Search Results (26)

The transition to environmentally friendly mobility inevitably requires users to use sustainable modes of transport. Rapid urbanization, along with the growing demand for efficient, inclusive, and ecological transport systems, has highlighted the urgent need for research and analysis into the acceptability and experiences of transitioning to sustainable modes of transport. This article proposes a six-step procedure to support the selection of vehicles for car-sharing fleets in cities. The analysis utilizes the Analytic Hierarchy Process method, which allows for the comparison and evaluation of five vehicle variants with different powertrains, taking into account various evaluation criteria: ecological and economic. To refine the research, criterion weights were determined based on original surveys among six car-sharing operators and eighty-seven experts in the field of decarbonization of urban transport. The results indicated that plug-in hybrid vehicles are the most advantageous option for car-sharing fleets, providing a balance between emissions, cost-effectiveness and operational flexibility. The solution obtained is in line with expectations, confirming that the proposed analytical approach is a reliable decision support tool that reduces the risk of making the wrong decision regarding the choice of powertrains. Full article

Introduction: Tuberculosis (TB) remains a public health concern, and people at risk for TB are hesitant to seek care. The first South African National TB prevalence survey, conducted in 2017–2019, found that most participants with TB symptoms did not seek care for TB. In 2022, an estimated 23% of people with TB in South Africa were undiagnosed, contributing to the country’s burden of “missing” TB cases. This study explores health-seeking behaviour among people with TB (PwTB) in South Africa, focussing on barriers and facilitators to care-seeking and the quantification of TB-related stigma from a patient and community perspective. Methods: We conducted a mixed-method study in the City of Johannesburg (COJ) Metropolitan Municipality from February to March 2022. PwTB aged 18 and older initiating TB treatment for microbiologically confirmed pulmonary TB were recruited from three primary healthcare facilities in the COJ. After providing written informed consent, they participated in a one-time, in-depth, face-to-face interview. The interviews were digitally recorded and conducted by trained facilitators. We used thematic analysis with deductive approaches to develop themes. We used the Van Rie TB stigma assessment scale to quantify perceived stigma. Results: We interviewed 23 PwTB with an overall median age of 39 years and 14 (61%) males. Patient-level barriers to accessing TB care included visiting traditional healers and pharmacists before their TB diagnosis; wrong or missed diagnosis by private doctors; work commitments; scarcity of resources to attend the clinic or walk long distances; perceived and experienced stigma; and a lack of TB knowledge. Facility-level barriers included long clinic queues and uncertainty about where to receive TB care in the clinic. Facilitators for TB care-seeking included being in contact with someone who had TB, receiving encouragement from family, or having knowledge about TB transmission and early diagnosis. The overall median total stigma score among 21 PwTB was 53 (IQR: 46–63), with median community and patient stigma scores of 25 (IQR: 22–30) and 31 (IQR: 21–36), respectively. Conclusions: We found important considerations for the TB programme to improve the uptake of services. Since PwTB consult elsewhere before visiting a facility for TB care, TB programmes could establish private–public partnerships. TB programmes could also increase TB awareness in the community, especially among males, and mobile clinics could be considered to assist with TB case detection and treatment provision. Applying behavioural design techniques and co-designing interventions with patients and providers could improve TB health-seeking behaviours. Full article

Over the past 10 years, the Korean government has declared its intention to achieve carbon neutrality to the international community and strived to create smart cities and green cities as a practice to achieve carbon neutrality. In the same context, the Smart Green City project, which began in earnest at the end of 2020, initially attracted national attention due to the strong will of the administration. However, limitations in its performance were revealed. This study aimed to identify the policy mechanisms that led the Korean government to pursue the Smart Green City project using a critical juncture approach and to derive useful implications for policy selection. A critical juncture framework was applied for in-depth analysis, and a descriptive case study based on critical thinking was conducted. The results confirmed that endogenous and exogenous environmental changes surrounding the government at critical junctures were mechanisms for establishing the Smart Green City project. In addition, the government’s attempt to differentiate itself from the policies of previous governments was confirmed to be a constraint that actually hindered the project’s progress. This study concludes that if a government facing a complex crisis makes hasty policy decisions, it is likely to repeat the policy pattern or institutional path of previous governments. It also argued that in this situation, policy choices that aim to cut a new path may cause the government to choose a wrong path, which may have irreversible consequences. Full article

Early identification of anomalies (such as leakages or sensor failures) in urban water distribution systems is critical to mitigating water scarcity in cities and is a challenge in water resource management. Several data-driven methods based on machine learning algorithms have been proposed in the literature for leakage detection in urban water distribution systems. Still, most of them are challenging to implement due to their complexity and requirements of vast amounts of reliable data for proper model generation. In addition, the required infrastructure and instrumentation to collect the data needed to train the models could be unaffordable. This paper presents the use and comparison of Autoregressive Integrated Moving Average models and Transfer Function models generated via the Box–Jenkins approach to modeling the water flow in water distribution systems for anomaly detection. The models were fit using water flow data from tanks operating in a branch of the water distribution system of Mexico City. The results showed that both methods helped select the best model type for each variable in the analyzed water branch, with Seasonal ARIMA models achieving a lower mean absolute percentage error than the fitted Transfer Function models. Furthermore, this methodology can be adjusted to different time windows to generate alerts at different rates and does not require a large sample size. The generated anomaly detection models could improve the efficiency of the water distribution system by detecting anomalies such as wrong measurements and water leakages. Full article

The level of clustering and the adjustment by cluster-robust standard errors have yet to be widely considered and reported in cross-sectional studies of tuberculosis (TB) in prisons. In two cross-sectional studies of people deprived of liberty (PDL) in Medellin, we evaluated the impact of adjustment versus failure to adjust by clustering on prevalence ratio (PR) and 95% confidence interval (CI). We used log-binomial regression, Poisson regression, generalized estimating equations (GEE), and mixed-effects regression models. We used cluster-robust standard errors and bias-corrected standard errors. The odds ratio (OR) was 20% higher than the PR when the TB prevalence was >10% in at least one of the exposure factors. When there are three levels of clusters (city, prison, and courtyard), the cluster that had the strongest effect was the courtyard, and the 95% CI estimated with GEE and mixed-effect models were narrower than those estimated with Poisson and binomial models. Exposure factors lost their significance when we used bias-corrected standard errors due to the smaller number of clusters. Tuberculosis transmission dynamics in prisons dictate a strong cluster effect that needs to be considered and adjusted for. The omission of cluster structure and bias-corrected by the small number of clusters can lead to wrong inferences. Full article

The success of the Australian COVID-19 vaccination strategy rested on access to primary healthcare. People experiencing or at risk of homelessness are less likely to access primary healthcare services. Therefore, leaders in homeless health service delivery in Sydney identified the need to develop a vaccine hub specifically for this vulnerable population. The aim of this study was to develop an evidenced based model of care to underpin the Vaccine Hub and optimize access to vaccination for people experiencing or at risk of homelessness. A mixed methods study was conducted that included interviews with key stakeholders involved in establishing and delivering the Inner City COVID-19 Vaccine Hub, and a survey with people receiving COVID-19 vaccination. Over the 6-month period of this study, 4305 COVID-19 vaccinations were administered. Participants receiving vaccination reported feeling safe in the Vaccine Hub and would recommend it to others. Stakeholders paid tribute to the collective teamwork of the Vaccine Hub, the collaboration between services, the ‘no wrong door’ approach to increasing access and the joy of being able to support such a vulnerable population in challenging times. The study findings have been populated into a Vaccination Hub Blueprint document that can be used as a template for others to improve access to vaccinations for vulnerable populations. Full article

Despite advancing Internet of Things (IoT) technologies, road projects often rely on inaccurate supplier data, making it difficult to determine the cost, quantity, quality, and transportation duration of the needed materials. The wrong choice of material suppliers can lead the supply chain to suffer losses, directly affecting the project’s performance. In this regard, many studies have devised material logistics optimization models for road projects. However, the majority based their decisions on inaccurate or outdated data. This paper studies this gap by introducing a framework that utilizes IoT technologies and smart construction to feed optimization models with accurate and dynamically updated material data. This IoT-powered framework considers only quantitative criteria as input data to the integrated linear programming optimization model, precisely selected suppliers, and optimally calculated costs using MS Excel Solver. The results reveal that the framework is sensitive to any dynamic data updates and can achieve up to 40% material cost savings in real runtime. The paper demonstrates the proposed outline framework with a case study of planning an alternative road between Riyadh and Madinah cities in Saudi Arabia. Full article

In recent years, artificial intelligence technology represented by deep learning has achieved remarkable results in image recognition, semantic analysis, natural language processing and other fields. In particular, deep neural networks have been widely used in different security-sensitive tasks. Fields, such as facial payment, smart medical and autonomous driving, which accelerate the construction of smart cities. Meanwhile, in order to fully unleash the potential of edge big data, there is an urgent need to push the AI frontier to the network edge. Edge AI, the combination of artificial intelligence and edge computing, supports the deployment of deep learning algorithms to edge devices that generate data, and has become a key driver of smart city development. However, the latest research shows that deep neural networks are vulnerable to attacks from adversarial example and output wrong results. This type of attack is called adversarial attack, which greatly limits the promotion of deep neural networks in tasks with extremely high security requirements. Due to the influence of adversarial attacks, researchers have also begun to pay attention to the research in the field of adversarial defense. In the game process of adversarial attacks and defense technologies, both attack and defense technologies have been developed rapidly. This article first introduces the principles and characteristics of adversarial attacks, and summarizes and analyzes the adversarial example generation methods in recent years. Then, it introduces the adversarial example defense technology in detail from the three directions of model, data, and additional network. Finally, combined with the current status of adversarial example generation and defense technology development, put forward challenges and prospects in this field. Full article

The objective of smart cities is to improve the quality of life for citizens by using Information and Communication Technology (ICT). The smart IoT environment consists of multiple sensor devices that continuously produce a large amount of data. In the IoT system, accurate inference from multi-sensor data is imperative to make a correct decision. Sensor data are often imprecise, resulting in low-quality inference results and wrong decisions. Correspondingly, single-context data are insufficient for making an accurate decision. In this paper, a novel compound context-aware scheme is proposed based on Bayesian inference to achieve accurate fusion and inference from the sensory data. In the proposed scheme, multi-sensor data are fused based on the relation and contexts of sensor data whether they are dependent or not on each other. Extensive computer simulations show that the proposed technique significantly improves the inference accuracy when it is compared to the other two representative Bayesian inference techniques. Full article

To assess water quality (WQ) online for assuring the safety of drinking water, a novel early warning system integrating a high-frequency monitoring system (HFMS) and data quality control (QC) was developed at Lake Qiandao. The HFMS was designed for monitoring water quality, nutrient inputs by main tributaries, water currents and meteorology at different sites at Lake Qiandao. The EWS focused on data availability, a QC method, a statistical analysis method and data applications instead of technological aspects for sondes, wireless data transfer and interface software development. QC was implemented before use to delete the abnormal values of outliers, to detect change points, to analyse the change trend, to interpolate discrete missing measurements, and find continuous missing or wrong observations caused by technical problems with the sonde. For demonstrating advantages and data availability, surface and profiling measurements at two sites were plotted. The plots show obvious seasonal and diel variations, demonstrating the success of integration of the system with advanced automated technology and good QC. This successfully developed system is now not only giving early warning signals, but also providing critical WQ information for the security of drinking water diverted to Hangzhou city through a tunnel of 110 km length. The automatic monitoring data with QC is also being used to produce initial conditions for WQ prediction based on a three dimensional hydrodynamic-ecosystem model. Full article

The right to the city concept is widely debated in academic discourse yet ambiguously executed in public discourse. In much of the discussion, the right to the city is advocated as a right that humans should claim—i.e., participating in urban space living. Nonetheless, constraints and limits are imposed on such advocacy, resulting in a tokenized implementation state. With such a background surmounting the COVID-19 pandemic era, this study is aimed at understanding the right to the city propagation and revealing the possible wrongs of such civic advocacy. Multiple cases in Malaysia were selected for analysis and as the discussion context representing the state-of-the-art aspect of right to the city in the context of an emerging country. Two potential misconceptions through the action of right to the city were identified: first, the concept of right to the city has the potential to infringe the centrality of power, which both citizens and the authority have to make clear; second, the lack of a sign of contribution from citizens poses a severe challenge to build a co-created urban space for all. This paper contributes to removing a blind spot—the possible wrong to the right to the city—and provides ideas to achieve authentic citizen participation. Full article

Cities are recognized as a major contributor to environmental pressures. Recently, organizational LCA (OLCA) has been found to align well with requirements for city-scale environmental decision support and a novel city-OLCA framework was introduced. City-OLCA combines two relevant aspects: It covers activities beyond public service provision (multi-stakeholder) and emissions beyond greenhouse gases (multi-impact). Its unique approach of acknowledging responsibility levels should help both city-managers and academia in performance tracking and to prioritize mitigation measures. The goal of this work is to test city-OLCA’s feasibility in a first case study with real city data from Vienna. The feasibility was confirmed, and results for 12 impact categories were obtained. As an example, Vienna’s global warming potential, ozone depletion potential, and marine eutrophication potential for 2016 were 14,686 kt CO₂ eq., 6796 kg CFC-11 eq., and 310 t N eq., respectively. Our results indicate that current accounting practices may underestimate greenhouse gas emissions of the entire city by up to a factor of 3. This is mainly due to additional activities not covered by conventional standards (food and goods consumption). While the city itself only accounts for 25% of greenhouse gases, 75% are caused by activities beyond public service provision or beyond governmental responsibilities. Based on our results, we encourage city managers to include an organizational based LCA approach in defining reduction strategies. This will reveal environmental blind spots and avoids underestimating environmental burdens, which might lead to setting the wrong focus for mitigation. Full article

In Korea, a greenhouse gas (GHG) environmental impact assessment (EIA) has been conducting since 2012, which sets the evaluation procedures and methods for GHG items during the EIA. However, the current EIA on GHG emissions can support wrong decision-making because the evaluation does not consider Scope 3 GHG emissions. Accordingly, this study proposed the life cycle EIA (LCEIA) method to identify changes in GHG emissions that need to be managed by considering Scope 3 GHG emissions in construction projects. The LCEIA method incorporates life cycle CO₂ (LCCO₂) including Scope 1, Scope, and Scope 3 GHG emissions using the concept of life cycle assessment (LCA) into the scoping step of the EIA process. The case study was conducted using existing EIA on GHG emission and LCEIA methodology for a development project in Gwangyang City. Scenario 1 is defined as an approach that calculates GHG emissions using the existing EIA method, and scenario 2 is also defined as a process using the LCEIA method. Results reveal that Scenario 2, including Scope 3 GHG emissions, had 46.4−51.2% more GHG emissions than Scenario 1. Sensitivity analysis for electricity and liquefied natural gas (LNG) density was also performed. Although the change in the carbon emission factor of electricity had a slightly sensitive effect on the research results, the LNG density was found to be less sensitive. This study believes the importance of switching to an EIA reflecting life cycle carbon dioxide (LCCO₂) to calculate the exact amount of GHG emissions for construction work. Full article

We report the unique design and prototype of a portable gas sensor module for monitoring greenhouse gases. The commercially available gas sensors (MQ-02, MQ-135, and TGS2602) were adopted in designing the module using Arduino Uno. Different locations in the city of Solapur, India (17.6599° N, 75.9064° E), were scanned for the usability of the developed prototype of the mobile gas sensor module. The choice of gas sensors in combination with Arduino Uno led to an excellent prototype for measuring the concentration of greenhouse gases, and therefore the wrong alarm for toxic gases. The prototype model and corresponding greenhouse gas concentrations (ppm) are described using an interplay of sensor design, software program, and greenhouse gases sites. Full article

The major challenge we face today in the energy sector is to meet the growing demand for electricity with less impact on the environment. South America is an important player in the renewable energy resource. Brazil accelerated the growth of photovoltaic installed capacity in 2018. From April of 2017 to April of 2018, the capacity increased by 1351.5%. It is expected to reach the value of 2.4 GW until the end of the year. The new Chilean regulation requests that 20% of the total electricity production in 2025 must come from renewable energy sources. The aim of this paper is to establish time series behavior changes between El Niño Southern Oscillation and the solar radiation resource in South America. The results can be used to validate the measured data of energy production for new solar plants. The method used to verify the behavior of the time series was the Detrended Fluctuation Analysis. Solar radiation data were collected in twenty-five cities distributed inside the Brazilian solar belt, plus six cities in Chile, covering the continent from east to west, in a region with high potential of solar photovoltaic generation. The results show the impact of El Niño Southern Oscillation on the climatic behavior of the evaluated data. It is a factor that may lead to the wrong forecast of the long-term potential solar power generation for the region. Full article