Optimism,Social Support,and Caregiving Burden among the Long-Term Caregivers:The Mediating Effect of Psychological Resilience

Background:As the elderly population grows,the demand for long-term care services is increasing.Despite significant investments in care quality and workforce training,long-term care workers often face challenges such as work fatigue,heavy workloads,and inadequate support.These issues can impact job satisfaction,mental health,and care quality,leading to staff turnover.This study examines how optimism,social support,and psychological resilience relate to caregiving burden,aiming to understand their effects on caregivers’well-being and performance to enhance the quality of long-term care services.Methods:The participants were 542 long-term care workers.Descriptive statistics,t-tests,one-way ANOVA,and hierarchical regression were used for data analysis.Results:(1)Optimism and social support were significantly and positively correlated with psychological resilience and significantly and negatively associated with caregiving burden.(2)Regarding differences in optimism,social support,psychological resilience,and caregiving burden among long-term care workers,females scored significantly higher than males in“social support;”married workers scored significantly higher than unmarried workers in“optimism,”“social support,”and“psychological resilience”;workers aged 45–65 scored significantly higher than those aged 25–45 in“optimism”;workers aged 25–45 scored significantly higher than those aged 45–65 in“caregiving burden”;social workers scored significantly higher than nursing staff in“optimism.”(3)Psychological resilience partially mediated the relationship between social support and caregiving burden concerning explanatory and predictive power.Conclusions:These findings suggest that optimism,social support,and psychological resilience are essential factors in reducing the caregiving burden among long-term care workers.The study highlights the importance of promoting psychological resilience and providing social support to alleviate the burden of caregiving.

Optimism in Crossing Brookly Ferry

Crossing Brookly Ferry is one of the most representative masterpieces composed by Walt Wiltman. In this pieces of writing, ones of the characteristics in this poem: optimism and unconventionality in its form are expressed in details.

Examining the Relationship of Optimism and Emotion Regulation Strategies with General Health among Students of University of Sistan and Baluchestan

Background and Objective: Individuals apply various emotion regulation strategies, some of which are adaptive and others are maladaptive affecting people’s general health. Moreover, individual life-orientation including favorable expectancies about future (optimism) is associated with health-related behaviors. The purpose of the present study was to investigate the relationship of optimism and emotion regulation strategies with general health of university students. Materials and Methods: This was a correlational study. In this regard, 182 students of University of Sistan and Baluchestan (70 males and 112 females) were chosen. The statistical population of the present study consisted of all undergraduate students of the university of Sistan and Baluchestan in the second semester of the 2009-2010 academic year. Considering the nature of the current study, the correlational method was applied. Based on Krejcie and Morgan’s table, a sample of 200 subjects was selected from students majored at different fields including human sciences, basic sciences and technical-engineering through applying multi-stage random sampling method. Eighteen incomplete questionnaire forms were excluded. Finally, data obtained from 182 subjects (112 females, 70 males) were analyzed. The mean age was 21.1 year-old and standard deviation of the sample was 2.06. Samplings were assessed using the Revised Life-Orientation Test (LOT-R), Emotion Regulation Questionnaire (ERQ) and General Health-28 Questionnaire (GHQ-28). Data were analyzed using the Pearson correlation coefficient and regression analysis. Results: Findings showed that there was a significant positive relationship between optimism and general health (r = 0.22, p < 0.01). Among all research variables, i.e. optimism and emotion regulation strategies (cognitive reappraisal and expressive suppression), only optimism was able to predict 0.06 percent of variance of general health (p < 0.001). Conclusion: Optimists have higher general health and consistent with other findings, optimism is associated with higher levels of applying coping strategies and lower levels of avoidance.

A NEW TECHNIQUE FOR COMPAIRING FUZZY NUMBERS USING AN INDEX OF OPTIMISM

Fuzzy numbers are convenient for representing imprecise numerical quantities in a vague environment, and their comparison or ranking is very important for application purposes. Despite many methods suggested in the literature, there is no single measure that is universally applicable to a wide variety of situations. This paper suggested a new method for comparing fuzzy numbers based on the combination of maximizing possibility and minimizing possibility using an index of optimism in [0,1] reflecting the decision makers’ risk taking attitude. The method is simple, but has many comparative advantages.

Investor Attention,Analyst Optimism,and Stock Price Crash Risk

This paper used the A-shares listed companies in China as samples,constructed a comprehensive indicator of investor attention,and conducted an empirical analysis on the correlations among investor attention,analyst optimism,and stock price crash risk.The results indicated that investor attention aggravates the stock price crash risk and has a positive effect on analyst optimism.Meanwhile,the analyst optimism plays a mediating role in the positive correlation between investor attention and stock price crash risk.In addition to that,institutional investor attention also has direct and indirect effects on the crash risk.

An Empirical Test of Optimism Bias in Capital Budgeting Decisions

Behavioral finance is a field that is scrutinizing the adequacy of traditional financial theories using insights from the disciplines of psychology and sociology. Many studies within its realm test the stock market behaviors, and behavioral phenomena are still to be tested in the area of corporate finance. This study aims to contribute to the behavioral corporate finance literature by a research in one of the psychological phenomena affecting the decision makers＇ abilities to reach conclusions rationally. In this study, it is aimed to investigate one of the biases, namely, the optimism bias in corporate capital budgeting decisions. Optimism in decision making can be associated with estimating lower costs and higher revenues. Thus, by assessing the forecasts of decision makers, the existence of optimism in their decisions is tried to be seen. This study aims at contributing to the literature in that it is conducted in an emerging country like Turkey.

Appraising the Manifestation of Optimism Bias and Its Impact on Human Perception of Cyber Security: A Meta Analysis

Cyber threats and risks are increasing exponentially with time. For preventing and defense against these threats and risks, precise risk perception for effective mitigation is the first step. Risk perception is necessary requirement to mitigate risk as it drives the security strategy at the organizational level and human attitude at individual level. Sometime, individuals understand there is a risk that a negative event or incident can occur, but they do not believe there will be a personal impact if the risk comes to realization but instead, they believe that the negative event will impact others. This belief supports the common belief that individuals tend to think of themselves as invulnerable, i.e., optimistically bias about the situation, thus affecting their attitude for taking preventive measures due to inappropriate risk perception or overconfidence. The main motivation of this meta-analysis is to assess that how the cyber optimistic bias or cyber optimism bias affects individual’s cyber security risk perception and how it changes their decisions. Applying a meta-analysis, this study found that optimistic bias has an overall negative impact on the cyber security due to the inappropriate risk perception and considering themselves invulnerable by biasing that the threat will not occur to them. Due to the cyber optimism bias, the individual will sometimes share passwords by considering it will not be maliciously used, lack in adopting of preventive measures, ignore security incidents, wrong perception of cyber threats and overconfidence on themselves in the context of cyber security.

Research progress on subjective well-being and dispositional optimism of the elderly

The subjective well-being of the elderly is an integral part of healthy aging.This review introduces the concept of subjective well-being of the elderly and the evaluation tools used,reviews the influencing factors of subjective well-being of the elderly,and summarizes the intervention measures of subjective well-being of the elderly.From the perspective of positive psychology,based on the introduction of the concept and evaluation tools of temperament optimism,this paper reviews the research status of temperament optimism and subjective well-being and the research progress of their correlation,so as to provide a theoretical basis for the intervention to improve the subjective well-being of the elderly.

采油厂管道工程预算(Optimism Bias)偏差研究

乐观偏差的英文为(Optimism Bias ,OB),在实务上,即是指工程在预算与工期方面,实际值与预估值的差异,即乐观偏差(%)=(实际值-估计值)÷估计值×100%,及指评估者对于完成工程目标所需成本或工期过度乐观预期,导致工程评估上的误差,而导致工程评估的可信度降低。乐观偏差分为前乐观偏差(OB Pre)与后乐观偏差(OB Post),前者主要在探讨工程预算的这部份,项目在一开始核定预算至施工工程发包的决标价的差异,后者则在探讨预算与工期的部份,而后乐观则是发生在合约签订后至工程完工这段期间与讨论完工成本与发包成本之间的差异。

Economic Optimism

Though facing challenges, the Chinese Government has kept a steady hand on the tiller and managed to sail the economy through the first half of 2016 relatively unscathed, sustaining steady economic growth and accelerating economic transformation. According to a report recently released by the National Bureau of Statistics, China＇s GDP growth in the first half of 2016 was 6.7 percent year on year. A slowing growth rate, in relation to past stellar economic performance of around double-digit growth, has, however, given rise to ques- tions about the development trend of the world＇s second largest economy, What will be the highlights of the second half of the year？ What will be the impact of China＇s economic growth on the global economy going forward？ Renowned experts in this field believe there is much resilience and potential for China＇s economy to maintain a stable and healthy growth, Some of their views follow：

Potential for Optimism

THE annual sessions of China’s National People’s Congress,the top legislature,and the National Committee of the Chinese People’s Political Consultative Conference,the top political advisory body,held in Beijing in March left little doubt that their deliberations and resolutions will have global ramifications.

A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.

Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real life.The development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of living.In a specific area,achieving higher signal coverage with fewer base stations has become an urgent problem.Therefore,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as ECPPSO.Introducing a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by PSO.ECPPSO also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization problems.To better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base stations.The experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches 80.These results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and effectiveness.Further ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

Prediction and optimization of flue pressure in sintering process based on SHAP

Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.

Implicit and explicit emotion regulation in adolescents with dispositional optimism

The development of adaptive emotion regulation(ER)plays a pivotal role in adolescent mental health and socio-emotional adaptation.Dispositional optimism,as an important protective factor for adolescent adjustment,may affect adolescent ER and subsequently influence adaptive outcomes.In this review,the changes and challenges,the role of ER in socio-emotional adjustment,and the developmental characteristics of implicit and explicit ER during adolescence are described.Subsequently,by employing the top-down model of personality,coping,and emotion,how dispositional optimism may affect psychological adjustment from the perspective of ER is analyzed.Furthermore,how the differences in adolescents’dispositional optimism may be reflected by the differences in implicit ER is discussed.Finally,recommendations for future research are outlined.

Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.

Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.

Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15￥/kWh or if the cost of the fuel cell drops below 4￥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.