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The purpose of this paper is to review some challenges of insuring weather risk in agriculture and to discuss potential remedies for these problems.

The paper is developed as a narrative on weather insurance based largely on existing literature.

Weather risks show characteristics that often violate classical requirements for insurability. First, some weather risks, particularly slowly emerging weather perils like drought, are spatially correlated and cause systemic risks. Second, climatic change may increase the volatility of weather variables and lead to non-stationary loss distributions, which causes difficulties in actuarial ratemaking. Third, limited availability of yield and weather data hinders the estimation of reliable loss distributions.

Some of the approaches discussed in this review, such as time diversification, local test procedures and the augmentation of observational data by expert knowledge, can be useful for crop insurance companies to improve their risk management and product design.

This study provides background and development information regarding weather insurance and also presents statistical tools and actuarial methods that support the assessment of weather risks as well as the design of weather and yield insurance products.

The purpose of this paper is to examine the aggregate demand for single- and multi-year crop insurance contracts and to discuss market potential for multi-year crop insurances.

In this paper the authors develop a dynamic discrete choice model of insurance alternatives, in which single- and multi-year insurance contracts are offered to heterogeneous risk averse farmers. The farmers determine their insurances choices based on inter-temporal utilities.

The results show that in a competitive insurance market with heterogeneous risk averse farmers, there is simultaneous demand for both insurance contracts. Moreover, the introduction of multi-year contracts enhances the market penetration of insurance products.

The effect of introducing multi-year crop insurance is moderate when applying the model to US corn production. In practice, however, the increase of insurance demand could be more pronounced because we did not consider marketing and administrative costs and thus ignore this cost reduction potential of multi-year insurance.

This study adds to the literature analyzing the feasibility of multi-year crop insurance and also shows that there is market potential for multi-year crop insurance.

This paper aims to adapt and computerize the Trail Making Test (TMT) to support long-term self-assessment of cognitive abilities.

The authors propose a divide-and-combine (DAC) approach for generating different instances of TMT that can be used in repeated assessments with nearly no discernible practice effects. In the DAC approach, partial trails are generated separately in different layers and then combined to form a complete TMT trail.

The proposed approach was implemented in a computerized test application called iTMT. A pilot study was conducted to evaluate iTMT. The results show that the instances of TMT generated by the DAC approach had an adequate level of difficulty. iTMT also achieved a stronger construct validity, higher test–retest reliability and significantly reduced practice effects than existing computerized tests.

The preliminary results suggest that iTMT is suitable for long-term monitoring of cognitive abilities. By supporting self-assessment, iTMT also can help to crowdsource the assessment processes, which need to be administered by healthcare professionals conventionally, to the patients themselves.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

Frictions in cylinder liner-piston ring often cause an inevitable loss of energy loss in the diesel engine. This study aims at evaluating the effect of depths in the cylinder liner groove texture on friction, wear and sealing performances.

Five depths of groove texture cylinder liners (50, 100, 150, 200, 250 µm) were fabricated, and experiments were carried out using a special-purpose diesel engine tester. Comparative analyses of cylinder liner contact resistances, piston ring wear losses and surface appearances were conducted with respect to different surface textures and applied loads.

Under no-load conditions, the cylinder liner with a 100 deep thread groove can significantly improve sealing and optimize its lubrication performance. On the other hand, the sealing is highly correlated with the depth of groove and the load within the cylinder liner. Under loaded conditions, the thread groove has less effect on the sealing performance.

The findings can provide feasible basis for the tribological design and production of diesel engines.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Gallic acid is a substance that is widely found in nature. Initially, it was only used as a corrosion inhibitor to retard the rate of corrosion of metals. In recent years, with intensive research by scholars, the modification of coatings containing gallic acid has become a hot topic in the field of metal protection. This study aims to summarize the various preparation methods of gallic acid and its research progress in corrosion inhibitors and coatings, as well as related studies using quantum chemical methods to assess the predicted corrosion inhibition effects and to systematically describe the prospects and current status of gallic acid applications in the field of metal corrosion inhibition and protection.

First, the various methods of preparation of gallic acid in industry are understood. Second, the corrosion inhibition principles and research progress of gallic acid as a metal corrosion inhibitor are presented. Then, the corrosion inhibition principles and research progress of gallic acid involved in the synthesis and modification of various rust conversion coatings, nano-coatings and organic resin coatings are described. After that, studies related to the evaluation and prediction of gallic acid corrosion inhibition on metals by quantum chemical methods are presented. Finally, new research ideas on gallic acid in the field of corrosion inhibition and protection of metals are summarized.

Gallic acid can be used as a corrosion inhibitor or coating in metal protection.

There is a lack of research on the synergistic improvement of gallic acid and other substances.

The specific application of gallic acid in the field of metal protection was summarized, and the future research focus was put forward.

To the best of the authors’ knowledge, this paper systematically expounds on the research progress of gallic acid in the field of metal protection for the first time and provides new ideas and directions for future research.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.