Increasing Agricultural Productivity Through Rural Infrastructure: Evidence From Oyo and Osun States, Nigeria

The study examined access to infrastructure and its effects on agricultural productivity in Surulere and Ife East Local Government Areas (LGAs) of Oyo and Osun States. Using multistage sampling procedure, data were collected through the use of structured questionnaires administered on one hundred and sixty respondents from the study areas. The data collected were analyzed using descriptive statistics and total factor productivity model to explain the effects of the available infrastructure on the farmers’ productivity. The findings on socio-economic characteristics of the respondents revealed that 92.9 and 86.3 percent of the respondents were male for Surulere and Ife East LGAs respectively. Above 56 and 35 percent of the respondents were in the age range of 41-50 years and have household sizes that were between 6 and 8 respectively in both LGAs. Majority of the respondents had formal education and took farming as primary occupation. The total factor productivity model used revealed that farm size and labour were positive and significantly affected productivity at 5% and 1% levels of probability respectively. It was however observed that the contribution of female labour in Ife East LGA was higher than that of male, thus introducing gender productivity differential into the production process. With regards to the infrastructural elements, improvement in soil practices and extension visits had positive significant effects on productivity and were statistically significant at 5 % level of probability in both LGAs. It is recommended that more infrastructure be provided to further improve the agricultural productivity of the rural farmers.Key words: Agricultural productivity, Rural infrastructure, Rural farmer

Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination R2 = 0.801), while the (NAR) model gave (RMSE = 93.4) and R2 = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity

Bureaucracy stifles medical research in Britain: a tale of three trials

<p>Abstract</p> <p>Background</p> <p>Recent developments aiming to standardise and streamline processes of gaining the necessary approvals to carry out research in the National Health Service (NHS) in the United Kingdom (UK), have resulted in lengthy and costly delays. The national UK governmental Department of Health’s Research Governance Framework (RGF) for Health and Social Care requires that appropriate checks be conducted before research involving human participants, their organs, tissues or data can commence in the NHS. As a result, medical research has been subjected to increased regulation and governance, with the requirement for approvals from numerous regulatory and monitoring bodies. In addition, the processes and outcomes of the attribution of costs in NHS research have caused additional difficulties for researchers. The purpose of this paper is to illustrate, through three trial case studies, the difficulties encountered during the set-up and recruitment phases of these trials, related to gaining the necessary ethical and governance approvals and applying for NHS costs to undertake and deliver the research.</p> <p>Methods</p> <p>Empirical evidence about delays and difficulties related to regulation and governance of medical research was gathered during the period 2009–2010 from three UK randomised controlled trials with sites in England, Wales and Scotland (1. SAFER 2- an emergency care based trial of a protocol for paramedics to refer patients directly to community based falls services; 2. COnStRUCT- a trial of two drugs for acute ulcerative colitis; and 3. Family Links - a trial of a public health intervention, a 10 week community based parenting programme). Findings and recommendations were reported in response to a call for evidence from The Academy of Medical Sciences regarding difficulties encountered in conducting medical research arising from R&D governance and regulation, to inform national policy.</p> <p>Results</p> <p>Difficulties and delays in navigating and gaining the appropriate approvals and NHS costs required to undertake the research were encountered in all three trials, at various points in the bureaucratic processes of ethical and research and information governance approvals. Conduct of each of the three trials was delayed by at least 12 months, with costs increasing by 30 – 40%.</p> <p>Conclusions</p> <p>Whilst the three trials encountered a variety of challenges, there were common issues. The processes for gaining approvals were overly complex and differed between sites and UK countries; guidance about processes was unclear; and information regarding how to define and claim NHS costs for undertaking the research was inconsistent. The competitive advantage of a publicly funded, open access health system for undertaking health services research and clinical trials within the UK has been outweighed in recent years by stifling bureaucratic structures and processes for governance of research. The recommendations of the Academy of Medical Sciences are welcomed, and the effects of their implementation are awaited with interest.</p> <p>Trial Registration numbers</p> <p>SAFER 2: ISRCTN 60481756; COnStRUCT: ISRCTN22663589; Family Links: ISRCTN 13929732</p

Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant

Prevalence and demographics of anxiety disorders: a snapshot from a community health centre in Pakistan

<p>Abstract</p> <p>Background</p> <p>The developing world is faced with a high burden of anxiety disorders. The exact prevalence of anxiety disorders in Pakistan is not known. There is a need to develop an evidence base to aid policy development on tackling anxiety and depressive disorders in the country. This is the first pilot study to address the prevalence of anxiety disorders and their association with sociodemographic factors in Pakistan.</p> <p>Methods</p> <p>A cross-sectional study was conducted among people visiting Aga Khan University Hospital (AKUH), a tertiary care facility in Karachi, Pakistan. The point prevalence of anxiety amongst the sample population, which comprised of patients and their attendants, excluding all health care personnel, was assessed using the validated Urdu version of the Hospital Anxiety and Depression Scale (HADS). The questionnaire was administered to 423 people. Descriptive statistics were performed for mean scores and proportions.</p> <p>Results</p> <p>The mean anxiety score of the population was 5.7 ± 3.86. About 28.3% had borderline or pathological anxiety. The factors found to be independently predicted with anxiety were, female sex (odds ratio (OR) = 2.14, 95% CI 1.36–3.36, p = 0.01); physical illness (OR = 1.67, 95% CI 1.06–2.64, p = 0.026); and psychiatric illness (OR = 1.176, 95% CI 1.0–3.1, p = 0.048). In the final multivariate model, female sex (adjusted odds ratio (AOR) = 2, 95% CI 1.28–3.22) and physical illness (AOR = 1.56, 95% CI 0.97–2.48) were found to be significant.</p> <p>Conclusion</p> <p>Further studies via nationally representative surveys need to be undertaken to fully grasp the scope of this emerging public health issue in Pakistan.</p

Seroprevalence of Toxoplasma gondii infection in arthritis patients in eastern China

Background: There is accumulating evidence for an increased susceptibility to infection in patients with arthritis. We sought to understand the epidemiology of Toxoplasma gondii infection in arthritis patients in eastern China, given the paucity of data on the magnitude of T. gondii infection in these patients. Methods: Seroprevalence of T. gondii infection was assessed by enzyme-linked immunosorbent assay using a crude antigen of the parasite in 820 arthritic patients, and an equal number of healthy controls, from Qingdao and Weihai cities, eastern China. Sociodemographic, clinical and lifestyle information on the study participants were also obtained. Results: The prevalence of anti-T. gondii IgG was significantly higher in arthritic patients (18.8%) compared with 12% in healthy controls (P < 0.001). Twelve patients with arthritis had anti-T. gondii IgM antibodies comparable with 10 control patients (1.5% vs 1.2%). Demographic factors did not significantly influence these seroprevalence frequencies. The highest T. gondii infection seropositivity rate was detected in patients with rheumatoid arthritis (24.8%), followed by reactive arthritis (23.8%), osteoarthritis (19%), infectious arthritis (18.4%) and gouty arthritis (14.8%). Seroprevalence rates of rheumatoid arthritis and reactive arthritis were significantly higher when compared with controls (P < 0.001 and P = 0.002, respectively). A significant association was detected between T. gondii infection and cats being present in the home in arthritic patients (odds ratio [OR], 1.68; 95% confidence interval [CI]: 1.24 – 2.28; P = 0.001). Conclusions: These findings are consistent with and extend previous results, providing further evidence to support a link between contact with cats and an increased risk of T. gondii infection. Our study is also the first to confirm an association between T. gondii infection and arthritis patients in China. Implications for better prevention and control of T. gondii infection in arthritis patients are discussed. Trial registration: This is an epidemiological survey, therefore trial registration was not required

Quality of antimalarial drugs and antibiotics in Papua New Guinea: A survey of the health facility supply chain

Background: Poor-quality life-saving medicines are a major public health threat, particularly in settings with a weak regulatory environment. Insufficient amounts of active pharmaceutical ingredients (API) endanger patient safety and may contribute to the development of drug resistance. In the case of malaria, concerns relate to implications for the efficacy of artemisinin-based combination therapies (ACT). In Papua New Guinea (PNG), Plasmodium falciparum and P. vivax are both endemic and health facilities are the main source of treatment. ACT has been introduced as first-line treatment but other drugs, such as primaquine for the treatment of P. vivax hypnozoites, are widely available. This study investigated the quality of antimalarial drugs and selected antibiotics at all levels of the health facility supply chain in PNG.Methods and Findings: Medicines were obtained from randomly sampled health facilities and selected warehouses and hospitals across PNG and analysed for API content using validated high performance liquid chromatography (HPLC). Of 360 tablet/capsule samples from 60 providers, 9.7% (95% CI 6.9, 13.3) contained less, and 0.6% more, API than pharmacopoeial reference ranges, including 29/37 (78.4%) primaquine, 3/70 (4.3%) amodiaquine, and one sample each of quinine, artemether, sulphadoxine-pyrimethamine and amoxicillin. According to the package label, 86.5% of poor-quality samples originated from India. Poor-quality medicines were found in 48.3% of providers at all levels of the supply chain. Drug quality was unrelated to storage conditions.Conclusions: This study documents the presence of poor-quality medicines, particularly primaquine, throughout PNG. Primaquine is the only available transmission-blocking antimalarial, likely to become important to prevent the spread of artemisinin-resistant P. falciparum and eliminating P. vivax hypnozoites. The availability of poor-quality medicines reflects the lack of adequate quality control and regulatory mechanisms. Measures to stop the availability of poor-quality medicines should include limiting procurement to WHO prequalified products and implementing routine quality testing