Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing.

Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.

Effect of Ti and Au buffer layers on controlling the density and wettability of well-aligned ZnO nanorod arrays grown on different substrates.

ZnO nanorod arrays (NRAs) have potential applications as building blocks for nanoscale electronic, optoelectronic, and sensing applications. The density of ZnO NRAs is controlled by a simple low-cost hydrothermal growth process. It is shown that Ti and Au thin buffer layers can be used to control ZnO NRA density up to an order of magnitude on a wide variety of substrates including bare glass AZO, ZnO seeded AZO, FTO and ITO substrates, respectively. We investigate surface morphological, structural and optical properties of ZnO NRAs by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Raman, and photoluminescence spectroscopy measurements, respectively. To highlight the importance of NRA density, wettability measurements show large dependence on density and static water contact angles range from as low as ∼23° to as large as ∼142°. These results indicate that the capability to control the density of ZnO NRAs, and thus their wettability, can have additional implications such as in their use in biosensors, field emission, dye-sensitized solar-cells (DSSCs), and photocatalytic activity in addition to potential light trapping effects over wide spectral ranges.

Elastic, optoelectronic and photocatalytic properties of semiconducting CsNbO3: first principles insights.

The cubic phase of CsNbO3 (CNO) perovskite has been hypothesized to investigate the elastic, electronic, photocatalytic, and optical properties for various technological applications using first-principles method. The pressure dependent structural stability has been confirmed from computed elastic constants. Relatively high value of elastic moduli, large hardness and toughness suggested that CNO would be applicable to design industrial machineries. The ductile to brittle transition is noticed at 20 GPa. The indirect bandgap of CNO proclaims its suitability for photovoltaic and IR photodetector applications. The total and partial density of states are calculated to show in evidence the contribution of individual atomic orbitals in the formation of bands. The pressure changes orbitals hybridization which can be substantiated by the change in the bandgap. Strong covalency of the Nb-O bond and antibonding character of Cs-O have been anticipated by the Mulliken population analysis and by the contour maps of electron charge density. The low carrier effective mass and high mobility carriers predict the good electrical conductivity of the material. The calculated values of conduction and valance band edge potential illustrate the excellent water-splitting and environmental pollutants degradation properties of CNO.

Handheld and benchtop vis/NIR spectrometer combined with PLS regression for fast prediction of cocoa shell in cocoa powder.

The fermented and dried cocoa beans are peeled, either before or after the roasting process, as peeled nibs are used for chocolate production, and shell content in cocoa powders may result from economically motivated adulteration (EMA), cross-contamination or misfits in equipment in the peeling process. The performance of this process is carefully evaluated, as values above 5% (w/w) of cocoa shell can directly affect the sensory quality of cocoa products. In this study chemometric methods were applied to near-infrared (NIR) spectra from a handheld (900-1700 nm) and a benchtop (400-1700 nm) spectrometers to predict cocoa shell content in cocoa powders. A total of 132 binary mixtures of cocoa powders with cocoa shell were prepared at several proportions (0 to 10% w/w). Partial least squares regression (PLSR) was used to develop the calibration models and different spectral preprocessing were investigated to improve the predictive performance of the models. The ensemble Monte Carlo variable selection (EMCVS) method was used to select the most informative spectral variables. Based on the results obtained with both benchtop (R2P = 0.939, RMSEP = 0.687% and RPDP = 4.14) and handheld (R2P = 0.876, RMSEP = 1.04% and RPDP = 2.82) spectrometers, NIR spectroscopy combined with the EMCVS method proved to be a highly accurate and reliable tool for predicting cocoa shell in cocoa powder. Even with a lower predictive performance than the benchtop spectrometer, the handheld spectrometer has potential to specify whether the amount of cocoa shell present in cocoa powders is in accordance with the Codex Alimentarius specifications.

Corrigendum to "Financial and functionality analysis of biogas a plant in Bangladesh" [Heliyon 8(9), September 2022, Article e10727].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e10727.].

Application analysis of digital neural network-based data mining method in maximizing the performance of sports training / Análise de aplicação do método de mineração de dados baseado em rede neural digital na maximização do desempenho do treinamento esportivo / Análisis de la aplicación del método de minería de datos basado en redes neuronales digitales para maximizar el rendimiento del entrenamiento deportivo

ABSTRACT Introduction: In today's rapid development of science and technology, digital network data mining technology is developing as fast as the expansion of the frontiers of science and technology allows, with a very broad application level, covering most of the civilized environment. However, there is still much to explore in the application of sports training. Objective: Analyze the feasibility of data mining based on the digital network of sports training, maximizing athletes' training. Methods: This paper uses the experimental analysis of human FFT, combined with BP artificial intelligence network and deep data mining technology, to design a new sports training environment. The controlled test of this model was designed to compare advanced athletic training modalities with traditional modalities, comparing the athletes' explosive power, endurance, and fitness. Results: After 30 days of physical training, the athletic strength of athletes with advanced fitness increased by 15.33%, endurance increased by 15.85%, and fitness increased by 14.23%. Conclusion: The algorithm designed in this paper positively impacts maximizing athletes' training. It may have a favorable impact on training outcomes, as well as increase the athlete's interest in the sport. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

RESUMO Introdução: No rápido desenvolvimento atual de ciência e tecnologia, a tecnologia de mineração de dados de rede digital desenvolve-se tão rápido quanto a expansão das fronteiras da ciência e tecnologia permitem, com um nível de aplicação muito amplo, cobrindo a maior parte do ambiente civilizado. No entanto, ainda há muito para explorar da aplicação no treinamento esportivo. Objetivo: Análise de viabilidade da mineração de dados com base na rede digital da formação esportiva, maximizar o treinamento dos atletas. Métodos: Este trabalho utiliza a análise experimental da FFT humana, combinada com a rede de inteligência artificial da BP e tecnologia de mineração profunda de dados, para projetar um novo ambiente de treinamento esportivo. O teste controlado deste modelo foi projetado para comparar modalidades avançadas de treinamento atlético com as modalidades tradicionais, comparando o poder explosivo, resistência e condição física do atleta. Resultados: Após 30 dias de treinamento físico, a força atlética dos esportistas com aptidão física avançada aumentou 15,33%, a resistência aumentou 15,85%, e o condicionamento físico aumentou 14,23%. Conclusão: O algoritmo desenhado neste artigo tem um impacto positivo na maximização do treinamento dos atletas. Pode ter um impacto favorável nos resultados do treinamento, bem como aumentar o interesse do atleta pelo esporte. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción: En el rápido desarrollo actual de la ciencia y la tecnología, la tecnología de extracción de datos de redes digitales se desarrolla tan rápido como lo permiten las fronteras en expansión de la ciencia y la tecnología, con un nivel de aplicación muy amplio que abarca la mayor parte del entorno civilizado. Sin embargo, aún queda mucho por explorar de la aplicación en el entrenamiento deportivo. Objetivo: Análisis de viabilidad de la minería de datos basada en la red digital de entrenamiento deportivo, maximizar la formación de los atletas. Métodos: Este trabajo utiliza el análisis experimental de la FFT humana, combinado con la red de inteligencia artificial BP y la tecnología de minería de datos profunda, para diseñar un nuevo entorno de entrenamiento deportivo. La prueba controlada de este modelo se diseñó para comparar las modalidades de entrenamiento atlético avanzado con las modalidades tradicionales, comparando la potencia explosiva, la resistencia y la forma física del atleta. Resultados: Después de 30 días de entrenamiento físico, la fuerza atlética de los atletas con un estado físico avanzado aumentó en un 15,33%, la resistencia aumentó en un 15,85% y el estado físico aumentó en un 14,23%. Conclusión: El algoritmo diseñado en este trabajo tiene un impacto positivo en la maximización del entrenamiento de los atletas. Puede tener un impacto favorable en los resultados del entrenamiento, así como aumentar el interés del atleta por el deporte. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Financial and functionality analysis of a biogas plant in Bangladesh.

Installation of a biogas plant in a rural region has become a viable alternative energy source with a variety of health and environmental advantages. Though Bangladesh has enormous resources for biogas production, biogas energy production is infancy stage in Bangladesh. The study aims to explore the economic aspect of household-level biogas plants and determine the relationship between biogas plant functionality and different factors. For doing this, 300 biogas plant owners were interviewed from fifteen Upazilla in Bangladesh and respondents were chosen by a two-stage random sampling technique. The study shows by measuring partial budgeting, USD 294.80 per year can be earned by a family by introducing biogas plant. Cost-benefit analysis showed that a small biogas plant (USD 143.07/year) was most profitable, followed by a large biogas plant (USD 142.17/year). In discounted cost-benefit analysis, medium size biogas plant was found to be the most beneficial investment, followed by a small size biogas plant. Average NPV, BCR, PBP, and IRR of Biogas plant were USD 1629.11, 1.77, 2.93, and 48% with subsidy where USD 1525.25, 1.77, 3.75, and 43% without subsidy. The measurement of carbon trading also highlights the economic benefit of a biogas plant in Bangladesh. The bivariate relationship between the functionality of biogas plants with different factors highlights that higher educated, trained plant owners with quality mason and follow up services ensured the efficient operation of the biogas plant.

A comparative study of the mechanical stability, electronic, optical and photocatalytic properties of CsPbX3 (X = Cl, Br, I) by DFT calculations for optoelectronic applications.

Organic free Cs-based perovskite materials are potential candidates for electronic and optoelectronic applications. A systematic comparative study of the mechanical, electronic, optical, and photocatalytic properties of CsPbX3 (X = Cl, Br, I) was conducted using density functional theory to compare the applicability of these materials in optoelectronic, photocatalytic, and photovoltaic (PV) devices. We calculated structural and elastic properties to determine the better agreement of damage-tolerance and electronic and optical responses for suitable device applications. Optimized lattice parameters and elastic constants showed excellent agreement with the experimental data whereas some properties were found to be much better than other theoretical reports. CsPbBr3 is thermodynamically more stable and more ductile compared to the other two perovskites. The hydrostatic pressure dependent mechanical stability showed that CsPbCl3 and CsPbBr3 sustained stability under low applied pressure, whereas the stability of CsPbI3 was very high. The electronic band gap calculations showed that CsPbCl3, CsPbBr3, and CsPbI3 are suitable for green, orange, and red emissions of optical spectra owing to the proper electronic band gaps. CsPbI3 can be shown as the best photocatalyst for the hydrogen evolution reaction and CsPbBr3 is the most stable photocatalyst due to its nearly balanced oxidation and reduction potentials, but CaPbCl3 is better for O2 production. The density of states and other optical properties have been reported in this study. Thus, our findings would be beneficial for experimental studies and can open a new window for efficient electronic, optoelectronic, and hydrogen production along with the biodegradation of polluted and waste materials.

IoT-Oriented 6G Wireless Network System for Smart Cities.

The smart city is an emerging concept that is based on the integration of various electronic devices and citizens that enhance the flow of information. IoT is an integral part for next generation wireless network infrastructure for acting as an interface of collecting data and controlling delivery of message which are using in smart cities. In this paper, an IoT-oriented relay assisted MIMO for beyond the fifth-generation wireless network system is proposed. The proposed system provides higher capacity and lower BER. The proposed system's BER results are compared with various combinations of transmission and receiving antennas at source, relay, and destination. It is found from BER performance that the developed scheme with relay does provide 1-17 dB gain with respect to direct connection. It is also found from mathematical analysis and simulation results that this scheme provides 3 to 9 b/s/Hz improvement in performance of capacity at 5 to 10 dB by adding a different combination of STBC and VBLAST. Simulation results are also presented to demonstrate the diversity and multiplexing gain that is a key to providing high data rates with reliable communication with many interferences for the IoT system. This system can also be used for massive antennas-based IoT system by raising the number of transmitting and receiving antennas with proposed encoding and decoding techniques explained in this paper.

Impact of Social Media on Geopolitics and Economic Growth: Mitigating the Risks by Developing Artificial Intelligence and Cognitive Computing Tools.

Social media is one of the most revolutionary innovations in computer science that facilitates connecting people in the world to share information, ideas, and thoughts. In recent years, social media has demonstrated tremendous growth, which has affected individuals, businesses, communities, and economies. The focus of the present study is to identify the impact of social media on geopolitics and economic growth. The study is based on a systematic review of previous literature on the subject. It has been revealed through the findings that social media impacts geopolitics by decreasing the level of censorship and increasing the spread of news or information, while it also enables the politicians to influence individuals over online social networks through the great level of access. On the other hand, it has been identified that social media has both positive and negative impacts on geopolitics and economic growth. Social media is able to unite diverse groups and individuals spread across the planet dedicated to specific issues. The formation of communities and the ability of social media to unite groups show how social media could contribute positively to geopolitics and economic growth. But it decreases the productivity level of the individuals; on the other hand, it does contain the potential to create a participatory economy, which can be beneficial for a particular country. Some argue that social sharing has encouraged people to use computers and mobile phones to express their concerns on social issues without actually having to engage actively with campaigns in real life. Their support is limited to pressing the "Like" button or sharing content. This study performs a thorough study selection exercise and a quality assessment to ensure that the present study is valuable to academia and the relevant stakeholders, especially the experts of computer science who can develop the smartest artificial intelligence and cognitive computing tools that can help mitigate those risks of social media for the geopolitically volatile, uncertain, complex, and ambiguous world and ensure smooth economic growth.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities.

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities.

Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.

Pattern of Casualties Attending in the Casualty Department of a Tertiary Level Hospital of Bangladesh.

A casualty is a loss resulting from an accident or someone who is hurt, wounded, diseased, detained or dead in an accident. The term casualty means a seriously injured patient. It is predominantly a military word, generally used for service for accidents. After a battle or accident the dead, the wounded, the sick are called together as "casualties". Casualty, in respect to personnel, any individual who is lost to his organization by reason of being declared dead, wounded, diseased, detained, captured, or missing. Hospital casualty service is not fully organized all over the Bangladesh. In view of the increasing workload and emerging need, functional casualty services have recently been introduced in our hospital to manage properly the accident patients. This retrospective observational study was carried out in the Casualty department of Mymensingh Medical College Hospital, Mymensingh, Bangladesh. Patients were enrolled total number of 69740 to investigate the quantity of patients and pattern of casualties. Patients were categorized according to their mode of injury. Total data was collected from hospital records of all patients attended in the Casualty Department of the hospital from November 19, 2017 to November 18, 2019. The modes of Casualties with demographic characteristics of patients were analyzed. Male and female ratio was 3:1. Patient attended in the Casualty department was average 96 per day, maximum was 176 and minimum was 33. According to age sub-division, 11-20 years age group attended in casualty was maximum and it was 48 in number. One day attended Road traffic accident (RTA) maximum was 65 and minimum was 3, maximum Non-RTA was 83 and minimum was 25, maximum physical assaults was 48 and minimum was 1. RTA and Injury due to fall were the common mode of casualty especially in the young population within the study area. We have seen that injury caused by fall from height was 43% among the all patients. Patients due to fall from tree was highest (35%) yearly in between April to June. Second to incidents of all fall was RTA which was 25%. Physical assaults (18%), machinery injury (9%) and others were 5%.

Outcome of Danis-Weber Type-B ankle fracture treated by Pre-contoured Distal Fibular Locking Plate.

Danis-Weber type-B ankle fracture is one of the most common injuries in young and active individual. This fracture offers a considerable challenge to orthopedic surgeon. Though there are several options for treating such type of fracture, ORIF by Pre-contoured Distal Fibular Locking Plate is the preferred option in the recent years. This descriptive type of observational study was performed from July 2017 to June 2019 in NITOR. Thirty (30) patients, 22 male and 8 female with an average age of 39 years with Danis-Weber type-B ankle fracture underwent ORIF by Pre-contoured Distal Fibular Locking Plate for fractured fibula and ORIF by 4.0mm cannulated cancellous screw for fractured medial malleolus. All the patients were initially managed by analgesic and short leg posterior slab. Average follow up was 24 weeks. Final outcome was assessed by AOFAS score. The main cause of injury was RTA (56.67%). Mean operation time was 1.2 hours. Mean duration of Hospital stay was 16.43±1.73 days. Superficial infection was in 3.33% and skin necrosis in 3.33% patient. Mean duration of radiological healing was 12.73±0.39 weeks. At final follow up, mean dorsi flexion was 10.93°±0.357° and plantar flexion was 50.93°±0.357°. Ninety percent (90%) patient had no difficulties in walking on any surface; 96.67% patient had stable ankle hind foot; 86.67% patient had good. Ten percent (10%) had fair and 3.33% patient had poor alignment of foot. The mean score in this study was 88.67±2.31. Satisfactory outcome was observed in 86.67% patients and 13.33% had unsatisfactory results. On the basis of results in the present study, it can be concluded that treatment of Danis-Weber type-B ankle fracture by Pre-contoured Distal Fibular Locking Plate is an effective and reliable method.

Financial and factor demand analysis of broiler production in Bangladesh.

Financial and factor demand analysis of broiler production has been estimated in this paper using a farm survey data of 210 farmers from four major broiler producing areas (i.e., Dhaka, Rajshahi, Mymensingh and Chittagong) of Bangladesh. Findings showed that broiler farming incurred most of its cost from its operating input, mainly feed. Broiler farming was financially profitable, but the performance of Mymensingh division was comparatively low, arising from a high unit cost of production and low unit price selling than the others. The net return was highest in Dhaka division, while Rajshahi division showed the highest ratio in returns on investment. However, in terms of cost (variable) and net return of broiler farming, no significant difference among the study areas was observed. The value of own price elasticity for feed, chick price, and labour price were negative and inelastic, which were -0.00249, -0.05718, and -0.13101, respectively. Besides, a complementary relationship was found between feed and day-old chick and feed and labour while day-old chick and labour were substitutes. The study also revealed that cross price elasticity was highly inelastic, and changes in the prices of inputs did not result in massive changes in the quantity demanded of other inputs for broiler farming.

Boosting Breast Cancer Detection Using Convolutional Neural Network.

Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

Comparison between Efficacy of Combined Use of Hyaluronidise and Triamcinolone versus Hyaluronidise, Triamcinolone Acetonide and Oral Colchicine in the Treatment of Oral Submucous Fibrosis.

Oral submucous fibrosis (OSF) is a chronic complex potentially pre-malignant condition caused by chewing areca nut and other irritants. It is an insidious process characterized by Juxta-epithelial deposition of fibrous tissue in the oral cavity and pharynx. OSF is very common in Southeast Asia and also now a days increase in Europe and North America. The aim of this study to compare the effectiveness of intralesional injection of triamcinolone and hyalurunidase versus intralesional injection of triamcinolone plus injection hyalurunidase with oral colchicine. The study included 60 patients of clinically diagnosed case of oral submucous fibrosis. Patients were divided into two Groups A and B. Group A patients received combination intralesionsl injection of triamcinolone acetonide 10mg/ml in 1ml with injection hyalurunidase 1500IU in 2ml with injection 2% lidocaine 7ml. 15 days interval in 3 months and Group B received intralesional injection of triamcinolone acetonide 10mg/ml in 1ml with injection hyalurunidase 1500IU in 2ml with injection 2% lidocaine 7ml in each 15 days interval for 3 months with oral colchicine 0.5mg twice daily for 3 months. Diagnosis based on burning sensation of mouth, blanching of mucosa, ulceration in oral cavity and also reduced mouth opening. Follow up assessment was done at intervals 1st follow up on 21st days after starting of treatment then 2nd follow up after 3 months and last 3rd follow up after 6 months. Before starting of treatment all patients were properly explained about the study and took their written consent. Much more improvement occurred in Group B patients, reducing in burning sensation and also increases in opening of mouth. In both groups blanching mucosae were improved. Treatment regimen of Group B is more effective in increasing mouth opening and improves burning sensation of oral cavity. No side effects were seen in both groups' patients.

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: "no actions," "lockdown," and "new medicines." The effect of interventions like lockdown and new medicines is compared with the "no actions" scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.

Bacteriology of Chronic Supporative Otitis Media (CSOM) at a Tertiary Care Hospital, Mymensingh.

Chronic suppurative otitis media (CSOM) is a notorious infection in developing countries causing serious local damage and threatening complications. It was a cross sectional observational study to isolate and identify aerobic bacteria and to analyze the susceptibility pattern of the aerobic bacterial isolates. It was carried out from March 2017 to July 2018 in the department of Microbiology, Mymensingh Medical College, Mymensingh, Bangladesh. Samples were collected from Outpatient of ENT department, MMCH. Out of a total 300 patients with CSOM were enrolled in this study and 209 were culture positive. Among them gram negative organisms were 129(61.72%) and gram positive organisms were 70(33.49%). The most frequently isolated organism in this study was Pseudomonas aeruginosa 72(34.44%), gram positive organisms S. aureus 63(30.14%), E. coli 21(10.04%), other Pseudomonas spp (other than P. aeruginosa) 15(7.17%), mixed bacterial infectios 10(4.78%), Proteus spp 9(4.30%), CoNS 7(3.34%), Klebsiela lspp 7(3.34%), Acinetobactor spp 5(2.39%). P. aeruginosa isolates had least resistant to imipenem and colistin, S. aureus were showed high sensitivity to Vancomycin and Linezolid and E. coli were sensitive to imipenem and amikacin, ciprofloxacin and amikacin respectively. Pseudomonas aeruginosa was the most common bacteria isolated from chronic discharging ears followed by Staphylococcus aureus. Piperacillin-Tazobactum, Ciprofioxacin, Gentamicin and Amikacin were found to be the most suitable drug for Pseudomonas aeruginosa, S. aureus and E. coli. The resistance against ceftriaxone and aztreonam was found to be very high.