Biomimetic Adaptive Building Façade Modeling for Sustainable Urban Freshwater Ecosystems: Integration of Nature's Water-Harvesting Strategy into Sun-Breakers.

Urban freshwater ecosystems have many critical functions, such as providing water to all living things and supporting biodiversity. Factors such as water pollution, increased water consumption, habitat loss, climate change, and drought threaten the health of urban freshwater ecosystems. Looking for solutions to these challenges, this article aims to recycle water and return it to its life cycle using a climate-sensitive water collection strategy. The model focuses on the biomimetic method as a basic strategy. In this regard, the concept of water-harvesting has been examined in detail by conducting a deep literature review, including architecture and engineering disciplines. With all these data obtained, a synthesis/integration study was carried out by developing a model proposal based on adaptive building façade elements to solve the water problems experienced in cities. The model proposal, which is directly related to the titles of "Clean Water and Sanitation (SDG 6)" and "Sustainable Cities and Communities (SDG 11)", which are among the Sustainable Development Goals (SDGs), aims to provide different perspectives on the disciplines with its superficial and functional features. In this context, it is anticipated that the article will become an indispensable resource for other researchers working on the subject.

The impact of determinants of health on the relationship between stigma and health in people living with HIV.

Determinants of health are important drivers of health states, yet there is little work examining their role in the relationship between HIV stigma and health. This study uses moderation analysis to examine how determinants of health affect the relationship between enacted, internalized, and anticipated stigma and mental health. Quantitative data was collected on 337 participants in Ontario, Canada at baseline (t1) between August 2018 and September 2019 and at follow-up (t2) between February 2021 and October 2021. Separate moderation models were created with each determinant of health (age, gender, sexual orientation, ethnicity, geographic region, education, employment, and basic needs) acting as the moderator between types of stigma at t1 and mental health at t2. Age was a significant moderator for the relationship between internalized and enacted stigma at t1 and mental health at t2. Region was a moderator for enacted and anticipated stigma and mental health. Sexual orientation was a moderator for anticipated stigma and mental health. Lastly, having basic needs was a moderator for enacted and anticipated stigma and mental health. Our findings suggest that intervention strategies may be more effective by incorporating supports for these determinants of health in addition to stigma reduction to improve mental health.

"Balancing work and movement": barriers and enablers for being physically active at Indian workplaces - findings from SMART STEP trial.

BACKGROUND: Non-communicable diseases are rising rapidly in low- and middle-income countries, leading to increased morbidity and mortality. Reducing sedentary behavior (SB) and increasing physical activity (PA) offer numerous health benefits. Workplaces provide an ideal setting for promoting SB/PA interventions; however, understanding the barriers and enablers is crucial for optimizing these interventions in workplace environments. METHODS: Nested within a cluster randomised controlled trial (the SMART-STEP trial), the present study employed in-depth interviews with 16 office workers who have completed 24 weeks of two distinct (technology assisted and traditional) workplace SB/PA interventions. Using a deductive analysis, semi-structured interviews were administered to explore the barriers and enablers to the SB/PA interventions at individual, interpersonal and organisational level using the socio-ecological model. RESULTS: Several individual (poor goal setting, perceived health benefits & workload, attitude, intervention engagement), interpersonal (lack of peer support) and organisational (task prioritisation, lack of organisational norm and material or social reward) barriers were identified. Indian women engaged in desk-based office jobs often find themselves burdened with intense home and childcare responsibilities, often without sufficient support from their spouses. A primary concern among Indian office workers is the poor awareness and absence of cultural norms regarding the health risks associated with SB. CONCLUSIONS: Raising awareness among workplace stakeholders-including office workers, peers, and the organization-is crucial before designing and implementing SB/PA interventions in Indian workspaces. Personalized interventions for Indian female office workers engaged in desk-bound work are warranted.

Leveraging virucidal potential of an anti-microbial coating agent to mitigate fomite transmission of respiratory viruses.

In the wake of the COVID-19 pandemic, respiratory tract infections have emerged as a significant global threat, yet their impact on public health was previously underappreciated. This study investigated the antiviral efficacy of the nano-coating agent BARRIER90, composed of silicon-quaternary ammonium compound and a naturally derived biopolymer, against three distinct respiratory viruses: Influenza A (H1N1), Adenovirus Type 1, and Enterovirus-Coxsackie B1. BARRIER90 exhibited robust and sustained virucidal activity, persisting up to 90 days post-coating, against the enveloped virus, Influenza A, with significant reduction in viral plaques. Contrastingly, its efficacy against non-enveloped viruses revealed transient activity against Enterovirus-Coxsackie B1, with almost no antiviral activity observed against Adenovirus Type 1. These findings indicate the potential of antimicrobial coatings in mitigating viral transmission through contaminated surfaces (fomites), which harbour pathogenic viruses for longer periods. Antimicrobial coatings may facilitate infection control in various settings, including healthcare facilities and shared workspaces.

Examining the Role of Passive Design Indicators in Energy Burden Reduction: Insights from a Machine Learning and Deep Learning Approach.

Passive design characteristics (PDC) play a pivotal role in reducing the energy burden on households without imposing additional financial constraints on project stakeholders. However, the scarcity of PDC data has posed a challenge in previous studies when assessing their energy-saving impact. To tackle this issue, this research introduces an innovative approach that combines deep learning-powered computer vision with machine learning techniques to examine the relationship between PDC and energy burden in residential buildings. In this study, we employ a convolutional neural network computer vision model to identify and measure key indicators, including window-to-wall ratio (WWR), external shading, and operable window types, using Google Street View images within the Chicago metropolitan area as our case study. Subsequently, we utilize the derived passive design features in conjunction with demographic characteristics to train and compare various machine learning methods. These methods encompass Decision Tree Regression, Random Forest Regression, and Support Vector Regression, culminating in the development of a comprehensive model for energy burden prediction. Our framework achieves a 74.2% accuracy in forecasting the average energy burden. These results yield invaluable insights for policymakers and urban planners, paving the way toward the realization of smart and sustainable cities.

Analyzing One Health governance and implementation challenges in Mexico.

Establishing a robust One Health (OH) governance is essential for ensuring effective coordination and collaboration among human, animal, and environmental health sectors to prevent and address complex health challenges like zoonoses or antimicrobial resistance. This study conducted a mixed-methods environmental scan to assess to what extent Mexico displays a OH governance and identify opportunities for improvement. Through documentary analysis, the study mapped OH national-level governance elements: infrastructure, multi-level regulations, leadership, multi-coordination mechanisms (MCMs), and financial and OH-trained human resources. Key informant interviews provided insights into enablers, barriers, and recommendations to enhance a OH governance. Findings reveal that Mexico has sector-specific governance elements: institutions, surveillance systems and laboratories, laws, and policies. However, the absence of a OH governmental body poses a challenge. Identified barriers include implementation challenges, non-harmonised legal frameworks, and limited intersectoral information exchange. Enablers include formal and ad hoc MCMs, OH-oriented policies, and educational initiatives. Like other middle-income countries in the region, institutionalising a OH governance in Mexico, may require a OH-specific framework and governing body, infrastructure rearrangements, and policy harmonisation. Strengthening coordination mechanisms, training OH professionals, and ensuring data-sharing surveillance systems are essential steps toward successful implementation, with adequate funding being a relevant factor.

Hemp Fiber-Reinforced Polymers Composite Jacketing Technique for Sustainable and Environment-Friendly Concrete.

This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRR layers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress-strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.

Using machine learning algorithms to enhance IoT system security.

The term "Internet of Things" (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

Effects of exercise training on bone health in adults living with HIV: a systematic review with meta-analysis.

We investigated the effects of exercise training on bone mineral density (BMD) in people living with Human Immunodeficiency Virus (PLHIV). Pubmed, Scopus, Cochrane Library, and ScienceDirect databases were searched for trials investigating exercise training-induced changes in BMD of PLHIV at baseline vs. post-intervention assessed by dual-energy X-ray absorptiometry (DXA). Hedge effect sizes (ES) were calculated incorporating fixed effects for BMD variation assumptions. Disaggregated comparisons were performed for trials with more than one intervention or BMD site assessment. Seven trials included 210 PLHIV and 35 non-HIV-infected controls. Methodological quality evaluated using the Physiotherapy Evidence Database (PEDro) scale ranged from poor to moderate. Interventions applied isolated resistance, combined aerobic and resistance, and multimodal exercise protocols performed 3 d/wk for 12-to 104 week. One controlled and another uncontrolled trial presented significant effects, reporting improvements at the femoral neck and total (ES 2.14 and 0.49, respectively). Magnitude of those specific ES influenced the overall effect (controlled and uncontrolled trials), which was small but significant (k = 12, ES 0.277, 95% confidence interval 0.120-0.434). Resistance training may promote favorable adaptations in BMD of PLHIV, particularly in femur. Future research should elucidate the optimal dose-response relationship and physiological mechanisms underlying exercise-induced adaptations on the BMD of PLHIV.

Achieving Carbon Mitigation with Economic Benefits through High-Resolution Analysis of Renewable Resource Integration in Global Coastal Cities.

Coastal regions, home to more than half of the global population and contributing over 50% to the global economy, possess vast renewable resources, such as seawater and solar energy. The effective utilization of these resources, through the seawater-cooled district cooling system (SWDCS), seawater toilet flushing (SWTF), and rooftop solar photovoltaic system (RTPV), has the potential to significantly reduce carbon emissions. However, implementing these technologies in different geographic contexts to achieve the desired carbon and economic outcomes at the city level lacks a clear roadmap. To address this challenge, we comprehensively analyzed 12 coastal megacities worldwide by integrating geospatial building data. Our study evaluated the potential energy savings, carbon mitigation, and levelized carbon abatement costs (LCACs) from a life cycle perspective. The results revealed that using seawater and solar energy within urban boundaries can reduce electricity consumption from 1 to 24% across these cities. The spatial distribution of the LCAC for seawater-based systems exhibited more variation compared to the RTPV. By applying specific LCAC thresholds ranging from 0 to 225 USD/tCO2e, all cities could achieve both carbon reductions and economic benefits. These thresholds resulted in up to 80 million tonnes of carbon emission reductions and 5 billion USD of economic benefits, respectively. Our study provides valuable insights into integrating renewable resource systems, enabling coastal cities to achieve carbon and economic advantages at the city scale simultaneously.

Comparative analysis of decarbonization of local public transportation: A real case study.

Climate disruptions have prompted institutions to invest in zero-emissions technologies, in recent years. As a result, the transportation sector has witnessed a shift from internal combustion engines to electric. Several public transport companies have initiated the "Zero-emissions" project to introduce alternatives to diesel in their bus service. This paper delves into the impacts of transitioning from diesel-powered to electric buses. It starts by estimating emissions produced by buses and comparing them. Subsequently, the analysis evaluates the implications of renewing the fleet on the service, considering the change in bus capacity. Additionally, a profitability analysis assesses the Total Cost of Ownership, factoring in helpful life and average distance (kilometres) driven annually. Overall, the findings indicate that switching to electric buses is a promising approach towards achieving environmental objectives. The study shows that investing in electric buses, particularly those measuring 9-m, offers significant economic benefits while aligning with sustainability goals. The research demonstrates that electric buses yield a substantial reduction in global and local emissions when compared to their diesel counterparts. Adopting a comprehensive "well-to-wheel" perspective, electric buses achieve an impressive 68 % reduction in emissions. However, concerning local emissions, certain specific lines recorded values exceeded legal limits. While the initial investment costs for electric buses may surpass diesel buses, the total cost of ownership analysis conducted over 15 years indicates that electric buses can become more cost-effective over time. This cost-effectiveness and their environmental advantages strengthen the case for adopting electric buses to pursue sustainable transportation systems.

Towards smart sustainable cities: Does technological innovation mitigate G7 CO2 emissions? Fresh evidence from CS-ARDL.

Sustainable cities are considered paradigmatic to achieve COP27 targets, but scant literature has analyzed the association with CO2 emission (CE) mitigation policies, especially for the G7 economies. This study traces the impacts of urbanization, innovation, income, and energy consumption on CE for the G7 countries from 1995 to 2019. The time period is important due to the rapid increase in urbanization, globalization, and, at the same time, technology diffusion. The study is focused on novel and relevant panel data econometric methods that tackle cross-section dependence, non-normality of data, and heterogeneity in slope coefficients. The empirical outcome first confirms the long-run cointegrating relationship among the variables in the presence of structural breaks by employing the advanced panel cointegration test. Technologies related to environmental management and energy consumption are positively linked with CE. However, the role of income per capita is found to be inverse with CE. In contrast, the effect of urbanization in different models is mixed for CE. Innovation and its interaction with urbanization are crucial for limiting CE and promoting sustainable cities in G7 economies. This indicates that urbanization with environment-friendly innovation can help G7 countries foster sustainable cities and achieve COP27 targets in general, specifically the Sustainable Development Goal of sustainable cities. The results were tested for the multiplicative effect of different variables with urbanization and innovation. Regarding policy insights, the study recommends focusing on smart and sustainable cities with environment-related innovations and transitioning towards cleaner energy from conventional energy consumption.

Views of adolescents living with perinatally acquired HIV on HIV status disclosure in Eswatini.

HIV is recognised as a multifaceted chronic disease, bearing psychosocial consequences that potentially impact on the personal and social well-being of those living with it. Adolescents and youth bear a significant proportion of the burden of the HIV epidemic but they have low rates of disclosure of their HIV status. This study aims to determine the views of adolescents living with perinatally acquired HIV on HIV status disclosure among 361 adolescents aged 15-19 in Eswatini. A cross-sectional study was conducted on adolescents who already knew their status, and data were collected using a structured questionnaire. A majority of the participants, 55% (n = 200), were females, and 44% (n = 160) were males. A low number of adolescents (22%) had disclosed their HIV status to anyone. Adolescents who found it difficult to understand disclosure themselves had low levels of onward disclosure (27%) compared to adolescents who better understood the disclosure event. Our results revealed that adolescents' prevalence of HIV status disclosure was low. This raises concerns as some of the adolescents were in relationships with partners who were not aware of their status. However, older adolescents displayed some level of disclosure self-efficacy in that a higher proportion of them disclosed better than younger adolescents.

Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels.

Unprecedented levels of urbanization have escalated urban environmental health issues, including increased air pollution in cities globally. Strategies for mitigating air pollution, including green urban planning, are essential for sustainable and healthy cities. State-of-the-art research investigating urban greenspace and pollution metrics has accelerated through the use of vast digital data sets and new analytical tools. In this study, we examined associations between Google Street View-derived urban greenspace levels and Google Air View-derived air quality, where both have been resolved in extremely high resolution, accuracy, and scale along the entire road network of Dublin City. Particulate matter of size fraction less than 2.5 µm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and carbon dioxide were quantified using 5,030,143 Google Air View measurements, and greenspace was quantified using 403,409 Google Street View images. Significant (p < 0.001) negative associations between urban greenspace and pollution were observed. For example, an interquartile range increase in the Green View Index was associated with a 7.4% [95% confidence interval: -13.1%, -1.3%] decrease in NO2 at the point location spatial resolution. We provide insights into how large-scale digital data can be harnessed to elucidate urban environmental interactions that will have important planning and policy implications for sustainable future cities.

Trees, Climate Change, and Health: An Urban Planning, Greening and Implementation Perspective.

The In Conversation: Boundary, Spanners, Thinkers and Policy Actors Round Table Series provides a platform for researchers, policy actors, and implementation experts to elevate discussion on emerging issues, present new and upcoming research, and facilitate conversations around impacts and possible solutions. This brief report, on trees, climate change, and health, reflects a conversation between the authors of this paper, along with supporting literature. It explores the potential of green spaces and trees as a viable strategy to address climate change challenges and simultaneously improve population health, well-being, and health equity. In particular, it highlights the public health benefits of trees and green space, the challenges faced in urban areas, and opportunities for the protection, maintenance and regeneration of urban green space.

Assessing the effectiveness of nature-based solutions-strengthened urban planning mechanisms in forming flood-resilient cities.

Cities have experienced rapid urbanization-induced harsh climatic events, especially flooding, inevitably resulting in negative and irreversible consequences for urban resilience and endangering residents' lives. Numerous studies have analyzed the effects of anthropogenic practices (land use changes and urbanization) on flood forecasting. However, non-structural mitigation's effectiveness, like Nature-Based Solutions (NBS), has yet to receive adequate attention, particularly in the Middle East and North Africa (MENA) region, which have become increasingly significant and indispensable for operationalizing cities efficiently. Therefore, our study investigated the predictive influence of incorporating one of the most common NBS strategies called low-impact development tools (LID) (such as rain gardens, bio-retention cells, green roofs, infiltration trenches, permeable pavement, and vegetative swale) during the urban planning of Alexandria, Egypt, which experiences the harshest rainfall annually and includes various urban patterns. City characteristics-dependent 14 LID scenarios were simulated with recurrence intervals ranging from 2 to 100 years using the LID Treatment Train Tool (LID TTT), depending on calibrated data from 2015 to 2020, by the Nash-Sutcliffe efficiency index and deterministic coefficient, and root-mean-square error with values of 0.97, 0.91, and 0.31, respectively. Our findings confirmed the significant effectiveness of combined LID tools on total flood runoff volume reduction by 73.7%, revealing that different urban patterns can be used in flood-prone cities, provided LID tools are considered in city planning besides grey infrastructure to achieve optimal mitigation. These results, which combined multiple disciplines and were not explicitly mentioned in similar studies in developing countries, may assist municipalities' policymakers in planning flood-resistant, sustainable cities.

Current Practices and Opportunities for More Sustainable Public Food Procurement: A Qualitative Study among Danish Municipalities and Regions.

Public food procurement has been emphasized as a powerful tool to promote a healthier and more sustainable food system, but there is still a long way to go to reach full potential. This study aimed to investigate practices and opportunities for sustainable and healthy public food procurement. A qualitative cross-sectional study was performed among Danish municipalities and regions stratified and randomly selected to cover standard practice (n = 17). In addition, interviews were performed among selected best-practice municipalities (n = 5) providing examples of ambitious goals and well-defined processes for obtaining sustainable food procurement. Large differences were observed in the cross-sectional analysis in relation to policy support and goals for sustainable food procurement, including organic purchase. Generally, there was a great attentiveness to reduce food waste and many valued the use of local food, especially among rural municipalities, whereas experience with climate impact reduction and shifts towards more plant-based menus was still in an early implementation stage. Results suggest a possible synergy effect between the use of organic food and efforts to reduce food waste and climate impact and emphasize the importance of local government policies to promote healthy and sustainable food procurement. Enabling factors to move sustainable food procurement forward are discussed.

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review.

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed "environmentally sustainable smart cities." This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991-2015, 2016-2019, and 2020-2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods-due to the acceleration of the digitalization and decarbonization agendas-thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time-some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

Entropy and Cities: A Bibliographic Analysis towards More Circular and Sustainable Urban Environments.

Cities are critical to a sustainable future for our planet; still, the construction and operation of cities rely on intensive resource and energy use and transformation, leading to the generation of waste, effluents, and pollution, representing negative externalities outside and inside the city. Within every process, transformation implies the use of energy and the increase of entropy. In an urban system, the transformation of energy and materials will trigger the creation of entropic landscapes, mainly in the informal city and in unguarded natural landscapes, even hundreds of kilometers away, which generates substantial economic, social, and environmental impacts. In this sense, cities are significant contributors to the environmental crisis. Upstream, degradation of landscapes and ecosystems is frequent. Cities' externalities and exogenous consumptions are directly linked with entropy and entropic landscapes, which are recognized as pollution (in the air, water, and land) or waste and in the degradation of natural ecosystems and communities. Through a systematic review of existing literature, this paper first outlines briefly how entropy has been applied in different disciplines and then focuses on presenting recent developments of how entropy has been defined, used, and characterized in urban studies concerning sustainability in cities and architecture, and presents a definition of the concept in relation to urban systems and key aspects to consider.