Farmers’ knowledge and adoption of improved mandarin orchard management practices in Syangja district, Nepal

Mandarin is one of the major fruit crops in the mid-hills of Nepal. Since improved farming practices appear to offer a significant chance to boost production and revenue, a study was conducted in Syangja, Nepal with the objective to assess farmers’ knowledge and adoption of improved mandarin orchard management practices and identify factors affecting their adoption. Primary data were collected by the use of pretested semi-structured questionnaire from randomly selected 97 respondents within the study area. Data were entered and analyzed using MS Excel, SPSS, and STATA, and the inferences were retrieved using the binary logistic regression model. Findings revealed that the respondents were familiar with most of the improved orchard management practices, however, there was variation in the scale of adoption of these practices. The majority (>90%) were found to practice pruning, FYM application, and weed control; however, there were relatively fewer adopters when it came to the use of Bordeaux paste (75.25%), chemical fertilizers (17.52%), mulching (57.74%), irrigation (44.33%), and soil testing and amendment (39.18%). The study showed that the adoption of Bordeaux paste and soil amendment differ significantly by gender (p=0.069 and p=0.041, respectively). Training has a positive impact on the use of Bordeaux paste (p=0.026), chemical fertilizers (p=0.075), and soil amendment practices (p=0.003). The usage of chemical fertilizers is more prevalent among people with formal education (p=0.075). Knowledge level also positively influences the adoption of mulching (p=0.014) and soil amendment practices (p=0.000). The number of trees is positively and significantly associated (p=0.008) with irrigation practice. It is recommended that expanding access to irrigation facilities and encouraging the use of the recommended amount of fertilizers, mulch, and Bordeaux paste must be prioritized in the study area

Herpetofauna of a Ramsar Site: The Beeshazar and Associated Lakes, Chitwan National Park, Nepal

The Nepalese herpetofauna has been poorly studied, and little is known about the distribution of the country’s amphibians and reptiles. We surveyed the herpetofauna at Beeshazar and associated lakes, a Ramsar site situated in the buffer zone of Chitwan National Park, Nepal. We conducted surveys between June and July 2015 using line transects, and nocturnal and diurnal visual encounter surveys (VES). We recorded 47 species comprising 13 anurans, 11 lizards, 18 snakes, four turtles, and one crocodilian. This document will serve as source material for outreach activities in conservation awareness of the herpetofauna in the buffer zone of the park. The present study suggests that the species composition of Beeshazar and associated lakes is likely to increase with additional systematic inventories

Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultra-wideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error

Type and extent of trans-disciplinary co-operation to improve food security, health and household environment in low and middle income countries: systematic review

Acknowledgements: We are grateful to Dr Steve Turner and Dr Adam Price for their insightful comments that improved the manuscript. We would like to thank Heather Clark and Bimbola Kalejaiye for their help in data extraction. We are also grateful to Melanie Bickerton and Dr Amudha Poobalan for their systematic review advice.Peer reviewedPublisher PD

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Waste management system using the internet of things : case study of Kathmandu Valley

Waste management is one of the arising issues in the populated city area. In a city like Kathmandu Valley, there is confusion between the collection of garbage and waste management or disposal. Both the collection as well as the disposal is the part of the waste management. The waste management consists of different other activities in the business organization like separation of different forms of waste, recycling, reducing reusing along with the disposal of solid waste. The internet of things is a system of interrelated computing devices which could be helpful for the management of waste. This paper is assessing the internet of things for the use of waste management in Kathmandu Valley. The Internet of things is offerings automation which could change the way of solid waste management in Kathmandu Valley. As the application of the Internet of Things involves constitutes of smart surveillance, smarter energy management, water distributions, urban security, and environmental monitoring. It possesses the best role in the waste management system of Nepal as IoT is known as the extensions of internet connectivity in relation to physical devices and everyday objects. Internet of things in waste management helps to save time, money manpower and fuel. IoT also reduces unnecessary collection from unfilled bins and help to identify best and effective routes for collection of the waste. The results also show the need and challenges along with disadvantages affiliated with the waste management in Kathmandu Valley. A requirement of the high initial cost during installment, reduce the requirement of man-power due to smart technology and high operational cost for the training at the beginning can create difficulties and challenges

Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression

Fingerprinting localization approach is widely used in indoor positioning applications owing to its high reliability. However, the learning procedure of radio signals in fingerprinting is time-consuming and labor-intensive. In this paper, an affinity propagation clustering (APC)-based fingerprinting localization system with Gaussian process regression (GPR) is presented for a practical positioning system with the reduced offline workload and low online computation cost. The proposed system collects sparse received signal strength (RSS) data from the deployed Bluetooth low energy beacons and trains them with the Gaussian process model. As the signal estimation component, GPR predicts not only the mean RSS but also the variance, which indicates the uncertainty of the estimation. The predicted RSS and variance can be employed for probabilistic-based fingerprinting localization. As the clustering component, the APC minimizes the searching space of reference points on the testbed. Consequently, it also helps to reduce the localization estimation error and the computational cost of the positioning system. The proposed method is evaluated through real field deployments. Experimental results show that the proposed method can reduce the offline workload and increase localization accuracy with less computational cost. This method outperforms the existing methods owing to RSS prediction using GPR and RSS clustering using APC