The coronavirus pandemic was declared by the World Health Organization (WHO) in 2020, and a global genetic vaccination program has been rapidly implemented as a fundamental solution. However, many countries around the world have reported that so-called genetic vaccines, such as those using modified mRNA encoding the spike protein and lipid nanoparticles as the drug delivery system, have resulted in post-vaccination thrombosis and subsequent cardiovascular damage, as well as a wide variety of diseases involving all organs and systems, including the nervous system. In this article, based on these circumstances and the volume of evidence that has recently come to light, we call the attention of medical professionals to the various risks associated with blood transfusions using blood products derived from people who have suffered from long COVID and from genetic vaccine recipients, including those who have received mRNA vaccines, and we make proposals regarding specific tests, testing methods, and regulations to deal with these risks. We expect that this proposal will serve as a basis for discussion on how to address post-vaccination syndrome and its consequences following these genetic vaccination programs.

The concept of a "circular economy" needs a more cogent theoretical anchor which will allow for transference of its goals and methods of attainment across cases. Tensions between ecological goals of this concept and the social implications of its implementation need to be addressed. This paper attempts to provide a theoretical framework for harnessing the strengths of a circular economy. Building on theories of social ecology which are predicated in Murray Bookchin's notion of "dialectical naturalism", the analysis presented here addresses some of the criticism of circular economic paradigms, such as their potential for stifling innovation or a neglect of human development challenges. A model for managing human "need" and "greed" within a circular economy framework is presented that also incorporates consumer choice and innovation. Planned obsolescence as a means of livelihood generation is also problematized with a view towards balancing durability of products on the one hand and ensuring throughput for manufacturing employment and innovation incentives on the other. Finally, the need for governance systems is considered to ensure that a planetary vision for a circular economy can be realized that efficiently harnesses local initiatives rather than an atomized and insular view of circularity.

The African society is one of the societies with rich culture and traditions. Apart from the indigenous religion of Africa, Christianity and Islam are worshiped as the major religions of the African society. Literature reflects a great amount of influence of religions on the existing societies, people and cultures. African literature often mirrors the clash of indigenous religion with Christianity. In the writings of African authors one can find the elements of Christian beliefs and practices. The present paper, however, is focused on the African woman novelist Buchi Emecheta’s selected four novels: Second-Class Citizen (1974), The Bride Price (1976), The Slave-Girl (1977) and The Joys of Motherhood (1979). The paper attempts to discuss the impact of Christianity on the social and cultural aspects of the African society with special focus on African women. The findings reveal the positive as well as negative impacts of the new religion on African people and on the position of African women through the characters present in the selected novels. With the medium of writing and through Christianity, Emecheta seek to educate her society and improve upon the position of the African women.

The article focuses on mining of non-energy mineral resources with minimum environmental impacts. It issues from research results of a project Competence Centre for Effective and Ecological Mining of Mineral Resources implemented at the Faculty of Mining and Geology at VŠB-Technical University of Ostrava, Czech Geological Survey, a company Watrad ltd., a state enterprise Diamo, a company RPS Ostrava plc and a company Sedlecký kaolin plc. The paper starts with a partial analysis of the existing legal norms related to mining and processing of mineral resources. Next, it analyses mineral resource mining options free of negative environmental impacts. The fundamental tool to assess potential environmental impacts of mining is the implementation of Environmental Impact Assessment (EIA) process for a given mineral resource. In the Czech Republic environmental impact assessment is carried out by course of Act 100/2001 Coll. Its important amendment is Act 39/2015 Coll. claiming, inter alia, that the environmental impact assessment is rigidly connected with other permits and procedures, such as the zoning process and building construction permits. The article describes the environmental impacts of mining of non-energy mineral resources, including the following factors: appropriation of land, impacts on surface water, ground water and soil, noise, influence on the landscape character, and air pollution. The paper also includes a case study summarizing information on the environmental factors that may play a role in potential underground mining of graphite in the deposit Český Krumlov - Městský Vrch and the deposit Lazec - Křenov.

Background & Objectives: The global pandemic caused by novel coronavirus SARS-CoV-2 has claimed several lives worldwide. With the virus gathering rapid spread, the world has witnessed increasing number of confirmed cases and mortality rate, India is not far behind with approximately 37,000 affected individuals as on May 2, 2020. The ongoing pandemic has raised several questions which need to be answered by analysis of transmission of the infection. The data has been collected on daily basis from WHO and other sites. We have represented the data collated graphically using statistical packages, R and other online softwares. The present study provides a holistic overview of the spread of COVID-19 infection in India. Methods: Real-time data query was done based on daily observations using publicly available data from reference websites for COVID-19 and other government official reports for the period (15^th February, 2020 to April 28^th, 2020). Statistical analysis was performed to draw important inferences regarding COVID-19 trend in India. Results: A decrease in growth rate of cases due to COVID-19 in India post lockdown and improvement in recovery rate during the month of April was identified. The case fatality rate was estimated to be 3.22% of the total reported cases. State-wise analysis revealed a deteriorating situation in states of Maharashtra and Gujarat among others as cases continued to increase rapidly there. A positive linear correlation between the number of deaths and total cases and exponential relation between population density and number of cases reported per square km was established. Interpretation & Conclusions: Despite early preventive measures taken up by the Government of India, the increasing number of cases in India is a concern. This study compiles state-wise and district-wise data to report the daily confirmed cases, case fatalities and strategies adopted in the form of case studies. Understanding the transmission spread of SARS-CoV-2 in a diverse and populated country like India will be crucial in assessing the effectiveness of control policies towards the spread of COVID-19 infection.

The coronavirus pandemic will deeply affect the tourism and travel sector. It is already clear now that its economic impact would be more severe that in the case of the Severe Acute Respiratory Syndrome (SARS) in 2002-2003. Although not as deadly as SARS, coronavirus infection has a longer incubation period and leaves about 85% of the infected without any (or with just mild) symptoms which makes it more difficult to track and to contain. Moreover, it appears to be much more contagious than its predecessor. The goods news is that most people recover from the disease and develop antibodies that can protect them from getting infected again (natural vaccination). Those cured might become the key element for the post-virus recovery strategies of tourism organisations. People with the acquired immunity to the virus would be capable of travelling freely without spreading the disease. Airlines, hotels and gastronomy should aim at this group offering them discounts and special offers. However, the problem is how to effectively ensure that everyone who claims to be cured from COVID-19 is telling the truth. Health tracking bracelets, apps, and other advanced technological solutions should be put in place. Recent best practices from Hong Kong, mainland China, or India might be applied.

Data on pathologic changes of the 2019 novel coronavirus disease (COVID-19) are scarce. To gain knowledge about the pathology that may contribute to disease progression and fatality, we performed post-mortem needle core biopsies of lung, liver, and heart in four patients who died of COVID-19 pneumonia. The patients’ ages ranged from 59 to 81, including 3 males and 1 female. Each patient had at least one underlying disease, including immunocompromised status (chronic lymphocytic leukemia and renal transplantation) or other conditions (cirrhosis, hypertension, and diabetes). Time from disease onset to death ranged from 15 to 52 days. All patients had elevated white blood cell counts, with significant rise toward the end, and all had lymphocytopenia except for the patient with leukemia. Histologically, the main findings are in the lungs, including injury to the alveolar epithelial cells, hyaline membrane formation, and hyperplasia of type II pneumocytes, all components of diffuse alveolar damage. Consolidation by fibroblastic proliferation with extracellular matrix and fibrin forming clusters in airspaces is evident. In one patient, the consolidation consists of abundant intra-alveolar neutrophilic infiltration, consistent with superimposed bacterial bronchopneumonia. The liver exhibits mild lobular infiltration by small lymphocytes, and centrilobular sinusoidal dilation. Patchy necrosis is also seen. The heart shows only focal mild fibrosis and mild myocardial hypertrophy, changes likely related to the underlying conditions. In conclusion, the post-mortem examinations show advanced diffuse alveolar damage, as well as superimposed bacterial pneumonia in some patients. Changes in the liver and heart are likely secondary or related to the underlying diseases.

Cyanobacteria are an ancient lineage of slow-growing photosynthetic bacteria and a prolific source of natural products with diverse chemical structures and potent biological activities and toxicities. The chemical identification of these compounds remains a major bottleneck. Strategies that can prioritize the most prolific strains and novel compounds are of great interest. Here, we combine chemical analysis and genomics to investigate the chemodiversity of secondary metabolites based on their pattern of distribution within some cyanobacteria. Planktothrix being a cyanobacterial genus known to form blooms worldwide and to produce a broad spectrum of toxins and other bioactive compounds, we applied this combined approach on four closely related strains of Planktothrix. The chemical diversity of the metabolites produced by the four strains was evaluated using an untargeted metabolomics strategy with high-resolution LC-MS. Metabolite profiles were correlated with the potential of metabolite production identified by genomics for the different strains. Although, the Planktothrix strains present a global similarity in term biosynthetic cluster gene for microcystin, aeruginosin and prenylagaramide for example, we found remarkable strain-specific chemo-diversity. Only few of the chemical features were common to the four studied strains. Additionally, the MS/MS data were analyzed using Global Natural Products Social Molecular Networking (GNPS) to identify molecular families of the same biosynthetic origin. In conclusion, we present an efficient integrative strategy for elucidating the chemical diversity of a given genus and link the data obtained from analytical chemistry to biosynthetic genes of cyanobacteria.

The importance of statistics in biological research is inevitable. The appliance of statistics is the most powerful tool to support the scientific hypothesis and to give credibility to biological research methodology, to interpret convoluted explanations and conclusions based on the research findings. The evidences from literatures point the persistent statistical errors, selection of non-proper statistical test with the consequence of misinterpretation of the scientific results which are published in international journals. One of the most extensively used statistical tests in the field of biology (preclinical and clinical) is analysis of variance (ANOVA). ANOVA test is in place of multiple T-tests to compare the means of more than two groups at a time; and there are some important points which biologists should be aware of them to avoid possible misinterpretation of results with the consequence of wrong conclusion. Accordingly, the aim of the current review is to help biologists to understand the basic concepts of ANOVA test and reach to a more valid interpretation of achieved results.

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) is an acquired disease with significant morbidity that affects both children and adults. Effective tools to assess functional capacity (FC) are severely lacking which has significant consequences for timely diagnosis, assessments for patient disability benefits and assessing the impact and effectiveness of interventions. In interventional research the inability to assess FC can result in an incomplete assessment of the potential effect of the intervention. Specifically of concern is that if an intervention is effective in reducing symptom load, patients may increase their activity level to reach a pre-intervention symptom load. Thus, if FC is not accurately assessed, beneficial treatment outcomes may be missed. To address this issue, using extensive, repeated patient feedback we have developed a new questionnaire, FUNCAP, to achieve optimal FC assessment in ME/CFS patients. The questionnaire covers eight domains and activity types: A. Personal hygiene / basic functions, B. Walking / movement, C. Being upright, D. Activities in the home, E. Communication, F. Activities outside the home, G. Reactions to light and sound, and H. Concentration. Through five rounds of anonymous web-based surveys and a further test - retest validation round, two versions of the questionnaire were developed; a longer version comprising 55 questions (FUNCAP55) to improve diagnostic and disability benefit/ insurance FC assessments and a shorter version (FUNCAP27) for interventional research and less extensive FC assessments. FUNCAP may also be useful in other conditions where fatigue and PEM is present, such as Long Covid.

The authors argue that despite significant numbers of foreign workers present in the Russian labor market Russia remains unattractive for highly skilled foreign workers. The economic crisis the country has faced since 2014 has resulted in a further outflow of foreign nationals from OECD countries. So Russia has to look for employees among newcomers from the former Soviet states, the majority of whom come from Central Asia. Russian politics with regard to immigration is characterized by the collision between “geopolitical” and domestic policy rationales. On one hand, seeking to maximize its influence in the post-Soviet space Russia provides preferential conditions of employment for citizens of partner countries in the framework of the Eurasian Economic Union. On the other hand, the Kremlin is afraid to liberalize its approach to immigration and the integration of immigrants through fear of losing popularity among the electorate. The second substantial feature of Russian immigration politics is an extreme level of securitization of migration issues. The government considers these issues primarily through a policing lens. One cannot claim that the Russian state does not take any steps to liberalize “migratory regulation”; however, the effect of these measures is being vastly reduced by new restrictions. In addition, liberally designed laws do not bring the expected results due to the corrupt practices that pervade the whole of migration regulation.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

In this data deposit, I describe a dataset that is the result of content mining 167,318 published articles for statistical test results. As a result of this content mining, 688,112 results from 50,845 articles were extracted. In order to provide a comprehensive set of data, the statistical results are supplemented with metadata from the article they originate from. The dataset is provided in a comma separated file (CSV) in long-format. For each of the 688,112 results, 20 variables are included, of which seven are article metadata and 13 pertain to the individual statistical results (e.g., reported and recalculated p-value). A five-pronged approach was taken to generate the dataset: (i) collect journal lists, (ii) spider journal pages for articles, (iii) download articles, (iv) add article metadata, and (v) mine articles for statistical results.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology and the role of interventions to upregulate autophagy, including compounds like spermidine, resveratrol, rapamycin, and metformin. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention for spike protein related diseases; with broad implications for human health. This review presents the therapeutic possibility of using autophagy to treat spike protein related diseases, and details the interventions to deploy this therapeutic modality.

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

Lyme disease and associated co-infections are increasing worldwide and approximately 20% of individuals develop chronic Lyme disease (CLD)/Post-Treatment Lyme Disease Syndrome (PTLDS) despite early antibiotics. A 7–8-week protocol of double dose dapsone combination therapy (DDDCT) for CLD/PTLDS results in symptom remission in approximately 50% of patients for one year or longer, with published culture studies indicating higher doses of dapsone demonstrate efficacy against resistant biofilm forms of Borrelia burgdorferi. The purpose of this study was therefore to evaluate higher doses of dapsone in the treatment of resistant CLD/PTLDS and associated co-infections. Twenty-five patients with a history of Lyme and associated co-infections, most of whom had ongoing symptoms despite several courses of DDDCT, took one or more courses of high dose pulsed dapsone combination therapy (200 mg dapsone X 3-4 days and/or 200 mg BID x 4 days), depending on persistent symptoms. The majority of patients noticed sustained improvement in 8 major Lyme symptoms, including fatigue, pain, headaches, neuropathy, insomnia, cognition, and sweating, where dapsone dosage, not just treatment length, positively affected outcomes. High dose pulsed dapsone combination therapy may represent a novel therapeutic approach for the treatment of resistant CLD/PTLDS, and should be confirmed in randomized, controlled clinical trials.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

Synergistic effects between reactions, reagents and catalysts can lead to minor heats of reaction and occur as an inherent result of multi-component reactions (MCRs) and their extensions. They enable syntheses to be performed at a low energy level and the number of synthesis steps to be drastically reduced in comparison with ‘classical’ two-component reactions. The very high potential for variability, diversity and complexity of MCRs additionally generates an extremely diverse range of products, thus bringing us closer to the aim of being able to produce tailor-made and extremely low-priced materials, drugs and libraries.

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Three patients with multi-year histories of relapsing and remitting Lyme disease and associated co-infections despite extended antibiotic therapy were each given double dose dapsone combination therapy (DDD CT) for a total of 7-8 weeks. At the completion of therapy, all three patients major Lyme symptoms remained in remission for a period of 25-30 months. In conclusion, Double dose dapsone therapy could represent a novel and effective anti-infective strategy in chronic Lyme disease/PTLDS, especially in those individuals who have failed regular dose dapsone combination therapy (DDS CT) or standard antibiotic protocols. A randomized, blinded, placebo-controlled trial is warranted to evaluate the efficacy of DDD CT in those individuals with chronic Lyme disease/PTLDS.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology. Additionally, we discuss factors that affect fasting, such as duration, type (dry vs. water), and the role of specific compounds like spermidine, resveratrol, rapamycin, and metformin. Furthermore, we analyse the interactions between fasting and other practices such as exercise, and highlight important considerations regarding participant characteristics, including pregnancy, breastfeeding, medication interactions, and metabolic disorders. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention with broad implications for human health.

Total 219 countries and territories globally suffering from the recent pandemic COVID-19 is now in its second wave with more brutality, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . It has several symptoms like as persistent fever; respiratory illnesses; cough; fatigue; shortness of breath; loss of appetite; persistent pain or pressure in the chest; dysgeusia; acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19 in both peoples who had died of due to acute respiratory distress syndrome (ARDS) or those who survived. Due to COVID-19, dysregulated immune response and wound repair mainly in elderly patients causes this secondary pulmonary fibrosis. Thus using anti-fibrotic agents could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article you will get to know about the lung fibrosis generation due to COVID-19 infection, about anti-fibrotic agents and the currents challenges of this field.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

As the increase threat of flood risk and environmental safety due to the urbanization, Sponge city research has been attracting extensive attention both in practical and theoretical research field. To date, there are only scattered studies about Sponge city. Moreover, vary names of Sponge city prevalent in different countries, which leads to disconnection of literature in the same field of Sponge city. In this paper, a thorough systematic literature mining of Sponge city is presented. A literature analysis system is created, which includes literature export from Web of Sciences and systematic analysis via NoteExpress and CiteSpace. Some literature statistical results are derived. Challenges and opportunities for future research are anticipated. Our goals are to promote this promising thought, summarize past research, and identify issues for future research to create impacts on the practice of Sponge city.

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

Sulfate, the main dissolved contaminant in acid mine drainage (AMD), is ubiquitous in watersheds affected by coal and metal mining operations worldwide. Engineered passive bioremediation systems (PBS) are low-cost technologies that remediate sulfate contamination by promoting (1) precipitation of sulfate-bearing compounds, such as schwertmannite and gypsum; and (2) microbially-mediated sulfate reduction (BSR) to sulfide with subsequent precipitation of sulfide minerals. In this study, chemical and sulfur isotopic data are used to infer multiple pathways for sulfate sequestration in the Tab-Simco PBS. By simultaneously monitoring sulfate concentrations and δ34SSO4 values at four sampling points across the PBS, we (1) identified that the organic layer within the bioreactor was the primary site of BSR processes contributing to sulfate sequestration; (2) observed seasonal variations of BSR processes; (3) estimated that initially the BSR processes contributed up to 30% to sulfate sequestration in the Tab-Simco bioreactor; and (4) determined that BSR contribution to sulfate sequestration continuously declined over the PBS operational lifetime. Together, our results highlight the utility of combining geochemical and microbial fingerprinting techniques to decipher complementary processes involved in sulfur cycling in a PBS as well as the value of adding the sulfur isotope approach as an essential tool to help understand, predict, prevent and mitigate sulfate contamination in AMD-impacted systems.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: madhavi@pitt.edu, madhavi@cs.cmu.edu Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

Work has begun in earnest to formulate a post-2020 Global Biodiversity Framework which will outline the vision and targets for the next decade of biodiversity conservation and beyond. However, the performance of the 2011-2020 Strategic Plan for Biodiversity suggests that even a meaningful target can fail to deliver if not accompanied by fit-for-purpose indicators. Here we provide a review of how ‘protected area’ effectiveness was addressed in the 2011-2020 plan and based on this, provide recommendations for fit-for-purpose indicators that will measure how such efforts contribute to the conservation of biodiversity. Indicators need to be built on quantitative data from site-level biodiversity monitoring of species and ecosystems combined with measurements of the state of nature in near-time, informed by remote-sensed products and other technologies. Additionally, indicators need to capture whether the essential elements of good management are in place including the identification of ecological values, threats, and objectives, equitable governance, and sufficient management resources and capacity. These fit-for-purpose indicators will require multilateral collaboration to galvanize support for, and resources to develop, the necessary infrastructure to collate and store information from countries.

Proteins are the primary effectors of function in biology, and thus complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants and post-translational modifications. This knowledge is foundational for the biological complexes and networks that control biology, yet remains largely unknown. We propose here an ambitious initiative to define the human proteome; that is to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented, along with a call for improved technologies in a two-pronged strategy to accomplish the Human Proteoform Project.

In order to study the petrogenesis and tectonic setting of Permian A-type granites and their relationships with hydrothermal mineralization along the Hegenshan-Heihe suture zone (HHSZ) in northeastern China, we select the newly discovered Hongyan Cu-polymetallic deposit in the northeastern part of the HHSZ that develops three stages of mineralization associated with the Shanshenfu alkali-feldspar granite (SAFG). The zircon U-Pb dating and whole rock geochemistry suggest that the SAFG is a typical A-type granite formed in the Early Permian. The zircon Hf isotopes and trace elements suggest that the SAFG has high Ti-in-zircon temperature (721–990℃), high magmatic oxygen fugacity and largely positive εHf(t) (+6.0 to +9.9). Therefore, we propose that the SAFG was derived from the crustal assimilation and fractional crystallization of the charnockitized juvenile crust. The high oxygen fugacity favors the chalcophile elements (e.g., Cu, Au, Ag) of the source region enriched in the fluid phases after magmatic fractional crystallization, consequently facilitating subsequent hydrothermal mineralization, which is also consistent with the characteristics of ore-forming fluids that changed from the initial high temperature, high salinity, high fO2 and CO2-rich magmatic-hydrothermal fluids of stage I to CO2-poor, dilute, and cooling meteoric fluids of stage III. Combined with regional geological background, the Permian A2-type granites along the HHSZ can be formed in post-collisional slab break-off process. In subsequent exploration for hydrothermal deposits along the HHSZ, the Permian A-type granites with arc-related juvenile crustal source and high fO2 have great potential and need more attention.

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

Wikipedia is the most popular and the largest user-generated source of knowledge on the Web. Quality of the information in this encyclopedia is often questioned. Therefore, Wikipedians have developed an award system for high quality articles, which follows the specific style guidelines. Nevertheless, more than 1.2 million articles in Polish Wikipedia are unassessed. This paper considers over 100 linguistic features to determine the quality of Wikipedia articles in Polish language. We evaluate our models on 500,000 articles of Polish Wikipedia. Additionally, we discuss the importance of linguistic features for quality prediction.

The prerogative to narrate the experience of marginal identities, particularly ethno-religious ones, appeared only in the post-occupation era in Iraqi fiction. Traditionally, secular Iraqi discourse struggled to openly address “sectarianism” due to the prevalent notion that sectarian identities are mutually exclusive and oppositional to national identity. It is distinctly in post-2003 Iraq—more precisely, since the sectarian violence of 2006–2007 began to cut across class, civil society, and urban identities—that works which consciously refuse to depict normative Iraqi identities with their mainstream formulations become noticeable. We witness this development first in the Western diaspora, where Iraqi novels exhibit a fascination with the ethno-religious culture of the Iraqi margins or subalterns and impart a message of pluralistic secularism. This paper investigates the origins of the taboo that proscribed articulations of ethno-religious subjectivities in 20^th-century Iraqi fiction, and then culls examples of recent diasporic Iraqi novels in which these subjectivities are encoded and amplified in distinct ways. In the diasporic novel, I argue, modern Iraqi intellectuals attain the conceptual and political distance necessary for contending retrospectively with their formative socialization experiences in Iraq. Through a new medium of marginalization—the diasporic experience of the authors themselves—they are equipped with a newfound desire to unmask subcultures in Iraq and to write more effectively about marginal aspects of Iraqi identity inside and outside the country. These new diasporic writings showcase processes of ethnic and religious socialization in the Iraqi public sphere. The result is deconstruction of mainstream Iraqi identity narratives and instrumentalization of marginal identities in a nonviolent struggle against sectarian violence.

Artisanal and small-scale gold mining (ASGM) has been an important source of income for communities in the Madre de Dios River Basin in Peru for hundreds of years. However, in recent decades, the scale of ASGM activities in the region has increased dramatically, and exposures to a variety of occupational and environmental hazards related to ASGM, including mercury, are becoming more widespread. The aims of our study were to: (1) examine patterns in the total hair mercury level of human participants in several communities in the region and compare these results to the 2.2 µg/g total hair mercury level equivalent to the World Health Organization (WHO) Expert Committee of Food Additives (JECFA)’s Provisional Tolerable Weekly Intake (PTWI); and (2), to measure the mercury levels of paco (Piaractus brachypomus) fish raised in local aquaculture ponds, in order to compare these levels to the EPA Fish Tissue Residue Criterion of 0.3 µg Hg/g fish (wet weight). We collected hair samples from 80 participants in four communities (one control and three where ASGM activities occurred) in the region, and collected 111 samples from fish raised in 24 local aquaculture farms. We then analyzed the samples for total mercury. Total mercury levels in hair were statistically significantly higher in the mining communities than in the control community, and increased with increasing distance away from the Madre de Dios headwaters (as the crow flies), did not differ by sex, and frequently exceeded the reference level. Regression analyses indicated that higher hair mercury levels were associated with residence in ASGM communities. The analysis of paco fish samples found no samples that exceeded the EPA tissue residue criterion. Collectively, these results align with other recent studies showing that ASGM activities are associated with elevated human mercury exposure. The fish farmed through the relatively new process of aquaculture in ASGM areas appeared to have little potential to contribute to human mercury exposure. More research is needed on human health risks associated with ASGM to discern occupational, residential, and nutritional exposure, especially through tracking temporal changes in mercury levels as fish ponds age, and assessing levels in different farmed fish species. Additionally, research is needed to definitively determine that elevated mercury levels in humans and fish result from the elemental mercury from mining, rather than from a different source, such as the mercury released from soil erosion during deforestation events from mining or other activities.

Syanyonja village in the gold district of Busia, South East of the Republic of Uganda contain geologically epigenetic gold quartz vein deposits in carbonate-altered mafic metavolcanic rocks, deposited as quartz reefs in mineralized shear zones. In supracrustal rocks, alluvial gold is obtained from weathered auriferous quartz veins, which are of late orogenic granitic activity. The Syanyonja gold deposits have long been subjected to artisanal and small-scale gold mining (ASGM) by the locals for livelihood. This study determined the amount of mercury discharged with tailings into Namukombe stream, a major water body in Syanyonja village and investigated the impacts of ASGM on the mining population and the environment. Quantitation of mercury discharged with tailings was done by mass balance method. Field survey at the mining sites was done followed by administration of questionnaires to 50 stampeders in the village. The study indicates that about 8% of mercury mixed with auriferous materials are lost in tailings, accounting for an annual mercury release of about 1.757 kg into the environment. Socio-demographic results indicate that the majority of the mining population (64%) are male and ASGM have left health and environmental footprints, which directly or indirectly affects the population. The most common health problems among miners are malaria (36%) and abdominal pain (20%). The standard of living of the miners are evidently low, and most mines are characterized by school dropouts, prostitutes and thieves. Mining sites have deplorably poor hygiene, with evident burning of amalgams to recover gold. ASGM have been accompanied by wanton mowing down of vegetation, land degradation as well as mercuric pollution of water, air, land and aquatic ecosystems. It is suggested that the Ugandan government should re-enforce committees to follow up on ASGM activities, train artisans on sustainable gold mining using borax, magnets, sluice boxes as well as take up farming actively as an alternative.

Background: Paracetamol may be use as antipyretic agent for the treatment of fever, as well as an analgesic in the treatment of mild to moderate pain on post vaccination in infants. The use of Paracetamol during fever may be or may not be recommended since it may alter natural human body immune response although it may reduce pain. Objectives: This study described the relevancy of Paracetamol use post infants vaccination based on data collection systematic review analyses. This study aims to describe the effectiveness of breastfeeding in reducing pain and Paracetamol in reducing fever and pain post infants vaccination. Data Sources and Study Selection: Electronic literature search by hand searching six (6) databases which include Ovid LWW Total Access Collection and Medline, CINAHL (Cumulative Index to Nursing and Alled Health Literature) Plus with Fulltext, Science Direct, Proquest Dissertations and Theses, Proquest Education Journal and Proquest Health and Medical Complete. Additionally, manual reference checks of all articles on Paracetamol and breastfeeding post infants vaccination published in English Language between 1978 and 2017. Two level of screening were used on 9614 citations which include screening of abstracts and titles followed by full text screening. Data Synthesis: Data synthesis were tabulated into study characteristics, quality and effects. Authors of trials were not contacted for further details or provision of original data if the published report contained insufficient information. The study findings, as reported by the authors, were included in this review. The data in this research cannot be pool due to not enough data regarding odd ratio or relative risk as well as confidence interval in each study. Results: Systematic review of breastfeeding included three (3) studies from 9614 of database searching. The reviews of all these three (3) studies found significant benefit from breastfed in pain score and duration of crying as well as behavioral changes. None study stated the unbeneficial of breastfeeding before, during and after immunization. Meanwhile, systematic review of Paracetamol effectiveness included four (4) studies from 1177 of database searching. The reviews of two (2) studies found significant benefit from prophylaxis Paracetamol in fever and only one (1) study found significant benefit from prophylaxis Paracetamol in fussiness. On the other hand, there was one (1) study found not signifiant benefit from prophylaxis Paracetamol in fever. Other than that, there were two (2) studies evaluate the safety of prophylactic Paracetamol which revealed different outcomes, in which study by Prymula et. al. in 2009 found that antibody responses to several antigens were reduced significantly, and the other study by Uhari et. al. in 1988 found that antibody titres to DTP bacteria of placebo and PCM not differ significantly. Thus, Paracetamol seems to be not relevant post infants vaccination and breastfeeding was found to be beneficial post infants vaccination. Conclusions: The relevancy of giving Paracetamol post all types of vaccination may be questionable since the safety issue of this intervention may be arised. Breastfeeding before, during and after immunization are recommended for pain reduction as it was proved effectively. Finally, in deciding Paracetamol to be of rational use following infants immunization, it may need for further research which include in depth quantitative and qualitative studies to identify specific problem and causes regarding this issue.

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things computing. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSN. Then, using the CUDA Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Although Cucurbita pepo is one of the most variable species of the plant kingdom, Zucchini morphotype has undergone intensive breeding that has led to a narrow genetic base making the crop vulnerable to pest and diseases. This vulnerability makes the knowledge of resistance genes of utmost importance. In this study, a data mining search of Zucchini summer squash genome database was conducted to identify and annotate members of the NBS-encoding gene family. In order to characterize the retrieved genes in detail, they have been studied in the bases of phylogenetic relationships, structural diversity, conserved protein motifs, gene duplications and promoter region analysis. Our study shows that the NBS-encoding gene family is relatively small in Zucchini (34 members, which are separated into non-TIR- and TIR-NBS-LRR subfamilies) with a significantly lower number of R-genes than in other species. Duplications have not played a major role in the expansion of this type of genes in C. pepo. Among the cis-regulatory elements presented in these sequences, six motifs are over-represented. These elements were reported to be involved in pathogens or plant stress induced responses. These results will contribute to the identification, isolation and characterization of candidate R-genes, thereby providing insight into NBS gene family evolution in the species.

We employ the solution concept of ex post Nash equilibrium to predict the interaction of a finite number of agents competing in a finite number of basic games simultaneously. The competition is called a multi-game. For each agent, a specific weight, considered as private information, is allocated to each basic game representing its investment in that game and the utility of each agent for any strategy profile is the weighted sum, i.e., convex combination, of its utilities in the basic games. Multi-games can model decision making in multi-environments in a variety of circumstances, including decision making in multi-markets and decision making when there are both material and social utilities for agents as, we propose, in the Prisoner's Dilemma and the Trust Game. Given a set of pure Nash equilibria, one for each basic game in a multi-game, we construct a pure Bayesian Nash equilibrium for the multi-game. We then focus on the class of so-called uniform multi-games in which each agent is constrained to play in all games the same strategy from an action set consisting of a best response per game. Uniform multi-games are equivalent to multi-dimensional Bayesian games where the type of each agent is a finite dimensional vector with non-negative components. A notion of pure type-regularity for uniform multi-games is developed and it is shown that a multi-game that is pure type-regular on the boundary of its type space has a pure ex post Nash equilibrium which is computed in constant time with respect to the number of the types and is independent of prior probability distributions. We then develop an algorithm, linear in the number of types of the agents, which tests if a multi-game is pure type-regular on the boundary of its type space in which case it returns a pure ex post Nash equilibrium for the multi-game.

Introduction: Despite more than one year passed since the first cases of SARS-CoV-2 were reported, there is still no consensus on the definition and clinical management of post-acute-COVID-19. The condition has heterogeneously been named as Chronic COVID syndrome, Post COVID-19 Syndrome, post-acute sequela of SARS-CoV-2 (PASC), and the more familiar long COVID. Method: In order to capture all relevant published studies, we undertook a multi-step search with no language restriction. The following four-step search strategy was utilized: First, a preliminary (limited) search was conducted on January 20, 2021, in Google Scholar and PubMed to identify the appropriate keywords. Then, on January 30, 2021, we adopted a search strategy of electronic databases from Cochrane Library, PsycINFO, PubMed, Embase, Scopus, and Web of sciences, using those keywords. Then, after duplicate removal, we screened all titles, abstracts, and full texts. This resulted in 66 eligible studies. Subsequently, after a forward and backward search of their references and citations an additional 54 publications were found, resulting in a total of 120 publications that formed the basis of the present analysis. The titles, abstracts, and full-texts of non-English articles were translated using Google Translate for further evaluation. We conducted our scoping review based on the PRISMA-ScR Checklist.Results: We found only one randomized clinical trial in our search. Of the 67 original studies, 22 were cohort and 28 were cross-sectional studies totaling 74.6% of the original studies. Of the total of 120 publications, 59 (49.1%) focused on signs and symptoms, 28 (23.3%) were focused on management, and 13 (10.8%) focused on pathophysiology. Ten (9%) publications focused on imaging studies. Ninety-one percent of the original investigations came from high and upper-middle-income countries, highlighting the scarcity of reports originating from low-income and lower-middle-income countries.Conclusion: The predominant symptoms among those with the so-called “Long COVID” were: fatigue, breathlessness, arthralgia, sleep difficulties, and chest pain. Recent reports also point to the risk of long-term sequela with cutaneous, respiratory, cardiovascular, musculoskeletal, mental health, neurologic, and renal involvement in those who survive the acute phase of the illness. The ambiguity and controversies in its definition have impaired proper recognition and management of those requiring additional support following the resolution of the acute phase of this infection. This has resulted in long-standing distress for the patients and their families. Our findings highlight the need for a multidisciplinary approach, support, and rehabilitation for these patients in terms of long-term mental and physical health.

The emergence of the COVID-19 pandemic has propelled the use of alcohol-based hand sanitizers to the fore as a SARS-CoV-2 control measure. To be effective these products must comply with relevant quality parameters such as alcohol concentration, methanol limits and purity. The current study was designed to determine the quality of alcohol-based hand sanitizer products in the Nairobi metropolitan area. For this purpose, 74 commercially marketed samples were collected and subjected to analysis by gas chromatography. Only three samples (4.1%) complied with the regulatory specifications for alcohol content, methanol limits and pH. Five samples (6.8%) complied with the specification for alcohol content but did not meet methanol or pH limits. A total of 44 (59.5%) samples had methanol levels that exceeded threshold limits. Eleven samples (14.9%) were found with methanol substitution (i.e., methanol, instead of ethanol or isopropanol, was the main alcohol component). The results show that users of alcohol-based hand sanitizers are being exposed to substandard and falsified products which in addition to being non-efficacious pose harm due to unacceptable levels of toxic impurities. Regular, routine post-market surveillance is needed to prevent such products from reaching the market.

Mercury has a strong environmental impact since both its organic and inorganic forms are toxic and it represents a pollutant of global concern. Liquid Hg is highly volatile and it can be released during natural and anthropogenic processes in the hydrosphere, biosphere and atmosphere. In this study the distribution of Gaseous Elemental Mercury (GEM) and the total and leached mercury concentrations on paints, plasters, roof tiles, concretes, metals, dust and wood structures were determined in the main buildings and structures of the former Hg-mining area of Abbadia San Salvatore (Siena, Central Italy). The mining complex (divided into 7 units) covers a surface of about 65 ha and contains mining structures and managers and workers buildings. In this work, nine surveys of GEM measurements were carried out from July 2011 to August 2015 for the buildings and structures located in the units 2, 3 and 6. Moreover, detailed measurements were performed in February, April, July, September and December 2016 in the edifices and mining structures of Unit 6. GEM concentrations showed a strong variability in terms of space and time mostly depending on the distance from the building hosting driers, furnaces and condensers and ambient temperature, respectively. In the Unit 2 surveys carried out in the hotter period (from June to September) showed GEM concentrations up to 27,500 ng m⁻³, while in the Unit 6 they were on average much higher and occasionally they saturated the GEM measurement device (>50,000 ng m⁻³). Concentrations of total (in mg kg⁻¹) and leached (in μg L⁻¹) mercury measured in different building materials (up to 46,580 mg kg⁻¹ and 4,470 mg L⁻¹ for total and leached mercury, respectively) showed for the same type of material highly variable values in dependence on the edifice or mining structure from which they were collected. The results obtained in this study are of relevant interest for the operational cleanings to be carried out during the reclamation activities.

There is growing recognition in research and policy of a mental health crisis among Canada’s paramedics but despite this, epidemiological surveillance of the problem is in its infancy. Just weeks before the emergence of the COVID-19 pandemic, we surveyed paramedics from a single, large, urban paramedic service in Ontario, Canada to assess for symptom clusters consistent with Post-Traumatic Stress Disorder (PTSD), major depressive disorder, and generalized anxiety disorder and to identify potential risk factors for each. In total, we received 589 completed surveys (a 97% completion rate) and found that 11% screened positive for PTSD, 15% for depression, and 15% for anxiety, with 1 in 4 active-duty paramedics screening positive for any of the three as recently as February 2020. In adjusted analyses, the risk of a positive screen varied as a function of employment classification, gender, self-reported resilience, and previous experience as a member of the service’s peer support team. Our findings support the position that paramedics screen positive for mental disorders at high rates – a problem likely to have worsened since the onset of the COVID-19 pandemic. We echo the calls of researchers and policymakers for urgent action to support paramedic mental health in Canada.

Building a social security system to ensure Singapore residents have peace of mind in funding for retirement has been at the top of Singapore Government’s policy agenda over the last decade. Implementation of the Lifelong Income For the Elderly (LIFE) scheme in 2009 clearly shows that the government spares no effort in improving its pension scheme to boost its residents’ income after retirement. Despite the recent modifications to the LIFE scheme, Singapore residents must still choose between two plans: the Standard and Basic plans. To enhance the flexibility of the LIFE scheme while maintaining its simplicity, we propose some plan modifications such that scheme members do not face a dichotomy of plan choices. Instead, they select two age parameters: the Payout Age and the Life-annuity Age. This paper provides an actuarial framework for determining members’ payouts and bequests based on the proposed age parameters. We analyze the net cash receipts and internal rate of return (IRR) for various plan-parameter configurations. This information helps members make their plan choices. To address cost-of-living increases we propose to extend the plan to accommodate an annual step-up of monthly payouts. By deferring the Payout Age from 65 to 68, members can enjoy an annual increase of about 2% of the payouts for the same first-year monthly benefits.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

In the current business environment, firms are eager to adopt new technology as they witness and perceive more and more successful business applications of new technologies, e.g., Big Data, Artificial Intelligence (A.I.), Cloud Computing, etc. As one of the disruptive technologies, Blockchain technology (BCT) is now drawing public attention owing to the cryptocurrency phenomenon (e.g., Bitcoin), for which Blockchain serves as the backbone technology. Given certain innovative features of BCT, especially its transparency, traceability, security, efficiency, confidentiality, and immutability, BCT holds out the promise of impacting supply chain operational and financial efficiencies. Recently, the burgeoning of Metaverse and Non-Fungible Token (NFT) has boosted the BCT as state-of-the-art technology to another notch. Motivated by the proliferating adoption of BCT, we conduct a holistic literature review with a focus on the status of research on BCT in the context of Supply Chain Management (SCM). In particular, this Blockchain-centered research reviews the research up to date on the Blockchain application in SCM. It provides holistic review in terms of (1) the functionality of BCT and its salient features; (2) the prevailing and potential applications of BCT; (3) and the business benefits and impact of BCT in SCM. Finally, we substantiate and highlight a variety of research directions.

Information has been and will be a vital element for a person or department groups in an organization. That is why there are technologies that help us to give them the proper management of data; Business Intelligence is responsible for bringing technological solutions that correctly and effectively manage the entire volume of necessary and important information for companies. Among the solutions offered by Business Intelligence are Data Warehouses, Data Mining, among other business technologies that working together achieve the objectives proposed by an organization. It is important to highlight that these business technologies have been present since the 50's and have been evolving through time, improving processes, infrastructure, methodologies and implementing new technologies, which have helped to correct past mistakes based on information management for companies. There are questions about Business Intelligence. Could it be that in the not-too-distant future it will be used as an essential standard or norm in any organization for data management, since it provides many benefits and avoids failures at the time of classifying information. On the other hand, Cloud storage has been the best alternative to safeguard information and not depend on physical storage media, which are not 100% secure and are exposed to partial or total loss of information, by presenting hardware failures or security failures due to mishandling that can be given to such information.

An understanding of the mechanism underlying fruit ripening is critical for fruit quality improvement. Although post-harvest ethylene application is known to enhance the onset of fruit ripening, exact mechanisms remain unclear. To characterize the fruit ripening process and mechanism, we investigated the effects of exposing kiwifruit cultivars ‘Hayward’ and ‘Gamrok’ to exogenous ethylene treatment post-harvest using comprehensive proteomic analyses. Comparative two-dimensional gel electrophoresis showed that most of the proteins aggregated in ethylene-treated samples compared to the control (non-treated). We observed that among all ethylene treatments, 95 proteins from ‘Hayward’ and 106 from ‘Gamrok’ were differentially expressed. Interestingly, among the elicited protein successfully identified by matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry, 50% from “Hayward’ and 60% from ‘Gamrok’ are associated with fruit ripening. Furthermore, 18% and 10% of proteins, respectively, are associated with defense response, whereas other major proteins are related to protein biosynthesis and photosynthesis/Calvin cycle. Interactions between identified proteins were demonstrated by bioinformatic analysis, providing insights into biological pathways and molecular functions in post-harvest kiwifruit ripening elicited by ethylene application. The present proteomic study in accordance with physiological analysis provides a quantitative evaluation of fruit ripening in response to exogenous ethylene in post-harvest kiwifruit.

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.

SARS-CoV-2 mRNA vaccination can entail chronic fatigue/dis-autonomy tentatively termed post-acute COVID-19 vaccination syndrome (PACVS). We explored receptor autoantibodies and interleukin-6 (IL-6) as somatic correlates of PACVS. Blood markers determined before and six months after first-time SARS-CoV-2-vaccination of healthy controls (N = 89, 71 females, mean/ median age 39/ 49 years) were compared with corresponding values of PACVS-affected persons (N= 191, 159 females, mean/median age: 40/39 years) exhibiting chronic fatigue/dis-autonomy (≥ three symptoms for ≥ five months after last SARS-CoV-2 mRNA vaccination) not due to SARS-CoV-2 infection and/or confounding diseases/medications. Normal vaccination response encompassed decreases of 11 receptor-antibodies (by 25 - 50 %, p < 0.0001), increases in two receptor-antibodies (by 15 - 25 %, p < 0.0001) and normal IL-6. In PACVS, serological vaccination-response appeared significantly (p < 0.0001) altered, allowing discrimination from normal post-vaccination state (sensitivity = 90%, p &lt; 0.0001) by increased angiotensin II type 1 receptor antibodies (cut-off ≤ 10.7 U/ml, ROC-AUC = 0.824 ± 0.027), decreased alpha-2B adrenergic receptor antibodies (cut-off ≥ 25.2 U/ml, ROC-AUC = 0.828 ± 0.025) and increased IL-6 (cut-off ≤ 2.3 pg/ml, ROC-AUC = 0.850 ± 0.022). PACVS is thus indicated as a somatic syndrome delineated/detectable by diagnostic blood markers

Mining for resources extraction may lead to several geological and associated environmental changes due to ground movements, collision with mining cavities and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e. subsidence, and degradation of vegetation health. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyse land-use and land-cover dynamics and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation health using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within the four years with 87.2% of growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that 66.5% of the vegetation of the study area was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide.

Connexins are tetraspan transmembrane proteins that form gap junctions and facilitate direct intercellular communication, a critical feature for the development, function and homeostasis of tissues and organs. In addition, a growing number of gap junction-independent functions are being ascribed to these proteins. The connexin gene family is under extensive regulation at the transcriptional and post-transcriptional level, and undergoes numerous modifications at the protein level, including phosphorylation, which ultimately affects their trafficking, stability and function. Here, we summarize these key regulatory events, with emphasis on how these affect their multi-functionality in health and disease.

This study is aimed to explore the environmental risk posed by the unsustainable mining activities in Mulawarman village, East Kalimantan, and articulate the disproportionate impact from the perspective of environmental justice on how mining regulations affect the lives of a vulnerable community. A qualitative comparative analysis based on the legislation and administrative rules on coal mining, and a case study of Mulawarman village were adopted. The information was framed based (participatory) observation, and in-depth interview, and purposively conducted to six selected respondents. The result shows how the laws and regulations disadvantage the community and expose them to unequal treatment. The adverse effects of mining activities change the socio-environmental dynamics in this village. Being the breadbasket in 1997, Mulawarman villagers experience the loss of food self-sufficiency, and turn to the government and mining company for social welfare, and clean water. Also, inconsistent and incomplete regulations pertaining to mining, favor to serve the business interests before the environment and the local community. This results in severe encroachment upon community rights and leads to long-term conflicts between mining companies and local communities, and has weakened the capacity of local authorities to help the affected community to recover their rights.

Alzheimer's disease (AD) detection acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. This paper goal a comparative study on the performance of data mining techniques on two datasets of Clinical and Neuroimaging Tests with AD. Our proposed model in the first stage, Apply clinical medical dataset to a composite hybrid feature selection (CHFS), for extract new features to select the best features due to eliminating obscures features, In parallel with Apply a novel hybrid feature extraction of three batch edge detection algorithm and texture from MRI images dataset and optimized with fuzzy 64-bin histogram. In the second stage, we applied a clinical dataset to a stacked hybrid classification(SHC) model to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. At the same stage of improving the classification accuracy of neuroimaging (MRI) dataset images by applying a convolution neural network (CNN) in comparison with traditional classifiers, running on extracted features from images. The authors have collected the clinical dataset of 426 subjects with (1229 potential patient sample) from oasis.org and (MRI) dataset from a benchmark kaggle.com with a total of around ~5000 images each segregated into the severity of Alzheimer's. The datasets evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏ lead to effectively reduced the false-negative rate with a relatively high overall accuracy with a stack hybrid classification of support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% of the previous result on a clinical dataset, Besides a compared model of CNN classification on MRI images dataset of 80.21%. The results showed the superiority of our CHFS model in predicting Alzheimer's disease more accurately with the clinical medical dataset in early-stage compared with the neuroimaging (MRI) dataset. The results of the proposed model were able to predict with accurately classify Alzheimer's clinical samples at a low cost in comparison with the MRI-CNN images model at the early stage and get a good indicator for high classification rate for MRI images when applying our proposed model of SHC.

Aphasia is one of the most socially disabling post-stroke deficits. Although traditional therapies have been shown to induce adequate clinical improvement, aphasic symptoms often persist. Therefore, new rehabilitation techniques which act as a substitute or as an adjunct to traditional approaches are urgently needed. The present review provides an overview of the efficacy and safety of the most innovative approaches which have been proposed over the last twenty years. First, we examined the effectiveness of the pharmacological approach, principally used as an adjunct to language therapy, reporting the mechanism of action of each single drug for the recovery of aphasia. Results are conflicting but promising. Secondly, we discussed the application of Virtual Reality (VR) which has been proved to be useful since it potentiates the ecological validity of the language therapy by using virtual contexts which simulate real-life everyday contexts. Finally, we focused on the use of Transcranial Direct Current Stimulation (tDCS), both discussing its applications at the cortical level and highlighting a new perspective, which considers the possibility to extend the use of tDCS over the motor regions. Although the review revels an extraordinary variability among the different studies, substantial agreement has been reached on some general principles, such as the necessity to consider tDCS only as an adjunct to traditional language therapy.

This paper considers two types of Protestant ethnic groups of some areas of Urals and Western Siberia. The first type consist of representatives of members of different ethnic groups consisting of well-educated professionals, incorporated into industrial society and associated with the intellectualism of Protestantism. The second type is represented by the indigenous peoples of the Polar Urals and Western Siberia, who use the Protestant religious organizations as tool for restoring life-sustaining elements of the native peoples’ traditional economy. I employed the inductive approach and the comparison method; during the fieldwork I used ethnographic participant observations, sociological structured interviews and closed-ended questionnaires. The empirical data have been collected in the Southern, Middle and Polar Urals and Western Siberia.

Lattice and code cryptography can replace existing schemes such as Elliptic Curve Cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex to handle longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code based cryptography. Handshakes between client devices containing the PUFs and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as High performance computing, and graphic processing units can further enhance security by preventing attackers to make this independent validation when only equipped with less powerful computers.

Current study was to estimation the post-harvest loss of avocado at the producer level using cross-sectional data obtained from 385 proportionately sampled respondents from Wolaita and Kembata Tembaro Zones, the SNNPR of Ethiopia. Descriptive and multiple regression analysis were used to the amount and determinant of post harvest loss of avocado fruits. Estimated total post harvest- loss of avocados was 24%; of which 5.7% unacceptable harvesting methods and 4.9% of improper storage practices. The respondent's sex, education, income, training, contact with extension agents, distance from the nearest road, and off-farm income were found to be negative determinants, whereas storage accessibility and avocado harvest techniques were found to be positive determinants of post-harvest loss of avocado fruits. Post-harvest management techniques for avocados need to be better understood and used by avocado growers if they are to minimize losses. Post-harvest losses contribute considerably to food insecurity and create a problem for the source of revenue for people and the economy of the country as well. Therefore, the use of a practical and effective post-harvest loss reduction technique could be a sustainable solution to increase food availability, eliminate hunger and improve farmers' livelihoods.

The novel coronavirus disease (COVID-19) pandemic has impacted health and wellbeing globally. To strengthen preventive and clinical care amid this pandemic, technological innovations like artificial intelligence (AI) are increasingly used in different contexts. This bibliometric study aimed to assess the current scholarly development and prominent research domains in applications of AI technologies in COVID-19 research. A total of 105 articles were retrieved from MEDLINE database that emphasized on the use of AI in the context of COVID-19. Most articles had multiple authors with a collaboration index of 7.18. Moreover, most of the articles were produced from the USA (22.86%) and China (21.9%), whereas developing countries were underrepresented among the contributing nations. Furthermore, several research domains were identified, including prevention and control, diagnostics, epidemiological characteristics, therapeutics, psychological conditions, and different areas of data sciences related to COVID-19. The current bibliometric evidence shows the early stage of development in this field, which necessitates equitable applications of AI in COVID-19 research emphasizing on health disparities, socio-legal issues, vaccine development, and applied public health research in this pandemic.

Strata and surface movement induced by mining under open-pit final slope is a huge threat to mine safety. Physical model test is an important method to study mining-induced strata and surface movement laws. Because of rock joints predominantly control rock mass deformation and failure, thus physical model test leaving out of consideration of rock joints is difficult to reflect the influence of rock joints on rock mass deformation. Therefore, this paper presents a three-dimensional physical model test considering simplified dominant rock joints. This test process includes the design of testing equipment, the construction of physical model with dominant rock joint sets, conduction of mining and deformation monitoring. And mining under eastern final slope of Yanqianshan iron mine was selected as a case to study the behavior of mining-induced strata and surface movement.

Lutein, a plant-derived xanthophyl-carotenoid, is an exceptional antioxidant and anti-inflammatory constituent found in food. Elevated concentrations of lutein found in human blood and plasma, due to high dietary intake, are beneficial against eye disease and improve cardiometabolic health. Lutein plays an important protective role against the development of neurological disorders, including Alzheimer’s disease (AD), multiple sclerosis (MS) and Parkinson’s disease (PD). It has also been shown to be beneficial for liver, kidney and respiratory health. Lutein, acting as a very strong antioxidant, can alleviate oxidative stress and downgrade reactive oxygen species (ROS). Oxidative stress is one of the key pathogenic mechanisms in post-COVID and mRNA vaccine injury syndromes. Recent in silico studies suggest that lutein and other naturally derived antioxidants, by docking at the site where the SARS-CoV-2 spike protein (SP) binds to the angiotensin enzyme type 2 (ACE2) receptor, may neutralize the SP-ACE2 interactions. Lutein can be added to a detoxification regimen to aid in clearing Spike protein and relieving symptoms. In agreement with Hippocrates’ dictum to “Let food be thy medicine,” this review establishes dietary lutein as a valuable therapy in the treatment of post-COVID syndrome, mRNA vaccine injury syndromes, and a wide range of other chronic illnesses.

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

In software development, developers received bug reports that describe the software bug. Developers find the cause of bug through reviewing the code and reproducing the abnormal behavior that can be considered as tedious and time-consuming processes. The developers need an automated system that incorporates large domain knowledge and recommends a solution for those bugs to ease on developers rather than spending more manual efforts to fixing the bugs or waiting on Q&A websites for other users to reply to them. Stack Overflow is a popular question-answer site that is focusing on programming issues, thus we can benefit knowledge available in this rich platform. This paper, presents a survey covering the methods in the field of mining software repositories. We propose an architecture to build a recommender System using the learning to rank approach. Deep learning is used to construct a model that solve the problem of learning to rank using stack overflow data. Text mining techniques were invested to extract, evaluate and recommend the answers that have the best relevance with the solution of this bug report.

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

Soccer is the most played sport worldwide, with over 265 million participants. It is an incredibly demanding sport, with many different technical skills and physical loads placed on the body. This makes post-match recovery strategies amongst high level soccer players of great importance. The aim of this review is to summarize the existing literature on stretching for post-match recovery, examining its relation to injury prevention, Delayed Onset Muscle Soreness (DOMS), and performance. Scientific evidence of the highest quality and relevance was extracted and reviewed. Despite its common practice, evidence does not support static stretching as a modality to improve recovery post-match amongst soccer players. Larger trials with important outcome measures are needed to determine if a post-match stretching regimen to facilitate recovery exists.

Background and Objectives: Due to huge demand and availability of Banana, innovative cost effective method is necessary to promote and smoothen the banana production among farmers commercially mitigating the demand. Method and Materials: In this study, we feed cow dung mixture along with Urea, TSP, MoP, water to the distal part of rachis after cutting down male bud as soon as the female flowers matured into fruits (T1). The effect of this method was then compared with two control groups; one with the same strategy except fertilizer applied on root following ring method (C1, Positive control) and another was male flower untouched without applying fertilizer on rachis or root (C2, Negative control). Results and Conclusion: T1 showed more than double increase in length than controls. In the same way, in case of shape (diameter), T1 (0.46 cm) showed twice as better growth in the C1 (0.22 cm) and C2 (0.18 cm). Trend analysis showed the test group T1 curve is much steeper than the control groups suggesting faster growth rate than the other two. Finally, the cost of fertilizers for T1 per plant was estimated 0.091 USD while for positive control C1 it was 2.9 USD. This study shows an approach to be effective and economic comparing to traditional method of fertilizer application, which can be adapted as a new method of banana production.

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.

An analysis of workplace accidents in the mining sector has been done using the database from the Spanish administration between the period 2005-2015 and applying data mining techniques. Data has been processed by means of the software Weka. Two scenarios were chosen regarding the accidents database, surface and underground mining. The most important variables involved in occupation accidents and their association rules have been determined. These rules are formed by several predictor variables that cause an accident, defining its characteristics and context. This study exposes the 20 most important association rules of the sector, either surface or underground mining, based on statistical confidence levels of each rule obtained by Weka. The outcomes display the most typical immediate causes with the percentage of accident basis of each association rule. The most typical immediate cause is body movement with physical effort or overexertion and type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident change in both scenarios. Data mining techniques have been proved as a very powerful tool to find out the root of the accidents, apply corrective measures and verify their effectiveness, either for public or private companies.

Understanding the role of risk regions identified by genome-wide association studies (GWAS) have made considerable progress lately referred to the post-GWAS era. Annotation of the genes to the GWAS and fine-mapped functional variants, and understanding their biological pathway/gene networks enrichments is expected to give rich dividend by elucidating the mechanisms underlying prostate cancer. To this aim, we compiled and analysed currently available post-GWAS data on genes identified through GWAS and validated through experimental studies in prostate cancer to investigate molecular biological pathways enriched for assigned functional genes. The results highlight some well-known cancer signalling pathways, antigen presentation process and enrichment in cell growth and development gene networks suggesting prostate cancer may result from the accumulation of the effects of functional variants through multiple gene sets and pathways. The upstream analysis identifies critical transcription factors, which supplements the results regarding the regulatory role of the post-GWAS genes. We also identified the common genes between post-GWAS and three well-annotated prostate cancer Oncomine data in patient samples in order to uncover possible main genes in prostate cancer development/progression. Post-GWAS generated knowledge of gene networks and pathways, if analysed further and targeted appropriately, will have an important impact on clinical management of the disease.

Mining is an important industry that provides products and services through infrastructure systems worldwide. However, the global development promotes the steady growth and accelerated demand for minerals, resulting in the accumulation of hazardous waste in land, sea and air environments and, consequently a series of environmental and health problems. Restoration techniques from mining tailing have become increasingly discussed among scholars due to their potential to offer benefits over reducing tailings levels, thereby reducing environmental pressure for the correct management and adding value to previously discarded waste. This review paper critically explores available literature on the main techniques of mining tailing recycling, and discusses leading recycling technologies, including the advantages and drawbacks, as well as future perspectives. The findings of this review contribute as a reference for scholars as well as support for decisionmakers concerning the related environmental issues.

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

Since January, the disease caused by the Sars Cov-2 virus has spread and following pandemic. In a few months, the virus is seriously affecting the health systems of the various countries of the world and placing people in difficult psychological conditions. Few scientific resources on mental health have been published but still no one has proposed an action plan to cope the future psychological problems.This manuscript provides a plan to easily frame the priorities of mental health area related to COVID-19 to be taken into consideration which should be disseminated and that should be known by all health professionals and also by the major administrators of public health.

In this paper the development process of a deployable modular sandwich panelized system for rapid assembly building construction is presented, and its structural performance under some different action effects is investigated. This system, which includes an innovative sandwich panel and its integrated connections, can be used as structural walls and floors in quickly assembled post-disaster housing, as well as load bearing panels for pre-fabricated modular construction and semi-permanent buildings. Panels and connections are composed of a pneumatic fabric formwork, and two 3-D high-density polyethylene (HDPE) sheets as the skins, filled with high-density rigid Polyurethane (PU) foam as the core. HDPE sheets manufactured with a studded surface considerably enhance the stress distribution, buckling performance and delamination strength of the sandwich panel under various loading conditions. The load-carrying behaviour of the system in accordance with some ASTM standards is presented here. The results show the system satisfies the codes criteria regarding semi-permanent housing.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a frequent, debilitating and still enigmatic disease. There is a broad overlap in the symptomatology of ME/CFS and the Post-COVID Syndrome (PCS). A fraction of the PCS patients develops the full clinical picture of ME/CFS. New observations in microvessels and blood from patients suffering from PCS have appeared and include microclots and malformed pathological blood cells. Capillary blood flow is impaired not only by pathological blood components but also by prothrombotic changes in the vascular wall, endothelial dysfunction, and expression of adhesion molecules in the capillaries. These disturbances can finally cause a low capillary flow and even capillary stasis. A low cardiac stroke volume due to hypovolemia and the inability of the capacitance vessels to adequately constrict to deliver the necessary cardiac preload generate an unfavorable low precapillary perfusion pressure. Furthermore, a predominance of vasoconstrictor over vasodilator influences exists, in which sympathetic hyperactivity and endothelial dysfunction play a strong role, causing constriction of resistance vessels and of precapillary sphincters which leads to a fall in capillary pressure behind the sphincters. The interaction of these two precapillary cardiovascular mechanisms causing a low capillary perfusion pressure is hemodynamically highly unfavorable in the presence of a primary capillary stasis already caused by the pathological blood components and their interaction with the capillary wall, to severely impair organ perfusion. The detrimental coincidence of the microcirculatory with the precapillary cardiovascular disturbances may constitute the key disturbance of the Post-COVID-19 syndrome and finally lead to ME/CFS in pre-disposed patients because the interaction causes a particular kind of perfusion disturbance - capillary ischemia-reperfusion - which has a high potential of causing mitochondrial dysfunction by inducing sodium- and calcium-overload in skeletal muscles. The latter in turns worsens the vascular situation by the generation of reactive oxygen species to close a vicious cycle from which the patient can hardly escape.

Abstract: COVID-19 Long Haulers, an estimated 3% to 12% of people infected globally with coronavirus having latter devasting symptoms 12 weeks after the initial infection is on the rise. We conducted a collaborative study with the long covid patient organization in Greece in order to estimate the prevalence, symptoms and problems that adult long haulers experience and then propose a management plan for these patients. Symptoms were obtained from 208 patients using unstructured qualitative free text entries in an anonymized online questionnaire. The majority of respondents (68.8%) were not hospitalized and had been diagnosed more than six months ago with lingering symptoms (66,8%). Eighteen different symptoms (fatigue, tachycardia, shortness of breath, parosmia etc) were mentioned in both hospitalized and community patients. Interestingly, patients with initial mild symptoms suffer from the same persistent symptoms as those who were hospitalized. Awareness of long covid sequelae seems to be low even among medical doctors. Treatment options incorporating targeted rehabilitation programs are either not available or still excluded from the management plan of long covid patients. Since long COVID is a multi-systemic entity, we propose a holistic interventional approach using a multidisciplinary medical team in order to securely and effectively diagnose and treat these specific patients. Academic and medical community must collaborate with long covid patients’ organizations so as to provide personalized medicine.

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling and other computational approaches that have been used to uncover natural products as inhibitors of DNA metiltransferases, a major family of epigenetic targets with significant potential for the treatment of cancer and several other diseases. Examples of these computational approaches include docking, similarity-based virtual screening, and pharmacophore modeling. It is also commented the chemoinformatic-based exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

Causative agent of the viral pneumonia outbreak in the World identified as SARS-CoV2 leads to a severe respiratory illness like SARS and MERS. The pathogen spreading has turned into a pandemic dissemination and increased the mortality rate. Therefore, useful information is urgently necessary for effective control of the disease. Our study shows the existence of unvarying sequence with no mutation, including ORF1ab regions in 134 high-quality filtered genome sequences of SARS-CoV2 downloaded from the GISAID database. We have detected this sequence stability by using MAUVE analysis and pairwise alignment with Global Needleman Wunsch algorithm for each two different sequences, reciprocally. They also confirmed all these results were also with the Clustal W analysis. The first 6500 bp including ORF1ab region is an unvarying sequence. According to the highest TM-score of predicted protein structure analysis, the results showed it is very similar to spike protein of feline infectious peritonitis virus strain UU4 (PDB 6JX7) depending on amino acid sequences encoded by this unvarying region, and N-acetyl-D-glucosamine is the ligand of this protein. These results have confirmed that N-acetyl-D-glucosamine could play an important role in controlling of SARS-CoV-2. Also, our molecular docking analysis data supports a strong protein-ligand interaction of N-acetyl-D-glucosamine with spike receptor-binding domain bound with ACE2 (PDB 6M0J) and RNA-binding domain of nucleocapsid phosphoprotein (PDB 6WKP) from SARS CoV-2. Therefore, binding of N-acetyl-D-glucosamine to these proteins could inhibit SARS CoV-2’s replication. In the present work, we have suggested providing a repurposing compound for further in vitro and in vivo studies and new insights for ongoing clinical treatments as a new strategy to control of SARS-CoV-2 infections.

Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms, and greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework, and tested in both business-like event logs as recorded in a higher educational ERP system, and a real case scenario involving a set of Dutch municipalities.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms and EDM tools from 2013 to June 2020. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 26 eligible articles from 2,050 articles. Some of the findings of this overview study are worth mentioning. First, only 2 studies explicitly stated their precise sample sizes with maths and science as the two most mentioned subject areas. Second, 16 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 26 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. For example, by using genotyping microarrays (e.g. collections of Single Nucleotide Polymorphism - SNP) it is possible to uncover the reasons (i.e. mutation in genes) because a treatment works properly in some patients (for example absence of mutated genes), but it does not work (presence of mutated genes) in others. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g. high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic software is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. Thus, to exploit all the potential of this massive amount of data in the short possible time (before that data becomes obsolete), the necessity to develop parallel software tools for efficient data collection and analysis arise. Moreover, due to the heterogeneity of the data produced by the different kinds of experimental platforms, it is necessary to automatize in a comprehensive software pipeline, the various steps that compose a bioinformatic analysis, such as: the preprocessing of raw data to remove noise or corrupted data; the annotation of data with external knowledge (e.g. Gene Ontology), and the integration of molecular data with clinical data. It should be noted that such steps are necessary to make statistical or data mining analysis more effective. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based SNP genotyping data. A case study in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data.

Post-viral fatigue syndrome (PVFS) encompasses a wide range of complex neuroimmune disorders of unknown cause characterized by disabling post-exertional fatigue, myalgia and joint pain, cognitive impairments, unrefreshing sleep, autonomic dysfunction, and neuropsychiatric symptoms. It includes myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and more recently post-COVID-19 condition (Long COVID). To date, there are no definitive clinical case criteria and no FDA-approved pharmacological therapies for PVFS. Given the current lack of effective treatments, there is a need to develop novel therapeutic strategies for these disorders. Mitochondria, the cellular organelles responsible for tissue energy production, have recently garnered attention in research into PVFS due to their crucial role in cellular bioenergetic metabolism in these conditions. Accumulating literature has identified a link between mitochondrial dysfunction and low-grade systemic inflammation in ME/CFS, FM, and Long COVID. To address this issue, this article aimed to critically review the evidence relating to mitochondrial dysfunction in the pathogenesis of these disorders; in particular, to evaluate the effectiveness of coenzyme Q10 supplementation on chronic fatigue and pain symptoms as a novel therapeutic strategy for the treatment of PVFS.

The pollen content of honey samples collected in the years 2017 and 2019 from experimental apiaries of Melipona seminigra pernigra Moure & Kerr 1950 installed in campo rupestre on canga (CRC) vegetation of the Serra dos Carajás, southeastern Amazonia, was analyzed to understand the local variability of floral resources occurring on natural and disturbed areas. Around one hundred pollen types were identified mainly belonging to Fabaceae, Myrtaceae and Euphorbiaceae (31, 6 and 5 types, respectively). The N5 mine presented the highest pollen richness with 95 pollen types identified, almost twice of those identified in the other areas, including the better preserved ones. Eighty percent of the pollen types are rare with concentrations ≤ 2,000 pollen grains/10 g; the remaining types are the most abundant and frequent, and are considered the primary bee sources (PBS). PBS correspond mostly to native plants such as Tapirira guianensis Aubl., Protium spp., Aparisthmium cordatum (A.Juss.) Baill., Mimosa acutistipula var. ferrea Barneby, Periandra mediterrânea (Vell.) Taub., Miconia spp., Pleroma carajasense K.Rocha, Myrcia splendens (Sw.) DC., Serjania spp. and Solanum crinitum Lam. All pollen types were identified during both seasons, but higher pollen concentration are related to the dry period (June-September). The statistical analysis indicated that there was no significant difference in honey pollen data between the natural and disturbed areas since the plant species considered as PBS in this work are intensively used in revegetation of degraded area (RDA) processes by mining activities.

. Identifying students who might have difficulty in their course of studies ahead of time is crucial. There can be many reasons for performance issues, such as personality, family, social, and/or economic. We advocate that educational systems should use machine learning to predict students’ performance based on performance factors. This would allow educational professionals and institutions to put in place a preventive plan to help students towards achievements of their educational goals and success. In this chapter, we propose a student performance prediction method and evaluate its performance. We provide a taxonomy of performance factors that help to gauge students performance from different perspectives and give insights on the categories and features that have more significant impact on students’ performance. The results of this work can be used by education institutions to put in place a student-centric approach to tackle performance issues before they create long-term effects on student’s life. In addition, it will help education policymakers to introduce a tailored approach for the population in specific areas.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

If the world’s countries seriously tackle the climate targets agreed in Paris, their citizens are likely to experience substantial changes in production, consumption and employment. We present a long-run post-Keynesian model for studying the potential implications of a major transition on macroeconomic stability and employment. It is a demand-led model in which firms have considerable but not absolute freedom to administer prices, while household consumption exhibits inertia. Firms continually seek input-saving technological improvements that, in the aggregate, tie technological progress to firms' cost structure. Together with firm pricing strategies and wage setting, the productivities of different inputs determine the functional income distribution. Saving and investment, and production and purchase of consumption goods, are undertaken by different economic actors, driven by income and capacity utilization, with the possibility that productive capacity exceeds, or falls short of, effective demand. The model produces business cycles and long waves driven by technological change. We present results for a “downshifting” scenario in which households voluntarily withdraw labor and discuss the implications of downshifting for stability, growth, and employment. We contrast the downshifting scenario with ones in which households reduce consumption without withdrawing from the labor pool.

Open pit mining is a common activity in the Yucatan peninsula for the extraction of limestone. This mining is known under the generic name of quarries, and regionally as sascaberas (sascab=white soil in Mayan language). These areas are characterized by the total removal of the natural vegetation cover and soil in order to have access to the calcareous material. The present study shows the composition and structure of the vegetation in five quarries after approximately ten years of abandonment, and the conserved vegetation near to each one of the quarries in southeastern Quintana Roo. Using a canonical correspondence analysis (CCA), the distribution of the species was determined in relation to the edaphic variables: soil depth, percentage of organic matter (OM), cationic exchange capacity (CEC), pH and texture. 26 families, 46 genera and 50 species were recorded in the quarries and 25 families, 45 genera and 47 species were recorded in the conserved areas. The dominant species in the quarries belong to the families Poaceae, Fabaceae, Rubiaceae and Anacardiaceae. The quarries with higher values of OM (1.63%), CEC (24.05 Cmol/kg), depth (11 cm) and sand percentage (31.33%) include the following species like Lysiloma latisiliquum, Metopium brownei and Bursera simaruba which are commonly found in secondary forests. On the other hand, quarries with lower values of OM (0.39%), CEC (16.58 Cmol/kg) and depth (5.02), and higher percentage of silt (42.44%) were dominated by herbaceous species belonging to the Poaceae family and by Borreria verticillata, which are typical in disturbed areas of southeastern Mexico. In all cases, the pH was slightly alkaline due to the content of calcium carbonate (CaCO3), characteristic of the soils of the region. The edaphic variables are significantly correlated with the development and distribution of vegetation, and with the structure of the communities.

Blackberry is a fruit that has high nutritional value, a factor that has expanded its consumption worldwide. However, due to the fragility of the fruits and the high incidence of postharvest diseases, the fruits have a short shelf life. Thus, the objective of this study was to evaluate whether the application of coatings based on microfibrillated cellulose (NC) and lemongrass essential oil (EO) nanoparticles can prolong the shelf life of blackberry fruits after harvest. EO-coated blackberry fruits at nanocellulose concentrations were analyzed as follows: 0; 0.2; 0.4; 0.6 and 0.8%, in addition to the control treatment of which neither essential oil nor nanocellulose was used, for each treatment five repetitions were used. The fruits were analyzed soon after the application of the cover and at three and six days after storage. Fruit quality was assessed by soluble solids (SS), titratable acidity (TA), pH, fresh weight loss (FWL) and colorimetric parameters such as luminosity, hue angle and fruit peel chroma. Coating on EO (1000 ppm) blackberries combined with NC at concentrations of 0.2, 0.4 and 0.6% is promising in preserving blackberry fruits and reducing the process color reversal, up to six days of storage. The 0.4% NC + 1000 ppm EO conjugated coating showed no SS changes in blackberry fruits during the six days of storage.

Land-cover classification that uses very-high-resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, there is still room for improvements in the accuracy and usability of classification systems. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed for improving the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. 1) An adaptive region is generated by extending gradually the region around a central pixel based on two predefined parameters (T1 and T2) in order to utilize the spatial feature of ground targets in a VHR image. 2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting (AMV). Each initial classified map is refined in this manner pixel by pixel. 3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated through two remote sensing images with high spatial resolutions of 1.0 and 1.3 m, respectively. Compared with the classical majority voting method and a relatively new post-processing method called general post-classification framework, the proposed D-AMVS can achieve a land-cover classification map with less noise and higher classification accuracies.

Failure characteristics induced by unloading disturbance and the corresponding mechanical mechanism of the coal seam floor are important theoretical bases for water-bursting prevention from the floor of the coal seam and rock burst alarm in deep mining. However, the existing two-dimensional ground-pressure-control theory based on shallow mining cannot sufficiently guide deep-mining practices. In this study, the redistribution of mining-induced stress field in rocks surrounding the longwall face and mechanical behaviors of strata in deep mining are investigated through a combination of numerical simulation, physical simulation, and field measurement. Results demonstrate that mining-induced stress fields in the floor of the longwall face differ in space and time. Vertical stress unloading from top to bottom of the floor and horizontal stress unloading are relatively low. A concentration zone of high horizontal stress exists at stope boundaries. The critical yield load of rock stratum in the floor is determined through thin plate yield theory. Under the combined effect of concentrated high horizontal and vertical resilience stresses, strata in the floor fracture from seam to seam if the load increases to the minimum critical buckling value. Fractured strata slide along the fracture surface, which leads to floor heave. The stope floor shows evident time-delay progressive failure characteristics. The stress shell in the stope floor in deep mining is found to be a sensitive mechanical parameter that produces three-dimensional ground-pressure behavior in the floor. This ground-pressure behavior in the stope floor is controlled by the existence of the corresponding stress shell and effects induced by its space–time evolution. This study provides theoretical basis for the dynamic control of a hazard-inducing environment in engineering and minimizing or altering disaster-occurrence conditions during the construction engineering of the coal seam floor.

This conceptual article is aimed at evaluating the COVID19 impacts on the global economy and to postulating an ‘economic paradigm shift’. We argue that the existing economic, political, and trade principles have been challenged due to the global pandemic issue. We also compare the COVID19 impact on Western countries and Eastern countries with their control mechanisms. The emerging of new economies is due to the travel restrictions and disrupt of the global economies are discussed that pave a gateway to the economic paradigm shift. The article finally assesses the advent of 5G technology and its implications to face any future health threats that will lead to the next economic paradigm shift in the modern world.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

The world is currently facing an outbreak of the monkeypox virus and confirmed cases have been reported from 74 countries. Following a recent “emergency meeting”, the World Health Organization just declared monkeypox a global health emergency. As a result, people from all over the world are using social media platforms, such as Twitter, for information seeking and sharing related to the outbreak, as well as for familiarizing themselves with the guidelines and protocols that are being recommended by various policy-making bodies to reduce the spread of the virus. This is resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. Mining this Big Data and compiling it in the form of a dataset can serve a wide range of use-cases and applications such as analysis of public opinions, interests, views, perspectives, attitudes, and sentiment towards this outbreak. Therefore, this work presents MonkeyPox2022Tweets, an open-access dataset of more than 255,000 Tweets related to the 2022 monkeypox outbreak that were posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.