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This article presents a new nine-level common ground (CG) type transformerless inverter (TL) structure with an integrated boost (IB) cell. The proposed structure eliminates the leakage current due to the CG structure between the input source and the grid neutral. The proposed topology is derived by combining a DC-DC boost converter and switched capacitor (SC) cell, capable of generating the nine-level output voltage over a wide range of input voltage variations. By maintaining the duty cycle of the IB converter switch at 50%, the proposed topology generates the output voltage with a boosting factor of 4. Besides, soft charging of the involved SCs and reduced inrush current is also achieved through the inductor, which ensures the permissible current stresses of the power switches. Furthermore, the voltage across the SCs is self-balanced, eliminating the need for complex independent control techniques. A 725-W laboratory prototype is built to test the performance of the proposed topology under various operating conditions, such as input and load variations. The measured efficiency of 96.9% at 725 W is obtained and is comparable with the simulation efficiency. Finally, the merits of the proposed topology are highlighted using a detailed comparison with the other recent nine-level inverter topologies. [ABSTRACT FROM AUTHOR]

Foraging activity of stingless bees are influenced by unpredictable environmental variables. The study on Influence of weather parameters on foraging activity of stingless bee, Tetragonula iridipennis Smith during winter was conducted from October, 2022 to January, 2023. Irrespective of winter months, peak activity of outgoing bees was observed from 1100-1600 hr, nectar carrying bees from 1100-1500 hr, pollen carrying bees from 1200-1400 hr and resin carrying bees from 1200-1500 hr. Hourly temperature and wind speed had positive correlation with foraging activity, whereas, the hourly relative humidity and precipitation negatively affected the foraging activity of T. iridipennis. Among forage carrying bees, the number of nectar carrying bees were maximum, followed by pollen and resin carrying bees (N>P >R) during winter. [ABSTRACT FROM AUTHOR]

This study aimed to determine whether autochthonous high-intensity D. repens microfilaremia leads to haematological changes in companion dogs. Dogs with hypermicrofilaremia presented to the District Veterinary Centre, Kannur were selected for the study. A total of 100 dogs were positive for hypermicrofilariaemia with D. repens based on peripheral blood smear examination and Knott's test. Of these, 3 dogs were concurrently positive for sheathed microfilaria (MF) of Brugia spp. Interestingly, an adult live worm of D. repens approximately eight cm in length was detected on a subcutaneous mass on the hind limb of a hypermicrofilaremic dog in the study. We compared the hematological parameters of 100 autochthonous hypermicrofilaremic dogs and 15 uninfected dogs. The hematological findings in the study are mild to moderate anemia, moderate to severe thrombocytopenia, leucocytosis with granulocytosis, lymphocytosis and monocytosis as compared to the uninfected group of dogs. The pathogenicity of naturally occurring hypermicrofilaremia is poorly studied, and knowledge of its epidemiology, host-parasite relationship and impact on various organs is warranted for better prevention and control, especially in hot-spot areas. [ABSTRACT FROM AUTHOR]

The grading of mangoes is an essential aspect of providing quality fruits to consumers and control the needs of the fruit processing industry. Manual visual inspection leads to inconsistencies, and it is human labour intensive. This paper is focused on improving the accuracy of the automatic mango grading system by doing multi-level grading using Deep Learning, Computer Vision and Image processing techniques. The proposed system is based on the mango maturity ripening stage, shape, texture features, colour and defects to identify the mango variety and classify based on quality. The maturity ripening stage of the mango is extracted using the Convolutional Neural Network (CNN). Computer Vision and Image processing techniques are used to extract shape, texture features and defects. The extracted features are input to the Random Forest classifier to identify the mango variety and grade the mango quality into three classes Notfit, Average and Good. The system has been validated on the dataset created for this study across three different varieties, Banganapalli, Neelam and Rumani, the most popular in Tamil Nadu. The proposed system using features extracted from CNN enhanced the system's efficiency with an accuracy of 93.23% for variety recognition and 95.11% for quality grading. Hence the proposed system is fully automated, commercially viable and has improved accuracy in variety recognition and quality grading of mangoes across different varieties. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a contemporary direction on the reduction of vibration and acoustic noise in Switched Reluctance Motor (SRM) using Soft Magnetic Composite (SMC) blank. Methods: Soft magnetic composite materials are composed of iron powder and resin. The particles are compacted, together with a lubricant and possibly a binder, at high pressure into a bulk material. Soft magnetic composite materials are characterized by three-dimensional isotropic ferromagnetic behaviour and very low eddy current loss. The competence of Soft Magnetic Composite (SMC) material in Switched Reluctance Motor (SRM) on vibration related issues has been investigated through Finite Element Analysis and experimentation. Results: To analyze the magnetic, vibration, and acoustic noise characteristics two motor configurations, viz., M19-SRM (M19- Switched Reluctance Motor) and SMC–SRM (Soft Magnetic Composite - Switched Reluctance Motor) are considered. Conclusion: The efficacy of SMC–SRM (Soft Magnetic Composite - Switched Reluctance Motor) prototype in vibration mitigation has been validated. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. Therefore, detecting epileptic activity is a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper evaluates machine learning classifiers' performance with their paradigms for classification of raw EEG signals into two classes, i.e., seizure and non-seizure. Here, the 13 descriptive features are taken into consideration and fed to the classifiers. Here, CHB-MIT Scalp EEG Database is used, which comprises paediatric subjects of 24 records. The performance of classifiers is evaluated categorically concerning gender and in total. The results confirmed that the fine KNN is the best classifier in males, females, and all subjects. [ABSTRACT FROM AUTHOR]

Aims To develop a healthy diet score that is associated with health outcomes and is globally applicable using data from the Prospective Urban Rural Epidemiology (PURE) study and replicate it in five independent studies on a total of 245 000 people from 80 countries. Methods and results A healthy diet score was developed in 147 642 people from the general population, from 21 countries in the PURE study, and the consistency of the associations of the score with events was examined in five large independent studies from 70 countries. The healthy diet score was developed based on six foods each of which has been associated with a significantly lower risk of mortality [i.e. fruit, vegetables, nuts, legumes, fish, and dairy (mainly whole-fat); range of scores, 0–6]. The main outcome measures were all-cause mortality and major cardiovascular events [cardiovascular disease (CVD)]. During a median follow-up of 9.3 years in PURE, compared with a diet score of ≤1 points, a diet score of ≥5 points was associated with a lower risk of mortality [hazard ratio (HR) 0.70; 95% confidence interval (CI) 0.63–0.77)], CVD (HR 0.82; 0.75–0.91), myocardial infarction (HR 0.86; 0.75–0.99), and stroke (HR 0.81; 0.71–0.93). In three independent studies in vascular patients, similar results were found, with a higher diet score being associated with lower mortality (HR 0.73; 0.66–0.81), CVD (HR 0.79; 0.72–0.87), myocardial infarction (HR 0.85; 0.71–0.99), and a non-statistically significant lower risk of stroke (HR 0.87; 0.73–1.03). Additionally, in two case-control studies, a higher diet score was associated with lower first myocardial infarction [odds ratio (OR) 0.72; 0.65–0.80] and stroke (OR 0.57; 0.50–0.65). A higher diet score was associated with a significantly lower risk of death or CVD in regions with lower than with higher gross national incomes (P for heterogeneity <0.0001). The PURE score showed slightly stronger associations with death or CVD than several other common diet scores (P < 0.001 for each comparison). Conclusion A diet comprised of higher amounts of fruit, vegetables, nuts, legumes, fish, and whole-fat dairy is associated with lower CVD and mortality in all world regions, especially in countries with lower income where consumption of these foods is low. [ABSTRACT FROM AUTHOR]

A slow evaporation method has grown the sodium 4-nitrophenolate dehydrate (S4NPD) crystals. FTIR and Raman spectra have analyzed the chemical structure of synthesized S4NPD. The single crystal XRD and Powder XRD studies confirmed S4NPD crystal contains the orthorhombic crystal system. The UV–Vis absorption and transmittance analysis reveal that the grown crystal possesses an optical transmittance in the visible and NIR regions. The cut-off wavelength of the S4NPD is 435 nm, and these features are more appropriate for emerging optical fields. The photoluminescence spectrum shows the peak absorbed at 517 nm, indicating green light emission. The nonlinear optical properties with the enhancing role of the growth crystal’s second and third-harmonic generation efficiencies. The laser threshold damage (LDT) value shows the suitability of crystal coupled with high-power laser output applications. A chemical etching study was carried out to examine the growth pattern of the S4NPD crystal. [ABSTRACT FROM AUTHOR]

Leptospirosis is a disease of zoonotic importance in worldwide scenario and is caused by the infection of Leptospira species. The present study was undertaken to estimate the seropositivity of leptospirosis in cats of Kerala. A total of 200 serum samples were collected from apparently healthy cats from different parts of Kerala such as Thiruvananthapuram, Kollam, Alappuzha, Pathanamthitta, Kottayam, Thrissur, Ernakulam, Malappuram, Kozhikode, Wayanad, and Kannur. Among these samples, 12 samples (6 %) were found to be positive with the MAT titre above 1:20. The serovar identified in this study was serovar Djasiman, Autumnalis, Icterohaemorrhagiae, Australis, Grippotyphosa, and Hardjo. The highest prevalence in cat was for serovar Djasiman (50%) followed by serovar Autumnalis (25%). The majority of cats were crossbreds, males and in age groups between one and two years (58.33%). The majority of animals had both indoor with outdoor access, the presence of rodents and other animals in the household or surroundings. [ABSTRACT FROM AUTHOR]

Introduction: The involuntary flow of urine from the bladder is known as Urinary Incontinence (UI). Men and women of any age can experience incontinence, but it is more prevalent in older women. Several chronic illnesses and environmental variables increases incidence of UI in elderly adults. Most people in this modern era avoid seeking medical intervention early, due to financial worries as the current diagnostic and intervention methods are expensive. Aim: To use low-cost urinary diaper method for assessing the severity of UI and to study the effect of Kegel and therapeutic exercises in reducing the severity of UI in elderly women. Materials and Methods: This interventional study was conducted in an Inshape Physiotherapy Centre in Chennai, India. The duration of the study was five months, from August 2021 to December 2021. A total of 36 women with UI aged 55-75 years were included. The severity of UI among the participants was assessed using onehour Diaper Test (DPT), Hamilton Anxiety Rating Scale (HAM-A) and their level of fitness was evaluated using Senior Fitness Test (SFT). Kegel exercise, pelvic floor exercises were implemented following American College of Sports Medicine (ACSM) standards. Statistical analysis was carried out using the Statistical Package for Social Sciences (SPSS) version 20.0, and data collected was subjected to descriptive statistics and paired t-test. Results: The mean age of the study participants was 69.43±3.4 years. As a result of the intervention, 13 out of 36 participants, who initially had severe UI showed improvement (10 participants improved to moderate UI and three participants improved to mild UI). Out of seven participants, who initially had moderate UI, three participants improved to mild UI. Eight out of the 36 participants showed no UI after intervention, indicating complete recovery. There is an association between the Body Mass Index (BMI), HAM-A and UI, the intervention results in the changes of BMI, and HAM-A thus, reducing the severity of UI. Conclusion: The present study showed that, cost-effective diagnostic and non invasive intervention methods reduced the severity of the UI. The study also recommends regular physical activities to reduce stress and anxiety levels, which in turn helps to improve UI. [ABSTRACT FROM AUTHOR]

Urban beekeeping is nowadays attracting immense popularity. Many people in large cities are installing hives on the rooftops. A significant drawback for beekeeping in urban areas of India is limited bee flora which may lead to poor honey yield and performance. As more people take to urban beekeeping, the competition for resources between honey bee colonies increases. Since the space covered by vegetation in the city remains relatively constant, each honey bee colony ends with less honey production per year. It slows the growth of individual urban honey bee colonies and affects the foraging potential of the honey bee colonies. Thus, urban beekeeping could be made effective by providing sufficient bee forage in surrounding beekeeping areas and by the selection of hives suitable for urban areas. Therefore, the present work was undertaken to study the diversity of nectariferous and polleniferous honey bee flora found in the major cosmopolitan city of India: Bengaluru. We recorded 58 major bee floras grown in the Bengaluru urban to make beekeeping activities throughout the year. In addition, hives with different frame numbers (5, 6 and 8) were evaluated for their performance in 3 different locations. Results clearly showed that hives with six frames are equally good compared to the conventional eight frame hive. Therefore, hives with six frames are more suitable for urban beekeeping, which would be efficient by reducing energy requirements in activities like comb construction of the colony. Appropriate hive selection was an important factor for successful beekeeping, especially in urban areas of Bengaluru. [ABSTRACT FROM AUTHOR]

The impressed current cathodic protection (ICCP) scheme is a more reliable and efficient method of corrosion prevention mechanism than the sacrificial method. Currently, the grid connected transformer rectifier units supported with a battery banks are used for the ICCP-based corrosion protection system in the desalination plant. This conventional method is entirely grid-dependent, more expensive, and suffers during prolonged grid failure. The present trend of industrialization is the application of multi-renewable energy sources based on a nanogrid to power the station's auxiliary power supply. This paper introduces a concept of distributed energy resources (DERs) operated integrated nanogrid (ING) system to provide a stable power supply solution to the ICCP scheme. A 100-million-litter per day capacity-based seawater desalination plant (SWDP) in India has been chosen as the test station. The conceptual hardware design and operational logic details for smooth integration of the integrated nanogrid module into the ICCP scheme of the Desalination plant is proposed. This research aims to investigate the behaviour of DERs during on-grid, off-grid and switching over from one mode of operation to another and vice-versa by using the accelerated Gauss–Seidel method in ETAP software (version 16.0.0). The simulation results confirm that the ING suffers from a high-frequency change rate during islanded operation, and in some cases, a complete blackout occurs. A PLC-based Smart Versatile ING Controller has been suggested to overcome the blackout issue. Finally, it has been proven that the stability of an industrial power system can be improved further by introducing the ING module into it. [ABSTRACT FROM AUTHOR]

The present investigation was aimed to study the anti-diabetic and hypolipidemic potential of Cinnamon cassia (Lauraceae family) bark in streptozotocin (STZ)-induced diabetic rats. The preliminary phytochemical analysis (hexane, petroleum ether, chloroform, ethanol, methanol, and aqueous extracts), GC-MS analysis (ethanol), in vitro (aqueous, ethanol and methanol), in vivo (ethanol) and in silico anti-diabetic activity with hypolipidemic effect of C. cassia bark was analysed. The ethanolic extract of the C. cassia bark has a fine inhibitory activity than the aqueous and methanolic extract. Out of 20 different compounds identified, seven compounds were biologically active, and 9-octadecenoic acid has highly interacted with PPARα/γ in docking studies. The levels of diabetic markers, enzymes, and lipid profiles were altered in STZ-induced rats, but after the treatment of C. cassia, the levels were returned to the normal. The study may prove the ethanolic extract of C. cassia has a powerful anti-diabetic and anti-hyperlipidemic activity. [ABSTRACT FROM AUTHOR]

Anaplasmosis is a fatal infectious disease affecting small ruminants all over the world. Haematological analysis and therapeutic evaluation of anaplasmosis were done in clinically affected goats from Thrissur district, Kerala in this study. A total of 100 blood samples from clinically affected goats were collected and microscopically examined using Field's staining technique for anaplasmosis which revealed 50 per cent positivity. Infected goats showed significant (p <0.05) decrease in their haematological variables like haemoglobin, MCH and platelets, compared to healthy animals. The effectiveness of long-acting oxytetracycline, long-acting enrofloxacin, and imidocarb dipropionate in treating anaplasmosis was 100%, 62.5%, and 50%, respectively. Long acting oxytetracycline was the most effective drug of choice for treating the disease. [ABSTRACT FROM AUTHOR]

Canine brucellosis or beagle fever is a zoonotic bacterial reproductive disease of dogs, caused by Brucella canis and occasionally by Brucella abortus, B. melitensis and B. suis. The actual seroprevalence of canine brucellosis in India is unknown and not yet studied in Kerala. A total of 131 animals presented to the outpatient unit of medicine, gynaecology and obstetrics of the two University Veterinary Hospitals at Mannuthy and Kokkala with clinical signs of epididymitis, orchitis, abortion, still birth, foetal resorption, foetal mummification, foetal maceration, neonatal death and infertility were randomly selected for the study. Paired sera samples were collected on the day of presentation and after three weeks of presentation for Rose Bengal Plate Test (RBPT). In this study, sera samples from forty-seven (35.88 per cent) infected dogs showed agglutination on RBPT using B. abortus S99 antigen. Out of forty-seven RBPT positive samples, 80.85 per cent (38/47) were female and 19.15 per cent (9/47) were male dogs. The high seroprevalence of canine brucellosis in this study is attributed to the endemicity of bovine brucellosis in the study area. [ABSTRACT FROM AUTHOR]

Hepatitis in dogs could be due to infectious, toxic or metabolic causes. Among infectious causes, leptospira, canine adenovirus (CAV 1) and babesia are responsible at varying levels, depending on the local geo-climatic conditions. This study, conducted on 40 dogs with clinical signs of hepatitis in Thrissur, Kerala, revealed occurrence of 2.5 per cent (1/40) for CAV 1, 20 per cent (8/40) for leptospirosis and 22.5 per cent (9/40) for babesiosis. The aetiologies were confirmed by polymerase chain reaction (PCR) targeting the E3 gene for CAV 1, microscopic agglutination test (MAT) for leptospirosis and peripheral blood smear examination for babesiosis. Haematological profile of CAV 1 infected animals revealed thrombocytopaenia, lymphopaenia, monocytopaenia and granulocytosis. Serum biochemical analysis divulged elevated alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total bilirubin and creatinine, with decreased total protein and albumin. Significant decrease in volume of packed red cells (VPRC), haemoglobin (Hb) and total erythrocyte count (TEC), with significant increase in total leukocyte count (TLC) were evident in dogs positive for leptospirosis. Serum biochemical analysis revealed significant increase in ALP, AST and blood urea nitrogen (BUN) and significant decrease in total protein. Severe anaemia and thrombocytopenia with mild leucocytosis and significant increase in ALP and total bilirubin, with hypoproteinemia were noticed in animals with babesiosis. [ABSTRACT FROM AUTHOR]

This paper presents a design of 4 bit differential current steering(DCS) DAC as a part of 16bit PAM transmitter (4-b/symbol) for achieving 4Gb/s data rate in 45-nm CMOS technology. The 4-bit folding flash ADC circuit is used in the receiver to complete the transceiver chain for the wireless communication. 4-b ADC and the binary to thermometer converter block will convert the single-ended continuous time input to 15-b thermometer code at 1 GHz clock. The 4-bit thermometer DCS DAC which takes the 15-b digital input and provides output current to drive the channel. The channel is terminated at the receiver with a terminating resistor (RT) and a terminating voltage (VT). All the IC design challenges are suitable with the CMOS technology for the communication devices and internet of things. Hence DAC is designed in 45-nm technology for the proposed work and overall achieved parameter results are compared with different topologies. For the 1 V applied voltage the whole system has the power consumption as 11.244mW and the ENOB of 3.953@29Mhz. [ABSTRACT FROM AUTHOR]

Background: The misconceptions about the dental treatment prevailing among the population was considered as one of the important barriers for non- utilization of oral health services. The purpose of the present study was to find out the prevalence of common myths related to oral health and in the treatment aspects among the rural population of Tamilnadu, India. Methods: A cross sectional questionnaire survey was conducted among the outpatients attending a dental college in Tamilnadu. The questionnaire included demographic details and 18 questions related to the traditional beliefs. Result: Out of 371 patients, 70 (18.9%) thought that the extraction of upper teeth will affect their eyesight. 49.1% felt that extraction during rainy season will cause infection. Around 38% believe scaling leads to weakening of the teeth. Chi-square test showed no statistically significant difference exists among the gender and as well as education. Conclusion: The myths related to dentistry still exist which requires further studies based on using appropriate technology to eliminate myths among the community. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify the primary bacteria associated with clinical mastitis in dairy cows and to assess the antimicrobial resistance pattern of the commonest isolate using phenotypic methods. The milk samples were collected from 83 domesticated dairy cows suffering from clinical mastitis (CM), from an organised dairy farm as well as cows presented with CM at the University Veterinary Hospital (UVH), Mannuthy, Thrissur, Kerala. Microbiological analysis, using morphological, cultural and biochemical properties as well as molecular identification using polymerase chain reaction (PCR) revealed that coagulase-negative staphylococci (CNS) were the most predominant (40.31 per cent) bacteria, followed by Staphylococcus aureus (32.83 per cent), Micrococci spp. (11.94 per cent), Klebsiella spp. (7.46 per cent) Escherichia coli (4.48 per cent) and Streptococci (2.98 per cent). The majority of the bacteria were resistant to more than one class of antimicrobials (aminoglycosides, β lactams, fluoroquinolones or tetracyclines). Furthermore, our findings revealed that the CNS is highly resistant to β-lactam family of antibiotics and that CNS may play a significant role in clinical mastitis of dairy cows. [ABSTRACT FROM AUTHOR]

This study was aimed to evaluate the prevalence of cystoisosporosis among domestic cats in Thrissur district, Kerala. A total of 155 cats were included in this study. Faecal samples collected from all the cats were subjected examination by direct smear method and floatation by Sheather's sugar solution. Of 155 faecal samples examined, oocysts were found in 28 cats (18.06 per cent). Among these, 12 were collected from cats with signs of gastrointestinal diseases and 16 from cats which are apparently healthy with firm faeces. Mixed infection with Cystoisospora spp and Toxocara, Ancylostoma and Cystoisospora were also detected in three (1.94 per cent) and one (0.65 per cent) cat respectively. Various risk factors associated with the infection were also studied. Prevalence was found to be higher in cats less than six months old. Gender and breed were not affected the prevalence. This study demonstrates that cystoisosporosis is common in cats. [ABSTRACT FROM AUTHOR]

Vector-borne diseases are an important cause of morbidity and mortality in domestic cat population and hepatozoonosis is one such infection. Incidence of hepatozoonosis has been described in a variety of animal species but information on cats is scarce. To investigate the occurrence of this pathogen in domestic cats, blood samples were collected from domestic cats from three districts of Kerala (Thrissur, Kannur and Wayanad). Field-stained blood smears were examined to detect the presence of gamonts of Hepatozoon spp. and the samples were subjected to molecular analysis by PCR amplification. Out of 122 blood samples screened, none of the sample revealed the presence of gamonts of Hepatozoon on microscopic examination. Polymerase chain reaction targeting 18S rRNA gene of Hepatozoon spp. demonstrated seven positive cases with a prevalence of 5.74 per cent. Results of this study indicate that hepatozoonosis is established within the domestic cats in Kerala and warrant the adoption of control measures. [ABSTRACT FROM AUTHOR]

Coagulase negative staphylococci (CNS) are emerging as the most prevalent causative agent of bovine mastitis. They are resistant to many commonly used antibiotics due to the presence of antimicrobial resistance (AMR) genes. A study was conducted to evaluate the AMR profiling of CNS isolated from bovine subclinical mastitis.Coagulase negative staphylococci were isolated from 49 (44.95 per cent) of the subclinical mastitis samples. Disc diffusion assay revealed that highest resistance was shown against gentamicin (42.85 per cent) followed by methicillin (32.6 per cent), ceftriaxone - tazobactam (24.48 per cent), enrofloxacin (20.4 per cent), tetracycline (16.32 per cent) and least resistance to cotrimoxazole (4 per cent). Genotypic characterisation of AMR genes such as mecA, aacA-aphD and norA by PCR was done for determining resistance to methicillin, gentamicin and fluroquinolone resistance. The CNS carried aacA-aphD, norA and mecA in 44.89 per cent, 32.65 per cent and 14.28 per cent, respectively. Comparison of phenotypic and genotypic characterisation of AMR in CNS was carried out by McNemar test and it was found that there was significant difference between the presence of mecA gene and methicillin resistance. There was no significant difference noticed forcharacterisation of phenotypic and genotypic AMR of CNS for gentamicin and fluroquinolone resistance. [ABSTRACT FROM AUTHOR]

Breast cancer rates are rising. It also remains the second principal reason for cancer-related mortality in females, and the mortality rate is also drastically rising. In recent years, MicroRNAs (miRNAs) have emerged to have a large potential as biomarkers because of their effective roles in human disease diagnosis (including breast cancer). miRNAs are small (short), regulatory, and evolutionarily conserved non-coding RNAs (ncRNAs) molecules (with a length of about 22 nucleotides) that are present in all eukaryotic cells. There are many studies available in the literature that focus on recent circulating miRNAs research, their relationships to human diseases, their role as a potential biomarker, etc. Therefore, in this study we used three key techniques for classification of breast cancer using miRNAs features: Linear Discriminant Analysis (LDA), Random Subspace Ensemble (RSE) and Bayesian Hyperparameter optimization (BHO). Linear Discriminant Analysis (LDA) is a simple but most practical and computationally attractive classification approach. Random Subspace Ensemble (RSE) is capable of producing a robust ensemble for classification. Some previous research showed applications of Bayesian optimization in many engineering optimization problems. Notably, it is a recently applied for hyperparameter tuning in various ensemble classifiers. Therefore, the potential application of the RSE of LDA classifiers (LDA as a base classifier) with BHO method to boost the predicting accuracy of breast cancer diagnosis using miRNAs profiling dataset, has been studied in this study. A publicly available dataset of serum miRNA profiles obtained from the GEO dataset (accession code GSE106817) has been applied for validation. A variety of output measurements were employed to determine the performances and efficiencies of the proposed model and other classifiers. The proposed approach exhibited successful overall performance. The results were directly compared with the individual LDA classifier and other established state-of-the-art classifiers. The outcomes point out that the approach is superior in terms of different efficiency indicators to the LDA and all established state-of-the-art models used in the study. Study simulations, outcomes, and mathematical investigations have illustrated that the technique presented is a practical and advantageous model for the classification of breast cancer from miRNA profiling. This model may usefully be employed in other cancer classifications from miRNA profiling. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) in association with cloud technologies are the raising stars in information technology industry, which provides a lot of innovative gadgets to several industries to automate their needs as well as monitoring the events properly without any human interventions. The most common and emerging needs now-a-days is the development of new gadgets to support healthcare industry to rectify the medical flaws and save the human life. In one side the booming technologies such as Internet of Things and Cloud platforms are available and the other hand a drastic need to introduce an intelligent gadget for medical oriented needs to save one's life in critical situations. This paper aims to create a bridge between the two and introduce a new device to combine healthcare with recent technological developments. The proposed approach introduces a new algorithm called iCloud Assisted Intensive Deep Learning (iCAIDL), which provides support to healthcare medium as well as patients by means of applying the intelligent cloud system along with machine learning strategies and this proposed algorithm is derived from the base of deep learning norms. An iCloud Assisted Intensive Deep Learning algorithm initially begins with the flow of collecting the existing health records from the data repository and train the system with deep learning principles. Once the data training phase ends the proposed algorithm begins to get the live data from patient and this data is assumed as a testing data, which will be processed by using intensive deep learning principles and store the resulting summary into the Cloud repository by means of enabling Internet of Things feature in association with the proposed algorithm called iCAIDL. This is transparent in both the state of users like doctors as well as the patients to monitor the health records in intelligent manner. A Smart Medical Gadget is designed to collect the health record from patients and maintain it into the medical repository for testing phase, in which it collects the patient heart rate, pressure level, blood flow in intensive manner. The logic of IoT is connected with the machine learning process such as: the data accumulated from the smart Medical Gadget needs to be send to the Server end for processing, here the processing is mentioned as the machine learning based processing, in which the received data is considered to be the testing data and the results are emulated accordingly. Once the results are emulated that also will be coming for training part for the upcoming testing data. So, that the data coming from the Medical Gadget is considered to be the testing data and once the processing is done it will be considered to be the training data for further medical summaries. The performance evaluations of the proposed approach is estimated based on the following metrics such as data transfer ratio from Smart medical Gadget to the server end, storage accuracy and the communication efficiency. Empirical results are attained using simulation, in which it produces a drastical improvement of healthcare parameters by merge the proposed algorithm called iCloud Assisted Intensive Deep Learning. [ABSTRACT FROM AUTHOR]

To look for early breast cancer signs and indications, mammography screening is one of the best approaches available. Screening mammograms are the most commonly recognized procedure and remain the gold standard for early breast cancer screening. But many times, a relatively low positive predictive rate of breast biopsy demonstrated by this diagnostic technique leads to unneeded biopsies for abnormal findings that are ultimately proven benign in many cases. Random Forest (RF)—which evolves from Decision Trees (DTs)—is one of the most practical and powerful ensemble learning concepts (or meta estimators). Breast Imaging Reporting and Data System (BI-RADS) is developed as a standardized system or tool for reporting breast mammograms. This technique is used to locate unusual findings into groups. In this study, the RF classifier with Chi-Square (χ2) and Mutual Information (MI) procedures of relevant Feature Selection (FS) has been applied successfully, in an attempt to predict cancer biopsy outcomes from BI-RAD findings and the patient's age. For validation purposes, the UCI Mammographic Mass dataset has been used and assessed using accuracy, AUC, and several other performance criteria through a 10-fold CV approach. The prediction findings from the proposed method were very encouraging (84.70% accuracy and AUC 0.9023). Similarly, the proposed system gave better results in terms of MCC and F1-score. The results were directly compared with the RF classifiers and other state-of-the-art classification methods. This comparative analysis indicates that the proposed model is superior in terms of various efficiency indicators to the RF classifier and all standard models used in the study. These findings also confirm that the χ2 and MI FS approaches correctly as well as efficiently obtained the relevant and discriminating feature subset. The result also points out that the suggested approach is a comparable approach to different classification models present in the relevant literature. It is an advantageous, practical, and sound method to predict cancer biopsy outcomes. [ABSTRACT FROM AUTHOR]

The medicinal properties of Cinnamon cassia (C. cassia) bark have been reported for their clinical importance for many diseases including diabetes. However, there is no clear evidence so far regarding dose selection for its hepato- and nephroprotective effect in diabetic condition. Hence, the present study aims at evaluating in vitro antioxidant activity, the acute toxicity, and dose fixation of C. cassia bark for their effective medicinal values in streptozotocin (STZ)-induced rats. All the extracts exhibited potential in vitro antioxidant activity and showed a dose-dependent (1000, 2000, 3000, 4000, and 5000 mg/kg BW) acute toxicity by in vivo model. The levels of aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), urea, and creatinine showed a significant elevation in animals treated with the highest dose. In further studies along with histopathological studies, animals treated with STZ (60 mg/kg BW) followed by a different dose (300, 400, and 500 mg/kg BW) of ethanolic extract of the C. cassia bark and glibenclamide (3 mg/kg BW) revealed that the altered level of mitochondrial enzymes, hepatic, and renal marker in STZ-induced animals were restored in C. cassia bark extract-treated group as of control. These results could be of scientific support for the use of the ethanolic extract of the C. cassia bark in folk medicine for the management of diabetes and its associated complications. [ABSTRACT FROM AUTHOR]

In Asian countries, there is high occurrence of tuberculosis (TB) among captive wild elephants due to close association with humans and other domestic livestock. The present study envisages on utilization of serological, molecular, and proteomic diagnostic assays for tuberculosis diagnosis. The usage of urine as a biological sample for the identification of biomarkers for tuberculosis forms the prime focus of the study. Seroprevalence of tuberculosis (TB) in elephants in Kerala were found to be 37.2% (n = 86) using Chembio DPP VetTB assay and nine (10.46%) were positive for acid fast bacilli using trunk wash sediment smear. On comparison with DPP VetTB assay, acid fast staining shows low sensitivity of 28.13%, specificity 100%, kappa statistics value of 0.329. The presence of Mycobacterium tuberculosis was confirmed in trunk wash using PCR targeting gene IS6110, at 245 bp amplicon size and 25 seropositive elephants (78.2%) were confirmed positive. Custom sequencing and phylogenetic analysis revealed that the isolate obtained were Mycobacterium tuberculosis. The urine of six TB confirmed elephants and six healthy elephants were selected for proteomic analysis. The protein in urine was precipitated, quantified, and subjected to SDS-PAGE, enzymatic digestion, and HRLCMS analysis. Mass spectrometry data analysis revealed 49 proteins identified in TB-infected group, 68 proteins identified in control group and Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase was the protein exclusively present in TB-infected group. Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase plays a major role in glycosaminoglycan biosynthesis. This is the first report of the presence of galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase protein in urine of elephant. [ABSTRACT FROM AUTHOR]

BACKGROUND: The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease. OBJECTIVE: To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI). METHODS: This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research. RESULTS: The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework. CONCLUSION: In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy. [ABSTRACT FROM AUTHOR]

Introduction: New and potentially threatening situations often cause anxiety in children. Distraction, which involves diverting children's attention away from painful stimuli during invasive dental procedures, helps to alleviate the child's fear and anxiety and appears to be safe and inexpensive technique. Aim: Comparative evaluation of anxiety levels in children while administering local anesthesia with two different distraction techniques - An in vivo study. Materials and Methods: Randomized controlled clinical trial. Sample size: 30 (15 in each group). Study groups: Group A - kaleidoscope and Group B - stress ball distraction. Following the standard protocol, inferior alveolar nerve block was given. In Group A, children were asked to see a kaleidoscope for various colors and patterns throughout the inferior alveolar nerve block (IANB) procedure. In Group B, children were instructed to keep pressing the stress ball throughout the IANB procedure. Evaluation: Self-reported anxiety at the time of injection was assessed using the Venham picture test (VPT). Physiological levels of anxiety were measured by recording oxygen saturation and pulse rate with a pulse oximeter at baseline and during the injection. Results: Group A showed significantly lower mean VPT scores and lower mean pulse rates than Group B during the IANB procedure. Oxygen saturation levels remained nonsignificant between the two groups. Conclusion: The findings of the study showed that kaleidoscope was more effective as compared to stress ball in reducing dental anxiety during IANB administration in children [ABSTRACT FROM AUTHOR]

Globally, breast cancer is considered a major reason for women's morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures. [ABSTRACT FROM AUTHOR]

The world is filled with exciting technologies and ideas; scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the artificial intelligence (AI) technology intervention. With technological development, specific processes or consultations are performed with doctors available worldwide. In this scenario, it could be noticed that health care is one of the world's expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker's issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. The proposed optimized neural network representation is differentiated with the available data conclusive process. From the outcomes, it is observed that the suggested representation achieves an accuracy of 98.89%, which is 4.76% higher than the existing model. [ABSTRACT FROM AUTHOR]

This paper elucidates a comparative analysis of hysteresis current control space vector modulation (HCC-SVM) and 3-dimensional space vector modulation (3D-SVM) for control of 3-level neutral point clamped (NPC) inverter. The NPC inverter topology is much suitable for single input applications and medium and high power applications. The detailed comparative analysis of total harmonic distortion (THD) for both modulation control strategies is analyzed. Among various modulation strategies, these two control strategies increased more attention due to the improved output compared to the other conventional methods. The simulation and hardware results are evident that 3D-SVM is the most efficient controller for capacitor balancing, common mode voltage minimization, THD minimization and attain low voltage stress across switch, but in terms of system design and complexity in switching time calculation is simple in the HCC-SVM method. The simulation and hardware results are verified using simulink and FPGA processor, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a novel deep learning-based underwater image dehazing method. The images taken under water are affected by light transportation properties like refraction, reflection, scattering, and absorption. Deep learning techniques give great results in the removal of haze in underwater degraded images. But the major challenge we are facing is the need of many images from the same location to train the model. The proposed system addressed this challenge by using CycleGAN, capable of producing synthetic underwater images from RGB in-air images, and is validated using images taken from different locations and with different characteristics. Qualitative evaluation has been done based upon visual analysis. Quantitative analysis is based upon the calculation of various parameters such as MSE, RMSE, and Euclidean distance. In addition, SSIM Loss has also been evaluated and compared. The model is tested for underwater images taken under various circumstances, and the quantitative and qualitative results obtained for these images are quite satisfactory. [ABSTRACT FROM AUTHOR]

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models. [ABSTRACT FROM AUTHOR]