine regeneration in stands and a key concern is how to regulate the light environment of different stand types to the requirements of the understory.In this study,we selected three stands typical in south China(a Cryptomeria japonica plantation,a Quercus acutissima plantation,and a mixed stand of both)and three thinning intensities to determine the best understory light environ-ment for 3-year-old Phoebe bournei seedlings.The canopy structure,understory light environment,and photosynthe-sis and growth indicators were assessed following thin-ning.Thinning improved canopy structure and understory light availability of each stand;species composition was the reason for differences in the understory light environ-ment.Under the same thinning intensity,the mixed stand had the greatest light radiation and most balanced spectral composition.P.bournei photosynthesis and growth were closely related to the light environment;all three stands required heavy thinning to create an effective and sustained understory light environment.In a suitable understory light environment,the efficiency of light interception,absorption,and use by seedlings was enhanced,resulting in a higher carbon assimilation the main limiting factor was stomatal conductance.As a shade-avoidance signal,red/far-red radia-tion is a critical factor driving changes in photosynthesis and growth of P.bournei seedlings,and a reduction increased light absorption and use capacity and height:diameter ratios.The growth advantage transformed from diameter to height,enabling seedlings to access more light.Our findings suggest that the regeneration of shade-tolerant species such as P.bournei could be enhanced if a targeted approach to thinning based on stand type was adopted.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Design of low-alloying and high-performance solid solution-strengthened copper alloys with element substitution for sustainable development

Solid solution-strengthened copper alloys have the advantages of a simple composition and manufacturing process,high mechanical and electrical comprehensive performances,and low cost;thus,they are widely used in high-speed rail contact wires,electronic component connectors,and other devices.Overcoming the contradiction between low alloying and high performance is an important challenge in the development of solid solution-strengthened copper alloys.Taking the typical solid solution-strengthened alloy Cu-4Zn-1Sn as the research object,we proposed using the element In to replace Zn and Sn to achieve low alloying in this work.Two new alloys,Cu-1.5Zn-1Sn-0.4In and Cu-1.5Zn-0.9Sn-0.6In,were designed and prepared.The total weight percentage content of alloying elements decreased by 43%and 41%,respectively,while the product of ultimate tensile strength(UTS)and electrical conductivity(EC)of the annealed state increased by 14%and 15%.After cold rolling with a 90%reduction,the UTS of the two new alloys reached 576 and 627MPa,respectively,the EC was 44.9%IACS and 42.0%IACS,and the product of UTS and EC(UTS×EC)was 97%and 99%higher than that of the annealed state alloy.The dislocations proliferated greatly in cold-rolled alloys,and the strengthening effects of dislocations reached 332 and 356 MPa,respectively,which is the main reason for the considerable improvement in mechanical properties.

Optimizing support performances of bolt reaming and anchoring in a coal drift

In current practice of bolt reaming and anchoring of roadways in soft coal and rock mass,resin cartridges bend easily under the strong pushing and stirring of bolts,and the resin accumulates in the bolt-reamed area and does not participate in the stirring.As a result,bolts encounter high drilling resistance and cannot reach the bottom of drillholes.The effective anchorage length is far less than the actual anchorage length.Bolts are not centered,and the shear is misaligned at the joint surface in the reaming area,which leads to cracking of the whole anchoring solid and large shear deformation of bolts.This study systematically analyzes the characteristics of roadway bolt reaming and anchoring.The influences of resin stirring force,bolt pull-out force,and reamingeanchoring solid strength on reamingeanchoring performance were analyzed theoretically.The main purpose is to develop a device that enhances reaming and anchoring.The mechanism through which the device strengthens the reamingeanchoring solid was analyzed theoretically.Numerical simulation and experiments were carried out to verify the improved performance of the small-pore reaming and anchoring using the proposed technology.The results showed that the stirring migration rate of the resin cartridge is greatly improved by adding the device to bolts.The reaction rate of the anchoring mixture,stirring pressure,pull-out force of the reaming and anchoring system,bolt concentricity,and shear and compressive strengths of the anchoring solid are also enhanced in the reaming area.This ensures that the resin cartridge in the reaming area is completely stirred,which greatly improves the shear resistance of the reamingeanchoring solid.Meanwhile,the drilling performance,torsional force,and stirring efficiency of bolts are maximized and prevail over those of conventional bolts.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Role of End-ring Configuration in Shaping IE4 Induction Motor Performance

The performance characteristics,particularly the starting performance of direct line-fed induction motors,which are mainly influenced by the design of the rotor,are crucial considerations for end-users.It is quite a challenging issue for motor manufacturers to enhance the starting performance of existing mass-produced motors with minimal modifications and expenses.In this paper,a simple and cost-effective method to improve the starting performance of a commercial squirrel-cage induction motor(SCIM)is proposed.The influence of geometric parameters of the end-ring on the performance characteristics,including starting(locked rotor)torque,pull-up and break down torque,starting current,rotor electric parameters,current density,power losses,and efficiency have been comprehensively investigated.It has been revealed that among the other end-ring design parameters,the ring thickness has a significant effect on the performance characteristics.An optimal end-ring thickness is determined,and its performance characteristics have been compared to those of its initial counterpart.Numeric and parametric analyses have been conducted using a 2D time-stepping finite element method(FEM).The FEM results were validated using experimental measurements obtained from an 11 kW SCIM prototype.

Unveiling Organic Electrode Materials in Aqueous Zinc-Ion Batteries:From Structural Design to Electrochemical Performance

Aqueous zinc-ion batteries(AZIBs)are one of the most compelling alternatives of lithium-ion batteries due to their inherent safety and economics viability.In response to the growing demand for green and sustainable energy storage solutions,organic electrodes with the scalability from inexpensive starting materials and potential for biodegradation after use have become a prominent choice for AZIBs.Despite gratifying progresses of organic molecules with electrochemical performance in AZIBs,the research is still in infancy and hampered by certain issues due to the underlying complex electrochemistry.Strategies for designing organic electrode materials for AZIBs with high specific capacity and long cycling life are discussed in detail in this review.Specifically,we put emphasis on the unique electrochemistry of different redox-active structures to provide in-depth understanding of their working mechanisms.In addition,we highlight the importance of molecular size/dimension regarding their profound impact on electrochemical performances.Finally,challenges and perspectives are discussed from the developing point of view for future AZIBs.We hope to provide a valuable evaluation on organic electrode materials for AZIBs in our context and give inspiration for the rational design of high-performance AZIBs.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Optimum Profiles of Endwall Contouring for Enhanced Net Heat Flux Reduction and Aerodynamic Performance

Successfully utilized non-axisymmetric endwalls to enhance turbine efficiencies(aerodynamic and turbine inlet temperatures)by controlling the characteristics of the secondary flow in a blade passage.This is accomplished by steady-state numerical hydrodynamics and deep knowledge of the field of flow.Because of the interaction between mainstream and purge flow contributing supplementary losses in the stage,non-axisymmetric endwalls are highly susceptible to the inception of purge flow exit compared to the flat and any advantage rapidly vanishes.The conclusions reveal that the supreme endwall pattern could yield a lowering of the gross pressure loss at the design stage and is related to the size of the top-loss location being productively lowered.This has led to diminished global thermal exchange lowered in the passage of the vane alone.The reverse flow adjacent to the suction side corner of the endwall is migrated farther from the vane surface,as the deviated pressure spread on the endwall accelerates the flow and progresses the reverse flow core still downstream.The depleted association between the tornado-like vortex and the corner vortex adjacent to the suction side corner of the endwall is the dominant mechanism of control in the contoured end wall.In this publication,we show that the non-axisymmetric endwall contouring by selective numerical shape change method at most prominent locations is advantageous in lowering the thermal load in turbines to augment the net heat flux reduction as well as the aerodynamic performance using multi-objective optimization.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Influence of the Nature of the Incoming Sludge on the Performance of a Vertical Flow Reed Beds in Dakar-Senegal

This work investigates the influence of the type sludge on drainage, plant development, purification performances and biosolids quality. Drainage properties were measured through the frequency of clogging, the percentage of leachate recovered and the dryness of accumulated sludge. Plant development was measured through the density, the height and the stem diameter. Purification performance was evaluated from the reduction rate. Biosolids quality was measured after 3 months of maturation. The results show that the clogging frequencies were 9.5%;0% and 3.7%;the volume of leachate recovered was 42.2%;20.4% and 24.7% and, the dryness was 33.4%;61.1% and 52.4% for FS-ST, FS-STT and SS respectively. Plants densities were about, with densities 197.1, 171.3 and 178.3 plants/m2 in beds fed respectively with FS-ST, FS-STT and SS. Despite the high removal rates, the concentrations of pollutants in the leachates are above the Senegalese standard NS 05-061 for discharge into the environment. The biosolids are all mature with C/N and NH4+/NO3?ratios lower than 12 and 1 respectively. The biosolids are also rich in organic and mineral elements. The concentrations of Ascaris eggs are higher than the WHO recommendations. These biosolids should be stored for additional time or composted.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

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.