Proposed Model to Study Effect of Lighting Parameters on Construction Sites

Inadequate lighting plans for the operations performed at night on construction sites can affect the quality of the implemented work, labor productivity, safety, as well as the project\u27s overall cost. Accordingly, to avoid inadequate lighting conditions, the location of the luminaries utilized to illuminate the chosen construction area and its configuration, including luminaire height and luminary angle, are crucial choices in the design of the lighting plan. Besides making sure that the chosen installation pattern is cost-effective and as much as possible meets the lighting levels needed for each point according to the nature of the implemented work and safety considerations, In this way, a maximal coverage location model using LINGO software is developed in order to investigate the best locations of luminaires, taking into account gradual and cooperative covering. The objective is to ensure that the selected allocation of luminaires minimizes the summation of the received illuminance by the points that exceed the demand and the received illuminance that is below the demand. The optimized results refer to that smaller luminary angles lead to large coverage and minimizes the objective function. Making sure that the selected luminaire height leads to the desired illuminance levels according to the inverse relationship between the height and the illuminance

Proposed Model to Study Effect of Lighting Parameters on Construction Sites

Inadequate lighting plans for the operations performed at night on construction sites can affect the quality of the implemented work, labor productivity, safety, as well as the project\u27s overall cost. Accordingly, to avoid inadequate lighting conditions, the location of the luminaries utilized to illuminate the chosen construction area and its configuration, including luminaire height and luminary angle, are crucial choices in the design of the lighting plan. Besides making sure that the chosen installation pattern is cost-effective and as much as possible meets the lighting levels needed for each point according to the nature of the implemented work and safety considerations, In this way, a maximal coverage location model using LINGO software is developed in order to investigate the best locations of luminaires, taking into account gradual and cooperative covering. The objective is to ensure that the selected allocation of luminaires minimizes the summation of the received illuminance by the points that exceed the demand and the received illuminance that is below the demand. The optimized results refer to that smaller luminary angles lead to large coverage and minimizes the objective function. Making sure that the selected luminaire height leads to the desired illuminance levels according to the inverse relationship between the height and the illuminance

Minimally invasive approach for the management of right atrial angiosarcoma; A case report

Cardiac angiosarcoma is a rare primary cardiac tumor. Outcomes of minimally invasive resection of cardiac angiosarcoma are rarely reported in the literature A male patient aged 28 years old presented with a right atrial mass compressing the superior vena cava and associated with pericardial effusion. Pericardiocentesis was done, and a preoperative workup revealed no distant metastasis. We planned excision of the mass through a right mini-thoracotomy approach. Intraoperatively, we found the mass invading the entire atrial wall thickness, and excision of the mass with a reconstruction of the right atrial wall was performed. Minimally invasive resection of atrial angiosarcoma could be feasible. Atrial angiosarcoma could present with vague signs and symptom

Real time data analysis and visualization for the breast cancer disease

Today, the amount of data that are digitally collected in the healthcare sector is tremendous and expanding rapidly, these data are inherently geospatial and temporal ranging from individual families to whole states and from minutes to decades. Therefore, they need sophisticated data management and analysis to be transformed into valuable knowledge. Healthcare professionals are faced with several challenges regarding extracting knowledge from this massive amount of data in order to support the decision-making process. To gain advantage of health care big data, big data analytics need to be exploited to utilize and understand patterns associations within these data thus make the right decision. In this research, an interactive data analysis and visualization tool is proposed to visually compare the performance of three machine learning algorithms on Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The proposed model consists of two phases: input phase and analysis/visualization phase. It aims to allow the user to interactively compare the performance of three different ML algorithms (KNN, SVM and NB) in terms of accuracy, sensitivity and error rate in a user-friendly way. Here, SVM classifier has proven its efficiency and it is concluded as the best classifier with the highest accuracy as compared to the other two classifiers

Healthcare analytics—A literature review and proposed research agenda

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field

Association Between Voice Handicap Index and Reflux Symptom Index: A cross-sectional study of undiagnosed general and teacher cohorts in Saudi Arabia

Objectives: This study aimed to assess potential associations between self-reported symptoms of laryngopharyngeal reflux (LPR) and voice disorders among two undiagnosed cohorts in Saudi Arabia. Methods: This cross-sectional study was conducted from February to April 2017 in Khobar, Saudi Arabia. Validated Arabic versions of the Reflux Symptom Index (RSI) and 10-item Voice Handicap Index (VHI-10) were distributed to 400 teachers at 13 schools and 300 members of the general population attending an ear, nose and throat clinic in Khobar. Scores of >13 and >11 on the RSI and VHI-10 indicated a potential subjective diagnosis of LPR and voice disorders, respectively. Results: A total of 446 individuals took part in the study, including 260 members of the general population (response rate: 86.7%) and 186 teachers (response rate: 46.5%). The mean age was 32.5 years. In total, 62.2% complained of voice and/or reflux problems, with the remaining 37.8% not reporting/unaware of any problems in this regard. Among the teachers, 30.6% and 18.3% had positive RSI and VHI-10 scores, respectively, while 43.1% and 14.6% of the individuals from the general population had positive RSI and VHI-10 scores, respectively. Overall, VHI-10 scores were significantly associated with RSI scores (P <0.001). Conclusion: A significant association between RSI and VHI-10 scores suggests that there may be an association between LPR and voice disorders. These tools would therefore be a valuable method of monitoring patients; however, they cannot be used to confirm a diagnosis. Thus, more detailed studies are needed to confirm this association using a larger sample size.Keywords: Voice Disorders; Laryngopharyngeal Reflux; Hoarseness; Diagnostic Self Evaluation; School Teachers; Saudi Arabia

Wearable artificial intelligence for anxiety and depression: A scoping review

Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services. Objective: The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods: We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis. Results: Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM). Conclusions: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression

Knowledge and Attitude among Doctors towards Use of Prophylactic Vitamin K in Neonatal Bleeding Disorders in Department of Obstetrics and Gynecology: Experience from Haj El-Safi Hospital, Sudan

Background: Vitamin K is crucial for neonates to prevent bleeding disorders. Raising awareness of vitamin K use would show positive outcomes. This study aimed to assess the knowledge and attitude of prophylactic use of vitamin K for bleeding disorders in neonates among doctors working in the Department of Obstetrics and Gynecology at Haj El-Safi Hospital, Sudan.Methods: A descriptive cross-sectional study was conducted in February 2019, involving 36 doctors selected by convenience sampling. Data were collected by an interview-based questionnaire designed to measure the knowledge level of doctors toward vitamin K deficiency classification and interactions, guidelines availability and adherence, and parents counseling. Data were analyzed and presented in tables. Results: The doctors involved in this study were 36, including 7 registrars, 6 medical officers, 23 house officers, mostly aged 20–25 years (n=29), and female (n=27). The level of knowledge about the classification of vitamin K deficiency and the interaction of vitamin K with other drugs among doctors was mostly good (n=14 and n=15, respectively). Registrars were the most in the good category (n=6, and n=7, respectively). Most doctors (n=23) were aware of vitamin K guidelines, while only 15 have continuous adherence, 16 of doctors counseled parents about the importance of prophylactic vitamin K.Conclusion: The level of knowledge towards prophylactic vitamin K use in neonatal bleeding disorders among doctors in the Department of Obstetrics and Gynecology at Haj El-Safi Hospital is good

Removal of diclofenac potassium from wastewater using clay-micelle complex

The presence of an ionized carboxyl group in the widely used non-steroidal anti-inflammatory (NSAID) drug diclofenac potassium results in a high mobility of diclofenac and in its low sorption under conditions of slow sand filtration or subsoil passage. No diclofenac degradation was detected in pure water or sludge during one month. Tertiary treatments of wastewater indicated that the effective removal of diclofenac was by reverse osmosis, but the removal by activated carbon was less satisfactory. This study presents an efficient method for the removal of diclofenac from water by micelle–clay composites that are positively charged, have a large surface area and include large hydrophobic domains. Adsorption of diclofenac in dispersion by charcoal and a composite micelle (otadecyltrimethylammonium [ODTMA] and clay [montmorillonite]) was investigated. Analysis by the Langmuir isotherm revealed that charcoal had a somewhat larger number of adsorption sites than the composite, but the latter had a significantly larger binding affinity for diclofenac. Filtration experiments on a solution containing 300 ppm diclofenac demonstrated poor removal by activated carbon, in contrast to very efficient removal by micelle–clay filters. In the latter case the weight of removed diclofenac exceeded half that of ODTMA in the filter. Filtration of diclofenac solutions at concentrations of 8 and 80 ppb yielded almost complete removal at flow rates of 30 and 60mLmin−1. One kilogram of ODTMA in the micelle–clay filter has been estimated to remove more than 99% of diclofenac from a solution of 100 ppb during passage of more than 100m3.Beit-Jala Pharmaceutical Co. is thanked for the supply of diclofenac potassium. Special thanks to Dr. Saleh Abu-Lafi for technical assistance. This work was supported by a grant from the USAID-MERC program