Program Performance Research Articles

A Survey on Autonomous Offline Path Generation for Robot-Assisted Spraying Applications

Robot-assisted spraying is a widespread manufacturing process for coating a multitude of mechanical components in an efficient and cost-effective way. However, process preparation is very time-consuming and relies heavily on the expertise of the robot programmer for generating the appropriate robot trajectory. For this reason, industry and academia investigate the possibility of supporting the end-user in the process by the use of appropriate algorithms. Mostly partial concepts can be found in the literature instead of a solution that solves this task end-to-end. This survey paper provides a summary of previous research in this field, listing the frameworks developed with the intention of fully automating the coating processes. First, the main inputs required for the trajectory calculation are described. The path-generating algorithm and its subprocesses are then classified and compared with alternative approaches. Finally, the required information for the executable output program is described, as well as the validation tools to keep track of program performance. The paper comes to the conclusion that there is a demand for an autonomous robot-assisted spraying system, and with a call-for-action for the implementation of the holistic framework.

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model.

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map. To assess the AI program's performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.

Causal evidence of the effect of a course specific mathematical learning and support center on economics students’ performance and affective variables

ABSTRACT The positive impact of Math Learning Support Centers (MLSCs) on student performance is well-documented in existing literature. However, we argue in this study that many of these evaluations could be improved methodologically. Using a more rigorous approach, we investigated the effect of an MLSC on students’ performance and affective variables in an economic study program. Our data, collected over six years, was analyzed using Propensity Score Matching and Difference-in-Differences methods, to control for observable and time-invariant unobservable characteristics. Contrary to a majority of the existing literature, we found no direct impact of students’ participation in the MLSC on students’ math performance. However, we identified positive effects on students’ test anxiety and other affective variables. Moreover, we found that students’ engagement in the learning process during the semester (e.g. by attending classes and solving exercise sheets) was a key driver of skill growth. These findings underscore the importance of more rigorous methodological approaches in future evaluations of MLSCs and other similar support systems.

High-performance Deterministic Concurrency Using Lingua Franca

Actor frameworks and similar reactive programming techniques are widely used for building concurrent systems. They promise to be efficient and scale well to a large number of cores or nodes in a distributed system. However, they also expose programmers to nondeterminism, which often makes implementations hard to understand, debug, and test. The recently proposed reactor model is a promising alternative that enables deterministic concurrency. In this article, we present an efficient, parallel implementation of reactors and demonstrate that the determinacy of reactors does not imply a loss in performance. To show this, we evaluate Lingua Franca (LF), a reactor-oriented coordination language. LF equips mainstream programming languages with a deterministic concurrency model that automatically takes advantage of opportunities to exploit parallelism. Our implementation of the Savina benchmark suite demonstrates that, in terms of execution time, the runtime performance of LF programs even exceeds popular and highly optimized actor frameworks. We compare against Akka and CAF, which LF outperforms by 1.86× and 1.42×, respectively.

Assessing IRS performance in a gender-integrated vector control programme on Bioko Island, Equatorial Guinea, 2010–2021

BackgroundIndoor residual spraying (IRS) is a common vector control strategy in countries with high malaria burden. Historically, social norms have prevented women from working in IRS programmes. The Bioko Island Malaria Elimination Project has actively sought to reduce gender inequality in malaria control operations for many years by promoting women’s participation in IRS.MethodsThis study investigated the progress of female engagement and compared spray productivity by gender from 2010 to 2021, using inferential tests and multivariable regression. Spray productivity was measured by rooms sprayed by spray operator per day (RSOD), houses sprayed by spray operator per day (HSOD), and the daily productivity ratio (DPR), defined as the ratio of RSOD to HSOD, which standardized productivity by house size.ResultsThe percentage of women participating in IRS has increased over time. The difference in DPR comparing male and female spray operators was only statistically significant (p < 0.05) for two rounds, where the value was higher for women compared to men. Regression analyses showed marginal, significant differences in DPR between men and women, but beta coefficients were extremely small and thus not indicative of a measurable effect of gender on operational performance.ConclusionsThe quantitative analyses of spray productivity are counter to stigmatizing beliefs that women are less capable than male counterparts during IRS spray rounds. The findings from this research support the participation of women in IRS campaigns, and a renewed effort to implement equitable policies and practices that intentionally engage women in vector control activities.

Barnyardgrass control in conventional and Clearfield rice grown under intermittent flooding

AbstractIn the mid‐southern United States, rice (Oryza sativa L.) is flooded primarily to suppress weed germination and growth. Two experiments were conducted to determine whether herbicide performance in conventional or Clearfield rice weed control programs was affected by intermittent flooding. The effects of flooding and herbicide program on barnyardgrass [Echinochloa crus‐galli (L.) P. Beauv] control, rice grain yield, and water applied were investigated at the Delta Research and Extension Center in Stoneville, MS, on Sharkey clay (very fine, smectitic, thermic Chromic Epiaquert). For herbicides commonly applied in conventional (clomazone, quinclorac, pendimethalin, thiobencarb, fenoxaprop‐ethyl, cyhalofop‐butyl, and bisypriboac sodium) or Clearfield rice weed control programs (imazethapyr plus bispyribac‐sodium followed by imazethapyr, imazamox or bispyribac‐sodium), intermittent flooding had no adverse effect on barnyardgrass control relative to a continuous flood. Moreover, initiating irrigation when the perched water table drops to 8 inches below the soil surface had no effect on rice grain yield and reduced water applied by 51%. Mid‐southern US rice producers can capture the water‐saving benefits of intermittent flooding while having no adverse effects on herbicide activity or crop productivity in conventional and Clearfield systems.

Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2.

The COVID-19 pandemic has created an urgent need for effective therapeutic and diagnostic strategies to manage the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the emergence of numerous variants of concern (VOCs) has made it challenging to develop targeted therapies that are broadly specific in neutralizing the virus. In this study, we aimed to develop neutralizing nanobodies (Nbs) using computational techniques that can effectively neutralize the receptor-binding domain (RBD) of SARS-CoV-2 VOCs. We evaluated the performance of different protein-protein docking programs and identified HDOCK as the most suitable program for Nb/RBD docking with high accuracy. Using this approach, we designed 14 novel Nbs with high binding affinity to the VOC RBDs. The Nbs were engineered with mutated amino acids that interacted with key amino acids of the RBDs, resulting in higher binding affinity than human angiotensin-converting enzyme 2 (ACE2) and other viral RBDs or haemagglutinins (HAs). The successful development of these Nbs demonstrates the potential of molecular modeling as a low-cost and time-efficient method for engineering effective Nbs against SARS-CoV-2. The engineered Nbs have the potential to be employed in RBD-neutralizing assays, facilitating the identification of novel treatment, prevention, and diagnostic strategies against SARS-CoV-2.

Engagement of community health workers to improve immunization coverage through addressing inequities and enhancing data quality and use is a feasible and effective approach: An implementation study in Uganda.

Uganda, like many other developing countries, faces the challenges of unreliable estimates for its immunization target population. Strengthening immunization data quality and its use for improving immunization program performance are critical steps toward improving coverage and equity of immunization programs. The goal of this study was to determine the effectiveness of using community health workers (CHWs) to obtain quality and reliable data that can be used for planning and evidence-based response actions. An implementation study in which 5 health facilities were stratified and randomized in two groups to (i) receive a package of interventions including monthly health unit immunization data audit meetings, and defaulter tracking and linkage and (ii) to serve as a control group was conducted between July and September 2020. Immunization coverage of infants in both arms was determined by a review of records three months before and after the study interventions. In addition, key informant and in-depth interviews were conducted among facility-based health workers and CHWs respectively, at the endline to explore the feasibility of the interventions. Overall, a total of 2,048 children under one year eligible for immunization were registered in Bukabooli sub-county by CHWs as compared to the estimated district population of 1,889 children representing a moderate variance of 8.4%. The study further showed that it is feasible to use CHWs to track and link defaulters to points of immunization services as more than two-thirds (68%) of the children defaulting returned for catch-up immunization services. At the endline, immunization coverage for the Oral Polio Vaccine third dose; Rotavirus vaccine second dose; Pneumococcal Conjugate Vaccine third dose increased in both the intervention and control health facilities. There was a decrease in coverage for the Measles-Rubella vaccine decreased in the intervention health facilities and a decrease in Bacillus Calmette-Guérin vaccine coverage in the control facilities. Difference in difference analysis demonstrated that the intervention caused a significant 35.1% increase in coverage of Bacillus Calmette-Guérin vaccine (CI 9.00-61.19; p<0.05)). The intervention facilities had a 17.9% increase in DTP3 coverage compared to the control facilities (CI: 1.69-34.1) while for MR, OPV3, and Rota2 antigens, there was no significant effect of the intervention. The use of CHWs to obtain reliable population estimates is feasible and can be useful in areas with consistently poor immunization coverage to estimate the target population. Facilitating monthly health unit immunization data audit meetings to identify, track, and link defaulters to immunization services is effective in increasing immunization coverage and equity.

Selection of Appropriate Portfolio Optimization Strategy

Portfolio optimization is a critical task in financial management, aiming to maximize expected returns while minimizing risk. This study compares the performance of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Dynamic Programming (DP), and Differential Evolutionary Algorithm (DEA) in optimizing portfolios in the NIFTY 50 market. Using daily stock data from March 2023 to May 2023, we evaluate the algorithms based on performance metrics including the Sharpe ratio, expected return, volatility and Sortino ratio. Then an integrated multi-criteria decision making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) methods has been used to compare the evolutionary algorithms for portfolio optimization. The results show that PSO outperforms the other algorithms in terms of Sharpe Ratio and Expected Return, while DEA exhibits the lowest portfolio risk. Furthermore, the efficient frontier analysis confirms PSO's ability to generate portfolios with higher expected returns at the same risk level. This research highlights the effectiveness of optimization algorithms in portfolio management and provides valuable insights for investors and portfolio managers in maximizing returns and managing risks.

Evaluation of Incremental Validity of Casper in Predicting Program and National Licensure Performance of Undergraduate Nursing Students: Protocol for a Mixed Methods Study.

Academic success has been the primary criterion for admission to many nursing programs. However, academic success as an admission criterion may have limited predictive value for success in noncognitive skills. Adding situational judgment tests, such as Casper, to admissions procedures may be one strategy to strengthen decisions and address the limited predictive value of academic admission criteria. In 2021, admissions processes were modified to include Casper based on concerns identified with noncognitive skills. This study aims to (1) assess the incremental validity of Casper scores in predicting nursing student performance at years 1, 2, 3, and 4 and on the National Council Licensing Examination (NCLEX) performance; and (2) examine faculty members' perceptions of student performance and influences related to communication, professionalism, empathy, and problem-solving. We will use a multistage evaluation mixed methods design with 5 phases. At the end of each year, students will complete questionnaires related to empathy and professionalism and have their performance assessed for communication and problem-solving in psychomotor laboratory sessions. The final phase will assess graduate performance on the NCLEX. Each phase also includes qualitative data collection (ie, focus groups with faculty members). The goal of the focus groups is to help explain the quantitative findings (explanatory phase) as well as inform data collection (eg, focus group questions) in the subsequent phase (exploratory sequence). All students enrolled in the first year of the nursing program in 2021 were asked to participate (n=290). Faculty will be asked to participate in the focus groups at the end of each year of the program. Hierarchical multiple regression will be conducted for each outcome of interest (eg, communication, professionalism, empathy, and problem-solving) to determine the extent to which scores on Casper with admission grades, compared to admission grades alone, predict nursing student performance at years 1-4 of the program and success on the national exam. Thematic analysis of focus group transcripts will be conducted using interpretive description. The quantitative and qualitative data will be integrated after each phase is complete and at the end of the study. This study was funded in September 2021, and data collection began in March 2022. Year 1 data collection and analysis are complete. Year 2 data collection is complete, and data analysis is in progress. At the end of the study, we will provide the results of a comprehensive analysis to determine the extent to which the addition of scores on Casper compared to admission grades alone predicts nursing student performance at years 1-4 of the program and on the NCLEX exam. RR1-10.2196/48672.

Fostering Computational Thinking and Problem-Solving in Programming: Integrating Concept Maps Into Robot Block-Based Programming

Computational thinking (CT) has gained considerable attention and in-depth discussion over the last two decades. Although the significance of CT has been highlighted, it could be challenging for educators to teach CT. Fortunately, adopting robots in education has been evidenced to be of benefit to promoting students’ learning motivation, CT, and higher-order thinking skills. However, several significant factors affecting students’ programming performances in robot-assisted learning activities have been identified, such as cognitive needs and organization. In this study, a CMR-BBP (concept map robot block-based programming) approach was designed by integrating concept maps into robot block-based programming to enhance students’ programming learning. Moreover, a three-group experiment was carried out in an elementary school to evaluate their learning outcomes. The experimental results revealed that the CMR-BBP approach benefited the students’ perceptions of their computational thinking and problem solving in comparison with the R-BBP (robot block-based programming) and C-BBP (conventional block-based programming) approaches. Furthermore, regarding cognitive load, both the CMR-BBP and R-BBP approaches enhanced the students’ germane cognitive load, while the CMR-BBP approach effectively reduced their extrinsic cognitive load. This study could be a notable reference for designing other courses in conjunction with programming learning activities.

How Much Safety Did the Social Safety Net Provide During the COVID-19 Pandemic?

This JAMA Forum discusses the successes, failures, and lessons learned regarding the performance of US social safety-net programs during the COVID-19 pandemic.

Intersectoral leadership on family planning programme performance: A prospective longitudinal study

Background: There had been no research on shared leadership at the team level in diverse organisation. Aims: This study aimed to investigate the effect of shared intersectoral leadership on family planning programme’s performance. Method: This was an observational study with a prospective longitudinal time series design. This study distributed questionnaires to 30 teams which were taken by simple random sampling to provide agreed answers. The measurement of the impact of shared leadership on performance was carried out three times. Linear regression was then used to analyse the data. The shared leadership team qualified as adequate. Result: The beta coefficient value indicated that shared leadership had a strong impact on the team’s performance of the family planning programme with a value of 0.817 - 0.824 (p = 0.000). Conclusion: Once the shared leadership approach is effectively utilised, the team performance of family planning programmes will improve.

The Relationship Between Motivation and Cadre Performance in the Healthy Fund Program in the Puskesmas Working Area Lhok Bengkuang, South Aceh District

Dana sehat (Healthy fund) is very important for the community because it can ease the burden of citizen, the majority of family living in Darussalam is catagorized as low income family whose wage is below the minimum province level. However, Dana Sehat requires high dedication from the managers. This study aimed for finding out the corelation between motivation and cadres working performance who participate in Dana Sehat program in the area of Puskesmas (comunity helath center) Lok Bengkuang in Aceh Selatan Regency. This is a corelational study with cross sectional design. The research population comprised of 39 cadres who participate in Dana Sehat program. The sampling tecnique used is total sampling. Data is collected using question. Then it is analyzed using chi square test. The result of research showed that motivation in Dana Sehat program is in low category as many as 22 people (56.4%), cadres’ performance in Dana Sehat program is less than 26 people (66.7%). The result of chi square test showed that p-value =0.004 (?=0.05), to cadres who participate in Dana Sehat program in work area of Puskesmas Lhok Bengkuang in Aceh Selatan Regency. It is recommended that the cadres who are working area of the Puskesmas to obtain and knowledge about the Dana Sehat Program.

Can people with disabilities obtain income by using skills learned from vocational trainings? Evidence from mainland China

PurposeThis study aims to examine the effects of vocational training programs for people with disabilities on their income.Design/methodology/approachIt conducted a multinomial regression analysis of 10,469 survey responses from 31 provincial administrative areas in mainland China.FindingsIt finds the following antecedents all influence the trainees’ self-reported income, including their perception of the quality of the program, the training subject, the degree of consistency between their current job and this subject, their employment sector, their motivation and access to training resources and the geographical location of the program.Research limitations/implicationsThe findings are not representative of people with disabilities across mainland China because the respondents were not randomly sampled.Practical implicationsThe findings suggest that to increase the income of people with disabilities, the training can be designed according to the needs of employers by teaching subjects relevant to the needs of the labor market, reaching out to motivated trainees and enhancing the quality of training. Training institutions, employers and governments are recommended to work together to integrate class-based learning with workplace-based learning and practices. More training resources can be devoted to the self-employed people with disabilities or those who operate in the informal sector and are in less-developed areas.Social implicationsThe improvement of employment opportunities and income of people with disabilities supports the safeguarding of their social economic rights and the building of an inclusive society.Originality/valueFew studies have empirically explored and explained the effects of vocational training programs on people with disabilities’ income. This article fills this gap by assessing the performance of China's vocational training programs funded by the Federation of Disabled Persons at all levels.

Cervical cancer in Region Skåne, Sweden 2017-2020 after the implementation of primary HPV screening: A quality assurance audit.

Primary human papilloma virus (HPV) screening to detect cervical cancer and dysplastic lesions was implemented in Region Skåne 2017 for women aged 30-70. The aim of this study was to characterize the screening history of women diagnosed with cervical cancer to evaluate the performance of the screening program, as well as to assess the cancer treatments given and shortcomings in the follow-up of women with cervical dysplasia. We performed a quality assurance audit. The data was collected from the National Cervical Cancer Prevention Registry, Region Skåne Labmedicin database and the Melior Journal system in 2017-2020. We identified 247 women diagnosed with invasive cervical cancer in Region Skåne in 2017-2020. Of these, 35 (14.2%) had a screening history over at least two screening rounds before diagnosis. There were 25 (10.1%) women diagnosed with cervical cancer in between screening intervals, i.e., interval cancer. The most common screening history in women with cervical cancer was irregular screening (143, 57.9%), followed by women being above screening age (44, 17.8%). HPV was detected in 96% of the cases, either in cervical cytology or in the tumor tissue. The screening program detected the disease in 96 (38.9%) of the patients, 149 (60.3%) were diagnosed through symptoms and two (0.80%) as a result of incidental findings. The most powerful tool in the prevention of cervical cancer is screening program attendance. Prolongation with HPV screening among elderly women will also reduce the incidence of cervical cancer. Today, such cancers are usually discovered when symptoms appear.

Performance of a multidisciplinary robotic surgery program at a university hospital (2012-2022).

Robotic-assisted surgery has become widely adopted for its ability to expand the indications for minimally invasive procedures. This technology aims to improve precision, accuracy, and outcomes while reducing complications, blood loss, and recovery time. Successful implementation of a robotic surgery program requires careful initial design and a focus on maintenance and expansion to maximize its benefits. This article presents a comprehensive study conducted at a University Hospital on the robotic surgery program from December 2012 to December 2022. Data from hospital databases, including patient demographics, surgical department, surgical time, operating room occupancy, and primary diagnosis, were analyzed. The analysis covered various time periods (surgical sessions, weeks, months, and years) to assess the program's evolution over time. Over the 10-year period, a total of 1847 robotic-assisted interventions were performed across five surgical services. Urology accounted for 57% of the cases, general surgery 17%, gynecology 16%, otorhinolaryngology 6%, and thoracic surgery 4%. The most frequently performed procedures included robotic prostatectomies (643 cases), hysterectomies (261 cases), and colposacropexies (210 cases). The weekly volume of interventions showed a notable increase, rising from 2 cases per week in 2013-2014 cases in 2022. Moreover, the average surgical duration per intervention exhibited a progressive decrease from 275min in 2013 to 184min in 2022. This study highlights the potential of a well-managed robotic surgery program as a viable alternative to conventional surgical approaches. Effective coordination and resource utilization contribute to the program's efficiency. The findings underscore the successful integration of robotic-assisted surgery in diverse surgical specialties.

RenderBench: The CPU Rendering Benchmark Suite Based on Microarchitecture-Independent Characteristics

This research addresses the issue of evaluating CPU rendering performance by introducing the innovative benchmark test suite construction method RenderBench. This method combines CPU microarchitecture features with rendering task characteristics to comprehensively assess CPU performance across various rendering tasks. Adhering to principles of representativeness and comprehensiveness, the constructed benchmark test suite encompasses diverse rendering tasks and scenarios, ensuring accurate capture of CPU performance features. Through data sampling and in-depth analysis, this study focuses on the role of microarchitecture-independent features in rendering programs, including instruction-level parallelism, instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance. The research findings reveal significant variations in rendering programs across these features. For instance, in terms of instruction-level parallelism, rendering programs demonstrate a high level of ILP (instruction-level parallelism), with an average value of 5.70 for ILP256, surpassing benchmarks such as Mibench and NAS Parallel Benchmark. Furthermore, in aspects such as instruction mix, branch prediction capability, register dependency distance, data flow stride, and memory reuse distance, rendering programs exhibit distinct characteristics. Through the application of the RenderBench method, a scalable and highly representative benchmark test suite was constructed, facilitating an in-depth exploration of CPU performance bottlenecks in rendering tasks. By delving into microarchitecture-independent features, this study provides profound insights into rendering program performance, offering valuable guidance for optimizing CPU rendering performance. The application of ensemble learning models, such as random forest, XGBoost, and ExtraTrees, reveals the significant influence of features like floating-point computation, memory access patterns, and register usage on CPU rendering program performance. These insights not only offer robust guidance for performance optimization but also underscore the importance of feature selection and algorithm choice. In summary, the results of feature importance ranking in this study provide beneficial directions and deep insights for the optimization and enhancement of CPU rendering program performance. These findings are poised to exert a positive impact on future research and development endeavors.

Does increasing auditors' independence lead to more forceful public auditing? A study of a Canadian internal audit reform

AbstractThe value of auditing as an instrument of accountability depends upon the independence of auditors. However, the extent to which auditors feel free to review the operation, compliance, and performance of public programs and objectively communicate findings to stakeholders has rarely been assessed. A Canadian public service reform in 2006 introduced institutional safeguards to bolster the independence of departmental internal auditors, but left legislative auditors working for the Auditor General's Office (i.e., Canada's Supreme Audit Institution) unaffected. We analyze and compare 2677 audit reports written by internal and legislative auditors before and after the reform. After the reform, the language used by internal auditors became more assertive and evaluative, but not more critical. This suggests that the safeguards had a meaningful impact on how auditors perform their accountability function but that factors other than independence may be driving their willingness to be more adversarial and critical when communicating audit findings.

Effects of the COVID-19 Pandemic on Legionella Water Management Program Performance across a United States Lodging Organization.

Legionella, the bacterium that causes Legionnaires' disease, can grow and spread in building water systems and devices. The COVID-19 pandemic impacted building water systems through reductions in water usage. Legionella growth risk factors can be mitigated through control measures, such as flushing, to address stagnation, as part of a water management program (WMP). A national lodging organization (NLO) provided WMP data, including Legionella environmental testing results for periods before and during the pandemic. The statistical analysis revealed an increased risk of water samples testing positive for Legionella during the pandemic, with the greatest increase in risk observed at the building's cold-water entry test point. Sample positivity did not vary by season, highlighting the importance of year-round Legionella control activities. The NLO's flushing requirements may have prevented an increased risk of Legionella growth during the pandemic. However, additional control measures may be needed for some facilities that experience Legionella detections. This analysis provides needed evidence for the use of flushing to mitigate the impacts of building water stagnation, as well as the value of routine Legionella testing for WMP validation. Furthermore, this report reinforces the idea that WMPs remain the optimal tool to reduce the risk of Legionella growth and spread in building water systems.