Remote Areas Research Articles

Revealing health and socioeconomic realities among individuals with sickle cell trait through case studies from remote areas of India

Revealing health and socioeconomic realities among individuals with sickle cell trait through case studies from remote areas of India

Economic Literacy and Human Development through Technology-Based Economic Education for Generation Z Students

Economic literacy and human development are two fundamental aspects in the effort to create a prosperous and competitive society. This study aims to explore the influence of economic literacy on human development through technology-based economic education for Generation Z students. Using a qualitative approach with a case study design, 43 students from the Faculty of Economics and Business at Universitas Negeri Makassar participated in this research. Data were collected through in-depth interviews and analyzed using a thematic analysis approach. The findings revealed that while most students possess a good basic understanding of economic concepts, challenges remain in applying these concepts to real-life situations. Technology in education has proven instrumental in enhancing economic understanding through tools like market simulations and e-learning platforms. However, key obstacles include unequal technological infrastructure, particularly in remote areas, and limited teacher competence in effectively integrating technology into the curriculum. Addressing these challenges is essential to ensure that economic literacy can significantly contribute to human development and the competitiveness of Generation Z in the future.

Unveiling Community Needs and Aspirations: Card Sorting as a Research Method for Developing Digital Learning Spaces

<p style="text-align:justify">This pilot study is part of a larger “Decolonization of Digital Learning Spaces” project, which aims to develop research tools for communities that are remote and/or excluded geographically, politically, economically, socially, culturally, and linguistically. The project’s ultimate goal is to work alongside these communities to design their own digital learning tools, networks, and online educational environments by accessing and leveraging their knowledge and skills. Testing the single-criterion card sorting method is the first step toward this goal. Card sorting is an easy, enjoyable, and cost-effective method for data collection and analysis, particularly for researchers working in remote areas with limited access to electricity or the Internet. The pilot explored single-criterion card sorting as a method to elicit knowledge from two diverse cultural and linguistic groups engaged in learning activities within their communities. These groups were from a Deaf and Hard of Hearing (DHH) community in Canada (engaged in a bow-making workshop) and a rural Kabyle community in Algeria (engaged in a traditional cooking lesson). Despite low participant numbers, distinct patterns emerged, indicating the method's effectiveness. The results, though anticipated, were non-random, demonstrating the potential of card sorting in producing patterns indicative of how individuals and/or communities categorize their world(s). Kabyle sortings focused on ingredients, highlighting older individuals as teachers passing along knowledge, while the DHH sortings emphasized face-to-face contact and hand movements in communication. The findings, though modest, established relationships, provided insights into the research context and offered logistical understanding, paving the way for further work with DHH and Kabyle communities towards the design of digital learning spaces.</p>

Compact highly sensitive photothermal RT-LAMP chip for simultaneous multidisease detection.

Developing instant detection systems with disease diagnostic capabilities holds immense importance for remote or resource-limited areas. However, the task of creating these systems-which are simultaneously easy to operate, rapid in detection, and cost-effective-remains a challenge. In this study, we present a compact highly sensitive photothermal reverse transcriptase-loop-mediated isothermal amplification (RT-LAMP) chip (SPRC) designed for the detection of multiple diseases. The nucleic acid (NA) amplification on the chip is achieved through LAMP driven by either LED illumination or simple sunlight focusing. SPRC performs sample addition and amplification within a limited volume and autonomous enrichment of NA during the sample addition process, achieving a limit of detection (LOD) as low as 0.2 copies per microliter. Through 120 clinical samples, we achieved an accuracy of 95%, with a specificity exceeding 97.5%. Overall, SPRC has achieved promising progress in the application of point-of-care testing (POCT) by using light energy to simultaneously detect multiple diseases.

Strong long ties facilitate epidemic containment on mobility networks

Abstract The analysis of connection strengths and distances in the mobility network is pivotal for delineating critical pathways, particularly in the context of epidemic propagation. Local connections that link proximate districts typically exhibit strong weights. However, ties that bridge distant regions with high levels of interaction intensity, termed strong long ties, warrant increased scrutiny due to their potential to foster satellite epidemic clusters and extend the duration of pandemics. In this study, strong long ties are identified as outliers on the joint distribution of distance and flow in the mobility network of Shanghai constructed from 1km×1km high-resolution mobility data. We propose a grid-joint isolation strategy alongside a reaction-diffusion transmission model to assess the impact of strong long ties on epidemic propagation. The findings indicate that regions connected by strong long ties exhibit a small spatial autocorrelation and display a temporal similarity pattern in disease transmission. Grid-joint isolation based on strong long ties reduces cumulative infections by an average of 17.1% compared to other types of ties. This work highlights the necessity of identifying and targeting potentially infected remote areas for spatially focused interventions, thereby enriching our comprehension and management of epidemic dynamics.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Computer-Aided Diagnosis of Early-Stage Retinopathy of Prematurity in Neonatal Fundus Images Using Artificial Intelligence.

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolution Neural Networks (CNN) based pre-trained networks. The characteristic demarcation lines and ridges in early stage ROP are segmented utilising a novel Swin U-Net. 2000 Scale Invariant Feature Transform (SIFT) descriptors were extracted from the segmented ridges and are dimensionally reduced to 50 features using Principal Component Analysis (PCA). Seven ROP-specific features, including six Gray Level Co-occurrence Matrix (GLCM) and ridge length features, are extracted from the segmented image and are fused with the PCA reduced 50 SIFT features. Finally, three ML classifiers, such as Support Vector Machine (SVM), Random Forest (RF), and k- Nearest Neighbor (k-NN), are used to classify the 50 features to predict the early-stage ROP from Normal images. On the other hand, the raw retinal images are classified directly into normal and early-stage ROP using six pre-trained classifiers, namely ResNet50, ShuffleNet V2, EfficientNet, MobileNet, VGG16, and DarkNet19. It is seen that the ResNet50 network outperformed all other networks in predicting early-stage ROP with 89.5% accuracy, 87.5% sensitivity, 91.5% specificity, 91.1% precision, 88% NPV and an Area Under the Curve (AUC) of 0.92. Swin U-Net Convolutional Neural Networks (CNN) segmented the ridges and demarcation lines with an accuracy of 89.7% with 80.5% precision, 92.6% recall, 75.76% IoU, and 0.86 as the Dice coefficient. The SVM classifier using the 57 features from the segmented images achieved a classification accuracy of 88.75%, sensitivity of 90%, specificity of 87.5%, and an AUC of 0.91. The system can be utilised as a point-of-care diagnostic tool for ROP diagnosis of neonates in remote areas.

Remote Assistance for Bone-Fractured Patients using Deep Learning Models

ABSTRACT Remote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in remote medical diagnostics is offering timely assistance to vulnerable groups like the elderly, disabled, mentally impaired individuals, and wounded military personnel in combat zones. This becomes particularly difficult in emergencies when rapid analysis of medical records is needed, especially if the data is stored on secure blockchain networks. The proposed work addresses these challenges by deploying a comprehensive framework for large-scale analysis, utilizing both document and image classification for dual validation. It integrates advanced techniques such as Inception V3, VGG-16, VGG-19, RESNET-50, and Densenet-201 for bone fracture detection, with Inception V3 achieving the highest accuracy of 95.1%. In addition, a Document Classification Analysis (DCA) method is proposed, which automatically classifies the severity of fractures. Object detection techniques are also introduced for detecting minor fractures using region-based image segmentation, ensuring precise diagnosis even for subtle injuries. This pioneering integration of technologies provides a holistic solution for remote medical diagnostics.

Environmental Factors to Malaria Incidence : A Literature Review

Malaria remains a major public health concern in Indonesia, particularly due to its high mortality rate among children under five, accounting for approximately two-thirds of all cases. The disease is transmitted by female Anopheles mosquitoes, which carry the Plasmodium parasite. In 2019, malaria caused 409,000 deaths globally, with 229 million cases reported, particularly in remote areas with poor access to healthcare, commonly in low-income countries. This literature review highlighted the environmental risk factors contributing to malaria incidence, based on studies from 24 scientific journals using data from the Sinta and Scopus databases. The study identified key environmental factors, including physical, chemical, biological, and socio-cultural elements, with the most significant risk linked to vector breeding sites. Among the reviewed articles, 62.5% (15 out of 24) found a statistically significant association between the presence of mosquito breeding sites and malaria incidence. Additional risk factors included the use of mosquito nets, housing conditions (specifically wall density), and nighttime outdoor activities. Effective malaria control measures require addressing these environmental factors, as they play a crucial role in the disease’s transmission. Strategies to disrupt the transmission cycle include eliminating mosquito breeding sites, applying larvicides to stagnant water, cleaning vector resting areas, promoting the use of insecticide-treated nets, and implementing Indoor Residual Spraying (IRS). A community-based, integrated approach to environmental management is essential to reducing malaria transmission and controlling its spread in affected regions.

Abstract 4129675: Effective Transcatheter Intracoronary Delivery of mRNA-Lipid Nanoparticle Targeting on The Heart

Introduction: mRNA has great potential to provide new medical innovations in the treatment of heart failure. Lipid nanoparticles (LNPs) are an established and effective mRNA delivery system. However, effectively delivering LNPs to the heart remains a significant challenge. We evaluated the efficacy of transcatheter intracoronary (IC) administration compared to intravenous (IV) and intramyocardial (IM) administration as methods for delivering. Methods: Normal rabbits were used to examine each route of administration. Fluorescence (ATTO 700)-labeled LNP encapsulating Firefly Luciferase (FLuc) mRNA (25 ug/kg) were used to confirm the in vivo mRNA-LNP dynamics. The LNP accumulation and FLuc expression were verified using an in vivo imaging system (IVIS) 4 hours post-administration, followed by immunohistochemical analysis. The same experiments were conducted in an ischemia-reperfusion (I/R) model. Results: In the normal model, IVIS spectrum data showed that LNPs accumulated in the order of IM, IC and IV groups, with FLuc expression significantly higher in the IC group than in the IV group and comparable to the IM group (A). Histological analysis revealed that FLuc-expressing cells were mainly found in troponin T-positive cardiomyocytes at the injection site in the IM group and throughout the heart in the IC group (B). In the I/R model, LNP accumulation and FLuc expression were increased in the IV group compared to the normal model, whereas, in the IC and IM groups LNP accumulation and FLuc expression levels were similar to those in the normal model (C). Histological analysis revealed more FLuc-expressing cells in the infarcted area in all groups, but higher FLuc expression was observed in remote areas in the IC group (D). Conclusions: IC administration effectively delivered mRNA-LNPs not only to the damaged area but also to the remote area (non-damaged area) in the diseased heart, suggesting a safe and useful method for delivery to a wider range of cardiomyocytes in the heart.

HoloLume: Point-of-Application Holographic Imaging Solution

Abstract Digital holographic imaging has emerged as a label-free, non-invasive, and powerful tool, offering a transformative method for the real-time investigation of morphology, density, and other physical and mechanical features of biological and non-biological samples. The rapid development of these portable, economical imaging systems for clinical/non-clinical setups, especially in remote areas, is of great interest. These systems are early point-of-care applications in diagnosis, research, education, and training. This article reports a general-purpose, fully automated portable imaging tool, "HoloLume," that offers high-resolution 3D imaging that facilitates the comprehensive understanding and analysis of different biological/non-biological samples. The proposed microscopic system HoloLume realizes the interferogram generation of samples using common-path optics configuration with Fresnel biprism, and its motorized stage precisely scans the whole sample with a step of 3.87 μm for quick identification of the region of interest, which makes sure a time-efficient and robust analysis. The prototype, assembled from off-the-shelf components, costs approximately USD 1700 and weighs around 3 kg. To validate and verify the performance of the HoloLume microscope, we have conducted experiments on both non-biological and biological samples. Thus, its non-invasive nature, high temporal stability, and economical lightweight prototyping make it an ideal choice for hands-on learning and quantitative analysis, reducing the need for time-consuming sample preparation and conventionally expensive microscopic systems. Furthermore, the adaptability of the proposed imaging tool to remote environments ensures wider accessibility and its easy integration with other imaging modalities enables the user to gain insight into advanced research and diagnostics.

Using School-Based Teleconsultation Services to Make Community Health Services Accessible in Semirural Settings of Pakistan: Sequential Explanatory Mixed Methods Study.

In Pakistan's remote areas, quality health care and experienced professionals are scarce. Telehealth can bridge this gap by offering innovative services like teleconsultations. Schools can serve as effective platforms for introducing these services, significantly improving health service access in semirural communities. This study aims to explore the feasibility of introducing school-based teleconsultation services (TCS) to strengthen community health in a semirural area of Karachi, Pakistan. This study used a mixed methods design. A total of 393 students were enrolled for the quantitative component, while 35 parents, teachers, and community stakeholders participated in the qualitative arm (focused group discussion). Proportional computation for the quantitative data was done using SPSS (version 24; IBM Corp), while qualitative data underwent thematic analysis. A total of 1046 successful teleconsultations were provided for 393 students over 28 months. The demographic data showed that the mean age of the students availing TCS was 9.24 (SD 3.25) years, with the majority being males (59.3%, 233/393). Only 1.24% (13/1046) of cases required referrals. The qualitative analysis yielded three themes: (1) transformation of the health care experience, (2) escalating demands for teleconsultation, and (3) the psychological aspect of care. This study demonstrated the efficacy of integrating TCS in a semiurban school in Karachi to address health care accessibility gaps. Implementing TCS through the school platform improved the overall health status of school children while reducing school absences and financial burdens on families. The study highlighted TCS's cost-effectiveness, time efficiency, and quality, with community support for 24/7 availability, expansion to adults, and a reimbursement model. School health nurse-led TCS offers a scalable solution to health care challenges, enhancing health outcomes for school-going children in Pakistan and globally, particularly in low- and middle-income countries, where accessibility is a major issue.

TRANSFORMATION OF ISLAMIC PHILANTHROPY BY THE IDRISIYYAH TARIQA IN THE DIGITAL ERA

<p align="center"><strong>Abstract</strong></p><p><strong> </strong></p><p><em>This article explores the transformational role of the Tarekat Idrisiyyah in the practice of Islamic philanthropy in the digital era through the Agen Care initiative. By utilizing information and communication technology, Agen Care has succeeded in increasing the efficiency of donation management, expanding the reach of social programs to remote areas, and increasing public awareness of the importance of social piety. Programs such as waqf, direct cash assistance, house renovation, education and health implemented by Care Agents have had a significant positive impact on the community. However, several technical and sustainability challenges are still faced. Implementation of these new strategies opens up opportunities to strengthen the role of the Idrisiyyah Tarekat as a relevant and effective agent of social change in the digital era. This article highlights how technology can strengthen and expand charitable activities, fulfill religious obligations, and contribute to creating a more prosperous and socially just society.</em></p><p> </p><p> </p>

The Challenges of the Kurikulum Merdeka Implementation in 3T Area

This study investigates the challenges of implementing the Kurikulum Merdeka in the disadvantaged, remote, and outermost (3T) areas of Maluku. The significance of this research lies in addressing the geographic, infrastructural, and educational accessibility issues in these 3T areas, which can hinder the effective implementation of the Curriculum. The research employs a descriptive qualitative methodology. The study engaged 40 respondents, including school principals, teachers from both public and private schools, training facilitators, and assistant program implementers. Data were collected through questionnaires and interviews and were then analyzed to identify recurring patterns, problems, challenges, and obstacles faced during implementation. The results indicate that despite their dedication to implementing the curriculum, nearly all schools struggle with the execution of the Kurikulum Merdeka. The main challenge identified is the inadequacy of teachers in designing and carrying out learning activities as required by the curriculum. This issue arises from various factors, including limited access to technology, insufficient training for principals and teachers, shortcomings in the mentoring mechanisms, and a lack of digital literacy among educators. Consequently, the government's mandate to implement this curriculum in all schools should be preceded by comprehensive preparation. Emphasis should be placed on enhancing teachers' capabilities through training sessions and workshops focused on planning, executing, and evaluating learning activities. Additionally, providing sufficient facilities and fostering a supportive learning environment are essential aspects that must be addressed. Keywords: implementation, Kurikulum Merdeka, disadvantaged, remote area

Advanced Control Scheme Optimization for Stand-Alone Photovoltaic Water Pumping Systems

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid.

Indigenous communities and tourism-integrated climate change outcomes on subsistence within the hinterlands: a development management perspective

PurposeThe study focused on rural commodities, climate change and tourism activities for socioeconomic welfare and enhanced productivity within disadvantaged indigenous communities and remote areas. It relates primary and secondary data details on the Musina Municipality’s rural biodiversity, tourism management and integrated pastoral livelihoods, alongside climate change issues locally and abroad.Design/methodology/approachThe study employs focus group discussions and interviews to gather data. Descriptive statistics, cross-tabulation analysis and central tendencies, along with manual data sorting, provide non-inferential data analysis.FindingsThis study discloses a harmful connection between unsustainable integrated rural activities and tourism products and climate change-linked environmental consequences within such environs. Ultimately, the study has highlighted the need for proper biodiversity resource management and an agricultural approach to diminish climate change hazards and permit the indigenous communities of the municipality. Thus, there is a need for awareness and practices in responsible tourism, decent rural ecotourism and agro-tourism for enhanced productivity and sustainability achievements.Originality/valueMany rural citizens in the world naturally live in low-income areas. Southern Africa and Africa, with specific reference to the Musina Municipality in Limpopo Province, South Africa, are no exception. Despite the abundance of assorted natural and cultural biodiversity and rural tourism qualities, such regions are nonetheless prone to climate change consequences and the deprivation of socioeconomic sustainability.

Magnetically Propelled Microrobots toward Photosynthesis of Green Ammonia from Nitrates.

Ammonia (NH₃) production is a critical industrial process, as ammonia is a key component in fertilizers, essential for global agriculture and food production. However, the current method of synthesizing ammonia, the Haber-Bosch process, is highly energy-intensive, and relies on fossil fuels, contributing substantially to greenhouse gas emissions. Moreover, the centralized nature of the Haber-Bosch process limits its accessibility in remote or resource-limited areas. Photochemical synthesis of ammonia, provides an alternate lower energy, carbon-free pathway compared to the prevailing industrial methods. The photoconversion of nitrate anions, often present in wastewater, offers a greener, more sustainable, and energy-efficient route for both ammonia-generation and wastewater treatment. Photochemical and chemical synthesis of ammonia requires intensive mass-transfer processes, which limits the efficiency of the method. To change the game, in this work, a key new technology of ammonia-generation, a catalytic ammonia generation (AmmoGen) microrobot, which converts nitrate to ammonia using renewable light energy is reported. The magnetic propulsion of the AmmoGen microrobots significantly enhances mass-transfer, and expedites the photosynthesis of ammonia. Overall, this "proof-of-concept" study demonstrates that microrobots can aid in catalytic small molecule activation and generation of value-added products; and are envisaged to pave the way toward new sustainable technologies for catalysis.

Cytogenetic data from remote areas of amazon: new karyotypes for spiny rats (Rodentia: Echimyidae)

Cytogenetic data from remote areas of amazon: new karyotypes for spiny rats (Rodentia: Echimyidae)

Smart Agriculture Crop Management Warehouse

In a developing country like India, agriculture is one the most important sectors in terms of financial gain and providing healthy foods to survive. Agriculture is the primary occupation in our country for ages and most of the population depend on it. Now-a-days farmers are facing huge losses due to some storage requirements, while storing agricultural products. A Farmer should have knowledge about the condition of their agricultural crops. Farmers are keeping their agricultural crops in warehouses to increase the lifetime of food grains. Food plays a significant vital role in life, once it involves food security that is littered with each food loss and food wastages. Reports suggests that almost fifty proportion of the food made world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation, or storage. One in all the point that threats to the farmers is that the loss throughout storage of crops in granaries and warehouses. If crop losses are reduced, it'll mechanically increase the number of food accessibility. To solve this problem one solution is that this project includes smart warehouse management and monitoring farming field, which includes temperature maintenance, humidity maintenance, fire alarms, stock measurement, Pest control. To assist and help the farmers who protects the crops by storing in warehouse, a fully automated enabled observation system is planned to be deployed in remote areas wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back food losses and increase food safety. Keywords: Agricultural crops monitoring and controlling, fully automated technology, Sensors, Warehouse management system.

HaptiScan: A Haptically-Enabled Robotic Ultrasound System for Remote Medical Diagnostics

Medical ultrasound is a widely used diagnostic imaging modality that provides real-time imaging at a relatively low cost. However, its widespread application is hindered by the need for expert operation, particularly in remote regional areas where trained sonographers are scarce. This paper presents the development of HaptiScan, a state-of-the-art telerobotic ultrasound system equipped with haptic feedback. The system utilizes a commercially available robotic manipulator, the UR5 robot from Universal Robots, integrated with a force/torque sensor and the Phantom Omni haptic device. This configuration enables skilled sonographers to remotely conduct ultrasound procedures via an internet connection, addressing both the geographic and ergonomic limitations faced in traditional sonography. Key innovative features of the system include real-time force feedback, ensuring that sonographers can precisely control the ultrasound probe from a remote location. The system is further enhanced by safety measures such as over-force sensing, patient discomfort monitoring, and emergency stop mechanisms. Quantitative indicators of the system’s performance include successful teleoperation over long distances with time delays, as demonstrated in simulations. These simulations validate the system’s control methodologies, showing stable performance with force feedback under varying time delays and distances. Additionally, the UR5 manipulator’s precision, kinematic, and dynamic models are mathematically formulated to optimize teleoperation. The results highlight the effectiveness of the proposed system in overcoming the technical challenges of remote ultrasound procedures, offering a viable solution for real-world telemedicine applications.