Coating of suitable materials having thickness of few atoms to several microns on a substrate is of great interest to the scientific community. Hard coatings develop under the significant composition of suitable-natured atoms where their force-energy behaviors when in certain transition state favour binding. In the binding mechanism of suitable atoms, electron belonging to outer ring filled state of gas-atom undertakes another clamp of energy knot belonging to outer ring unfilled state of solid-atom. Set process conditions develop the binding of different-natured atoms when processing their suitable composition in a system. Atoms of different nature develop structure in the form of hard coating by locating their ground points between the original ones. Here, gas-natured atoms increase the potential energy under decreasing levitational force of electrons, whereas, solid-natured atoms decrease the potential energy under decreasing gravitational force of electrons. In TiN coating, Ti–Ti atoms bind under the difference of expansion of their lattices, called nets of energy knots, where one atom just lands on the already landed atom. An adhered N-atom to a Ti-atom forms its position among four Ti-atoms where N-atom occupies the interstitial site of Ti-atoms. Two oppositely working force-energy behavior atoms deposit in the form of coating at substrate surface as per set conditions of the process. The rate of ejecting (or dissociating) solid-natured atoms depend on the nature of their source (target), process parameters and processing technique. In random arc-based vapor deposition system, depositing differently natured atoms at substrate surface depends on the input power. In addition to intrinsic nature of atoms, different properties and characteristics of coatings emerge as per engaged forces under their involved energy. The present study sets new trends in the field of coatings involving the diversified class of materials and their counterparts.

We discuss the fate of the correspondence principle beyond quantum mechanics, specifically in quantum field theory and quantum gravity, in connection with the intrinsic limitations of the human ability to observe the external world. We conclude that the best correspondence principle is made of unitarity, locality, proper renormalizability (a refinement of strict renormalizability), combined with fundamental local symmetries and the requirement of having a finite number of fields. Quantum gravity is identified in an essentially unique way. The gauge interactions are uniquely identified in form. Instead, the matter sector remains basically unrestricted. The major prediction is the violation of causality at small distances.

Space-based Interferometric Synthetic Aperture Radar (InSAR) applications have been widely used to monitor the cryosphere over past decades. Because of temporal decorrelation, interferometric coherence often severely degrades on fast moving glaciers. In addition, higher sensitivity ambiguity occurs in large baseline configurations, which are needed for extracting topographic information over low relief areas such as the surface of a glacier. TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) observations, which overcome the temporal decorrelation because of their simultaneous measurements by two satellite constellations, have used a short baseline sufficient for generation of excellent digital elevation models in most locations around the world. However, it remains difficult to estimate detailed topographic characteristics over low slope glacier surfaces because of the relatively less sensitive height ambiguity from the small baselines. In this study, we used the TanDEM-X pursuit monostatic mode with large baseline formation following a scientific phase timeline to develop highly sensitive topographic elevation models of the Petermann Glacier of Northwest Greenland. As expected, coherent interferometric phases (> 0.8) were well maintained over the glaciers despite their fast movement thanks to the nearly simultaneous TanDEM‐X measurements. The height ambiguity, which defined as the altitude difference correspondent to phase change of flattened interferogram, of the dataset was 10.63 m, which is favorable for extracting topography in a low relief region. We validated the TanDEM‐X derived glacial topography by comparing it to the SAR/Interferometric radar altimeter observations acquired by CryoSat‐2 and the IceBridge Airborne Topographic Mapper laser altimeter measurements. Both observations showed very good correlation within a few meters of the offsets (‐12.5 – ‐3.1 m) with respect to the derived glacial topography. Because of highly sensitive ambiguity, we could successfully extract detailed geomorphological features on the glaciers. Routine TanDEM-X observations will be very useful to better understand the dynamics of glacial movements and topographic change.

The latest developments in digital humanities have increasingly enabled the construction of large data sets which can easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with temporal information and are usually based on a strict hierarchy (country, city, street, house number, etc.) that is hard, if not impossible, to use with historical data. Indeed, historical data are full of uncertainties (temporal, textual, positional accuracy, confidence in historical sources) that can not be ignored or entirely resolved. We propose an open source, open data, extensible solution for geocoding that is based on gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address searched by the user. The matching criteria are customisable and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that geocoding results can be checked and collaboratively edited. The system has been tested on the city of Paris, France, for the 19th and the 20th centuries. It shows high response rates and is fast enough to be used interactively.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Although solar photovoltaic (PV) system costs have declined, capital cost remains a barrier to widespread adoption. Do-it-yourself (DIY) system designs can decrease costs by about 50% by reducing labor costs, but if not attached to a building structure demand ground penetration for conventional footings. This is not technically and economically feasible at all sites. To overcome these challenges, this study details systems designed to eliminate drilling holes and pouring concrete. Two different designs for both fixed tilt and variable tilt PV racking are investigated to assess their structural integrity, constructability, and economic cost when fabricated with a ballast-supported foundation. The ballast-supported foundations are analyzed for eight such systems by proposing two separate ballast designs: one for single line of post systems, and one for double line of post systems built on a 4-kW basis. The results of the analysis found that both designs are slightly more expensive than typical in-ground concrete systems by 25%, but the overall DIY systems costs remain economically advantageous. Sensitivity analyses are conducted to show how modifications to the dimensions including clearances and distances between gabions influence the weight of the system and thus change the economic value of the design, so users can trade dimensional freedom for cost-savings, and vice versa. Overall, all the wood-based PV racking system designs provide users with cost-effective and easy DIY alternatives to conventional metal racking, and the novel ballast systems presented provide more versatility for PV systems installations.

In recent years, the number of open preservation surgeries for treatment of laryngeal Cancer has increased. The purpose of this surgery is to avoid the highly mutilating intervention of a total laryngectomy (TL) and to maintain laryngeal function while ensuring oncological radicality. The aim of this study was to assess the oncologic results of OPHL I and II and to identify prognostic factors that could impact patient survival and local failure rates. This study was conducted on 182 patients with laryngeal squamous cell carcinoma treated with OPHLs between 2005 and 2015. The survival rates of a group of patients treated with TL between 2004 and 2014 were taken into con-sideration to compare survival outcomes. The DSS in pT2 and pT3 tumors in relation to the type of surgery showed no statistically significant difference between OPHLs and TL (p=0.54 and p=0.63 respectively). The 5-years T-recurrence free survival showed that pT4 tumors, perineural/vascular positive invasion and OPHL IIb + ARY treated tumors were associated with a higher risk of tumor recurrence (p<0.0001, p=0.0005 and p=0.016 respectively). OPHLs represent an excellent alterna-tive to TL considering the characteristics of the tumor and the general conditions of the patient.

This study investigates the transition from surface to underground mining at the Sepon Gold mine. The stability of surface slopes is assessed prior to commencing underground operations. Stope mining is adopted based on ore body characteristics and geological features. Finite element numerical analysis, employing the Generalized Hoek-Brown criterion, evaluates slope stability for surface slopes and opening stopes. The pit design comprises a 70° slope angle, 20 m height, and 10-15 m safety berm, meeting stability requirements with a factor of safety of 2.46. Underground mining design includes a 62° ore body dip, 50 m thick crown pillar to prevent surface subsidence, and 5 m wide, 5 m high stopes. Sill pillars are left for support after each level excavation. Rock bolts are employed in specific stope areas where is necessary, with continuous monitoring for surface deformation. The study analyzes the influence of stope sizes on pit wall and pit bottom stability, identifying slope failures near the hanging wall close to the pit bottom during underground mining. A slight increase in the displacement zone is observed on the upper crest of the top footwall. Overall, the findings demonstrate successful stability in the transition from surface to underground mining at Sepon Gold mine.

Judo is a popular Japanese martial art and an Olympic sport. Recently, an increasing interest has been observed in the application of judo as a multicomponent exercise, with a growing body of evidence showing that multicomponent exercise improves physical and cognitive performance in older adults. Therefore, this review highlights the benefits of judo training in preventing physical and cognitive decline in older adults. Specifically, first, this review outlines the basic characteristics of judo (philosophy, match, and training). Next, previous studies investigating the effects of judo training on physical and cognitive aspects of older adults are reviewed. Thereafter, the brain mechanisms underlying the effects of judo training in improving physical and cognitive performance are discussed. Throughout this review, judo training demonstrated some positive effects on physical (gait and balance, among others) and cognitive (memory and executive function) function in older adults. These positive effects are attributed to a variety of changes in the brain (e.g., increased neurotrophic factor expression and increased cerebral blood flow, among others), that affect different brain regions and networks both functionally and structurally. From these findings, this review concludes that judo training can be an effective way to maintain and prevent physical and cognitive decline in older adults.

Background and purpose: Total elbow arthroplasties (TEA) aim to replicate anatomy and provide stability in the treatment of distal fractures of the humerus. In the presence of an aging population with higher functional demand, improving patients’ well-being is crucial. This study aimed to analyze patients’ reported outcomes and functional outcomes for TEA in comminuted fractures of the distal humerus and to compare these outcomes with their counterpart patients who have been treated with open reduction and internal fixation (ORIF). In addition, this study aims to compare the secondary procedures rate between the two groups.Patients and Methods: Eligible patients were those who underwent TEA or ORIF of the distal humerus and completed several patient-reported outcome (PRO) questionnaires including the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, 12-Item Short Form Health Survey Physical and Mental components (SF-12 P and SF-12 M, respectively) scores, visual analog scale (VAS) score for pain, and patient satisfaction ratings (1-5). A physical examination including range of motion, instability, and strength was performed for all patients. Results: This study found that patients following TEA have shown significantly lower pain levels (TEA: 3.72±2.8; ORIF 5.2±2.98, P=0.019) and higher satisfaction levels (TEA: 4.18±1.17; ORIF 3.57±1.46, P=0.035) compared with patients following ORIF. DASH score (TEA: 33.7±29.4; ORIF 39.75±24.6, P=0.31) and SF-12 score (TEA: 31.7±9.67; ORIF 31.25±10.2, P=0.85) were not statistically different between TEA and ORIF.Patients following TEA have demonstrated an advantage in flexion compared with patients following ORIF (P=0.045). Both patients following TEA and ORIF demonstrated no statistically significant difference in pronation and supination of the operated elbow compared to the contralateral side. Although, a decreased range in extension and flexion of the operated elbow compared with the contralateral side was demonstrated in both groups (extension P=0.005, flexion P<0.001). The grip Test showed no significant difference between the patients who were treated by TEA or ORIF (P=0.99). Moreover, ORIF in comminuted fractures of the distal humerus in elders may be associated with a higher complication rate compared with TEA.Conclusion: TEA following comminuted fractures of the distal humerus is associated with favorable satisfaction, pain levels, and range of motion in flexion compared with patients following ORIF of the distal humerus. Additionally, TEA may be associated with a lower rate of secondary procedures.

The environmental issues we are currently facing require long-term prospective efforts for sustainable growth. Renewable energy sources seem to be one of the most practical and efficient alternatives in this regard. Understanding a nation's pattern of energy use and renewable energy production is crucial for developing strategic plans. No previous study has been performed to explore the dynamics of power consumption with the change in renewable energy production on a country-wide scale. In contrast, a number of deep learning algorithms demonstrated acceptable performance while handling sequential data in the era of data-driven predictions. In this study, we developed a scheme to investigate and predict total power consumption and renewable energy production time series for eleven years of data using a Recurrent Neural Network (RNN). The dynamics of the interaction between the total annual power consumption and renewable energy production are investigated through extensive Exploratory Data Analysis (EDA) and a feature engineering framework. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.084. Higher performance is achieved through the increase in the number of epochs and hyperparameter tuning. The proposed framework can be used and transferred to investigate the trend of renewable energy production and power consumption and predict the future scenarios for different communities. Incorporation of the cloud-based platform into the proposed pipeline may lead to real-time forecasting.

The ability to self-direct a research program determines graduate degree completion. Yet, research on incompletion of graduate physics programs assume students’ present level of self-direction adequate and neglects to recognize a lack of self-directed learning as key. One theoretical mathematical physicist focused on changing this challenge of physics graduate education by promoting self-directed learning through the type research flow that has been found to bring the greatest satisfaction to researchers with respect to their insights. This he provided through his space, time, open mindedness and theoretical contributions with his students and in collaboration with his colleagues. A self-directed learner himself, David J. Rowe developed methods of mentoring for encouraging physics graduate students to recognize symmetry as valuable in identifying solutions to problems quickly—helping these students take the lead in finding insightful resolutions to complex, multidimensional, mathematical physics uncertainties. How Rowe set about supporting self-directed learning in his graduate physics education interactions will be examined with the use of narrative research to interpret the texts and conversations with the author he made available. His techniques will be presented and recommendations made regarding how Rowe’s work in this regard can be modeled to improve self-direction in STEM graduate education.

The importance of measuring sustainability in tourism has been significantly advancing in recent years, following the need to manage the impact of tourism on territories and hosting communities. It was further boosted by the pandemic, where sustainability has been defined as one of the central elements to restart global tourism. The ETIS model, developed by the European Commission, is a point of reference based on self-assessment, data collection and analysis by the destinations themselves. The application of ETIS toolkit has faced many challenges, especially at sub-national level, mostly related to the lack of available and updated data to feed the model. The hypothesis explored by the authors is to solve the implementation issues, developing an indicator based on the use of the Sentiment Analysis to frame e-reputation and tourism satisfaction, and further combining it with other open data sources. The Tourism Sustainability Index (TSI) can provide a scalable and geo-referenced evaluation of tourism sustainability, measuring the four pillars and sub-components referenced to ETIS criteria, applicable to any tourism destination. Results show that the TSI can be seen as a consistent and valid tool for destinations to analyze sustainability, monitor its evolution through time periods and sub-areas, and compare it to other benchmark or competitive areas.

Glaucoma is one of the most common causes of irreversible vision loss worldwide. It is an insidious disease with a multifactorial pathogenesis. Despite progress in treatment methods, prevention and lifestyle modifications may be useful in slowing the progression of this disease. This systematic review aimed to evaluate the influence of diet, oxidative stress, and disturbances in blood serum levels of nutrients on the incidence and severity of glaucoma based on scientific reports on the role of nutrition in the pathogenesis and course of glaucoma. This paper presents an analysis of the above issues; however, further research is required to develop this topic. Future clinical trials are needed to assess the influence of nutrition and to develop nutritional management strategies for patients with glaucoma.

Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

This research paper is divided into two aspects: first part describes the modelling techniques which are referred for handling infectious diseases in public health domain, second part describes the modelling techniques which are referred for building epidemiology cases in public health domain. To illustrate further, first part helps in understanding various modeling ordinary differential equations have been defined to develop the definition, described to know the terminologies involved in developing the equations and henceforth established to understand the end-to-end process. Second part helps in understanding various aspects of epidemiology in public health domain and how they help in addressing the public health domain policy-making.

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: a) Time-Domain apparatus, particularly for physical measurements; b) Frequency Domain apparatus, particularly for chemical analysis, c) NMR Cryoporometry apparatus for measuring pore-size distributions, and d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held size NMR spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently the NMR Spectrometer is very suitable for a), NMR time-domain relaxation and c), NMR Cryoporometry. With a suitable high-homogeneity magnet it is also appropriate for simple use b), spectral analysis, or, with a suitable gradient set, use d), MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make an excellent NMR Cryoporometers, as demonstrated by this paper, and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials-science.

The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis of responses where participants indicated their biggest concern related to COVID-19 showed that the average subjectivity in responses by the age group of 41-50 decreased from April 2020 to June 2020, the average subjectivity in responses by the age group of 71-80 drastically increased from May 2020, and the age group of 11-20 indicated the least level of subjectivity in their responses between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, and 81-90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11-20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21-30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31-40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41-50, 51-60, 61-70, 71-80, and 81-90. Finally, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The deep learning model used the responses to multiple questions in the context of fear, preparedness, and response related to COVID-19 from the dataset and achieved an overall performance accuracy of 91.62% after 500 epochs.

This study explores the advanced integration of Bi2Te3-based thermoelectric systems in Ocean Thermal Energy Conversion (OTEC) to optimize performance. OTEC, a promising renewable energy source, needs help in efficiency maximization. We address this by implementing Bi2Te3 thermoelectric generators (TEGs), known for their high thermoelectric efficiency at low-temperature differences. Our research encompasses a comprehensive simulation and analysis of these TEGs in OTEC systems, examining material properties, system design, and environmental impact. We present a novel approach to integrating these TEGs, optimizing their arrangement, and interfacing with the OTEC cycle to enhance overall efficiency. The study also investigates the durability and cost-effectiveness of Bi2Te3 TEGs in marine environments. Results demonstrate a significant improvement in energy conversion efficiency, highlighting the potential of Bi2Te3 TEGs in sustainable ocean energy exploitation. This work advances material science in renewable energy, providing valuable insights into the practical application of thermoelectric materials in large-scale energy systems. It paves the way for future research on material optimization and system integration, essential for developing efficient and sustainable OTEC systems.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

Abstract There is no research that combines behavioural and fMRI research approaches to investigate the mediating and moderating effects of working memory, verbal IQ, visuospatial and verbal reasoning on students’ performance of maths and science. This paper investigates all these multiple relationships between working memory (WM), verbal IQ, visuospatial and verbal reasoning with performance in maths and science accuracy. The multimethod research consists of a behavioural survey and a fMRI neuroscience investigating the interactive and multiple relationships of all cognitive processing independent variables (IVs) with the dependent variables (DVs) of maths and science performance. Using structural Equation Modelling (SEM) and Warp Partial Least Squares (Warp PLS) analytical approach, the research discusses the findings of both, the behavioural and fMRI data. The adolescent sample (N=34) for the behavioural and fMRI study, was recruited from consenting schools in London, UK, and consisted of equal numbers of girls and boys between 11-15 years old. The hypothesised behavioural SEM model predicted positive relationships between all the independent variables (IV) of visuospatial matrix reasoning (VSMatAR), verbal analogical reasoning (VerbAR), verbal working memory (VWM), visuospatial working memory (VSWM) and verbal IQ, with performance in maths and science accuracy. The findings support all hypotheses except two and identifies verbal IQ, Verbal WM and VerbAR as the three strongest predictors of maths and science accuracy. The neuroscience fMRI data identified four brain regions of interest, namely, the bilateral DLPFC, Parietal, Temporal and pre-supplementary motor area (PSMA) as being most relevant to maths and science outcomes. The hypothesised fMRI SEM, produced mixed results which supported some of the hypothesised relationships between the IVs of verbal AR and VSMatrix AR associations with all four brain regions and in turn the associations with maths and science performance. The paper makes two original contributions, firstly, by testing the hypothesis that Verbal WM mediates the independent variables’ effects on maths and science outcomes. And secondly, by using Warp PLS to test the SEM (behavioural and fMRI) models’ multiple non-linear interactions simultaneously between the IVs with the DVs of maths and science accuracy. The Warp PLS testing of all IVs and DVs produced some interesting new insights regarding the relationships between Verbal analogical reasoning, which are deemed important to assist academics and educational practitioners in improving maths and science performance of adolescent students. The combination of behavioural and neuroscience fMRI data provides more reliable empirical evidence regarding the multiple interactive influences impacting maths and science performance for adolescents.

During virus outbreaks in the recent past web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbreaks. The recent outbreak of the Marburg Virus disease (MVD), the high fatality rate of MVD, and the conspiracy theory linking the FEMA alert signal in the United States on October 4, 2023, with MVD and a zombie outbreak, resulted in a diverse range of reactions in the general public which has transpired in a surge in web behavior in this context. This resulted in “Marburg Virus” featuring in the list of the top trending topics on Twitter on October 3, 2023, and “Emergency Alert System” and “Zombie” featuring in the list of top trending topics on Twitter on October 4, 2023. No prior work in this field has mined and analyzed the emerging trends in web behavior in this context. The work presented in this paper aims to address this research gap and makes multiple scientific contributions to this field. First, it presents the results of performing time series forecasting of the search interests related to MVD emerging from 216 different regions on a global scale using ARIMA, LSTM, and Autocorrelation. The results of this analysis present the optimal model for forecasting web behavior related to MVD in each of these regions. Second, the correlation between search interests related to MVD and search interests related to zombies (in the context of this conspiracy theory) was investigated. The findings show that there were several regions where there was a statistically significant correlation between MVD-related searches and zombie-related searches (in the context of this conspiracy theory) on Google on October 4, 2023. Finally, the correlation between zombie-related searches (in the context of this conspiracy theory) in the United States and other regions was investigated. This analysis helped to identify those regions where this correlation was statistically significant.

Background: ChatGPT is becoming a new reality. Where do we go from here? Objective: to show how we can distinguish ChatGPT-generated publications from counterparts produced by scientist. Methods:By means of a new algorithm, called xFakeBibs, we show the significant difference between ChatGPT-generated fake publications and real publications. Specifically, we triggered ChatGPT to generate 100 publications that were related to Alzheimer’s disease and comorbidity. Using TF-IDF, using the real publications, we constructed a training network of bigrams comprised of 100 publications. Using 10-folds of 100 publications each, we also 10 calibrating networks to derive lower/upper bounds for classifying articles as real or fake. The final step was to test xFakeBibs against each of the ChatGPT-generated articles and predict its class. The algorithm successfully assigned the POSITIVE label for real ones and NEGATIVE for fake ones. Results: When comparing the bigrams of the training set against all the other 10 calibrating folds, we found that the similarities fluctuated between (19%-21%). On the other hand, the mere bigram similarity from the ChatGPT was only (8%). Additionally, when testing how the various bigrams generated from the calibrating 10-folds against ChatGPT we found that all 10 calibrating folds contributed (51%-70%) of new bigrams, while ChatGPT contributed only 23%, which is less than 50% of any of the other 10 calibrating folds. The final classification results using the xFakeBibs set a lower/upper bound of (21.96-24.93) number of new edges to the training mode without contributing new nodes. Using this calibration range, the algorithm predicted 98 of the 100 publications as fake, while 2 articles failed the test and were classified as real publications. Conclusions: This work provided clear evidence of how to distinguish, in bulk ChatGPT-generated fake publications from real publications. Also, we also introduced an algorithmic approach that detected fake articles with a high degree of accuracy. However, it remains challenging to detect all fake records. ChatGPT may seem to be a useful tool, but it certainly presents a threat to our authentic knowledge and real science. This work is indeed a step in the right direction to counter fake science and misinformation.

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.

Harbor porpoise are typically seen in small groups of 1-3 individuals, with aggregations of 20+ individuals treated as rare events. Since the 1990s, the harbor porpoise population in the Salish Sea has seen a significant recovery, which has led to an increased number of observed aggregations that exceed the more usual small group sizes. By combining the observational data of United States and Canadian research organizations, community scientists, and whale watch captains or naturalists, we demonstrate that harbor porpoise aggregations appear to be more common than previously known, with 160 aggregations documented in 2022 alone. Behavioral data also indicated that foraging behaviors were common and social behaviors, like mating, were seen more often during these encounters compared to small groups. Other behaviors that are considered to be rare or unknown, were also observed during these encounters, including cooperative foraging and vessel approach. These aggregations are likely important foraging and social gatherings for harbor porpoises. This holistic approach integrating data from two countries and multiple sources provides an ecosystem level assessment that more effectively reflects the habitat use of harbor porpoise in this region that do not recognize the socio-political boundaries imposed upon the natural world.

Abstract: This paper summarises an early and successful piece of citizen science, performed within The University of Queensland Seismograph Stations (UQSS) observatory, in cooperation with colleagues at CSIRO. It was designed to en-courage young STEM students from Brisbane high schools to engage in “real” re-search, back in 1995. Having completed the project report, their (analog) results sat in a cupboard until the report was dusted off and the project was re-analysed in 2022 by an honors student, considering timely climate change applications for the study. This is a time when science is changing considerably from analog to digital medium and operational methods. The original project was called Earthquake generated T phases on BRS Seismograph (Brisbane, Q’ld)- a predictor for Tasman Sea Tsunamis? [1] Fortunately, seismology is a very collaborative field. The research question has since changed. There is a lot of data analysis involved in the science of recording earthquake signals, with auxiliary definitive catalogues, observers logbooks, housing of the recordings themselves (analog and digital) and the software mediums that change over time. In other words, a lot of headaches can be encountered in longitudinal data collection study such as this. The citizen science students used a pre-prepared decadal collection (1980-90) derived from the BRS observatory data catalogue. BRS is part of the global World-Wide Seismograph Station Network (WWSSN). Currently in Australia, university earth science observatories have diminished, and in their place, public seismic networks (PSN) have evolved, either in back-yard sheds or school science labs. The level of expertise required fits the role of advancing citizen science for a real science advantage. This is a topical citizen disaster preparedness action area for today’s climate emergency, which is threatening the globe and all lifeforms. Citizen engagement or mobilisation is an essential preparedness strategy. Keywords: citizen science 1; seismology 2; earth observation 3; climate change 4; education for sustainable development 5; disaster preparedness 6; disaster alerts 7

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

Drinking water quality is a major concern, especially in African countries. This manuscript aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the chemical elements' composition and origin. Therefore, chemometrics techniques such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while au-tocorrelation analysis (AA) supplemented by simple spectral density analysis (SDA) is used for TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, three groups were controlled by TDS and EC. G1 reveals a close association between SO42−, K+, Ca2+, and TDS; G2 reveals a close association between Na+, Cl−, Mg2+, and EC; G3 shows the dissociation of bicarbonates HCO3− and NO3− from other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. None-theless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several dis-tinct cyclic mechanisms. The methods enabled the authors to conclude that the geochemical processes influencing the chemical composition are: (i) dissolution of evaporated mineral depos-its, (ii) water-rock interaction, and (iii) evaporation process. In addition, Groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42-, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. The major participation of salts and chlorides is in the electrical conductivity of water. The dominance of the lithological factor in the overall mineralization of the Plio-Quaternary surface aquifer waters. The origins of HCO3− and NO3− are different. Indeed, carbonated for HCO3- has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42-, Cl-, Na+, K+, and EC. Ca2+, HCO3− and NO3− are resulted from human activity fertilizers, the carbonate facies outcrops, and domestic sewage.

Cities are undergoing huge shifts in technology and operations in recent days, and `data science' is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting insights or actionable knowledge from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the study and analysis of actual happenings with historical data using a variety of scientific methodology, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore ``Smart City Data Science", where city data collected from various sources like sensors and Internet-connected devices, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, various machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world services of today's cities. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, professionals, as well as policy-makers of a country, particularly, from the technological point of view.

Artificial Intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus AI-based modeling is the key to building automated, intelligent, and smart systems according to today's needs. To solve real-world issues various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

In the current age of the Fourth Industrial Revolution ($4IR$ or Industry $4.0$), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding real-world applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world applications areas, such as cybersecurity, smart cities, healthcare, business, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for not only the application developers but also the decision-makers and researchers in various real-world application areas, particularly from the technical point of view.

Objective: To highlight opportunities for future nutrition intervention research within early childhood and education care (ECEC) settings, with a focus on generating evidence that has applicability to real-world policy and practice. Methods: An overview of opportunities to progress the field was developed by authors using a collaborative writing approach and informed by recent research in the field. The group developed a list of recommendations aligned with the reach, effectiveness, adoption, implementation and maintenance (RE-AIM) framework. Pairs of authors drafted individual sections of the manuscript, which was then reviewed by a separate pair. The first and senior author consolidated all sections of the manuscript and sought critical input on draft iterations of the manuscript. Results: Interventions that employ digital platforms (Reach) in ECEC settings, as well as research in the family day care setting (Effectiveness) were identified as areas of opportunities. Research understanding the determinants of and effective strategies for dissemination (Adoption), implementation of nutrition programs, in addition to de-implementation (Implementation) of inappropriate nutrition practices, is warranted. For maintenance, there is a need to better understand sustainability and sustainment of interventions, in addition to undertaking policy-relevant research. Conclusions: The ECEC setting is prime for innovative and practical nutrition intervention research.

We continue to analyze basic constraints on human's decision making from the viewpoint of quantum measurement theory (QMT). As has been found, the conventional QMT based on the projection postulate cannot account for combination of the question order effect (QOE) and the response replicability effect (RRE). This was an alarm signal for quantum-like modeling of decision making. Recently, it was shown that this objection to quantum-like modeling can be removed on the basis of the general QMT based on quantum instruments. In the present paper we analyse the problem of combination of QOE, RRE, and the famous QQ-equality (QQE). This equality was derived by Busemeyer and Wang and it was shown (in the joint paper with Solloway and Shiffrin) that statistical data from many social opinion polls satisfies it. Now, we construct quantum instruments satisfying QOE, RRE, and QQE. The general features of our approach are formalized with postulates which generalize {\it Wang-Busemeyer} postulates for quantum-like modeling of decision making. Moreover, we show that our model closely reproduces the statistics of the famous Clinton-Gore Poll data with a prior belief state independent of the question order. This model successfully removes the order effect from the data to determine the genuine distribution of the opinions in the Poll. The paper also provides a psychologist-friendly introduction to the theory of quantum instruments - the most general mathematical framework for quantum measurements. We hope that this theory will attract attention of psychologists and will stimulate further applications.

The IRRESISTIBLE Project (FP7, Grant 612367) had the aim of involving teachers, students and the public in the discussion on Responsible Research and Innovation (RRI), promoting both the construction of knowledge on cutting-edge (and controversial) research topics and the discussion about the criteria that these research/innovation processes should respect in order to be considered as responsible. These criteria also represent a strong contribution to a more sustainable future for all. This quantitative research evaluates the impact of IRRESISTIBLE’s student-curated exhibitions – about the RRI dimensions of cutting-edge research topics (socio-scientific issues) – on students’ perceptions regarding their scientific competences and the science classes. A pre and post-questionnaire was developed, validated and applied to students from 10 countries. The overall results of the statistical analysis indicate that students improved their perceptions regarding their competences for developing exhibitions in science classes as a way of creating awareness on topics relating science-technology-society. This activity reinforced students’ perceptions that in science classes they: a) discuss current issues and how they impact their lives; b) develop socially and relevant projects; and c) learn how to influence other citizens’ decisions about social issues related to science, technology and environment with the aim of assuring a more sustainable future.

The purpose of this paper is to critique the definition of falsification as research misconduct according to the Public Health Service (PHS) in order to better understand what it entails. In support of this purpose, the approach decided upon for analysis was philosophical including framing the issue borrowing from both mereological and epistemological perspectives. Through the consideration given to parthood relations of mereology, we gained insight from a cognitive imperfection standpoint about similarities that exist between the epistemic constraints on knowledge and the nature of violations concerning research misconduct. Findings from the examination of a case study include the significance of accuracy in representation in falsification as misconduct and the core dimensions comprising an instance of falsification, which are Deliberateness, Alteration, and Inclusion. Given that either behavior or actions must occur that violate these three aspects in order to qualify as an instance of misconduct under falsification, the author proposes that, at a minimum, any revisions made to the definition of falsification stipulate what he refers to as the Violation Imperative.

Low-frequency electromagnetic induction (EMI) is a non-invasive geophysical method that is based on the induction of electromagnetic (EM) waves into the subsurface to quantify changes in electrical conductivity. In this study we present an open (design details and software are accessible) and modular system for the collection of EMI data. The instrument proposed allows to adjust the separations between the transmitter and up to four receiving antennas as well as the acquisition frequency (in the range between 3 and 50 kHz) to permit measurements with variable depth of investigation. The sensor provides access to raw data and the software described in this study allows control of the signal processing chain. The design specifications permit apparent conductivity measurements in the range of between 1 mS/m and 1000 mS/m, with a resolution of 1.0 mS/m and with a sampling rate of up to 10 samples per second. The sensor allows for a synchronous acquisition of a time stamp and a location stamp for each data sample. The sensor has a mass of less than 5 kg, is portable and suitable for 1-person operation, provides 4 hours of operation time on one battery charge, and provides sufficient rigidity for practical field operations.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

Background: The purpose of this study was to compare the immediate and long-term complications that are associated with the utilized techniques for the insertion of indwelling central venous catheters, that is the open surgical technique, the ultrasound-guided technique and the transcutaneous technique based on external anatomical landmarks in the right internal jugular vein, to a pediatric population. Methods: This was a prospective randomized trial based on a pediatric patient population, under 16 years of age, of a tertiary pediatric-oncological hospital. Procedure was performed by a medical team with varying experience regarding the percutaneous and open insertion methods. We studied the outcome of our procedure, based on the immediate and delayed complication rate, as well as the needed time in order to complete the procedure and mean duration of line use. Results: The patients that were inserted in our protocol were divided into three subgroups, based on the selected technique for the insertion of the central venous catheter. A total number of 88 insertions (25.4%) (out of 346) were based on the technique that was using external anatomical landmarks, 121 insertions were based on the ultrasound-guided transcutaneous technique (34,9%), whereas in 137 cases (39.5%) the open surgical technique was preferred. All cases that were related to catheter re-insertion were excluded from our study. We performed a statistical analysis regarding the catheter dwell time between the three subgroups of patients and no significant difference was recorded. Moreover, the development of thrombosis was investigated, and we mentioned that the higher percentage of this complication was related with the transcutaneous external landmark and open surgical technique. Also, the incidence of infection was taken into consideration, which manifested an increased incidence when the transcutaneous technique based on external landmarks was used. Conclusions: Ultrasound guided percutaneous insertion was considered to be a safe and effective technique for the insertion of central venous catheters. Our study also demonstrated a decrease in operating times, when performed by operators with increasing expertise, increased preservation of the diameter of the venous lumen, and no increase in complication rates when the ultrasound-guided technique was selected.

Abstract: The glucocorticoid receptor (GR), including both alternative spliced isoforms (GRa and GRb), has been implicated in the development of primary open-angle glaucoma (POAG) and iatrogenic glucocorticoid-induced glaucoma (GIG). POAG is the most common form of glaucoma, which is the leading cause of irreversible vision loss and blindness in the world. Glucocorticoids (GCs) are commonly used therapeutically for ocular and numerous other diseases/conditions. One serious side effect of prolonged GC therapy is the development of iatrogenic secondary ocular hypertension (OHT) and OAG (i.e. GC-induced glaucoma (GIG)) that clinically and pathologically mimics POAG. GC-induced OHT is caused by pathogenic damage to the trabecular meshwork (TM), a tissue involved in regulating aqueous humor outflow and intraocular pressure. TM cells derived from POAG eyes (GTM cells) have lower expression of GRb, a dominant negative regulator of GC activity, compared to TM cells from age-matched control eyes. Therefore, GTM cells have a greater pathogenic response to GCs. Almost all POAG patients develop GC-OHT when treated with GCs, in contrast to a GC responder rate of 40% in the normal population. Increased expression of GRb can block GC-induced pathogenic changes in TM cells and reverse GC-OHT in mice. Endogenous expression of GRb in the TM may relate to differences in the development of GC-OHT among the normal population. A number of studies have suggested increased levels of endogenous cortisol in POAG patients as well as differences in cortisol metabolism, suggesting that GCs may be involved in the development of POAG. Additional studies are warranted to better understand the molecular mechanisms involved in POAG and GIG in order to develop new disease modifying therapies to better treat these two sight threatening forms of glaucoma.

We investigate the innovational determinants of “Patent Applications” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS and Dynamic Panel. We found that the variables that have a deeper positive association with “Patent Applications” are “Human Resources” and “Intellectual Assets”, while the variables that show a more intense negative relation with Patent Applications are “Employment Share in Manufacturing” and “Total Entrepreneurial Activity”. A cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient has been realized. The results show the presence of two clusters. A network analysis with the distance of Manhattan has been performed and we find three different complex network structures. Finally, a comparison is made among eight machine learning algorithms for the prediction of the future value of the “Patent Applications”. We found that PNN-Probabilistic Neural Network is the best performing algorithm. Using PNN the results show that the mean future value of “Patent Applications” in the estimated countries is expected to decrease of -0.1%.

In this article we have estimated the value of “Opportunity Driven Entrepreneurship” in Europe. We use data from European Innovation Scoreboard-EIS of the European Commission for 36 countries in the period 2010-2019. We use Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, Pooled OLS, and Dynamic Panel. Our results show that “Opportunity Driven Entrepreneurship” is positively associated, among others, to “Innovation Friendly Environment” and “Turnover Share Large Enterprises”, while it is negatively associated, among others, to “Sales Impacts” and “R&D Expenditure Business Sectors”.

In this article we estimate the value of “Non-R&D Innovation Expenditures” in Europe. We use data from the European Innovation Scoreboard-EIS of the European Commission from the period 2010-2019. We test data with the following econometric models i.e.: Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. We found that “Non-R&D Innovation Expenditures” is positively associated among others to “Innovation Index” and “Firm Investments” and negatively associated among others to “Human Resources” and “Government Procurement of Advanced Technology Products”. We use the k-Means algorithm with either the Silhouette Coefficient and the Elbow Method in a confrontation with the network analysis optimized with the Distance of Manhattan and we find that the optimal number of clusters is four. Furthermore, we propose a confrontation among eight machine learning algorithms to predict the level of “Non-R&D Innovation Expenditures” either with Original Data-OD either with Augmented Data-AD. We found that Gradient Boost Trees Regression is the best predictor for OD while Tree Ensemble Regression is the best Predictor for AD. Finally, we verify that the prediction with AD is more efficient of that with OD with a reduction in the average value of statistical errors equal to 40,50%.

In this article we investigate the determinants of “New Doctorate Graduates” in Europe. We use data from the EIS-European Innovation Scoreboard of the European Commission for 36 countries in the period 2010-2019 with Pooled OLS, Dynamic Panel, WLS, Panel Data with Fixed Effects and Panel Data with Random Effects. We found that “New Doctorate Graduates” is positively associated, among others, with “Human Resources” and “Government Procurement of Advanced Technology Products” and negatively, associated among others, with “Total Entrepreneurial Activity” and “Innovation Index”. We apply a clusterization with k-Means algorithm either with the Silhouette Coefficient either with the Elbow Method and we found that in both cases the optimal number of clusters is three. Furthermore, we use the Network Analysis with the Distance of Manhattan, and we find the presence of seven network structures. Finally, we propose a confrontation among ten machine learning algorithms to predict the value of “New Doctorate Graduates” either with Original Data-OD either with Augmented Data-AD. Results show that SGD-Stochastic Gradient Descendent is the best predictor for OD while Linear Regression performs better for AD.

In this article we investigate the determinants of the European “Most Cited Publications”. We use data from the European Innovation Scoreboard-EIS of the European Commission for the period 2010-2019. Data are analyzed with Panel Data with Fixed Effects, Panel Data with Random Effects, WLS, and Pooled OLS. Results show that the level of “Most Cited Publications” is positively associated, among others, to “Innovation Index” and “Enterprise Birth” and negatively associated, among others, to “Government Procurement of Advanced Technology Products” and “Human Resources”. Furthermore, we perform a cluster analysis with the k-Means algorithm either with the Silhouette Coefficient and the Elbow Method. We find that the Elbow Method shows better results than the Silhouette Coefficient with a number of clusters equal to 3. In adjunct we perform a network analysis with the Manhattan distance, and we find the presence of 4 complex and 2 simplified network structures. Finally, we present a confrontation among 10 machine learning algorithms to predict the level of “Most Cited Publication” either with Original Data-OD either with Augmented Data-AD. Results show that the best machine learning algorithm to predict the level of “Most Cited Publication” with Original Data-OD is SGD, while Linear Regression is the best machine learning algorithm for the prediction of “Most Cited Publications” with Augmented Data-AD.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

The article affords the question of lifelong learning in Europe using data from the European Innovation Scoreboard-EIS in the period 2010-2019 for 36 countries. The econometric analysis is realized using WLS, Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects and Random Effects. The results show that lifelong learning is, among other variables, positively associated to “Human Resources” and “Government procurement of advanced technology products” and is negatively associated, among others, to “Average annual GDP growth” and “Innovation Index”. A clusterization is realized using the k-Means algorithm with a confrontation between the Elbow Method and the Silhouette Coefficient. Subsequently, a Network Analysis was applied with the distance of Manhattan. The results show the presence of 4 complex and 2 simplified network structures. Finally, a comparison was made among eight machine learning algorithms for the prediction of the value of lifelong learning. The results show that the linear regression is the best predictor algorithm and that the level of lifelong learning is expected to growth on average by 1.12%.

In this article we investigate the determinants of marketing or organizational innovators in Europe for 36 countries in the period 2010-2019. We have used data from the European Innovation Scoreboard-EIS of the European Commission. We perform different econometric models i.e. Dynamic Panel, Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. Results show that the level of marketing or organizational innovators in positively associated, among others variables to “Innovation Index”, “Innovators” and “Knowledge Intensive Service Exports”, while is negatively associated with “Sales Impacts”, “Foreign Controlled Enterprises Share of Value Added” and “Government procurement of advanced technology products”.

In the following article, the value of the "Knowledge Intensive Services Exports in Europe" in 36 European countries is estimated. The data were analyzed through a set of econometric models or: Poled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects, WLS. The results show that “Knowledge Intensive Services Exports” is negatively associated, among others, with "Buyer Sophistication", "Government Procurement of Advanced Technology Products", and positively associated with the following variables i.e. "Innovation Index", "Sales Impacts" and "Total Entrepreneurial Activity". Then a clusterization with k-Means algorithm was made with the Elbow method. The results show the presence of 3 clusters. A network analysis was later built and 4 complex network structures and three structures with simplified networks were detected. To predict the future trend of the variable, a comparison was made with eight different machine learning algorithms. The results show that prediction with Augmented Data-AD is more efficient than prediction with Original Data-AD with a reduction of the mean of statistical errors equal to 55,94%.

The following article indicates the determinants of “Internet User Skills” among European countries based on the application of the database deriving from the DESI-Index. The data were analyzed using the following econometric models, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, WLS corrected for heteroskedasticity. The Elbow method and the Silouette coefficient method were compared for the optimization of the number of clusters obtained by the k-Means algorithm. The result shows the presence of 5 clusters. A network analysis was carried out using the Euclidean distance with the result of identifying two network structures between some analyzed countries. subsequently a comparison was made between six different machine learning algorithms for the prediction of the future value of the variable of interest. The result shows that the best predictor algorithm is Gradient Boosted Tree Regression with an expected value of the predicted variable increasing by a value of 1.75%. Later a further comparison was made by comparing 6 algorithms with the increased data. The result shows that the best predictor is Simple Regression Tree. The interest variable is predicted to decrease by an amount equal to -6.099%. Statistical errors improve on average by 32.43% in the transition between the original data and the increased data.

The following article analyzes the determinants of the innovation index in Europe. The data refer to the European Innovation Scoreboard-EIS of the European Commission for the period between 2010 and 2019 for 36 countries. The data are analyzed using the following econometric techniques: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel Data, Pooled OLS, WLS. The results show that the Innovation Index is negatively connected to some variables, among which the most significant are "GDP per capita", "R&D expenditure public sector", "Venture capital", "Tertiary education", and positively connected to some variables among which the most relevant are: "Government procurement of advanced technology products", "Average annual population growth", "Finance and support", "Human resources", "Marketing or organisational innovators", "Linkages". A clustering was then carried out using the unsupervised k-Means algorithm optimized with the Silhouette coefficient which shows the presence of 2 clusters per value of the Innovation Index. Eight machine learning algorithms has been used for prediction with real data. The Tree Ensemble Regression algorithm has been chosen as best performer. A further prediction has been made with the augmented data. The result shows that the best performing algorithm is Linear Regression with an innovation index value predicted to grow by approximately 3.38%.

The following article estimates the value of ICT Specialists in Europe between 2016 and 2021 for 28 European countries. The data were analyzed using the following econometric techniques, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, WLS and Pooled OLS. The results show that the value of ICT Specialists in Europe is positively associated with the following variables: "Desi Index", "SMEs with at least a basic level of digital intensity", "At least 100 Mbps fixed BB take-up" and negatively associated with the following variables: "4G Coverage","5G Coverage", "5G Readiness", "Fixed broadband coverage", "e-Government", "At least Basic Digital Skills", "Fixed broadband take-up", "Broadband price index", "Integration of Digital Technology". Subsequently, two European clusters were found by value of "ICTG Specialists" using the k-Means clustering algorithm optimized by using the Silhouette coefficient. Finally, eight different machine learning algorithms were compared to predict the value of "ICT Specialists" in Europe. The results show that the best prediction algorithm is ANN-Artificial Neural Network with an estimated growth value of 12.53%. Finally, "augmented data" were obtained through the use of the ANN-Artificial Neural Network, through which a new prediction was made which estimated a growing value of the estimated variable equal to 3.18%.

The following article analyzes the determinants of e-government in 28 European countries between 2016 and 2021. The DESI-Digital Economy and Society Index database was used. The econometric analysis involved the use of the Panel Data with Fixed Effects and Panel Data with Variable Effects methods. The results show that the value of “e-Government” is negatively associated with “Fast BB (NGA) coverage”, “Female ICT specialists”, “e-Invoices”, “Big data” and positively associated with “Open Data”, “e-Government Users”, “ICT for environmental sustainability”, “Artificial intelligence”, “Cloud”, “SMEs with at least a basic level of digital intensity”, “ICT Specialists”, “At least 1 Gbps take-up”, “At least 100 Mbps fixed BB take-up”, “Fixed Very High Capacity Network (VHCN) coverage”. A cluster analysis was carried out below using the unsupervised k-Means algorithm optimized with the Silhouette coefficient with the identification of 4 clusters. Finally, a comparison was made between eight different machine learning algorithms using "augmented data". The most efficient algorithm in predicting the value of e-government both in the historical series and with augmented data is the ANN-Artificial Neural Network.

This article analyzes the determinants of the "Broadband Price Index" in Europe. The data used refer to 28 European countries between 2016 and 2021. The database used is the Digital, Economy and Society Index-DESI of the European Commission. The data were analyzed using the following econometric techniques, namely Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled OLS, WLS and Dynamic Panel. The value of the "Broadband Price Index" is positively associated with the DESI Index, and "Connectivity" while it is negatively associated with "Fixed Broadband Take Up", "Fixed Broadband Coverage", "Mobile Broadband", "e-Government", "Advanced Skills and Development", "Integration of Digital Technology", "At Least Basic Digital Skills ", "Above Basic Digital Skills "," At Least Basic Software Skills ". A cluster analysis was carried out below using the k-Means algorithm optimized with the Silhouette coefficient. The analysis revealed the existence of three clusters. Finally, an analysis of the machine learning algorithms was carried out to predict the future value of the "Broadband Price Index". The result shows that the most useful algorithm for prediction is the Artificial Neural Network-ANN with an estimated value equal to an amount of 9.21%.

The determinants of the presence of “Foreign Doctorate Students” among 36 European Countries for the period 2010-2019 are analyzed in this article. Panel Data with Fixed Effects, Random Effects, WLS, Pooled OLS, and Dynamic Panel are used to investigate the data. We found that the presence of Foreign Doctorate Students is positively associated to “Attractive Research Systems”, “Finance and Support”, “Rule of Law”, “Sales Impacts”, “New Doctorate Graduates”, “Basic School Entrepreneurial Education and Training”, “Tertiary Education” and negatively associated to “Innovative Sales Share”, “Innovation Friendly Environment”, “Linkages”, “Trademark Applications”, “Government Procurement of Advanced Technology Products”, “R&D Expenditure Public Sectors”. A cluster analysis was then carried out through the application of the unsupervised k-Means algorithm optimized using the Silhouette coefficient with the identification of 5 clusters. Finally, eight different machine learning algorithms were used to predict the value of the "Foreign Doctorate Students" variable. The results show that the best predictor algorithm is the "Tree Ensemble Regression" with a predicted value growing at a rate of 114.03%.

The storage of biochemical information, which is a prerequisite for the development of the first cell, is an unsolved problem affecting all concepts of the origin of life. However, if the protected environment in the continental crust is taken into account, completely new possibilities emerge for identifying processes that may have been crucial for the formation of the first cell. Under this background, we can hypothesize that, before cellular life began, a self-sustaining cycle of molecular reaction steps with information storage in RNA existed outside of a cell. This cycle was made possible in an open system bound to gas-permeable tectonic fracture zones with a high proportion of CO2 and N2. The formation of peptides and vesicles in supercritical CO2 and the chemical evolution of peptides have already been proven for the upper continental crust. Further considerations include the interactions of vesicles with catalytic peptides and the emergence of proto-tRNA. In combination with the formation of proto-tRNA synthetases, which consist of only two amino acid species and associated proto-tRNAs, the first RNA as an information storage system could have been formed with the information of proto-enzymes.

Wearable and mobile technology has advanced in leaps and bounds in the last decade with technological advances creating a role from enhancing healthy living to monitoring and treating disease. However, the discussion about the ethical use of such commercial technology in the community, especially in minors, is lacking behind. In this paper, we first summarize the major ethical concerns that arise from the usage of commercially available wearable technology in children, with a focus on smart watches, highlighting issues around the consent process, mitigation of risk and potential confidentiality and privacy issues, as well as the potential for therapeutic misconceptions when used without medical advice. Then through a relevant thought experiment we move on to outline some further ethical concerns that are connected to the use of wearables by minors, to wit the issue of informed consent in the case of minors, forcing them to live in the spotlight, and compromising their right to an open future. We conclude with the view that mitigating potential pitfalls and enhancing the benefits of wearable technology especially for minors requires brave and comprehensive moral debates.

The present investigation is focused on the forecasting visual observation of the impact of anthropogenic activity on the pilgrimage places located along the coastal environments in Tamil Nadu, India. Devotees performing the unregulated ritual ceremonies, open defecation, waste materials dumping and local municipality discharging wastewater contamination levels were assessed from direct visual surveillance, and by taking photographs and baseline information collected from five different pilgrimage sites. Results showed that ritual ceremonies, wastewater discharges and debris highly contaminated site-III, and found open defecation at site-I. The lack of coastal regulation, pollution awareness, insufficient sanitation facilities and failure to control the commercial and recreational activities have major deleterious effects on the present and future environments of the coastal areas. This is the first attempt conducted by visual assessment of the coastal pollution in pilgrimage places. The results immensely support the recommendation for proper regulation of ritual activities, arrangement of basic sanitation facilities and prohibition of wastewater discharges to prevent waterborne diseases as well as to strictly follow the regional and national level of coastal regulation policy to protect the biological resources of the Gulf of Mannar marine ecosystems.

Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. Central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this paper, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with Random Forest classifier using Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

1) Background: A population-based cross-sectional study was conducted to understand how green cover and accessibility of common public open spaces in compact urban areas affect physical activeness of resident. 2) Methods: A total of 554 residents completed a structured questionnaire on quality-of-life, physical activity level, and healthy eating practice. 3) Results: The sampled population lived with green cover averaged 10.11 ± 7.95% (ranged 1.56–9.88), whereas majority (90%) of the residents performed physical activities at medium and high levels. Metabolic Equivalent of Task (MET)-minutes/week was associated with the green cover percentage (Pearson r = 0.092; p < 0.05). Irrespective of age and physical activity level, active residents commonly used public open spaces within district for performing exercise, especially parks and promenade were mostly used by older residents while sports facilities by the younger groups. 4) Conclusions: Current findings suggested promotion of exercise could be achieved by the design or redesign of built environment to include more parks accessible to the residents with the increase of vegetation.

Although distributed additive manufacturing can provide high returns on investment the current markup on commercial filament over base polymers limits deployment. These cost barriers can be surmounted by eliminating the entire process of fusing filament by 3-D printing products directly from polymer granules. Fused granular fabrication (FGF) (or fused particle fabrication (FPF)) is being held back in part by the accessibility of low-cost pelletizers and choppers. An open-source 3-D printable invention disclosed here provides for precise controlled pelletizing of both single thermopolymers as well as composites for 3-D printing. The system is designed, built and tested for its ability to provide high tolerance thermopolymer pellets from a number of sizes capable of being used in a FGF printer. In addition, the chopping pelletizer is tested for its ability to chop multi-materials simultaneously for color mixing and composite fabrication as well as precise fractional measuring back to filament. The US$185 open-source 3-D printable pelletizer chopper system was successfully fabricated and has a 0.5 kg/hr throughput with one motor, and 1.0 kg/hr throughput with two motors using only 0.24 kWh/kg during the chopping process. Pellets were successfully printed directly via FGF and indirectly after being converted into high-tolerance filament in a recyclebot.

A study of Bianchi Type I cosmological model is undertaken in the framework of creation of particles. To accommodate the creation of new particles, the universe is regarded as an Open thermodynamical system. The energy conservation equation is modified with the incorporation of a creation pressure in the energy momentum tensor. Exact solutions of the field equations are obtained (i) for a particular choice of the particle creation function and (ii) by considering the deceleration parameter to be constant. In the first model the behavior of the solution at late times is investigated. The physical aspects of the model have also been discussed. In the case of the second model we have restricted our analysis to the power law behaviour for the average scale factor. This leads to a particular form for the particle creation function. The behavior of the solution is investigated and the physical aspects of the model have also been discussed for the matter dominated era.

Background: Video assisted thoracoscopic surgery (VATS) has become the recommended approach for treatment of resectable lung cancer. However, no large randomized clinical trial has been conducted formally comparing surgical resections completed by VATS to those done by open thoracotomy (OT) in low volume centers. The current study sought to assess differences in recurrence-free survival (RFS), overall survival (OS), positive margins and postoperative length of stay (LOS) between VATS and OT lobectomies in our center. Method: A single institution retrospective chart review from May 2005 through May 2015 was conducted. All patients diagnosed with stage I through III lung cancer who underwent surgical resection were selected. Patient and tumor characteristics recorded included age at diagnosis, sex, tobacco use, tumor location (side and lobe), stage, size and receipt of chemotherapy or radiotherapy. Chis-square and Wilcoxon-Mann-Whitney tests were used to compare demographics, tumor characteristics and LOS. Multiple logistic and Cox regression analyses were used to compute relative risk (RR) for positive margins and mortality hazard ratios along with 95 percent confidence intervals (95%CI), respectively. Results: Of the 235 patients, 101 subjects had VATS while OT was performed in 134 patients. Age at diagnosis, sex, tobacco use, tumor location, and size were comparable for VATS and OT. No significant difference was observed in the relative risk of positive margins for VATS versus OT, RR = 0.56 (95%CI = 0.26, 1.05). However, VATS had shorter median LOS compared to OT (4 vs. 6 days, respectively), p = 0.002. A comparison of VATS versus OT showed no significant difference in the risk of recurrence, HR = 1.21 (95%CI = 0.74, 2.00), or death, HR = 1.34 (95%CI = 0.88, 2.06), in the intent-to-treat population. Similarly, no significant differences in recurrence or mortality risk were observed between VATS versus OT for analyses conducted separately for each cancer stage group or those limited to patients with negative margins. Conclusion: Our study indicates that compared to OT, VATS leads to shorter LOS while achieving comparable margins status, recurrence-free and overall survival regardless of tumor stage at diagnosis.

The aim of this work was to determine if the development of low-cost or no-cost finite element analysis (FEA) software has advanced to the point where it can be used in place of trusted commercial FEA packages for linear static structural analyses using isotropic material models. Nonlinear structural analysis will be covered in a separate paper. Several suitable packages were identified, these underwent a process of systematic elimination when they were unable to meet the minimum imposed qualitative criteria. Three packages were chosen to be subjected to performance benchmarking, namely: Code_Aster/Salome Meca; Mecway and Z88 Aurora. SimScale, a browser-based analysis package was included as well because it met all the baseline criteria and has the potential to offer a completely cloud-based approach to computer aided engineering, potentially reshaping the way an engineering business views its operational capabilities. This paper presents the test cases and simulation results for packages that fall under the linear static structural analysis type.

The integration of AI in educational settings, particularly through Large Language Models (LLMs), is accelerating, reshaping pedagogical approaches due to students' interactions with new learning tools. This study assesses the impact of generative AI on the educational experiences of computer and data science students at the Center for Informatics, University of Paraíba (CI/UFPB), Brazil. Through Exploratory Factorial Analysis (EFA) of five psychometric scales, the research examines students' acceptance of LLMs, their associated burnout levels, technology anxiety, and the prevalence of metacognitive and dysfunctional learning strategies associated with LLMs. Results indicate significant adoption of AI technologies among students, accompanied by a low incidence of technology anxiety, manifesting as fears of losing jobs to AI. However, a significant correlation was observed between academic burnout and dysfunctional learning strategies, which could likely be attributed to the rigorous academic environment. Additionally, the employment of metacognitive strategies in conjunction with LLMs reflects an advanced learning approach, yet challenges with functional learning strategies persist. This study contributes to the discourse on AI in education, highlighting the need for educational frameworks that support effective AI adoption while addressing the psychological demands on students.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 to August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, a brief analysis of specific features of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis.

Domestic cats (Felis catus) are widespread worldwide as the most popular pets. This medium-sized carnivore have well-known negative effects on biodiversity, but there is still need to better understand the proximate causes of its predation. Based on a citizen science project, we assessed the role of spatiotemporal (i.e., latitude, longitude and seasons), climatic (i.e., rainfall), anthropogenic (i.e., human footprint, HFI) and individual (i.e., sex and age) variables on the number of preys returned home by cats in metropolitan France. Over the 5 048 cats monitored between 2015 and 2022, prey from 12 different classes (n= 36 568) were returned, being 68% mammals, 21% birds and 8% squamates. Shrews brought home by cats peaked during summer, while rodents were recorded during summer-autumn. Birds brought home by cats peaked in spring-summer and in autumn, and lizards in spring and in late summer. Lower HFI was associated with more voles and mice brought home and the opposite trend was observed for lizards and birds. Younger cats were more prone to bring home shrews, birds and reptiles. Although environmental factors play a minor role in prey brought home by cats, some geographical characteristics of prey species distribution partly explains the hunting behaviour of cats.

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of the study is to propose an artefact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of the study is that NB-IoT and LoRaWAN technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.

Analysing and understanding donor behaviour in Non-profit Organisations (NPOs) is challenging due to the lack of human and technical resources. Machine learning (ML) techniques can analyse and understand donor behaviour at a certain level; however, it remains to be seen how to build and design an Artificial Intelligence enabled Decision Support System (AI-enabled DSS) to analyse donor behaviour. Thus, this paper proposes an AI-enabled DSS conceptual design to analyse donor behaviour in NPOs. A conceptual design is created following a Design Science Research approach to evaluate an AI-enabled DSS's initial DPs and features to analyse donor behaviour in NPOs. The evaluation process of the conceptual design applied formative assessment through conducting interviews with stakeholders from NPOs. The interviews were conducted using the Appreciative Inquiry framework to facilitate the process of interviews. The results of analysis based on the interviews provide insightful information not only about the proposed conceptual design for an AI-enabled DSS, but also about what is required to analyse donor behaviour NPOs using DSS. Evaluating the conceptual design results recommend efficiency, effectiveness, flexibility, and useability in the requirements of the AI-enabled DSS. This research contributes to the design knowledge base of AI-enabled DSS for analysing donor behaviour in NPOs. Future research will combine theoretical components to introduce a practical AI-enabled DSS for analysing donor behaviour in NPOs. This research is limited to such analysis on donors who donate money or volunteer time for NPOs.

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 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.

Place-based education (PBE) and active learning are effective strategies for geoscience education. However, traditional PBE via field trips requires significant resources, time, physical abilities, and expertise of teachers. We provide an alternative PBE experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location specific videos about Earth events to create a virtual PBE experience. Using Google Earth, GEGVL organizes place-based videos by locations and links pertinent non-place-based videos and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower division undergraduates show that the design logic of GEGVL is promising virtual PBE organizer for interesting students in and helping them learn about Earth sciences.

Considering the significance of thermodynamic cycles in the global energy system, it is necessary to develop new general classes of thermodynamic cycles to relieve current energy and environmental problems. Inspired by the relationship between power cycles and refrigeration cycles, we realize that general classes of thermodynamic cycles should occur in pairs with opposite functions. Here we reverse class 1 heating cycles to obtain another new general class of thermodynamic cycles named class 2 heating cycles (HC-2s). HC-2s have two basic forms, and each contains six thermodynamic processes. HC-2s present the simplest and most general approach to utilizing the temperature difference between a medium-temperature heat source and a low-temperature heat sink to achieve efficient high-temperature heating. HC-2s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in the global sustainable energy system.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

Animals, with their complex and obligate multicellularity, evolved from microbial eukaryotes that were likely obligately or facultatively unicellular. The nature of the unicellular progenitors of animals has intrigued biologists since the late 19^th century, coinciding with the parallel spread of the cell theory and the theory of evolution. However, views on the ancestry of animals have been extremely varied. The huge diversity of single-celled organisms, the tremendous plasticity of animal cellular phenotypes, and the difficulties of organizing both into clear phylogenies in the pre-molecular era allowed a wide range of hypotheses to flourish, with nearly every major single-celled lineage, at one time or another, having been proposed as the precursor of animals. Most of these hypotheses never gained followers beyond their originator (such as the ideas that animals evolved directly from either bacteria, Volvox or fungi) and will not be discussed here. Three concepts, however, have been enduring and influential: (1) the amoeboid theory; (2) the flagellate theory; and the (3) the ciliate theory – to which a fourth category can now be added: (4) a mixed model, in which the ancestor was phenotypically plastic. We will discuss their origin, history, and current relevance.

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Agrivoltaic agrotunnels are currently designed for high density grow walls that are not amenable to bush berries or root crops. Commercial grow bins provide deeper substrates for produce with more root systems but have high costs per unit growing area. To overcome the economic limitations of grow bins, this study applies the distributed manufacturing open-source design paradigm to develop four designs for low-cost open source structures. The designs target root vegetables and bush fruit specifically to be adopted by remote communities with limited or no outdoor growing environment to offset the market price for imported fresh produce. The indoor growing designs provide the necessary structure for supporting grow light and grow bins and enabled the transplanted berry plants to flower and produced fruits. They provide a comparable amount (110L) or more of grow volume from 106 to 192 L. The water reservoir volume for the commercial system (62 L) and grow area (0.5 m³) is surpassed by all new designs that range from 64 to 192 L and 0.51 to 0.76 m³, respectively. These superior properties are possible with material costs for all four designs that save more than 90% of the economic cost of the commercial systems.

Fusing data from many sources helps to achieve higher results of analysis. In this work, we present a new algorithm to fuse data from multiple cameras with data from multiple lidars. This algorithm was developed to increase the sensitivity and specificity of autonomous vehicle perception systems, where the most accurate sensors measuring the vehicle’s surroundings are cameras and lidar devices. Perception systems based on data from one type of sensor do not use complete information and have lower quality. The camera provides two-dimensional images; lidar produces three-dimensional point clouds. We developed a method for matching pixels on a pair of stereoscopic images using dynamic programming inspired by an algorithm to match sequences of amino acids used in bioinformatics. We improve the quality of the basic algorithm using additional data from edge detectors. We also improve the algorithm performance by reducing the size of matched pixels determined by available car speeds. We perform point cloud densification in the final step of our method, fusing lidar output data with stereovision output. We implemented our algorithm in C++ with Python API, and we provided the open-source library named Stereo PCD. This library very efficiently fuses data from multiple cameras and multiple lidars. In the article, we present the results of our approach to benchmark databases in terms of quality and performance. We compare our algorithm with other popular methods.

This paper presents an extensive performance analysis of open-ended waveguide elements for direct radiating arrays with high scan angle (± 50º/60º). The evaluated designs are based on square and hexagonal apertures loaded with ridges. Both square and triangular lattices are considered in the framework of Ka-band downlink design requirements for future LEO mega-constellations. The parameter space defined by the monomodal condition has been explored to find an optimum value for each structure. The analyses are validated with both infinite and finite full-wave models in terms of active reflection coefficient, scan loss and cross-polar discrimination.

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley, one in the west/northwest, the other in the southeast. Wavelet analysis revealed the annual cycle to be most persistent temporal mode of PM10 variability in both subregions, with intermittent signals observed at time scales of around 120, 30~90 and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

This research examines how rainfall and mining affect the slope damage resulting from the transition from open-pit mining to underground mining. Using unmanned aerial vehicle (UAV), the Huangniu slope of Dexing Copper Mine was fully characterized, and experiments were conducted on rock samples from appropriate sites. First, the mechanical properties of the samples were measured. Then, the parameters of the similarity simulation experiments were derived based on the similarity theory. Subsequently, the rainfall, rock slope, data acquisition and monitoring systems were designed. Finally, the rock mass failure with different slope angles was analyzed, and the deformation and damage patterns under the coupling effect were obtained. The results show that rainfall increases pore water pressure and moisture content. Rainfall and slope-slip water have more impact on the open-pit platform. The pore water pressure values on the upper rock mass rise faster than inside. In the open-pit mining stage, the rock mass shifts slightly to the upper left. In the room mining stage, vertical fractures and goaf sinking occur. The fractures above the mine room form a semi-ellipse. In the pillar mining stage, the overlying rock displacement is evident, and fractures persist. In the continuous pillar mining stage, the overlying rock collapses massively. The 65° slope model is the most damaged, while the 55° slope model is the least damaged. The results also suggest that the UAV guides sample selection.

Abstract: Agriculture uses plastic products for containers, packaging, tunnels, drip irrigation tub-ing, mulches. Large amounts of plastics are used as mulches on the soil surface for vegetable pro-duction (tomato, cucumber, watermelon, strawberry, vine) to reduce the weed competition, in-crease water and fertilizer use efficiency and enhance crop yield. Portugal uses around 4,500 t/year of polyethylene plastic to cover approximately 23,000 ha of agricultural land and only a small amount is recovered for recycling or secondary uses because of dirt with soil, vegetation, pesticide and fertilizers. Cleaning and decontaminating polyethylene mulch are costly and commercial technology is often not accessible nor economically viable in the current economic Portuguese situation. Most plastic mulch is composed of polyethylene that degrades slowly and produces a large quantity of residues in soil with negative impact in the environment. In the present study, the effects of long-term cultivation of blueberry using green 100% high density polyethylene mulch, in the south Portugal were evaluated for soil chemical and biological changes. High density green plastic mulch did not contaminate the topsoil with di(2-ethylhexyl) phthalate and heavy metals, buttotal nitrogen and organic carbon concentrations, electric conductivity and microbial activity were reduced in the planting row compared with bare soil, without mulching. Further-more, the presence of plastic mulch did not affect negatively the presence of nematodes, and the Rhabditida (bacterial feeders) increased in the planting and covered row.

The loop and knot securities of 2 braided and 2 monofilament sutures of four diameters (3.0, 2.0, 0, 1) were evaluated with a tensiometer for four 4-throw knots known to be secure with a 2.0 polyfilament suture. Loop security of monocryl 1 is low, being 14.7 + 3.0 Newton (N) for a 3-throw half-knot (H3) and 15.4 + 2.4 N and 28.3+ 10 N for 2 (SSs) and 4-throw (SSsSsSs) symmetrical sliding half-hitches. This is lower than 18, 24, and 46 N for similar knots with vicryl. Polyfilament sutures have excellent knot security for all diameters. Occasionally, some slide to open with slightly lower knot security, especially for larger diameters, although not clinically problematic. Knot se-curity of monofilament sutures was unpredictable for all 4 knots, especially for larger diameters, resulting in many clinically insecure knots. A secure monofilament knot requires a 6-throw knot with 2 symmetrical sliding half-hitches or 2 symmetrical half-knots secured with 4 asymmetric blocking half-hitches. In conclusion, with polyfilament sutures, 4 or 5-throw half-knot or half-hitch sequences result in secure knots. For monofilament sutures, loop and knot security is much less, half-knot combinations should be avoided, and secure knots require 6-throw knots with 4 asym-metric half hitches. This conclusion is valid for gynecologic laparoscopic and open surgery, and for surgery in general.

This study propounds a sustainable alternative to petroleum-based polyurethane (PU) foams aiming to curtail this nonrenewable resource's continued and uncontrolled use. Coconut fatty acid distillate (CFAD) and crude glycerol (CG), both wastes from vegetable oil processes, were utilized for bio-based polyol production for rigid PU foam application. The raw materials were subjected to catalyzed glycerolysis with alkaline-alcohol neutralization and bleaching. The resulting polyol possessed properties suitable for rigid foam application with an average OH number of 215 mg KOH/g, acid number of 7.2983 mg KOH/g, and Gardner color value of 18. The polyol was used to prepare rigid PU foam, and its properties were determined using FTIR, TGA/DTA, and UTM. Additionally, the cell foam morphology was investigated by scanning electron microscope (SEM) in which most of its structure revealed an open-celled network and quantified at 92.71% open cell content using pycnometric testing. The PU foam thermal and mechanical analysis results showed an average compressive strength of 70.59 kPa, thermal conductivity of 46.50 mWm-1K-1, and density of 32.79 kgm-3. These properties showed its applicability as thermal insulating foam, thus, demonstrating the potential use of CFAD and CG in commercial polyol and PU foam production.

We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; 2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and 3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.

Biocultural heritage preservation relies on ethnobotanical knowledge and the paleoethnobotanical data used in (re)constructing histories of human-biota interactions. Biocultural heritage, defined as the knowledge and practices of Indigenous and Local peoples and their biological relatives, is often guarded information, meant for specific audiences and withheld from other social circles. As such, these forms of heritage and knowledge must also be included in the ongoing data sovereignty discussions and movement. In this paper we share the process and design decisions behind creating an online database for ethnobotanical knowledge and associated paleoethnobotanical data, using a content management system designed to foreground Indigenous and local perspectives. Our main purpose is to suggest the Mukurtu content management system, originally designed for physical items of cultural importance, be considered as a potential tool for digitizing and ethically circulating biocultural heritage, including paleoethnobotanical resources. With this database, we aim to create access to biocultural heritage and paleoethnobotanical considerations for a variety of audiences while also respecting the protected and sensitive natures of Indigenous and local knowledges.

The inherent value and use of hydrocarbon from waste plastics and solvents can be extended through open-loop chemical recycling as this process converts plastic to range of non-plastic materials. This process is enhanced by first creating plastic-solvent-combinations from multiple sources which are then streamlined through single process stream. We report on the relevant mechanics for streamlining industrially relevant polymers such as polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE) and acrylonitrile butadiene styrene (ABS) into chemical slurries mixed with various organic solvents such as toluene, xylene and cyclohexane. The miscibility of the polymer feedstock within the solvent was evaluated using the Relative Energy Difference method, and the dissolution process was evaluated using the “Molecular theories in a continuum framework” model. These models were used to design a batch process yielding 1 tonne/h slurry by setting appropriate assumptions including constant viscosity of solvents, disentanglement-controlled dissolution mechanism and linear increase of the dissolved polymer’s mass fraction over time. Solvent selection was found to be the most critical parameter for the dissolution process. The characteristics of the ideal solvent are high affinity to the desired polymer and low viscosity. This work serves as a universal technical guideline for open-loop chemical recycling of plastics avoiding the growth of waste plastic in a circular economy framework.

At least six small alternate-frame open reading frames (ORFs) overlapping well-characterized SARS-CoV-2 genes have been hypothesized to encode accessory proteins. Researchers have used different names for the same ORF or the same name for different ORFs, resulting in erroneous homological and functional inferences. We propose standard names for these ORFs and their shorter isoforms, developed in consultation with the Coronaviridae Study Group of the ICTV. We recommend calling the 39 codon Spike-overlapping ORF ORF2b; the 41, 57, and 22 codon ORF3a-overlapping ORFs ORF3c, ORF3d, and ORF3b; the 33 codon ORF3d isoform ORF3d-2; and the 97 and 73 codon Nucleocapsid-overlapping ORFs ORF9b and ORF9c. Finally, we document conflicting usage of the name ORF3b in 32 studies, and consequent erroneous inferences, stressing the importance of reserving identical names for homologs. We recommend that authors referring to these ORFs provide lengths and coordinates to minimize ambiguity due to prior usage of alternative names.

Ascorbate; Pars Plana Vitrectomy; Open Angle Glaucoma; Oxidative Stress; Vitreous Me-tabolism 0. How to Use This Template

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.

Distribution System Operators (DSOs) are adapting to changing energy demands and the shift toward renewable and decentralized energy. Circularity is increasingly relevant for DSOs as their distribution systems age and need replacing. Taking circular action, however, is generally regarded as challenging as the current circular economy (CE) landscape is highly fragmented and rarely touches the implementation level. A longitudinal case study at Dutch DSO Liander on the circu-larity of distribution transformers was used to understand empirical success factors for taking circular action and to generate prescriptive knowledge on the operationalization of circularity. Six design propositions for circular action were formulated that suggest to 1) initiate small-scale cir-cularity experiments, 2) involve technical and strategic experts, 3) synergize circularity with more urgent, primary goals 4) translate circular initiatives bottom-up and top-down, 5) collaborate with other DSOs, and 6) create multidisciplinary teams. At DSO Liander, selecting the right interventions depending on the changing context contributed to operationalizing circularity for distribution transformers and achieving circularity targets. The generalizable design propositions could assist other DSOs in optimizing their strategy toward circular action.

A proposed interpretation of the characteristics and causes of the variation in science dynamics for technological evolution is described. The theoretical approach focuses on variability in research topics that guide scientific and technological evolution. Statistical method verifies the proposed approach by applying entropy, measures of dispersion and variance decomposition. The statistical evidence, using data of four main research fields in quantum technologies, suggests the following empirical properties: a) the growth of heterogeneity in research topics is inversely proportional to the age of research fields: recent research fields have a higher variability between topics with uncertainty in evolutionary dynamics, whereas older research fields have a lower variability and more concentration in specific research topics with stable evolution towards clear directions; b) the nature of research fields has a systematic effects in variability guiding the evolution of technologies. The theoretical approach and findings clarify main characteristics and causes of the evolution of science and technologies considering the underlying homogeneity or heterogeneity in research topics, providing main implications to improve the scientific and technological forecasting that may support appropriate R&amp;D investments towards promising research fields and technologies

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

In this paper, a distinction is first made between environmental, sustainable and green chemistry; the last two are then examined in relation to the more general problem of the environmental education. A brief historical digression on the STS (Science, Technology and Society) movement tries to dissect the reasons why chemistry is seen by the general public as a problem, not as a decisive resource for the realization of the ecological transition. Although sustainable and green chemistry can be decisive in overcoming the insularity of chemical disciplines in the high school, it fails to effectively embed itself in educational practices. Specific operational goals, supporting a real scientifical and technological literacy in sustainable and green chemistry, can help chemical educators. They are provided at the end of an examination of the founding axes of sustainable and green chemistry, according to the criteria of scientific and technological literacy.

This paper presents the results of a study aimed at understanding how technology maturity level influences the incidence of university technology transfer to the private sector. The study examined the topic from the perspective of private sector organizations. It used data from a random sample of patent applications filed with the United States Patent and Trademark Office and a theoretically guided sampling of multiple cases of private sector organizations that contemplated obtaining and assimilating technologies created at universities in the United States. The patent application data were analyzed using nonparametric statistical techniques and the case data were analyzed using qualitative comparative analysis (QCA). The findings of the study suggest that the typical maturity level of technologies created at U.S. universities is a TRL-5 or lower on as scale adapted from the NASA technology readiness level (TRL) scale. A technology maturity level of TRL-6 or higher is likely an insufficient but necessary part of at least one unnecessary but sufficient configuration of conditions that tends to result in the occurrence of university technology transfer. However, under certain circumstances, a technology maturity level of at least TRL-6 could be a sufficient but unnecessary condition for the occurrence of university technology transfer. These findings have several important implications. First, they provide support for the notion that university technology transfer is subject to causal complexity. Moreover, it may be possible to increase the incidence of university technology transfer in the United States by implementing public policy and practices that explicitly take technology maturity level into consideration.

Integrated landscape approaches (ILA) aim to reconcile multiple, often competing, interests across agriculture, nature conservation, and other land uses. Recognized ILA design principles provide guidance for their implementation, yet application remains challenging, and a strong performance evidence-base is yet to be formed. A comprehensive literature review and focus group discussions with practitioners identified considerable diversity of ILA in actors, temporal, and spatial scales, inter alia. This diversity hampers learning from and steering these integrated planning approaches because of its intractable nature. Therefore, we developed a tool—an ‘ILA mixing board’—to structure the complexity of ILA into selectable and scalable attributes in a replicable way to allow planning, diagnostics, and comparative assessment of ILA. The ILA mixing board tool presents seven qualifiers, each representing a key attribute of ILA design and performance such as project flexibility, inclusiveness of the dialogue, and the centrality of the power distribution. Each qualifier has five (non-normative) outcome indicators that can be registered as present or absent. This process in turn guides planners, evaluators and other participating stakeholders involved in landscape management to diagnose the ILA type, and or its performance. We apply the ILA mixing board as a diagnostic tool to three ILA cases in Nicaragua, Madagascar, and the Congo Basin to show some of the many possible configurations of qualifiers on the mixing board. Overall, the tool allows comparative analyses of the complexity of ILA in a structured and manageable way.

During planetary exploration mission operations, one of the key responsibilities of the instrument teams to determine data viability for subsequent analysis. During the 2019 CanMoon Lunar Sample Return Analogue Mission, the Lead Raman Specialist manually examined each spectra to provide quality assurance/validation. This non-trivial process requires years of experience to complete accurately. With the proven efficacy of Convolutional Neural Networks (CNNs) in classification tasks, and the increased use of automation and control loops on planetary space platforms for navigation and science targeting, an opportunity presents itself to approach this validation problem utilising CNNs. We present the Generalised Raman Validation Network (GRaVN), an neural network focused specifically on extracting the generalised structure of Raman spectra for quality assurance/validation. This work demonstrates the viability of utilising a CNN network in validation activities for Raman spectroscopy. Utilising only two hidden layers, a configuration was developed that provided good levels of accuracy on a manually curated dataset. This indicates that such a system could be useful as part of an autonomous control loop during planetary exploration activities.

The first two decades of the 21st-century have seen the emergence of the modern citizen science movement, increased demand for niche eco and wildlife tourism experiences, and the willingness of people to voluntarily share information and photographs online. To varying extents, the rapid growth of these three phenomena has been driven by the availability of portable smart devices, access to the Web 2.0 internet from almost anywhere on the planet, and the development of applications and services, including social media/networking sites (SNSs). In addition, the number of peer-reviewed publications that explore how text and images shared on SNSs can be data-mined for academic research has surged in recent years. This systematic quantitative review has two goals. The first goal is to provide an oversight of how the photographs that ecotourists share online are contributing to wildlife tourism research. The second goal is to promote the emerging photovoice technique as a theoretical context for social research based on the photographs and comments that ecotourists share on SNSs. From the perspectives of community benefits, conservation behaviours, and environmental education, there are many similarities between authentic ecotourism experiences and quality ecological citizen science programs. Much of the literature regarding the theory and practice of citizen science reports on the difficulties of attracting, training, motivating and retaining community members. The synthesis of this review is that crowdsourcing wildlife and tourism data from comments and photographs that ecotourists share on SNSs is a credible method of research that provides a self-replenishing pool of citizen scientists.

We describe how clinical researchers can exploit the Android cell phone as an economic platform for the gathering of data from clinical trial participants. The aim was to provide a solution with the shortest possible learning curve for researchers who are comfortable with setting up web pages. The additional requirement is that they extend their skills to the installation of a local webserver on the cell phone and then use four simple PHP templates to construct the clinical research data collection and processing forms. Data so collected is automatically written to local csv files on the cell phone. These csv phones can be retrieved from the device by the researcher simply by plugging the cell phone into their desktop PC and accessing the cell phone memory in just the same way as they would a USB memory stick. The results are presented as a list of recommended Android Apps along with settings that have proved to provide a stable combination likely to be easily used by clinical research participants. We have made a limited ‘user trial’ of this approach with satisfactory feedback received. We have concluded that this approach will reward researchers with a solution that is user friendly, will provide transcription free data and that is more than cost competitive with the conventional error prone/poor compliance ‘paper based participant form – researcher transcription’ cycle.