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.

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.

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.

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.

中日两国哲学术语optimism译词的形成与演变

哲学术语optimism源于拉丁文optimus,于19世纪中后期传入中日两国。通过调查中日两国多种近现代词典与两国语料库发现,在译介到两国之初时,日语先后译为「楽天教」「萬物皆善説」「楽観主義」「楽天主義」等,其后译词传入汉语。而现代,两国在译词选择上出现了不同倾向,中方较统一地以“乐观主义”对译;日方则较为复杂,同时存在多个译词且较不固定,但整体上来说以「楽天」为语素的译词占优。这与汉日语中的同形词“乐观/楽観”“乐天/楽天”的语义及使用相关,同时也与汉日语分别使用反义词“乐观—悲观”和「楽天―厭世」对译哲学术语optimism与pessimism有密切关联。

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

Influence of layer thickness on formation quality,microstructure,mechanical properties,and corrosion resistance of WE43 magnesium alloy fabricated by laser powder bed fusion

Laser powder bed fusion(L-PBF)of Mg alloys has provided tremendous opportunities for customized production of aeronautical and medical parts.Layer thickness(LT)is of great significance to the L-PBF process but has not been studied for Mg alloys.In this study,WE43 Mg alloy bulk cubes,porous scaffolds,and thin walls with layer thicknesses of 10,20,30,and 40μm were fabricated.The required laser energy input increased with increasing layer thickness and was different for the bulk cubes and porous scaffolds.Porosity tended to occur at the connection joints in porous scaffolds for LT40 and could be eliminated by reducing the laser energy input.For thin wall parts,a large overhang angle or a small wall thickness resulted in porosity when a large layer thicknesses was used,and the porosity disappeared by reducing the layer thickness or laser energy input.A deeper keyhole penetration was found in all occasions with porosity,explaining the influence of layer thickness,geometrical structure,and laser energy input on the porosity.All the samples achieved a high fusion quality with a relative density of over 99.5%using the optimized laser energy input.The increased layer thickness resulted to more precipitation phases,finer grain sizes and decreased grain texture.With the similar high fusion quality,the tensile strength and elongation of bulk samples were significantly improved from 257 MPa and 1.41%with the 10μm layer to 287 MPa and 15.12%with the 40μm layer,in accordance with the microstructural change.The effect of layer thickness on the compressive properties of porous scaffolds was limited.However,the corrosion rate of bulk samples accelerated with increasing the layer thickness,mainly attributed to the increased number of precipitation phases.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).