Effect of Neighbourhood Characteristics on Resident's Satisfaction in Doya Area of Bauchi Metropolis

This study determines the effect of neighbourhood characteristics on residents’ satisfaction in Doya area of Bauchi metropolis to reveal the significant relationship of the effect. Field data were gathered using a structured, close-ended questionnaire containing 5 Likert scales administered to the household head of Doya area of Bauchi metropolis using simple random sampling. A total number of one hundred and twenty-five (125) valid questionnaires were used for the analysis. The data were analyzed using descriptive statistics (Mean score and frequency table) and linear regression method through SPSS. The study found that electricity, water, drainage, availability of schools, availability of hospital, economic activities, neighbourhood security, sanitary services, recreational facilities and accessibility are factors affecting resident’s satisfaction. It further found that satisfaction with proximity to work, water, and educational facilities were striking the highest mean score. Finally linear regression model reveals that neighbourhood condition significantly affects resident’s satisfaction. The study suggests a need for the government to provide more social amenities in the study area. Proper routine management of social amenities should be done to enhance the resident’s satisfaction in the study area

Handling multicollinearity and outliers using weighted ridge least trimmed squares

Common problems in multiple linear regression models are multicollinearity and outliers. In this paper, we will propose a robust ridge regression. It is based on weighted ridge least trimmed squares (WRLTS). The proposed method (WRLTS) has been compared to some different estimation methods, namely the Ordinary Least Squares (OLS), Ridge Regression (RR),Robust Ridge Regression (RRR) such as Ridge LeastMedian Squares (RLMS), Ridge Least Trimmed Squares (RLTS) regression based on LTS estimator and Weighted Ridge (WRID) with respect to Standard Error. Two examples are used to illustrate the proposed method. In both examples, WRLTS is found to be the best estimator among the other methods in this paper

Robust PC with wild bootstrap estimation of linear model in the presence of outliers, multicollinearity and heteroscedasticity error variance

The regression model estimator is considered efficient if it is robust and resistant to the presence of heteroscedasticity variance, multicollinearity or unusual observations called outliers. However, in regard to these problems, the wild bootstrap and robust wild bootstrap are no longer efficient since they could not produce the smallest variance. Hence this research investigates the use of robust PC with wild bootstrap techniques on regression model as an estimator for real and simulation data in a situation where multicollinearity, heteroscedasticity and multiple outliers are present. This paper proposed a robust procedure based on the weighted residuals which combined the Tukey bisquare weighted function, principal component analysis (PCA) to remedy the multicollinearity problems, least trimmed squares (LTS) estimator, robust location and scale, and the wild bootstrap sampling procedure of Wu and Liu that remedy the heteroscedasticity error variance. RPCWBootWu and RPCWBootLiu were obtained through a modified version of RBootWu and RBootLiu. Finally, based on the real data and simulation study, the performance of the RPCWBootWu and RPCWBootLiu is compared with the existing RBootWu, RBootLiu and also with BootWu, BootLiu using the biased, RMSE and standard error. The numerical example and simulation study shows that the RPCWBootWu and RPCWBootLiu techniques have proven to be a good alternative estimator for regression model with lower standard error values

Obstructive urolithiasis in a 11/2 – year old Ouda–Yankasa ram: case report

Obstructive urolithiasis is the retention of urine subsequent to the lodgement of calculi in the urinary tract from the kidney up to the urethral orifice. This report describes the post-mortem and chemical analysis findings of the calculi in an 18-month old Ouda-Yankasa cross ram presented at the Large Animal Clinic of the Veterinary Teaching Hospital, Usmanu Danfodiyo University, Sokoto. The patient was presented on 20th of March, 2013 with reports of anorexia, disinclination to drink water and anuria that developed four days before presentation at the clinic. On clinical examination, urethral blockage and mild ascites were observed. Cystocentesis was performed to relieve the patient and plain radiograph taken but was not diagnostic. The patient died before definitive diagnosis was made for rational treatment. The post - mortem findings include frothy exudate in the trachea and bronchial airways, congestion of the lungs, hydroperitoneum with recovered fluid measuring 2,350 ml, splenomegaly, hydronephrosis, distended urinary bladder, severe haemorrhagic cystitis, urinary calculi in the bladder and throughout the urethral length and urethral stricture. The urinary calculi recovered were white, friable and amorphous, ranging from small particles to 5mm in diameter. Histopathologic section of the kidney showed atrophied glomeruli. It can thus be concluded that the atrophied glomeruli in turn impaired glomerular filtration which invariably pre-disposed the patient to uraemia leading to its death. The chemical analysis of the calculi showed that the calculi was either oxalate, phosphate or silicate, or any of these mixtures.Keywords: calculi, obstruction, phosphate, uraemi

Estimation parameters using bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers

This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares RLTS and ridge least absolute value (RLAV) respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Bisquare ridge regression (BRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estimate the parameters coe±cients. The results of Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) are compared with existing methods using real data and simulation study. The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the diÆerent disturbance distributions and degrees of multicollinearity

Pediatric Blood Culture Isolates and Antibiotic Sensitivity Pattern in a Nigerian Tertiary Hospital

Introduction: There is a significant variation in the bacterial pathogens implicated in childhood septicemia and their antibiotic sensitivity patternfrom place to place. Sustained monitoring of this dynamics is therefore critical to rational antibiotic use. Materials and Methods: This study was thus conducted to determine the etiology of childhood septicemia and their antibiotic sensitivity pattern. Blood culture results (contaminants excluded), age, and sex of all pediatric patients with suspected septicemia between January 2013 and December 2014 were retrieved. Data were analyzed using SPSS version 20. Results: Over a 2‑year period, a total of 3680 blood samples were processed. Pathogenic bacteria were isolated in 701 samples (19%).    Staphylococcus aureus was the most common isolate (41.4%) and was most sensitive to ampicillin‑sulbactam (89%). Klebsiella species (21.7%),  coagulase‑negative Staphylococcus (14.7%), and Pseudomonas aeruginosa (11%) were other common organisms isolated. Virtually, all the isolates demonstrated a reliable susceptibility to ciprofloxacin except for S. aureus and Klebsiella species which were most sensitive to ampicillin‑sulbactam and imipenem, respectively. Conclusion: In conclusions, S. aureus is the leading cause of childhood septicemia in this locale. The significant rate of isolation of the supposedly less virulent organisms calls for an urgent review of potential risk factors and an appraisal of the hospital infection control policies and structures. Keywords: Antibiotics, isolates, paediatri

Detection of mycobacteria in raw cow milk sold in Bwari Area Council, Abuja FCT

Bovine tuberculosis (bTB) is an important zoonotic disease worldwide and hence it is of great public health significance. It is present in most developing countries where surveillance and control activities are often inadequate or unavailable. This study was designed to detect mycobacteria in raw milk of cows using ZN-stain, PCR, and cultural techniques to determine the prevalence of bTB in Bwari area council of FCT Abuja. Out of the 145 raw milk sampled, 6.89% tested positive by ZN-stain and culture while 1.3% were positive by PCR. The herd prevalence per satellite town based on ZN-stain technique was 8.89%, 10.0%, 3.33% and 5.00% for Bwari, Dei-Dei, Kubuwa and Ushafa respectively. While by cultural method, the prevalence was 2.22%, 10.00%, and 5.00% for Bwari, Dei-Dei, Kubuwa and Ushafa respectively. PCR revealed the prevalence of Mycobacterium species for Bwari and Dei-Dei as 2.22% and 3.33% respectively. Detection of Mycobacteria in raw (unpasteurized) pose a serious public health risk to raw milk consumers in Bwari area council.Keywords: Raw milk, bTB, ZN stain, PCR, Cultur

Obstructive urolithiasis in ouda-yankasa ram: case report

Obstructive urolithiasis is the retention of urine subsequent to the lodgement of calculi inthe urinary tract from the kidney up to the urethral orifice. This report describes the postmortem findings and chemical analysis findings of the calculi in an 18-month old Ouda-Yankasa cross ram presented at the Large Animal Clinic of the Veterinary TeachingHospital, Usmanu Danfodiyo University, Sokoto. The patient was presented on 20th ofMarch, 2013 with reports of anorexia, disinclination to drink water and anuria thatdeveloped four days before presentation at the clinic. On clinical examination, urethralblockage and mild ascites were observed. Cystocentesis was performed to relieve thepatient and plain radiograph taken but was not diagnostic. The patient died beforedefinitive diagnosis was made for rational treatment. The post mortem findings includefrothy exudate in the trachea and bronchial airways, congestion of the lungs,hydroperitoneum with recovered fluid measuring 2,350 ml, splenomegaly,hydronephrosis, distended urinary bladder, severe haemorrhagic cystitis, urinary calculiin the bladder and throughout the urethral length and urethral stricture. The urinarycalculi recovered were white, friable and amorphous, ranging from small particles to 5mm in diameter. Histopathologic section of the kidney showed atrophied glomeruli. Itcan thus be concluded that the atrophied glomeruli in turn impaired glomerular filtrationthat invariably pre-disposed the patient to uraemia leading to its death. The chemicalanalysis of the calculi showed that the calculi was either oxalate, phosphate or silicate, orany of these mixtures.Keywords: Ouda-Yankasa ram, Obstructive urolithiasis, Calculi, Urethral blockage, Ascites, Uraemi

Performance of robust wild bootstrap estimation of linear model in the presence of outlier and heteroscedasticity errors

The regression model estimator is considered efficient if it is robust and resistant to the presence of heteroscedasticity variance, multicollinearity or unusual observations called outliers. However, in regard to these problems, the wild bootstrap and robust wild bootstrap are no longer efficient since they could not produce the smallest variance. Hence this research investigates the use of robust PC with wild bootstrap techniques on regression model as an estimator for real and simulation data in a situation where multicollinearity, heteroscedasticity and multiple outliers are present. This paper proposed a robust procedure based on the weighted residuals which combined the Tukey bisquare weighted function, principal component analysis (PCA) to remedy the multicollinearity problems, least trimmed squares (LTS) estimator, robust location and scale, and the wild bootstrap sampling procedure of Wu and Liu that remedy the heteroscedasticity error variance. RPCWBootWu and RPCWBootLiu were obtained through a modified version of RBootWu and RBootLiu. Finally, based on the real data and simulation study, the performance of the RPCWBootWu and RPCWBootLiu is compared with the existing RBootWu, RBootLiu and also with BootWu, BootLiu using the biased, RMSE and standard error. The numerical example and simulation study shows that the RPCWBootWu and RPCWBootLiu techniques have proven to be a good alternative estimator for regression model with lower standard error values

Deep Sequence Models for Text Classification Tasks

The exponential growth of data generated on the Internet in the current information age is a driving force for the digital economy. Extraction of information is the major value in an accumulated big data. Big data dependency on statistical analysis and hand-engineered rules machine learning algorithms are overwhelmed with vast complexities inherent in human languages. Natural Language Processing (NLP) is equipping machines to understand these human diverse and complicated languages. Text Classification is an NLP task which automatically identifies patterns based on predefined or undefined labeled sets. Common text classification application includes information retrieval, modeling news topic, theme extraction, sentiment analysis, and spam detection. In texts, some sequences of words depend on the previous or next word sequences to make full meaning; this is a challenging dependency task that requires the machine to be able to store some previous important information to impact future meaning. Sequence models such as RNN, GRU, and LSTM is a breakthrough for tasks with long-range dependencies. As such, we applied these models to Binary and Multi-class classification. Results generated were excellent with most of the models performing within the range of 80% and 94%. However, this result is not exhaustive as we believe there is room for improvement if machines are to compete with humans