Search Results (232)

Design Thinking (DT) has emerged as a relevant methodology for addressing global challenges aligned with the United Nations Sustainable Development Goals (SDGs). This study presents a systematic literature review, conducted following PRISMA 2020 guidelines, which analyzes 42 peer-reviewed publications from 2013 to 2023. Through inductive content analysis, 10 core DT principles—such as empathy, iteration, user-centeredness, and systems thinking—I identified and thematically mapped to specific SDGs, including goals related to health, education, innovation, and climate action. The study also presents five real-world cases from diverse sectors such as technology, healthcare, and urban planning, illustrating how DT has been applied to address practical challenges aligned with the SDGs. However, the review identifies persistent gaps in the field: the lack of standardized evaluation frameworks, limited integration across SDG domains, and weak adaptation of ethical and contextual considerations, particularly in vulnerable communities. As a response, this paper recommends the adoption of structured impact assessment tools (e.g., Cities2030, Responsible Design Thinking), integration of design justice principles, and the development of participatory, iterative ecosystems for innovation. By offering both conceptual synthesis and applied insights, this article positions Design Thinking as a strategic and systemic approach for driving sustainable transformation aligned with the 2030 Agenda. Full article

The global rise in urban-related health issues poses significant challenges to public health, particularly in cities facing socio-economic crises. In Lebanon, 70% of the population is experiencing financial hardship, and healthcare costs have surged by 172%, exacerbating the strain on medical services. Given these conditions, improving the quality and accessibility of green spaces offers a promising avenue for alleviating mental health issues in urban areas. This study investigates the psychological impact of nine urban public spaces in Beirut through a comprehensive survey methodology, involving 297 participants (locals and tourists) who rated these spaces using Likert-scale measures. The findings reveal location-specific barriers, with Saanayeh Park rated highest in quality and Martyr’s Square rated lowest. The analysis identifies facility quality as the most significant factor influencing space quality, contributing 73.6% to the overall assessment, while activity factors have a lesser impact. The study further highlights a moderate positive association (Spearman’s rho = 0.30) between public space quality and mental well-being in Beirut. This study employs a hybrid methodology combining Research for Design (RfD) and Research Through Designing (RTD). Empirical data informed spatial strategies, while iterative design served as a tool for generating context-specific knowledge. Design enhancements—such as sensory plantings, shading systems, and social nodes—aim to improve well-being through better public space quality. The proposed interventions support mental health, life satisfaction, climate resilience, and urban inclusivity. The findings offer actionable insights for cities facing public health and spatial equity challenges in crisis contexts. Full article

The goal of single-image super-resolution (SISR) tasks is to generate high-definition images from low-quality inputs, with practical uses spanning healthcare diagnostics, aerial imaging, and surveillance systems. Although cnns have considerably improved image reconstruction quality, existing methods still face limitations, including inadequate restoration of high-frequency details, high computational complexity, and insufficient adaptability to complex scenes. To address these challenges, we propose an Omni-dimensional Dynamic Convolutional Network (ODConvNet) tailored for SISR tasks. Specifically, ODConvNet comprises four key components: a Feature Extraction Block (FEB) that captures low-level spatial features; an Omni-dimensional Dynamic Convolution Block (DCB), which utilizes a multidimensional attention mechanism to dynamically reweight convolution kernels across spatial, channel, and kernel dimensions, thereby enhancing feature expressiveness and context modeling; a Deep Feature Extraction Block (DFEB) that stacks multiple convolutional layers with residual connections to progressively extract and fuse high-level features; and a Reconstruction Block (RB) that employs subpixel convolution to upscale features and refine the final HR output. This mechanism significantly enhances feature extraction and effectively captures rich contextual information. Additionally, we employ an improved residual network structure combined with a refined Charbonnier loss function to alleviate gradient vanishing and exploding to enhance the robustness of model training. Extensive experiments conducted on widely used benchmark datasets, including DIV2K, Set5, Set14, B100, and Urban100, demonstrate that, compared with existing deep learning-based SR methods, our ODConvNet method improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the visual quality of SR images is also improved. Ablation studies further validate the effectiveness and contribution of each component in our network. The proposed ODConvNet offers an effective, flexible, and efficient solution for the SISR task and provides promising directions for future research. Full article

Africa’s rapid urbanization often exceeds the capacity of governments to provide essential services and infrastructure, exacerbating structural inequalities and exposing vulnerable populations to serious health risks. This paper examines the case of Douala, Cameroon, to demonstrate that health inequities in African cities are not simply the result of urban growth but are shaped by spatial inequities, historical legacies, and systemic exclusion. Disadvantaged neighborhoods are particularly impacted, becoming epicenters of health crises. Using a mixed-methods approach combining spatial analysis, household surveys and interviews, the study identifies three key findings: (1) Healthcare services in Douala are unevenly distributed and dominated by private providers, which limits access for low-income residents. (2) Inadequate infrastructure and environmental risks in informal settlements lead to a higher disease burden and an overflow of demand into better-equipped districts, which overwhelms public health centers across the city. (3) This structural mismatch fuels widespread reliance on informal and unregulated care practices. This study positions Douala as a microcosm of broader public health challenges in rapidly urbanizing African cities. It highlights the need for integrated urban planning and health system reforms that address spatial inequalities, strengthen public health infrastructure, and prioritize equity—key principles for achieving the third Sustainable Development Goal (ensuring good health and well-being for all residents) in sub-Saharan Africa. Full article

This study investigates the accessibility of public healthcare services in Olsztyn County, a major urban center in the Warmia and Mazury region of Poland. The aim was to develop a methodological framework using Geographic Information System (GIS) tools and spatial data to assess the local availability of healthcare infrastructure. The analysis included key facilities such as hospitals, clinics, pharmacies, and specialized outpatient services. A spatial accessibility indicator was constructed to evaluate and compare access levels across municipalities. The results show a clear disparity between urban and rural areas, with significantly better access in cities. Several rural municipalities were found to have limited or no access to essential healthcare services. These findings highlight the uneven spatial distribution of medical infrastructure and point to the need for targeted strategies to improve service availability in underserved areas. The proposed methodological approach may support future studies and inform local and regional planning aimed at reducing healthcare inequalities and improving access for all residents, regardless of their location. This research contributes to the growing body of evidence emphasizing the role of spatial analysis in assessing public service accessibility and supports the development of more equitable healthcare systems at the local level. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

In the era of new urbanization and shared prosperity, addressing the entrenched contradiction between unequal urban–rural public services and consumption disparities is central to achieving sustainable urban–rural development. As the world’s largest developing country, China faces a pronounced urban–rural consumption gap, underscoring the urgency of narrowing this divide through improved urban–rural public services. This study constructs a theoretical framework to analyze how urban–rural public service gaps influence consumption disparities, developing an evaluation index system for public service gaps across three dimensions: basic education, healthcare, and social security. Using panel data from 26 Chinese provinces (2011–2023), we employed fixed effects (FE) estimation, two-stage least squares (2SLS), and two-step system GMM models to examine the impact of public service gaps on consumption disparities and explore heterogeneous effects across inter-period dynamics and economic catching-up levels. Findings show that the coefficients of the three public service gaps (education, healthcare, social security) on the consumption gap are positive and statistically significant. This indicates that further widening of urban–rural public service gaps will exacerbate consumption disparities. The urban–rural consumption gap exhibits a reinforcing effect: gaps in the previous period strengthen current-period disparities, forming a vicious cycle that hinders sustainable development. Heterogeneity analysis across time reveals that the impacts of healthcare and social security gaps on consumption disparities tend to weaken, while the effect of compulsory education gaps increases significantly. From the perspective of economic catching-up heterogeneity, regions with higher catching-up levels exhibit a stronger impact of public service gaps on consumption disparities compared to lower catching-up regions. Full article

This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchain’s foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development. Full article

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management. Full article

Background/Objectives: The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems, especially in ensuring continuity of care for patients with chronic diseases. Rehabilitation services, essential for recovery following SARS-CoV-2 infection, were among the most disrupted. This exploratory study aimed to assess Romanian patients’ perceptions of the accessibility and quality of post-COVID-19 rehabilitation services, focusing on individuals with chronic conditions. Methods: This exploratory cross-sectional study was conducted over a 12-month period in 2024. Data were collected from 76 adult patients diagnosed with at least one chronic condition (hypertension, diabetes mellitus, ischemic heart disease, cancer, or chronic obstructive pulmonary disease) and with confirmed prior SARS-CoV-2 infection. Most participants were recruited during outpatient specialty consultations, with a smaller number included from hospital settings, all located in Bucharest. A structured questionnaire was administered by the principal investigator after obtaining informed consent. Quantitative data were analyzed using non-parametric methods following confirmation of non-normal distribution via the Shapiro–Wilk test (p < 0.05). Satisfaction scores were reported as medians with interquartile ranges (IQR), and group comparisons were performed using the Mann–Whitney U test. A mixed-methods approach was employed, including thematic analysis of open-ended responses. Results: Patient satisfaction with rehabilitation services was consistently low. The median satisfaction scores [IQR] were accessibility 1.0 [0.0–2.0], quality of services 0.0 [0.0–4.0], staff empathy 0.0 [0.0–5.0], and perceived effectiveness 0.0 [0.0–5.0]. The median score for perceived difficulties in access was 1.0 [1.0–2.0], indicating widespread barriers. No statistically significant differences were observed between urban and rural participants or across chronic disease categories. Thematic analysis (n = 65) revealed key concerns including lack of publicly funded services, cost barriers, limited physician referral, service scarcity in rural areas, and demand for home-based rehabilitation options. Conclusions: Romanian patients with chronic illnesses and previous SARS-CoV-2 infection continue to face substantial barriers in accessing post-COVID-19 rehabilitation services. These findings highlight the need for more equitable and integrated recovery programs, especially for vulnerable populations in underserved settings. Full article

Background: Adherence to iron supplementation is influenced by systemic barriers, including poor healthcare infrastructure, shortage of healthcare providers, and limited access to antenatal care (ANC) services. These challenges are more pronounced in rural areas. However, evidence on urban–rural disparities in non-adherence to iron supplementation remains limited, particularly in sub-Saharan Africa. This study examined these regional differences, stratified by income levels and national contexts. Method: This analysis utilised Demographic Health Survey (DHS) data conducted between 2015 and 2023 from 26 sub-Saharan African countries, including 287,642 women from urban (n = 91,566) and rural areas (n = 196,076). The outcome of this study was non-adherence to iron supplementation, defined as taking iron supplementation for less than 90 days during pregnancy. This study examines urban–rural differences in non-adherence stratified by country income levels based on World Bank 2022 income classifications and national context. A chi-square test was used to assess urban–rural differences, with a p-value of <0.05 considered statistically significant. Results: Non-adherence was significantly higher in rural areas (68.42%) than in urban areas (51.32%) (p < 0.001), with the disparity more pronounced in low-income countries (LICs). Ethiopia, Madagascar, Uganda, and Burundi were among the countries with the highest rural non-adherence, reflecting severe poverty and limited access to ANC. In contrast, Zimbabwe showed an inverse trend, where rural adherence was higher than urban. Conclusions: Rural sub-Saharan Africa has significantly higher non-adherence to iron supplementation, particularly in LICs, likely driven by systemic barriers such as poor infrastructure and limited access to healthcare. This non-adherence in rural areas undermines efforts to improve pregnancy and birth outcomes across the region. Targeted interventions, like those in Zimbabwe, can help address these inequities and improve maternal health outcomes. Full article

Rapid urbanization and the rising demand for sustainable living have encouraged the growth of smart cities, which incorporate innovative technologies to ameliorate environmental sustainability, optimize resource management, and improve living standards. The convergence of blockchain (BC) technology and the Internet of Things (IoT) presents transformative convenience for managing smart cities and achieving sustainability goals. In fact, blockchain technology combined with IoT devices provides a decentralized, transparent, and safe framework for managing massive volumes of data produced by networked sensors and systems. By guaranteeing accountability, minimizing fraud, and maximizing resource use, blockchain not only facilitates the smooth operation of smart city infrastructures but also encourages sustainable habits. The various uses of blockchain technology in smart city management and its contribution to sustainability objectives are examined in this study. Through an examination of important domains like energy distribution, waste management, transportation systems, healthcare, and governance, the research shows how blockchain promotes effective data exchange and data security, builds stakeholder trust, and makes it possible to establish decentralized organizations to improve decision-making. Full article

The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations. This study proposes a method for vital sign detection using an integrated sensing and communication (ISAC) system where a unified orthogonal frequency division multiplexing (OFDM) signal was adopted, and it is capable of sensing life signs and carrying out communication simultaneously. An ISAC demonstration system based on software-defined radios (SDRs) was initiated to detect respiratory and heartbeat rates while maintaining communication capability in a typical office environment. The specially designed OFDM signals were transmitted, reflected from a human subject, received, and processed to estimate the micro-Doppler effect induced by the breathing and heartbeat of the human in the environment. According to the results, vital signs, including respiration and heartbeat rates, have been accurately detected by post-processing the reflected OFDM signals with a 1 MHz bandwidth, confirmed with conventional contact-based detection approaches. The potential of dual-function capability of OFDM signals for sensing purposes has been verified. The principle and method developed can be applied in wider ISAC systems for search and rescue purposes while maintaining communication links. Full article

Background: This study examines disparities in HIV-related healthcare access, support, and treatment adherence between rural and urban Black/African American populations in Georgia, USA, emphasizing structural, socioeconomic, and stigma-related barriers affecting health outcomes. Methods: A cross-sectional quantitative study was conducted using structured surveys administered via RedCap from August to December 2023. Participants (n = 55) were recruited through community-based HIV organizations, healthcare providers, and advocacy networks. The survey assessed demographic factors, healthcare access, adherence to treatment, support systems, and experiences with stigma. Data were analyzed using IBM SPSS Statistics, version 28.0 (IBM Corp., Armonk, NY, USA), with chi-square tests examining associations between geographic location and HIV-related outcomes. Results: Findings indicate significant disparities in HIV care access and support. Urban participants were more likely to receive family and friend support (p < 0.01), financial assistance through the Ryan White Program (p = 0.01), and timely linkage to care within one week of diagnosis (p < 0.05). Rural participants reported lower educational attainment, income levels, and limited healthcare access, contributing to poorer health outcomes. No significant differences were observed in PrEP or condom use. Conclusions: The study underscores the need for targeted interventions. Expanding telehealth, enhancing community outreach, and reducing stigma through policy reforms are critical to improving HIV-related health outcomes in rural Georgia. Full article

Transit deserts, defined by insufficient public transit provision relative to demand, aggravate socio-economic inequalities by restricting access to employment, education, and healthcare. With increasing urbanization and growing disparities in public transport accessibility, identifying transit deserts is critical for equitable mobility planning. As urban populations expand, addressing transit accessibility requires advanced data-driven approaches. This study applies machine learning (ML) models, decision trees (DTs), logistic regression (LR), and random forest (RF), within an Intelligent Transport System (ITS) framework to detect transit deserts in Lucknow, India. Employing a 100 × 100 m spatial grid data, the models classify transit accessibility based on economic status, trip frequency, population density, and service access. The results indicate that RF achieves superior classification accuracy, while DT offers interpretability with slightly lower recall. LR underperforms due to its linear assumptions. The findings reveal the spatial clustering of transit deserts in socio-economically disadvantaged areas, highlighting the need for targeted interventions. This study advances ML-driven ITS analytics, offering a novel approach for classifying transit accessibility patterns at a granular level, thereby aiding policy interventions for improved urban mobility. Full article