Effectiveness of simulation-based learning regarding management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students:A quasi-experimental study

Objective:To assess the effectiveness of simulation-based learning regarding the management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students.Methods:This was a quasi-experimental study conducted among 1152nd-year nursing students.The participants were selected by a simple random sampling technique.The participants were divided into an experimental(n=56)and a comparison group(n=59)by a random table method.Data were analyzed using descriptive and inferential statistics with SPSS version 20.Results:There were significant differences in mean post-test knowledge scores(P=0.03)and mean post-test self-efficacy scores(P=0.001)between the experimental and the comparison groups while the difference in mean post-test clinical decision-making ability scores between the two groups was non-significant(P=0.07).A positive correlation was found between knowledge and clinical decision-making ability in pre-test(P=0.03)and in post-test(P<0.001)and a non-significant correlation was found between pre-test knowledge and self-efficacy score(P=0.52)among the experimental group.Conclusions:Simulation-based learning regarding the management of post-COVID complications is effective among nursing students.Simulation labs should be established in health care settings where simulation training can be provided for updating the knowledge,clinical decision-making ability,and self-efficacy of nursing personnel during program installment and continuous nursing education.

Business Blockchain Suitability Determinants: Decision-Making through an Intuitionistic Fuzzy Method

Blockchain is one of the innovative and disruptive technologies that has a wide range of applications in multiple industries beyond cryptocurrency.The widespread adoption of blockchain technology in various industries has shown its potential to solve challenging business problems,as well as the possibility to create new business models which can increase a firm’s competitiveness.Due to the novelty of the technology,whereby many companies are still exploring potential use cases,and considering the complexity of blockchain technology,which may require huge changes to a company’s existing systems and processes,it is important for companies to carefully evaluate suitable use cases and determine if blockchain technology is the best solution for their specific needs.This research aims to provide an evaluation framework that determines the important dimensions of blockchain suitability assessment by identifying the key determinants of suitable use cases in a business context.In this paper,a novel approach that utilizes both qualitative(Delphi method)and quantitative(fuzzy set theory)methods has been proposed to objectively account for the uncertainty associated with data collection and the vagueness of subjective judgments.This work started by scanning available literature to identify major suitability dimensions and collected a range of criteria,indicators,and factors that had been previously identified for related purposes.Expert opinions were then gathered using a questionnaire to rank the importance and relevance of these elements to suitability decisions.Subsequently,the data were analyzed and we proceeded to integrate multi-criteria group decision-making(MCGDM)and intuitionistic fuzzy set(IFS)theory.The findings demonstrated a high level of agreement among experts,with the model being extremely sensitive to variances in expert assessments.Furthermore,the results helped to refine and select the most relevant suitability determinants under three important dimensions:functional suitability of the use case,organizational applicability,and ecosystem readiness.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

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.

ZnO Additive Boosts Charging Speed and Cycling Stability of Electrolytic Zn–Mn Batteries

Electrolytic aqueous zinc-manganese(Zn–Mn) batteries have the advantage of high discharge voltage and high capacity due to two-electron reactions. However, the pitfall of electrolytic Zn–Mn batteries is the sluggish deposition reaction kinetics of manganese oxide during the charge process and short cycle life. We show that, incorporating ZnO electrolyte additive can form a neutral and highly viscous gel-like electrolyte and render a new form of electrolytic Zn–Mn batteries with significantly improved charging capabilities. Specifically, the ZnO gel-like electrolyte activates the zinc sulfate hydroxide hydrate assisted Mn^(2+) deposition reaction and induces phase and structure change of the deposited manganese oxide(Zn_(2)Mn_(3)O_8·H_(2)O nanorods array), resulting in a significant enhancement of the charge capability and discharge efficiency. The charge capacity increases to 2.5 mAh cm^(-2) after 1 h constant-voltage charging at 2.0 V vs. Zn/Zn^(2+), and the capacity can retain for up to 2000 cycles with negligible attenuation. This research lays the foundation for the advancement of electrolytic Zn–Mn batteries with enhanced charging capability.

Whole-process case management effects on mental state and selfcare ability in patients with liver cancer

BACKGROUND Regarding the incidence of malignant tumors in China,the incidence of liver cancer ranks fourth,second only to lung,gastric,and esophageal cancers.The case fatality rate ranks third after lung and cervical cancer.In a previous study,the whole-process management model was applied to patients with breast cancer,which effectively reduced their negative emotions and improved treatment adherence and nursing satisfaction.METHODS In this single-center,randomized,controlled study,60 randomly selected patients with liver cancer who had been admitted to our hospital from January 2021 to January 2022 were randomly divided into an observation group(n=30),who received whole-process case management on the basis of routine nursing mea-sures,and a control group(n=30),who were given routine nursing measures.We compared differences between the two groups in terms of anxiety,depression,the level of hope,self-care ability,symptom distress,sleep quality,and quality of life.RESULTS Post-intervention,Hamilton anxiety scale,Hamilton depression scale,memory symptom assessment scale,and Pittsburgh sleep quality index scores in both groups were lower than those pre-intervention,and the observation group had lower scores than the control group(P<0.05).Herth hope index,self-care ability assessment scale-revision in Chinese,and quality of life measurement scale for patients with liver cancer scores in both groups were higher than those pre-intervention,with higher scores in the observation group compared with the control group(P<0.05).CONCLUSION Whole-process case management can effectively reduce anxiety and depression in patients with liver cancer,alleviate symptoms and problems,and improve the level of hope,self-care ability,sleep quality,and quality of life,as well as provide feasible nursing alternatives for patients with liver cancer.

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.

Influencing factors of self-management ability among dry eye patients in west China

AIM:To investigate the current situation and influencing factors of self-management ability in dry eye patients in west China.METHODS:A total of 265 patients clinically diagnosed with dry eye received a convenience survey questionnaire at West China Hospital of Sichuan University.All participants completed the rating scale of health self-management skill for adults(AHSMSRS),Huaxi Emotional-Distress Index(HEI),e-health literacy scale(e-HEALS)and Brief Illness Perception Questionnaire(Brief-IPQ).A generalized linear model was employed to establish a multivariate linear model with demographic data,psychological state,e-HEALS,and illness perception as independent variables and health selfmanagement skill score as the dependent variable.RESULTS:The mean score for health self-management skill was 165.58±15.79.Multivariate analysis revealed that advanced age,better illness perception and improved psychological state were associated with better health selfmanagement ability among dry eye patients.Furthermore,the health self-management ability of patients with a disease duration less than 1y was found to be higher compared to those with a disease duration exceeding 1y.CONCLUSION:The health self-management ability of dry eye patients in west China is relatively high.Age,duration of disease,illness perception and psychological state are the influencing factors on the health selfmanagement ability of dry eye patients.

Experimental Study on Wire Melting Control Ability of Twin-Body Plasma Arc

The twin-body plasma arc has the decoupling control ability of heat transfer and mass transfer,which is beneficial to shape and property control in wire arc additive manufacturing.In this paper,with the wire feeding speed as a characteristic quantity,the wire melting control ability of twin-body plasma arc was studied by adjusting the current separation ratio(under the condition of a constant total current),the wire current/main current and the position of the wire in the arc axial direction.The results showed that under the premise that the total current remains unchanged(100 A),as the current separation ratio increased,the middle and minimum melting amounts increased approximately synchronously under the effect of anode effect power,the first melting mass range remained constant;the maximum melting amount increased twice as fast as the middle melting amount under the effect of the wire feeding speed,and the second melting mass range was expanded.When the wire current increased,the anode effect power and the plasma arc power were both factors causing the increase in the wire melting amount;however,when the main current increased,the plasma arc power was the only factor causing the increase in the wire melting amount.The average wire melting increment caused by the anode effect power was approximately 2.7 times that caused by the plasma arc power.The minimum melting amount was not affected by the wire-torch distance under any current separation ratio tested.When the current separation ratio increased and reached a threshold,the middle melting amount remained constant with increasing wire-torch distance.When the current separation ratio continued to increase and reached the next threshold,the maximum melting amount remained constant with the increasing wire-torch distance.The effect of the wire-torch distance on the wire melting amount reduced with the increase in the current separation ratio.Through this study,the decoupling mechanism and ability of this innovative arc heat source is more clearly.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

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.

Effect of Y element on atomic structure, glass forming ability,and magnetic properties of FeBC alloy

The atomic structure of amorphous alloys plays a crucial role in determining both their glass-forming ability and magnetic properties. In this study, we investigate the influence of adding the Y element on the glass-forming ability and magnetic properties of Fe_(86-x)Y_xB_7C_7(x = 0, 5, 10 at.%) amorphous alloys via both experiments and ab initio molecular dynamics simulations. Furthermore, we explore the correlation between local atomic structures and properties. Our results demonstrate that an increased Y content in the alloys leads to a higher proportion of icosahedral clusters, which can potentially enhance both glass-forming ability and thermal stability. These findings have been experimentally validated. The analysis of the electron energy density and magnetic moment of the alloy reveals that the addition of Y leads to hybridization between Y-4d and Fe-3d orbitals, resulting in a reduction in ferromagnetic coupling between Fe atoms. This subsequently reduces the magnetic moment of Fe atoms as well as the total magnetic moment of the system, which is consistent with experimental results. The results could help understand the relationship between atomic structure and magnetic property,and providing valuable insights for enhancing the performance of metallic glasses in industrial applications.

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.

Cultivation Path of Nursing Undergraduates’Scientific Research and Innovation Ability

Objective: The cultivation of the innovation ability and scientific research is one of the nursing learning objectives for undergraduate students. To explore the method and effect of training system of scientific research innovation ability of nursing undergraduates based on “3332”. Methods: Three course learning modules are constructed: stage-based course learning module, systematic project practice training module and comprehensive practice training module. A practical training platform for scientific research innovation projects is built, and undergraduate scientific research innovation ability training is carried out from both in-class and out-of-class lines. Results: Since 2017, the students have obtained 7 national innovation and entrepreneurship training programs, 52 university-level undergraduate scientific research projects, published more than 10 academic papers, and obtained 2 patent authorization. Conclusions: The training system of scientific research innovation ability of nursing undergraduates based on “3332” is conducive to the development of scientific research innovation ability of nursing students, and to cultivate nursing talents who can adapt to the development of the new era and have better post competence.

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.

Combining ability of Egyptian cotton(Gossypium barbadense L.)reveals genetic potential for improved yield and fiber quality

Background As the most widely cultivated fiber crop,cotton production depends on hybridization to unlock the yield potential of current varieties.A deep understanding of genetic dissection is crucial for the cultivation of enhanced hybrid plants with desired traits,such as high yield and fine fiber quality.In this study,the general combining ability(GCA)and specific combining ability(SCA)of yield and fiber quality of nine cotton parents(six lines and three testers)and eighteen F1 crosses produced using a line×tester mating design were analyzed.Results The results revealed significant effects of genotypes,parents,crosses,and interactions between parents and crosses for most of the studied traits.Moreover,the effects of both additive and non-additive gene actions played a notably significant role in the inheritance of most of the yield and fiber quality attributes.The F1 hybrids of(Giza 90×Aust)×Giza 86,Uzbekistan 1×Giza 97,and Giza 96×Giza 97 demonstrated superior performance due to their favorable integration of high yield attributes and premium fiber quality characteristics.Path analysis revealed that lint yield has the highest positive direct effect on seed cotton yield,while lint percentage showed the highest negative direct effect on seed cotton yield.Principal component analysis identified specific parents and hybrids associated with higher cotton yield,fiber quality,and other agronomic traits.Conclusion This study provides insights into identifying potential single-and three-way cross hybrids with superior cotton yield and fiber quality characteristics,laying a foundation for future research on improving fiber quality in cotton.

Research on the Effect of Dance Therapy on Improving Social Communication Ability of Children with Autism

Research motivation:Through the 12 weeks dance therapy intervention for children with autism,the purpose is to explore the intervention model of dance therapy for children with autism and the changes in motor ability,social ability,and communication ability of children with autism after dance therapy intervention.The results of the research are expected to expand the intervention mode of dance therapy in my country and provide practical reference for rehabilitation intervention of children with autism.Research methods:24 autistic boys aged 6 to 12 with mild to moderate symptoms were recruited and screened through the Internet as the subjects of this study.We randomly divided them into experimental group(N=12)and control group(N=12).All children with autism have an autism diagnosis certificate issued by Children’s Hospital or a tertiary hospital,excluding other mental diseases(such as epilepsy,major physical disability,mental illness,no history of drugs and other interventions,etc.).We used the paired sample t-test to compare the score difference between the dance treatment group and the control group before and after the two groups,and used the observation method to record the basic communication behavior and the number of active communication behaviors in the experimental group during the intervention process.All data analysis is used in SPSS 20.0.Research results:After 12 weeks of dance therapy intervention,there were statistically significant differences in the gross movement,balance,and coordination abilities of the children in the experimental group compared with those before the intervention(p<0.01).There was no significant difference between the children in the control group(p>0.05).After 12 weeks of dance therapy intervention,there were statistically significant differences in the scores of the social response scale for children with autism in the experimental group(p<0.05).There was no significant change in the scores of each item of the SRS scale before and after intervention in the control group and the dance treatment group(p>0.05).