Determining the Essential Criteria for Choosing Appropriate Methods for Maintenance and Repair of Iraqi Healthcare Building Facilities

Today, building maintenance and repair (M&R) is a neglected aspect of the construction business throughout a building’s entire life cycle. Selecting appropriate M&R strategies is crucial, particularly for emerging economies like Iraq with severely constrained resources. This study seeks to identify the primary selection criteria for M&R methods of healthcare building facilities (HBFs) in Iraq. A comprehensive desktop literature analysis was undertaken to extract and determine the essential selection criteria for the most suited M&R approaches to buildings in general. Then, two rounds of the Delphi survey were conducted to consolidate the specific selection criteria to suit the circumstances of Iraq and HBFs. A total of 21 sub-criteria were identified and divided into six main groups. The main criteria and the associated sub-criteria were then analyzed and ranked using the fuzzy analytic hierarchy process (FAHP) technique. The ranking of the various main criteria revealed that the “cost” criterion was ranked first in terms of importance, followed by the “human resources” and “quality” criteria. The fourth, fifth, and sixth main criteria are “reliability/flexibility”, “safety/risk/environment”, and “facilities/technology”, respectively. The overall ranking of the sub-criteria placed “optimization and cost reduction” in the first position and “extending the life of the equipment and preserving their initial quality” in the bottom place. It is anticipated that the key findings and effective recommendations of this study will considerably contribute to the improvement of building maintenance and repair management practices in developing nations while enhancing different stakeholders’ understanding of the most important selection criteria for M&R methods, particularly with regard to healthcare building facilities in Iraq

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

Background: Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival. Objective: To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.Material and Methods: In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection. Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).  Conclusion: Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically

Optimum salinity/composition for low salinity water injection in carbonate rocks:A geochemical modelling approach

The brine-dependent recovery process mainly known as low salinity/ smart water injection (LSWI/SWI) is of great interest to the oil industry for enhanced oil recovery (EOR), especially for carbonate reservoirs due to their complex rock properties. In-depth understanding of fluid/ carbonate rock interactions helps to better understand carbonate reservoirs’ behavior with respect to low salinity water injection. Mineral dissolution/ precipitation and multi-ion exchange (MIE) are generally known to be key factors in brine/ carbonate interactions, controlling the rock wettability and consequently the performance of low salinity water injection. However, the effect of the aforementioned mechanisms is not fully understood. In this paper we investigate the carbonate/ brine interactions, using geochemical modelling, and study the competition between all active mechanisms which results in an optimum point in water salinity. This optimum point is the best salinity of injected water, leading to the most effective alteration in the wettability towards the water-wet conditions. The simulation outputs are then validated against experimental results previously reported. Finally, a sensitivity analysis of the potential determining ions (PDIs) e.g., calcium, magnesium, and sulfate is performed to systematically understand the effect of each ion on optimum water salinity. Generally, for rocks containing anhydrite, both MIE and dissolution curves have a monotonous trend. However, for free-anhydrite rocks, MIE considered as the dominant mechanism controlling the performance of low salinity water injection. MIE mechanism mainly depends on CaSO4- surface concentration and as sulfate concentration increases a higher fold of dilution would result in a better performance of LSWI. However, calcium and magnesium have not shown significant influence on the dissolution and MIE mechanisms

The score trend of each criterion by times.

ObjectivesCaffeine’s potential benefits on multiple sclerosis (MS), as well as on the ambulatory performance of non-MS populations, prompted us to evaluate its potential effects on balance, mobility, and health-related quality of life (HR-QoL) of persons with MS (PwMS).MethodsThis single-arm pilot clinical trial consisted of a 2-week placebo run-in and a 12-week caffeine treatment (200 mg/day) stage. The changes in outcome measures during the study period (weeks 0, 2, 4, 8, and 12) were evaluated using the Generalized Estimation Equation (GEE). The outcome measures were the 12-item Multiple Sclerosis Walking Scale (MSWS-12) for self-reported ambulatory disability, Berg Balance Scale (BBS) for static and dynamic balance, Timed Up and Go (TUG) for dynamic balance and functional mobility, Multiple Sclerosis Impact Scale (MSIS-29) for patient’s perspective on MS-related QoL (MS-QoL), and Patients’ Global Impression of Change (PGIC) for subjective assessment of treatment efficacy. GEE was also used to evaluate age and sex effect on the outcome measures over time. (Iranian Registry of Clinical Trials, IRCT2017012332142N1).ResultsThirty PwMS were included (age: 38.89 ± 9.85, female: 76.7%). Daily caffeine consumption significantly improved the objective measures of balance and functional mobility (BBS; P-valueConclusionsCaffeine may enhance balance, functional mobility, and QoL in PwMS. Being male was associated with a sharper increase in self-reported ambulatory disability over time. The effects of aging on balance get more pronounced over time.Trial registrationThis study was registered with the Iranian Registry of Clinical Trials (Registration number: IRCT2017012332142N1), a Primary Registry in the WHO Registry Network.</div

Descriptive statistics of demographic and MS-related variables.

Descriptive statistics of demographic and MS-related variables.</p

IRCT registry, English Language: Clinical Trial Protocol Registry (English).

IRCT registry, English Language: Clinical Trial Protocol Registry (English).</p

Study Protocol, English Language: Study Protocol (English).

Study Protocol, English Language: Study Protocol (English).</p

Research summary.

Abbreviations: MSWS-12: 12-item Multiple Sclerosis Walking Scale, BBS: Berg Balance Scale, TUG: Timed Up-and-Go, MSIS-29: Multiple Sclerosis Impact Scale, PGIC: Patients’ Global Impression of Change, QoL: Quality of life, PwMS: Persons with multiple sclerosis.</p