STR-827: SHEAR STRENGTH OF CIRCULAR CONCRETE BEAMS REINFORCED WITH GFRP BARS

This paper presents the results of an investigation of the shear strength and behavior of three circular concrete beams reinforced with glass-FRP (GFRP) bars and spirals. The beams, which measured 3,000 mm in length by 500 mm in diameter, were tested under four-point bending. The test parameters included the GFRP-spiral-reinforcement ratio (different spiral spacings [150 and 200 mm] and spiral diameters [13 and 15 mm]). As designed, the beams failed in shear due to GFRP-spiral rupture. The test results indicated that the higher the GFRP spiral reinforcement ratio, the higher the enhancement of the shear strength due to the confinement, which controls shear cracks and improves aggregate interlocking

Some biological aspects of four marine fish species in Iraqi marine waters, northwest Arabian Gulf

The study described some biological characteristics of four commercially important fish species in Iraqi marine waters including Planiliza klunzengeri, P. subviridis, Acanthopagrus arabicus and Otolithes ruber from February 2020 to January 2021. The length and weight of individual fish caught were measured. The length-weight relationships were W= 0.027L2.715 for P. klunzengeri,  W= 0.034L2.670 for P. subviridis,  W= 0.030L2.867 for A. arabicus and W= 0.023L2.755 for O. ruber, and all species indicated negative allometric growth. The lower values of the relative condition factor (Kn) were found at the mid-lengths of P. klunzingeri and P. subviridis, and the large individuals for A. arabicus and O. ruber. Scales were used for age determination and measurements, and the back-calculated lengths-at-ages were calculated using the body proportional formula. Asymptotic total lengths (L∞) estimated were 29.6, 32.8, 44.9 and 70.6 cm for P. klunzengeri, P. subviridis, A. arabicus and O. ruber, respectively. These results could assist in fisheries management for the sustainable exploitation of these commercially important species in Iraqi marine waters

Tissue trauma and inflammatory response following laparoscopic versus abdominal hysterectomy: a prospective randomized clinical trial

Background: Laparoscopic surgery is associated with more favorable clinical outcome than conventional open surgery. This might be related to the magnitude of tissue trauma and tissue stress response. This study compares the intensity of tissue injury by assessing plasma C-reactive protein (CRP), lactic dehydrogenase (LDH), and cancer antigen 125 (CA 125) in patients undergoing laparoscopic hysterectomy compared to abdominal hysterectomy.Methods: This study was conducted at Ain Shams University Maternity Hospital from May 2018 to February 2020. 74 women candidate for hysterectomy were recruited and randomized into two groups: group A included 37 cases who underwent abdominal hysterectomy, and group B included 37 cases that underwent laparoscopic hysterectomy.Results: CRP, LDH and CA125 significantly increased post-operatively in both groups. Postoperative mean serum CRP, LDH and CA125 were significantly higher in the laparotomy group (10.84±2.47, 262.21±76.77, and 13.41±2.6 respectively) compared to laparoscopy group (7.92±2.25, 148.53±43.56, 11.79±2.77 respectively) (p<0.05).Conclusions: Surgery is a significant cause of tissue injury that can be assessed by specific markers. We suggest that laparoscopic surgery causes less tissue damage as assessed by lower postoperative CRP and LDH values, which might account for the earlier recovery and reduced hospital stay in laparoscopic surgery

Cyclic Self-Organizing Map for Object Recognition

Object recognition is an important machine learning (ML) application. To have a robust ML application, we need three major steps: (1) preprocessing (i.e. preparing the data for the ML algorithms); (2) using appropriate segmentation and feature extraction algorithms to abstract the core features data and (3) applying feature classification or feature recognition algorithms. The quality of the ML algorithm depends on a good representation of the data. Data representation requires the extraction of features with an appropriate learning rate. Learning rate influences how the algorithm will learn about the data or how the data will be processed and treated. Generally, this parameter is found on a trial-and-error basis and scholars sometimes set it to be constant. This paper presents a new optimization technique for object recognition problems called Cyclic-SOM by accelerating the learning process of the self-organizing map (SOM) using a non-constant learning rate. SOM uses the Euclidean distance to measure the similarity between the inputs and the features maps. Our algorithm considers image correlation using mean absolute difference instead of traditional Euclidean distance. It uses cyclical learning rates to get high performance with a better recognition rate. Cyclic-SOM possesses the following merits: (1) it accelerates the learning process and eliminates the need to experimentally find the best values and schedule for the learning rates; (2) it offers one form of improvement in both results and training; (3) it requires no manual tuning of the learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyper-parameters and (4) it shows promising results compared to other methods on different datasets. Three wide benchmark databases illustrate the efficiency of the proposed technique: AHD Base for Arabic digits, MNIST for English digits, and CMU-PIE for faces

AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt

The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%

Forced evictions and their social and health impacts in Southern Somalia: a qualitative study in Mogadishu Internally Displaced Persons (IDP) camps

Background: Forced evictions are common in conflict-affected settings. More than 500 internally displaced persons (IDPs) are evicted daily in Mogadishu. Context specific research is necessary to inform responsive humanitarian interventions and to monitor the effective- ness of these interventions on IDPs health. Objective: This study explored the causes of forced evictions and their health impacts among IDPs in southern Somalia. Methods: We used a qualitative approach, conducting 20 semi-structured interviews, six key informant interviews and four focus group discussions. We used maximum variation sampling to include a wide range of participants and used the framework approach and Nvivo software to analyse the data. Results: In this context, landlords often rented land without proper tenure agreements, resulting in risk of forced evictions. Informal tenure agreements led to fluctuations in rent, and IDPs were evicted because tenancy laws were inadequate and failed to protect their rights. IDP settlements often increased the value of land by clearing scrub, and landlords often sought to profit from this by evicting IDPs at short notice if a buyer was found for the land. The effect of eviction on an already marginalised population was wide ranging, increasing their exposure to violence, loss of assets, sexual assault, disruption of livelihoods, loss of social networks, and family separation. Evicted IDPs reported health issues such as diarrhoea, malaria, pneumonia, measles and skin infections, as well as stress, anxiety, psychological distress and trauma. Conclusion: Forced evictions remain one of the biggest challenges for IDPs as they exacerbate existing vulnerabilities. Prioritizing implementation of legal protection for IDP tenure rights is necessary to prevent unlawful evictions of IDPs. Humanitarian agencies should aim to respond more effectively to protect evictees and provide support to prevent poor health outcomes. Further quantitative research is needed to examine the relationship between forced evictions and health outcomes