Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control  and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving   throughput, but not considering  delay. However, delay is the main factors for trasnmiitng NRT packets.  This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.&nbsp

Efficacy and Safety of Long Acting Beta Agonist/Long Acting Muscarinic Antagonist/Inhaled Corticosteroid Along with Rosuvastatin in Moderate to Severe COPD Patients

BACKGROUND: Chronic obstructive pulmonary disease is characterized by persistent airflow restriction that is usually progressive and linked with chronic inflammatory response in the airway and the lung to toxic particles and gases. Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines give three criteria which are needed to diagnose the disease.1)A post-bronchodilator FEV1:FVC ratio of less than 70%, 2)Appropriate symptoms like dyspnea, sputum production, chronic cough, or wheezing, 3)Significant exposures to toxic environmental stimuli. Statins inhibit HMG-CO A (3-hydroxy-3-methyl glutaryl coenzyme A reductase). It is strong inhibitors of cholesterol biosynthesis and have greatly enhanced the management of coronary artery disease. Statins are now becoming accepted as powerful anti inflammatory agents that have favorable effects beyond low-density lipoprotein cholesterol reduction(LDL). A relation between statin use and occurrence of an abnormal Th1 subset of T lymphocytes, CD4 +CD28 null which often expanded in unstable angina. We aimed in this study to assess anti inflammatory effects of statin in COPD patients. METHODS: The study is undertaken to compare the Efficacy and safety of long acting beta agonist/long acting muscarnic antagonist/inhaled corticosteroid along with Rosuvastatin in moderate to severe COPD patients. 60 patients who were attending Respiratory medicine outpatient department Govt .Stanley Medical College satisfying inclusion &exclusion criteria were included in the study, the study subject was randomly allocated into 2 groups of 30 patients each. Group 1 patients (control) were treated with LABA/LAMA/ICS once daily. Group 2 patients (case) were treated with LABA/LAMA/ICS with Rosuvastatin 10mg once daily. Patients was followed for 12 weeks. Efficacy by Pulmonary Functions Test (Spirometry) parameters like FEV1, FVC and PEFR were recorded. Clinical COPD questionnaires for symptoms reduction. Safety by adverse drug reactions reported voluntarily by the patients, observed or enquired were recorded. The statistical analysis done by using the SPSS version 23 and Excel software. The difference between FVC, FEV1,PEFR, AST CPK was found by the "t" test. RESULTS: In our study 22 were females(37%) and 38were males(63%).the mean age was 52(+/-6) years both control and case. In our study 22 were females(37%) and 38were males(63%). The mean age was 52(+/-6) years both control and case. Forced expiratory volume (FEV1) Mean baseline values of case1.13 L (+/-.32) ,control 1.13 L ( +/-.44) At the end of study Mean value of case 1.63L(+/-.31), control 1.65 L(+/-.33). There was no significant change in both groups. On comparing PEFR of both groups, there was a difference of 8L/min in median change from baseline in rosuvastatin group as compared to control at 12weeks which was statistically significant (P=0.001). On comparing both group mean change in CCQ symptom score was not significant during study period (12 weeks). DISCUSSION: There was no change in pulmonary function test such as FVC and FEV1,CCQ questionnaire symptom score Laboratery investigations [AST,CPK,HB%] in both the groups. In case group there was significant improvement in PEFR 8L/min from the baseline which is statistically significant(0.001). Study done by Chogtu [et. al.] on Rosuvastsin in COPD and Pulmonary hypertension also shows PEFR difference of 10L/min from baseline with Rosuvastatin group as compared to placebo at 12 weeks which was statistically significance(P-0.04)154 CONCLUSION: From this study we are concluding that Rosuvastatin 10mg with LABA/LABA/ICS for 12week regimen in moderate to severe COPD patients. There was no change in FEV and FEV1.In study group there is significant improvement in PEFR 8L/min which is statistically significant. In our study rosuvastatin was well tolerated, safe has significant therapeutic effect in patients with moderate to severe COPD

COMPARATIVE STUDY OF EFFICACY AND SAFETY OF AZITHROMYCIN ALONE AND IN COMBINATION WITH PROBIOTIC IN THE TREATMENT OF IMPETIGO IN CHILDREN

Objective: Impetigo is a superficial infection of the skin that involves only the epidermis. It affects mostly children, usually on exposed areas of the body (eg. The face and the legs). Staphylococcus aureus is the most important causative organism. Streptococcus pyogenes (i.e.) group A beta-hemolytic streptococcus) causes fewer cases, either alone or in combination with S. aureus. The objective of this study is to find out the efficacy and safety of azithromycin alone and in combination with probiotic among children suffering from impetigo.Methods: This prospective, randomized, single-blinded interventional study was conducted for a period of 6 mo in pediatric OPD and dermatology OPD in Rajah Muthiah Medical College and Hospital. A total of 100 patients, randomly divided into two groups with 50 patients in each group. Group, I patients treated with Azithromycin 10 mg/kg/d for 5 d. Group II patients treated with Azithromycin 10 mg/kg/d for 5 d with probiotic (50 million spores of Lactobacillus sporegens, Streptococcus faecalis 30 million spores, clostridium butyricum 2 million spores, Bacillus mesentericus 1 million spores) twice daily for 5ds.Results: Reduction in a number of lesions and wound area, clinical response were highly significant in Azithromycin with the probiotic-treated group.Conclusion: In this study, probiotic bacteria may counteract the inflammatory process beyond the intestinal milieu. The results of this study indicate that Azithromycin with probiotic is effective in the treatment of impetigo

EVALUATION OF DRUG-DRUG INTERACTIONS IN PATIENTS OF GENERAL MEDICINE, ICU and EMERGENCY DEPARTMENTS AT A TERTIARY CARE HOSPITAL

Objective: To evaluate the drug-drug interactions in General medicine, ICU and Emergency departments.Methods: It is a prospective-observational study. This study included hospital In-patients treated in General medicine, ICU and Emergency. Data were collected from the medical records of In-patients which includes patient's demographic details, medical history, social history, treatment chart, status and drug-drug interaction is assessed by using Lexicompand Medscape and other tertiary resources and documented in a suitably designed data collection form.Results: A total of 250 subjects were enrolled in the study. The majority (23.6%) of cases were identified in the age group 51–60 y old. Higher rate of DDIs was identified in the male gender prescriptions. 70% moderate interactions, 33.6% Minor interactions, 6.4% serious interaction found in General medicine, ICU and Emergency departments.Conclusion: Polypharmacy, age and comorbid condition were took part major role in drug-drug interactions

Trained neural network to predict paddy yield for various input parameters in Tamil Nadu, India

The major objective of the present study was to explore if Artificial Neural Network (ANN) models with back propagation could efficiently predict the rice yield under various climatic conditions; ground-specific rainfall, ground-specific weather variables and historic yield data. The back propagation algorithm will calculate each expected weight using the error rate as the activity level of a unit was altered.  The errors in the model during the training phase were solved during the back-propagation. The paddy yield prediction took various parameters like rainfall, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, and the long-time paddy yield recorded using Artificial Neural Networks. The R2 value on the test set was found to be 93% and it showed that the model was able to predict the paddy yield better for the given data set. The ANN model was tested with learning rates of 0.25 and 0.5. The number of hidden layers in the first layer was 50 and in the second hidden layer was 30. From this, the testing value of R square was 0.97. The observations with the ANN Model showed that i) the best result for the test set was  R2 value of 0.98, ii) the two hidden layers kept with 50 neurons in the first layer and 30 neurons in the second one, iii) the learning rate was of 0.25. With all these configurations, maximum yield is possible from the paddy crop

IMPACT OF CONTINUOUS PATIENT COUNSELLING ON KNOWLEDGE, ATTITUDE, AND PRACTICES AND MEDICATION ADHERENCE OF DIABETIC PATIENTS ATTENDING OUTPATIENT PHARMACY SERVICES

ABSTRACTObjective: The morbidity and morbidity associated with diabetes can be drastically reduced by the knowledge about diabetes mellitus and appropriateattitude toward the disease. A study was conducted to assess the level of knowledge, attitude, and practices (KAP) and medication adherence patternsof diabetic patients and effect of pharmacist‑led patient education on KAP and medication adherence patterns in these patients.Methods: 400 diabetic patients of either sex, aged above 18 years were divided randomly into two groups of 200 each as control and the interventiongroups. At the baseline, patients in both the groups were assessed for KAP using KAP Questionnaire and medication adherence using MoriskyAdherence Questionnaire. Patients in the intervention group were counseled both verbally and by distribution of a patient education leaflets at baselineand at three consecutive follow‑ups (1st, 2nd, and 3 months), and patients in the control group were counseled both verbally and by distribution ofpatient education leaflets at the baseline and then on the follow‑up after 3 months. Both the groups were assessed repeatedly for KAP and medicationadherence using same questionnaires after each counseling sessions. The mean scores of KAP and medication adherence, and the fasting blood sugarlevels (FBS) at the baseline and on the follow‑up for control and the intervention groups were analyzed statistically using independent sample t‑testand Mann–Whitney U‑test.rdResults: Of 200 patients in each group, 178 females and 22 males in the intervention group (mean age 57.80±9.878 years) and 179 females and21 males in the control group (mean age 57.57±9.438 years). A statistically significant improvement in the mean KAP and adherence scores wasobserved from the baseline to the final follow‑up in both groups (p≤0.001). The increase in the KAP and medication adherence scores from baselineto the follow‑up in the intervention group was found to be significantly higher than the control group. There was a reduction in the mean FBS frombaseline to the follow‑up in both the groups but a statistically significant higher reduction in the mean FBS was found in the intervention group frombaseline to the final follow‑up when compared to the control group (p < 0.001).Conclusion: A better KAP of diabetic patients about their disease can improve the medication adherence behavior which in turn can improve clinicaloutcomes. The patient education should be a continuous process, and patients should be assessed at every subsequent visit for medication adherenceto achieve better health outcome.Keywords: Diabetes, Adherence, Knowledge, attitude and practices, Patient education

Android application development for identifying maize infested with fall armyworms with Tamil Nadu Agricultural University Integrated proposed pest management (TNAU IPM) capsules

Several pests and diseases wreak havoc on maize crops worldwide. Novel and rapid methods for detecting pests and diseases in real-time will make monitoring them and designing effective management measures easier. In the recent past, maize has been imperilled by fall armyworms (Spodoptera frugiperda), which have caused substantial yield losses in maize. This study aimed to create an Android mobile application via  DCNN (Deep Convolutional Neural Network)-based AI pest detection system for maize producers. Everyone benefits from the deployment of these CNN models on mobile phones, especially farmers and agricultural extension professionals because it makes them more accessible. Automatic diagnosis of plant pest infestations from captured images through computer vision and artificial intelligence research is feasible for technological advancements. Therefore, early detection of maize fall armyworm (FAW) infestation and providing relevant recommendations in maize could result in intensified maize crop yields. . An Android mobile application was created to identify fall armyworm infection in maize and included the recommendations given by Tamil Nadu Agricultural University proposed Integrated Pest Management (TNAU IPM ) capsules in the mobile app on as to how to deal with such a problem. Digital and novel technology was chosen to address these issues in maize. Deep convolutional neural networks (DCNNs) and transfer learning have recently moved into the realm of just-in-time crop pest infestation detection, following their successful use in a variety of fields. The algorithm accurately detects FAW (S. frugiperda) infected areas on maize with 98.47% training accuracy and 93.47% validation accuracy

Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.)

Maize (Zea mays L) is one of the most saleable cereal crops grown worldwide and a dominant staple food in many developing countries. The severe outbreak of fall armyworm in maize causes massive yield loss. Modern technologies, including smartphones, can assist in detecting recognising the fall armyworm infestation in maize. The objective of this study was to develop an automated Artificial Intelligence Powered Expert System (AIPES) for identifying fall armyworm infestation in maize. In addition, it put forward a deep learning-based model that is trained on photographs of healthy and fall armyworm infested leaves, cobs and tassels from a dataset and furnished an application that will be detecting maize fall armyworm infestation using Convolutional Neural Network (CNN) architecture and Mobile Net V 2 framework model. The study developed an Artificial Intelligence (AI) based maize fall armyworm infestation detection system using a DCNN (Deep Convolutional Neural Network) to support maize cultivating farmers. The model executed the objective by accurately identifying the fall armyworm infested maize plant and also classified them vis-c-vis the healthier crop. The deep learning models were trained to detect and recognise fall armyworm infection using more than 11000 images of fall armyworm infested leaves, cobs, and tassels. The created application (AIPES for identifying fall armyworm infestation in maize) using CNN detected and recognised the fall armyworm infestation in maize with a 100 per cent training accuracy rate and 87 per cent validation accuracy. So, the detection of maize fall armyworm and the treatment of fall armyworm-infested maize could lead to a higher maize crop yield.      

Ghrelin and its Association with Nutritional and Inflammatory Status of Patients on Maintenance Hemodialysis in a South Indian Tertiary Care Hospital

Background: Malnutrition and inflammation are associated with morbidity and mortality in patients on maintenance hemodialysis (MHD). Ghrelin, an orexigenic peptide hormone, is speculated to be associated with nutritional and inflammatory status in MHD. Aim: To assess the serum total ghrelin levels and its possible relationship with inflammation and nutritional status in patients on MHD. Subjects and Methods: The study was conducted on 90 patients on MHD for 6 months and above (56 males, 34 females, mean age 52.6 [11.7] years; mean dialysis vintage 20.9 [12.1] months) and 70 healthy volunteers as control (5 males, 25 females, mean age 50.6 [9.7] years). Demographics were obtained for the study population, and dialysis‑related data were collected for cases. Anthropometry, biochemical parameters, serum total ghrelin and inflammatory markers tumor necrosis factor‑alpha (TNF‑α), and high‑sensitivity C‑reactive protein (hsCRP) were assessed for cases and control. Self‑reported appetite (five questions of appetite and diet assessment tool) and nutritional status (subjective global assessment‑dialysis malnutrition score) were assessed for cases. Results: Ghrelin (242.5 [62.3] pg/mL vs. 80.2 [19.6] pg/mL; P < 0.001), TNF‑α (39.8 [15.2] pg/mL vs. 6.5 [1.2] pg/mL; P < 0.001), hsCRP (10.2 [2.8] mg/L vs. 2.7 [0.54] mg/L; P < 0.001) were significantly elevated in cases versus control, anthropometry, and biochemical parameters were significantly decreased in hemodialysis patient. Of 90 cases, (13/90 [14.4%]) were well‑nourished, (28/90 [31%]) mild to moderately malnourished, and (49/90 [54.4%]) were moderate to severely malnourished. Appetite was very good for 14.4%, good and fair for 47.8%, poor and very poor for 37.8% patients. There was a significant difference in appetite with respect to nutritional status (P < 0.001). Ghrelin had positive correlation with inflammatory markers and negative correlation with nutritional status (P < 0.001). Conclusion: The study identified the association of ghrelin with appetite, nutritional, and inflammatory status of the patients on MHD.Keywords: Appetite, Ghrelin, Hemodialysis, Inflammation, Nutritional statu

Influence of elevated carbon dioxide concentrations on methane emission and its associated soil microflora in rice ecosystem

The dynamics of methane emission and its associated soil microflora in rice ecosystem as a response to elevated CO2 concentrations were studied in open top chamber (OTC) conditions. The treatments consisted of three levels of CO2 (396, 550 and 750 µmol mol-1) and three levels of nitrogen (0, 150 and 200 kg ha-1) and replicated five times in a completely randomized design. The data showed that elevated [CO2] significantly (P ? 0.01) increased the DOC throughout the cropping period with the values ranging from 533 to 722 mg L-1 and 368 to 501 mg L-1 in C750 and Camb, respectively. Methane emission rates were monitored regularly during the experiment period and it was revealed that elevated [CO2] had increased the methane emissions regardless of stages of crop growth.  It was observed that methane emissions were significantly higher under [CO2] of 750 µmol mol-1 by 33 to 54 per cent over the ambient [CO2] of 396 µmol mol-1. Consistent with the observed increases in methane flux, the enumeration of methanogens showed a significant (P ? 0.01) increase under elevated [CO2] with the population ranging from 5.7 to 20.1 x 104 CFU g-1 of dry soil and 5.1 to 16.9 x 104 CFU g-1 of dry soil under C750 and Camb concentrations, respectively. Interestingly, even though higher methanotrophs population was recorded under elevated [CO2], it could not circumvent the methane emission. Overall, the results of OTC studies suggest that methane mitigation strategies need to be explored for the future high CO2 environments.