The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

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%.

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.

Gut flora in multiple sclerosis:implications for pathogenesis and treatment

Multiple sclerosis is an inflammatory disorder chara cterized by inflammation,demyelination,and neurodegeneration in the central nervous system.Although current first-line therapies can help manage symptoms and slow down disease progression,there is no cure for multiple sclerosis.The gut-brain axis refers to complex communications between the gut flo ra and the immune,nervous,and endocrine systems,which bridges the functions of the gut and the brain.Disruptions in the gut flora,termed dys biosis,can lead to systemic inflammation,leaky gut syndrome,and increased susceptibility to infections.The pathogenesis of multiple sclerosis involves a combination of genetic and environmental factors,and gut flora may play a pivotal role in regulating immune responses related to multiple scle rosis.To develop more effective therapies for multiple scle rosis,we should further uncover the disease processes involved in multiple sclerosis and gain a better understanding of the gut-brain axis.This review provides an overview of the role of the gut flora in multiple scle rosis.

A Review on Vibration Control of Multiple Cylinders Subjected to FlowInduced Vibrations

The fatigue damage caused by flow-induced vibration(FIV)is one of the major concerns for multiple cylindrical structures in many engineering applications.The FIV suppression is of great importance for the security of many cylindrical structures.Many active and passive control methods have been employed for the vibration suppression of an isolated cylinder undergoing vortex-induced vibrations(VIV).The FIV suppression methods are mainly extended to the multiple cylinders from the vibration control of the isolated cylinder.Due to the mutual interference between the multiple cylinders,the FIV mechanism is more complex than the VIV mechanism,which makes a great challenge for the FIV suppression.Some efforts have been devoted to vibration suppression of multiple cylinder systems undergoing FIV over the past two decades.The control methods,such as helical strakes,splitter plates,control rods and flexible sheets,are not always effective,depending on many influence factors,such as the spacing ratio,the arrangement geometrical shape,the flow velocity and the parameters of the vibration control devices.The FIV response,hydrodynamic features and wake patterns of the multiple cylinders equipped with vibration control devices are reviewed and summarized.The FIV suppression efficiency of the vibration control methods are analyzed and compared considering different influence factors.Further research on the FIV suppression of multiple cylinders is suggested to provide insight for the development of FIV control methods and promote engineering applications of FIV control methods.

The Extended Hybrid Carrier-Based Multiple Access Technology for High Mobility Scenarios

The hybrid carrier(HC)system rooted in the carrier fusion concept is gradually garnering attention.In this paper,we study the extended hybrid carrier(EHC)multiple access scheme to ensure reliable wireless communication.By employing the EHC modulation,a power layered multiplexing framework is realized,which exhibits enhanced interference suppression capability owing to the more uniform energy distribution design.The implementation method and advantage mechanism are explicated respectively for the uplink and downlink,and the performance analysis under varying channel conditions is provided.In addition,considering the connectivity demand,we explore the non-orthogonal multiple access(NOMA)method of the EHC system and develop the EHC sparse code multiple access scheme.The proposed scheme melds the energy spread superiority of EHC with the access capacity of NOMA,facilitating superior support for massive connectivity in high mobility environments.Simulation results have verified the feasibility and advantages of the proposed scheme.Compared with existing HC multiple access schemes,the proposed scheme exhibits robust bit error rate performance and can better guarantee multiple access performance in complex scenarios of nextgeneration communications.

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.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Multiple factors influencing high-purity indium electrolytic refining

The effects of various contaminants in the electrolytic refinement of indium were investigated using a glow discharge mass spectrometer(GDMS).The effects of several factors such as the indium ion(In3+)concentration,the sodium chloride(NaCl)concentration,the current density,the gelatin concentration,the pH,and the electrode distance,were examined.Significant variations in impurity levels concerning gelatin concentration were observed.Both the gelatin and In3+concentration were moderately positively correlated with the Pb content.The Sb concentration was associated positively with the NaCl concentration,while the Ti concentration had an adverse correlation with the NaCl concentration.The Bi element content was positively linked to the electrode distance.As the current density increased,Cu,Pb,and Bi impurities initially rose and then eventually declined.Notably,a critical current density of 45 A·m^(-2) was identified in this behavior.

Clinical features and possible pathogenesis of multiple evanescent white dot syndrome with different retinal diseases and events:a narrative review

●Multiple evanescent white dot syndrome(MEWDS)is a rare fundus disease,characterized by acute vision loss and visual field defects.Many previous studies have explained the possible pathogenesis and clinical features of primary MEWDS.However,as the number of reported cases increases,secondary MEWDS occurs in other related retinal diseases and injuries,exhibiting some special characteristics.The associated retinal diseases include multifocal choroiditis/punctate inner choroidopathy(MFC/PIC),acute zonal occult outer retinopathy,best vitelliform macular dystrophy,pseudoxanthoma elasticum,and ocular toxoplasmosis.The related retinal injury is laser photocoagulation,surgery,and trauma.Although primary MEWDS often have a self-limiting course,secondary MEWDS may require treatment in some cases,according to the severity of concomitant diseases and complications.Notably,MEWDS secondary to MFC/PIC that is prone to forming choroidal neovascularization and focal choroidal excavation,needs positive treatment with corticosteroids.The possible underlying pathogenesis of secondary MEWDS is the exposure of choroidal antigen after the disruption of Bruch’s membrane.The MEWDS-related features in secondary MEWDS are still evanescent under most circumstances.Its prognosis and treatment depend on the severity of complications.Current studies propose that the etiology is associated with immune factors,including viral infection,inflammation in choroid and Bruch’s membrane,and antigen exposure caused by retinal and/or choroidal insults.More pathogenic studies should be conducted in the future.Accurate diagnosis for secondary MEWDS could benefit patients in aspects of management and prognosis.

Three-dimensional magnetic reconnection in complex multiple X-point configurations in an ancient solar-lunar terrestrial system

Magnetic reconnection processes in three-dimensional(3D)complex field configurations have been investigated in different magneto-plasma systems in space,laboratory,and astrophysical systems.Two-dimensional(2D)features of magnetic reconnection have been well developed and applied successfully to systems with symmetrical property,such as toroidal fusion plasmas and laboratory experiments with an axial symmetry.But in asymmetric systems,the 3D features are inevitably different from those in the 2D case.Magnetic reconnection structures in multiple celestial body systems,particularly star-planet-Moon systems,bring fresh insights to the understanding of the 3D geometry of reconnection.Thus,we take magnetic reconnection in an ancient solar-lunar terrestrial magneto-plasma system as an example by using its crucial parameters approximately estimated already and also some specific applications in pathways for energy and matter transports among Earth,ancient Moon,and the interplanetary magnetic field(IMF).Then,magnetic reconnection of the ancient lunar-terrestrial magnetospheres with the IMF is investigated numerically in this work.In a 3D simulation for the Earth-Moon-IMF system,topological features of complex magnetic reconnection configurations and dynamical characteristics of magnetic reconnection processes are studied.It is found that a coupled lunar-terrestrial magnetosphere is formed,and under various IMF orientations,multiple X-points emerge at distinct locations,showing three typical magnetic reconnection structures in such a geometry,i.e.,the X-line,the triple current sheets,and the A-B null pairs.The results can conduce to further understanding of reconnection physics in 3D for plasmas in complex magnetic configurations,and also a possible mechanism for energy and matters transport in evolutions of similar astrophysical systems.

Infection in Multiple Myeloma: Microbiological Profile and Prognosis in Senegalese Patients

Introduction: Infections are additional factors of morbidity and mortality in multiple myeloma (MM), and the current recommendation is antibiotic prophylaxis. In sub-Saharan Africa, few data on infectious complications of MM are available. We aim to describe the microbiological features of infections in MM, and their impact on survival in Senegalese patients. Methods: A retrospective (January 2005-January 2022), analytic, multicenter study on infections in patients followed for MM (IMWG criteria) in Senegalese clinical hematology services. The socio-epidemiological, diagnostic, microbiological, evolutionary and survival aspects were analyzed. Results: The study included 106 patients with multiple myeloma who had an infection at admission or during the treatment. Ten patients have the comorbidity (hypertension, lupus, type 2 diabetes). These patients had 136 infectious events identified at diagnosis (79.2%) or during chemotherapy (20.8%). The sites of infection are lung (42.6%), urinary (29.4%), dermatological (6.6%), digestive (5.2%), osteoarticular (4.4%), ear, nose and throat (3.7%), central nervous system (1.5%), or without site. We recorded 26.4% of patients with multi-site infections. The causal pathogens are bacteria (Gram-negative bacilli: 22.1%;Gram positive bacilli: 9.5%, Mycobacterium tuberculosis: 13.3%), parasitique (plasmodium falciparum 6.6%), viruses (SARS-COV2: 2.9%, VZV: 2.2%) and fungal (2.9%). Survival was reduced in patients who had an infection at the time of multiple myeloma diagnosis (p: 0.189) and those who had multiple infectious foci (p: 0.011). Conclusion: Infections in multiple myeloma are more frequent at diagnosis. The germs are varied and mostly bacteria, particularly gram-negative bacteria, and Kochs bacillus. Our study reveals that multiple infectious foci are a poor prognosis factor. It is necessary to evaluate the infectious risk early, and to adopt an antibiotic prophylaxis based on our tropical environment.

MULTIPLE INTERSECTIONS OF SPACE-TIME ANISOTROPIC GAUSSIAN FIELDS

Let X={X(t)∈R^(d),t∈R^(N)}be a centered space-time anisotropic Gaussian field with indices H=(H_(1),…,H_(N))∈(0,1)~N,where the components X_(i)(i=1,…,d)of X are independent,and the canonical metric√(E(X_(i)(t)-X_(i)(s))^(2))^(1/2)(i=1,…,d)is commensurate with■for s=(s_(1),…,s_(N)),t=(t_(1),…,t_(N))∈R~N,α_(i)∈(0,1],and with the continuous functionγ(·)satisfying certain conditions.First,the upper and lower bounds of the hitting probabilities of X can be derived from the corresponding generalized Hausdorff measure and capacity,which are based on the kernel functions depending explicitly onγ(·).Furthermore,the multiple intersections of the sample paths of two independent centered space-time anisotropic Gaussian fields with different distributions are considered.Our results extend the corresponding results for anisotropic Gaussian fields to a large class of space-time anisotropic Gaussian fields.

Development of a cell adhesion-based prognostic model for multiple myeloma:Insights into chemotherapy response and potential reversal of adhesion effects

Multiple myeloma(MM)is a hematologic malignancy notorious for its high relapse rate and development of drug resistance,in which cell adhesion-mediated drug resistance plays a critical role.This study integrated four RNA sequencing datasets(CoMMpass,GSE136337,GSE9782,and GSE2658)and focused on analyzing 1706 adhesionrelated genes.Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes,including KIF14,TROAP,FLNA,MSN,LGALS1,PECAM1,and ALCAM,which demonstrated the strongest associations with poor overall survival(OS)in MM patients.To comprehensively evaluate the impact of cell adhesion on MM prognosis,an adhesion-related risk score(ARRS)model was constructed using Lasso Cox regression analysis.The ARRS model emerged as an independent prognostic factor for predicting OS.Furthermore,our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs,protein kinase inhibitors,and drugs targeting the PI3K/Akt/mTOR signaling pathway.Nevertheless,we identified promising drug candidates,such as tirofiban,pirenzepine,erlotinib,and bosutinib,which exhibit potential in reversing this resistance.In vitro,experiments employing NCIH929,RPMI8226,and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs.By employing siRNA-mediated knockdown of the key ARRS model gene KIF14,we observed suppressed proliferation of NCIH929 cells,along with decreased adhesion to BMSCs and fibronectin.This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM.Additionally,potential molecular mechanisms underlying adhesion-related resistance are proposed,along with viable strategies to overcome such resistance.These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Enhanced Precision Therapy of Multiple Myeloma Through Engineered Biomimetic Nanoparticles with Dual Targeting

Multiple myeloma(MM)is the second most prevalent hematological malignancy.Current MM treatment strategies are hampered by systemic toxicity and suboptimal therapeutic efficacy.This study addressed these limitations through the development of a potent MM-targeting chemotherapy strategy,which capitalized on the high binding affinity of alendronate for hydroxyapatite in the bone matrix and the homologous targeting of myeloma cell membranes,termed T-PB@M.The results from our investigations highlight the considerable bone affinity of T-PB@M,both in vitro and in vivo.Additionally,this material demonstrated a capability for drug release triggered by low pH conditions.Moreover,T-PB@M induced the generation of reactive oxygen species and triggered cell apoptosis through the poly(ADP-ribose)polymerase 1(PARP1)-Caspase-3-B-cell lymphoma-2(Bcl-2)pathway in MM cells.Notably,T-PB@M preferentially targeted bone-involved sites,thereby circumventing systemic toxic side effects and leading to prolonged survival of MM orthotopic mice.Therefore,this designed target-MM nanocarrier presents a promising and potentially effective platform for the precise treatment of MM.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.