The Role of Underemployment in Employee’s Overall Job Satisfaction: The Alabama Case.

Job satisfaction is an important measure of utility that employees derive from their jobs and is related to various features of the job such as pay, security, intrinsic values of work, working conditions, career growth opportunities, working hours, and the like. This paper analyzes the relationship between underemployment and overall job satisfaction among other personal and job characteristics of the workforce in Alabama using survey data from Alabama workforce development regions. A logistic model is used to analyze the determinants of job satisfaction in Alabama including underemployment. Estimation results show a negative relationship between underemployment and job satisfaction. Personal and work-related attributes such as education, age, work hours, and gender are also shown to influence employee job satisfaction.Community/Rural/Urban Development, Industrial Organization, Labor and Human Capital,

Do demand side variables influence financial inclusion? Lessons from South Punjab – Pakistan

Financial inclusion helps to eradicate poverty and unemployment and improves the livelihoods of the public. This research aims to examine the demand side variables that influence financial Inclusion in south Punjab, Pakistan. This research has examined the influence of education, income, accessibility, and religious belief on financial inclusion. This study has used a quantitative research design. The Likert scale questionnaire was distributed among two hundred and eighty respondents through convenience sampling.  SPSS 16 has been used for data analysis in this research. Results showed that income, education, and accessibility have a significant and positive impact on financial inclusion, but no significant relationship between religious belief and financial inclusion is found due to the availability of Islamic banking in Pakistan. This research is significant for financial inclusion policymakers and for financial institutions as it will help them to make programs to promote financial inclusion accordingly. On the supply side, a lot of work has been done but negligible work has been done on the demand side of financial inclusion. This study investigated the demand side variables that influence financial inclusion

Biokinetics Of microbial consortia using biogenic sulfur as a novel electron donor for sustainable denitrification

In this study, the biokinetics of autotrophic denitrification with biogenic S0 (ADBIOS) for the treatment of nitrogen pollution in wastewaters were investigated. The used biogenic S0, a by-product of gas desulfurization, was an elemental microcrystalline orthorhombic sulfur with a median size of 4.69 µm and a specific surface area of 3.38 m2/g, which made S0 particularly reactive and bioavailable. During denitritation, the biomass enriched on nitrite (NO2–) was capable of degrading up to 240 mg/l NO2–-N with a denitritation activity of 339.5 mg NO2–-N/g VSS·d. The use of biogenic S0 induced a low NO2–-N accumulation, hindering the NO2–-N negative impact on the denitrifying consortia and resulting in a specific denitrification activity of 223.0 mg NO3–-N/g VSS·d. Besides Thiobacillus being the most abundant genus, Moheibacter and Thermomonas were predominantly selected for denitrification and denitritation, respectively

Biologically-inspired transport of solid spherical nanoparticles in an electrically-conducting viscoelastic fluid with heat transfer

Bio-inspired pumping systems exploit a variety of mechanisms including peristalsis to achieve more efficient propulsion. Non-conducting, uniformly dispersed, spherical nano-sized solid particles suspended in viscoelastic medium forms a complex working matrix. Electromagnetic pumping systems often employ complex working fluids. A simulation of combined electromagnetic bio-inspired propulsion is observed in the present article. Currents formation has increasingly more applications in mechanical and medical industries. A mathematical study is conducted for magnetohydrodynamic pumping of a bi-phase nanofluid coupled with heat transfer in a planar channel. Two-phase model is employed to separately identity the effects of solid nanoparticles. Base fluid employs Jeffery’s model to address viscoelastic characteristics. The model is simplified using of long wavelength and creeping flow approximations. The formulation is taken to wave frame and non-dimensionalize the equations. The resulting boundary value problem is solved analytically, and exact expressions are derived for the fluid velocity, particulate velocity, fluid/particle temperature, fluid and particulate volumetric flow rates, axial pressure gradient and pressure rise. The influence of volume fraction density, Prandtl number, Hartmann number, Eckert number and relaxation time on flow and thermal characteristics is evaluated in detail. The axial flow is accelerated with increasing relaxation time and greater volume fraction whereas it is decelerated with greater Hartmann number. Both fluid and particulate temperature are increased with increment in Eckert and Prandtl number whereas it is reduced when the volume fraction density increases. With increasing Hartmann, number pressure rise is reduced. Furthermore, pressure is reduced with greater relaxation time in the retrograde pumping region whereas it is elevated in the co-pumping and free pumping regions. The number of the trapped boluses is decreased whereas the quantity of boluses increases with a rise in volume fraction density of particles

Rust resistance in faba bean (Vicia faba L.) : status and strategies for improvement

Faba bean (Vicia faba L.) is an important grain legume used as food and feed. Its production is threatened by abiotic stresses and diseases, of which rust (Uromyces viciae-fabae) is one of the major diseases in East and North Africa, China and the northern grain growing region of Australia. Understanding the genetic and physiological mechanisms of rust resistance in faba bean is in an early phase. The presence of seedling and adult plant resistance genes has been observed. The resistance most frequently utilised in applied plant breeding is race-specific, where the interaction between resistance genes in the host and avirulence genes in the pathogen confers resistance. The main drawback of using race-specific resistance is lack of durability, when deployed singly. Slow rusting or partial resistance, controlled by multiple genes of small effect, is generally non-race specific, so it can be more durable. We present the current knowledge of host resistance and pathogen diversity and propose rational breeding approaches aided with molecular markers to breed durable rust resistance in faba bean.Peer reviewe

An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19

Cough-based diagnosis for Respiratory Diseases (RDs) using Artificial Intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias Free Network (RBFNet), an end to end solution that effectively mitigates the impact of confounders in the training data distribution. RBFNet ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID19 dataset in this study. This approach aims to enhance the reliability of AI based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks is proposed for the feature encoder module of RBFNet. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (cGAN) which helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBFNet is demonstrated by comparing classification performance with State of The Art (SoTA) Deep Learning (DL) model (CNN LSTM) after training on different unbalanced COVID-19 data sets, created by using a large scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectivelyComment: 13 pages, 7 figures, 5 table

Viability of Split Thickness Autogenous Skin Transplantation in Canine Distal Limb Reconstruction – An Experimental Evaluation

Distal limb reconstruction is complicated by the paucity of local tissues and the frequent association of orthopedic injury with cutaneous loss. Though, second-intention healing or skin stretching techniques are used for wounds involving less than a 30% circumference of the limb, however, skin grafts are recommended for reconstruction of larger superficial wounds. The present study was designed to clinically evaluate the viability of split thickness autogenous skin transplantation (STAST) in dogs. Standardized surgical defects of variable size i.e. 3×3, 4×4 and 5×5 sq cm were made on the left middle radial area (forearm) of 15 mongrel dogs assigned to Group A, B and C, respectively having 5 dogs each. Split thickness autogenous skin grafts were harvested from mid thorax and placed in these defects through several simple interrupted sutures. Results indicated a success rate of 80% with no clinical difference in the survival rate of three different sizes of grafts used. Hence, STAST can successfully be used for canine distal limb reconstruction

Epidemiology of patients presenting to a pediatric emergency department in Karachi, Pakistan

Background: There is little data describing pediatric emergencies in resource-poor countries, such as Pakistan. We studied the demographics, management, and outcomes of patients presenting to the highest-volume, public, pediatric emergency department (ED) in Karachi, Pakistan.Methods: In this prospective, observational study, we approached all patients presenting to the 50-bed ED during 28 12-h study periods over four consecutive weeks (July 2013). Participants’ chief complaints and medical care were documented. Patients were followed-up at 48-h and 14-days via telephone. Results: Of 3115 participants, 1846 were triaged to the outpatient department and 1269 to the ED. Patients triaged to the ED had a median age of 2.0 years (IQR 0.5–4.0); 30% were neonates (\u3c 28 days). Top chief complaints were fever (45.5%), diarrhea/vomiting (32.3%), respiratory (23.1%), abdominal (7.5%), and otolaryngological problems (5.8%). Temperature, pulse and respiratory rate, and blood glucose were documented for 66, 42, and 1.5% of patients, respectively. Interventions included medications (92%), IV fluids (83%), oxygen (35%), and advanced airway management (5%). Forty-five percent of patients were admitted; 11 % left against medical advice. Outcome data was available at time of ED disposition, 48-h, and 14 days for 83, 62, and 54% of patients, respectively. Of participants followed-up, 4.3% died in the ED, 11.5% within 48 h, and 19.6% within 14 days.Conclusions: This first epidemiological study at Pakistan’s largest pediatric ED reveals dramatically high mortality, particularly among neonates. Future research in developing countries should focus on characterizing reasons for high mortality through pre-ED arrival tracking, ED care quality assessment, and post-ED follow-up

Correction to: Epidemiology of patients presenting to a pediatric emergency department in Karachi, Pakistan

An amendment to this paper has been published and can be accessed via the original article

Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity