Influence of exogenous factors on water demand forecasting models during the COVID-19 period

Water scarcity has urged the need for adequate water demand forecasting to facilitate efficient planning of municipal infrastructure. However, the development of water consumption models is challenged by the rapid environmental and socio-economic changes, particularly during unforeseen events like the COVID-19 pandemic. This study investigated the impact of COVID-19 on the efficiency of water demand prediction models, considering the lockdown measures and various exogenous features, such as previous consumption (PC) and socio-demographic (SDF), seasonal (SF), and climatic (CF) factors. Multiple ensemble models, gradient-boosting machines (GBM), extreme-gradient-boosting (XGB), light-gradient-boosting, random forest (RF), and stack regressor (STK) were examined, compared to other machine-learning techniques, multiple -linear regression (MLR), decision trees, and neural networks. The models were tested using 3-year metering records for 128,000 consumers in Dubai. The feature importance analysis indicated that PC and SDF had a significant impact on consumption rates with correlation coefficients of 0.95 and 0.74, respectively, as opposed to SF and CF, which had negligible effect. The results showed that, before COVID, RF and STK outperformed other models with a coefficient-of-determination (R2) and root-mean-squared-error (RMSE) of 0.928 and 0.039, followed by XGB at 0.923 and 0.041, respectively. However, MLR achieved the highest prediction accuracy amid COVID with R2 and RMSE of 0.90 and 0.05, followed by GBM and XGB equally at 0.83 and 0.06, respectively. An ensemble-based error prediction model was applied, resulting in up to 9.2% improvement in predictions. Overall, this research emphasized the efficiency of ensemble models in handling fluctuating data with a high degree of nonlinearity.

Information Quality Management for the Medical Wearable Devices: Validation Study

Researchers have focused on wearable devices as viable Internet of Things (IoT) solutions in the recent decade. After the epidemic of COVID-19, they are receiving increasing attention. Wearable Internet of Things (IoT) systems must have high information quality (IQ) to be useful. In earlier publications, we developed a comprehensive framework for systematically managing the total information quality of IoT systems. By performing a comparative study, the framework was first shown to be accurate and reliable. In the last publication, medical wearable systems were selected as a single-case experiment to test the framework's usefulness by focusing on the assessment phase. This paper effectively uses the single case to test the rest of the framework phases: awareness and action. In addition, this work clearly illustrates the significance of the framework's three pillars: data quality, IoT, and IQ management procedures.

Effective cancer care delivery during natural calamity!

Introduction: COVID19 has placed unprecedented constraints on healthcare services. Colorectal cancer (CRC) care was one of the many areas predicted to suffer due to these additional pressures. We believe that despite the challenges posed by COVID19, we have continued to deliver a standard of care for elective and emergency CRC resection that compares favorably with the national average. Method: We conducted an analysis of the elective and emergency CRC resections carried out at a tertiary center over a 10-month period (Feb-Dec 2020). Data was collated from patient, operative, and theatre records and compared to the national average as defined by the 2020 National Bowel Cancer Audit (NBOCA). Results: A total of 227 patients underwent surgery (189 elective and 38 emergencies), with a median age of 69. Of these, 153 were laparoscopic (67%), 57 open (27%), and 17 robotic (7%). The median length of stay was less than the national average;5 days for elective surgery (NBOCA: 6) and 8 days for emergencies (NBOCA: 10). Within 30 days, overall unplanned readmissions rate was 6.5% (NBOCA: 11.6%) and return to theatre was 3.2% (NBOCA: 8.4%). Elective surgery had a 90-day mortality of 1% (NBOCA: 3%) compared to 7.8% for emergencies (NBOCA: 10.5%). Conclusion: Despite the added constraints of the COVID19 pandemic, CRC resection in our unit remains safe with better outcomes than the national standard. We have demonstrated that with adequate precaution and a concerted team effort, delivery of safe care with reasonable outcome is achievable.

Operational excellence using gap analysis: UAE IT centers case study

Purpose: There is a high demand on distance learning due to COVID 19 pandemic. As a result, the information technology centers of any educational institution are playing important role in maintaining the quality of education. It is very vital to assess and enhance the IT service performance. The motivation behind this research paper is to measure the operational excellence by applying gap analysis technique. The SERVQUAL tool is used in this research study in the information technology centers in three universities in United Arab Emirates. Design/methodology/approach: The research study is conducted by calculating the perception and expectation scores (performance-based model). The gap scores (expected scores minus perceived-based model) are examined using SERVQUAL tool. The methodology is using survey questionnaire to collect data from 200-250 users of IT service centers from each university. The questionnaire has 22 questions, which represent the 22 items of five SERVQUAL dimensions. The survey participants concluded that the SERVQUAL is a useful tool for IT center service quality in the three educational institutions presented in this paper. Findings: The SERVQAUL identified the gaps in service quality of IT centers for these institutions. The perception and expected scores of SERVQUAL in three IT centers of these institutions are also illustrated. The perception results are tabulated versus the expected results as well as the gaps are calculated. Moreover, we demonstrated the comparison between the average perception and the expected dimension scores results for each university. As a result, the averages of each dimension's items is calculated and the benchmarking between universities is done in terms of average expected and perception scores. Research limitations/implications: We can conclude that the three universities should focus on the responsiveness dimension as it gets the lowest average gap scores. This study is cross sectional that is done on the users only. In addition, the decision makers' and service providers' feedback can be studied and more elaborated. Originality/value: These scores can be used in business excellence models' criteria. Some of these models can be Malcolm Baldrige or European Foundation for Quality Management (EFQM). SERVQUAL can be integrated within the Malcolm or EFQM to enhance performance and continuous improvement. © 2020 Universidade do Minho. All rights reserved.