Performance Monitoring Tool Research Articles

Effectiveness of health partners coordination for COVID-19 pandemic response in Nepal.

Nepal established the health partner coordination for COVID-19 response based on national and international plans and framework to support information-sharing for decision- making and course correction. This paper aims to assess the performance of COVID-19 Health Partner Coordination response in Nepal by adopting coordination best practices and tools from the international humanitarian cluster system. Secondary data from unpublished documents on health partner coordination for COVID-19 response in Nepal was collected and analysed from April 2023 to May 2023. The secondary data were from the review of the health partner coordination meetings conducted for COVID-19 response using a researcher-developed document review tool, responses of health partners on a survey tool adapted from the cluster coordination performance monitoring tool from the Global Health Cluster, and transcript of Focused Group Discussion among health partners. Descriptive analysis of quantitative information and thematic analysis with predefined themes of qualitative information were performed using MS Excel and MS Word respectively. A written approval from the Ministry of Health and Population and an ethical clearance from the Nepal Health Research Council was obtained before conducting the study. More than three-fifths of the meetings showed good results in conduction, process, participation, and documentation of meeting action points with improvement required for follow-up (22.2%). Assessment of health partner coordination subfunctions resulted in either 'good' (>75.0%) or 'satisfactory' (50.1%-75.0%) except for prioritization based on analyses, which was 'unsatisfactory' (<50%). Partners admired good practices of health partner coordination, pointed out some issues, and provided recommendations. COVID-19 has highlighted the importance of effective coordination of health sector for response to a pandemic. Continuation of this practice after addressing the areas for improvement will contribute to health preparedness and readiness for future disasters or public health emergencies.

Alumni Rating Index and Monitoring Dashboard Technology to Improve University’s Performance and Service Quality

Limited number of university ranking systems have considered the perspectives of alumni or individuals who have availed themselves of the educational programs or services provided by the institutions. In Malaysia, there is a lack of comprehensive rankings or assessments that primarily focus on assessing university performance through the lens of student experiences. In the absence of authentic user experiences, prospective students and other relevant parties commonly rely on conventional academic rankings such as the QS or THE to ascertain the quality of a university. The primary aims of this research endeavour encompass the identification of pivotal variables for the evaluation of higher learning institution’s performance, the creation of an alumni rating index, and the establishment of a performance monitoring tool utilizing dashboard technology. The goals are to enhance performance and service quality of institutions of higher education. The present study utilizes a quantitative research approach and centres its investigation on many cohorts of graduates originating from a prominent Malaysian university. A rigorous cluster sampling procedure was implemented. The assessment of alumni rating is conducted across twelve dimensions, with each component being evaluated using appropriate variables that enable the calculation of the desired index and facilitate the execution of intricate studies. The analytics that were performed and the alumni rating index that was calculated were subsequently shared with stakeholders through the use of an analytical dashboard. The dashboard was developed on user experience (UX) design principles. The evaluation of the produced dashboard is thereafter conducted by a group of stakeholders who have direct involvement in enhancing alumni engagement. The findings suggest that the university attained elevated ratings in the domains of social involvement, educational resources, and financial value. However, the scores for financial aid, student development, and student well-being were significantly lower. Despite the provision of various financial support, the respondents hold the belief that these forms of assistance are inadequate. The paper concludes by demonstrating the implementation of an alumni rating dashboard. The research is expected to facilitate the development of a localise university rankings and ratings, as well as informed policy-making efforts at the ministry level.

P.O.L.A.R. Star: A New Framework Developed and Applied by One Mid-Sized Pharmaceutical Company to Drive Digital Transformation in R&D.

Digital transformation has become a cornerstone of innovation in pharmaceutical research and development (R&D). Pharmaceutical companies now have an imperative to embrace transformation, including mid-sized and small-sized companies despite resource limitations that do not allow economies of scale compared with larger organizations. This article describes the journey undertaken by Chiesi to develop an efficient framework to drive digital transformation along its R&D value chain with the objective of building and refreshing a clear roadmap and relevant priorities, together with identifying and enabling new digital capabilities and skills within R&D, defining tools and processes that will guide Chiesi activities in the space up to mid-long term. This work has led so far to five main achievements, which align with the steps in the framework: a strategically aligned roadmap with key focus areas for digital transformation and a dedicated team to lead the effort; a common language for data across the R&D value chain; an internal mindset that's open to innovation and participation in key external networks and consortia; a set of quick-win use cases for the new framework; and a defined set of Key Performance Indicators (KPIs) and monitoring tools for digital transformation. The work presented here demonstrates that R&D digital transformation should represent an ongoing process to enable cross-functional collaboration and integration within complex corporate environments that face an ever-growing volume of diverse data, to efficiently support business needs, and to ensure a positive impact on patient care.

Progress Toward Tuberculosis Elimination and Tuberculosis Program Performance - National Tuberculosis Indicators Project, 2016-2022.

Elimination of tuberculosis (TB) is defined as reducing TB disease incidence in the United States to less than 1 case per million persons per year. In 2022, TB incidence in the United States was 2.5 TB cases per 100,000 persons. CDC's TB program developed a set of national TB indicators to evaluate progress toward TB elimination through monitoring performance of state and city TB program activities. Examining TB indicator data enables state- and city-level TB programs to identify areas for program evaluation and improvement activities. These data also help CDC identify states and cities that might benefit from technical assistance. The 5-year period for which the most recent data were available for each of five indicators: 1) overall TB incidence (2018-2022), 2) TB incidence among non-U.S.-born persons (2018-2022), 3) percentage of persons with drug susceptibility results reported (2018-2022), 4) percentage of contacts to sputum acid-fast bacillus (AFB) smear-positive TB patients with newly diagnosed latent TB infection (LTBI) who completed treatment (2017-2021), and 5) percentage of patients with completion of TB therapy within 12 months (2016-2020). The National TB Indicators Project (NTIP) is a web-based performance monitoring tool that uses national TB surveillance data reported through the National TB Surveillance System and the Aggregate Reports for TB Program Evaluation. NTIP was developed to facilitate the use of existing data to help TB program staff members prioritize activities, monitor progress, and focus program improvement efforts. The following five indicators were selected for this report because of their importance in Federal TB funding allocation and in accelerating the decline in TB cases: 1) overall TB incidence in the United States, 2) TB incidence among non-U.S.-born persons, 3) percentage of persons with drug susceptibility results reported, 4) percentage of contacts to sputum AFB smear-positive TB cases who completed treatment for LTBI, and 5) percentage of patients with completion of TB therapy within 12 months. For this report, 52 TB programs (50 states, the District of Columbia, and New York City) were categorized into terciles based on the 5-year average number of TB cases reported to National TB Surveillance System. This grouping allows comparison of TB programs that have similar numbers of TB cases and allocates a similar number of TB programs to each category. The following formula was used to calculate the relative change by TB program for each indicator: [(% from year 5 - % from year 1 ÷ % from year 1) × 100]. During the 5-year period for which the most recent data were available, most TB programs had improvements in reducing overall TB incidence (71.2%) and increasing the percentage of contacts receiving a diagnosis of LTBI who completed LTBI treatment (55.8%); the majority of programs (51.0%) also had improvements in reducing incidence among non-U.S.-born persons. The average percentage of persons with drug susceptibility results reported in most jurisdictions (28 of 52, [53.9%]) met or exceeded the 5-year national average of 97% (2018-2022). The percentage of contacts to sputum acid-fast bacillus (AFB) smear-positive TB patients with newly diagnosed latent TB infection (LTBI) who completed treatment increased in 29 of 52 (55.8%) jurisdictions from 2017 to 2021, signifying that, for most jurisdictions, steps have been taken to enhance performance in this area. The average percentage of patients with completion of TB therapy within 12 months was at or above the national average of 89.7% in approximately two-thirds (32 of 52 [61.5%]) of jurisdictions. This report is the first to describe a 5-year relative change for TB program performance. These results suggest that TB programs are making improvements in activities that help identify persons with TB and LTBI and ensure patients complete treatment in a timely manner. Use of NTIP data from individual TB programs enables a more detailed examination of trends in program performance and identification of areas for program improvement. Assessing indicator trends by TB program provides an opportunity to gain a better understanding of program performance in comparison to other programs. It can also facilitate communication between programs regarding successes and challenges in program improvement. This information is valuable for TB programs to allocate resources effectively and provide additional context on TB control for public health policymakers.

SOLDIERS HEALTH MONTORING AND TRACKING USING GSM AND GPS

In enemy territory soldiers not only have to deal with the physical threat, but also with stress andfatigue caused by protracted operations or lack of sleep. So for the security purpose we need a tool for remote soldier performance and health monitoring. So in this project a tool is implemented using biosensors for health monitoring purpose. Also a GPS system is used in order to track the location of soldier. Additionally a GSM modem is also used to make the system wirelessly compatible. Any abnormalities in the readings of wireless body area sensor network (WBASNs) is considered as a trigger for GSM to establish the connection between the soldier and base unit and send current location and health status to the receiver. By using all this equipments we had tried to implement the basic guarding system for the soldier in low cost, light weighted, portable and precise device.

Developing of Tomato Distribution Process Monitoring System Using Internet of Things (IoT)

Technological developments are used to provide product quality assurance and provide goods and services to consumers. Product quality is a condition related to the product, where the product could meet the needs and satisfaction of consumers. Product quality assurance, could be supported from raw materials, processes to distribution. This condition requires a tool that is able to control or monitor agricultural products by utilizing technology, namely the Internet of Things (IoT) using temperature, humidity, vibration, carbon dioxide (CO2) sensors and GPS that is able to show the location when the distribution process takes place in real time. The purpose of this study was to design and performance monitoring tools for temperature, humidity, vibration, carbon dioxide (CO2) and location during the distribution process. The results of the field test showed that the GPS sensor was able to show the location in real time according to the delivery route. The results of recording the highest temperature data was 30.8°C and the lowest temperature was 27.8°C, while the lowest humidity was 80% RH and the highest was 94% RH. The recording results from the SW-420 sensor showed that 51.04% for small vibration, 19.79% for medium vibration, and 28.64% for large vibration. The recording results from the MQ-2 sensor was able to show the carbon dioxide value around the tomatoes, with the lowest carbon dioxide with the value of 10% and the highest with the value of 23%.

The concept of food waste and food loss prevention and measuring tools.

Food waste (FW) has become a global concern, with an estimated 1.3 billion tonnes lost annually, costing about $1 trillion. Environmental and social consequences of FW are significant, contributing to 6% of European Unions' greenhouse gasemissions and affecting global food security. FW occurs is a complex issue occurring at various stages of the food supply chain (FSC) and is influenced by multiple factors such as infrastructure, available knowledge and socio-economic conditions. Developed countries FW is more prevalent at the consumption stage, whereas in the developing countries losses occur in agricultural production, post-harvest and distribution stage. Accurate quantification of FW across the supply chain is crucial and monitoring key performance indicators helps identify areas for improvement. The European Union mandates FW measurement, aligning with sustainable development goals, emphasizing the need for effective waste prevention measures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach was utilized to conduct a systematic literature review on FW key performance indicators (KPIs) and monitoring tools. The research identified 22 KPIs, categorized into three levels of the FSC: primary, secondary and tertiary. The most common KPIs included FW per capita, FW per portion and FW percentage. The study further discusses FW prevention measures and essential monitoring tools for addressing FW throughout the supply chain.

Insights into the Physicochemical Parameters, Microbial Community Structure, and Functional Variations in Biodegradation of N-Alkane Derivatives from Fischer–Tropsch Wastewater

This study provides a theoretical baseline on the application of chemical and microbiological indicators as rapid system performance monitoring tools that will allow for timely corrective measures to maintain and improve the bioremediation performance of the Fischer–Tropsch wastewater (FTWW) treatment plants. Microorganisms isolated from the sediments and water samples collected from site 1 of Blesbokspruit wetland exhibited the highest biodegradation efficiency of up to 98.04% and 92.85%, respectively, in 96 h reaction time using batch culture media spiked with 300 ppm short chain n-alkane derivatives. The highest COD reduction rate was observed during the first 24 h of biodegradation, and it steadily declined thereafter. The decline in pH from 7.0 to 6.3 was observed in the 96 h reaction time and was attributed to the production of acidic secondary metabolites and the entrapment of the produced CO2 within the batch media. The ORP also declined from the aerobic zone to the anaerobic zone within 24 h (day 1) reaction time. The EC and TDS results were also indicative of the rate of consumption of essential nutrients during the biodegradation process, which could be related to biochemical reactions involved in biodegradation of n-alkane derivatives. Proteobacteria and Firmicutes were the prevalent phyla during the biodegradation of the n-alkane derivatives. Enterococcus and Escherichia genera were more dominant on most days of biodegradation, therefore, indicating that these genera were actively involved in the biodegradation process of the n-alkane derivatives. These genera displayed a positive correlation with EC, ORP, pH and TDS in the four days of biodegradation for batch cultures inoculated with microorganisms from the water and sediments samples collected from the Blesbokspruit wetland. The results obtained demonstrated that physicochemical and microbiological indices can be used to infer the biodegradation rates, patterns and system operations in FTWW bioremediation.

A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer

Background and PurposeClinical Artificial Intelligence (AI) implementations lack ground-truth when applied on real-world data. This study investigated how combined geometrical and dose-volume metrics can be used as performance monitoring tools to detect clinically relevant candidates for model retraining. Materials and MethodsFifty patients were analyzed for both AI-segmentation and planning. For AI-segmentation, geometrical (Standard Surface Dice 3 mm and Local Surface Dice 3 mm) and dose-volume based parameters were calculated for two organs (bladder and anorectum) to compare AI output against the clinically corrected structure. A Local Surface Dice was introduced to detect geometrical changes in the vicinity of the target volumes, while an Absolute Dose Difference (ADD) evaluation increased focus on dose-volume related changes. AI-planning performance was evaluated using clinical goal analysis in combination with volume and target overlap metrics. ResultsThe Local Surface Dice reported equal or lower values compared to the Standard Surface Dice (anorectum: (0.93 ± 0.11) vs (0.98 ± 0.04); bladder: (0.97 ± 0.06) vs (0.98 ± 0.04)). The ADD metric showed a difference of (0.9 ± 0.8)Gy for the anorectum D1cm3. The bladder D5cm3 reported a difference of (0.7 ± 1.5)Gy. Mandatory clinical goals were fulfilled in 90 % of the DLP plans. ConclusionsCombining dose-volume and geometrical metrics allowed detection of clinically relevant changes, applied to both auto-segmentation and auto-planning output and the Local Surface Dice was more sensitive to local changes compared to the Standard Surface Dice. This monitoring is able to evaluate AI behavior in clinical practice and allows candidate selection for active learning.

The experiences of preparation and engagement of educators in teaching e-portfolio

IntroductionA teaching e-portfolio is used to organize the collation and presentation of documents about teaching for the development and evaluation of educators. The current study was aimed at describing teaching e-portfolio components at Shahid Sadoughi University of Medical Sciences. As well, we examined the performance and experience of educators in engaging in the teaching e-portfolio.Materials and methodsThis study was conducted at Shahid Sadoughi University in 2018–2022 in three main stages: (1) Development of the teaching e-portfolio; (2) Implementing teaching e-portfolio, and evaluating teaching documentation quantitatively; and (3) Exploration of educators’ experiences by a conventional content analysis introduced by Graneheim and Lundman. The teaching e-portfolio was developed from the perspective of the components, domains, and criteria of scholarship of teaching and learning. The teaching e-portfolio documented the educational activities of educators in 12 areas, including philosophy of education (1 activity), curriculum planning (4 activities), evaluation (7 activities), teaching and learning (1 activity), e-learning (1 activity), professional development in education (1 activity), scholarly activities (2 activities), mentoring and counseling (3 activities), educational leadership (2 activities), education research (6 activities), education reform project (1 activity), and production of scholarship of teaching and learning (13 activities). The educators recorded the documentation of educational activities in their teaching e-portfolio. Their documentation was reviewed by two peers. The reviewers delivered constructive feedback to improve the educators’ performance. The quantitative performance of educators in different activities in teaching e-portfolio was examined by descriptive tests (frequency and percentage). The experiences of educators were explored by the conventional content analysis approach which was introduced by Graneheim and Lundman.ResultsIn the present study, 148 educators registered in the teaching e-portfolio. A total of 1488 documents of educational activities were registered in the e-portfolio from 2018 to 2022, and 55.24% of the activities received feedback in the peer review process. The experience of participants was categorized into a theme “fear and hope in utilizing teaching e-portfolio”. This theme consisted of three categories: “motivational roadmap for personal and professional development in the future”, “concern about the consequences of continuous monitoring”, and “restriction of resources and capability as resistance sources”.ConclusionThe current study showed that the participation of educators in a teaching e-portfolio was at an acceptable level. Support systems and educational policies played an effective role in guiding educators to participate in educational development activities. The educators perceived the teaching e-portfolio as a two-faceted tool. Teaching e-portfolio can provide a road map for their personal and professional development to achieve excellent teaching. As well, the teaching e-portfolio was recognized as a tool for continuous performance monitoring and detection of the inefficiency of teaching quality activities. This perception, along with limited resources such as time, weak technological literacy, and difficulty in working with electronic devices and systems, led to resistance from educators to involve in teaching e-portfolio.

Buffer-Based High-Coverage and Low-Overhead Request Event Monitoring in the Cloud

Request latency directly affects the performance of modern cloud applications. Due to various causes in hosts and networks, can suffer from latency anomalies (), which may violate the Service-Level Agreement. However, existing performance monitoring tools have incomplete coverage and inconsistent semantics for monitoring and cannot accurately diagnose . This paper presents, a high-coverage and low-overhead event monitoring system, which monitors <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">buffers</i> to capture most -related abnormal events with consistent -level semantics in the end-to-end datapath of . First, models the datapath of as a buffer chain and defines events based on three properties of buffers, so as to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end monitor</i> the root causes of . Then, to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistent semantics</i> for captured events, designs a -level semantics injection mechanism to make events captured in networks have the victim requests’ ID. Finally, offloads the semantics operations and event collection in software to SmartNICs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low CPU overhead</i> . We have implemented on commodity SmartNICs and programmable switches. Evaluation results show that can diagnose 98% with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 0.08% network bandwidth overhead and 0.6% application throughput decline.

Comparison of Single Control Loop Performance Monitoring Methods

Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes.

Feasibility of implementing a performance monitoring system for health information technology students during the internship

Introduction: In universities of medical sciences, due to the key role of human resources in people's lives, their training during the internship period is of particular importance. Considering the role of effective assessment of the quality of students' performance, this research has been carried out with the aim of feasibility of implementing a system for monitoring the performance of health information technology students during internship.&#x0D; Methods: This research is descriptive-cross-sectional and among applied studies that was conducted in 1401. The data collection tool was a researcher-made questionnaire with 5 sections including background information and technical, organizational, human and legal dimensions, and the research population was the professors of the health information technology department at the country. The questionnaire was sent via email and finally, the data of 20 completed questionnaires were analyzed using SPSS software version 16 by descriptive statistics method.&#x0D; Results: The highest average scores for the possibility of implementing the performance monitoring system during internship was related to the legal dimension with 82.3%. And then there were the technical dimension with 80.6%, the organizational dimension with 79.6% and finally the human resource dimension with an average of 76.6%.&#x0D; Conclusion: It is possible to implement the performance monitoring system of health information technology students during internship from technical, organizational, legal and human resources aspects and considering the importance of the internship period and the effective role of performance monitoring tools in the management of activities, it is suggested that necessary measures be taken to implement this system.

Exploring TVET Institution Directors’ Barriers in Managing Malaysian TVET Institutions-Industry Partnership

Fordeveloping nations to meet the demand for skills in the twenty-first century, TVET is essential. The TVET institution actively creates a skilled workforce to meet the needs of the nation's labourmarket to meet those demands. As a result, efforts are being made to improve the existing TVET institutions. This study looked into the difficulties faced by TVET institution managers when handling their relationships with collaboration. The quantitative approach used in this study was the survey design approach. The right respondents are chosen for this study using purposeful sampling techniques. A total of 53 directors of TVET institutions in Malaysia and 30 representatives from industry were chosen as respondents for this study. The Rasch measurement model was used to analyze the data. This study identified managerial skills and common barriers as the two main obstacles to the collaboration between Malaysian TVET institutions and industries. Technical, human, and conceptual skills are the three categories used tocategorize managerial skill barriers. Technically, directors are unable to put knowledge into practice because they lack computer proficiency, managerial knowledge and experience, and methods and techniques for performance monitoring tools. Conceptually, directors struggle to plan long-term objectives, articulate strategies and comprehend the organizational structure of their industry. Finally, directors lack the ability to manage relationships between organizations and the drive to launch partnerships with the sector. While this was going on, the common TVET institution-industry collaboration barriers were grouped into four categories: governance issues, funding and financial issues, management issues in collaboration, and industry-institutionculture issues.

Predicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models

The main objective of the present paper is to demonstrate the feasibility of machine-learning-based surrogate models for predicting low-velocity impact behaviour considering void content and location generated during the resin transfer moulding process. Generating reliable experimental datasets for training those models is almost impossible, therefore an adapted finite element model was implemented providing reliable results to generate the synthetic datasets. The optimum hyperparameter combination for training the Random Forest model was found based on the grid search technique. The accuracy of the classification, single-output regression and multi-output regression models was sufficient. It was concluded that the multi-output regression model, which predicts the force-time, displacement-time, and energy-time curves, provides the best information, and is sufficiently accurate (R2 > 0.995) and fast (5 s per sample) as an online structural performance monitoring tool.

Workflow aware analytical model to predict performance and cost of serverless execution

SummaryServerless computing has emerged as a powerful deployment model based on the Function‐as‐a‐Service (FaaS) paradigm, where applications are orchestrated through a set of independent functions. The function orchestration within an application can be represented through a serverless workflow, which defines the overall execution plan of the application. To ensure the quality of service for serverless computing platforms, it is essential to develop performance and cost models that can predict the service quality that can be obtained from deploying and executing applications in the cloud platform. While several analytical models have been developed for various cloud deployment frameworks in recent years, there has been a lack of performance and cost analysis models for serverless computing platforms. The existing performance and cost monitoring tools available in serverless frameworks face several challenges, such as complexity, lack of transparency, and incomplete monitoring data. In this paper, we fill the gap by proposing an efficient workflow‐based analytical model that can estimate the end‐to‐end response time and cost of the serverless execution plan. The proposed model can handle complex structures like loop, cycles, self‐loop, and parallel substructures that exist in serverless workflows. Additionally, we propose a heuristic optimization algorithm to identify the optimal resource configuration to achieve the optimal response time under a given budget constraint. We evaluated the effectiveness of the proposed model by considering seven serverless applications in both AWS Lambda and Microsoft Azure platforms. We compared the accuracy of the proposed model with the real values of response time and cost obtained in AWS Lambda and Microsoft Azure serverless platforms. The proposed performance and cost model in the AWS Lambda platform has been observed to have an average accuracy of 99.2% and 98.7% respectively. In the Microsoft Azure platform, the average accuracy of the performance and cost model has been observed to be 98.6% and 98.2% respectively.

English

The development of renewable energies is essential to meet the energy demand which is growing year after year. Africa has a strong solar potential which is, however, little exploited. The development of solar photovoltaic (PV) energy will be more favored with the democratization of performance monitoring and characterization tools for PV systems. Monitoring systems are very important because they allow the monitoring of the operation and control of PV installations in order to reduce maintenance costs and avoid undesirable power supply interruptions. There are several monitoring systems, more or less efficient, proposed on the market but they are often too expensive and not adapted to all existing systems. This paper presents a low-cost electronic board for monitoring and characterizing photovoltaic systems. The designed board is based on the ATMega328 microcontroller of the open-source development platform Arduino. It is composed of four blocks that allow the acquisition of electrical and environmental parameters. The board has been tested in monitoring mode with the follow-up of a small PV solar installation. The results of these experiments are consistent with what is found in the literature. Its use in characterization mode has allowed to obtain, in real time, the current-voltage and power-voltage characteristics. Compared to the results of the IV-500W tracer, the characterization results are consistent with an RMSE of 0.81 in voltage and 0.15 in current on all points measured. The design work is completed with the PCB design on Altium designer which resulted in the Gerber file. Key words: Microcontroller, photovoltaic (PV) monitoring, PV characterization, PCB.

Remote Real-Time Optical Layers Performance Monitoring Using a Modern FPMT Technique Integrated with an EDFA Optical Amplifier

Fiber performance monitoring using modern online technologies in the next generation of intelligent optical networks allows for identifying the source of the degeneration and putting in protective steps to increase remote optical network stability &amp; reliability. In this paper, the performance of the fiber performance monitoring tool (FPMT) technique was improved by integrating it with optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures at the online remote rather than disrupting the data flow with a measurement accuracy for defect location of up to ~99.9%, small tolerance of up to ~1 m, the longest distance to detecting optical line defects of up to ~300km, and enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB. The proposed integration method provides better results with an excellent and efficient solution at fault location measurement &amp; detection in real-time with good financial implications of the technique. The competitiveness of the improved technique over the actual optical networks has been successfully confirmed through application to Huawei labs infrastructure nodes and displayed experimental simulation results.

Assessment of energy performance and GHG emissions for the urban water cycle toward sustainability

Abstract This study presents a holistic approach to evaluate energy performance and greenhouse gas (GHG) emissions from urban water supply and sanitation stages, which are important for sustainable water management and climate change mitigation. The study was conducted for Antalya city of Turkey to compare baseline and improved scenario conditions using the Energy Performance and Carbon Emissions Assessment and Monitoring (ECAM) tool. The current application of urban water and wastewater services was defined as the baseline scenario. For the improved urban water cycle, the reduction of non-revenue water, onsite sanitation prevention, increase in energy efficiency, biogas production and reuse of treated wastewater were investigated. Water supply and sanitation stages contributed to approximately 26 and 74% of total GHG emissions and 70 and 30% of energy consumption for the baseline scenario, respectively. GHG emissions were determined approximately as 52,423 tCO2eq/year for CO2 (40%), 47,029 tCO2eq/year for CH4 (35%) and 33,006 tCO2eq/year for N2O (25%) for the baseline scenario. The total GHG emissions of 132,457 tCO2eq/year and energy consumption of 136,328 MWh/year were reduced by 27.65% for GHG emissions and 16.48% for energy consumption for the improved urban water cycle. The outcomes of this research are expected to achieve sustainable cities and combat climate change.

Implementing SMS reminders for routine immunization in Northern Nigeria: a qualitative evaluation using the RE-AIM framework

BackgroundShort Message Service (SMS) reminders have improved vaccine uptake in low- and middle-income countries (LMICs). However, the limited use of SMS reminders in LMICs requires evaluating the intervention’s internal and external validity to improve adoption and sustainability. Using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we qualitatively assessed the impact of a SMS reminder intervention implemented in Kebbi State, Northwest Nigeria between May 20, 2019 and May 31, 2020. This will guide and inform future SMS reminder interventions to improve childhood immunization uptake in LMICs.MethodsIn June 2020, we conducted 14 focus group discussions, 13 in-depth interviews, and 20 key informant interviews among 144 purposively selected participants from five local government areas of Kebbi State. For analysis, we used a deductive approach to develop preliminary codes based on the RE-AIM framework and the inductive approach to generate themes that emerged from the interviews.ResultsThe perceived importance and impact of the SMS reminder in improving demand and uptake for vaccinations were the consistent contributing factors that encouraged participants’ participation. Other facilitators included the involvement of health workers in supporting SMS reminder registration and community gatekeepers using existing structures to convey messages on scheduled immunization services. Policymakers adopted the intervention because it aligns with the state’s priority to improve immunization coverage. Similarly, the SMS reminder appealed to health workers and program managers because it reduced their workload and served as a performance monitoring tool to track immunization and intervention defaulters. Despite these, low mobile phone ownership and the inability to read text messages due to the low literacy level were the main barriers during implementation. Finally, data availability on cost-effectiveness and the intervention’s impact on improving coverage was critical for scalability.ConclusionsOur study demonstrated that SMS reminders in local languages could improve vaccination demand and uptake in resource-constrained settings due to their perceived importance and impact. Addressing the cited implementation barriers and promoting the facilitators is critical to its adoption and sustainability. Costing and impact data are needed to collaborate findings on the effectiveness of the SMS reminder to improve childhood vaccination uptake.