Remote Areas Research Articles

A robust neural network for privacy-preserving heart rate estimation in remote healthcare systems

In this study, we propose a computationally-light and robust neural network for estimating heart rate in remote healthcare systems. We develop a model that can be trained on consumer-grade graphics processing units (GPUs), and can be deployed on edge devices for swift inference. We propose a hybrid model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architectures for estimating heart rate from Electrocardiogram (ECG) and Photoplethysmography (PPG) signals. Considering the sensitive nature of the ECG signals, we ensure a formal privacy guarantee, differential privacy, for the model training. We perform a tight accounting of the overall privacy budget of our training algorithm using the Rényi Differential Privacy technique. We demonstrate that our model outperforms state-of-the-art networks on a benchmark dataset for both ECG and PPG signals despite having a much smaller number of trainable parameters and, consequently, much smaller training and inference times. Our CNN-BiLSTM architecture can also provide excellent heart rate estimation performance even under strict privacy constraints. We develop a prototype Arduino-based data collection system that is low-cost, efficient, and useful for providing access to modern healthcare services to people living in remote areas.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

Chemical contamination affecting filter-feeding bivalves in no-take marine protected areas from Brazil

Marine protected areas (MPAs) are zones geographically delimited under pre-defined management goals, seeking to reduce anthropogenic threats to biodiversity. Despite this, in recent years reports of MPAs affected by chemical contamination has grown. Therefore, this study addresses this critical issue assessing legacy and current chemical contamination in filter-feeder bivalves obtained in very restrictive no-take MPAs from Brazil. The detected pollutants encompass polycyclic aromatic hydrocarbons (PAHs), linear alkylbenzenes (LABs), and persistent organic pollutants (POPs) like dichlorodiphenyltrichloroethane (DDTs) and polychlorinated biphenyls (PCBs). Despite protective measures, bivalves from nine MPAs exhibited high LABs (13.2–1139.0 ng g−1) and DDTs levels (0.1–62.3 ng g−1). PAHs were present in low concentrations (3.1–29.03 ng g−1), as PCBs (0.7–6.4 ng g−1), hexachlorobenzene (0.1–0.2 ng g−1), and Mirex (0.1–0.3 ng g−1). Regardless of the sentinel species, MPAs and management categories, similar accumulation patterns were observed for LABs, DDTs, PAHs, and PCBs. Based on the limits proposed by Oslo Paris Commission, the measured levels of PAHs, PCBs and were below the environmental assessment criteria. Such findings indicate the no biological effects are expected to occur. However, they are higher considering background conditions typically measured in remote or pristine areas and potential simultaneous exposure. Such findings indicate an influence of anthropogenic sources, emphasizing the urgency for monitoring programs guiding strategic management efforts to safeguard these areas.

Transforming communities: Analyzing the effects of infrastructure and tourism development on social capital, livelihoods, and resilience in Gilgit-Baltistan, Pakistan

This study explores the dynamic relationship between social capital, livelihoods, and tourism development in Gilgit-Baltistan, Pakistan, focusing on the indigenous social capital influenced by cultural perspectives. Given the rapid infrastructure and tourism developments in Gilgit-Baltistan, understanding their profound impacts on local communities is crucial, as these changes pose unique challenges and offer unprecedented opportunities for sustainable development. It particularly assesses the effects of infrastructure developments, such as the Karakoram Highway and the Belt and Road Initiative, from 1978 to 2022. Through non-probabilistic sampling and contextual textual analysis, this qualitative research investigates how these developments transform community social capital, affect livelihoods and resilience, and alter community perceptions and adaptations. The findings highlight transformative impacts on livelihoods and community dynamics, intensified by international and domestic tourism and infrastructure growth. These transformations lead to challenges like cultural commodification and authenticity issues, with unregulated tourism growth straining local infrastructure and exacerbating sustainability challenges—evident during the COVID-19 pandemic. The study underscores the critical interplay between infrastructure, tourism, and community resilience, advocating for regulated development to balance economic growth with cultural and environmental preservation. Contributing significant theoretical insights and practical guidelines, the research stresses managing the pace and mechanisms of tourism development to maintain the resilience and sustainability of these isolated communities while outlining future research directions on vulnerabilities and resilience strategies in remote tourism areas.

Techno-economic study of a hybrid photovoltaic-diesel system for a remote area in southwest Algeria

Exploiting natural sun and wind resources in remote and isolated areas is undoubtedly an excellent decision to generate electrical energy due to their availability and cleanliness. Various systems were used to generate this energy, such as photovoltaics (PV), wind turbine sand other energy systems. Moreover, for optimum energy use, some of these systems are combined either with each other or with other conventional systems, such as diesel generators with PV systems (i.e., hybrid systems). This work aims to present a technical-economic study of PV/diesel autonomous hybrid systems to supply electrical power for an isolated house located in a hot desert climate, Adrar. For optimizing the hybrid systems, hourly input data of solar radiation and load were used according to two configurations, where the annual load is 11.2 kWh/day. The findings showed that the diesel system had a high cost, with a cost for energy of $0.407/kWh and a fuel price of $0.140/l. Among the hybrid power systems but with significant pollution, the proposed hybrid system 2 kw photovoltaic and diesel generator with 2.3 kW has important economic feasibility, where the energy cost amounted to $0.172/kWh. In addition, CO2 emissions are reduced by approximately 5 tons every year compared to an independent diesel generator system.

An exploration of barriers to access to healthcare in Hancock County, Tennessee: A qualitative study.

Explore barriers to healthcare access in Hancock County, Tennessee using a conceptual framework for access to healthcare. We collected data from 30 participants in Hancock County during 1 week in April 2023 using a combination of network and purposive sampling. We analyzed the data using thematic analysis and the conceptual framework of healthcare access. All dimensions of the conceptual framework of healthcare access presented barriers to healthcare access for participants of the study. A lack of acceptability of local healthcare among participants manifested in a perceived lack of availability of healthcare. This resulted in participants travelling or considering it necessary to travel long distances for care, even in a life-threatening emergency, despite the local availability of a hospital with an emergency department. A lack of acceptability can create healthcare access barriers similar to a lack of availability of healthcare facilities. The research team met several times with the leader of a local community organization to discuss this research in Hancock County. These conversations helped to inform the study design and provided necessary background to conduct in-depth interviews. Members of the community organization helped identify individuals to interview and provide access to Remote Area Medical clinic patients. The research team discussed the final themes with the primary community collaborator.

Innovative leadership strategies for sustainable tourism: A case study of Moroccan Daraa-Tafilalt oases

In the contemporary digital era, sustainable tourism development in remote regions, particularly in the Moroccan Daraa-Tafilalt Oases, necessitates innovative leadership strategies. This study investigates how leadership fosters innovation and resilience within these remote areas to promote sustainable tourism. Employing a mixed-methods approach, the research delves into effective leadership strategies, stakeholder engagement, and the integration of innovative practices in the digital age, before and after strategies implementation. The results for each stage underscore the importance of visionary leadership, community involvement, and adaptive approaches in navigating the complexities of remote tourism development amidst digital transformations. Additionally, the study identifies cultural sensitivity, environmental factors, and the rational exploitation of local resources as key factors contributing to successful leadership in fostering sustainable tourism. The insights gleaned from this research offer valuable guidance for policymakers, industry stakeholders, and local communities striving for sustainable development in remote oasis regions in the digital era.

Understanding experiences, unmet needs and priorities related to post-stroke aphasia care: stage one of an experience-based co-design project

ObjectivePeople with post-stroke aphasia (language/communication impairment) and their supporters report mixed satisfaction with stroke and aphasia care. To date, however, their journey of care and the key service interactions that...

Prevalence of dyslipidemia, hypertension and diabetes among tribal and rural population in a south Indian forested region.

While NCDs are in rise globally, tribal and rural populations residing near to reserve forests with limited exposure to modern lifestyles may bear a unique burden. This study investigates the prevalence and risk factors of hypertension, diabetes, and dyslipidemia among these communities. We conducted a cross-sectional study between 2018 and 2020 in the forest-dwelling population of Chamarajanagar, India. Using multistage stratified sampling based on caste and remoteness, we enrolled 608 participants aged over 18 years, including 259 non-tribal and 349 tribal individuals. Data collection includes the administration of STEPS questionnaire and measurement of fasting blood sugar, lipid levels, and blood pressure. The prevalence of diabetes, hypertension, and dyslipidemia were 4.6%, 28.8%, and 85.7%, respectively, among the study population. We also found abnormal levels of low-density lipoprotein (LDL), high-density lipoprotein (HDL), Triglycerides (TGA), Total cholesterol (TC), and very low-density lipoprotein (VLDL)in 4.9%, 82.4%, 22.7%, 5.8%, and 7.4% of participants, respectively. Significant differences were observed in diabetes, LDL abnormality, TGA abnormality, VLDL abnormality, and TC abnormality, but not in hypertension, dyslipidemia, or HDL abnormality, across the Socio Geographic Discrimination Index. We found a significant difference in diabetes and HDL abnormality, but not in hypertension, dyslipidemia, LDL abnormality, TGA abnormality, TC abnormality, or VLDL abnormality, between tribal and non-tribal populations living in the forest-dwelling area. Waist circumference was a significant independent predictor of diabetes among tribal participants, while wealth index, age, and waist circumference were significant predictors of hypertension. There were no significant predictors for dyslipidemia among tribal participants. Our study suggests that tribal population living in a remote area are at a lower risk of developing diabetes compared to non-tribal populations living in the same geographic area. However, the prevalence of hypertension and dyslipidemia among tribal populations remains high and comparable to that of the general population.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

UAV-Based Delivery Systems: A Systematic Review, Current Trends, and Research Challenges

The rising popularity of drones significantly impacts package delivery services, offering both unique opportunities and challenges. This survey explores the diverse applications of drones for last-mile deliveries, highlighting their capacity to access remote areas and create new business prospects. Use cases, ranging from critical medical deliveries to addressing COVID-19 pandemic needs, underscore the transformative potential of drone technology. While recognizing drones’ eco-friendly attributes in eliminating harmful gas emissions, the survey addresses battery constraints, necessitating an investigation into physical energy models to extend flight autonomy. This becomes crucial for operational capabilities, especially in adverse weather conditions. A reliable communication infrastructure is crucial for the success of drone operations in package delivery, especially during unexpected events, as seamless connectivity plays a key role in facilitating efficient control and monitoring between ground stations and drones. This enables dynamic rerouting, enhancing overall delivery reliability. The survey explores innovative approaches, including collaborations with other vehicles like trucks, trains, and buses, optimizing the last-mile delivery process. Despite the transformative potential, concerns about privacy, security, safety, and risk management in drone delivery are acknowledged. The work also emphasizes responsible and ethical implementation, considering diverse concerns associated with widespread adoption. In contrast to existing survey articles focused on specific technical aspects, this comprehensive survey broadens its scope. It covers ethical issues, sustainability aspects, healthcare systems, physics models, innovative approaches, reliable communications, security and safety concerns, and real test-beds in drone-based delivery systems. The survey not only identifies potential applications and tackles technical challenges but also integrates broader considerations. In addition, this work extensively explores the motivations, lessons learned, and future directions in the realm of drone delivery. Analyzing existing literature, it provides valuable insights for researchers, industry professionals, policymakers, and stakeholders keen on understanding the dynamic evolution of drone technology in the package delivery domain.

The Past, Present, and Future of the Internet: A Statistical, Technical, and Functional Comparison of Wired/Wireless Fixed/Mobile Internet

This paper examines the quantitative and qualitative situation of the current fixed and mobile Internet and its expected future. It provides a detailed insight into the past, present, and future of the Internet along with the development of technology and the problems that have arisen in accessing and using broadband Internet. First, the number of users and penetration rate of the Internet, the various types of services in different countries, the ranking of countries in terms of the mean and median download and upload Internet data speeds, Internet data volume, and number and location of data centers in the world are presented. The second task introduces and details twelve performance evaluation metrics for broadband Internet access. Third, different wired and wireless Internet technologies are introduced and compared based on data rate, coverage, type of infrastructure, and their advantages and disadvantages. Based on the technical and functional criteria, in the fourth work, two popular wired and wireless Internet platforms, one based on optical fiber and the other based on the 5G cellular network, are compared in the world in general and Australia in particular. Moreover, this paper has a look at Starlink as the latest satellite Internet candidate, especially for rural and remote areas. The fifth task outlines the latest technologies and emerging broadband Internet-based services and applications in the spotlight. Sixthly, it focuses on three problems in the future Internet in the world, namely the digital divide due to the different qualities of available Internet and new Internet-based services and applications of emerging technologies, the impact of the Internet on social interactions, and hacking and insecurity on the Internet. Finally, some solutions to these problems are proposed.

IoT-based Tracking Cattle Health Monitoring System Using Wireless Sensors

Maintaining the health of dairy cows is essential to raising the production of dairy products worldwide. Due to the numerous health problems that cows face, the unpredictability of disease outbreaks, and the high cost of producing new animals, dairy farmers are beginning to lose faith in the industry. To increase milk production, producers must thus employ effective technological methods for monitoring the health of their cows. This research examined many automated wireless sensor-based dairy cow health monitoring systems. The primary goal of smart surveillance systems based on wireless sensor networks (WSNs) in agricultural optimization is to continuously monitor the health of dairy cows. For interested farmers to keep an eye on their cattle's whereabouts throughout the day from many locations, this monitoring device needs to be deployed in both local and remote agricultural areas. A database would hold the information gathered by the automated system. Farmers can then use farm automation to acquire data so they can implement efficient farm management practices. Moreover, WSN is a low-cost tool designed specifically to detect diseases in dairy cows. This technological advancement in agricultural automation would help increase output by lowering the need for human intervention. This page provides an overview of all livestock tracking methods along with a summary of the problems and difficulties they face

Evaluation of the Effectiveness of Government Policy on MSMEs Through Empirical Data Analysis in Lampung

Micro, Small, and Medium Enterprises (MSMEs) play an important role in the Indonesian economy, including in Lampung Province. Although the government has issued various policies to support the development of MSMEs, the effectiveness of these policies still needs to be evaluated in depth. This research aims to evaluate the effectiveness of government policies in supporting MSMEs in Lampung through empirical data analysis. The research uses an empirical data analysis approach to evaluate the effectiveness of MSME support policies in Lampung. The analytical method in this research uses literature study through collecting and analyzing secondary data in the form of journal articles, government documents, and other scientific articles about entrepreneurship and MSMEs sourced from libraries. It is hoped that the results of this research will provide a comprehensive evaluation of the effectiveness of government policies in supporting MSMEs in Lampung, as well as identifying supporting and inhibiting factors for policy success. This research found that government policy in supporting MSMEs in Lampung had a positive impact in encouraging the growth of the MSME sector in Lampung. However, there are still challenges that still need improvement, namely lack of socialization, and limited access for MSMEs in remote areas. Keywords: empirical analysis, government policy, MSMEs

Ultrasonography screening of hepatic cystic echinococcosis in sheep flocks used for evaluating control progress in a remote mountain area of Hejing County, Xinjiang

BackgroundAlthough ultrasonography (US) has been widely used in the diagnosis of human diseases to monitor the progress of cystic echinococcosis (CE) control, the screening method for hepatic CE in sheep flocks requires adjustment. In this study, we used a US scanner to screen sheep flocks and evaluated the efficacy of dosing dogs once a year with praziquantel for 7 years from 2014 to 2021.MethodsAll sheep in the three flocks were screened using an ultrasound scanner in 2014 and compared with the prevalence of infection in 2021 in Bayinbuluke, Xinjiang, China. Sheep age was determined using incisor teeth. Cyst activity and calcification were determined using US images. The dogs were dewormed with praziquantel once a year to control echinococcosis in the community.ResultsThree flocks had 968 sheep in 2014, with 13.22%, 22.62%, 18.7%, 27.27%, 11.88%, and 6.3% of sheep aged 1, 2, 3, 4, 5, and ≥ 6 years old, respectively. US scanning revealed that the overall CE prevalence was 38.43% (372/968), with active cysts and calcified cysts present in 9.40% (91/968) and 29.02% (281/968) of the sheep, respectively. For the young sheep aged 1 and 2 years, the prevalence of active and calcified cysts was: 1.56% and 0.91%, and 10.94% and 18.72%, respectively. Approximately 15.15% and 16.52% of the 4- and 5-year-old sheep, respectively, harbored active cysts. There was no significant difference in the infection rates of sheep between 2014 and 2021 (P > 0.05).ConclusionsUS is a practical tool for the field screening of CE in sheep flocks. One-third of the sheep population in the flocks was 1–2 years old, and these sheep played a very limited role in CE transmission, as most of the cysts were calcified. Old sheep, especially culled aged sheep, play a key role in the transmission of CE. Dosing dogs once a year did not affect echinococcosis control.

Feasibility of Wind Energy Utilization for Sustainable Power Generation in Ilorin, Kwara State, Nigeria's North-Central Region

The escalating energy demands across Nigeria, especially in remote rural areas, have outpaced the capacity of the national electricity grid, necessitating the development of independent and sustainable energy sources. Among the renewable options, wind energy stands out as a promising solution. This study focuses on assessing the potential of wind energy in Ilorin, located in Kwara State, within Nigeria's north-central region. Utilizing data collected from 2007 to 2021 by the Nigerian Meteorological Agency, the research examines monthly average wind speeds at two specific coordinates in Ilorin, considering variations in air density. The study utilizes a 15-year set of monthly average wind velocities obtained from the Nigerian Meteorological Agency (NiMet) Headquarters in Abuja, measured at a height of 10 meters above ground level. By employing the 2-coefficient Weibull statistical model and extrapolation principles across different altitudes ranging from 150 to 900 meters above ground level, the study reveals distinct seasonal patterns of wind speeds ranging from 1.1 to 5.1 m/s in Ilorin. Furthermore, wind power density values ranging from 6.7 to 39.20 W/m2 are identified, with optimal wind attributes observed at altitudes exceeding 900 meters. These findings provide valuable insights for assessing the feasibility of wind energy utilization and designing efficient systems in Nigeria's north-central regions, aiding in the sustainable energy transition.

Enzyme-Linked Immunosorbent Assay Using Henipavirus-Receptor EphrinB2 and Monoclonal Antibodies for Detecting Nipah and Hendra Viruses

The Nipah virus (NiV) and the Hendra virus (HeV) are highly pathogenic zoonotic diseases that can cause fatal infections in humans and animals. Early detection is critical for the control of NiV and HeV infections. We present the development of two antigen-detection ELISAs (AgELISAs) using the henipavirus-receptor EphrinB2 and monoclonal antibodies (mAbs) to detect NiV and HeV. The NiV AgELISA detected only NiV, whereas the NiV/HeV AgELISA detected both NiV and HeV. The diagnostic specificities of the NiV AgELISA and the NiV/HeV AgELISA were 100% and 97.8%, respectively. Both assays were specific for henipaviruses and showed no cross-reactivity with other viruses. The AgELISAs detected NiV antigen in experimental pig nasal wash samples taken at 4 days post-infection. With the combination of both AgELISAs, NiV can be differentiated from HeV. Complementing other henipavirus detection methods, these two newly developed AgELISAs can rapidly detect NiV and HeV in a large number of samples and are suitable for use in remote areas where other tests are not available.

Exploring wind energy for small off-grid power generation in remote areas of Northern Brazil

Exploring wind energy for small off-grid power generation in remote areas of Northern Brazil

Use of digital technologies for staff education and training programmes on newborn resuscitation and complication management: a scoping review

BackgroundPoor-quality care is linked to higher rates of neonatal mortality in low-income and middle-income countries (LMICs). Limited educational and upskilling opportunities for healthcare professionals, particularly those who work in remote...

Establishment of a reverse transcription–recombinase polymerase amplification–lateral flow dipstick method for the dual detection of Israeli acute paralysis virus and chronic bee paralysis virus

IntroductionAs an important social insect, honey bees play crucial roles in agricultural production, sustainable development of agricultural production, and the balance of the natural environment. However, in recent years, Israeli acute paralysis virus (IAPV) and chronic bee paralysis virus (CBPV), the main pathogens of bee paralysis, have continuously harmed bee colonies and caused certain losses to the beekeeping industry. Some beekeeping farms are located in wild or remote mountainous areas, and samples from these farms cannot be sent to the laboratory for testing in a timely manner, thereby limiting the accurate and rapid diagnosis of the disease.Methods and resultsIn this study, we used a reverse transcription–recombinase polymerase amplification–lateral flow dipstick (RT–RPA–LFD) method for the dual detection of IAPV and CBPV. RPA primers and LFD detection probes were designed separately for their conserved genes. Primers and probes were screened, and the forward and reverse primer ratios, reaction times, and temperatures were optimized. According to the results of the optimization tests, the optimal reaction temperature for RT–RPA is 37°C, and when combined with LFD, detection with the naked eye requires <20 min. The developed RPA–LFD method specifically targets IAPV and CBPV and has no cross-reactivity with other common bee viruses. In addition, the minimum detection limit of the RT–RPA–LFD method is 101 copies/μL.ConclusionBased this study, this method is suitable for the detection of clinical samples and can be used for field detection of IAPV and CBPV.