Mereotopology is a concept rooted in analytical philosophy. The phase-field concept is based on mathematical physics and finds applications in materials engineering. The two concepts seem to be disjoint at a first glance. While mereotopology qualitatively describes static relations between things like x isConnected y (topology) or x isPartOf y (mereology) by first order logic and Boolean algebra, the phase-field concept describes the geometric shape of things and its dynamic evolution by drawing on a scalar field. The geometric shape of any thing is defined by its boundaries to one or more neighboring things. The notion and description of boundaries thus provides a bridge between mereotopology and the phase-field concept. The present article aims to relate phase-field expressions describing boundaries and especially triple junctions to their Boolean counterparts in mereotopology and contact algebra. An introductory overview on mereotopology is followed by an introduction to the phase-field concept already indicating first relations to mereo- topology. Mereotopological axioms and definitions are then discussed in detail from a phase-field perspective. A dedicated section introduces and discusses further notions of the isConnected relation emerging from the phase-field perspective like isSpatiallyConnected, isTemporallyConnected, isPhysicallyConnected, isPathConnected and wasConnected. Such relations introduce dynamics and thus physics into mereotopology as transitions from isDisconnected to isPartOf can be described.

The purpose of the article is to determine the peculiarities of using of teaching elements of research-based training at the Institute of Human Sciences of Borys Grinchenko Kyiv University. Based on the focus group methodology, the authors identify the key methodological characteristics of research-based training, which have been put into basis of analysis of educational programs for the purpose of determining the application of tasks that contribute to the development of research skills of students. The study used a method of focus group. Its purpose was to obtain the necessary information from the participants to describe the methodological basis and justification of methods, forms, indicators, etc. of research-based training system among people who are competent, have experience in this field. After that, the method of "theoretical sampling" was used, which enabled to formulate generalized characteristics according to the results of focus groups. The practical value of the study is determination of the methodological characteristics of research-based training which is the basis for the application of tasks by university teachers that promote the development of research competence of students. The research is one of the first attempts to determine the methodological characteristics of research-based training in Ukraine.

Participation by local communities in wildlife conservation projects have long been advocated since it is socially just and is effective to reach conservation and development goals. Socio–economic variables that drive participation and impact of participation have been studied, but the contextual process that stir up local community participation remains understudied. In this paper, we studied factors facilitating community participation in three wildlife conservation projects in Northeast India. Through ethnographic fieldwork at these sites we identified conservation actors and examined interactions between them.We found common modes of participation at these sites and these were related to gaining material incentives, providing labour, attending consultative workshops. Levels of interaction and coercion were found to be different in three sites. Three critical factors that drive participation were: (1) trigger, (2) negotiation and (3) sustenance. Trigger factors kickstart participation through establishment of a crisis narrative and facilitation by external actors. Negotiation factors emerge from day–to–day interaction between local community and external actors and involve effective entry stage activities, income opportunity, mediating voices within the community and intra–community dynamics. Sustenance factors affect the long term participation by community in the conservation project and involve tangible/intangible results, capability development of locals, funding and availability to critical information. In our paper we argue that investment of time and fund to understand the stakeholders and their concept of participation, periodic feedback sessions, capacity development of locals for self–mobilization, innovative information dissemination and securing long term funding are necessary for effective local community participation.

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

Developments in technology have enabled envisioning the derivation of materials and products from renewable biomass, as an alternative to finite fossil-based resource consumption. Therefore, bioeconomy is regarded as an opportunity for sustainable economic growth. Countries are formulating strategies in accordance with their goals to attain from bioeconomy. Proper measurement, monitoring and reporting of the outcomes of these strategies is crucial for long-term success. This study aims to critically evaluate the national methods used for the measurement, monitoring and reporting of bioeconomy contribution to the total economy. For this purpose, research and surveys have been conducted on selected countries (Argentina, Germany, Malaysia, the Netherlands, South Africa and the United States). The results reveal that the bioeconomy targets set up in the strategies often reflect country’s priorities and comparative advantages. However, comprehensive approaches to measure and monitor bioeconomy progress are frequently lacking. Most countries only measure the contribution to gross domestic product (GDP), turnover and employment of the sectors included in their bioeconomy definition, which may provide an incomplete picture. In addition, this study identifies the mismatch between the targets and measurement methods, as the environmental and social impacts of bioeconomy are often foreseen, but not measured. It is concluded that existing global efforts towards a sustainable bioeconomy monitoring can be strengthened and leveraged to measure progress towards sustainable goals.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

Since the outbreak of the novel coronavirus disease (COVID-19) at the end of 2019, the clinical presentation of the disease showed a great heterogeneity with a diverse impact between different subpopulations. Emerging evidence from different parts of the world showed significantly poor outcome among males compared to female patients. A better understanding of the molecular mechanisms behind this difference might be a fundamental step for a more effective and targeted response to the outbreak. For that reason, here we try to investigate the molecular basis of the gender variations in mortality rates related to COVID-19 infection. To achieve this, we used our in-house pipeline to process publicly available lung transcriptomic data from 141 females compared to 286 males. After excluding Y specific genes, our results showed a shortlist of 73 genes that are differentially expressed between the two groups. Our results showed downregulation of a group of genes that are involved in the regulation of hydrolase activity including (AGTR1, CHM, DDX3X, FGFR3, SFRP2, and NLRP2), which is also believed to be essential for lung immune response and antimicrobial activity in the lung tissues in males compared to females. In contrast, our results showed an upregulation of angiotensin II receptor type 1 (AGTR1), a member of the renin-angiotensin system (RAS) that plays a role in angiotensin-converting enzyme 2 (ACE2) activity modulation. Interestingly, recent reports and experimental animal models highlight an important role of this receptor in SARS-Coronavirus lung damage as well as pulmonary edema, suggesting a possible role of its blockers like losartan and olmesartan as potential therapeutic options for COVID-19 infection. Finally, our results also showed a differential expression of different genes that are involved in the immune response including the NLRP2 and PTGDR2, further supporting the notion of the sex-based immunological differences. Taken together, our results provide an initial evidence of the molecular mechanisms that might be involved in the differential outcomes observed between both genders during the COVID-19 outbreak. This might be essential for the discovery of new targets and more precise therapeutic options to treat COVID-19 patients from different clinical and epidemiological characteristics with the aim of improving their outcome.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

There is increasing demand from stakeholders for tools to support outcome-based approaches in environmental management. For such tools to be useful, understanding user requirements is key. In Scotland, UK, stakeholders were engaged in the development of an Environmental Decision Support System (EDSS) to support the management of land and freshwater resources for multiple policy outcomes. A structured participatory engagement process was employed to determine stakeholder requirements, establish development principles to fulfil these requirements and road-test prototypes. The EDSS specification that emerged from this bottom-up process was spatially-explicit, free at the point of use, and touch and mobile device compatible. This application, which is under development, does not closely resemble most existing published EDSS. We suggest that there is a mismatch between the way scientists typically conceptualise EDSS and the kinds of applications that are likely to be useful to decision-makers on the ground. Interactive mobile and web-based geospatial information services have become ubiquitous in our daily lives, but their importance is not reflected in current literature on EDSS. The current focus in environmental management on adaptive, stakeholder-centred strategies based on outcomes offers an opportunity to make better use of these new technologies to aid decision-making processes.

Gene editing tools such as CRISPR-Cas9 have created unprecedented opportunities for genetic studies in plants and animals. We designed a course-based undergraduate research experience (CURE) to train introductory biology students in the concepts and implementation of gene editing technology as well as develop their soft skills in data management and scientific communication. We present two versions of the course that can be implemented with twice-weekly meetings over a five-week period. In the remote-learning version, students perform homology searches, design guide RNAs and primers, and learn the principles of molecular cloning. This version is appropriate when access to laboratory equipment or in-person instruction is limited, such as closures that have occurred in response to the Covid-19 pandemic. In the in-person version, students design guide RNAs, clone CRISPR-Cas9 constructs, and perform genetic transformation of the model plant Arabidopsis thaliana. The highly parallel nature of the CURE makes it possible to target dozens to hundreds of genes, depending on the number of course sections available. Applying this approach in a sensitized mutant background enables focused reverse genetic screens for genetic suppressors or enhancers. The course can be readily adapted to other organisms or projects that employ gene editing.

Recently, selection in pigs has been focused on improving the lean meat content in carcasses; this focus has been most evident in breeds constituting a paternal component in breeding. Such sire-breeds are used to improve the meat quantity of cross-breed pig lines. However, even in one breed, a significant variation in the meatiness level can be observed. In the present study, the comprehensive analysis of genes and microRNA expression profiles in porcine muscle tissue was applied to identify the genetic background of meat content. The comparison was performed between whole gene expression and miRNA profiles of muscle tissue collected from two sire-line pig breeds (Piertain, Hampshire). The RNA-seq approach allowed the identification of 627 and 416 differentially expressed genes (DEGs) between pig groups differing in terms of loin weight between Pietrain and Hampshire breeds, respectively. The comparison of miRNA profiles showed differential expression of 57 microRNAs for Hampshire and 34 miRNAs for Pietrain pigs. Next, 43 genes and 18 miRNAs were selected as differentially expressed in both breeds and potentially related to muscle development. According to Gene Ontology analysis, identified DEGs and microRNAs were involved in the regulation of the cell cycle, fatty acid biosynthesis and regulation of the actin cytoskeleton. The most deregulated pathways dependent on muscle mass were the Hippo signalling pathway connected with the TGF-beta signalling pathway and controlling organ size via the regulation of ubiquitin-mediated proteolysis, cell proliferation and apoptosis. The identified target genes were also involved in pathways such as the FoxO signalling pathway, signalling pathways regulating pluripotency of stem cells and the PI3K-Akt signalling pathway. The obtained results indicate molecular mechanisms controlling porcine muscle growth and development. Identified genes (SOX2, SIRT1, KLF4, PAX6 and genes belonging to the transforming growth factor beta superfamily) could be considered candidate genes for determining muscle mass in pigs.

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby animal welfare, productivity, and sustainability of the farming industry.

The influence and importance of research and development (R&D) for business sustainability have gained increasing interests, especially in the high-tech sector. However, the efforts of R&D might cause complex and mixed impacts on the financial results considering the associated expenses. Thus, this study aims to examine how R&D efforts may influence business to improve its financial performance considering the dual objectives: the gross and the net profitability. This research integrated a rough-set-based soft computing technique and multiple criteria decision-making (MCDM) methods to explore this complex and yet valuable issue. A group of public listed companies from Taiwan, all in the semiconductor sector, was analyzed as a case study. Initially, more than 30 variables were considered, and the adopted soft computing technique retrieved 14 core attributes—for the dual profitability objectives—to form the evaluation model. The importance of R&D for pursuing superior financial prospects is confirmed, and the empirical case demonstrates how to guide an individual company to plan for improvements to achieve its long-term sustainability by this hybrid approach.

Focal adhesion kinase (FAK) and its close paralogue, proline-rich tyrosine kinase 2 (PYK2), are key regulators of aggressive spreading and metastasis of cancer cells. While targeted small-molecule inhibitors of FAK and PYK2 are showing promising antitumor activity, their clinical long-term efficacy may be undermined by the strong capacity of cancer cells to evade anti-kinase drugs. In healthy cells, the expression and/or function of FAK and PYK2 is tightly controlled through modulation of gene expression, competing alternatively spliced forms, non-coding RNAs, and proteins that directly or indirectly affect kinase activation or protein stability. The molecular factors involved are frequently deregulated in cancer cells. Here, we review the endogenous mechanisms controlling FAK and PYK2, and discuss how these mechanisms could inspire or improve anticancer therapies.

Structure-based design and synthesis of two biphenyl-N-acyl-β-D-glucopyranosylamine derivatives as well as their assessment as inhibitors of human liver glycogen phosphorylase (hlGPa, a pharmaceutical target for type 2 diabetes) is presented. X-ray crystallography revealed the importance of structural water molecules and that the inhibitory efficacy correlates with the degree of disturbance caused by the inhibitor binding to a loop crucial for the catalytic mechanism. The in silico derived models of the binding mode generated during the design process corresponded very well with the crystallographic data.

List-only entity linking is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional entity linking task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, list-only entity linking can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions, which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in entity linking, which is yet currently neglected. In this work, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which Personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of list-only entity linking, but also opens up the bridge between list-only entity linking and conventional entity linking solutions.

Cycling reliability is crucial for Si-based materials due to severe volume change during cycles that results in the fast capacity fading. Though the binder only occupies a very low amount of the total mass of anodes, it is proved to perform a key parameter to improve the cycle performance of Si-based anodes. Because they are eco-friendly and cost saving, water-based binders styrene-butadiene rubber (SBR) and carboxymethyl cellulose (CMC) are regarded as the better binder to substitute Poly (vinylidene difluoride) (PVDF) as the binder for Si-based anode. In this study, the anodes are fabricated by simply mixing the active materials (naso Si, graphite and conductive additive) together and using the mixture of SBR and CMC as a binder. The results showed that the retention capacities of the anodes are more than 440 mAh/g after 400 cycles. It indicates that it is an easy and simple way to make high performance anodes.

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix materials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNTs content. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both static and dynamically varying compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

Exogenous miRNAs derived from dietary substances have been shown to be orally transferred to the mammalian system and proven to remain active to regulate host-gene expression. This way they have become an active area of research as functional food components and aspects for dietary supplementation. They are being studied as a new class of metabolically targeted therapeutics that work through diet manipulation and may hold promise for a therapeutic approach in reducing the risk of life-threatening diseases. However, a substantial amount of evidence also defies this dietary miRNA concept in terms of their absorption, bioavailability, cellular uptake and its physiological effects in the mammalian system. But recent advances in the identification of some unique sequence and structural characteristics of dietary miRNAs and a deeper understanding of their stability in host peripheral blood for its cellular uptake have strengthened the whole concept. The review comprehensively summarizes the mechanism for miRNA extracellular transport, absorption through the gastrointestinal tract (GI), stability in peripheral blood, and cellular uptake in mammalian cells. It recapitulates the shreds of evidence, related to the influence of dietary miRNAs on gene expression based on the source of the origin (plant vs animal), and compares their cross-kingdom behaviour in terms of their unique sequence and stem-loop structure properties that help them to get stabilized in the mammalian system. The review also summarizes the parameters required for maintaining the sustainable uptake and bioavailability of the dietary miRNAs with existing examples of successful in-vivo and in-vitro delivery of dietary miRNA for augmented therapy. Lastly, it provides an overview of the available and required databases, webserver, and tools that can be used for the successful identification of potential dietary miRNA candidates.

Hospital-based programs democratize mRNA therapeutics by facilitating the processes to translate a novel RNA idea from the bench to the clinic. Because mRNA is essentially biological software, therapeutic RNA constructs can be rapidly developed. The generation of small batches of clinical grade mRNA to support IND applications and first-in-man clinical trials, as well as personalized mRNA therapeutics delivered at the point-of-care, is feasible at a modest scale of cGMP manufacturing. Advances in mRNA manufacturing science and innovations in mRNA biology, are increasing the scope of mRNA clinical applications.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

Since their discovery in 1993, numerous microRNAs (miRNAs) have been identified in humans and other eukaryotic organisms, and their role as key regulators of gene expression is still being elucidated. It is now known that miRNAs not only play a central role in the processes that ensure normal development and physiology, but they are often dysregulated in various diseases. In this review, we present an overview of the role of miRNAs in normal renal development and physiology, in maladaptive renal repair after injury, and in the pathogenesis of renal parenchymal diseases. In addition, we describe methods used for their detection and their potential as therapeutic targets. Continued research on renal miRNAs will undoubtedly improve our understanding of diseases affecting the kidneys and may also lead to new therapeutic agents.

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

High levels ofmeat consumption are increasingly being criticised for ethical, environmental, 2 and social reasons. Plant-based meat substitutes have been identified as healthy sources of protein in 3 comparison to meat. This alternative offers several social, environmental and health benefits and may 4 play a role in reducing meat consumption. However, there has been a lack of research on how specific 5 meat substitute attributes can influence consumers to replace or partially replace meat in their diets. 6 Research demonstrates that in many countries consumers are highly attached to meat.They consider 7 it as an essential and integral element of their daily diet. For these consumers which are not interested 8 in vegan or vegetarian alternatives to meat, so-called meathybrids could be a low-threshold option 9 for a more sustainable food consumption behaviour. In meathybrids only a fraction of the meat 10 product (e.g. 20% to 50%) is replaced with plant-based proteins. In this paper, the results of an online 11 survey with 500 German consumers are presented with focus on preferences and attitudes relating 12 to meathyrids. The results show that more than fifty percent of consumers substitute meat at least 13 occasionally. Thus, about half of the respondents reveal an eligible consumption behaviour in respect 14 to sustainability and healthiness to a certain degree. Concerning the determinants of choosing either 15 meathybrid or meat it becomes evident that the highest effect is exerted by the health perception. The 16 healthier meathybrids are perceived, the higher is the choice probability. Thus, this egoistic motive 17 seems to outperform altruistic motives like animal welfare or environmental concerns when it comes 18 to choice for this new product category.

Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

Over 130 countries have committed to reaching net-zero CO2 or GHG emissions by 2050, yet this ambition is rarely underpinned by robust policies. By applying a detailed integrated assessment modelling approach for Brazil, we assess, for the first time, the extent to which the existing and planned local policies could put Brazil on the path to its net zero pledge. This includes quantifying the role of nature-based solutions, such as protection and restoration, and engineered solutions, such as bioenergy with carbon capture and storage (BECCS). We show protection is the single most important climate mitigation measure at relatively low costs, whereas relying heavily on engineered solutions would jeopardise Brazil’s chances of achieving its net zero pledge. We also show that the mismatch between Brazil's short- and long-term climate targets reflects current weak environmental governance. Our analysis reinforces the urgent need for Brazil to eliminate illegal deforestation and go beyond to help fight climate change whilst curbing biodiversity loss.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

Background: Few studies have investigated the longitudinal association between cervical spondylosis (CS) and migraine by using a nationwide population-based database. Methods: We conducted a retrospective cohort study from 2000 to 2011 identifying 27,930 cases of cervical spondylosis and 111,720 control subjects (those without cervical spondylosis) from a single database. The subjects were frequency-matched on the basis of sex, age, and diagnosis date. The non- cervical spondylosis cohort was four times the size of the cervical spondylosis cohort. To quantify the effects of cervical spondylosis on the risk of migraine, univariate and multivariate Cox proportional hazard regression analyses were used to calculate the hazard ratio (HR) and 95% confidence interval (CI). Results: After a 10-year follow-up controlling for potential confounding factors, overall migraine incidence was higher in the cervical spondylosis cohort than in the non- cervical spondylosis cohort (5.16 and 2.09 per 1,000 people per year, respectively; crude hazard ratio = 2.48, 95% confidence interval = 2.28–2.69) with an adjusted hazard ratio of 2.03 (95% confidence interval = 1.86–2.22) after accounting for sex, age, comorbidities, and medication. Individuals with myelopathy in the cervical spondylosis cohort had a 2.19 times (95% confidence interval = 1.80–2.66) higher incidence of migraine compared than did those in the non- cervical spondylosis cohort. Conclusion: Individuals with cervical spondylosis exhibited a higher risk of migraine than those without cervical spondylosis. The migraine incidence rate was even higher among individuals with cervical spondylotic myelopathy.

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Introduction: Dementia is a cognitive decline with patients often exhibit behavioural and psychological symptoms, severely affecting the quality of life and placing a heavy burden on caregivers. Acupressure has reported benefits for dementia. This study aims to critically review the available evidence for its use as a non-pharmacological therapy. Methods: Systematic search of major research databases for human clinical trials using acupressure as an intervention for dementia patients was conducted. Results were synthesised for the effects of acupressure on various outcome measures of interest for dementia.Results: Twelve clinical trials (N=973), including eight randomised control studies, were included in this review. The study sample was predominantly institutionalised residents with moderate to severe dementia. Baihui (GV20), Shenmen (HT7), Fengchi (GB20), Neiguan (PC6), Sanyinjiao (SP6), and Yingtang (EX-HN3) were the most used acupoints for intervention. Acupressure techniques employed in these clinical trials vary greatly with no standardised approach. This review finds inconsistent evidence in the effectiveness of acupressure in reducing agitation and behavioural disturbances. However, the treatment appears to improve their ease of care and reduce physical stress. Affixing acupressure devices on selected acupoints can also potentially improve psychiatric pain, anxiety, and depression. Long-term (6 months) treatment can potentially improve the cognitive function, activities of daily living, and quality of life of patients with mild to moderate dementia. The effect of acupressure on sleep disturbances remains unclear. Conclusion: More high-quality research on acupressure is needed to fill the gaps in knowledge and inform better care for dementia patients in the future.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).

Background: Evaluation of nutrition programs is essential to guarantee the effectiveness of community-based management of acute malnutrition (CMAM). Methods: The Rainbow Project Supplementary Feeding Programs (SFPs) in Zambia were evaluated between years 2015-17, following implementation of new recommendations based on previous evaluations (years 2012-14). Outcomes of the program were compared with International Standards and with those of 2012-14. Cox proportional risk regression analysis was performed to identify predictors of mortality and defaulting. Results: Data for 900 under age 5 years malnourished children (48.8% male; mean age 19.7months ±9.9) were analyzed. Rainbow 2015-17 program outcomes met International Standards, for general malnutrition or stratified moderate acute malnutrition (MAM) and severe acute malnutrition (SAM). When comparing with 2012-14 outcomes, better performance was noted: mortality rates were reduced by half (5.6% vs 3.1%, p = 0.01; for SAM: 12.4% vs 6.7%, p = 0.006), with significant improvement in average weight gain and mean length of stay (p<0.001), and increased awareness of HIV status (+30%; p < 0.001). HIV infection (5.5; 1.9–15.9), WAZ < −3 at baseline (4.6; 1.3–16.1) and kwashiorkor (3.5; 1.2–9.5) remained the major predictors of mortality. Conclusion: The effectiveness of the Rainbow SFPs for child malnutrition treatment and prevention in Zambia has significantly improved after evaluation and implementation activities, with impressive outcomes which resulted in a 50% reduction in mortality.

In this paper, we present the design, manufacturing and characterization of a soft textile-based clutch (TBC) that switches between locking and unlocking of its linear displacement by exploiting vacuum stimulation. The applied vacuum locks the relative sliding motion between two elaborated textile webbings covered by an elastic silicone rubber bag. Based on different fabrication techniques, such as silicone casting on textile, melt embossing for direct fabrication of miniature patterns on textile and sewing, we developed three groups of TBC samples based on friction and interlocking principles and we compared their performance in blocking configuration. The clutch with interlocking mechanism presented the highest withstanding force (150 N) respect to the one (54 N) recorded for the friction-based clutch. The simple and compact structure of the proposed clutch, together with the intrinsic adaptability of fabric with other clothing and soft materials, make it a proper solution for applications in soft wearable robotics and generally as locking and variable stiffness solution for soft robotic applications.

With the development of IoT technology, various information resources, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of IoT information Service model gradually change people's daily life. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet real-time search needs of users. However, IoT search requires to use a large number of user privacy Information, such as personal health information, location information, social relations information, to provide personalized services. User privacy information will meet security problems if an effective access control mechanism is missing during the IoT search process. Access control mechanism can effectively monitor the access activities of resources, and ensure authorized users to access information resources under legitimate conditions. This survey examines the growing literature on access control for IoT search. Problems and challenges of access control mechanism are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provides theoretical, methodological and technical guidance for IoT search access control mechanism in large-scale dynamic heterogeneous environment. Based on the literature study, we also analyzed future development direction of access control in the age of IoT.

This paper provides a concise review of remote sensing of ocean fronts and its applications in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms/techniques: Canny (1986), Cayula and Cornillon (1990, 1992, 1995), Miller (2004, 2009), Shimada et al. (2005), Belkin and O’Reilly (2009), and Nieto et al. (2012). A case is made for feature-based approach in marine ecology and fisheries that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

A new research stream emerged in the 2000s dedicated to flexible work arrangements in public and private organizations, called “new ways of working” (NWW). This article aims to examine NWW from both a theoretical and empirical perspective, focusing on outcomes of this new concept and the debate between “mutual gains” vs. “conflicting outcomes.” Through a literature review, it examines this research field’s innovation and its rather vague theoretical foundations. Findings demonstrate that NWW definitions are diverse and somewhat imprecise, leading to fragmented research designs and findings; the research stream’s theoretical foundations should be better addressed. Findings also highlight the current lack of empirical data, which therefore does not allow any real conclusions on NWW’s effects on employees’ and organizations’ well-being and performance.

Atherosclerosis (ATH) and Coronary Artery Disease (CAD) are chronic inflammatory diseases with an important genetic background which derive from the cumulative effect of multiple common risk alleles, most of them located in genomic non-coding regions. These complex diseases behave as non-linear dynamical systems that show a high dependence on their initial conditions, so that long-term predictions of disease progression are unreliable. One likely possibility is that the non-linear nature of ATH could be dependent on non-linear correlations in the structure of the human genome. In this review we show how Chaos theory analysis highlighted genomic regions that shared specific structural constraints that could have a role in ATH progression. These regions were shown to be enriched in repetitive sequences of the Alu family, genomic parasites which colonized the human genome, which show a particular secondary structure and have been involved in the regulation of gene expression. We also review the impact of Alu elements on the mechanisms that regulate gene expression, especially highlighting the molecular mechanisms by which the Alu elements could alter the inflammatory homeostasis. We devise especial attention to their relationship with the lncRNA ANRIL, the strongest risk factor for ATH, their role as miRNA sponges, and their ability to interfere with the regulatory circuitry of the NF-kB response. We aim to characterize ATH as a non-linear dynamic system in which small initial alterations in the expression of a number of repetitive elements are somehow amplified to reach phenotypic significance.

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

Information regarding the cost of a construction project is available to the investor and project participants in order to determine the subsequent success of a project, given that the information they collect has an impact on the decisions they make. Cost calculations, especially in the initial phase of a project, often generate large errors. This paper presents the new approach based on a combination of the Case Based Reasoning method (CBR) with the originally selected criteria for the description of a construction project (as a result of Pearson correlation coefficient and Spearman's rank correlation coefficient) and Building Information Modeling (BIM) technology. The CBR method fulfils expectations for a simple and fast system supporting the cost estimation process. It does not require any specialist knowledge, so it will be comprehensible to cost estimation practitioners. The BIM-based model gives the opportunity for the calculation of quantity take-offs and enables the use of the information contained in the BIM model in the cost estimation process. In order to prepare the model an appropriate relational database had to be developed. With extensive research, a database of 173 construction projects, including the construction of a sports field, was obtained. There were 14 variables defined originally by authors; however, only 10 (as a result of the correlation analysis) were used for the calculation. Data related to the project were collected in the BIM model. Results estimating the project’s unit price, using the CBR method, were presented and discussed. The Mean Absolute Estimate Error was used to evaluate the model.

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several dierent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho,  and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

Aquaporin-3 (AQP3) is one of the aquaglyceroporins, which is expressed in the basolateral layer of the skin membrane. Studies have reported that human skin squamous cell carcinoma overexpresses AQP3 and inhibition of its function may alleviate skin tumorigenesis. In the present study, we have applied a virtual screening method that encompasses filters for physicochemical properties and molecular docking to select potential hit compounds that bind to the Aquaporin-3 protein. Based on molecular docking results, the top 20 hit compounds were analyzed for stability in the binding pocket using unconstrained molecular dynamics simulations and further evaluated for binding free energy. Furthermore, examined the ligand-unbinding pathway of the inhibitor from its bound form to explore possible routes for inhibitor approach to the ligand-binding site. With a good docking score, stability in the binding pocket, and free energy of binding, these hit compounds can be developed as Aquaporin-3 inhibitors in the near future.

Most of the municipalities in the Gulf region are facing performance related issues in their municipal solid waste management (MSWM) systems. They lack to possess a deliberate inter-municipality benchmarking processes. Instead of identifying the performance gaps for their key components (e.g., personnel productivity, operational reliability, etc.) and adopt proactive measures, the municipalities primarily rely on an efficient emergency response. A novel hierarchical modeling framework, based on deductive reasoning, is developed for performance assessment of MSWM systems. Fuzzy rule based modeling using Simulink-MATLAB was used for performance inferencing at different levels, i.e., component, sub-components, etc. The model is capable of handling the inherent uncertainties due to limited data and imprecise knowledge base. The model’s outcomes can exclusively assist the managers working at different levels of organizational hierarchy for effective decision-making. Performance of the key component, assists the senior management to assess the overall compliance level of performance objectives. Subsequently, operation management can hone in the sub-components to acquire useful information for intra-municipality performance management. While, individual indicators are useful for inter-municipality benchmarking. The model has been implemented on two municipalities operating in Qassim Region, Saudi Arabia. The results demonstrate the model’s pragmatism for continuous performance improvement of MSWM systems in the country and elsewhere.

The detection and quantification of protein–protein interactions (PPIs) is a crucial technique that often involves the use of recombinant proteins with fusion-protein tags, such as maltose-binding protein (MBP) and glutathione-S-transferase (GST). In this study, we improved the cohesive and sticky properties of gelatinized starch by supplementing it with agarose, resulting in a harder gel that could coat the bottom of a microtiter plate. The resulting gelatinized starch/agarose mixture allowed for the efficient immobilization of MBP-tagged proteins on the coated plates, enabling the use of indirect ELISA-like PPI assays. By using the enzymatic activity of GST as an indicator, we succeeded in determining the dissociation constants between MBP-tagged and GST-tagged proteins on 96-well microtiter plates and a microplate reader without any expensive specialized equipment.

This paper presents a novel 3-degrees-of-freedom (3-DOF) haptic master with rubber bands for self-resetting. The mechanical design avoids coupling between three directions mechanically by using three perpendicular axis intersecting at one point. Bevel gear transmission is adopted to increase the compactness of the overall structure. VR-based interactive system is designed and built by incorporating the proposed haptic master. The proposed haptic device can generate force feedback along 3-degree-of-freedom motion using motors and provide command signals to the avatar in the virtual environment. In order to analyze the performance of the developed device in terms of haptic feedback operation, ergonomics assessments are designed and experimentally implemented. Preliminary studies on the influencing factor including the guidance force, the reset force, the speed of the avatar and the arm the length have been conducted. The results of this paper are of great significance for the design of the haptic master and interactive system.

Coronavirus Disease-19 (COVID-19) is presently wreaking havoc on public health and socio-economic development. Besides the upper and lower respiratory tract involvement, gastrointestinal symptoms are also reported in COVID-19 patients through gut-lung axis. Finding its way through the feces of infected individuals and other sources, the genetic material of SARS-CoV-2 (ssRNA) is reported widely in wastewater and is being used as a fingerprint for its detection. With millions of cases arriving every day, there is a need to level up the testing speed efficiency. Due to the restricted sampling potential of testing laboratories, clinical testing is unable to track all the symptomatic and asymptomatic cases. Wastewater-based epidemiology (WBE) bestows an auxiliary monitoring tool that will contribute in community level screening. Sample collection, concentration, RNA extraction, quantification and data analysis are the main steps involved in implementation of WBE that can be relied upon as an alarm call for an upcoming wave, emergence of a new variant or any future pandemic. WBE can be a cheaper and more practical alternative to high end and sophisticated clinical testing for community transmission detection. Worldwide, there are more than 300 reports entailing the occurrence of SARS-CoV-2 in wastewater exhibiting unique temporal trends with five of them in India. This review aims to address the present knowledge on surveillance of SARS-CoV-2 in wastewater and its implications.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

Carbon supported nanoparticles of monometallic Ni catalyst and binary Ni-Transition Metal (Ni-TM/C) electrocatalytic composites were synthesized via chemical reduction method, where TM stands for the doping elements Fe, Co, and Cu. The chemical composition, structure and morphology of the Ni-TM/C materials were characterized by X-ray photoelectron spectroscopy (XPS), X-ray diffractometry (XRD), transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDS). The electrochemical properties towards hydrogen oxidation reaction in alkaline medium were studied using the rotating disc electrode and cycling voltammetry methods. A significant role of the TM dopant in the promotion of the hydrogen electrooxidation kinetics of the binary Ni-TM/C materials were revealed. A record-high in exchange current density value of 0.060 mA cm²_Ni was measured for Ni₃Fe₁/C, whereas the monometallic Ni/C counterpart has only shown 0.039 mA cm²_Ni. In order to predict the feasibility of the electrocatalysts for hydrogen chemisorption, density functional theory was applied to calculate the hydrogen binding energy and hydroxide binding energy values for bare Ni and Ni₃TM₁.

This article investigates the prospect of implementing multidisciplinary and multicultural student teamwork (MMT) involving Case-based Learning (CBL) and Problem-based Learning (PBL) as a sustainable teaching practice. Based on a mixed methods approach, which includes direct observation (both physical and virtual), questionnaire distribution and focus-group interviews the study reveals that MMT through CBL and PBL can both facilitate and hinder sustainable learning. Our findings show that while MMT enhances knowledge sharing, it also poses a wide range of challenges, raising questions about its social significance as a sustainable teaching practice. The study suggests the implementation of certain mechanisms, such as ‘Teamwork Training’ and ‘Pedagogical Mentors’, aiming to strengthen the sustainable orientation of MMT through CBL and PBL.

This study investigated the roles of low-molecular-weight fucoidan (LMWF) in enhancing the anti-cancer effects of fluoropyrimidine-based chemotherapy. HCT116 and Caco-2 cells were treated with LMWF and 5-FU. Cell viability, cell cycle, apoptosis, and migration were analyzed in both cell types. Potential mechanisms underlying how LMWF enhances the anti-cancer effects of fluoropyrimidine-based chemotherapy were also explored. The cell viability of HCT116 and Caco-2 cells was significantly reduced after treatment with a LMWF-5-FU combination. In HCT116 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through the 1) induction of cell cycle arrest in the S phase and 2) late apoptosis mediated by the Jun-N-terminal kinase (JNK) signaling pathway. In Caco-2 cells, LMWF enhanced the suppressive effects of 5-FU on cell viability through both c-mesenchymal–epithelial transition (MET)/ Kirsten Rat Sarcoma virus (KRAS)/ extracellular signal-regulated kinase (ERK) and c-MET/ phosphatidyl-inositol 3-kinases (PI3K)/ protein kinase B (AKT) signaling pathways. Moreover, LMWF enhanced the suppressive effects of 5-FU on tumor cell migration through the c-MET/ matrix metalloproteinase (MMP)-2 signaling pathway in both HCT116 and Caco-2 cells. Our results demonstrated that LMWF is a potential complementary therapy for enhancing the efficacies of fluoropyrimidine-based chemotherapy in colorectal cancers (CRCs) with the wild-type or mutated KRAS gene through different mechanisms. However, in vivo studies and in clinical trials are required to validate the results of the present study.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins and carbohydrates in their seeds, distinctly dominate modern agronomic practice. Indeed, these plants play an essential role in the food industry and fuel production. Therefore, the seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, not only seed development and germination, but also age- and stress-related alterations in seed vigor, longevity, nutritional value and safety can be addressed by a broad panel of analytical, biochemical and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes, accompanying plant development, senescence and response to biotic or environmental stress. Among individual methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. Here we discuss the main methodological approaches employed by seed proteomics in the context of physiological changes related to seed development, ageing and response to environmental stress.

The objective of the study was to investigate the gap between data and evidence-based decisions among healthcare professionals considering the enormous amount of individual and aggregate data collected. Our study assessed the capacity, skills, and knowledge of the Ministry of Health leadership staff to understand data management, analysis, utilization, and dissemination. Three key components were assessed: 1) Knowledge through true/false questions, 2) Level of Skill (and Competency) using a Likert scale, and 3) Understanding of Key Concepts and Tools based on a Likert scale. The 183 study respondents were diverse healthcare professionals from Kenya, Tanzania, and Rwanda. Majority of respondents had not received any training on data management, analysis, interpretation, and utilization techniques, further there was a significant difference between those who had received training versus those who had not(p=0.005). The respondents were competent in work-related experiences but lacked skills and knowledge on: data concepts and tools, study designs, and types of data analysis. These findings explain the gap between data management, analysis, utilization, and dissemination among health professional’s cadre. To enhance service delivery and optimal provision of health care, it is imperative to have all health care professionals receive a well-designed training on data management, analysis, interpretation, and utilization.

Ecologists use the net biotic interactions among plants to predict fundamental ecosystem features. Following this approach, ecologists have built a giant body of theory founded on observational evidence. However, due to the limitations that a phenomenological approach raises both in empirical and theoretical studies, an increasing number of scientists claim the need for a mechanistic understanding of plant interaction outcomes, and a few studies have taken such a mechanistic approach. In this synthesis, we propose a modeling framework to study the plant interaction mechanistically. We first establish a conceptual ground to frame plant-plant interactions, and then, we propose to formalize this research line theoretically developing a family of individual-based, spatially-explicit models in which biotic interactions are an emergent property mediated by the interaction between plants’ functional traits and the environment. These models allow researchers to evaluate the strength and sign of biotic interactions under different environmental scenarios and thus constitute a powerful tool to investigate the mechanisms underlying facilitation, species coexistence, or the formation of vegetation spatial patterns.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

The key issue in the post-genomic era is how to systematically describe the association between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens up the possibility of investigating the relationships between different molecules from a global perspective. In this article, a Molecular Associations Network(MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attributes of the node itself (e.g. sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the manner of the node in the complex network (associations with other nodes). Random Forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieves a superb performance with 0.9735 AUC in 5-fold cross-validation, which show that the proposed method can provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

The past decade has witnessed enormous progress, which has seen the noncoding RNAs (ncRNAs) turn from the so called dark matter RNA to critical functional molecules, influencing most physiological processes in development and disease contexts. Many ncRNAs interact with each other and are part of networks that influence the cell transcriptome and proteome and consequently the outcome of biological processes. The regulatory circuits controlled by ncRNAs have become increasingly more relevant in cancer. Further understanding of these complex network interactions and how ncRNAs are regulated, is paving the way for the identification of better therapeutic strategies in cancer.

Traditional refractory materials such as nickel-based superalloys have been gradually unable to meet the performance requirements of advanced materials. The Mo-Si-based alloy, as a new type of high temperature structural material, has entered the vision of researchers due to its charming high temperature performance characteristics. However, its easy oxidation and even "pesting oxidation" at medium temperatures limit its further applications. In order to solve this problem, researchers have conducted large numbers of experiments and made breakthrough achievements. Based on these research results, the effects of rare earth elements like La, Hf, Ce and Y on the microstructure and oxidation behavior of Mo-Si-based alloys were systematically reviewed in the current work. Meanwhile, this paper also provided an analysis about the strengthening mechanism of rare earth elements on the oxidation behavior for Mo-Si-based alloys after discussing the oxidation process. Furthermore, the research focus about the oxidation protection of Mo-Si-based alloys in the future was prospected to expand the application field.

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

Background: In Transcranial Direct Current Stimulation (tDCS) the injected current gets distributed across the brain areas. The motive is to stimulate the target region-of-interest (ROI), while minimizing the current in non-target ROIs. For this purpose, determining the appropriate current-dose for an individual is difficult. Aim: To introduce Dose-Target-Determination-Index (DTDI) to quantify the focality of tDCS and examine the dose-focality relationship in three different populations. Method: Here, we extended our previous toolbox i-SATA to the MNI reference space. After a tDCS montage is simulated for a current-dose, the i-SATA(MNI) computes the average (over voxels) current density for every region in the brain. DTDI is the ratio of average current density at target ROI to the ROI with maximum value (peak region). Ideally target ROI should be the peak region, so DTDI shall range from 0 to 1. Higher the value, the better the dose. We estimated the variation of DTDI within and across individuals using T1-weighted brain images of 45 males and females distributed equally across three age groups- (a) Young adults (20 ≥ x ˂ 40 years), (b) Mid adults (40 ≥ x ˂ 60 years), and (c) Older adults (60 ≥ x ˂ 80 years). DTDI’s were evaluated for the frontal montage with electrodes at F3 and right supra-orbital for three current doses 1mA, 2mA, and 3mA with the target ROI at left middle frontal gyrus. Result: As the dose is incremented, DTDI may show (a) increase, (b) decrease, and (c) no change across the individuals. The focality decreases with age and the decline is stronger in males. Higher current dose at older age can enhance the focality of stimulation. Conclusion: DTDI provides information on which tDCS current dose will optimize the focality of stimulation. DTDI recommended dose should be prioritised based on the age (> 40 years) and sex (especially males) of an individual. The toolbox i-SATA(MNI) is freely available.

A series of D–π–A diketopyrrolopyrrole (DPP)-based small molecules have been designed for organic light-emitting diodes (OLEDs) and organic solar cells (OSCs) applications. Appling the PBE0/6-31G(d,p) method, the ground state geometry and relevant electronic properties were investigated. The first excited singlet state geometry and the absorption and fluorescent spectra were simulated at the TD-PBE0/6-31G(d,p) level. The calculated results reveal that the photophysical properties are affected through the introduction of different end groups. Furthermore, the electronic transitions corresponding to absorption and emission exhibit intramolecular charge transfer feature. It was disclosed that the designed molecules act not only as luminescent for OLEDs, but also donor materials in OSCs. Moreover, they also can be used as potential electron transfer materials using for OLEDs and OSCs.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Although the regulatory function of miRNAs and their targets have been characterized in model plants, a possible underlying role in the cotton response to herbivore infestation has not been determined. To investigate this, we performed small RNA and degradome sequencing between resistant and susceptible cotton cultivar following infestation with the generalist herbivore whitefly. In total, 260 miRNA families and 241 targets were identified. Quantitative-PCR analysis revealed that several miRNAs and their corresponding targets exhibited dynamic spatio-temporal expression patterns. Moreover, 17 miRNA precursors were generated from 29 long intergenic non-coding RNA (lincRNA) transcripts. Genome-wide analysis also led to the identification of 85 phased small interfering RNA (phasiRNA) loci. Among these, nine PHAS genes were triggered by miR167, miR390, miR482a, and two novel miRNAs, including those encoding a leucine-rich repeat (LRR) disease resistance protein, an auxin response factor (ARF) and MYB transcription factors. Through combined modeling and experimental data, we explored and expanded the miR390-tasiARF cascade during the cotton response to whitefly. Virus-induced gene silencing (VIGS) of ARF8 in whitefly-resistant cotton plants increased auxin and jasmonic acid (JA) accumulation, resulting in an increased tolerance to whitefly infestation. These results highlight the provides a useful transcriptomic resource for plant-herbivore interaction.

The present work evaluates the food effect on the absorption of rivaroxaban (Riva), a BCS II drug, from the orally administered commercial immediate-release tablet (Xarelto IR) using physiologically based pharmacokinetic (PBPK) and conventional in vitro- in vivo correlation (IVIVC) models. The bioavailability of Riva upon oral administration of Xarelto IR tablet is reported to exhibit a positive food effect. The PBPK model for Riva was developed and verified using the previously reported in vivo data for oral solution (5 and 10 mg) and Xarelto IR tablet (5 and 10 mg dose strength). Once the PBPK model was established, the in vivo performance of the tablet formulation with the higher dose strength (Xarelto IR tablet 20 mg in fasted and fed state) was predicted using the experimentally obtained data of in vitro permeability, biorelevant solubility and in vitro dynamic dissolution data using United States Pharmacopeia (USP) IV flow-through cell apparatus. In addition, the mathematical IVIVC model was developed using the in vitro dissolution and in vivo profile of 20 mg strength Xarelto IR tablet in fasted condition. Using the developed IVIVC model, the pharmacokinetic (PK) profile of the Xarelto IR tablet in fed condition was predicted and compared with the PK parameters obtained via the PBPK model. A virtual in vivo PK study was designed using a single-dose, 3-treatment cross-over trial in 50 subjects to predict the PK profile of the Xarelto® IR tablet in the fed state. Overall, the results obtained from the IVIVC model were found to be comparable with that from the PBPK model. The outcome from both the model pointed to the positive food effect on the in vivo profile of the Riva. The developed models thus can be effectively extended to establish bioequivalence for the marketed and novel complex formulations of Riva such as amorphous solid dispersions.

Metal hydrides are one of the types of functional materials that allow safe and compact storage of a large amount of hydrogen, which is increasingly used today as an alternate fuel or energy source. The possibility of obtaining the initial energy necessary for the production of hydrogen by electrolysis process from renewable energy sources, such as solar panels and wind generators, makes hydrogen energetic quite attractive and rapidly developing industry sector. Solid form of hydrogen storage with the possibility of reversible sorption, gives opportunity for creation autonomous energy storage systems. La-Ni based alloys allow hydrogen storing at ambient temperatures and pressure not higher than 15 bar, which makes the application of these alloys quite practical, interesting and prospects for analysis and modifications on the ways of stored hydrogen capacity increasing, alloys price reducing and application for renewable energy storage.

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance (LME) exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data is an important technology to enable patients to perform exercises independently in remote settings, e.g. their own home. In this paper we first report on a comparison of traditional approaches to exercise recognition and repetition counting, corresponding to supervised machine learning and peak detection from inertial sensing signals respectively, with more recent machine learning approaches, specifically Convolutional Neural Networks (CNNs). We investigated two different types of CNN: one using the AlexNet architecture, the other using time-series array. We found that the performance of CNN based approaches were better than the traditional approaches. For exercise recognition task, we found that the AlexNet based single CNN model outperformed other methods with an overall 97.18% F1-score measure. For exercise repetition counting , again the AlexNet architecture based single CNN model outperformed other methods by correctly counting repetitions in 90% of the performed exercise sets within an error of ±1. To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. In addition to reporting our findings, we also make the dataset we created, the INSIGHT-LME dataset, publicly available to encourage further research.

TWIST1 is a transcription factor that affects cell behavior during development and cell differentiation. Yet, it is better known for its roles in neoplasia through regulation of cell plasticity. The pathological contributions of TWIST1 in tumor initiation, angiogenesis, invasion, metastasis, and chemo-resistance have been the focus of much research. To-date, the only way to quantitatively measure the abundance of TWIST is by immunoblots. Yet, no bioassay exists that can detect TWIST1 activity. Thus, we present here a TWIST1 cell-based assay that allows measuring the amount of active TWIST1 non-invasively in living cells. The bioassay was characterized against previously described TWIST1 “inhibitors”, as well as by epigenetic modulators of TWIST1 gene expression. Moreover, we tested multiple cell lines, showing that the level of TWIST1 mRNA resembles that of the bioassay. We show that prostate cancer cells (PC3) undergoing EMT, migrate out of 3D-spheroids and have increased TWIST1 activity. This fast and reliable system to detect active TWIST1 in different biological conditions allows a detailed analysis of this factor, as well as it can be used for drug discovery, since TWIST1 is a potential target for cancer chemotherapeutics.

COVID-19 is characterized by an unprecedented abrupt increase in the viral transmission rate (SARS-CoV-2) relative to its pandemic evolutionary ancestor, SARS-CoV (2003). The complex molecular cascade of events related to the viral pathogenicity is triggered by the Spike protein upon interacting with the ACE2 receptor on human lung cells through its receptor binding domain (RBD_Spike). One potential therapeutic strategy to combat COVID-19 could thus be limiting the infection by blocking this key interaction. In this current study, we adopt a protein design approach to predict and propose non-virulent structural mimics of the RBD_Spike which can potentially serve as its competitive inhibitors in binding to ACE2. The RBD_Spike is an independently foldable protein domain, resilient to conformational changes upon mutations and therefore an attractive target for strategic re-design. Interestingly, in spite of displaying an optimal shape fit between their interacting surfaces (attributed to a consequently high mutual affinity), the RBD_Spike–ACE2 interaction appears to have a quasi-stable character due to a poor electrostatic match at their interface. Structural analyses of homologous protein complexes reveal that the ACE2 binding site of RBD_Spike has an unusually high degree of solvent-exposed hydrophobic residues, attributed to key evolutionary changes, making it inherently ‘reaction-prone’. The designed mimics aimed to block the viral entry by occupying the available binding sites on ACE2, are tested to have signatures of stable high-affinity binding with ACE2 (cross-validated by appropriate free energy estimates), overriding the native quasi-stable feature. The results show the apt of directly adapting natural examples in rational protein design, wherein, homology-based threading coupled with strategic ‘hydrophobic ↔ polar’ mutations serve as a potential breakthrough.

Over the last decade, a significant literature has emerged that advocates the potential of different Additive manufacturing (AM) technologies and printable polymeric materials. Nevertheless, large scale printing and complex geometric shapes, with curvatures and non-planar layer deposition, are a challenging process for the traditional gantry-based machine. The 3 degrees of freedom cartesian configuration restricted their capability to planar layered printing and restricted part dimensions. To date, many researchers have used industrial robots to overcomes this limitation. This review gives the reader a good overview of the FDM technique due to its scalability, cost efficiency and a wide range of material printability. A strong emphasis is laid on the PLA and PLA-based composites as promising materials for the FDM process applications. The second part of this paper links the successful use of these materials in the traditional printing process to large scale printing using the robot-based FDM process. This survey presents representative setups for robot-based AM and works that have been used these setups for non-planar material deposition. Finally, we conclude this paper by identifying opportunities for realizing new functional capabilities by exploiting robot-based AM, and we also present the future trends in this area.

Purpose: Now traditional lecture-based teaching and learning have been affected by the COVID-19. The objectives of this article are to design the novel educational technique called ‘dynamic-based learning’ (DBL) that provides the combination of online teaching-learning methods and student’s creativity, to evaluate primary dynamic-based learning function, and to propose dynamic-based learning for higher education. Methods: DBL composes of four steps, including, preparation, homework, classroom, and evaluation, which was designed, and taught in medical and dental schools. Online support materials included mobile phone, email, Facebook Messenger, Line Messenger, Cisco Webex, and Zoom Meetings applications were recruited for this novel method. Results: A total of 32 third-year medical students and 26 sixth-year dental students was treated by DBL similarly. three subjects, including, Innovation in Dentistry, Basic Medical Research, and Principles of Pathology and Forensic Medicine were selected in this article. The results showed students could create their knowledge, ideas, and creativity during the online classes.Conclusion: DBL can be used as an alternative learning mode during the COVID-19 crisis. The benefits of DBL also include high flexibility, dynamic process, active learning, and high creativity. DBL should be tested with other disciplines such as engineering school, laws school, health sciences school, and should be compared with other traditional teaching and learning modes in the future. This method may support the global higher education systems to move forward the COVID-19 pandemic to set a novel standard of a future normal.

Universitas Sebelas Maret (UNS) through SMART UNS Company has conducted research and development of e-motorcycle conversion using Li-ion battery pack as a substitute for ICE energy source from the conventional motorcycle. Currently, the battery-pack that used for e-motorcycle conversion is in the development phase towards commercialization. The challenge of estimating production costs is the complicated production process and storing hidden expenses that can be a problem. This hidden cost is often a missing or varied factor that costs less or more expensive. This study presents an integrated parametric cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each activity. Activity-based costs break the production process into a specific cost element for each step. Each activity's cost is put into a parametric cost estimation model to calculate the cost of each activity into the total cost of production. Cost estimation results will be analyzed using a regression method to determine which variables most affect the production cost of Li-ion battery packs for the conversion of e-motorcycles in the SMART UNS company.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

The effect of microstructure and chemistry of passive films on the kinetics of passive layer growth and passivity breakdown of some Ti-based alloys, namely Ti-6Al-4V, Ti-6Al-7Nb and TC21 alloys was studied. The rate of pitting corrosion was evaluated using cyclic polarization measurements. Chronoamperometry was applied to assess the passive layer growth kinetics and breakdown. Microstructure influence on the uniform corrosion rate of these alloys was also investigated employing Tafel extrapolation and dynamic electrochemical impedance spectroscopy. Corrosion studies were performed in 0.9% NaCl solution at 37 oC, and the obtained results were compared with ultrapure Ti (99.99%). The different phases of the microstructure were characterized by X-ray diffraction and scanning electron microscopy. Chemical composition and chemistry of the corroded surfaces were studied using X-ray photoelectron analysis. For all studied alloys, the microstructure consisted of α matrix, which was strengthened by β phase. The highest and the lowest values of the β phase’s volume fraction were recorded for TC21 and Ti-Al-Nb alloys, respectively. The uniform corrosion rate and pitting corrosion resistance (R_pit) of the studied alloys were enhanced following the sequence: Ti-6Al-7Nb < Ti-6Al-4V << TC21. The corrosion resistance of Ti-Al-Nb alloy approached that of pure Ti. The obvious changes in the microstructure of these alloys, together with XPS findings, were adopted to interpret the pronounced variation in their corrosion rates.

The use of naturally available materials not conforming to traditional specifications or standards, in the base and sub-base layers of road pavement structures, stabilised with New-age (Nano) Modified Emulsions (NME), have been tested, implemented and successfully verified through Accelerated Pavement Testing (APT) in South Africa. This was made possible through the development and use of a design procedure addressing fundamental principles and based on scientific concepts, which are universally applicable. The understanding of and incorporation of the chemical interaction between the mineralogy of the materials and a NME stabilising agent (compatibility between the chemistry of the reactive agents and material mineralogy) into the design approach is key to achieving the required engineering properties. Stabilised materials evaluation is done using tests indicative of the basic engineering properties (physics) of compressive strengths, tensile strengths and durability. This article describes the basic materials design approach developed to ensure that organofunctional nano-silane modified emulsions can successfully be used for pavement layer construction utilising naturally available materials, at a low risk. The enablement of the use of naturally available materials in all pavement layers can have a considerable impact on the unit cost and life-cycle costs of road transportation infrastructure. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. Closed forms of multiplicative degree-based topological indices which are numerical parameters of the structure and determine physico-chemical properties of the concerned molecular compound. In this article, we compute and analyze many multiplicative degree-based topological indices of silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q].

The recovery of precious metals is a project with both economic and environmental significance. In this paper, it presents how to use bacterial mineralization to selectively recover gold from multi-ionic aqueous systems. The Bacillus licheniformis FZUL-63, separated from a landscape lake in FuZhou University, was shown to selectively mineralize and precipitate gold from coexisting ions in aqueous solution. The removal of Au(III) was almost happened in first hour, and FTIR data show that the amino, carboxyl and phosphate groups on the surface of the bacteria are related to the adsorption of gold ions. XPS results implied that Au(III) ions are reduced to monovalent, and then the Au(I) was adsorbed on the bacterial surface at the beginning stage(first hour). XRD results showed the gold biomineralization began about 10 hours after the interaction between Au(III) ions and bacteria. The Au(III) mineralization has been rarely influenced by other co-existing metal ions. TEM analysis shows the gold nanoparticles are polyhedral structure with a particle size of ~20 nm. The Bacillus licheniformis FZUL-63 could selectively mineralize and recover 478 mg/g(dry biomass) gold from aqua regia-based metal wastewater through four cycles. It could be of great potential in the practical application.

In the present work, the state of the art of the most common additive manufacturing (AM) technologies used for the manufacturing of complex shape structures of graphene-based ceramic nanocomposites, ceramic and graphene-based parts is explained. A brief overview of the AM processes for ceramic, which are grouped by the type of feedstock used in each technology, is presented. The main technical factors that affect the quality of the final product were reviewed. The AM processes used for 3D printing of graphene-based materials are described in more detail; moreover, some studies in a wide range of applications related to these AM techniques are cited. Furthermore, different feedstock formulations and their corresponding rheological behaviour were explained. Additionally, the most important works about the fabrication of composites using graphene-based ceramic pastes by Direct Ink Writing (DIW) are disclosed in detail and illustrated with representative examples. Various examples of the most relevant approaches for the manufacturing of graphene-based ceramic nanocomposites by DIW are provided.

Nature-based Solutions (NbS) have rapidly been gaining traction across the research, policy, and practice spheres, touted as transformative actions to jointly address biodiversity loss and climate change. However, there are multiple, alternative ways to imagine NbS in those three spheres. To inform the NbS discourses across these three spheres, we critically reflect on the prevailing framing of NbS and consider the potential of a different framing of NbS to support transformations towards regenerative relationships between humans and nature. Such reflection is urgently needed to ensure that research, policy, and practice delivers on the transformative ambitions of NbS. We propose a novel “core framing” of NbS, charting two pathways for how such a framing can support a human value-based transformation – first through influencing individual beliefs and values, and second through the communication and application of the NbS concept in research, policy, and practice. We argue that for NbS to support transformation, it must support a reframing of human-nature relationships, one where the interdependencies between people and nature are recognized as essential for social and environmental well-being. We elaborate on how such a framing is key to support inclusivity and collaboration between diverse research perspectives, policy objectives across scales, and implementation practices, to deliver successful NbS.

The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

We report on an immunofluorescent paper-based assay for the detection of severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) humanized antibody. The paper-based device was fabricated by using lamination technique for easy and optimized handling. Our approach utilises a two-step strategy that involves (i) initial coating of the paper-electrode with recombinant SARS-CoV-2 nucleocapsid antigen to capture the target SARS-CoV-2 specific antibodies, and (ii) subsequent detection of SARS-CoV-2 antibodies using fluorophore-conjugated IgG antibody. The fluorescence readout was observed with fluorescence microscopy. The images were processed and quantified using a MATLAB program. The assay can selectively detect SARS-CoV-2 humanized antibodies spiked in PBS and healthy human serum samples with the relative standard deviation of approximately 6.4% (for n = 3). It has broad dynamic ranges (1 ng to 50 ng/µL in PBS and 5 to 100 ng/µL in human serum samples) for SARS-CoV-2 humanized antibodies with the detection limits of 2 ng/µL (0.025 IU/mL) and 10 ng/µL (0.125 IU/mL) in PBS and human serum samples, respectively. We believe that our assay has the potential to be used as a simple, rapid, and inexpensive paper-based diagnostic device with a portable fluorescent reader to provide point-of-care diagnosis. This assay can be used for rapid examination of a large batch of samples toward clinical screening of SARS-CoV-2 specific antibodies as a confirmed infected active case or to evaluate the immune response to a SARS-CoV-2 vaccine.

Recent results have shown that the human malaria-resistant hemoglobin S mutation originates de novo more frequently in the gene and in the population where it is of adaptive significance, namely, in the hemoglobin subunit beta gene compared to the non-resistant but otherwise identical 20A>T mutation in the hemoglobin subunit delta gene, and in sub-Saharan Africans, who have been subject to intense malarial pressure for many generations, compared to Northern Europeans, who have not. This finding raises a fundamental challenge to the traditional notion of accidental mutation. Here we address this finding with the replacement hypothesis, according to which pre-existing genetic interactions can lead directly and mechanistically to mutations that simplify and replace them. Thus, a gradual evolutionary process under selection can hone in on interactions of importance for the currently evolving adaptations, from which large-effect mutations follow that are relevant to these adaptations. We exemplify this hypothesis using multiple types of mutation, including gene fusion mutations, gene duplication mutations, A>G mutations in RNA-edited sites and transcription-associated mutations, and place it in the broader context of a system-level view of mutation origination called Interaction-based Evolution. Potential consequences include that similarity of mutation pressures may contribute to parallel evolution in genetically related species, that the evolution of genome organization may be driven by mutational mechanisms, that transposable element movements may also be explained by replacement, and that long-term directional mutational responses to specific environmental pressures are possible. Such responses need to be further tested by future studies in natural and artificial settings.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

The Sustainable Development Goals, while complex at first sight, express a simple narrative about the relationships between people and nature. This paper illustrates this in the context of a coral reef land or seascape supporting coastal people. Coral reefs, their health described by measures of coral and fish diversity and abundance, provide key services and benefits to people. These services directly support 10s of millions of jobs in multiple economic sectors in coastal and distant states, protect and harbor communities and cities across tropical coastlines, sustain use of living and non-living resources, provide transport infrastructure and valuable natural products, and in future may provide energy solutions. Through these multiple benefits, coral reefs contribute to reducing hunger and poverty, thus improving health, and potentially strengthening gender and social equality. However, access and use result in pressures that may drive decline in coral reef health. Broader land and seascape factors also affect reef health and therefore delivery of benefits, including land-use change and altered freshwater flows, as well as climate change. Managing this complex system requires appropriate awareness and knowledge, governance mechanisms and investments by stakeholders. This ‘SDG narrative’ can be used from local to global levels, motivating actions and policy at and across these scales to sustain ecosystem function and use, for the oceans what is also increasingly called a blue economy.

This paper aims to propose a new approach of territorial competitiveness assessment revisited from the resource-based view, as the combination of location-specific resources and capabilities can improve the territorial socio-economic development. A territorial competitiveness index is calculated in order to assess the potential of renewable energy sources to improve the sustainable development in islands. Different sources of information and methodologies have been employed to measure the variables included in the model, thus ensuring a rigorous process in the index calculation. In order to quantify the basic resources, for example, a methodology based on a multicriteria analysis (MCA) with geographic information system (GIS) is suggested, with the objective of obtaining an indicator called index of available territorial resources. This index synthesizes the map information through a numerical value that allows integrating the territorial resource with other indicators of the model. The results of the study show that capability development is a key factor to better exploit the territorial resource endowment in order to achieve a competitive advantage.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with themselves, their environment, and hosts via metabolites and signaling molecules. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metagenomics methods, and modeling formalisms. Furthermore, the review explains model building, examples of microbiome model applications for prediction, optimization, and control. It concludes with guidelines, software, and repositories for modeling. Each section provides entry-level information, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

In South Korea, landslides are caused by localized heavy rainfall and typhoons, which often occur in the summer season at natural slopes in mountainous areas and artificial slopes in urban surroundings. Flow-type landslides frequently occur in mountainous areas. To evaluate flow-type landslides, it is essential to identify the physical characteristics of soil, giving focus to the soil on the top layers of various types of slope. This study conducts a survey and an analysis of the characteristics of landslides that occurred in the study area with different geological conditions of granite and gneiss. The characteristics of soil in the area and its surroundings that have or have not undergone landslides for every geological condition is also evaluated. Based on these characteristics and a statistics method, it extracts the triggering factors, permeability coefficients (k), and shearing strength with cohesion (c) and internal friction angel (φ) of soils that are highly related to landslides around weathered soil layers. As a result, the permeability coefficients show significant relevance with void ratio (e), the effective size of grains (D10), and uniformity coefficient (cu), while the shearing strength with the proportion of fine-grained soil (Fines), uniformity coefficient (cu), degree of saturation (S), dry weight density (rd), and void ratio (e). By obtaining this result, the study uses the regression analysis to suggest models to estimate the permeability coefficients and shearing strength. For the gneiss area, the statistics-based estimation model (SEM) is proposed as k_gn = (1.488 × 10^-02 × e) + (1.076 × D₁₀) + (-1.629 × 10^-04 × c_u) - (1.893 × 10^-02) for permeability coefficients; cgn = (-0.712 × Fines) + (-0.131 × c_u) + 15.335 for cohesion; and φgn = (27.01 × r_d) + (-12.594 × e) + 6.018 for internal frictional angle of soils. For the granite area, the statistics-based estimation model (SEM) is proposed as kgr = (8.281 × 10^-03 × e) + (0.639 × D₁₀) + (-2.766 × 10^-05 × c_u) - (9.907 × 10^-03) for permeability coefficients; c_gr = (-0.689 × Fines) + (-0.0744 × S) + 18.59 for cohesion; and φ_gr = (33.640 × r_d) + (-0.875 × e) - 9.685 for internal frictional angle of soils. The use of statistics-based estimation models (SEMs) for landslide-triggering factors that trigger landslides will support the simple calculation of permeability coefficient and shearing strength (cohesion and internal frictional angle), only requiring information about the physical properties of soil at the natural slopes that have different geological features such as gneiss and granite areas.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

The simplified, combined protocol admits children with a mid-upper-arm circumference (MUAC) of <125 mm or edema to malnutrition treatment with ready-to-use therapeutic food (RUTF) prescribing 2 daily RUTF sachets to children with MUAC <115 mm or edema and 1 daily sachet to those with MUAC ≥115 mm but <125 mm. This treatment has previously been shown to result in non-inferior programmatic outcomes compared to standard treatment. We aimed at observing its effectiveness in a routine setting at scale, including via delivery by community health workers (CHW). A total of 27 601 children were admitted to the simplified, combined treatment. Treatment resulted in 96% overall recovery with a mean LOS of 40 days and a mean RUTF consumption of 63 sachets per child treated. Among children admitted with MUAC <115 mm or edema 94% recovered with a mean LOS of 55 days and consuming an average of 97 RUTF sachets. Recovery in all sub-groups studied exceeded 90%.Treatment by CHWs resulted in similar (96%) recovery as treatment by formal health care workers (96%). The simplified, combined protocol results in high recovery and low RUTF consumption per child treated, and can safely be adopted by CHWs to provide treatment in the community-level.

Previous research has either neglected to consider misbehaviour or misconduct in the virtual workplace during the current COVID-19 pandemic or highlighted some challenges encountered with remote working or work from home but in isolation, e.g., misbehaviours or ethical concerns associated with improper use of technology when accessing clients’ confidential information, without understanding their aetiology and context in a holistic manner. This has impacted the ability of existing research in providing recommendations organisations can practically implement to improve the sustainability and productivity of the virtual workplace, especially as they seek to navigate the current pandemic and prepare for the global business environment in the next decade. Instead, this study, for the first time, proposes a novel holistic methodology to identify the aetiologies of ethical and performance challenges with focus to the Information Technology consulting sector, as well as developing a set of recommendations companies can adopt to manage such behavioural and productivity challenges. Thus, this study illustrates the key issues associated with remote working implemented to respond to the current COVID-19 pandemic, which mine not only the individual performance in an organisation but also its overall productivity, as well as suggests the main performance and reward management strategies that can be leveraged to mitigate them. Tailored strategies are described and discussed as referred to this industry to help in navigating a more unstable yet competitive market to manage the virtual employees’ misbehaviours in the workplace and drive growth for individuals and organisations alike.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

A project-based module on Sustainable Development Goal number 5, Gender Equality, was im-plemented on 5 different groups of Business English students consisting of a total number of 62 students in higher education. The main purpose of this project was to raise awareness of this goal by means of a flipped method in which students were required to carry out some research on specific areas of the aforementioned goal and work in teams to elaborate oral presentations. Once their findings were shared in class, students were expected to answer a written questionnaire of open-ended questions which were part of a qualitative analysis. Results of this survey showed that not only 90% of the students gained in depth knowledge of this goal, but also 85% had built a positive attitude to take initiative and 80% were optimistic about future gender equality. Finally, 70% of students suggested further social action to curb the problem of gender discrimination. On the whole, the flipped classroom method of learning combined with project-based group work have proven to be an effective way to raise awareness of this goal, create a more positive attitude, in-crease their willingness to take action as well as widening their English lexical resources.

Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of our world. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness characteristic, biometrics systems will become also vulnerable, privacy being one of the most important challenge. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The following work paper represents an effort to present the main cryptographic techniques and algorithms that can give us the possibility to raise a certain level of secureness for privacy. We will show their own challenges (strengths and weaknesses). We will demonstrate how we can use the most common and well-known techniques and algorithms in order to get a maximum efficiency and a high level in assuring the integrity of the biometrics data.