C-type lectin CD209/DC-SIGN and CD209L/L-SIGN proteins are distinct cell adhesion and pathogen recognition receptors that mediate cellular interactions and recognize a wide range of pathogens including, viruses such as SARS, SARS-CoV-2, bacteria, fungi and parasites. Pathogens exploit CD209L family proteins to promote infection and evade the immune recognition system. CD209L and CD209 are widely expressed in SARS-CoV-2 target organs and can contribute to infection and pathogenesis. CD209L family receptors are highly susceptible to alternative splicing and genomic polymorphism, which may influence virus tropism and transmission in vivo. The carbohydrate-recognition domain (CRD) and the neck/repeat region represent the key features of CD209L family proteins that are also central to facilitating cellular ligand interactions and pathogen recognition. While, the neck/repeat region is involved in oligomeric dimerization, the CRD recognizes the mannose containing structures present on specific glycoproteins such as those found on the SARS-CoV-2 spike protein. Considering the role of CD209L and related proteins in diverse pathogen recognition, this review article discusses the recent advances in the cellular and biochemical characterization of CD209 and CD209L and their roles in viral uptake, which has important implications in understanding the host-pathogen interaction, the viral pathobiology and driving vaccine development of SARS-CoV-2.

Limnologists often adhere to a discretized view of waterbodies—they classify them, divide them into zones, promote discrete management targets, and use research tools, experimental designs, and statistical analyses focused on discretization. This approach to limnology has profoundly benefited the way we understand, manage, and communicate about waterbodies. But the research questions and the research tools in limnology are changing rapidly with consequences for the relevance of our current discretization schemes. Here, I examine how and why we discretize and argue that selectively rethinking the extent to which we must discretize gives us an exceptional chance to advance limnology in new ways.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

In cosmology dark energy and dark matter are included in the ΛCDM model, but they are still completely unknown. Because in black holes Lorentz invariance seems not to be applicable for curved space-time, we introduce a model for a reduced speed of light in black holes due to quantum gravity effects and the Heisenberg uncertainty relation. Then black holes are a source for a scalar field with dark energy characteristics. This model has no information paradox for black holes because particles / radiation entering the black hole are redshifted in their wavelength that far that the wavelength has the size of the Schwarzschild radius and thus they are in some way "frozen" in the black hole. We show that the scalar field also has characteristics of dark matter shortly after Planck time, when we use a Big Bounce model. This model also presents an alternative to cosmological inflation with the possibility to solve the flatness and horizon problem and the problem of density fluctuations.

The increase in the number of firms manipulating financial reports has misled shareholders' investment decisions and resulted in an indelible blot on foreign investors’ trust. Due to earnings management (EM) practice, managers' inefficiency, and lack of transparency in Iraq companies. This study tested the influence of the corporate governance mechanisms (CG), (board independence, audit committee, meeting frequency) on EM based on agency theory, as well, to link between EM and firm's performance (FP) in Iraqi listed companies and the impact of moderating role of corporate social responsibility (CSR) based on the Stakeholder Theory. The study's sample consists of 65 companies for the 2013-2018 financial years. Data were collected mainly from the annual reports (secondary data) of the Iraqi listed firms. This study uses the M-score model to detect EM practices as practical techniques in detecting earnings manipulation practices. The panel static model estimators. Hence, this paper adds to the CG literature from the perspective of stakeholder theory using Iraq's unique industrial environment. Based on the research results, policy-makers might use the study‘s findings to recognize the essential roles of several CG mechanisms in alleviating the opportunistic practices in Iraq. Further, companies should also be encouraged to enhance the CSR disclosure quality.

A close inspection of Zwicky's seminal papers on the dynamics of galaxy clusters reveals that the discrepancy discovered between the dynamical mass and the luminous mass of clusters has been widely overestimated in 1933 as a consequence of several factors, among which the excessive value of the Hubble constant $H_0$, then believed to be about seven times higher than today's average estimate. Taking account, in addition, of our present knowledge of classical dark matter inside galaxies, the contradiction can be reduced by a large factor. To explain the rather small remaining discrepancy of the order of 5, instead of appealing to a hypothetic exotic dark matter, the possibility of a inhomogeneous gravity is suggested. This is consistent with the ``cosmic tapestry" found in the eighties by De Lapparent and her co-authors, showing that the cosmos is highly inhomogeneous at large scale. A possible foundation for inhomogeneous gravitation is the universally discredited ancient theory of Fatio de Duillier and Lesage on pushing gravity, possibly revised to avoid the main criticisms which led to its oblivion. This model incidentally opens the window towards a completely non-standard representation of cosmos, and more basically calls to develop fundamental investigation to find the origin of the large scale inhomogeneity in the distribution of luminous matter

Modern experimental astroparticle physics features large-scale setups measuring different messengers, namely high-energy particles generated by cosmic accelerators (e.g. supernova remnants, active galactic nuclei, etc): cosmic and gamma rays, neutrinos and recently discovered gravitational waves. Ongoing and future experiments are distributed over the Earth including ground, underground/underwater setups as well as balloon payloads and spacecrafts. The data acquired by these experiments have different formats, storage concepts and publication policies. Such differences are a crucial issue in the era of big data and of multi-messenger analysis strategies in astroparticle physics. We propose a service ASTROPARTICLE.ONLINE in the frame of which we develop an open science system which enables to publish, store, search, select and analyse astroparticle physics data. The cosmic-ray experiments KASCADE-Grande and TAIGA were chosen as pilot experiments to be included in this framework. In the first step of our initiative we will develop and test the following components of the full data life cycle concept: (i) describing, storing and reusing of astroparticle data; (ii) software for performing multi-experiment and multi-messenger analyses like deep-learning methods; (iii) outreach including example applications and tutorial for students and scientists outside the specific research field. In the present paper we describe the concepts of our initiative, and in particular the plans toward a common, federated astroparticle data storage.

Abstract: Intensity of natural and man-made disasters is increasing day by day. Disaster is one of the major threats that affects the sustainable development of tourist attractions. Big data and Internet of Things(IoT) will greatly improve the disaster management. Based on the Big Data and IoT, a tourism attraction disaster management system is designed, divided into several stages namely pre-disaster early warning prevention, disaster mitigation, recovery and reconstruction after disaster and updating disaster planning. Then, the system flow is analysed, as well as the system structure is constructed. Additional, system function and its operation flow are introduced, including disaster warning, disaster relief, disaster assessment, real-time monitoring and supporting disaster planning functions. Finally, an application case is introduced. Research intends to improve tourism area disasters management.

CVDs are a leading cause of death globally. In CVDs, the heart is unable to deliver enough blood to other body regions. Since effective and accurate diagnosis of CVDs is essential for CVD prevention and treatment, machine learning (ML) techniques can be effectively and reliably used to discern patients suffering from a CVD from those who do not suffer from any heart condition. Namely, machine learning algorithms (MLAs) play a key role in the diagnosis of CVDs through predictive models that allow us to identify the main risks factors influencing CVD development. In this study, we analyze the performance of ten MLAs on two datasets for CVD prediction and two for CVD diagnosis. Algorithm performance is analyzed on top-two and top-four dataset attributes/features with respect to five performance metrics –accuracy, precision, recall, f1-score, and roc-auc – using the train-test split technique and k-fold cross-validation. Our study identifies the top two and four attributes from each CVD diagnosis/prediction dataset. As our main findings, the ten MLAs exhibited appropriate diagnosis and predictive performance; hence, they can be successfully implemented for improving current CVD diagnosis efforts and help patients around the world, especially in regions where medical staff is lacking.

In the past decades, a significant rise in the adoption of streaming applications has changed the decision-making process for the industry and academia sectors. This movement led to the emergence of a plurality of Big Data technologies such as Apache Storm, Spark, Heron, Samza, Flink, and other systems to provide in-memory processing for real-time Big Data analysis at high throughput. Spark Streaming represents one of the most popular open-source implementations which handles an ever-increasing data ingestion and processing by using the Unified Memory Manager to manage memory occupancy between storage and processing regions dynamically, which is the focus of this study. The problem behind memory management for data-intensive stream processing pipelines is that the incoming data is faster than the downstream operators can consume. Consequently, the backpressure of Spark acts in the opposite direction of downstream operators. In such a case, the incoming data overwhelms the memory manager and provokes memory leak issues. As a result, it affects the performance of applications generating, e.g., high latency, low throughput, or even data loss. In such a case, the initial intuition motivating our work is that memory management became the critical factor in keeping processing at scale and system stability of Spark. This work provides a deep dive into Spark backpressure, evaluates its structure, presents the main characteristics to support data-intensive streaming pipelines, and investigates the current in-memory-based performance issues.

The World Health Organization(WHO) in 2016 considered mHealth as: “the use of mobile wireless technologies including smart devices such as smartphones and smartwatches for public health” as an important resource for health services delivery and public health given their ease of use, broad reach and acceptance. WHO emphasizes the potential of this technology to increase access to health information, services and skills as well as promoting positive changes in health behaviors and management of diseases. In this regard, the capability of smartphones and smartwatches for m-health monitoring as well as verification of the patient the signal has become an important component of mHealth system. Most of the smartwatches could extract more than one bioelectrical signal therefore, therefore they provide suitable platform for extracting health data for e-monitoring. The existing approaches have not considered the integrity of data obtained from these smart devices. Therefore, it is important that the integrity of the collected data be verified continuously through user authentication. This could be done using any of the bioelectrical signals extracted and transmitted for e-monitoring. In this article, a smartwatch is used for extracting bioelectrical signal before decomposing the signal into sub-bands of Detail and Approximation Coefficient for user authentication. To select suitable features using biorthogonal wavelet decomposition of signal from a non-intrusive extraction, a detailed experiment is conducted extracting suitable statistical features from the bioelectrical signal from 30 subjects using different biorthogonal wavelet family. Ten features are extracted using Biorthogonal wavelet to decompose the signal into three levels of sub-band Detail and Approximation Coefficient and features extracted from each level the decomposed Detail and Approximation Coefficients. Comparison analysis is done after the classification of the extracted features based on the Equal Error Rate (EER). Using Neural Network (NN) classifier, Biorthogonal Wavelet Detail Coefficient Sub-band level 3 of bior1.1 achieved the best result of EER 13.80% with the fusion of the best sub-band three levels of bior1.1 achieving a better result of 12.42% EER.

The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media & Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

The article provides detailed information about the new technologies based on cluster computing Hadoop and Apache Spark. The experimental task of processing logistic regression with the help of these technologies is considered. The findings on the comparison of the performance of cluster computing of Hadoop and Apache Spark are revealed and substantiated.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

A variant of the von Neumann-Wigner Interpretation is proposed. It does not make use of the familiar language of wave functionsand observers. Instead it pictures the state of the physical world as a vector in a Fock space and, therefore not, literally, a functionof any spacetime coordinates. And, rather than segregating consciousness into individual points of view (each carrying with it asense of its proper time), this model proposes only unitary states of consciousness, Q(t), where t represents a fiducial time withrespect to which both the state of the physical world and the state of consciousness evolve. States in our world's Fock space areclassified as either 'admissible' (meaning they correspond to definite states of consciousness) or 'inadmissible' (meaning they donot). The evolution of the state vector of the world is such as to always keep it restricted to 'admissible' states. Consciousness istreated very much like what Chalmers calls an "M-Property." But we try to show that problems with the quantum Zeno effect do not arise from this model.

Detection of PLA2R and THSD7A among primary membranous glomerulonephritis (MGN) patients transformed the diagnosis, treatment monitoring and prognosis. Anti-PLA2R can be detected in 70-90% of primary MGN patients while anti-THSD7A in 2-3% of anti-PLA2R negative primary MGN patients depending on the technique used. Serum and urine samples are less invasive and non-invasive respectively and can detect the presence of anti-PLA2R and anti-THSD7A with higher sensitivity and specificity, significant in patients’ monitoring and prognosis better than exposing patients to frequent biopsy which is an invasive procedure. Different techniques of detection of PLA2R and THSD7A in patients’ urine and sera were reviewed with the aim of providing newer and alternative techniques. We proposed the use of biomarkers (PLA2R and THSD7A) in making the diagnosis, treatment decision and follow up of patients with primary MGN. We also reviewed other prognostic renal biomarkers like retinol binding protein (RBP) and beta-2 microglobulin in order to detect progression of renal damage for early intervention.

We examine the global legal entity identifier (LEI) system for the identification of participants in financial markets. Semi-structured interviews with data professionals reveal the many ways in which the LEI can improve both business process efficiency and counterparty and credit risk management. Larger social benefits, including monitoring of systemic financial risk, are achievable if it becomes the accepted universal standard for legal entity identification. Our interviews also review the substantial co-ordination and investment barriers to LEI adoption. To address these, a clear regulatory-led road map is needed for its future development with widespread application in regulatory reporting.

As its capital, Jakarta plays a critical role in boosting Indonesia’s economic growth and setting the precedent for broader change outside of the city. One crucial avenue of inquiry to better understand, and prepare for, the future of a country so heavily impacted by disastrous weather events is understanding the effects of climate change through data. This study investigates meteorological data collected from 1996 to 2021 and compares the application of the SARIMA and the Holt-Winters methods to predict the future influence of climatic parameters on Jakarta’s weather. The performance of the SARIMA method is proven to provide better results than the Holt-Winter models and both methods showed the best performances when forecasting the humidity data. The results of the forecast are able to demonstrate the characteristic of the climate in Jakarta, with dry season ranging from May to October and wet season ranging from November to April.

Here we studied Bianchi type-III string cosmological models generated by means of a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional space-time. To obtain the exact solutions of field equations we consider that the shear scalar and the scalar expansion are proportional, σ∞θ which leads to D=cn and secondly we adopt the assumption considering the Reddy String Condition, ρ+λ=0. From the two different cases obtained here, first case leads to the Bianchi type-III string cosmological model in Lyra geometry in five-dimensional space-time and the second case leads to the five-dimensional vacuum universe in general relativity. The Geometrical and physical properties of the model universe are studied comparing with the present day’s observational findings. The model universe obtained here starts with the big bang and as time progresses both particle density ρp and energy density ρ decreases with expansion of our Universe.

The e-commerce platform in the digital economy era has evolved into a data platform ecosystem built around data resources and data mining technology systems. The most typical applications of big data are also concentrated in the field of e-commerce. E-commerce companies should first grasp the interactive relationship among the three major factors of data, technology and innovation, e-commerce platform operation is a multidisciplinary research field. It is not easy for researchers to obtain a panoramic view of the knowledge structure in this field. Knowledge graph is a kind of graph that shows the development process and structure relationship of knowledge with the field of knowledge as the object. It is not only a visual knowledge mapping, but also a serialized knowledge pedigree, which provides researchers with a quantitative research method for the development trend of statistics and academic status. The purpose of this research is to help researchers understand the key knowledge, evolutionary trends and research frontiers of current research. This study uses Citespace bibliometric analysis to analyze the data of the Science Net database and finds that: 1) The development of the research field has gone through three stages, and some representative key scholars and key documents have been recognized; 2) the common knowledge mapping of literature The co-occurrence of citations and keywords shows research hotspots; 3) The results of burst detection and central node analysis reveal research frontiers and development trends. Today, the visualization of big data brings different challenges. The abstraction between the world and today's data visualization occurs when the data is captured. Every user sees his own visualization data generated by standardized calculations. At the same time, there are still many controversies in the theoretical model, structure and structural dimensions. This is the direction that future researchers need to further study.

World Health Organization (WHO) has announced the monkeypox (MPX) epidemic a global public health emergency due to its re-emergence, remarkable increase in the number of MPX cases worldwide, and its potential spread. This paper introduces the symptoms, complications, and features of MPX; its transmission, diagnosis and testing, vaccines and treatment; MPX and sexually transmitted diseases, especially the human immunodeficiency virus (HIV); possible natural hosts or reservoirs of the monkeypox virus (MPXV). A useful tool for MPX and surgical safety recommendations are presented. The challenges in fighting the MPX epidemic, One Health strategy, and future research are discussed.

Non-Orthogonal Multiple Access (NOMA) is the key technology promised to be applied in next-generation networks in the near future. In this study, we propose a multi-points cooperative relaying (MPCR) NOMA model instead of just using a relay as the previous studies. Based on the channel state information (CSI), the base station (BS) selects a closest user equipment (UE) and sends a superposed signal to this UE as a first relay node. We have assumed that there are N UEs in the network and Nth UE, which is farthest from BS, has the poorest quality signal transmitted from the BS compared other UEs. Nth UE received the forwarded signal from N-1 relaying nodes that are UEs with better signal quality. At the ith relaying node, it detect its own symbol by using successive interference cancellation (SIC) and will forward the composite signal to the next closest user, namely i+1th UE, and include an excess power which will use for energy harvesting (EH) intention at the next UE. By these, the farthest UE in network can be significantly improved. In addition, closed-form expressions of outage probability for users over both the Rayleigh and Nakagami-m fading channels are also presented. Analysis and simulation results performed by Matlab software which are presented accurately and clearly show that the effectiveness of our proposed model and this model consistents with the multi-access wireless network in future.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

Abstract:Global ship demolition is mostly concentrated in south Asian countries, namely Bangladesh, India, Pakistan and China, since 1990’s, having competitive advantage for their high natural tide, and low environmental and social costs. Due to high social and environmental externalities, stakeholders increase monitoring of the externalities and continue to prescribe improvement towards sustainability, which put pressures on profitability and competitiveness. As a consequence, also seen in the past, a leakage effect may emerge, leading to shift of this activity to a region, with relatively less monitored and less stricter on social and environmental impacts. Unfortunately, the leakage effect is never predicted in shipbreaking in order to understand the level of push compatible in the given socio-economic contexts. In this study, we have attempted to predict the future ship demolition landscape, applying machine learning technique to 34,531 in-service vessels worldwide, larger than 500 gross tonnage (GT), which is run against a learning model based on 3500 demolished vessels from 2014. This study shows that redistribution may occur among the top recycling nations: India may emerge out to be a dominant player in shipbreaking, surpassing Bangladesh by a margin of two-fold, while Pakistan and China are in decreasing trend. In addition, the leakage effect is observed, in that Vietnam is predicted to be the fourth largest ship demolition country, while China and Pakistan recede from the third and fourth place to 6th and 8th. Turkey is predicted to advance from fifth position to third position by vessel count but stays same in term of total GT dismantled. Although it is not clear if any leakage is to be observed in the near future, this study may be a model for future predictive analytics and help stakeholders take evidence-based business decisions.

We discuss a possible scale of gravitational origin at around 10 MeV, or 10⁻¹² cm, which arises in the MacDowell-Mansouri formalism of gravity due to the topological Gauss-Bonnet term in the action, as pointed out by Bjorken several years ago.~A length scale of the same size emerges also in the Kodama solution in gravity, which is known to be closely related to the MacDowell-Mansouri formulation. We particularly draw attention to the intriguing incident that existence of six compact extra dimensions originated from TeV-scale quantum gravity as well points to a length scale of 10⁻¹² cm, as the compactification scale. The presence of six such extra dimensions is also in remarkable consistency with the MacDowell-Mansouri formalism; it provides a possible explanation for the factor of ~10¹²⁰ multiplying the Gauss-Bonnet term in the action. We also comment on the relevant implications of such a scale regarding the thermal history of the universe motivated by the fact that it is considerably close to 1–2 MeV below which the weak interactions freeze out, leading to Big Bang Nucleosynthesis.

This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using banking system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was significantly improved by 1.84% points based on the Kolmogorov–Smirnov statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.

Cyclin dependent kinase 1 (CDK1) has been primarily identified as a key cell cycle regulator in both mitosis and meiosis. Recently, an extramitotic function of CDK1 emerged when evidence was found that CDK1 is involved in many cellular events that are essential for cell proliferation and survival. In this review we summarize the involvement of active CDK1 in the initiation and elongation steps of protein synthesis in eukaryotes. During its activation CDK1 influences the initiation of protein synthesis, promotes the activity of specific translational initiation factors and affects the functioning of a subset of elongation factors. Our review provides insights into gene expression regulation during the transcriptionally silent cell cycle/M-phase and describes quantitative and qualitative translational changes based on the extramitotic role of the cell cycle master regulator CDK1, to optimize temporal synthesis of proteins to sustain division-related processes: mitosis and cytokinesis.

The Square Kilometre Array (SKA) will be both the largest radio telescope ever constructed and the largest Big Data project in the known Universe. The first phase of the project will generate on the order of 5 zettabytes of data per year. A critical task for the SKA will be its ability to process data for science, which will need to be conducted by science pipelines. Together with polarization data from the LOFAR Multifrequency Snapshot Sky Survey (MSSS), we have been developing a realistic SKA-like science pipeline that can handle the large data volumes generated by LOFAR at 150 MHz. The pipeline uses task-based parallelism to image, detect sources, and perform Faraday Tomography across the entire LOFAR sky. The project thereby provides a unique opportunity to contribute to the technological development of the SKA telescope, while simultaneously enabling cutting-edge scientific results. In this paper, we provide an update on current efforts to develop a science pipeline that can enable tight constraints on the magnetised large-scale structure of the Universe.

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

Studies have previously described the occurrence of multidrug-resistant (MDR) Escherichia coli in human and veterinary medical settings, livestock, and, to a lesser extent, in the environment and food. While they mostly analyzed foodborne E. coli regarding phenotypic and sometimes genotypic antibiotic resistance and basic phylogenetic classification, we have limited understanding of the in vitro and in vivo virulence characteristics and global phylogenetic contexts of these bacteria. Here, we investigated an E. coli strain (PBIO3502) isolated from a pork sausage in Germany in 2021 in-depth. Whole-genome sequence analysis revealed sequence type (ST)58, which is an emerging international high-risk clonal lineage. In addition to its MDR phenotype that mostly matched the genotype, PBIO3502 demonstrated pronounced virulence features, including in vitro biofilm formation, siderophore secretion, serum resilience, and in vivo mortality in Galleria mellonella larvae. Along with the genomic analysis indicating close phylogenetic relatedness of our strain with publicly available, clinically relevant representatives of the same ST, these results suggest the zoonotic and pathogenic character of PBIO3502 with the potential to cause infection in humans and animals. Also, our study highlights the necessity of the One Health approach while integrating human, animal, and environmental health, as well as the role of meat products and food chains in the putative transmission of MDR pathogens.

This paper provides an in-depth exploration of the role of Big Data and Artificial Intelligence (AI) in advancing dairy farming towards net zero emissions, a critical goal in the face of the global climate crisis. The study emphasizes how these technologies significantly enhance the management of greenhouse gas (GHG) emissions and optimize resource use, thereby contributing to environmental sustainability in agriculture. A key aspect of this transition is the alignment with international climate commitments, such as the Paris Agreement, which are instrumental in steering global efforts toward emission reduction and mitigating climate change. The integration of Big Data and AI in dairy farming emerges as a powerful tool to reduce the sector's environmental impact while sustaining economic growth. The paper delves into the specific applications of these technologies in emission management, including predictive analytics for feed optimization, manure management, and energy efficiency enhancements. It also addresses the broader implications of technological integration in dairy farming, considering aspects like benchmarking standards, data privacy, and the role of policy in fostering sustainable practices. The study underscores the challenges inherent in adopting these advanced technologies, including the need for improved farmer training, data quality, and compatibility with existing systems. It also advocates for enhanced policy frameworks that support sustainable practices, encourage technological adoption, and balance economic viability with environmental responsibility. This comprehensive analysis offers valuable insights into harnessing digital technologies for climate change mitigation and delineates a path for the dairy industry towards achieving net zero emissions, thereby contributing significantly to global environmental sustainability efforts.

Authorities define cities – or human settlements in general – through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations – all building footprints of America (mainland) or their centroids more precisely – to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities – in contrast to government defined city boundaries – constitute a valuable data source for city-related research.

Background: We aimed to assess the risk of chemotherapy- and radiotherapy-related cognitive impairment in colorectal cancer patients. Methods: We randomly selected 40% of colorectal cancer patients from Korean National Health Insurance Database (NHID), 2004-2018 (N=148,848). Patients with one or more ICD-10 diagnostic codes for dementia or mild cognitive impairment was defined as cognitive impairment cases. Patients who were aged 18 or younger, diagnosed with cognitive impairment before colorectal cancer (N=8,225) and did not receive primary resection (N=45,320) were excluded. The effects of each chemotherapy agent on cognitive impairment were estimated. We additionally estimated the effect of radiotherapy in rectal cancer patients. Time-dependent competing risk Cox regression was conducted to estimate overall and age-specific hazard ratios (HR) separately for colon and rectal cancer. Results: In colon cancer, capecitabine and irinotecan was associated with higher cognitive im-pairment, while 5-fluorouracil was not. In rectal cancer, no chemotherapy agents increased the risk of cognitive impairment, nor did radiotherapy. Hazardous association of irinotecan was estimated larger in elderly patients compared with younger counterparts. Conclusion: Heterogeneous associations between various chemotherapy agents and cognitive impairment were observed. Elderly patients were more vulnerable to possible adverse cognitive effects. Radiotherapy did not increase the risk of cognitive impairment.

Climate change is currently one of the main problems facing agriculture to achieve sustainability. It causes situations such as drought, increased rainfall, and increased diseases, causing a decrease in food production. In order to combat these problems, Agricultural Big Data contributes with tools that allow improving the understanding of complex, multivariate, and unpredictable agricultural ecosystems through the collection, storage, processing, and analysis of vast amounts of data from diverse heterogeneous sources. This research aims to discuss the advancement of technologies used in Agricultural Big Data architectures in the context of climate change. The study aims to highlight the tools used to process, analyze, and visualize the data and discuss the use of the architectures in the crop, water, climate, and soil management, especially to analyze the context, whether it is in Resilience Mitigation or Adaptation. The PRISMA protocol guided the study, finding 33 relevant papers. Despite the advances in this line of research, few papers were found that mention the components of the architectures, in addition to the lack of standards and the use of reference architectures, which allow the proper development of Agricultural Big Data in the context of climate change.

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. 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. The paper also briefly outlines some potential applications in the fields of Big Data, Data Mining, Natural Language Processing, and their related disciplines, with a specific focus on online learning during this Omicron wave that may be studied, explored, and investigated by using this dataset.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

Space-time evolution is briefly explained by using the 3-dimensional quantized space model (TQSM) based on the 4-dimensional (4-D) Euclidean space. The energy (mass), charges and absolute time are newly defined based on the 4-D Euclidean space. The photons and gravitons are understood as the 2-D space fluctuations along the space axes and 1-D time fluctuations along the time axis, respectively. This indicates that the electromagnetic (EM) and gravitational (G) waves can be unified into the so called gravityelectromagnetic (GEM) wave which has both space and time fluctuations. The photon with zero force fields is the 2EM wave because an EM wave corresponds to a charged fermion. The dark matter bosons, weak force bosons, strong force bosons are the 1-D time fluctuations which can be expressed by the G waves. This indicates that all five forces are unified by the GEM wave. It is called as the super unification theory in the present work. And the force carrying bosons and mesons are, for the first time, proposed as the possible candidates of the magnetic monopoles like the fermions and baryons are the electric monopoles. The signs of the magnetic charges quantized as q_m=cq are newly defined. The big bang is understood by the space-time evolution of the 4-D Euclidean space but not by the sudden 4-D Minkowski space-time creation. The big bang process created the matter universe with the positive energy and the partner anti-matter universe with the negative energy from the CPT symmetry.

In the paper we consider conformal mappings of Riemannian spaces onto Ricci-2-symmetric Riemannian spaces and geodesic mappings of spaces with affine connections onto Ricci-2-symmetric spaces. The main equations for the mappings are obtained as a closed system of differential equations of Cauchy-type in covariant derivatives. We have found the number of essential parameters which the solution of the system depends on. Similar approach was applied for the case of conformal mappings of Riemannian spaces onto Ricci-m-symmetric Riemannian spaces and geodesic mappings of spaces with affine connections onto Ricci-m-symmetric spaces.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

A novel type of phosphazene containing additive that act both as catalyst and as flame retardant for benzoxazine binders is presented in this study. The synthesis of a derivative of hexachlorocyclotriphosphazene (HCP) and meta-toluidine was carried out in the medium of the latter, which made it possible to achieve complete substitution of chlorine atoms in the initial HCP. Thermal and flammability characteristics of modified compositions are revealed. The modifier catalyzes the process of curing and shifts the beginning of reaction from 222.0 C for pure benzoxazine to 205.9 C for composition with 10 phr of modifier. The additive decreases the glass transition temperature of compositions. Achievement of the highest category of flame resistance (V-0 in accordance with UL-94) is ensured both by increasing the content of phenyl residues in the composition and by the synergistic effect of phosphorus and nitrogen. Brief research of the curing kinetics disclosed the complex nature of the reaction. An accurate two-step model is obtained using extended Prout-Tompkins equation for both steps.

Emulsion stabilisation of base layers surfaced with chip seals often proves problematic with chips punching into the base and early distress. This can be aggravated by the use of modified binders that restricts the evaporation of moisture from pavement layers. The introduction of New-age (Nano) Modified Emulsion (NME) stabilisation has the advantage that water is chemically repelled from the stabilised layer resulting in an accelerated development of strength. A need was identified to evaluate the early life performance of selected chip seals, together with identified binders. Three different chip seal surfacings with unconventional modified binders were constructed and evaluated using Accelerated Pavement Testing (APT) with the MMLS3. The objectives of the experimental design and testing were to evaluate binder performance, early loss of chips before chip orientation at low temperatures, punching of the chips into the NME stabilised base, deformation characteristics of a Cape seal and the effect of the use of a standard normal modified binder. This paper contains details of the NME base layer, the binder and seal selection and the test results. It is shown that a cost-effective thin chip seal in combination with a suitable binder can be used on a NME stabilised base with confidence.

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

The most widely distributed bla_CTX-M gene on a global scale is bla_CTX-M-15. The dissemination has been associated with clonal spread and different types of mobile genetic elements. This study aimed to review and describe the genetic environments of bla_CTX-M-15 gene detected from Enterobacteriaceae in published literature from Africa. A literature search for relevant articles was done through PubMed, and Google Scholars electronic databases, 43 articles from 17 African countries were included in the review based on the eligibility criteria. Insertion sequences were reported as part of the genetic environment of bla_CTX-M-15 gene in 32 studies, integrons in 13 studies, and plasmids in 23 studies. In this review, five insertion sequences including ISEcp1, IS26, orf447, IS903, and IS3 have been detected associated with the genetic environment of bla_CTX-M-15 in Africa. Seven different genetic patterns were seen in bla_CTX-M-15 genetic environment. Insertion sequence ISEcp1 was commonly located upstream of the end of the bla_CTX-M-15 gene while insertion sequence orf477 was located downstream. In some studies, ISEcp1 was truncated upstream of bla_CTX-M-15 by insertion sequences IS26 and IS3. Class 1 integron (Intl1) was most reported to be associated with bla_CTX-M-15 (13 studies), with Intl1/dfrA17–aadA5 being the most common gene cassette array. IncFIA-FIB-FII multi-replicons and IncHI2 replicon types were the most common plasmid replicon types that horizontally transfer bla_CTX-M-15 gene. Aminoglycoside modifying enzymes, and plasmid-mediated quinolone resistance genes were commonly collocated with bla_CTX-M-15 gene on plasmids. This review revealed the predominant role of ISEcp1, Intl1and IncF plasmid in the mobilization and continental dissemination of the bla_CTX-M-15 gene in Africa.

This article addresses the question why initiatives in the field of green business and sustainable development often fail. Therefore, it dismantles some typical patterns of failure and shows – as a case study – how these patterns can be challenged through an innovative educational concept: the green business and sustainable development school. The applied methodology is a real-life project that is designed through blended, interdisciplinary elements from business model canvas, Theory U, participation and design thinking. The results of the school initiative are discussed and evaluated by four distinctive stakeholder groups and outline the school’s supporting potential to overcome typical patterns of failure by the younger generation in the future. This article concludes with ideas to enhance the school concept reaching out to even more stakeholder-groups to increase its reliability and viability.

A dynamic view is conjectured for not only the universe but also the underlying theories in contrast to the convectional pursuance of single unification theory. As the 4 -d spacetime evolves dimension by dimension via the spontaneous symmetry breaking mechanism, supersymmetric mirror models consistently emerge one by one at different energy scales and scenarios involving different sets of particle species and interactions. Starting from random Planck fluctuations, the time dimension and its arrow are born in the time inflation process as the gravitational strength is weakened under a 1-d model of a “timeron” scalar field. The “ timeron” decay then starts the hot big bang and generates Majorana fermions and U(1) gauge bosons in 2-d spacetime. The next spontaneous symmetry breaking results in two space inflaton fields leading to a double space inflation process and emergence of two decoupled sectors of ordinary and mirror particles. In fully extended 4-d spacetime, the supersymmetric standard model with mirror matter before the electroweak phase transition and the subsequent pseudo-supersymmetric model due to staged quark condensation as previously proposed are justified. A set of principles are postulated under t his new framework. In particular, new understanding of the evolving supersymmetry and Z2 or generalized mirror symmetry is presented.

Extraversion, a Big Five personality trait, has been identified as a significant factor in COVID-19 positive coping—yet, relevant research is conflicting. Studies have not situated the influence of extraversion within a geographical and historical context. Thus, a sound base is lacking for assessing when opposing results regarding extraversion and COVID-19 positive coping are likely to result. Furthermore, extraversion with respect to COVID-19 coping has not been considered concerning other-directed learning in contrast to self-directed learning. To establish a sufficiently sound base, an examination of the range of high-ranking Google Scholar results on extraversion and COVID-19 coping from different countries during the pandemic’s various waves—pre and post vaccine introduction—is undertaken. The same are then considered for insights into public opinion. Following, extraversion is compared with other-directed learning and differentiated from self-directed learning. An understanding is thus presented for assessing when extraversion will necessarily be an effective personality trait for COVID-19 positive coping and when it will not. Extraversion’s effect will be found inherently inconsistent for identifying COVID-19 positive coping as a result, because of its dependence on other-directed learning. The conclusion: COVID-19 positive coping stability is contingent on personal values that guide self-directed learning, rather than other-directed extraversion.

This article analyses three recent shifts in what [1] called the geography of ‘Big Things’ meaning the contemporary functions and adaptability of modern city centre architecture. We periodise the three styles conventionally into the fashionable ‘Starchitecture’ of the 1990s, the repurposed ‘Agritecture’ of the 2000s and the parodising ‘Parkitecture’ of the 2010s. Starchitecture was the form of new architecture coinciding with the rise of neo-liberalism in its brief era of global urban competitiveness prevalent in the 1990s. After the Great Financial Crash of 2007-8 the market for high-rise emblems of iconic, thrusting, skyscrapers and giant downtown and suburban shopping malls waned and online shopping and working from home destroyed the main rental values of the CBD. In some illustrious cases ‘Agritecture’ caused re-purposed office blocks and other CBD accompaniments to be re-purposed as settings for high-rise urban farming, especially aquaponics and hydroponic horticulture. Now, Covid-19 has further undermined traditional CBD property markets causing some administrations to decide to bulldoze their ‘deadmalls’ and replace them with urban prairie landscapes, inviting the designation ‘Parkitecture’ for the bucolic results. The paper presents an account of these transitions by reference to questions raised by urban cultural scholars like Jane M. Jacobs and Jean Gottmann to figure out answers in time and space to questions their work poses.

In this article, we assume that the beginning of the universe was before the Big Bang. In the beginning, all matter in the universe was combined in an infinitesimal spherical shape. This sphere was compressed to an incomprehensible value for a period, and then exploded and expanded time and space. We are referring to the negative time before the Big Bang. The evolution of the universe before the Big Bang, passing through the moment of the explosion to the end of the universe at the Big Rip, has been studied. In this article, we try to answer the questions; did the universe exist before the Big Bang? What is the origin of the universe and how did it arise? What are the stages of the evolution of the universe until the moment of the Big Rip? What is the length of time for the stages of this development?

The study sought to determine the impact of the Big Five personality characteristics on aggressiveness among school children aged 12 to 16 in the Jenin governorate. The study employing the Big five-factor scale of Macri and Costa and the scale of aggressive behaviour in three areas [aggressive behaviour against oneself, aggressive behaviour against others, and aggressive behaviour against property]. The sample size in this study was 314 students. The quantitative study tool [the questionnaire] revealed the following correlation between the five major personality factors and aggression: neuroticism [+], openness [-], conscientiousness [-], extraversion [-], agreeableness [-]. The most prevalent main personality variables were conscientiousness and neuroticism. The degree of aggressiveness in the Jenin governorate was relatively low on the overall scale.

Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (C_c) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, C_c was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e₀). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate C_c such as oedometer test. This study presents a novel prediction model for C_c of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate C_c based on LL, PL, and e₀. The performance of developed GEP-based model was evaluated through coefficient of determination (R²), root mean squared error (RMSE), and mean average error (MAE). High R² and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R², RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining C_c of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation

Background. Predisposition to HIV+ is influenced by a wide range of correlated economic, environmental, demographic, social, and behavioral factors. While evidence among a candidate handful have strong evidence, there is lack of a consensus among the vast array of variables measured in large surveys. Methods. We performed a comprehensive data-driven search for correlates of HIV positivity in >600,000 participants of the Demographic and Health Survey (DHS) across 29 sub-Saharan African countries from 2003 to 2017. We associated a total of 7,251 and of 6,288 unique variables with HIV+ in females and males respectively in each of the 50 surveys. We performed a meta-analysis within countries to attain 29 country-specific associations. Results. We identified 344 (5.4% out possible) and 373 (5.1%) associations with HIV+ in males and females, respectively, with robust statistical support. The identified associations are consistent in directionality across countries and sexes. The association sizes among individual correlates and their predictive capability was low to modest, but comparable to established factors. Among the identified associations, variables identifying being head of household among females was identified in 17 countries with a mean odds ratio (OR) of 2.5 (OR range: 1.1-3.5, R² = 0.01). Other common associations were identified with marital status, education, age, and ownership of land or livestock. Conclusions. Our continent-wide search for variables has identified under-recognized variables associated with HIV+ that are consistent across the continent and sex. Many of the association sizes are as high as established risk factors for HIV+, including male circumcision.

Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these data combined form ‘big data’ that can be utilized to optimize treatments for each unique patient (‘precision medicine’). To achieve this precision medicine, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often have problems with sharing their data, even though the patient is actually the owner of his/her own health data, and the sharing of data is associated with increased citation rate. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. The idea that society benefits the most if the patient’s data are shared as soon as possible so that other researchers can work with it, has not taken root yet. There are some publicly available datasets, but these are usually only shared after studies are finished and/or publications have been written based on the data, which means a severe delay of months or even years before others can use the data for analysis. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here we discuss several aspects of data sharing in the medical domain: publisher requirements, data ownership, support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing.

With the influence of Big Data culture on qualitative data collection, acquisition, and processing, it is becoming increasingly important that social scientists understand the complexity underlying data collection and the resulting models and analyses. Systematic approaches for creating computationally tractable models need to be employed in order to create representative, specialized reference corpora subsampled from Big Language Data sources. Even more importantly, any such method must be tested and vetted for its reproducibility and consistency in generating a representative model of a particular population in question. This article considers and tests one such method for Big Language Data downsampling of digitally-accessible language data to determine both how to operationalize this form of corpus model creation, as well as testing whether the method is reproducible. Using the U.S. Nuclear Regulatory Commission's public documentation database as a test source, the sampling method's procedure was evaluated to assess variation in the rate of which documents were deemed fit for inclusion or exclusion from the corpus across four iterations. The findings of this study indicate that such a principled sampling method is viable, thus necessitating the need for an approach for creating language-based models that account for extralinguistic factors and linguistic characteristics of documents.

Current commercial, pre-commercial, and experimental in ovo techniques for the sex determination of fertilised eggs employ either minimally invasive biomolecular assays (extracting fluid via a small laser-drilled window in the eggshell, for detection of genetic or hormonal biomarkers), analysis of volatile compounds emitted from the eggshell, visible imaging, and reflectance or transmission spectroscopic analysis exploiting molecular optical fluorescence, polarisation, and scattering phenomena, including various combinations of these modalities. , to date no endeavour employing the NIR and FTIR based spectroscopic techniques has resulted in a commercially sustainable solution to the egg sexing problem. Besides achieving only subpar performance in overall accuracy, specificity, and sensitivity, the least invasive of the current state-of-the-art optical methods still requires, creating a transmission window (fenestration) of 12–15 mm diameter through to the mammillae layer of the shell, proximal to the external shell membrane, which can affect the incubation or post-hatch development viability of up to 10% of incubated eggs. Multimodal solution combining Raman spectroscopy and hyperspectral imaging has strong prospects to overcome the hard barriers existing before the perfection of a non-invasive in-line process for high reliability and rapid throughput for sex determination of eggs within 3 days of incubation. The method for sexing of chicken embryos needs to take a multipronged approach in collecting and analyzing spectral data that points to biomarkers using the machine learning approaches to look for nanomolar to picomolar concentrations of these in the fluid.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

In the field of machine learning, cluster analysis has always been a very important technology for determining useful or implicit characteristics in the data. However, the current mainstream cluster analysis algorithms require comprehensive analysis of the overall data to obtain the best parameters in the algorithm. As a result, handling large-scale datasets would be difficult. This research proposes a distributed related clustering mechanism for Unsupervised Learning, which assumes that if adjacent data are similar, a group can be formed by relating to more data points. Therefore, when processing data, large-scale datasets can be distributed to multiple computers, and the correlation of any two datasets in each computer can be calculated simultaneously. Later, results are processed through aggregation and filtering before assembled into groups. This method would greatly reduce the pre-processing and execution time of the dataset; in practical application, it only needs to focus on how the relevance of the data is designed. In addition, the experimental results show the accuracy, applicability, and ease of use of this method.

In 2007, the excavation of the M-30 ring road located in Madrid and the creation of a green corridor either side of the Manzanares river brought significant change to the metropolitan area. The corridor and linear park which it provided were designed to contribute to the regeneration of the fluvial ecosystem, establish links among residents on each side of the river and promote cultural and leisure activities. This paper provides a sustainability analysis of the excavation of the M-30 (involving the socio-economic and environmental impact) 14 years after its construction. In order to show such an impact, an analysis of the area both prior to the project and after completion, as well as a hypothetical solution that uses improved materials, has been performed. This entails use of the multi-criteria decision-making model named MIVES (initials in Spanish, modelo integrado de valor para una evaluación sostenible). The MIVES method is based on the application of value functions of sustainability indicators selected by socio-economic and environmental criteria, chosen by experts. Results from analysis showed that the excavation of the M-30 considerably improved the sustainability of the area (sustainable index 3.43 and 6.26 both before and after the excavation works). However, use of improved materials in contrast with the application of conventional materials slightly improved the sustainability of the work (Sustainability Index 6.26 and 6.74, respectively, of the conventional materials).

Maternal and child nutrition has been a critical component of health, sustainable development, and progress in low- and middle-income countries (LMIC). While a decrement in maternal mortality is an important indicator, simply surviving pregnancy and childbirth does not imply better maternal health. One of the fundamental obligations of nations under international human rights law is to enable mothers and teenage girls to endure pregnancy and delivery as an aspect of their enjoyment of reproductive and sexual health and rights and live a dignified life. The aim of this study was to discover the correlation between the Maternal Observation and Motivation (MOM) program and m-Health support for maternal and newborn health. A Comparative study was done among 109 pregnant mothers (study group-94; control group-102 mothers) with not less than 20 weeks of gestation. Maternal outcomes such as Hb, weight gain and newborn results like birth weight and crown- heel length was obtained on the baseline, 28 and 36 weeks of gestation. Other secondary data collected were abortion, stillbirth, low birth weight, major congenital malformations, twin or triplet pregnancies, physical activity and maternal wellbeing. The MOM intervention included initial face to face education, three in-person visits and eight virtual health coaching by WhatsApp. The baseline data on Hb of the mothers show that 31(32.98%) vs 27(28.72%) of the study and control group had anaemia, which improved to 27.66% and 14.98% among study group mothers at 28 and 36 weeks of gestation (p<0.001). The weight gain (p< 0.001), level of physical activity (p< 0.001), and maternal wellbeing (p< 0.01) also had significant differences after the Intervention. Even after controlling for potentially confounding variables, the maternal food practices regression model revealed that birth weight was directly correlated with consumption of milk (p 0.001), fruits (p 0.01), and green vegetables (p 0.05).As per the physical activity and maternal wellbeing regression model, the birth weight and crown heel length were strongly related with the physical activity and maternal wellbeing of mothers at 36 weeks of gestation (p <0.05). Combining the MOM intervention with standard antenatal care is a safe and effective way to improve maternal welfare while upholding pregnant mothers' human rights.

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

The global pandemic caused by the new coronavirus has largely changed established business practices. The aim of the study is to present the results of eighteen-month intensive research into the effects of the pandemic on e-consumer behavior. In one of the most active e-commerce markets in Europe, the Czech Republic, we analyzed a sample of more than one and a half million Facebook users in terms of their C2B interactions on the B2C activities of the five major e-commerce market players. The measurements were carried out in three periods, which corresponded to the onset of the first wave, peak, and fading of the second wave of the pandemic. This enabled us to monitor the effect of seasonality and the stabilization of patterns of consumer behavior during the coronavirus crisis. The results suggest that a specific panic pattern of e-consumer behavior was developed at the time of the onset of the pandemic. However, as the pandemic progressed, the market has adapted to a new normal, which, as evidenced by the change in trends, appears to be a combination of the pre-pandemic and pandemic behavioral patterns. Using a statistical analysis, it was possible to identify delta of changes within the patterns of consumer behavior, thus fulfilling the final condition for creating an empirical model of the COVID-19 pandemic impacts on e-consumer behavior presented in this study.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyzes have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials. As a consequence, utilization of big data in acute pain and regional anesthesia research considerably increased over the last decade. Multicentric clinical registries and administrative databases (e.g., healthcare claims databases) have collected millions of cases until today, on which basis several important research questions were approached. In acute pain research, big data was used to assess postoperative pain outcomes, opioid utilization, and the efficiency of multimodal pain management strategies. In regional anesthesia, adverse events and potential benefits of regional anesthesia on postoperative morbidity and mortality were evaluated. This article provides a narrative review on the growing importance of big data for research in acute postoperative pain and regional anesthesia.

Here we mathematically model black holes following dynamics similar to RC electrical model, focusing on their similarities at the singularity. We use this mathematically modelling to hypothesize the origin and growth of a black Hole. Or model consists of several steps defined by: (1) the formation of a black hole following general relativity equations; (2) growth of the black hole modelled as a resistance-capacitance-like electrical circuit. Based on the mathematical modeling of a black hole following dynamics similar to an RC circuit, for a circular motion with constant acceleration, in which the condition is fulfilled, Vt = ω r, we are going to calculate the amount of dark matter in the Milky Way galaxies and the Andromeda galaxy and subsequently the rotation curve of both galaxies to compare them with observed or measured values.

In order to perceive the behavior presented by the multiphase chemical reactors, the ant colony optimization algorithm was combined with computational fluid dynamics (CFD) data. This intelligent algorithm creates a probabilistic technique for computing flow and it can predict various levels of three-dimensional bubble column reactor (BCR). This artificial ant algorithm is mimicking real ant behavior. This method can anticipate the flow characteristics in the reactor using almost 30 % of the whole data in the domain. Following discovering the suitable parameters, the method is used for predicting the points not being simulated with CFD, which represent mesh refinement of Ant colony method. In addition, it is possible to anticipate the bubble-column reactors in the absence of numerical results or training of exact values of evaluated data. The major benefits include reduced computational costs and time savings. The results show a great agreement between ant colony prediction and CFD outputs in different sections of the BCR. The combination of ant colony system and neural network framework can provide the smart structure to estimate biological and nature physics base phenomena. The ant colony optimization algorithm (ACO) framework based on ant behavior can solve all local mathematical answers throughout 3D bubble column reactor. The integration of all local answers can provide the overall solution in the reactor for different characteristics. This new overview of modelling can illustrate new sight into biological behavior in nature.

Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, a new technique to improve the robustness of speech/music classifier for 3GPP enhanced voice service (EVS) using long short-term memory (LSTM) is proposed. For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. Experiments show that LSTM-based speech/music classification produces better results than conventional EVS under a variety of conditions and types of speech/music data.

Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

The effects of the 2008 financial crisis undoubtedly caused problems not only to the banking sector but also to the real economy of the developed and the developing countries in almost all around the globe. Besides, as is widely known, every banking crisis entails the corresponding cost to the economy of each country affected by it, which results from the shakeout and the restructuring of its financial system. The purpose of this research is to investigate the consequences of the financial crisis and the COVID-19 health crisis and how these affected the course of the four systemic banks (Eurobank, Alpha Bank, National Bank, Piraeus Bank) through the analysis of ratios for the period of 2015-2020.

Business Intelligence, defined by [1] as "the ability to understand the interrelations of the facts that are presented in such a way that it can guide the action towards achieving a desired goal", has been used since 1958 for the transformation of data into information, and of information into knowledge, to be used when making decisions in a business environment. But, what would happen if we took the same principles of business intelligence and applied them to the academic environment? The answer would be the creation of Academic Analytics, a term defined by [2] as the process of evaluating and analyzing organizational information from university systems for reporting and making decisions, whose characteristics allow it to be used more and more in institutions, since the information they accumulate about their students and teachers gathers data such as academic performance, student success, persistence, and retention [5]. Academic Analytics enables an analysis of data that is very important for making decisions in the educational institutional environment, aggregating valuable information in the academic research activity and providing easy to use business intelligence tools. This article shows a proposal for creating an information system based on Academic Analytics, using ASP.Net technology and trusting storage in the database engine Microsoft SQL Server, designing a model that is supported by Academic Analytics for the collection and analysis of data from the information systems of educational institutions. The idea that was conceived proposes a system that is capable of displaying statistics on the historical data of students and teachers taken over academic periods, without having direct access to institutional databases, with the purpose of gathering the information that the director, the teacher, and finally the student need for making decisions. The model was validated with information taken from students and teachers during the last five years, and the export format of the data was pdf, csv, and xls files. The findings allow us to state that it is extremely important to analyze the data that is in the information systems of the educational institutions for making decisions. After the validation of the model, it was established that it is a must for students to know the reports of their academic performance in order to carry out a process of self-evaluation, as well as for teachers to be able to see the results of the data obtained in order to carry out processes of self-evaluation, and adaptation of content and dynamics in the classrooms, and finally for the head of the program to make decisions.

Background: Multidrug-resistant bacteria present resistance mechanisms against β-lactam antibiotics, such as Extended-Spectrum Beta-lactamases (ESBL) and Metallo-β-lactamases enzymes (MBLs) operon encoded in Gram-negative species. Likewise, Gram-positive bacteria have evolved other mechanisms through mec genes, which encode modified penicillin-binding proteins (PBP2). This study aimed to determine the presence and spread of β-lactam antibiotic resistance genes and the microbiome circulating in Quito’s Public Transport (QTP). Methods: A total of 29 station turnstiles were swabbed to extract the surface environmental DNA. PCRs were performed to detect the presence of 13 antibiotic resistance genes and to identify 16S rDNA barcoding, followed by clone analysis, Sanger sequencing and BLAST search. Results: ESBL genes blaTEM-1 and blaCTX-M-1 and MBL genes blaOXA-181 and mecA were detected along QPT stations. Two subvariants were found for blaTEM-1, blaCTX-M-1, and blaOXA-181. Almost half of the circulating bacteria found at QPT stations were common human microbiota species including those classified by the WHO as pathogens of critical and high-priority surveillance. Conclusions: β-lactam antibiotic resistance genes are widely spread throughout QPT. This is the first report of blaOXA-181 in environmental samples in Ecuador. Moreover, we detected a new putative variant of this gene. Some commensal coagulase-negative bacteria may have a role as mecA resistance reservoirs

This article proposes a measurement solution designed to monitor instantaneous frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping. A program in Python takes care of receiving the data, calculating the frequency, and finally transferring the measurement results to a database. The frequency is calculated with two different methods, which are compared in the article. The stored data is visualized using the Grafana platform, thus demonstrating its potential for comparing scientific data. The system as a whole constitutes an efficient low cost solution as a data acquisition system.

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

children is challenging. An important advance is the 15-questions Pictorial Personality Traits Questionnaire for Children. A study on students from 10 to 13 years old in Poland validated the questionnaire but with some observations. Thus, there is a need for replication and stronger evi-dence in this age group. In Chile, we replicated the study with 3,423 4th-graders (9 to 12 years old). Teachers, in regular sessions, applied the questionnaire to their entire classes. We found similar results, including that asexual pictograms worked well in both genders. We also found positive relationships between conscientiousness, openness, and extraversion with mathematical performance. Furthermore, a combination of these three traits has a relationship with math performance twice as big as each trait alone. Moreover, students with the lowest scores in this combination of personality traits (6.6% of students) have 0.27 standard deviations less in mathematical performance than those with the highest score, which is 74.3% of students. To the best of our knowledge, this is the first time that a study finds a strong relationship between a combination of personality traits gathered with a 15-question questionnaire and fourth-graders´ math perfor-mances.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The Ten simple rules for using public biological data are: 1) use public data purposefully in your research, 2) evaluate data for your use case, 3) check data reuse requirements and embargoes, 4) be aware of ethics for data reuse, 5) plan for data storage and compute requirements, 6) know what you are downloading, 7) download programmatically and verify integrity, 8) properly cite data, 9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share, and 10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that are able to elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose an hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.

As the additive manufacturing industry grows, it is compounding the global plastic waste problem. Distributed recycling and additive manufacturing (DRAM) offers an economic solution to this challenge, but it has been relegated to either small-volume 3D printers (limiting waste recycling throughput) or expensive industrial machines (limiting accessibility and lateral scaling). To overcome these challenges, this paper provides proof-of-concept for a novel open-source hybrid 3D printer that combines a low-cost hanging printer design with a compression screw-based end-effector that allows direct extrusion of recycled plastic waste in large expandable printing volumes. Mechanical testing of the resultant prints from 100% waste plastic, however, showed that combining challenges of non-uniform feedstocks and a heavy printhead for a hangprinter reduced the strength of the parts compared to fused filament fabrication. The preliminary results are technologically promising, however, and provide opportunities to improve on the open source design to help process the volumes of waste plastic needed for DRAM to address the negative environmental impacts of global plastic use.

Chinese divination of I Ching has a history of thousands of years. The six lines changes in 64 hexagrams have exceeded one billion scenarios, and the inherent laws among them have not been well revealed to this day. By using big data's technology and coin toss method, this paper simulates the change of 64 hexagrams, and explores the probability and proportion of each hexagram during the change from the perspective of quantification, as well as the maximum and minimum conversion rate of hexagram change. This paper summarizes the basic characteristics and the basic law of hexagram change, and accordingly constructs the spatial form of hexagram change, and reveals the hidden secrets of the ancient Book of changes (I Ching). To achieve this goal, we randomly toss three coins 600 million times to generate 100 million hexagrams. According to the basic rules of hexagram divination, we calculate the hexagram changes. The results showed that: (1) Changes of things are mostly simple changes. The probability of 1 billion randomly generated hexagrams from one to three dynamic lines is close to 80%. (2) About 17% of the 64 hexagrams have no dynamic lines, which means that a significant proportion of the 64 hexagrams are invariant. (3) Small probability changing hexagram certainty and large probability changing hexagram uncertainty. (4) After generating one billion hexagrams at random, the topographic map with the probability of changing hexagrams has axial symmetry and fractal geometric characteristics, and the fractal characteristics are mainly manifested in that the changes on both sides of the symmetry are presented based on the triangle background. These results reflect the obvious characteristics and internal regularity of the changes in the 64 hexagrams of the I Ching. This article provides new ideas for scientific exploration of the internal laws of the 64 hexagrams in the I Ching.

As RPG has high sales and profits, lots of developers have supplied various RPG to market but it changed to mass production type with sensational advertising, low quality and excessive charging and similar contents which affects game market and users’ game play experience. The author of this paper studied ways to improve mobile RPG by collecting and analyzing users’ reviews using crawling on Google Play Store. The author of this paper used topic modeling that uses text mining technique and LDA (Latent Dirichlet Allocation) to extract meaningful information from collected big data and visualized it. Inferring users’ reviews, figuring out opinions objectively and seeking ways to improve games are helpful in improving mobile RPG that can be played continuously.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

Epilepsy is characterized by repeated spontaneous reactions caused by hyper-excitability and neurons firing in high synchronization in the central nervous system. It seriously affects the quality of life of epileptic patients and nearly 30% of individuals are refractory to treatment of antiepileptic drugs. Therefore, there is an urgent need to develop new medicines to manage and control the refractory epilepsy. Cannabinoid ligands including selective cannabinoid receptor subtype (CB1 or CB2 receptor) ligands and non-selective cannabinoid (synthetic and endogenous) ligands may serve as the novel candidates for this need. Cannabinoid systems appear to regulating seizure activity in the brain through the activation of CB1 and CB2 cannabinoid receptors (CB1R and CB2R). An abundant series of cannabinoid analogues have been tested in various animal models, including a rat pilocarpine model of acquired epilepsy, in vitro hippocampal neuronal culture models of acquired epilepsy and status epilepticus, a pentylenetetrazole model of myoclonic seizures in mice and a penicillin-induced model of epileptiform activity in the rats. The accumulating lines of evidence show that cannabinoid ligands exhibit significant benefits to control seizure activity in different epileptic models. For this reason, we summarize the relationship between brain CB2 receptors and seizures, and emphasize the potential mechanisms of their therapeutic effects involving affecting neurons, astrocytes, and microglia cells. The unique features of CB2Rs, such as lower expression levels under physiological conditions and high inducibility under epileptic conditions, make it an important target for future research on drug-resistant epilepsy.

Using a simple model for internal heat evolution and mass-internal heat relations found for rocky exo planetary objects in the inner solar system we have inferred the phases of volcanism at earth ( EA) or host star ages (SA) in 52 potentially habitable rocky exo planets. We have also calculated the internal tidal heat contributions and magnetic moments of these exo planets. Based on these results we have inferred the probability of existence of life favouring geophysical conditions in the above exo planets at EA and SA. In M star associated exo planets, occurrences of super flares and long night periods may pose problems for the development of advanced life

Tuberculosis (TB) remains a major public health threat globally, especially in sub-Saharan Africa. Both human and Mycobacterium tuberculosis (MTBC) genetic variation affect TB outcomes, but few studies have examined if and how the two genomes interact to affect disease. We hypothesize that long-term coexistence between human genomes and MTBC lineages modulate disease to affect its severity. We examined this hypothesis in our TB household contact study in Kampala, Uganda, in which we identified 3 MTBC lineages of which one, L4.6-Uganda, is clearly derived and hence recent. We quantified TB severity using the Bandim TBscore and examined the interaction between MTBC lineage and human single nucleotide polymorphisms (SNPs) genome-wide, in two independent cohorts of TB cases (N=149 and N=127). We found a significant interaction between a SNP in PPIAP2 and the Uganda lineage (combined p=4x10-8). PPIAP2 is a pseudogene that is highly expressed in immune cells. Pathway and eQTL analyses indicated potential roles between coevolving SNPs and cellular replication and metabolism as well as platelet aggregation and coagulation. This finding provides further evidence that host-pathogen interactions affect clinical presentation differently than host and pathogen genetic variation independently, and that human-MTBC coevolution is likely to explain patterns of disease severity.

A convenient and fast multiresidue method for the efficient identification and quantification of 72 pesticides belonging to different chemical classes in red and white grape wines have been developed. The analysis was based on gas chromatography tandem quadrupole mass spectrometric determination (GC–MS/MS). An optimization strategy involved the selection of MWCNTs amount and cleanup procedure cycle times for m-PFC was performed to achieve ideal recoveries and reduce sample matrix compounds in the final extracts. The optimized procedure obtained consistent recoveries between 70.2 and 108.8% (70.2% and 108.8% for white wine, 72.3% and 106.0% for red wine) , with relative standard deviations (RSDs) generally lower than 8.3% at three spiking levels of 0.01, 0.05 and 0.2 mg/kg. Linearity was studied in the range between 0.005 and 0.2 mg/kg using pesticide standards prepared both in pure solvent and in the presence of matrix, showing coefficients of determination (R²) higher than 0.985 for all the pesticides. To improve accuracy, matrix matched calibration curves were used for calculating the quantification results. Finally, the method was used successfully in detecting pesticide residues in commercial gape wines.

Background Although high-density lipoprotein has cardioprotective effects, the association between serum high-density lipoprotein cholesterol (HDL-C) and hypertension is poorly understood. Objective We investigated whether low and high concentrations of HDL-C are associated with hypertension using a large healthcare dataset. Methods In a community-based cross-sectional study of 1,493,152 Japanese people aged 40–74 years who underwent a health checkup, blood pressures and clinical parameters, including nine HDL-C concentrations (20–110 mg/dL or over) were investigated. Results A crude U-shaped relationship was observed between the nine HDL-C concentrations and blood pressure in males (n = 830,669), while a left-to-right inverted J-shaped relationship was observed in females(n = 662,483). An age-adjusted logistic regression analysis showed J-shaped relationships (left-to-right inversion in females) between HDL-C and odds ratios for hypertension (≥140/90 mmHg), with lower limits of 60–79 mg/dL in males and 90–99 mg/dL in females, which were unchanged after adjusting for smoking, habitual exercise, alcohol consumption, and pharmacotherapy for hypertension, dyslipidemia, and diabetes. However, further adjustment for body mass index and serum triglyceride concentration revealed latent positive linear associations between HDL-C and hypertension, although the association between extremely high HDL-C (≥100 mg/dL) and hypertension was attenuated in non-alcohol drinkers. Conclusion Both low and extremely high HDL-C concentrations are associated with hypertension. The former association may be dependent on excess fat mass, which is often concomitant with low HDL-C, whereas the latter association may be dependent on frequent alcohol consumption.

This paper analyzes the potential strategies for a bank merger from a Sustainable Development Goals (SDG) perspective, focusing on three key criteria: profitability, market share, and service quality. In the short-term, the merged bank should prioritize optimizing financial performance through cost management, revenue stream identification, and risk management practices. Market share expansion can be achieved through targeted customer acquisition and retention efforts, market research, and competitive analysis. Service quality can be enhanced through improved customer service, efficient complaint resolution processes, and leveraging technology. These short-term plans align the merged bank's operations with the identified criteria and promote responsible banking practices that contribute towards the SDGs. In the long-term, the merged bank should focus on diversifying revenue streams, expanding its customer base, and optimizing cost structure. Long-term strategies should include establishing a strong brand presence, customer loyalty programs, and continuous improvement in service quality. The paper emphasizes the importance of monitoring progress, making necessary adjustments, and aligning with SDGs for sustained profitability and long-term success in the marketplace. The findings of this study provide valuable insights for banks considering a merger and highlight the significance of considering SDGs in their strategic planning.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Good health and the promotion of well-being for all is the third of the 17 Global Goals included in the 2030 Agenda for Sustainable Development. Contributing to this goal, the current study aimed to examine the relationships between one kind of athletes’ well-being, namely state organic self-talk, with personality traits, and basic psychological need satisfaction and frustration within their sport. Athletes (N = 691; mean age 21.65) from a variety of individual (n = 270) and team sports (n = 421) completed a multisection questionnaire capturing the targeted variables. Three-step hierarchical regression analyses revealed that: In step 1, all personality traits were to some extent a significant predictor of athletes’ organic, spontaneous self-talk dimensions and goal-directed self-talk functions. In step 2, need satisfaction significantly contributed to all spontaneous self-talk dimensions and goal-directed self-talk functions (except for creating functional deactivated states) over and above personality. Finally, in step 3, need frustration significantly contributed to negative spontaneous self-talk dimensions, and to all goal-directed self-talk functions (except for instruction) over and above personality and need satisfaction. Overall, our results indicate the importance of personality traits as personal antecedents, and perceptions of basic psychological need satisfaction and frustration as social-environmental antecedents, in shaping athletes’ state organic self-talk.

It is strongly believed that technology can reap the best only when it can be tamed by all stakeholders. Big data technology has no exception for this and even after a decade of emergence, the technology is still a herculean task and is in nascent stage with respect to applicability for many people. Having understood the gaps in the technology adoption for big data in the contemporary world, the present exploratory research work intended to highlight the possible prospects of big data technologies. It is also advocated as to how the challenges of various fields can be converted as opportunities with the shift in the perspective towards this evolving concept. Examples of apex organizations like (IMF and ITU) and their initiatives of big data technologies with respect to the Sustainable Development Goals (SDGs) are also cited for a broader outlook. The intervention of the responsible organizations along with the respective governments is also much sought for encouraging the technology adoption across all the sections of the market players.

Here we mathematically model black holes and the early universe following dynamics similar to RLC electrical models, focusing on their similarities at the singularity. We use this mathematically modelling to hypothesize the evolution of an expanding universe as the result of a black hole collapse followed by its evaporation. Or model consists of several steps defined by: (1) the formation of a black hole following general relativity equations; (2) growth of the black hole modelled as a resistance-capacitance-like electrical circuit; (3) expansion of space-time following the disintegration of the black hole, modelled by RLC-like dynamics. In updating this article, version 2, we will expand by presenting three additional examples related to the theory of the generalization Boltzmann's constant in curved space-time and the theory of general and especial relativity. We will calculate the critical mass to produce a black hole in the LHC, the existence of a high temperature Bose-Einstein condensate and finally we will demonstrate the existence of a tangential force to the repulsion force in the disintegration of subatomic particles. In the update of this article, version 3, we will expand to develop two examples. First, we will present the relationship between M-theory, extra dimensions with the theory of the generalization of Boltzmann's constant in curved space-time. Secondly, we will propose the idea of ​​updating the Lambda-CDM model, considering the primordial gravitational waves to correctly describe the evolution of the universe.

Astronauts are at risk of losing 1.0 to 1.5% of their bone mass for every month they spend in space despite their adherence to high impact exercise training programs and dietary regimens designed to preserve their musculoskeletal system. This loss is the result of microgravity-related impairment of osteocyte and osteoblast function and the consequent upregulation of osteoclast-mediated bone resorption. This review describes the ontogeny of osteoclast hematopoietic stem cells, the contributions of macrophage colony stimulating factor, activator of NFkB and the calcineurin pathways make in osteoclast differentiation, and provides details of bone formation, the osteoclast cytoskeleton, the immune regulation of osteoclasts, and osteoclast mechanotransduction on Earth, in the microgravity of space, and in conditions of simulated microgravity. The article discusses the need to better understand how osteoclasts are able to function in zero gravity and reviews current and prospective therapies that may be used to treat osteoclast-mediated bone disease.

We find solutions of a magnetically charged non-singular black hole in some modified theory of gravity coupled with nonlinear electrodynamics. The metric of a magnetized black hole is obtained which has one (an extreme horizon), two horizons, or no horizons (naked singularity). Corrections to the Reissner-Nordstrom solution are found as the radius approaches to infinity. The asymptotic of the Ricci and Kretschmann scalars are calculated showing the absence of singularities. We study the thermodynamics of black holes by calculating the Hawking temperature and the heat capacity. It is demonstrated that phase transitions take place and we show that black holes are thermodynamically stable at some range of parameters.

How crucial is big data in contemporary molecular design? In this publication we elucidate fundamental concepts and terminology in this field, critically addressing overlooked issues. We thoroughly examine the size, accessibility, quality, and structural aspects of big data alongside the primary methodologies employed for their analysis. Within chemical compounds, properties and descriptors represent two distinct data types, forming the basis for categorizing molecular big data. The primary objective of chemistry is property production, which means we are searching for novel drugs or materials rather than chemical compounds, and big data is the central issue of this philosophy. The increasing availability of data in computer-aided technology propels advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL). Accordingly, a broad chemical audience must comprehend these methods to understand data-centered chemistry. Thus, we aim to systemize big data issues through a simple illustrative framework with fundamental descriptor categories: coding, computer-generated descriptors, and property correlates. Although we employ computer-generated descriptors as big data for predictions, the measured data are irreplaceable for achieving high-quality and reliable outcomes and controlling molecular effects. The scarcity of property data remains a significant hurdle limiting comprehensive studies on the structure-property relationships within big data. Accordingly, guided by pragmatics not-so-big data is an option for drug design. We presented also a brief review of the recent big data literature.

The local climate effects of utility-scale photovoltaic (PV) plants have also attracted more and more attention with the rapid development of PV industry. Turbulent fluxes and intensity characteristics for the PV plant and the adjacent reference site are investigated in the gobi area in Wujianqu in Xinjiang. Results indicate that near surface boundary layer is more unstable during the daytime while is more stable at the night in the PV plant than the reference site. The average roughness is 0.089m and 0.041m for the PV plant and the reference site respectively. The turbulence intensity in the vertical direction (I_w) of the PV plant is higher than the reference site especially during the daytime. The turbulent kinetic energy of the PV plant during the daytime is lower than the reference site.The momentum in the PV plant is higher than the reference site, especially during the daytime. Compared to the reference site, the PV plant has higher sensible heat flux and less latent heat flux. Site specific turbulent intensity relationships were developed for the PV plant and the reference site by analyzing wind components as a function of stability parameter (z/L). The turbulent components of wind followed 1/3 power law in the unstable conditions and stable conditions in the PV plant and the reference site.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

In this paper, we first define the concepts of r-fuzzy soft α-open (semi-open and δ-open) sets on fuzzy soft topological spaces based on the article Aygunoglu et al. (Hacet. J. Math. Stat., 43 (2014), 193-208), and the relations of these sets with each other are established. In addition, we introduce the concepts of fuzzy soft δ-closure (δ-interior) operators, and study some properties of them. Also, the concept of r-fuzzy soft δ-connected sets is introduced and studied with help of fuzzy soft δ-closure operators. Thereafter, we define the concepts of fuzzy soft α-continuous (β-continuous, semi-continuous, pre-continuous and δ-continuous) functions, which are weaker forms of fuzzy soft continuity, and some properties of these functions along with their mutual relationships are discussed. Moreover, a decomposition of fuzzy soft α-continuity and a decomposition of fuzzy soft semi-continuity is obtained. Finally, as a weaker form of a fuzzy soft continuity, the concepts of fuzzy soft almost (weakly) continuous functions are defined, and some properties are specified. Additionally, we explore the notion of continuity in a very general setting called, fuzzy soft (L, M, N, O)-continuous functions and a historical justification is introduced.

In the information age today, data are getting more and more important. While other industries achieve tangible improvement by applying cutting edge information technology, the construction industry is still far from being enough. Cost, schedule, and performance control are three major functions in the project execution phase. Along with their individual importance, cost-schedule integration has been a significant challenge over the past five decades in the construction industry. Although a lot of efforts have been put into this development, there is no method used in construction practice. The purpose of this study is to propose a new method to integrate cost and schedule data using big data technology. The proposed algorithm is designed to provide data integrity and flexibility in the integration process, considerable time reduction on building and changing database, and practical use in a construction site. It is expected that the proposed method can transform the current way that field engineers regard information management as one of the troublesome tasks in a data-friendly way.