The purpose of the current research is to advance further the D-Partitioning method and emphasize on its practical application. It has the objective to clarify it in a user friendly manner in order to simplify its implementation. By applying the basic initial ideas of the method, the main line of the research is the development of a generalized stability analysis tool and demonstrating its application. With the aid of this tool, proper parameter values can be chosen for a desirable performance and stability of a system. The analysis tool can be practically used when one, two or more system’s parameters are varied independently or simultaneously. Basically this tool defines regions of stability in the space of the system’s parameters.

New generation technological improvements lead patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to develop a machine learning model to suit in different availability of users. Most of the current systems allow people to choose related symptom in web interfaces. In addition to these applications it is aimed to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday language statements besides medical expression of symptoms for corresponding symptoms which is also supporting initial clinical decision system. Extracted terms are analyzed for matching diagnosis where an accuracy of 90% has been accomplished.

In this paper, we study the existence of solutions for non-linear fractional q-difference equations of order 2 ?? ? 3 involving the p-Laplacian operator with various boundary value conditions. By using the Banach contraction mapping principle, we prove that, under certain conditions, the suggested non-linear fractional boundary value problem involving the p-Laplacian operator has a unique solution. Finally, we illustrate our results with some examples.

A wealth of data in public health care systems has been collected and meanwhile there are plenty of new technological improvements which have considerable influence on current data pool. Nevertheless, important obstacles are challenging to utilize existing clinical data. Enhanced technological improvements lead patients to search their symptoms and corresponding diagnosis on online resources. In this study, it is aimed to develop a machine learning model to suit in different availability of users. Most of the current systems allow people to choose related symptom in web interfaces or Q&A forums. In addition to these applications it is aimed to implement a new technique which extracts the text-based symptoms and its related parameters such as, severity, duration, location, cause, accompanied by any other indicators. This study is applicable for patient`s everyday language statements besides medical expression of symptoms for corresponding symptoms. Extracted terms are used as an input of the model and analyzed for matching diagnosis where an accuracy of 72.5% has been accomplished.

This work was conducted to assess the microbial profile and quality attributes of unbranded groundnut oil sold at Keffi. A total of 25 samples of unbranded groundnut oil were collected from different locations and subjected to microbial and quality assessment. The total viable bacteria count ranged from 2.1– 7.2 × 105 cfu/ml, while the total faecal coliform count ranged from 2.2–6.2 × 105 cfu/ml. The Salmonella/Shigella count ranged from 1.4–4.2 × 105 cfu/ml and the fungal count ranged from 3.6 – 8.2 × 105 cfu/ml. The microbial isolates obtained were Mucor spp., Aspergillus flavus, Aspergillus niger, Rhizopus spp., Penicillium spp., Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus spp., Micrococcus spp., E. coli and Salmonella spp. Anti-biogram of the bacterial isolates revealed a varying level of resistance/susceptibility to the antibiotics tested. The result of mineral contents analysis showed that all samples had high detectable levels of Zn, Mn, Fe, Cu, Cd and Pb. These results indicated values that exceeded the maximum limits set by regulatory agencies, thereby making these oils unsafe for consumption. It can therefore be concluded that it is imperative for the manufacturers of these products to adopt good manufacturing practices and ensure proper quality assurance of their products.

The undertaking determined the community awareness on typhoon and assesses the hazard mitigation practices with an end view of drawing the suggested activities to the office of the disaster information and risk reduction programs of the Local Government Unit of Malvar. This descriptive study covered the household heads in the municipality. They gathered data through a researcher-made questionnaire. The results revealed that majority of the respondents were aware on typhoon and assessed the hazard mitigation practices of the municipality as good. Considering the result, it was recommended that the locality may conduct seminars, workshops and orientation programs on terminologies regarding typhoon and other environmental issues. Drills and simulations in the school and community may be exercised. In addition, they may also raise funds to provide the needed supplies of the community. The aforementioned activities may be given emphasis by the concerned authorities or agencies for maximum implementation.

This paper details the use of Electroencephalography, a methodology commonly applied for medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI (Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier than anticipated, and therefore need frequent rotation from such activities.