Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

The public health emergency has changed the environment and conditions of art teaching. Based on the abnormal teaching background, we can use this as an opportunity to explore new teaching forms. Relying on the unique functions of the network platform, the Art Cloud Classroom explores a new style of home-based art learning that is vivid, autonomous and interactive, develops students' art skills, develops positive interests and emotions, and makes every life a better place in the nourishment of art.

Our study is based on the premise that every crisis has historical precedents and antecedents. First, we analyze past crises, beginning with the experiences of the Dutch tulip bulb crisis. Then, we review major cataclysms, such as World War I, the Spanish flu crisis, the Great Depression of 1929–1933, World War II and the subsequent transition to socialism, the 1973 oil shock, the regime change of 1989, and the 2008–2009 global financial crisis from both general and corporate perspectives. Throughout history, periods of crisis have alternated with phases of development. During times of crisis, people’s behavior changes as they search for solutions and support. This pattern is evident across all levels of economic activity, where governments, organizations, and individuals do their utmost to achieve a quick recovery. Sometimes, they look to external aid, forgetting that lessons from the past may provide guidance for crisis management. Without claiming to be exhaustive, we have identified points worthy of consideration. Our goal is to offer guidance for business organizations, complemented by thoughts addressed to individuals and governments alike. Organizations must pay attention to the first signs of crises and either proceed according to a pre-developed fitting strategy or revise it according to specific circumstances. They cannot avoid the consequences, but they can mitigate the negative effects.

Under the concept of independent maintenance proposed by the Meteorology, Climatology, and Geophysics Agency (BMKG) for operational equipment, a thorough analysis of its management processes is necessary. Leadership involvement at various levels can affect maintenance outcomes, impacting sustainability. This research creates a thinking model that connects responsible leadership (RL) with sustainable performance (SP) through agile organization (AO) mediation and maintenance management implementation (MMI) in the management of leading operations equipment. The method used was a survey of 366 respondents who were BMKG employees, and explanatory analysis was analyzed based on descriptive statistical analysis using SmartPLS. The research results show that the third hypothesis proposed is acceptable, and the two mediator variables are partial mediation. The discussion of the study results shows some theoretical and practical implications for achieving the goals of SP, where organizations should encourage RL behavior that can implement current practices regarding AO and MMI. The test results show that AO and MMI have a significant role as mediators in encouraging the influence of RL on SP. This study is the first step in examining the relationship of RL to SP using AO and MMI mediation. Furthermore, this model can be developed and analyzed in other sectors or fields to increase knowledge.

Preserving roads involves regularly evaluating government policy through advanced assessments using vehicles with specialized capabilities and high-resolution scanning technology. However, the cost is often not affordable due to a limited budget. Road surface surveys are highly expected to use low-cost tools and methods capable of being carried out comprehensively. This research aims to create a road damage detection application system by identifying and qualifying precisely the type of damage that occurs using a single CNN to detect objects in real time. Especially for the type of pothole, further analysis is to measure the volume or dimensions of the hole with a LiDAR smartphone. The study area is 38 province’s representative area in Indonesia. This research resulted in the iRodd (intelligent-road damage detection) for detection and classification per type of road damage in real-time object detection. Especially for the type of pothole damage, further analysis is carried out to obtain a damage volume calculation model and 3D visualization. The resulting iRodd model contributes in terms of completion (analyzing the parameters needed to be related to the road damage detection process), accuracy (precision), reliability (the level of reliability has high precision and is still within the limits of cost-effective), correct prediction (four-fifths of all positive objects that should be identified), efficient (object detection models strike a good balance between being able to recognize objects with high precision and being able to capture most objects that would otherwise be detected-high sensitivity), meanwhile, in the calculation of pothole volume, where the precision level is established according to the volume error value, comparing the derived data to the reference data with an average error of 5.35% with an RMSE value of 6.47 mm. The advanced iRodd model with LiDAR smartphone devices can present visualization and precision in efficiently calculating the volume of asphalt damage (potholes).