A Feasibility Study of Public Private Partnership in Sustainable Ethiopia’s Coffee Quality Improvement Programme

The rainforests of the south western Ethiopian highlands are recognised asthe birthplace of Coffea arabica. Ethiopia is the only country in the worldwhere Coffea Arabica (L.) can be found in the wild. The forests that oncedominated the highlands are considerably diminished during the lastdecades. This poses a massive threat to the survival of the genetic resources of Coffea Arabica, 99.8% of the genetic diversity of which is in Ethiopia. As a result, the diversity at origin of some 2,000 or more coffee varieties – unique in the world – is in danger of being irretrievably lost. The monastery islands and environ of Lake Tana had coffee from the South west forests planted 400 years ago by King Fassil of Gonder. This has created a natural ex-situ gene bank in an area otherwise covered with forest. Civilian population pressure since 1974 has reduced the forest for firewood and timber incomes and coffee has been neglected especially during the low price periods. In order to address these issues with sustainable livelihoods, the feasibility of a PPP programme is being studied, incorporating the governments with NGOs and private partners. This paper comprehensively analyses the approaches to study the feasibility and status of Public Private Partnerships (PPPs) in Amhara region specially Zegey peninsula and Lake Tana Island coffee quality improvement. To collect the required information interviews were conducted with concerned stakeholders and a simple questionnaire also prepared and distributed to coffee producing farmers in Amharic language.Key words: Quality Coffee Berries, Pre & Post Harvesting method, Amhararegion- Zegey Peninsula, Feasibility of PPP assistanc

Lightweight Object Detection Ensemble Framework for Autonomous Vehicles in Challenging Weather Conditions

The computer vision systems driving autonomous vehicles are judged by their ability to detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing this ability of a self-driving car to distinguish between the elements of its environment under adverse conditions is an important challenge in computer vision. For example, poor weather conditions like fog and rain lead to image corruption which can cause a drastic drop in object detection (OD) performance. The primary navigation of autonomous vehicles depends on the effectiveness of the image processing techniques applied to the data collected from various visual sensors. Therefore, it is essential to develop the capability to detect objects like vehicles and pedestrians under challenging conditions such as like unpleasant weather. Ensembling multiple baseline deep learning models under different voting strategies for object detection and utilizing data augmentation to boost the models' performance is proposed to solve this problem. The data augmentation technique is particularly useful and works with limited training data for OD applications. Furthermore, using the baseline models significantly speeds up the OD process as compared to the custom models due to transfer learning. Therefore, the ensembling approach can be highly effective in resource-constrained devices deployed for autonomous vehicles in uncertain weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and were able to identify objects from the images captured in the adverse foggy and rainy weather conditions. The applied techniques demonstrated an increase in accuracy over the baseline models and reached 32.75% mean average precision (mAP) and 52.56% average precision (AP) in detecting cars in the adverse fog and rain weather conditions present in the dataset. The effectiveness of multiple voting strategies for bounding box predictions on the dataset is also demonstrated. These strategies help increase the explainability of object detection in autonomous systems and improve the performance of the ensemble techniques over the baseline models

Review and Analysis of Failure Detection and Prevention Techniques in IT Infrastructure Monitoring

Maintaining the health of IT infrastructure components for improved reliability and availability is a research and innovation topic for many years. Identification and handling of failures are crucial and challenging due to the complexity of IT infrastructure. System logs are the primary source of information to diagnose and fix failures. In this work, we address three essential research dimensions about failures, such as the need for failure handling in IT infrastructure, understanding the contribution of system-generated log in failure detection and reactive & proactive approaches used to deal with failure situations. This study performs a comprehensive analysis of existing literature by considering three prominent aspects as log preprocessing, anomaly & failure detection, and failure prevention. With this coherent review, we (1) presume the need for IT infrastructure monitoring to avoid downtime, (2) examine the three types of approaches for anomaly and failure detection such as a rule-based, correlation method and classification, and (3) fabricate the recommendations for researchers on further research guidelines. As far as the authors\u27 knowledge, this is the first comprehensive literature review on IT infrastructure monitoring techniques. The review has been conducted with the help of meta-analysis and comparative study of machine learning and deep learning techniques. This work aims to outline significant research gaps in the area of IT infrastructure failure detection. This work will help future researchers understand the advantages and limitations of current methods and select an adequate approach to their problem

Virtual clinics in glaucoma care: face-to-face versus remote decision-making

BACKGROUND/AIMS: To examine the agreement in clinical decisions of glaucoma status made in a virtual glaucoma clinic with those made during a face-to-face consultation. METHODS: A trained nurse and technicians entered data prospectively for 204 patients into a proforma. A subsequent face-to-face clinical assessment was completed by either a glaucoma consultant or fellow. Proformas were reviewed remotely by one of two additional glaucoma consultants, and 12 months later, by the clinicians who had undertaken the original clinical examination. The interobserver and intraobserver decision-making agreements of virtual assessment versus standard care were calculated. RESULTS: We identified adverse disagreement between face-to-face and virtual review in 7/204 (3.4%, 95% CI 0.9% to 5.9%) patients, where virtual review failed to predict a need to accelerated follow-up identified in face-to-face review. Misclassification events were rare, occurring in 1.9% (95% CI 0.3% to 3.8%) of assessments. Interobserver κ (95% CI) showed only fair agreement (0.24 (0.04 to 0.43)); this improved to moderate agreement when only consultant decisions were compared against each other (κ=0.41 (0.16 to 0.65)). The intraobserver agreement κ (95% CI) for the consultant was 0.274 (0.073 to 0.476), and that for the fellow was 0.264 (0.031 to 0.497). CONCLUSIONS: The low rate of adverse misclassification, combined with the slowly progressive nature of most glaucoma, and the fact that patients will all be regularly reassessed, suggests that virtual clinics offer a safe, logistically viable option for selected patients with glaucoma

Collecting multiple data linkage consents in a mixed mode survey: evidence and lessons learnt from next steps

Linking survey responses with administrative data is a promising practice to increase the range of research questions to be explored, at a limited interview burden, both for respondents and interviewers. This paper describes the protocol for asking consent to data linkage on nine different sources in a large-scale nationally representative survey of young adults in England: the Next Steps Age 25 Survey. Evidence from preparatory qualitative research, piloting and from the main stage of the study is presented. This research constitutes a novel contribution to the literature, discussing the practicalities of implementing a data linkage protocol asking consent both retrospectively and prospectively, on multiple domains, and in the context of a mixed-mode survey

A simulation tool for better management of retinal services

Background: Advances in the management of retinal diseases have been fast-paced as new treatments become available, resulting in increasing numbers of patients receiving treatment in hospital retinal services. These patients require frequent and long-term follow-up and repeated treatments, resulting in increased pressure on clinical workloads. Due to limited clinic capacity, many National Health Service (NHS) clinics are failing to maintain recommended follow-up intervals for patients receiving care. As such, clear and robust, long term retinal service models are required to assess and respond to the needs of local populations, both currently and in the future. Methods: A discrete event simulation (DES) tool was developed to facilitate the improvement of retinal services by identifying efficiencies and cost savings within the pathway of care. For a mid-size hospital in England serving a population of over 500,000, we used 36 months of patient level data in conjunction with statistical forecasting and simulation to predict the impact of making changes within the service. Results: A simulation of increased demand and a potential solution of the 'Treat and Extend' (T&E) regimen which is reported to result in better outcomes, in combination with virtual clinics which improve quality, effectiveness and productivity and thus increase capacity is presented. Without the virtual clinic, where T&E is implemented along with the current service, we notice a sharp increase in the number of follow-ups, number of Anti-VEGF injections, and utilisation of resources. In the case of combining T&E with virtual clinics, there is a negligible (almost 0%) impact on utilisation of resources. Conclusions: Expansion of services to accommodate increasing number of patients seen and treated in retinal services is feasible with service re-organisation. It is inevitable that some form of initial investment is required to implement service expansion through T&E and virtual clinics. However, modelling with DES indicates that such investment is outweighed by cost reductions in the long term as more patients receive optimal treatment and retain vision with better outcomes. The model also shows that the service will experience an average of 10% increase in surplus capacity.Peer reviewedFinal Published versio

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

Adverse reactions to intravenous iodinated contrast media: a prospective study

Background: Adverse reactions to intravenous iodinated contrast media may be classified as general and organ-specific, such as contrast-induced nephrotoxicity. General adverse reactions may be sub classified into acute and delayed types. Acute general adverse reactions can range from transient minor reactions to life-threatening severe reactions. This study was done to determine clinical adverse effects of the iodinated contrast media.Methods: Data of 899 consecutive patients at C.U. Shah Medical College and Hospital, Surendranagar, who received sodium meglumine diatrizoate intravenous iodinated contrast media during the period of May 2011 to April 2012, were collected for any adverse drug reactions.Results: Out of 899, 189 patients developed adverse contrast reactions. The incidences of mild, moderate and severe adverse reactions were 19.47%, 1.33% and 0.28%, respectively. There were no differences in the incidence of adverse reactions according to gender (males 21.1%; females 20.7%; p= >0.05) or age (p= >0.05). The incidence of adverse reactions was significantly higher in patients with a history of previous reactions (50%) than in those with no history (21.25%; p= <0.05).Conclusions: The skin was the most commonly affected site of reactions. In reactions, mild forms were more common compared to moderate and severe

Qualitative investigation of patients' experience of a glaucoma virtual clinic in a specialist ophthalmic hospital in London, UK

OBJECTIVES: To explore how patients felt about delivery of care in a novel technician-delivered virtual clinic compared with delivery of care in a doctor-delivered model. DESIGN: A qualitative investigation using one-to-one interviews before and after patients' appointments at either the standard outpatient glaucoma clinic or the new technician-delivered virtual glaucoma clinic (Glaucoma Screening and Stable Monitoring Service, GSMS). SETTING: A glaucoma clinic based in a tertiary ophthalmic specialist hospital in London. PARTICIPANTS: 43 patients (38 Caucasian, 5 African/Afro-Caribbean) were interviewed prior to their glaucoma appointment; 38 patients were interviewed between 4 and 6 weeks after their appointment. Consecutive patients were identified from patient reception lists and telephoned prior to their appointment inviting them to participate. RESULTS: Trust in the patient-provider relationship emerged as a key theme in patients' acceptance of not being seen in a traditional doctor-delivered service. Patients who were well informed regarding their glaucoma status and low risk of progression to sight loss were more accepting of the GSMS. Patients valued the reassurance received through effective communication with their healthcare practitioner at the time of their appointment. CONCLUSIONS: This study suggests that patients are accepting of moving to a model of service delivery whereby the doctor is removed from the consultation as long as they are informed about the status of their condition and reassured by the interaction with staff they meet. This study highlights the importance of patient engagement when introducing new models of service delivery