The Embodied Cognitive Behavior Therapy (ECBT) approach for the treatment of emotional disorders in clinical settings is presented. The model integrates cognitive behavioral theory, neuroscience and embodied cognition. ECBT draws from evidence of bidirectional effects between modes of bottom up (sensori-motor simulations giving rise to important basis of knowledge) and top down (abstract mental representations of knowledge) processes in psychotherapy. The paper first describes the dominance of the traditional mentalistic view of cognition and its limitations. Evidence for the embodied model of cognition and emotion is reviewed whilst highlighting its advantages as a complimentary process model to deepen and broaden talking therapies. An overview is given of the switch (e.g., the technique of balancing) between top-down and bottom-up orientation in the ECBT model as well as a clear description of the method for emotional regulation, acceptance of unwanted emotions and emotional mastery. ECBT builds on and extends the unconscious processes of the ‘Interpersonal Synchrony’ (IS) model identified by Koole and Tschacher [1], to enhance the therapeutic alliance for emotional co-regulation. A new idea is proposed that both embraces and extends the IS model: embodiment techniques of imitation and movement synchronization in the Emotional Field of our method be used in a conscious way to speed up the calming effects of co-regulation and the client’s self-regulatory capacity. The paper ends with an outline of the criteria needed to become an embodied therapist. A case study is given highlighting these aspects.

Polymeric microparticles of polyethyleneglycol-polylactic acid-co-glycolic acid (PEG-PLGA) are widely used as drug carriers for a variety of applications due to their unique characteristics. Herein, we developed a novel method for the synthesis of uniformly sized microparticles via coaxial flow-phase separation. The study evaluated the effect of various process parameters on microparticle size and polydispersity including polymer concentration, stirring rate, surfactant concentration, and the organic/aqueous phase flow rate and volume ratio. The results demonstrated that stirring rate and polymer concentration had the most significant impact on the mean particle size and distribution whereas surfactant concentration had the most substantial impact on the morphology of particles. Several microparticle formulations yielding particle sizes in the range of (5-50 µm), morphology, and concentration were synthesized as a demonstration of the tunability and scalability of this method. Notably, by controlling the process parameters microparticles of less than ~ 7 μm could be made using polymer concentrations varying by an order of magnitude. Finally, we demonstrated the tunability and scalability of this method by showing a 10-fold increase in encapsulation efficiency, a 3-fold increase in drug loading of a model hydrophilic drug, and modified release kinetics in microparticle formulations of comparable sizes but different polymer concentrations.

Oxidative stress is defined as an imbalance between the production of free radicals and reactive oxygen species (ROS) and the ability of the body to neutralize them by antioxidant defense systems. Cells produce ROS as a control of physiological processes, but increasing ROS becomes pathological leading to non-specific and irreversible damage to biological molecules, such as DNA, lipid, and protein. Endogenous ROS are mainly produced by mitochondria during both physiological and pathological conditions, and enzymes, such as nicotinamide adenine dinucleotide phosphate oxidase (NOX), xanthine oxidase (XO), lipoxygenase (LOX), myeloperoxidase (MPO) and monoamine oxidase (MAO). To neutralize ROS, the body employs enzymatic and non-enzymatic defense systems. The dietary intake of bioactive phenols, such as quercetin (Que), is a non-enzymatic system that can quench ROS and protect from pro-oxidative damage. In this review, we evaluate the ability of Que to target endogenous oxidant enzymes involved in ROS production and explore the mechanisms of action underlying its antioxidant properties. Que not only acts as a free radical scavenger by donating electrons through the negative charges in its phenolic and ketone groups, but it can effectively inhibit the activity of several endogenous oxidative enzymes, binding them with high affinity and specificity. Among all targets, Que showed the best results in molecular docking simulations with XO, followed by MAO-A, 5-LOX, NOX, and MPO. Taken together, these findings highlight the potential of Que as a natural antioxidant therapy for oxidative stress-related diseases.

A big race for the search for novel lead has begun due to the emergence of COVID-19 across the globe. More than 6,00,000 cases of afflicted patients worldwide has been reported till date with high mortality and morbidity. At present no approved drugs are known for COVID-19. Phylogenetic analysis present strong nucleotide sequence similarity of around 80% with SARS-CoV. Therefore, the drugs used for treating SARS-CoV and MERS are being used for SARS-CoV-2 also. Recently, the crystal structure of COVID-19 is reported and hence, we have used this tom predict the binding affinity with SARS-CoV-2-main protease and prepared a pharmacophore that may be used for future design of novel inhibitors.

Automatic control to any of robot manipulators, some kind of issues are being observed. A numerical method for solution generation to the inverse kinematics problem of redundant robotic manipulators is presented to obtain the smoothest algorithm as possible, leading to a robust iterative method. After the primary objective of the reachability of end-effectors to the target point is achieved, the aim is set to resolve the redundant degrees of freedom of redundant manipulator. This method is numerically stable since it converges to the correct answer with virtually any initial approximation, and it is not sensitive to the singular configurations of the manipulator. In addition, this technique is computationally effective and able to apply for serial manipulators with any DOF applications. A planar 3R-DOF serial link redundant manipulator is considered as exemplar problem for solving. Also, the continuum approach for resolving more complex structure with variable DoF is illustrated here and their brief applicability to support surgeries and adaptive use of artificial linkage moments is also calculated.

UN Convention on Climate Change 2015 mandates reducing carbon footprint to reduce global warming. Considering environmental concerns, Electric Vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an “EV only” scenario by 2030. Two wheelers dominate the Indian automobile industry with almost 80% market share. With the transportation sector accounting for 70% of world’s fossil fuels use, the need for a sustainable supply chain which addresses environment, economy and societal issues, has, of late, aroused interest in the business academic community and industries. Sustainability in business decision-making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimises risk management strategy, and promotes profitability. This study intends to develop a conceptual framework after examining research gaps in sustainable supply chain management (SSCM) and allied topics like sustainability, green supply chain management, and vendor partnerships through collaborative efforts. Even the UN vide Goal 9 of sustainable goals pertaining to industry, highlights the importance of this framework.

Fluorescein anisotropy, which is a widely used technique to study the folding state of proteins or affinity of ligands, is used in the present work to study the temperature sensing of fluid in a microchannel, by adding fluorophore in the fluid. Fluorescein was used as a temperature probe, while glycerol-aq. ammonia was used as a working fluid. Fluorescence anisotropy of fluorescein were measured by varying various parameters. Apart from this, a comparison of fluorescence anisotropy and fluorescence intensity is also performed.

Vaccination is an important method of immunological preventive care required for health management of all animal including fish. More particularly, immunization is important in in-land fisheries for the management of diseases in fish brood stocks and healthy seed production. According to the latest statistics in 2020, 90.3 million tonnes of captured fish production was achieved from the aquaculture sector. Out of the above, 78.8 million tonnes from marine and 11.5 million tonnes from inland waters aquaculture sectors. About 4% decline in fish production was achieved in 2020 than 2018 from inland fisheries sectors. On the other hand, the digestive protein content, healthy fats and nutritional values of fish products are comparitively more affordable as compared to other meat sources. In 2014, about 10% aquatic cultured animals were lost (costing > 10 billion $ global annual loss) due to infectious diseases. Therefore, vaccination in fish especially in brood stocks is one of the most important approaches to stop such losses in the fisheries sector. Fish vaccines consist of either whole killed pathogens, subunit of a protein, a recombinant protein, DNA vaccines or live attenuated vaccines. Challenges existing are adaption of vaccination in fisheries sector, route of administration, use of effective adjuvants and most importantly lack of effective results. Use of autogenous vaccines, vaccination via intramuscular, intraperitoneal or oral routes, and most importantly adding vaccines in feed using top dressing methods or as a constituent in fish feed are now in emerging. These method shall lower the risk of using antibiotics in cultured water which in turn will reduce the environmental contamination.

Unsupervised metric learning has been generally studied as a byproduct of dimensionality reduction or manifold learning techniques. Manifold learning techniques like Diusion maps, Laplacian eigenmaps has a special property that embedded space is Euclidean. Although laplacian eigenmaps can provide us with some (dis)similarity information it does not provide with a metric which can further be used on out-of-sample data. On other hand supervised metric learning technique like ITML which can learn a metric needs labeled data for learning. In this work propose methods for incremental unsupervised metric learning. In rst approach Laplacian eigenmaps is used along with Information Theoretic Metric Learning(ITML) to form an unsupervised metric learning method. We rst project data into a low dimensional manifold using Laplacian eigenmaps, in embedded space we use euclidean distance to get an idea of similarity between points. If euclidean distance between points in embedded space is below a threshold t₁ value we consider them as similar points and if it is greater than a certain threshold t₂ we consider them as dissimilar points. Using this we collect a batch of similar and dissimilar points which are then used as a constraints for ITML algorithm and learn a metric. To prove this concept we have tested our approach on various UCI machine learning datasets. In second approach we propose Incremental Diusion Maps by updating SVD in a batch-wise manner.

Most popular machine learning algorithms like k-nearest neighbour, k-means, SVM uses a metric to identify the distance(or similarity) between data instances. It is clear that performances of these algorithm heavily depends on the metric being used. In absence of prior knowledge about data we can only use general purpose metrics like Euclidean distance, Cosine similarity or Manhattan distance etc, but these metric often fail to capture the correct behaviour of data which directly affects the performance of the learning algorithm. Solution to this problem is to tune the metric according to the data and the problem, manually deriving the metric for high dimensional data which is often difficult to even visualize is not only tedious but is extremely difficult. Which leads to put effort on \textit{metric learning} which satisfies the data geometry.Goal of metric learning algorithm is to learn a metric which assigns small distance to similar points and relatively large distance to dissimilar points.

Soccer is the leading cause of all sports-related ocular injuries in Europe and Israel and leads to blindness at a disproportionately high rate, especially among children (Thompson et al., 2019). With soccer being the most popular sport in the world, it is surprising the only solution to ocular injuries are sports glasses which can cause direct damage to the eye and impair peripheral vision by 15%, calling for an alternative (Smith & Jones, 2020). This work, based on an imported facial model, focuses on both the qualitative and quantitative aspects of building a personalized, low-cost (through optimizing material needed), superior apparatus to conventional sports glasses. The quantitative side examines a modified and model-validated (through a SRS of thirty unique extrapolated points) multivariate linear regression equation, based on horizontal and vertical rotation angles (n=60) trained with residual and computer simulation data, that can accurately extrapolate and quantify ocular impact from all horizontal and vertical rotation angles and a modified Hertzian Contact Stress Equation to determine the thickness of the apparatus using the elasticity modulus, impact force, and geometrical facial and soccer-ball features. The qualitative side examines where firstly, ocular impact is most prevalent, and secondly, where most impact is seen from rotating the ball horizontally and vertically through analysis of Pearson’s Correlation Coefficient at the supraorbital, medial canthus, infraorbital, and lateral canthus regions from both regression equations as well as a cost-benefit analysis for comparison with conventional sports glasses. The findings discovered in this work lay the groundwork for future research to build a customized low-cost ocular injury prevention device which can simultaneously ensure complete ocular safety while enhancing athletic performance for over 250,000,000 players (Johnson & Garcia, 2022).

Phylogenomics has enriched our understanding that the Tree of Life can have network-like or reticulate structures among some taxa and genes. Non-vertical modes of evolution—such as hybridization/introgression and horizontal gene transfer—deviate from a strictly bifurcating tree model, causing non-treelike patterns. Here, we present a brief overview of a phylogenomic workflow for inferring organismal histories and compare methods for detecting reticulate evolution. We discuss how the timing of coalescent events can help disentangle introgression from incomplete lineage sorting and how horizontal gene transfer events can help determine the relative timing of speciation events. In doing so, we identify pitfalls of certain methods and discuss how to extend their utility across the Tree of Life. Workflows, methods, and future directions discussed herein underscore the need to embrace reticulate evolutionary patterns for understanding the timing and rates of evolutionary events, providing a clearer understanding of life’s history.

Inorganic chemistry has contributed to the progress of human civilization in several different ways. Its activities at the interface with other scientific disciplines has led to emergence of several new fields like organometallic chemistry, bio-inorganic chemistry, solid-state chemistry, environmental chemistry, etc. In the past few decades, it has made inroads in yet another dimension of immense relevance, i.e. nano-science and –technology. In this assay an attempt has been made to present an overview of utility of inorganic and organometallic compounds as precursors for the synthesis of nano-materials with reference to catalysis and materials science. Applications of metal nanoparticles (NPs) and metal chalcogenide NPs, generated through molecular precursor route, in catalysis have been discussed. Synthesis of ternary and quaternary metal chalcogenide nano-materials by molecular precursor route has been reviewed.

The article reviews the constituent’s physico-chemical parameters in the water of the Mansagar Lake of the Jaipur City. The water quality is also investigated using evaluated physico-chemical parameters with in-depth study of their distribution as per sample location-wise and season-wise. A detailed discussion on the associationship among parameters (pH, Conductivity, turbidity, dissolved Oxygen, chemical and Biochemical Oxygen demand, etc.) using the Pearson’s analytical method is also presented. The strength of the article is the graphical presentation of evaluated physico-chemical parameters and the water quality indices for sample station-wise and the season-wise. To our best of understanding, first time such details have been clubbed together and discussion in brief for studied lake.

With this article, impact of effluent mixed water with the quality of the Mansagar Lake has been established by evaluating the physico-chemical parameters and the heavy-metal contents experimentally. The process of calculating water quality and the metal pollution quality indices is also explained in brief with respect to selected sample locations and varied environmental conditions (Pre- and Post-Monsoon season). Distribution trends of Pearson‘s correlation factor have also been discussed to establish their relation among the physico-chemical parameters and the heavy-metal contents for varied environmental conditions. In the end, detailed discussion on observations made during this study and useful recommendations are also elaborated in details. With this article, we intend to present a document for better understanding of the water quality of this lake in view of futuristic management strategies to be adopted to maintain it heritage values.

Here, using the theory of electrical-quantum modelling of the neutron and proton as a three-phase alternating current electrical generator, we will analyse the proton decay and the inverse neutron decay and determine the following subatomic particles, Dproton, Protoniu, Dneutron and Neutroniumd.

We are going to demonstrate the origin of dark energy, using a Hardwire similarity, a test that is carried out in terrestrial seismic with vibroseis in the search for gas and oil. In the first instance, we are going to analyse the mathematical function of Dirac's delta and compare it with the detonation of an explosive that is carried out in the search for gas and oil. Let's understand that in both cases a frequency spectrum is produced. We will also explain the meaning of a non-causal, zero-phase system; a causal system, of minimum phase, in order to determine the mathematical behaviour that corresponds to the Big Bang. Next, following the line of seismic, we will analyse a Hardwire Similarity to demonstrate that a vibroseis source, which generates a sweep, produces, in addition to the ideal sweep of fundamental frequencies, spurious harmonic frequencies and additional noise. We will show that this additional energy generated by the spurious harmonic frequencies and additional noise adds to the energy of the fundamental frequency sweep in analogy as dark energy does.

Delay-tolerant networks (DTN) is an approach to deal with scarce network connectivity found in sparse mobile ad-hoc networks (MANETs) which makes the problem of routing messages a challenging task. DTNs have find their usefulness in many challenging environments such as tactical networks, underwater sensor networks, wildlife monitoring, disaster recovery etc. Pocket Switched Networks (PSNs) have emerged as a new application of the delay tolerant networks where network nodes are computing devices carried by humans. Hence, the study of how humans interact in their day-to-day life, the places they visit frequently, the people they meet frequently, the social groups in which they participate on regular basis etc. can help improve routing process in PSNs. This type of routing inspired from the way humans interact with each other is referred to as social based routing and had been a recent topic of research in the field of DTNs. This paper presents a comprehensive survey of the various social-based algorithms that have been designed for Delay Tolerant Networks.

A naked human eye can perceive objects down to a millimeter length. While lenses and microscopes have overcome this limit, the human mind still lacks perspective when navigating conventional scales (1), especially in the range that are less palpable to naked human eye (2,3). This problem is particularly acute in the context of science communication, where the conventional scale bar units facilitate little comprehension regarding the perception for factorial size differences (3). Here we aim to bridge the gap of scale factors and perspectives using a universal toolkit of objects, which can help comprehend the relative change in length dimensions up to 13 orders of magnitude difference. We further have demonstrated the use of such a universal object toolkit as a length perceptive scale by illustrating and narrating biological phenomena. The meter to picometer ‘length perceptive scale’ proposed here has the potential to cover majority of length scales present in the biological realm, and is analogous to the time compression methods widely used in explaining cosmos timeline (4). Our toolkit can also be calibrated according to the users need in their scientific communication and illustrations, which will aid the readers’ benefit in understanding the length scale perception of illustrated phenomenon.

Intratumoral heterogeneity can exist along multiple axes: Cancer Stem Cells (CSCs)/non-CSCs, drug-sensitive/drug-tolerant states and a spectrum of epithelial-hybrid-mesenchymal phenotypes. Further, these diverse cell-states can switch reversibly among one another, thereby posing a major challenge to therapeutic efficacy. Therefore, understanding the origins of phenotypic plasticity and heterogeneity remains an active area of investigation. While genomic components (mutations, chromosomal instability) driving heterogeneity have been well-studied, recent reports highlight the role of non-genetic mechanisms in enabling both phenotypic plasticity and heterogeneity. Here, we discuss various processes underlying phenotypic plasticity such as stochastic gene expression, chromatin reprogramming, asymmetric cell division and the presence of multiple “attractors”. These processes can facilitate a dynamically evolving cell population such that a subpopulation of (drug-tolerant) cells can survive lethal drug exposure and recapitulate population heterogeneity on drug withdrawal, leading to relapse. These drug-tolerant cells can be both pre-existing and also induced by the drug itself through cell-state reprogramming. The dynamics of cell-state transitions both in absence and presence of the drug can be quantified through mathematical models. Such a dynamical systems approach to elucidating patterns of intratumoral heterogeneity by integrating longitudinal experimental data with mathematical models can help design effective combinatorial and/or sequential therapies for better clinical outcomes.

Leishmaniasis is a tropical disease caused by a protozoan parasite Leishmania that is transmitted via infected female sandflies. At present, leishmaniasis treatment mainly counts on chemotherapy. The currently available drugs against leishmaniasis are costly, toxic, with multiple side effects, and limitations in the administration route. The rapid emergence of drug resistance has severely reduced the potency of anti-leishmanial drugs. As a result, there is a pressing need for the development of novel anti-leishmanial drugs with high potency, low cost, acceptable toxicity, and good pharmacokinetics features. Due to the availability of preclinical data, drug repurposing is a valuable approach for speeding up the development of effective antileishmanial through pointing to new drug targets in less time having low costs and risk. Metabolic pathways of this parasite play a crucial role in the growth and proliferation of Leishmania species during the various stages of their life cycle. Based on available genomics/proteomics information, known pathways-based (sterol biosynthetic pathway, purine salvage pathway, glycolysis, GPI biosynthesis, hypusine, polyamine biosynthesis) Leishmania-specific proteins could be targeted with known drugs that were used in other diseases, resulting in finding new promising anti-leishmanial therapeutics. The present review discusses various metabolic pathways of the Leishmania parasite and some drug candidates targeting these pathways effectively that could be potent drugs against leishmaniasis in the future.

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed. The data set consists of 20 000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enables the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

Sustainability transition theories analyse a systematic shift towards sustainability at micro (niche), meso (regime) and macro (landscape) level. The assessment of technological systems and structures at the firm level in sustainability transition literature is scant. The present study, taking the technological assessment perspective at the firm level, finds answers to questions like (a) how do established corporations move towards sustainable practices? (b) what role does technological innovation play in the firm’s transition towards sustainability? (c) what technological modes are adopted for sustainability transition? We find answers to these questions through an in-depth case analysis of two multi-national companies in the consumer goods industry. Internally developed and externally acquired technologies by firms in the last 15 years are plotted using qualitative and quantitative indicators on pre-designed templates. Technologies for all three sustainability dimensions, namely, environmental, social and economic, are mapped and the impact assessed. The analysis finds a sustainability transition landscape that shows the use of protected (patents, trademarks, designs) and unprotected technologies (open innovation) to generate impacts like production efficiency, consumption reduction, emission reduction, reduce-recycle-reuse among others. Companies implementing sustainable technologies do observe positive impacts. Implementation of reduce-reuse-recycle (3R)-based technologies enhance the achievement of sustainable development targets. Furthermore, the use of trademarks seems common in differentiating their technologies and identities. These and other results are detailed and used to comment on the role of managing intellectual property and harnessing the effect of technological innovations in sustainability transition.

Image synthesis poses a challenging problem that researchers in computer vision and machine learning have been grappling with for several decades. Numerous machine learning techniques have emerged and proven effective in generating high-fidelity artificial images. This study breaks new ground by exploring image synthesis through generative learning using the D-Wave 2000Q quantum annealer, marking the first attempt to address the issue of generative image synthesis on a Quantum Processing Unit (QPU). Alongside executing image synthesis on the quantum annealer, this research also compares its performance with existing classical models and delves into resolving the Generative Learning Trilemma.

In Wuhan city of China, an episode of novel coronavirus (COVID-19) happened. during late December and it has quickly spread to all places in the world. Until May 29, 2020, cases were high in the USA with 1.7 Million, Russia with approximately 387 thousand, the UK with 271 thousand confirmed cases. Everybody on the planet is anxious to know when the coronavirus pandemic will end. In this scourge, most nations force extreme medication measures to contain the spread of COVID-19. Modeling has been utilized broadly by every national government and the World Health Organization in choosing the best procedures to seek after in relieving the impacts of COVID-19. Many epidemiological models are studied to understand the spread of the illness and its prediction to find maximum capacity for human-to-human transmission so that control techniques can be adopted. Also, arrangements for the medical facilities required such as hospital beds and medical supplies can be made in advance. Many models are used to anticipate the results keeping in view the present scenario. There is an urgent need to study the various models and their impacts. In this study, we present a systematic literature review on epidemiological models for the outbreak of novel coronavirus in India. The epidemiological dynamics of COVID-19 is also studied. Here, In addition, an attempt to take out the results from the exploration and comparing it with the real data. The study helps to choose the models that are progressive and dependable to predict and give legitimate methods for various strategies.

Glaucoma is a disease associated with retina of eye. Presently, millions of human being is suffering from this disease. Early detection of these diseases can save the people from blindness. Therefore, various methods have been developed for its detection. In this paper, we have studied the reported methods and summarized their performance in terms of accuracy of detection.

In an effort to discover an effective and selective antitumour agent, synthesis and anti-cancer potential of 4-(pyridin-4-yl)-6-(thiophen-2-yl)pyrimidin-2(1H)-one (SK-25), which has been reported earlier by us with significant cytotoxicity towards MiaPaCa-2 malignant cells, with an IC₅₀ value of 1.95 μM and was found to instigate apoptosis. In the present study, the antitumour efficacy of SK-25 was investigated on Ehrlich ascites tumour (solid), Sarcoma 180 (solid) tumour and Ehrlich ascites carcinoma. The compound was found to inhibit tumour development by 94.71% in Ehrlich ascites carcinoma (EAC), 59.06% in Ehrlich tumour (ET, solid) and 45.68% in Sarcoma-180 (solid) at 30 mg/kg dose. Additionally, SK-25 was established to be non-toxic at a maximum tolerated dose of 1000 mg/kg in acute oral toxicity in Swiss-albino mice. Computer-based predictions also show that the compounds could have an interesting DMPK profile. The current study provides insight for further investigation of the antitumour potential of the molecule.

Cloudburst is one of the most devastating and frequently occurring natural hazardous events in Indian Himalayan region. Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Whenever, such events occur, lead to flash floods causing landslides, house collapses, dislocation of traffic, and human casualties on a large scale. Therefore, it is necessary to predict the cloudburst inundation zones accurately to avoid damage associated with them. For this, high resolution Digital Elevation Model generated from CartoSat-1 (Stereo pair) were integrated in MIKE 11 Hydrodynamic 1D model to generate longitudinal profile of the study area and to find water level, peak discharge, flow velocity, flow width at different reaches along the Asi ganga and Bhagirathi river, to know the Cloudburst flood inundation scenario. On 3^rd August 2012 one of the major Cloudburst event occurred in Asi Ganga Valley in Indian Himalayan region which was considered for simulation of hydrodynamic model. For a Cloudburst event, 100 mm/hr rainfall was considered for the simulation of the hydrodynamic model. It is observed that the discharge rise from 50 m³/s to 549.164 m³/s (an abrupt increase of about 10 times) within 1 hr at Sangamchetty in Asiganga river and at Joshiyara area rise from 600 m³/s to 3378.69 m³/s (an abrupt increase of about 5 times) within 4 hr in Bhagirathi river. Similarly the water level rises around 3 m and 6m in Asi Ganga and Bhagirathi rivers respectively. Flash Flood inundation areas due to Cloudburst on 3^rd August 2012 were demarcated from the simulation results in GIS environment.

Screen viewing time is the total time spent by a child on any digital/electronic device. The objective of the present study was to determine the incidence and predictors of excessive screen viewing time in children in Ujjain, India. This cross-sectional, community-based study was conducted through house-to-house survey by using the three-stage cluster sampling method in 36 urban wards and 36 villages of Ujjain district, India. Excessive screen viewing time was defined as screen viewing for > 2 h/day. The prevalence of excessive screen viewing time was 17.83%. Risk factors identified using the multivariate logistic regression model were: age (OR: 1.5, P < 0.001); mobile phone use before bedtime (OR: 3.17, P = 0.008); parents’ perception about the child habituated to screen (OR: 14.03, P < 0.001); television in bedroom (OR: 48.69, P < 0.001); morning mobile screen viewing time (OR: 9.27, P < 0.001); not reading books other than textbooks (OR: 9.71, P < 0.001); and lack of outdoor play for >2 h (OR: 4.20, P < 0.001). Presence of eye pain was a protective factor for excessive screen viewing time (OR: 0.12, P = 0.011). The study identified multiple modifiable risk factors for excessive screen viewing time.

Background: Persistent up regulation of NF-κB leads to chronic inflammation and subsequent microglial activation and takes neurons towards death by activating death receptor domains and the p53 pathway. Thus, inhibition of NF-κB may lead to more effective treatment for Parkinson’s disease. Therefore, we have used mangiferin, specific inhibitor of NF-κB in this study. Method: The study utilized male Wistar rats weighing 200-250 gm (n=8 in each group). Stereotactic surgery of rats was done to induce 6-OHDA lesioning in rats. On day 42, rats were subjected to behavioural studies to evaluate effect of mangiferin and their brains were taken out after euthanasia to perform biochemical and molecular studies. Results: Mangiferin significantly increases locomotor parameters in 6-OHDA lesioned rats. It also decreases activity of Cyclooxygenase enzyme which then leads to decrease concentration of inflammatory cytokines. Microglial inflammation was also substantially reduced by reducing MPO concentration. Oxidative stress burden was also reduced after treatment with mangiferin as indicated by increase in Total Antioxidant Capacity, SOD and Catalase and reduction in concentration of MDA. Treatment with mangiferin also reduces burden of oxidative stress by increasing the activity of NRF2/ARE pathway. Activity of Caspase 3 and 9 was also significantly reduced after treatment with mangiferin. Significant decrease in activity of both Cox1 and Cox 2 was also observed. Maximum improvement in all parameters was observed in rats treated with grouping of mangiferin 45mg.kg-1 and levodopa 10mg.kg-1. Treatment with levodopa alone has no significant effect on biochemical and molecular parameters though it significantly improves behavioural parameters. Conclusion and Implications: Results of this study suggest that mangiferin has protective effect in hemi-parkinsonian rats by inhibiting NF-κB. Current treatment of Parkinson’s disease does not target the underlying problem of the disease. Therefore, combination therapy of mangiferin and levodopa can be helpful in better management of Parkison’s.

Here, applying the theory of the generalization of the Boltzmann constant in curved space-time, we will model the mechanism by which elementary particles are formed. We will determine the relationship between gravity and each of the elementary particles that form the standard model following analogies to that of stellar bodies. In order to achieve our goal, we will propose new models for photons, quarks and gluons; with this, we will demonstrate why there are stable and unstable elementary particles; why the first family of elementary particles forms hadrons and why the second and third families cannot form hadrons; why the fermions comply with the Pauli exclusion principle and why the bosons do not comply with the Pauli exclusion principle, etc. Finally, we will analyse the generalization of the ADS/CFT correspondence and propose a theory of everything (T.O.E.).

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

As the digital era advances, many industries continue to expand their use of digital technologies to support company operations, notably at the customer interface, bringing new commercial opportunities and increased efficiencies. However, there are new sets of responsibilities associated with the deployment of these technologies, encompassed within the emerging concept of corporate digital responsibility (CDR), which to date has received little attention in the academic literature. This exploratory paper thus looks to make a small contribution to addressing this gap in the literature. The paper adopts a qualitative, inductive research method, employing an initial scoping literature review followed by two case studies. Based on the research findings, a simple model of CDR parameters is put forward, and the article concludes with a discussion of a number of emergent issues - fair and equitable access, personal and social well-being, environmental implications, and cross-supply chain complexities - that suggest possible directions for future research.

Background: During the stay in the neonatal intensive care unit, parents play a crucial role in the care of their infants. Recent studies reported a decrease in parental participation due to Coronavirus Disease (COVID-19) pandemic that determined restricted access policies in hospitals. The aim of this study is to describe the barriers to a good parents’ participation during the stay in the neonatal intensive care unit in the COVID-19 era. Methods: A quantitative observational study was carried out. Results: 270 parents participated in this study. Mothers’ participation in care seems to be higher as compared to fathers (p = 0.017). Parents who lived the birth of their first child reported a better level of participation in care when compared to those who lived the birth of their second born (p = 0.005). Parents of extremely preterm neonates reported a lower interaction with their infant if compared to parents of term newborns (p < 0.001). Conclusions: Some more disadvantaged categories have reported lower scores: cultural and linguistic minorities, parents of multiple children and fathers. COVID-19 pandemic made several Family Centred Care activities not possible with a higher impact on those who benefited the most of these facilities. This study was prospectively registered by the IRB-CRRM of the University “G. d’Annunzio” Chieti-Pescara on the 23/01/2024 with registration number CRRM;2023_12_07_01.

Neuron degeneration is the most common old age complications worldwide. Alzheimer's disease (AD) is one of the most common neurodegenerative condition with limited drug options and significant adverse effects. Hence, there is a growing interest in exploring natural products for their potential pharmacological activities against AD. This study investigates the efficacy of Verbesina encelioides flower extract (VFE) in alleviating neurobehavioral damage induced by Scopolamine in rat mimicking AD model. GC-MS analysis showed that V. encelioides flower extract is rich in carotenoids like spirilloxanthin. Various behavioural models were used to assess the rats' learning and memory capabilities. Additionally, biochemical tests were conducted on the AD rat models to evaluate oxidative stress in the brain, including superoxide dismutase (SOD), nitric oxide (NO), reduced glutathione (GSH), and anti-acetylcholinesterase (AChE) enzyme assays. Findings from this study suggest that the VFE significantly improved the rats' cognitive aptitude and alleviated them from oxidative stress by reducing free radical generation. Based on the results obtained from GC-MS analysis, in silico interaction analysis of gingkolide A and spirilloxanthin with acetylcholinesterase was carried out. In summary, this study suggests that V. enceloides flower extract improves learning abilities and mitigates cognitive deficits caused by scopolamine. Thus V. enceloides could be potentially seen as natural therapeutic intervention against neurodegenerative disorders likes Alzheimer’s disease and dementia.

Nurses have experienced several psychological and work-related issues during the COVID-19 pandemic including pandemic burnout and job burnout. The aim of the study was to examine the impact of social support and resilience on COVID-19 pandemic burnout and job burnout among nurses. We conducted a cross-sectional study in Greece. Study population included 963 nurses. We measured social support, resilience, COVID-19 pandemic burnout, and job burnout with the Multidimensional Scale of Perceived Social Support, Brief Resilience Scale, COVID-19 burnout scale, and single item burnout measure respectively. Nurses received high levels of social support, while their resilience was moderate. Additionally, nurses experienced moderate levels of COVID-19 burnout and job burnout. Increased social support (adjusted beta = -0.075, 95% CI = -0.125 to -0.024) and increased resilience (adjusted beta = -0.399, 95% CI = -0.491 to -0.308) were associated with reduced COVID-19 pandemic burnout. We found a negative relationship between social support and job burnout (adjusted beta = -0.263, 95% CI = -0.405 to -0.121). A similar negative relationship was found between resilience and job burnout (adjusted beta = -0.529, 95% CI = -0.785 to -0.272). Social support and resilience can act as protective factors against COVID-19 pandemic burnout and job burnout among nurses. Policy makers should develop and implement appropriate strategies to improve nurses’ social support and resilience since they are the backbone of healthcare systems worldwide.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

Dilated cardiomyopathy (DCM) is a common form of cardiomyopathy, characterized by ventricular chamber dilatation and systolic dysfunction, in the absence of coronary artery disease, arterial hypertension, valvular or congenital diseases. DCM is a heterogeneous group of disorders of the myocardium caused by genetic factors, environmental factors, or a combination of both. DCM affects mainly men aged between 20 and 50 years, being considered one of the main causes of heart failure (HF) and the main indication for heart transplantation. Guideline-based HF treatment is the mainstay of management for patients with DCM. In recent years, gene therapy and induced pluripotent stem cells have become promising strategies. In this review, we summarize the relevant clinical issues, and current treatment of DCM patients, including the role of genetic evaluation.

The Anthropocene has created a new cartography where various discursive levels are intertwined. It unites two fields of knowledge: geology and anthropology. In the 19th century, Romanticism challenged the separation between natural sciences and spirit sciences. With the Anthropocene a geological era is established, but with an epistemological dimension: environmental catastrophes are not a passive "object", they become an agent of social and political change. Images of the world (Weltbild) turn nature into an animated whole that challenges the dual vision: observer and observed. There is no nature without "observer", nor geology without anthropology. The Anthropocene modifies the foundations of our view of the world where we had excluded life. This is how concepts such as symbiogenesis, homeostasis, etc., which make visible and try to explain phenomena that are otherwise inexplicable. The Naturwissenschaft by J.W. Goethe is a point of support, with all these ideas that develop in the 20th century and anticipate the Anthropocene term of the 21th century. While the concepts of "belief" and "science" continue to be sharpened, rehabilitating "old quarrels" around anthropology, cosmology, theology, etc. The dignity of man is at stake.

The remarkable success of taxonomic discovery, powered by culturomics, genomics and metagenomics, creates a pressing need for new bacterial names, while holding a mirror up to the slow pace of change in bacterial nomenclature. Here, I take a fresh look at bacterial nomenclature, exploring how we might create a system fit for the age of genomics, playing to the strengths of current practice, while minimising difficulties. Adoption of linguistic pragmatism, obeying the rules while treating recommendations as merely optional will make it easier to create names derived from descriptions, from people or places or even arbitrarily. Simpler protologues and a relaxed approach to recommendations will also remove much of the need for expert linguistic quality control. Automated computer-based approaches will allow names to be created en masse before they are needed, while also relieving microbiologists of the need for competence in Latin. The result will be a system that is accessible, inclusive and digital, while also fully capable of naming the unnamed millions of bacteria.

In the digital era in which over 4 billion people regularly access the internet, the conventional process of publishing scientific articles in academic journals following peer review is undergoing profound changes. Following physics and mathematics scholars who started to publish their work on the freely accessible arXiv server in the early 1990s, researchers of all disciplines increasingly publish scientific articles in the form of freely accessible and fully citeable preprints before or in parallel to conventional submission to academic journals for peer review. The full transition to open science, I argue in this study, requires to expand the education of students and young researchers to include scholarly communication in the digital era.

The goal of germplasm enhancement is to introgress traits from wild crop relatives into cultivated material and eventually cultivars. It seeks to restore genetic diversity that has been lost over time or to augment cultivated material with novel alleles that improve parents in breeding programs. This paper discusses potato germplasm enhancement efforts in the past, focusing on effective examples such as disease resistance and processing quality. In addition, it outlines new strategies for enhancement efforts, shifting the focus from evaluating phenotypes to tracking and manipulating specific DNA sequences. In the genomics era, germplasm enhancement will increasingly be focused on identifying and introgressing alleles rather than traits. Alleles will come from a broad pool of genetic resources that includes wild species relatives of potato, landraces, cultivated potato itself, and distantly related species. Genomics tools will greatly increase the efficiency of introgressing multigenic traits, and will make it possible to identify rare alleles and utilize recessive alleles.

The research work explores the impact of temperature on Silicon photovoltaic (PV) panels considering Nigeria as case study. It is found that high solar radiation in Nigeria increases surface temperature of PV panel above 25oC of optimal operating temperature of PV panels. Redundant energy gain from incident solar energy is dissipated at the rear of PV panels as heat, which reduces the efficiency of PV panel. Cooling mechanism is needed to cool the PV panels to increase the efficiency. In this study we demonstrated a unique design of a bifunctional photovoltaic-thermal (PVT) system by employing heat exchanger at the rear of PV panel and water is used as a working fluid that is circulated through the heat exchanger to remove the excess heat. It is observed from the simulation results that Maiduguri peak annual electrical power output gives 1907kWh/kWp which is the highest, because of high solar radiation average of 727W/m2 in a year. And for Makurdi the peak annual electrical power output of 1542kWh/kWp while for Port Harcourt the peak power output of 1355kWh/kWp respectively. Interestingly, Polycrystalline Si-PV surface temperature decrease from 49.25oC to 38.38oC. Electrical power increased from 1526.83W to 1566.82W in a day and efficiency increase from 13.99% to 15.01%.

Glioblastoma multiforme (GBM) is a glioma and the most aggressive type of brain tumor with a dismal average survival time, despite the standard of care. One promising alternative therapy is boron neutron capture therapy (BNCT), which is a noninvasive therapy for treating locally invasive malignant tumors, including glioma. BNCT involves boron-10-released radiation directly into tumor cells with minimal damage to healthy tissues. This therapy lacks clinically approved targeted blood-brain-barrier-permeating delivery vehicles for therapeutic drug/ boron-10. Nanoparticles, particularly gold nanoparticles (GNPs), are selective and effective drug-delivery vehicles because of their desirable properties, facile synthesis, and biocompatibility. This review discusses historical and current GNP therapeutic applications as a drug delivery vehicle for treating glioma. Furthermore, GNPs loaded with therapeutic drugs, imaging agents, and GBM-targeting antibodies/ peptides to treat GBM will be discussed. Additionally, the importance of upper and lower limits on therapeutic efficacy will be described and explored followed by highlighting their advantages, limitations, and possible toxicity. Recent research and selected important findings for GNP-mediated pharmaceutics in GBM will also be considered.

Abstract: A. baumannii exhibits resilience in harsh environments and progressively develops resistance to diverse antibacterial agents over time. This adaptability poses a significant challenge in treating an infection caused by this bacterium within healthcare settings. The presence of peptidoglycan, a crucial precursor for cell wall synthesis, contributes to this challenge. Peptidoglycan serves not only as a structural support in bacterial cells but also provides protection against adverse environmental conditions. The synthesis of the peptidoglycan layer involves a group of substrates such as N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc), within a three-partitioned process occurring in the membrane, periplasmic space, and cytoplasm of each bacterium. Initiated in the cytoplasm and extending to the inner cell membrane, this synthesis starts with the production of nucleotide precursors. The first step involves the synthesis of UDP-GlcNAc, orchestrated by Glm (GlmS, GlmM, and GlmU) enzymes using fructose-6-phosphate. Subsequently, enzymes from the Mur family, namely MurB, MurA, MurD, MurC, MurI, MurF, and MurE, synthesize UDP-N-acetylmuramyl-pentapeptide (UDP-Mpp) from UDP-GlcNAc. Concurrently, on the cytosolic side, the production of membrane-surrounded undecaprenyl phosphate occurs, involving membrane-side proteins MraY and MurG in the biosynthesis of peptidoglycan. This review emphasizes updated information on Mur family proteins, encompassing physicochemical properties, nature of binding site, number and type of binding site residues, functional domain details, three-dimensional structural aspects, and their functions within bacterial cells. Special attention is directed towards their enzymatic role in peptidoglycan biosynthesis due to the limited availability of precursors in the market. Additionally, the review provides a comprehensive summary of existing inhibitors and ongoing efforts in identifying new targeted inhibitors, recognizing these enzymes as potential candidates for antibacterial drug design and discovery.

C. auris is a newly emerging yeast, raising public health concerns due to its outbreak potential, lack of protocols for decontamination and isolation of patients or contacts, increased resistance to common antifungals and associated high mortality. This research aimed to describe the challenges related to identifying the outbreak, limiting further contamination, and treating affected individuals. We retrospectively analysed all cases of C. auris detected between October 2022 and August 2023, but our investigation focused on a three-month-long outbreak in the department of cardio-vascular surgery and the related intensive care unit. Along with isolated cases in different wards, we identified 13 patients who became infected or colonized in the same area and time, even though the epidemiological link could be traced only in 10 patients, according to the epidemiological investigation. In conclusion, our study emphasizes the substantial challenge encountered in clinical practice when attempting to diagnose and limit the spread of an outbreak. Therefore, it is crucial to promptly apply contact precaution measures and appropriate environmental cleaning, from the first case detected positive.

ABSTRACT Education is the most important instrument in human life because education can increase human dignity. In addition, education can also be a benchmark for human quality and an example of the progress of a nation. The history of education in Indonesia has evolved from the colonial era to the digital era. These developments have influenced government policies in every era. In the 21st century, the development of education in Indonesia is starting to show progress. This is because it is supported by the rapid growth of information and technology. This research article aims to analyze the educational policies carried out by the government from the colonial era to the digital era to be used as evaluation material at this time so that in the future education in Indonesia is even better. To achieve this goal, this research focuses on the question of how is the transformation of education in Indonesia from the colonial era to the digital era? and what is the paradigm of education in Indonesia when viewed from a historical perspective?. The research method used is the Literature Review method from 27 sources in journal articles, websites, and data reports for 2019-2021. The results of this study found that changes in the Indonesian education system from time to time have a positive influence on the progress of the Indonesian nation. The development of education in Indonesia also has an impact on increasing the Human Development Index (HDI). This shows that the quality of Indonesian society is increasing. Based on the results obtained, it is hoped that it can provide information about the transformation of education in Indonesia from the colonial era to the digital era from a historical perspective. This article is suitable as a reference source for education observers in Indonesia to know the history of education and its policies from the colonial era to the digital era and useful for academics to know the history of education in Indonesia. This research has limitations, namely this research is only limited to the development of education in Indonesia from the colonial era to the digital era and the paradigm of education development in Indonesia from a historical perspective.

Novel Coronavirus (2019-nCOV) causes inflammatory response with worsening symptoms. Classification of potential anti-viral and anti-inflammatory drugs in managing the symptoms of the COVID-19 and reducing morbidity is important. The objective of this study is to identify a group of drugs, best suited for COVID-19 treatment based on recent developments in clinical trials, FDA drug evaluation, directions and developments and from drug therapies globally. Online literature search was done on Medline, PubMed and google scholar databases for studies on various treatments and drug therapies for COVID-19 and relevant studies were identified and the identified drugs are described in detail as per their Pharmacological, pharmaceutical properties of the drugs, mechanism of action, current COVID-19 drug therapy, contraindications and drug-drug interactions Certain drugs can inhibit action against viral infection and protect lungs from severe inflammatory response. This article summarizes several drugs like Hydroxychloroquine, Chloroquine, Remdesivir, Favipiravir, Lopinavir, Ritonavir, Dexamethasone, Ivermectin, Baricitinib, Casirivimab / imdevimab, Bamlanivimab along with auxiliary treatment like convalescent plasma transfusion. Remdesivir is first drug approved by FDA. Hydroxychloroquine, dexamethasone and remdesivir are showing results against COVID-19 but it is important to test the efficacy and safety of such drugs though some drugs have shown remarkable results.

This paper analyzes the main variables which determines the relation between disinformation and the youth responsibility during the last stage of Covid-19 pandemic in Spain. The document presents relevant results on this subject. At the Introduction the reader will find the theoretical framework of the following concepts: disinformation, responsibility, credibility, and youth responsibility variables. The greatest interest, considering the authors specialization, falls in communication factors. The methods applied had been the reference review of the literature found about this subject, as well as a qualitative opinion research through discussion groups with young University students from Communication Schools in Madrid, both public and private. This recent study, held in June 2021, provide a very rich material for this paper. The main results and findings are the facts of being not satisfied with the information received about Covid-19; the knowledge about their most credible sources; the connection between information and responsibility, and some of the solutions said by youth to be more responsible in this context. As a conclusion, this paper confirms the first hypothesis of considering the disinformation as a variable which causes the lack of youth responsibility. Regarding the second hypothesis it is checked that young people consider communication as a solution for being more responsible.

The antibiotic or antimicrobial resistance is rapidly spreading in microbes relevant to human health. Two visible major contributory factors have been the indiscriminate overuse of antimicrobials for preventing diseases in human and to enhance the productivity in agriculture sector. To mitigate the potential threat posed by post-antibiotic era, the global health stakeholders have been making extra efforts at a war footing to formulate and implement global and national plans of action. In the current article, an endeavour is made to provide a perspective to look beyond the current focus on just use of the antimicrobials. Attention has been drawn towards various obvious and not-so-obvious self-preservation infection-prevention practices in vogue from the pre-antibiotic era whose usage has been on decline in the antibiotic era for various reasons. Particularly, the practices with a clear potential to effectively decrease the spread of pathogens through contact, curtail the evolution and dissemination of the antimicrobial resistance in local environment and its introduction into the global community, should be Identified and strengthened to make them part of comprehensive hygiene and quarantine practices. Broadly, the suggestions pertaining to the personal and community hygiene including bereavement practices, isolation and quarantine of suspected pathogen carriers, and water and environment security have been made to invoke a constructive debate and discussion among various stakeholders for their evaluation and implementation to effectively delay the development of antimicrobial resistance wherever possible and disrupt its spread to pathogens.

Very little is known about laboratory confirmed blood stream infections (LCBIs) in neonatal intensive care units (NICUs) in resource-limited settings. The aim of this cohort study was to determine the incidence, risk factors, and causative agents of LCBIs in a level-2 NICU in India. The diagnosis of LCBIs was established using the Centre for Disease Control, USA criteria. A predesigned questionnaire containing risk factors associated with LCBIs was filled-in. A total of 150 neonates (43% preterm) were included in the study. The overall incidence of LCBIs was 31%. The independent risk factors for LCBIs were: preterm neonates (relative risk (RR) 2.23), duration of NICU stay more than 14 days (RR 1.75), chorioamnionitis in the mother (RR 3.18), premature rupture of membrane in mothers (RR 2.32), neonate born through meconium-stained amniotic fluid (RR 2.32), malpresentation (RR 3.05), endotracheal intubation (RR 3.41), umbilical catheterization (RR 4.18), and ventilator-associated pneumonia (RR 3.17). The initiation of minimal enteral nutrition was protective from LCBIs (RR 0.22). The predominant causative organisms were gram-negative pathogens (58%). The results of the present study can be used to design antibiotic interventions to reduce LCBIs in resource-limited settings.

Asthma is a prevalent disease that around 300 million worldwide, resulting in substantial morbidity, mortality, and economic burden on a global scale. New clinical and laboratory research has shed light on the immunology causing asthma. Asthma is now recognized as a heterogeneous disease. A personalized medicine is based on the classification of asthma by endotype by linking observable characteristics to immunological mechanisms. Identifying endotype mechanisms is essential for better characterizing patients and personalizing therapeutic approaches with novel biological agents that target specific immune pathways. This article provides a summary of the major immunological mechanisms involved in the pathogenesis and emergence of the disease's phenotypic features, as well as the individualized treatment for severe asthma subtypes

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large [email protected]} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}

Antimicrobial resistance (AMR) is a growing public health issue worldwide that affects the world economy. The concern associated with this issue increased during the COVID-19 pandemic. The misuse of antibiotics during the COVID-19 pandemic will have catastrophic implications for the control of AMR. In 2019, the coronavirus illness COVID-19 first appeared in China. COVID-19 infection is a surface-to-surface communicable disease. It is considered the most vital global health disaster of the century. It has rapidly spread around the world and is the greatest challenge for humankind. This chapter discusses the origin, symptoms, transmission, treatment, and recommendations of the COVID-19 disease.

Flares, sometimes accompanied by coronal mass ejections (CMEs), are the result of sudden changes in the magnetic field of stars with high energy release through magnetic reconnection, which can be observed across a wide range of the electromagnetic spectrum from radio waves to the optical range to X-rays. In our review, we attempt to collect some fundamental new results, which can largely be linked to the Big data era that has arrived due to the expansion of space photometric observations of the last two decades. We list the different types of stars showing flare activity, their observation strategies, and discuss how their main stellar properties relate to the characteristics of the flares (or even CMEs) they emit. Our goal is to focus, without claiming to be complete, on those results that may in one way or another challenge the "standard" flare model based on the solar paradigm.

Emerging infectious diseases (EIDs) continue to be a problem all across the world, but in certain areas the threat is greater than in others. The detection of new diseases will aid in the focus of surveillance efforts. Specifically, identifying a "patient zero" is critical step to help limit further transmission. In fact, several factors have been found to contribute to emerging infectious diseases emergence and transmission. Antigenic drift is an important factor that affects vaccine effectiveness and it is important for scientists and healthcare providers alike to understand how this process works in order to ensure that people receive effective vaccinations against infectious diseases. However, recent advances in biotechnology, immunology and artificial intelligence have enabled us to make progress in diagnosis, treatment, prevention and control of infectious diseases. The fight against infectious diseases will always be ongoing, but the advancement of bioscience offers a promising outlook for the near future. In the last two decades, several high impact zoonotic disease outbreaks have been linked to bat-borne viruses. These include SARS coronavirus, Hendra virus and Nipah virus. In addition, it has been suspected that ebolaviruses and MERS coronavirus are also linked to bats. It is being increasingly accepted that bats are potential reservoirs of a large number of known and unknown viruses, many of which could spillover into animal and human populations. In this article, we discuss the potential risk factors that contribute in emerging infectious diseases. Also, we discuss the management of disease prevention and control.

In this work we present a new algorithm that achieves the perfect Shannon secret by means of the XOR function and a method that we call multiple key reuse. The algorithm has two execution modes: message authentication and data encryption. The XOR encryption scheme allows for batch encryption and exhibits Perfect Forward Secrecy (PFS). Furthermore, based on our fundamental algorithm, we have developed a new strategy for blockchain implementation that does not require Proof of Work (PoW), but defines a fair mechanism for miner selection and secure addition of blocks to the chain. Since our method is mainly based on the Boolean XOR function, the strength of the cryptosystem can be directly established thanks to its mathematical properties. Due to the risk that quantum computers represent for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era for the security of communications between Internet of Things devices as well as Blockchain technology.

In this COVID-19 pandemic, the use and dependency on Internet has grown exponentially. The number of people doing online activities such as e-learning, remote working, online shopping and others have increased. This has also led to increased vulnerability to cyber crimes. Cyber security attacks have become a serious problem. The common types of cyber security attacks are phishing, malware, ransomware, social engineering, identity theft and denial-of-service. The attackers target the victims in order to get their credential information or financial benefits. Those people who are doing online activities are vulnerable to cyber threats. This is because the network is not safe. The attackers are able to code according to the weaknesses of the Internet. Once the attackers hack into the devices, they have the root access and can do whatever they want to do with the device. In this research paper, the concept of cyber security attack and detailed research about real attacks are discussed. This is followed by detailed review about the recent cyber security attacks with a critical analysis. Moreover, the research paper will be proposing the latest research contribution of cyber security during COVID-19 and the implementation scenario which will give the examples about how the companies maintain privacy as well as the limitations. Then, the paper will be discussing the reasons that people are vulnerable to cyber security and the unique solution to the problems stated. Finally, this paper will conclude with an in-depth analysis and future direction for cyber security research.

The current Covid-19 pandemic has hugely disrupted the delivery of routine and established medical care. Patients can develop a wide range of clinical signs and symptoms from a cough and fever to severe respiratory failure. There is an ongoing argument on a concise investigative pathway to ensure the safety of all healthcare workers. The stethoscope can help with any clinical respiratory assessment but the risk of cross infection is high. Computer tomography should not be routinely performed. There is a potential place for lung ultrasound but outcomes are not yet determined.

Obtaining a good socket fit is an iterative process dependent on the skill and experience of the prosthetist creating it, and requiring individualisation based on the size and shape. There is no standard measurement system used to aid prosthetic socket creation, despite the severe impacts on physical health and quality of life if one is ill-fitting. Pressure sensors embedded in a prosthetic socket were used to collect data at the socket-residuum interface. To choose an interpolation method, a 2D grid was used, with previously collected walking test pressure data, to simplify the sensor array with a border for extrapolation. Four multivariable interpolation methods were evaluated to create a colour map of the pressure data. Radial Basis Function interpolation was chosen as it produced a clear image with a graduated interpolation between data points and was used to create a colour map across the surface of a 3D prosthetic socket model. For the model to be accessible to clinical audiences, a desktop application was created using PyQt to view the model. The created application allowed for connection to the sensors via Bluetooth, with the pressure data updating the colour map on the 3D model in real-time. The created application shows the potential for a clinical product, however further development informed by feedback from rehabilitation clinicians and prosthesis users is required

Precision farming is transforming how antibiotics get used on farms. It fights the overuse that creates superbugs and harms nature. Farmers now have high-tech tools to use antibiotics ultra-precisely. Drones, AI, and advanced watering equipment help them pinpoint only sick plants and animals needing treatment. This shields crops while avoiding environmental damage. More careful antibiotic use makes livestock healthier and boosts harvests too. And it prevents resistant germs, where antibiotics stop working altogether. The approach helps farms in multiple ways, using fewer resources for even better results. There are some growing pains, like high costs for small farms. And questions around data privacy also cause concern. But overcoming these hurdles could help precision farming spread far and wide. This technology promises safer, greener, and more productive agriculture. Targeted use of antibiotics protects both crops and people from drug-proof germs. And nature benefits when less medicine spreads into soil and water. The future looks bright for farming with precision.

Although learning analytics (LA) holds great potential to improve teaching and learning, LA research and practice are currently riddled with limitations that impact every stage of the LA life cycle. The present paper offers an overview of these challenges before proposing strategies to overcome them and exploring how the recent innovations brought forth by language models can improve LA research and practice. In particular, we encourage the empowerment of teachers during LA development, as this would strengthen the theoretical foundation of LA solutions and increase their interpretability and usability. Furthermore, we provide examples of how process data can be used to understand learning processes and generate more interpretable LA insights. Furthermore, we explore how LLMs could come into play in LA to generate interpretable insights, timely and actionable feedback, increase personalization, and support teachers’ tasks more broadly.

This paper reviews the low-resilience problem in many cities, poor designs of cities to cope with disasters, and the need for tolerance of urban constructions. It explores answers concerning the question of how shall we build cities resiliently? The method of this applied research is a multiphase process that considers all physical and socioeconomic elements of a city. It introduces six indicator groups of urban management (M), economy (E), built environments (U), Infrastructures (I), natural environments (N), and health protection (H). The groups include 55 indicators as variables in the mathematical calculations in this paper. This paper builds a mathematical model to maximize the profitability of resilient buildings by optimizing investments in the required projects. The projects will upgrade the firmness and tolerance of cities against nature-based and human-made dangers and risks. There is a linear programming in 55 variables to select optimal solutions from fifty-five factorial alternatives. Then, the programming will develop into non-linear programming. The unique innovation of this paper is its linear programming interpretation by non-linear to give optimal solutions for the problem. Applying the Lagrange function in the Kuhn-Tucker conditions proves the accuracy of the hypothesis that post-COVID urbanization requires maximum resilience. Only in this way, the urban economies will be free of risks. Outcomes in this paper will assist in the pre-planning, design, and building of built environments everywhere resilient and sustainable.

Pancasila is the identity of the Indonesian nation, which means that Pancasila is a national characteristic that cannot be found in any country. Pancasila points are taken from the noble values of the Indonesian state. These principles contain culture, customs, and national character known as manners, cooperation, and high nationalism. As the successor to Indonesian culture, the young generation must be able to preserve and develop Pancasila. In this era of globalization, there are many challenges for the younger generation in preserving Pancasila. This study aims to provide an overview of the decline of Pancasila identity among the younger generation due to the impact of globalization. The research question that has been determined is how the practice behaviour of the younger generation towards the implementation of Pancasila, the solutions and efforts that all Indonesian people can make regarding the crisis of Pancasila identity in the era of globalization. This research method uses a literature review. The research sources were conducted from the latest journals and books published in 2019 until 2021 related to the theme of Pancasila and the millennial generation. The results showed that globalization has greatly facilitated foreign cultures to enter Indonesia. The younger generations use this foreign culture as a lifestyle without filtering it, whether it contains Pancasila values or not. This causes Pancasila has been forgotten and no longer applied in their everyday life. Young people can make various efforts to preserve the national identity, namely Pancasila. Apart from the role of the young generation itself, Indonesian society can also play an essential role in supporting the younger generation to continue implementing Pancasila in their lives. By conducting this research, it is hoped that Indonesia's young generation can understand, select, and stay away from the negative impacts of globalization that enter Indonesia freely. The solution that can be given from this research is that the younger generation must love their own culture more and practice every principle in Pancasila. Parents, educational institutions, and the government must also support the younger generation to maintain national identity, Pancasila values. The limitation of this research is that the researcher did not conduct any primary data, which means the data were not directly from the source. This causes the accuracy of this study to be less guaranteed. The recommendation that the researchers can give for further research is to obtain primary data to ensure accuracy and explore research topics regarding the fading of Pancasila identity in the younger generation in this era of globalization.

In most world’s countries, scholarship evaluation for tenure and promotion continues to rely on conventional criteria of publications in journals of high impact factor and grant funding. Continuing to hire and promote scholars for their achievements in research and in securing research funds exposes universities at risk because students, directly and indirectly through government funds, are the main source of revenues for academic institutions, whereas talented young researchers are those who actually carry out most of the published research. Purposeful scholarship evaluation needs to include all three areas of scholarly activity: research, teaching and mentoring, and service to society. Young scholars seeking tenure and promotion benefit from the practice of open science because it provides better and more impactful results with respect to each of the three areas of scholarship.

Smartphone has become one of the most revolutionary devices in the history of computing. With various kinds of applications, the scope of smartphone has been significantly broadened in the past few years including almost every aspect in our daily life. However, due to the limited on-board resources such as CPU, storage, network bandwidth and battery power, smartphones and the mobile network serving them bring new challenges that have not been encountered in the traditional computing and networking environments. This dissertation focuses on the research areas of improving the network architecture and enhancing the current applications on smartphones. It mainly investigates the areas in the following two directions for three representative categories of mobile services. - In the first direction, the dissertation aims to develop new communication models for smartphone Ad-Hoc networks to achieve efficient communication in the proximity. It is motivated by the fact that smartphone Ad-Hoc networks can help improve the current location-based services and propel new applications. Moreover, the new communication models provide complementary alternatives to the traditional infrastructure-based wireless networks. - In the second direction, the dissertation focuses on improving the other two categories of services, cloud storage services and real-time video streaming services for mobile devices. In the field of cloud storage, we introduce a cloud-assisted approach to provide a set of advanced file operations, such as encryption, decryption and compression, on smartphones. Furthermore, by utilizing the on-board Near Field Communication(NFC) module, we develop an algorithm to securely share the files between mobile devices. For the services of real-time video streaming, we propose approaches that identify a user’s status by analyzing the accelerometer data and then, dynamically adjust the buffer mechanism to save network bandwidth on smartphones.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

In this work we introduce an end-to-end multi-modal neural network to segment the Gross Tumor Volume (GTV) from 3D-CBCT’s during radiotherapy. We improve the tumor segmentation by using a U-net which takes additional information such as the tumor mask generated at the planning phase along with the CBCT volume. The mask contains relevant information about the tumor’s location and can guide the model to use this knowledge appropriately to give a better prediction. This technique could become an alternative to produce segmentation masks of GTV in CBCT automatically during radiotherapy as in the traditional RT-pipeline, they are not segmented. We have evaluated our model on a dataset of 82 patients who have undergone radiotherapy. We compare the results of registered target volumes from planning CT as mask seed with 2 different types of multi-modal architectures. Our model shows a DSC of 0.706±0.002 with Late Fusion and 0.702±0.015 with Early Fusion using the GTV Mask. The performance of the two models on this mask is similar, so we perform further experiments with different types of masks which suggest that Late Fusion model produces a better segmentation of the tumor than the Early Fusion model. We also provide an ablation study consisting of a single modality U-net and a metric based on the Planning CT mask registration. It indicates a clear advantage of using our model to produce segmentation for this type of imaging.

The rapid development of quantum computing devices promises powerful machines with capabilities that solve a wide range of problems that traditional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is considered to be the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, like security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is presented also, and focuses on their strengths and weaknesses.

Functional genomics, a multidisciplinary subject, investigates the functions of genes and their products in biological systems to better understand diseases and find new drugs. Drug repurposing is an economically efficient approach that entails discovering novel therapeutic applications for already-available medications. Genomics enables the identification of illness and therapeutic molecular characteristics and interactions, which in turn facilitates the process of drug repurposing. Techniques like gene expression profiling and Mendelian randomization are helpful in identifying possible medication candidates. Progress in computer science allows for the investigation and modelling of gene expression networks that involve large amounts of data. The integration of data from DNA, RNA, and protein activities has resemblance to pharmacogenomics, which is of utmost importance in the development of cancer drugs. Functional genomics in drug discovery, particularly for cancer, is still not thoroughly investigated, despite the existence of a significant amount of literature on the subject. Next-generation sequencing and proteomics present highly intriguing opportunities. Publicly available databases and mining techniques facilitate the development of cancer treatments based on functional genomics. Expanding the study and application of functional genomics has great promise to advance the process of discovering and repurposing drugs, especially in the field of oncology.

(1) Background: Colon cancer is one of the leading causes of cancer morbidity and mortality globally. It is a multi-step process that involves genetic and epigenetic modifications leading to histological and morphological changes. Several complementary therapeutic options have been analyzed, shedding light on plant-based medication as a potential treatment for colon cancer. Flavonoids such as quercetin are known to have anti-cancer and anti-inflammatory properties. This in vitro study examines quercetin's anti-inflammatory, anti-apoptotic, anti-angiogenesis effects and antioxidant properties in colon cancer cells. (2) Methods: The antioxidant capacity of quercetin-treated cells was investigated using biochemical assays, and angiogenesis and cytokine levels were assessed using enzyme-linked immunosorbent assay (ELISA). The epigenetic modulation and differential expression of aging, apoptotic, and proliferation genes, and Histone deacetylases (HDACs) were also investigated. (3) Results: In this study, the quercetin-treated group significantly reduced the antioxidant enzymes, cytokines, and VEGF levels, altering the expression of epigenetic factors. Quercetin also induced significant senescence in colon cancer cells. Moreover, a considerable increase was observed in the apoptotic and hTERT genes. In contrast, a decrease in p53, proliferation genes, and HDACs was observed, providing a basis for the clinical use of quercetin in cancer treatment. (4) Conclusion: In vitro studies showed that quercetin treatment efficiently induces senescence and apoptosis in colon cancer cells. We also found that quercetin effectively modulated the expression of p53, Wnt1, CTNNB1, and HDACs, indicating that it could be used to treat colon cancer.

The Genotype-Phenotype Working Group was established in November 2021 as part of the AgBioData Consortium (https://www.agbiodata.org) with the goal of identifying current challenges in annotating and integrating large-scale genotype and phenotype data. Over the course of the year, the members of this working group identified different types of data sets, explored experimental platforms and methods for data generation, and examined how these data are annotated including the metadata requirements. We conducted a thorough review of publicly funded repositories for raw and processed data for each data type. We also examined several secondary databases and knowledgebases that enable the integration of heterogeneous data types in the context of the Genome Browser, Pathway Networks and tissue-specific gene expression. The review revealed a need for additional infrastructural support, standards, and tools to connect Genotype to Phenotype data and enhance data interoperability for knowledge synthesis and to foster translational research.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Innovation has emerged as a critical driver of economic growth in the digital era, revolutionizing industries and reshaping economies worldwide. As traditional funding avenues continue to be dominated by risk-averse lenders and investors, entrepreneurs are increasingly turning to alternative funding mechanisms to fuel their innovative ideas and ventures. This article delves into the evolving landscape of financing innovation in the digital era, with a primary focus on the role of alternative funding mechanisms. By drawing on a comprehensive review of existing literature and empirical evidence, we aim to provide a conceptual framework that elucidates the interplay between alternative finance platforms, innovation ecosystems, and entrepreneurial activities. Moreover, this article examines the unique characteristics and challenges associated with financing innovation through crowdfunding, venture capital, and angel investing. The insights from this research contribute to a better understanding of how these alternative funding mechanisms can effectively support and catalyze innovation across industries and economies.

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

This article examines how digitalisation is impacting the management of IT. Phase 1 of the re-search focuses on the German automotive industry, and adopts a qualitative inductive approach, based on feedback from 19 semi-structured interviews with IT managers and professionals. Five key aspects of digitalisation are identified, and a new model for the management of IT is devel-oped. Phase 2 then reviews the deployment of digital technologies in eight organisations from other industry sectors and assesses the overall impact on the management of IT. The results suggest that the transition to new IT management structures in the German automotive industry may be the forerunner of a change process that will impact more widely in other industries, in particular where digital technologies are now embedded in an organisation’s products or services. The results also provide some new perspectives on the conceptualisation of digitalisation and digital transformation, and represent a contribution to the developing theory around new tech-nology deployment and IT management in the digital era.

Technology Readiness Levels (TRLs) is a well-established and widely used approach for defining the readiness of new technology. To this extent, it assesses technology maturity against specific benchmarks, ranging from 1 (concept) to 9 (market solution). Although this is a useful classification service, allowing us to establish a common language, there are cases where we find that this conceptual approach fails to adequately highlight the maturity of certain innovative endeavors and effectively steer their development to higher TR levels. We will present an empirical case where the TRL approach presented a critical shortcoming in highlighting the true and effective readiness of a specific technological development and could not suggest the next natural step in ascending the maturity ladder. We will seek to generalize for the case of co-creation at large, analyze why co-creation may be poorly serviced by the current TRL model and suggest an amendment that would allow the observed shortcoming of the traditional TRL approach to be overcome and its use extended also in such co-creative settings.

The classical human satellite DNAs, also referred to as human satellites 1, 2 and 3 (HSat1, HSat2, HSat3, collectively HSat1-3) constitute the largest individual arrays of tandemly repeated DNA sequences in the genome. Even though they were among the first human DNA sequences to be isolated and characterized at the dawn of molecular biology, HSat1-3 have been left behind in the genomics era and remain among the most enigmatic sequences in the human genome. Although HSat1-3 total roughly 3% of the genome on average, they were almost entirely missing from the human genome reference assembly for 20 years. Recently, the Telomere-to-Telomere Consortium produced the first truly complete assembly of a human genome, including the enormous HSat1-3 arrays, opening them up for a new wave of discovery. Towards this end, here, I provide an account of the history and current understanding of HSat1-3 genomics, evolution, and roles in disease.

Introduction: Industrial revolution 4.0 in the current era of globalization plays an active role in the modern era in Indonesia. The influence of increasingly advanced technology affects the millennial generation as the nation's next generation to make Pancasila the basis of the State or the foundation of a State. In addition, the millennial generation has begun to shift the guidelines of life from Pancasila so that the appreciation of Pancasila values is starting to disappear along with the development of the times and in line with the progress of Science and Technology (IPTEK). If this continues, then the values of Pancasila will be replaced by a new ideology that will cause behavioural changes in society. Purpose: this study aims to provide the proper application to millennial life using Pancasila values, knowing the behaviour of the millennial generation towards the implementation of Pancasila values, and making Pancasila a way of life. Methods: the method used in this research is Literature Review and takes sources from journals published in 2019 to 2021 related to the Pancasila theme and the millennial generation. Results: the study results show that with the existence of Pancasila as the ideology and principle of the State, it is expected to strengthen the attitude and nature of the community in accepting things outside the norms that exist in Indonesia. In addition, the younger generation is expected to be able to apply Pancasila values along with the development of science and technology in the modern era and current globalization. The research question is how to make the millennial generation implement Pancasila in life in the contemporary period and what solutions can continue to instil Pancasila values ​​in the 4.0 industrial revolution. Recommendation: the conclusion drawn is that Pancasila can become a unifying tool for the nation if the current generation or millennial generation still desires to maintain its values ​​, even though they are in a modern era and many influences from its outside cultures. Limitation: the limitation of this research is not getting all the data directly from the source. The researcher recommends using primary data for the following study to ensure its truth and accuracy better because the researcher directly obtains it.

The SARS-CoV-2 virus that causes the COVID-19 disease has wreaked havoc on the world community in terms of every imaginable parameter. The research output on COVID-19 has been nothing short of phenomenal, especially in the medical and biomedical sciences, where the search for a potential vaccine is being conducted in earnest. Much of the advanced research has been distributed in the leading medical journals, including the Journal of the American Medical Association (JAMA), where the latest research is distributed on a daily basis. The purpose of this paper is to provide some perspectives on 44 interesting and highly topical research papers that have been published in JAMA, at the time of writing, within the past two weeks. The diverse topics include public health, general medicine, internal medicine, oncology, paediatrics, geriatrics, and biostatistics.

Periodontal diseases significantly impact about half of the global population, and their treatment often encompasses relieving symptoms as well as regenerating the destroyed tissues. Revolu-tionary research in the management of periodontal disease includes biomaterials, a boon to re-generative dentistry owing to their excellent biological properties, non-toxic, an-ti-inflammatory, biocompatible, biodegradable, and adhesion. This study aimed to fabricate an injectable fucoidan containing chitosan hydrogel and prove its effectiveness in periodontal bone regeneration. The injectable hydrogel was prepared using the sol-gel method and loaded into 2ml syringes. 40 periodontitis patients with bony defects were recruited and randomized into two groups, to re-ceive fucoidan-chitosan hydrogel and concentrated growth factor, respectively. Customized acrylic stents were used to guide the hydrogel placement into the defect site. Post-surgical changes in clinical parameters were assessed at 3, 6 and 9 months to appreciate the soft & hard tissue changes using repeated measures analysis of variance and Bonferroni’s posthoc test. Significance was kept at 5%. The fucoidan-chitosan hydrogel exhibited significantly lower probing depth values and a higher mean reduction in clinical attachment level as compared to the CGF group at the end of 3 and 6 months (p<0.05). The mean defect fills in the fucoidan-chitosan group was 1.20 at the end of 9 months (p<0.001). The presence of fucoidan in the hydrogel significantly contributed to bone re-generation in humans, thus strengthening its potential in tissue engi-neering. Fucoidan- Chitosan will be a promising biomaterial for bone tissue regeneration.

Gut microbiota and its metabolites like short chain fatty acids (SCFAs) are linked with pathology of Alzheimer’s disease (AD)- a debilitating public health problem in older adults. However, strategies to beneficially modulate gut microbiota and its sensing signaling pathways remain largely unknown. Here, we screened, validated and established the agonists of free fatty acid receptor 2 (FFAR2) signaling, which senses beneficial signals from SCFAs produced by microbiota in the gut. We demonstrated that inhibition of FFAR2 signaling increases amyloid-beta (Aβ) stimulated neuronal toxicity. Thus, we screened FFAR2 agonists, using in-silico library of more than 144,000 natural compounds, and 15 compounds were selected based on binding with FFAR2 agonist sites. Further, cell culture toxicity and FFAR2 stimulatory experiments demonstrated that Fenchol (a natural compound commonly present in basil) was potent FFAR2 agonist in neuronal cells. Interestingly, we also demonstrated that Fenchol protects Aβ-stimulated neurodegeneration in FFAR2 dependent manner. In addition, Fenchol reduced AD like phenotypes such as Aβ-accumulation and, learning and memory behaviors in Caenorhabditis (C.) elegans. Fenchol increased Aβ-clearance by increasing proteasome/lysosome activity and reduced senescence in neuronal cells. These results demonstrated that the inhibition of FFAR2 signaling promotes Aβ-induced neurodegeneration, while activating it by Fenchol as a natural agonist reverse it by promoting Aβ-clearance and reducing cellular senescence; thus stimulation of FFAR2 signaling can be a therapeutic approach to prevent/ treat AD.

Brucellosis is one of the most common etiologies of laboratory-acquired infections worldwide, and handling of living brucellae should be performed in a Class II biological safety cabinet. The low infecting dose, multiple portals of entry to the body, the great variety of potentially contaminated specimens, and the unspecific clinical manifestations of human infections facilitate the unintentional transmission of brucellae to laboratory personnel. Work accidents such as spillage of culture media cause only a small minority of exposures, whereas >80% of events result from unfamiliarity with the phenotypic features of the genus, misidentification of isolates, and unsafe laboratory practices such as aerosolization of bacteria and working on an open bench without protective goggles or gloves. Although the bacteriological diagnosis of brucellae by traditional methods is simple, the Gram stain and the biochemical profile of the organism, as determined by commercial kits, can be misleading, resulting in inadvertent exposure and contagion. The use of novel identification technologies is not hazard-free. The MALDI-TOF technology requires an initial bacterial inactivation step, while the instruments’ reference database may misidentify Brucella as belonging to other Gram-negative species. The rapid identification by the FISH method mistakes brucellar isolates for members of the closely related Ochrobactrum genus.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

Delve into the ebb and flow of human ingenuity as the researcher traces the evolution of writing from papyrus to pixels. Discover how humble instruments like quill pens and typewriters have propelled human into the digital frontier, forever reshaping the art of expression in the modern age. This article examines the evolution of writing tools from traditional pen and paper to digital writing in the modern age. It explores the development of writing materials such as papyrus, parchment, and paper, as well as the invention of writing tools including quill pens, fountain pens, and typewriters. The rise of digital writing tools, including word processing software and smartphones, is also discussed, along with their benefits such as increased efficiency and productivity, enhanced collaboration and sharing capabilities, and greater accessibility and flexibility. However, the article also highlights the challenges of writing in the digital age, including the impact of digital distractions on writing focus and quality, the issue of online plagiarism and copyright infringement, and the potential loss of traditional writing skills. The article concludes with recommendations for educators, school administrators, students, and researchers to strike a balance between digital and traditional writing practices, to embrace digital writing tools while preserving traditional writing skills, and to understand the importance of the evolution of writing. The theoretical frameworks of Social Construction of Technology (SCOT) Theory, Media Ecology Theory, and Activity Theory are also discussed in relation to the evolution of writing tools. Overall, this article provides valuable insights into the history, benefits, and challenges of writing in the digital age, as well as suggestions for navigating the future of writing and its tools.

Objectives: The aim of this study was to assess the knowledge, attitude and practice of healthcare professionals regarding adverse drug reaction [ADR] monitoring and pharmacovigilance [PV] in India. Materials and Methods: It was a questionnaire based cross sectional observational study. Data was collected with the help of data collection Google form that included the demographics and twenty two survey based questions. Data were analysed by using Microsoft Excel sheet, further analysed for results, including frequency, percentage, mean and standard deviation. Result: The questionnaire was filled by two hundred ten healthcare professionals in which 52.9 % were male and 47.10% of female. Most of the respondents were pharm d students (50.47%). Out of the total 91.4% responded to the definition of pharmacovigilance correctly. 87.6% participants said all ADR should be reported. 86.20% participants think Pharmacovigilance should be taught in detail to healthcare professionals. Most of the respondents (43.8%) always informed the patients about ADR while prescribing the medicines. Conclusion: Study revealed most of the participants have good knowledge about ADR and pharmacovigilance. Difficult to decide whether ADR occur or not and extra work load being major factors responsible for under reporting.

The utilization of fermented foods has been a longstanding practice in human civilization worldwide for example sauerkraut from the Roman Empire, Jiangshui, and PaoCai, which are popular traditional foods in China. In Korea, Russia, and Mongolia, Kimchi and Kefir are widely used. Similarly, Japan, Indonesia, and Pakistan have their traditional fermented foods such as Miso, Natto, Tempeh, and sourdough, respectively. In America and Europe, fermented alcoholic beverages made from sorghum and maize are among the most common. Nevertheless, the full potential of fermented foods to enhance the bioavailability of bioactive compounds and restore probiotic communities has yet to be thoroughly explored. In this review, we focus on the bioactive compounds and probiotic stability in food fermented with probiotic bacterial strains. Probiotic fermented food improves the bioactive compound contents and has been gaining interest in basic and clinical research. Bioactive compounds, including phenolic, alkaloids, terpenoids, flavonoids, stilbenes, coumarins, tannins, anthocyanidins, flavones, isoflavonoids, and polyphenols, along with beneficial bacteria such as Lactobacilli, Bifidobacteria, Pediococcus, and Weissella demonstrate increased levels and restoration in probiotic fermented foods. These bioactive compounds, combined with a thriving microbiota, play a role in preventing viral infections by targeting influenza, noroviruses (NoVs), Murine norovirus-1 (MNV-1), and COVID-19, while also stimulating the host's immune function. Clinical and pre-clinical investigations are warranted to explore the dose-response and duration efficacy of probiotic fermented foods against viral infections.

The prevalence of digital devices in modern society has raised concerns about the potential neg-ative effects of blue light emissions on eye health and biological rhythms. Research into blue light emissions from digital devices and their potential impact on eye health emphasizes the importance of understanding and quantifying the extent and scope of blue light emissions produced by commonly used screens (smartphones, tablets and computers). This comparative study specifically evaluates three popular Apple devices: iPhone 12 mini®, iPad Pro 12.9® and MacBook Pro®. The devices' spectral power was measured using a spectroradiometer while displaying text and a game at different brightness levels. The study also examines the effectiveness of blue-blocking lenses from well-known brands. The research highlights the importance of quantifying blue light emissions and understanding their potential impact on eye health. By doing so, appropriate measures can be developed to mitigate adverse effects on ocular structures. Above all, this research provides valuable information about the risks associated with exposure to blue light, showing that all tested devices have blue wavelength peaks between 445 nm and 455 nm. By recognizing and addressing the potential negative impacts of blue light, the risks of eye health problems can be reduced and ensure more responsible and conscious use of digital devices.

Antibody-drug conjugates (ADCs) have provided new therapeutic options and significant promise for patients with cancers, particularly where existing treatments are limited. Substantial effort in ADC development is underway globally, with 13 ADCs currently approved and many more in development [1]. Therapeutic benefits of ADCs leverage the ability to selectively target cancer cells through antibody binding, resultant relative sparing of non-malignant tissues, and the targeted delivery of a cytotoxic payload. Consequently, this drug class has demonstrated activity in multiple malignancies refractory to standard therapeutic options [1-4]. Despite this, limitations exist, including narrow therapeutic windows, unique toxicity profiles, development of therapeutic resistance, and appropriate biomarker selection [5-7]. This review will describe the development of ADCs, their mechanisms of action, pivotal trials, and approved indications and identify common themes. Current challenges and opportunities will be discussed for this drug class in cancer therapeutics at a time when significant developments in antibody therapies, immunotherapy and targeted agents are occurring.

Globalization plays a very important role in the advancement of science and technology today, especially in Indonesia. Globalization has also succeeded in changing all aspects of human life. In addition to having a positive impact on the development of science and technology in Indonesia, globalization also has a negative impact that is quite visible on the character of today's youth. The crisis of the character of Indonesian youth is a serious matter and is reflected in the behavior of Indonesian youth today which does not coincide with the identity of the Indonesian nation and the values ​​of Pancasila. This paper examines the lack of implementation of Pancasila values ​​in the life of the globalization era and also how globalization affects moral decadence among the younger generation. The method used is the Literature Review method, which is a qualitative research method by collecting data from the literature. The lack of practice of Pancasila values ​​and the emergence of globalization is increasingly diminishing the character of Indonesian youth. The implementation of Pancasila values ​​among teenagers needs to be increased so that the nation's moral character is not eroded by the currents of globalization.

We live in extraordinary times, where COVID-19 pandemic has brought the whole world to a screeching halt. Tensions and contradictions that surround the pandemic ridden world include the availability, and the lack thereof, various facial protection measures to mitigate the viral spread. Here, we comprehensively explore the different type of facial protection measures, including masks, needed both for the pubic and the health care workers (HCW). We discuss the anatomy, the critical issues of disinfection and reusability of masks, the alternative equipment available for the protection of the facial region from airborne diseases, such as face shields and powered air purifying respirators (PAPR), and the skin-health impact of prolonged wearing of facial protection by HCW. Clearly, facial protection, either in the form of masks or alternates, appears to have mitigated the pandemic as seen from the minimal COVID-19 spread in countries where public mask wearing is strictly enforced. On the contrary, the healthcare systems, that appear to have been unprepared for emergencies of this nature, should be appropriately geared to handle the imbalance of supply and demand of personal protective equipment including face masks. These are two crucial lessons we can learn from this tragic experience.

The demand for improving the nuclear waste management has since long been identified as one of the major hurdles for widespread use of nuclear energy. Nuclear waste management, through partitioning and transmutation (P&T) has been researched since the 1990ies with partitioning being a pre-requisite for the process. Recently, an innovative approach of reactors directly operating on spent nuclear fuel, iMAGINE, has been proposed which could deliver on the aims of P&T as a side effect to more efficient nuclear energy production in the future. The results shown here confirm that it is possible to achieve the aims of P&T without prior partitioning, but it is certainly less efficient since the high concentration of MAs, required for efficient burning, are impossible to obtain in a short operational time. The proposed nuclear waste management approach will be a long-term effort when it is accomplished without partitioning/separation technologies. However, none of the analyses contradicts this effort – the key points are a) when the technology for treating the waste is possible and reliable, the time horizon will not be a major concern b) the waste management is now intrinsically linked with energy production instead of requiring dedicated costly facilities, delivering a promising economic basis c) the waste management is now associated with long-term energy production and massively improved resource utilization. In summary, the objectives of P&T are achievable without prior partitioning – this is a ground-breaking result. However, the approach will require continued nuclear energy production for a very long time. In case of a nuclear phase-out decision, a much more efficient transmutation scenario would have to be envisaged as already discussed several years ago for Germany.

Nuclear technologies have a strong potential and a unique role to play in delivering reliable low carbon energy for a future net-zero society. However, to assure the sustainability required for the long-term success, nuclear will need to deliver innovative solutions as proposed in iMAGINE. One of the most attractive features, but also a key challenge for the envisaged highly integrated nuclear energy system is the need for a demand driven salt clean-up system. The work described provides an insight into the interplay between a potential salt clean-up system and the reactor operation in a plutonium started core in a dynamic approach. The results presented will help to optimize the parameters for the salt clean-up process as well as to understand the differences which appear between a core started with enriched Uranium and Plutonium as the fissile material. The integrated model is used to investigate the effects of the initial fissile material on core size, achievable burnup, and long term operation. Different approaches are tested to achieve a higher burnup in the significantly smaller Pu driven core. The effects of different clean-up system throughputs on the concentration of fission products in the reactor salt and its consequences are discussed for general molten salt reactor design. Finally, an investigation of how a plutonium loaded core could be used to provide fuel for future reactors through fuel salt splitting is presented with the outcome that one Pu started reactor of the same size as a uranium started core could deliver fuel for 1.5 new cores due to enhanced breeding. The results provide an essential understanding for the progress of iMAGINE as well as the basis for inter-disciplinary work required for optimizing iMAGINE.

The past years saw the rise of genomic biobanks and mega-scale meta-analysis of genomic data that promise to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limit the global understanding of disease risk and intervention efficacy, but also inhibit viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared, and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable, computable, operate without access to raw data due to privacy concerns. But they must be comparable, both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of commonly used genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks, locally and internationally, increase the accuracy of association analyses, and inform developmental efforts.

Nuclear technologies have the potential to play a major role in the transition to a global net-zero society. Their primary advantage is the capability to deliver controllable 24/7 energy on demand. However, as a prerequisite for successful worldwide application, significant innovation will be required to create the nuclear systems of the 21st century, the need of the hour. The pros and cons of nuclear are discussed and analysed at different levels – the societal and public recognition as well as the scientific/engineering and economic level – to assure a demand driven development. Based on the analysis of the different challenges a vision for the nuclear system of the 21st century is synthesised consisting of three pillars – unlimited nuclear energy, zero waste nuclear, and accident free nuclear. These three combined visions are then transformed into dedicated and verifiable missions which are discussed in detail regarding challenges and opportunities. In the following a stepwise approach for the development of such a highly innovative nuclear system is described. Essential steps to assure active risk reduction and the delivery of quick progress are derived as answer to the critique on the currently observed extensive construction time and cost overruns on new nuclear plants. The 4-step process consisting of basic studies, experimental zero power reactor, small scale demonstrator, and industrial demonstrator is described. The 4 steps including sub-steps deliver the pathway to a successful implementation of such a ground breaking new nuclear system. The potential sub-steps are discussed with the view not only onto the scientific development challenges, but also as an approach to reduce the regulatory challenges of a novel nuclear technology.

In an era of global connectivity, language and literature education faces the challenge of navigating multilingualism, cultural diversity, and technological advancements. This paper explores the challenges and opportunities in language and literature education in the era of global connectivity. Through a qualitative research approach, interviews, focus group discussions, and classroom observations were conducted with 30 Arts and Sciences students and teachers. The findings reveal significant themes related to cultural diversity, multilingualism, and the integration of technology in language learning. The study emphasizes the importance of inclusive pedagogies, critical thinking development, and the incorporation of authentic and diverse literary works. Recommendations are provided for educators and policymakers to promote intercultural understanding and enhance language and literature education. The limitations of the study suggest areas for future research, including long-term language proficiency and diverse contextual considerations. Overall, this paper calls for ongoing innovation and collaboration to meet the evolving needs of language and literature education in an interconnected world.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

Pseudomonas aeruginosa is an adaptable bacterial pathogen that infects various organs, including the respiratory tract, vascular system, urinary tract, and central nervous system leading to high morbidity and mortality. Our primary focus of this study was to characterize P. aeruginosa clinical strains on the basis of pigment color production, determine its association to multidrug resistance behavior and ability to form biofilm. We identified yellow (30.1%), green (39.8%) and no pigment (30.1%) producing strains from a total of 143 clinical isolates. Yellow pigment producing strains presented significant resistance to a class of antibiotics including β-lactam (91.5%), aminoglycosides (70.5%), and carbapenems (51.9%) compared to green and non-pigmented strains. Importantly, 16.3% of yellow pigment producing strains was resistant to colistin where only 2.3% of non-pigmented and 1.8% of green pigmented strains were resistant to this agent. Moreover, yellow pigment producing strain were frequent producers of β-lactamase group of enzymes, ESBL (55.6%), MBL (55.6%), and AmpC (50%) and displayed higher frequency of efflux positive group (64.2%) compared to green (7.14%) and non-pigmented one (28.5%). Notably, green pigment producing strains when compared to non-pigmented groups also displayed antibiotic susceptibility behavior similar to yellow pigment producing strains. Although yellow pigment producing strains were strong biofilm producers, no significant association was identified between pigment and biofilm formation. Among pigmented and non-pigmented strains, majority of yellow pigment producing strains have shown MIC levels greater than the green and non-pigmented strains. Our study has demonstrated the impact of pigment coloration on susceptibility to antimicrobial agents where yellow pigment producing strains represent considerably a serious problem as due to lack of alternative agents against such transformed strain may collectively be associated with multidrug resistance development.

Plant viral diseases constitute a major contributor to agricultural production losses, significantly impacting the economies of exporting countries by more than $30 billion annually. Understanding and researching the biology and genomics of viruses is crucial for developing virus-resistant, genetically edited or genetically modified plants. Genetic modifications can be targeted to specific regions within genes of target plants which are important or essential for the virus to establish a systemic infection, thus fostering resistance or enabling plants to effectively respond to invading agents while preserving their yield. This review provides an overview of viral incidence and diversity in tropical fruit crops and aims to examine the current state of the knowledge on recent research efforts aimed at reducing or eliminating the damage caused by viral diseases, with emphasis on genetically edited products that have reached the market in recent years.