Testing Approach Research Articles

Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries

BackgroundIn 2022, fewer than half of persons with tuberculosis (TB) had access to molecular diagnostic tests for TB due to their high costs. Studies have found that the use of artificial intelligence (AI) software for chest X-ray (CXR) interpretation and sputum specimen pooling can each reduce the cost of testing. We modeled the combination of both strategies to estimate potential savings in consumables that could be used to expand access to molecular diagnostics.MethodsWe obtained Xpert testing and positivity data segmented into deciles by AI probability scores for TB from the community- and healthcare facility-based active case finding conducted in Bangladesh, Nigeria, Viet Nam, and Zambia. AI scores in the model were based on CAD4TB version 7 (Zambia) and qXR (all other countries). We modeled four ordinal screening and testing approaches involving AI-aided CXR interpretation to indicate individual and pooled testing. Setting a false negative rate of 5%, for each approach we calculated additional and cumulative savings over the baseline of universal Xpert testing, as well as the theoretical expansion in diagnostic coverage.ResultsIn each country, the optimal screening and testing approach was to use AI to rule out testing in deciles with low AI scores and to guide pooled vs individual testing in persons with moderate and high AI scores, respectively. This approach yielded cumulative savings in Xpert tests over baseline ranging from 50.8% in Zambia to 57.5% in Nigeria and 61.5% in Bangladesh and Viet Nam. Using these savings, diagnostic coverage theoretically could be expanded by 34% to 160% across the different approaches and countries.ConclusionsUsing AI software data generated during CXR interpretation to inform a differentiated pooled testing strategy may optimize TB diagnostic test use, and could extend molecular tests to more people who need them. The optimal AI thresholds and pooled testing strategy varied across countries, which suggests that bespoke screening and testing approaches may be needed for differing populations and settings.

Conceptional design of photoneutralization test system for negative ion-based neutral beam injection

Neutral beam injection is one of the effective heating methods in the field of magnetic confinement fusion, and neutralization is the most crucial link in the case of negative ions. To further increase the neutral beam injection power, improve the long pulse operation capability, and optimize the efficiency of the NNBI system, further research and verification about the neutralization mode are needed. Theoretically, photoneutralization can achieve more than 90% neutralization efficiency. However, maintaining stable operation of the megawatt laser cavity over extended periods poses corresponding challenges. Additionally, the cost associated with laser target surpasses the benefit gained from increased neutralization efficiency, leading to its lack of practical application thus far. This paper proposes a solution to these issues by designing a single-channel, multi-fold photoneutralization verification system utilizing the CRAFT NNBI one-quarter and one-half size negative source test equipment. An outline of the system's test and diagnostics approach is provided. Key parameters such as laser target thickness, negative ion energy, beam shape and efficiency of the photoneutralization system are numerically calculated. Combined with the experimental data of the negative source test platform, theoretical calculations show that the neutralization efficiency can achieve 63% with the system efficiency exceeding 40%. Even by increasing the incident laser power or the number of reflections, neutralization efficiency can be increased to 95%, with a simultaneous increase in system efficiency to 60%. Maintaining efficiency while increasing incident laser power could reduce the number of reflections to approximately ten, reaching an acceptable threshold. However, this adjustment will increase the irradiation density of a single mirror from 660W/mm2 increases to 3000W/mm2. This paper methodically designs a practical laser neutralization verification platform, which is expected to substantially improve the neutralization efficiency, and facilitate practical application and validation.

Enhancing the Decision Tree Algorithm to Improve Performance Across Various Datasets

Background: The Village Fund is an initiative by the central government to promote equitable regional development. However, it has also led to corruption. Many Indonesians share their opinions on the Village Fund on social media platforms like X, and news coverage is extensive on portals like detik.com. Objective: This study aims to classify data from social media and news coverage to enhance understanding. Methods: The research improves the decision tree algorithm by integrating other algorithms and techniques such as XGBoost and SMOTE. Ensuring high accuracy is vital for the credibility of machine learning classifications among the public. The study uses two different datasets, necessitating varied testing approaches. For the news portal dataset, a single test with seven labels is conducted, followed by enhancement with XGBoost. The X dataset undergoes two tests with datasets of 1200 and 3078 entries, using three labels. Conclusion: The evaluation results indicate that the highest accuracy achieved with the news portal data was 82%, thanks to a combination of decision tree algorithms with various parameters and the balancing effect of SMOTE. For the Twitter dataset with 3078 entries, the highest accuracy reached 95%, attributed to the application of ensemble techniques, particularly boosting.

Five key actions for redesigning adult skills assessments from learners, employees, and educators

Summative assessments provide data to evaluate the effectiveness of schools, teachers, and educational policies, contributing to a robust accountability system worldwide. However, educators and scholars argue that summative assessments do not effectively promote ongoing learning, enhance teaching practices, or align with the skills required in the workplace. Furthermore, traditional summative testing approaches lack culturally responsive practices and overlook the diverse needs of adult learners. This article summarizes research conducted through focus groups, interviews, and listening sessions to gain insight into learners’, employees’, and educators’ perceptions of assessment needs in adult education and workforce development in the United States. We conducted six focus groups, four interviews with diverse learners and employees, and two listening sessions with educators and administrators. In-person and online formats were used in Spanish and English. The results indicate that the backgrounds and needs of learners, employees, and educators in this study are diverse, but all agree there is a strong need for open assessment resources for mid- and lower-skilled and culturally diverse learners that provide them with useful information to help them achieve their personal and occupational goals. Educators in this study needed assessment information connected to the standards and curriculum that guide their instruction, and employees and their employers needed tools to identify and train adult learners for in-demand skills within industries and occupations. Assessing adult skills requires addressing these needs by designing tests that focus on learners’ characteristics and goals and are integrated with educators’ and employers’ needs and goals. The research findings suggest that assessments should be personalized to learners, integrated with instruction or job training, and embrace the cultural diversity of the adult learner population better to serve learners, employees, and educators.

Development of Indicator for Piled Pier Health Evaluation in Vietnam Using Impact Vibration Test Approach

Development of Indicator for Piled Pier Health Evaluation in Vietnam Using Impact Vibration Test Approach

Machine Learning-Based Prediction Approach for COVID-19 Detection via Chest X-Ray Scans

Objectives: In medical research, the application of AI technologies has become a desirable approach for disease diagnosis due to their ability to increase diagnosis speed and accuracy. One major application of these technologies is in diagnosing diseases using medical imagery data such as X-ray and CT scan images. The objective of this study is to implement a COVID-19 diagnosis approach based on ML and compare it with laboratory testing approaches. Methods: The study utilizes the VGG16 architecture to extract features from X-ray and CT scan images, which are then used to build an ANN classifier to predict images as either COVID-positive or normal. Real-world datasets of varying difficulty were employed to evaluate the system's performance under different stressors, while comparative analysis with other classifiers was conducted to assess the model's improvement in accuracy and time complexity. Findings: The proposed architecture correctly classified 98.41% of the testing images. Among the images from infected patients in the testing set, 97.54% were accurately identified, while 98.72% of the normal cases were correctly classified. The model demonstrated good precision, with 96.49% of predicted COVID cases truly being from COVID-19 patients. Additionally, the F1 score for the model is 98.41%, indicating a good balance in predicting both categories. Showcasing its ability to handle multiple predictions quickly. The model's execution time for classifying a single case was computed as 0.0039 seconds, showcasing its ability to handle multiple predictions quickly. The proposed approach exhibits superior performance on datasets with balanced outcomes, surpassing the accuracy of SVM, DT, Logistic Regression, and Random Forest models. The approach outperforms laboratory-based testing approaches in terms of accuracy cost and assay times. Novelty: This study not only implements a robust ML-based approach for COVID-19 diagnosis but also provides a unique comparative analysis with traditional laboratory-based testing methods, such as Reverse Transcription Polymerase Chain Reaction (RT-PCR), reported in other literature. This dual focus on ML performance and its comparison with established testing methods highlights the practical advantages of ML in terms of diagnosis speed, accuracy, and cost-efficiency, offering a comprehensive perspective not commonly addressed in existing research. Keywords: COVID-19, Machine Learning techniques, F1 score, Classifier, Specificity, Accuracy, Sensitivity, Precision

Steroidomics-Based Screening for Primary Aldosteronism: Impact of antihypertensive Drugs.

Diagnosis of primary aldosteronism (PA) is complicated by the need to withdraw antihypertensive medications that interfere with test results, particularly renin. This study examined whether machine learning-based steroid-probability scores offer a renin measurement-independent approach for testing less prone to interference than the aldosterone-to-renin ratio (ARR). This prospective multicenter cohort study involved the use of plasma steroidomics and the ARR in 839 patients tested for PA, including 190 with and 578 without PA (71 indeterminate). Receiver operating characteristic curves for steroid-probability scores and the ARR were examined with and without interfering medications. Impacts of individual medications on plasma aldosterone, 18-oxocortisol, 18-hydroxycortisol, steroid-probability scores, renin, and ARRs were examined by multivariable and paired analyses in patients with and without PA. Receiver operating characteristic curves indicated a significant impact of interfering antihypertensive medications on the diagnostic performance of the ARR and minimal impact on steroid-probability scores. Mineralocorticoid receptor antagonists increased plasma aldosterone, 18-oxocortisol, and 18-hydroxycortisol in patients without PA and resulted in false-positive test results for steroid-probability scores and false-negative results for the ARR. Diuretics increased aldosterone, 18-oxocortisol, and steroid-probability scores in patients without PA, whereas angiotensin-converting enzyme inhibitors decreased aldosterone, steroid-probability scores, and ARRs. Beta-adrenoceptor blockers, dihydropyridine calcium channel blockers, and angiotensin receptor blockers had negligible impact on mineralocorticoids and steroid-probability scores. Among antihypertensive drugs that impact plasma aldosterone, 18-oxocortisol, and 18-hydroxycortisol, mineralocorticoid receptor antagonists stood out as a cause of false-positive results for derived steroid-probability scores. Other antihypertensives have minimal or no impact, an advantage for use of steroid-probability scores over the ARR when those medications cannot be withdrawn. URL: https://drks.de/search/en/trial/DRKS00017084; Unique identifier: DRKS00017084.

Multi-sensor approach for pulsed eddy current testing: Design, simulation, and non-destructive testing potential

Multi-sensor approach for pulsed eddy current testing: Design, simulation, and non-destructive testing potential

Reflex Xpert MTB/XDR Testing of Residual Rifampicin-Resistant Specimens: A Clinical Laboratory-Based Diagnostic Accuracy and Feasibility Study in South Africa.

The World Health Organization-approved Xpert MTB/XDR test detects Mycobacterium tuberculosis and resistance to isoniazid, fluoroquinolones, ethionamide, and injectable drugs directly in specimens. This pragmatic, laboratory-based study assessed the diagnostic accuracy and feasibility of a reflex testing approach, where Xpert MTB/XDR was performed on residual specimens previously processed for Xpert MTB/RIF Ultra. Routine respiratory specimens, processed for Xpert MTB/RIF Ultra, were stored in sample reagent buffer at 2°C-8°C. If rifampicin resistant, the residual specimen was assessed for adequate volume (≥2 mL) and tested with Xpert MTB/XDR, with storage time recorded. A second specimen was used for routine and reference standard testing (culture and sequencing). Specimens (99% sputum) from 763 participants submitted to 2 large routine laboratories were included. Xpert MTB/XDR yielded valid resistance detection results in 639 (84%), compared with 507 (66%) for routine testing (difference [95% CI], 18% [13%-22%]). The median turnaround time for results was 23 hours for Xpert MTB/XDR and 15 days for routine testing. While 748 specimens (98%) were ≥2 mL, only 102 (13%) were stored for ≤4 hours. By the reference standard, 284 of 394 (72%) were isoniazid resistant, and 57 of 380 (15%) were fluroquinolone resistant. The sensitivities of Xpert MTB/XDR were 94% (95% CI, 91%-97%) for isoniazid and 91% (81%-97%) for fluoroquinolone resistance detection. The specificities were 98% (94%-100%) and 100% (98%-100%), respectively. Xpert MTB/XDR performed favorably compared with the reference, and the reflex testing approach increased results availability over routine testing, while dramatically decreasing turnaround time from weeks to hours. Laboratory workflow precluded testing within the manufacturer-recommended 4-hour storage time, but longer storage did not appear detrimental.

New Digital Divide Shaped by Algorithm? Evidence from Agent-Based Testing on Douyin’s Health-Related Video Recommendation

While recommendation algorithms have significantly empowered human communication process, there is an emerging scholarly and societal concern regarding the potential discrimination inherent in algorithmic decision-making. The present study employs a novel agent-based testing approach to conduct an automated audit of the Douyin algorithm’s recommendations for health-related videos, aiming to investigate its relationship with information inequalities between individuals who are socioeconomically advantaged and disadvantaged. Our findings imply the possible existence of a new digital divide, wherein the algorithm tends to recommend a smaller proportion of authenticated health-related videos on cheap phone models, representing users in low socioeconomic group. Furthermore, this divide was mitigated when both groups demonstrated the same watching strategy for authenticated information. This study implies that recommendation algorithms actively perpetuate and exacerbate discriminatory social structures in reality. We also advocate for increased attention and education among the low socioeconomic group to enhance their health information literacy and understanding of algorithmic mechanisms.

Connecting audiences with climate change: Towards humanised and action-focused data interactions

This paper investigates the relationship between design research and humancomputer interaction (HCI) in the context of climate change communication and engagement. We discuss current practices in climate change communication and the decrease in concern and engagement caused by “crisis fatigue”. Through Research through Design (RtD), we set out to investigate data humanism and how users react to climate change data, testing approaches to improve engagement. With this purpose, we designed and evaluated Finding Arcadia, an interactive data story that uses data humanism to shift the dialogue from crisis-focused to action-focused. One study with the original IMF visualisations (N = 17) and two studies in public spaces (N = 12 and N = 64) point to the contextualization of the data and presenting actionable solutions helping in engaging users with climate change issues; help in creating solution-focused narratives and interpreting and relating with climate data. From these results, we derive insights for designing empowering interactive data visualizations for resilient climate change engagement.

Current microbiological testing approaches and documented infections at febrile neutropenia onset in patients with hematologic malignancies

ObjectivesThis study aims to identify infection etiology in febrile neutropenia (FN) is vital. This study explores different microbiological approaches and their impact on diagnosing infections in patients with hematologic malignancies and FN. MethodsThis is a retrospective analysis conducted at the Hospital Clinic of Barcelona details microbiological testing strategies used to diagnose infections at FN onset between January 2020 and July 2022. ResultsA total of 4520 microbiological tests were ordered in 462 FN episodes, achieving a 10% test positivity rate, with 200 (43.3%) episodes showing microbiological documentation of infection. Blood cultures (40.4%), non-culture blood tests (21.2%), and respiratory tract samples (16.2%) were the most requested. Blood cultures exhibited the highest (16.9%) test positivity rates, whereas non-culture blood tests showed the lowest (3.3%). Bacterial infections were present in 149 of 462 (32.3%) FN episodes. Viral infections (66 of 462, 14.3%)—notably, respiratory viruses—were also frequent. Mortality rate at 60 days was 9.1%; documented infections were associated with a higher risk (15%). ConclusionsIn the current landscape of antimicrobial diagnostics, our findings revealed the highest reported rate of microbiologically documented infections at FN onset. Bacterial infections are common; however, our data reiterate the significance of viral infections in causing fever. Optimizing FN management during respiratory viral infections remains a challenge for antimicrobial de-escalation. The low positivity rates observed in certain diagnostic tests emphasize the need for cost-effective diagnostic stewardship.

Auditory hair cells and spiral ganglion neurons regenerate synapses with refined release properties in vitro

Ribbon synapses between inner hair cells (IHCs) and type I spiral ganglion neurons (SGNs) in the inner ear are damaged by noise trauma and with aging, causing "synaptopathy" and hearing loss. Cocultures of neonatal denervated organs of Corti and newly introduced SGNs have been developed to find strategies for improving IHC synapse regeneration, but evidence of the physiological normality of regenerated synapses is missing. This study utilizes IHC optogenetic stimulation and SGN recordings, showing that, when P3-5 denervated organs of Corti are cocultured with SGNs, newly formed IHC/SGN synapses are indeed functional, exhibiting glutamatergic excitatory postsynaptic currents. When using older organs of Corti at P10-11, synaptic activity probed by deconvolution showed more mature release properties, closer to the specialized mode of IHC synaptic transmission crucial for coding the sound signal. This functional assessment of newly formed IHC synapses developed here, provides a powerful tool for testing approaches to improve synapse regeneration.

FedGMMAT: Federated generalized linear mixed model association tests.

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for the confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for confounding fixed and additive polygenic random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

Computational Test for Conditional Independence

Conditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference. CI testing is difficult, especially when testing involves categorical variables conditioned on a mixture of continuous and categorical variables. Current parametric and non-parametric testing methods are designed for continuous variables and can quickly fall short in the categorical case. This paper presents a computational approach for CI testing suited for categorical data types, which we call computational conditional independence (CCI) testing. The test procedure is based on permutation and combines machine learning prediction algorithms and Monte Carlo cross-validation. We evaluated the approach through simulation studies and assessed the performance against alternative methods: the generalized covariance measure test, the kernel conditional independence test, and testing with multinomial regression. We find that the computational approach to testing has utility over the alternative methods, achieving better control over type I error rates. We hope this work can expand the toolkit for CI testing for practitioners and researchers.

Evaluating diagnostic accuracy of an RT-PCR test for the detection of SARS-CoV-2 in saliva

Background and objectiveSaliva has been proposed as a potential more convenient, cost-effective, and easier sample for diagnosing SARS-CoV-2 infections, but there is limited knowledge of the impact of saliva volumes and stages of infection on its sensitivity and specificity.MethodsIn this study, we assessed the performance of SARS-CoV-2 testing in 171 saliva samples from 52 mostly mildly symptomatic patients (aged 18 to 70 years) with a positive reference standard result at screening. The samples were collected at different volumes (50, 100, 300, and 500 µl of saliva) and at different stages of the disease (at enrollment, day 7, 14, and 28 post SARS-CoV-2 diagnosis). Imperfect nasopharyngeal (NP) swab nucleic acid amplification testing was used as a reference. We used a logistic regression with generalized estimating equations to estimate sensitivity, specificity, PPV, and NPV, accounting for the correlation between repeated observations.ResultsThe sensitivity and specificity values were consistent across saliva volumes. The sensitivity of saliva samples ranged from 70.2% (95% CI, 49.3–85.0%) for 100 μl to 81.0% (95% CI, 51.9–94.4%) for 300 μl of saliva collected. The specificity values ranged between 75.8% (95% CI, 55.0–88.9%) for 50 μl and 78.8% (95% CI, 63.2–88.9%) for 100 μl saliva compared to NP swab samples. The overall percentage of positive results in NP swabs and saliva specimens remained comparable throughout the study visits. We observed no significant difference in cycle number values between saliva and NP swab specimens, irrespective of saliva volume tested.ConclusionsThe saliva collection offers a promising approach for population-based testing.

Developing a Passive Dosing Method for Acute Aquatic Toxicity Tests of Cationic Surfactant Benzalkoniums (BACs).

Benzalkonium chlorides (BACs) have been of environmental concern due to their widespread use and potential harm. However, challenges arise in defining and controlling the exposure concentration (Cw) in aquatic toxicity tests involving BACs with a long alkyl chain (i.e., #C > 14). To address this, a novel passive dosing method was introduced in the 48 h-acute ecotoxicity test on Daphnia magna and compared to the conventional solvent-spiking method in terms of Cw stability and toxicity results. Among 13 sorbent materials tested for their sorption capacity, poly(ether sulfone) (PES) membrane was an optimal passive dosing reservoir, with equilibrium desorption of BACs to water achieved within 24 h. The Cw of BACs remained constant in both applied dosing methods during the test period. However, the Cw in solvent-spiking tests was lower than the nominal concentration for long-chain BACs, particularly at low exposure concentrations. Notably, the solvent-spiking tests indicated that the toxicity of BACs increased with alkyl chain length from C6 to 14, followed by a decline in toxicity from C14 to 18. In contrast, the passive dosing method displayed similar or slightly increasing toxicity levels of BACs from C14 to C18, indicating higher toxicity of C16 and C18-BACs than that inferred by the solvent spiking test. These findings emphasize the potential of applying this innovative passive dosing approach in aquatic toxicity tests to generate reliable and accurate toxicity data and support a comprehensive risk assessment of cationic surfactants.

Meta-analysis: High pooled positive predictive value of the European Society of Paediatric Gastroenterology, Hepatology and Nutrition no-biopsy approach for coeliac disease testing in children.

The European Society of Paediatric Gastroenterology, Hepatology and Nutrition established guidelines in 2012 for a no-biopsy approach to diagnose coeliac disease in children. This guideline required symptoms suggestive of coeliac disease, positive human leukocyte antigen (HLA) DQ2/DQ8 haplotypes, tissue transglutaminase type-2 immunoglobulin A antibody titre at levels greater than 10 times the upper limit of normal, and positive endomysial immune-globulin A antibody test. An updated 2020 guideline excluded the need for symptoms and positive HLA. To assess the pooled positive predictive value (PPV) of the no-biopsy approach with small bowel biopsy (SBB) data as the reference standard for comparison. Database searches (October 2023) provided data that we combined using a random-effects meta-analysis to provide a pooled PPV, representing the probability that a positive test result means that an individual truly has the condition. We included 23 studies. Study sample sizes totalled 23,769 but only 3007 children had comparative SBB. The proportion of coeliac disease confirmed by the no-biopsy approach and SBB ranged from 79.2% to 100%, with an overall pooled PPV of 97.4% (95% confidence interval 96.0, 98.6). Sensitivity analysis showed higher PPV for the criteria that included HLA (98.5% vs. 96.8%; p = 0.017). Both no-biopsy criteria exhibit high PPV when compared to the reference standard. These results provide a consistent message of accuracy and feasibility to inform change and improve outcomes.

Exploring and field-demonstrating geofence-based occupancy-centric control in residential buildings

Several studies have highlighted the thermal comfort challenges associated with occupancy-centric control (OCC) when occupants first arrive, despite OCC demonstrating energy savings potential in buildings. To address this challenge, a geofence-based OCC (Geo-OCC), where the virtual boundary of the occupant’s physical location is infused into the smart thermostat, is explored to pre-condition buildings, aiming to alleviate thermal discomfort. This research adopted an apples-to-apples field testing approach to quantify how Geo-OCC impacts the thermal comfort and energy consumption of the heating, ventilation, and air-conditioning (HVAC) system in two 1,200 ft2 lab homes located in Texas, USA. These two homes were designed and built as identically as they could be. Two side-by-side experimental scenarios were designed to compare different control strategies through integrations with smart thermostats. In scenario 1, Geo-OCC was implemented in the east home, while a baseline case without OCC (Non-OCC) was executed in the west home. In scenario 2, Geo-OCC was employed in the east home, and a traditional OCC based on occupancy presence sensors (Sensed-OCC) was implemented in the west home, demonstrating Geo-OCC’s ability to alleviate thermal discomfort. The field-testing results of scenario 1 suggested that Geo-OCC achieved a 46.41 % reduction in heating energy consumption compared to Non-OCC over the testing duration of 25 days, resulting in a maximum unmet degree (UD) of 3.83 °C in a 5-minute sampling frequency, in contrast to a maximum UD of 0.83 °C in Non-OCC mode. The results from the field testing in scenario 2 indicated that Geo-OCC reduced the maximum UD to 1.94 °C in a 5-minute sampling frequency, compared to 2.28 °C in Sensed-OCC, without an increase in heating energy consumption. The results of the scenario 1 experiment suggested the OCC decreased the HVAC energy consumption while having a slightly adverse effect on thermal comfort during occupied periods, especially upon the first arrival of occupants. The field testing of scenario 2 exhibited the capability of Geo-OCC to enhance thermal comfort with minor effects on the HVAC energy consumption. In addition, Geo-OCC displayed the potential to reduce the peak demand increase caused by traditional Sensed-OCC.

Effect of SIM RS GOS on Outpatient Waiting Time in selected hospitals, Indonesia

Background: Hospital Management Information System must be used by every hospital in Indonesia to improve the quality of health services, one of which is the SIM RS GOS application owned by the Indonesian Ministry of Health. Outpatient waiting time is a measure of the quality of health services. Objective: The objective of this study was to analyze the effect of SIM RS GOS application on waiting time in the outpatient polyclinic of RSUD dr. Adnaan WD. Methods: The research conducted was quantitative research using a quasi-experimental design with a one group pre-test and post test approach. The population in this study were patients at the outpatient polyclinic of RSUD dr. Adnaan WD, with a sample size of 52 respondents. The data collection instrument used in this study was an observation sheet containing an observation guide for outpatient waiting time. The test analysis used is Wilcoxon because the data is not normally distributed. Results: The results showed that there was an effect of the SIM RS GOS application on waiting time (p=0.000). Conclusion: SIM RS GOS Application can reduce patient waiting time at the outpatient polyclinic.