Time Data Research Articles

Efficient Soil Health Monitoring and Irrigation Management System Using Fuzzy Logic

Abstract: The need for sustainable agriculture is acutely felt due to ever increasing global water crisis. Efficient irrigation management is crucial in this regard. The proposed system employs a network of soil moisture sensor distributed over a agricultural field, where continuous acquisition of real time data on soil moisture levels is done. Soil moisture measurement is a vital parameter in terms of precision agriculture, which affects the irrigation scheduling for ensuring optimal crop growth. Traditional methods related face challenges adapting to changing soil patterns at different conditions. The system proposes application of fuzzy logic algorithm for processing of data. Key advantages of using fuzzy logic in soil moisture measurement include improved precision in moisture detection, enhanced ability to make more informed irrigation decisions. The proposed system delivers a model for promoting water conservation, enhancing crop productivity and maintaining soil health.

A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of manufacturing. The process variables collected by sensors in real industrial systems typically exhibit diverse time scales and data features. To overcome the above issues, we propose a comprehensive model known as adaptive multiscale CNN and enhanced highway LSTM (ACEL). To begin with, multiscale features under variable operating conditions are automatically extracted by constructing the parallel convolutional module. Then, to selectively focus on the feature representation within the channels, efficient channel attention is introduced to downplay the channels with less important information. In addition, the bidirectional LSTM is designed to generate more fine-grained hybrid features based on contextual information and local features learned by the CNN. An enhanced highway configuration is designed to be used to bolster the global temporal dependencies. Finally, ACEL is applied to the Tennessee Eastman benchmark and CSTR simulation, and multiple statistical metrics show that the proposed model outperforms other advanced fault diagnosis methods.

Kontrol Temperatur dan Kelembapan pada Ingkubator Bayi Menggunakan Platform Antares

The condition of premature birth is a condition where a baby needs an incubator to maintain the baby's body temperature, because premature babies do not have many weaknesses to regulate body temperature so that they are prone to hypothermia. Setting the temperature and humidity in the baby incubator is an important aspect of caring for premature babies. The use of baby incubators in hospitals requires a high cost of up to Rp. 500,000 per night, even though premature babies need care in an incubator for up to one month, therefore a tool is needed to control humidity and temperature based on the Internet of Things. Control the humidity and temperature of the air used in the patient's home using a microcontroller where the microcontroller will detect the humidity and temperature inside the baby and the temperature measurement results can be viewed via the internet of things which can display temperature and humidity reading data remotely. The results of the comparison between LYNX32 and the platform between the displayed temperature and humidity values have an error of 0%. The Antares platform display displays temperature and humidity data in real time and has historical data that can be downloaded via the Antares platform, making it easier for nurses to monitor conditions.

Enhancing the delivery of comprehensive care for people living with HIV in Canada: insights from citizen panels and a national stakeholder dialogue.

People living with human immunodeficiency virus (HIV) are living longer with health-related disability associated with ageing, including complex conditions. However, health systems in Canada have not adapted to meet these comprehensive care needs. We convened three citizen panels and a national stakeholder dialogue. The panels were informed by a plain-language citizen brief that outlined data and evidence about the challenge/problem, elements of an approach for addressing it and implementation considerations. The national dialogue was informed by a more detailed version of the same brief that included a thematic analysis of the findings from the panels. The 31 citizen panel participants emphasized the need for more prevention, testing and social supports, increased public education to address stigma and access to more timely data to inform system changes. The 21 system leaders emphasized the need to enhance person-centred care and for implementing learning and improvement across provinces, territories and Indigenous communities. Citizens and system leaders highlighted that policy actions need to acknowledge that HIV remains unique among conditions faced by Canadians. Action will require a national learning collaborative to support spread and scale of successful prevention, care and support initiatives. Such a collaborative should be grounded in a rapid-learning and improvement approach that is anchored on the needs, perspectives and aspirations of people living with HIV; driven by timely data and evidence; supported by appropriate decision supports and aligned governance, financial and delivery arrangements; and enabled with a culture of and competencies for rapid learning and improvement.

Dynamic Sensor Selection for Efficient Monitoring of Coupled Multidisciplinary Systems

Abstract Coupled multidisciplinary systems involve different disciplines/subsystems with feedback-coupled interactions, illustrating the complex interdependencies inherent in real-world engineering systems. Effective monitoring of a coupled multidisciplinary system is crucial for real-time assessment of the interactions between various disciplines within the system. This monitoring provides the data necessary for detecting and addressing issues in a timely manner and facilitates adaptive decision-making for taking reliable design or control actions. However, processing and analyzing data in real time is computationally intensive, and limited resources, such as computational power, sensor capabilities, and budget, may constrain the extent to which a system can be monitored comprehensively. To address this, this paper develops a particle-based approach that dynamically selects a subset of sensors that provides the highest information about the state of the system in real time. The proposed approach first predicts the amount of uncertainty in the estimation of the state of the system given noisy measurements from different subsets of available sensors. Then, it selects the sensors that reduce this uncertainty the most, enhancing the precision and efficiency of the monitoring process. The efficacy of the proposed framework is demonstrated via two coupled multidisciplinary systems in the numerical experiments.

Home Automation Based on Internet of Things

In today's world, home automation systems have become an essential part of our daily lives. The Internet of Things (IoT) has provided us with the ability to control and monitor various appliances in our homes remotely. This paper proposes an IoT based home automation system that measures electric current used by a particular electric appliance using UNO 328, wifi ESP 8266, LCD 1602, current sensor ACS 712, 5v power supply. With the help of this project, we can easily identify which electric appliance uses how much electric current, enabling us to remove such devices which consumes lots of energy, reduce the cost of bills, save energy, and reduce human effort. The system is user-friendly and provides real- time data, which makes it easy to monitor the energy consumption of each appliance. Keywords— Internet of Things, home automation, electric current, UNO328, wifi ESP8266, LCD1602, energy consumption

Spatial distribution of NORMs concentrations and assessing potential risk to inhabitants in western Chattogram, Bangladesh

ABSTRACT This study represents a pioneering endeavour in consolidating previous measurements of naturally occurring radioactive materials (NORMs) within the Chattogram division, Bangladesh. Employing a meticulous approach, data was overlaid onto a comprehensive grid map covering the entire Chattogram region, comprising 523 grids, each of 8.05 km x 8.05 km. The analysis revealed numerous areas where NORMs concentrations had not been previously measured. To bridge this gap, soil samples were collected from 56 previously unmeasured locations across Cumilla and Chandpur districts and analysed using high purity Germanium (HPGe) detector technology. Results indicated average activity concentrations of Ra-226, Th-232, and K-40 at 27.30 ± 5.41, 46.09 ± 7.88, and 362.50 ± 48.10 Bq.kg−1 respectively, with no detectable presence of Cs-137 or other anthropogenic radionuclides, affirming the region’s absence of fallout or accidental releases. Additionally, measured values for radiation hazard indices are well within acceptable limits set by United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Spatial distribution maps of radionuclide concentrations and absorbed dose rates were generated not only for the immediate study area but also for the entire Chattogram division, integrating previously collected data for the first time. These distribution maps, alongside the comprehensive findings of this study, establish a pivotal dataset for future research and regulatory efforts, providing invaluable insights into the region’s radiological landscape.

Assessing the Suitability of Automation Using the Methods–Time–Measurement Basic System

Due to its high complexity and the varied assembly processes, hybrid assembly systems characterized by human–robot collaboration (HRC) are meaningful. Suitable use cases must be identified efficiently to ensure cost-effectiveness and successful deployment in the respective assembly systems. This paper presents a method for evaluating the potential of HRC to derive automation suitability based on existing or to-be-collected time data. This should enable a quick and favorable statement to be made about processes, for efficient application in potential analyses. The method is based on the Methods–Time–Measurement Basic System (MTM-1) procedure, widely used in the industry. This ensures good adaptability in an industrial context. It extends existing models and examines how much assembly activities and processes can be optimized by efficiently allocating between humans and robots. In the process model, the assembly processes are subdivided and analyzed with the help of the specified MTM motion time system. The suitability of the individual activities and sub-processes for automation are evaluated based on criteria derived from existing methods. Two four-field matrices were used to interpret and classify the analysis results. The process is assessed using an example product from electrolyzer production, which is currently mainly assembled by hand. To achieve high statement reliability, further work is required to classify the results comprehensively.

Live Intersection Data Acquisition for Traffic Simulators (LIDATS)

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling

The management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution. This paper proposes a model to estimate daily average broiler weights using time and weight data collected through scales. In the proposed model, a method of self-adjusting weights in the bandwidth calculation formula is employed, and the daily average weight representative value is estimated using KDE. The focus of this study is to contribute to the individual weight management of broilers by intensively researching daily fluctuations in average broiler weight. To address this, weight and time data are collected and preprocessed through scales. The Gaussian kernel density estimation model proposed in this paper aims to estimate the representative value of the daily average weight of a single broiler using statistical estimation methods, allowing for self-adjustment of bandwidth values. When applied to the dataset collected through scales, the proposed Gaussian kernel density estimation model with self-adjustable bandwidth values confirmed that the estimated daily weight did not deviate beyond the error range of ±50 g compared with the actual measured values. The next step of this study is to systematically understand the impact of the broiler environment on weight for sustainable management strategies for broiler demand, derive optimal rearing conditions for each farm by combining location and weight data, and develop a model for predicting daily average weight values. The ultimate goal is to develop an artificial intelligence model suitable for weight management systems by utilizing the estimated daily average weight of a single broiler even in the presence of error data collected from multiple weight measurements, enabling more efficient automatic measurement of broiler weight and supporting both farms and broiler demand.

Dynamic Monitoring and Analysing Method of Construction Project Performance Based on Digital Twin

Higher-level management and monitoring systems must be developed to maintain increasing global civil infrastructure projects. One such innovation in the construction industry is the Digital Twin (DT), which helps businesses address the evolving needs for smart city development and infrastructure growth through enhanced project management and execution. In this paper, we introduce a DT framework that will digitally describe construction areas in real-time through the use of Internet of Things (IoT) sensors, Building Information Modelling (BIM), and machine learning (ML). The computer program periodically collects and analyses data using sensors associated with the environment along with 3D imaging sensors (LiDAR, photogrammetric cameras). More Effective project management and monitoring are the result of linking this data with BIM systems, allowing for reports on the construction development. In order to promote proactive management, this system uses ML algorithms to analyze patterns and predict future risks. For superior prediction and decision-making by the DT, this system's dual-input layout processes both spatial and temporal data. The framework enables a method to collect data in real time and use them to achieve early detection of issues, improved resource management, and enhanced project outcomes.

#1336 Serotonin levels in hemodialysis patients with Long COVID-associated cognitive impairment

Abstract Background and Aims Long COVID comprises a broad range of persistent symptoms that emerge after COVID-19 and can last for months or years and have a direct impact on daily life. Cognitive impairment and loss of short-term memory are examples of symptoms associated with long COVID. To date, there are neither generally agreed-upon diagnostic criteria nor effective treatments available. A recent study found an association of serotonin deficiency and long COVID in the general population [1], leading to vagal dysfunction and impaired cognition. In this study, we aimed to evaluate longitudinal serotonin levels in hemodialysis (HD) patients with or without cognitive symptoms associated with long COVID. Method We analyzed routinely collected serum samples (n = 102) from 16 HD patients collected between 60 days before and 60 days after a positive SARS-CoV-2 RT-PCR. Metabolites were extracted using cold methanol and subjected to reverse phase liquid chromatography/mass spectrometry (LC/MS). Serotonin was identified by matching retention time, accurate mass, and fragmentation data against an in-house LC/MS library. Log2 of serotonin signal intensity was used for quantification. The COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) was administered shortly after positive RT-PCR test, as well as 1- and 6-months post-COVID. Patients were classified as having cognitive symptoms if they reported short-term memory loss or difficulties with concentration or communication problems as per C19-YRS. Unpaired t-test or Fisher's exact tests were performed to compare characteristics of patients with and without cognitive symptoms. We defined three study periods relative to the date of COVID-19 diagnosis: –60 to –15 days (baseline); –14 to +14 days (acute COVID); and +15 to +60 days (post-COVID). Differences in serotonin signal intensity (log2) between patients with and without cognitive symptoms were evaluated with unpaired t-tests. Linear mixed effects models with random intercepts were constructed to determine changes in serotonin levels over time with the baseline period as the comparator. This research was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK130067). Results Demographic data are presented in Table 1a. Of the 16 patients, 7 (44%) were classified with cognitive symptoms associated with long COVID. There were no differences in age, sex, race, ethnicity, vintage, diabetes, and hypertension between patients with and without cognitive symptoms. Serotonin levels in patients with vs. without cognitive symptoms did not significantly differ at baseline and during acute COVID (Fig. 1). Of note, in the post-COVID phase, serotonin levels were significantly lower in the group with impaired cognition (p = 0.012) compared to symptoms-free patients (Fig. 1). Within each group, serotonin levels remained unchanged at acute-COVID or post-COVID periods compared to baseline (Table 1b). Conclusion We show that HD patients with impaired cognition have lower serotonin in the post-COVID phase compared to patients without cognitive symptoms. These results corroborate findings in the general population. Further investigation in a larger cohort is warranted to evaluate the potential of serotonin as a target for the prevention or as a marker of neurocognitive symptoms associated with long COVID.

Space–time exploration of the east–west gap in economic development and digital financial inclusion: evidence from China pre and post-COVID-19

The East–West imbalance in China’s economic development has profoundly affected all aspects of society, but the emergence of digital financial inclusion offers potential opportunities to address this issue. This study examines 31 provinces in China from 2011 to 2020, and Exploratory Space–time Data Analysis is employed to specify the temporal and spatial changes in the East–West disparity in economic development and digital financial inclusion. The main findings are: First, inequality in economic development is associated with a decline in spatial autocorrelation. With the acute exacerbation of inequalities after the pandemic, enhancing spatial dependence through regional cooperation would be conducive to reducing disparities. Second, economic development, digital financial inclusion, and its components (usage and coverage) have hot spots in the East and cold spots in the West. Before the pandemic, significant advancements in the West mitigated the East–West gap, but the East–West divergence in economic development worsened after the COVID-19 explosion. Third, distinct from economic development, digital financial inclusion and its key elements (usage) not only have a growing regional dependence, but also that the pandemic has not disrupted improvements in the West. More importantly, there is a significant regional overlap between digital financial inclusion, usage, and economic development, implying spatial and attributable connections among them. Consequently, governments should consider digital financial inclusion as a policy tool to relieve the worsening inequality in economic development between the East and West during the post-pandemic stage.

Fate and transport of viable bacillus anthracis simulant spores in ambient air during a large outdoor decontamination field exercise.

Following an incident involving a release of Bacillus anthracis spores or other biological threat agent into the outdoor environment, understanding the factors that may affect the bioagent's fate and transport can help predict viable contaminant spread via the ambient air. This article provides scientific data for the first time on ambient air concentrations of bacterial spores over time and location during different phases of a field test in which Bacillus atrophaeus (surrogate for B. anthracis) spores were released outdoors as part of a full-scale study on sampling and decontamination in an urban environment. This study advances the knowledge related to the fate and transport of bacterial spores (such as those causing anthrax disease) as an aerosol in the outdoor environment over the course of three weeks in a mock urban environment and has exposure and health risk implications. The highest spore air concentrations occurred at the beginning of the study (e.g. during inoculation of surfaces and characterization sampling), and in the downwind direction, but diminished over time; few B. atrophaeus spores were detected in the air after several weeks and following decontamination. Therefore, in an actual incident, potential reaerosolization of the microorganism and subsequent transport in the air during surface sampling and remediation efforts should be considered for determining exclusion zone locations and estimating potential risk to neighboring communities. The data also provide evidence suggesting that the large-scale decontamination of outdoor surfaces may reduce air concentrations of the bioagent, which is important since exposure of B. anthracis via inhalation is a primary concern.

Dose finding for ZSP1601 in patients with nonalcoholic steatohepatitis using population pharmacokinetics and exposure–response approach

IntroductionZSP1601 is a novel pan-phosphodiesterase inhibitor developed in China specifically for the treatment of nonalcoholic fatty liver disease (NAFLD). AimThe aim is to develop a population pharmacokinetic (pop PK) model for ZSP1601 by integrating data from two clinical studies. This undertaking aims to deepen our understanding of the clinical factors that influence ZSP1601 exposure while simultaneously investigating exposure–response (ER) relationships related to efficacy and safety. The goal is to guide formulating optimal dosage strategies in the subsequent phases of clinical trials. MethodsAnalysis of pooled concentration–time data from 95 subjects, with 2647 observations from two clinical trials involving healthy volunteers and NAFLD patients, employed a nonlinear mixed-effects modeling approach to characterize ZSP1601 pharmacokinetics. Covariate impact on ZSP1601 pharmacokinetics was investigated, and relationships between ZSP1601 exposure, efficacy and safety endpoints were explored. ResultsA two-compartment model featuring sequential zero-order then first-order absorption and first-order elimination effectively described ZSP1601′s pharmacokinetic profile. Covariate analyses identified body weight as a statistically significant factor affecting drug central volume, while FED (food consumption) influenced absorption rate constant and duration. The Sigmoid Emax model aptly captured exposure–response relationships for ALT (alanine aminotransferase), AST (aspartate aminotransferase), and LFC (liver fat content) percentage changes relative to baseline and ZSP1601 exposure levels (AUCss) on the 29th day. ZSP1601 exposure levels (Cmax1) exhibited a significant exposure–response relationship with headaches (p < 0.001). ConclusionThe PopPK model and ER analysis, based on available data, comprehensively characterizes ZSP1601′s pharmacokinetic, safety and efficacy profile, aiding informed decisions regarding dosage selection for the drug's complete developmental trajectory. The exposure-response (ER) analysis yields quantitative insights into the optimal balance of efficacy and safety within different dosage regimens for patient administration. In light of these findings, the dose regimen of 100 mg administered twice daily is proposed for subsequent clinical investigations.

Crystal Violet Removal Using Algae-based Activated Carbon and Its Composites with Bimetallic Fe0-Cu

Abstract The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of water pollution. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe0-Cu), algae-activated carbon, and their composites (AC-Fe0-Cu), employed as adsorbents. In this paper, synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3 - 10), time intervals (15 – 180) minutes, and initial dye concentrations (50 - 500 ppm). Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the optimum conditions for improving dye removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency.&amp;#xD;

NDNOTA: NDN One-Time Authentication

Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher’s signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers’ identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages.

Barriers and benefits of mHealth for community health workers in integrated community case management of childhood diseases in Banda Parish, Kampala, Uganda: a cross-sectional study

BackgroundLow-quality data presents a significant challenge for community health workers (CHWs) in low and middle-income countries (LMICs). Mobile health (mHealth) applications offer a solution by enabling CHWs to record and submit data electronically. However, the barriers and benefits of mHealth usage among CHWs in informal urban settlements remain poorly understood. This study sought to determine the barriers and benefits of mHealth among CHWs in Banda parish, Kampala.MethodsThis qualitative study involved 12 key informant interviews (KIIs) among focal persons from Kampala City Council Authority (KCCA) and NGOs involved in data collected by CHWs, and officials from the Ministry of Health (MOH) and two mixed-sex Focused Group Discussions (FGDs) of CHWs from Banda parish, Kampala district. Data analysis utilised Atlas Ti Version 7.5.7. Thematic analysis was conducted, and themes were aligned with the social-ecological model.ResultsThree themes of institutional and policy, community and interpersonal, and individual aligning to the Social ecological model highlighted the factors contributing to barriers and the benefits of mHealth among CHWs for iCCM. The key barriers to usability, acceptability and sustainability included high training costs, CHW demotivation, infrastructure limitations, data security concerns, community awareness deficits, and skill deficiencies. Conversely, mHealth offers benefits such as timely data submission, enhanced data quality, geo-mapping capabilities, improved CHW performance monitoring, community health surveillance, cost-effective reporting, and CHW empowering with technology.ConclusionDespite limited mHealth experience, CHWs expressed enthusiasm for its potential. Implementation was viewed as a solution to multiple challenges, facilitating access to health information, efficient data reporting, and administrative processes, particularly in resource-constrained settings. Successful mHealth implementation requires addressing CHWs’ demotivation, ensuring reliable power and network connectivity, and enhancing capacity for digital data ethics and management. By overcoming these barriers, mHealth can significantly enhance healthcare delivery at the community level, leveraging technology to optimize resource utilization and improve health outcomes. mHealth holds promise for transforming CHW practices, yet its effective integration necessitates targeted interventions to address systemic challenges and ensure sustainable implementation in LMIC contexts.

Autonomous Trash Tracking System

Our revolutionary waste management project introduces a comprehensive solution leveraging cutting-edge technology to transform traditional waste disposal systems. By integrating a smart mobile application and sensor-equipped trash cans, our initiative aims to streamline the waste disposal process, addressing challenges such as timing issues, accessibility concerns, and data management inefficiencies. The mobile app empowers users with real-time information on bin availability, optimal disposal times, and recycling practices, fostering a sense of responsibility and environmental sustainability among communities. Sensor-equipped trash cans strategically placed in public spaces communicate with the mobile app and municipal waste management systems via the Internet of Things (IoT), providing accurate, up-to-date data on bin statuses. This data-driven approach allows for dynamic optimization of waste collection routes, minimizing operational costs and environmental impact. Furthermore, municipalities can utilize real-time data insights to allocate resources efficiently, schedule maintenance based on usage patterns, and implement targeted educational campaigns. Overall, our technology-driven solution not only enhances efficiency in waste management but also contributes to reducing environmental pollution and promoting sustainable waste disposal practices. Keywords—Smart dustbins, proximity sensors, moisture sensors, App development, software and hardware integration