Quality Of Life For Residents Research Articles

Real-Time Navigation Roads: Lightweight and Efficient Convolutional Neural Network (LE-CNN) for Arabic Traffic Sign Recognition in Intelligent Transportation Systems (ITS)

Smart cities are now embracing the new frontier of urban living, with advanced technology being used to enhance the quality of life for residents. Many of these cities have developed transportation systems that improve efficiency and sustainability, as well as quality. Integrating cutting-edge transportation technology and data-driven solutions improves safety, reduces environmental impact, optimizes traffic flow during peak hours, and reduces congestion. Intelligent transportation systems consist of many systems, one of which is traffic sign detection. This type of system utilizes many advanced techniques and technologies, such as machine learning and computer vision techniques. A variety of traffic signs, such as yield signs, stop signs, speed limits, and pedestrian crossings, are among those that the traffic sign detection system is trained to recognize and interpret. Ensuring accurate and robust traffic sign recognition is paramount for the safe deployment of self-driving cars in diverse and challenging environments like the Arab world. However, existing methods often face many challenges, such as variability in the appearance of signs, real-time processing, occlusions that can block signs, low-quality images, and others. This paper introduces an advanced Lightweight and Efficient Convolutional Neural Network (LE-CNN) architecture specifically designed for accurate and real-time Arabic traffic sign classification. The proposed LE-CNN architecture leverages the efficacy of depth-wise separable convolutions and channel pruning to achieve significant performance improvements in both speed and accuracy compared to existing models. An extensive evaluation of the LE-CNN on the Arabic traffic sign dataset that was carried out demonstrates an impressive accuracy of 96.5% while maintaining superior performance with a remarkably low inference time of 1.65 s, crucial for real-time applications in self-driving cars. It achieves high accuracy with low false positive and false negative rates, demonstrating its potential for real-world applications like autonomous driving and advanced driver-assistance systems.

Preferences and Quality of Life in Nursing Home Residents: A Mixed Methods Study.

Delivery of person-centered care (PCC) is the standard in nursing homes (NHs) and demonstrates a positive impact on resident quality of life (QOL). PCC inherently recognizes and prioritizes resident preferences; however, preferences, and their degree of importance among residents, demonstrate a variable relationship with QOL that remains underexplored. Therefore, the current study examined the association between preferences and QOL among NH residents. A mixed methods study incorporating surveys was conducted among 144 residents, with semi-structured follow-up interviews with 11 residents. Findings confirm a variable relationship between resident preferences and QOL. Higher order preferences not captured within the preference assessment may influence QOL. High numbers of unimportant and can't do/no choice preferences were linked to resident acclimatization to the NH. QOL was at risk if care was not consistent with preferences. In-depth assessments and robust measures of preferences and QOL should be integrated into care delivery and future research. [Journal of Gerontological Nursing, 50(5), 7-13.].

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Garbage Collection Android Application

Abstract: The "Android Garbage Collection Application" stands as an innovative solution tailored for Android devices, with the objective of revolutionizing waste management in smart cities. Conventional waste collection methods frequently result in missed pickups, litter accumulation, and potential public health risks. This project harnesses seamless integration with Firebase to create a user-friendly Android app. It provides real-time notifications to residents as garbage collection trucks approach their locations. A central feature of this app is its live tracking system, enabling users to monitor the precise location of the garbage truck in real-time. Additionally, it offers estimated arrival times, effectively reducing waiting times for residents and optimizing the waste collection process. What truly sets this application apart is its focus on community engagement. By actively involving residents in the waste management process, it encourages shared responsibility and cooperation among city dwellers, fostering a cleaner environment and ensuring timely and efficient waste collection. Furthermore, this project serves as a testament to the transformative power of modern technology in enhancing traditional municipal services. It exemplifies how integrating cuttingedge technology can effectively address longstanding challenges and enhance the quality of life for residents in smart cities. The scalability and adaptability of this application make it a valuable asset for optimizing waste management in various urban settings. Its data analytics capabilities provide city officials with valuable insights, enabling more informed decision-making and a proactive approach to waste management

Research on Green Space Ecosystem Services under the Guidance of Healthy City Orientation

Research on Green Space Ecosystem Services under the Guidance of Healthy City Orientation

QUALITY OF LIFE (QOL) AND ACADEMIC PERFORMANCE: THE CASE OFCHILDRENSCHOOLING IN AN ISLAND

Good quality of life (QoL) may provide conducive environment for school children to studyand perform better. Studies indicate that school children residing in isolated areaswith discontented quality of life did not perform academically. However, theissue of QoL and its relation to academic performance in a secluded island of Pulau Tuba is interesting to study. Thisis because, while Pulau Tuba is an isolated island, being part and partial of tourism industry inLangkawi had caused for the island to be unique and different. Therefore, this study investigated the possible relation between the residents QoL and the academicperformance of their children. An adapted questionnaire was used to collect the required data for the study. Several interviews with the local residents were also conducted to acquire the necessary data in order to supportthefindings of the study. The study found that the villagers are enjoying their good quality of life. Nevertheless, that could not contribute much in better academic performance of the school children. Tri-angulation using interviews was conducted to validate the findings and dig out the answers to the anomaly. Recommendations were figured out as the ways to improve theacademic performance of the school children.

Geo-spatial technology based on a multi-criteria evaluation technique used to find potential landfill sites in the town of Bule Hora in southern Ethiopia

ABSTRACT Solid waste has surfaced as an eminent and critical concern of environmental and social significance on a global scale, and Ethiopia, a developing country with limited income, has also encountered unfavorable outcomes due to substandard waste management practices. When pinpointing a fitting landfill location in the town of Bule Hora, various ecological, economic, and societal aspects must be considered; these may result in discord and exacerbate a multifaceted and lengthy process. Hence, this research aims to identify prospective landfill sites within the town and utilize geospatial methods, such as Multi-Criteria Evaluation and Analytic Hierarchy Process, to accomplish its objectives. The utilization of geospatial technology and multi-criteria evaluation provides an efficient manner to simultaneously address all bottlenecks involved in the selection of an appropriate landfill location. Geospatial technology evaluates and manages environmental constraints, whereas multi-criteria assessment categorizes choices based on their desirability. Furthermore, by employing a restriction map adhering to established standards, seven landfill sites have successfully been identified within the town. The Land Suitability Index assesses site suitability based on ecological factors, while the Total Hauling Distance evaluates sites within an economic framework. AHP determines weightings through 25.4 pairwise comparisons, resulting in a consistency ratio of 1.95%. The cartographic analysis is conducted using ESRI ArcGIS version 10.8 software. The findings of this study reveal that 98.69% of the area under study is subject to restrictions. The study recommends the implementation of geospatial methods for identifying suitable landfill sites, which would aid in the decision-making process and prevent hasty decisions from triggering environmental degradation. Proper waste disposal would augment the quality of life for residents by diminishing health hazards. The study endeavors to serve as a reference for other developing countries in selecting appropriate landfill sites. Implications: The town of Bule Hora also faces the problem of waste disposal; there is no scientifically selected suitable landfill. Residents of the town of Bule Hora practice waste disposal in open fields, near settlements, water bodies, roads, agricultural land, and other places. The main sources of solid waste in the town are homes, shops, hotels, restaurants, open markets, hospitals, educational institutions, private clinics, etc. Water pollution can potentially lead to the spread of waterborne diseases. According to reports from the Bule Horas Health Department, many people are affected by water-related diseases every year. These open landfill systems with no regard for settlement, topography, geology, surface, or groundwater conditions are the consequences of these unsuitable habitats and health problems. To reduce these problems, this study plays an important role in determining the suitability of landfills for the town and proposing alternative measures that can minimize negative environmental impacts from waste. This study aims to apply geospatial-based technology to a multi-criteria assessment technique to select perfectly suitable landfill sites that are environmentally friendly, economically cost-effective, and socially responsible; examine the town’s current waste management system; calculate the selected parameter weights for feature ranking; and delineate solid waste landfills.

INDUSTRIAL TERRITORIES IN DNIPRO CITY: TRANSFORMATION STRATEGY INTO RECREATIONAL ASSETS

In the modern world, with increasing urbanization, efficient land use becomes critically important for the sustainable development of cities. The reorganization of outdated industrial territories emerges as a key direction in this process, requiring a combination of various approaches to preserve industrial function, reprofile enterprises, and adapt production to new conditions. This study is built upon a broad methodological foundation, encompassing both general scientific and specialized methods within the fields of architecture, urban planning, and urban studies. Specifically, methods such as literary, statistical, and cartographic analyses, synthesis, and generalization are employed for data collection and processing. Specialized methods, including experimental design, mental mapping, and phenomenological analysis, are also utilized to achieve the research objectives. A cluster approach was employed in developing the concept of spatial development for recreation on former industrial territories. Taking into account various factors such as economic opportunities, sociocultural factors, urban planning considerations, ecological and infrastructural requirements, as well as legal aspects, is crucial for the successful reorganization of industrial territories for recreational purposes. Achieving a balance among these factors enables the creation of resilient, functional, and culturally rich spaces that contribute to improving the quality of life for residents and the development of cities. The research encompasses a strategy for planning the reorganization of industrial territories for recreational use, which involves the identification and analysis of territorial potential and condition, planning functional transformations, architectural transformation, sustainability and ecological considerations, public participation, infrastructure transformations, economic resilience, multimodality, and adaptability. The cluster approach involves the formation of flexible entities enriched with innovative, dynamic spaces encompassing various functional zones, while preserving industrial heritage and creating green, environmentally sustainable spaces that align with the needs of modern society. Community engagement and project flexibility are key elements in creating inclusive and sustainable ecological spaces that enhance the quality of life and attract new visitors. The article analyzes the urban planning situation in the city of Dnipro, identifying shortcomings in the formation of industrial zones. The study emphasizes the need to develop strategies to address issues related to the placement of production zones in the city, which lead to limitations in territorial development and the fragmentation of the urban fabric. Based on this, a concept of a recreational cluster for industrial tourism within the industrial hub "Fabrichny" is developed, envisioning the transformation of industrial territory into recreational facilities. An analysis of the site revealed valuable architectural objects that could become the main attractors of the recreational cluster. These objects served as the basis for the development of a conceptual project for the considered area.

Enhancing Public Transit System Through AI and IoT

The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.

Counting what counts: assessing quality of life and its social determinants among nursing home residents with dementia

BackgroundMaximizing quality of life (QoL) is a major goal of care for people with dementia in nursing homes (NHs). Social determinants are critical for residents' QoL. However, similar to the United States and other countries, most Canadian NHs routinely monitor and publicly report quality of care, but not resident QoL and its social determinants. Therefore, we lack robust, quantitative studies evaluating the association of multiple intersecting social determinants with NH residents’ QoL. The goal of this study is to address this critical knowledge gap.MethodsWe will recruit a random sample of 80 NHs from 5 Canadian provinces (Alberta, British Columbia, Manitoba, Nova Scotia, Ontario). We will stratify facilities by urban/rural location, for-profit/not-for-profit ownership, and size (above/below median number of beds among urban versus rural facilities in each province). In video-based structured interviews with care staff, we will complete QoL assessments for each of ~ 4,320 residents, using the DEMQOL-CH, a validated, feasible tool for this purpose. We will also assess resident’s social determinants of QoL, using items from validated Canadian population surveys. Health and quality of care data will come from routinely collected Resident Assessment Instrument – Minimum Data Set 2.0 records. Knowledge users (health system decision makers, Alzheimer Societies, NH managers, care staff, people with dementia and their family/friend caregivers) have been involved in the design of this study, and we will partner with them throughout the study. We will share and discuss study findings with knowledge users in web-based summits with embedded focus groups. This will provide much needed data on knowledge users' interpretations, usefulness and intended use of data on NH residents’ QoL and its health and social determinants.DiscussionThis large-scale, robust, quantitative study will address a major knowledge gap by assessing QoL and multiple intersecting social determinants of QoL among NH residents with dementia. We will also generate evidence on clusters of intersecting social determinants of QoL. This study will be a prerequisite for future studies to investigate in depth the mechanisms leading to QoL inequities in LTC, longitudinal studies to identify trajectories in QoL, and robust intervention studies aiming to reduce these inequities.

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) > 3D UMPs (32.51%) > POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents.

Comparative Evaluation of Depression, Anxiety and Quality of Life between Clinical and other than Clinical Branch Postgraduate Medical Students: A Cross-sectional Study

Introduction: Medical training has been reported to be stressful. Clinical branch residents were found to be more anxious when compared to other than clinical branch residents. Heavy workloads and long working hours usually contribute to stress, resulting in fatigue, depression, and anxiety. Aim: To compare depression, anxiety, and Quality of Life (QoL) between clinical and other than clinical branch postgraduate medical students. Materials and Methods: The present cross-sectional study was conducted in the Department of Psychiatry at the Institute of Medical Sciences (IMS), Banaras Hindu University (BHU), in Varanasi, Uttar Pradesh, India. The data were collected from 150 residents through face-to-face interviews and by applying the Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HDRS), and World Health Organisation Quality of Life Brief Version (WHOQOL-BREF). The data were analysed using International Business Machines (IBM) Statistical Package for the Social Sciences (SPSS) Statistics 23.0 software. Results: The present study found that the majority of residents were male 108 (72%), belonged to the Hindu religion (134, 89.33%), were unmarried (150, 83.33%), and resided in the hostel (118, 78.67%) during their residency tenure. The prevalence of depression among clinical branch residents was 52 (50.0%), and in other than clinical branch residents, it was 17 (36.96%) (p=0.633). The prevalence of anxiety among clinical branch residents was 65 (62.5%), and in other than clinical branch residents, it was 18 (39.14%) (p=0.002). QoL was better in other than clinical branch residents compared to clinical branch residents in physical health, social, and environmental domains (p<0.04). Conclusion: The study concluded that clinical branch residents were experiencing significantly higher levels of anxiety. Overall, the QoL of residents in other than clinical branches was better than their counterparts in clinical branches.

Residents’ Perceptions of the Impacts of Tourism on the Quality of Life in a Tourist Island Destination: Evidence from Tobago

Tobago, the smaller island of the twin-island Republic of Trinidad and Tobago is heavily tourist penetrated and almost exclusively reliant on tourism for its sustenance. Tobago is touted as beautiful, peaceful and tranquil and is generally referred to as ‘The Capital of Paradise’. However, the moniker ‘The Capital of Paradise’ is being dissipated due to the increasing reliance on tourists and tourism which has the attendant ‘blessings and blights’, including, but not limited to, economic development, increased crime and impacts on the quality of life. While tourism has a great potential to enhance residents’ quality of life, a limited number of studies have been conducted on the issue. Additionally, while there is some scholarship on the tourism-quality of life nexus, the views of residents in tourist island destinations are hardly used to inform these studies. As a result of this lacuna, the current article presents the perceptions of residents of Tobago, a tourist island destination in the Caribbean, regarding their perceptions of the impacts of tourism (local and foreign) on the quality of life (socio-cultural, economic, and environmental) on the island. Data were gathered from thirty (N=30) residents of Tobago using a qualitative approach via semi-structured interviews, analysed using thematic analysis and presented in a descriptive manner. The results indicate that tourism had not improved the quality of life for residents in Tobago, crimes committed in Tobago were conducted by homegrown criminals, and that increased crime in Tobago was not a result tourism. Four themes emanated from the study (increased crime, growth and development, localized criminals, and fear of crime). Implications for policy and practice are also discussed.

야간 시간대 보행자의 범죄 두려움 감소를 위한 CPTED 적용 방안

Fear of crime is a deeply rooted source of anxiety in modern society, making it essential to reduce this fear to improve the quality of life for residents. Previous studies have indicated that pedestrians, especially during nighttime hours, often experience a significant level of fear of crime. Therefore, this study focuses on identifying vulnerable points for fear of crime during nighttime pedestrian activity in Jangyang 2-dong, Gwangjin-gu, Seoul. It also proposes crime prevention strategies based on Crime Prevention Through Environmental Design (CPTED) principles, considering road environments and inter-individual spacing metrics.
 Firstly, using K-means Clustering on reported crime data for Jangyang 2-dong, nighttime hours were defined as 21:00 to 01:00. Subsequently, social network analysis were applied to identify areas with low centrality, categorizing them into Cluster 1 (Jangyangbyeong-ro 6, Dukseom-ro 44, Dukseom-ro 50) and Cluster 2 (Jayang-ro 3, Dukseom-ro 58) as locations vulnerable to fear of crime. Finally, tailored crime prevention designs were proposed for each cluster. This study is significant for its use of social network analysis on safety data to identify fear of crime vulnerable areas and for suggesting crime prevention measures tailored to the characteristics of the region from a CPTED perspective.

Empowering Communities for Environmental Change: Waste Segregation Solutions in Alido Heights

One of the significant environmental challenges we face is improper waste segregation. Poor waste disposal practices hinder the progress of integrated solid waste management in households. This study aims to identify problems encountered during implementation and provide specific solutions to enhance the quality of life for residents and the Homeowners Association and establish adequate waste segregation practices that promote environmental cleanliness. The study utilizes quantitative and qualitative methods with a descriptive design to evaluate the waste segregation system in Alido Heights Subdivision, Bulihan, City of Malolos, Bulacan. The researchers designed a survey questionnaire with questions constructed and developed based on the issues identified by the residents. Simultaneously, interviews were conducted with an officer of the homeowners’ association to gather their knowledge and experiences. The results indicate that the respondents effectively manage household waste and segregation. Furthermore, the efficiency of the waste segregation system implemented by the subdivision reveals inadequacies in designated waste disposal equipment and the location of the dumping site, resulting in inefficient waste segregation within the subdivision. The improvement programs recommended by the subdivision’s residents include providing more garbage bins, allocating additional waste disposal sites, and implementing a campaign program to establish a more efficient segregation system among the residents in the area.

Impacts of Tourism on Development of Urban Areas in Indian Cities: A Systematic Literature Review

The paper aims to review the impact of tourism on the development of urban areas in Indian cities. The study explores the various ways in which tourism acts as a catalyst for the development of any city in terms of sectors like the economic, social, and physical development of urban areas. In the context of India, there are many cities which are already heavily dependent on tourism activity, and they are flourishing as tourism cities. This also examines the challenges the cities face in managing tourism's growth and the impact it has on the local communities. The findings suggest that while tourism has the potential to bring significant benefits to urban areas, it also has negative effects on the environment, cultural heritage, and quality of life for residents. This paper is based on a systematic literature review and content analysis.

THE ASSOCIATION OF COVID-19 OUTBREAKS AND OTHER FACTORS WITH NURSING HOME RESIDENTS’ QUALITY OF LIFE

Abstract Nursing home (NH) residents’ Quality of life (QoL) is an important goal of care. However, it is understudied, particularly during the COVID-19 pandemic. Our objective was to examine whether COVID-19 outbreaks, care aide emotional exhaustion, and lack of resident access to geriatric professionals were associated with NH residents’ QoL. In this cross-sectional study (Jul-Dec 2021), we purposefully selected 9 NHs in Alberta, Canada based on their COVID-19 exposure (no or minor/short outbreaks vs repeated or extensive outbreaks). Using a validated questionnaire (DEMQOL-CH), we assessed dementia-specific QoL of 689 residents from 18 care units through video-based interviews with care aides. Independent variables included COVID-19 outbreak in the NH in the last 2 weeks (health authority records), proportion of care aides on a care unit with high emotional exhaustion scores (9-item short form Maslach Burnout Inventory), and resident access to geriatric professionals (validated facility survey). We ran mixed-effects regression models, adjusted for facility and care unit characteristics (validated facility and care unit surveys), and resident covariates (Resident Assessment Instrument – Minimum Data Set 2.0). COVID-19 outbreaks within two weeks of the data collection (β=0.189, 95% confidence interval [CI]: 0.058;0.320), higher proportions of emotionally exhausted care aides on a care unit (β=0.681, 95%CI: 0.246;1.115) and lack of access to geriatric professionals (β=0.216, 95%CI: 0.003;0.428) were significantly associated with poorer resident QoL. Policies aimed at reducing infection outbreaks, better supporting care staff, and increasing access to geriatric specialists, may help to mitigate negative effects of COVID-19 NH residents’ QoL.

CORPORATE PERSPECTIVES OF A NATIONAL NURSING HOME PRAGMATIC TRIAL AND FACTORS AFFECTING IMPLEMENTATION FIDELITY

Abstract Implementation fidelity is concerning for embedded pragmatic clinical trials (ePCTs), where effectiveness is evaluated in real-world settings instead of a structured clinical environment. Healthcare facilities paused ePCT research activities during the COVID-19 pandemic to address related staffing issues and infections. We examined a 54-nursing-home ePCT implementing Music & Memory, a nonpharmacological intervention using music to improve quality of life for residents living with dementia. Our qualitative study describes factors influencing implementation fidelity across four nursing home corporations. The interview guide assesses corporate perceptions of implementation barriers, facilitators, and participation incentives. Themes were organized within the Conceptual Framework for Implementation Fidelity’s potential modifying factors: 1) context, 2) participant responsiveness, 3) intervention complexity, 4) facilitation strategies, 5) quality of delivery, and 6) recruitment. Interviewees (n=4) lead corporate-wide dementia initiatives. 1) Context: COVID-19-related capacity issues and local “champion” turnover impacted success. 2) Participant responsiveness: Developing playlists strengthened staff-resident relationships, although time-consuming and requiring staff competency. Residents experienced decreased agitation while staff satisfaction increased. 3) Intervention complexity: Brown’s research team adapted processes to save staff time. 4) Facilitation strategies: The relationship with Brown promoted problem-solving. 5) Quality of delivery: The ePCT was an effective, non-pharmacological approach addressing dementia symptoms. 6) Recruitment: Corporate leadership makes decisions regarding individual nursing homes’ recruitment in research. Financial incentives did not drive participation, although some voiced appreciation for offsetting time and resources invested. Recommendations for future efforts included in-person champion training and site-specific quality improvement efforts. Researchers maintaining relationships with corporate and local leadership promotes sustainable engagement.

INITIAL FINDINGS ON THE BENEFITS OF THE TOVERTAFEL: REDUCING BEHAVIORS IN PERSONS WITH DEMENTIA

Abstract Technologies have been developed to reduce behaviors in persons with dementia by addressing unmet needs through activities that stimulate multiple senses. The Tovertafel (“magic table”) is a novel device from the Netherlands that enables purposeful play using interactive games designed to promote social, physical, and cognitive engagement in people with dementia. This study is investigating whether an intervention using the Tovertafel improves quality of life for residents with moderate to severe dementia living in long-term care communities in the Midwest. Twelve white women with an average BIM score of 6 completed the study at the initial facility. The 4-week intervention consisted of 30-minute game sessions with 2 to 6 individuals each weekday. Participants attended 2.5 sessions per week on average and displayed increased engagement during the games per facilitator reports. Direct care staff completed the Revised Memory and Behavior Problems Checklist–Nursing Home and the Apathy Evaluation Scale–10 for all participants. Data collected eight times over six consecutive weeks during the control (2 weeks) and intervention (4 weeks) periods were analyzed. After checking assumptions, paired-sample t-tests were conducted that indicated significantly fewer memory-related behaviors (p=.047) (e.g., repetitive behaviors and questions) and instances of apathy (p=.027), during the intervention period than the control. Despite reductions in depressive and disruptive behaviors, no statistically significant differences were found. Initial findings suggest that the Tovertafel could offer innovative ways to provide activities to people with moderate to severe dementia in long-term care settings, increase their quality of life, and improve interactions with staff.