Elements of system's models (and subsystems) are the amplitude and temporal parameters of oscillatory processes. Similarity of these elements has been observed at all levels of living things (cellular, organ, systemic- organismic), i.e., oscillatory process parameters are the elements of electro- physiological sub-systems of the overall (whole) system - the entire organism.

In this paper we presented an Ebola Virus Disease fractional order model in the Caputo sense with eighteen compartments. This model considered vaccination, condom use, quarantine, use of treatment drugs and isolation as control measures combined together. We showed that the model has non-negative solution. The stability of the equilibria was discussed and Laplace Adomian Decomposition Method is used to obtain the analytic or approximate solution of the system of non-linear fractional differential equation. It was observed or shown that with the Laplace Adomian Decomposition Method an approximate solution of a fractional order Ebola Virus Disease Model as large as eighteen compartments can be obtained and the result is close to the exact solution.

Keywords: Laplace Adomian Decomposition Method; Ebola Virus Disease; Fractional Order Model; Analytic solutions; Approximate Solutions

The anti-COVID-19 policies were implemented in order to mitigate and contain the health, economic and social crisis. The objective of the present work was to establish the learning neural network of the studies related to anti-COVID-19 policies. A documentary, cross-sectional, exploratory and correlational work was carried out with a sample of findings published in indexed journals. A structure was found whose centrality, grouping and trajectories suggest that the anti-COVID-19 policies initiated the mitigation process and were shaped through risk communication and assimilation. In reference to the studies reported in the literature, there is a line of research regarding the establishment of a discussion agenda, agreements and co-responsibility.

Keywords: Structure; Sustainability; Rationality; Decision and Action

The rhinovirus, which is the primary cause of the common cold, is the most prevalent viral infection in humans. It thrives in temperatures ranging from 33 to 35 °C (91 to 95 °F), commonly found in the nasal region. Belonging to the Enterovirus genus in the Picornaviridae family, rhinoviruses consist of three species (A, B, and C) with approximately 165 recognized types characterized by variations in surface antigens or genetics. These viruses, exhibiting lytic behavior, are among the smallest viruses, measuring about 30 nanometers in diameter. To provide context, they are significantly smaller than other viruses like smallpox and vaccinia, which are approximately ten times larger at around 300 nanometers, and influenza viruses, which range from 80 to 120 nm in size.

     Numerous researchers (Dines A., Cribb A., 1993; Downie R.S., Tannahill C., Tannahill A., 1996; Ewles L., Simnett I., 1999; Marmot M., Wilkinson R.G., eds, 1999; Adams L., Amos M., Munro J., 2002; Aslanov D.I., 2011; eds) interpret the definition of «human health» and «norm», treating them as an arbitrary or as conditions, «though epistemologically conceivable, but expressing nothing definite in real life» (Tsaregorodtsev G.I., 1998).

     There are at least five common approaches to the norm understanding: medical; behavioral; educational; focused on the client (patient); social - ecological.

The escalating incidence of viral conditions such as COVID-19, flu, and various respiratory disorders presents a considerable obstacle for healthcare systems worldwide. Prompt identification and forecasting assessments are essential for efficacious treatments and control measures. Conventional diagnostic techniques like PCR assays and X-ray scans either take too long or lack the requisite precision, adding complexity to an already difficult situation. This study seeks to exploit the potential of Convolutional Neural Networks (CNN) for scrutinizing CT images in the early recognition and prediction of viral illnesses. We utilized an array of CNN designs, including ResNet, VGG, and Inception among others, and conditioned them on an extensive collection of CT images marked for several types of viral infections. We grappled with issues such as inconsistent data, unequal class distribution, and the demand for instantaneous analyses. Additionally, ethical questions tied to using patient information for algorithmic learning present further significant hurdles. Initial findings suggest that models based on CNN surpassed conventional methods in diagnostic efficacy, with accuracy levels exceeding 95%. This emerging technology offers a promising avenue to transform the way we diagnose viral diseases, providing a swifter, more reliable, and more precise approach.

Keywords: Convolutional Neural Networks (CNN); Viral Diseases; CT Scan Images; Early Detection; Prediction and Diagnostic Methods

Over the past decades, Afghanistan has encountered several serious healthcare difficulties. One of these occurrences, a lethal endemic of Crimean Congo Hemorrhagic Fever (CCHF), hit Afghanistan in 1998. A broad assessment of the literature reveals a number of characteristics that put the people of Afghanistan at a very high risk of developing CCHF. First off, the nation's poor healthcare system is now ill-equipped to handle difficulties of this kind. Medical institutions and healthcare personnel lack the necessary tools to correctly diagnose, treat, and prevent CCHF. Second, a significant percentage of people is ignorant about the vector's propagation and methods of control. Afghanistan has a major agricultural industry, and as a result, many individuals are active in caring for animals and handling cattle, which can result in the spread of the CCHF virus. Finally, during the past 20 years of the republican government of Afghanistan after the initial tenure of the Taliban's government as a result of the Afghan war, a sizable number of Afghan refugees returned to Afghanistan from Pakistan, a nation where CCHF is endemic. This increased Afghanistan's vulnerability. This literature review identifies probable causes of high-risk CCHF and offers suggestions that Afghan policy-makers may desire to take into account in order to ameliorate the existing situation.

Keywords: Crimean Congo Hemorrhagic Fever; Afghanistan; Zoonotic Diseases; Infectious Virus; Endemic; CCHF; Outbreak

Mastitis in cattle is a complex and multi-factorial disease caused by an interaction between three major factors: the animal, pathogens, and the environment. A cross-sectional study was conducted from June 2022 to November 2022 to estimate the prevalence of mastitis in lactating cows, to assess the risk factors for bovine mastitis, and to isolate and identify coliform and gram-positive cocci bacteria involved in the mastitis cases from dairy farms in Dire Dawa, Eastern Ethiopia. Detection of mastitic animal was done based on physical examination of udders and CMT test. Bacterial culture and biochemical tests were employed to identify the target pathogens. A total of 366 dairy cows and 1,464 quarters were screened for mastitis. Overall prevalence of mastitis at cow and quarter levels were 24.04 and 13.5%%, respectively. Age, parity, cows udder position, history of mastitis, barn floor, milking sequences of clinically mastitic cows and leg and udder hygiene scores were found to be risk factors significantly (P<0.05) associated with mastitis. From the 191 mastitis-positive milk samples, 82.7% (158/191) were culture positive. Out of the isolates from clinical cases (n=59) and isolates from sub clinical cases (n=99), Staphylococcus aureus (22%) and E. coli (15.7%) were predominant isolate. The other bacterial isolate in order of abundance, Coagulase Negative Staphylococcus (10.5%), Streptococcus agalactiae (6.8), Streptococcus dysgalactiae (5.8%), Staphylococcus intermedius (4.7%), Staphylococcus hyicus (4.2%), Klebsiella pneumoniae (3.1%), Micrococci spp (2%), Streptococcus uberis (1.6%), Enterobacter aerogenes (1%), and Enterococci spp (0.5%). The study showed that high parity number (OR = 19.5; p = 0.005), moderate parity (OR = 10.9; p = 0.022) and history of mastitis in preceding lactation (OR = 28.4; p = 0.001) were the major risk factors which are significantly associated with higher prevalence of S. aureus. History of mastitis in preceding lactation (OR = 3.7; p = 0.021) and very dirty (OR = 3.9; p = 0.005) udder and legs were the major risk factors which are significantly associated with higher prevalence of E. coli. Therefore, hygienic milking practice, adequate sanitation of the dairy environment, proper attention to the health of mammary glands and regular screening tests should get emphases as control strategies.

Keywords: Associated risk factor; CMT; Coliform; Isolation; gram positive cocci; Mastitis; prevalence

The foundation of human health and food security lies in ensuring a sufficient and safe food supply. To obtain this a comprehensive study on food microbiology is essential. The term “Food Microbiology” relates to the study of microorganisms that exist, contaminate, and ultimately results in the spoilage of food. The food system encompasses all stages from farm to fork, which includes raw materials, their processing, storage, transportation, and final dispatch till it reaches the end consumer. This holistic comprehension is pivotal in addressing and minimizing microbial spoilage within the food industry.

Clinical microbiology has undergone transformative advancements with the integration of Artificial Intelligence (AI), particularly Machine Learning (ML), in image analysis. This revolutionary approach has played a pivotal role in enhancing diagnostics, pathogen identification, and understanding microbial structure, offering promising prospects for improved patient outcomes and healthcare efficiency. Machine Learning algorithms, a subset of AI, are adept at identifying patterns and learning from data. In the realm of clinical microbiology, these algorithms have been employed to analyze microbial images, enabling the rapid and accurate identification of pathogens. Through techniques such as Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), ML models are trained to recognize specific characteristics of bacteria, viruses, and other microorganisms, thereby aiding in the swift diagnosis of infectious diseases. The different applications of clinical Microbiology are Pathogen Identification, Antimicrobial Susceptibility Testing, Disease Prediction and Prognosis, and Automated Analysis and Workflow Integration. ML models are instrumental in the identification of various pathogens by analyzing microscopic images. These models can distinguish between different bacterial strains, detect antibiotic resistance, and predict the potential virulence of the identified microorganisms, facilitating timely and targeted therapeutic interventions. Despite the promising applications of Machine Learning in clinical microbiology, challenges persist. These include the need for large, annotated datasets for model training, addressing model interpretability, and ensuring the generalizability of models across diverse clinical settings. Ongoing research and development are imperative to address these challenges and further refine ML models for robust and reliable applications in clinical microbiology. Moreover, the continuous evolution of ML algorithms and advancements in imaging technologies pave the way for exploring novel applications, such as real-time monitoring of microbial growth and the development of predictive models for emerging infectious diseases. The integration of Machine Learning for image analysis in clinical microbiology marks a significant stride towards enhancing diagnostic accuracy and efficiency. While challenges remain, the potential of ML in pathogen identification, antimicrobial susceptibility testing, and disease prognosis is vast. The ongoing advancements in AI and imaging technologies hold the promise of further elevating the capabilities of clinical microbiology, contributing to improved healthcare outcomes and proactive management of infectious diseases.

Background: Cytomegalovirus (CMV) is a large encapsulated double-stranded DNA virus belonging to the family Herpesvirida subfamily beta herpes viral.

Objectives: To investigate the prevalence of the human cytomegalovirus in cancer patients.

Result: A total 100 patients. Blood samples were tested for IgM and IgG antibodies using ELISA according to gender (Male 27% positive CMV IgM 73% Negative CMV IgM, 45% positive CMV IgG 55% Negative IgG) Female 7% positive CMV type o IgM 93% Negative CMV IgM ,59% positive CMV IgG 41%Negative CMV IgG. In Breast cancer patient 1% positive CMV IgM and 9 % Negative, 5% positive CMV IgG 5% Negative IgG. In prostatic cancer 4% positive CMV IgM and 3% Negative CMV IgM, 4% positive CMV IgG 3% Negative CMV IgG. In CML 9% positive CMV IgM 38% Negative CMV IgM 22% positive CMV IgG 25% Negative CMV IgG. In AML 8%positive CMV IgM 28% Negative CMV IgM 18% positive CMV IgG 18% Negative CMV IgG. According to chemotherapy 18% positive CMV IgM 58% Negative CMV IgM 36% positive CMV IgG 40% Negative CMV IgG. And according to history Blood transfusion 19%positive CMV IgM 17%Negative CMV IgM 25% positive CMV IgG 11% Negative CMV IgG while in patients with no history of blood transfusion 3% positive CMV IgM 61% Negative CMV IgM 24% positive CMV IgG 40% Negative CMV IgG.

Conclusion: According to the result of this study and the results reported by the previous studies in Sudan among the blood donors and other target groups (Haemodialysis, Organ transplant patients, and pregnant women) prove that the Cytomegalo virus was highly distributed among Cancer patients in Sudan.

Keywords: Cytomegalovirus; cancer; IgG; IgM; Blood samples; ELISA

As cyber-attacks multiply rapidly and malware becomes more intricate, conventional signature-focused detection methods find it challenging to keep up. In light of this, deep learning approaches have emerged as a potent solution to boost malware detection efficacy. This study delves into sophisticated deep-learning strategies to spot and categorize malware in modern cybersecurity data. We present a combined model that merges the strengths of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), harnessing both the spatial and sequential attributes of the data. When pitted against classic deep learning setups, our model showcases remarkable enhancements in detection precision, marking a 98.7% success rate and a mere 0.5% rate of false positives using the XYZ dataset. Additionally, we deploy specific data augmentation methods designed for cybersecurity data, augmenting our model's adaptability and resilience to new threats. The results indicate that employing cutting-edge deep learning designs can fortify malware detection mechanisms, providing superior defense in a constantly shifting cyber-threat environment.

Keywords: Deep Learning; Malware Detection; Signature-based Detection; Convolutional Neural Networks(CNN); Recurrent Neural Networks (RNN) and Data Augmentation

A cross-sectional study was conducted from June 2022 to April 2023 to isolate of staphylococci in bovine subclinical mastitis, to establishing prevalence, to identify risk factors and to assess on hygienic handling practice of raw cow’s milk in each selected dairy farm in Eastern Ethiopia. A semi-structured questionnaire survey and observation, California mastitis test (CMT), bacterial isolation and identification were conducted during the study. A total of 379 dairy cattle from dairy farms were screened for SCM by CMT. The prevalence of SCM in the study areas was 206(54.3%). Milk samples were collected from 206 cows free of clinical mastitis but positive in CMT. The prevalence of staphylococcal species among lactating cows with subclinical mastitis was 82.5%. The prevalence 92(44%) of Coagulase Negative Staphylococcus was higher than the Staphylococcus Aurous 78(37%). Among the risk factors studied, SCM due to staphylococci was significantly higher (P < 0.05) in older cows (94.3%) than younger (58.6%), in cows during late lactation (91.9%), in cows which gave more than 5 births (87%), in cross-breed cows (65.3%) than local breed cows (25.7%). Respondents with the age group ranging from 25 to 50 years and better educational level were acquainted that drinking raw milk is possible sources for staphylococci. This suggesting that more work on awareness creation is required in society with lower educational level compared to the society with better educational level. In addition, this is an opportunity to create awareness towards the prevention and control of zoonosis in actively involved group of society. Observational assessment of the milk handling practices in dairy farms showed that about (55.5%) of them were not washed their hands before milking. The study showed that the hygienic status of the udder and houses of lactating cows in the majority of dairy farms in the study area are poor, and the prevalence of subclinical mastitis and staphylococcal species are high. Based on results, creating the awareness, careful hygienic milking practice and regular health monitoring should be practiced to reduce prevalence of subclinical mastitis and staphylococcal infection.

Keywords: Hygiene; Lactating cow; Milk; Prevalence; Subclinical Mastitis; Staphylococci

     Microbiology has come a long way since its humble beginnings, when Antonie van Leeuwenhoek discovered the hidden world of microorganisms through his self-crafted microscopes. Today, thanks to the ingenuity and dedication of researchers over the centuries, microbiology is a thriving field with ground-breaking discoveries that have transformed our understanding of life on Earth.

     Today, we celebrate the remarkable progress in microbiology and explore the exciting frontiers that lie ahead. One area that has captured the imagination of many is the human micro-biome, the trillions of microorganisms that reside in our bodies and influence our health in myriad ways. Researchers are unravelling the intricate interactions between the micro-biome and our bodies, holding the promise of innovative therapies and personalized medicine.

This paper put forwards afresh concept to improvise the conventional JL-TFET. The choice and placement of dielectric are crucial in determining the efficiency of the device. Thus, asymmetric hetero-dielectric is found to improve the gate control and also aid in lowering capacitance. The concept of triple gate material has been incorporated which resulted in better gate control and thereby improving ON-current. The proposed structure reports a current ratio of 4.4 x1010A and promising values for Subthreshold Swing (SS) 9 mV/dec (point) and 48 mV/dec (average).This improvised structure helps in development of devices suitable for low power applications. The simulation based study was performed in July 2021 and compared with the results available.

Microorganisms is believed to be the causative agent of various problems in human health but with the development of microbiological techniques these problems could be prevented to some extent by researchers who have successfully developed pathways for the use of the microorganisms in ways beneficial for the society. Microorganisms and their microbial based products play an important role in enhancing plant growth and crop yield by several mechanisms such as antibiosis and plant-microbe interaction thereby increasing the demand in market and using microorganism as a biological control in agriculture. The increases in world population demands sustainable agricultural production but however over exploitation of crops lead to the use of chemical fertilizers and pesticides resulting to a human health hazard. Microorganisms can be classified into two categories- some are beneficial microorganism that can improve agricultural production and some are negative microorganisms since they induce severe health problems.

Ganoderma lucidum (Ling Zhi), a basidiomycete white rot macrofungus has been used extensively for therapeutic use in China, Japan, Korea and other Asian countries for 2,000 years. Macrofungi are distinguished as important natural resources of immunomodulating and anticancer agents and with regard to the increase in diseases involving immune dysfunction, cancer, autoimmune conditions in recent years, applying such immunomodulator agents especially with the natural original is vital. In the last three decades, numerous polysaccharides and polysaccharide-protein complexes have been isolated from mushrooms and used as a source of therapeutic agents In the present study glucan was isolated from G. lucidum was isolated for studying its therapeutic properties. The fruiting bodies of G. lucidum were collected from the outskirts of Thrissur district, Kerala, South India. The type specimen was deposited in the herbarium of Center for Advanced Studies in Botany, University of Madras, Chennai, India (HERB. MUBL. 3175). Glucan was isolated by the method of Mizuno with slight modification [1]. The confirmation of glucan was done by anthrone and phenol sulphuric acid test. Structural confirmation of glucan was done by infrared radiation (IR) and nuclear magnetic resonance (NMR) spectrum which were recorded at Sophisticated Analytical Instrument Facility, Indian Institute of Technology, Bombay, India. The molecular wt of glucan was determined by gel filtration chromatography. Identification of sugar components (complete hydrolysis and chromatography) was done by paper chromatography. In the HNMR spectrum, H.sup.-1 signals were observed at less than 4.8 ppm (4.762, 4.683, 4.667, 4.658, 4.402 ppm), which suggest that component sugars have beta configuration. From gel filtration chromatography, the molecular weight of BG was found to be 1.5×106 Daltons. The Rf values of monosaccharides D-glucose, D-fructose, D-galactose, D-mannose and D-rhamnose were 0.47, 0.54, 0.44, 0.53 and 0.65 respectively. Comparing the Rf values, the sugar present in the Ganoderma glucan was found to be glucose, mannose and rhamnose.

In this expeditiously era, strong waste management issues rose to new rights of common interest in few regions of the India because of increasing strong waste production, reducing management capacity, rusting disposal costs, and open enemies to the sitting of current management facilities. Moreover, Poor solid waste management will cause a slew of issues in terms of health, environment, and socio – economics [1]. In addition, difficulties in controlling SWM have resulted in improper waste disposal methods, such as open dumping and open burning of waste, which are adversely affect the ecology. The rising managing waste appropriately is crucial for establishing sustainable and habitable cities, though it remains a challenge for many developing nations and cities. Effective waste management is wealthy, much containing 20%-50% of domestic budgets. Running the important domestic service requires intergraded systems that are effective, sustainable and socially supported. The goal of this paper is to have sustainable solid waste management, so estimates have been made that project waste generation per capita per day in Surat in the form of bio-degradable, non-biodegradable, and recyclable wastes in the next 30 years, as well as how these wastes can be treated in the form of compost, biofuel, fuel pallets, and generating power from waste. It gives an overview of solid waste management in the world, India, and Surat, as well as how to address the problem by offering sustainable Solid Waste Management.

Cancer is the second most fatal disease faced by Indians and it takes the lives of around 2.5 million people annually. Around 70% of the cases have been treated but survival rates remain diminished. Indian context dictates that the most prominent cancers have been lung cancer, breast cancer, stomach and cervical cancer and are attributed to poor to moderate living conditions, improper dietary and lifestyle choices, inadequate medical facilities and pollution. Hence, India faces a grave public health concern in the form of cancer with growing pollution and attitudinal shifts in the population.

Synechocystis sp. PCC 6803 is a potential producer of lipids, alcohols, and biofuels. Genome-scale models (GSM) has been used to examine potential knockout to optimize specific metabolite (such as, ethanol) production. Besides from a metabolic production perspective, GSMs can also be used examine the effects of genes from the perspective of genotype-phenotype relationship. However, most GSMs are reaction-based rather than gene-based. Hence, GSMs can be used for reactome-phenome mapping where each reaction may be the result of one or more genes. In this study, we examine the reactome-phenome map of Synechocystis sp. PCC 6803 using its GSM model, iJN678, by performing single knockouts to each of its 863 reactions. Our results suggest that 37.3% to 39.7% (322 to 343 reactions) of the knockouts have minimal impact on the phenome as they were clustered together with wildtype phenotype and 53.5% (462 reactions) are essential. The rest of the 58 to 79 reactions can be clustered into 9 to 33 phenotypic clusters. Moreover, the fluxome variation within wildtype cluster is significantly larger than that of essential reaction cluster (t ≥ 3.26, p-value ≤ 1.3E-3). This suggests that individual reaction knockout may have measurable effects on the fluxes; which may be useful in metabolic engineering.

As worldwide turbulences and disruptions amass, focus on human sciences should remain at the core of interest of the scientists. Uncertainty, and depleted resources can sometimes distract human advancement, but this should not be the case, especially when human well-being is at stake. Thus, scientific dialogue and research is now, more than ever, a crusade to be encouraged by the scientific community. Experts in Microbial pathogenesis, Clinical microbiology to name but a few have a substantial role in addressing pandemics like the COVID-19 that human race is still enduring. Wisdom of crowds should be solicited and encouraged though respected research and publication to forecast and predict future pandemic for better preparedness. Marine, aquatic, and environmental microbiology, and ionizing radiation and nuclear microbiology effects are also pushing their way to the forefront as priorities in response to the state of war between Russia and Ukraine. Historically, world turbulences have hampered human wellbeing research while other areas of innovation and research like weapon of nuclear destruction were funded and well supported. Scientists and researchers should not fall victims of world turbulences again and should remain focused not losing the momentum of advancing research for human well-being. Medicon Microbiology (MCMI) is the ideal platform to facilitate peer-reviewed open-access space to support researchers and scientists in publishing expert analysis. Medicon Microbiology (MCMI) editorial board wishes to extend gratitude to experts input of authors and is confident that the esteemed reader would benefit and consider partaking a role in the betterment of human sciences and wellbeing. 

The coastal area has experienced critical changes of waste issues throughout recent years. Hence, this study means to review individuals’ points of view on the elements of waste in coastal regions and our goal is to make an essential that intend to manage the plastic contamination that addresses a major test for beach front urban areas, and to diminish the utilization of plastics in the public area to make urban areas liberated from plastic. Plastic marine litter is a worldwide natural issue with things of floating in water and affecting environments. Plastic has unfortunate results for untamed life, for economies and more on to human wellbeing. Plastic requests for earnest requirement for activity on a worldwide scale as it influences the marine climate. Fumble of strong waste is a grave concern whenever observed can assist with lessening plastic marine litter. Beachfront urban communities are situated on the connection point or change regions among land and ocean, including huge inland lakes. The marine climate incorporates the waters of oceans and estuaries, the seabed and its soils, and all marine natural life and its ocean and waterfront territories. The marine climate is an indispensable asset for life on earth. Marine environments play out various key natural capacities - they manage the environment, forestall disintegration, and circulate sunlight based energy, assimilate carbon dioxide, and keep up with organic control. The recognizable proof of the beginning of the plastics that arrive at the coast is by all accounts of vital significance. This article plans to safeguard the climate and advance asset effectiveness, can go past the issues of beach front zones, for example, distinguishing areas of action subject to a critical utilization of plastics and the objective that is being given. Making successful metropolitan waste administration frameworks is quite possibly the main activity to forestall plastic contamination. Deficient and overpowered seepage frameworks, open unloading, littering, and flood/overflow at assortment
focuses and landfills are the fundamental drivers of land-based plastics spilling into waterways and marine conditions. In this review, future drives are worked to framework for assortment, decrease and isolation of waste at the source should zero in on sea shores. To resolve the issue of plastic contamination in the marine climate, legislatures should initially assume a functioning part in resolving the issue of plastic waste by acquainting regulation with control the wellsprings of plastic trash and the utilization of plastic added substances.

The monopoly in the field of studying the cardinal problems concerning the optimization of controlling activities and the complex security of human vital functions in all its spheres of being for a long time used to belong to psychology. But during the last decades of the 20th century a new conception has arrived - that is: for solving such global problems we need to organize complex scientific research, based on the interconnection of different sciences. Carrying out these kinds of researches is making absolutely new requirements to the methodology, and at the same time forming new kinds of approaches for solving the methodological problems.

The Revitalarised Transitional Government of National Unity of South Sudan formed in January, 2020 creates a peaceful space and environment to increase economic growth, reconstruction and resilience in South Sudan. About 90% of the total area is arable, with 50% considered as good and prime agricultural land. Out of the total prime agricultural land, only about 5% is currently being utilised and 12-15% is covered by arid and semi- arid lands (ASALs). Livestock has been listed to have a great potential in addition to the agricultural productivity in to contribute to the economic wealth and GDP of South Sudan. Over the years, the severity and occurrence of droughts have increased and their effects are exacerbated by the increasing wonders of unreliable rainfalls, land degradation, global warming and climate change. These circumstances have created conditions of chronic vulnerability in the country with persistent food insecurity, widespread economic hardships, conflicts, migration and unspeakable human sufferings, all affecting the pastoral and agro-pastoral communities that inhabit the ASALs. 

Helicobacter pylori (H. pylori) is a mysterious spiral bacterium that is colonized as gastric-flora in 50% of the world’s population [1]. Introduction of this bacterium in 1982 by Barry Marshall and Robin Warren was one of the most revolutionary discoveries in the field of gastroenterology; Todays, H. pylori is considered as the causative agent for chronic gastritis, peptic ulcer disease, and gastric adenocarcinoma as well as extra-gastrointestinal diseases [2-3].

A field experiment was conducted on the Influence of Boron Molybdenum and Nickel on Biochemical traits and quality of blackgram (Vigna mungo L. Hepper) at Agricultural college farm, Bapatla during Rabi 2017-18 in randomized block design with eight treatments of micronutrient application viz., control (T₁), B (T₂), Mo (T₃), Ni (T₄), B+Mo (T₅), B+Ni (T₆), Mo+Ni (T₇) and B+Mo+Ni (T₈) replicated thrice. The results revealed that leghaemoglobin content in nodules, activity of urease in leaves and nodules and protein content in seed increased with B, Mo and Ni application individually and in combination. Activity of NR in leaves increased with T₂ to T₈ except Ni (T₄) application alone.

An animal is an ideal tool using a non-human species handed down in medical research because it can mimic the attitude of a disease found in humans. Animal models are used to bring to successful completion of the information about a disease and its avoidance, diagnosis, and treatment.

Viruses are smallest, ultramicroscopic, non-cellular, obligate intracellular infectious agents that cannot reproduce outside their host cell. Their genome contains either DNA or RNA as genetic material. Their genome is enclosed in a protein shell known as capsid, which may be surrounded by a lipid containing membrane called envelope. The entire infectious unit is called “Virion”- an extracellular infectious virus particle. Viruses are connecting link between living and non livings because it act as non living particle outside the host cell while reproduce inside host cell like living organisms does.

COVID-19 emerged from Wuhan, China in late 2019. A new strain of coronavirus, it was not previously identified in humans. Coronaviruses are a large family of viruses which cause illness ranging from a mild cold to severe disease, for instance Severe Acute Respiratory Syndrome (SARS-CoV).

Data science mining methods are utilized in the field of medication for different purposes. Mining affiliation rule is one of the intriguing points in information mining which is utilized to produce continuous itemsets. It was first proposed for market bushel examination. Analysts proposed varieties in methods to create incessant itemsets. Creating huge number of incessant itemsets is a tedious cycle. In this paper, the creators contrived a strategy to anticipate the danger level of the patients having coronary illness through incessant itemsets. The dataset of different coronary illness patients is utilized for this exploration work. The information mining strategies-based frameworks could vitally affect the workers’ way of life to anticipate heart sicknesses. There are numerous logical papers, which utilize the strategies of information mining to anticipate heart infections. Nonetheless, restricted logical papers have tended to the four cross-approval methods of dividing the informational index that assumes a significant part in choosing the best procedure for foreseeing coronary illness. Pick the ideal blend between the cross-approval methods and
the information mining, order strategies that can upgrade the exhibition of the forecast models. This paper means to apply the four-cross-approval methods (holdout, k-overlay cross approval, separated k overlap cross-approval, and rehashed irregular) with the proposed techniques Extended Support Vector Machine and Extended KNN to work on the precision of coronary illness expectation and select the best forecast models. It investigates these procedures on a little and huge dataset gathered from various information sources like Kaggle and the UCI AI archive. The assessment measurements like exactness, accuracy, review, and F-measure were utilized to quantify the presentation of forecast models. Experimentation is performed on two datasets, and the outcomes show that when the dataset is epic (50000 records), the ideal mix that accomplishes the most noteworthy precision is holdout cross-approval with the neural organization with an exactness of 71.82%. Simultaneously, Repeated Random with Random Forest considers the ideal blend in a little dataset (303 records) with a precision of 89.01%. The best models will be prescribed to the doctors in business associations to help them anticipating coronary illness in workers into one of two classifications, cardiovascular and non-heart, at a beginning phase. Successive itemsets are produced dependent on the picked indications and least help esteem. The separated successive itemsets assist the clinical professional with settling on indicative choices and decide the danger level of patients at a beginning phase. The proposed strategy can be applied to any clinical dataset to anticipate the danger factors with hazard level of the patients dependent on picked factors. An exploratory outcome shows that the created
technique distinguishes the danger level of patients effectively from continuous itemsets. The early recognition of heart illnesses in representatives will further develop efficiency in the business association. 
Keywords: Data Mining; Heart Disease; Feature Selection; Cross-Validation Methods; Data Preprocessing; Classification Algorithms; Productivity; Business Organizations; Frequent Itemsets; Heart Disease Prediction; Association Rule Mining; Data Mining; Medical Data Mining; SVM; ESVM; ANN and EKNN

It has long been a challenge to control infectious diseases by destruction, decrease in their number or inhibition of microorganisms. It can be carried out with different methods depending on the place to be applied and the degree of microbial eradication that is intended to be achieved.

Contact tracing is an activity of paramount importance during the work with an individual either symptomatic or asymptomatic diagnosed with the infectious disease of interest. The aim of contact tracing is to identify, provide support for those contacts as well as to prevent further infective dissemination. Contact tracing enables us to adequately identify, voluntary quarantine or self-quarantine and monitor those COVID-19 suspect cases.
A contact is defined as an individual exposed to a case or a case’s environment such that they had an opportunity to acquire the infection. A high-risk subpopulation is a population that has the risk of infection or severe disease. 
The incubation period is defined as the interval between the onset of invasion by an infective particle and the first sign/symptom of the infective process. In the case of SARS-CoV-2, the incubation period is on average 4-5 days but may be as long as 14 days.
The infectious period is a time during which a case can transmit a disease to others and starts 2 days before someone develops symptoms.
During an isolation a case needs to be separated from others. During a quarantine a close contact is separated from others to prevent the disease transmission to the susceptible individuals and contact’s activities are restricted for the duration of their incubation period.
Contact tracing is carried out by the adequately educated staff. They should possess a qualitative background, language skills, cultural access to social and medical support for the suspected individuals.
Occupational Health and Safety (OHS) efforts aim to ensure a healthy and safe working environment, prevent work-related diseases and promote health. OHS issues may affect the Rapid Response Team (RRT) as well as the incident being investigated. Risk assessment should consider the tasks, the environment, the exposure as well as preventive controls available.
The current coronavirus pandemic 2019 (COVID-19) represents a major public healthcare crisis with epidemiologic models predicting hazardous consequences. The major concern is a high morbidity rate. Contact tracing using of smartphone apps is a powerful approach to control and reduce the lethal outcomes of the COVID-19 pandemic. However, the use of smartphone technology is limitated due to the potential exposure of sensitive personal information.
Keywords: COVID-19; Contact tracing; Data protection; App

Scrub typhus represents a robust Public Health ailment that is Prevalent in the Asia-Pacific region that is inclusive of although not restricted to Korea, China, India, Taiwan Indonesia, Thailand, Sri Lanka, as well as the Phillipines, This area has been referred to as the tsutsugumashi triangle. One billion people have a threat globally to get afflicted, that results in a disease generation in one million individuals. Scrub typhus alias tsutsugumashi disease occurs every year. Here we have stressed on the epidemiology world wise besides the significance of Scrub typhus in Indian scenario that presents as an acute febrile illness having life threatening consequences. We conducted a Here we conducted a systematic review utilizing search engine pubmed, google scholar; web of science; embase; Cochrane review library utilizing the MeSH terms like Scrub typhus; other rickettsios is; differential diagnosis with other acutefebrile illnesses; epidemiology of Orientiatsutsugumashi (etiologic agent); malaria; Indian scenario; Leptrombidiumbitesllarvae; mites; serology; WeilFelix reaction; immuofluoresceceassay; met genomic next generation sequencing (mNGS), Scrub typhus cell surface display technology; treatment; doxycycline; azithromycin; rifampicin; chloramphenicol; innovative early diagnostics. We found a total of 3500 articles out of which we selected 51 articles for this update. Nometa-analysis was done. Since this disease has reemerged it is usually not kept in mind as a differential diagnosis in acute febrile illnesses that is why we chose to review and update this life hreateningisease having central nervous system (CNS), Gastrointestinal Tract (GIT), complications, myocarditis, besides usually diagnostic eschar. Avoidamce is equally important specifically for farmers other vegetation areas etc.
Keywords: Scrub typhus; epidemiology; complications; avoidance; Public Health problem

The period between 1850 and 1915 known as the Golden Age of Microbiology is possibly returning to 2020s. Information Technology had its beginning of upward growth from 1990s and Growth of Social media increased tremendously in the decade of 2010 and both still continues to grow.

Anthropogenic, non-degradable materials are well-known for fragmentation in the coastal and marine environments which lead to formation of micro litter particles. Due to the buoyant and persistent properties of micro litters, they have a potential to become widely dispersed in the coastal marine environment through the hydrodynamic processes and ocean currents. Ingestion of these micro litters in marine organisms has been reported hence they play a vital role in disturbances in the marine food webs. Therefore, we have assessed the distribution and abundance of micro litters in ten selected beaches along the Gulf of Mannar between Dhanuskodi and Thoothukudi, southeast coast of India during August 2013 and February 2014.
Overall, the total micro litters segregated in the Gulf of Mannar were 27.9 item m^{3 -1}. The sampling period wise record showed that August 2013 found highest record of 17.67 item m^{3 -1} while it was only 10.23 item m^{3 -1} in Feb. 2014. Overall Gulf of Mannar recorded >92.6% occurrences of both hard (40.5%) and soft (52.1%) plastic type micro litters, whereas no single record of litter types of glass and cloth type micro litters were noticed. Nevertheless, overall, 2.6-5.0 mm size groups accounted maximum numbers in the Gulf of Mannar beaches. As litter groups, overall mean abundance of all five micro litter categories were higher in
Mugundarayar Chadram beach (17.0%) in Aug. 2013 and in Kundukal (15.2%) during Feb. 2014. As micro litter size categories, >10 mm sizes found to be dominated (1.56 item m^{3 -1}) in Muguntharayar Chadram beach and Pudumadam beach during Aug. 2013 and Feb. 2014, respectively.
The present study concludes that, the abundance of micro litters found to be varied between surveys, beaches, litters’ group and litters’ sizes. The micro litters of soft (SP) and hard plastics (HP) contributed more than 92.6% and mostly by fragments of fishing nets, nylon ropes, food wrappers, plastic buoys etc. The present study concludes that there is a possibility of migration of micro litters to the coastal and coral reef environments of Gulf of Mannar due to hydrodynamic process thus enter the marine food chain including corals via engulfment as food materials. Therefore, a proper disposal plan should be followed to control solid waste management in the Coastal Marine Environment.
Keywords: Marine Litter; Micro Plastics; Beaches; Hydrodynamic process; Gulf of Mannar

One hundered and fifty soil samples were collected from different city of Babylon/Iraq. Thirty three Actinomycetes isolates were obtained, purified and identified according to morphological properties and biochemical ltests. All isolates were tested for the irantimicrobial activities and screened for production of some important enzymes such as amylase, catalase, protease, cellulose and lipase. There salts obtained that 13 isolated have such potential and all 13 isolates have amylase activity. The most active
isolates in production of study enzymes were KO-AG18, HI-SA24 and KO-RS27. Most 13 Actinomycetes isolates were showed antimicrobial against some Gram-positive bacteria (Staphylococcus albus, Staphylococcus aureus and Streptococcus pyogenes) and Gram- negative (Klebsella pneumoniae, Escherichia coli, Serratia marcescens, Pseudomonas aeruginosa and Aeromonas hydrophila due to inhibition zone diameter (mm) ranged from 4-37 against gram negative and from 6-37 against gram positive bacteria.
Keywords: Isolates; Antimicrobial Activity become isolates; antimicrobial; activity

It is not possible, therefore, to understand the phenomenon of life without understanding its fundamental component: WATER. The UN (UN) in accordance with the WHO (WHO), adopts on December 22, 1993 and by Resolution A/RES/47/193, that every year, every March 22, the call is commemorated: World Day of the water. Likewise, an invitation was sent to all member states to consecrate this day, as the promotion of public awareness and through the production and dissemination of documentaries, conferences, round tables, seminars and exhibitions related to the conservation and development of natural resources. hydric.

Dolphins and whales are collectively referred to as cetaceans which have until recently remained unstudied in Ghana and within the Gulf of Guinea. In recent times, the growing increase in the landings and stranding of cetaceans have raised concern to environmentalists and the general public even though Ghana has enacted several legislations intended to foster the conservation of biodiversity and protection of the environment. 

The browse species are an important source of nutrients for goats in arid range-land. However, the chemical compositions of browse species used as goat feed in Bena-Tsemay district have not studied and documented for future long-term utilization which target to properly balance their inclusion levels in animal diets. Thus, this study was conducted to assess chemical compositions of selected browse species browsed by goats. The leaves of fourteen browse species samples were collected from range-land and analyzed for chemical compositions in completely randomized block design by using the Generalized Linear Model (GLM) procedures of SAS. The higher (p<0.05) crude protein (CP) (198g/kg, DM) was observed in A. marina leaves, while the lower CP levels (125g/kg, DM) and (126g/kg, DM) were observed in A. tortilis and G. tenax leaves, respectively. The S. birrea leaves had higher (p<0.05) estimated dry matter intake (94.05g/kg, DM), while it was lower (58.57g/kg, DM) for A. tortilis leaves. However, the digestible dry matter (DDM), total digestible nutrients (TDN), relative feed value (RFV) and metablisable energy (ME) values were similar (p<0.05) among the all studied browse plant species. Based on result from this study, it can be concluded that all studied browse plants had high-quality-protein to supplement poor-quality-roughage feeds for enhanced livestock production. Also, it is suggested that future research will consider the supplementation effects of browse plant on animal
performances (meat, milk and growth rate). 
Keywords: Crude protein; Digestible dry matter; Dry matter intake; Relative feed value; Metabolizable energy

Undoubtedly, the year 2020 will be marked by fire as the year of the COVID-19 pandemic, a situation that, on the other hand, we are still suffering and we do not foresee that it can conclude at least in the short term since we outlined these lines. To assume that everything is known about Sars-Cov 2 [1] (Severe acute respiratory syndrome - coronavirus 2) is a fallacy, since every day, we know less for sure than we do not know about it.