In the realm of the five-category classification endeavor, there has been limited exploration into applied techniques for classifying Arabic text. These methods have primarily leaned on singletask learning, incorporating manually crafted features that lack robust sentence representations. Recently, the Transformer paradigm has emerged as a highly promising alternative. However, when these models are trained using single-task learning, they often face challenges in achieving outstanding performance and generating robust latent feature representations, especially when dealing with small datasets. This issue is particularly pronounced in the context of the Arabic dialect, which has a scarcity of available resources. Given these constraints, this study introduces an innovative approach to dissecting sentiment in Arabic text. This approach combines Inductive Transfer (INT) with the Transformer paradigm to augment the adaptability of the model and refine the representation of sentences. By employing self-attention SE-A and feed-forward sub-layers as a shared Transformer encoder for both the five-category and three-category Arabic text classification tasks, this proposed model adeptly discerns sentiment in Arabic dialect sentences. The empirical findings underscore the commendable performance of the proposed model, as demonstrated in assessments of the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, By using methods that can process information and recognize patterns, for example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, three-stage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances.

In recent times, models like the Transformer have showcased remarkable prowess in tasks related to natural language processing. However, these models tend to be excessively intricate and demand extensive training. Additionally, while the multi-head self-attention mechanism in the Transformer model aims to capture semantic connections between words in a sequence, it encounters limitations when handling short sequences, thereby limiting its effectiveness in 5-polarity Arabic sentiment analysis tasks. The switch-transformer model has recently emerged as a high-performing alternative. Nevertheless, when these models are trained using single-task learning, they often fall short of achieving exceptional performance and struggle to generate robust latent feature representations, especially when working with compact datasets. This challenge is particularly pronounced in the case of the Arabic dialect, which is considered a low-resource language. Given these constraints, this research introduces a novel approach to sentiment analysis in Arabic text. This method leverages multitask learning in tandem with the switch-transformer shared encoder to enhance model adaptability and refine sentence representation. By introducing a mixture of expert (MoE) mechanism that break down the problem into smaller, more manageable sub-problems, the model becomes adept at handling lengthy sequences and intricate input-output relationships, benefiting both five-point and three-polarity Arabic sentiment analysis tasks. This proposed model effectively discerns sentiment in Arabic dialect sentences. The empirical results highlight the outstanding performance of the suggested model, as evidenced in evaluations on the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Herpesviridae is a family of enveloped double-stranded DNA viruses that cause a wide range of diseases in hosts. Among the various components of herpesvirus particles, tegument proteins residing between the envelop and nucleocapsid play crucial roles in viral replication, immune evasion, and modulation of host cellular pathways. Understanding the three-dimensional structure of herpesvirus tegument proteins help to reveal the molecular mechanism underlying the crosstalk with other viral and cellular components, and thus is crucial for the investigation of their biological and pathological functions. In this review, we summarize the current knowledge of the structural features of herpesvirus tegument proteins, highlighting the structure-based functional implications, including the potential as targets for antiviral drug development.

Semantic interoperability is a foundational concept in modern healthcare, enabling seamless communication and meaningful data exchange across diverse EHR systems. At the heart of this concept lies archetype clinical information models, which serve as standardized templates to structure and encode clinical data, ensuring consistency and shared understanding among various healthcare stakeholders. This review paper delves into the intricacies of Electronic Health Records (EHRs) within the context of semantic interoperability and the pivotal role played by archetype clinical information models. Drawing from a wealth of recent research, this review paper examines the theoretical underpinnings of semantic interoperability. By surveying the literature, it synthesizes the diverse approaches and methods employed to achieve semantic interoperability within EHR systems. Furthermore, this review does not shy away from addressing the significant challenges that persist in this arena. These challenges encompass issues related to data quality, governance, resistance to change, and the scalability and integration hurdles that healthcare organizations encounter in their quest for interoperable EHR systems. Through a comprehensive review of relevant research papers from esteemed sources including PubMed, ScienceDirect, and Taylor & Francis, the paper offers a comprehensive understanding of the approaches and methods employed in this domain. This analysis encompasses a time frame ranging from 2015 to 2023, ensuring an up-to-date assessment of the field. By presenting a well-rounded picture of the field, this review paper offers valuable insights for healthcare stakeholders, researchers, and practitioners. It underscores the enduring importance of semantic interoperability in EHR systems, not only in advancing patient care, clinical decision support, and healthcare operations but also in driving data-driven innovations in the dynamic and ever-evolving medical healthcare sector. This comprehensive examination sets the stage for future advancements in the pursuit of truly interoperable and data-driven healthcare systems.

The commonly quoted bistable Higgs potential is not a proper description of the Higgs field because, among other technical reasons, one of its stable states acquires a negative expectation value in vacuum. We rely on formal catastrophe theory to derive the form of the Higgs potential that admits only one positive mean value in vacuum. No symmetry is broken during the ensuing phase transition that assigns mass to the Higgs field; only gauge redundancy is “broken” by the appearance of phase in the massive state, but this redundancy is not a true symmetry of the massless field. Furthermore, a secondary, certainly amusing conclusion is that, in its high-energy state, the field oscillates about its potential minimum between positive and negative masses; but it is doubtful that such evanescent states can survive below the critical temperature of 159.5 GeV, where the known particles were actually created.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

Hearing loss stands as the most prevalent sensory deficit among humans, posing a significant global health challenge. Projections indicate that by 2050, approximately 10% of the world's population will grapple with disabling hearing impairment. While approximately half of these cases have a genetic etiology, traditional interventions such as hearing aids and cochlear implants do not completely restore normal hearing. The absence of biological treatment has prompted significant efforts in recent years, with a strong focus on gene therapy to address hereditary hearing loss. Although several studies have exhibited promising recovery of common forms of genetic deafness in mouse models, existing challenges must be overcome to make gene therapy applicable in the near future. Herein, we summarize the primary gene therapy strategies employed over past years, provide an overview of the recent achievements in the preclinical studies for genetic hearing loss, and outline the current key obstacles to cochlear gene therapy.

As a service business models have been ranked by management consultants the management innovation of 2020, and yet are scarcely theorized. Therein, value stipulations in customer contracts lead to the shift from product to service. However, there is no available research about the nature, formula and decisional process about these value stipulations. The motive for this research is to fill in this gap. The goal is to provide first insight about value stipulations in as a service business models: their formula and decisional methodology. The article is a multiple theory building case study on leading platform as a service providers, which aims to contruct the paradigm of the value decision methodology to invest in this technology by purchasing it from dedicated vendors. The methodology works to identify the management instruments involved in the decisional activities to invest in digital technology, where each management instrument is assumed to match an activity at seller and buyer; the sequence of the activities using the Critical Path Method gives the decisional process. Dependent variables are the elements of the as a service business contracts: the customer offering (product-service solution) and the value created, captured and delivered by the solution. Independent variables are given by empirical data analysis. Findings show a relationship between dependent variable value in the as a service business model contracts and independent variables governance objective, strategy, as a service business model type, activity management techniques (roadmap, program, project, operations, lean start-up), capital allocation techniques (equity, debt, options, venture capital typology). If innovation is exploitative, then the customer offering is known and value is determinable via roadmap – project management techniques (program, project, an operations) and capital allocation techniques for precictable environment, If innovation is radical, then the customer offering is not known and value is a target used to architect the solution via lean start-up innovation techniques funded by venture capital. The contribution is a new theoretical construct and deep insight into the practice of digital transformation considering all inherent issues.

The anterior cruciate ligament (ACL) injury in the knee is one of the most frequent injuries in the biomechanical environment during jumping exercises; however, repeated jumping, like jumping rope, has not been reported to have a significant impact on the ACL injury. Because of this, this study aimed to compare and examine any potential biomechanical risk factors that can arise during various jumping rope activities, including the basic bounce (BB), forward-backward jump (FB), side-to-side jump (SS), and high knee jump (HK). Thirty participants were recruited. A motion capture system was used to obtain synchronized kinematic and kinetic data for four different jumping rope techniques. The OpenSim modeling system was used to determine the biome-chanical knee factors. The paired samples t-test was used to compare the mean difference in each jumping rope technique of initial contact (IC) and maximum knee flexion (MKF), and a one-way ANOVA was used to compare the mean difference between four different jumping rope techniques in the IC-MKF phase. Between IC and MKF, knee flexion angle, moment, joint ground reaction forces (GRF), and muscle forces of four jumping rope techniques were significantly different (p <.05). Also, in the IC-MKF phase, knee flexion angle and knee extension moment; HK was lower than others; vertical GRF; HK and FB were higher than others; anteroposterior, mediolateral GRF, and muscle forces; SS was higher than others (p <.05). Depending on the jumping rope techniques, the lower knee flexion angle in HK, the higher knee vertical GRF in HK and FB, the higher knee extension moment, anteroposterior, mediolateral GRF, and muscle forces on SS could be a pos-sibility of ACL injury risk compared to others. Therefore, it is recommended that SS be used in risk assessment of ACL in future studies, and BB will be suitable for normal people in daily exercises or will be the alternative intervention program for knee rehabilitation with the lowest risk of injury.

Resistive random-access memory has emerged as a promising non-volatile memory technology, garnering substantial attention due to its potential for high operational performance, low power consumption, and scalability. Two-dimensional nanostructured materials play a pivotal role in RRAM devices, offering enhanced electrical properties and physical attributes that contribute to overall device improvement. In this study, self-rectifying switching behavior in RRAM devices is proposed based on molybdenum disulfide nanocomposites decorated with palladium on SiO2/Si substrates. The integration of Pd and MoS2 at the nanoscale effectively mitigates leakage currents decreasing from cross-talk in the RRAM array, eliminating the need for a separate selector device. The successful demonstration of the expected RRAM switching behavior follows the application of a Pd nanoparticle coating. The fabricated Pd-MoS2 synaptic device showed a high current ratio for forward/reverse current higher than 60 at a low resistance state and observed a memory on/off ratio of 103 performing stable resistance switching behavior.

In federated learning (FL), clients train models locally without sharing raw data, ensuring data privacy. In particular, federated distillation transfers knowledge to clients regardless of the model architecture. However, when groups of clients with different data distributions exist, sharing the same knowledge among all clients becomes impractical. To address this issue, this paper presents an approach that clusters clients based on the output of a client model trained using their own data. The clients are clustered based on the predictions of their models for each label on a public dataset. Prior knowledge of the number of groups is not required. Evaluations on the MNIST dataset showed that our method accurately identified these group structures and improved accuracy by 15–75% compared with traditional federated distillation algorithms when distinct group structures were present. Additionally, we observed significant performance improvements in smaller client groups, bringing us closer to fair FL.

The electronic cigarette or e-cigarette is the new alternative method to deliver nicotine. In 2007, the United States introduced vaping devices on the market. Due to their attractive taste and lack of adverse effects, these vaping devices have gained popularity among youth and adults. However, there has been increasing evidence that vaping can cause severe lung injury, and these devices release toxic chemicals into the body. E-cigarettes are recognized as a global threat to public health. Nicotine, the most prevalent chemical in vaping devices, is associated with neurological problems and cognitive decline. Long-term use of e-cigarettes can cause adverse health problems, including respiratory illnesses, asthma, bronchitis, and lung inflammation. In 2019, the Centers for Disease Control and Prevention (CDC) officially declared vaping-associated illness EVALI (e-cigarette or Vaping Product Associated Lung Injury). The US Surgeon General published an alarm in December 2016 that e-cigarette consumption among adolescents and teens in the US is a "major public health concern" and that more young people are now vaping than smoking traditional cigarettes and cigars. Since its commercial introduction in 2003, modern electronic cigarettes (or E-cigarette s) have become increasingly popular with smokers, non-smokers, and those who used to smoke who have switched to them. With such a rise in consumption, people are becoming more aware of the irritating, harmful, and perhaps cancer-causing effects on the lungs. (Chadi et al., 2019; Dinardo & Rome, 2019; Lyzwinski et al., 2022; Marrocco et al., 2022; O’Callaghan et al., 2022)

An overview of the link between nonequilibrium thermodynamics and complexity theory is offered. It is shown how the rate of entropy production can be quantified through the spectrum of the Lyapunov exponents. It was shown how the entropy production per unit of time meets the necessary and sufficient conditions to be a Lyapunov function and constitutes per se an extremal principle. The entropy production fractal dimension conjecture was established. It is shown how the rate of entropy production as a non-extremal criterion represents an alternative way of sensitivity analysis of differential equations. Finally, an extension to biophysical-chemical systems, on the one hand, the use of the dissipation function is shown, as a thermodynamic potential out of equilibrium, in the characterization of biological phase transitions. On the other hand, it was evidenced how the rate of entropy production represents a physical quantity to evaluate the complexity and robustness of cancer.

The time-dependent behavior and long-term stability of deep-buried tunnels in soft rocks have received lots of considerations in tunnel engineering and allied sciences. To better explore and deepen the engineering application of rock creep, extensive research studies are still needed, although fruitful outcomes have already obtained in many related investigations. In this article, the Weilai Tunnel in China’s Guangxi province is studied taking its host rocks as the main research object. In fact, aiming at forecasting the time-varying deformation of this tunnel, a novel elasto-visco-plastic creep constitutive model with two variants is proposed, by exploiting the typical complex load-unload process of rock excavation. The model is well validated and good agreements are found with the relevant experimental data. Moreover, the time-dependent de-formation rules are properly established for the surrounding rocks, by designing two new closed-form solutions based on the proposed creep model and the Hoek-Brown criterion. The convergence deformations calculated from the closed-form solutions conform well to the on-site monitoring data. In only 27 days after excavation, the creep deformation of the Weilai tunnel overtakes 400 mm, which is enormous. To guarantee the long-term stability of this tunnel, a ro-bust support scheme and its long-term monitoring with appropriate remote sensors are strongly suggested.

With the intensification of population aging in rural areas, it becomes increasingly important to analyze the post-retirement migration intention of rural residents and the variables influencing these intentions. In this study, we focus on rural residents aged 45 to 60 and investigate the main variables that influence the post-retirement migration intention of rural residents, using survey data collected from 164 households in three different rural areas and the logistic regression model. From the result of this study, we found that gender, part-time employment, savings level, children's residence and occupation stability, and interest in urban living positively affect migration intention. In contrast, the number of rural companions, relationships with others in rural areas, and evaluation of rural living have a negative effect. In addition, we employ age and the proportion of mobile income as control variables to examine the variables that influence the post-retirement migration intention in different age groups and mobile income groups. The analysis reveals that, the variables influencing post-retirement migration intention varied across age groups and mobile income groups, and this variation can be attributed to the differences between groups' characteristics.

Bundle recommendation is provided to users by combining related items into a bundle, enhancing the user's shopping experience and increase the merchant's sales revenue. Despite the success of existing solutions based on Graph Neural Networks(GNN), there are still some significant challenges: (1)It is demanding to explicitly model multiple complex associations using standard graph neural networks. (2)Numerous additional nodes and edges are introduced to approximate higher-order associations. (3)The user-bundle historical interaction data is highly sparse. In this work, we propose a Global Structural Hypergraph Convolution model for Bundle Recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles(three entities) into a relational hypergraph without introducing additional nodes and edges. Higher-order associations are already involved in the hypergraph structure which alleviate the training burden of neural networks and the dilemma of scarce data. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, hypergraph convolution propagation between user and bundle nodes generates the learned representations. Experimental results on two real-world datasets demonstrate that SHCBR outperforms the state-of-the-art baselines by 11.07%-25.66\% on Recall and 16.81%-33.53% on NDCG. We publicly release the codes and datasets at https://github.com/Lxtdzh/SHCBR-master/.

Calcium (Ca2+) ions act as second messenger, regulating several cell functions. Mitochondria are critical organelles for the regulation of intracellular Ca2+. Mitochondrial calcium (mtCa2+) uptake is ensured by the presence in the inner mitochondrial membrane (IMM) of the mitochondrial calcium uniporter (MCU) complex, a macromolecular structure composed of pore-forming and regulatory subunits. MtCa2+ uptake plays a crucial role in the regulation of oxidative metabolism and cell death. Many evidences demonstrate that dysregulation of mtCa2+ homeostasis can have serious pathological outcomes. In this review, we briefly discuss the molecular structure and the function of the MCU complex and then we focus our attention on human diseases in which a dysfunction in mtCa2+ has been showed.

Medicinal and aromatic plants are gaining more importance because of their potential application in food, pharmaceutical and fragrance industry. Medicinal and aromatic plants have been used in cosmetics, perfumery, pharmaceuticals and food flavoring since ancient times, because of the presence of essential oils and different components. Seeds are of immense economic and biological importance, and they contain high oil, protein and starch reserves. The seeds of medicinal and aromatic plants are stores of important and active secondary metabolites that have been economically and commercially beneficial and helpful for medicine and pharmacy. The seeds of fennel are highly nutritious, and the seeds contain different minerals such as calcium, magnesium, and potassium. The seeds are helpful in weight loss and cancer prevention. They can also improve digestive health, regulate blood pressure, improve skin appearance, and promotes lactation. The main chemical components of lavender are linalool, linalyl acetate, 1,8-cineole, β-ocimene, camphor and terpinen-4-ol. The lavender essential oil has anxiolytic, anti-inflammatory, antimicrobial, antioxidant and antinociceptive activities.The main components of thyme are p-cymene, γ-terpinene and thymol. Thyme can help to improve eyesight, and it has high anti-bacterial and anti-inflammatory activities. The main compounds of Echinacea species are chlorogenic acid, caftaric acid, cynarin, echinacoside and cichoric acid. The keyword searches for Fennel, Lavender, Thyme, Echinacea, Seed biology, Traditional medicinal science and seed anatomy were performed by using Web of Science, Scopus, PubMed and Google scholar. The aim of this article review is to survey the pharmacological and health benefits of seeds of important medicinal plants.

The main purpose is to create a model for managing sustainability in the metro and its entities in Gauteng. Within this context, the study will focus on the issue of measures, outcomes, and impacts in the City of Johannesburg (CoJ). The study will clarify a public sector application in municipal entities of the CoJ because sustainability is not well understood or used in the public sector. This article investigates the sustainability phenomena in a metropolitan setting, including the CoJ and its municipal entities. The purpose is rather two-fold. A focus group discussion was held with CoJ senior executive managers to provide feedback on the sustainability model. The information and insights were used to refine the sustainability model to manage sustainability in Coj and its enti-ties. The data was analysed using thematic analysis to refine the sustainability model. The re-sults indicated that sustainability is a function of having a shared understanding of sustainabil-ity, the ability to mainstream and integrate sustainability thinking into all programmes and ac-tivities, leadership commitment, requisite competencies, and monitoring and evaluation of sus-tainability. The implication for leadership is to ensure the alignment of sustainability to the strategic objec-tives of CoJ and to foreground any planning in CoJ with a sustainability lens. Achieving this focus requires a shared understanding, development of competencies, committed leadership to imple-ment measurement and impact framework, and adjusting plans based on monitoring and evalua-tion outcomes.

Peripheral arterial diseases (PAD) are complex cardiovascular conditions influenced by environmental factors and somatic mutations in multiple genes involved in hematopoiesis and inflammation. While traditional risk factors such as smoking, hypercholesterolemia, and hypertension have been extensively studied, the role of somatic mutations in PAD progression remains underexplored. This article aims to provide a comprehensive review of the molecular mechanisms, genetic landscape, prognostic significance, and clinical implications of somatic mutations in PAD. The expansion of clonal hematopoiesis of indeterminate potential (CHIP) clones in the circulating blood, named clonal hematopoiesis (CH), leads to the infiltration of these clones into atherosclerotic plaques and the production of inflammatory cytokines, increasing the risk of cardiovascular diseases including PAD. Furthermore, recent experimental evidence has demonstrated the involvement of somatically mutated TP53 genes with a high variant allele frequency (VAF) in PAD development and prognosis. The review delves into the relationship between CH and PAD, elucidating the prevalence, impact, and underlying mechanisms of this association. This understanding paves the way for novel therapeutic approaches targeting CHIP to promote tissue regeneration and improve outcomes in PAD patients. It emphasizes the need for further re-search to fully unravel the genetic footprint of the disease and highlights potential clinical implications. The findings presented in this article lay the foundation for personalized medicine approaches and open avenues for the development of targeted therapies based on somatic mutation profiling.

Heavy metal ions (HMIs) have acute toxic effects on the health and are dangerous for human ex-istence and the ecosystem. Therefore, its sensitive and selective detection is of great importance. In recent years various nanocomposite materials have been used by researchers for the detection of HMIs by using various modalities of electrochemical techniques. This review article summarizes the recent advances in developing electrochemical sensors based on numerous nanocomposite mate-rials for detecting heavy metal ions. Nanocomposites materials, such as Metal-Organic Framework (MOF), organic conducting polymers (OCP), carbon nanotubes, graphene/reduced graphene oxide, graphitic carbon nitride, metal oxide, chitosan, mxenes, metal nanoparticle-based nanocomposites, etc. have been explored by various researchers to improve the sensing properties of electrochemical sensors. This review emphasized the synthesis and characterization techniques of nanocomposite materials, modalities for HMI detection by electrochemical technique and the electrochemical sensors. Moreover, this review highlights the development of portable biosensors for detecting HMIs in real-world scenarios, such as environmental monitoring, food safety, and clinical diagnosis. This review also demonstrates the importance of electrochemical sensors-based on nanocomposite ma-terials as a reliable, sensitive, and selective tool for detecting HMIs.

Forest ecosystems provide diverse services and values that contribute to human well-being. Although high proportion of species is still undiscovered, tropical forests alone are thought to host more than 50% of the world’s biodiversity. Beyond supporting via direct use of products like timber, fiber and biomass energy, they play an important role in providing a wide range of ecosystem services such as regulating water flow and quality, water purification, improving infiltration, fresh water, erosion control, carbon sequestration and sedimentation control. They are one of the important parts of terrestrial and the largest carbon pool, occupying an integral position in the global carbon cycle. However, these days, deterioration of nature and forest biodiversity is a severe danger to the global environment. This will be accompanied by increased strains on food supply, causing increased pressure on already delicate ecological systems. Thus, better forest management strategies are necessary for managing forests sustainably. Nevertheless, little is known regarding the existing forest management approaches and their associated benefits and challenges. Multidisciplinary approaches are needed to achieve sustainable management of forests. These can be accomplished if suitable forest management innovations are put in place. Various technologies can also be adopted through applying different forest assessment tools like remote sensing using light detection and ranging technology (LiDAR) in forest management. Harnessing such technologies will definitely result in providing increased socio-economy as well as improved environmental sustainability. However, the speed of transformation depends much on level of commitment of stakeholders including policy makers. As a result, this review is intended to explore existing forest management innovations which are suitable both ecologically as well as socio-economically. This shows that while there is a lot to be improved, there are already developed forest management innovations that are already in use including PFM and MFM, which helps to achieve SFM.

This paper presents the results of simulations of particle-laden air-solid jet flow in long straight tubes using CFD-DEM, along with an analysis of coarse-graining. Although previous studies have used CFD-DEM for similar flows, these have typically been in a dilute flow regime where uncoupled simulations are accurate. However, fully coupled simulations can introduce issues, necessitating validation studies to ensure that all coupling parameters are effectively used and that the physics is accurately represented. This paper validates the simulations against two different experimental studies, with fluid Reynolds numbers between 10,000 and 40,000 and Stokes numbers between 5.6 and 50. Interestingly, the profiles of mean particle velocity exhibit fewer discrepancies as the Stokes number increases but more discrepancies for the root mean square velocity compared to the experiments. Particle number flux is consistent with the experiments after the nozzle exit. Coarse-graining is also applied to the same simulations, achieving relatively accurate results. However, as expected, scaling of contact collision frequencies, forces, and stresses cannot be achieved, meaning that coarse-graining may be useful for comparing designs or operating parameters on an industrial scale but falls short when measuring the total energy dissipation of one experiment.

With the emergence of new VR/AR technologies, the question arises of how they can be used in human-swarm interaction. Where are the limitations and boundaries of conventional interfaces, and how can VR/AR overcome them? To find out, a user study was conducted. The subjects were able to interact with an autonomously acting swarm of drones using both a conventional desktop interface (Desktop-HSI) and a newly developed VR interface (VR-HSI).The resulting assessments are used as a basis for determining the extent to which this new technology can be applied in human-swarm interaction. The technical basis was an already tested simulation software that was expanded with a network interface to allow interacting with a drone swarm using connected HSIs (Human-Swarm Interfaces). Standardized communication was emphasized to enable the development of HSIs with different requirements. Specifically for this work, a VR-HSI was developed with Unity and a desktop-HSI with Java and their performance regarding human-swarm interaction was evaluated.

At the entrance of some churches in Tuscany (Italy), the reproduction of an apparently undecipherable inscription can be found. At least from the 18^th century, this epigraphic puzzle has originated a debate on its interpretation. This study proposes a hypothesis based on the Latin alphabet used in texts contemporary to the churches where the inscription is reproduced and a possible interpretation of the message consistent with the official religious doctrine. The proposed deciphering is extended to the full text, including some signs that were previously considered as geometric forms or a specific elaboration of letters not attested in other contemporary documents.

A large number of studies have indicated that plant community modulate the yield, improve growth and promote development of host plants, and among them rhizosphere growth-promoting bacteria play an important role in increasing crop yield. Bacteria benefit from the plant nutrients provided by the roots, but plants can benefit from their rhizobacteria as well. Rhizobia are considered as the most important candidate for plant growth promotion in comparison with rhizobacteria due to their endophytic nature and nitrogen fixing ability. They can also provide auxins which play a notable function in plant growth and plant-microbe interactions. Bacillus spp., the sporulating Gram-positive bacteria, have important roles in plant growth promotion and induction of systemic resistance. Pseudomonas are Gram-negative aerobic chemoheterotroph and are usually found in the environment. Streptomyces are important groups of soil bacteria from the actinomycetes family. Most of them are efficient rhizosphere and rhizoplane colonizers, and they can also be endophytes colonizing inner tissues of host plants. The present review aims to present the most up-to-date findings and results regarding the effects of plant growth promoting bacteria on yield of different crops, especially sorghum. Furthermore, the mechanisms of the actions of different bacteria briefly illustrated, aiming to present future needs to be addressed in sustainable crop production. Plant growth promoting rhizobacteria inoculation with different plants or treating plants with microbe-to-plant signal constituents can be an effectual technique to stimulate crop growth. They are also active in protecting crops from phytopathogens and environmental stress, plant pathogens and they have positive roles in nutrient cycling.

This paper presents high-resolution digital measurements at low cost, combining oversample and averaging. Experiments of processing data between samples and between multiple temperature sensors in the same environment are conducted. They provide results with effective increase in the analog to digital converter bits, in correspondence with the minimization on the effects of noise. It is shown that averaging a set of small, low-cost sensors at a small microcontroller can give flexible access to improved measurement accuracy.

Starting from the fact that in sensorless control systems of Permanent Magnet Synchronous Motor (PMSM) the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, and thus increased ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the sensorless PMSM control system based on LADRC controller with ESO/DO type observer are presented, as well as its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. Also, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

In resent years, China’s digital economy has achieved significant global recognition for its instrumental role in supporting sustainable economic and social development. Based on the panel data from China's provinces between 2013-2021, this paper investigates the impact and transmission mechanism of financial deepening on digital economy development. The study finds that financial deepening significantly improves digital economy development. Heterogeneity analysis indicates that financial deepening offers greater support to digital economy development in central and western regions than in the east. Further analysis shows that financial deepening improves technological innovation, which in turn promotes the digital economy development. After applying a test of robustness, the results proved significant.

The temperature-dependent energy storage properties of four tungsten bronze phase compounds are studied together with an investigation of their structure and temperature-dependent permittivity response, i.e., Ba6Ti2Nb8O30 (BTN), Ba6Zr2Nb8O30 (BZN), Sr3TiNb4O15 (STN) and Sr3ZrNb4O15 (SZN) ceramics. It was found that BZN has smaller grains and a more porous structure than BTN. SZN shows no clear grain boundaries with the most porous structure among all samples, exhibiting a much lower permittivity response than other samples with no signs of phase transitions from room temperature to 400 °C. Though the energy storage response of those samples is generally quite low, it exhibits rather good temperature stability. It was suggested that by obtaining a denser structure through chemical modification or other methods, those tungsten bronze ceramics with good temperature stability could be promising as energy storage devices when improved energy storage properties are achieved.

This work presents a design and model of the thermo-hydrodynamic behavior applying CFD for pin-fin heat sink with square shape (rectangular fin profile), through a novel pin arrangement that improves heat removal in the system. The novelty consist in the insertion of a deflector row that will help to direct the flow, so it hits the rectangular cross-sectional fins the presents in the system. The heat sink model is placed in channel designed in which air flows as suitable medium, and its overall performance is investigated. In addition, a constant heat flux is applied to the bottom wall of the heat sink model, equal values and amounts that​ correspond to the heat fluxes generated by current electronic equipment and devices. The numerical results of the global thermal resistance, the pressure drop and the Nusselt number are reported. The results allow determining best arrangement or geometry configuration, location and functionality of the fin-deflectors were collocated for evaluate thermal-hydraulic efficiency of system.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

The mouse visual system recently became the most popular model to study the cellular and circuit mechanisms of sensory processing. However, the importance of eye movements in mice only started to be appreciated recently. Eye movements provide a basis for active sensing and deliver insights into various brain functions and dysfunctions. A plethora of knowledge on the central control of eye movements and their role in perception and behaviour arose from work on primates. However, an overview of the known eye movement types in mice and a comparison to primates is missing.Here, we review the eye movement types described to date in mice and compare them to those observed in primates. We discuss the central neuronal mechanisms for their generation and control. Furthermore, we review the mounting literature on eye movements in mice during head-fixed and freely moving behaviours. Finally, we highlight gaps in our understanding and suggest future directions for research.

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural networks and an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

Treatment of neurological disease is hampered by the lack of validated specific and sensitive biomarkers, resulting in delayed diagnosis and nonspecific disease modifying therapies. For example, in ALS the lack of a sensitive and specific biomarker impedes the ability to administer a treatment prior to or at the onset of motor neuron dysfunction. Although viral or other infectious etiologies have been proposed as a contributing factor to neurodegeneration, it can be argued that these are casual and not causal associations. In the case of ALS, evidence for direct causality would require in vivo validation of specific nucleotide sequences of microbial origin which are known to be neuroinvasive with tropism for motor neurons, such as polio and non-polio. Several viral enteropathogens recapitulate the pathological events in sporadic ALS, including the ability to cleave TDP-43 resulting in accumulation of misfolded proteins in the cytoplasm. This review provides supporting evidence that motor neuron disease is causally related to specific enteroviruses and argues that prospective validation is imperative for effective prevention and treatment.

Knowledge about pharmacological benefits of different seeds is an important factor for cultivation and application of medicinal herbs and plants. The seeds of medicinal plants are stores of valuable and active secondary metabolites that have been commercially and economically beneficial and helpful for medicine and pharmacy. The major parameter of reproduction and preservation of plants are also seeds which have a functional role in the distribution and establishment of plant in different regions. Five important seeds which have tremendous medicinal and pharmacological benefits are anise, basil, borage, cilantro and chamomile. Anise seed is used as a spice, either whole or ground, and its essential oil and extract are also obtained from the seeds. Basil seeds have a long history of usage in Chinese and Ayurvedic medicine, which are the good source of minerals, high in fiber including pectin, rich is flavonoids and other polyphenols. Borage seed oil is used for skin disorders such as seborrheic dermatitis, atopic dermatitis, and neurodermatitis. Cilantro is an annual herb that is part of the Apiaceae family. The seeds are rich in iron, zinc, copper and essential minerals which can decrease bad cholesterol and improves good cholesterol in the body. Chamomile can be considered for treatment of insomnia, hemorrhoids, anxiety, diarrhea and may help with wound healing and skin irritation. The keyword searches for Anise, Seed, Basil, Borage, Cilantro, Chamomile, Seed biology, Traditional medicinal science and seed anatomy were performed by using Scopus, Web of Science, PubMed and Google scholar. The aim of this article review is to survey the pharmacological and health benefits of seeds of five important medicinal plants.

Considering the differences between the European and African continents concerning the management of the mining production sector, we decided to carry out this study with the main objective of demonstrating that, in Africa, mining can positively change the quality of life of the populations where it develops and, at the same time, it is possible to respect the environment, which is our main wealth. To achieve these objectives, it is necessary to present the mining activity of the continent, emphasizing both the negative aspects and its strong points. The most important thing is to make a good diagnosis of the situation, which will allow us to cure our "patient", that is, African mining production.

The widely conserved RACK1 protein is a WD-40 type scaffold protein that regulates diverse environmental stress signal transduction pathways. Arabidopsis RACK1A has been reported to interact with various proteins in salt stress and Light-Harvesting Complex (LHC) pathways. However, the mechanism of how RACK1 contributes to the photosystem and chlorophyll metabolism in stress conditions remains elusive. Using T-DNA-mediated activation tagging transgenic rice (Oryza sativa L.) lines, we show that leaves from rice RACK1B gene (OsRACK1B) gain-of-function (RACK1B-OX) plants exhibit the stay-green phenotype under salinity stress. In contrast, leaves from down-regulated OsRACK1B (RACK1B-UX) plants display an accelerated yellowing. qRT-PCR analysis revealed that several genes encoding chlorophyll catabolic enzymes (CCEs) are differentially expressed in both RACK1B-OX and RACK1B-UX rice plants. In addition to CCEs, STAY-GREEN (SGR) is a key component that forms the SGR-CCE complex in senescing chloroplasts, which causes LHCII complex instability. Transcript and protein profiling revealed a significant upregulation of OsSGR in RACK1B-UX plants than that in RACK1B-OX rice plants during salt treatment. The results imply that senescence-associated transcription factors (TFs) are altered in accordance with altered OsRACK1B expression, indicating a transcriptional reprogramming by OsRACK1B and a novel regulatory mechanism involving the OsRACK1B-OsSGR-TFs complex. Our findings suggest that ectopic expression of OsRACK1B negatively regulates chlorophyll degradation, leads to the steady level of LHC-II isoform Lhcb1, an essential prerequisite for the state transition of photosynthesis for adaptation, and delays salinity-induced senescence. Taken together, these results provide important insights into the molecular mechanisms of salinity-induced senescence, which can be useful in circumventing the effect of salt on photosynthesis and in reducing the yield penalty of important cereal crops, like rice, in global climate change conditions.

Open System Interconnection (OSI) model is the best means to understand the way the internet works and the communication of computers over a network system. It is considered as the standard model for network communication. This article describes the OSI model with an example and provides a brief description of the seven layers of the OSI model.

MicroRNAs (MiRNAs) are a class of non-coding single-stranded RNA molecules of approximately 20-24 nucleotides in plants that play an important regulatory role in a variety of biological processes such as plant growth and development and response to various abiotic stresses. For example Drought, Salt, Cold, High temperature, Heavy metals and Nutrition. MiRNAs affect gene expression by manipulating the cleavage, translational expression or DNA methylation of target mRNAs. This review describes the current progress made on the way miRNAs are produced and regulated and the way miRNA/target gene is used in plant responses to abiotic stresses. Studying the molecular mechanism of action of miRNAs downstream target genes can optimize the genetic manipulation of crop growth and development conditions that provide a more theoretical basis for improving crop production.

The cytoplasmic membrane is the fundamental component of all living cells, which participates in various physiological processes, such as material exchange, stress response, cell recognition, signal transduction, cellular immunity, apoptosis, pathogenicity, etc. The normal function of a cytoplasmic membrane requires stable organization of transmembrane protein-lipid microdomains, transmembrane protein-cell wall microdomains, and cytoskeleton-transmembrane protein microdomains. Here, we review the mechanisms and functions of various membrane lipid components, fatty acid content and saturation, membrane curvature, and cell wall and cytoskeleton in plasma membrane homeostasis affecting the pathogenicity of pathogenic fungi. Pathogenic fungi maintains plasma membrane homeostasis and contributes to fungal virulence by maintaining plasma membrane assembly, structural and functional integrity of pathogenic fungi at various stages of cell development through interactions among lipid components of cytoplasmic membranes, transmembrane proteins, cytoskeleton and cell wall components, etc.

The Neurodegenerative Diseases are, according to recent studies, one of the main causes of disability and death worldwide. Interest in molecular genetics has started to have an exponential growth thanks to numerous advancements in tech, shifts in the understanding of the disease as a phenomenon, and the change of perspective regarding gene editing and the upsides of this action. The aim of this paper is to analyse the newest approaches in genetics and molecular sciences regarding four of the most important neurodegenerative disorders: Alzheimer’s Disease, Parkinson’s Disease, Huntington’s Disease and Amyotrophic Lateral Sclerosis. We intend through this review to focus on the newest treatment, diagnosis and predictions plans regarding this large group of diseases, in order to obtain a better accuracy analysing and spotting the emerging signs that could lead to a better outcome in order to increase both the quality and the life span of the patient. Moreover, this review could provide future evidence of possible novelty therapies that target the specific genes and could be useful to be taken into consideration when the classical approaches fail to shed light.

Chitosan's favourable characteristics, such as biocompatibility, biodegradability, and bioactivity, have made it a popular biomaterial and the subject of numerous studies over the last years. This study investigates the enhancement of mechanical properties of chitosan fibres through wet impregnation using tripolyphosphate (TPP) as cross-linker. A 1% w/v solution of TPP was prepared and five separate chitosan fibre specimens were immersed individually in TPP solution for varying times (1, 2, 4, 6, and 8 hours). The specimens were then characterized using FTIR, SEM, and dynamometry to evaluate their physical, chemical, and mechanical properties. The FTIR results indicated successful interaction between the protonated amine groups of chitosan and TPP ions, with the crosslinking reaction reaching completion after 2 hours of immersion. SEM images showed that the surface of the fibres became rougher after the crosslinking reaction, suggesting the formation of new chemical bonds. Mechanical testing revealed a significant improvement in tensile strength for the treated samples compared to untreated ones, indicating successful strengthening through crosslinking. The fibres demonstrate significant potential for use in wound healing dressings.

The highly specialized structure and function of neurons depend on a sophisticated organization of the cytoskeleton, which supports a similarly sophisticated system to traffic organelles and cargo vesicles. Mitochondria sustain crucial functions by providing energy and buffering calcium where is needed. Accordingly, the distribution of mitochondria is not even in neurons and is regulated by a dynamic balance between active transport and stable docking events. This system is finely tuned to respond to changes in environmental conditions and neuronal activity. In this review, we summarize the mechanisms by which mitochondria are selectively transported in different compartments taking into account the structure of the cytoskeleton, the molecular motors and the metabolism of neurons. Remarkably, the motor proteins driving the mitochondrial transport in axons have been shown to mediate also their transfer between cells. This so-named intercellular transport of mitochondria is opening new exciting perspectives in the treatment of multiple diseases.

Energy is needed by cancer cells to stay alive and communicate with their surroundings. The primary organelles for cellular metabolism and energy synthesis are mitochondria. Researchers recently proved that cancer cells can steal immune cells' mitochondria using nanoscale tubes. This finding demonstrates the dependence of cancer cells on normal cells for their living and function. It also denotes the importance of mitochondria in cancer cells’ biology. Emerging evidence has demonstrated how mitochondria are essential for cancer cells to survive in the harsh tumor microenvironment, evade the immune system, obtain more aggressive features, and resist treatments. For instance, functional mitochondria can improve cancer resistance against radiotherapy by scavenging the released reactive oxygen species. Therefore, targeting mitochondria can potentially enhance oncological outcomes, according to this notion. The patients' reactions to radiation are varied, ranging from a complete response to even cancer progression during treatment. This concept illustrates how different levels of mitochondrial metabolism might contribute to this heterogeneity. Considering this notion can help to improve personalized oncological treatments. This article outlines the importance of mitochondrial metabolism in cancer biology and personalized treatments.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In the paper, we explore the notion of global digital citizenship, particularly with respect to museums and cultural heritage organizations. In this perspectives study, we explore current examples of how museums can adapt to the tenets of global digital citizenship necessary to navigate and participate in increasingly interconnected digital worlds and culture, and to collectively address global challenges.

Purpose: This research compared the dissociated phoria at near and distance fixation in free space using three techniques: the Howell test, alternate Cover test, and Thorington test. Methods: 220 healthy Mexican children (age 5.06-17.11) participated in this study. Phorias were quantified at both distances using each test, from the least to the most disruptive. The stereopsis degree and near point of convergence (break/recovery) were analyzed to understand their role on the visual system's sensorimotor balance. Results: For the exophoria/esophoria at near, the alternate Cover and Thorington tests showed similar values. The exophoria at distance, was similar with the alternate Cover and Howell tests, while esophoria, with the alternate Cover and Thorington tests. The break value and near exophoria relates to each other and affect the stereopsis degree, whereas age is associated with the stereopsis degree and break value. Conclusions: The analysis revealed that alternate Cover and Thorington tests can substitute one another, but not with the Howell test. The test's specific dissociation method may explain these results. Analyzing the binocular visual system provides information about its sensorimotor balance and fragility when faced to the technique’s degree of dissociation.

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) enables determination of room-temperature structures without causing radiation damage. Using an optical pump-probe or mix-and-injection, SFX enables the intermediate state visualization of a molecular reaction. In SFX experiments, serial and stable microcrystal delivery to the X-ray interaction point is vital for reasonable data collection and efficient beam time. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) facility established SFX instruments at a nanocrystallography and coherent imaging (NCI) experimental station. Various sample delivery methods, including injection, fixed-target scanning, and hybrid methods, have been developed and applied to collect XFEL diffraction data. Herein, we review the currently available sample delivery methods for SFX at the NCI experimental station at the PAL-XFEL. This article will help PAL-XFEL users access the SFX system for their experiments.

AI is extraordinarily useful technology but it is becoming increasingly powerful with rapidly growing capabilities to disrupt and harm human society. We call on international and national organizations and individuals to join forces in banning the development of superintelligent AI and introducing regulations to prevent and mitigate AI-caused harm.

Environmental regulation has dual effects on water resource utilization performance through various channels such as the "three red lines" and the "Porter hypothesis". Based on the detailed analysis of the impact path of environmental regulation on water resource utilization performance, the two-step system GMM method was adopted to test the dual effects of environmental regulation on water resource utilization performance by using panel data of 30 provinces and cities from 2004 to 2020. The results show that: (1) Environmental regulation has a significant direct impact on water resource utilization performance, and is currently in the stage of positive relationship of an inverted u-shaped relationship. (2) Environmental regulation has a significant indirect impact on water resource utilization Environmental regulation has dual effects on water resource utilization performance through various channels such as the "three-red lines" and the "Porter hypothesis". In accordance with a detailed examination of the influence path of environmental regulation on the performance of water resource utilization, the double effects of environmental regulation on water resource utilization performance were examined using panel statistics from 30 provinces and cities from 2004 to 2020 using the two-step system GMM approach. The results demonstrate that: (1) Environmental regulation has a direct effect on water resource utilization performance which is currently in the stage of the positive relationship of an inverted u-shaped relationship. (2) The influence of environmental regulation on water resource utilization performance through government intervention and industrial structure adjustment is indirect, among which government intervention can significantly improve water resource utilization performance, while the growth of the tertiary industry as a percentage can lower water resource utilization performance. (3) Both direct and indirect impacts indicate that enhancing the efficiency of the use of water resources is sustainable, and the water resource utilization performance of the preceding period had a major positive impact over the present phase. Therefore, the paper sets out a few policy suggestions, such as choosing reasonable environmental regulation tools, actively guiding the market-oriented reform with constraints, and upgrading the industrial structure in a green way.

This qualitative research study explores the experiences of three Emirati women who have achieved significant success in their higher education institutes in the (UAE). The study aims to understand how childhood and adolescent experiences shape the leadership development of these women, both positively and negatively. The study also includes interviews with family members and close colleagues of the participants to gain additional perspectives. The findings the importance of family support, role models, and mentors in shaping the leadership development of women in the UAE. The study also sheds light on the challenges and barriers that women face in leadership positions, including gender bias and discrimination. Overall, the study contributes to a deeper understanding of the experiences of Emirati women in leadership positions and provides insights into how to support their leadership development.

Less than half of e-waste plastics is sorted worldwide and this rate is likely to decline as major processing countries have banned importation of e-waste plastics. This forces the development of decentralized processing facilities, also known as microfactories. The present work investigates the recyclability of different grades of acrylonitrile-butadiene-styrene (ABS) copolymer, polycarbonate, and polypropylene, which were found to be very abundant in a recycling site in the UK. The determination of the matrix relied on the resin identification code imprinted in the e-waste plastic and subsequent FTIR analysis. The melt-blend extrusion technology enabled the valorization of the wasted thermoplastics as 3D filament without significant degradation of the polymers. The recycled materials maintained the tensile strength around 2.5 MPa in agreement with the specifications offered by virgin polymers. Further characterization was done by means of laser microscope, thermogravimetric analysis, and XRF to determine the commercial viability of the recycled filament. A modified solvent-based method was developed with acetone to remove the brominated flame retardants: 25g/100mL, 30 minutes of contact time, and 4 extraction steps. The FTIR results show that the degradation of the rubbery dispersed phase corresponding to the butadiene can be accumulated in the less soluble fraction of the waste ABS.

Monitored infection and vaccination rates during past past Corona waves are used to infer a posteriori two key parameter of the SIRV-epidemic model, namely the real time variation of the (i) ratio of recovery to infection rate and (ii) ratio of vaccination to infection rate. We demonstrate that using the classical SIR model the ratio between recovery and infection rates tends to overestimate the true ratio, that is of relevance in predicting the dynamics of an epidemics in the presence of vaccinations.

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively

Background Community health workers (CHWs) drawn from the general population are an important human resource in health care systems, preventing non-communicable diseases (NCDs) and contributing to an increase in healthy life expectancy in Japan. Thus, we have developed the COmmunity health workers perceptual and behavioral Competency Scale for preventing Non-communicable diseases (COCS-N) to measure CHWs’ competence in preventing NCDs. The purpose of this study is to examine individual and community factors affecting CHWs’ COCS-N scores. Methods Municipal public health nurses and other public health professionals are responsible for training and supporting CHWs in Japan. Therefore, the existence of CHWs and their willingness to participate in the study were confirmed with the municipalities, who were asked to distribute the self-administered questionnaire to CHWs where consent was obtained (N = 6,480). Variables used included demographic characteristics, COCS-N scores, and individual- and community-related factors. Logistic regression analysis was used to assess associations between variables. Results A total of 3,120 people completed the questionnaire, a valid response rate of 48.1%. The respondents’ mean age was 67.0 years (standard deviation = 9.0), and 88.0% were female. Comparison of the high- and low- competence groups in terms of NCD prevention based on COCS-N scores identified 13 factors associated with significant differences, including years spent working as a CHW (p < 0.001), subjective sense of health (p = 0.005), European Health Literacy Survey Questionnaire (HLS-EU-Q47) scores (p < 0.001), and community commitment scale (CCS) scores (p < 0.001). Logistic regression analysis revealed that HLS-EU-Q47 scores (odds ratio [OR]: 1.02, 95% confidence interval [CI]: 1.02–1.03) were a significant individual factor, while CCS scores (OR: 1.14, 95% CI: 1.11–1.16) were a significant community factor. Conclusions We found that the COCS-N score was associated with the individual factors overall health literacy (HL), perceptions of HL, and subjective sense of health, and with the community factor CCS scores. These results suggest that strengthening individual factors such as HL and subjective sense of health, and community factors such as sense of community is an effective strategy for increasing CHWs’ competence in preventing NCDs.

The optimization of the pile driver frame is very important to the overall performance of the pile driver. This paper takes the large body structure of a model of hydraulic static pile driver as the research object. ANSYS APDL finite element software is used to perform static analysis and sensitivity analysis on the large body structure of the pile driver. On the premise of meeting the design requirements, according to the most dangerous working conditions of the pile driver, the size optimization of the frame structure of the pile driver is carried out based on sensitivity analysis. The mathematical model is established with the plate thickness as the design variable, the strength and stiffness of the body structure as the constraints, and the minimum mass of the body structure as the objective function. The optimization results show that the optimization design model based on sensitivity analysis not only meets the strength and stiffness conditions of the large structure, but also reduces the structural quality by 22.1% and greatly saves the production cost.

The enzymatic activities of bacterial isolates have been widely studied, but a spatial distribution of this activity is rarely focused. New antibiotic-producing microorganisms can be discovered in a more efficient manner if spatial statistical techniques are applied to the distribution of the activity of bacterial isolates in different terrains. This study is focused to generate a series of maps illustrating the spatial distribution of different soil parameters and the inhibition against relative-safe pathogens (like Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Salmonella, Pseudomonas fluorencens and Enterobacter cloacae) in an urban park in València (Spain). This spatial data has demonstrated a higher proportion of isolates was specifically allocated. This routine of data presentation is valuable for understanding bacterial population dynamics at a local level.

The integrity of the genome is crucial for the survival of all living organisms. However, genomes need to adapt to survive certain pressures and for this propose use several mechanisms to diversify. Chromosomal instability (CIN) is one of the main mechanism leading to the creation of genomic heterogeneity by altering the number of chromosomes as well as changing their structures. In this review we will discuss the different chromosomal patterns and changes observed in speciation, in evolutional biology as well as during tumor progression. By nature, human genome shows induction of diversity during gametogenesis but as well during tumorigenesis that can conclude in drastic changes such as whole genome doubling to more discrete changes as indels as well as the recent discovered complex chromosomal rearrangement chromothripsis. More importantly, changes observed during speciation are strikingly similar to the genomic evolution observed during tumor progression and resistance to therapy. The different origins of CIN will be treated as the importance of double strand breaks (DSB) or the consequences of micronuclei. We will also explain the mechanisms behind the controlled DSBs and recombination of homologous chromosomes observed during meiosis, to explain how errors lead to similar pattern observed during tumorigenesis. Then, we will also list several diseases associated to CIN resulting in fertility issue, miscarriage, genetic rare diseases and cancer. Understanding better chromosomal instability as a whole is primordial for the understanding of mechanisms leading to tumor progression.

The gap junction-coupled astroglial network plays a central role in the regulation of neuronal activity and synchronization, but its involvement in the pathogenesis of neuronal diseases is not yet understood. Here we present the current state of knowledge about the impact of impaired glial coupling in the development and progression of epilepsy and discuss whether astrocytes represent alternative therapeutic targets. We focus mainly on temporal lobe epilepsy (TLE), which is the most common form of epilepsy in adults, characterized by high therapy resistance. Functional data from TLE patients and corresponding experimental models point to a complete loss of astrocytic coupling, but preservation of the gap junction forming proteins connexin43 (Cx43) and connexin30 (Cx30) in hippocampal sclerosis. Several studies further indicate that astrocyte uncoupling represents a causal event in the initiation of TLE, as it occurs very early in epileptogenesis, clearly preceding dysfunctional changes in neurons. However, more research is needed to fully understand the role of gap junction channels in epilepsy and to develop safe and effective therapeutic strategies targeting astrocytes.

Monitored differential infection rates of past Corona waves are used to infer, a posteriori, the real time variation of the ratio of recovery to infection rate as key parameter of the SIR-epidemic model. From monitored Corona waves in five different countries it is found that this ratio exhibits a linear increase at early times below the first maximum of the differential infection rate before the ratios approach a nearly constant value close to unity at the time of the first maximum with small amplitude oscillations at later times. The observed time dependencies at early times and at times near the first maximum agree favorably well with the behavior of the calculated ratio for the Gaussian temporal evolution of the rate of new infections, although the predicted linear increase of the Gauss ratio at late times is not observed.

This paper looks at key elements of global culture that are driving a new paradigm shift in museums causing them to question their raison d'être, their design and physical space, recognizing the need to accommodate visitor interaction and participation, and to reprioritize institutional outcomes and goals reexamining their priorities. As heritage sharing in online spaces reaches across national, political, and social boundaries on platforms and networks, this has been driven by museum engagement with Internet life during the pandemic. Museum relationships and interactions with communities both local and global, continue to challenge core values and precepts, leading to radical changes in how museums define their roles and responsibilities. In this new cultural landscape, museums are responding to human digital identity in a tidal wave of human interactions on the Internet, from social media to online sharing of images and videos. This is revealing shared perspectives on cultural conflict as being tied to freedom of expression of one’s heritage embedded in digital identity.

Two meters is the ratio of the distance between two points according to the standard 1-meter ruler saved somewhere in France, and this comparative ratio is really equal to dimensionless number 2. We can easily repeat such comparative ratios for kilograms and any other quantities that modern physics believes they carry units. When we think about it, all physical quantities are dimensionless comparative ratios referred to standard units saved somewhere in France (never mind more complicated, derived units, they still depend on the particular items saved in France). Once this important fact is established for dimensional units, we realize how we should deal with dimensionless constants in physics: we need to determine one such constant by experiment, and then all other related dimensionless constants fall in place by simple ratios, just as has been done for dimensional units over many years in the past. For the forces of nature, we advocate the fine-structure constant as the dimensionless quantity that will serve as the baseline for other dimensionless constants representing the coupling of forces. Then, we can extend gravity in the atomic world and quantum physics into large-scale cosmology, both quite flawlessly.

The pressure exerted on the heart and blood vessels because of blood flow is considered as an important parameter for the cardiovascular function. It determines sufficient blood perfusion as well as transportation of nutrition, oxygen and other essential factors to every organ. Pressure in the primary arteries located near the heart and the brain, known as central blood pressure (CBP), while in peripheral arteries, known as peripheral blood pressure (PBP). Normally, CBP and PBP are correlated; however, cardiovascular disorders interfere their regulation and affect the blood flow in vital organs and accessory organs, differently. Therefore, understanding each of them in normal and disease conditions is essential for managing various cardiovascular disorders and increasing their treatment outcomes. In this review, we have described the control systems (neural, hormonal, osmotic and cellular) of the blood pressure and its regulation in hypovolemic shock using centhaquine (Lyfaquin®) as a resuscitative agent.

The present-day financial system is being influenced by the rapid development of Fintech (financial technologies), which are technologies created to improve and automate traditional forms of finance for businesses and consumers. The topic of Fintech as a financial disruptor is gaining popularity in line with the swift spread of digitalization across the banking industry, whereby this paper contributes to the field by presenting a novel bibliometric analysis of academic literature related to Fintech as a financial disruptor. The analysis is based on metadata extracted from the Scopus database through the VOSviewer and Biblioshiny software. The bibliometric analysis of 363 documents identified the most impactful sources of publication, keywords, authors, and most cited documents on the topic of Fintech as a financial disruptor. As our analysis demonstrates the number of publications on the given topic increases, defining both the interest among academia and potential for future research.

(1) Background: Twitch.tv is a live video content website. As of 2022, Twitch users are generally adolescents and young adults, with estimates of the percentage of users aged 16-24 between 22.3% and 41%, predominantly males with estimates ranging from 65% to 78.36%. In recent years, “slotstream” content has become increasingly popular, where streamers gamble online while users watch them. (2) Methods: from July 2022 to November 2022, we researched articles related to Twitch, live streaming, gambling, casino, slot, gambling online, modeling, social media influencers, conditioning, and celebrities, searching for relevant studies in the databases LexisNexis Academic, Business Source Complete, PubMed, Web of Science, Freedom Collection, Health &amp; Medical Collection, Elsevier Journal, Springer, APA PsycARTICLES, Wiley, and other single journals. (3) Results: we took into consideration Gambling Disorder, Online Gambling Disorder, and Internet Gaming Disorder on the one hand, and consumer components such as persuasive communication and influencer-audience relationship on the other; we then hypothesized that the presence of these variables within the slotstream format is positively correlated with the development of pathological gambling, especially in an adolescent audience. (4) Conclusion: we assume that there is a positive correlation between watching slotstream content and the probability of developing both pathological and non-pathological gambling behavior.

Transmembrane transport of small organic and inorganic molecules is one of the cornerstones of cellular metabolism. Among transmembrane transporters, solute carrier (SLC) proteins form the largest, albeit very diverse, superfamily with over 400 members. It has been early on recognized that xenobiotics can directly interact with SLCs and that this interaction can fundamentally determine their efficacy, including bioavailability and intertissue distribution. Apart from the well-established prodrug strategy, the chemical ligation of transporter substrates to nanoparticles of various chemical composition has recently been used as a means to enhance their targeting or absorption. In this review, we summarize efforts in drug design exploiting interactions with specific SLC transporters to optimize their therapeutic effects. Furthermore, we describe current and future challenges as well as new directions for the advanced development of therapeutics that target SLC transporters.

Teacher burnout has been shown to be one of the most common negative consequences of the COVID-19 pandemic. This study aimed to identify distinct psychological resources and burnout risk profiles of teachers and examine their association with Kolb’s educator roles and their professional experience. Methods: The survey data were collected from 330 preschool and primary school (84 males, Mage = 38.3, SD = 9.14) teachers using a convenience sampling method. Results: The two-step cluster analysis revealed two distinct profiles. The first profile, “high psychological resources, no burnout risk,” was characterized by absent symptoms of burnout and increased levels of well-being, self-control, and positive emotionality. The second profile, “moderate psychological resources, mild burnout,” was associated with medium levels of well-being, self-control, and positive emotionality accompanied by mild burnout. Our findings highlighted that cluster one had a significantly higher score for the facilitator role and cluster two for the expert and coach roles. Additionally, teachers with less professional experience were more likely to belong to cluster one, considering their adequate skills in digital literacy. Conclusions: These findings provide new insights into the explanation of teacher burnout and the design of intervention programs.

The paper presents a new conception of quasi-distributed sensor for simultaneous measurement of several physical fields and the results of an experimental study of this sensor. A distinctive feature of the sensor defined by the sensitive RC elements connected in the original tree-like structure. The proposed is the structure of the sensor and measurement system as well as corresponding measurement algorithm. The high accuracy demonstrated by the sensor’s prototype gives possibility to effective use the proposed sensors in many technical and scientific applications.

The discovery of first ATP-binding cassette (ABC) transporter, whose overexpression in cancer cells is responsible for exporting anticancer drugs out of tumor cells, initiated enormous efforts to overcome tumor cell multidrug resistance (MDR) by inhibition of ABC-transporter. Because of its many physiological functions, diverse studies have been conducted on the mechanism, function and regulation of this important group of transmembrane transport proteins. In this review, we will focus on the structural aspects of this transporter superfamily. Since the resolution revolution of electron microscope, experimentally solved structures increased rapidly. A summary of the structures available and an overview of recent structure-based studies are provided. More specifically, the artificial intelligence (AI)-based predictions from AlphaFold 2 will be discussed.

As of July 7, 2022, the total number of confirmed cases caused by COVID-19 has reached 544,324 million, and the total number of 6.333 million deaths (WHO). Older people were globally the most vulnerable during the pandemic. This paper examined the mortality and psychological crisis of older people during the pandemic in four cities, namely Wuhan, Milan, London, and Hong Kong. The selection of cities was based on different degrees of social connectedness of older people and chronologically to cover the whole period of the outbreak from January 2020 to August 2022. Older people in Milan and Wuhan tended to rely on the close family relationship during times of crisis. Meanwhile, older people in Hong Kong and London were more dependent on social services. Both quantitative and qualitative approaches were employed to analyze the situations of older people under different government responses and the strengths and weaknesses of respective healthcare systems. Government reports and official statistics were used to illustrate the seriousness of each city's COVID-19 outbreak while stories reported by the press, NGOs, and journal articles were used to reflect the reactions of the older people. Interactions of social culture, health care provisions, and government responses to the pandemic were discussed from the perspective of health welfare regimes. On one hand, results showed that governments have not learned from the experiences of other countries. On the other hand, older people have not successfully coped with the life-threatening stress by breaking institutional boundaries and redefining cultural norms.

Molecular autism research is evolving towards a biopsychosocial framework that is more informed by autistic experiences. In this context, research aims are moving away from correcting external autistic behaviors and towards alleviating internal distress. Autism Spectrum Conditions (ASCs) are associated with high rates of depression, suicidality and other comorbid psychopathologies, but this relationship is poorly understood. Here, we integrate emerging characterizations of internal autistic experiences within a molecular framework to yield insight into the prevalence of psychopathology in ASC. We demonstrate that descriptions of social camouflaging and autistic burnout resonate closely with the accepted definitions for early life stress (ELS) and chronic adolescent stress (CAS). We propose that social camouflaging could be considered a distinct form of CAS that contributes to allostatic overload, culminating in a pathophysiological state that is experienced as autistic burnout. Autistic burnout is thought to contribute to psychopathology via psychological and physiological mechanisms, but these remain largely unexplored by molecular researchers. Building on converging fields in molecular neuroscience, we discuss the substantial evidence implicating mitochondrial dysfunction in ASC to propose a novel role for mitochondrial allostatic load in the relationship between autism and psychopathology. An interplay between mitochondrial, neuroimmune and neuroendocrine signaling is increasingly implicated in stress-related psychopathologies, and these molecular players are also associated with neurodevelopmental, neurophysiological and neurochemical aspects of ASC etiology. Together, this suggests an increased exposure and underlying molecular susceptibility to ELS that increases the risk of psychopathology in ASC. This article describes an integrative framework shaped by autistic experiences that highlights novel avenues for molecular research into mechanisms that directly affect the quality of life and well-being of autistic individuals. Moreover, this framework emphasizes the need for increased access to diagnoses, accommodations, and resources to improve mental health outcomes in autism.

Cannabis has been used as an herbal remedy for thousands of years and recent research indicates promising new uses in medicine. Researchers have been particularly interested in the potential uses of cannabinoids in treating cancer due their ability to regulate cancer-related cell cycle pathways, leading to many beneficial effects such as tumor growth prevention, cell cycle obstruction, and cell death. The aim of this review is to summarize current knowledge on mechanisms of cannabinoids and their role in treating chemotherapy-induced nausea and vomiting, relieving cancer-associated pain, and obstructing tumor cell growth.

The endothelium plays a key role in blood vessel health. At the interface of the blood, it releases several mediators that regulate local processes and that protect against the development of cardiovascular disease. In this interplay, there is increasing evidence for a role of extracellular nucleotides and endothelial purinergic P2Y receptors (P2Y-R) in vascular protection. Recent advances have revealed that endothelial P2Y1-R and P2Y2-R mediate nitric oxide-dependent vasorelaxation as well as endothelial cell proliferation and migration, which are processes involved in the regeneration of damaged endothelium. However, endothelial P2Y2-R, and possibly P2Y1-R, have also been reported to promote vascular inflammation and atheroma development in mouse models, with endothelial P2Y2-R also being described as promoting vascular remodeling and neointimal hyperplasia. Interestingly, at the interface with lipid metabolism, P2Y12-R has been found to trigger HDL transcytosis through endothelial cells, a process known to be protective against lipid deposition in the vascular wall. Better characterization of the role of purinergic P2Y-R and downstream signaling pathways in determination of the endothelial cell phenotype in healthy and pathological environments has clinical potential for the prevention and treatment of cardiovascular diseases.

Restrictive cardiomyopathy is a rare cardiac disease causing severe diastolic dysfunction, ventricular stiffness and dilated atria. In consequence, it induces heart failure often with preserved ejection fraction and is associated with a high mortality. Since a poor clinical prognosis, patients with restrictive cardiomyopathy require frequently heart transplantation. Genetic as well as non-genetic factors contribute to restrictive cardiomyopathy and a significant portion of cases remains of unknown etiology. However, the genetic forms of restrictive cardiomyopathy and the involved molecular pathomechanisms are only partially understood. In this review, we summarize the current knowledge about primary genetic restrictive cardiomyopathy and describe its genetic landscape, which might be of interest for geneticists as well as for cardiologists.

Neurological / neurovascular disorders constitute the leading cause of disability and second leading cause of deaths in the world. Some of the major neurological / neurovascular disorders or diseases include stroke, Alzheimer’s disease, spinal cord injury, neonatal hypoxic-ischemic encephalopathy, etc. Their pathophysiology is considered as highly complex and has been blamed as the main obstacle in the development of any drugs for these diseases. In this review, we have described the endothelin system, their involvement in neurovascular disorders, importance of endothelin B receptors (ETBRs) as a novel potential drug target, and its agonism by IRL-1620 (INN - sovateltide), which we are developing as a drug candidate for treating the above-mentioned neurological disorders / diseases. We have highlighted the results of our pre-clinical and clinical studies related to these diseases. The phase I, safety and tolerability study of sovateltide has shown it as a highly safe and well tolerable compound at tested dosages, while pre-clinical and clinical phase II studies have demonstrated its efficacy in treating acute ischemic stroke, where it is currently being developed as a “First in Class” drug. Its testing in Alzheimer’s disease (AD), acute spinal cord injury and neonatal hypoxic-ischemic encephalopathy (HIE) is currently being carried out and are at different stages of drug development. Successful completion of these studies will prove the involvement of ETBRs signaling in neurological / neurovascular diseases and help in development of novel neurological drugs in future.

Natural cycles underpin the very stuff of life. In this commentary we consider unnatural cycles: that is, anthropogenic activities which have a circularity, but whose nature is to have a detrimental effect on human health, exacerbating existing problems. Natural cycles have feedback loops, some of which have recently come to light, with an understanding that everything is connected in some way. In health, feedback loops are imperative in homeostatic mechanisms. However, in the unnatural cycle the feedback loops serve to reinforce (and in some cases amplify) negative problems. We offer a commentary on an unnatural cycle moving from air quality to lung function and back to air quality; we call this the lung disease unnatural cycle. We suggest where links occur, and where wider consideration of interactions between various disciplines can lead to breaking this unnatural (or vicious) cycle, changing it to a healthy cycle where individual health can be improved, along with better global scale outcomes. We suggest that many activities within this unnatural cycle occur within silos. However, the improved cycle incorporates joint activities at geological, health, and financial levels, to the mutual benefit of all, breaking the unnatural cycle, and improving health, life and financial costs.

The temporal evolution of pandemics described by the susceptible-infectious-recovered (SIR)-compartment model is sensitively determined by the time dependence of the infection (a(t)) and recovery (u(t)) rates regulating the transitions from the susceptible to the infected and from the infected to the recovered compartment, respectively. Here approximated SIR-solutions for different time dependencies of the infection and recovery rates are derived which are based on the adiabatic approximation assuming time-dependent ratios k(t)=u(t)/a(t) varying slowly in comparison to the typical time characteristics of the pandemic wave. For such slow variations the available analytical approximations from the KSSIR-model, valid for a stationary value of the ratio k, are used to insert a-posteriori the adopted time-dependent ratio of the two rates. Instead of investigating endless different combinations of the time dependencies of the two rates a(t) and u(t) a suitably parameterized reduced time dependence of the ratio k(tau) is adopted. Together with the definition of the reduced time this parameterized ratio k(tau) allows us to cover a great variety of different time dependencies of the infection and recovery rates. The agreement between the solutions from the adiabatic approximation in its four different studied variants and the exact numerical solutions of the SIR-equations is remarkably good providing strong confidence in the accuracy of the proposed adiabatic approximation.

Nanoscale structuring/printing is of interest for range of applications in 3D subtractive and additive manufacturing (3D^+/-). Basic principles of light field enhancement and control at the nanoscale are overviewed in this section/chapter for bulk, surface, and localised plasmons (1D, 2D, and 3D localisation, respectively). All these plasmons are resonant phenomena which have common Lorentzian spectral lineshape which relates refractive and absorption properties as well as defining the phase of transmitted and scattered light. Localisation of light at the nanoscale creates the possibility of modification with matching resolution. Harnessing this light enhancement can be demonstrated as a "nano-pen" for direct write nanolithography.

The 2019 Coronavirus Disease (COVID-19) is a major cause of morbidity and mortality worldwide. Since late November 2021, Omicron variant has emerged as the primary cause of COVID-19 and caused a huge increase in the reported incidence around the world. To date, 32-34 spike mutations have been reported, 15 of which were located in the receptor-binding domain that interacts with the cell surface of the host cells, while the rest were located in the N-terminal domain and around the Furin cleavage site. At present, both the genomic and clinical profiles of this novel variant are being investigated. Here, we aim to discuss the recent reports on the transmissibility and severity of Omicron variant from both the genetic and clinical perspectives. Afterward, we also take the chance to deliver our personal view on the topic.

Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signature-based ransomware detection technique falls behind because it can only detect known anomalies. When it comes to new and non-familiar ransomware traditional system unveils huge shortcomings. For detecting unknown patterns and sorts of new ransomware families,behavior-based anomaly detection approaches are likely to be the most efficient approach. In the wake of this alarming condition, this paper presents an ensemble classification model consisting of three widely used machine learning techniques that include Decision Tree (DT), RandomForest (RF), and K-nearest neighbor (KNN). To achieve the best outcome ensemble soft voting and hard voting techniques are used while classifying ransomware families based on attack attributes. Performance analysis is done by comparing our proposed ensemble models with standalone models on behavioral attributes based ransomware dataset..

One of the most important challenges in the Machine and Deep Learning areas today is to build good models using small datasets, because sometimes it is not possible to have large ones. Several techniques have been proposed in the literature to address this challenge. This paper aims at studying the different available Deep Learning techniques and performing a thorough experimentation to analyze which technique or combination thereof improves the performance and effectiveness of the models. A complete comparison with classical Machine Learning techniques was carried out, to contrast the results obtained using both techniques when working with small datasets. Thirteen algorithms were implemented and trained using three different small datasets (MNIST, Fashion MNIST, and CIFAR-10). Each experiment was evaluated using a well-established set of metrics (Accuracy, Precision, Recall, F1, and the Matthews correlation coefficient). The experimentation allowed concluding that it is possible to find a technique or combination of them to mitigate a lack of data, but this depends on the nature of the dataset, the amount of data, and the metrics used to evaluate them.

A structure-based view on mesons is given, based upon the concept of an archetype quark, described as a pointlike source producing an energy flux, the spatial description of which is derived from Dirac’s second dipole moment. This enables to conceive the archetype meson (pion) as a structure that behaves as a one-body anharmonic quantum mechanical oscillator. All mesons appear being excitations of the archetype, thereby allowing a calculation of the mass spectrum without the use of empirical parameters for the masses of the quark flavors. This includes a physically comprehensible analysis of the spin-spin interaction between quarks. It also provides a solution for the eta-etaprime puzzle. Next to this, it is shown that quite some particles that are presently regarded as elementary, have a common root and can be traced back to a few archetypes only.

The Covid‐19 pandemic, evolving needs of students & mentors, and the drive for global educational equality are collectively shifting how courses are packaged/distributed, ushering a more holistic approach and blending of fields. We recently created interdisciplinary courses in chemical biology aimed at massive open online and small private levels. These courses cover biology, chemistry, & physics, and concepts underlying modern chemical‐biology tools. We discuss what we learned while creating/overseeing these courses: content optimization and maintaining material freshness while fostering a stimulating learning environment. We outline mechanisms that help sustain student attention throughout rapidly‐moving courses, how to integrate adaptability to students’ needs in the short & long term, and speculate how we could have improved. We believe this will be an important guide for anyone wanting to develop online learning formats ideal for nurturing interdisciplinary scientists of tomorrow.

For better export marketing strategies (EMS), companies mobilize their internal resources, which are managerial commitment, firm experience, and product uniqueness. However, Small businesses with constrained resources cannot be well explained with this view. So, more research on how small business come up with EMS have been called for. To explain how resource-restricted firms which rely heavely on entrepreneur, this study adopted the concept of dynamic managerial capabilities (DMCs) and resource versatility to better explain small business exports. We analyzed small businesses in Mongolia with qualitative research methods, including interviews with entrepreneurs and support organizations, site visits, and group discussions. We suggest international dynamic marketing capabilities (IDMCs), which are entrepreneurial orientation, networking capability, and versatile dynamic capability for small business. Theoretical and managerial implications are discussed.

Excitation of the acoustic field leading to the Blaha effect affects the plasticity of the material significantly in ultrasonic vibration-assisted forming. In a micro-forming field, the effects are more significant in the deformation in surface of materials [1]-[3], in which reduction of the surface roughness based on the increasing of plastic deformation of surface asperity was effective [4]. On the other hand, the effect on deformation behavior of the bulk region indicted reduction in the yield stress of materials, and not only acoustic effect [5], but also impact effect is found to generate a large amount of dislocation and produce plastic deformation [6][7]. However, the effect on the bulk is more significant as that on the surface. Differences in the effect on the surface and the bulk are not clarified. In this study, the mechanism of the deformation in the surface of the material with ultrasonic vibration assistance is investigated and compared with that in the bulk. Forging tests using a newly developed ultrasonic vibrator were carried out on pure Cu foils with various process conditions. The longitudinal vibration frequency of the ultrasonic transducer is 60∓2kHz, and the vibration amplitude is in an adjustable range of 0~10μm. Forging test was carried out at different initial stress, specimen size and amplitude. The difference in acoustic softening and impact effects on the surface and the bulk was discussed.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Four discrete models using the exact spectral derivative discretization finite difference (ESDDFD) method are proposed for a chaotic five-dimensional, conformable fractional derivative financial system incorporating ethics and market confidence. Since the system considered was recently studied using the conformable Euler finite difference (CEFD) method and found to be hyperchaotic, and the CEFD method was recently shown to be valid only at fractional index , the source of the hyperchaos is in question. Through numerical experiments, illustration is presented that the hyperchaos previously detected is in part an artifact of the CEFD method as it is absent from the ESDDFD models.

Museums increasingly recognize the need to address advances in digital culture which impact the expectations and needs of their audiences. Museum collections of real objects need to be presented both on their own premises and digitally online, especially as social media becomes more and more influential in people’s everyday lives. We investigate these challenges magnified by advances in digital and computational media and culture looking particularly at recent and relevant reports on changes in the ways museums interact with the public. We find that the Covid-19 pandemic has accelerated many of the changes driving museum transformation. We believe that museums must be more prepared than ever to adapt to unabated technological advances set in the midst of cultural and social revolution, now intrinsic to the digital landscape in which museums are inevitably connected and participating across the global digital ecosystem where they inevitably find themselves entrenched.

The main idea of this paper is to investigate the exact solutions and dynamic properties in optical nanofibers, which is modeled by space-time fractional perturbed nonlinear schr\"odinger equation involving Kerr law nonlinearity with conformal fractional derivative. Firstly, by the complex fractional traveling wave transformation, the traveling wave system of the original equation is obtained, then a conserved quantity, namely the Hamiltonian is constructed, and the qualitative analysis of this system is conducted via this quantity by classifying the equilibrium points. Moreover, the prior estimate of the existence of the soliton and periodic solution is established via the bifurcation method. Furthermore, all exact traveling wave solutions are constructed to illustrate our results explicitly by the complete discrimination system for polynomial method.

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

The global spread of multidrug-resistant (MDR) microbial infections is currently one of the most severe risks to global public health, with 10 million fatalities expected by 2050 unless action is taken. Nanotechnology has revolutionized science and medicine. The reliance on nanotechnology is growing. Nanoparticles have distinct properties that improve biological, chemical, and physical properties studied for various uses. A significant area of attention in the synthesis of nanoscale modulators is the utilization of crude formulations, retro-synthesized, and pure chemicals, mainly from herbal sources, with fewer adverse effects. Green chemistry has devised a tangential technique for synthesizing metals and metal oxides to produce nanoparticles. Plant extracts (leaves, stems, and shoots) and microorganisms (bacteria, fungus, and yeast) are used as reducing intermediates to make nanoparticles. Studies in microbiology have shown that nanoparticles kill bacteria, fungi, viruses, and protozoa. These green nanoparticles contain antibacterial, antifungal, and anti-inflammatory effects. Most nanoparticles have high antibacterial properties, indicating they may be used to combat diseases and biological contaminants. These nanoparticles have antibacterial action against pathogenic microorganisms that cause serious illnesses, including multidrug-resistant pathogens. The current research will pave the way for future applications and improved methods for producing nanoparticles, paving the way for an innovative route in nano-life sciences with widespread recognition.

Amongst the advantages of using Worldwide Interoperability Microwave Access (WiMAX) technology at the last-mile level as access technology include an extensive range of 50 km Line of Sight (LOS), 5 to 15 km Non-Line of Sight and few infrastructure installations compared to other wireless broadband access technologies. Despite positive investments in ICT fibre infrastructure by developing countries, including Botswana, servicing end-users is subjected to high prices and service disparities. The alternative, the Wi-Fi hotspot initiative by the Botswana government, falls short as a solution for last-mile connectivity and access. This study used OPNET simulation Modeller 14,5 to investigate whether Botswana’s national broadband project could adopt WiMAX IEEE 802.16e as an access technology. Therefore, using the simulation method, this paper evaluates the WiMAX IEEE 802.16e/m over three subscriber locations in Botswana. The results obtained indicate that the deployment of the WiMAX IEEE 802.16e standard can solve most of the deployment issues and access at the last-mile level. Although the findings suggest that WiMAX IEEE 802.16e is more suitable for high-density areas, it could also solve rural areas’ infrastructure development challenges and provide the required high-speed connectivity access. However, unlike the Wi-Fi initiative, which requires more infrastructure deployment and less on institutional and regulatory frameworks, the deployment of WiMAX IEEE802.16e requires institutional and regulatory standards.

Dirofilaria immitis infection is one of the most severe parasitic diseases of dogs. Prevention is achieved by the administration of drugs containing macrocyclic lactones (MLs). These products are very safe and highly effective, targeting the third and fourth larval stages (L3, L4) of the parasite. Until 2011, claims of ineffectiveness of MLs, reported as “Lack of Efficacy” (LOE), were generally attributed to owners’ non-compliance, or other reason for inadequate preventative coverage. There was solid argumentation that a resistance problem is not likely to occur because of i) the great extent of refugia, ii) the complexity of resistance development to MLs, and iii) the possible big number of genes involved in resistance selection. Nevertheless, today it is unequivocally proven that ML resistant D. immitis strains exist, at least in the Lower Mississippi region, USA. Accordingly, tools have been developed, to evaluate and confirm the susceptibility status of D. immitis strains. A simple, in-clinic, microfilariae suppression test, 14-28 days after ML administration, and a “decision tree” (algorithm), including compliance and preventatives’ purchase history, and testing gaps, may be applied for assessing any resistant nature of the parasite. On the molecular level, specific SNPs may be used as markers of ML resistance, offering a basis for validation of clinically suspected resistant strains. In Europe, no LOE/resistance claims have been reported so far, and the existing conditions (stray dogs, rich wildlife, majority of owned dogs not on preventive MLs treatment) do not favor selection pressure on the parasites. Considering the genetic basis of resistance and the epizootiological characteristics of D. immitis, ML resistance neither establishes easily nor spreads quickly, a fact confirmed by the current known dispersion of the problem, which is limited. Nevertheless, ML resistance may propagate from an initial geographical point, via animal and vector mobility, to other regions, while it can also emerge as an independent evolutionary process in a new area. For these reasons and considering the current chemoprophylaxis recommendations and increasing use of ML endectoparasiticides as a potential selection pressure, it is important to remain vigilant for timely detection of any ML LOE/resistance, in all continents where D. immitis is enzootic.

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

An association of food sources with odors prominently guides foraging behavior in animals. To understand the interaction of olfactory memory and food preferences, we used glucose-averse (GA) German cockroaches. Multiple populations of cockroaches evolved a gustatory polymorphism where glucose is perceived as a deterrent and enables GA cockroaches to avoid eating glucose-containing toxic baits. Comparative behavioral analysis using an operant conditioning paradigm revealed that learning and memory guide foraging decisions. Cockroaches learned to associate specific food odors with fructose (phagostimulant, reward) within only a 1 hr conditioning session, and with caffeine (deterrent, punishment) after only three 1 hr conditioning sessions. Glucose acted as reward in wild type (WT) cockroaches, but GA cockroaches learned to avoid an innately attractive odor that was associated with glucose. Olfactory memory was retained for at least 3 days after three 1 hr conditioning sessions. Our results reveal that specific tastants can serve as potent reward or punishment in olfactory associative learning, which reinforces gustatory food preferences. Olfactory learning therefore reinforces behavioral resistance of GA cockroaches to sugar-containing toxic baits. Cockroaches may also generalize their olfactory learning to baits that contain the same or similar attractive odors even if they do not contain glucose.

Based on hospital capacities, facts from past experience with the coronavirus disease 2019 (COVID-19) virus and the number of dark infections during the second wave (DII = 2D2), a reasonable limiting value of 140/D2 for the 7-day incidence per 100,000 persons (MSDIHT) and a second wave herd immunization threshold fraction value of 0.26 in Germany were calculated. If the MSDIHT is held below this limiting value, the German hospital system can cope with the number of new seriously infected persons without any triage decisions. On the basis of the SIRV epidemics model, the classical threshold values for herd immunization were calculated for 18 countries. For these countries, the dates regarding when herd immunization against the second COVID-19 wave will be reached were estimated

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be a very powerful for the solution of problems in complex planning. We introduce mctreesearch4j, a standard MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demon- strate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP’s. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.