Vibrations observed as a result of moving vehicles can potentially affect both buildings and the people inside them. The impacts of these vibrations are complex, affected by a number of parameters, like amplitude, frequency, and duration, as well as by the properties of the soil beneath. These factors together lead to various effects, from slight disruptions to significant structural damage. Occupants inside affected buildings may experience discomfort, disrupted sleep patterns, and increased stress levels due to the pervasive nature of vibrations. Low-frequency vibrations, typically ranging from 5 to 25 Hz, are of particular concern since they can exacerbate these effects by resonating with internal human organs. To effectively mitigate these issues, a comprehensive approach is required, starting with some interventions at the source. This may involve strategic choices in road construction materials and advancements in vehicle design to reduce the transmission of vibrations through the ground to the surrounding environment. Understanding the complexities of vibration dynamics is essential in urban planning, serving as a fundamental consideration in the development of modern infrastructure that prioritizes the well-being and safety of its inhabitants. Therefore, the aim of the present study is to consider artificial neural networks to assess the potential impact of traffic-induced vibrations on a building's residents. The results of the study indicate that the proposed method of utilizing machine learning can be effectively applied for such purposes.

In this study, the Pseudomonas fluorescens-based lipase enzyme was utilized to enhance the comfort and hydrophilic properties of polyester/cotton blend fabric. The experiment was set up, the major influencing elements were examined, and the appropriate operational parameter levels were established using the Box-Behnken design approach. To express the wettability and moisture regain of treated fabrics, temperatures, lipase enzyme concentrations, and treatment time were taken as independent variables. The ideal optimum lipase enzyme treatment parameters were found to be 30 degrees C temperature, 14% lipase concentration, and 50 min of treatment time. At optimal operating conditions, the moisture regain and wettability for lipase-treated p/c blend fabrics enhanced to 1.8 +/- 0.02% and 6.3 cm capillary rise(2-s drop test and 2-s sinking time), respectively. The lipase enzyme-treated p/c blend fabrics are characterized by a totally reduced susceptibility to fabric pilling which is 4-5 as well as a restricted ability to combine oily impurities and a high oil-soil-release capability of stain removal index of 95% and also showed a surface resistivity decreased by one order of magnitude under normal conditions which is 470 s of half-life decay time. Generally, the effects of the lipase enzyme treatment on the fabric properties were then assessed by FT-IR, TGA, DSC, moisture regain, tensile strength, stain repellency, pilling resistance and anti-static charge generation.

Introduction Nature-based solutions are increasingly recognized as vital components of urban resilience strategies, particularly within the framework of green infrastructure. This study aims to propose an approach that fosters symbiosis between green and gray infrastructure to address the challenges posed by climate change in urban environments.Methods We conducted a comprehensive review of guidelines and scientific literature to inform the selection of species and the design of root containers for urban tree planting. Additionally, we performed a multicriteria analysis and assessed water comfort to guide decision-making regarding species selection in specific city areas.Results The methodology was applied to a case study in Bogota, yielding insights applicable to any city with basic knowledge of suitable species for planting in built public spaces. Crucial criteria for selecting local species for sidewalks were identified, including size, permeability, soil compaction characteristics, and climatic adaptability. A list of desirable species adapted to all humidity zones of the case study city was generated. Hydrological sizing methods proposed are contingent upon both the species to be planted and the geometry of the streets.Discussion The approach and findings presented in this study promote the development of trees and their ecosystem services while mitigating potential damage to surrounding infrastructure.Conclusion Implementing strategies that facilitate symbiosis between green and gray infrastructure contributes to urban resilience and aids in climate change adaptation efforts.

Tourism plays an important role in Kashgar's socio-economic development. Climate change has a profound impact on the development of tourism. However, basic research on climate change and its impact on tourism remains insufficient in Kashgar. Using the atmospheric reanalysis data ERA5 and the universal thermal climate index (UTCI) model, climate change and climate comfort were evaluated from 1979 to 2018. The annual mean UTCI was -2.3 degrees C, i.e., at the coolish level, with moderate cold stress, illustrating that Kashgar's tourism climate was weak. The obvious increase in the annual mean air temperature led to an obviously increased UTCI and a lengthened comfortable period, which provides possibilities to exploit an attractive climate and the potential for tourism. The poor climate conditions imply that the development of Kashgar's tourism should depend more on the diversification and uniqueness of tourism products and the quality of tourism services. Therefore, the development of increased tourism products and the expansion of tourism regions, in the context of improving climate conditions, were focused on. We expect this case study to provide a reference for consumer travel decision-making and a necessary scientific basis for the planning and implementation of a tourism-based national promotional strategy in western China.