Behavioral Decision-Making of Key Stakeholders in Public-Private Partnerships:A Hybrid Method and Benefit Distribution Study

Public-private partnerships(PPPs)have been used by governments around the world to procure and construct infrastructural amenities.It relies on private sector expertise and funding to achieve this lofty objective.However,given the uncertainties of project management,transparency,accountability,and expropriation,this phenomenon has gained tremendous attention in recent years due to the important role it plays in curbing infrastructural deficits globally.Interestingly,the reasonable benefit distribution scheme in a PPP project is related to the behavior decisionmaking of the government and social capital,aswell as the performance of the project.In this paper,the government and social capital which are the key stakeholders of PPP projects were selected as the research objects.Based on the fuzzy expected value model and game theory,a hybrid method was adopted in this research taking into account the different risk preferences of both public entities and private parties under the fuzzy demand environment.To alleviate the problem of insufficient utilization of social capital in a PPP project,this paper seeks to grasp the relationship that exists between the benefit distribution of stakeholders,their behavioral decision-making,and project performance,given that they impact the performance of both public entities and private parties,as well as assist in maximizing the overall utility of the project.Furthermore,four game models were constructed in this study,while the expected value and opportunity-constrained programming model for optimal decision-making were derived using alternate perspectives of both centralized decision-making and decentralized decision-making.Afterward,the optimal behavioral decision-making of public entities and private parties in four scenarios was discussed and thereafter compared,which led to an ensuing discussion on the benefit distribution system under centralized decision-making.Lastly,based on an example case,the influence of different confidence levels,price,and fuzzy uncertainties of PPP projects on the equilibrium strategy results of both parties were discussed,giving credence to the effectiveness of the hybrid method.The results indicate that adjusting different confidence levels yields different equilibriumpoints,and therefore signposts that social capital has a fair perception of opportunities,as well as identifies reciprocal preferences.Nevertheless,we find that an increase in the cost coefficient of the government and social capital does not inhibit the effort of both parties.Our results also indicate that a reasonable benefit distribution of PPP projects can assist them in realizing optimum Pareto improvements over time.The results provide us with very useful strategies and recommendations to improve the overall performance of PPP projects in China.

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.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

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.

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.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

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.

Effect of Cognitive Behavioral Intervention on Plasma Cortisol in Patients with Breast Cancer PTSD

Objective: To investigate the effect of cognitive behavioral intervention on plasma cortisol in patients with breast cancer PTSD before surgery. Methods: SCL-90, EPQ-N and cognitive behavior intervention were performed on 30 patients with PTSD, 30 patients without PTSD and 30 normal subjects, and their fasting blood was collected to analyze cortisol in the morning. Results: The scores of SCL-90 and EPQ-N in patients with breast cancer PTSD were significantly higher than those in non-PTSD patients and normal subjects (P < 0.01). After cognitive behavioral intervention, the plasma cortisol level of breast cancer PTSD patients was significantly decreased (P Conclusion: Compared with non-PTSD breast cancer patients and normal control group, breast cancer PTSD patients had more serious psychological problems, worse emotional stability and higher plasma cortisol level. Cognitive-behavioral intervention can effectively reduce plasma cortisol levels in breast cancer patients with PTSD.

Distributional and behavioral responses of the wintering Oriental Storks to drought in China’s largest freshwater lake

Extreme droughts are increasing in frequency and severity globally as a result of climate change.Developing understanding of species’responses to drought is crucial for their conservation,especially in regions experi-encing increased aridity.Although numerous studies have investigated birds’responses to drought,the emphasis has primarily been on landbirds.Drought can significantly alter the wetland environments that waterbirds inhabit,but the response of waterbirds to drought remains understudied.In this study,we surveyed the distri-bution and behavior of Oriental Storks(Ciconia boyciana)in Poyang Lake,which is the largest freshwater lake in China.Results indicate that drought-induced catchment areas at the lowest water level limited the total popu-lation size of Oriental Storks in the sub-lakes.Sub-lakes with large catchment areas at the lowest water level demonstrated a capacity to support a larger population of wintering Oriental Storks.Over time,Oriental Storks exhibited a gradual concentration in Changhu Lake,characterized by larger catchments,after resource depletion in sub-lakes with smaller catchments.Additionally,the duration of Oriental Storks’vigilance and moving be-haviors decreased significantly compared with that observed before the drought.After the drought,Oriental Storks increased their foraging efforts,as evidenced by increased presence in deeper water and reaching their heads and necks into deeper water to forage,higher search rates,but lower foraging rates.In accordance with area-restricted search theory,reductions in habitat quality resulting from drought,including extensive fish die-offs,forced Oriental Storks to increase their foraging efforts.Sustaining a specific water area in sub-lakes during droughts can preserve resource availability,which is crucial for the conservation of Oriental Storks.Imple-menting measures such as water level control and micro-modification of lake bottoms in sub-lakes might mitigate the impact of drought on the piscivorous Oriental Storks.

Mindfulness and mindful parenting:Strategies for preschoolers with behavioral issues

The behavior issues of preschoolers are closely related to their parents'parenting styles.This editorial discusses the value and strategies for solving behavior issues in preschoolers from the perspectives of mindfulness and mindful parenting.We expect that upcoming studies will place greater emphasis on the behavioral concerns of preschoolers and the parenting practices that shape them,particularly focusing on proactive interventions for preschoolers'behavioral issues.

Understanding Behavioral Manifestations of Obsessive-Compulsive Disorder in People with Intellectual Disabilities—A Qualitative Study

Background: There is limited knowledge about obsessive-compulsive disorder (OCD) in people with intellectual disabilities (IDs). This paper describes the manifestation of compulsive behaviors associated with OCD at the behavioral level in people with ID in institutionalized settings. The aim was to gain nuanced insight into appropriate understanding and classification in this specific context, and derive implications for research and practice. Methods: Individual cases of people with ID (n = 7) were studied to assess compulsive symptoms through two days of on-site observation of the person with ID within the institution, guided group discussions (n = 28), and semi-structured interviews with key informants and caregivers of the person with ID (n = 20). Caregiver ratings of the compulsive behavior checklist were compiled. Data were analyzed using qualitative content analysis. Results: All forms of OCD were present. Characteristics of compulsive behaviors in people with ID at the behavioral level included less complex and more obvious compulsive acts, immediate responses, signs of tension, motor restlessness, facial expression changes, repetition, need for predictability, time-consuming behaviors, and aggressive reactions when these acts were interrupted. Some of the compulsive behaviors corresponded to the ICD-11 OCD code 6B20, and others to compulsions as a psychological symptom (MB23.4). Conclusions: OCD may manifest atypically at the behavioral level in people with ID, posing significant challenges for accurate classification due to symptom ambiguity. Follow-up differential diagnostic studies are needed to more accurately identify and differentiate OCD symptoms in people with ID. Further, disorder-specific guidelines for recognizing OCD in people with ID are needed for institutionalized settings without psychiatric-psychotherapeutic expertise.

Information-motivation-behavioral guided nursing for stroke patients with pulmonary dysfunction:A randomized controlled trial

BACKGROUND Patients with stroke frequently experience pulmonary dysfunction.AIM To explore the effects of information-motivation-behavioral(IMB)skills modelbased nursing care on pulmonary function,blood gas indices,complication rates,and quality of life(QoL)in stroke patients with pulmonary dysfunction.METHODS We conducted a controlled study involving 120 stroke patients with pulmonary dysfunction.The control group received routine care,whereas the intervention group received IMB-model-based nursing care.Various parameters including pulmonary function,blood gas indices,complication rates,and QoL were assessed before and after the intervention.RESULTS Baseline data of the control and intervention groups were comparable.Post-intervention,the IMB model-based care group showed significant improvements in pulmonary function indicators,forced expiratory volume in 1 sec,forced vital capacity,and peak expiratory flow compared with the control group.Blood gas indices,such as arterial oxygen pressure and arterial oxygen saturation,increased significantly,and arterial carbon dioxide partial.pressure decreased significantly in the IMB model-based care group compared with the control group.The intervention group also had a lower complication rate(6.67%vs 23.33%)and higher QoL scores across all domains than the control group.CONCLUSION IMB model-based nursing care significantly enhanced pulmonary function,improved blood gas indices,reduced complication rates,and improved the QoL of stroke patients with pulmonary dysfunction.Further research is needed to validate these results and to assess the long-term efficacy and broader applicability of the model.

Cognitive Behavioral Therapy for Tinnitus in Adults: A Review of Efficacy in Quality-of-Life Enhancement

Background: Tinnitus, characterized by the perception of sounds without an external source, significantly affects quality of life. Cognitive Behavioral Therapy (CBT) has emerged as a promising approach for managing tinnitus-related distress and enhancing psychological well-being. Objectives: This review aims to analyze the effectiveness of CBT in tinnitus management, focusing on alleviating distress, enhancing coping mechanisms, and improving overall well-being. Methods: PubMed and World of Science databases were systematically searched using keywords related to tinnitus, CBT, and quality of life. English, peer-reviewed studies focusing on adult populations were included. Studies involving pediatric populations or not meeting inclusion criteria were excluded. Data extraction was performed using PRISMA guidelines, with a narrative synthesis approach for analysis. Methodological quality and risk of bias were assessed using appropriate tools. The search engine initially identified 155 studies that met the inclusion criteria for the systematic review. However, upon further evaluation, 140 of these studies were excluded due to their non-randomized design. Of the remaining 15 studies, 11 were found to be partially accessible but ultimately excluded from the review as they did not meet the full accessibility criteria. Therefore, only four studies remained in the review, deemed suitable for inclusion based on their randomized design and full accessibility. Results: Studies by Beukes et al. [1]-[3] and Simoes et al. [4] evaluated CBT’s effectiveness. With internet-based CBT, Beukes et al. demonstrated reductions in tinnitus distress, negative cognitions, and comorbidities. Simoes et al. proposed combination treatments for tinnitus management. The review outcome suggests that CBT is an effective treatment for tinnitus, as it can help reduce tinnitus distress and improve quality of life. However, limitations in sample sizes and follow-up durations highlight the need for further research to establish CBT’s long-term efficacy and optimal parameters. Integrating internet-based CBT into comprehensive care strategies can enhance the well-being of individuals affected by tinnitus.

Effects of comprehensive nursing with cognitive behavioral therapy in orthodontic osteodilated arch treatment

BACKGROUND This work explored the effects of cognitive behavior therapy(CBT)-based comprehensive nursing intervention(CNI)mode in arch expansion to treat patients with orthodontic osteodilated arch(OOA).AIM To explore the application effect of CBT-based CNI model in orthodontic expansion arch treatment.METHODS Using convenient sampling method,81 patients with OOA were selected and rolled into a control group(Ctrl group,40 cases)and an observation group(Obs group,41 cases).During the treatment,patients in the Ctrl group received routine nursing intervention mode,and the those in the Obs group received CBT mode on the basis of this.Before and after intervention,the incidence of oral mucositis,the mastery rate of correct arch expansion method,self-rating anxiety scale score,soft scale index,and plaque index were compared for patients in different groups.In addition,satisfaction and complications were comparatively analyzed.RESULTS Incidence of oral mucositis in the Obs group was lower(14.6%vs 38.5%),and the mastery rate of correct arch expansion method was obviously higher(90.2%vs 55.0%)was obviously higher(all P<0.05).Meanwhile,the soft scale index and plaque index in the Obs group were much lower(P<0.05).The compliance(90.24%)and satisfaction(95.12%)in the Obs group were greatly higher(P<0.05).CONCLUSION The CBT-based CNI mode greatly improved the mastery rate of correct arch expansion method during arch expansion in treating patients with OOA and enhanced the therapeutic effect of arch expansion and the oral health of patients,improving the patient compliance.

Research on Public Engineering Emergency Decision-Making Based on Multi-Granularity Language Information

To effectively deal with fuzzy and uncertain information in public engineering emergencies,an emergency decision-making method based on multi-granularity language information is proposed.Firstly,decision makers select the appropriate language phrase set according to their own situation,give the preference information of the weight of each key indicator,and then transform the multi-granularity language information through consistency.On this basis,the sequential optimization technology of the approximately ideal scheme is introduced to obtain the weight coefficient of each key indicator.Subsequently,the weighted average operator is used to aggregate the preference information of each alternative scheme with the relative importance of decision-makers and the weight of key indicators in sequence,and the comprehensive evaluation value of each scheme is obtained to determine the optimal scheme.Lastly,the effectiveness and practicability of the method are verified by taking the earthwork collapse accident in the construction of a reservoir as an example.