RESUMEN

The severe dependence of traditional phase change materials (PCMs) on the temperature-response and lattice deficiencies in versatility cannot satisfy demand for using such materials in complex application scenarios. Here, we introduced metal ions to induce the self-assembly of MXene nanosheets and achieve their ordered arrangement by combining suction filtration and rapid freezing. Subsequently, a series of MXene/ K+/paraffin wax (PW) phase change composites (PCCs) were obtained via vacuum impregnation in molten PW. The prepared MXene-based PCCs showed versatile applications from macroscale technologies, successfully transforming solar, electric, and magnetic energy into thermal energy stored as latent heat in the PCCs. Moreover, due to the absence of binder in the MXene-based aerogel, MK3@PW exhibits a prime solar-thermal conversion efficiency (98.4%). Notably, MK3@PW can further convert the collected heat energy into electric energy through thermoelectric equipment and realize favorable solar-thermal-electric conversion (producing 206 mV of voltage with light radiation intensity of 200 mw cm-2). An excellent Joule heat performance (reaching 105 °C with an input voltage of 2.5 V) and responsive magnetic-thermal conversion behavior (a charging time of 11.8 s can achieve a thermal insulation effect of 285 s) for contactless thermotherapy were also demonstrated by the MK3@PW. Specifically, as a result of the ordered arrangement of MXene nanosheet self-assembly induced by potassium ions, MK3@PW PCC exhibits a higher electromagnetic shielding efficiency value (57.7 dB) than pure MXene aerogel/PW PCC (29.8 dB) with the same MXene mass. This work presents an opportunity for the multi-scene response and practical application of PCMs that satisfy demand of next-generation multifunctional PCCs.

RESUMEN

Land use change is an important factor affecting the performance of ecosystem services (ESs). Therefore, understanding the impact of land use change on ESs is of great significance for promoting the coordination of regional human-land relationships. In this study, random forest and cellular automata were used to simulate and predict the characteristics of land use change in the Yangtze River Economic Belt, and diversified land use evolution patterns were formed in combination with China's strategic development needs. The effects of habitat suitability on ESs were analyzed by using a multiscenario land use change model. The results demonstrated that the driving factors selected in this article had a good induction effect on the law of land use evolution, and the simulated land use change had high credibility. Under the mode of ecological protection and cultivated land protection, the expansion of construction land was greatly affected and was not conducive to social and economic development. Under the natural evolution mode, farmland was greatly encroached upon, and food security was greatly threatened. The regional coordination model had relative advantages, and all kinds of land use needs were met to a certain extent. The water production function of ESs was strong, but the carbon storage function was weak. The relationship between the habitat suitability index and ES changes under land use change revealed that there were significant differences in ES changes caused by ecological quality changes in mountainous and plain areas. This study provides a reference for promoting social and economic development and ecosystem integrity. Integr Environ Assess Manag 2023;19:1473-1484. © 2023 SETAC.

Asunto(s)

Ecosistema , Ríos , Humanos , Conservación de los Recursos Naturales , China , Políticas

RESUMEN

In urban areas, logistic transportation operations often run into problems because travel speeds change, depending on the current traffic situation. If not accounted for, time-dependent and stochastic travel speeds frequently lead to missed time windows and thus poorer service. Especially in the case of passenger transportation, it often leads to excessive passenger ride times as well. Therefore, time-dependent and stochastic influences on travel speeds are relevant for finding feasible and reliable solutions. This study considers the effect of exploiting statistical information available about historical accidents, using stochastic solution approaches for the dynamic dial-a-ride problem (dynamic DARP). The authors propose two pairs of metaheuristic solution approaches, each consisting of a deterministic method (average time-dependent travel speeds for planning) and its corresponding stochastic version (exploiting stochastic information while planning). The results, using test instances with up to 762 requests based on a real-world road network, show that in certain conditions, exploiting stochastic information about travel speeds leads to significant improvements over deterministic approaches.

RESUMEN

The problem of transporting patients or elderly people has been widely studied in literature and is usually modeled as a dial-a-ride problem (DARP). In this paper we analyze the corresponding problem arising in the daily operation of the Austrian Red Cross. This nongovernmental organization is the largest organization performing patient transportation in Austria. The aim is to design vehicle routes to serve partially dynamic transportation requests using a fixed vehicle fleet. Each request requires transportation from a patient's home location to a hospital (outbound request) or back home from the hospital (inbound request). Some of these requests are known in advance. Some requests are dynamic in the sense that they appear during the day without any prior information. Finally, some inbound requests are stochastic. More precisely, with a certain probability each outbound request causes a corresponding inbound request on the same day. Some stochastic information about these return transports is available from historical data. The purpose of this study is to investigate, whether using this information in designing the routes has a significant positive effect on the solution quality. The problem is modeled as a dynamic stochastic dial-a-ride problem with expected return transports. We propose four different modifications of metaheuristic solution approaches for this problem. In detail, we test dynamic versions of variable neighborhood search (VNS) and stochastic VNS (S-VNS) as well as modified versions of the multiple plan approach (MPA) and the multiple scenario approach (MSA). Tests are performed using 12 sets of test instances based on a real road network. Various demand scenarios are generated based on the available real data. Results show that using the stochastic information on return transports leads to average improvements of around 15%. Moreover, improvements of up to 41% can be achieved for some test instances.