This article aimed at providing some extension of the modal square of opposition in the 1 light of Ockham’s account of the modal operators. Moreover, we shall set forth some significant 2 remarks on the de re-de dicto distinction and on the modal operator of contingency by means of a 3 Boolean algebra called Bitstring Semantics. This generalisation starting from Ockham’s account of 4 modalities will allow us to take into consideration whether Ockham’s thought on this matter holds 5 water or not, and in which case it should be changed in order to make it coherent.

Multivariate predictive analysis of the Stream-Water (SW) parameters (discharge, water level, temperature, dissolved oxygen, pH, turbidity, and specific conductance) is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the meteorological and climatic features have a significant impact on the temporal distribution of the SW variables in recent days making the SW variables forecasting even more complicated for diversified water-related issues. To predict the SW variables, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-informed analysis and prediction, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, a comprehensive Explorative Data Analysis (EDA) and feature engineering were performed to prepare the dataset to obtain the best performance of the predictive model. Long Short-Term Memory (LSTM) neural network regression model is trained using over several years of daily data to predict the SW variables up to one week ahead of time (lead time) with satisfactory performance. The performance of the proposed model is found highly adequate through the comparison of the predicted data with the observed data, visualization of the distribution of the errors, and a set of error matrices. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time SW variables prediction.

The increasing prevalence of methicillin-resistant S. aureus (MRSA) in the dairy industry has become a fundamental concern. Endolysins are bacteriophage-derived peptidoglycan hydrolases that induce the rapid lysis of host bacteria. We investigated endolysin candidates with lytic activity against MRSA and evaluated the lytic activity of the endolysin candidate against S. aureus and MRSA. To identify endolysins, we used the following bioinformatic strategy: (1) retrieval of genetic information, (2) annotation, (3) selection of MRSA, (4) selection of endolysin candidates, and (5) evaluation of protein solubility. We then characterized the endolysin candidate under various conditions. Approximately 67% of S. aureus was detected as MRSA and a total of 114 putative endolysins were found. The 114 putative endolysins were divided into three groups based on their combination of conserved domains. Considering the protein solubility, we selected putative endolysins 177 and 117. Putative endolysin 117 was successfully overexpressed and renamed LyJH1892. LyJH1892 lysed approximately 85% of S. aureus. LyJH1892 showed more potent lytic activity against MRSA than normal S. aureus (relative lytic activity > 125%). LyJH1892 showed broad lytic activity against coagulase-negative Staphylococci (CNS). In conclusion, These findings provide a rapid and useful strategy for the development of specific endolysins against antibiotic-resistant bacterial strains.

Artificial Intelligence (AI) has the power to generate scenarios and make predictions through the use of advanced algorithms and machine learning techniques. OpenAI’s GPT-3 AI is a state-of-the-art language model that has been trained on a large dataset of text, which allows it to generate hu-man-like text, and it can generate scenarios for different fields. However, GPT-3 was trained on data available up until June 2021, had no access to more recent data nor was connected to the internet. In this study, we investigated the capability of OpenAI’s GPT-3 AI to predict the Ukrainian war es-calation, which started in 2022 and had massive geopolitical effects. We used GPT-3´s capability to generate future scenarios, to check those scenarios for internal consistency, and to create a proba-bility estimate. The results showed that although GPT-3 described an open war as one of the low probability scenarios, its capability on predicting the future was limited. Furthermore, it became evident that checking internal consistency of the generated scenarios could be improved. We ap-preciated GPT-3 as very useful and powerful for generating future scenarios, but also concluded that its prediction capabilities of real-world events are limited, should be used with caution, and require further development.

Cempo Ireng (Oryza sativa L. var. Cempo Ireng) is one of the black rice variety in Indonesia that consumed as functional food, not as a staple food because of the black rice contains anthocyanin pigment which has beneficial to human health. High concentration of anthocyanin reaching 393.93 ppm cause black color in the pericarp layer of Cempo Ireng which make this variety as an antioxidant source, low glycemic index, and free of gluten, leading to have higher price in the rice market. However, most of the farmers are not cultivate Cempo Ireng due to the longer vegetative phase up to 150 days compared to the white rice varieties which only takes 90 days, the taller phenotype of Cempo Ireng cause problems in cultivation, and also the purple color of Cempo Ireng attract birds and earhead bug (Leptocorisa oratorius) to feed on black rice caryopsis that cause severe yield loss. Transgenic methods, mutation technique, intensification systems, and physical treatment have been applied in Cempo Ireng to overcome those lack characteristics. In the future, Cempo Ireng also has a big potential to become a parent in cross breeding program because of the positive characters. The aim of this review is to describe the potential of Cempo Ireng in rice breeding program for improving food sustainability.

The Van der Pol oscillator is a chaotic non-linear system. Small perturbations in initial conditions may result in wildly different trajectories. Because of its chaotic nature, controlling, or forcing, the behavior of a Van der Pol oscillator is difficult to achieve through traditional adaptive control methods. Connecting two Van der Pol oscillators together where the output of one oscillator, the driver, drives the behavior of its partner, the responder, is a proven technique for controlling the Van der Pol oscillator. Deterministic AI (DAI) is an adaptive feedback control method that leverages the known physics of the Van der Pol system to learn optimal system parameters for the forcing function. We assessed the performance of DAI employing three different online parameter estimation algorithms. Our evaluation criteria include mean absolute error (MAE) between the target trajectory and the response oscillator trajectory over time. RLS with exponential forgetting (RLS-EF) had the lowest MAE overall, with a 2.46% reduction in error. However, another method was notable. Least Mean Squares with normalized gradient adaptation (LMS-NG) had worse initial error in the first 10% of the simulation, but after that point had consistently better performance. We found that over the last 90% of the simulation, DAI with LMS-NG had a 48.7% reduction in MAE compared to feedforward alone.

This manuscript explores unmanned aerial vehicle DC motor control performance efficacy of deterministic artificial intelligence in comparison to model-following adaptation, particularly a direct self-tuner with filtering. The deterministic artificial intelligence model made use of self-awareness statements to overcome error in response to permutations of the multi-duty cycle square wave that served as the system input. It can be seen that (despite equivalently powerful estimation techniques) deterministic artificial intelligence provided far superior results: a reduction in peak initial transient error of 55%, and a mean error reduction over 81% with over 65% reduction in error standard deviation compared to a state-of-the-art nonlinear adaptive control method. Deterministic artificial intelligence also was able to very closely track at the switching of the input control, while the benchmark nonlinear adaptive control failed to respond as quickly at these points.

Phytoseiidae inhabit a wide range of herbs, shrubs and trees. Aesculus hippocastanum is an important ornamental tree in Europe and is likely reservoir of these mites. We therefore assessed the species composition and the spatial and seasonal variability in the abundance of Phytoseiidae in city parks in South Bohemia, Czech Republic. Leaf samples were randomly collected from horse chestnut tree branches at eight sites, five times during the vegetation season in 2013. The mites were collected by washing technique and mounted on slides for identification. In total, 13,903 specimens of phytoseiid mites were found, and eight species were identified: Amblyseius andersoni, Euseius finlandicus, Kampimodromus aberrans, Neoseiulella tiliarum, Phytoseilus macropilis, Paraseiulus talbii, Paraseiulus triporus and Typhlodromus (Typhlodromus) pyri. Paraseiulus talbii and P. macropilis were recorded on the leaves of horse chestnut trees for the first time in the Czech Republic in this study. The predominant species was E. finlandicus (96.25%). The number of mites per compound leaf was, on average, 2.53, 10.40, 23.54, 11.59 and 9.27 on the sampling dates in each month between May and September, respectively. The mite density was significantly affected by the sampling site and date.

Portable Document Format (PDF) is one of the most widely used files types worldwide in data exchange, this has encourage hackers to utilize such files to spread any malicious content through PDF, utilizing different methods and techniques to accomplish that, on the other hand, security researches kept trying to improve detection methods to cope up to the rapidly increasing number of malwares daily, one of the commonly used detection technique nowadays is by utilizing artificial intelligence and Machine learning classificat; thision to help detecting PDF Malwares, in this paper, we utilize machine learning classifier Random Forest on a newly released PDF Malware dataset CIC-Evasive-PDFMal2022 to achieve the main goal of detecting malicious PDF documents, results showing a detection accuracy of around 99.5%

The extracellular matrix (ECM) is a ubiquitous member of the body and is key to the maintenance of tissues and organs integrity. Initially thought to be a by-stander in many cellular processes, the extracellular matrix has been shown to have diverse components that regulate and activate many cellular processes and ultimately influence cell phenotype. Importantly, the ECM composition, architecture, and stiffness/elasticity influence cellular phenotypes. Under normal conditions and during development, the synthesized ECM is constantly undergoing degradation and remodeling processes via the action of matrix proteases that maintain tissue homeostasis. In many pathological conditions including fibrosis and cancer, the ECM synthesis, remodeling, and degradation is dysregulated causing its integrity to be altered. Both physical and chemical cues from the ECM are sensed via receptors including integrins and play key roles in driving cellular proliferation and differentiation and in various disease progression such as cancers. Advances in ‘omics’ technologies have seen an increase in studies focussing on bi-directional cell-matrix interactions and here we highlight emerging knowledge on the role played by the ECM during normal development and in pathological conditions. This review summarizes current ECM-targeted-therapies that can modify tumor ECM to overcome drug resistance and better cancer treatment.

Artificial intelligence (AI) has the potential to revolutionize research by automating data analysis, generating new insights, and supporting the discovery of new knowledge. The top 10 contribution areas of AI towards public health were gathered in this feasibility study. We utilized the “text-davinci-003” model of GPT-3, using OpenAI playground default parameters. The model was trained with the largest training dataset any AI had, limited to a cut-off date in 2021. This study aimed to test the ability of GPT-3 to advance public health and to explore the feasibility of using AI as scientific co-author. The AI was asked for input including scientific quotations and the human authors reviewed responses for plausibility. We found that GPT-3 was able to assemble, summarize, and generate plausible text blocks relevant for public health concerns, elucidating valuable areas of application for itself. However, most quotations were invented by GPT-3 and thus, invalid. Ac-cording to today’s rules, we conclude that AI can contribute to public health research as a team member. Nevertheless, good scientific practice needs to be also followed for AI contributions, and a broad scientific discourse on AI contributions is needed. Policies for good scientific practice should be updated timely following this discourse.

Water quality is the measure of chemical, physical and biological suitability of water in relation to natural effects and intended purpose which may affect human health and aquatic life. Assessment of water quality is very essential for the management of water resources and human health. Traditionally, in-situ measurements have been used to obtain the water quality parameters of the water bodies. However, with the availability of satellite images, researchers have shown that satellite images are a reliable tool that can be used to estimate water quality. Satellite image-derived water quality parameters provide extensive spatial extent and large temporal variations when compared to traditional in situ sample collection and laboratory measurements. The present work estimated several parameters for quality of water in the Kamuzu reservoir of Lilongwe River for the 2013-2020 period using Sentinel-2 and Landsat-8 satellite images. The band ratio algorithms were used to retrieve Chlorophyll a (Chl-a), Turbidity, Total Suspended Matter (TSM), Secchi depth, Coloured Dissolved Organic Matter (CDOM), and Cyanobacteria from the reservoir. Turbidity and TSM were compared with the in-situ data collected over the same period. The comparison indicated R² of 0.9 and 0.69 for TSM and Turbidity respectively from Sentinel-2 images whereas R² of 0.56 and 0.61 was obtained using Landsat 8 images which are quite encouraging. The other set of results included the spatial distribution maps of water quality parameters using Landsat-8 and Sentinel-2 satellite data. It was observed that the spatial distribution of water quality parameters, except for CDOM and Cyanobacteria, showed very good distribution and matches with the theoretical results. However, for CDOM and Cyanobacteria, the distribution was almost similar for the entire study area and the band ratio algorithm may not be able to estimate them quite reasonably. This research reiterates the need for the use of remote sensing in estimating the water quality parameters and may be a substitute to the in-situ data, in terms of spread and frequency, which is very common to most of the water bodies, across the globe.

This paper examines the potential of Artificial Intelligence (AI) to address societal megatrends, with a specific focus on OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3). To do this, we con-ducted an analysis using GPT-3 in order to explore the benefits of AI for digitalization, urbanization, globalization, climate change, automation and mobility, global health issues and aging population. We also looked at emerging markets as well as sustainability in this study. Interaction with GPT-3 was done solely through prompt questions and generated responses were analyzed. Our results indicate that AI can significantly improve our understanding of these trends by providing insights into how they develop over time and what solutions could be implemented. Further research is needed to determine how effective AI will be in addressing them successfully but initial findings are encouraging. Our discussion focuses on the implications of our findings for society going forward and suggests that further investigation should be done into how best to utilize new technologies like GPT-3 when tackling these challenges. Lastly, we conclude that while there is still much work left to do before any tangible effects can be seen from utilizing AI tools such as GPT-3 on societal mega-trends; early indications suggest it may have a positive impact if used correctly.

DNA is stored in the nucleus of a cell in a folded state; however, only the necessary genetic information is extracted from the required group of genes. The key to extracting genetic information is chromatin ambivalence. Depending on the chromosomal region, chromatin is characterized into low-density "euchromatin" and high-density "heterochromatin", with various factors being involved in its regulation. Here, we focus on chromatin regulation and gene expression by the yeast FACT complex, which functions in both euchromatin and heterochromatin. FACT is known as a histone H2A/H2B chaperone and initially reported as an elongation factor associated with RNA polymerase II. In budding yeast, FACT activates promoter chromatin by interacting with the transcriptional activators SBF/MBF via the regulation of G1/S cell cycle genes. In fission yeast, FACT plays an important role in the formation of higher-order chromatin structures and transcriptional repression by binding to Swi6, an HP1 family protein, at heterochromatin. This FACT property, which refers to the alternate chromatin-regulation depending on the binding partner, is an interesting phenomenon. Further analysis of nucleosome regulation within heterochromatin is expected in future studies.

Digital transformation of companies is often referred to as a future track and a strategic path towards ensuring the survival and sustainability of companies. In this paper, we aim to outline this transformation process in Tunisian companies and identify its driving factors and finally explain its key success factors. To this end, we examine a sample of 70 companies operating in all economic sectors. The results indicate that such a digital strategy has a significant and a positive effect on the success of digital transformation of companies. Leadership has an effect at a low level of digital maturity. On the other hand, corporate culture does not have a significant effect on digital transformation. These results contribute to explaining this emerging phenomenon by focusing on the driving factors as well as the factors that contribute to its success.

A drone performs comparable function to a laser scanner in the construction quality monitoring, following Scan-to-BIM process. Both technologies digitally capture the as-is environment into the computer and the data captured is transferred to a BIM world to create accurate as-built models. Although the laser scanner is the dominant method of the Scan-to-BIM process, a number of digital professionals point drawbacks of the method and present the drone is an alternative that can improve the drawbacks thereby leading to UAV-to-BIM process in parallel with the Scan-to-BIM. Korean construction industry plans to utilize the two technologies for monitoring construction operation quality in major public projects by 2025. While contractors need competent engineers to be competitive in the projects, the two technology applications are not so popular to the construction projects in Korea and very few experts skillful and knowledgeable of the technologies are available. Korean universities are requested to develop the curriculum of the technologies for the contractors. To be successful in progressing the curriculum, it is very essential to implement a preliminary study with the technologies minimizing the potential failure in operating the curriculum later on. This study performs empirical research on the technologies and identify valuable lessons beneficial to develop the UAV-to-BIM curriculum for the construction engineers.

This article is some review of results that were obtained at 2007-2021 years development of “The Information as Absolute” concept and the informational physical model, which is based on the concept; including a number of fundamental physical problems are briefly considered in framework of the conception and the model. Recently in physics there are several publications, that present lists of the problems. However, those lists are essentially incomplete, for at least two reasons. Firsts of all, a number of phenomena are studied traditionally by philosophy, and so corresponding problems are usually considered to be “metaphysical”. However, they relate also to some concrete physical phenomena. For example, physics evidently studies Matter, and so the metaphysical problems “what is ontology of Matter”, “what is “Space”, “Time” and a few other physical phenomena and notions as well, are really a Meta-physical problems “what does physics study?” There are other fundamental physical problems, which are not considered as such in physics, and are absent in the “fundamental problems lists”. Those include the problems, which really exist, yet are incorporated into standard physical theories, and so are fundamental “implicitly”, which in physics are “solved by default” ‒ and mostly erroneously. Note, though, that a number of “Meta-physical”, and concrete fundamental, problems more in detail are considered in the paper “The Informational Conception and Basic Physics”, https://arxiv.org/abs/0707.4657, v5 (2021), so this paper is, in certain sense, an expanded conclusion of this paper, which includes, correspondingly, more in detail consideration of some more general physical problems; and, besides, in this article, the problem “what are Gravity and Electric Forces” is essentially clarified comparing with the arXiv 2021 paper version above, and additionally initial model of Nuclear Force is presented. Besides, the concrete problem “What is Life”, and the rational cosmological model, where a few vague points in standard cosmology rather probably are rationally clarified, while the fundamental problem “matter – antimatter asymmetry” in Matter is solved with rather large probability, are considered, and one of recently published rather complete “lists of fundamental problems” is commented in Appendix.

The purpose of this article is to address two questions that will aid researchers in understanding FinTech’s inception, development, and potential impact on the stability of the financial system. First, it explains why financial technology is a current phenomenon. While many of the underlying technology for FinTech breakthroughs have been around for some time, it is only recently that financial institutions and entrepreneurs have begun to apply them to financial goods and services. Supply and demand factors in "conventional" financial innovation have been studied, and they have been found to converge, leading to a high rate of innovation. And second, this article explains why FinTech is being covered in greater depth than other types of innovation. This study introduces the concept of "depth" of innovation as a means of answering this question. The more fundamental the innovation, the more it will affect the financial sector. In this paper, we demonstrate that many recent developments in the field of financial technology (FinTech) are truly revolutionary advances, with far-reaching implications for the financial services industry. In addition to the benefits of more adaptability, higher transformational potential might bring increased risk to economic security.

The population of Bifidobacterium spp in faecal samples from breast-fed and formula-fed infants were investigated and developed antibiogram. The common bacterial growth media such as Man Rogos Sharpe (MRS), Brain Heart Infusion (BHI), Luria Bertani (LB) broth and Bifidobacteria agar were evaluated by the enumeration of Bifidobacterium spp. in faecal material. The results demonstrated that the population of Bifidobacterium spp. in the faecal samples of breast-fed infants is numerically high and, low in formula-fed infants. Phylogenetic analysis of the 16S rRNA, (Xylose/Fructose-6-phosphate phosphoketolase) xfp, and RFLP map of Bifidobacteria isolates suggested a presence of possible new and different Bifidobacterium spp. The intensity of the reddish brown colour formed during the f6ppk (Fructose-6-phosphate phosphoketolase) assay confirms the presence of variable Bifidobacteria. The highest recovery and diversity of Bifidobacteria were obtained from Bifidobacteria agar media. Growth in various media resulted in the formation of small (SCV) and big colony variants (BCV). We understand that the various antibiotic minimum inhibitory concentration (MIC) values obtained by applying different media, growth conditions, after bile salt treatment, and at low pH can vary. This study concludes that the variation of microbiological conditions differentiating resistant and susceptible strains is influenced by the test conditions as well.

We analyze the behavior of relativistic spherical objects within the context of modified $f(R,T)$ gravity considering Tolman VI spacetime, where gravitational lagrangian is a function of Ricci scalar(R) and trace of energy momentum tensor(T) i.e,$ f(R,T)= R+ 2\beta T$, for some arbitrary constant $\beta$. For developing our model, we have chosen $\pounds_{m} = -p$, where $ \pounds_{m}$ represents matter lagrangian. For this investigation, we have chosen three compact stars namely PSR J1614-2230 [Mass=(1.97$\pm$ 0.4)M$_\odot$; Radius= 9.69$_{-0.02} ^{+0.02}$ Km] ,Vela X-1 [Mass=(1.77$\pm$ 0.08)M$_\odot$; Radius= 9.560$_{-0.08} ^{+0.08}$ Km] and 4U 1538-52 [Mass=(9.69)M$_\odot$; Radius= 1.97 Km]. In this theory the equation of pressure isotropy is identical to standard Einstein's theory. So all known metric potential solving Einstein's equations are also valid here. In this paper, we have investigated the effort of coupling parameter ($\beta$) on the local matter distribution. Sound of speed and adiabatic index are higher with grater values of $\beta$ while on contrary mass function and gravitational redshift are lower with higher values of $\beta$ . For supporting the theoretical results, graphical representation are also employed to analyze the physical viability of the compact stars.

IoT is a computational perception that defines a situation, where smart objects are interconnected to the internet/ network. In the last few years, IoT objects have increased in all sectors/ fields and use in all domains of our lives. As the number of devices increases, the privacy of data becomes an important issue. To deal with this issue different techniques have been used by researchers in this field. Unfortunately, these methods have less traceability, privacy of data, and accountability. This paper aims to design a blockchain-based network architecture for Traceability, Data privacy, and Accountability (TDA). Technologies of blockchain are known as a distributed ledger of transactions record, which record the life span information with a timestamp. The major features of blockchain are persistency, decentralization, and audibility. These features help to decrease the budget and increase efficiency. Moreover, to strengthen the TDA architecture, this paper also discusses the performance of the proposed architecture.

There have been several developments in the field of nanopore biosensor development and sequencing applications that address previous limitations that restricted widespread nanopore use. These innovations, paired with the large-scale commercialization of biological nanopore sequencing by Oxford Nanopore Technologies, are making these platforms a mainstay in contemporary research labs. Equipped with the ability to provide long and short-read sequencing information with quick turn-around times and simple sample preparation, nanopore sequencers are quickly improving our understanding of unsolved genetic, transcriptomic, and epigenetic problems. However, there remain some key obstacles that have yet to be improved. In this review, we provide a general introduction to nanopore sequencing principles, discussing biological and solid-state nanopore developments, obstacles to single-base detection, and library preparation considerations. We conclude with examples of important clinical applications to give perspective on the potential future of nanopore sequencing in the field of molecular diagnostics.

The problems of formation and propagation of surface seismic waves in vertically layered and horizontally inhomogeneous media are studied. A mathematical model has been developed for the propagation of surface seismic waves in an inhomogeneous medium based on the use of the fundamental principles of wave dynamics and Fourier transforms. The existence of various types of groups of these waves is proved, and their main properties are studied. It has been established that the reflection coefficients of different groups of a dispersive wave on models of half-spaces (a layered, inhomogeneous medium) with a sharp velocity differentiation are characterized by the same dependence on the angle of incidence as in the absence of dispersion. In the region of the critical angle, which depends on the period of the incident wave, a local decrease in the intensity of wave reflection is observed, which is associated with the origin of the head dispersive wave. It has been found that the reflection coefficients of Rayleigh waves from low-velocity layers, thin (d / λ2 = 0.205) and thick (d / λ2 = 0.82) are approximately 0.2 ± 0.04 and practically do not change with increasing angle of incidence. On the basis of theoretical studies, it was revealed that reflected surface waves carry information both about the elastic properties of the reflecting medium and about the dimensions of the reflecting horizontal inhomogeneity. An experimental dispersion curve of the group velocity of Rayleigh waves on the Central Asia-Caucasus path has been obtained. The seismic stress state of the "subgrade-base" system was studied. Based on the analysis of the results of numerical experiments, it has been established that in order to obtain a complete picture of the seismically stressed state of the subgrade, it is necessary to take into account the contribution to it of each component of the accelerogram of a real earthquake.

Background: Mental health difficulties during teenage years e are common and are a risk factor for later mental and physical health problems. Rural young people are at greater risk for mental health difficulties and have less access to services than their urban counterparts. The purpose of this study was to explore young people and their carers’ experiences of mental health support provided by a rural mobile service, and to identify access enablers from the service users’ perspective. Methods: A qualitative descriptive approach was used to analyse twelve interviews with current service users and eight interviews with family members of young people who had accessed the service.Results: Three main themes were identified: (a) Access and flexibility, (b) Clinicians’ qualities and strategies, and (c) Experiences of change. The mobile service was perceived to be effective in producing positive change in mental health, relationships and attainment of life goals. Key enablers to access included the flexibility of the mobile service, the variety of service delivery modes and therapeutic methods offered, the ease of access facilitated by location in schools, and young people’s autonomy in how they chose to utilise the service. Conclusion: This study provides information about what is important to rural young people and their families in mental health service provision. The findings have implications for changing the way services are organized and operated. Healthcare policy and services could support user-led model design that incorporates the access and use enablers and removes the barriers to rural mental health support.

In this paper, we have investigated the impact of an early learning assessment on students' motivation for improving their performance throughout the semester. An observation analysis was conducted on an entry level mechanical engineering course in which students are enrolled in during their first semester of engineering work. This study analyzes the effect that a first exam, with an average below a passing grade, has on student's outcome in the course. It was hypothesized that students were motivated to achieve their desired grade outcomes following inadequate performance on the first exam. This was investigated by diving into the results of the course and referencing initial performance to the remaining exam and assessment outcomes. Students were placed into grade bands ranging from 0 to 100 in 20% increments. Their results were tracked and it was shown that for the second mechanics exam, averages jumped 43.333%, 35.35%, and 30.055% for grade bands of 0 to 20, 20 to 40, and 40 to 60 respectively. Assessment grades increased as well with the remaining assessments being averaged to a score of 91.095%. Variables contributing to student performance came from both with-in and outside the classroom. Learning communities, material differentiation, and student and professor adaptation all contributed to the rise in performance. It was concluded that the internal and external variables acted in combination with one another to increase student dedication to achieve success.

Ion channels are integral membrane proteins that facilitate the movement of ions across cell membranes, playing a key role in a range of biological processes. The high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose. In our previous work, we demonstrated the effectiveness of protein language models for ion channel prediction, using a logistic regression classifier to distinguish ion channels from non-ion channels (TooT-BERT-C) and transporters from non-transporters (TooT-BERT-T). In this study, we build upon this approach by using a combination of classical machine learning classifiers and a Convolutional Neural Network (CNN) with fine-tuned representations from ProtBERT, ProtBERT-BFD, and MembraneBERT to discriminate ion channels from non-ion channels. The results of our experiments demonstrate that TooT-BERT-CNN-C, a combination of the representations from ProtBERT-BFD and a CNN, outperforms existing state-of-the-art methods for predicting ion channels, with a Matthews Correlation Coefficient (MCC) of 0.86 and an accuracy of 98.35% on an independent test set.

Design for Manufacture and Assembly (DfMA) in architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current needs for further research and development. The DfMA's conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concept of DfMA and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures associated with Dfab and DfAM, as well as its AM assembly process, were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. Large research gaps in the DfMA for Dfab and DfAM can be filled to increase operational efficiency and sustainable practices.

Transparent conductive electrodes (TCEs) are of enormous significance to the emergence of flexible and wearable electronics and continued growth of modern devices. Versatile and tunable TCEs, featuring with not only high optical transmittance but also intriguing features of electrochemical energy-storage capability, remain a significant challenge. Here we develop capacitive active films comprised of graphene-conjugated V2O5@poly (3,4-ethylene dioxythiophene) ternary composite (V2O5@PEDOT/rGO) on silver nanowire coated substrates as solid-state super/pseudocapacitors. The constructed electrodes exhibit improved electrolyte ions interaction with effective graphene layer, achieving high areal capacitance 0.6-1.2 mF.cm−2 with 0.5M LiCl electrolytes at optical transparency >60% with record durability. As demonstrated, the kinetic blocking of PEDOT layer and anchoring capability of graphene upon amphoteric soluble vanadium ions from layered V2O5 nanoribbons/nanobelts contribute synergistically to the unusual electrochemical stability, also shown using scanning electrochemical microscopy (SECM) providing electroactivity sites and ion transportation rates. As-fabricated symmetric solid-state supercapacitors delivered broad potential window >1.4 V under two different electrolyte environments (aqueous LiCl and LiCl/PVA gel) and demonstrated higher power and energy density (0.27 μWh.cm−2) outperforming previously reported devices at <0.1 μWh.cm−2. The electrochemical properties are also discussed in terms of solvation in polymer gel electrolyte ions.

Objectives: One of the home-made herbal remedy being traditionally used against gout is evaluated to identify any new and characteristic marker compound which may be used as characteristic analytical marker to standardize this polyherbal remedy. Materials and method: A distinct and remarkable peak in the HPLC chromatogram of remedy was identified which was further isolated by fraction collector. This fraction was then evaporated and characterized for structure elucidation using set of various techniques such as RP-HPLC,UV, FTIR, TLC, EIMS and NMR. Results: The compound characterized was non polar compound, ester of betulin and palmitoleic acid. Although the remedy has been standardized using set of four markers as discussed in literature but this newly developed marker compound can be used to further standardize this remedy and other herbal preparations comprising of this marker compound.Conclusion: Hence the isolated compound can be used as novel analytical marker in the standardization of this herbal remedy and the other herbal preparations containing such marker compound

A steel damper column is an energy-dissipating member that is suitable for reinforced concrete (RC) buildings and multistory housing. To assess the seismic performance of buildings with steel damper columns, the peak displacement of the whole building and the energy dissipation demand of the dampers must be evaluated. This article proposes an energy-based prediction procedure for the peak and cumulative response of an RC frame building with steel damper columns. The proposed procedure considers two energy-related seismic intensity parameters, namely the maximum momentary input energy and the total input energy. The peak displacement is predicted considering the energy balance during a half cycle of the structural response, using the maximum momentary input energy. The energy dissipation demand of the dampers is then predicted considering the energy balance during a whole response cycle using the total input energy. The local responses (e.g., peak drift, maximum plastic rotation of beams, maximum shear strain, and energy dissipation demand of dampers) are predicted using pushover analysis. Numerical analysis results for 8- and 16-story RC buildings show that the proposed prediction method achieves satisfactory accuracy.

The central dogma of molecular biology dictates that, with only a few exceptions, information proceeds from DNA to protein through an RNA intermediate. Examining the enigmatic steps from prebiotic to biological chemistry, we take another road suggesting that primordial peptides acted as template for the self-assembly of the first nucleic acids polymers. Arguing in favour of a sort of archaic “reverse translation” from proteins to RNA, our basic premise is a Hadean Hearth where key biomolecules such as amino acids, polypeptides, purines, pyrimidines, nucleosides and nucleotides were available under different prebiotically plausible conditions, including meteorites delivery, shallow ponds and hydrothermal vents scenarios. Supporting a protein-first scenario alternative to the RNA world hypothesis, we propose the primeval occurrence of short peptides termed “selective amino acid- and nucleotide-matching oligopeptides” (henceforward SANMAOs) that noncovalently bind at the same time the polymerized amino acids and the single nucleotides dispersed in the prebiotic milieu. We describe the chemical features of this hypothetical oligopeptide, its biological plausibility and its virtues from an evolutionary perspective. We provide a theoretical example of SANMAO’s selective pairing between amino acids and nucleosides, simulating a poly-Glycine peptide that acts as a template to build a purinic chain corresponding to the glycine’s extant triplet codon GGG. Further, we discuss how SANMAO might have endorsed the formation of low-fidelity RNA’s polymerized strains, well before the appearance of the accurate genetic material’s transmission ensured by the current translation apparatus.

With the development of deep learning technology, various structures and research methods for super-resolution restoration of natural images and document images have been introduced. In particular, a number of recent studies have been conducted and developed in image restoration using generative adversarial network. Super-resolution restoration is ill-posed problem because of some complex restraints such as a lot of high-resolution images being restored for the same low-resolution image and also difficulty in restoring noises like edges, light smudging, and blurring. In this study, we utilized the spatially adaptive de-normalization (SPADE) structure for document image restoration to solve previous problems such as edge unclearness, hardness to catch features of texts, and the image color transition. Consequently, it can be confirmed that the edge of the character and the ambiguous stroke are restored more clearly when contrasting with the other previously suggested methods. Also, the proposed method’s PSNR and SSIM scores are geting 8% and 15% higher, respectively, compared to the previous methods.

This paper considers the application of two selected algorithms (Cuckoo Search CS, Firefly Algorithm FA) to determine and maximize the reliability of a two complex system known from the literature. This system operates with resource constraints. In addition, several classical methods for determining the reliability of systems have been applied. Although the CS algorithm is known for its effectiveness as it uses the Lévy flight, for the presented optimization tasks the FA firefly algorithm in the considered parameter range proved to be more effective. The presented results broaden the existing discussion on the application of nature-inspired algorithms for solving RRAP (Reliability Redundancy Allocation Problem) problems. From this point of view, two described test scenarios can be considered as a suitable tool for validating other algorithms in RRAP problems. Not only the values of the criterion functions can be compared, but also other introduced parameters and statistical analyse of variance (ANOVA) with post-hoc RIR Tuckey test was discussed.

The global transport sector of the world economy contributes about 15% of the Greenhouse Gas (GHGs) emissions in the world today. The University of Saskatchewan has pursued the green energy transition over the years. They have spearheaded diverse sustainability projects and agendas, due to the importance of curbing climate change and advancing sustainability. The transport system in the university campus is one area of focus where the Sustainability Office plans to introduce some innovations, as a way of curbing GHG emissions while also advancing sustainability practice in the university campus. The study carried out an economic benefit analysis on the campus fleet (consisting of 91 ICE vehicles) to determine if it is economically or financially feasible to transition from Internal Combustion Engine (ICE) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert software for analyzing renewable energy technology projects. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), Benefit-Cost-ratio, GHG emission reduction potential, etc. were analyzed. The findings revealed that the GHG emission from the campus fleet will be reduced by 100% (this will result in the removal of about 298.1 tCO₂ from the environment). Also, the fleet manager will save approximately $129,049 (88.9%) in fuel costs. Apart from these, the return on investment will be achieved in year 5 (all things being equal), but can be reduced to year 2 if the vehicles are put into constant and active use (eliminating most idle times. Also, the Sustainability Office will be making a GHG reduction revenue of $14,906.

Background/Objectives: The purpose of the proposed study is disclosure of correlations between the dynamics of the Breathe Rate (BR) and Heart Rate (HR) when performing the intermittent physical operation at maximum power on a cycle ergometer.Methods: The stage on the study of the General functional athlete readiness (GFAR) performed using the sports standard “Red Engine” and the cycle ergometer in 16 volunteers (10 men, 6 women) whose average age was 21±1.17 years. To determine the sports potential of the volunteers in this study, we used own Coefficient of Anaerobic Capacity (CANAC Q, beats). Continuous recording of the heart rate and respiratory rate of volunteers in the maximum power sports test was carried out by the “RheoCardioMonitor” device with an athlete functional readiness module based on the method of Transthoracic electrical impedance rheography (TEIRG).Results: The degree of correlation of functional indicators (M, HR_M, GFAR) with CANAC Q for the group in full (n=80) occurred at a very high level, which confirmed the effectiveness of using the Coefficient of Anaerobic Capacity (CANAC Q) in assessing the general functional athlete readiness of the volunteers. Conclusions: CANAC Q measured in “beats” of the heart and recorded very accurately using the method of transthoracic electrical impedance rheography (TEIRG). For this reason as promising sports PSM-system, CANAC Q can replace the methods for determining the functional athlete readiness by blood lactate concentration and maximum oxygen consumption.

This manuscript compares deterministic artificial intelligence to model following control applied to DC motor control, including evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. The performance is evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (benchmark selection). However, the performance gets optimized with the step size decreased. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 seconds, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, the continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles since it is superior to all the approaches with multiple computation rates.

Chaotic systems are systems whose results are sensitive to the initial conditions. Chaotic systems generally have nonlinearities that can be difficult to model. One example of a chaotic system with nonlinearities is the Lorenz system. The Lorenz system can be used to model weather, wind disturbances, and electronic circuit design, among other applications. In this manuscript, the Lorenz system is modeled and various control methods are applied in an effort to dictate the system’s state and rate trajectories. The combination of the linear feedback and nonlinear feedforward controllers can show over an 80% improvement in state trajectory errors when compared to the baseline run. However, the improved state trajectory performance comes at a cost of as much as 1800% error in the rate trajectories.

We consider an alternative hypothesis of crater formation -- through an electric discharge machining (EDM) of the celestial bodies' surfaces. The source of the energy and the current itself may lie in the plasma infalling on a celestial body (from an extremely strong solar outburst or a nearby supernova). This mechanism may have been even more important than impact cratering in the early Solar System, with potential implications to the history of the dynamics of the System itself and solar activity in particular. In this light, the conclusion that shocked and metamorphosed minerals found within crater sites are evidence of hypervelocity impacts might be reconsidered. There are ambiguities that remain among sources of mineralogical formations. This study is ongoing with details of investigations supporting a new approach to crater formation not yet exhausted. Apart from mineralogical analysis and theoretical considerations regarding the large scale crater formation, we present a description of experimental studies of EDM acting at low pressures. Experimental results are made with crude setups and could easily be replicated on a larger scale with valuable research potential, as there is a well understood relation between the crater size and the energy needed for its production.

The hitologiy image of mouse xenograft of olfactory ensheating cells ( OECs) in to Albino Swiss Strain rats corticospinal tract lesion shows immunorejection after 10 days in grafted tissue . Moreover, the images of postoperative xenotransplanted OECs under daily subcutaneous injection of 100 ml of a dosage of 6 mg/kg/day avoid immunorejection in the misplace of GFP labeled transplanted OECs with the survival time of 4 weeks and there is no sign of inflammation. the result shows that the daily injection of 100ml diluted cyclosporine avoids immune attack and reduces inflammatory cells infiltrate in delayed CST ipsilateral rat model and also our study proved that stop of cyclosporine injection would not prevent DFR function and the regenerated fibers can still be visualised.

Theories on life’s origin generally acknowledge the advantage of a semi-permeable vesicle (protocell) for enhancing the chemical reaction-diffusion processes involved in abiogenesis. However, more and more evidence indicates that the origin of life concerned the photo-chemical dissipative structuring of the fundamental molecules under UV-C light. In this paper, we analyze the Mie UV scattering properties of such a vesicle made from long chain fatty acids. We find that the vesicle could have provided early life with a shield from the faint, but dangerous, hard UV-C ionizing light (180-210 nm) that probably bathed Earth’s surface from before the origin of life and until perhaps 1,500 million years after, until the formation of a protective ozone layer as a result of the evolution of oxygenic photosynthesis.

Digital wellbeing concerns the balance and health we may experience in digital use, and the existing studies have focused on adolescents and adults. However, young children are more vulnerable to digital overuse and addiction than adults; thus, their digital wellbeing deserves empirical exploration. This scoping review synthesized and evaluated 35 collected studies on young children’s digital use and their wellbeing that were published until October of 2022 to understand the definitions, measurements, contributors, and interventions. The synthesis of evidence revealed that: (1) there was no consensus about its definition; (2) there were no effective measurements of young children’s digital wellbeing; (3) both child factors (duration and place of digital use, child demographic characteristics) and parent factors (digital use, parental perception, and mediation) contribute to young children’s wellbeing; and (4) there were some effective applications and interventions. This review contributes to the theoretical development by mapping the existing work on young children's digital wellbeing, proposing a model, and identifying the research gaps for future studies.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Advancements in genomics, bioinformatics and genome editing have uncovered new dimensions in gene regulation. Post-transcriptional modifications by the alternative splicing of mRNA transcripts are critical regulatory mechanisms of mammalian gene expression. In the heart, there is an expanding interest in elucidating the role of alternative splicing in transcriptome regulation. Substantial efforts have been directed towards investigating this process in heart development and failure. However, few studies have shed light on alternative splicing products and their dysregulation in congenital heart defects (CHDs). While elegant reports have shown the crucial roles of RNA binding proteins (RBPs) in orchestrating splicing transitions during heart development and failure, the impact of RBPs dysregulation or genetic variation on CHDs has not been fully addressed. Herein, we review the current understanding of alternative splicing and RBPs’ roles in heart development and CHDs and discuss the impacts of perinatal splicing transition and its dysregulation in CHDs. We further summarize discoveries made of causal splicing variants in key transcription factors that have been implicated in CHDs. Improved understanding of the roles of alternative splicing in heart development and CHDs may potentially inform novel preventive and therapeutic advancements for newborn infants with CHDs.

Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, adaptive and learning methods are implemented to control DC motors that are used for actuating control surfaces of unmanned underwater vehicles. Adaptive control is a method in which the controller is designed to adapt the system with parameters which vary or are uncertain. Parameter estimation is the process of computing the parameters of a system using a model &amp; measured data. Adaptive methods have been used in conjunction with different parameter estimation techniques. Deterministic artificial intelligence, a learning-based approach that uses the process dynamics defined by physics, is also applied to control the output of the DC motor to track a specified trajectory. This work goes further to evaluate the performance of the adaptive &amp; learning techniques based on different discretization methods. The results are evaluated based on the absolute error mean between the output &amp; the reference trajectory and the standard deviation of the error. The first order-hold method of discretization and surprisingly large sample time of seven tenths of a second yields over sixty percent improvement over the results presented in the prequel literature.

This study offers a concise summary of the research that has been done on alternative methods of HRM. A survey of 47 articles on the topic of creative human resource management uncovered three primary schools of thinking. At its core, innovation in HRM entails nothing more than that. One such topic worthy of investigation is the way human resource management approaches new situations. Third, research on inventiveness in the business world has given rise to the concept of "innovative HRM." Each of these three approaches, as well as the theoretical underpinnings of each, will be covered in this article. The note wraps up with a discussion of the significance of the findings for next research as well as clinical practice.

For each $t\in(-1,1)$, exact values of the least upper bounds $$ H(t)=\sup_{\E X=t,\,\E X^2=1} \frac{\E\abs{X}^3}{\E \abs{X-t}^3},\quad \sup_{\E X=t,\,\E X^2=1} \frac{L_1(X)}{L_1(X-t)} $$ are obtained, where $L_1(X)=\E|X|^3/(\E X^2)^{3/2}$ is the non-cental Lyapunov ratio. It is demonstrated that these values are attained only at two-point distributions. As a corollary, S.\,Shorgin's conjecture is proved that states that the exact value is $$ \sup\frac{L_1(X)}{L_1(X-\E X)}= \frac{\sqrt{17 + 7\sqrt7}}{4} = 1.4899\ldots, $$ where the supremum is taken over all non-degenerate distributions of the random variable $X$ with the finite third moment. Also, in terms of the central Lyapunov ratio $L_1(X-\E X)$, an analog of the Berry--Esseen inequality is proved for Poisson random sums of independent identically distributed random variables with the constant $$ 0.3031\cdot H\left(\frac{\E X}{\sqrt{\E X^2}}\right) \left(1-\frac{(\E X)^2}{\E X^2}\right)^{3/2} \le 0.3031\cdot \frac{\sqrt{17 + 7\sqrt7}}{4}<0.4517.$$ where $\Law(X)$ is the common distribution of the summands.

Leaders are essential in encouraging and facilitating creative thinking among members of a group or organization. A leader must also monitor the group's initiatives to encourage innovation and fresh thinking. In this article, we take a look at the circumstances under which there is a link between leadership and original problem-solving. Thirty separate studies served as the basis for this sample. Many of these analyses see leadership independently from the concept of innovation. Both moderating and mediating factors were discussed, and two additional factors were identified for which no firm conclusions could be drawn. This investigation reveals three new potential mediators or moderators of the connection between leadership and inventiveness.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

In our day-to-day life, Lie detection has a significant concern. We human beings are very much inaccurate while detecting the liars and We believe in what we are told. Lie detection is important in today’s life, because Concealing the information or faking it can sometimes take you to huge problems. In any areas like airport management[2], criminal investigations, counterterrorism, etc this concept has great importance. It is an evergreen challenging and changing topic. This paper presents the common technique which was followed up till now and why it was not considered effective and a review of Robust solutions to detection of deception. People generally do not[3] always believe on what someone says but also try to visualize their facial expressions. While in Robust solution these facial micro-expressions are identified, which are tiny, natural expressions seen on the individual’s face, when they try to conceal or suppress emotions. In addition, the article also provides the year-wise assessment and analysis of research articles published in the area of Lie detection from 2011 to 2022. In the end, our proposed framework for lie detection system is also presented. This paper cover up current issues as well as challenges that could be helpful to resolve in future research works. The review paper closes up by supporting future directions.

Unusual outbreak of Covid 19 has put the whole new challenges around the globe. In a short span of time, the world has been changed, lifestyles, social events, cultural, political events everything has been put on hold as number of countries has enforced partial or complete lockdown that resulted positive in context of controlling spread and contamination of COVID-19. Such activities have also put some effects on environment in both positive and negative way. In this study only positive impacts of COVID-19 on Environment have been highlighted that played role in regeneration of natural systems i.e., reduced air emissions, cleaner air, beaches &water bodies, reduced noise levels, biodiversity rehabilitation etc.

In this study, the influence of the environment on soil conditions, e.g. heavy metals and soil magnetic susceptibility, on the total number of bacteria and the species of Bradyrhizobium in the field cultivation of the soybean variety Lissabon, the seeds of which had been inoculated with HiStick®Soy, were evaluated. Field experiments were carried out with the random plot method on the Cambisol with a texture of silt loam. The experiment consisted of 24 objects in six combinations and four replications using sewage sludge and fly ash. In the soil samples, the total number of bacteria and the number of the species of Bradyrhizobium were determined using the culture-plate method. The total content of heavy metals (Cu, Zn, Cd, Ni, Pb, Cr, Hg, Fe) in the analyzed objects, compared to the limit values, allowed them to be classified as uncontaminated soils despite the application of sewage sludge and fly ash according to the experimental scheme, showed a significant difference between them. The study of the magnetic parameter χ indicated a significant differentiation between objects from 34.0 to 65.8 × 10-8m3kg-1. High correlation coefficients between χ and Fe (r = 0.789), Zn (r = 0.653), Cr (r = 0.617) and TOC (r = 0.949) indicated that the source of these elements was external organic matter. Biological tests (total content of bacteria and species of Bradyrhizobium) in different experimental variants indicated significant relationships between them and showed the resistance of the microbial community in the field cultivation of Lissabon soybean to heavy metal contamination from sewage sludge and fly ash.

The final assembly of appliances and automotive industries is a rigorous process, that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies The many parts and sub-assemblies that compose the final assembly, make full traceability a challenging fit, that is almost unsurmountable. Such full traceability along the entire supply-chain is not existent today and must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has a great value. This article expounds a framework that harnesses block-chain and smart-contract technology to offer traceable and protected documentation of the assembly process. We expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system.

In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification. However, the proposed network models have a higher model complexity and do not provide high classification accuracy if few-shot learning is used. This paper pre-sents an HSI classification method that combines random patches network (RPNet) and re-cursive filtering (RF) to obtain informative deep features. The proposed method first convolves image bands with random patches to extract multi-level deep RPNet features. Thereafter, the RPNet feature set is subjected to dimension reduction through principal component analysis (PCA) and the extracted components are filtered using the RF procedure. Finally, HSI spectral features and the obtained RPNet-RF features are combined to classify the HSI using a support vector machine (SVM) classifier. In order to test the performance of the proposed RPNet-RF method, some experiments were performed on three widely known datasets using a few training samples for each class and classification results were compared with those obtained by other advanced HSI classification methods adopted for small training samples. The comparison showed that the RPNet-RF classification is characterized by higher values of such evaluation metrics as overall accuracy and Kappa coefficient (https://github.com/UchaevD/RPNet-RF).

COVID-19 is the first known coronavirus pandemic. Nevertheless, the seasonal circulation of the four milder coronaviruses of humans – OC43, NL63, 229E and HKU1 – raises the possibility that these viruses are the descendants of more ancient coronavirus pandemics. This proposal arises by analogy to the observed descent of seasonal influenza subtypes H2N2 (now extinct), H3N2 and H1H1 from the pandemic strains of 1957, 1968 and 2009, respectively. Recent historical revisionist speculation has focussed on the influenza pandemic of 1889-1892, based on molecular phylogenetic reconstructions that show the emergence of human coronavirus OC43 around that time, probably by zoonosis from cattle. If the “Russian influenza”, as The Times named it in early 1890, was not influenza but caused by a coronavirus, the origins of the other three milder human coronaviruses may also have left a residue of clinical evidence in the 19th century medical literature and popular press. In this paper, we search digitised 19th century British newspapers for evidence of previously unsuspected coronavirus pandemics. We conclude that there is little or no corpus linguistic signal in the UK national press for large-scale outbreaks of unidentified respiratory disease for the period 1785 to 1890.

This review discusses the distribution pattern, nesting style, mating behavior, and colony structure of the Asian weaver ants (Oecophylla smaragdina) in Asia. Recent findings suggested that weaver ant occurrences are not only limited to tropical rainforests biome, agroforestry and large monoculture fields, but have encroached human rural habitation including densely populated urban areas. Comparatively, O. longinoda and O. smaragdina are taxonomically classified as two distinct species, but the main differences between them are strongly dependent on the allopatric nature or geographical speciation of their distribution. Although weaver ants are dominant ubiquitous and conspicuous arboreal insects with a predilection for habitation in trees canopies, viable nests colonies on the ground have been reported in Thailand. O. smaragdina usually construct their polydomous nests (multiple satellites nests arrangement within a single host but diverse plants species) by weaving tree-leaves using their larval silk. Knowledge on mating behavior is rudimentary; hence more studies are needed especially in understanding how weather parameters affect nuptial flight swarming act. At the colony organization level, comprehensive reports about minor and major workers contrasts with the poorly documented but significant intermediate size of workers caste. The versatile impact of Asian Oecophylla is offering important ecological subsistence services to both the nature and humans. This is by combining positive economic implications to food security concern with a provision of organic nutrients for host plants and highly healthful diet enhancer (nourishing-medicinal). Despite its wide presence in large oil palms monoculture, only one report had exposed weaver ants’ potential positive ecological impact (i.e. predation on bagworms Pteroma pendula) in Southeast Asia.

This work communicates cavity ring-down spectroscopy (CRDS) of methylidyne (CH) in a chemiluminescent plasma that is produced in a microwave cavity. Of interest are the rotational lines of the 0-0 vibrational transition for the A-X band and the 1-0 vibrational transition for the B-X band. The reported investigations originate from CH-radical research in 1996 that constituted the first case of applying CRDS to the CH radical. The report also includes recent analysis that shows excellent agreement of measured and computed data, and it communicates CH line strength data.

Wireless communications are gaining massive demand for faster video streaming, virtual reality, and 5G-based IoT (Internet of Things) applications. Due to this network congestion increases hastily. The demand for user-specific services, machine learning, forcing the radio access network (RAN) to provide improvement in utilization of spectrum efficiency, minimizing power consumption, and improving data rate. For LTE/LTE-A network specifications provided by third-generation partnership project (3GPP), guarantees to provide qualitative of service (QoS). Release 15/16 utilizes two-dimensional (2D) active antenna array systems (AAS). This kind of AAS provides beam forming in both azimuth and elevation also termed as Full Dimensional MIMO (FD-MIMO). It enhances the radiation pattern of the antenna array element by adjusting the angular power spectra at the base station (BS), it also allows selecting the minimum number of antennas for effective beam forming toward specific user equipment’s (UEs). Spectrum allocation challenge can be dealt with at mm wave band having FR2 (>6GHz) frequency band specified by International Telecommunication Union (ITU-R) in line with IMT-2020 specifications. The spectrum is free at mm Wave for communication of 5G. However, the main challenge is, it faces path loss and atmospheric loss electromagnetic energy absorbed by gases like oxygen. In this work, the 5G new RAN power spectrum and power delay profile at 10GHz carrier frequency with 20MHz to 100MHz channel bandwidth is observed for line of sight (LOS) and non-line of sight (NLOS) communication for guaranteed QoS. To demonstrate the path loss and power delay profile (PDP) in directional and Omni-directional antennas NYUSIM channel simulator is utilized.

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant, and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

Expanded polystyrene (EPS)- block geofoam is an extremely lightweight fill material utilized in many geotechnical applications such as roadway and railway embankments. This study provides a methodology for analyzing the load-bearing capacity and horizontal sliding failure mechanisms for seismic internal stability analysis of EPSblock geofoam embankments. The seismic analysis procedure is demonstrated by analysis of one of the EPS-block geofoam embankments that Tennessee Department of Transportation is proposing a partial replacement of an existing bridge in Memphis, Tennessee in the USA. The objective of the seismic analysis procedure is to determine stresses from anticipated earthquakes to select the proper type of EPS considering both the 1% and 2% strain criteria that can support the anticipated increase in stress and potential horizontal displacements due to dynamic and static stresses. The study revealed that the 2% criterion provides an economical advantage compared to the 1% criterion.

The multiple regression method is still an important tool for establishing precipitation forecast models with a lead time of one season. This study developed a flexible statistical forecast model for July precipitation over the middle-lower reaches of the Yangtze River (MLYR) based on the prophase winter sea surface temperature (SST). According to the characteristics of observed samples and related theoretical knowledge, some special treatments (i.e., more flexible and better–targeted methods) were introduced in the forecast model. These special treatments include a flexible MLYR domain definition, the extraction of indicative signals from the SST field, artificial samples, and the amplification of abnormal precipitation. Rolling forecast experiments show that the linear correlation between prediction and observation is around 0.5, more than half of the abnormal precipitation years can be successfully predicted, and there is no contradictory prediction of the abnormal years. These results indicate that the flexible statistical forecast model is valuable in real-life applications. Furthermore, sensitivity experiments show that forecast skills without these special treatments are obviously decreased. This suggests that forecast models can benefit from using statistical methods in a more flexible and better-targeted way.

Sustainable teacher development is the key to the success in the program of Master of Translation and Interpretation (MTI) but there was limited research on their current situation and needs in China. In this study 514 teachers from 32 provinces, municipalities and autonomous regions across China were investigated by means of quantitative questionnaires and online interviews concerning their current situation of career development and this research presented a comprehensive description of their age, academic qualifications and professional title. Also this study expounded the current situation and difficulties in academic research, teaching, translation practice and staff training. The findings were as follows. 1：In spite of reasonable age structure, approximately 50% of them had less than three years’ teaching experience in MTI. 2: There was a rise in the number of MTI teachers with doctoral degree but they found it hard to offer students professional guidance. 3: A large proportion of MTI teachers were in a slow stream of promotion in the title of professional post. 4: The proportion of the academic achievements related to translation in all the research projects and papers was small. 5: A majority of them worked as part-time translators. 6: About half of the teachers were not very satisfied with the effectiveness in their staff training program. Therefore it is suggested that more opportunities be provided for the MTI teachers to conduct translation practice in the professional sector and more researches be needed to thoroughly understand their requirement in terms of career development with a view to bridging the gap between content in the teacher training and their actual needs.

During the response of plants to adverse stresses, aquaporin (AQP) plays a prominent role in membrane water transport based on received upstream signals. In addition, they have various physical parts for dealing with environmental stresses. Due to the importance of the AQP gene family, studies have been conducted investigating the function and regulatory system of these genes. However, many of their molecular aspects are still unknown. This study aims to carry out a genomic-wide investigation of the AQP gene family in durum wheat using bioinformatics tools and to investigate the expression patterns of some members in response to salt stress. Our results showed that there are 80 TtAQP genes in durum wheat, which are classified into four main groups based on phylogenetic analysis. Many duplications were observed between the members of the TtAQP gene family, and high diversity in response to post-translational modifications was observed between TtAQP family members. The expression pattern of TtAQP genes disclosed that these genes are primarily upregulated in response to salt stress. Besides, qPCR data revealed that TtAQPs are more induced in delayed responses to salinity stress. Overall, our findings illustrate that TtAQP members are diverse in terms of their structure, regulatory systems and expression levels.

Phylogenetic trees of coronaviruses are difficult to interpret because they undergo frequent ge-nomic recombination. Here, we propose a new method, named coloured genomic bootstrap (CGB) barcodes, to highlight the polyphyletic origins of human sarbecoviruses and understand their host and geographic ori-gins. The results indicate that SARS-CoV and SARS-CoV-2 contain genomic regions of mixed an-cestry originating from horseshoe bat (Rhinolophus) viruses. First, different regions of SARS-CoV share exclusive ancestry with five Rhinolophus viruses from Southwest China (RfYNLF/31C: 17.9%; RpF46: 3.3%; RspSC2018: 2.0%; Rpe3: 1.3%; RaLYRa11: 1.0%) and 97% of its genome can be related to bat viruses from Yunnan (China), supporting its emergence in Rhinolophus species of this province. Second, different regions of SARS-Cov-2 share exclusive ancestry with eight Rhi-nolophus viruses from Yunnan (RpYN06: 5.8%; RaTG13: 4.8%; RmYN02: 3.8%), Laos (RpBA-NAL103: 3.3%; RmarBANAL236: 1.7%; RmBANAL52: 1.0%; RmBANAL247: 0.7%), and Cam-bodia (RshSTT200: 2.3%), and 98% of its genome can be related to bat viruses from northern Laos and Yunnan, supporting its emergence in Rhinolophus species of this region. Although CGB barcodes are very useful to retrace the origins of human sarbecoviruses, further investigations are needed to better apprehend the diversity of coronaviruses in bats from Cambo-dia, Laos, Myanmar, Thailand and Vietnam.

In this paper, the authors consider the influence of the type of power supply and the internal parameters of the DC-DC converter on the stability of the system at constant power load. The stability of the system under consideration is important in the design and development of the power supply system for an autonomous consumer. Such a typical system can be a DC microgrid. In the work, when analyzing the stability of the system, the power source is not considered an ideal voltage source, but the active-inductive nature of the voltage source is taken into account, which is equivalent to using a synchronous machine with permanent magnets. The Rauss-Hurwitz algebraic criterion was used as a criterion for analyzing the stability of the system. This will make it possible to build and analyze the areas and boundaries of the system's stability. As a result, the areas and boundaries of the stability of the system were presented. The general trend towards the behavior of the zone of instability of the system was given. At the same time, dependences describing the boundaries of instability are presented, which are combinations of parameters of the DC-DC converter, beyond which the system becomes unstable. The presented definitions of the instability limits can be used as a corrective device of the control system to ensure the stability of the system in the upper limits of the power consumption.

We analyze the information that can be retrieved from the tracking parameters produced by an innovative wearable eye tracker. The latter is based on a permanent-magnet marked corneal lens and by an array of magnetoresistive detectors that measure the magnetostatic field in several positions in the eye proximity. We demonstrate that, despite missing information due to the axial symmetry of the measured field, physiological constraints or measurement conditions make possible to infer complete eye-pose data. Angular precision and accuracy achieved with the current prototypical device are also assessed and briefly discussed. The results show that the instrumentation considered is suitable as a new, moderately invasive medical diagnostics for the characterization of ocular movements and associated disorders.

Microstructure evolution in metal additive manufacturing (AM) is a complex multi-physics and multi-scale problem. Understanding the impact of AM process conditions on the microstructure evolution and the resulting mechanical properties of the printed part is an active area of research. At the meltpool scale, the thermo-fluidic governing equations have been extensively modeled in the literature to understand the meltpool conditions and the thermal gradients in its vicinity. In many phenomena governed by partial differential equations, dimensional analysis and identification of important dimensionless numbers can provide significant insights into the process dynamics. In this context, a novel strategy using dimensional analysis and the method of linear least squares regression to numerically investigate the thermo-fluidic governing equations of the Laser Powder Bed Fusion AM process is presented in this work. First, the governing equations are solved using the Finite Element Method, and the model predictions are validated by comparing with experimentally estimated cooling rates, and with numerical results from the literature. Then, through dimensional analysis, an important dimensionless quantity - interpreted as a measure of heat ab- sorbed by the powdered material and the meltpool, is identified. This dimensionless measure of heat absorbed, along with classical dimensionless quantities such as Peclet, Marangoni, and Stefan numbers, is used to investigate advective transport in the meltpool for different alloys. Further, the framework is used to study the variations of thermal gradients and the solidification cooling rate. Important correlations linking meltpool morphology and microstructure evolution related variables with classical dimensionless numbers are the key contribution of this work.

Basketball is a highly anaerobic game, that consists of high-intensity efforts followed by lower-intensity efforts. Postural deformities can be a limiting factor in achieving maximal performances in basketball. Flat feet are one of the most common deformities and they can lead to poorer performances, discomfort and pain. In this research we made an attempt to link the degree of feet flatness with sports performances among Icelandic adolescent basketball players. A sample of 100 basketball players was tested on a podoscope to establish the degree of feet flatness and all participants performed countermovement jump, 20m sprint and pushups in 30 seconds. No significant correlation was observed between feet status and sports performances. No differences were found between groups with and without flat feet, which indicates that feet flatness was not a limiting factor in achieving better results. However, we state that only strong and healthy feet might lead to excellent performance.

Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Suicide may be prevented if prompt intervention is taken to mitigate risk. Machine learning and artificial intelligence-based modelling, such as natural language processing (NLP) and other text analytics approaches, has the potential to become a major technique for the detection, diagnosis, and treatment of people who are suffering from mental health issues. The primary aims of this research are to determine whether NLP techniques have been utilised in the field of suicide prevention, and if so, were they effective? What were their limitations? Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. Thematic analysis was used to synthesise and analyse the included studies. Findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, and the Mixed Methods Appraisal Tool (MMAT) was used in assessing paper quality. Result: The preliminary search of five databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Also, to reduce suicides, people with mental problems must be continuously and passively monitored. Further, NLP and other machine learning/artificial intelligence technologies can be used to address health inequities and electronic health records provide valuable data for creating suicide risk tools. Finally, Online, social media, and smartphone applications can be leverage in detecting people with suicide ideation. Conclusion: The use of artificial intelligence and machine learning opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention. To summarise, there is substantial evidence that NLP is useful in identifying people who have suicide ideation.

The Marchenko method is a data-driven way which makes it possible to calculate Green's functions from virtual points in the subsurface by the reflection data at the surface, only requiring a macro velocity model. This method requires collocated sources and receivers. However, in practice, subsampling of sources or receivers will cause gaps and distortions in the obtained focusing functions and Green's functions. To solve this problem, this paper proposes to integrate sparse inversion into the iterative Marchenko scheme. Specifically, we add sparsity constraints to the Marchenko equations and apply sparse inversion during the iterative process. Our work not only reduces the strict requirements on acquisition geometries, but also avoids the complexity and instability of direct inversion for Marchenko equations. This new method is applied to a two-dimensional numerical example with irregular sampled data. The result shows that it can effectively fill gaps of the obtained focusing functions and Green's functions in the Marchenko method.

The dentate gyrus (DG) is part of the hippocampal formation and is essential for important cognitive processes such as navigation and memory. The oscillatory activity of the DG network is believed to play a critical role in cognition. DG circuits generate three main rhythms: theta, beta, and gamma, which participate in the specific information processing performed by DG neurons. In the temporal lobe epilepsy (TLE), cognitive abilities are impaired, which may be due to drastic alterations in the DG structure and network activity during epileptogenesis. The theta rhythm and theta coherence are especially vulnerable in dentate circuits; disturbances in DG theta oscillations and their coherence may be responsible for general cognitive impairments observed during epileptogenesis. Some researchers suggested that the vulnerability of DG mossy cells is a key factor in the genesis of TLE, but others did not support this hypothesis. The aim of the review is not only to present the current state of the art in this field of research but to help pave the way for future investigations by highlighting the gaps in our knowledge to completely appreciate the role of DG rhythms in brain functions. Disturbances in oscillatory activity of the DG during TLE development are described in detail that may be a diagnostic marker in the treatment of this disease.

Fabry disease is a lysosomal storage disorder caused by the deficiency of the α-galactosidase-A enzyme. Cardiac, renal, and neurological involvement significantly reduces life expectancy. Until a few years ago, treatment options for Fabry disease were limited to enzyme replacement therapy with agalsidase alfa or beta administered by intravenous infusion every 2 weeks. Migalastat (Galafold®) is an oral pharmacological chaperone that increases enzyme activity of “amenable” mutations. The safety and efficacy of migalastat were supported in the phase III FACETS and ATTRACT studies, compared to available enzyme replacement therapies; showing a reduction in left ventricular mass, and stabilization of kidney function and plasma Lyso-Gb3. Similar results were confirmed in subsequent extension publications, both in patients who started migalastat as their first treatment and in patients who were previously on enzyme replacement therapy and switched to migalastat. In this review we describe the safety and efficacy of switching from enzyme replacement therapy to migalastat in patients with Fabry disease and “amenable” mutations, referring to publications available to date.

This article describes the transition of an undirected active network, used for numerical and logical analysis, and made up of communicating operators and nodes, and being self-extensible, into a network of semantic structures, and the extensions necessary to handle language. The network, called Active Structure, was infused with knowledge in the form of tens of thousands of definitions taken from a human-readable dictionary. Where the dictionary fell short, from circularity, lack of a hierarchical form for its senses and sub senses, or inadequate detail around verbs, the entries were extensively curated. The result is a machine which has access to every meaning of every word in its vocabulary, and needs to manage the right meaning for every use of a word, a task that our unconscious minds now handle. To demonstrate that such a network can aspire to human-level tasks, we have begun loading an Anti-Money Laundering Act so that the machine can devise and test strategies against money launderers. There is a higher goal being aimed at, the breaking of the Four Pieces Limit. That is, the network is not intended to save money on human cognitive labor, but to raise an absurdly low limit on it.

As China put forward its “carbon emissions peak and carbon neutrality” goals, how to achieve carbon reduction had become a key for China’s goal. The manufacturing industry is an important source of carbon dioxide emissions. For a manufacturing country like China, adjustments in various aspects of the industry would have a huge impact on carbon emissions. As an important reform of contemporary production mode, the process of production automation in China will inevitably affect China's carbon emissions. Therefore, the analysis of the impact of production automation on carbon dioxide emissions was an important basis for judging the future carbon reduction in China. Refer to the traditional study of carbon Kuznets curve, this paper analyzed the impact of average wage on production automation and the role of production automation in the carbon Kuznets curve(CKC). This paper proposed that production automation plays a mediating role in the process of carbon emissions, and gives a verification model of the mediating role. By analyzing the relationship between average wage and production automation process, the U-shaped curve relationship between them was verified. By examining the relationship between carbon dioxide emission data and production automation industry in China, we verified that production automation plays a partial mediating role in the change of carbon Kuznets curve. Combined with the analysis of the two parts, this paper believed that with the continuous development of China's intelligent manufacturing industry, China's carbon reduction prospects were more optimistic, and there was a good industrial foundation to achieve the “carbon peaking and carbon neutrality” goals. Finally, this paper proposes policy suggestions as increase research investment in production automation, help promote the application of production automation, encourage the research and application development of low-carbon technology, especially encourage modular design, so as to give full play to the role of production automation in the process of carbon neutrality in China.

The by-products of the juice and cider industry in forms of pomace and peels are rich sources of pectin which further can be used in the food, cosmetic and non-food industries. The aim of the research was to use by-products from fruits and berries grown in Latvia: Rhubarb (Rheum rhabarbarum); Apples (Malus domestica); Plums (Prunus domestica); Red Currants (Ribes rubrum); Black Currants (Ribes nigrum); Gooseberries (Ribes uva-crispa); Sour Cherries (Prunus cerasus); Pumpkins (Cuccurbita spp.) for pectin extraction. Citric acid (0.1 N) was used for the hot water extraction. The yield of pectin (PY = 4.47 – 17.8 % DM) and physicochemical parameters of obtained pectin were determined: methoxy degree (ME = 4.27 – 8.13 %), esterification degree (DE = 45.16 % – 64.06 %), anhydrouronic acid content (AUA = 38.23.% - 84.74 %); moisture content (5.2 – 7.4 %), ash content (1.42 - 2.88 g 100 g-1), total phenolic content (TPC = 2.076 – 4.668 GAE, mg L-1) and antiradical scavening activity (DPPH Method (0.56 – 37.29 %)). Pectin analysis was performed by FT-IR and the rheological properties of pectin gels were determined.

Ciherang rice variety was released in year 2000 and become a superior variety in Indonesia. In a decade, due to the excellent agronomic characteristics and high productivity, Ciherang gradually replace the mega-variety IR-64 and widely cultivate in almost 40% rice production area in Indonesia. In addition to its high grain yield, short straw, early maturity, and high productive tiller number, it had superior grain quality, such as long and slender grain shape, clean yellow color, aromatic, and high milled rice percentage with excellent eating quality, matching with farmers and rice consumers demands. It has intermediate glycemic index and delicious taste. It is intolerance to flooding condition and susceptible to several diseases and pests, including rice blast, bacterial blight, brown planthopper, and yellow stem borer. Because of Ciherang has been well-characterized genetically, many quantitative trait loci (QTLs) and candidate genes which associated with certain traits have been identified. It has also been used as a parent in rice breeding program to develop mapping populations for genetic analysis. In order to increase the flooding tolerant and disease resistant in Ciherang, many valuable genes have been introduced into Ciherang through backcrossing breeding technique and transgenic approach. The optimal rice productivity in Ciherang also supported by the application of intensification system in Ciherang cultivation. Unravelling the complex genetic control of agronomic characteristics, grain yield traits, and other desirable traits which valuable for rice farmers and consumers are very important.

The endothelium is a monolayer of cells lining the inner blood vessels. Endothelial cells (ECs) play indispensable roles in angiogenesis, homeostasis, and immune response under normal physiological conditions, and their dysfunction is closely associated with pathologies such as cardiovascular diseases. Abnormal EC metabolism, especially fatty acid (FA) dysfunctional metabolism, contributes to the development of many diseases including pulmonary hypertension (PH). In this review, we focus on discussing the latest advances in FA metabolism in ECs under normal and pathological conditions with an emphasis on PH. We also highlight areas of research that warrant further investigation.

The current theories of consciousness do not address the two major problems:What is the solution to the hard problem of consciousness or What is the experience of self-existence? and How is conscious perception computed? We address these questions using our hypothesized theory:The Self Simulation theory and propose a model of computation of consciousness.The components of the model are an interconnected network of auxiliary processors or Auxiliary Neural Networks(AN) which receives input from the external sensory modalities,computes a representation of the information and relays it to the NCC(Neural Correlates of Consciousness), the NCC – a processing unit which can be neuro anatomically distributed in location but computationally connected to each other and to the Auxiliary Networks,and a working memory which the NCC references for further computation of information.Computation of conscious perception is achieved via the decoding of the content of the memory by the NCC.The self simulation theory accounts not only for the various phenomenal experiences but also experimentation paradigms conducted in the past.

Metal halide perovskites (MHPs), in particular lead-based halide perovskites have earned a recognized fame in several fields for their outstanding optoelectronics properties including direct generation of free charge carriers, optimal ambipolar charge carrier transport properties, high absorption coefficient, point-defect tolerance, and compositional versatility. Nowadays, this class of materials represents a real and promising alternative of silica for the photovoltaic technologies. This worthy success led to a growing interest in the exploration of MHP materials in other hot research fields such as the solar-driven photocatalytic water splitting towards hydrogen production, CO2 reduction to CO and CH4, useful organic reactions such as organic synthesis (formation of C-C, C-N, and C-O bonds), and pollutants and dye degradation. Nevertheless, many of these perovskite materials showed air and moisture instability problems that considerably hinder their practical application for photocatalytic water splitting and photodegradation of CO2. Moreover, if the chemical instability is a problem that can be in part mitigated by the optimization of the lattice structure, the presence of lead represents a real problem for the practical application of these type of materials in commercial devices. To successfully overcome this problem, lead-free metal halide perovskites (LFMHPs) have gained increasing interest thanks to their promising optoelectronic properties, comparable to lead-based materials, and their eco-friendly nature. Among all the lead-free perovskite alternatives, this mini review will focus the attention on the bismuth-based perovskites and perovskite derivatives with specific focus on solar-driven photocatalysis application for H2 evolution.

This paper presents the life cycle assessment results of a study of plastic recycling via hydrothermal upgrading (HTU), a chemical recycling technology. It was investigated due to its potential to address current gaps in the plastic recycling system, largely due to several plastic packaging materials and formats that cannot be processed by traditional mechanical recycling technologies (primarily due to technoeconomic reasons). As society transitions towards a net-zero-based circular economy, assessments should be conducted with a futuristic outlook, preventing costly mistakes by employing the right technologies in the right areas. HTU can generate up to 76% reduction in climate change impacts when compared with comparable end-of-life treatment technologies whilst conserving material with the system. Additionally, further reductions could be achieved by changes in electricity consumption within the study. This represents a new insight to chemical recycling of polymers by establishing a prospective life cycle assessment study that looks to introduce a step-change in the recycling system. By creating a transparent cut-off model with consistent allocation, it highlights the benefits of introducing this technology as opposed to the current model of disposal through incineration or landfill.

In the context of the new crown epidemic, remote physical training will already be in order under the guidance of the Ministry of Education. There is a global focus on current lifestyle behaviors and the future health and well-being of youth. Youth physical activity, aerobic activity and muscle fitness levels are declining.[1] Research on the effects of youth physical training and group attitudes is still in its infancy. This study conducted in-depth interviews with online physical training students, parents, teachers involved in the construction of online physical training, and sports practitioners through interview methods, documentation, and mathematical statistics. The results of the interviews were qualitatively coded using NVivo to summarize the attitudes of different subjects toward physical fitness training and the influencing factors, (1) students were basically satisfied with the teaching effect of online physical fitness training (2) parents thought the teaching effect of online physical fitness training was average (3) physical education teachers thought the effect of online physical fitness training was average (4) physical education practitioners thought the effect of online teaching was poor.

We present large eddy simulations of a mid-latitude open ocean front using a modified state of the art computational fluid dynamics code. We investigate the energy and information fluxes at the submesoscale/small-scale range in the absence of any atmospheric forcing. We find submesoscale conditions (Ro∼1, Ri∼1) near surface, within baroclinic structures related with partially imbalanced frontogenetic activity. Near surface, the simulations show a significant scale coupling on scales larger than ∼103(m). This is manifested as a strong direct energy cascade and intense mutual communication between scales, where the latter was evaluated using an estimator based on Mutual Information Theory. At scales smaller than ∼103(m) the results show near-zero energy flux, however, at this scale range, the estimator of mutual communication still shows values corresponding with a significant level of communication between them. This fact motivates to investigate the nature of the self-organized turbulent motion at this scale range with weak energetic coupling but where communication between scales is still significant and to inquire into the existence of synchronization or functional relationships between scales with emphasis on eventual underlying non-local processes.

Teaching kinematic rotations is a daunting task for even some of the most advanced mathematical minds. However, envisioning and explaining the three-dimensional rotations can be highly simplified by changing the paradigm. This paradigm change allows for a high school student with a understanding of geometry to be able to not only develop the matrix but also explain the rotations at a collegiate level. The proposed method includes the assumption of a point (P) within the initial three-dimensional frame with axes (x ̂_i, y ̂_i, z ̂_i). It then utilizes a two-dimensional rotation view (2DRV) to measure how the coordinates of point P translate after the initial axis is rotated instead of using the established Euler's formula. The equations are used in matrix notation to develop a direction cosine matrix (DCM) for future equations. This method provides a high school student with an elementary and repeatable process to compose and explain kinematic rotations.

It is now widely accepted the climate change is having a profound impact on the weather systems around the world. These, in turn, have a considerable effect on two important elements of the Tuscan economy: wine producing and tourism. This case study sought to explore the relationship between the perception of Tuscan wine-producing agritourism owners of the potentially abstract notion of climate change and their concrete experiences as entrepreneurs. Semi-structured interviews were conducted with eight wine-producing agritourism owners or managers in Val d’Orcia, a small area of Siena, Tuscany and analyzed thematically. The impact of climate change on the area’s viticulture is undeniable but the responses to the challenges are more nuanced. Political leadership on the climate crisis appears absent and perhaps as a consequence, these small-scale operators lack knowledge and funds to enable them to plan ahead: they react often day-to-day to the immediate weather conditions rather than planning long term. While recognizing the difficulties they face from climate change as viticulturists, as agrotourism owners they welcome the longer seasons which enable them to open in the formally barren shoulder seasons but struggle with last-minute cancellations due to unpredictable weather in the area.

Dynamic Realistic Failure Process Analysis (DRFPA) is a numerical software based on FEM it is firstly validated by comparing with the experimental test. Such validation is carried out under two consistent concrete specimens under uniaxial compression. After validation, DRFPA is used to study the influence of two parallel pre-existing crack inclination angles on the fracture of concrete under dynamic loads. The evolution of wing cracks and secondary cracks is investigated by observing the initiation, coalescence, and propagation of them. Meanwhile, the relationship between failure mode and inclination angles is discussed via analyzing the acoustic emission events. Finally, it is concluded the relationship between inclination angles of pre-cracks and performance/stability of concrete structure.

The proportion of human cells to the microbial cell is 1:1. These procaryotes use efflux pumps and enzymes to prevent cellular intoxication of ions and compounds respectively. There is promising evidence on the role of the gut microbiome and its enzymes in metabolizing xenobiotics. The genetic potential of oral bacteria in drug and xenobiotic metabolism is yet to be unveiled. This study aimed to characterize the bacteriome associated with oral fibroepithelial polyps (FEP) and to predict the genetic potential. A representative sub-sample of 22 clinically diagnosed oral FEP (the control group) was selected from a main case-control study. Amplification of nucleotides of extracted DNA from frozen tissues was performed for the V1 to V3 region and sequencing of the amplicon with Illumina’s 2 X 300–bp chemistry. Classification of high-quality nonchimeric merged reads was done to the species level with a prioritized BLASTN-based algorithm. Downstream compositional analysis was performed with QIIME (Quantitative Insights into Microbial Ecology). Functional prediction of bacteriome was obtained by PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States). Rothia mucilaginosa, Streptococcus mitis, Gamella haemolysans, Streptococcus sp. oral taxon 431, and Rothia dentocariosa accounted for the top five taxa among 810 bacterial species according to the percentage of average relative abundance. Rothia mucilaginosa was elevated statistically significantly (p< 0.05). The genetic potential of xenobiotics and drug metabolism catalyzed by the P450 enzymes was observed for the first time as an attribute of bacteriome associated with oral FEP tissues dominated by R. mucilaginosa. This finding needs further investigation.

The persistence of diseases that affect construction workforce as a result of activities on construction sites poses a danger to the sustainable development of the industry. This resulted to a huge loss of skilled labour and economic development of the industry and the entire country. The arrival of the fourth industrial revolution (4IR) technologies urges an urgent need to assess the effect of the technology’s biological driver on the construction occupation related diseases. Therefore, this study is aimed at assessing the effect of 4IR on the construction occupation related diseases in Gauteng, South Africa. The study is quantitative in design and questionnaire survey were administered to project and Health and Safety (H&S) managers in Gauteng, South African construction sector using a proportionate simple random sampling technique. For data analysis, the Warp PLS-SEM 8.0 software algorithm was used for the analysis of the collated data. The study found that the effects of the 4IR’s biological driver variables ranges between moderate to high effects for genome sequencing (GENSE) and Neurotechnology (NEURO) respectively. The combined predictive relevance of the two (2) variables predicts 64% of the construction occupation related diseases. This implies that the adoption of the driver would help reduce the causes of construction-related diseases. Hence, implies that continuous deployment of 4IR technologies would ensure that construction occupation related diseases are easily identified and put on alert.

The field of applied behavior analysis has been directly involved in both research and applications of behavioral principles to improve the lives of captive zoo animals. Thirty years ago, Forthman and Ogden (1992) wrote one of the first papers documenting some of these efforts. Since that time, considerable work has been done using behavioral principles and procedures to guide zoo welfare efforts. The current paper re-examines and updates Forthman and Ogden’s original points, with attention to the five categories they detailed: (1) promotion of species-typical behavior, (2) reintroduction and repatriation of endangered species, (3) animal handling, (4) pest control, and (5) animal performances. In addition, we outline three current and future directions for behavior analytic endeavors: (i) experimental analyses of behavior and the zoo, (ii) applied behavior analysis and the zoo, and (iii) within-subject methodology and the zoo. The goal is to provide a framework that can guide future behavioral research in zoos, as well as create applications based on these empirical evaluations.

Background: Dynapenia and non-alcoholic fatty liver disease (NAFLD) are common, especially in the middle and advanced-age diabetic male population. We aimed to examine the clinical features, NAFLD severity, and parameters associated with the presence of dynapenia in type 2 diabetes mellitus (T2DM) cases. Material and Methods: One hundred thirty-five male patients diagnosed with T2DM between 45 and 65 years of age were included. Patients were staged by ultrasonography according to NAFLD status. Results: There were significant differences in muscle strength, upper arm circumference, calf circumference, and up-and-go test scores between the NAFLD groups (p<0.001 for all). The frequency of dynapenia was lower, and arm and calf circumferences were higher in patients without NAFLD. The muscle strength, upper arm circumference, calf circumference, and up-and-go test scores were significantly lower in the dynapenic group compared to the non-dynapenic group (p<0.005 for all). The prevalence of dynapenia increased along with the increase in NAFLD stages (p<0.001). Conclusions: We detected a significant association between NAFLD and dynapenia in middle-aged men with T2DM. As muscle strength decreases, the amount of fat in the liver increases, and as the fat in the liver increases, muscle strength decreases.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.

Classical and optimal control architectures for motion mechanics with fusion of noisy sensors use different algorithms and calculations to perform and control any number of physical demands, to varying degrees of accuracy, precision, and cost. Their performances are tested for the purpose of comparison through the means of a Monte Carlo simulation that simulates how different parameters might vary under noise, representing real-world imperfect sensors. We find that improvements in one figure of merit often come at a cost in the performance in the others, especially depending on the presence of noise in the system sensors. If sensor noise is negligible, open-loop optimal control performs the best. However, in the overpowering presence of sensor noise, using a control law inversion patching filter performs as the best replacement, but has significant computational strain.

At present, diversified and highly concurrent businesses in the Internet industry often require heterogeneous databases formed by multiple databases to meet the needs. This report introduces database kernel SG-ColBase we developed. After achieving read and write concurrency control, data rollback, atomic log writing, and downtime data redo to ensure complete transaction support. The parallelism of database kernel execution is extended through field level locks and snapshot reads. Use the Bloom filter, resource cache pool, memory pool, skip list, non blocking log cache, and asynchronous data writing mechanism to improve the overall execution efficiency of the system. In terms of data storage, column storage, logical key and LSM-tree are introduced. While improving the data compression ratio and reducing data gaps, all disk data operations are written in incremental order. With the characteristics of asynchronous batch operation, the data writing speed is greatly improved. Thanks to the continuous feature of vertical data brought by column storage, the disk scanning brought by vertical traversal is reduced, which is a qualitative leap in efficiency compared with traditional relational databases in the big data analysis scenario. SG-ColBase can reduce the use of heterogeneous databases in business and improve R&D efficiency.

Predominantly rural areas make up half of Europe and account for about 20 per cent of the population. Yet most of them are among the least privileged regions in the European Union, with a GDP per capita significantly below the European average and with an average population age higher than in urban areas, although this gap will only slowly begin to narrow in the next decade. Together with a lack of connectivity, inadequate infrastructure, lack of diversified job opportunities and limited access to services, this makes rural areas a less attractive place to live and work. At the same time, however, rural areas actively participate in the EU's green and digital transition. Achieving the EU's digital goals for 2030 can offer more opportunities for the sustainable development of rural areas in areas other than agriculture, livestock and forestry, opening up new perspectives for the growth of manufacturing and, even more, service industries, and contributing to a better geographical distribution of services and industries. In this context, the objective of this work is the construction of a synthetic index of the welfare of European nations, through the Wroclaw taxonomic method and through the use of logit models, for the identification of best practices of local realities and the interpretation in a more im-mediated way of the fair and sustainable welfare of each European nation at a rural level

In this review, the following information describes the manifestation of sleep deprivation by human beings and its adverse effect on their health. Sleep deprivation has been demonstrated into namely two types known as REM sleep and NREM sleep affecting our health in so a problematic way that it is making our body immune to many diseases leading to lethal problems. Therefore, great research by many scientists has discovered that the turmeric “Curcuma longa” which is been used in every Indian kitchen since ancient times, has shown a remarkable effect on the problem caused by sleep deprivation but due to its poor solubility and low bioavailability drawn it into a great disadvantage. But the help of the study of nanotechnology and the evolution of curcumin into nano–curcumin made the possibility of the remarkable effect by making the curcumin more potent and enhancing its stability. Immunological changes due to sleep deprivation lead to Alzheimer’s disease, glioma, neuropathic pains, and many more. Therefore, this review has been summarized as it is been providing information related to curcumin and its affection for sleep deprivation.

In the field of low power wireless networks, one of the techniques on which many researchers are putting their efforts is related to positioning methodologies such as fingerprinting in dense urban areas. This paper presents an experimental study aimed at quantifying the mean location estimation error in densely urbanized areas.Using a dataset made available by the University of Antwerp, a neural network was implemented with the aim of providing the position of the end-devices. In this way it was possible to measure the mean location estimation error in an area with high urban density. The results obtained show an accuracy in the localization of the end-device of less than 150 meters.This result would make it possible to use the fingerprint instead of alternative, energy consuming, methodologies such as GPS in IoT (Internet of Things) applications where battery life is the primary requirement to be met.

Background: Acute pancreatitis is a common emergency. Morbidity control requires early detection of disease severity. METHODS: A total of 131 AP patients were analyzed retrospectively. Patients were divided into two groups mild AP (MAP: Ranson score <3) and severe AP (SAP: Ranson score ≥3), according to Ranson’s criteria. Demographic data, hospitalization duration, PNI, CRP, and RDW levels were compared. Any p-value below 0.05 (p<0.05) was accepted as statistically significant. RESULTS: Study included 67 (51.15%) males and 64 (48.85%) females. The age average was 59.74 (19-90) years. 95 (72.52%) patients had MAP, and 36 (27.48%) patients had SAP. Mean hospitalization time, PNI, and CRP differed significantly between the two groups (p=0.010, p<0.05, p<0.05, respectively). The RDW (p=0.380) level difference was insignificant. For SAP prediction; the sensitivity, specificity and cut-off value according to Ranson code cut-off point for PNI were determined as 80.0% (95% CI:54.8-85.8), %72.2 (95% CI:70.5-87.5) and ≤45.6 (gr/L) + (mm3), respectively, and 94.7% (95% CI:57.8-87.9), %75.0 (95% CI:88.1-98.3) and >105,1 mg/L, respectively for CRP. CONCLUSION: PNI and CRP values (but not RDW values) were compatible with the disease severity determined by the Ranson criteria.

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.

Dry eye disease (DED) is one of the most common eye diseases. There is at least one DED patient in almost every five people. AI-based research methods increasingly become the focus of DED diagnosis research. This study utilizes a systematic review method on DED AI-based diagnosis. 2112 unduplicated records are extracted from Google Scholar, Web of Science (WOS), PubMed, China National Knowledge Infrastructure (CNKI), and Scopus databases. The most contributed countries, institutions, authors, journals, references, and disciplines are recognized. Keyword distribution and hot topics are identified. Popular databases of ophthalmic images, videos, and electronic demographic medical records are discussed. The DED diagnosis, classification, and grading criteria are identified. The major diagnosing methods are clustered, compared, and investigated. Findings show that diagnosing method research could be classified into three categories based on the relationship between AI techniques, which are (1) ground truth and/or comparable standards for AI DED diagnosis (TBUT, S Ⅰ T, TMH, and OSDI), (2) potential methods for AI-based methods have a great advantage(DED detection based on meibometry Images, CASPs, IVCM Images, OCT Images, blink videos and ultrasonic imaging), (3) and the potential direction and supplemented methods for AI-based DED detection (DED detections based on tear osmolarity, proteomic analysis, TCM and demographic information). AI-based approaches based on digital ophthalmologic images play an important role in early screening. Challenges and future perspectives are discussed at the end of this article, academically and practically.

Atmospheric correction is the processes of converting radiance measured at a spectral 1 sensor to the reflectance of the materials in a multispectral or hyperspectral image. This is an 2 important step for detecting or identifying the materials present in the pixel spectra. We present 3 two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 4 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns 5 the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian 6 Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We 7 compare both methods for estimating gain in the correction model to the well-know QUAC method 8 of assuming a constant mean endmember reflectance. Prediction of reflectance using the Gaussian 9 process model outperforms the other methods in terms of both accuracy and reliability.

Introduction. A needle is inserted into a joint for arthrocentesis or injection of a therapeutic medication(s), commonly a corticosteroid. The aim of this paper is to discuss the first theme identified from a scoping review of corticosteroid injections for the pathology of the first metatarsophalangeal joint. Pathology. The two most common pathologies affecting the first metatarsophalangeal joint are osteoarthritis and bunions. An arthritic joint is regularly injected with a corticosteroid, but bunions are not. Other pathologies that may receive an injection include rheumatoid arthritis, gout, sesamoiditis and post-operative arthrofibrosis. Discussion. Most available evidence discusses corticosteroid injections for osteoarthritis, but there is a paucity of high-quality evidence, especially for corticosteroid use in other pathological conditions. Conclusion. Whilst the evidence base suggests that corticosteroid injections are safe short- and mid-term treatment options for a range of soft tissue and joint pathology, the specific indications, and short/long-term outcomes in the first metatarsophalangeal joint pathologies are not clear and warrant further study.

In this empirical article, we argue that, while emergency remote teaching (ERT) may have achieved its goal of saving the academic years during the COVID-19 pandemic, it also constructed unintended pedagogical consequences that were possibly overlooked at the time of advocating it. The research question that the article attempted to answer is, what unintended pedagogical consequences did students and lecturers suffer because of the move to ERT at rural-based universities (RBUs) in South Africa? Drawing on students' and lecturers’ lived experiences of ERT, this article foregrounds unintended pedagogical consequences that arose at one RBU in South Africa during the transition from face-to-face teaching to ERT. Underpinned by the tenets of critical realism philosophy, as well as student integration theory, in-depth interviews with three lecturers and six students were conducted. The findings of the study indicate that home conditions, individual characteristics, pre-COVID-19 blended learning experiences, university training and support, and teaching, learning, and assessment practices and policies altogether contributed to the construction of unintended pedagogical consequences of ERT presented in this article. These consequences include (1) exclusion of low-income students in active teaching and learning, (2) equipping middle-class students with better chances of success than working-class students. (3) distressing female students and lecturers more than their male counterparts, and (4) unproductive assessment practices. This study may be beneficial to academics and policymakers from similar contexts in their plight to continue with remote teaching and assessment (RTA) beyond the pandemic.