The purpose of this study is to bring out a scientometric analysis of the advances of digital transformation in the global banking sector. For this, a background search of the term "Digital Transformation Banking " has been brought out from the Web of Science (Wos) Database, covering records published between 2002 and 2023. A set of 312 publications was identified, which mainly includes a set of 264 academic articles, 29 articles published without cataloging a specific volume of a journal, 9 articles under review, 5 editorial material documents, 4 conference articles, and 1 document is a book chapter. Nowadays the scientific production is low but showing growing development presenting 123 publications and 7,754 citations for the year 2022. Among the scientific production, 43 publications from the Republic of China (8.8%) and 35 publications from Ukraine (7.8%) stand out. ,2%). It is also worth mentioning that the most relevant authors on this topic are Ricardo Palomo, professor and dean of San Pablo University, and Robert J Kauffman, professor at Singapore Management University in terms of influence. Main institutions in terms of scientific production, mainly: the Massachusetts Institute of Technology (MIT) of the United States and the Russian Academy of Sciences of Russia.

Lagerstroemia is a genus of deciduous and evergreen shrubs and trees belonging to the family Lythraceae. Due to the attention paid to environmental and health issues, Lagerstroemia has become increasingly important in ecological environment protection, drug development, genetic resource protection, and application research. In order to gain an in-depth understanding of the current status and cutting-edge trends of Lagerstroemia research in the world, and objectively reflect the scientific capabilities and influence of relevant research countries, institutions, authors, and journals in this field. Using the visual analysis software VOSviewer and CiteSpace, a statistical analysis of the literature on Lagerstroemia in the Web of Science database was performed from 2002 to 2022. The results showed that the number of articles published on Lagerstromia in the world was increasing. Four of the five articles with the highest citation frequency were from the United States, and the number of articles published in Hortscience was the highest. In terms of subject distribution, the most frequently published subjects were plant sciences, pharmacology and pharmacy. These two disciplines focused on the cultivation of Lagerstroemia species and medicinal research on Lagerstroemia extracts. In terms of categories, science accounted for 41% of the top 10 subjects. Technical science accounted for a relatively large proportion. However, the amount of relevant literature in engineering science needs to be improved. India, the United States, and China were the main research forces in this field. In terms of international cooperation, China had close collaborations with many countries such as the United States, Japan, India, Russia, and the United Kingdom. Beijing Forestry University ranked first in terms of total number of publications, and the Chinese Academy of Sciences had the highest total citations and average citations. Meanwhile, most of the authors at the top of the list were Chinese scholars. Cluster analysis showed that the research of keywords such as “oxidative stress”, “protein” and “corosolic acid” were the research hotspots in recent years. Research on the chemical components and pharmacological effects of Lagerstroemia will be the focus of the research on Lagerstroemia.

(1) Background: Surgical phases form the basic building blocks for surgical skill assessment, feedback and teaching. Phase duration itself and its correlation to clinical parameters has not yet been investigated. Novel commercial platforms provide phase indications but have not been assessed for accuracy yet. (2) Methods: We assess 100 robot-assisted partial nephrectomy videos for phase duration based on previously defined proficiency metrics. We develop an annotation framework and subsequently compare our annotations to an existing commercial solution (Touch Surgery, Medtronic™). We subsequently explore clinical correlations between phase durations and peri-operative parameters. (3) Results: Objective and uniform phase assessment requires precise definitions derived from an iterative revision process. Comparison to a commercial solution shows large differences in definitions across phases. BMI and duration of renal tumor identification correlate positively, as well as tumor complexity and both tumor excision and renorraphy duration. (4) Conclusions: Surgical phase duration can be correlated with certain clinical outcomes. Further research should investigate if retrieved correlations are also clinically meaningful. This requires increasing dataset sizes and facilitation through intelligent computer vision algorithms. Commercial platforms can facilitate this dataset expansion and help unlock the full potential, provided the disclosure of phase annotation details.

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

The goal of this study is to develop a global analysis, based on data from 2015 to 2022, that clarifies the impact of containment policies (e.g., lockdown and quarantine) for Coronavirus Disease-2019 (COVID-19) on the air pollution between countries of different continents. In this context, average changes of CO, NO2, SO2, O3, PM2.5, and PM10 concentrations based on measurements at ground level in January, February, and March for 2019, 2020, 2021, and 2022 are compared with average values of 2015-2018 period between 300 cities of 19 countries in 5 continents. Results show that the maximum reduction in pollutant concentrations during this period is given by: CO (-4,367.5%) in France, NO2 (-150.5%) in China and Australia, SO2 (-154.1%) in Israel, O3 (-94.1%) in China, PM2.5 (-41.4%) in Germany and PM10 (-157.4%) in Turkey. Findings reveal that the effects of containment policies on air quality vary significantly between countries depending on different geographical characteristics of regions. This study has main environmental policy implications because it clarifies the critical role of severe control measure to reduce air pollution and support sustainable environment and development.

Since streptomycin discovery, actinomycetes were the main source for new antibiotics, but after the Golden age (1950-1960^th) the discovery rate significantly decreased. The high probability to rediscover well-known antibiotics led to a reduction in interest in soil bacteria as a source for new antibiotics. At the same time, actinomycetes remain a very promising reservoir for searching for new active molecules. In this work, we present several reporters containing eye-visible fluorescent protein genes, which can be used to increase the efficiency of determining the mechanism of antibiotics at the very initial stage of screening. Presented reporters and the following pipeline were optimized given the involvement of citizen scientists without specialized skills and equipment in order to utilize the reservoir of soil bacteria in the search for new antibiotic producers. The combination of mechanism-based approaches and civil science has proved its effectiveness in practice revealing a significant increase in the screening rate. Two new strains Streptomyces sp. KB-1 and BV113 were found to produce antibiotics pikromycin and chartreusin, respectively, demonstrating the efficiency of the pipeline.

Despite the acknowledged relevance of renewable energy sources, biofuel production supported by food-related agriculture has faced severe criticism. One way to minimize the considered negative impacts is the use of sources of non-food biomass or wastes. Synthetic biology (SB) embraces a promising complex of technologies for biofuel production from non-edible and sustainable raw materials. Therefore, it is pertinent to identify the global evolution of investments, concepts, and techniques underlying the field in support of policy formulations for sustainable bioenergy production. We mapped the SB scientific knowledge related to biofuels using software that combines information visualization methods, bibliometrics, and data mining algorithms. The United States and China have been the leading countries in developing SB technologies. Technical University of Denmark and Tsinghua University are the institutions with higher centrality and have played prominent roles besides UC-Los Angeles and Delft University Technology. We identified six knowledge clusters under the terms: versatile sugar dehydrogenase, redox balance principle, sesquiterpene production, Saccharomyces cerevisiae, recombinant xylose-fermenting strain, and Clostridium saccharoperbutylacetonicum N1-4. The emerging trends refer to specific microorganisms, processes, and products. Yarrowia lipolytica, Oleaginous yeast, E. coli, Klebsiella pneumoniae, Phaeodactylum tricornutum, and Microalgae are the most prominent microorganisms, mainly from the year 2016 onwards. Anaerobic digestion, synthetic promoters, and genetic analysis appear as the most relevant platforms of new processes. Improved biofuels, bioethanol, and N-butanol are at the frontier of the development of SB-derived products. Synthetic biology is a dynamic interdisciplinary field in environmentally friendly bioenergy production pushed by growing social concerns and the emergent bioeconomy.

Today, across the most critical problems faced by hospitals and health centers are those caused by the existence of patients who do not attend their appointments. Among others, this practice generates waste of resources and increases the patients’ waiting list. To handle these problems, hospitals are actively trying to implement methods to reduce the idle time caused by patient no-shows. Many scheduling systems developed require predicting whether a patient will show up for an appointment or not. Although, a challenging problem resides in obtaining these estimates precisely. The goal of this work is to analyze how objective factors influence a patient not to attending their appointment, to identify the main causes that contribute to a patient’s decision, and to be able to predict whether or not the patient will attend the scheduled appointment. As a result, the obtained model is tested on a real dataset collected in a health center linked to the University of Vale do Itajaí (UNIVALI), which includes 25 features and about 5000 samples. The algorithm that produced the best results for the available dataset is the Random Forest classifier. It reveals the best recall rate (0.91), since it measures the ability of a classifier to find all the positive instances and achieves a receiver operating characteristic curve rate of 0.969.

Preprints are regularly cited in peer reviewed journal articles, books and conference paper. Are preprint citations somehow less important than citations to peer reviewed research papers? This study investigates citation patterns between 2017 and 2020 for preprints published in three preprint servers, one specializing in biology (bioRxiv), one in chemistry (ChemRxiv), and another hosting preprints in all disciplines (Research Square). As evaluation of scholarship continues to largely rely on citation-based metrics, this analysis and its outcomes will be useful to inform new research-based education in today’s scholarly communication.

Clearly policy makers should consider the impacts of any decisions they might make before making them. Science can provide estimates of various economic, ecologic, environmental, and even social impacts of alternative policies, impacts that determine how effective any particular policy will be. These impact estimates can be used to compare and evaluate alternative policies in the search for identifying the best one to implement. Among all scientists providing inputs to policy making processes are analysts who develop and apply models that provide these estimated impacts and, possibly, their probabilities of occurrence. But just producing them is not a guarantee that they will be considered by policy makers. This paper discusses ways scientists, including systems analysts, can effectively contribute to and inform those involved in making water management decisions. Brief descriptions of a variety of past and on-going water management policy making processes illustrate both some successes and failures of science informing policy.

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based studies using these datasets require thoughtful modification to sample selection and analytic strategies, relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings. This paper offers some practical guidance to the full analytical process, demonstrates methods for estimating causal treatment effects on several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying repeated measures outcomes, and aims to increase transparency and reproducibility. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

This paper presents and discusses the pedagogical implication of teaching Shared Value, presenting a case study about a contamination lab namely the case of the Shared Value Living Lab (SVLL) which took place at the University of Torino (UniTO) in Italy. The paper analyzes the pedagogical side of CSV (arguments, topics, learning methodologies, etc.) in the framework of recent theories and approaches of teaching sustainability in business school and in the cooperation between industry and academia, as well. Our research methodology relies on the analysis and comparison of one case study under an intrinsic as it enables researchers to find “interactivity” and connectedness between the individuals participating. The SVLL case is an interesting example of co-creation of social value between academia and its stakeholders. First, it represents an inclusive project linking society and business; second, SVLL acted as a hub putting in contact different interlocutors; third, SVLL training stimulated the acquisition of soft skill in students (interviews, managing relations, represents the project outside university, explaining the project to people, research curiosity) through contamination activities. With our study, we demonstrate the change in students participating in the SVLL may not only be cognitive but also affective, making students feel hopeful, empowered and liberated, and ready to contribute to a more profound change towards the vast array of pro-sustainability behaviors.

Innovative teaching strategies are designing a new and promising landscape in education. They fill up the lessons with creativity and imagination either for the students and teachers. This article addresses an attempt to make easier the approach to science in a non-scientific environment: primary education at university level. Gamification methodologies were combined with flipped classroom in order to free up in-class time and engage the students with the taught courses. A qualitative study was merged with quantitative measures of emotional and motivational parameters. These results were improved with four semi-structured interviews. The results clearly showed a raise in the students' motivational level, an acknowledgment of good teaching practice and an evident enhancement of felt positive emotions toward science teaching and scientific issues.

We are witnessing a dramatic transformation in the way we do science. In recent years, significant flaws with existing scientific methods have come to light, including lack of transparency, insufficient involvement of stakeholders, disconnection from the public, and limited reproducibility of research findings. These concerns have sparked a global movement to revolutionize scientific practice and the emergence of Open Science. This new approach to science extends principles of openness to the entire research cycle, from hypothesis generation to data collection, analysis, replication, and translation from research to practice. Open Science seeks to remove all barriers to conducting high quality, rigorous, and impactful scientific research by ensuring that the data, methods, and opportunities for collaboration are open to all. Emerging digital technologies and "big data" (see "Ten simple rules for responsible big data research") have further accelerated the Open Science movement by affording new approaches to data sharing, connecting researcher networks, and facilitating the dissemination of research findings. Open scientific practices are also having a profound impact on the health sciences and medical research, and specifically how we conduct clinical research with human participants. Human health research necessitates careful considerations for practicing science in an ethical manner. There is also a particular urgency to human health research since the goal is to help people, so doing good science takes on a different meaning than simply doing science well. It also implores the scientist to reassess the conventional view of human health research as a pursuit conducted by scientists on human subjects, and lays a greater emphasis on inclusive and ethical practices to ensure that the research takes into account the interests of those who would be most impacted by the research. Openness in the context of human health research also raises greater concerns about privacy and security and presents more opportunities for people, including participants of research studies, to contribute in every capacity. At the core of open health research, scientific discoveries are not only the product of collaboration across disciplines, but must also be owned by the community that is inclusive of researchers, health workers, and patients and their families. To guide successful open health research practices, it is essential to carefully consider and delineate its guiding principles. This editorial is aimed at individuals participating in health science in any capacity, including but not limited to people living with medical conditions, health professionals, study participants, and researchers spanning all types of disciplines. We present ten simple rules that, while not comprehensive, offer guidance for conducting health research with human participants in an open, ethical, and rigorous manner. These rules can be difficult, resource-intensive, and can conflict with one another. They are aspirational and are intended to accelerate and improve the quality of human health research. Work that fails to follow these rules is not necessarily an indication of poor quality research, especially if the reasons for breaking the rules are considered and articulated (see rule 6: document everything). While most of the responsibility of following these rules falls on researchers, anyone involved in human health research in any capacity can apply them.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

The development and generalization of Digital Volume Correlation (DVC) on X-ray computed tomography data highlight the issue of long term storage. The present paper proposes a new model-free method for pruning the DVC data. The size of the remaining sampled data can be user-defined, depending on the needs concerning storage space. The data pruning procedure is deeply linked to hyper-reduction techniques. The DVC data of a resin-bonded sand tested in uniaxial compression is used as an illustrating example. The relevance of the pruned data is tested afterwards for model calibration. A new Finite Element Model Updating (FEMU) technique coupled with an hybrid hyper-reduction method is used to successfully calibrate a constitutive model of the resin bonded sand with the pruned data only.

New approaches to collect in-situ data are needed to complement the high spatial (10~m) and temporal (5-day) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowd-sourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: 1) what is the spatial availability of these images across the European Union (EU)? and 2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary - the largest open-access platform for such images - against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337031 points. For 37.78% of the LUCAS points a crowd-sourced image is available within a 2-km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowd-sourced image within 300-m from a LUCAS point, illustrating the huge potential of crowd-sourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785710 Dutch parcels and the pictures' meta-data such as camera orientation and focal length. Availability of crowd-sourced imagery looking at parcels was assessed for 8 different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowd-sourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas.

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time open science becomes increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a toolbox to construct comprehensive energy system models and has been published open source under a free license. With a collaborative development based on open processes the framework seeks for a maximum level of participation and transparency to facilitate open science principles in energy system modelling. Based on a generic graph based description of energy systems it is well suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems ranging from an urban to a transnational scale.

Despite the strong links between sleep, circadian rhythms, and mental health, sleep education has been neglected in mental healthcare provider training programs. The current pilot study examined the potential efficacy and acceptability of a sleep education workshop for trainee psychologists, called the Sleep Psychology Workshop. Eleven students completing their Master of Psychology degrees (90% female, 24.4 ± 1.6 years old) attended the Sleep Psychology Workshop as part of their Health Psychology course, delivered as three, two-hour lectures (total six hours). Trainees’ sleep psychology knowledge quiz scores (% correct) demonstrated significant improvement from pre- (M = 60%, SD = .09) to post-workshop (M = 79%, SD = .08), t (6) = -5.18, p = .002. Trainees also reported increased self-efficacy to use common sleep-related assessment instruments and empirically supported interventions to manage sleep and circadian rhythm disturbances, along with increased confidence to manage insomnia disorder in clinical practice (all p<.02). Trainees also endorsed the workshop as an acceptable sleep education program for trainee psychologists via a post-workshop feedback survey, focus group, and six-month follow-up survey. This pilot study provides preliminary evidence for the Sleep Psychology Workshop as an effective and acceptable sleep education program for trainee psychologists.

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.

During the last years, significant initiatives have been launched for the dissemination of Open Access as part of the Open Science movement. Nevertheless, the other major pillars of Open Science such as Open Research Data (ORD) and Open Peer Review (OPR) are still in an early stage of development among the communities of researchers and stakeholders. The present study sought to unveil the perceptions of a medical and health sciences community about these issues. Through the investigation of researchers’ attitude, valuable conclusions can be drawn, especially in the field of medicine and health sciences, where an explosive growth of scientific publishing exists. A quantitative survey was conducted based on a structured questionnaire, with 51.8% response rate (215 responses out of 415 electronic invitations). The participants in the survey agreed with the ORD principles However they ignored basic terms like FAIR (Findable, Accessible, Interoperable and Reusable) and appeared incentive to permit the exploitation of their data. Regarding OPR, participants expressed their agreement, implying their interest for a trustworthy evaluation system. Conclusively, researchers urge to receive proper training for both ORD principles and OPR processes which combined with a reformed evaluation system will enable them to take full advantage of the opportunities that arise from the new scholar publishing and communication landscape.

Pandemic scenario has significantly changed several factors of life, including teaching, and learning at university. The development of suitable teaching materials to support university studies is mandatory to overcome distance learning difficulties and improve traditional teaching methodologies. This work explains a novel method for the preparation of teaching materials for medical sciences, but also plausible for other learning fields. An encephalon was extracted and prepared by using this methodology for teaching purposes. More than 200 students evaluated several factors of the material prepared this way, indicating a better understanding (up to 80%) of theoretical contents related to this human section, together with a high usability and good physical appearance

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

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.

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.

Nuʻupia Ponds, a traditional Hawaiian fishpond system, are located at Marine Corps Base Hawaii (MCBH) and part of the Nuʻupia Ponds Wildlife Management Area, a wetland refuge for native, endangered, and protected birds and Hawaiian green sea turtles, as well as many native fish species. Currently, there is uncertainty regarding the ecological status and condition of the fishponds following prior modification of wetland habitats in and around the ponds. This study examines circulation dynamics and characterizes water exchange, pond volume, and residence time across the full tidal spectrum at Nuʻupia fishponds. Our results indicate a general west to east gradient in current flow; with higher flushing rates and lower residence times of fishponds in the western ponds of the Nu’upia system compared to the eastern ponds. We further found low flushing rates at several sites causing limited water exchange with Kāne’ohe Bay, as well as within Nu’upia Pond system. Sufficient water circulation plays a fundamental role in maintaining a healthy balance of fishpond flora and fauna and preserve ecosystem health. The results from this study provide a baseline of current physical water circulation dynamics and implications for ecosystem health, as well as inform science-based conservation and management strategies moving forward.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

Conventional data visualization software have greatly improved the efficiency of the mining and visualization of biomedical data. However, when one applies a grid computing approach the efficiency and complexity of such visualization allows for a hypothetical increase in research opportunities. This paper will present data visualization examples presented in conventional networks, then go into higher details about more complex techniques related to leveraging parallel processing architecture. Part of these complex techniques include the attempt to build a basic general adversarial network (GAN) in order to increase the statistical pool of biomedical data for analysis as well as an introduction to the project utilizing the decentralized-internet SDK. This paper is meant to show you said conventional examples then go into details about the deeper experimentation and self contained results.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) “blockchain” to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (“TradSci”), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific “metaverse”. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (“NFTs”). The so enabled, highly integrative approach, termed decentralized science (“DeSci”), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.

In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.

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 field of microbial pathogenesis seeks to identify the agents and mechanisms responsible for disease causation. Since Robert Koch introduced postulates that were used to guide the characterization of microbial pathogens, technological advances have substantially increased the capacity to rapidly identify a causative infectious agent. Research efforts currently focus on causation at the molecular level with a search for virulence factors (VFs) that contribute to different stages of the infectious process. We note that the quest to identify and characterize VFs sometimes lacks scientific rigor, and this suggests a need to examine the epistemology of VF characterization. We took this premise as an opportunity to explore the epistemology of VF characterization. In this perspective, we discuss how the characterization of various gene products that evolved to facilitate bacterial survival in the broader environment have potentially been prematurely mischaracterized as VFs that contribute to pathogenesis in the context of human biology. Examples of the reasoning that can affect misinterpretation, or at least premature assignment of mechanistic causation, are provided. Our aim is to refine categorization of VFs by emphasizing a broader biological view of their origin.

Surfactants are widely used in the synthesis of nanoparticles, as they have a remarkable ability to direct their growth to obtain well-defined shapes and sizes. However, their post-synthesis removal is a challenge, and the methods used often result in morphological changes that defeat the purpose of the initial controlled growth. The cleaning methods could be classified as thermal or non-thermal. In general, the structure of materials can be better preserved by non-thermal treatments. After the removal of surfactants, the highly active surfaces of nanomaterials may undergo structural reconstruction by exposure to a different environment. Thus, ex situ characterization after air exposure may not reflect the effect of the cleaning methods. In situ characterization is required to better understand the impact of various cleaning procedures. Combining X-ray photoelectron spectroscopy, in situ infrared reflection absorption spectroscopy, and environmental transmission electron microscopy measurements with CO probe experiments, we investigated different surfactant-removal methods to produce clean metallic Pt nanoparticles from surfactant-encapsulated ones. Non-thermal plasmas show the best results. It was demonstrated that both UV-ozone treatment and room temperature O2 plasma treatment lead to the formation of Pt oxides on the surface after the removal of the surfactant. These oxides are reduced to metallic Pt during in situ CO probe experiments. On the other hand, when H2 was used for plasma treatment, the Pt0 oxidation state and nanoparticle size distribution were preserved. In addition, H2 plasma treatment can reduce Pt oxides after O2-based treatments, resulting in metallic nanoparticles with clean surfaces. Thermal reduction in hydrogen leads to carbon species emerging onto nanoparticle surfaces after heating and agglomeration of Pt nanoparticles. Particularly, O2-based thermal treatments result in the formation of aggregates consisting of small nanoparticles. These findings provide a better understanding of the various options for surfactant removal from metal nanoparticles and point toward non-thermal plasmas as the best route if the integrity of the nanoparticle needs to be preserved.

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. Onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skills levels, potentially starting novices with graphical IDEs before introducing Emacs. Key takeaways include literate programming aids comprehension but requires rigorous onboarding and reinforcement; Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.

Evaluating the economic impact of airports is crucial for understanding the benefits they bring to a region. However, when an area has more than one airport, it becomes essential to analyze each airport's contribution to the local economy to make informed investment and policy decisions. Thus, studying economic models that can distinguish each airport's impact on the region's economy becomes essential. In this context, this paper aims to compare three different approaches to determine the economic contribution of airports in a given region and identify their social and economic benefits. The International Civil Aviation Organization recommends using Input-Output analysis in this context. The study considered three weight factors for the Input-Output basic model: circular buffer, displacement time, and Huff's gravitational model. The analysis was performed using the three largest airports in São Paulo's state, Brazil, due to their proximity and influence on the surrounding area. The models were compared based on their efficiency and accuracy in reflecting the reality of the case study context. The study identified the most suitable model for establishing correlations between investments made in airport infrastructure and the generation of gross domestic product, employment, and added value. This study fills a gap in the existing literature by proposing improvements to the methods for evaluating airports' economic and social benefits. In recent times, airport investors, both in the government and private sectors, have become increasingly demanding in their need for accurate analyses before making investments. Therefore, the results of this paper will provide valuable insights into the benefits of investing in airport infrastructure and help policymakers and investors make informed decisions.

The biodiversity loss in urban areas has attracted public concern, which is one of the urgent global environmental issues. This study used bibliometric methods to analyze 3351 publications from 1995-2021 that retrieved from the Web of Science and visually represented the state-of-the-art of researches in city biodiversity field. The prolific authors, journals(sources), institutions, and countries are clearly identified. The most cited and influential paper proposed a conceptual framework of associations between urban green space, and ecosystem and human health, and then concluded that green infrastructure could physically and psychologically benefits people by ecosystem services it provides, and make a better socio-economic benefit. The theme hotspots are urbanization, urban ecology, ecosystem services, urban planning, green infrastructure, urban forest and urban park et al. Ingo Kowarik is the most productive author in terms of number of publications. Michael L. McKinney is the most cited authors by the publications in analyzed corpus, who identified how urbanization harms native ecosystems, however, a well ecologically educated population could greatly enhance species richness in all ecosystems. Urban park, garden, fragmented green spaces and green corridor networks could help enhance city biodiversity. In general, the city biodiversity research presents a trend that involves intensive global cooperation, and become more comprehensive.

Bayesian models allow us to investigate children’s belief revision alongside physiological states like “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy-violations may be predictive of belief revision. How can probabilistic models inform interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs – suggesting stronger surprise occurs following unlikely events. In contrast, Kullback-Leibler divergence considers the “dissimilarity” between prior beliefs and updated beliefs following observations – with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or to evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback-Leibler divergence and children’s pupillometry responses only when children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and updated, more accommodating beliefs.

Plant breeding is central to agriculture, and shifts in plant breeding practices (e.g., hybrid development) and selection goals (e.g., response to synthetic fertilizer) have catalyzed monumental and persistent changes in agricultural production systems of all scales with social, political, economic, and environmental repercussions. While plant breeders are largely trained in the sciences of biology, genetics, and statistics, we posit an ethical imperative to examine the degree of equity with which the benefits of new research and plant varieties are distributed. In the United States, the history of plant breeding parallels the colonial history of agriculture, which compels reflection by current plant breeders about their role in shaping our agricultural system. In this perspective essay, we examine longstanding ideas about equitable food systems through the lens of public plant breeding in the United States. We propose a framework for equitable public plant breeding with respect to both its process and outcomes, and we intend for the ideas presented herein to catalyze reflection, discussions, and actions as the plant breeding community seeks greater equity in the food and seed systems our work supports.

One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.

Ramsey’s theory (RAM) from combinatorics and network theory goes looking for regularities and repeated patterns inside structures equipped with nodes and edges. RAM represents the outcome of a dual methodological commitment: by one side a top-down approach evaluates the possible arrangement of specific subgraphs when the number of graph’s vertices is already known, by another side a bottom-up approach calculates the possible number of graph’s vertices when the arrangement of specific subgraphs is already known. Since natural neural networks are often represented in terms of graphs, we suggest to utilize RAM for the analytical and computational assessment of a peculiar structure supplied with neuronal vertices and axonal edges, i.e., the human brain connectome. We discuss how a RAM approach in neuroscientific issues might be able to locate and trace unexplored motifs shared between different cortical and subcortical subareas. Furthermore, we will describe how notable RAM outcomes, such as the Ramsey’s theorem and the Ramsey’s number, could contribute to uncover still unknown anatomical connexions endowed in neuronal networks and unexpected functional interactions among grey zones of the human brain.

The objective of this article: analysis of research trends on the topic "Materials Science for Energy Engineering" for the period 2012-2021. Materials and methods: bibliometric analysis of data from the Scopus abstract database.Results. It is shown that this topic is developing faster than the general energy topics.The key feature of the research trends is a significant increase in work on the development of materials for renewable energy and a decrease in the number of publications related to nuclear power.The primary goal of many studies is to increase the efficiency of renewable energy production through the use of new materials.The importance of the topic is evidenced by the emergence of new journals that quickly entered the first quartile of the abstract databases Scopus and Web of Science, for example: ACS Applied Energy Materials.There has been a significant increase of interest in perovskite solar cells, layered semiconductors, triboelectric nanogenerators, cobalt compounds, sulfur compounds, and oxygen and hydrogen evolution reaction during Electrolysis.Publications on electric batteries are well represented in all time intervals. The focus is on electrode materials and battery efficiency.

Geoscientists developed geoethics, an intra-disciplinary field of applied philosophical studies, during the last decade. Reaching beyond the sphere of professional geosciences, it led to professional, cultural, and philosophical approaches to handle the social-ecological structures of our planet ‘wherever human activities interact with the Earth system’. Against the backdrop of the COVID-19 and considering geoscientists’ experiences dealing with disasters (related to hazards like tsunamis, floods, climate changes.), this essay (1) explores the geoethical approach, (2) re-casts geoethics within western philosophical systems, such as the Kantian imperatives, Kohlberg scale of moral adequacy, Jonas’ imperative of responsibility, and (3) advances a ‘geoethical thesis’. The latter takes the form of a hypothesis of a much broader scope of geoethics than initially envisioned. That hypothesis appears by suspecting a relationship between the relative successes in the COVID-19 battle with the positioning of agents (individual, collective, institutional) into ethical frameworks. The turmoil caused by the COVID-19 pandemic, calls for the transfer of experiences between different disciplinary domains to further sustainable governance, hence generalising the geoethical approach. It is emphasized that only when behaving as responsible and knowledgeable citizens, then people of any trade (including [geo-]scientists) can transgress the boundaries of ordinary governance practices with legitimacy.

Despite good intentions, the increasing number of plastic bag bans aimed at alleviating marine plastic pollution saw a correlated increase in the number of unintended consequences that emerged alongside the bans, suggesting that human behavior towards plastic bag consumption have not changed, but merely shifted, and are feeding into other major international environmental catastrophes. Nudge theory, which helps people make better choices for themselves without inhibiting their free will, is a potential solution that has been shown to play a subtle but important role in providing options under circumstances where complex information needs to be streamlined for the wider community, avoiding any unintended consequences and behavioural shifts that might arise from instruments that diminishes autonomy. It is therefore timely to look into the insights of nudge theory to encourage a positive behavioural change to reduce plastic bag consumption. Here we apply a systematic literature review to show how successful applications of nudges in supermarkets can be leveraged to reduce plastic bag consumption. We find that the current applications of nudges in various industries worldwide, including supermarkets have produced positive and encouraging results, as well as producing lasting behavioural change among the wider community. Supermarkets are identified as a powerful deployment site of these nudges due to their positioning as a dominant provider of plastic bags to the wider community, as well as being the largest and leading provider of daily food needs. Finally, we synthesise our findings to produce a coherent and testable framework of actionable interventions that supermarkets can employ to nudge customers towards reduced plastic bag reliance, accompanied with a visual timeline of a customer shopping in a supermarket experiencing these nudges.

This paper proposes a re-conceptualization of the port supply chain as a smart service system, according to the theory of the Service science. Starting from a short literature review about the port supply chain approach and the Service science, a new comprehensive framework is provided to better understand the seaport dynamics and the creation of competitive port supply chains. The methodology used is the case study approach. The authors examined the port of Salerno (Italy), and re-conceptualized it as a smart port service systems. Both theoretical and practical implications are provided to enrich the literature about the port supply chain and to support the port operators.

Beaked whales (Ziphiidae) are rarely reported in the Arabian Sea. Four new cases (five individuals) were documented in deep waters offshore Pakistan through a pilot programme in 2015-2018 where trained fishers video-recorded net entanglements in the pelagic tuna drift gillnet fishery. Videos were analysed frame-by-frame. The large body size (est. 5-6m) of one specimen, its moderately bulbous melon, long tubular rostrum and a large falcate dolphin-like dorsal fin, indicated Longman’s beaked whale Indopacetus pacificus. It represents the first record for Pakistan (EEZ), and with a stranding at Gujarat, India, a second for the northern Arabian Sea. The other 4 ziphiids were significantly smaller (est. 3– 4.5m), with a decidedly non-bulbous melon, variable short to moderately short rostra, falcate to subtriangular dorsal fin and a nondescript greyish colouration, identified as Mesoplodon spp. Video quality was poor but none of the specimens showed tusks, arched mandible lines or noticeable linear tooth rakes, practically excluding adult males. The successful release of all net-entangled beaked whales is unprecedented. The simultaneous bycatch of two mesoplodonts in the same net set is equally exceptional. This citizen science strategy adds to our understanding of the distribution of I. pacificus and mesoplodonts, which may be more common in the Arabian Sea than the scarce literature suggests. If significant bycatch of beaked whales is confirmed, the massive tuna gillnet fishing effort in the Arabian Sea could have negative implications for their conservation status.

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 growth of social media has changed the face of many aspects of marketing such as online, digital, etc. It also has changed the way modern human communicates and connects with others. Moreover, the behavior on this platform could not and should not be justified with strategies of other marketing channels and media. Due to the nature of social media, they are rich in precise and lean data, but processing these data and extracting knowledge and insights from them are problematic. Evaluating the effectiveness of a marketing endeavor is also a task related to these data. The current research attempts to assess the effectiveness of an advertising campaign on Instagram via advertising cost and sentiment classification of audience opinion regarding the campaign. The methodology used in this research is the standard process of data mining, i.e., CRISP-DM. Furthermore, multiple machine learning models and approaches were studied to train a prediction model based on data. In order to find the most accurate algorithm, grid search was used among the trained models and different algorithms with different combinations of hyper-parameters. The obtained results revealed that although the number of not profitable advertising media was higher than the profitable media, the overall status of the campaign was profitable, both in the cost-effectiveness approach and sentiment analysis approach. The other valued outcome of this research was important general and specific insights which can be used to shape a better-performing and effective advertising campaign on Instagram.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Emphasis on primary health care (PHC) is the most efficient and economical approach to achieving universal health coverage. The aim of the study is to examine PHC publications from 2011 to 2023 in the Asian Pacific region (APR) using the Web of Science (WoS) database. Academic papers in the WoS PHC subject category published in the APR from 2011 to 2023 were retrieved and analyzed. A total of 4,283 papers were published in the WoS PHC category from APR onwards. The annual number of published papers and their citations increased from 2011 to 2023. Australia led PHC publications in APR with 3,049 publications (71.19% of APR regional output). New Zealand had the highest number of citations per paper (13.76) and the highest mean impact factor (3.23±1.80). Publications in the WoS PHC category also covered a wide range of topics, including Medicine General Internal (64.77%), Health Care Sciences Services (21.88%), Health Policy Services and Public Environmental Occupational Health (21.36%), demonstrating the breadth of PHC research. Australia dominates the output of PHC publications in the APR. Australian Family Physician and Australian Journal of Primary Health are the leading journals publishing PHC papers in the APR.

Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.

Introduction: The first velvet worm (phylum Onychophora) was described nearly two centuries ago, by a British priest, in the tiny Caribbean island of Saint Vincent. He thought it was a new type of mollusk, a slug with tiny feet. Objective: Here I present a brief biographical account of the field of onychophorology, marking nearly two hundred years since the first scientific description of velvet worms. Method: I synthetize historical records, publications, personal experience and biographical sketches to construct an encompassing narrative of the field's development through the lives of researchers who have shaped it. Results: The narrative begins with Lansdown Guilding's initial discovery and extends to include pivotal figures like Jean Victor Audouin, Charles Émile Blanchard, Adolph Eduard Grube, Lorenzo Camerano, Eugène Louis Bouvier, Sidnie Milana Manton, Sylvia S. Campiglia, Roger Lavallard, Hilke Ruhberg, and Bernal Morera-Brenes. Contributions from Noel Tait, Robert Mesibov, Georg Mayer, Cristiano Sampaio, Ivo de Sena Oliveira, and José Pablo Barquero-González are also acknowledged. The article highlights their individual contributions to onychophorology, considering their scientific achievements and personal journeys, and presents portraits of nearly all of them. Conclusion: Our current knowledge of onychophorans is the results of hundreds of contributions from a large number of people who have lived varied, sometimes fascinating lives, but all united in their interest in velvet worms.

Modern research applies the Open Science approach that fosters the production and sharing of Open Data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In the geospatial context this is generally achieved through the setup of OGC Web services that implements open standards that satisfies the FAIR requirements. Nevertheless, the requirement of Findability is not fully satisfied by those services since there’s no use of persistent identifiers and no guarantee that the same dataset used for a study can be immutably accessed in a later period: a fact that hinders the replicability of research. This is particularly true in recent years where data-driven research and technological advances have boosted frequent updates of datasets. Here, we review needs and practices, supported by some real case examples, on frequent data or metadata updates in geo-datasets of different data types. Additionally we assess the currently available tools that support data versioning for databases, files and log-structured tables. Finally we discuss challenges and opportunities to enable geospatial web services that are fully FAIR: a fact that would provide, due to the massive use and increasing availability of geospatial data, a great push toward open science compliance with ultimately impacts on the science transparency and credibility.

The case of naturalization of the zebra mussel, Dreissena polymorpha (Pallas), in coun-tries lying beyond its native Ponto-Caspian range is remarkable as one of the first instances, when the scientific community as early as the mid-19th century was fully aware of the non-indigenous status of a particular species as well as of the need for the study and monitoring of this process. Based on a study of contemporary sources, I reconstruct the early response of the European naturalists (including those that today would be called ‘citizen scientist’) to the invasion of Dreissena and describe their attitudes to the problem, including the divergence of opinion about the origin and the means of dispersal of this bivalve species. An analysis of pa-pers of the English, French, German, and Russian authors published between 1774 and 1920 has shown that the invasion of D. polymorpha was by no means ‘silent’; quite opposite, it provoked an immediate reaction of naturalists, and the scientific agenda for the study of the new invader was proposed in England as early as 1838

Background: Citicoline, a medical food, lacks proven efficacy for ischemic stroke and has uncertainties around its harms. The continued prescription should be reevaluated given the more robust, high-quality Evidence against that medical food. This essay is based on a recent Cochrane review and uses only one all-cause mortality as the primary outcome. Therefore, the essay should be considered as something other than an updated Cochrane review but like a reflection based on Evidence-based Medicine and Philosophy of Science analysis. Question Research: Why citicoline (a medical food) should not be prescribed to treat people with acute ischemic stroke: The certainty of the Evidence.? Objective: Demonstrate from evidence-based medicine and philosophy of science perspective that citicoline should not be prescribed for acute ischemic stroke due to lack of efficacy and harm uncertainties. Search publications: We searched in PubMed and Cochrane Library from 2020 until 30 October 2023. We, furthermore, used engineering machines Bing and Google Scholar to detect additional papers. Additionally, we also reviewed reference lists of the retrieved publications and review articles and searched the websites of the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Selection criteria: We included systematic reviews, meta-analyses, randomized clinical trials, clinical guidelines focused on acute ischemic stroke and comparing citicoline versus placebo or no intervention. We excluded narrative reviews, observational studies and ongoing trials. Data collection and analysis: We identified only new randomized clinical trials and assessed the risk of bias in seven domains. The other eight trials were already included in the mentioned Cochrane review. The systematic reviews with or without meta-analyses were assessed using McMaster University guidelines. We estimated risk ratios (RRs) for that outcome. We measured statistical heterogeneity using the I² statistic. We conducted our analyses using the fixed effect model. We did not use the GRADE approach due to what is shown in a Cochrane review published in 2020 by Martí-Carvajal et al. We used the RevMan 5.4 software from Cochrane Collaboration to conduct the forest plot. We used a Trial Sequential Analysis with Copenhagen Trial Unit Software. We estimated a Bayes factor from the relative risk and 95% confidence interval. Results: We identified only one new RCT (N = 99) reported mortality data and three clinical guidelines. We conducted a new meta-analysis with nine trials (N = 4461) having a high risk of bias and showing little to no difference in mortality between citicoline and placebo (17.1% vs 18.4%; RR 0.94, 95% CI 0.82 to 1.06; I2 = 0%). The Bayes factor was 0.7, indicating weak Evidence for the null over the alternative hypothesis. Trial sequential analysis suggested sufficiency of Evidence for mortality. No guidelines recommend citicoline. Conclusions: This essay reassessed citicoline for acute ischemic stroke after the 2020 Cochrane review. Adding a new RCT further supported the lack of mortality benefit with citicoline. The overall evidence quality could be better. Analyses using evidence-based medicine and philosophy of science approaches do not support prescribing citicoline due to a lack of efficacy substantiation and potential harms.

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

Semi-structured complex numbers H was a number set developed to enable division by zero in ordinary algebraic equations. Its utility has been shown in mathematics and engineering. However, very little has been done to show its usefulness in computer science. Consequently, the aim of this paper was to show the utility of semi-structured complex numbers in computer science by developing a division by zero calculator. First two computer programs were written, one for a standard (STD) calculator and the other for a division by zero (DBZ) calculator. The programs were fed 20000 randomly generated arithmetic equations of varying lengths and the space and time complexity associated with processing these equations were measured and compared to determine the efficiency of each calculator. In the process, three major contributions were made: (1) A representation for semi-structured complex numbers that enables it to be easily used by a computer was developed; (2) It was demonstrated that the DBZ calculator outperforms the STD calculator in terms of efficiency; and, (3) It was shown that the number set H reduced the amount of error handling required to run a computer program. These results provide a firm foundation to advance the number set as a useful tool in computer science.

A novel technique using a W-band metasurface for the purpose of transmissive fine powder layer sensing is presented. The proposed technique may allow for the detection, identification, and characterization of inhomogeneous ultrafine powder layers which are effectively hundreds of times thinner than the incident wavelengths used to sense them. Such a technique may be useful during personnel screening processes (i.e., at an airport) and in industrial manufacturing environments where early detection and quantization of harmful airborne particulates can be a matter of security or safety. To our knowledge, the requisite science involved in such a novel W-band metasurface sensing technique has not been fully explored.

In the last years, interactive exhibitions based on digital technologies are becoming widely common, thanks to their flexibility and effectiveness in engaging visitors and creating memorable experiences. One of the topics in which digital technologies can be particularly effective is the communication of abstract concepts that are difficult for the human mind to imagine. An emblematic example is the astronomy discipline, which requires us to imagine and understand phenomena far away from our everyday life. In this paper, the authors present a research project, MARSS, in which digital technologies are used effectively to enhance the Users’ Experience of the Museo Astronomico di Brera located in Milan. Specifically, the MARSS project aims at designing and developing a new digital journey inside the museum to allow different categories of visitors to enjoy the exhibition in an engaging and interactive way. The paper presents the design and development phases of the experience and its evaluation with users. The results of the evaluation indicate that the digital interactive experience has been appreciated by users and is successful in translating the content of high scientific value into more engaging and easily understandable elements.

Gender and identity issues permeate society as a whole. Therefore, the matters involving transgender individuals should be analised in order to understand the difficulties experienced by this population and the social practices implemented. In this sense, the objective of this study was to investigate the strategic themes and their evolution in relation to the theme. For this, a bibliometric performance and network analysis (BPNA) was carried out with the existing data in the Web of Science database between 1954 and march 2021. Twenty-three thousand and four hundred and seventy-one (23,471) articles were identified, which were included in the SciMAT software to perform a bibliometric analysis, resulting in the graph of the thematic evolution structure and the strategic diagram, in which 8 motor themes and a cross-cutting theme of great magnitude are highlighted, which are discussed in depth. The results show the relation between the transgender theme and gender, identity, sexual orientation, hormone therapy and gender-affirming surgery. It is concluded that, despite the large number of associated researches, some areas of study are still incipient, such as the inclusion of transgender people in the formal labor market and in the prison context, thus opening field for further studies.

Time is a component quantity of various measurements and is also used on its own to sequence events, to compare the duration of events or the intervals between them, and to quantify rates of change of quantities in material reality or in the conscious experience [1]. The measuring equipment well might happen to be a clock. By making a presupposition that clock is Time, it is perceived experimentally that “Time exhibits wave-like properties”

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

Velvet worms, or onychophorans, include placental species and, as a phylum, have survived all mass extinctions since the Cambrian. They capture prey with an extraordinary adhesive net that appears in an instant. The first naturalist to formally mention them was Lansdown Guilding (1797-1831), a British priest from the Caribbean island of Saint Vincent. His life is as little known as the history of the field he initiated, onychophorology. This is the first general history of onychophorology, and I have divided it into half century periods. The beginning, 1826-1879, was defined by former students of great names in the history of biology, like Cuvier and von Baer. This generation included Milne-Edwars and Blanchard, and the greatest advances came from France, with smaller but still important contributions from England and Germany. In the 1880-1929 period, work concentrated in anatomy, behavior, biogeography and ecology, but of course the most important work was Bouvier’s mammoth monograph. The next half century, 1930-1979, was important for the discovery of Cambrian species; Vachon’s explanation of how ancient distribution defined the existence of two families; Pioneer DNA and electron microscopy from Brazil; and primitive attempts at systematics using embryology or isolated anatomical characteristics. Finally, the 1980-2020 period, with research centered in Australia, Brazil, Costa Rica and Germany, is marked by an evolutionary approach to everything, from body and behavior to distribution; for the solution of the old problem of how they form their adhesive net and how the glue works; the reconstruction of Cambrian onychophoran communities, the first experimental taphonomy; the first country-wide map of conservation status (from Costa Rica); the first model of why they survive in cities; the discovery of new phenomena like food hiding, parental feeding investment and ontogenetic diet shift; and for the birth of a new research branch, Onychophoran Ethnobiology, founded in 2015. While a few names appear often in the literature, most knowledge was produced by a mass of researchers who entered the field only briefly.

Attitudes and behaviours towards the natural environment have been extensively studied in certain cultural settings during the last 40 years. In education, the teacher's ability to grasp the fundamentals of an academic subject may define his or her own attitudes towards that discipline; certainly the reverse is also valid. The correlations between affective and cognitive domains appear to play a significant role in teaching-learning dynamics. In this study we seek to assess whether the affective posture towards school sciences of a cohort of teachers in rural communities shows an association with their cognitive competence in the disciplines. The results of this study provide evidence that there is a statistically significant correlation between the cognitive and affective domains for in-service teachers. The affective domain, therefore, could serve as a predictor for cognitive competency and self-efficacy expectancies with respect to both content and career fulfilment.

We explore the class of positive integers n that admit idempotent factorizations n=pq such that lambda(n) divides (p-1)(q-1), where lambda(n) is the Carmichael lambda function. Idempotent factorizations with p and q prime have received the most attention due to their cryptographic advantages, but there are an infinite number of n with idempotent factorizations containing composite p and/or q. Idempotent factorizations are exactly those p and q that generate correctly functioning keys in the RSA 2-prime protocol with n as the modulus. While the resulting p and q have no cryptographic utility and therefore should never be employed in that capacity, idempotent factorizations warrant study in their own right as they live at the intersection of multiple hard problems in computer science and number theory. We present some analytical results here. We also demonstrate the existence of maximally idempotent integers, those n for which all bipartite factorizations are idempotent. We show how to construct them, and present preliminary results on their distribution.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

Abstract: Background: The practice of menstrual hygiene(MH) is a public health issue and depends on various factors. Taboos and incorrect MH practices can lead to health hazards. This study aimed at assessing the Knowledge, Attitude, and Practice of MH among the female students and faculty of Medical and Health Sciences University. Materials and methods: This cross-sectional questionnaire survey between February and June 2023 using consecutive sampling. Univariate analysis and comparisons were done with a significant p-value <.05. Results: A total of 253 women participated in the study. The knowledge and practices of MH were good. There were significantly more irregular and painful periods in younger females (p=.005,.0001). 89% of women used disposable pads and least used menstrual cups. Younger women were unaware of menstrual cloth. The attitude towards menses varied, with younger females feeling annoyed (p=.002), and older females embarrassed (p=.0001) at menarche. About 1/3rd of participants perceived menstrual blood as unhygienic. Younger women were more open about talking about menses (p=.005). Conclusion: There was good knowledge and practices of MH among participants. There were differences in menstrual history and attitude among older and younger women. The attitude and beliefs vary among the participants, due to sociocultural factors and age differences.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

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

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.

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

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.

As a pioneering transdisciplinary field, innovationology transcends the traditional boundaries of academic disciplines and development practices to address persistent global challenges. This article provides a comprehensive exploration of innovationology, delving deep into its philosophical foundations, ontological commitments, and ethical imperatives. By unpacking the core tenets of this innovative approach, the paper positions it as a robust and holistic science capable of catalyzing transformative innovation and sustainable development worldwide. Innovationology represents a paradigm shift that moves beyond conventional innovation and development theories, embracing a systems-oriented, community-centric, and decolonial praxis. Drawing on a synthesis of theoretical perspectives and empirical insights, the article examines innovationology's ontological commitment to marginalized communities as active agents of change, its epistemological grounding in pluralistic knowledge systems, and its ethical imperatives of social justice, environmental sustainability, and inclusive innovation. This in-depth analysis strengthens the conceptual and theoretical foundations of innovationology, providing critical guidance for its practical implementation and continued refinement as a cutting-edge transdisciplinary science for global transformation.

A revitalized practice of natural philosophy can help people to live a better life and promote a flourishing ecosystem. Such a philosophy is natural in two senses. First, it is natural by seeking to understand the whole of nature, including mental phenomena, In particular, a comprehensive natural philosophy should address the phenomena of sentience by embracing first- and second-person methods of investigation. Moreover, to expand our understanding of the world, natural philosophy should embrace a full panoply of explanations, similar to Aristotle’s four causes. Second, such a philosophy is natural by being grounded in human nature, taking full account of human capacities and limitations. Future natural philosophers should also make use of all human capacities, including emotion and intuition as well as reason and perception, to investigate nature. Finally, since the majority of our brain's activities are unconscious, natural philosophy should explore the unconscious mind with the aim of deepening our relation to the rest of nature and enhancing well-being.

The diversity and inclusion discussion permeates many sectors of society. Within this dialogue, science and scientists are acutely aware of the value of diversity and the need for inclusion. While demographic diversity in science has received considerable recent attention, very little research and understanding exists on inclusion. Our study presents empirical data on the meaning of inclusion using a crowdsourcing approach that sought responses to the question “What does inclusion mean to you?”. The most prominent concepts were those of empathy, warmth, support, love, acceptance and curiosity; diverse perspectives; and participation. We clustered conceptual elements of inclusion into four themes: access and participation, embracing diverse perspectives, a welcome environment, and team belonging. On the basis of these data, we theorize a conceptual framework model from which inclusion may be put into practice. Our model suggests a dynamic process of inclusion operating from principal structural elements of 1) a foundation that involves place, access and participation, and space; 2) reciprocal engagement as an engine for inclusion; and 3) expression of inclusion as culture. The framework model demonstrates a means by which the practice of diversity can be more than shifts in demographic statistics, and instead promote the full expression of benefits derived when the many dimensions of diversity are truly included.

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.

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.

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.

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.

Open science, accessibility and knowledge sharing, especially of articles and monographs stemming from a publicly funded research, seem to be quite a positive direction in the development of science and have received almost unanimous approval from a scientific community. However, when it comes to data sharing, the data obtained by qualitative methodology deserve special attention and treatment. Although FAIR principles provide ways to anonymise the data and interlocoutors (as explained in Celjak et al 2020) individuals coming from smaller communities or even communities of practice can sometimes be easily regcognized by members of the same communitiy, especially if the data refers to audio files! The researcher is obliged to thoroughly describe the ways in which the data will be managed and used, and if it will state that the data will be managed in line with FAIR principles, this will inevitably impact the narratives collected. The prerogative to make all the data open will inevitably lead to autocesorship i.e. in creating a kind of FAIRy Tales for the future, interlocutors will share with researchers. Having all that in mind I argue for a more vigilant approach when embracing the idea that all the data should be made FAIR. Not all the data should be made FAIR because this could, in the end, compromise the research process itself. Some data must remain in the space of trust created between main actors in the research process. These data should be treated with CARE principles.

The current practices of the technology transfer profession seem to reflect the belief that the chances for success are greatly improved when faculty inventors employ their social capital to facilitate the process. However, there is a gap in the university technology transfer literature regarding our understanding of faculty inventor social capital in the technology transfer process. The aim of this study was to understand whether the use of faculty inventor social capital is a causal condition for successful university technology transfer. This question was examined using a multiple case study approach and the qualitative comparative analysis (QCA) method. The results of the study suggest that the use of faculty inventor social capital is not a necessary, sufficient, or INUS condition for the occurrence of university technology transfer.

Individuals are pupils of society, and students represent individuals within the societal context. It is the noble aspiration of every educator to cultivate students with well-rounded personalities for the benefit of society. School curricula are instrumental in shaping the children who will, in turn, shape the future of society, and it is posited that students with well-rounded personalities are invariably those who embody love. The challenge lies in how to enable children to possess, retain, extend, refine, and be filled with love—a task necessitating the collaborative and synergetic efforts of both the educational and social systems to achieve a societal milestone. Pedagogy of love focuses on nurturing the capacity for love within students, who, through self-awareness of their indoctrination in love, transform techniques of self-love into capabilities of love for others. This process continuously generates the energy of love within social relations, thereby fostering social production, improving the relations of production, and enhancing the creative capacities, vitality, and welfare of a socialist society's wealth and capital accumulation.

Historically, the lack of comprehensive and real-time data is the main challenge in city sustainability assessments. However, with the increase in the availability of crowdsourced data over the last decade, a rich and real-time data source provides detailed images of urban systems. This has opened up new opportunities for practitioners, including geographers, social researchers, and data scientists, to gather and utilize social media data as a core source for studying cities and assessing their sustainability. This paper proposes an assessment model for the sustainability of cities based on crowdsourced data and the principles of General System Theory. The model aims to utilize crowdsourced data for the analysis and decision-making processes to enhance the sustainability and resilience of smart future cities.

A novel electrophoretic technique to improve the sensing capabilities of charged particles in solution is presented. The proposed technique may improve the ability of metasurfaces to sense charged particles in solution by forcing them to preferentially sediment within metasurface regions of greatest sensitivity. Such a technique may be useful in various sensing applications, such as in biological, polymer, or environmental sciences, where low concentration particles in solution are of interest. The electrophoretic technique was simulated and experimentally tested using latex nanoparticles in solution. The results suggest that, using this technique, one may theoretically increase the particle density within the metasurface regions of greatest sensitivity by nearly 1900% in comparison to random sedimentation due to evaporation. Such an increase in particle density within the regions of greatest sensitivity may facilitate more precise material property measurements and enhance identification and detection capabilities of metasurfaces to low concentration particles in solution. It was experimentally verified that the electrophoretic technique enabled the preferential gathering of latex nanoparticles within the most sensitive metasurface regions, resulting in 900% - 1700% enhancements in metasurface sensing capabilities.

A case study is presented which demonstrates the value and validity of a novel approach to the use of consolidated amateur (‘ham’) radio reception reports as indicators of the presence of intense ionospheric sporadic E (Es). It is shown that the use of amateur data can provide an important supplement to other techniques, allowing the detection and tracking of Es where no suitable ionosonde or other measurements are available. The effectiveness of the approach is demonstrated by reference to ionosonde data, and the advantages and limitations of the technique are discussed.

Building on recent directions in religion-related social and political science, our essay addresses a need for location-specific and religion-specific scientific research that might contribute directly to local and regional interreligious peacemaking. Over the past 11 years, our US-Pakistani research team has conducted research of this kind: a social scientific method for diagnosing the probable near-future behavior of religious stakeholder groups toward other groups. Integrating features of ethnography, linguistics, and semiotics, the method enables researchers to read a range of ethno-linguistic signals that appear uniquely in the discourses of religious groups. Examining the results, we observe, firstly, that our religion and location-specific science identifies features of religious group behavior that are inevident in broader, social scientific studies of religion and conflict; we observe, secondly, that our science integrates constative and performative elements: it seeks facts and it serves a purpose. We conclude that strictly constative, fact-driven sciences may fail to detect certain crucial features of religious stakeholder group behavior.

Thermodynamic cycles are not only the core concepts of thermal science, but also key approaches to energy conversion and utilization. So far, power cycles and refrigeration cycles have been the only two general classes of thermodynamic cycles. While diverse types of systems have been developed to perform thermodynamic cycles, no new general classes of thermodynamic cycles have been proposed. Based on the basic principles of thermodynamics, here we propose and analyze a new general class of thermodynamic cycles named class 1 heating cycles (HC-1s). Two basic forms of HC-1s are obtained by connecting six essential thermodynamic processes in the proper order and forming a thermodynamic cycle. HC-1s present the simplest and most general approach to utilizing the temperature difference between a high-temperature heat source and a medium-temperature heat sink to achieve efficient medium-temperature heating and/or low-temperature cooling. HC-1s fill the gaps that have existed since the origin of thermal science, and they will play significant roles in energy conservation and emission reduction.

Regulatory agencies across the Latin American Region have strengthened the regulatory science through the development of new tools, standards and various other related parameters to evaluate and assess safety, efficacy, quality and performance. The former have been implemented to promote and incorporate new drugs and technologies, which still, are a challenge to well-established regulatory frameworks. Furthermore, in today’s environment, the existing regulatory framework protecting public’s health creates barriers for market entry of novel drugs and medical devices. This article aims to the pioneering work that Cuban Regulatory Agency (CECMED) has been developing with the aim to build a strong regulatory framework geared to accelerated innovation and the successful transition from research and development to clinical development. The Office of Innovation recently established at the CECMED is the first flagship in Latin America and the Caribbean region. Its aim is to play a leading role as a driving force for the national and regional biopharmaceutical innovation. This article will discuss the Office of Innovation its conceptualisation and management taking into account the Latin American regional and national Cuban context.

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Science (ESS) are no exception. However, within ESS specialized visualization requirements and data models --- particularly for those data arising from numerical models --- often make general-purpose visualization packages difficult, if not impossible, to effectively use. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR's utility in the study of a numerically simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

This paper presents the robotic platform, PiBot, that has been developed and that is aimed at improving the teaching of Robotics with vision to secondary students. Its computational core is the Raspberry Pi 3 controller board, and the greatest novelty of this prototype is the support developed for the powerful camera mounted on board, the PiCamera. An open software infrastructure written in Python language was implemented so that the student may use this camera, or even a WebCam, as the main sensor of this robotic platform. Also, higher level commands have been provided to enhance the learning outcome for beginners. In addition, a PiBot 3D printable model and the counterpart for the Gazebo simulator were also developed and fully supported. They are publicly available so that students and educational centers that do not have the physical robot or can not afford the costs of these, can nevertheless practice and learn or teach Robotics using these open platforms: DIY-PiBot and/or simulated-PiBot.