Acute Malnutrition and Under-5 Mortality, Northeastern Part of India.

We assessed the prevalence of childhood acute malnutrition and under-five mortality rate (U5MR) in Darbhanga district, India, using a two-stage 49-cluster household survey. A total of 1379 households comprising 8473 people were interviewed. During a 90-day recall period, U5MR was 0.5 [95% confidence interval (CI), 0.2-1.4] per 10 000 per day. The prevalence of global acute malnutrition among 1405 children aged 6-59 months was 15.4% (NCHS) and 19.4% (2006 WHO references). This survey suggests that in Darbhanga district, the population is in a borderline food crisis with few food resources. Appropriate strategies should be developed to improve the overall nutritional and health status of children

Securing the Cloud: A Critical Appraisal of Data Security Strategies

Cloud computing, a paradigm shift in the evolution of the internet, has garnered significant attention. However, security remains a primary concern, hindering its widespread adoption. Cloud computing essentially transfers user data and applications to remote data centers, where users relinquish control, and data management practices may not always adhere to the highest security standards. This unique characteristic of cloud computing raises a multitude of security concerns that warrant careful consideration and understanding. One of the most crucial and prevalent security concerns is the potential exposure of user data and applications stored on service provider premises. In this article, an attempt is made to review the literature in this area of research

Friction of Pneumatic Rubber Tyres on Sand

The paper describes an apparatus for determining the rolling friction of pneumatic rubber tyres on sandy surfaces at different loads for different inflation pressures. The coefficient of friction is dependent on the size and shape of the tyre. The results refer only to measurements at a very low speed. Tyres having a flat tread and low inflation pressure are preferred on sand

Three decades of using of gypsum under sodic water irrigation in coarse textured soils

The aim of this study was to determine the long term use of sodic waters and gypsum on physico-chemical properties of coarse textured loamy sand soil. The study consisted of three natural occurring underground sodic waters and amendment gypsum in various cropping systems. The results demonstrated that sodic water irrigation significantly decreased infiltration rate (1.46 & 1.09 cm hr-1) and soil porosity (35.66 & 33.26 %) and increased soil strength (17.49 & 17.67 kg cm-1), pH (9.52 & 9.66), exchangeable sodium percentage (48.00 & 55.00), sodium adsorption ratio (45.14 & 54.10 (mmol/l)1/2) and calcium carbonate content (2.15 & 2.44 %). The gypsum application significantly improved infiltration rate (2.20 cm hr-1) and soil porosity (38.7 %) and reduced soil strength (16.74 kg cm-1), soil pH (9.35) exchangeable sodium percentage (39.00), sodium adsorption ratio (36.93 (mmol/l)1/2) over a period of thirty years. A significant CaCO3 build up in soil was also observed with gypsum application (3.28 % 4.56 %) as compared to its content at the start of study. Thus, it is concluded that in coarse textured soils of North west India, sodic waters up to RSC 12.0 me l-1 could safely be used crop production in combination with gypsum in loamy sand soil without any adverse effect on the physico-chemical characteristics of soil

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles

Enhancement of Ethanol Production in Electrochemical Cell by Saccharomyces cerevisiae (CDBT2) and Wickerhamomyces anomalus (CDBT7)

Bioethanol (a renewable resource), blended with gasoline, is used as liquid transportation fuel worldwide and produced from either starch or lignocellulose. Local production and use of bioethanol supports local economies, decreases country's carbon footprint and promotes self-sufficiency. The latter is especially important for bio-resource-rich land-locked countries like Nepal that are seeking alternative transportation fuels and technologies to produce them. In that regard, in the present study, we have used two highly efficient ethanol producing yeast strains, viz., Saccharomyces cerevisiae (CDBT2) and Wickerhamomyces anomalous (CDBT7), in an electrochemical cell to enhance ethanol production. Ethanol production by CDBT2 (anodic chamber) and CDBT7 (cathodic chamber) control cultures, using 5% glucose as substrate, were 12.6 ± 0.42 and 10.1 ± 0.17 mg·mL−1 respectively. These cultures in the electrochemical cell, when externally supplied with 4V, the ethanol production was enhanced by 19.8 ± 0.50% and 23.7 ± 0.51%, respectively, as compared to the control cultures. On the other hand, co-culturing of those two yeast strains in both electrode compartments resulted only 3.96 ± 0.83% enhancement in ethanol production. Immobilization of CDBT7 in the graphite cathode resulted in lower enhancement of ethanol production (5.30 ± 0.82%), less than free cell culture of CDBT7. CDBT2 and CDBT7 when cultured in platinum nano particle coated platinum anode and neutral red-coated graphite cathode, respectively, ethanol production was substantially enhanced (52.8 ± 0.44%). The above experiments when repeated using lignocellulosic biomass hydrolysate (reducing sugar content was 3.3%) as substrate, resulted in even better enhancement in ethanol production (61.5 ± 0.12%) as compared to glucose. The results concluded that CDBT2 and CDBT7 yeast strains produced ethanol efficiently from both glucose and lignocellulosic biomass hydrolysate. Ethanol production was enhanced in the presence of low levels of externally applied voltage. Ethanol production was further enhanced with the better electron transport provision i.e., when neutral red was deposited on cathode and fine platinum nanoparticles were coated on the platinum anode