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This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

The present investigation goals to empirically test the role of expert cloud on team performance and employee creativity. Here, the expert cloud comprises cloud application, cloud management, cloud infrastructure and cloud resources. The present study aims to identify important and key criteria and examine the relationships among them. In other words, the purpose of this study to find out the impact and relationship between cloud application, cloud management, cloud infrastructure and cloud resources and team performance and employee creativity.

Today, human societies’ rapid growth and the environmental changes that surround us every day are clearly visible. They highly affect our activities. In today’s highly complex organizations, people alone cannot handle all the issues that have arisen. As organizational managers are faced with diverse cultures in the governance of organizations, the need to use work teams with different abilities and specializations to achieve the goals of organizations leads managers to use teamwork and focus on employee creativity. On the other hand, the expert cloud makes it possible for human societies like universities, firms, industries, institutes, businesses and colleges to pool and share their human resources’ skills, knowledge and experiences to meet the competitive era’s demands. Therefore, the current investigation examines the impact of expert cloud on team performance and employee creativity. The research information is collected using an online questionnaire. The data collected is analyzed using AMOS and SMART PLS software.

All of the formulated hypotheses are supported. The results have shown that cloud application, cloud management, cloud infrastructure and cloud resources positively and significantly affect team performance and employee creativity.

Managers must be conscious of the vital role that the professional cloud plays in team performance and the innovation of workers. This paper would also make executives more conscious of the powerful tools in the field of cloud computing. Firms can use the outcomes of this paper investigation to improve team performance and employee creativity.

It is considered one of the initial efforts to demonstrate the impacts of expert cloud on team performance and employee creativity. This study’s value relies on that practitioners and academics may have supporting evidence on the role played by novel technology such as an expert cloud.

The purpose of this paper is to establish a Process-aware FMEA (PAFMEA) development environment in order to face the main Failure Mode Effect Analysis (FMEA) deficiencies concerning failure analysis in maintenance.

The proposed framework integrates Delphi methodology to obtain consensus of specialists’ opinions, analytic hierarchy process (AHP) to perform multiple criteria-based risk assessment and a business process management system to instantiate the development cycle. A conceptual model is presented and analyzed through a case study.

PAFMEA reveals a new perception in the evaluation and prioritization of failure modes during maintenance failure analysis, such as risk definition and resource availability, dealing with conflicting characteristics in decision-making approaches.

The PAFMEA environment includes requirements that are grouped with a process instantiation of an AHP structure, providing a high degree of applicability and performance to the development cycles of the FMEA. The new method confronts the classical risk assessment approach and contributes to the literature, adding new perspectives to the FMEA analysis.

PAFMEA brings new and promising perspectives to the FMEA development cycle, which, in short, means adding on a multi-criteria failure analysis method (AHP) through a process-aware platform, with performance impacts in FMEA knowledge sharing, decision making and delivery.

Food labels are increasingly used to provide information to consumers. As a common design strategy used for food package labels globally, label frame is often used to expand the perceived breadth of a brand and create a broader brand image. We evaluated the effect of the presence or absence of a non-genetically modified organism (non-GMO) label frame on consumers' preferences for non-GMO foods.

This study collected data from 120 MBA students at a university in Sichuan, China, and 126 foreign volunteers in a shopping mall in Chengdu, Sichuan Province. The study investigates the effect of the presence or absence of non-GMO label frame (i.e. label with or without an outline) on non-GMO food preferences through a field survey and two controlled experiments. To empirically analyse the psychological mechanisms by which non-GMO label frames affect consumers' preferences for non-GMO food, we set up the mediating variable of food association of safety.

For ordinary consumers, a framed non-GMO label is more likely to evoke food association of safety and further enhance consumer preference for non-GMO foods. It facilitates consumers' choice of healthier foods. This finding did not otherwise vary across demographic characteristics.

This study is the first to examine the influence of non-GMO label frames on consumers' non-GMO food preferences, which is an innovative research question. The findings of this study are instructive for food manufacturers and policymakers to better design and use non-GMO label frames to attract more consumers to choose non-GMO foods.

This study aims to use a thermal elastohydrodynamic lubrication (EHL) algorithm incorporating an Eyring flow model to solve a steady-state contact in simple sliding motion.

A theoretical model was used to investigate the effect of starvation on the surface dimple phenomenon by gradually reducing the thickness of the inlet oil layer.

The increase in the starvation degree reduces the dimple depth, film thickness, the pressure peak and the temperature rise. Under the severe starvation condition, the dimple is eliminated so that the EHL contact becomes partly parched. In elliptical results, for the same starvation parameters, the oil replenishment is stronger than that in circular contact.

This paper fulfils an exploration to study how the oil starvation influences the surface dimple phenomenon.

Reliability centered maintenance (RCM) is a process used to determine activities to be taken to ensure an asset continues to perform asset's function in asset's present operating context by identifying asset's function, failure modes that could preclude performing asset's intended function, prioritizing failure modes and determining effective preventative maintenance tasks that can be cost effectively and efficiently implemented to reduce the likelihood of a failure.

A comprehensive survey of literature was undertaken to examine the current industry state of practice. Various industries were examined to better understand applications of RCM within the various industry sectors and determine those industries that RCM has not historically been readily adopted. A case study example of RCM applied to radial gates for water control in open channel canals for water conveyance is presented to demonstrate a civil infrastructure application.

The results found that RCM has been used since RCM's inception in the airline industry during the 1960s to reduce the cost of maintaining aircrafts. Over the past 40 years, an assortment of industries has begun implementing cost effective preventative maintenance tasks identified during RCM analysis. However, there is a noticeable lack of civil assets being analyzed by RCM, such as water conveyance systems and other civil infrastructure systems vital to the health and well-being of today's societies.

The comprehensive literature review of the current state of practice will provide a better understanding of the various applications of RCM to facilitate RCM's application to other industries, thereby reducing failure due to early identification of maintenance tasks. An example RCM demonstrates the application to a radial gate, used in water conveyance for the drinking water and irrigation sectors, which have not historically used RCM for developing maintenance strategies.

The human body has the same basic size data but has different surface morphology, resulting in the unfitness even under the same size specification. The purpose of this study was to solve the local fitness problems by representing and quantifying the human surface morphological difference.

Firstly, the 3D point cloud for 323 female students was scanned, and the cross-section layers of the “waist-to-thigh” zone were determined. Secondly, the space vector based on the space Euclidean distance was extracted to represent and quantify the surface morphological difference. And the Principal Component Analysis and K-means were adopted to subdivide the target zone. Thirdly, the pattern based on the subdivision results and surface flattening was generated. Additionally, the fitness was evaluated by the subjective and objective assessments, separately.

The space vector could represent and quantify the shape morphology of the “waist-to-thigh” zone. It had successfully achieved the human body subdivision and corresponding pattern generation for the “waist-to-thigh” zone. And the pattern based on the shape subdivision and surface flattening of the space vector could effectively improve the wearing fitness. Particularly in the waist and crotch area of trousers, the obvious wrinkles had been solved because the space vector is more in line with the shape morphology characteristics.

The proposed method could represent and quantify the difference in human surface morphology in a 3D manner. It solved the unfitness problem caused by the same body size but different shape surface morphology. And it will contribute to the fitness improvement of the trousers.

Constructing porous wind barriers is one of the most effective approaches to increase the running safety of trains on viaducts in crosswinds. This paper aims to further improve the wind-sheltering performance of the porous wind barriers.

Improved delayed detached eddy simulations based on the k-ω turbulence model were carried out, and the results were validated with wind tunnel tests. The effects of the hole diameter on the flow characteristics and wind-sheltering performance were studied by comparing the wind barriers with the porosity of 21.6% and the hole diameters of 60 mm–360 mm. The flow characteristics above the windward and leeward tracks were analyzed, and the wind-sheltering performance of the wind barriers was assessed using the wind speed reduction coefficients.

The hole diameters affected the jet behind the wind barriers and the recirculation region above the tracks. Below the top of the wind barriers, the time-averaged velocity first decreased and then increased with the increase in the hole diameter. The wind barrier with the hole diameter of 120 mm had the best wind-sheltering performance for the windward track, but such barrier might lead to overprotection on the leeward track. The wind-sheltering performance of the wind barriers with the hole diameters of 240 mm and 360 mm was significantly degraded, especially above the windward track.

The effects of the hole diameters on the wake and wind-sheltering performance of the wind barriers were studied, by which the theoretical basis is provided for a better design of the porous wind barrier.

This paper aims to investigate the variations in the flow fields induced by transition regions in the windbreak structures between the flat ground and the cutting along a railway and to propose mitigation measures to improve the windproof ability of the windbreak.

The improved delayed detached eddy simulation method was used to simulate the impact of the windbreak transition on flow structures of the high-speed railway under different wind angles, and also the accuracy of the numerical results was validated with those of the wind tunnel test.

The results showed that the original windbreak transition region resulted in a dimensionless peak wind velocity of 0.62 and 0.82 for railway line-1 at wind angles of 90° and 75°, respectively, and the corresponding values were 0.81 and 0.97 for railway line-2. The flow structure analysis revealed the reason for the mismatched height in the transition region, and the right-angle structures of the windbreaks resulted in ineffective protection and sudden changes in the wind speed and direction. Two mitigation measures – oblique structure (OS) and circular curve structure (CCS) transition walls – were developed to reduce the peak wind speed. The OS provided superior protection. The peak value of dimensionless wind velocity was all less than 0.2 for OS and CCS.

The flow field deterioration mechanism induced by the inappropriate form of a windbreak transition at different wind angles was examined, and effective mitigation and improvement measures were proposed and compared with the original transition.

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU.

The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms' purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality.

The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases.

The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms' decisions to purchase D&O insurance.