The Influence of Climate Factors on the Stability of Infrastructures: Case of Forest Ecosystem in Southern Cameroon

The southern forest ecosystem of Cameroon extends from latitude 2° to 6°N and from 10° to 16°E. In order to investigate about the influence of climate factors on the infrastructures, the main parameters of local climate of that ecosystem have been evaluated. The results show that, the factors are stable and have a common trend. But, the abundance of precipitations;the important hydrographic network and the presence of dense forest appear as the majors climatic constraints that may be taken in account to ensure the stability of the infrastructures in forest ecosystem.

An Appraisal of Water Availability, Infrastructures for Faecal Disposal and the Potential of Spread of Infectious Diseases in the Traveling Agencies and Motor Parks in the City of Yaounde (Cameroon)

The number of interurban and urban-rural travelers in the city of Yaounde has recently exceeded one hundred thousand per year, yet surprisingly few studies have assessed travelers’ behavior, illness, and risk factors in a sanitary risks setting. Particularly scarce are surveys of data spanning travel, return, and follow-up of the same cohort in traveling agencies and parks. This study examines behavior and illness among travelers moving from Yaounde to other urban and rural areas of Cameroon and beyond. Patterns of behavior connected to a type of travel and illness are characterized in this study so as to identify risks factors and provide background data for pre-travel advice in our traveling agencies. Assessing the susceptibility that imported cases could pose to the national response to infectious diseases depends both on public health, health services and infrastructures. The six agencies assessed: Nvan, Mokolo, Tongolo, Biyem Assi, Mimbowman and Etoudi reveal that the sanitary infrastructures for faecal and urinary disposal are not sufficient and more so, some agencies do not keep enough cleanliness, let alone provide adequate hand washing material as prescribed by national and international concerns to combat the global health challenges. This could be an impediment to travellers going out or inside of the city, and could be a preponderant mechanism for the spread of infectious disease as presented by the infectious diseases isolated and identified in the health districts around the travelling agencies.

Reliability analysis of geotechnical infrastructures: Introduction

In the design of geotechnical infrastructure,engineers have to deal with naturally occurring soils and rocks which are subjected to spatial variability as well as other uncertainties such as errors in measurement and in modeling methods.Reliability assessment which provides a systematic approach for quantifying the risk of failure has been shown to be a promising tool for solving these challenging geotechnical engineering problems.The method provides a more consistent measure of the level of safety or“structural reliability”through the evaluation of a reliability index and the associated“failure”probability,and is a method that satisfies the need to clearly convey safety issues to the public and regulatory authorities.Various methods for calculating the reliability of geotechnical infrastructures with regard to the assessment of the ultimate and serviceability limit states have been proposed by many researchers and these approaches include:the direct Monte Carlo Simulation,Bayesian and other sampling techniques,the first-order reliability method and the second-order reliability method,the random field method,the response surface method and other surrogate models with the related probabilistic procedures.In this special issue of Geoscience Frontiers,we assemble eleven invited papers which provide insights on the latest developments and challenges in applying probabilistic and reliability methods to geotechnical infrastructure design.

A Blockchain-Based Architecture for Enabling Cybersecurity in the Internet-of-Critical Infrastructures

Due to the drastic increase in the number of critical infrastructures like nuclear plants,industrial control systems(ICS),transportation,it becomes highly vulnerable to several attacks.They become the major targets of cyberattacks due to the increase in number of interconnections with other networks.Several research works have focused on the design of intrusion detection systems(IDS)using machine learning(ML)and deep learning(DL)models.At the same time,Blockchain(BC)technology can be applied to improve the security level.In order to resolve the security issues that exist in the critical infrastructures and ICS,this study designs a novel BC with deep learning empowered cyber-attack detection(BDLE-CAD)in critical infrastructures and ICS.The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network.In addition,the presented enhanced chimp optimization based feature selection(ECOA-FS)technique is applied for the selection of optimal subset of features.Moreover,the optimal deep neural network(DNN)with search and rescue(SAR)optimizer is applied for the detection and classification of intrusions.Furthermore,a BC enabled integrity checking scheme(BEICS)has been presented to defend against the misrouting attacks.The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects.The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.

An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

A Diachronic Perspective on the Use of French Spatial Data Infrastructures

Despite the recent development of many worldwide initiatives, there is still a need for the development of observation frameworks that will provide a comprehensive view of SDI’s use. Amongst the many challenges left, a thorough analysis of the information flows between existing SDIs as well as their respective uses and the way that those evolve over time is an important issue to explore. The research presented in this paper introduces a methodological framework oriented to the study of the SDIs use from a diachronic perspective. The approach is based on a Social Network Analysis (SNA) and questionnaires collected by online surveys. We develop a structural and diachronic analysis based on a series of graph-based measures identifying the main patterns that appear over time. The methodological framework is applied to a series of French SDIs and users involved in environmental management. The study identifies a series of structural differences in the data flows that emerge between the users and SDIs. Last, the diachronic network analysis provides an overall understanding on how data flows evolve over time at different institutional levels.

Research e-infrastructures for open science: The national example of CSTCloud in China

This paper focuses on research e-infrastructures in the open science era.We analyze some of the challenges and opportunities of cloud-based science and introduce an example of a national solution in the China Science and Technology Cloud(CSTCloud).We selected three CSTCloud use cases in deploying open science modules,including scalable engineering in astronomical data management,integrated Earth-science resources for SDG-13 decision making,and the coupling of citizen science and artificial intelligence(AI)techniques in biodiversity.We conclude with a forecast on the future development of research e-infrastructures and introduce the idea of the Global Open Science Cloud(GOSC).We hope this analysis can provide some insights into the future development of research e-infrastructures in support of open science.

Numerical evaluation of enhanced green infrastructures for mitigating urban heat in a desert urban setting

The cities of desert climates are anticipated to recognize a synergy of urban heat island(UHI)and severe heat waves during summertime.To improve the urban thermal environment,the present study aims quantitatively explore a strategically designed network of vegetation patches called green infrastructure(GI)in subtropical desert cities such as Dubai.To achieve a more comfortable temperature environment,we built and simulated four GI situations with higher GI fractions,GI25,GI50,GI75,and GI100.Using a mesoscale urban model,the mosaic approach is utilized to test potential thermal improvement and urban climate impact,and a portion of each urban grid cell in the model domain is altered with various species of urban vegetation patches by 25%,50%,75%,and 100%.The daily peak reduction in ambient temperature at 17:00LT is similar to 0.0168℃ per unit of GI increase when compared to the untreated scenario;however,the maximum anticipated daytime summer temperature decline for GI25,GI50,GI75,and GI100 is 0.6℃,1.1℃,1.4℃,and 1.7℃,respectively.The associated reduction in nighttime ambient temperature per unit increase in the GI is 0.0432℃,with a maximum temperature drop of around 2.4℃ for the GI100 scenario.Increased GI reduces the height of the planetary boundary layer(PBL)by up to 468 m,which might lead to greater pollution concentrations.While GI-based cooling has a significant influence on delayed sea breeze and humidity,it may raise the risk of heat discomfort in the indoor building environment.This study adds to our understanding of the potential for GI mitigation as well as the seasonal impact of developing GIs on the desert urban boundary layer.

Research on Logistics Network Infrastructures Based on DEA-PCA Approach:Evidence from the Yangtze River Delta Region in China

Compared with the extensive research on logistics network infrastructures(LNIs)in the developed world,empirical research is still scarce in China.In this paper the theory of LNIs is firstly overviewed.Then a new evaluation index system for LNIs is set up which contains factors that reflect the economic development level,transportation accessibility and turnover volume of freight traffc.An empirical study is carried out by using data envelopment analysis(DEA)and principal component analysis(PCA)approach to classify LNIs into 4 clusters for 25 cities in the Yangtze River Delta Region of China.According to the characteristics of the 4 clusters,suggestions are proposed for improving their LNIs.Finally,after comparing different LNIs of 25 cities in the Yangtze River Delta Region of China,this paper proposes that different LNIs including hub,central distribution center or cross docking center,regional distribution center or distribution center should be built reasonably in order to meet the customer's requirement in the four different cluster cities.

Impacts of road on plant invasions in the Middle Mountain region of central Nepal

Biological invasion is triggered by human development activities such as the construction and expansion of road networks.Road verges serve as important habitats and corridors for the distribution of invasive alien plant species(IAPS)between geographically distant habitats.However,the trajectory of plant invasion and the data regarding the impact of roads on IAPS distribution are relatively poor in Nepal.Here,we surveyed two road types(main roads and feeder road)in the Middle Mountain region of central Nepal in order to investigate how different road types are driving the dispersal of IAPS along road verges and the adjacent natural habitats.Systematic sampling was conducted at ca 2.5 km intervals along the roads.At each sampling site,paired plots(25 m×4 m)were sampled:one adjacent to and along the road,and another 20 m away and parallel to it in the interior habitat.Our results revealed that the main road verges had a higher cover(33%)and a larger number of IAPS(14 species)than the feeder road(25%;10 species).The IAPS cover and richness were significantly higher along verges than in the adjacent interior habitats for both road types,indicating that roads are contributing as corridors for the dispersal of IAPS in the Middle Mountain areas of central Nepal.Further,elevation,tree canopy,and disturbances(grazing/mowing/trampling)were found to be the key factors that determine spatial distribution of IAPS along road verges.We emphasize that regular monitoring of vegetation along the road verges can help with the early detection and control of potential IAPS in the region before they become problematic.

Enhancing the Trustworthiness of 6G Based on Trusted Multi-Cloud Infrastructure:A Practice of Cryptography Approach

Due to the need for massive device connectivity,low communication latency,and various customizations in 6G architecture,a distributed cloud deployment approach will be more relevant to the space-air-ground-sea integrated network scenario.However,the openness and heterogeneity of the 6G network cause the problems of network security.To improve the trustworthiness of 6G networks,we propose a trusted computing-based approach for establishing trust relationships inmulti-cloud scenarios.The proposed method shows the relationship of trust based on dual-level verification.It separates the trustworthy states of multiple complex cloud units in 6G architecture into the state within and between cloud units.Firstly,SM3 algorithm establishes the chain of trust for the system’s trusted boot phase.Then,the remote attestation server(RAS)of distributed cloud units verifies the physical servers.Meanwhile,the physical servers use a ring approach to verify the cloud servers.Eventually,the centralized RAS takes one-time authentication to the critical evidence information of distributed cloud unit servers.Simultaneously,the centralized RAS also verifies the evidence of distributed RAS.We establish our proposed approach in a natural OpenStack-based cloud environment.The simulation results show that the proposed method achieves higher security with less than a 1%system performance loss.

Thornthwaite moisture index and depth of suction change under current and future climate‒An Australian study

Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Characterization of Wastewater in School Environments for an Ecological Treatment Solution: A Case Study of Ndiebene Gandiol 1 School

The study conducted at Ndiebene Gandiol 1 school in Senegal has unveiled serious environmental and public health challenges. The wastewater analysis revealed high levels of Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and fecal coliforms, signaling potential risks to the well-being of students and staff. This situation mirrors a wider issue in rural educational settings, where inadequate sanitation persists. Intensive wastewater treatment options are known for their effectiveness against high pollutant loads but are resource-intensive in both energy and cost. Conversely, extensive treatment systems, while requiring more land, provide a sustainable alternative by harnessing natural processes for pollutant removal. The research suggests a hybrid treatment approach could serve the school’s needs, balancing the robust capabilities of intensive methods with the ecological benefits of extensive systems. Such a solution would need to be tailored to the specific environmental, financial, and logistical context of the school, based on comprehensive feasibility studies and stakeholder engagement. This study’s findings underscore the urgency of addressing sanitation in schools, as it is intrinsically linked to the health and academic success of students. Quick, effective, and long-term strategies are vital to secure a healthier and more prosperous future for the youth. With proper implementation, the school can transform its sanitation facilities, setting a precedent for rural educational institutions in Senegal and similar contexts globally.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Study on the Safety and Prevention Technology of Coal Mining under the River in Xingyuan Coal Mine

Coal mining-induced surface subsidence poses significant ecological and infrastructural challenges, necessitating a comprehensive study to ensure safe mining practices, particularly in underwater conditions. This project aims to address the extensive impact of coal mining on the environment, infrastructure, and overall safety, focusing on the Shigong River area above the working face. The study employs qualitative and quantitative analyses, along with on-site engineering measurements, to gather data on crucial parameters such as coal seam characteristics, roof rock lithology, thickness, water resistance, and structural damage degree. The research encompasses a multidisciplinary approach, involving mining, geology, hydrogeology, geophysical exploration, rock mechanics, mine surveying, and computational mathematics. The importance of effective safety measures and prevention techniques is emphasized, laying the foundation for research focused on the Xingyun coal mine. The brief concludes by highlighting the potential economic and social benefits of this project and its contribution to valuable experience for future subsea coal mining.

Assessment of Barriers and Challenges to the Screening and Linkages of Non-Communicable Diseases by Community Health Volunteers in Nyeri County, Kenya

Non-communicable diseases (NCDs) are a significant global health challenge, contributing to 50% of worldwide morbidity and 63% of mortality. The burden is particularly substantial in low—and middle-income countries (LMICs), where 80% of NCD-related deaths occur. A quasi-experimental study addressed this challenge from May 2022 to March 2023. This study utilized a non-equivalent pre-and post-test design, with 300 participants in the quantitative and 70 in the qualitative. The study employed multistage cluster and random sampling to select ten community units, resulting in 150 community health volunteers (CHVs) in the control unit and 150 in the intervention group. Data collection was facilitated through the KOBO app. Qualitative data analysis involved six homogeneous focus group discussions (FGDs) and ten key informant interviews (KIIs), audio-recorded, transcribed, and analyzed using N-Vivo 12. Despite efforts to implement screening programs and improve linkages to care, significant barriers persist. This article reviews these barriers, drawing on current literature and empirical evidence. Key obstacles identified include limited awareness, inadequate healthcare infrastructure, cultural beliefs, financial constraints, fragmented healthcare systems, and challenges linking individuals to appropriate care services. The article explores strategies to overcome these barriers, emphasizing the importance of collaborative approaches involving stakeholders at various levels. Addressing these challenges aims to strengthen NCD screening and linkages to care, ultimately improving health outcomes for populations globally. Several recommendations emerge from the study’s findings and literature review. Raising awareness about NCDs and preventive measures is crucial and can be achieved through targeted health education campaigns and community outreach programs. Addressing healthcare infrastructure deficiencies, such as inadequate facilities and workforce shortages, is essential to ensure access to quality care. Cultural beliefs and practices also play a significant role in shaping health-seeking behavior. Engaging with local communities and incorporating cultural sensitivity into healthcare delivery can help bridge the gap between traditional beliefs and modern healthcare practices. Financial constraints pose a significant barrier to healthcare services, particularly in LMICs. Innovative financing mechanisms, such as health insurance schemes or subsidies, can help alleviate this burden and improve access to care. Furthermore, the fragmented nature of healthcare systems can hinder effective NCD management. Enhancing coordination and integration between primary care providers, specialists, and community health workers is essential to ensure seamless care delivery and patient follow-up. Finally, strengthening linkages between screening programs and care services is critical for the timely diagnosis and management of NCDs. This requires establishing robust referral systems and ensuring continuity of care for patients throughout their healthcare journey. In conclusion, addressing the multifaceted barriers to NCD screening and care linkage is essential for improving health outcomes globally. By implementing targeted interventions and fostering collaboration among stakeholders, progress can be made towards reducing the burden of NCDs and promoting population health.

Exploring University Campus Community’s Perceptions and Barriers toward Biking: A Case Study of the University of Tennessee Knoxville

Bicycling constitutes an integral component of sustainable non-motorized means of travel. Despite the immense benefits of bicycling, the modal share of bicycling is quite low, specifically among young and low-income commuters e.g., college and university students. This study explores the perceptions of and barriers to bicycling of the campus community at the University of Tennessee, Knoxville with the aim to identify the factors that could enhance bike use among students at the university campus as well as highlight the barriers that keep young population away from biking. An online survey about bike use at the university campus was administered and responses to the survey were recorded. On the methodological front, a binary logit model was estimated with students’ willingness to use bikes at campus as the dependent variable. The survey results revealed that about 47% of the students considered vehicular traffic as the reason for feeling unsafe while biking and emphasized having separate bike routes. Most of the respondents highlighted the need to change clothes after biking in summer or carry things as the reason for not selecting bikes as a mode of transport at the campus, whereas inadequate bike lanes, paths, or trails, insufficient lighting along bike routes, and the lack of adequate bicycle parking were highlighted as the major barriers deterring bike use within the campus. The study suggests development of a campus bike network with proper bike lanes, bike runnels, installation of safety cameras and better lighting at the campus, and bike-supporting infrastructure such as communal showers to be provided in campus buildings to encourage bike use among university students. The study findings can assist campus transportation planners in devising a sustainable campus transportation plan incorporating the influencing factors and deterrents of bike use in a university campus setting.

Strategic Placement of Charging Stations for Enhanced Electric Vehicle Adoption in San Diego, California

California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.

Metropolitan Housing Development in Urban Fringe Areas-A Case Study of Three Metropolitan Cities of South Africa: Johannesburg, Ekurhuleni and Tshwane

Purpose:The metropolitan cities of Johannesburg,Ekurhuleni and Tshwane play an important role in the economy of the Gauteng province in South Africa.The region constitutes to 22.4 percent of the total population of South Africa and has a strong presence and contributes in areas of manufacturing sector,financial and business services,retail and wholesale trade,etc.The rapid urban population,increase in the informal settlements and socio-economic opportunities has resulted in considerable urban sprawl in and around the urban fringe areas of these metropolitan cities.The urban fringe areas of these metros often come under the influence of rapid urbanization process and pressures.Coupled with the economical and potential land dynamics and lack of priority of spatial development guidelines,these areas attract rapid and haphazard development from communities and developers.Research Design/Methodology:This research is based on a qualitative approach through a comprehensive literature review that included content analysis of key documents on housing sector such as IDPs(Integrated Development Plans),Municipal Annual Reports,Growth Development Strategies,and among other sectoral documents on housing sector.Some of the key priority issues considered in the housing sector included:eradication of housing backlogs,spatial restructuring of housing,provision of choice in terms of location,tenure and housing typology.Findings:The current paper discusses the approaches of metropolitan housing development processes in three metropolitan cities of South Africa from Gauteng region,namely:Johannesburg,Ekurhuleni and Tshwane.The paper discusses the existing housing sectoral scenario along with the fringe areas in three cities with focus on:formal and informal settlements,housing segregation and the backlogs,current institutional arrangements,role of public private participation,and scope for alternate mechanisms.The paper concludes in discussion on sustainable development options for housing development in urban fringe areas.