USING ARTIFICIAL NEURAL NETWORKS MODELS FOR PREDICTING WHEAT YIELD PRODUCTIVITY

Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range

Evidence-based integrated environmental solutions for secondary lead smelters: Pollution prevention and waste minimization technologies and practices

An evidence-based methodology was adopted in this research to establish strategies to increase lead recovery and recycling via a systematic review and critical appraisal of the published literature. In particular, the research examines pollution prevention and waste minimization practices and technologies that meet the following criteria: (a) reduce/recover/recycle the largest quantities of lead currently being disposed of as waste, (b) technically and economically viable, that is, ready to be diffused and easily transferable, and (c) strong industry interest (i.e., industry would consider implementing projects with higher payback periods). The following specific aims are designed to achieve the study objectives: Aim 1 – To describe the recycling process of recovering refined lead from scrap; Aim 2 – To document pollution prevention and waste management technologies and practices adopted by US stakeholders along the trajectory of LAB and lead product life cycle; Aim 3 – To explore improved practices and technologies which are employed by other organizations with an emphasis on the aforementioned criteria; Aim 4 – To demonstrate the economic and environmental costs and benefits of applying improved technologies and practices to existing US smelting operations; and Aim 5 – To evaluate improved environmental technologies and practices using an algorithm that integrates quantitative and qualitative criteria. The process of identifying relevant articles and reports was documented. The description of evidence was presented for current practices and technologies used by US smelters as well as improved practices and technologies. Options for integrated environmental solutions for secondary smelters were introduced and rank ordered on the basis of costs (i.e., capital investment) and benefits (i.e., production increases, energy and flux savings, and reduction of SO2 and slag). An example was provided to demonstrate the utility of the algorithm by detailing the costs and benefits associated with different combinations of practices and technologies. The evidence-based methodology documented in this research reveals that it is technically and economically feasible to implement integrated environmental solutions to increase lead recovery and recycling among US smelters. The working example presented in this research can be confirmed with US stakeholders and form the basis for implementable solutions in the lead smelter and product industries to help reverse the overall trend of declining life-cycle recycling rates

Evidence-based integrated environmental solutions for secondary lead smelters: Pollution prevention and waste minimization technologies and practices

An evidence-based methodology was adopted in this research to establish strategies to increase lead recovery and recycling via a systematic review and critical appraisal of the published literature. In particular, the research examines pollution prevention and waste minimization practices and technologies that meet the following criteria: (a) reduce/recover/recycle the largest quantities of lead currently being disposed of as waste, (b) technically and economically viable, that is, ready to be diffused and easily transferable, and (c) strong industry interest (i.e., industry would consider implementing projects with higher payback periods). The following specific aims are designed to achieve the study objectives: Aim 1 – To describe the recycling process of recovering refined lead from scrap; Aim 2 – To document pollution prevention and waste management technologies and practices adopted by US stakeholders along the trajectory of LAB and lead product life cycle; Aim 3 – To explore improved practices and technologies which are employed by other organizations with an emphasis on the aforementioned criteria; Aim 4 – To demonstrate the economic and environmental costs and benefits of applying improved technologies and practices to existing US smelting operations; and Aim 5 – To evaluate improved environmental technologies and practices using an algorithm that integrates quantitative and qualitative criteria. The process of identifying relevant articles and reports was documented. The description of evidence was presented for current practices and technologies used by US smelters as well as improved practices and technologies. Options for integrated environmental solutions for secondary smelters were introduced and rank ordered on the basis of costs (i.e., capital investment) and benefits (i.e., production increases, energy and flux savings, and reduction of SO2 and slag). An example was provided to demonstrate the utility of the algorithm by detailing the costs and benefits associated with different combinations of practices and technologies. The evidence-based methodology documented in this research reveals that it is technically and economically feasible to implement integrated environmental solutions to increase lead recovery and recycling among US smelters. The working example presented in this research can be confirmed with US stakeholders and form the basis for implementable solutions in the lead smelter and product industries to help reverse the overall trend of declining life-cycle recycling rates

Towards in vitro selection studies for salinity tolerance in Canino apricot cultivar. Effect of gamma irradiation on in vitro mutation and selection for salt-tolerance

In vitro mutation method was used to obtain salt-tolerant clone in apricot. Small propagules of Canino apricot cultivar were irradiated with gamma ray at doses of 0, 10, 25, 35, 50, 75 and 100 Gy. After 30 days from treatment, both the radio sensitivity and post-irradiation recovery were assessed as the number of proliferated shoots per explants, fresh weight of cultures, shoot length and productivity of irradiated explants. A sudden and sharp decrease in the survival percentage occurred with the dose 75 Gy, while the highest dose (100 Gy) was lethal for all propagules. A marked decline in the number of regenerated shoots per explant and fresh weight of produced cultures was associated with an increase of irradiation doses. Doses in the range of 10-75 Gy, which preserved high survival percentage of irradiated explants, seemed to be more suitable for in vitro mutation in Canino apricot cultivar. Irradiated shoots were exposed to different concentrations of NaCl which were added to the multiplication medium at the rates of 25, 50, 75, 100, 125 mM and after 30 days, vigorous shoots were selected from salinity treatments. In conclusion, apricot tissues exposed to different doses of gamma irradiation in the range of 10-75 Gy, followed by culturing the plantlets produced in a medium containing additional salts (ranging from 25 to 125 mM) can be considered a good method to identify the most tolerant mutants to salts in apricot cultivars

In Vivo Positional Analysis of Implantable Collamer Lens Using Ultrasound Biomicroscopy

Purpose. To evaluate the anterior segment, the anatomical position of the implantable collamer lenses (ICL), and its relationship to adjacent ocular structures using Ultrasound Biomicroscopy (UBM). Methods. In a prospective study, 142 myopic eyes of 93 patients implanted with Visian ICL were subjected to UBM examination between March 2010 and January 2015. The relative position of ICL to the adjacent structure and the overall iris configuration were evaluated. The machine calibers were used to measure the minimum central distance between the ICL and anterior lens capsule (vault) and the vertical central distance between the corneal endothelium and the ICL (E-ICL). Results. The mean ICL vault was 376±105 μm. The mean E-ICL was 2826±331 μm. Contact between ICL and the posterior epithelium of the iris was present in all eyes. The overall iris configuration was flat in 89 eyes. Central anterior convexity was present in 41 eyes and mild peripheral iris bombe in 12 eyes. The haptics could be imaged in the ciliary sulcus in 112 eyes and at least one haptic resting on the lens periphery and zonules in 30 eyes. Conclusion. UBM can provide valuable anatomical information that allows detailed postoperative in vivo assessment of ICL

Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: A machine-learning approach.

BackgroundPatients with atrial fibrillation (AF) usually have a heterogeneous co-morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine-learning approach, accounting for the dynamic nature of comorbidity risks and incident AF.MethodsUsing machine-learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non-AF cohort of 1 077 833 enrolled in the 4-year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non-AF), diverse multi-morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine-learning techniques.ResultsComplex inter-relationships of various comorbidities were uncovered for AF cases, leading to 6-fold higher risk of heart failure relative to the non-AF cohort (OR 6.02, 95% CI 5.72-6.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c-indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923-0.925), stroke (0.871, 95%CI 0.869-0.873), myocardial infarction (0.901, 95% CI 0.899-0.903), major bleeding (0.700, 95%CI 0.697-0.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the "treat all" strategy.ConclusionsComplex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making

Using Kinect sensor in observational methods for assessing postures at work

This paper examines the potential use of Kinect range sensor in observational methods for assessing postural loads. Range sensors can detect the position of the joints at high sampling rates without attaching sensors or markers directly to the subject under study. First, a computerized OWAS ergonomic assessment system was implemented to permit the data acquisition from Kinect and data processing in order to identify the risk level of each recorded postures. Output data were compared with the results provided by human observers, and were used to determine the influence of the sensor view angle relative to the worker. The tests show high inter-method agreement in the classification of risk categories (Proportion agreement index = 0.89 k = 0.83) when the tracked subject is facing the sensor. The camera’s point of view relative to the position of the tracked subject significantly affects the correct classification of the postures. Although the results are promising, some aspects involved in the use of low-cost range sensors should be further studied for their use in real environmentsDiego-Mas, JA.; Alcaide Marzal, J. (2014). Using Kinect sensor in observational methods for assessing postures at work. Applied Ergonomics. 1-10. doi:10.1016/j.apergo.2013.12.001S11

Evaluation of existing control measures in reducing health and safety risks of engineered nanomaterials

While the risk management of engineered nanomaterials (ENMs) receives significant attention, there is still a limited understanding of how to select optimal risk management measures (RMMs) for controlling and mitigating the risks associated with exposure to ENMs. Clearly, there exists a need to expand current risk management practices to ensure safe production, handling and use of ENMs. Moreover, the performance of the existing RMMs should be re-evaluated for ENMs since control options that are proven to be effective for preventing or limiting risks associated with traditional particles might give unsatisfactory results in the case of nano-scale particles. This paper has brought together the evidence on the adequacy of traditional controls to minimize potential health and environmental risks resulting from exposure to ENMs. The aim here is to advance our understanding of the risk management approaches relevant for ENMs, and ultimately to support the selection of the most suitable RMMs when handling ENMs. To that end, evaluative evidence collected from the review of relevant literature and survey of nanotechnology institutions are combined and summarised to understand the level of protection offered by each control measure, as well as the relative costs of their implementation. The findings suggest that most relevant risk control options are based on isolating people from hazard through engineering measures (e.g. ventilation and chemical fume hoods) or personal protective equipment (PPE), rather than eliminating hazard at source (e.g. substitution). Although control measures related to the modification of ENMs have high efficiency in the occupational risk control hierarchy, they are not widely employed since there is currently a high degree of uncertainty regarding the impact of manipulating nano-characteristics on the performance of final product. Lastly, despite its low cost, PPE is the least effective category in the occupational risk control hierarchy and should not be used on its own when significant risk reduction is required. Clearly, further quantitative data is needed to fully assess the feasibility and cost-effectiveness of risk control options to prevent risks from exposure to ENMs. When there is little information on the efficiency of control measures specific to ENMs, the default efficiencies can be used for initial assessment purposes although it should not be considered exhaustive

Prevalence of hallux valgus in the general population: a systematic review and meta-analysis

BACKGROUND: Hallux valgus (HV) is a foot deformity commonly seen in medical practice, often accompanied by significant functional disability and foot pain. Despite frequent mention in a diverse body of literature, a precise estimate of the prevalence of HV is difficult to ascertain. The purpose of this systematic review was to investigate prevalence of HV in the overall population and evaluate the influence of age and gender. METHODS: Electronic databases (Medline, Embase, and CINAHL) and reference lists of included papers were searched to June 2009 for papers on HV prevalence without language restriction. MeSH terms and keywords were used relating to HV or bunions, prevalence and various synonyms. Included studies were surveys reporting original data for prevalence of HV or bunions in healthy populations of any age group. Surveys reporting prevalence data grouped with other foot deformities and in specific disease groups (e.g. rheumatoid arthritis, diabetes) were excluded. Two independent investigators quality rated all included papers on the Epidemiological Appraisal Instrument. Data on raw prevalence, population studied and methodology were extracted. Prevalence proportions and the standard error were calculated, and meta-analysis was performed using a random effects model. RESULTS: A total of 78 papers reporting results of 76 surveys (total 496,957 participants) were included and grouped by study population for meta-analysis. Pooled prevalence estimates for HV were 23% in adults aged 18-65 years (CI: 16.3 to 29.6) and 35.7% in elderly people aged over 65 years (CI: 29.5 to 42.0). Prevalence increased with age and was higher in females [30% (CI: 22 to 38)] compared to males [13% (CI: 9 to 17)]. Potential sources of bias were sampling method, study quality and method of HV diagnosis. CONCLUSIONS: Notwithstanding the wide variation in estimates, it is evident that HV is prevalent; more so in females and with increasing age. Methodological quality issues need to be addressed in interpreting reports in the literature and in future research