Low Resource Research Articles

Peranan Pengelolaan Keuangan sebagai Pertanggungjawaban dalam Pembelanjaan Dana Desa Sesuai Standar Akuntansi Pemerintah di Desa Bah Jambi III Kecamatan Tanah Jawa Kabupaten Simalungun

This study aims to identify and describe the Role of Financial Management as Accountability in Spending Village Funds According to Government Accounting Standards in Bah Jambi III Village, Tanah Jawa District, Simalungun Regency. Method The approach in this research is a qualitative approach with a descriptive research type. The data sources used are primary data obtained by interviews and direct observation and secondary data obtained from related documents. The research location is located in Bah Jambi III Village, Tanah Jawa District, Simalungun Regency. The results of this study indicate that: (1) The village financial management mechanism is regulated in Law Number 6 of 2014 concerning Villages which explains that there are 5 cycles of financial management mechanisms, namely. planning, implementation, administration, reporting, accountability. (2) While the obstacles faced by the village government are low human resource capacity, disbursement of village funds, government. (3) In Application of Government Accounting Standards No. 01 in village financial accountability. Reporting and financial accountability have not implemented Government Accounting Standards No. 01, because the Financial Report for accountability made by Bah Jambi III Village

Cellular Internet of Things: Principles, Potentials and Use Cases

Internet of things (IoT) can either be deployed over existing cellular networks or their own custom-built standalone networks. Based on the infrastructure, IoT can be classified into two types: cellular and non-cellular categories. In the cellular form, IoT network needs the support of cellular infrastructure of the mobile service providers. Currently, three forms of cellular IoT are being deployed across the world. They are: narrowband Internet of things (NBIoT), extended coverage GSM (EC-GSM) and long term evolution for machines (LTE-M). Out of these three, NBIoT and EC-GSM are low energy and low resource consuming versions of cellular IoT. They need narrow bandwidths for their operations. Their energy consumption is also very low and thus suitable for low energy applications. Both NBIoT and EC-GSM are compatible with all types of cellular communication infrastructure such as 2G, 3G, 4G and 5G. They can cover a large area with a very small amount of power. Both these forms are popular low power wide area (LPWA) technologies. Due to their LPWA features, they are popular for the connected living applications at home and workplace surroundings. Their LPWA features make them popular green technology for digital transformation. LTE-M uses comparatively larger bandwidth and higher power. It is suitable for higher bandwidth and higher data rate applications. We survey the recent literature on cellular IoT and present their key principles, potentials and applications. We provide their main characteristics, deployment options, standards, and some specific applications in different sectors.

A Cross Sectional Study of the Prevalence of Retinopathy in Patients with Chronic Kidney Disease on Hemodialysis in a Tertiary Care Centre in Chengalpattu District, South India

Context: Many studies have reported varying proportions of retinopathy among cases with chronic kidney disease (CKD). Cases with severe grades of CKD were more likely to develop ocular complications, especially diseases which occurs due to small vessel pathology. Aim: To estimate the prevalence of various types of retinopathies in patients with CKD undergoing hemodialysis in a tertiary care centre in South India. Settings and Design: The outpatient Department of Ophthalmology and Hemodialysis unit of Nephrology, Karpaga Vinayaga Institute of Medical Sciences from January 2022 to June 2023 A cross-sectional hospital-based observational study in a low resource setting. Methods and Material: The study consists of 28 patients who were known cases of CKD on hemodialysis. Patients underwent complete ocular evaluation and findings were noted. The pre hemodialysis values of blood urea and serum creatine, hemoglobin, liver function tests and vital parameters were also noted. Data analysis was done utilising version 20 of Statistical Packages for the Social Sciences (SPSS). Results: 56 eyes of 28 patients on hemodialysis were studied. The mean age was 53 years + 10.9 with a male preponderance. 31.6% had retinopathy of which 26.3% had hypertensive retinopathy and 5.3% had diabetic retinopathy. This is contrary to the previous studies that have reported a higher incidence of diabetic retinopathy in patients with end stage CKD. Hypertension resulted in CKD earlier than diabetes. Conclusion: The results emphasize the importance of frequent eye examination in patients with CKD to identify retinopathy earlier, thus potentially averting permanent vision impairment.

Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe.

Step counting devices were previously shown to be efficient in a variety of applications such as athletic training or patient's care programs. Various sensor placements and algorithms were previously experimented, with a best mean absolute percentage error (MAPE) close to 1% in simple mono-activity walking conditions. In this study, an existing running shoe was first instrumented with an inertial measurement unit (IMU) and used in the context of multi-activity trials, at various speeds, and including several transition phases. A total of 21 participants with diverse profiles (gender, age, BMI, activity style) completed the trial. The data recorded was used to develop a step counting algorithm based on a deep learning approach, and further validated against a k-fold cross validation process. The results revealed that the step counts were highly correlated to gyroscopes and accelerometers norms, and secondarily to vertical acceleration. Reducing input data to only those three vectors showed a very small decrease in the prediction performance. After the fine-tuning of the algorithm, a MAPE of 0.75% was obtained. Our results show that such very high performances can be expected even in multi-activity conditions and with low computational resource needs making this approach suitable for embedded devices.

Uncovering the silent epidemic: Insulin resistance and some associated risk factors among pregnant women with gestational diabetes mellitus (GDM) in a low resource setting

The present study sought to identify insulin resistance and related risk factors in pregnant women with gestational diabetes mellitus (GDM). One hundred and eighty-one pregnant women (34 with GDM and 147 without GDM) participated in the study. GDM screening involved a 75g OGTT test, while a structured questionnaire was used to gather socio-demographics data, and HOMA IR was used to assess insulin resistance. The study identified a 19.2% prevalence of GDM. Multiple regression analysis revealed that the studied risk factors accounted for 94% of GDM development. Additionally, the ROC curve demonstrated the wealth index as a significant factor for GDM development. The study found a high prevalence of GDM among pregnant women in low-resource settings, highlighting the need for targeted interventions to address the elevated occurrence. Socioeconomic factors such as low income and education were found to increase the risk of GDM

Decoding healthcare strategies: providing comprehensive care for type II diabetes in Ecuador

Abstract Background Diabetes is a major global health challenge, impacting millions and disproportionately affecting low- and middle-income countries (LMICs), where approximately three-quarters of affected individuals reside. Despite abundant guidance on diabetes management, most evidence is derived from high-resource settings, potentially impeding effective application in low-resource settings. This study explored issues related to the application of the current clinical practice guidelines (CPG) for diabetes management in Ecuador, from the perspective of local health professionals. Methods We developed 5 focus groups in Quito, the capital of Ecuador, in a low-income district. The groups included doctors, nurses, and health promoters; they discussed four sections of the CPG concerning their daily practice. Qualitative results are contrasted with quantitative research based on patient records. Results The Ecuadorian health system considers patients with diabetes a priority group, yet a patient may wait up to 4 months to receive their first appointments and once they receive their diagnosis several criteria set out in the CPG are not followed due to lack of time and resources, particularly the delivery of multidisciplinary attention. Access to specialized consultations, such as an ophthalmological assessment is severely restricted, and it can take up to a year to get an appointment. Clinicians expressed difficulties in adapting the nonpharmacological recommendations to different socioeconomic backgrounds, while at times, prescription choices are determined by medicine stock regardless of the patient’s actual needs. Conclusions Healthcare professionals have developed a series of ad-hoc strategies to ensure healthcare for their patients, pointing out the difficulties of applying CPG within a health system that lacks basic resources. Healthcare is intertwined with social inequalities among the Ecuadorian population that have worsened since the covid19 pandemic. Key messages • We identified significant challenges in applying the CPG in low resource settings. Clinicians adapt by introducing novel ways to overcome limitations at times to the detriment of the patient’s health. • CPGs need to be tailored to the context considering socioeconomic realities of the population and limited resources in the health setting.

Respiratory Syncytial Virus Infection in Children with Bronchiolitis

Background: Respiratory Syncytial Virus (RSV) is the most common organism causing bronchiolitis. Bronchiolitis is almost always diagnosed clinically. As a result, the availability of bedside sensitive rapid RSV antigen tests is critical for timely RSV acute respiratory infection diagnosis. Methods and materials: This study was conducted to detect RSV antigen positivity and the sociodemographic profile of children with RSV antigen positive bronchiolitis. From October 2022 to March 2023, this observational study was conducted in the pediatrics department of Chattogram Medical College Hospital. Ninety-two nasopharyngeal swabs from children younger than two years, were tested for RSV antigen. The mean and standard deviation were used to express the demographic data. Chi-square analysis was used to examine differences in group proportions. Results: RSV antigen positivity was detected in 45.7% of the 92 cases, with a mean age of 4.18±3.08 months. Severe bronchiolitis cases were more common (22.8%) among antigen positive cases and had the longest hospital stay. During the months of November (42.4%) and December (29.3%), RSV infection levels peaked. This study describes that during the seasonal epidemics, RSV was the main concern, as the number of positive cases decreased in the months that followed. The signs and symptoms of RSV positive and negative cases were not significantly different. Conclusion: A simple, low-cost and bedside RSV test can be considered as a valuable diagnostic tool in a low resource setting. J Bangladesh Coll Phys Surg 2024; 42: 368-378

Diagnostic and prognostic value of ECG-predicted hypertension mediated left ventricular hypertrophy using machine learning

Abstract Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH). Using cardiac magnetic resonance (CMR) imaging, four distinct hypertension mediated LVH phenotypes have been reported: normal LV, LV remodelling, eccentric LVH and concentric LVH. Early detection of hypertensives with LVH can enable timely initiation of therapy however a CMR based screening strategy is costly, particularly in low resource settings. The electrocardiogram (ECG) is an inexpensive screening tool and can detect LVH but its capacity to discriminate between the 4 phenotypes is unknown. Purpose To classify hypertension mediated LVH phenotypes from the ECG using machine learning (ML) and test for associations of ECG predicted LVH phenotypes with incident cardiovascular outcomes. Methods ECG markers with a known physiological association with LVH (including QRS markers, Sokolow-Lyon, and Cornell) were extracted from the 12-lead ECG of 20,439 hypertensive participants in UK Biobank. Three classification models, logistic regression, support vector machine (SVM) and random forest, were trained in 80% of the participants using extracted ECG markers and clinical variables. The remaining 20% individuals were included in the test set for performance measurement. External validation was sought in 877 hypertensives from the Study of Health in Pomerania (SHIP). Subsequently, we tested for associations between the four ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure using Cox proportional hazard regression in the UK Biobank test set with median follow-up of 4.2 years. Results Among UK Biobank participants (mean age ± standard deviation 65 ± 7.4 years), 23,190 had normal LV, 894 LV remodelling, 218 eccentric and 103 concentric LVH. Classification performance of the three models was comparable, but SVM showed consistent results (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) and superior prediction of eccentric LVH (AUC 0.86). In SHIP (55 ± 12.7 years), 704 had normal LV, 134 LV remodelling, 12 eccentric and 24 concentric LVH. In the external validation performed in SHIP, SVM had accuracy of 0.75 (sensitivity 0.51, specificity 0.85, AUC 0.65), with superior prediction of eccentric LVH (AUC 0.76), as shown in Figure 1. Ventricular rate and QRS amplitude (P<0.001) were the features most strongly associated with LVH. In the test set of UK Biobank, setting normal LV as the reference group, the ECG predicted eccentric LVH group was associated with heart failure (Hazard Ratio 3.42, confidence interval 1.06-9.86), there were no associations with MACE (Figure 2). Conclusions ECG-based ML classifiers were able to discriminate the four hypertension mediated LVH phenotypes with external validation demonstrating robustness. This automated approach enhances capabilities of non-specialists and potentially represents an accessible screening strategy for the early detection of hypertensives with LVH.

Family Vulnerability: From concept to a scale validity and application

Abstract This activity will last 20 minutes. An interactive presentation will be structured at three steps. First, a debate will be facilitated to engage participants on the following questions: i. What is family vulnerability? ii. Why should we measure family vulnerability at the context of Primary Health Care (PHC)? Second, once concepts are aligned, a brief presentation about the Vulnerability Family Scale for Brazil (EVFAM-BR) will be performed. At this time, participants will have the opportunity to know about the process of development, validation and the final version of EVFAM-BR (Souza et al., 2023, available at https://doi.org/10.1371/journal.pone.0280857). Third, presenters will share step by step how EVFAM-BR can be implemented into PHC, considering two real-world experiences in Brazil, Southeast and North regions, and will discuss about possible strategies to tailor it at different cultural and geographic context, from settings with higher vulnerability and low resources to high technology settings.

Physical unclonable functions and QKD-based authentication scheme for IoT devices using blockchain

As the number of Internet of Things (IoT) devices is increasing exponentially, strong security measures are needed to guard against different types of cyberattacks. This research offers a novel IoT device authentication technique to mitigate these challenges by integrating three cutting-edge technologies namely blockchain technology, Quantum Key Distribution (QKD), and Physically Unclonable Functions (PUFs). By utilizing the distinctive qualities of PUFs for device identification and the unrivaled security of QKD for key exchange, the proposed approach seeks to address the significant security issues present in IoT environments. Adopting blockchain technology ensures transparency and verifiability of the authentication process across distributed IoT networks by adding an unchangeable, decentralized layer of trust. An examination of the computing and communication costs reveals that the proposed protocol is effective, necessitating low computational resources that are critical for IoT devices with limited resources. The protocol’s resistance against a variety of attacks is demonstrated by formal proofs based on the Real-Or-Random (ROR) model and security evaluations using the Scyther tool, ensuring the integrity and secrecy of communications. Various threats are analyzed, and the protocol is proven to be secure and efficient from all forms of attacks.

Working conditions of commercial drivers: a scoping review of psychosocial work factors, health outcomes, and interventions

BackgroundPsychosocial work factors significantly influence both organisational and worker health. Poor management of these factors can create precarious working conditions, risking drivers’ health. This review maps evidence on the health impact of these factors and health interventions targeting the working conditions and unhealthy habits of commercial drivers.MethodThe search was conducted in four main databases (PubMed, Central, JSTOR and Dimensions Ai) and other sources like Google Scholar. In All, 28,039 articles were retrieved and through a rigorous screening process, 68 records were included in this scoping review.ResultsThis review found that drivers work in precarious conditions like long driving hours, low job resources and social support, low job control, poor remuneration, workplace abuse and sexual harassment, work-family conflict, lone driving hour, irregular shift work, lack or insufficient breaks during work hours and difficult access to social protection and sanitation facilities. These precarious working conditions may expose drivers to mental health issues, cardiovascular diseases, HIV/AIDs, stroke, chronic fatigue, kidney and bladder issues and musculoskeletal pains. Most health promotion interventions target behaviour at the individual level, such as the benefits of a healthy diet and exercise, with little effort to improving working conditions.ConclusionEmployers in the road transport sector need to implement health promotion interventions that focus on drivers’ well-being. Additionally, improving working conditions and enforcing occupational health and safety standards in the road transport sector are essential for creating a safe and healthy workplace for all commercial drivers.

Barriers and facilitators for universal gestational diabetes Mellitus screening in a low resource setting: a cross-sectional study in Sri Lanka

Gestational Diabetes Mellitus (GDM) is associated with adverse maternal and perinatal outcomes and increased risk of developing diabetes in later life. Sri Lanka adopted universal GDM screening at two time points in pregnancy (before 12 weeks and between 24 and 28 weeks) in 2014 and its utilization has not been assessed. This study assessed the utilization of GDM screening services and associated factors among antenatal mothers in Southern Sri Lanka. A cross-sectional study was carried out in a consecutive sample of 420 postpartum mothers who delivered at three major hospitals in Sri Lanka. Data were collected on socio-demographics, pregnancy, availability and utilization of screening services and logistics using an interviewer-administered questionnaire and a data record sheet. Data was analysed using SPSS software. Chi-square test and binary logistic regression was used to assess the association between variables. The coverage of first and second screening tests were 91.4% and 94.5% and timeliness were 72.4% and 59.5%, respectively. The median period of amenorrhoea at the first and second screening were 10.0 (inter-quartile range: 2.5) weeks and 28.0 (inter-quartile range: 1.5) weeks. A higher utilization was associated with higher income (OR = 3.4, 95% CI: 1.1–10.5) and planned pregnancy (OR = 4.9, 95% CI: 2.2–10.7) for the first screening test. Primiparity (OR = 0.3, 95% CI: 0.1–0.9) and proximity of the nearest laboratory (OR = 3.5, 95% CI: 1.0–12.0) were positively associated with the second screening test. The timeliness of both screening tests was associated with planned pregnancy (OR = 2.1, 95% CI: 1.1-4.0 and OR = 2.3, 95% CI: 1.1–4.5) and being non-employed during the pregnancy (OR = 2.5, 95% CI: 1.4–4.3 and OR = 2.3, 95% CI: 1.3–4.1). A high utilization of GDM screening was observed. Timeliness should be ensured through improved availability and accessibility of screening facilities.

Low Resource Summarization using Pre-trained Language Models

With the advent of Deep Learning-based Artificial Neural Network models, Natural Language Processing (NLP) has witnessed significant improvements in textual data processing in terms of its efficiency and accuracy. However, the research is mostly restricted to high-resource languages such as English, and low-resource languages still suffer from a lack of available resources in terms of training datasets as well as models with even baseline evaluation results. Considering the limited availability of resources for low-resource languages, we propose a methodology for adapting self-attentive transformer-based architecture models (mBERT, mT5) for low-resource summarization, supplemented by the construction of a new baseline dataset (76.5k article, summary pairs) in a low-resource language, Urdu. Choosing news (a publicly available source) as the application domain has the potential to make the proposed methodology useful for reproducing in other languages with limited resources. Our adapted summarization model urT5 with up to 44.78% reduction in size as compared to mT5 can capture contextual information of the low-resource language effectively with an evaluation score (up to 46.35 ROUGE-1, 77 BERTScore) on par with state-of-the-art models in the high-resource language of English (PEGASUS: 47.21, BART: 45.14 on XSUM Dataset) . The proposed method provided a baseline approach toward extractive as well as abstractive summarization with competitive evaluation results in a limited resource setup.

Two decades of progressive cost reduction: A paradigm shift for distributed solar photovoltaics and energy efficiency

The increasing deployment of renewable energy resources has led to massive energy cost reductions worldwide in the past decade. The emergence of this cost revolution led electricity consumers to increasingly adopt distributed renewable energy resources to decrease dependence on traditional power grids. This paper applies the integrated resource planning framework, the objective of which is to design a least-cost electricity system by looking at renewable energy resources, efficient appliances, and demand response management strategies to reduce electricity bills. The results show that the commercial entity can save its electricity bill by $0.16 if it installs 6 MW solar PV over the lifetime of the solar PV plants. Besides, it was observed that wind turbines were not economically feasible to install at the site because of low wind resources. Biogas power plant is too expensive mainly because of the cost of fuel (waste). It also shows that by retrofitting 2000 compact fluorescent lamps (CFLs) with light-emitting diodes (LEDs), the company can save $0.15 million. By shifting between 0.5 MW and 1.4 MW of heating and cooling demand to periods of low tariff costs, the company can save $0.013 million annually. The climate transition plan for this company relies on PV, efficiency interventions and demand response. The study also demonstrates that an integrated resource planning framework can be used to plan a mini-grid.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

Can Game-Based Learning Help to Increase the Awareness of Water Management in Uganda? A Case Study for Primary and Secondary Schools

In the Ugandan curriculum, theoretical teaching predominates in conveying water-related topics due to its efficiency and low resource requirements. However, this teacher-centred approach limits student interaction and collaborative learning. Conversely, game-based learning can enhance social skills, critical thinking, and engagement, yet it remains underutilised in Ugandan education. To address this gap, this study developed three educational games: Water Conservation Snakes and Ladders (WCSL), Water Awareness Quartet Cards (WAQC), and Water Pollution Puzzle (WPP). These games were implemented in four schools—three primary and one secondary—where students engaged in competitive, team-based play. The impact of these games on learning outcomes was assessed through pre- and post-test questionnaires. Descriptive and thematic analysis indicated a significant positive effect, with WAQC showing a 25% greater influence on performance than WCSL (18%) and WPP (14%). The findings also underscored the importance of age in determining game acceptability and enjoyment. To enhance education on water-related issues, it is recommended that these topics be integrated into both Science and Social Studies curricula and taught consistently across academic terms. Ultimately, the Ugandan government should consider incorporating game-based learning strategies into schools to improve student engagement and learning in water resource management.

Loneliness in Older Adults Living in Puerto Rico During the COVID-19 Pandemic: An Underexamined Public Health Concern

Few studies have examined the mental health of older adults in low resource settings during the COVID-19 pandemic. We used a negative binomial regression with a log linear link function to model the effects of demographic, health, and social factors and common pandemic-related experiences on loneliness in older adults in Puerto Rico. Data are from a 2021 Knowledge, Attitudes, and Practices survey about COVID-19 with persons aged 60+ who reside in Puerto Rico ( N = 213). Almost 40% of study participants met the loneliness cutoff score (≥6) on the UCLA Three-Item Loneliness Scale. Age, psychological distress, and psychological sense of community were associated with loneliness. We discuss cultural and contextual factors (e.g., familism, outmigration after Hurricane María, under-resourced healthcare system) that may influence loneliness. We conclude that culturally consonant and contextually feasible public health measures should focus on key correlates in developing loneliness interventions for older adults in Puerto Rico.

Political Labeling on Social Media: a Netnographic Study of “SDM Rendah” in Indonesia’s 2024 Presidential Election on X (Twitter)

The 2024 presidential election campaign in Indonesia has produced various unique phenomena, including the emergence of the "Low Human Resources'' (SDM Rendah) label directed at the supporters of candidate pair 02 by the supporters of candidate pairs 01 and 03. The existence of this label has become a spotlight because it reflects the dynamics of politics and social interaction within the community. In conducting the netnographic research strategy in this study, the primary focus is on gaining a deep understanding of the underlying reasons for this labeling, as well as how it affects public perception and the overall political dynamics. The social media platform X was selected as the research subject due to its crucial role in facilitating communicative interaction, including the labeling process. It is expected that the results of this research can provide deeper insights into the phenomenon of labeling in the political realm on the X social media platform, as well as provide a better understanding for politicians and the public regarding the importance of using social media platforms wisely in the political context. This research also has significant academic implications in the field of communication science, particularly in the theory of Computer-Mediated Communication (CMC), which can contribute to a deeper understanding of the role of social media in contemporary politics. The practical implications cannot be overlooked either, especially in the efforts to raise awareness of the power and impact of social media platforms in the political context during the 2024 presidential campaign in Indonesia.

The implications of vibration and sound therapy in modern medicine: A literary review

The following literary review studies the overall science behind vibration-based medicine and healing techniques. Specifically, the effects that 432 hertz has on the body as well as the implications of such healing techniques in western medicine and hospitals. Through studies and literature on the use of sound/music in ancient healing techniques, it is found that sound frequencies (different hertz. levels) can be a healing technique in modern medicine as well. This literary review was found using a multitude of articles and novels on the background of sound therapy, various mediums of implementation, as well as studies on the specific effects on the human body - neurologically and physically. There were 19 articles found through research and 14 were used. The major key words used were frequency healing, modern medicine, sound therapy, 432 hertz neurological impacts, and healing medicine. After reading abstracts of various articles and gathering background information, a culmination of authors’ analysis and studies were used to make a nuanced argument on the implementation of sound therapy in the modern medical industry. After analyzing the data gathered, it was found that there is a direct correlation between frequencies and their ability to help treat diseases throughout the body. Moreover, there were clear links between specific frequencies and how they can be applied to different areas to even diagnose diseases years before current technology can. Building upon that, this healing technique is very versatile in that it can be used for a multitude of problems ranging from physical pain to sleep insomnia to cardiovascular diseases. Furthermore, the research found can be integrated into the western healthcare sector to provide easier, low-cost, and low resource treatment that is effective for all.

Advancements in Natural Language Understanding- Driven Machine Translation: Focus on English and the Low Resource Dialectal Lusoga

This review explores recent advancements in Natural Language Understanding-driven Machine Translation (NLU-MT) with a focus on English and the low-resource dialectal Lusoga. A Low-resource language, such as Lusoga, faces significant challenges in Machine Translation (MT) due to the scarcity of high-quality parallel corpora, the complex morphology inherent in Bantu languages, and the dialectal variations within Lusoga itself, particularly between Lutenga and Lupakoyo. This paper examines the role of NLU-based MT systems in overcoming these challenges by shifting from word-for-word mapping to meaning-based translations, enabling better handling of these dialectal differences. We highlight the success of leveraging linguistic similarities between Lusoga and related languages, such as Luganda, to improve translation performance through multilingual transfer learning techniques. Key advancements include the use of transformer-based architectures such as Multilingual Bidirectional and Auto-Regressive Transformer (mBART) and Multilingual Text-To-Text Transfer Transformer (mT5), specifically selected for their effectiveness in NLU-driven contexts, which have shown promise in enhancing translation accuracy for African low-resource languages. However, the review also identifies ongoing obstacles, including historical low demand and the lack of well-developed corpora, which hinder scalability. The paper concludes by emphasizing the potential of hybrid approaches that combine community-driven corpus-building initiatives with improved model architectures to drive further progress in low-resource MT. Ultimately, NLU-MT is positioned as a crucial tool not only for bridging communication gaps but also for preserving linguistic diversity and cultural heritage.