As a part of our ongoing research on endophytic fungi, we have isolated a sesterterpene mycotoxin, fusaproliferin (FUS), from Fusarium solani strain associated with the plant Aglaonema hookerianum Schott. FUS showed rapid and sub-micromolar IC₅₀ against pancreatic cancer cell lines. Time dependent survival analysis and microscopy imaging showed rapid morphological changes in cancer cell lines 4 hours after incubation with FUS. This provides a new chemical scaffold that can be further developed to obtain more potent synthetic agents against pancreatic cancer.

In the past few decades, the transmission of data over an unsecure channel resulted in an increased rate of hacking. Therefore, the requirement to make multimedia data more secure is increasing day by day. Numerous algorithms are developed to improve efficiency and robustness. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of having less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using substitution-permutation and one-time-pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as Key Space Analysis, Complexity Analysis, Sensitivity Analysis, Strict Avalanche Criteria (SAC), Histogram Analysis, Entropy Analysis, Mean of Absolute Deviation (MAD) Analysis, Correlation Analysis, Contrast Analysis, and Homogeneity to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

In a densely populated country like Bangladesh, universities grapple with the challenge of efficiently addressing myriad queries from a large student body, leading to a heightened workload for university stakeholders. To tackle these challenges, we introduce PixieGPT, a tailor-made Generative Pre-Trained Transformer for Bangladeshi universities. PixieGPT significantly mitigates workload by adeptly handling common university-related queries, thereby enhancing user experience. The hierarchical structure plays a crucial role in managing diverse queries from thousands of students about the university system. The solution introduces a modular hierarchical knowledge base (KB) with simpler complexities, addressing the intricacies of efficiently managing large volumes of queries. PixieGPT is designed in a modular way so that the solution is also adaptable for the implementation of other universities worldwide based on the requirements of a particular administrative system. The modular nature facilitates easy adaptation with minor changes based on specific university requirements, ensuring a seamless integration process. This paper delves into the intricacies of PixieGPT's design, emphasizing its pivotal role in mitigating workload challenges for university stakeholders in Bangladesh. The incorporation of BERT for Natural Language Understanding(NLU) and GPT models for Natural Language Generation(NLG) enhances PixieGPT's capabilities, contributing to the scalability and efficiency of the system. The presented use case underscores the practical benefits of PixieGPT, positioning it as a promising solution for universities globally with similar operational frameworks.

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

The underlying physical concept of computing out-of-time-ordered correlation (OTOC) is a significant new tool within the framework of quantum field theory, which now-a-days is treated as a measure of random fluctuations. In this paper, by following the canonical quantization technique we demonstrate a computational method to quantify the two different types of Cosmological auto-correlated OTO functions during the epoch when the non-equilibrium features dominates in Primordial Cosmology. In this formulation, two distinct dynamical time scales are involved to define the quantum mechanical operators arising from cosmological perturbation scenario. We have provided detailed explanation regarding the necessity of this new formalism to quantify any random events generated from quantum fluctuations in Primordial Cosmology. We have performed an elaborative computation for the two types of two-point and four-point auto-correlated OTO functions in terms of the cosmological perturbation field variables and its canonically conjugate momenta to quantify random auto-correlations in the non-equilibrium regime. For both the cases we found significantly distinguishable non-chaotic, but random behaviour in the OTO auto-correlations, which was not pointed before in this type of studies. Finally, we have also demonstrated the classical limiting behaviour of the mentioned two types of auto-correlated OTOC functions from the thermally weighted phase space averaged Poisson Brackets, which we found exactly matches with the large time limiting behaviour of the auto-correlations in the super-horizon regime of the cosmological scalar mode fluctuation.

The concept of out-of-time-ordered correlation (OTOC) function is treated as a very strong theoretical probe of quantum randomness, using which one can study both chaotic and non-chaotic phenomena in the context of quantum statistical mechanics. In this paper, we define a general class of OTOC, which can perfectly capture quantum randomness phenomena in a better way. Further we demonstrate an equivalent formalism of computation using a general time independent Hamiltonian having well defined eigenstate representation for integrable supersymmetric quantum systems. We found that one needs to consider two new correlators apart from the usual one to have a complete quantum description. To visualize the impact of the given formalism we consider the two well known models viz. Harmonic Oscillator and one dimensional potential well within the framework of supersymmetry. For the Harmonic Oscillator case we obtain similar periodic time dependence but dissimilar parameter dependences compared to the results obtained from both microcanonical and canonical ensembles in quantum mechanics without supersymmetry. On the other hand, for one dimensional potential well problem we found significantly different time scale and the other parameter dependence compared to the results obtained from non-supersymmetric quantum mechanics. Finally, to establish the consistency of the prescribed formalism in the classical limit, we demonstrate the phase space averaged version of the classical version of OTOCs from a model independent Hamiltonian along with the previously mentioned these well cited models.

The out-of-time-ordered correlation (OTOC) function is an important new probe in quantum field theory which is treated as a significant measure of random quantum correlations. In this paper, with the slogan "Cosmology meets Condensed Matter Physics" we demonstrate a formalism using which for the first time we compute the Cosmological OTOC during the stochastic particle production during inflation and reheating following canonical quantization technique. In this computation, two dynamical time scales are involved, out of them at one time scale the cosmological perturbation variable and for the other the canonically conjugate momentum is defined, which is the strict requirement to define time scale separated quantum operators for OTOC and perfectly consistent with the general definition of OTOC. Most importantly, using the present formalism not only one can study the quantum correlation during stochastic inflation and reheating, but also study quantum correlation for any random events in Cosmology. Next, using the late time exponential decay of cosmological OTOC with respect to the dynamical time scale of our universe which is associated with the canonically conjugate momentum operator in this formalism we study the phenomena of quantum chaos by computing the expression for {\it Lyapunov spectrum}. Further, using the well known Maldacena Shenker Stanford (MSS) bound, on Lyapunov exponent, λ≤2π/β, we propose a lower bound on the equilibrium temperature, T=1/β, at the very late time scale of the universe. On the other hand, with respect to the other time scale with which the perturbation variable is associated, we find decreasing but not exponentially decaying behaviour, which quantifies the random correlation at out-of-equilibrium. Finally, we have studied the classical limit of the OTOC to check the consistency with the large time limiting behaviour.

Background: Malignant pleural mesothelioma (MPM) is an atypical, belligerent tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Pleural mesothelioma is a common type of mesothelioma that accounts for about 75 percent of all mesothelioma diagnosed yearly in the United States. Diagnosis of mesothelioma takes several months and is expensive. Given the difficulty of diagnosing MPM, early identification is crucial for patient survival. Our study implements artificial intelligence and recommends the best fit model for early diagnosis and prognosis of MPM. Method: We retrospectively retrieved patient’s medical reports generated by Dicle University, Turkey and implemented multi-layered perceptron (MLP), voted perceptron (VP), Clojure classifier (CC), kernel logistic regression (KLR), stochastic gradient decent SGD), adaptive boosting (AdaBoost), Hoeffding tree (VFDT), and primal estimated sub-gradient solver for support vector machine (s-Pegasos). We evaluated the models, compared and tested using paired T-test (corrected) at 0.05 significance based on their respective classification accuracy, f-measure, precision, recall, root mean squared error, receivers characteristic curve (ROC), and precision-recall curve (PRC). Results: In phase-1 SGD, AdaBoost.M1, KLR, MLP, VFDT generates optimal results with the highest possible performance measures. In phase-2, AdaBoost with a classification accuracy of 71.29% outperformed all other algorithms. C-reactive protein, platelet count, duration of symptoms, gender, and pleural protein were found to be the most relevant predictors that can prognosticate mesothelioma. Conclusion: This study confirms that data obtained from biopsy and imagining tests are strong predictors of mesothelioma but are associated with high cost, however, can identify mesothelioma with optimal accuracy. Predictive analytics without using biopsy results can diagnose mesothelioma with acceptable accuracy. Implementation of phase-2 followed by phase-1 can address diagnosis expenses and maximize disease prognosis. Additionally, results indicate improved MPM diagnosis using AI methods dependent upon the specific application.

Time series forecasting has been a challenging area in the field of Artificial Intel-ligence. Various approaches such as linear Neural Networks, Recurrent Linear Neural Networks, Convolutional Neural Networks and recently Transformers have been attempted for the time series forecasting domain. Although, Trans-former-based architectures have been outstanding in the Natural Language Pro-cessing domain, especially in autoregressive language modeling, the initial at-tempts to use transformers in the time series arena have met with mixed success. One of the recent papers demonstrated that modeling the multi-variate time se-ries problem as independent channels on a transformer architecture produced lower mean square and mean absolute errors on benchmarks, however, another recent paper claimed that a simple linear neural network out-performs previous approaches including transformer-based designs. We investigate this paradox in detail comparing the linear Neural Network and Transformer based designs, provide insights to why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear Neural Network based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on majority of the popular benchmarks.

This manuscript aims to provide a nuanced study of the idea of rights and duties prevalent in ancient Vedic society through Vedic literature and Dharmaśāstras . This manuscript delves into the exegesis of the Védas and Dharmaśāstras to accomplish this. The archaic Vedic literature and Dharmaśāstra texts are the origin and backbone of Sanskrit literature. They have a plethora of ideas that, if accepted, could be quite useful for the protection of any person's human rights. In Védas and Dharmaśāstras, rights and duties complement each other, and rights are integrated by duties. According to these texts, rights and duties are correlated and the relationship between rights and duties leads to the core concept of dharma (constitutional laws). Dharma is a systematic Sanskrit concept that includes traditions, obligation, morals, laws, order, and justice. It was a unique concept of dharma that kept checks and balances on sovereign officials and prevented them from becoming autocratic and anarchist. It also provided the common man with a protective shield against the dictatorship of sovereign officials. Ordinary citizens had more privileges and fewer responsibilities relative to the state's highest officials. The greater the authority, the less his privileges were, and the more extensive his responsibilities became. This research is an exegetical analysis of ancient Indian Vedic and later Vedic literature and is primarily aimed at deciphering some of the essential ideas of the rights found in these texts, which are akin to contemporary human rights. It endeavours to discern and explain the tenets of human rights obnubilated in the pristine mantras of Antediluvian Vedic and Smṛti texts of India. The essay further attempts to add a much needed non-western perspective to the historiography of human rights.

Despite the importance of few-shot learning, the lack of labeled training data in the real world, makes it extremely challenging for existing machine learning methods as this limited data set does not represent the data variance well. In this research, we suggest employing a generative approach using variational autoencoders (VAEs), which can be used specifically to optimize few-shot learning tasks by generating new samples with more intra-class variations. The purpose of our research is to increase the size of the training data set using various methods to improve the accuracy and robustness of the few-shot face recognition. Specifically, we employ the VAE generator to increase the size of the training data set, including the basic and the novel sets while utilizing transfer learning as the backend. Based on extensive experimental research, we analyze various data augmentation methods to observe how each method affects the accuracy of face recognition. We conclude that the face generation method we proposed can effectively improve the recognition accuracy rate to 96.47% using both the base and the novel sets.

The recent COVID-19 pandemic has showcased the implications of using virtual private networks (VPNs) as home-working is now common. Establishing the current state of knowledge on VPNs and their processes is vital. This corroborates an up to date understanding on the fundamentals of VPNs and their usage in today’s society; it informs on how to better use VPNs too. Insight into the security issues VPNs face and possible solutions to these allows for the identification of gaps for potential future research. Addressing these gaps would then indicate how to further improve VPNs, making sure VPNs are indeed beneficial to users.

E-waste is considered the fastest-growing global waste stream and has become an emerging environmental challenge. The widespread usage of digital devices has generated a large amount of e-waste. According to the Ministry of Commerce, India annually generates ~14.5 lakh metric tons of e-waste and recycles only ~5 % of e-waste. Annually, the global production of e-waste has been expected to increase by 4–5 %. Advancements in IT and communication divisions have improved the usage of electronic equipment. Heavy metals such as (Pb), mercury (Hg), arsenic (As), tin (Sn), and organic chemicals such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated biphenyls (PBBs), polybrominated dibenzo-p-dioxins (PBDDs), polybrominated diphenyl ethers (PBDEs) are subsequently released from e-wastes and contaminates soil and water sources. These toxic compounds are hazardous to human health and the environment. They are capable of entering various biological systems and causing alterations in respiratory functions of the lungs, DNA damage, impaired functioning of the thyroid, and increased risk of chronic diseases such as cancer and cardiovascular ailments. Such concerns raise the quintessential need to manage e-waste to prevent future hazards. This manuscript critically reviews the sources, health and environmental impact, disposal techniques, recycling, and regulation of e-waste.

Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.

The present work is focused on functionalizing Montmorillonite (Mt) with poly dimethyl siloxane (PDMS) to develop a hybrid membrane reactor for the removal of Co (II) and Ni (II) ions from model wastewater samples. The specific surface area of the novel material was found to be 444 m2 /g. X-Ray Diffraction (XRD) and Fourier Transform Infra-Red (FTIR) spectra reveal the formation of exfoliated composite material. The efficiency of this membrane material was determined by subjecting it to continuous filtration experiments. It was able to remove 50% of Co (II) and 75% of Ni (II) ions, respectively, in one cycle of filtration in less than three minutes of operation with a permeate flux rate of 675 L/m2/H and 975 L/m2/H respectively, for both the ions.

In the NCBI database, as on June 6, 2020, total number of available complete genome sequences of SARS-CoV2 across the world is 3617. The envelope protein of SARS-CoV2 possesses several non-synonymous mutations over the transmembrane domain and (C)-terminus in 0.414\% of these 3617 genomes. The C-terminus motif DLLV has been changed to DFLV and YLLV in the proteins QJR88103 (Australia: Victoria) and QKI36831 (China: Guangzhou) respectively, which might affect the binding of this motif with the host protein PALS1.

Previous incidental findings of an increase of suicidal risk among subjects with low cholesterol levels have drawn attention to the role of lipids in suicidal behavior. To date, multiple lines of evidence acquired from clinical studies have confirmed an association between low cholesterol levels and suicidal behavior, but the involvement of dimensional traits including impulsivity and aggression in this association remains elusive. In this narrative review, we aimed to address and synthesize the literature regarding the involvement of lipids in the neurobiology of suicidal behavior and its underlying psychological substrates, impulsivity and aggression. An electronic database search was performed using different combinations of relevant keywords. Both preclinical and clinical studies matching the scope of this article were reviewed and filtered through an inspection of the abstracts to recruit the most suitable articles that contributed essential and substantial findings to the literature. Although subject characteristics and study designs vary across studies, current research has demonstrated that impulsivity and aggression might have shared neurobiologic substrates involved in altered serotonergic neurotransmission. Despite the association between low serum lipid levels and suicidal behavior being well documented, the involvement of lipid subtypes in the pathophysiology of impulsive and aggressive traits remains elusive. Further work is warranted to recognize the roles of lipids in neuronal membrane functions and serotonin metabolism, promote a greater appreciation of identifying biomarkers that could be used to determine at-risk individuals, and develop potential interventions to disrupt the pathogenesis of behavioral phenotypes of suicide.

House is the haven that keeps people from natural and human conditions, it gives them trust, safety, and steadiness. It is one of the most basic human needs this became a serious function which cities offer, and became one of the most important aspects which caught urban researchers interest, they take into consideration a wide range of architectural, social, and economic indicators. The study aims to provide an overall conception of Rwandz residential functions, using a collection of parameters and some GIS and statistical techniques, to help establish plans and future projects to improve the growth of this city and other towns and cities in that area. The study found that the old parts of Rwandz city which are located in the core, differ from the outer parts which are relatively newer in many properties, generally, the core is more densely populated than the outer, bigger family size, more illiteracy, and unemployment, few incomes, older houses, smaller houses, in the opposite of the outer parts. Besides, the study tested the correlation coefficient between the criteria; it found some strong statistical relationships between them, which reflected some real-life properties of the residential function. Lastly, the study designed a regression model to predict the main residential function criteria.

Impure and pure casts have collected reasonable attentions over all the world. In this paper, Aluminum (Al) and Copper (Cu) cast alloys are considered to be studied. Multiple impure and pure Al-Cu cast alloys are established under the condition of very high temperatures. Then, all of the established alloys are examined by applying mechanical tests. More specifically, tests of hardness are exploited. Moreover, different quenching conditions are employed and analyzed. These are the water, air and oil. Consistent results are separately obtained for the impure and pure materials.

This study investigates how a range of economic, environmental, and social indicators have influenced the concept of inclusive growth in 19 member countries of the Asian Cooperation Dialogue over the period spanning from 1995 to 2021. To analyze these relationships, the research employs the Driscoll Kraay's Standard Errors regression technique, designed to account for factors such as cross-sectional dependence, hetero-scedasticity, and autocorrelation within the data. Furthermore, various preliminary tests were conducted to assess the data for cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. Additionally, the Westerlund cointegration test was employed to evaluate the potential long-term equilibrium relationships among the variables. The findings reveal that the influence of independent variables on the dependent variable, which in this case is the level of inclusive growth (ING), differs significantly among three distinct income categories: lower-middle-income countries (LMYCs), upper-middle-income countries (UMYCs), and high-income countries (HYCs). Notably, the results underscore the overall statistical significance and robust fit of the regression model for all three income groups. One of the primary contribu-tions of this research is the provision of empirical evidence concerning the role played by fishery and aquaculture production in fostering inclu-sive growth in the Asian context. This research also highlights the trade-offs between economic development and environmental sustainability in terms of trade openness, agriculture, forestry and fishing, ecological footprint, and renewable energy utilization. Enhancing inclusive growth in Asia requires improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. The paper suggests some future work directions for extending the analysis to other regions and indicators, as well as incorporating dynamic panel data models and causality tests. The paper also suggests some policy implications for fostering inclusive growth in Asia through regional cooperation, capacity building, technology transfer and green financing.

This paper is regarding design and program an Micro-controller Arduino Uno board by using Arduino software to work as a photo-sensor(Active) single axial solar tracker system(SASTS). A solar panel, two photo-resistors (LDR) in two sides (north/south) of the photo-voltaic(PV) and a servo motor are connected to the Uno board, which is running a code that prepared by Arduino software IDE in advanced then it works as a tracking system. Here, the LDRs send the signal of presence or absence of the light to the board and based on that sent signal the Uno reflects a new signal to the servo motor to rotate and finds the light source. Lastly, the photo-sensor single axis tracker is made while Continuously, the system tries to face the panel to the sun and whilst changing the irradiance intensity it starts searching to find the angle of highest irradiance. Based on results that are extracted from the data, the tracker system significantly boosts the output efficiency of the solar panel. By using the Micro-controller Uno board, LDRs, servo motor and special designed mechanical base, the tracking system is constructed, based on acquired data the influence of the STS on the increasing the solar panel efficiency is more obvious. Significantly, the tracker system rises the efficiency of the PV .

Dendrobium okinawense is an endangered epiphytic orchid, and there has been no scientific report so far on its propagation. Protocorm is mass of cell, and protocorm-like bodies (PLBs) look alike protocorm produced by vegetative explants in vitro. Regeneration of PLBs is the most efficient technique for the orchids micro-propagation. We used different light emitting diodes (LEDs) for the efficient PLB organogenesis of D. okinawense. PLBs regenartion under green and red LED surpassed respectively 81.1% and 71.6% in numbers, and respectively 80.8% and 57.8% in fresh weight over white fluorescent light. We manipulated the culture media by different concentrations of PCIB and HMI. PLBs organogenesis promoted by low concentration, it increased respectively 35.9% and 19.3% over control by 0.01 mg/L PCIB and 0.01 ml/L HMI in numbers. Green LED and PCIB independently produced mostly similar numbers of new PLBs. Interestingly, culture media with 0.01 mg/L of PCIB further increased 8.5% of the numbers of PLBs under green LED, whereas the culture media with 0.01 mg/L HMI reduced the number of PLBs under green LED. PLBs culture under green LED with very low concentrations of PCIB manipulated culture media can significantly increase their organogenesis of D. okinawense.

This paper evaluates norms and assesses the level of knowledge in air conditioning project management within the construction industry. A total of 25 questions were distributed to multiple candidates, who were filtered based on pre-established inclusion and exclusion criteria. Thirty-nine candidates were ultimately approved to participate in the survey. The questions were designed to address five hypotheses, with each set of five questions corresponding to one hypothesis. The results were obtained after pre-processing the data using Matlab software. The data was pre-processed using Matlab software, and the results were analyzed using the Delphi method. The analysis revealed that only two hypotheses were approved: No matter whether there are nationalized safety rules or not, the impact of data sciences and smart technologies, including air conditioning management systems, is critical for human life in the building business.

The COVID-19 pandemic, which has affected the entire world, has not only created a number of emerging issues for each country, especially in the field of public health, but has also provided a number of opportunities for risk management, alternative strategies and completely new ways of looking at challenges. This paper examines the COVID-19 pandemic response in Türkiye and the possible implications of the experience for future responses to other health emergencies and disaster risk management, based on the lessons learned. This study uses publicly available literature from government, private sector, and academic sources to analyse the conflicts, changes and lessons learned during the COVID-19 pandemic, which are components of the World Health Organization (WHO) Health Emergency and Disaster Risk Management (Health EDRM) framework. The COVID-19 experience in Türkiye has several aspects, including the significant role of healthcare workers, the existence of an effective health system accustomed to emergencies, applications based on information technologies, partial transparency of public authorities in providing information, and socio-cultural environment related to cooperation on prevention strategies, including wearing masks and vaccination. Challenges in Türkiye include distance learning in schools, lockdowns that particularly affect the elderly, ensuring environmental sustainability, hesitation about the effectiveness of social/financial support programmes, socio-cultural trivialisation of pandemics after a while, and relaxation of prevention strategies. Lessons learned from the COVID-19 pandemic include the value of transparency in public health/healthcare information, the strengthening of all aspects of the health system in terms of health workers, and the importance of a balanced economy prepared for foreseeable risks.

In this research, we investigate the bending of photon light and/or time-like geodesics within the context of four-dimensional space-time geometry, specifically in the presence of topological defects. These topological defects manifest as cosmic string space-time configurations, encompassing screw dislocation, spiral dislocation, and spacelike dislocation. Our primary objective is to determine the extent of deflection experienced by light-like and time-like geodesics and analyze how this deflection is affected by the cosmic string parameter, screw dislocation parameter, and spacelike dislocation parameter. To accomplish this, we employ a derivation technique that centers on the observation that photon rays (light-like geodesics) and/or time-like geodesics follow paths parallel to the z-axis while maintaining a constant radius along the cylinder.

Computation of circuit complexity has gained much attention in the Theoretical Physics community in recent times to gain insights about the chaotic features and random fluctuations of fields in the quantum regime. Recent studies of circuit complexity take inspiration from the geometric approach of Nielsen, which itself is based on the idea of optimal quantum control in which a cost function is introduced for the various possible path to determine the optimum circuit. In this paper, we study the relationship between the circuit complexity and Morse theory within the framework of algebraic topology using which we study circuit complexity in supersymmetric quantum field theory describing both simple and inverted harmonic oscillators up to higher orders of quantum corrections. The expression of circuit complexity in quantum regime would then be given by the Hessian of the Morse function in supersymmetric quantum field theory, and try to draw conclusion from their graphical behaviour. We also provide a technical proof of the well known universal connecting relation between quantum chaos and circuit complexity of the supersymmetric quantum field theories, using the general description of Morse theory.

Legumes develop root nodules that harbour endosymbiotic bacteria, rhizobia. These rhizobia convert nitrogen to ammonia by biological nitrogen fixation. A thorough understanding of the biological nitrogen fixation in legumes and its regulation is key to develop sustainable agriculture. It is well known that plant hormones affect nodule formation; however, most studies are limited to model legumes due to their suitability for in vitro, plate-based assays. Specifically, it is almost impossible to measure the effects of exogenous hormones or other additives during nodule development in crop legumes such as soybean as they have huge root system in soil. To circumvent this issue, the present research develops suitable media and growth conditions for efficient nodule development under in vitro, soil free conditions in an important legume crop, soybean. Moreover, we also evaluate the effects of all major phytohormones during soybean nodulation under identical conditions. This versatile, inexpensive, scalable and simple protocol provides several advantages over previously established methods. It is extremely time-and resource-efficient, does not require special training or equipment, and produces highly reproducible results. The approach is expandable to other large legumes as well as for other exogenous additives.

Robotics and autonomous technologies enable space travel. Robotics change space colonization. We study space-based robotics. Human-centric missions need robotics. Vision, decision-making, movement, and manipulation follow. These developments influence resource utilization, extravehicular activities, planetary surface exploration, long-duration space missions, and colonization. Robots improve research, productivity, and astronaut safety but present problems. Highlighting research and knowledge gaps. Space ethics, human-robot interaction, and failure recovery are gaps. This extensive study showed how robots and autonomous systems may transform space exploration and colonization. Researchers, engineers, and mission planners may benefit. Robotics and autonomy will improve space exploration.

Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture’s future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.

Bone homeostasis depends on the interplay between bone resporption by osteoclasts and bone formation by osteoblasts. Any Imbalance of this tightly regulated process can cause diseases such as osteoporosis. Therefore, the knowledge about the factors that regulate communication between osteoclasts and osteoblasts are critical to bone cell biology.Osteoporosis is a progressive systemic skeletal disease characterized by low bone mass density and deterioration of bone tissue. Mature miRNAs are about 22 nucleotide long non coding RNA molecules that are involved in regulatory processes intracellularly. A number of scientific studies have revealed a comprehensive and evidential knowledge about miRNAs that affect the bone metabolism by influencing bone formation and resorption processes. In this short review we have summrized the regulatory role of some selective miRNAs in bone formation.

This research investigates the compatibility of conventional air conditioning with the principles of green building, highlighting the need for systems that enhance indoor comfort while aligning with environmental sustainability. Conventional cooling systems, though proficient in regulating indoor temperatures, encounter several issues when incorporated into green buildings. These include energy waste, high running costs, and a misalignment with eco-friendly practices, which may also lead to detrimental environmental effects and potentially reduce occupant comfort, particularly in retrofit situations. Given the emphasis on sustainability and energy conservation in green buildings, there is a pressing demand for heating, ventilation, and air conditioning (HVAC) solutions that support these goals. This study emphasizes the critical need to reconsider traditional HVAC strategies in the face of green building advances. It advocates for the adoption of innovative HVAC technologies designed for eco-efficiency and enhanced comfort. These technologies should integrate seamlessly with sustainable construction, use greener refrigerants, and uphold environmental integrity, driving progress towards a sustainable and occupant-friendly built environment.

The aim of this article is introduced the concept of double framed T-soft fuzzy set (DFT-soft fuzzy set) which is the combination of soft set and fuzzy set. We also defined the notions and apply this concept in BCK/BCI-algebras. By using example, we also discussed the concept of double framed T-soft fuzzy algebra (DFT-soft fuzzy algebra) and double framed B-soft fuzzy algebra (DFB-soft fuzzy algebra) and also investigated their properties. Each double framed T-soft fuzzy algebra is double framed B-soft fuzzy algebra but by using example, we proved that converse may or may not be possible.

Background. Rheumatoid arthritis (RA) is a chronic inflammatory and autoimmune disorder which affects the joints in the wrists, fingers, and knees. RA is often associated with depressive and anxiety symptoms as well as chronic fatigue syndrome (CFS)-like symptoms.Aim. To examine the association between depressive symptoms (measured with the Beck Depression Inventory, BDI), anxiety (Hamilton Anxiety Rating Scale, HAMA), and CFS-like (Fibro-fatigue Scale) symptoms and immune-inflammatory, autoimmune, and endogenous opioid system (EOS) markers, and lactosylceramide in RA. Methods. The serum biomarkers were assayed in fifty-nine RA and fifty-nine patients without increased psychopathology (PP) and fifty healthy controls.Results. There were highly significant correlations between the BDI, FF, and HAMA scores and severity of RA, as assessed with the DAS28-4, clinical and disease activity indices, the number of tenders and swollen joints, and patient and evaluator global assessment scores. A common latent vector (reflective model) could be extracted from the PP and RA-severity scales, which showed excellent psychometric properties. Partial least squares analysis showed that 69.7% of the variance in this common core underpinning PP and RA symptoms could be explained by the regression on immune-inflammatory pathways, rheumatoid factor, anti-citrullinated protein antibodies, CD17, and mu-opioid receptor levels. Conclusions. Depression, anxiety, and CFS-like symptoms due to RA are reflective manifestations of the phenome of RA and are mediated via the effects of the same immune-inflammatory, autoimmune, and EOS pathways and lactosylceramide that underpin the pathophysiology of RA. These PP symptoms are clinical manifestations of the pathophysiology of RA.

Background. There is strong comorbidity between atherosclerosis (ATS) and depression which is attributed to increased atherogenicity, insulin resistance (IR), and immune and oxidative stress.Aim of the study. To examine the role of the above pathways and mu opioid receptor (MOR), β-endorphin, zinc, copper, vitamin D3, calcium, and magnesium in depression due to ATS / unstable angina (UA).Methods. Biomarkers were assayed in 58 controls and 120 ATS patients divided into those with moderate and severe depression according to the Beck Depression Inventory (BDI)-II score > 19 and > 29, respectively. Results. Neural network and logistic regression models showed that severe depression due to ATS/UA was best predicted by IL-6, UA, MOR, zinc, β-endorphin, calcium and magnesium and that moderate depression was associated with IL-6, zinc, MOR, β-endorphin, UA, atherogenicity, IR, and calcium. These neural networks yielded a significant discrimination of severe and moderate depression with an area under the ROC curve of 0.831 and 0.931, respectively. Using Partial Least Squares analysis, 66.2% of the variance in a latent vector extracted from the ATS/UA clinical features, BDI-II scores, atherogenicity, and IR could be explained by the regression on IL-6, IL-10, zinc, copper, calcium, MOR, and age. The BDI-II scores increased from controls to ATS to UA class III to UA class IV.Conclusions. Depression due to ATS/UA is a reflective manifestation of increased atherogenicity and IR, which are modulated by immune activation, aberrations in the endogenous opioid system, antioxidants, trace elements, and macrominerals.

Fuel gas network (FGN) synthesis is a systematic method for reducing fresh fuel consumption in a chemical plant. In this work, we address the synthesis of fuel gas network using block superstructure originally proposed for process design and intensification (Demirel et.al. [1]). Instead of a classical source-pool-sink superstructure, we consider a superstructure with multiple feed and product streams. These blocks interact with each other through direct flows that connect a block with its adjacent blocks and through jump flows that connect a block with all blocks. The blocks with feed streams are viewed as fuel sources and the blocks with product streams are regarded as fuel sinks. Addition blocks can be added as pools when there exists intermediate operations among 9 source blocks and sink blocks. These blocks can be arranged in a I × J two-dimensional grid with I = 1 for problems without pools, or I = 2 for problems with pools. J is determined by the maximum number of pools/sinks. With this representation, we formulate fuel gas network synthesis problem as a mixed-integer nonlinear (MINLP) problem to optimally design a fuel gas network with minimal total annul cost. We present a real-life case study from LNG plant to demonstrate the capability of the proposed approach.

Cybercrimes are becoming a bigger menace to both people and corporations. It poses a serious challenge to the modern digital world. According to a press release from 2019 Cisco and Cybersecurity Ventures, Cisco stopped seven trillion threats in 2018, or 20 billion threats every day, on behalf of its clients. According to Cybersecurity Ventures, the global cost of cybercrime will reach $6 trillion annually by 2021, which is significantly more than the annual damage caused by all natural disasters and more profitable than the global trade in all major illegal narcotics put together. Botnets are the most common and have a significant negative impact on any civilization among malware programmes. As a result, this study will explore various P2P botnet detection algorithms by outlining their essential characteristics, advantages and disadvantages, obstacles, and future research.

The Circular Economy of plastics is a promising concept that has the potential to reduce pollution and close the loop on plastic waste. However, further research is needed to develop more efficient and environmentally friendly methods of recycling plastic. This review article discusses the Circular Economy of plastics, its potential benefits and drawbacks, and the challenges that need to be addressed to make it a reality. Some case studies are also examined to explore how the Circular Economy of plastics has been implemented across the globe.

The fuel cell particularly the Proton Exchange Membrane Fuel Cell (PEMFC) is a new energy technology that plays a critical role in achieving market viability. The PEMFC has high power density, zero CO₂ emissions, and high conversion efficiency. However, with uniform distribution of fuel in PEMFC, we can improve the performance and reduce the cost. The PEMFC will be more efficient and effective in terms of renewable energy as a byproduct only water. Metal foam is used as an alternative for the channels in PEMFC. The model of PEMFC was established by using the ANSYS Fluent 3D Model. Then we proposed four configurations of PEMFC. The outcome demonstrates that Case IV provided outstanding performance if it is compared with the other three cases. In terms of power density, Case IV provided a high power density that is double that of Case I. The porosity of metal foam was investigated. The 0.6 porosity value is found as the optimal value. The uniform distribution of the fuel can be achieved at this value which leads to the outstanding current density.

Rigid-body visual tracking is an active research field with many practical applications including visual surveillance and intelligent transport system. In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to detect and track each leaf of a plant for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking and generation of a leaf status report. The proposed method uses a graph theoretic approach to reliably detect and track individual leaves by overcoming the challenge of leaf-losses based on temporal image sequence analysis for automatically generating the leaf status report containing the following phenotypes, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf stopped growing, and the length and relative growth rate of individual leaves. The proposed method demonstrates high accuracy in detecting leaves and tracking them through the early vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.

Regeneration of lost tail is of great importance to lizards. Anolis carolinensis, a green lizard, is capable of regenerating its tail efficiently after autotomy. Hence, it is considered as a model organism in regeneration study. A. carolinensis shed its tail in order to distract the predator’s attention and thus makes a way to escape. Restoring of the amputated tail takes several days and the mechanism is currently clearly understood. Although save its life, tail regeneration is associated with the impairment of several vital functions in Anoles. In addition, various differences have been observed between original and regenerated tail in terms of mechanism and structure. To date, very little work has been conducted on tail autotomy and regeneration at molecular and genetic level. The genes responsible for regeneration in anoles are identified recently. These genes are evolutionarily conserved through all tetrapod vertebrates. They are, however, in a state of ‘switched-off’ in other vertebrates including humans. Consequently, a throughout study of these so called ‘switched-off’ genes may provide a way of restoring lost organs in human, and thus could revolutionize the modern medical science.

Beta thalassemia major (β-TM) disorder characterized by the lack, or severe reduction in the production of hemoglobin β-globin chains. The standard protocol for the management of β-TM is blood transfusion and iron chelation therapy to reduce the iron overload state. The present study aimed to investigate the relationships between two iron regulatory hormones, hepcidin (HEPC) and erythroferrone (ERFE) levels and iron status parameters (ISPs) in Iraqi patients with β-TM. ISPs and hormones were measured in sixty patients and compared with thirty healthy controls. The results indicated significant changes in different iron status parameters, while ferritin (FRT) with the ~11 fold increase showed the most change. Significant reduction in HEPC and increase in ERFE levels were detected in patients as compared to the control group, while no direct correlation was identified with the other measured ISPs. Receiver operating characteristic (ROC) analysis showed that the z-score of the composite of ERFE+FRT has a full diagnostic ability for β-TM. In conclusion, our finding indicated the correlation between different ISPs, FRT as the leading predictor of iron overload and tow main iron regulatory hormones.

This study investigates effective strategies for second language education in primary schools, analyzing the efficacy of immersive and interactive methods, digital literacy tools, and mixed-method approaches. The findings reveal significant improvements in language proficiency and student engagement through these methodologies, with immersive and interactive strategies showing substantial benefits in oral proficiency and engagement, digital tools enhancing accessibility and engagement, and mixed-method approaches yielding the highest overall effectiveness. The study underscores the importance of a balanced, flexible approach to language education, integrating traditional and modern methods to cater to diverse learner needs. These strategies not only advance linguistic skills but also contribute to the broader cognitive and emotional development of young learners, emphasizing the critical role of effective language education in preparing students for a globalized world.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

The outbreak of Coronavirus, as well as its emerging potential consequences, has become a global challenge worldwide, demanding effective and controlled therapeutic strategies. Potential drug candidates could achieve that with minimal toxicity. The current investigation selected 2-Deoxy-D-glucose prescribed by Defense Research and Development Organization (DRDO), India. The derivative and modified form was tested through in silico analysis against COVID-19 main protease complex with N3 inhibitor. The derived form of 2-Deoxy-D-glucose was generated by replacing the hydroxy group with a hydrogen atom, and Cypate 2-Deoxy-D-glucose was chosen as a derivative against the COVID-19 main protease complex. A molecular docking approach was adopted to identify the stable and competent form among the modified and derivative forms of 2-Deoxy-D-glucose based on binding energy. By further promoting the stabilized complex, these compounds' toxicity was also scrutinized through ADMET analysis to predict the potential candidate. The current investigation suggested that the modified version of 2-Deoxy-D-glucose was more stable with minimal toxicity against COVID-19 main protease.

Although buildings constitute 35% of global energy use and 38% of carbon emissions from energy, the amount of energy and energy-related carbon emissions are increasing rapidly. On the other hand, non-renewable usable energy resources are decreasing on the contrary. New buildings are being built due to the increasing population and the existing building stock not meeting the needs or losing its lifespan. Some of the existing building stock is demolished and renewed for structural reasons and some for reasons such as usage performance. It is possible to reduce both energy consumption and greenhouse gas emissions with strategic energy uses in existing building stocks. There are various techniques to measure the maximum performance of sustainable and energy-efficient use of existing building stocks with which changes. Building Information Modeling (BIM) technology is one of these techniques that provides wide opportunities to users in recent years, and it is almost a virtual laboratory of structures. Building stocks can be modeled with BIM, and alternative shell and system suggestions can be used to measure how building performance has changed and to achieve the best results. In this research study, improvement scenarios that optimize energy use in existing building stocks are investigated on a sample residential building by using the virtual laboratory facilities of BIM. After measuring the current energy performance of the existing building with BIM tools, 192 alternative results were obtained with 6 variables (external walls, roof, insulation layers, transparent surface materials, lighting, and photovoltaic panels). The obtained alternatives were compared with the current state of the existing building. The material expenditures and amortization periods required for these alternative scenarios are also calculated. The results obtained were evaluated according to annual / lifetime energy consumption, fuel, electricity, initial investment costs, energy use intensity, and carbon emission amounts. With the optimization of the current situation of the sample building, annual fuel consumption decreased by 61% and electricity consumption by 64%. The amortization period of the optimum improvement was determined as 12 years.

The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi and 2G/3G/4G cellular are insufficient for the IoT networks because they are designed to serve appliances with immense processing capabilities such as laptops and PCs. Moreover, most of these technologies are centralized and use an existing infrastructure. Currently, the new communication technologies such as Z-Wave, 6LowPAN, and Thread are dedicated to the IoT and have been developed to meet its requirements. These technologies can handle many factors such as range, data requirements, security, power demands, and battery life. Nevertheless, the security issues in IoT systems have major concerns and matters because vulnerabilities in such systems may result in fatal catastrophes. In this paper, an enhanced IoT security framework for authentication and authorization is proposed and implemented to protect the IoT protocols from different types of attacks such as man-in-the-middle attack, reply attack, and brute force attack. The proposed framework combines an enhanced token authentication that has identity verification capabilities and a new sender verification mechanism on the IoT device side based on time stamp, which in turn can mitigate the need for local identity verification methods in IoT devices. The proposed IoT security framework is tested using security analysis with different types of attacks compared with previous related frameworks. The analysis shows the high capability of the proposed framework to protect IoT networks against many types of attacks compared with current available security frameworks. Finally, the proposed framework is developed using Windows application to simulate the framework phases, check its validity through the real network, and calculate the payload time is adds.

New research points to a possible link between Autism Spectrum Disorder and the gut microbiota as many autistic children have co-occurring gastrointestinal problems. This review focuses on specific alterations of gut microbiota mostly observed in autistic patients. Particularly, the mechanisms through which such alterations may trigger the production of the bacterial metabolites or leaky gut in autistic people are described. Various altered metabolite levels were observed in autistic children, many of those were of bacterial origin such as short chain fatty acids (SCFAs), indoles and lipopolysaccharides. A less integrative gut-blood-barrier is abundant in autistic individuals. This explains the leakage of bacterial metabolites into the patients triggering new body responses or altered metabolism. Some other co-occurring symptoms such as mitochondrial dysfunction, oxidative stress in the cells, altered tight junctions in the blood brain barrier and structural changes in cortex, hippocampus, amygdala and cerebellum were detected. Moreover, this paper suggests that autism is associated with an unbalanced gut microbiota (dysbiosis). Although the cause-effect relationship between autism and gut microbiota is not yet well established, consumption of specific probiotics may represent a powerful tool to re-establish gut homeostasis and promote gut health. Diagnostic and therapeutic value of new biomarkers leading to the perturbation in the phenylalanine metabolism will be discussed.

As we know, the economy of science is to achieve optimal gains using scarce resources. Similarly, a problem where economies of the world are trying to resolve it is the issue of how to achieve an optimal economic contribution from limited tourism resources. According to the fact that global studies and statistics indicate that good governance is considered as the most important factor in developing countries and is considered as a strong governance of the development and as Information and Communication Technology (ICT) is effective in the interactions and economic and social exchanges of any society, it is the link between the tourism industry and the technology under the form of a robust tool and utilitarian in the tourism process called E-tourism can play a significant role. Therefore, the aim of this study was to investigate the effect of network Readiness Index and good governance on income from tourism industry. In this study, data and information about 14 selected countries were collected from Southwest Asia in the annual period of 2016-2011 in the form of panel data and the proposed model was used and estimated by the generalized torques system method. The estimated results of the model indicate that both hypotheses have a positive and significant impact on the amount of Network Readiness Index in the income of tourism industry, as well as the positive and significant effect of the index the amount of income from the tourism industry is confirmed, and their impact has been positive and significant, as well as exchange rate variables, revenue and commercial openness, also have a positive and significant effect on revenues of the tourism industry.

More than fifty million people over the age of fifty have osteoporosis issues due to low bone mass, disease contribution, and not exercising continuously. Certain most important risk factors and causes of osteoporosis contain age, gender, hormones, visits to inexperienced doctors, and certain medical conditions. In this manuscript, we diagnosed certain frank operational laws under the availability of complex intuitionistic fuzzy (CIF) information. Furthermore, using the evaluated operational laws, we pioneered the theory of CIF frank weighted averaging (CIFFWA), CIF frank ordered weighted averaging (CIFFOWA), CIF frank hybrid averaging (CIFFHA), CIF frank weighted geometric (CIFFWG), CIF frank ordered weighted geometric (CIFFOWG), CIF frank hybrid geometric (CIFFHG) operators, and described their beneficial and valuable results with certain useful properties. Moreover, we aim to evaluate the most dangerous type of osteoporosis based on their symptoms, causes, and risk factors using diagnosed approaches. Finally, to enhance the worth of the evaluated operators, we compare the diagnosed result with certain prevailing results and illustrated their geometrical representation with the help of MATLAB.

Particle-mediated elasticity/viscoelasticity imaging has potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density, Poisson’s ratio, and viscosity. Here, we investigated elasticity imaging based on the use of small particles located within the tissue and at the tissue interface exposed to a static and dynamic external load. First, we discuss elasticity/viscoelasticity imaging methods based on the use of particles (bubbles and rigid spheres) located within the tissue. Elasticity/viscoelasticity imaging techniques based on the use of particles (bubbles, rigid and soft spheres) located at the tissue interface are then presented. Based on new advances, we updated some of the models for the responses of the particles located within the tissue and at the tissue interface available in the literature. Finally, we compared the mathematical models for the particles located within the tissue and at the tissue interface and evaluated the elasticity/viscoelasticity imaging methods based on the use of small particles. Therefore, instead of the use of the term elasticity imaging, we suggest the use of the term viscoelasticity imaging.

Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021-2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime, on weekends, and in the fall season. Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham's transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

In the last years evidence has emerged that neurodegenerative diseases (NDs) are strongly associated with the microbiome composition in the gut. Parkinson’s disease (PD) is the most intensively studied neurodegenerative disease in this context. In this review, we performed a systematic evaluation of published literature comparing changes in colonic microbiome in PD to the ones observed in other NDs including Alzheimer’s Disease (AD), Multiple system atrophy (MSA), Multiple sclerosis (MS), Neuromyelitis optica (NMO) and Amyotrophic lateral sclerosis (ALS). To warrant comparability of different studies, only human case-control studies were included. Several studies showed an increase of Lactobacillus, Bifidobacterium, Verrucomicrobiaceae and Akkermansia in PD. A decrease in PD was observed of Faecalibacterium spp., Coprococcus spp., Blautia spp., Prevotella spp. and Prevotellaceae. On low taxonomic resolution, like phylum level, the changes are not disease specific and inconsistent. However, on higher taxonomic resolution like genus or species level, a minor overlap was observed between PD and MSA, both alpha synucleinopathies. We show that a methodical standardization of sample collection and analysis is necessary for ensuring the reproducibility and comparability of data. We also provide the evidence, that assessing the microbiota composition at high taxonomic resolution, reveals changes in relative abundance, that may be specific or characteristic for one disease, or a disease-group and might evolve discriminative power. The interactions between bacterial species and strains and moreover the co-abundances must be more deeply investigated before assumptions of the effects of specific bacteria on the host can be made with certainty.

Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

Contemporary financial systems, despite their inherent complexity and resilience, face significant challenges related to transparency, efficiency, and security. Notable deficiencies in transparency and emerging security vulnerabilities underscore the pressing need for innovative solutions in current financial practices. Hence, this paper introduces a financial distribution system based on a consortium blockchain. Given organizations’ preference for keeping sensitive data private, the partially decentralized nature of consortium blockchains plays a pivotal role in the proposed system’s architecture. Moreover, within this system, the proposed smart contract serves a dual role. It functions not only as a transactional tool but also as a specialized solution designed to improve transactional procedures. Its multifaceted capabilities include ensuring data accuracy, facilitating swift execution, and ensuring adherence to regulatory requirements within the dynamic financial distribution landscape. Through rigorous testing and empirical analysis utilizing the Hyperledger Besu platform, it has been observed that the performance of the proposed system surpasses traditional blockchain frameworks. The results unequivocally demonstrate the proposed system’s proficiency in handling high-frequency financial transactions with minimal computational burden.

The solid-state technique was utilized to make a single phase of delafossite CuAlO2, along with 1%, 2%, 3% and 4% silver doped CuAlO2 samples in this study. The content of the samples was investigated using energy dispersive X-ray spectroscopy (EDS) equipped with a field emission scanning electron microscope (FE-SEM). The polycrystalline nature of all of the analyzed cases was confirmed by structural analyses utilizing an X-ray diffraction (XRD) pattern with a simple typical peak of CAO rhombohedral phase. Raman, FT-IR and UV–Vis spectroscopy were used to study the structural, and optical energy band gap (Eg) of Ag doped CuAlO2 nanoparticles. The Kubelka–Munk function was used to calculate the optical band gap of ACAO alloys using diffuse reflectance spectra, and it altered as the Ag ion concentration increased. A. c. impedance spectroscopy was used to investigate the dielectric characteristics of Ag doped CuAlO2 nanoparticles.

Growing evidence suggests a possible involvement of the intestinal microbiota in generating new neurons, but a detailed breakdown of the microbiota composition is lacking. In this report, we systematically reviewed preclinical rodent reports addressing the connection between the composition of the intestinal microbiota and neurogenesis and neurogenesis-affecting neurotrophins in the hippocampus. Various changes in bacterial composition from low taxonomic resolution at the phylum level to high taxonomic resolution at the species level were identified. As for neurogenesis, studies predominantly used doublecortin (DCX) as a marker of newly formed neurons or bromodeoxyuridine (BrdU) as a marker of proliferation. Brain-derived neurotrophic factor (BDNF) was the only neurotrophin found researched in relation to the intestinal microbiota. Phylum Actinobacteria, genus Bifidobacterium and genus Lactobacillus found the strongest positive while phylum Firmicutes, phylum Bacteroidetes and family Enterobacteriaceae as well as germ-free status, showed the strongest negative correlation towards neurogenesis or BDNF mRNA expression. Age, short-chain fatty acids (SCFA), obesity and chronic stress were recurring topics in all studies identified. Overall, these findings add to the existing evidence of a connection between microbiota and processes in the brain. To better understand this interaction, further investigation based on analyses of higher taxonomic resolution and clinical studies would be a gain to the matter.

The Pashmina and Barbari are two famous goat breeds found in the wide areas of the Indo-Pak region. Pashmina is famous for its long hair-fiber (Cashmere) production while Barbari is not-selected for this trait. So, the mRNA expression profiling in the skin samples of both breeds would be an attractive and judicious approach for detecting putative genes involved in this valued trait. Here, we performed differential gene expression analysis on publicly available RNA-Seq data from both breeds. Out of 44,617,994 filtered reads of Pashmina and 55,995,999 of Barbari which are 76.48% and 73.69% mapped to the ARS1 reference transcriptome assembly respectively. A pairwise comparison of both breeds resulted in 47,159 normalized expressed transcripts while 8,414 transcripts are differentially expressed above the significant threshold. Among these, 4,788 are upregulated in Pashmina while 3,626 transcripts are upregulated in Barbari. Fifty-nine transcripts harbor 57 genes including 32 LOC genes and 24 are annotated genes which were selected on the basis of TMM counts > 500. Genes with ectopic expressions other than uncharacterized and LOC symbol genes are Keratins (KRT) and Keratin Associated Proteins (KRTAPs), CystatinA&6, TCHH, SPRR4, PPIA, SLC25A4, S100A11, DMKN, LOR, ANXA2, PRR9 and SFN. All of these genes are likely to be involved in keratinocyte differentiation, sulfur matrix proteins, dermal papilla cells, hair follicles proliferation, hair curvature, wool fiber diameter, hair transition, hair shaft differentiation and its keratinization. These differentially expressed reported genes are critically valuable for enhancing the quality and quantity of the pashmina fiber and overall breed improvement. This study will also provide important information on hair follicle differentiation for further enrichment analyses and introducing this valued trait to other goat breeds as well.

Cyanobacterial mass developments in eutrophic ponds and lakes are a major concern for lake management, as many cyanobacteria produce a huge variety of toxic secondary metabolites, e.g. microcystins. The aim of this research was to culture a strain of the cyanobacterium Microcystis sp strain BM25, to observe its biomass production and to isolate and purify protease inhibitors from this cyanobacterial biomass. Different secondary metabolites were isolated following a standard bioassay-guideline. Isolation was performed, with an enzymatic protease assay as bioassay. High performance liquid chromatography was used to identify different fractions of secondary metabolite from the strain BM25. Moreover, protease homogenates were isolated from Daphnia magna in order to test the inhibitors against naturally occurring major digestive proteases trypsin and chymotrypsin. It was measured that 60% MeOH and the 80% MeOH C18-SPE fraction inhibits chymotrypsin activity 98% (6 nmol pNA min^-1 mg^-1) and 99 % (4 nmol pNA min^-1 mg^-1), respectively. In contrast, trypsin activity was not inhibited by methanolic extracts of this cyanobacterium strain.

The latest research cumulates staggering information about the correlation between the microbiota-gut-brain axis and neurodevelopmental disorders. This review aims to shed light on the potential influence of the microbiome on the development of the most prevalent neurodevelopmental disease, attention-deficit-hyperactive disorder (ADHD). As the etiology and pathophysiology of ADHD are still unclear, finding viable biomarkers and effective treatment still represent a challenge. Therefore, we focused on factors that have been associated with a higher risk of developing ADHD while simultaneously influencing the microbial composition. We reviewed the effect of a differing microbial composition on neurotransmitter concentrations important in the pathophysiology of ADHD. Additionally, we deduced factors that correlate with a high prevalence of ADHD while simultaneously affecting the gut microbiome such as emergency c-sections, and premature birth as the former leads to a decrease of the gut microbial diversity and the latter causes neuroprotective Lactobacillus levels to be reduced. Also, we assessed nutritional influences such as breastfeeding, ingestion of short-chain fatty acids (SCFAs) and polyunsaturated fatty acids (PUFAs) on the host’s microbiome and development of ADHD. Finally, we discussed the potential significance of Bifidobacterium as a biomarker for ADHD, the importance of preventing premature birth as prophylaxis and nutrition as a prospective therapeutic measurement against ADHD.

Acute kidney injury (AKI) is a common complication post-cardiac surgery, impacting short- and long-term survival. Free light chains (FLCs) are being explored as markers for assessing kidney damage risk. A study aimed to validate early AKI detection by studying FLCs (kappa, lambda, and kappa/lambda ratio) in serum and saliva samples. Clinical data was collected from 149 patients who underwent cardiac surgery in Baghdad. Ninety of these patients provided saliva samples in addition to serum samples for free light chain evaluation. Patients were grouped by proteinuria severity. Serum kappa levels were significantly higher in severe proteinuria patients compared to mild or no proteinuria. Salivary kappa levels were significantly higher in mild proteinuria patients compared to severe or no proteinuria. Salivary lambda levels decreased with increasing proteinuria severity. Serum kappa increase could be due to immune system activation and kidney compromise. Both serum and salivary kappa showed good predictive ability for post-surgery AKI prognosis compared to lambda and the kappa/lambda ratio, depending upon ROC performance. The study suggests FLCs, especially kappa, could be valuable in early AKI detection post-cardiac surgery, indicating potential for improved patient outcomes through proactive monitoring and intervention.

The succinate receptor, SUCNR1, has been attributed to tumor progression, metastasis, and im-mune response modulation upon its activation via the oncometabolite succinate. Nonetheless, lit-tle is known about SUCNR1 prognostic relevance and its association with tumor immune infil-trates and microbiota in renal cell carcinoma (RCC). Herein, publicly available platforms in-cluding Human Protein Atlas, cBioPortal, TIMER2, and TISIDB were utilized to depict a diver-gent implication of SUCNR1 in the immune microenvironment of clear cell RCC (KIRC) and pa-pillary RCC (KIRP); the two major subtypes of RCC. Our results showed that SUCNR1 expression level was augmented in RCC compared to other solid cancers, yet with opposite survival rate predictions in RCC subtypes. Consequently, higher expression level of SUCNR1 was associated with a good disease-specific survival rate (p=5.797e-5) in KIRC patients albeit a poor prognostic prediction in KIRP patients (p=1.9282e-3). Intriguingly, SUCNR1 was mainly correlated to im-munomodulators and diverse immune infiltrates in KIRP. Whereas the receptor was mostly asso-ciated with a repertoire of microbes including beneficial bacteria that likely influenced a better disease-specific survival rate in KIRC. Our findings illustrate a significant subtype-specific role of SUCNR1 in RCC which potentially modulates tumor immune infiltration and microbiome sig-nature, hence altering the prognosis of cancer patients.

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

Around the world, there is no population clear from urinary tract infection (UTI), particularly among women. UTI is considered the most predominant bacterial infection. This study aimed to detect the incidence of the most common major uropathogens in patients severe from urinary tract infection with antibiotic sensitivity tests that assist urologist doctors for appropriate antimicrobial empirical therapy.Methods: This study was carried in a private laboratory in Babil city, Iraq from May 2019 to May 2020. Totally 70 individuals suffering from clear symptoms of UTI, as well as, 20 healthy persons participated in this study as a control group. Then, the standard microbiological methods carried out to isolate and identify bacterial species. Antimicrobial susceptibility tests were performed using different antimicrobial discs by applying the Kirby–Bauer disc diffusion method.Results: Totally, 90 specimens were obtained from them 20 control group, 19 with no growth, and 51 patients with bacterial growth distributed as 43 (83%) females and 8 (17%) males. E. coli were the most common predominant organisms. All isolates were showed a high rate of resistance to evaluated cephalosporins 100% and 82% to cefotaxime and ceftriaxone respectively, while very low resistance recorded in Aminoglycosides 20% and 13% to Gentamicin and amikacin respectively. Most age group infected with UTI was 21-40 years old.Conclusion: The current study showed an increasing burden of urinary tract infection caused by various bacteria implicated in UTI that causes changeable sensitivity to various antimicrobial agents. Therefore, in clinical use appropriate medications should be selected based on the data obtained from antimicrobial susceptibility tests.

Since December 2019, a rapid increase in the number of SARS-CoV-2 (COVID-19) cases was reported worldwide, despite strict infection control and lock down measures. Current paper investigated the actual facts behind this rapid increase in the number of cases. Study of genomic sequence reveals that domestic and wild animals were likely ancestors and zoonotic source for SARS-CoVs, MERS-CoVs, and SARS-CoV-2. Strong evidence suggest that these viruses already existed and replicated in animals and humans during past several decades, exhibiting diverse mutations, evolutions and self-limiting diseases, except during outbreaks. Serious zoonotic reservoir investigations are required to investigate animal transmission of SARS-CoVs and SARS-CoV-2 to limit current pandemic. This might be the reason of increasing number of cases via animals. SARS-CoV-2 has been retrospectively isolated in different studies in August 2019, several months before Wuhan announced. Hence, there is a possibility that viruses existed, went undetected, infecting subclinically, in past several years, and SARS-CoV-2 antigens and neutralizing antibodies may have been present in humans since long time. This might be another reason of increasing number of cases by screening as mass screening and antigen or antibody testing was not carried out in the past years. Randomized controlled trials are required to investigate human to human transmission by touch, as the current evidence is limited with conflicting results. As all SARS-CoVs are basically respiratory viruses, droplet precautions and infection control measures are essential, especially for hospital staff. Increased number of SARS-CoV-2 asymptomatic, or subclinical cases are detected worldwide. This silent phase of transmission can be beneficial for humans. Lack of symptoms eventually lessen virus transmission and reduce the pathogen's long-term survival and provide humoral herd immunity up to several years. Hence, seropositivity with diverse antibodies develops against mutating SARS-CoVs which will confer strong immunity during epidemics. Strategies such as identification, contact tracing and quarantine are costly and practically difficult. Hence, asymptomatic persons can continue their work with droplet precautions and standard infection control procedures, while symptomatic or sick persons can isolate themselves in their homes without the need for strict quarantine until clinical recovery, with reduced hospital visits and minimizing chances of hospital acquired infections. RT-PCR has low sensitivity and specificity, carries a high risk of handling live virus antigens, and requires difficult protocols. As viral load also sharply declines after few days of onset of infection, this technique might overlook infection. Furthermore, SARS-CoV-2 infection may be present in blood when oropharyngeal swabs are negative by RT-PCR. Additionally, RT-PCR usually gives false negative and false positive results and must be interpreted cautiously. This might be again a reason of increasing number of cases by false positive RT-PCR reporting. Moreover, antibodies against SARS-CoVs develop robustly in serum even by reduced amount of antigens. In contrast to RT-PCR, ELISA for diagnosing antibodies against SARS-CoV-2 demonstrates 100% specificity and 100% sensitivity, even in clinically asymptomatic individuals. These antibodies can be used for serologic surveys, monitoring and screening. However, screening tests for SARS-COV-2 should be avoided in unhygienic public places by nasopharyngeal swabs, which carry a high risk of further transmission, co-infection or superinfection. Such highly infectious virus must be isolated and tested in highly sterilized laboratory. Further strict international laws and policies are required to stop the possible spread of experimental viruses, biological warfare and bioterrorism.

This study investigated riverbank protection strategies along the dynamic Jamuna River in Bangladesh, a braided river prone to erosion and sedimentation. It reviews past countermeasures and emphasizes the effectiveness of groyne-type structures in redirecting flow and preventing erosion. Notably, the left bank exhibited greater stability than the right bank, emphasizing the need for effective groyne installations. A systematic methodology utilizing numerical modeling using International River Interface Cooperation (iRIC Nays2DH) ensured accuracy in assessing morphological impacts. This study presents novel countermeasures incorporating groyne installations in Zone 1 (from Singrabari to Meghai) and Zone 2 (from Pachthakuri to Subhagacga), accompanied by simulations for various flood conditions. Zone 2, identified as highly erosion-vulnerable, showed the greatest susceptibility. Simulations (Cases_0 1, 2, and 3) revealed groynes in Zone 1 alone ineffective, while Zone 2 installations successfully diverted oblique flow, reducing erosion risk. Although Case_3's combined approach was effective, it was uneconomical for Zone 1. Case_2, focusing on Zone 2 groynes, emerged as the optimal solution, redirecting flow and shaping sandbars to mitigate Jamuna River's riverbank erosion. It provides a practical and economical solution. Further research is recommended for groyne length and spacing variations. This study enhances Jamuna River protection, emphasizing groyne-type structures' importance and promoting a holistic understanding of morphological dynamics for future river management and effective countermeasures.

Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. Therefore, the primary objective of this study was to evaluate the impact of crossbreeding Bangladeshi local cattle with the exotic Holstein breed on their body characteristics and milk quality. To achieve the goal, data pertaining to body traits and milk samples were gathered from a total of 981 cows from 19 dairy farms located in the northwestern region of Bangladesh. A trained evaluator measured body condition score (BCS), udder score, locomotion score, and body conformation traits. Milk yield information was acquired from official records, while milk composition details were determined through milk analysis. The average measurements for body traits were as follows: wither height (WH) at 138 cm, body length (BL) at 155 cm, and heart girth (HG) at 203 cm. Notably, crossbred cows (Holstein x Local cattle) exhibited greater values for WH, BL, HG, BCS, and udder score than their Bangladeshi local counterparts. Furthermore, the average daily milk yield of sampled cows was recorded at 8.7 kg, with fat and protein contents of 3.78% and 3.67%, respectively. It is worth noting that crossbred cows produced a higher volume of milk compared to Bangladeshi local cattle. However, milk from crossbred cows displayed lower fat and protein content, although their somatic cell score (SCS) remained similar. Additionally, milk from crossbred cows exhibited a longer coagulation time when compared to that of Bangladeshi local cattle. A more comprehensive assessment of the crossbreeding scheme, particularly in comparison to Holstein, would necessitate further research, including an examination of its impact on cheese yield traits, health, fertility, and longevity.

Drought is treated as a key natural disaster that affects numerous segments of the natural environment and economy throughout the world. Drought indices (DIs) were computed for Potwar region (PR) in Punjab-Pakistan, using DrinC software which are deciles, Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). Drought situation of 12, 9, 6 and 3 months was estimated on temporal basis. DIs obtained by deciles technique showed that for the last 39 years, 8-years are with drought severity in a cycle and are occurring every 2 to 7-years just the once repetitively. The RDI and SPI index showed the analogous trends as of deciles. Though, for RDI and SPI, the extremely dry and severely dry class was merely two years and rest of the drought affected years with respect to deciles were normally and intermediately dry. SPI is better as compared to deciles as the severity is better understood in the context of SPI. Regression analysis revealed that the RDI and SPI indices are mutually interrelated and if first 3 month precipitation is obtainable one can forecast yearly RDI. This investigation is valuable to devise future development plans to contest vulnerable drought incidents, its mitigation and impacts on socio-economic sectors.

The aim of the study was the evaluation of the efficiency of selected abiotic elicitors, i.e. silver and cadmium ions, ultrasound, and UV-C irradiation, in the stimulation of triterpenoid biosynthesis, accumulation, and saponin secretion in Calendula officinalis hairy root cultures. Apart from the possible enhancement of triterpenoid production, the relationship between primary and secondary metabolism (represented respectively by sterols and pentacyclic triterpenes), modifications of the sterol compositional profile, and fluctuations in the total triterpenoid content were monitored in the performed experiments. The main phenomenon observed as a response to heavy metal treatment was the stimulation (up to 12-fold) of the secretion of saponins, accompanied by significant changes in sterol composition. Ultrasound stimulated the secretion of saponins (up to 11-fold); however, it exerted diverse influences on the triterpenoid content in hairy root tissue (stimulating or decreasing) depending on the duration of the exposure to the elicitor. UV-C radiation caused a slight increase in the content of both sterols and saponins in hairy root tissue, and stimulated saponin secretion up to 8.5-fold. The expected symptoms of the competition between the biosynthetic pathways of sterols and pentacyclic triterpenoids were less evident in reactions to abiotic stressors than those reported previously for biotic elicitors

Coronaviruses are a large family of RNA viruses which cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and COVID-19. This article highlights some key findings based on a thorough scanning of genes of 475 SARS-CoV2 genomes, including the co-presence of ORF7a and ORF8 over the 256 SARS-CoV2 genomes and the absence of the gene ORF7b over the 219 SARS-CoV2 genomes collected from various countries including India. The presence of the gene ORF7b is found in the SARS-CoV2 genomes containing the L-type strain which is reported to having much higher virulence as compared to the S-type strain.

Different drought indices are used to quantify its characteristics. This research applied many approaches to assessing the uncertain SPI and SPEI and the most capturing index of drought. Machine learning algorithms are used to predict drought; TBATS and ARIMA models run diverse input sources including observations, CSIC, and CMIP6-ssp126 datasets. The longest drought duration was 14 months. Drought severity and average intensity were found -24.64 and -1.76, -23.80 and -1.83, -23.57 and -1.96, -23.44 and -2.0 where the corresponding drought categories were SPI 12 -Sweileh, SPI 9 Sweileh, SPI 12 Wadi Dhullail, SPI 12 Amman-Airport. The dominant drought incident occurred between Oct 2020 and Dec 2021. CMIP6 can capture the drought occurrence and severity by measuring SPI but did not capture the severity magnitude same as from observations (-2.87 by observation and -1.77 by CMIP6). Using observed SPI and historical CMIP6, ARIMA was the most accurate than TBATS. Regarding SPEI forecast, ARIMA was the most accurate model to forecast drought index using the observed historical SPEI and CSIC over all stations. The performance metrics ME, RMSE, MAE, and MASE implied significantly promising forecasting models; -0.0046, 0.278, 0.179, & 0.193 respectively for ARIMA and -0.0181, 0.538, 0.416, & 0.466 respectively for TBATS. Hybrid modelling is suggested for more consistency and robustness of forecasting approaches.

Medicinal plants possess a surplus of novel and biologically active secondary metabolites that are responsible for counteracting diseases. Traditionally, Gomphandra tetrandra (Wall.) Sleumer is used to treat mental disorders. The present research was designed to explore phytochemicals from the ethanol leaf extract of Gomphandra tetrandra (Wall.) Sleumer to identify the potential pharmacophore(s) in the treatment of neurological disorders. The chemical compounds of the experimental plant were identified through GC-MS analysis. Besides, in-vitro antioxidant activity was assessed using different methods. Furthermore, in-vivo neurological activity was assessed in Swiss-albino mice. Computer aided analysis was appraised to determine the best-fit phytoconstituent of a total of fifteen identified compounds in the experimental plant extract against beta-amyloid precursor protein. The experimental extract revealed fifteen compounds in GC-MS analysis and the highest content was 9, 12, 15-octadecatrienoic acid (Z, Z, Z). Also, the extract showed potent anti-oxidant activity in in-vitro assays. Furthermore, in in-vivo neurological assays, the extract disclosed significant (p<0.05) neurological activity. The most favorable phytochemicals as neurological agents were selected via ADMET profiling and molecular docking was studied with beta-amyloid precursor protein. Moreover, in the computer aided study, 1, 5-Diphenyl-2H-1, 2, 4-triazoline-3-thione (Pub Chem CID: 2802516) was more active than other identified compounds with strong binding affinity to beta-amyloid precursor protein. The present in vivo and in silico studies revealed neuropharmacological features of G. tetrandra leaves extract as a natural agent against neurological disorders, especially Alzheimer’s disease.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

Wheat (Triticum aestivum L.) being a staple food crop is an important nutritional source providing protein and minerals. It is important to fortify staple cereals like wheat with essential minerals to overcome the problems associated with malnutrition. The experiment was designed to evaluate the status of 11 micronutrients including grain iron (GFe) and zinc (GZn) in 62 wheat cultivars released between 1911 and 2016 in Pakistan. Field trials were conducted over two years and GFe and GZn were quantified by both inductively coupled plasma optical emission spectroscopy (ICP-OES) and energy dispersive X-ray fluorescence spectrophotometer (EDXRF). The GZn ranged from 18.4 to 40.8 mg/kg by ED-XRF and 23.7 to 38.8 mg/kg by ICP-OES. Similarly, GFe ranged from 24.8 to 44.1 mg/kg by ICP-OES and 26.8 to 36.6 mg/kg by EDEXR. The coefficient of correlation was higher for GZn (r=0.90), compared to GFe (r=0.68). Modern cultivars like Zincol-16 and AAS-2011 showed higher GFe and GZn along with improved yield components. Old wheat cultivars WL-711, C-518 and Pothowar-70 released before 1970 also exhibited higher value of GFe and GZn, however their agronomic performance was poor. Multivariate analysis using ten micronutrients (Al, Ca, Cu, K, Mg, Mn, Na and P) along with agronomic traits, and genome-wide SNP markers identified the potential cultivar with improved yield, biofortification trait and wider genetic diversity. Genetic gain analysis identified significant increase in grain yield (0.4% year-1), while there was negative gain for GFe (-0.11% year-1) and GZn (-0.15% year-1) over the span of 100 years. The Green Revolution Rht-B1 and Rht-D1 genes had strong association with plant height, and grain yield (GY), while semi-dwarfing alleles had negative effect on GFe and GZn contents. This study provided a valuable insight into biofortification status of wheat cultivars deployed historically in Pakistan and is a valuable source to initiate a breeding strategy for simultaneous improvement in wheat phenology and biofortification.

This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.

Fossil fuels are non-renewable sources of energy, used particularly in developing countries. There are insufficient fossil fuels and their availability is gradually declining. This leads to a steady increase in prices. Renewable energy is clean, cost-effective and limitless. The considerable population growth and industrial growth have made the construction of solar power plants essential in developing countries. The study used Boolean logical-AHP techniques to select a suitable site for solar power in Duhok Governorate. The result indicated that 68.5% of the area in the governorate of Duhok is available for solar power station construction, especially the most appropriate zone which has 132.2 sq. km and can provide more than 8000 megawatts of solar energy. Most of the appropriate areas are in the south and southwest regions of the governorate, located mostly in the districts of Bardarash, Shekhan and Semel, situated between the major cities of Mosul, Erbil and Duhok. These locations can supply a significant amount of clean, renewable energy for these areas.

Humans have extremely variable skin pigmentation and melanin production influenced by genetics, UV exposure, and some medications. A significant number of skin illnesses that result in pigmentary abnormalities have an impact on patients’ physical appearance as well as their psychological and social well-being. Skin pigmentation can be divided into two basic categories: hyperpigmentation, where pigment appears to overflow, and hypopigmentation, where pigment is reduced. Albinism, melasma, vitiligo, Addison's disease, and post-inflammatory hyperpigmentation, which can be brought on by eczema, acne vulgaris, and drug interactions, are the most common skin pigmentation disorders in clinical practice. Anti-inflammatory medications, antioxidants, and medications that inhibit tyrosine, which prevents the production of melanin, are all possible treatments for pigmentation problems. Skin pigmentation can be treated orally and topically with medications, herbal remedies, and cosmetic products, but a doctor should always be consulted before beginning any new medicine or treatment plan. This review article explores the numerous types of pigmentation problems, their causes, and treatments, as well as the 25 plants, four marine species, and 17 topical and oral medications now on the market that have been clinically tested to treat skin diseases.

Effective pandemic management requires understanding the level of community knowledge, attitude of people and preparedness towards COVID-19. This study aimed to assess the knowledge, attitude and preparedness toward COVID-2019 among Bangladeshi general people. A cross sectional survey was conducted from 20th March 2020 to 20th April 2020 among 1007 Bangladeshi adult people. A convenience sampling strategy was utilized and the data was collected through a self-administered questionnaire. Chi-square test was used to identify the gender differences regarding knowledge, attitude and preparedness towards COVID-19. Respondents predominately used social media to inform themselves about COVID-19 (68.10%) and female used social media more than male (p<0.001). Female had more correct knowledge about staying home with sickness and/or symptoms to contain the COVID-19 transmission (p=.02). While male had more negative attitude about staying out during the pandemic than female (<0.001) and men were less likely to take preventative measures than female counterparts. A number of people were still believing the myths like “COVID-19 can transmit via mosquito” (9.14%) and male had more incorrect knowledge regarding this (p<0.001). Moreover, 17.81% of the respondents thought that COVID-19 can spread in warm weather. Some findings are directing us to a knowledge gap among general population in Bangladesh which calls for uncovering those aspects. Adequate and effective communication are necessary so that general people can stay alert with positive attitude and hygienic practices to fight against this ongoing crisis.

This article will briefly review the progress of h-BN based solid-state metal semiconductor metal (MSM) neutron detectors. In the last decade, several groups have been working on hexagonal boron nitride (h-BN) based solid-state neutron detectors. Recently, the detection efficiency of 59% has been reported. Efficient, low-cost neutron detectors made from readily available materials are essential for various applications. Neutron detectors are widely used to detect fissile materials and nuclear power plants for security applications. The most common and widely used neutron detectors are 3He based, which are sometimes bulky, difficult to transport, have high absorption length, need relatively high bias voltage (>1000 V), and have low Q-value (0.764 MeV). Also, 3He is not readily available material. Thus, there is a strong need to find an alternative detection material. 10B isotope has a high neutron absorption cross-section, and it has been tested as a coating on the semiconducting materials. Due to the two-step process, neutron capture through 10B, and then electron-hole pair generation in a typical semiconducting material, the efficiency of these devices is not up to the mark. The progress in h-BN based detectors requires a review to envision the further improvement in this technology.

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

Sustainable manufacturing practices are becoming increasingly necessary due to the growing concerns regarding climate change and resource scarcity. Consequently, material recycling technologies have gradually become preferred over conventional processes. This study aimed to recycle waste polylactic acid (PLA) from household-disposed cups and lids to create 3D-printed parts and promote sustainable manufacturing practices. To achieve this, the current study utilised virgin and post-consumer PLA (sourced from household waste) blends. The PC-PLA wastes were shredded and sorted by size with the aid of a washing step, resulting in a filament with a 1.70 ± 0.5 mm diameter without fragmentation or dissolution. A 50:50 wt.% blend of virgin PLA (vPLA) and PC-PLA was selected as the standard recycling percentage based on previous research and resource conservation goals. The study investigated the impact of three 3D printing parameters (layer height (LH), infill density (I), and nozzle temperature (NT))on the quality of 3D-printed parts using a three-level L9 Taguchi orthogonal array. The findings revealed that blending PC-PLA with vPLA led to significant improvements in the tensile, flexural, and impact strengths by 18.40%, 8%, and 9.15%, respectively, compared to those of recycled PLA. This conclusion was supported by the investigation of the fracture surface area, which revealed fractographic features associated with printing parameters, such as plastic deformation and interfilament debonding. An ANOVA analysis revealed a positive influence of larger layer height and high nozzle temperature on the mechanical properties. Subsequently, the optimal printing parameters (LH: 0.3 mm, I: 100%, and NT: 215 °C) were determined using the S/N ratio, and a confirmation test using the optimum printing parameters exhibited a strong correlation with statistically predicted outcomes. Finally, the study used optimum printing parameters to fabricate 100% post-consumer recycled PLA 3D printed parts, demonstrating their potential for low-strength applications. Based on these findings, it can be concluded that virgin and post-consumer recycled PLA blended filament for fabricating 3D printed components is an effective way to support plastic recycling in the context of a circular economy.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

Malaysia is a developing country and agro-industries contribute country’s economy. However, it is largely unknown the scenarios of agricultural practice and food supply over the years. To understand the fact, this study was designed to explore the agricultural land use changes and domestic food supply for last 50 years in Malaysia. The result showed that oil palm plantation area and yield, both are increased by folds, conforming “oil palm” mono-culture practice. Rice cultivation area remained almost unchanged but the yield increased. However, both area of plantation and yield in case of rubber, coca and coconut were decreased. When, it comes to domestic food supply, in 1973 food grain (Rice, Wheat, Maize) import was lower than domestic production. However, the import increased more than 3 times than the domestic production in 2013. Domestic production of vegetables and vegetables import was almost 50:50. Fruits import increased almost 3 times of domestic production, though domestic production was increased. In conclusion, it is understood that Malaysian agriculture economy is standing on “oil palm” wearers domestic food supply is import dependent. It is high time to reshuffle the policy for sustainable development and achieving food sovereignty.

This communication reports an improvement of the quality of the electrical data obtained from the application of electrical resistivity tomography method on archaeological studies. The electrical contact between ground and electrode enhances significantly by using carbomer-based gel during the electrical resistivity tomography measurements. Not only does the gel promote the conservation of the building surface under investigation, but it also virtually eliminates the necessity of conventional spike electrodes, which in many archaeological studies are inadequate or not permitted. Results evidenced an enhancement in the quality of the electrical data obtained in the order of thousands of units compared with those without using the carbomer-based gel. The potential and capabilities of this affordable gel make it appropriate to be applied to other geoelectrical studies beyond archaeological investigations. Moreover, it might solve corrosion issues on conventional spike electrodes, and electrical multicore cables usually provoked for added saltwater attempting to improve the electrical contact.

There is an urgent need to better understand the mechanisms involved in scar formation in brain. It is well known that astrocytes are critically engaged in this process. Here we analyze in-cipient scar formation one week after a discrete ischemic insult to the cerebral cortex. We show that the infarct border zone is characterized by pronounced changes in the organization and subcellular localization of the major astrocytic protein AQP4. Specifically there is a loss of AQP4 from astrocytic endfoot membranes that anchor astrocytes to pericapillary basal laminae and a disassembly of the supramolecular AQP4 complexes that normally abound in these membranes. This disassembly may be mechanistically coupled to a downregulation of the newly discovered AQP4 isoform AQP4ex. AQP4 has adhesive properties and is assumed to facilitate astrocyte mo-bility by permitting rapid volume changes at the leading edges of migrating astrocytes. Thus, the present findings provide new insight in the molecular basis of incipient scar formation.

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.

Pathologic angiogenesis is responsible for much of the vision loss that stems from several chorioretinal vascular diseases including neovascular age-related macular degeneration (nAMD), diabetic retinopathy (DR), and retinal vein occlusion (RVO). Because vascular endothelial growth factor (VEGF) serves as a pivotal angiogenic molecule, it has emerged as the primary target for therapeutic drugs. Current anti-VEGF therapy is dominated by bevacizumab, ranibizumab, and aflibercept, all of which have been available for more than a decade, but new drugs, both biosimilars (Byooviz and Cimerli) and drugs whose primary aim is to decrease treatment burden (brolucizumab, faricimab, and the ranibizumab port delivery system), have been approved. Recent failures (conbercept, abicipar, and KSI-301) emphasize the complexity of drug development and the difficulties faced by innovators. Most therapies under development, including tyrosine kinase inhibitors and gene therapy, include VEGF-A blockade as their primary function and aim to extend durability rather than increase peak efficacy. But even as many new drugs look to extend durability, cost containment with mandated use of biosimilars serves to balance the therapeutic landscape.

Shape memory alloys (SMAs) are increasingly used in the fields of aviation, automotive and biomedicine due to their unique properties. Nickel-Titanium (NiTi) alloy materials, which are one of the shape memory alloys, are among the most frequently used alloy materials. The shape memory and super elastic effects of NiTi alloys, high ductility and deformation hardening make it difficult to shape burr. An additional problem is the formation of a white layer during machining. In this study, surface milling operations were performed in dry cutting conditions with uncoated cutting tools with different nose radii. The processing parameters were determined based on the experience gained as a result of the preliminary tests. Tungsten carbide cutting tools with different nose radii (0.4mm and 0.8mm) were used for the milling operations. Milling was carried out at three different cutting speeds (20, 35, 50 m/min), feed rates (0.03, 0.07, 0.14 mm/tooth), and a constant axial cutting depth (0.7 mm). As a result of our experimental studies, the best tool life was found to be in 0.8 mm nose radius cutting tools at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.264 mm). The minimum average surface roughness was found after milling with 0.8 mm nose radius cutting tool at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.346 μm). It has been determined that increasing the cutting tool nose radius reduces both the flank wear over the cutting tool and the average surface roughness.

In the context of the pandemic caused by Covid-19, an important aspect that was affected and changed was the way people communicate. Communication efficiency can be improved by using modern technologies, for example: mobile devices, digital audio and video platforms, text platforms, social media and more. For further economic success, digital skills are needed not only for executives, but also for managers, in order to be able to lead teams and avoid situations of stress and tension in the virtual environment. This scientific paper includes a primary research of how the pandemic affected the communication at the workplace. For this a qualitative analysis was first performed, in which the interview was used as a working tool. Next, on the basis of a questionnaire, a hypothesis was formulated and validated afterwards through multiple linear regression. This research highlighted the particularly important role of communication and, in this context, the role of the remote team leader. In order to keep the team motivated and interested in the virtual environment, the manager must use modern tools to motivate, support and express his appreciation when the team, respectively the members of his team, achieve their goals successfully.

This paper addresses a wildlife monitoring problem using a team of UAVs for efficient monitoring of wildlife. The state-of-the-art technology using UAVs has been an increasingly popular tool to monitor wildlife compared to the traditional methods such as satellite imagery-based sensing or GPS trackers. However, there still exist unsolved problems as to how the UAVs need to cover a spacious domain to detect animals as many as possible. In this paper, we propose the optimal transport-based wildlife monitoring strategy for a multi-UAV system, to prioritize monitoring areas while incorporating complementary information such as GPS trackers and satellite-based sensing. Through the proposed scheme, the UAVs can explore the large-size domain effectively and collaboratively with a given priority. The time-varying nature of wildlife due to their movements is modeled as a stochastic process, which is included in the proposed work to reflect the spatio-temporal evolution of their position estimation. In this way, the proposed monitoring plan can lead to efficient wildlife monitoring with a high detection rate. Various simulation results including statistical data are provided to validate the proposed work.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

The rise in dependency upon information present on web and social networks has increased the importance of content credibility systems. These sys- tems can help the users to make a right decision related to buying a product, utiliz- ing a service and etc. Application areas including social network blogs of different subjects such as health, food, education, politics and product review/ratings some- times suffer incredible and wrong information flow. The content credibility sys- tems can help users to identify the credible information on these various forums. Recently, researchers have proposed certain mechanisms for these systems, out of which reputation based systems have gained most attention. However, reputa- tion systems are vulnerable to reputation based attacks, like Sybil, Slandering, and Whitewash promoting spread of wrong information. The authors have proposed a defense mechanism against these attacks that is based upon Bayesian Model. The proposed defense mechanism provides protection against these attacks so that fake or wrong information could be prevented. The authors evaluated the proposed mechanism in three different scenarios and presented the results in terms of pre- cision, recall and rate of change in rank. The results reveal almost 88%prevention against these attacks in comparison to the baseline systems.

The SARS coronavirus 2 (SARS-CoV-2) is the causative agent of the 2019 coronavirus disease (COVID-19) pandemic that has executed 6.9 million people and infected over 765 million. It’s become a major worldwide health alarm and is also known to cause abnormalities in various systems, including the hematologic system. COVID-19 infection primarily affects the lower res-piratory tract and can lead to a cascade of events, including a cytokine storm, intravascular thrombosis, and subsequent complications such as arterial and venous thromboses. COVID-19 can cause thrombocytopenia, lymphopenia, and neutrophilia, which are associated with worse out-comes. Prophylactic anticoagulation is essential to prevent complication and death rate associated with the virus's effect on the coagulation system. It is crucial to recognize these complications early and promptly start therapeutic anticoagulation to improve patient outcomes. While rare, COVID-19-induced disseminated intravascular coagulation exhibits some similarities to DIC induced by sepsis. LDH, D-dimer, ferritin, and CRP biomarker are often increase in serious COVID-19 cases and poor prognosis. Understanding the pathophysiology of the disease and identifying risk factors for adverse outcomes is critical for effective management of COVID-19.

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

Since the first coronavirus patient was identified in Bangladesh on March 8, the most controversial issue is about the exact level of the infection in Bangladesh. Conformity with the population density the number of COVID-19 tests is inadequate. As the number of tests increases, so does the number of infections, making it difficult to predict the spread of COVID-19 in Bangladesh. In this case, the unplanned initiatives are particularly responsible in other for unplanned measures, lack of public awareness, and lack of proper knowledge. In this case, the Ministry of Health has made three major mistakes, three important features of the medical system in Bangladesh have been mentioned. It is more effective to prevent COVID-19 by isolating the infected person by further testing COVID-19 until effective treatment is available and to provide adequate and effective masks and personal protective equipment (PPE). In this case, the COVID-19 testing kit invention has received a good response in many countries of the world. This study focuses on the comprehensive data verification, selection, and evaluation of COVID-19 in Bangladesh and its implications for the future, what to do to address and prevent the COVID-19 challenge, and effective treatment against the coronavirus (COVID-19). It is hopeful that the discussion of the material mentioned in this research paper will help to strike a balance between the government, citizens, and experts which will be feasible in improving the current situation in COVID-19 Bangladesh and reducing its severity.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

The illicit use of fentanyl has led to hundreds of thousands of opioid-related deaths worldwide. Therefore, the detection of fentanyl by law enforcement and recreational users is of utmost importance. However, current detection methods are expensive, time-consuming, require special storage conditions and necessitate complex instrumentation that is generally unportable and requires skilled personnel to operate. An alternative approach would be using molecularly imprinted polymers (MIPs) as the recognition component of a handheld sensor, testing strip, or color-based assay. In this work, a molecularly imprinted polymer was made by using methacrylic acid (MAA) and ethyleneglycol dimethacrylate (EGDMA) as the functional monomer and cross-linking monomer respectively, and benzylfentanyl (Bfen) as the template. The use of benzylfentanyl is advantageous because it closely resembles the structure of fentanyl but does not have any physiological effects as a narcotic. Important studies herein determined the optimum ratio of the template:functional monomer, with subsequent evaluations of selectivity versus the commonly encountered narcotics such as heroin, cocaine, and methamphetamine. The data obtained from the HPLC analysis showed that the Bfen-MIP was successful in selectively binding the template and actual fentanyl, better than other common narcotics.

The tumor microenvironment plays a pivotal role in the behavior and development of solid tumors as well as shaping the immune response against them. As the tumor cells proliferate, the space they occupy and their physical interactions with the surrounding tissue increases. The growing tumor tissue becomes a complex dynamic structure, containing connective tissue, vascular structures, and extracellular matrix that facilitates stimulation, oxygenation, and nutrition, necessary for its fast growth. Mechanical cues such as stiffness, solid stress, interstitial fluid pressure, matrix density, and microarchitecture influence cellular functions and ultimately tumor progression and metastasis. In this fight, our body is equipped with T cells as its spearhead against tumors. However, the altered biochemical and mechanical environment of the tumor niche affects T cell efficacy and leads to their exhaustion. Understanding the mechanobiological properties of the tumor microenvironment and their effects on T cells is key for developing novel adoptive tumor immunotherapies.