Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Soil Key Fertility Attributes Using a Portable X-ray Fluorescence: A Simple Method to Overcome Matrix Effect

The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA).
Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Recently, the prediction of photovoltaic (PV) power has become of paramount importance to improve the expected revenue of PV operators and the effective operations of PV facility systems. Additionally, the precise PV power output prediction in an hourly manner enables more sophisticated strategies for PV operators and markets as the electricity price in a renewable energy market is continuously changing. However, the hourly prediction of PV power outputs is considered as a challenging problem due to the dynamic natures of meteorological information not only in a day but also across days. Therefore, in this paper, we suggest three PV power output prediction methods such as artificial neural network (ANN)-, deep neural network (DNN)-, and long and short term memory (LSTM)-based models that are capable to understand the hidden relationships between meteorological information and actual PV power outputs. In particular, the proposed LSTM based model is designed to capture both hourly patterns in a day and seasonal patterns across days. We conducted the experiments by using a real-world dataset. The experimental results show that the proposed ANN based model fails to yield satisfactory results, and the proposed LSTM based model successfully better performs more than 50% compared to the conventional statistical models in terms of mean absolute error.
Zigzag-Division Multiple Access for Wireless Networks With Long and Heterogeneous Delays

A new random channel access framework, named as zigzag-division multiple access (ZDMA), is proposed for wireless networks with long and heterogeneous propagation delays to achieve system throughput significantly larger than 1. The key idea is to allow simultaneous transmissions at the transmitters and employ zigzag decoding at the receiver. After studying the zigzag decoding, we present three new random access protocols. Extensive numerical studies show that incorporating ZDMA in existing slotted Aloha and tree splitting protocols can significantly increase system throughput. The third protocol of greedy scheduling with ZDMA achieves the highest throughput performance.
Antimicrobial Susceptibility Pattern and Bacterial Isolates Profile in Septicaemia Suspected Patients Attending FMC. Yenagoa

Background: Infection of the blood stream by bacteria represents a considerable public health problem and it is an important cause of morbidity and mortality in hospitalised patients. The aim of this study was to appraise the prevalence of bacterial isolates from septicaemia suspected patients and their antimicrobial susceptibility pattern amongst patients attending the Federal Medical Centre {FMC), Yenagoa. Methods: Three hundred and thirty-five (335) blood samples were collected, cultured and processed following standard microbiological techniques as part of the routine clinical management of the patient with suspected septicaemia in the Medical Microbiology Laboratory of the Hospital. Antibiotic susceptibility testing was done on pure culture isolates employing disc-diffusion method for the commonly used antibiotics. The obtained data were analysed by using SPSS version 20 and the results were presented in tabular and graphical forms. Results: Out of 335 blood cultured, 138(41.19%) were culture positive. 122(88.41%) were aerobic bacterial while 16(11.59%) were anaerobes. Of the aerobes, the gram negative and grampositive bacteria constituted 67(54.90%) and 55(45.10%) of the culture isolates; respectively. The predominant aerobic bacteria isolated from the cultured blood were Coagulase negative staphylococci27(22.00%), followed by S. aureus 17(14.20%) and Klebsiellapneumoniae16 (13.05%), Serretia marcescens 15(12.35%) Acinetobacter spp 13(10.75%), E.coliand Enterococcus spp. , 9 (7.19%) each, Pseudomonas aeruginosa and Salmonella typhi 7 (5.76%) each, and Bacillus subtilis 2(1.57%). Among the anaerobes, the most frequently isolated were Bacteroides fragilis 8(50.00%), Clostridiumperfringens4(25.00%), Peptostreptococcus spp. 2(12.50%), and Fusobacterium spp. 2(12.50%). All the isolates showed high rates of resistance to most antibiotics tested. Conclusions: In the current study most of the pathogens isolated from blood culture presented high rate of resistance to most commonly antibiotics used to treat bacterial infections thus limiting therapeutic options. Therefore, routine bacteriological profile examination along with their antibiotic resistance patterns must be an essential component in the management of sepsis. A knowledge of these patterns is essential when local polices on the use of antibiotics are being formulated.
Dual-Task Integrated Network for Fast Pedestrian Detection in Crowded Scenes

SUMMARY Pedestrian detection is a critical problem in computer vision with signiﬁcant impact on many real-world applications. In this paper, we introduce an fast dual-task pedestrian detector with integrated segmentation context ( DTISC ) which predicts pedestrian location as well as its pixel-wise segmentation. The proposed network has three branches where two main branches can independently complete their tasks while useful representations from each task are shared between two branches via the integration branch. Each branch is based on fully convolutional network and is proven e ﬀ ective in its own task. We optimize the detection and segmentation branch on separate ground truths. With reasonable connections, the shared features introduce additional supervision and clues into each branch. Consequently, the two branches are infused at feature spaces increasing their robustness and comprehensiveness. Extensive experiments on pedestrian detection and segmentation benchmarks demonstrate that our joint model improves the performance of detection and segmentation against state-of-the-art algorithms.
Rice Farmers’ Adoption and Economic Benefits of Integrated Pest Management in South Sulawesi Province, Indonesia

The study examined the adoption levels and economic benefits of IPM among rice farmers in South Sulawesi Province, Indonesia. The research was conducted in two main rice producing areas of the province, Soppeng and Maros Regencies. Simple random sampling was employed in selecting 20% of the total populations, resulting in sample sizes of 69 repondents in Soppeng and 52 respondents in Maros. A structured questionnaire was used in direct interview with the selected respondents to obtain data on their socio-economic profile, IPM implementation, and profit gained from IPM. Percentage was used to present the data. In both regencies, overall IPM adoption rates were high with most respondents were categorized as medium and high IPM adopters with adoption rates of 85.1% in Soppeng and 88.5% in Maros. The medium and high IPM adopters gained more economic profits of 26.9% in Soppeng and 16%% in Maros in comparison to the low IPM adopters. The high adoption rates indicated that although the nationwide IPM Program has ended in late 1990’s, there was a continuous technological transfer from older farmers who participated in IPM-FFS to younger farmer generation. In order to improve IPM implementation by the younger farmers, they need to be provided with intensive extension support for IPM innovations. Keywords: Integrated pest management, rice farmers, adoption, economic benefit
To Study the Effect of Pre-Emptive Dose of Pregabalin for Postoperative Analgesia in Patients Undergoing Surgery under General Anaesthesia

DOI: 10.21276/sjams.2018. 6.11. 38 Abstract: Successful recovery after surgery depends on complication free postoperative period. Pre-emptive use of analgesic agents reduces total analgesic consumption after surgery and increases patients comfort. Our hypothesis is that preemptively given oral pregabalin reduces postoperative pain in patients scheduled for surgery under general anaesthesia. This randomized control study was conducted on 60 patients of ASA grade 1 and 2 after taking ethics committee approval. Patients were divided into two groups. In Group P (n=30), patients who received 300 mg of Pregabalin orally one and half hours prior to surgery and in group C, patients received Placebo orally one and half hours prior to surgery. Diastolic, systolic and mean blood pressure, heart rate were measured throughout the procedure. Time of rescue analgesia and VAS score was recorded postoperatively. oral pregabalin given pre-emptively provides postoperative analgesia. Mean duration of first rescue analgesia in group P was that was 58.69±25.21 in comparison to group C 38.40±24.61. Time of second rescue analgesia in group P was 132.06±23.77 and in group C was 100.86±30.26 showing better analgesia in group P when compared with group C (p<0.05). oral pregebalin given pre-emptively reduces postoperative pain in patients scheduled for surgery under general anaesthesia.
Observing Parent Behavior: Reconciling Theoretical Concepts with Empirical Reality.

AUTHOR Ge, Xiaojia TITLE Observing Parent Behavior: Reconciling Theoretical Concepts with Empirical Reality. SPONS AGENCY John D. and Catherine T. MacArthur Foundation, Chicago, IL. ; National Inst. of Mental Health (DHHS), Bethesda, Md. ; National Inst. on Drug Abuse (DHHS/PHS), Rockville, Md. PUB DATE Mar 93 CONTRACT DA05347; MH00567; MH43270; MH48165 NOTE 49p. ; Paper presented at the Biennial Meeting of the Society for Research in Child Development (New Orleans, LA, March 25-28, 1993). PUB TYPE Speeches/Conference Papers (150) Reports Research /Technical (143)
Simulation model for shapiro time delay and light deflection experiments

The time delay experiment proposed by I.I. Shapiro in 1964 and conducted in the seventies was the most precise experiment of general relativity until that time. Further experimentation has improved the accuracy level of both the time delay and the light deflection experiments. A simulation model is proposed that involves only a simple mass and time transformation factor involving velocity of light. The light deflection and the time delay experiments are numerically simulated using this model that does not use the general relativistic equations. The computed values presented in this paper compare well with recent levels of accuracy of their respective experimental results.
Growth of Water Yam (Dioscorea alata L.) under Alkaline Soil Conditions

Water yam (Dioscorea alata L.) are adaptable to cultivation under different soil conditions. This study aimed to reveal the effects on the growth of water yam under natural alkaline soil conditions (called Shimajiri Mahaji locally) at the Miyako Islands, Okinawa Prefecture in 2011 and under three fertilizer levels and varied pH in the greenhouse at Setagaya, Tokyo, Japan in 2012. The Shimajiri Mahaji soil for this experiment was of poor fertility with a pH of 7.9. The application of a larger quantity of fertilizer did not affect plant growth. The lack of effect of N: P: K fertilizer on the yield of ‘Okinawa white’ yam might have been due to the disturbance of mineral absorption by the alkaline soil conditions. The effect of alkaline soil conditions (pH 8.0) on the growth of five water yam cultivars including ‘Okinawa white’ was investigated at Tokyo, Japan. Plant growth varied among cultivars under alkaline soil conditions, in which some cultivars grew well. No Fe or Mn deficiency symptoms were observed on any tested cultivars under alkaline soil condition. The dry weights of tops and roots of ‘Okinawa white’ were smaller compared to control in alkaline soil, but the dry weight of tubers was not significantly different. Water yam could adapt to alkaline soil conditions, depending on the cultivar. Thus, different fertilization guidelines for the cultivation of adaptable cultivars of water yam on alkaline soils such as Shimajiri Mahaji should be developed.
Blood pressure pattern following a combined exercise session in hypertensive older women

Abstract Objective: The aim of the present study was to verify the behavior of hemodynamic variables in hypertensive older women following a combined exercise session and to identify the intensity of effort achieved in this session. Method: The study included 14 hypertensive older women (72.0±6.7 years old) who regularly practiced physical activities. The participants underwent a one-hour combined exercise session performed in a group, with intensity of effort monitored by accelerometry (Actigraph, model GT3X+). The hemodynamic parameters (systolic blood pressure, diastolic blood pressure, heart rate, stroke volume, cardiac output and peripheral vascular resistance) were individually measured by finger photoplethysmography (Finometer) before and after (60 min) the exercise session. Results: The sessions consisted predominantly of low-light intensity activities (60%) and 20% of class time was spent on sedentary behavior. None of the evaluated variables exhibited changes when the pre and post-exercise values were compared (p>0.05). Systolic blood pressure behavior correlated positively with time spent in sedentary behavior (r=0.541; p=0.045). Conclusion: The combined exercise sessions, as conducted, did not generate post-exercise hypotension. In addition, the time spent in typically sedentary activities during the classes positively correlated with changes in blood pressure, suggesting that combined exercise programs, applied in the form of collective aerobic exercises, should be composed of higher intensity activities.
The Effects of Performance Report Layout on Managers’ Subjective Evaluation Judgments

Managers tend to provide subjective performance evaluations that are relatively high (leniency) and not very dispersed (compression). This paper reports on an experiment that investigates whether the layout of performance reports affects the leniency and compression of managers’ subjective evaluations. Relying on psychology theory, we predict that subjective ratings will be higher and more compressed if performance reports contain alphabetically listed indicators rather than categorically listed indicators (as in a balanced scorecard). Moreover, we predict that ratings will be higher and more compressed if performance reports present indicator target and actual values in tables than when this information is presented in graphs. The results from the experiment provide support for the hypothesis that performance ratings are higher if measures are listed in alphabetical order as opposed to presented in a four-category balanced scorecard format. However, there is no support for the other hypotheses. We discuss the implications of the study for accounting research and practice.
Automated Mapping of Woody Debris over Harvested Forest Plantations Using UAVs, High-Resolution Imagery, and Machine Learning

Surveying of woody debris left over from harvesting operations on managed forests is an important step in monitoring site quality, managing the extraction of residues and reconciling differences in pre-harvest inventories and actual timber yields. Traditional methods for post-harvest survey involving manual assessment of debris on the ground over small sample plots are labor-intensive, time-consuming, and do not scale well to heterogeneous landscapes. In this paper, we propose and evaluate new automated methods for the collection and interpretation of high-resolution, Unmanned Aerial Vehicle (UAV)-borne imagery over post-harvested forests for estimating quantities of fine and coarse woody debris. Using high-resolution, geo-registered color mosaics generated from UAV-borne images, we develop manual and automated processing methods for detecting, segmenting and counting both fine and coarse woody debris, including tree stumps, exploiting state-of-the-art machine learning and image processing techniques. Results are presented using imagery over a post-harvested compartment in a Pinus radiata plantation and demonstrate the capacity for both manual image annotations and automated image processing to accurately detect and quantify coarse woody debris and stumps left over after harvest, providing a cost-effective and scalable survey method for forest managers.
An Integrative Approach to Assess Subjective Well-Being. A Case Study on Romanian University Students

Subjective well-being (SWB) has presented long-lasting interest for researchers and the recent focus on the economic approach to SWB led to increased awareness of the topic. Despite the significant number of studies, conceptualizing and assessing SWB, along with finding predictors of SWB, need further empirical exploration. Following this rationale, using statistical and econometric methods (correlation analysis, Principal Component Analysis (PCA), Multinomial Logistic Regression (MLR)) applied on data collected via a survey on students from Bucharest University of Economic Studies (363 respondents), this study explores and provides insights that support a better understanding of defining and measuring SWB. Additionally, the study offers valuable information on the main determinants of SWB for a particular group, in this case, Romanian business students. According to findings, we argue that: (1) when assessing perception of life satisfaction and happiness, Romanian students tend to make slight distinctions between these two concepts; (2) question order effect is not significant, whereas negative sentiments (such as pessimism) impact self-assessment of happiness, but not of life satisfaction; (3) the main predictors for SWB are satisfaction with current activities, level of optimism/pessimism, health, and safety of the neighborhood. This paper proposes a new approach to modeling SWB by MLR, which features expressing the dependent variable with respect to the principal factors obtained by PCA.
Validation of the forecast skill of the Global Modeling and Assimilation Office Observing System Simulation Experiment

A global Observing System Simulation Experiment (OSSE) framework has been developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO). The OSSE uses a 13‐month integration of the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational model as the Nature Run, and the Goddard Earth Observing System version‐5 (GEOS‐5) forecast model with Gridpoint Statistical Interpolation (GSI) data assimilation as the forecast model. Synthetic observations have been developed with correlated observation errors to replicate the observing network from 2005–2006.
Analysis of a robust finite element approximation for a parabolic equation with rough boundary data

The approximation of parabolic equations with nonhomogeneous Dirichlet boundary data by a numerical method that consists of finite elements for the space discretization and the backward Euler time discretization is studied. The boundary values are assumed in a least squares sense. It is shown that this method achieves an optimal rate of convergence for rough (only L2) boundary data and for smooth data as well. The results of numerical computations which confirm the robust theoretical error estimates are also presented.
A New Literature Review Of Automatic Generation Control In Deregulated Environment

One in all the foremost vital issue is Automatic Generation Control (AGC) in electrical power system design and operation. The main objective of AGC in a power system is to take care of the frequency of the particular power area. If interconnected facility is taken into account then the tie-line power is to be unbroken near to regular values by adjusting the MW outputs the AGC generators therefore on accommodate unstable load demands. This paper reviews numerous control strategies for elegance of Automatic Generation Control (AGC) within the deregulated power system environment. Models and control procedures / structures of power structure that concern the AGC prevention program and application issues tend to be and are fragmented. Further, discussion on challenges and benedictions related to the reviewed Control techniques/ structures have been conferred. ————————————————————
The Effect of Mind Mapping-Aided Scientific Approach and Learning Style on Student’s Reasoning

This study aimed at analyzing: (1) the effect of mind mapping-aided scientific approach on student’s reasoning, (2) the effect of learning style on student’s reasoning, and (3) the effect of the interaction between mind mapping-aided scientific approach and learning style on student’s reasoning. This study used a quasi-experiment non-equivalent posttest only control group design. The data were collected by using reasoning test and the data on learning style by using a standard test. The data were analyzed the two-way ANOVA statistical analysis. Based on the analysis it was found out that 1) there was a significant effect of the mind mapping-aided scientific approach on the student’s reasoning (p<0.05). 2) there was a significant effect of field independent and field dependent learning styles on the student’s reasoning (p<0.05). 3) there was no significant effect of the interaction between mind mapping-aided scientific approach and learning style on the student’s reasoning (p> 0.05).