Resource-constrained Environments Research Articles

Enhanced Intracellular Delivery via a Titanium-Coated TiO2 Microstructure Device: Leveraging an Infrared Laser for Optimal Efficiency.

This study presents a novel optoporation technique using a titanium-coated TiO2 microstructure (TMS) device activated by an infrared diode laser for highly efficient intracellular delivery. The TMS device, fabricated with 120 nm titanium coating on a titanium dioxide (TiO2) microstructure containing microneedles (height ∼2 μm and width ∼4.5 μm), demonstrates enhanced biocompatibility and thermal conductivity compared to the conventional TiO2 microstructure (MS). Exposure to the TMS device with an IR diode laser (980 nm) generates heat, forming photothermal bubbles that disrupt the cell membrane and create transient pores for biomolecular delivery. Unlike traditional optoporation methods, which rely on large, vibration-sensitive lasers, the IR diode laser-assisted TMS device-based optoporation technique offers a compact, cost-effective, and portable alternative, making it suitable for clinical and research applications in resource-constrained environments. The performance of the TMS and MS devices was compared in various cancer cell lines (HeLa, L929, and N2a), with the TMS device showing superior delivery success rates for biomolecules of varying molecular sizes. Notably, the TMS device achieved a 99.30% delivery success rate for the smallest molecule, PI dye, and an 85.17% success rate for the largest studied molecule, β-galactosidase enzyme-Cy5. Furthermore, the TMS device consistently provided a higher delivery success rate at lower laser power, minimizing cellular stress and preserving cell survivability. Moreover, using Western Blot analysis, the TMS device demonstrated lower levels of apoptosis compared to the MS device, with statistically significant differences, highlighting its potential for efficient intracellular delivery while minimizing cellular stress and damage. These results highlight the potential of the TMS device as an advanced tool for large-size intracellular biomolecular delivery, offering significant improvements in stability, efficiency, and cell survivability.

FeatherFace: Robust and Lightweight Face Detection via Optimal Feature Integration

Face detection in resource-constrained environments presents challenges, due to the computational demands of state-of-the-art models and the complexity of real-world conditions, such as variations in scale, pose, and occlusion. This study introduces FeatherFace, a lightweight face-detection architecture with only 0.49 M parameters, designed for high accuracy and efficiency in such environments. Leveraging MobileNet-0.25 as a backbone, FeatherFace incorporates advanced feature-integration strategies, including a bidirectional feature pyramid network (BiFPN), a convolutional block attention module (CBAM), deformable convolutions, and channel shuffling. Evaluated on the WIDERFace dataset, FeatherFace achieves an overall average precision (AP) of 87.2%, with notable performance gains of 4.0% AP on the Hard subset compared with the baseline. Ablation studies highlight the critical role of multiscale feature integration and the strategic placement of attention mechanisms in addressing detection challenges such as small or occluded faces. With its compact design and reduced inference time, FeatherFace bridges the gap between the reliability of computationally intensive models and the need for deploying robust models in highly resource-constrained environments, such as edge devices and embedded systems. This work provides valuable insights for developing robust and lightweight models suited to challenging real-world applications.

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

Enhancing seismic risk assessment through probabilistic analysis of liquefaction potential index: a computational intelligence approach

ABSTRACT This study proposes a novel computational intelligence approach for enhancing seismic risk assessment through probabilistic analysis of the liquefaction potential index (LPI). The methodology leverages a hybridised model that combines backpropagation neural networks (BcNN) with an improved firefly algorithm (IFF) to predict LPI values accurately and efficiently. The BcNN-IFF model was trained and validated using a comprehensive dataset comprising the geotechnical and seismic parameters from various earthquake-prone regions worldwide. Feature importance analysis revealed that total stress (σv’) and peak ground acceleration (amax) were the most influential factors in the LPI prediction. The BcNN-IFF model demonstrated superior performance compared to other hybrid models, achieving 91% accuracy in the training phase and 85% accuracy in the testing phase, with low root mean square error and mean absolute error values. The practical implications of the BcNN-IFF model highlights its potential for reliable liquefaction susceptibility assessments, particularly in resource-constrained environments. Future research directions are outlined, emphasising the need for uncertainty quantification, data augmentation, and computational efficiency enhancements to improve the applicability of the model in real-world scenarios. This study advances state-of-the-art computational modelling and provides a foundation for integrating such tools into geotechnical engineering practices for effective seismic risk assessment and infrastructure resilience.

Quality of Experience-Oriented Cloud-Edge Dynamic Adaptive Streaming: Recent Advances, Challenges, and Opportunities

The widespread adoption of dynamic adaptive streaming (DAS) has revolutionized the delivery of high-quality internet multimedia content by enabling dynamic streaming quality adjustments based on network conditions and playback capabilities. While numerous reviews have explored DAS technologies, this study differentiates itself by focusing on Quality of Experience (QoE)-oriented optimization in cloud-edge collaborative environments. Traditional DAS optimization often overlooks the asymmetry between cloud and edge nodes, where edge resources are typically constrained. This review emphasizes the importance of dynamic task and traffic allocation between cloud and edge nodes to optimize resource utilization and maintain system efficiency, ultimately improving QoE for end users. This comprehensive analysis explores recent advances in QoE-driven DAS optimization strategies, including streaming models, implementation mechanisms, and the integration of machine learning (ML) techniques. By contrasting ML-based DAS approaches with traditional methods, this study highlights the added value of intelligent algorithms in addressing modern streaming challenges. Furthermore, the review identifies emerging research directions, such as adaptive resource allocation and hybrid cloud-edge solutions, and underscores potential application areas for DAS in evolving multimedia systems. With the aim of serving as a valuable resource for researchers, practitioners, and decision-makers in addressing the challenges of resource-constrained edge environments and the need for QoE-centric solutions, this comprehensive analysis endeavors to promote the development, implementation, and application of DAS optimization. Acknowledging the crucial role of DAS optimization in improving the overall QoE for the end users, we hope to facilitate the continued advancement of video streaming experiences in the cloud-edge collaborated environment.

Vehicle-to-Everything-Car Edge Cloud Management with Development, Security, and Operations Automation Framework

Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention.

Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investigates the application of CHERI (Capability Hardware Enhanced RISC Instructions) to enhance the security of Intrusion Detection Systems (IDSs) in automotive networks. By leveraging CHERI’s fine-grained memory protection and capability-based access control, the IDS ensures the robust protection of rule configurations against unauthorized access and manipulation. Experimental results demonstrate a 100% detection rate for spoofed IP packets and unauthorized rule modification attempts. The CHERI-enabled IDS framework achieves latency well within the acceptable limits defined by automotive standards for real-time applications, ensuring it remains suitable for safety-critical operations. The implementation on the ARM Morello board highlights CHERI’s practical applicability and low-latency performance in real-world automotive scenarios. This research underscores the potential of hardware-enforced memory safety in mitigating complex cyber threats and provides a scalable solution for securing increasingly connected and autonomous vehicles. Future work will focus on optimizing CHERI for resource-constrained environments and expanding its applications to broader automotive security use cases.

Rapid and visual detection of hepatitis B virus using the ERA/Cas12f1_ge4.1-based lateral flow assay system.

Hepatitis B virus (HBV) is a major pathogen posing significant challenges to global public health, making early diagnosis critical for preventing severe liver diseases. We previously developed a fluorescent biosensor named PAM-dependent dsDNA Target-activated Cas12f1 Trans Reporter (PDTCTR). However, its reliance on specialized fluorescence equipment and lack of visual readout limited its application in resource-limited settings. To address these limitations, we developed a lateral flow assay (LFA) that integrates enzymatic recombinase amplification (ERA) with the Cas12f1_ge4.1 system. This approach enables the specific amplification of the HBV target gene through ERA and leverages the precise cleavage activity of Cas12f1_ge4.1 for enhanced signal amplification. The entire detection process is completed within 50 minutes, with results readily interpretable through visual inspection. The assay achieves a minimum detection limit of 100 copies per μL and demonstrates high specificity, showing no cross-reactivity with related viruses. In a validation study involving 71 clinical samples, the system achieved a sensitivity of 94.23%, specificity of 100%, and a kappa value of 0.90 compared to quantitative PCR (qPCR), indicating high reliability. This method thus shows promise as an effective tool for early HBV diagnosis, particularly suited for rapid, on-site detection in resource-constrained environments, and holds broad potential for diverse applications.

A digital twin solution for fault detection in time-critical IIoT applications

ABSTRACT IIoT sensor data plays a pivotal role in monitoring the industrial system’s health and identifying potential faults. However, traditional fault detection approaches often face challenges such as network latency, limited accuracy, and resource-intensive processing. This paper introduces an end-to-end Digital Twin solution that enhances fault detection for IIoT systems. The solution is powered by two key innovations: the integration of a Digital Twin architecture that leverages a collaborative cloud-edge approach for real-time monitoring, and the use of a lightweight two-phased machine-learning ensemble model optimized for resource-constrained environments. The great performance achieved across various fault scenarios demonstrates the effectiveness of the proposed approach. The model provides an average accuracy of 99.71% with a mere 4.8 ms of average estimation delay. These advancements ensure both high accuracy and rapid response times, providing a robust solution for proactive fault detection in dynamic industrial environments.

AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis

The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.

Control VS Flexibility: the Moderating Role of Culture on Leadership and Performance Measurement Relationship Toward SDGs

Objective: The objective of this study is to investigate the impact of organisational culture (OC) and leadership (LS) on performance measurement (PM) in the Yemen constriction sector. It aims to examine the direct effects of control-oriented culture (COC) and flexible-oriented culture (FOC) on PM and their moderating influence on the relationship between LS and PM. Theoretical Framework: This topic presents the main concepts and theories that underpin the research. The Competing Values Framework (CVF), leadership theory, and contingency theory stand out, providing a solid basis for understanding how OC and LS affect PM in the dynamic construction sector. Method: This research adopted a quantitative approach using a questionnaire survey tool. Data was collected from managers and engineers of 171 construction firms in Yemen and analysed using partial least squares structural equation modelling (SEM-PLS) and SPSS tools. Results and Discussion: The results reveal that while COC and FOC significantly affect PM, LS has no direct impact. In addition, the study found that COC plays a moderating role in the relationship between LS and PM. However, FOC does not have such an effect. Research Implications: The results underscore the importance of adopting culturally aligned leadership strategies to improve PM and address the unique challenges in Yemen’s construction industry. These strategies also contribute to achieving sustainable development goals (SDGs), especially improving infrastructure and creating job opportunities. Originality/Value: This study contributes to the theory by emphasising the complementary roles of flexibility and control in shaping PM. It offers practical recommendations for enhancing leadership and performance measurement in dynamic, resource-constrained environments.

A hybrid human fall detection method based on modified YOLOv8s and AlphaPose

To address the challenges of low detection accuracy of small objects and weak applicability of the multi-person fall action recognition applications, we propose a hybrid fall detection method based on modified YOLOv8s and AlphaPose called HFDMIA-Pose. Firstly, we use the modified Yolov8s as object detector. It uses SPD-Conv to preserve small object features and adds a small object detection layer, while using BCIOU as the loss function. These methods can effectively improve the accuracy of small object detection and significantly reduce the model size. Secondly, we improve the fall recognition accuracy by introducing a hybrid fall detection algorithm based on human skeletal nodes. Lastly, we build a multi-person fall detection dataset (MPFDD) to test the model’s effectiveness in multi-person scenarios. Validated on datasets Le2i and MPFDD, our method improves accuracy by 4.30%, F1 by 4.57%, and FPS by 37.50% faster than the AlphaPose. Compared with other models, our model improves accuracy by 5.33% on average, F1 by 5.51%, and FPS by 43.05% faster on average. Therefore, HFDMIA-Pose has significantly improved performance compared to the original model and it also demonstrates strong competitiveness over other advanced human fall detection models. Furthermore, it has the advantages of high detection accuracy, fewer model size, and fast speed, which makes it more suitable for resource constrained edge environments and can meet industrial and daily scenarios.

Joey: Supporting Kangaroo Mother Care with Computational Fabrics

Kangaroo Mother Care (KMC) is a low-cost, evidence-based intervention with proven benefits to both infants and their families. It refers to holding the infant with the infant's chest skin against the caregiver's chest skin in an upright positionc[2]. Based on a report from the World Health Organizationc[3], KMC benefits are evidenced by a 32% reduction in neonatal mortality. It has been adopted as a standard of care for infants with low birth weights worldwide, especially in resource-constrained environments. Continuous monitoring of KMC practices is clinically important. In particular, the duration of KMC sessions and infant vital signs during KMC hold significant clinical values.

SURGICAL SITE INFECTIONS IN LOW-RESOURCE SETTINGS: A FOCUSED STUDY ON THE INCIDENCE, CONTRIBUTING FACTORS, AND PREVENTION STRATEGIES FOR SURGICAL SITE INFECTIONS IN A RESOURCE-CONSTRAINED ENVIRONMENT

Background: Surgical Site Infections (SSIs) are a significant concern in healthcare, particularly in low-resource settings, where they contribute disproportionately to postoperative morbidity and mortality. These infections arise from systemic, procedural, and patient-related factors, with limited access to infection prevention resources exacerbating the issue. Addressing these challenges is essential to improving surgical outcomes and ensuring safer care in resource-constrained environments. This study aimed to explore the incidence, contributing factors, and prevention strategies for SSIs in a low-resource setting. Objective: To assess the incidence, contributing factors, and prevention strategies for surgical site infections in a resource-constrained environment. Methods: This descriptive observational study was conducted at Bolan Medical College, Quetta, from August to October 2024. Data were collected from 138 patients undergoing surgical procedures, excluding those with pre-existing infections or minor outpatient procedures. Patient demographics, comorbidities, nutritional status, surgical details, and infection control practices were recorded. SSIs were classified based on the CDC criteria and monitored for 30 days postoperatively through in-person evaluations and telephone follow-ups. Statistical analysis using SPSS v26 included descriptive statistics and logistic regression to identify associations between risk factors and SSI incidence. Results: The average patient age was 45.3 ± 12.7 years, with 75 males and 63 females. Comorbidities included diabetes (29%) and malnutrition (25%). SSIs occurred in 21.7% of patients, with superficial infections being most common (53.3%), followed by deep (33.3%) and organ/space infections (13.3%). Malnutrition had the highest SSI rate (40%), followed by diabetes (35%) and age over 50 years (30%). Procedure-related factors, such as surgery duration over 3 hours and emergency procedures, showed SSI rates of 35% and 28%, respectively. Contaminated and dirty wounds had SSI rates of 40% and 50%, respectively. Proper wound care and prophylactic antibiotics reduced SSI rates to 12% and 15%, respectively. Conclusion: SSIs pose a significant burden in low-resource settings due to systemic, procedural, and patient-related factors. Targeted interventions, such as improving infection prevention practices, healthcare infrastructure, and patient education, are critical to reducing SSI rates and enhancing surgical outcomes in resource-constrained environments.

Next-Generation Point-of-Care Diagnostics: Silver Nanoparticle-Enhanced 3D-Printed Multiplex Electrochemical Biosensor for Detecting Dengue and Chikungunya Viruses.

In recent years, the increasing prevalence of viral infections such as dengue (DENV) and chikungunya (CHIKV) has emphasized the vital need for new diagnostic techniques that are not only quick and inexpensive but also suitable for point-of-care and home usage. Existing diagnostic procedures, while useful, sometimes have limits in terms of speed, mobility, and price, particularly in resource-constrained environments and during epidemics. To address these issues, this study proposes a novel technique that combines 3D printing technology with electrochemical biosensors to provide a highly sensitive, user-friendly, and customizable diagnostic platform. This study focuses on a unique 3D-printed electrode cassette made with fused deposition modeling technology, which ensures strong structural alignment and improved performance under a variety of environmental conditions. When combined with paper-based electrodes loaded with silver nanoparticles, the platform dramatically enhances the detection sensitivity and reliability. The biosensor uses cyclic voltammetry and electrochemical impedance spectroscopy to detect DENV and CHIKV antigens within a linear range of 1 × 102 to 1 × 106 ng/mL. Results were delivered in 20 s and stable for 30 days. The device's performance was verified by testing with blood serum samples containing both DENV and CHIKV antigens, demonstrating its capacity to properly identify coinfections. This novel diagnostic tool represents a huge step forward in accessible and efficient healthcare solutions, bridging important gaps in the global battle against arboviral infections.

Impact of Imaging on Surgical Management of Penetrating Chest Trauma: Experience From a High-Volume Trauma Center in a Resource-Constrained Environment

Impact of Imaging on Surgical Management of Penetrating Chest Trauma: Experience From a High-Volume Trauma Center in a Resource-Constrained Environment

A Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation.

Video instance segmentation, a key technology for intelligent sensing in visual perception, plays a key role in automated surveillance, robotics, and smart cities. These scenarios rely on real-time and efficient target-tracking capabilities for accurate perception and intelligent analysis of dynamic environments. However, traditional video instance segmentation methods face complex models, high computational overheads, and slow segmentation speeds in time-series feature extraction, especially in resource-constrained environments. To address these challenges, a Dual-Channel and Frequency-Aware Approach for Lightweight Video Instance Segmentation (DCFA-LVIS) is proposed in this paper. In feature extraction, a DCEResNet backbone network structure based on a dual-channel feature enhancement mechanism is designed to improve the model's accuracy by enhancing the feature extraction and representation capabilities. In instance tracking, a dual-frequency perceptual enhancement network structure is constructed, which uses an independent instance query mechanism to capture temporal information and combines with a frequency-aware attention mechanism to capture instance features on different attention layers of high and low frequencies, respectively, to effectively reduce the complexity of the model, decrease the number of parameters, and improve the segmentation efficiency. Experiments show that the model proposed in this paper achieves state-of-the-art segmentation performance with few parameters on the YouTube-VIS dataset, demonstrating its efficiency and practicality. This method significantly enhances the application efficiency and adaptability of visual perception intelligent sensing technology in video data acquisition and processing, providing strong support for its widespread deployment.

A Lightweight Person Detector for Surveillance Footage Based on YOLOv8n.

To enable person detection tasks in surveillance footage to be deployed on edge devices and their efficient performance in resource-constrained environments in real-time, a lightweight person detection model based on YOLOv8n was proposed. This model balances high accuracy with low computational cost and parameter size. First, the MSBlock module was introduced into YOLOv8n. Then, a series of modifications were made to the MSBlock structure. Next, a heterogeneous PAFPN with improved MSBlock was formed using heterogeneous convolution kernels. Finally, AKConv, a variable kernel convolution, was applied to further reduce the number of parameters and the computational cost while improving accuracy. A series of experiments demonstrated that, due to these improvements, the proposed lightweight model achieved a reduction of nearly 10% in parameter size and 5% in the floating-point computational cost compared to the original YOLOv8n. Additionally, on a custom surveillance dataset, the model shows a 1.4% improvement in mAP@0.5:0.95, and on a more complex subset of the PASVOC public dataset, the model achieved a 2.8% improvement in mAP@0.5 and a 1.2% improvement in mAP@0.5:0.95, proving the high accuracy and generalization ability of the improved lightweight model.

Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa

Background There is limited knowledge regarding African genetic drivers of disease due to prohibitive costs of large-scale genomic research in Africa. Methods We piloted a scalable virtual genotyped cohort in South Africa that was affordable in this resource-limited context, cost-effective, scalable virtual genotyped cohort in South Africa, with participant recruitment using a tiered informed consent model and DNA collection by buccal swab. Genotype data was generated using the H3Africa Illumina micro-array, and phenotype data was derived from routine health data of participants. We demonstrated feasibility of nested case control genome wide association studies using these data for phenotypes type 2 diabetes mellitus (T2DM) and severe COVID-19. Results 2267346 variants were analysed in 459 participant samples, of which 229 (66.8%) are female. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. Of the 343 samples that passed QC, 93 participants had T2DM and 63 had severe COVID-19. For 1780 previously published COVID-19-associated variants, 3 SNPs in the pre-imputation data and 23 SNPS in the imputed data were significantly associated with severe COVID-19 cases compared to controls (p<0.05). For 2755 published T2DM associated variants, 69 SNPs in the pre-imputation data and 419 SNPs in the imputed data were significantly associated with T2DM cases when compared to controls (p<0.05). Conclusions The results shown here are illustrative of what will be possible as the cohort expands in the future. Here we demonstrate the feasibility of this approach, recognising that the findings presented here are preliminary and require further validation once we have a sufficient sample size to improve statistical significance of findings. We implemented a genotyped population cohort with virtual follow up data in a resource-constrained African environment, demonstrating feasibility for scale up and novel health discoveries through nested case-control studies.

Implementation of a Low-Cost, Real-Time Assessment System for Primary School Classrooms

The integration of technology in educational surroundings has evolved from a supplemental enhancement to an essential element of modern education. In response to this shift, there is a growing need for cost-effective tools that can increase student engagement and streamline the assessment process for primary students. This study presents the design and implementation of a low-cost, real-time assessment system tailored specifically for primary school classrooms. Utilizing Arduino-based receivers and student-operated remote controllers, the system provides an affordable and scalable alternative to traditional student response systems. Field tests were conducted with 26 students aged 9 to 10 in a public primary school, where the system's effectiveness was evaluated through teacher observations and student feedback. Results indicate that the system significantly increased student participation, focus, and enjoyment during assessments compared to traditional paper-based methods. Despite some challenges, the system demonstrates great potential as a practical tool for resource-constrained educational environments, with future development focusing on addressing scalability and technical limitations.