Establishment and parasitism efficiency of Trichogramma principium (Sugonyaev et Sorokina) on Helicoverpa armigera (Hb.) infesting cotton in Sudan

      This study was conducted at the Gezira Research Station (GRS) farm during 2010/11 season to verify establishment and parasitism efficiency of Trichogramma principium (Sugonyaev et Sorokina) (Hymenoptera: Trichogrammatidae) in eggs of Helicoverpa armigera (Hb.) (Lepidoptera: Noctuidae) infesting the Sudanese cotton cultivars "Barac 67B", "Hamid", "Burhan" and "Abdin". Trichogramma was acquired from the Rearing Unit, Agricultural Research Corporation (ARC), at preimaginal (prepupal) stage, in eggs of the rice moth Corcyra cephalonica (Stainton) (Lepidoptera: Pyralidae). Depending on numbers of H. armigera eggs / 100 plants, one release was done on each of Barac and Hamid and two on each of Abdin and Burhan. The release rate was 24,000 parasitoids /fed, at 7x7m distance between release points and 14-day intervals. Evaluation was done between treated plots with Trichogramma versus untreated. Observations consisted of the percentage of emerged parasitoids, percentage of parasitism and the numbers of the African bollworm larvae /100 plants. At the first release, the percentage of emerged parasitoids ranged between 71% in Barac and 86.4% in Hamid; the cultivars` average ranged between 60.5% and 94.8% and the overall average was 79.2%. The percentage of parasitized H. armigera eggs ranged between 22.2% and 60%. At the second release, the percentage of emerged adults ranged between 73.2% in Burhan and 82.1% in Abdin; the cultivars average ranged between 46.6 and 96.7% and the overall average was 77.7%. The percentage of parasitized H. armigera eggs ranged between 22.2% and 77.7%. The high level of parasitoid emergence declared a good viability of the released material, tolerance of the parasitoid to the local weather conditions and acceptance of the crop. The levels of parasitism reported were quite acceptable for this introductory release and first occurrence of the parasitoid in this new cotton agroecosysytem. A positive signal of migration from treated to untreated plots was observed through parasitized H. armigera eggs detected in the latter plots. The numbers of H. armigera larvae were negligible in both treatments. Accordingly, for proving potential capacity of establishment and parasitism efficiency against H. armigera, T. principium is strongly recommended for use on Sudanese cotton cultivars.   المقدرة على الاستيطان والكفاءة التطفلية ل  Trichogramma principium (Sugonyaev et Sorokina)  ضد Helicoverpa armigera (Hb.)   على القطن في السودان      أجريت هذه الدراسة بمزرعة محطة بحوث الجزيرة في موسم 2010-2011 للتثبت من المقدرة على  الاستيطان و الكفاءة التطفلية ل  Trichogramma principium (Sugonyaev et Sorokina) (Hymenoptera: Trichogrammatidae) على بيض دودة اللوز الأفريقية Helicoverpa armigera (Hb.) (Lepidoptera: Noctuidae) على أصناف الأقطان السودانية "باراك B 67"، "حامد"، "برهان"، و"عابدين". تم الحصول على طفيل التريخوجراما من وحدة الإكثار بهيئة البحوث الزراعية في طور ماقبل التشرنق بداخل بيض فراشة الأرز  Corcyra cephalonica (Lepidoptera: Pyralidae) . اعتمادا على عدد بيض دودة اللوز / 100 نبات، فلقد أجريت إطلاقه واحدة على كل من الصنفين باراك و حامد و اطلاقتان على كل من الصنفين عابدين و برهان بمعدل 24000 طفيل للفدان و على مسافات 7x7م بين نقاط الإطلاق و بفاصل 14 يوما بين الإطلاقات. تمت المقارنة بين الحقل المعامل بالتريخوجراما و الغير معامل. اشتمل التقييم على تحديد النسبة المئوية لبزوغ الأطوار المكتملة من الطفيل، النسبة المئوية للتطفل و عدد اليرقات في ال 100 نبات. عند الإطلاقة الأولى،  تراوحت النسبة المئوية لبزوغ الأطوار المكتملة بين 71% كما في باراك و 86.4% كما في حامد، و المتوسط  للأصناف بين 60.5% و 94.8% و المتوسط العام لهم 79.2 % و تراوحت النسبة المئوية للتطفل بين 60% و 22.2%. عند الإطلاقة الثانية،  تراوحت النسبة المئوية لبزوغ الأطوار المكتملة بين 73.2%% كما في برهان و 82.1% كما في عابدين، و المتوسط للأصناف بين 46.6%  و 96.7% و المتوسط العام لهم 77.7% ، و تراوحت النسبة المئوية للتطفل بين 77.7% و 22.2%. يدل هذا المستوى العالي من بزوغ الأطوار المكتملة على الحيوية الجيدة للطفيل المطلق، تحمله للظروف الجويه وتقبله للمحصول العائل.  تعتبر المستويات المتحصل عليها من التطفل مناسبة لهذا الإطلاق الإستهلالى و تواجد الطفيل لأول مرة على  هذا الوسط البيئي الزراعي الجديد للقطن بالسودان. هنالك مؤشرات لبداية هجرة الطفيل من المساحات المعامله لغير المعاملة تمثل في تواجد بيض متطفل علية في الأخيرة. كان عدد يرقات ديدان اللوز قليلا في المعاملتين. و عليه، و لقابليته على الاستيطان و كفاءته التطفلية المقبولة على ديدان اللوز الأفريقية فان ال T. Principium موصى باستعماله بشدة على أصناف الأقطان السودانية.      &nbsp

Performance evaluation of uplink shared channel for cooperative relay based narrow band internet of things network

– Low Power Wide Area Network (LPWAN) is one of the fastest growing network techniques provides efficient communciations for smart cities, e-Health, industry 4.0 and other applications. LPWAN enables long-rang communcaitons for M2M and cellular IoT networks. Narrowband-IoT (NB-IoT) is a type of LPWAN developed by 3GPP to connect a wide stream of IoT services and devices. NB-IoT systems rely on the mechanism of repeating the same signal every specified period of time in order to improve radio coverage better than it is in LTE systems. Repetition process is used to enhance the coverage of NB-IoT and for upgrade throughput as well. However, increasing the repetition of the signal significantly may give a negative result relative to the bandwidth limits. A cooperative relay (CoR) can be used beside repetition mechanism to helps reduce bandwidth stress. Moreover, the use of CoR for NB-IoT in physical uplink shared channel with repetitions will enhance the throughput. This paper will evaluate the performance of the CoR to enhance physical uplink shared channel in NB-IoT. The NB-IoT system model is simulated bu MATLAB to demonstrate the use of Cooperative relay (CoR) scheme in NPUSCH for NB-IoT for performance evaluation and comparison of using CoR scheme by considering metrics like data rate, throughput, and delay. The results conclude that in using CoR in NB-IoT gives high performance in overall NoT network throughput

Economic impact of clinical pharmacist interventions in a general tertiary hospital in Qatar

Background With an increasingly strained health system budgets, healthcare services need to continually demonstrate evidence of economic benefits. This study sought to evaluate the economic impact of interventions initiated by clinical pharmacists in an adult general tertiary hospital. Methods A retrospective review of clinical pharmacist interventions was carried out throughout followup durations in March 2018, July/August 2018, and January 2019 in Hamad General Hospital (HGH) at Hamad Medical Corporation (HMC) in Qatar. The study included clinical pharmacy interventions data of patients admitted to the internal medicine, critical care, and emergency wards. Included interventions were documented by clinical pharmacists or clinical pharmacy specialists, and approved by physicians. Interventions by non-clinical pharmacists or with missing data were excluded. Adopting the perspective of HMC, we calculated the total economic benefit, which is the sum of the cost savings and the cost avoidance associated with the interventions. Cost savings was defined as the reduced cost of therapy associated with therapy changes minus the cost of intervention and cost avoidance was the cost avoided by eliminating the occurrence of adverse drug events (ADEs). Sensitivity analyses were performed to assess the robustness of results against uncertainties. Results A total of 852 interventions, based on 340 patients, were included. The analysis projected an annual total benefit of QAR 2,267,036 (USD 621,106) based on a negative cost-savings of QAR-175,139 (USD-47,983) and a positive cost avoidance of QAR741,898 (USD203,260) over the 3-month follow-up period. The uncertainty analysis demonstrated the robustness of outcomes, including a 100% probability of positive economic benefit. Conclusions The clinical pharmacist intervention was associated witScopu

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

An IoT-Platform-Based Deep Learning System for Human Behavior Recognition in Smart City Monitoring Using the Berkeley MHAD Datasets

Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model’s simulation results achieved 98% accuracy for the citizen behavior recognition system

Vehicle detection for vision-based intelligent transportation systems using convolutional neural network algorithm

Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. )erefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset

An IoT-platform-based deep learning system for human behavior recognition in smart city monitoring using the Berkeley MHAD datasets

Internet of Things (IoT) technology has been rapidly developing and has been well utilized in the field of smart city monitoring. The IoT offers new opportunities for cities to use data remotely for the monitoring, smart management, and control of device mechanisms that enable the processing of large volumes of data in real time. The IoT supports the connection of instruments with intelligible features in smart cities. However, there are some challenges due to the ongoing development of these applications. Therefore, there is an urgent need for more research from academia and industry to obtain citizen satisfaction, and efficient architecture, protocols, security, and services are required to fulfill these needs. In this paper, the key aspects of an IoT infrastructure for smart cities were analyzed. We focused on citizen behavior recognition using convolution neural networks (CNNs). A new model was built on understanding human behavior by using the berkeley multimodal human action (MHAD) Datasets. A video surveillance system using CNNs was implemented. The proposed model’s simulation results achieved 98% accuracy for the citizen behavior recognition system