Representação social da Covid-19 para a população de uma cidade de pequeno porte / Social representation of Covid-19 for the population of a small-sized city / Representación social del Covid-19 para la población de un pequeño pueblo

Objetivo: analisar a representação social da Covid-19 para a população geral de uma cidade de pequeno porte do Estado do Rio de Janeiro. Método: estudo qualitativo, apoiado na abordagem estrutural das representações sociais. Participaram 100 usuários de serviços de saúde. Os dados foram coletados por questionário sociodemográfico de evocações livres de palavras e roteiro de entrevista semiestruturada. Os dados foram analisados com o auxílio dos softwares Excel, EVOC 2005 e análise de conteúdo temático-categorial para contextualização das evocações respectivamente. Resultados: os termos do possível núcleo central foram: morte, sofrimento, cuidados, ansiedade-angústia e vacina. Na primeira periferia: medo e prevenção. À segunda periferia: informação-desinformação; desgoverno; ter-fé e proteção. A zona de contrate: doença; isolamento-social; dificuldades; catástrofe-mundial; desemprego e pandemia. Considerações finais: marcaram essa representação os impactos psicossociais negativos resultantes da desestruturação da vida e das mortes ocasionadas pela nova doença, no entanto o grupo aderiu as medidas de cuidados de proteção.

Objective: to analyze the social representation of Covid-19 among the general population of a small-sized city in the State of Rio de Janeiro. Method: Qualitative study, based on the structural approach of social representations. One hundred healthcare service users participated. Data were collected through a sociodemographic questionnaire, free word evocation, and a semi-structured interview guide. The data were analyzed using Excel software, EVOC 2005, and thematic-categorical content analysis for contextualization of the evocations, respectively. Results: the terms of the possible central core were: death, suffering, care, anxiety-distress, and vaccine. In the first periphery: fear and prevention. In the second periphery: information-misinformation; mismanagement; having faith and protection. The contrast zone: disease; social isolation; difficulties; global catastrophe; unemployment; and pandemic. Final considerations: this representation was marked by the negative psychosocial impacts resulting from the disruption of life and the deaths caused by the new disease; however, the group adhered to protective care measures.

Objetivo: analizar la representación social del Covid-19 para la población general de una pequeña ciudad del Estado de Río de Janeiro. Método: estudio cualitativo, basado en el enfoque estructural de las representaciones sociales. Participaron 100 usuarios de servicios de salud. Los datos se recolectaron mediante un cuestionario sociodemográfico con evocación libre de palabras y una guía de entrevista semiestructurada. Los datos fueron analizados utilizando lo software Excel y EVOC 2005 y análisis de contenido temático-categórico para contextualizar las evocaciones respectivamente. Resultados: los términos del posible núcleo central eran: muerte, sufrimiento, cuidados, ansiedad-angustia y vacuna. En la primera periferia: miedo y prevención. En la segunda periferia: información-desinformación; desgobierno; tener fe y protección. La zona de contraste: enfermedad; aislamiento-social; dificultades; catástrofe-mundial; desempleo y pandemia. Consideraciones finales: esta representación se caracterizó por los impactos psicosociales negativos derivados de la desestructuración de la vida y de las muertes causada por la nueva enfermedad, sin embargo, el grupo adhirió a las medidas de protección.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region.

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

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.

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

Ten steps towards integrated decision making for ecological restoration in cities: Rewilding the European beaver (Castor fiber) in Berlin, Germany.

Ensuring a livable city for all within the more-than-human discourse, restoration of urban ecosystems requires careful consideration of both human and non-human needs. However, traditional assessments and therefore most management plans usually fail to include the latter as a core planning requirement. This article presents and explains a 10-step method which simultaneously and actively considers both to identify potential restoration areas within urban ecosystems. To do so, a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis for the multispecies needs identification is combined with a Multicriteria Spatial Decision Support System (MCSDSS) for the spatial assessment. To validate this method, a case study of Berlin, Germany, an explicitly urban case, is presented. The aim of the study was to evaluate the ecosystem restoration (rewilding) potential of the city's riparian and riverine ecosystems through the enhancement of Eurasian beaver habitats.•Method combining SWOT analysis with MCSDSS for an integrated spatial assessment•Well-suited for multispecies (human and non-human) perspective on urban nature restoration.

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.

Impact of the digital economy on carbon dioxide emissions in resource-based cities.

With the rapid development of the digital economy, its environmental impact, particularly on carbon dioxide emissions in resource-based cities, has emerged as a vital research topic. Resource-based cities, often central to traditional industries, are confronted with the dual challenges of environmental pollution and economic transformation. This study employs empirical analysis to examine the influence of the digital economy on carbon dioxide emissions in these cities. The findings reveal that the digital economy significantly reduces carbon dioxide emissions, with this impact being more pronounced in the early stages of digital economic development and gradually diminishing thereafter. In the mechanism analysis, we found that the digital economy can reduce carbon dioxide emissions in resource-based cities by raising public concern about the environment. Moreover, the study highlights significant variations in carbon reduction effects among different types of resource-based cities, noting that stronger environmental regulations further enhance these effects. These insights not only provide a new theoretical perspective but also offer practical guidance for policymakers in promoting sustainable development within the digital economy.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China.

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Optically Transparent Frequency Selective Surfaces with Wideband Capability for IoT Applications: A Polarization-Independent Double-Layer Design.

This study proposes wide-band frequency selective surfaces (FSS) with polarization-independent characteristics that are tailored for IoT applications. The design consists of two different layers with band-stop characteristics that target key frequency bands in sub-6 GHz: 3.7 GHz (n77) and 4.5 GHz (n79), offering a 1.39 GHz bandwidth spanning from 3.61 GHz to 5.0 GHz. This study also presents a double-layer structure with a WB property with a fractional bandwidth of 32%. Simulations have been conducted to observe variations in insertion loss across incident and polarization angles ranging from 0 to 60 degrees for both TE and TM modes in the suggested FSS structures. These simulations demonstrate the design's polarization independence. Transparent polyvinyl chloride with a dielectric constant of 2.77 and a thickness of 1.48 mm has been utilized as the substrate material. The optical transmittance is calculated to be 96.7% for Layer 1, 95.7% for Layer 2, and 92.4% for the double-layer structure, and these calculated optical transmittance values were found to be higher compared to the studies in the literature. The proposed design is well-suited for sub-6 GHz IoT applications due to their high transparency, cost-effectiveness, robust high-performance capabilities in suppression, and polarization-independent features. The results of 3D full-wave simulations were compared with measurement and the equivalent circuit model outcomes, and a good agreement between the results was observed.

Field performance of 15 rain gardens in different cities in Taiwan.

Rain gardens are widely used for low impact development (LID) or as a nature-based solution (NbS). They help to reduce runoff, mitigate hot temperatures, create habitats for plants and insects, and beautify landscapes. Rain gardens are increasingly being established in urban areas. In Taiwan, the Ministry of Environment (MoE) initiated a rain garden project in Taipei city in 2018, and 15 rain gardens have since been constructed in different cities. These Taiwanese-style rain gardens contain an underground storage tank to collect the filtrated rainwater, which can be used for irrigation. Moreover, the 15 rain gardens are equipped with sensors to monitor temperature, rainfall, and underground water levels. The monitoring data were transmitted with Internet of Things (IoT) technology, enabling the capture and export of real-time values. The water retention, temperature mitigation, water quality, and ecological indices of the rain gardens were quantified using field data. The results from the young rain gardens (1-3 years) showed that nearly 100 % of the rainfall was retained onsite and did not flow out from the rain gardens; however, if the stored water was not used and the tanks were full, the rainwater from subsequent storms could not be stored, and the tanks overflowed. The surface temperatures of the rain garden and nearby impermeable pavement differed by an average of 2-4 °C. This difference exceeded 20 °C in summer at noon. The water in the underground storage tanks had very low levels of SS and BOD, with averages of 1.6 mg/L and 5.6 mg/L, respectively. However, the E. coli concentrations were high, and the average was 6283 CFU/100 mL; therefore, washing or drinking water is not recommended. The ecological indices, i.e., the Shannon and Simpson indices, demonstrated the good flora status of the rain gardens after one year. Although the weather differed by city, the performance of the rain gardens in terms of water retention, temperature mitigation, rainwater harvesting, and providing biological habitats was consistent. However, maintenance influences rain garden performance. If the stored water is not frequently used, the stored volume is reduced, and the stored water quality degrades.

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.

Spatiotemporal distribution of oxidative potential in PM2.5 and its key components across six Chinese cities.

Increasing evidence has supported that oxidative potential (OP) serves as a crucial indicator of health risk of exposure to PM2.5 over mass concentration. However, there is a lack of comparative studies across multiple cities, particularly on a fine temporal scale. In this study, we aim to investigate daily variation of ambient PM2.5 OP through simultaneous samplings in six Chinese cities for one year. Results showed that more than 60 % of the sampling days exhibited non-zero ranking difference between volume-normalized oxidative potential (OPv) and mass concentration among the six cities. Key components contributing to OPv inculde Mn, NO3-, and K+, followed by Ca2+, Al, SO42-, Cl-, Fe, and NH4+. Based on these chemical components, we developed a stepwise multivariable linear regression model (R2: 0.71) for OPv prediction. The performance of the model is comparable to both species- and sources-based ones in the literature. These findings suggest that a relatively lower daily-averaged mass concentration of PM2.5 does not necessarily indicate a lower oxidative risk. Future studies and policy developments on health benefits should also consider OPv rather than mass concentration alone. Priority could be given to sources/species that contribute significantly to oxidative potential of ambient PM2.5. SYNOPSIS: This study highlights inclusion of oxidative potential as a complementary metric for air pollution assessment and control.

Contrasting nature of aerosols over South Asian cities and its surrounding environment.

Cross-country assessment of aerosol loading was made over several South Asian megacities using multiple high-resolution remote-sensing database to assess how aerosols vary within the city and its suburbs. Parameters sensitive to aerosol optical and microphysical properties were processed over city-core and its surrounding, separated by a buffer. Cities across the Indo-Gangetic Plain (IGP; AOD:0.52-0.72) along with Mumbai (0.47) and Bangalore (0.46) denote comparatively high aerosol loading against non-IGP cities. City-core specific AOD was invariably high compared to surrounding, however with varying gradient having robust geographical signature. Exceptions to this general trend were in Kathmandu (ΔAOD: 0.07) and Dhaka (ΔAOD: 0.01) while strong positive AOD gradient was noted in Bangalore (+0.11), Colombo (+0.08) and in Mumbai (+0.07). While all mainland cities exhibited robust intraannual variability, distinction between city-core and its surrounding AOD exhibited varying seasonality. City-specific geometric coefficient of variation indicated insignificant association with mean AOD as opposed to European and American cities. Both pixel-based and city-specific analysis revealed a strong increasing trend in AOD with highest magnitude in Varanasi and Bangalore. Aerosol sub-types based on aerosols' sensitivity to UV-absorption and particle size denotes higher relative abundance of carbonaceous smoke aerosols within city-core, without having significant distinction for mineral dusts and urban aerosols.

Knowledge, attitudes, and perceptions of farmers towards urban agroecology in Malaysia.

Adopting agroecological approaches to build resilient urban food systems has recently gained traction around the world, but there is little to no reliable literature on the knowledge, attitudes, and perspectives of urban farmers towards these nature-based solutions in many developing nations, including Malaysia. The present study conducted an online survey to determine the extent to which local urban farmers understand and employ agroecology, as well as to assess their awareness and views on using agroecological practices and sustainable farm management. We found that the majority of respondents are unfamiliar with agroecological principles, with 79 % agreeing or strongly agreeing that implementing sustainable agricultural practices is challenging. However, more than 90 % of respondents are aware of the environmental consequences of excessive input utilisation. Our findings highlight the need for improved initiatives to promote agroecological approaches among farmers by sharing knowledge and best practices. In light of the growing threat posed by urban heat islands and the rapid urbanisation, this study offers novel insights into the knowledge gaps and perceptions about agroecological approaches among urban farmers, challenges that must be addressed to promote sustainable agriculture, and the potential role of farmers in achieving the three fundamental pillars of sustainability-planet, people, and prosperity.

Leveraging Generative AI Models in Urban Science.

Since the late 2000s, cities have emerged as the primary human habitat across the globe, and this trend is anticipated to continue strengthening in the coming decades. As we increasingly inhabit human-designed urban spaces, it becomes crucial to understanding better how these environments influence human behavior and how individuals perceive the city. In this chapter, we begin by examining the interplay between urban form and social behavior, highlighting key indicators of urban morphology, and presenting state-of-the-art methodologies for data collection. Subsequently, we harness the computational capability of foundation models, the latest Artificial Intelligence (AI) generation, to simulate interactions between individuals and urban built environments in a diverse group of 21 cities across the globe. Through this exploration, we scrutinize the models' capacity to encapsulate the intricate complexities of how individuals behave and perceive cities. These examples demonstrate the potential of advanced AI systems to assist urban scientists in understanding cities, emphasizing the necessity for a meticulous evaluation of their capabilities and limitations for the optimal application of Generative AI in urban research and policymaking.

Is Having Urban Green Space in the Neighborhood Enough to Make a Difference? Insights for Healthier City Design.

Background: Prior research indicates that engagement with nature is associated with mental well-being; however, the impact of accessibility to urban green spaces (UGS) with suitable infrastructure for visitation and physical activities, like leisure or recreation, remains underexplored, particularly in developing countries. Purpose: This study delves into whether merely having green space in the neighborhood is sufficient to impact residents' mental health in Brazilian metropolitan regions. Method: Utilizing a cross-sectional survey, data were collected from 2136 participants. The analyzed variables included the intensity, duration, and frequency of nature engagement, suitability of UGS for visitation and physical activities, and mental well-being indicators measured by the DASS-21 scale. Multivariate statistical analyses and multiple regression models were employed to verify hypothetical relationships. Results and conclusions: Higher intensity, duration, and frequency of nature engagement in UGS were significantly associated with lower depression, anxiety, and stress scores. Notably, having urban UGS in the neighborhood alone was not enough to reduce mental health issues. Practical implications: The findings point out the need for urban planning policies that prioritize the development of high-quality, accessible green spaces to maximize mental well-being benefits. These insights could inform city designs that foster healthier urban environments. Future directions: Longitudinal studies are needed to establish causality between nature engagement and mental health improvements. Further research should incorporate objective measures of nature engagement and explore more aspects of green space quality, such as biodiversity and amenities.

Synchronizing smart city nodes using Skew Integrated Timestamp (SIT).

Time synchronization among smart city nodes is critical for proper functioning and coordinating various smart city systems and applications. It ensures that different devices and systems in the smart city network are synchronized and all the data generated by these devices is consistent and accurate. Synchronization methods in smart cities use multiple timestamp exchanges for time skew correction. The Skew Integrated Timestamp (SIT) proposed here uses a timestamp, which has time skew calculated from the physical layer and uses just one timestamp to synchronize. The result from the experiment suggests that SIT can be used in place of multiple timestamp exchanges, which saves computational resources and energy.

Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities.

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities' IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

[To assess Resilience of Russian cities under international sanctions and the consequences of COVID-19: a sociological study].

The paper presents the results of in-depth interviews and a questionnaire survey of the Russian cites administration representatives about cities resilience under the sanctions pressure and COVID-19. The survey was conducted by the Center for Territorial Changes and Urban Development of IPEI RANEPA in March-May 2023, it was attended by representatives of the administration of more than 50 cities of the Russian Federation. We found overall situation as stable: social programs are being implemented in full, unemployment is decreasing, construction of municipal facilities continues, problems with failures in the supply of spare parts, equipment and components are being solved. At the same time, the sanctions have affected the urban economy in completely different ways: while in some cities show significant negative effect, in others the impact of sanctions is insignificant. Cites face number of new challenges: disruption of supply chains, refusal to supply paid equipment, inability to find analogues of imported equipment with the necessary characteristics, rising prices for spare parts. components and construction materials, the rupture of established sales channels to unfriendly countries, a drop in municipal budget revenues, etc. The heads of the city administration work overtime to solve emerging problems, organize interaction between enterprises, establish and deepen business contacts with friendly countries, put forward proposals to improve the situation at the federal level. New tasks successfully solved, although it requires serious efforts. To respond to new challenges, we need a new, more decentralized and local-oriented style of public administration, a process of well-established feedback.