Search Results (137)

Urban building energy retrofit (UBER) is a critical strategy for advancing the low-carbon and climate-resilience transformation of cities. The integration of machine learning (ML), data-driven clustering, and multi-objective optimization (MOO) is a key aspect of artificial intelligence (AI) that is transforming the process of retrofit decision-making. This integration enables the development of scalable, cost-effective, and robust solutions on an urban scale. This systematic review synthesizes recent advances in AI-driven MOO frameworks for UBER, focusing on how state-of-the-art methods can help to identify and prioritize retrofit targets, balance energy, cost, and environmental objectives, and develop transparent, stakeholder-oriented decision-making processes. Key advances highlighted in this review include the following: (1) the application of ML-based surrogate models for efficient evaluation of retrofit design alternatives; (2) data-driven clustering and classification to identify high-impact interventions across complex urban fabrics; (3) MOO algorithms that support trade-off analysis under real-world constraints; and (4) the emerging integration of explainable AI (XAI) for enhanced transparency and stakeholder engagement in retrofit planning. Representative case studies demonstrate the practical impact of these approaches in optimizing envelope upgrades, active system retrofits, and prioritization schemes. Notwithstanding these advancements, considerable challenges persist, encompassing data heterogeneity, the transferability of models across disparate urban contexts, fragmented digital toolchains, and the paucity of real-world validation of AI-based solutions. The subsequent discussion encompasses prospective research directions, with particular emphasis on the potential of deep learning (DL), spatiotemporal forecasting, generative models, and digital twins to further advance scalable and adaptive urban retrofit. Full article

Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including graph convolutional network (GCN), GraphSAGE, and several physics-informed graph attention network (GAT) variants—against conventional artificial neural network (ANN) baselines, using both shape coefficient and energy use intensity (EUI) stratification across three distinct residential districts. Extensive ablation and cross-district generalization experiments reveal that models explicitly incorporating interpretable physical edge features, such as inter-building distance and angular relation, achieve significantly improved prediction accuracy and robustness over standard approaches. Among all models, GraphSAGE demonstrates the best overall performance and generalization capability. At the same time, the effectiveness of specific GAT edge features is found to be district-dependent, reflecting variations in local morphology and spatial logic. Furthermore, explainability analysis shows that the integration of domain-relevant spatial features enhances model interpretability and provides actionable insight for urban retrofit and policy intervention. The results highlight the value of physics-informed GNNs (PINN) as a scalable, transferable, and transparent tool for urban energy modeling, supporting evidence-based decision making in the context of aging residential building upgrades and sustainable urban transformation. Full article

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

We propose a conceptual framework to analyze how crises dynamically affect the operations, performance, and strategic choices of digital platforms and how these effects impact their sustainability. Drawing on the theory of two-sided platforms, we propose a framework that considers two key dimensions: (1) the nature of shocks affecting each side of the platform and (2) the time horizon of their impact. We apply this framework to evaluate how sharing-economy platforms responded to the COVID-19 crisis, focusing on Airbnb, Uber Eats, and Prosper as illustrative cases. Full article

The immobilization of a laccase from Pycnoporus sanguineus UEM-20 via the formation of cross-linked enzyme aggregates (CLEAs) was optimized through a central composite design (CCD) of response surface methodology (RSM). Both free and immobilized enzymes were investigated for their physico-chemical characteristics, and their adequacy in removing bisphenol A (BPA) and decolorizing malachite green dye in solution was evaluated. The immobilization caused only minor differences in thermostability. Upon immobilization, the enzyme experienced some changes in its kinetic properties. The V_max decreased by a factor of 1.1, and the K_M increased by a factor of 1.89. These kinetic changes did not modify in any remarkable way the capacity of the immobilized enzyme in degrading BPA and decolorizing malachite green dye. Its sensitivity to NaCl was also minimally affected by immobilization. However, its sensitivity to sodium sulfate was substantially decreased. After 1 month’s conservation, the activity of the free form had suffered a drastic drop. The immobilized form, by contrast, remained 100% active after 6 months. All these findings predict that the immobilized laccase from P. sanguineus UEM-20 may be useful in the enzymatic bioremediation of pollutants such as endocrine disruptors and synthetic dyes. Full article

Jatobá (Hymenaea courbaril) is a native tree abundant in Brazil. The fruit coat is an industrial by-product of jatobá flour processing, typically discarded. Presently, within the circular bioeconomy concept, there are efforts underway that aim at finding economically viable applications for the bio-residues of jatobá. Within this context, the present work attempts to find possible applications for the jatobá coat in glycemic control through inhibition of α-amylase activity. Aqueous and hydroethanolic extracts were used. In vitro experiments included detailed kinetic studies with an α-amylase catalyzed reaction. Starch absorption in vivo was assessed by means of a starch tolerance test in mice. Both extracts inhibited α-amylase. The IC₅₀ values for the aqueous and hydroalcoholic extracts were 81.98 ± 3.53 µg/mL and 51.06 ± 0.42 µg/mL, respectively. The inhibition was of the non-competitive type. Both extracts reduced hyperglycemia caused by starch administration in mice, the aqueous extract being effective over a larger dose range. This action can be attributed to the α-amylase inhibition. In silico studies suggested that procyanidin dimers, taxifolin 7-O-rhamnoside, and quercetin 7-rhamnoside contribute, but several other not-yet-identified substances may be involved. The findings suggest that aqueous and hydroalcoholic extracts from jatobá coat warrant further investigations as potential modulators of glycemia following starch ingestion. Full article

The sustainable management of energy resources is fundamental in addressing global environmental and economic challenges, particularly when considering biofuels such as ethanol and gasoline. This study evaluates advanced forecasting models to predict consumption trends for these fuels in Brazil. The models analyzed include ARIMA/SARIMA, Holt–Winters, ETS, TBATS, Facebook Prophet, Uber Orbit, N-BEATS, and TFT. By leveraging datasets spanning 72, 144, and 263 months, the study aims to assess the effectiveness of these models in capturing complex temporal consumption patterns. Uber Orbit exhibited the highest accuracy in forecasting ethanol consumption among the evaluated models, achieving a mean absolute percentage error (MAPE) of 6.77%. Meanwhile, the TBATS model demonstrated superior performance for gasoline consumption, with a MAPE of 3.22%. Our models have achieved more accurate predictions than other compared works, suggesting ethanol demand is more dynamic and underlining the potential of advanced time–series models to enhance the precision of energy consumption forecasts. This study contributes to more effective resource planning by improving predictive accuracy, enabling data-driven policy making, optimizing resource allocation, and advancing sustainable energy management practices. These results support Brazil’s energy sector and provide a framework for sustainable decision making that could be applied globally. Full article

Multi-sided platforms (MSPs) can enable multiple user groups to create coordinated value. Like all transformative business models, these platforms emerged to resolve platform-related issues. Among the well-known MSPs, this research has focused on the ride-hailing platform InDrive as a successful case of MSP in Pakistan. Despite the presence of major companies like Uber and Careem, InDrive has gained recognition in a short period and has become the most downloaded ride-hailing application in Pakistan. InDrive focuses on empowering riders and drivers with greater fare-setting autonomy through negotiation; this peer-to-peer pricing model distinguishes it from its counterparts (Uber and Careem). This research examines the strategic features and innovations of InDrive’s business model to create a comprehensive framework for evaluating the effectiveness of strategic management, focusing on generating value by balancing the well-being of all stakeholders, ensuring equity, boosting productivity, and enhancing the impact of network effects. Notably, ride-hailing services (RHSs) are highly dynamic, as the features and offerings of these platforms may evolve. Therefore, balancing the sustainability of MSPs requires ongoing effort and an iterative approach. Full article

Objectives: To evaluate the offer of children’s menus offered in fast food restaurants present in the Uber Eats delivery application through the Kids Menu Healthy Score ‘KIMEHS’ in Greater Santiago. Methods: Observational, descriptive, cross-sectional. Research in fast food restaurants present in the Uber Eats delivery app. A total of 858 restaurants were selected. The KIMEHS was used to assess menu quality. KIMEHS index and descriptive statistics were calculated. Results: 558 restaurants were evaluated through the app; 57 offered children’s menus, yielding 114 children’s menu options from 18 different municipalities. The common offer was based on fried and/or processed lean meat accompanied by French fries in 71%. Moreover, 99% of the menus assessed obtained the minimum score in the KIMEHS placing them in the ‘not healthy at all’ category. When associations were made between foods and the lowest KIMEHS score quartile, the presence of chips had the strongest association (OR; 40.36: CI95% 11.43–201.08). Conclusions: Most restaurants offer a children’s menu of low nutritional quality and poor balance, where their dishes are commonly based on fried and processed products, pointing to the urgent need for legislation on guidelines to be applied on the different actors influencing the food offered to children. Full article

Laccase from Trametes versicolor was immobilized via the formation of interlinking enzyme aggregates (CLEA). Its free and immobilized enzymes were characterized, and its efficiency was tested via the removal of bisphenol A (BPA) in aqueous solution. The resistances against thermal denaturation and pH variations were improved upon immobilization. Although the optimal pH of the enzyme was not modified by immobilization, the latter considerably increased its stability in the pH range of 2.0 to 8.0. The immobilized form was still 50% active after 6 months of storage, while the free form took 1 month to suffer a similar drop in activity. Both free and immobilized T. versicolor laccases were efficient in removing 200 µM BPA from aqueous solutions. The free laccase removed 79% and 92.9% of the compound during the first hour of reaction when 0.1 and 0.2 U were used, respectively. The immobilized form, on the other hand, removed 72% and 94.1% of 200 µM BPA during the first hour of reaction when 0.2 and 0.5 U were used, respectively. The immobilized enzyme allowed seven reuse cycles in the oxidation of ABTS and up to four cycles in the degradation of BPA. The results suggest that the laccase from T. versicolor may be useful in biological strategies aiming at degrading endocrine disruptors, such as BPA. Full article

The aggregation of ride-sharing platforms has forced traditional ride-sharing platforms to decide whether to join or leave these emerging platforms. This study presents a stylized model analyzing the demand, supply, and profit of two self-operated platforms, C2C platforms (such as DiDi and Uber) and B2C platforms, considering aggregation platform awareness and commissions. The study investigates the conditions under which the self-operated platforms should employ the entry strategy based on the optimization method and Cournot game theory, as well as exploring the reasons why self-operated platforms choose to withdraw after joining. The results show that in order to avoid competition, B2C platforms adopt an entry strategy, while C2C platforms adopt a non-entry strategy. Only during the off-peak period, when the awareness of the aggregation platform is very high and the level of competition between the two types of platforms is very intense, will both types of platforms adopt an entry strategy, but C2C platforms may experience a significant loss of market share, leading to a decline in social welfare. Furthermore, even if the self-operated platform chooses to withdraw, social welfare will still increase if the two self-operated platforms adopt the best strategy. The study contributes to sustainable development by promoting efficient resource allocation, reducing redundant competition, and improving overall market efficiency, thereby fostering a more sustainable urban transportation system. Full article

In the field of autonomous vehicle (AV) acceptance and opinion studies, questionnaires are widely used. Additionally, AV experiments and driving simulations are utilized. However, few AV studies have investigated social media, and fewer studies have analyzed the impact of AV crashes on public opinion, often relying on limited social media datasets. This study aims to address this gap by exploring a comprehensive dataset of six million tweets posted over a decade (2012–2021), and neural networks, sentiment analysis and knowledge graphs are applied. The results reveal that tweets predominantly convey negative sentiment (40.86%) rather than positive (32.52%) or neutral (26.62%) sentiment. A binary segmentation algorithm was used to distinguish an initial positive sentiment period (January 2012–May 2016) followed by a negative period (June 2016–December 2021), which was initiated by a fatal Tesla accident and reinforced by a pedestrian killed by an Uber AV. The sentiment polarity exhibited in the posted tweets was statistically significant (U = 24,914,037,786; p value < 0.001). The timeline analysis revealed that the negative sentiment period was initiated by fatal accidents involving a Tesla AV driver and a pedestrian hit by an Uber AV, which was amplified by the mainstream media. Full article

This study explores the sharing economy through the lens of service ecosystem theory, focusing on how resource integration and value co-creation enhance competitiveness. Using a conceptual framework and case study analysis of platforms like Airbnb and Uber, the research examines the systemic interactions among actors within these ecosystems. The findings highlight the dual role of these platforms as drivers of innovation and instruments of platform capitalism. Practical and theoretical contributions include strategies for fostering resilience and sustainability in dynamic market environments. Full article

Veterans living in rural areas of the United States face various health challenges that demand timely access to care to improve their well-being and quality of life. Telehealth (i.e., the use of telecommunications technology to connect people with care providers remotely) has become vital in addressing the accessibility gap for people constrained by vehicle ownership, income, geographic isolation, and limited access to specialists. This study aims to examine the current evidence on rural veterans’ use of telehealth for their healthcare needs, evaluates the cost savings associated with telehealth, as well as veterans’ use of telehealth during COVID-19. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted on three databases (Google Scholar, PubMed, and Scopus) to select relevant articles published from 2017 to 2023. A total of 36 articles met the inclusion criteria and were categorized into three objectives: veterans’ medical conditions managed through telehealth (n = 24), veterans’ transportation cost savings using telehealth (n = 4), and telehealth use during the COVID-19 pandemic (n = 8). The results indicated that telehealth is a viable option for managing various medical conditions of rural veterans, including complex ones like diabetes and cancer. Additionally, telemedicine was a useful platform in bridging the healthcare accessibility gap during disasters or pandemics like COVID-19 evident from its increased usage during the pandemic. Lastly, telehealth was associated with cost and time savings between USD 65.29 and USD 72.94 per visit and 2.10 and 2.60 h per visit, respectively. However, the feasibility of telehealth for veterans’ medical conditions such as rheumatism, cancer, HIV, and diabetes is underexplored and calls for further investigation post-COVID-19. Lastly, the limited literature on rural veterans’ transportation cost savings using different mobility options—taxi, Uber, public transportation, and rides from friends and family—is another critical gap. Full article

The sharing economy (SE) is a nascent phenomenon representing a socio-economic process to optimise underutilised resources through digital platforms. This process facilitates the shared consumption of resources to maximise resource utilisation while supporting the circularity of resources. However, the successful operation of SE practices is hindered by the lack of identification of effective strategies for enhancing the SE implications, which are essential to comprehending SE practices and developing more sophisticated applications. Therefore, this research aims to provide the first insights into the strategies that enhance SE practices across diverse industries and identify knowledge gaps and future research directions. A systematic literature review (SLR) was conducted by selecting articles published in the 2014–2023 period in Scopus and Web of Science databases. Selected articles were subjected to descriptive and NVivo 14-supported thematic analyses. The descriptive analysis showed that, despite considering articles published in the last 10 years, all relevant articles were published in the last 5 years. Developed and developing countries showed almost equal contributions, while China was recognised as the country with the highest number of publications. Accommodation and transportation sectors were reported as the sectors with the highest number of publications. A cross-analysis was conducted to recognise the varying utilisation of different strategies across diverse industries and sectors. Ten different categories were identified through the thematic analysis that enhance SE practices: economic; environmental; geographic; governance; health, safety, and security; marketing; people; product/services; research, training, education; and technology-related strategies. Each category was discussed along with its relevant strategies, resulting in identifying a total of 84 strategies. These strategies were then presented alongside the responsible parties tasked with their implementation. The study contributes to the SE literature by providing an SLR for contemporary strategies utilised to enhance SE practices, specifically focusing on elucidating the most appropriate categorisation of these strategies. Moreover, this comprehensive SLR provides the first insights into the effective strategies that enhance SE practices across diverse industries. Full article