Analysis of immediate effects of the COVID-19 pandemic on SMEs in Rwanda aims to assess and quantify the economic and financial impacts of the pandemic during the lockdown and presents options adopted by SMEs to reopen their business opera-tions. It also seeks to recommend options for sustainable and resilient economic recovery from the scars of COVID-19. This analysis used data collected from nearly 220 SMEs located across the country and applied an Exploratory Data Analysis (EDA) and multivariate linear regression models to measure the pandemic effects. The study revealed that firms downsized employment by 36.4 per cent, but data depict significant deviation within SMEs by their size. The COVID-19 pandemic has had a greater magni-tude of negative effects on sales levels among small businesses. Tax amounts were reduced amidst the crisis however, data do not show any significant variations among SMEs. Additionally, SMEs owners applied various coping mechanisms during re-opening. The study used firm-level data, providing detailed and reliable effects of the shocks on business performance. This analysis recommends establishing the medium-term financing and technical support for SMEs to ensure steady and sustainable recovery from pandemic effects as well as strengthening their resilience to any other socio-economics shocks.

Community centers play a crucial role in urban environments, providing physical and educational services to their surrounding communities, particularly for students. Among the many benefits for students are enhanced academic outcomes, improvement of behavioral problems, and increased school attendance. Such centers are also particularly vital for low-income and racial minority students as they are pivotal in giving them outside-of-school learning opportunities. However, determinants influencing attendance at community centers remain largely unexplored. Using census data, Boston Centers for Youth and Families (BCYF) attendance data, and specific center attributes, we develop human mobility gravitational models to predict attendance across the BCYF network. Using those models, we investigate the potential effects on general and student attendance by changing center attributes, such as facilities and operating hours. We also research the impact of changing the walking accessibility to those centers on their respective attendance patterns. After the analysis, we find that the most cost-effective policy to increase BCYF attendance is changing each center’s educational and recreational offerings far beyond any accessibility interventions. Our results provide insights into potential policy changes that could optimize the attendance and reach of BCYF Community Centers to underserved populations.

The conditions which allow sustained growth of a protocell population are investigated in the case of asymmetrical division. The results are compared to those of previous studies concerning models of symmetrical division, where synchronization (between duplication of the genetic material and fission of the lipid container) was found under a variety of different assumptions about the kinetic equations and about the place where molecular replication takes place. Such synchronization allows a sustained proliferation of the protocell population. In the asymmetrical case there can be no true synchronization, since the time to duplication may depend upon the initial size, but we introduce a notion of homogeneous growth which actually allows sustained reproduction of a population of protocells. We first analyze Surface Reaction Models, defined in the text, and we show that in many cases they undergo homogeneous growth under the same kinetic laws which lead to synchronization in the symmetrical case. This is the case also for Internal Reaction Models (IRMs), which however require a deeper understanding of what homogeneous growth actually means, as discussed below.

The relationship between culture and the individual is a central focus of social scientific research. This paper examines motivations that mediate between shared culture norms and individual actions. Inspired by the works of Leon Festinger and Melford Spiro, we posit that social network conformation (the perceived adherence of one’s social network with norms) and internalization of cultural norms (incorporation of cultural models with the self-schema) will differentially shape behavior (cultural consonance) depending on the domain and individual characteristics. For the domain of gender roles among Brazilian men, religious affiliation results in different configurations of the individual and culture. Our findings suggest that due to changing and competing cultural models, religious men are compelled to reflexively “think” about what masculinity means to them, rather than subconsciously conform to social (hegemonic) expectations. This study demonstrates the importance of considering the impetus of culturally informed behaviors, and in doing so, provides a methodological means for measuring and interpreting such motivations, an important factor in the relationship between culture and the individual.

In this study, we conducted a comprehensive analysis of the spatial distribution of soil organic carbon stock (SOC stock) and the associated uncertainties in two soil layers (0–10 cm and 0–30 cm; SOC stock 10 and SOC stock 30 respectively), in Valchiavenna, an alpine valley located in northern Italy . We employed the digital soil mapping (DSM) approach within different machine learning models, including multivariate adaptive regression splines (MARS), random forest (RF), support vector regression (SVR), and elastic net (ENET). Our dataset comprised soil data from 110 profiles, with SOC stock calculations for all sampling points based on bulk density (BD), whether measured or estimated, considering the presence of rock fragments. As environmental covariates for our research we utilized environmental variables, in particular geomorphometric parameters derived from a digital elevation model (with a 20 m pixel resolution), land cover data, and climat-ic maps. To evaluate the effectiveness of our models, we evaluated their capacity to predict SOC stock 10 and SOC stock 30 using the coefficient of determination (R2). The results for the SOC stock 10 were as follows: MARS 0.39, ENET 0.41, RF 0.69, and SVR 0.50. For the SOC stock 30, the corresponding R2 values were: MARS 0.45, ENET 0.48, RF 0.65, and SVR 0.62. Additionally, we calculated the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for further assessment. To map the spatial distribution of SOC stock and address uncertainties in both soil layers, we chose the RF model, due to its better performance, as indicated by the highest R2 and the lowest RMSE and MAE. The resulting SOC stock maps using the RF model demonstrated an accuracy of RMSE = 1.35 kg.m-2 for the SOC stock 10 and RMSE= 3.36 kg.m-2 for the SOC stock 30. To further evaluate and illustrate the precision of our soil maps, we conducted an uncertainty assessment and mapping by analyzing the standard deviation (SD) from 50 iterations of the best-performing RF model. This analysis effectively highlighted the high accuracy achieved in our soil maps. The maps of uncertainty demonstrated that the RF model better predicts the SOC stock 10 compared to the SOC stock 30.

Semantic interoperability is a foundational concept in modern healthcare, enabling seamless communication and meaningful data exchange across diverse EHR systems. At the heart of this concept lies archetype clinical information models, which serve as standardized templates to structure and encode clinical data, ensuring consistency and shared understanding among various healthcare stakeholders. This review paper delves into the intricacies of Electronic Health Records (EHRs) within the context of semantic interoperability and the pivotal role played by archetype clinical information models. Drawing from a wealth of recent research, this review paper examines the theoretical underpinnings of semantic interoperability. By surveying the literature, it synthesizes the diverse approaches and methods employed to achieve semantic interoperability within EHR systems. Furthermore, this review does not shy away from addressing the significant challenges that persist in this arena. These challenges encompass issues related to data quality, governance, resistance to change, and the scalability and integration hurdles that healthcare organizations encounter in their quest for interoperable EHR systems. Through a comprehensive review of relevant research papers from esteemed sources including PubMed, ScienceDirect, and Taylor & Francis, the paper offers a comprehensive understanding of the approaches and methods employed in this domain. This analysis encompasses a time frame ranging from 2015 to 2023, ensuring an up-to-date assessment of the field. By presenting a well-rounded picture of the field, this review paper offers valuable insights for healthcare stakeholders, researchers, and practitioners. It underscores the enduring importance of semantic interoperability in EHR systems, not only in advancing patient care, clinical decision support, and healthcare operations but also in driving data-driven innovations in the dynamic and ever-evolving medical healthcare sector. This comprehensive examination sets the stage for future advancements in the pursuit of truly interoperable and data-driven healthcare systems.

$\phi$CDM models provide an alternative to the standard $\Lambda$CDM paradigm, while being physically better motivated. These models lead to a time-dependent speed of sound for dark energy that is difficult to replicate by $w$CDM parametrizations. We review the most up-to-date status of observational evidence for the $\phi$CDM models in this paper. We start with an overview of the motivation behind these classes of models, the basic mathematical formalism, and the different classes of models. We then present a compilation of recent results of applying different observational probes to constraining $\phi$CDM model parameters. Over the last twenty years, the precision of observational data has increased immensely, leading to ever tighter constraints. A combination of the recent measurements favors the spatially flat $\Lambda$CDM model, but a large class of $\phi$CDM models is still not ruled out.

This work deals with a systematic review of literature data concerning the theme of the integrated approach to occupational health and safety management, with particular reference to the programming of assistance plans, which guide the company's organizational choices also by aiming at the principles of the Total Worker Health. In the current regulatory framework on this issue, the UNI ISO 45001: 2018 standard assumes particular relevance, which defines the dynamic approach to occupational health and safety management systems, paying particular attention to external contextual factors that may influence corporate organizational decisions. The adoption of these systems is not mandatory, but allows companies to fulfill their duties in terms of health and safety at work, through an organizational approach aimed at awareness, involvement and participation of all subjects of the company prevention system, overcoming the phase of the mere technological and prescriptive approach, towards a holistic vision of prevention, which places the person at the center of preventive actions. In this context, the role of institutional networks and socio-economic partnerships assumes particular importance, in the activation of territorial assistance interventions to support companies for the improvement of risk management levels. To this end, the importance of verifying the effectiveness of assistance interventions emerges from the scientific debate, through the use of indicators such as quantitative ones for measuring the performance of all phases of the intervention with particular attention to the effects in terms of improved solutions developed.

Several chemistry-climate models (CCM) underestimate the total column ozone (TCO) over the polar region in the Southern Hemisphere during wintertime. To evaluate potential causes of the problem, we exploit CCM SOCOLv3 to study the TCO over Antarctica sensitivity to the: (1) photo-dissociation rates of ozone for large solar zenith angles; (2) rates of the stratospheric heterogeneous reactions, and (3) intensity of the meridional flux into the polar regions due to sub-grid scale mixing processes in the model. Comparisons of the model results with the satellite-based IASI (Infrared Atmospheric Sounder Interferometer) sensor measurements showed that the most important processes for the improvement of the polar ozone simulation results are photolysis and horizontal mixing. The reasonable tuning of these factors has allowed us to improve the model representation of the ozone annual cycle over the southern polar region. The proposed increase of the horizontal mixing can be recommended for the CCMs with relatively low horizontal resolution.

(1) Background: Colon polyps are common protrusions in the colon’s lumen with potential risks of developing colorectal cancer. Early detection and intervention of these polyps are vital for reducing colorectal cancer incidence and mortality rates. This research aims to evaluate and compare the performance of three machine-learning image classification models’ performance in detecting and classifying colon polyps. (2) Methods: The performance of three machine learning image classification models, Google Teachable Machine (GTM), Roboflow3 (RF3), and You Only Look Once version 8 (YOLOV8), in the detection and classification of colon polyps were evaluated. The study used a dataset of colonoscopy images of normal colon, polyps, and resected polyps. The study assessed the models’ ability to correctly classify the images into their respective classes using precision, recall, and F1 score generated from confusion matrix analysis and performance graphs. (3) Results: All three models successfully distinguished between normal colon, polyps, and resected polyps in colonoscopy images. GTM achieved the highest accuracies: 0.99, with consistent precision, recall, and F1 scores of 1.00 for the `normal’ class, 0.97 - 1.00 for `polyps,’ and 0.97 - 1.00 for `resected polyps.’ While GTM exclusively classified images into these three categories, both YOLOV8 and RF3 extended their capabilities to detect normal colonic tissue, polyps, and resected polyps, with YOLOV8 and RF3 achieving overall accuracies of 0.84 and 0.87, respectively. (4) Conclusions: Machine learning, particularly models like GTM, shows promising results in ensuring comprehensive detection of polyps during colonoscopies.

Diverse development models, including waterfall development, iterative development, and agile development, have been put forth and implemented across real-world contexts. When engaging in discussions on project management, the examination and exploration of development models assume paramount importance and are integral. This paper embarks upon an investigation and scrutiny of these development models, culminating in the proposition of "Quasi" Development Models: Quasi-Waterfall and Quasi-Agile.

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

The methylene blue (MB) adsorption was performed on a natural material powder of Cynarascolymus as identified by Cs. To analyze the Cs material, FTIR, SEM, isoelectric point (pHpzc) analysis, TGA, and DRX were used. The maximum experimental adsorption capacity of the Cs material was 203.333, 192.187, and 179,380 mg•g-1 at 298, 303, and 313 K, respectively. The correlation coefficients (R2) and average percentage errors APE(%) values for the kinetic and isotherms models indicated that the adsorption kinetics fol-lowed a pseudo-nth order model and that the Traditional isotherm model Red-lich-Peterson (R-P) correctly described the experimental data obtained at 298, 303, and 313 K, respectively. The steric, energetic, and thermodynamic characteristics of the most rele-vant advanced model (double-energy single-layer model (AM 2)) were analyzed in detail. The number of active sites for the first receptors (n1) was determined to be 0.129, 0.610, and 6.833; whereas the number of second active sites (n2) was determined to be 1.444, 1.675, and 2.036 at 298, 303, and 313 K, respectively. This indicated the presence of both multi–docking and multimolecular modes for the first style of MB ions (n1); while only a multi-molecular mode for the second style of MB ions (n2). Thermodynamic characteristics demonstrated that MB adsorption onto the Cs adsorbent is spontaneous and feasible.

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given the approximately 8,000 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially-available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies compared to in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus have virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

Drought is considered one of the severest natural disasters and it is difficult to predict it. This review article aimed to display the state of the art of methods used to predict and monitor types of droughts. We examine more than 30 indices and models to identify the strengths and weaknesses of methods and identify gaps remaining in this field. Examples of examined indies are Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI). The research found improvement in drought modeling, however, more focus and improvement are required to monitor and predict drought types. It also found that some methods outperform others such as PDSI, SPI, SPEI, EVI, NDVI, NDWI, VCI and TCI.

This article is a theoretical contribution to mental model research, which currently has different threads. Whereas some researchers focus on the perceived causal structure, others also include decision policies and decisions. We focus on the link between recognized causal structure (“mental models of dynamic systems”) and policies, proposing Johnson-Laird’s theory of mental models as the link. The resulting framework hypothesizes two types of systematic mental model errors: (1) misrepresentation of the system’s structure and (2) failure to deploy relevant mental models of possibilities. Examination of three experiments through this lens reveals errors of both types. Therefore, we propose that the cognitive theory of mental models opens a path to better understand how people construct their decision policies and develop interventions to reduce such mental model errors. The article closes by raising several questions for empirical studies of the reasoning process leading from mental models of dynamic systems to decision policies.

This paper presents additional results of the generalized bathtub model for urban networks, including a simpler derivation and exact solutions for uniformly distributed trip lengths. It is shown that in steady state this trip-based model is equivalent to the more parsimonious accumulation-based model, and that the trip-length distribution has merely a transient effect on traffic dynamics, which converge to the same point in the macroscopic fundamental diagram (MFD). To understand the statistical properties of the system, a queueing approximation method is proposed to compute the network accumulation variance. It is found that (i) the accumulation variance is much larger than predicted by traditional queueing models, due to the nonlinear dynamics imposed by the MFD, (ii) the trip-length distribution has no effect on the accumulation variance, indicating that the proposed formula for the variance might be universal, (iii) the system exhibits critical behavior near the capacity state where the variance diverges to infinity. This indicates that the tools from critical phenomena and phase transitions might be useful to understand congestion in cities.

A major challenge for the dissemination, replication, and reuse of epidemiological forecasting studies during COVID-19 pandemics is the lack of clear guidelines and platforms to exchange models in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner, facilitating reproducibility of research outcomes. During the beginning of pandemics, models were developed in diverse tools that were not interoperable, opaque without traceability and semantics, and scattered across various platforms - making them hard to locate, infer and reuse. In this work, we demonstrate that implementing the standards developed by the systems biology community to encode and share COVID-19 epidemiological models can serve as a roadmap to implement models as a tool in medical informatics, in general. As a proof-of-concept, we encoded and shared 24 epidemiological models using the standard format for model exchange in systems biology, annotated them with cross-references to data resources, packed up all associated files in COMBINE archives for easy sharing, and finally, disseminated the models through BioModels repository to significantly enhance their reproducibility and repurposing potential. We recommend the use of systems biology standards to encode and share models of epidemic and pandemic forecasts to improve their findability, accessibility, interoperability, reusability, and reproducibility.

: Predicting long-term student learning is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, formative assessment often involved open-ended questions that students answer in writing. These types of questions in formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low-SES schools. We constructed a classifier to detect incoherent responses to open-ended mathematics questions. We then used it in a model to predict scores on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students’ open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on formative assessments.

Responding to the influences of climate change, on the one hand, and selected benefits of digital technology, on the other hand, an energy transition of global scale appears to be underway. Many observers project that a significant element of the energy transition will be a growing dependence on electricity, a dependence possibly doubling by 2050. Such a transformation, however, would likely require re-configuring the architecture of complex, centralized electricity grids, an artifact of a context of more than a century ago. In concert with the energy transition, we argue to modify the objective of the electricity grid to enable efficient, pervasive optimization in local service areas that provides incentives for users to be efficient in their energy use. At the core of our argument is the presentation of economic incentives denominated in an electricity-backed commodity currency such that incumbent electricity generators could augment their economic purpose of electricity production and electricity distribution to include financial intermediation. A direct consequence of this institutional transformation is the opportunity for all users to generate wealth. There are others who have been inspired to conjure ways that energy could be a candidate currency. Our argument is distinctive, though, in exploiting how an institution (the power grid system) could be repositioned and how all agents in the system could benefit by the institutionalization of electricity as money.

The present paper is concerned with the study of the quality of life index. Such an index has become an important index for measuring the well-being of individuals. However, the quality of life index is always a subjective, intangible, and often hard to quantify with precision due to the lack of quantitative models dealing with. The main goal of the present paper is thus to propose a mathematical, quantitative model for the measurement of a quality of life index. The main novelty is firstly the construction of a wavelet dynamic multiscale model to quantify and investigate the effect of time scale on the quality of life index measuring. The proposed procedure is acted empirically on a sample corresponding to Saudi Arabia as a case of study during the period from 2003 to 2020 as part of the 2030-vision plan. Saudi Arabia has implemented the so-called 2030-vision plan where the quality of life improvement is one of the main goals to be attempted. The findings show that wavelets are capable to localize the time-wise behavior of the index contrarily to classical studies which estimate a global view of the index. Moreover, the study shows the link between the quality of life behavior and many other indices.

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.

In this paper we investigate the geometric structure and control of exponential families depending on additional parameters, called external parameters. These generalized exponential families emerges naturally when one applies the maximum entropy formalism to derive the equilibrium statistical mechanics framework. We study the associated statistical model, compute the Fisher metric and introduce a natural fibration of the parameter space over the external parameter space. The Fisher Riemannian metric allows to endow this fibration with an Ehresmann connection and to study the geometry and control of these statistical models. As an example, we show that horizontal lift of paths in the external parameter space corresponds to an isentropic evolution of the system. We apply the theory to the example of a ideal gas in a rotating rigid container. Most of the results are expressed in local coordinates; in the appendices we hint at possible global extensions of the theory.

With the Paris Agreement, countries are obliged to report greenhouse gas (GHG) emission reduc-tions, which will ensure that the global temperature increase is maintained well below 2C. The Parties will report their Nationally Determined Contributions in terms of plans and progress to-wards these targets during the postponed COP26 in Glasgow in November 2021. These commit-ments however do not take significant portions of the consumption related emissions related to countries imports in to account. Similarly, the majority of companies that report their emissions to CDP also do not account for their embodied value-chain related emissions. Municipalities, on the path towards carbon neutrality in accordance with the methods outlined by C40, also do not in-clude imported and embodied CO2e in their total emission tallies. So, who is responsible for these emissions - the producer or the consumer? How can we ensure that the NDC's, municipalities and companies reduction targets share the responsibility of the emissions in the value-chain thus en-suring that targets and plans become, sustainable, climate fair, and just in global value chains? Today the responsibility lays with the producer, which is not sustainable. We have the outline for the tools needed to quantify and transparently share the responsibility between producers and consumers at corporate, municipal and national level based on an improved understanding of the attendant sources, causes, flows and risks og GHG emissions globally. Hybrid LCA/EEIO models can for example be further developed. This will, in the end, enable everyday consumption to support a more sustainable, green and low carbon transition of our economy.

The Sustainable Development Goals (SDGs) is a global strategy that aims to obtain a more equitable and just world. These objectives are organized in 17 SDGs, which are detailed in 169 targets. Different international institutions have emphasized education's relevance to developing citizens who contribute to the SDGs' achievement for 2030. However, a review focused on Physical Education exclusively has not been performed yet. Therefore, the objective of this work is double. First, to analyze and select the specific goals of the SDGs that can be implemented in the subject of Physical Education. And second, to relate these specific goals to the different models based on Physical Education practices. This review showed how three institutional documents have previously related sport, physical exercise and physical education to the specific goals of the SDGs. Based on the search done, this document selects those goals that could be integrated into the educational context through Physical Education. The bibliographic and narrative analysis carried out in this research shows that of the 169 specific goals proposed in the SDGs, only 24 could be worked on in Physical Education. In addition, after this previous analysis, a proposal for the relationship between the practice-based models and these 24 goals is presented. The contributions made in this paper will allow teachers to establish links between PE sessions and SDGs while raising awareness to develop students who contribute to a more sustainable world.

Medical and translational scientific research requires the use of animal models as an initial approach to the study of new therapies and treatments, but when the objective is an exploration of translational potentialities, classical models fail to adequately mimic problems in humans. Among the larger animal models that have been explored more intensely in recent decades, small ruminants, namely sheep and goats, have emerged as excellent options. The main advantages associated to the use of these animals in research works are related to their anatomy and dimensions very similar to those of humans in most physiological systems, in addition to their low maintenance and feeding costs, tendency to be docile, long life expectancies and few ethical complications raised in society. The most obvious disadvantages are the significant differences in some systems such as the gastrointestinal, and the reduced amount of data that limits the comparison between works and the validation of the characterization essays. Despite everything, recently these species have been increasingly used as animal models for diseases in different systems, and the results obtained open doors for their more frequent and advantageous use in the future. The purpose of this review is to summarize the general principles related to the use of small ruminants as animal models, with focus on regenerative medicine, to group the most relevant works and results published recently and to highlight the potentials for the near future in medical research.

Over the last decade, there has been a renewed interest in oral health workforce planning. The purpose of this review is to examine oral health workforce planning models on supply, demand and needs, mainly in respect to their data sources, modelling technique and use of skill mix. A search was carried out on PubMed, Web of Science, and Google Scholar databases for published scientific articles on oral health workforce planning models between 2010 to 2020. No restrictions were placed on the type of modelling philosophy, and all studies including supply, demand or needs based models were included. Rapid review methods guided the review process. Twenty-three studies from 15 different countries were included in the review. A majority were from high income countries (n=17). Dentists were the sole oral health workforce group modelled in 13 studies; only five studied included skill mix (allied dental personnel) considerations. The most common application of modelling was a workforce to population ratio or a needs-based demand weighted variant. Nearly all studies presented weaknesses in modelling process due to the limitations in data sources and/or non availability of necessary data to inform oral health workforce planning. Skill mix considerations in planning models were also limited to horizontal integration within oral health professionals. Planning for the future oral health workforce is heavily reliant on quality data being available for supply, demand and needs models. Integrated methodologies that expand skill mix considerations and account for uncertainty are essential for future planning exercises.

Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.

Isotopic stable analysis (SIA) is a powerful tool in the assessment of different types of ecological and physiological studies. For that, different preservative methods for the samples are commonly used prior to isotopic analysis. The effects of various preservative methods (drying, freezing, ethanol and formaldehyde) have been analyzed for C:N ratio, δ13C and δ15N on a variety of tissues including dorsal fins (three seahorse and two pipefish species), seahorse newborns (three seahorses species), and prey (copepods and different stages of Artemia) commonly used to feed the fishes in rearing conditions. The aims of the study were to: (i) evaluate isotopic effects of preservation methods across tissues; and (ii) construct the first conversion models available in syngnathid fishes. The preservation in ethanol and to a lesser extend in formaldehyde significantly affected δ13C values, whereas δ15N signatures were not affected significantly. Due to their low lipid content, the isotopic signals in fish fins were almost unaffected, supporting the suitability of dorsal fins as a convenient tool in isotopic studies on vulnerable species such as syngnathids. The regression equations provided permit the successful conversion of δ13C and δ15N values between preservative treatments. The conversion models can be applied to isotopic studies in the field and in the laboratory.

Periodic travelling waves are observed in various brain activities including visual, motor, language, sleep, and so on. There are several neural field models describing periodic waves assuming nonlocal interaction and, possibly, inhibition, time delay, or some other properties. In this work we study the influence of asymmetric connectivity functions and of time delay on the emergence of periodic waves and on their properties. Nonlinear wave dynamics is studied, including modulated and aperiodic waves. Multiplicity of waves for the same values of parameters is observed. External stimulation in order to restore wave propagation in a damaged tissue is discussed.

Rough volatility models are popularized by \cite{gatheral2018volatility}, where they have shown that the empirical volatility in the financial market is extremely consistent with rough volatility. Fractional Riccati equation as a part of computation for the characteristic function of rough Heston model is not known in explicit form as of now and therefore, we must rely on numerical methods to obtain a solution. In this paper, we give a short introduction to option pricing theory and an overview of the current advancements on the rough Heston model.

Atopic dermatitis (AD) is a chronic inflammatory skin disease, which can cause skin barrier function damaged. Although co-incubation with docosahexaenoic acid (DHA) exerts a positive effect in deficient skin model, there is no study to investigate the effects of topical treatment with DHA in inflammatory reconstructed human epidermis (RHE) model. The effects of DHA on monolayer normal human epidermal keratinocyte (NHEK) cells were evaluated via CCK-8, qPCR and ELISA. The skin related barrier function was assessed by hematoxylin-eosin (HE) staining, western blot (WB), Immunohistofluorescence (IF) and ELISA in normal and inflammatory RHE models. DHA upregulated filaggrin and loricrin expression at mRNA levels in addition to suppress overexpression of TNF-α，IL-1α and IL-6 stimulated by poly I:C plus LPS (stimulation cocktail) in cultured NHEK cells. After topical treatment with DHA, cocktail induced inflammatory characteristics of skin diseases including barrier morphological, differentiation proteins and TSLP secretion, which were alleviated in RHE models. Supplementation with DHA can improved related barrier function and have anti-inflammation effects in monolayer keratinocytes and RHE models, which indicated that DHA may have a potential value for the treatment of inflammation-associate skin diseases.

Monin-Obukhov similarity theory (MOST) overestimates wind shear in some atmospheric stable conditions, i.e. Richardson number $R_f<0.25$. The overestimated wind shear that leads to an under-predicted friction wind speed and a lower ambient turbulence intensity for a given hub-height reference wind speed and a given roughness length, could influence wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we firstly show that MOST predicts a larger wind shear for the atmospheric stability parameter $\zeta>0.1$ and proposes new flow similarity functions without constraining $R_f$ to limit the overestimated wind shear by MOST. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier–Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in larger wake deficits and slower wake recovery rate with a square root of the mean-squared-error (RSME) of wake deficit in the range of 0.07-0.18. This overestimated wake effect is improved by applying the new similarity functions and the RSME of wake deficit is averagely reduced by 0.05. Finally, we check the role of the under-predicted turbulence intensity playing in the larger wake deficit predicted by models based MOST. Additional numerical simulations using the modified turbulence model are carried out, in which the roughness length is reduced to impose a hub-height ambient turbulence intensity equivalent to the MOST case. Simulation results show that reducing turbulence intensity enhances wake effects, however, it cannot reproduce the large wake deficit predicted by models based on MOST, which suggests that the overestimated wake effect by MOST could be also related to the overestimated wind shear.

Duplex Stainless Steels (DSS) and Superduplex Stainless Steels (SDSS) are an important class of stainless steels because they combine the benefits of austenite and ferrite phases, resulting in steels with better mechanical properties and higher corrosion resistance. Due to these characteristics are widely employed in various industries. However, the appearance of deleterious phases in their microstructure impairs the properties of DSS and SDSS. Among the deleterious phases, the main one is the sigma phase (σ), which can be nucleated when the steel is exposed to the temperature range between 650 °C and 900 °C, reducing its toughness and resistance to corrosion. In a previous work, Fonseca and collaborators used two descriptors of the microstructural path to analyze the formation of sigma phase (σ), SV, interfacial area per unit volume between sigma phase and austenite, and <λ>, mean chord length of sigma, both in function of the VV, volume fraction of sigma, known in the literature as microstructural partial path (MP). In this work, the contiguity ratio is applied for the first time to describe the microstructural path in the study of sigma phase precipitation in SDSS. The contiguity ratio showed that the distribution of the ferrite/sigma boundaries is homogeneous. Thus, it is reasonable to infer that one has a uniform distribution of sigma phase nuclei within the ferrite. About the kinetics of sigma phase formation, the DSS can be described by the classical JMAK equation, whereas for the SDSS, the kinetics tends to follow the Cahn model for grain edge nucleation. Finally, we present the 3D reconstruction of the sigma phase in SDSS. The results demonstrate that the sigma phase nucleates at the edges of the ferrite/austenite interfaces. Moreover, the sigma phase grows consuming the ferrite, but it is not fully interconnected.

This study compared the performance of five Family Generalized Auto-Regressive Conditional Heteroscedastic (fGARCH) models (sGARCH, gjrGARCH, iGARCH, TGARCH and NGARCH) in the presence of high positive autocorrelation. To achieve this, financial time series was simulated with autocorrelated coefficients as ρ = (0.8, 0.85, 0.9, 0.95, 0.99), at different time series lengths (as 250, 500, 750, 1000, 1250, 1500) and each trial was repeated 1000 times carried out in R environment using rugarch package. And the performance of the preferred model was judged using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results from the simulation revealed that these GARCH models performances varies with the different autocorrelation values and at different time series lengths. But in the overall, NGARCH model dominates with 62.5% and 59.3% using RMSE and MAE respectively. We therefore recommended that investors, financial analysts and researchers interested in stock prices and asset return should adapt NGARCH model when there is high positive autocorrelation in the financial time series data.

Coastal areas in the South Coast of West Java Province have the potential to develop marine ecotourism, one of which is the Pangandaran area which must be transferred into economic value by not damaging natural resources. Marine ecotourism development is not only intended to raise foreign exchange for local governments, but are also expected to play a role in maintaining natural resources sustainably. This research aims to analyze the sustainable synergistic marine ecotourism development model. The method used in this research using quantitative descriptive method. The Quantitative descriptive method is used to describe the general condition of the research area, using primary and secondary data. The technique of taking respondents using accidental sampling as many as 50 respondents consisting of tourists, public figures, fishermen who have side jobs as a provider of marine ecotourism services. The analysis tool used is through a Rapfish model approach to measuring the synergistic model of sustainable development of marine ecotourism. Based on the results of a research on a sustainable synergistic marine ecotourism development model by measuring the ecological dimensions of environmental services in high conditions, the economic dimension of marine ecotourism is in moderate condition. Marine ecotourism technology in low conditions and social dimensions of marine ecotourism in low conditions. Model development of sustainable marine ecotourism synergistic with regard to the dimension of environmental, economic and social institutions should be able to form integrated from infrastructure to support marine ecotourism up to raise the level of income of fishermen who have a second job as a marine ecotourism providers. The infrastructure and regulatory dimensions are recommended to use the technology information to promote marine ecotourism optimally and regulations need to make marine ecotourism zoning rules and infrastructure improvements.

We discuss the influence played by different statistical models in the prediction of porosity and litho-fluid facies from logged and post-stack inverted acoustic impedance (Ip) values. We compare the inversion and classification results obtained under three different a-priori statistical assumptions: an analytical Gaussian distribution, an analytical Gaussian-mixture model and a non-parametric mixture distribution. The first model assumes Gaussian distributed porosity and Ip values, thus neglecting their facies-dependent behaviour caused by different lithologic and saturation conditions. Differently, the other two statistical models relate each component of the mixture to a specific litho-fluid facies, so that the facies-dependency of porosity and Ip values is taken into account. Blind well tests are used to validate the final predictions, whereas the analysis of the maximum-a-posteriori (MAP) solutions, the coverage ratio and the contingency analysis tools are used to quantitatively compare the inversion outcomes. This work points out that the correct choice of the statistical petrophysical model could be crucial in reservoir characterization studies. Indeed, for the investigated zone it turns out that the simple Gaussian model constitutes an oversimplified assumption, while the two mixture models provide more accurate results, although the non-parametric one yields slightly superior predictions with respect to the Gaussian-mixture assumption.

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network contents and the company analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

RO membrane fouling is one of the main challenges that membrane manufactures, the scientific community and industry professionals have to deal with. The consequences of this inevitable phenomenon have a negative effect on the performance of the desalination system. Predicting fouling in RO systems is key to evaluating the long-term operating conditions and costs. Much research has been done on fouling indices, methods, techniques and prediction models to estimate the influence of fouling on the performance of RO systems. This paper offers a critical review evaluating the state of industry knowledge in the development of fouling indices and models in membrane systems for desalination in terms of use and applicability. Despite major efforts in this field, there are gaps in terms of effective methods and models for the estimation of fouling in full-scale RO desalination plants. In existing models applied to full-scale RO desalination plants, neither the spacer geometry of membranes nor the efficiency and frequency of chemical cleanings - which play an important role in the performance of this process - are considered.

Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from 8 Global Climate Models. The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J and the Sacramento model were chosen for this study. During model evaluation, 10,000 parameter sets have been generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997-2003) and the validation (2004-2010) period respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment while the GR4J and IHACRES-CMD over and under estimate low flow respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended along with other methods of sustainable river basin managements.

Long memory plays an important role in many fields by determining the behaviour and predictability of systems; for instance, climate, hydrology, finance, networks and DNA sequencing. In particular, it is important to test if a process is exhibiting long memory since that impacts the accuracy and confidence with which one may predict future events on the basis of a small amount of historical data. A major force in the development and study of long memory was the late Benoit B. Mandelbrot. Here we discuss the original motivation of the development of long memory and Mandelbrot's influence on this fascinating field. We will also elucidate the sometimes contrasting approaches to long memory in different scientific communities

The work proposes a backtesting analysis in comparison between the Lee-Carter and the Cairns-Blake-Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975-2014, over which we computed back-projections evaluating the performances of the models in comparisons with real data. We propose three different backtest approaches, evaluating the goodness of short-run forecast versus medium-length ones. We find that both models were not able to capture the improving shock on the mortality observed for the male population on the analyzed period. Moreover, the results suggest that CBD forecast are reliable prevalently for ages above 75, and that LC forecast are basically more accurate for this data.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

The Sunyaev-Zeldovich effect (SZe) is a global distortion of Cosmic Microwave Bckground (CMB) spectrum as result of its interaction with a hot electron plasma in the intracluster medium for large gravitational virialized structures such as galaxy clusters. Furthermore, this hot gas of electrons emits X-Rays due to its fall in the gravitational potential well of the cluster. The analysis of SZe and X-Ray data, provide a method for calculating distances to galaxy clusters at any redshift (Angular diameter distance (d_A) and gas mass fraction (f_gas)). On the other side, many of these galaxy clusters produce a Strong Gravitational Lens effect (SGL), which has become an useful astrophysical tool for cosmology. We use these cosmological tests, in addition to the more traditional ones (SNIa, CMB, BAO), to constraint alternative models of dark energy (ωCDM, CPL, IDE, EDE) and study the history of expansion through the cosmographic parameters (H(z), q(z), j(z)). Using Akaike and Bayesian Information Criterion (AIC, BIC) we find that the ωCDM and ΛCDM models are the most favored by the observational data. In addition, we found that at low redshift appears an peculiar behavior of slowdown of aceleration, which occurs only on dynamical dark energy models using only galaxy clusters (d_A,clusters + f_gas).

Information-theoretic measures such as the entropy, cross-entropy and the Kullback-Leibler divergence between two mixture models is a core primitive in many signal processing tasks. Since the Kullback-Leibler divergence of mixtures provably does not admit a closed-form formula, it is in practice either estimated using costly Monte-Carlo stochastic integration, approximated, or bounded using various techniques. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy and the Kullback-Leibler divergence of mixtures. We illustrate the versatile method by reporting on our experiments for approximating the Kullback-Leibler divergence between univariate exponential mixtures, Gaussian mixtures, Rayleigh mixtures, and Gamma mixtures.

Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

Implementing digital technologies, such augmented reality and 3D models, to various cultural sectors is an ongoing procedure, especially favorable after COVID-19 pandemic. Even though such technologies are familiar to museums, galleries and archaeological places all over the world, little research or practical cases have been developed when it comes to theaters and theatrical costumes. Proposed research article describes the methodology of both reference material collection and applications development, while an analysis of functions and aesthetic results are described, especially when it comes to 3D models. The paper’s aim to reveal that theatrical operations (as part of cultural sector) as well as structured parts (such as acts, performances and costumes) can be preserved and served to larger audiences via technological means, such as an augmented reality application.

Gaucher disease (GD) is a lysosomal storage disorder stemming from biallelic mutations in GBA1, characterized by glucocerebrosidase dysfunction and glucocerebroside and glucosylsphingosine accumulation. Since phenotypes of murine models of GD often differ from those in patients, careful characterization of Gba1 mutant mice is necessary to establish their ability to model GD. We performed side-by-side comparative biochemical and pathologic analyses of four murine Gba1 models with genotypes L444P/L444P (p.L483P/p.L483P), L444P/null, D409H/D409H (p.D448H/p.D448H), and D409H/null, along with matched wildtype mice, all with the same genetic background and cage conditions. All mutant mice exhibited significantly lower glucocerebrosidase activity (p < 0.0001) and higher glucosylsphingosine levels than wildtype, with the lowest glucocerebrosidase and highest glucosylsphingosine levels in mice carrying a null allele. Although glucocerebrosidase activity in L444P and D409H mice was similar, D409H mice showed more lipid accumulation. No Gaucher or storage-like cells were detected in any of the Gba1 mutant mice. Quantification of neuroinflammation, dopaminergic neuronal loss, alpha-synuclein levels and motor behavior revealed no significant findings, even in aged animals. Thus, while the models may have utility for testing the effect of different therapies on enzymatic activity, they did not recapitulate the pathological phenotype of patients with GD, and better models are needed.

The stochastic modelling of chemical physical processes is essentially based on a coarse-grained formulation of fluctuations, usually described by means of Wiener processes. By taking the paradigmatic example of chemical reaction kinetics, we propose a simple representation of microscopic stochastic dynamics grounded on the use of distributional derivatives of counting processes that account for elementary reactive events. This approach is consistent with the statistical analysis based on the Chemical Master Equation, and provides the formal setting for the existing algorithmic approaches (Gillespie algorithm). Some practical advantages of this formulation are addressed, and several other examples are discussed in connection with cluster formation, velocity fluctuations in liquid systems, and quantum transitions (radiative interactions) at the molecular level.

Since alternative methods (AAs), such as in vitro cultures, organs in silico, or mathematical methods, are not fully able to reproduce the complexity of the human organism, particularly neu-ral processes, and brain structures, animal experimentation remains the only way in which re-search can obtain the necessary knowledge to suggest drug therapies or targeted therapeutic in-terventions. Particularly in neuroscience, transgenic animals allow us to model neurological dis-eases, study the relationship between the brain and behavior, and decipher the function of genes involved in pathological conditions. However, it should never be forgotten that animals are "sen-tient beings" capable of feeling pleasure and pain (Art. 13 of the 2007 Lisbon Treaty). For this rea-son, it is crucial to prioritize the welfare of animals involved in testing by considering their phys-ical, psychological, adaptive, and relational well-being. This is where the concept of a Culture of Care comes in, where the responsibility of care extends beyond the veterinary aspects to include everything that can safeguard the welfare of animals before, during, and after experimentation. This chapter explains the advantages of using the animal model while practicing the culture of care in neuroscience, as both animals and research outcomes benefit from this approach.

Time series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in an oilfield. As one of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin are selected as a typical study area. Firstly, we used SAR images collected by the Sentinel-1A satellite from January 2021 to December 2022 and applied the Multidimensional Small Baseline Subset (MSBAS) method to obtain vertical and east-west deformation measurements. On this basis, a nonlinear Bayesian inversion method was applied to model the shallow reservoir in a series of complex deformation areas, based on a single-source model and a multi-source model, respectively. As a result, the ground deformation monitoring results obtained by long time series InSAR clearly reflect the uneven ground deformation caused by the oil extraction and water injection operation process. There is slight subsidence in the Huatugou oilfield, while significant uplift deformation occurred in the Ganchaigou oilfield and the Youshashan oilfield, with a maximum uplift rate of 48 mm/year. Further analysis indicates that the introduction of the 2D deformation field helps to improve the robustness of oilfield reservoir parameter inversion. Moreover, the dual-source model is more suitable than the single-source model for inverting complex deformation reservoir parameters. This study not only fills the blank of InSAR deformation monitoring for the oilfields in the western Qaidam Basin but also provides a theoretical reference for model and method selection of reservoir parameter inversion in other oilfields.

Gravitational data are the only ones able to provide otherwise hidden details of what happens below the surface. In this paper, several models are implemented to constrain them with the outer structure of Mars: firstly a Bouguer inversion to reduce gravitational residual, obtaining a crustal depth in the range 23.19-94.56 km. Successively, it was considered an Airy model providing for local buoyancy. Given that its configuration does not permit to evaluate properly the different flexural responses of the lithosphere, and that it overcompensates for the crust of the highest volcanic conformations, a flexural response function was used to filter this model. It uses the lithosphere elastic thickness T_e to scale especially the short wavelengths, correcting for local phenomena. Finally, an optimization was implemented to find the best-fitting values for T_e, using both an infinite plate and a thin shell approximation, and then obtain a global map for crustal density and thickness. Even if the Martian dichotomy is basically confirmed by all the models, along with the well-known anomalies localized at the major basin craters and volcanic Paterae, the Bouguer model supplies the best representation of the state-of-the-art knowledge about the outer layers of Mars and with further improvements can represent a future benchmark for the research on the Red Planet.

The spin-1/2 quantum transverse Ising model, defined on a ladder structure, with nearest-neighbor and four-spin interaction on a plaquette, is studied by using exact diagonalization and finite-size-scaling procedures. The quantum phase transition between the ferromagnetic and paramagnetic phases has been obtained in the thermodynamic limit. The critical transverse field decreases as the antiferromagnetic four-spin interaction increases, and reaches a multicritical point. However, the exact diagonalization approach was not able to capture the essence of the dimer phase beyond the multicritical transition.

This article presents the impact analysis of space harmonics (SH) presence on a single-sided three-phase linear induction motor, along with a comprehensive parametric investigation. The presence of SH often reduces the LIM performances. The electromagnetic phenomena are governed by Maxwell's equations associated with equivalent circuit modelling. The chosen mathematical model uses 2D formulation with magnetic vector potential A. The model implementation is performed with finite element method on Gmsh-GetDP free platform. The electromagnetic thrust is calculated in current excitation case by a numerical model (FEM) with and without term generating SH in order to highlight the effect of SH. The adaptation to voltage supply operation is obtained by equivalent electric circuits (EEC), through calculation of the operational impedance. The main objective of the study consists in a comparative exploration of SH presence using two numerical models (single and multi-harmonics), and an assessment of parameters effect on space harmonic presence and various machine characteristics such as thrust, efficiency, and power factor. The motor’s characteristics (ie: thrust, efficiency and power factor) strongly depend on parameters such as the pole pair number and conductivity. Improving the operation and maximizing the performances of such a machine for a given specification requires the use of optimization algorithms.

Escalation of commitment explains the choice for investing in an option for which great investments have previously been made even though better choices became available. The last 25 years of producing innumerous versions of Alzheimer Disease animal models with virtually no predictive validity should have exposed the fact that using animals for studying AD is clearly a failing course of action and as such should have been abandoned long ago, giving room to other alternatives to flourish. In this assay, I explore the idea that the investment in new animal models for studying Alzheimer (AD) represents a classic case of escalation of commitment, and sunk cost dilemma, in the biomedical research.

The Earth’s synthetic gravitational and density models can be used to validate numerical procedures applied for a global (or large-scale regional) gravimetric forward and inverse modelling. Since the Earth’s lithospheric structure is better constrained by tomographic surveys than a deep mantle, most of existing 3-D density models describe only a lithospheric density structure, while 1-D density models are typically used to describe a deep mantle density structure below the lithosphere-asthenosphere boundary. The accuracy of currently available lithospheric density models is examined in this study. The error analysis is established to assess the accuracy of modelling the sub-lithospheric mantle geoid, while focusing on the largest errors (according to our estimates) that are attributed to lithospheric thickness and lithospheric mantle density uncertainties. Since a forward modelling of the sub-lithospheric mantle geoid comprises also numerical procedures of adding and subtracting gravitational contributions of similar density structures, the error propagation is derived for actual rather than random errors (that are described by the Gauss’ error propagation law). Possible systematic errors then either lessen or sum up after applying particular corrections to a geoidal geometry that are attributed to individual lithospheric density structures (such as sediments) or density interfaces (such as a Moho density contrast). The analysis indicates that errors in modelling of the sub-lithospheric mantle geoid attributed to lithospheric thickness and lithospheric mantle density uncertainties could reach several hundreds of meters, particularly at locations with the largest lithospheric thickness under cratonic formations. This numerical finding is important for a calibration and further development of synthetic density models of which mass equals the Earth’s total mass (excluding the atmosphere). Consequently, the (long-to-medium wavelength) gravitational field generated by a synthetic density model should closely agree with the Earth’s gravitational field.

This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov-Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results which are also applied to two sets of real data.

Machinery parts gradually wear out over time due to regular usage. To improve machinery health and prevent critical issues, a reliable prognosis framework can be implemented by monitoring the behaviour of machinery parts and issuing warnings before they reach a critical state. To achieve this, vibration data from roller bearings experiencing various fault conditions have been collected. Different techniques from the literature were combined to analyze the distinct configurations in the vibration data sets and identify the main defects in roller bearings. The significant features extracted from this analysis were then used to create optimized stochastic model equations, separately regressing inner and outer race fault features to healthy bearing features under random conditions. These models can help engineers design more dependable systems, optimize their performance, and minimize the risk of failures and downtime.

In the Cerrado-Amazonian ecotone in the State of Mato Grosso, intensely altered by anthropic action, the knowledge and processes of energy conversion and energy balance are still incipient, making the monitoring and modeling of diffuse radiation essential for several applications. The objective of this study was to evaluate the seasonality of the diurnal evolution and estimate the hourly diffuse radiation (Hdh) incident radiation in the horizontal plane, between June 2011 and October de 2016. The instantaneous measurements (5 minutes) of diffuse radiation underwent geometric, astronomical, and anisotropic corrections, with subsequent hourly integrations. The seasonality of diffuse radiation and its radiometric fractions was evaluated. The estimates were made considering total and seasonal data groupings (water stations in the region) and in different cloudiness classes (atmospheric transmissivity index - KTh). The diurnal behavior of diffuse radiation (Hdh) was similar to that of global radiation and at the top of the atmosphere, with maximum values at solar noon. The correlations between Kdh and KTh showed third-order polynomial behavior, with maximum observed values of Kdh ranging from 0.8 to 0.9, for KTh less than 0.2. Estimation equations based on radiometric fractions underestimated the values of diffuse radiation, with a better performance presented by models adjusted in annual data groupings. Among the parameterized models for estimating diffuse radiation obtained in the literature, those calibrated regionally in this study, together with those developed for tropical regions, presented better statistical performances.

Stochastic mortality models seek to forecast future mortality rates; thus, apparently the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models - in terms, that is, of their goodness-of-fit and prediction accuracy - is often based on the logarithmic scale of the mortality rate. In this article, we examine whether the same forecast outcomes are obtained when the performance of mortality models is assessed based on the original and log scales of the mortality rate. We compare four different stochastic mortality models: the original Lee-Carter model, the Lee-Carter model with (log-)normal distribution, the Lee-Carter model with Poisson distribution and the median Lee-Carter model. We show that the preferred model will depend on the scale of the objective variable, the selection criteria measure and the range of ages analysed.

To handle the problem of solving the results of aircraft’s visual navigation with scene matching, this paper take the measure of the spherical EPnP positioning posture solving by measuring the central angle threshold value and approaches for constructing a measuring model. The detailed steps are as follows: firstly, this approach needs to construct a positioning coordinate model for the earth surface, makes sure the expression for the 3-dimensional coordinate of the earth surface and solves the positioning of constructing data model with EPnP positioning posture solving algo-rithm. Secondly, by contrasting and analyzing the positioning posture value of approximate plane coordinates, the critical value is acquired, which can be recognized as plane calculation. Lastly, this method should construct a theoretical model of measurement for the visual height and central angle with the decided central angle threshold value. The simulation experiment shows that the average positioning precision of taking the spherical coordinates as input is 16.42 percent way higher than taking the plane coordinates as input. When the central angle is less than 0.05 degrees and the surface district is less than 5585 square meters, the positioning precision of the plane co-ordinates is pretty much equal to the spherical coordinates. At this moment, the sphere can be seen as flat. The conclusion of this essay can theoretically guide the further study of positioning posture solving of the scene matching, which is also of vital significance for theory research and engineering application.

Exosomes have shown promising potential as a therapeutic approach for wound healing. Nevertheless, the translation from experimental studies to commercially available treatments is still lacking. To assess the current state of research in this field, a systematic review was performed to examine the methodological heterogeneity among studies conducted over the past five years. Additionally, the review analyzed the suitability of animal models used and their relevance to human medicine. A PubMed search was performed for english-language, full-text available papers published from 2018 to June 2023, focusing on exosomes derived from mammalian sources and their application in wound healing, particularly those involving in vivo assays. Out of 531 results, 148 papers were selected for analysis. The findings revealed significant variability in terms of cell sources and types, biomaterials, and administration routes under investigation, indicating the need for further research in this field. Additionally, a comparative examination encompassing diverse cellular origins, types, administration pathways, or biomaterials is imperative. Furthermore, the predominance of rodent-based animal models raises concerns, as there have been limited advancements towards more complex in vivo models and scale-up assays. These constraints underscore the substantial efforts that remain necessary before attaining commercially viable and extensively applicable therapeutic approaches using exosomes.

Biogeochemical models estimate soil organic carbon (SOC) sequestration, crop growth, and yield. The DeNitrification and DeComposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice-wheat rotation under organic/inorganic fertilizations with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC under-predicted harvestable grain C-biomass of rice by 29.22% to 42.14% and over-simulated grain C-biomass of wheat by 55.01% with equal amounts of NPK and animal manure applied under CT. However, after calibration by adjusting default values of soil/crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (only -2.81% to -6.17% less). DNDC also predicted effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha-1). DNDC simulated SOC trends for rice-wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice-wheat systems.

Activated sludge process is a well known method to treat municipal and industrial waste water. In this complex process, the oxygen concentration in the reactors plays a critical role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory (LSTM) network to identify an input-output model suitable for the design of an oxygen concentration controller. The model is identified from easy-accessible measures collected from a real plant. This dataset covers almost a month. The performances achieved with the proposed LSTM model are compared with the ones obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models catch the oscillation and the overall behaviour (ARX ρ=0.833 , LSTM ρ=0.921), but, while the ARX model fails in reaching the correct amplitude (FIT=41.20%), the LSTM presents satisfactory performance (FIT=60.56%).

Information cascades (tree-like structures formed by posts, comments, likes, replies, etc.) constitute the spine of the public online information environment, reflecting its various trends, evolving with it and, importantly, affecting its development. While users participate in online discussions, they display their views and thus contribute to the growth of cascades. At the same time, users’ opinions are influenced by cascades’ elements. The current paper aims to advance our knowledge regarding this social phenomenon by developing an agent-based model in which agents participate in a discussion around a post on the Internet. Agents display their opinions by writing comments on the post and liking them (i.e., leaving positive assessments). The result of these processes is dual: on the one hand, agents develop an information cascade; on the other hand, they update their views. Our purpose is to understand how agents’ activity, openness to influence, and cognitive constraints (that condition the amount of information individuals are able to proceed with) affect opinion dynamics. We also control for social contagions (when people’ perception of a message may depend not only on the message’s opinion, but also on how other individuals perceive this object, with more positive evaluations increasing the probability of adoption) and ranking algorithms that steer the order in which agents learn new messages. Among other things, we demonstrated that replies to disagreeable opinions are extremely effective for promoting your own position. In contrast, likes have a tiny effect on this issue.

In the era of big data with increasingly complex data structures and ever-larger data scales, matrix-type data are becoming highly valued and their applications in the fields of medicine, industry, education, geography, and astronomy are growing in extent. In recent years, significant progress has been made in the practical use of matrix variable t-distribution finite mixture models for handling data in order to address the issues of multi-subgroup structures and long data tails. In this paper, the expectation-maximization (EM) algorithm with penalized maximum likelihood is proposed to resolve the problem of the unbounded nature of the likelihood function applied to the model by considering the degeneracy of the variance-covariance matrix of this model. Our data were analyzed through simulations and real data, and the results demonstrate that our model is effective in both preventing the likelihood function from being unbounded and in ensuring the accuracy of the estimated parameters of the EM algorithm.

Science evolves over a gentle arc spanning centuries, with scientists building upon and extending the hypotheses and discoveries of their forebears when nurturing their own work from ideation to crystallization and finally implementation. However, evidence suggests several limitations of our modern academic pursuits including major inertia and epistemological biases to implement even major advancements. For instance, the transformative uncertainty principles of quantum mechanics are yet to be satisfactorily integrated in modern analyses and publications, even almost a century after Heisenberg received the Nobel prize for these. Another example is ever expanding reliance on mathematics to validate the hypotheses of Physics, and undermine the opinions to the contrary. In addition, modern science limits itself to the era post fifteenth century and hastily rejects premodern achievements despite glaring examples. This reluctance and inertia to capitalize on existing knowledge is a challenge that imperils our intellectual pursuits. A salient facet of science is "the willingness to admit ignorance". Only on this foundational principle can science meaningfully evolve. It is time we take a step back to evaluate widely accepted and foundational premises of modern science and institute structured processes to implement the treasure trove of knowledge amassed by our predecessors. This essay highlights some of the opportunities that can and should be availed capitalizing upon the recent developments of computational and analytical capabilities along with artificial intelligence.

(1) Background: Ventilator-associated pneumonia (VAP) causes high mortality among patients with respiratory disease and imposes major burdens on healthcare infrastructure. Models that use electronic health record data to predict the onset of VAP may spur earlier treatment and improve patient outcomes. We developed and studied the performance of interpretable machine learning (ML) models that predict the onset of VAP from electronic health records (EHRs); (2) Methods: We trained Logistic Regression (LR), full feature Explainable Boosting Machine (fEBM), and eXtreme Gradient Boosting (XGBoost) ML models on data from the MIMIC- III (v1.3) database. Model performance was measured by area under the receiver operating characteristic curves (AUCs). We trained a minimal-feature EBM model (mEBM) with features derived from white blood cell (WBC) counts, duration of ventilation, and Glasgow Coma Scale (GCS). Finally, model robustness was evaluated on randomly sparsified EHR datasets; (3) Results: The fEBM model outperformed the XGBoost and LR models at 24 hours post-intubation. The mEBM model maintained an AUC of 0.893. The fEBM model performance remained robust on sparsified datasets; (4) Conclusions: Our novel interpretable ML algorithm reliably predicts the onset of VAP in intubated patients. Integration of this EBM-based model into clinical practice may enable clinicians to better anticipate and prevent VAP.

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict the individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall rating of ROB. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether 42 studies were assessed, of which a vast majority (n=34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our results of the temporal analysis showed a significant reduction in the number of studies with high ROB for the domain analysis. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity. Neurobiologically, ADHD impairments arise from abnormalities in different circuits involving the prefrontal cortex. In face of high rates of diagnosis, alternative/complementary pharmacological therapeutic approaches for ADHD are needed. Although the number of publications that study the potential effects of caf-feine consumption on ADHD treatment have been accumulating over the last years, and caffeine has recently been used in ADHD research in the context of animal models, an updated evi-dence-based systematic review on the effects of caffeine on ADHD-like symptoms in animal stud-ies is missing. To provide insight and value at the preclinical level, a systematic review based on PRISMA guidelines was performed for all publications available up to September 1, 2021. Caffeine treatment increases attention, improves learning, memory and olfactory discrimination, without altering blood pressure and body weight. These results are supported at the neuronal level. Nonetheless, the implication of caffeine in modulating ADHD-like symptoms of hyperactivity and impulsivity is contradictory, raising discrepancies that require further clarification. Our results strengthen the hypothesis that caffeine cognitive effects found in animal models could be trans-lated to human ADHD, particularly during adolescence.

The paper presents section models for analysis of the resistance of RC members subjected to bending moment with or without axial force. To determine the section resistance the nonlinear stress-strain relationship for concrete in compression is assumed, taking into account the concrete softening. It adequately describes the behavior of RC members up to failure. For the reinforcing steel linear elastic-ideal plastic model is applied. For the ring cross-section subjected to bending with axial force the normalized resistances are derived in the analytical form by integrating the cross-sectional equilibrium equations. They are presented in the form of interaction diagrams and compared with the results obtained by testing conducted on RC columns under eccentric compression. Furthermore, the ultimate normalized bending moment has been derived for the rectangular cross-section subjected to bending without axial force. It was applied in the cross-sectional analysis of steel and concrete composite beams, named BH beams, consisting of the RC rectangular core placed inside a reversed TT welded profile. The comparisons made indicated good agreements between the proposed section models and experimental results.

This study presents the results of a research project financed by the Lazio Regional Government. The research focused on defining an integrated model of recent alluvial deposits in the Tiber River. To achieve this objective, geological boreholes were made to monitor the aquifer and in situ and laboratory tests carried out. The data obtained was used to detail stratigraphic aspects and improve the comprehension of water circulation beneath the recent alluvial deposits of the Tiber River in the urban area of Rome, between the Ponte Milvio bridge and the Tiber Island. The stratigraphic intervals recognised in the boreholes were parameterised based on their litho-technical characteristics. The new data acquired, and integrated with existing data in the CNR IGAG database, made it possible to produce a three-dimensional model of the lithologies in the study area.The model of the subsoil, simplified for applied reasons, was described in hy-drostratigraphic terms: three different lithotypes were subjected to piezometric levels monitor-ing. Finally, the research generated a numerical hydrological level in a stationary regime. In general, this study demonstrates how a numerical hydrogeological model calibrated by piezo-metric monitoring data can support the construction of a geological model, discarding or con-firming certain hypotheses and suggesting other means of reconstructing sedimentary bodies.

In this research, we are attempting to review the relationship between users' models of using internet and bonding social capitals in Iran. The theoretical framework of the research are based on theoretical approaches of Dearnly and Feder, Velleman, Katz in the field of internet and models of using it and views of Putnam, Woolcock regarding social capital. The method used in this research is a qualitative – quantitative mixed method and the sampling method which has been used in the qualitative method is the purposive sampling method (theoretical sampling) and in the quantitative method, a combination of clustering, systematic and stratified sampling method in proportion with age and gender has been used. The statistical population of all persons who are 15 years old and more in Kerman city has been estimated to be 515114 persons in 2019 and the research sample has been estimated to be about 400 persons. Research results indicate that the rate of citizens' usage of internet in Kerman city is very low (less than 5 hours per week). Other results of the research indicate that regarding the model based on information associated with news, mostly filtered and unpermitted news sites such as VOA, BBC and other networks have been used. Research data regarding social identity is indicative of formation of identity evolutions in the contemporary society of Iran. The results of the path model test of the research indicate that news and economic information based models have about (0.11) direct and positive impact and ethnic identity has a direct and positive impact (0.189) and group identity has about (-0.131) impact and entertainment based model has about (0.130) impact on social capital. The results of variables' indirect impacts have also been expressed in the research

Most hair relaxers or straighteners used for Afro-textured hair have a pH above 11. This pH, according to international occupational health and safety guidelines, is corrosive to the skin. Further, relaxers are associated with a 3-6 times increased risk of alopecia. The skin also gets into contact with these hair straightening products during application resulting in skin irritation. Using animals as test subjects to determine these products' potential risk and safety margin is standard practice; however, with the European Union (EU) ban on animal testing, the testing method needs to be replaced by newer in vitro laboratory technologies. This study used previously and newly established in vitro cell-based technologies and human-skin derived cells as laboratory models to evaluate relaxer-induced cytotoxicity. These technologies include endpoint (MTT, CCK-8 and flow cytometry) and real-time assays (real-time cell analysis (RTCA) and extracellular flux analysis). Cellular toxicity was evident following treatment of keratinocytes and fibroblasts with acceptable concentrations of thioglycolic acid (TGA, HSCH2COOH), ammonium bisulphite (NH4HSO3), lithium hydroxide (LiOH) and sodium hydroxide (NaOH) based relaxers. Real-time assays showed significant reductions (P<0.001) in cell index and ATP production following treatment of keratinocytes with as little as one-tenth of the acceptable concentrations. Increased apoptosis (47.9%, 58.0%, 76.7% and 80.3%) also occurred in cells after treatment with TGA, NH4HSO3, NaOH and LiOH, respectively. In conclusion, this study demonstrated that using skin cells in conjunction with advanced cell-based technologies could serve as alternatives to laboratory animals for accessing the toxicity margin of hair relaxers, straighteners and other cosmetics.

Alcohol and nicotine are widely-abused legal substances worldwide. Relapse to alcohol or tobacco seeking and consumption after abstinence is a major clinical challenge, and is often evoked by cue-induced craving. Therefore, disruption of the memory for the cue-drug association is expected to suppress relapse. Memories have been postulated to become labile shortly after their retrieval, during a “memory reconsolidation” process. Interference with the reconsolidation of drug-associated memories has been suggested as a possible strategy to reduce or even prevent cue-induced craving and relapse. Here, we surveyed the growing body of studies in animal models and in humans assessing the effectiveness of pharmacological or behavioral manipulations in reducing relapse by interfering with the reconsolidation of alcohol and nicotine/tobacco memories. Our review points to the potential of targeting the reconsolidation of these memories as a strategy to suppress relapse to alcohol drinking and tobacco smoking. However, we discuss several critical limitations and boundary conditions, which should be considered to improve the consistency and replicability in the field, and for development of an efficient reconsolidation-based relapse prevention therapy.

The emergence of Airbnb along with an increase in urban tourism has intensified the pressure on urban areas while adding a new dimension in the dynamics of the housing distribution especially in historic cities. These dynamics affect both local economies and alter significantly the characteristics of urban space arising the necessity to create not only policies that foster sustainable tourism development but also to advance urban models that explore the relation between Airbnb and the traditional rental and accommodation sector. Through the case of Venice, the present study sheds light on the potential evolution of Airbnb housing in comparison to the traditional rental and homeowner market. In particular, it seeks to understand whether a potential equilibrium between these uses exists and if so, at which point in regards to this equilibrium the historic center of Venice is now. To tackle this question, methods deriving from the field of game theory and specifically evolutionary game theory are used. With the agents (players) being the housing units, the designed theoretical model explores the population dynamics of the housing units in Venice given the three options of homeownership or long-term rental (residential), short term rental over Airbnb (airbnb) or no use (vacant). The findings of our theoretical population game model are validated and discussed against a dataset describing the use patterns in the city of Venice during the past 20 years. A verification of the outcome through further case studies could eventually provide insights on future behavior of tourism pressure in historic urban areas.

Excitation-inhibition imbalance of GABAergic interneurons is predisposed to develop chronic temporal lobe epilepsy (TLE). We have previously shown that virtually every neuronal nitric oxide synthase (nNOS)-positive cell is a GABAergic inhibitory interneuron in the denate gyrus. The present study was designed to quantify the number of nNOS-containing hilar interneurons using stereology in pilocapine- and kainic acid (KA)-exposed transgenic adult mice that expressed GFP under the nNOS promoter. In addition, we studied the properties of miniature excitatory postsynaptic current (mEPSC) and paired-pulse response ratio (PPR) of evoked EPSC in nNOS interneurons using whole cell recording techniques. Results showed that there were fewer nNOS-immunoreactive interneurons of chronically epileptic animals. Importantly, patch-clamp recordings revealed reduction in mEPSC frequency, indicating diminished global excitatory input. In contrast, PPR of evoked EPSC following the granule cell layer stimulation was increased in epileptic animals suggesting reduced neurotransmitter release from granule cell input. In summary, we propose that impaired excitatory drive onto hippocampal nNOS interneurons may be implicated in the development of refractory epilepsy.

As the global human population increases, animal agriculture must adapt to provide more animal products while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this review is to discuss the digitalization of animal farming with Precision Livestock Farming (PLF) technologies, specifically biosensors, big data, and block chain technology. Biosensors are noninvasive or invasive sensors that monitor an animal’s health and behavior in real time, allowing farmers to monitor individual animals and integrate this data for population-level analyses. The data from the sensors is processed using big data-processing techniques such as data modelling. These technologies use algorithms to sort through large, complex data sets to provide farmers with biologically relevant and usable data. Blockchain technology allows for traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. With these PLF technologies, animal agriculture can become more transparent and regain consumer trust. While the digitalization of animal farming has the potential to address a number of pressing concerns, these technologies are relatively new. The implementation of PLF technologies on farms will require increased collaboration between farmers, animal scientists, and engineers to ensure that technologies can be used in realistic, on-farm conditions. These technologies will call for data models that can sort through large amounts of data while accounting for specific variables and ensuring automation, accessibility, and accuracy of data. Issues with data privacy, security, and integration will need to be addressed before there can be multi-farm databases. Lastly, the usage of blockchain technology in animal agriculture is still in its infancy; blockchain technology has the potential to improve the traceability and transparency of animal products, but more research is needed to realize its full potential. The digitalization of animal farming can supply the necessary tools to provide sustainable animal products on a global scale.

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of CoV disease 2019 (COVID-19) is a highly pathogenic and transmissible CoV that is presently plaguing the global human population and economy. No proven effective anti-viral therapy or vaccine currently exist, and supportive care remains to be the cornerstone treatment. Through previous lessons learned from SARS-CoV-1 and MERS-CoV studies, scientific groups worldwide have rapidly expanded the knowledge pertaining to SARS-CoV-2 virology that included in vitro and in vivo models for testing of anti-viral therapies, and randomized clinical trials. In the present narrative, we review SARS-CoV-2 virology, clinical features, pathophysiology, and animal models with a specific focus on anti-viral and adjunctive therapies currently being tested or require testing in animal models and randomized clinical trials.

The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 20May 2020. Applying Holt’s second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generated 10-day ahead forecasts of the likely number of infected cases and deaths in India until 29 May2020. Our results show that the number of cumulative cases in India will rise to169109 [PI 95% (14426, 19455)], concurrently the number of deaths may increase to 4863 [PI 95% (4221, 5551)] by 29 May 2020.Further, we have marked the states (e.g. Delhi, Uttar Pradesh, Rajasthan, Madhya Pradesh, Maharashtra, Gujarat, and Tamil Nadu) where outburst is expected by considering the cases above three standard deviations. Under the worst-case scenario, Maharashtra is likely to be the most affected state with around 62628 [PI 95% (52840, 73555)] cumulative cases by 29 May 2020. However, Kerala and Karnataka are likely to remain in the lesser affected region. The presented results mark the states where lockdown by 1 June2020, can be loosened.

The prevention, mitigation and treatment of movement impairments, ideally, requires early diagnosis or identification. As the human movement system has physiological and functional redundancy, movement limitations do not promptly arise at the onset of physical decline. A such, prediction of movement limitations is complex: it is unclear how much decline can be tolerated before movement limitations start. Currently, the term ‘homeostatic reserve’ or ‘physiological reserve’ is used to refer to the redundancy of the human biological system, but these terms do not describe the redundancy in the muscle architecture of the human body. The result of functional redundancy is compensation. Although compensation is an early predictor of movement limitations, clear definitions are lacking and the topic is underexposed in literature. The aim of this article is to provide a definition of compensation and emphasize its importance. Compensation is defined as an alteration in the movement trajectory and/or altering muscle recruitment to complete a movement task. Compensation for capacity is the result of a lack in neuromusculoskeletal reserve, where reserve is defined as the difference between the capacity (physiological abilities of the neuromusculoskeletal system) and the task demand. Compensation for movement objectives is a result of a shift in weighting of movement objectives, reflecting changing priorities. Studying compensation in biomechanics requires altered protocols in experimental set-ups, musculoskeletal models that are not reliant on prescribed movement, and inclusion of alternative movement objectives in optimal control theory.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

A thermodynamic analysis of population dynamics and of sustainability provides rigor to many important issues. In this work, the “system Society” is analysed in connection with the “system Environment” using an exergy metric, and the method includes an internalization of the Externalities (Capital, Labour, Environmental effects) conducted on the basis of a “system+ environment” balance. In this perspective, this study investigates the Late Pleistocene extinction of the Homo Neandertalensis, which took place in a geologically short time and in the presence of a competing species, the Homo Sapiens. The case in study is not trivial, and its choice not casual: in those times, the only factor that could lead to an advantage of one group over the other was their respective resource use intensity. A specific indicator, the Exergy Footprint, is here applied to measure the total amount of primary resources required to produce a certain (material or immaterial) commodity, including the resources needed for the physical survival of the individuals. On the basis of the available data, the results show that the EF of the Neandertal was higher than that of the Sapiens, and that, both species sharing the same ecological niche in a time of dwindling resources, the less frugal was also more fragile in an evolutionary sense.

The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

The Batch process of Adsorption were conducted using Congo red (CR) as the adsorbate and Jatropha curcas seed (chaff) as the adsorbent material. Adsorption kinetics and isotherms analysis were conducted and results obtained confirmed the adsorption process as highly dependent on effects such as; contact time, adsorbent dosage, initial dye concentration and the particle sizes of adsorbate. However, the sorption equilibrium for Congo red dye unto jatropha curcas seed (chaff) was reached within 180 minutes, more so, the Adsorption efficiency was recorded at 82.05%. The process of the experimental sorption kinetics followed a pseudo-second-order kinetic model while Freundlich isotherm model was best applicable for obtaining the equilibrium of the parameters. These is an effective confirmation and validation of jatropha curcas seed (chaff) application as environmentally friendly and low-cost biomaterial for dye removal from aqueous solutions and industrial effluents.

It is a common practice to detect outliers in a financial time series in order to avoid the adverse effect of additive outliers. This paper investigated the performance of GARCH family models (sGARCH; gjrGARCH; iGARCH; TGARCH and NGARCH) in the presence of different sizes of outliers (small, medium and large) for different time series lengths (250, 500, 750, 1000, 1250 and 1500) using root mean square error (RMSE) and mean absolute error (MAE) to adjudge the models. In a simulation iteration of 1000 times in R environment using rugarch package, results revealed that for small size of outliers, irrespective of the length of time series, iGARCH dominated, for medium size of outliers, it was sGARCH and gjrGARCH that dominated irrespective of time series length, while for large size of outliers, irrespective of time series length, gjrGARCH dominated. The study further leveled that in the presence of additive outliers on time series analysis, both RMSE and MAE increased as the time series length increased.

In this paper new upper limits on the parameters of the Continuous Spontaneous Localization (CSL) collapse model are extracted. To this end the X-ray emission data collected by the IGEX collaboration are analyzed and compared with the spectrum of the spontaneous photon emission process predicted by collapse models. This study allows to obtain the most stringent limits within a relevant range of the CSL model parameters, with respect to any other method. The collapse rate $\lambda$ and the correlation length $r_C$ are mapped, thus allowing to exclude a broad range of the parameter space.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multiscale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review mathematical modelling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multiscale picture of T-cell development.

In this paper, a proof similar to the Pusey-Barrett-Rudolph (PBR) theorem is given to prove that time in quantum theory is not epistemic but has ontological reality. For this, we first discuss how time can be encoded as an intrinsic property of a quantum system. We prepare a quantum state containing time as an intrinsic property and show by a PBR-type proof that time cannot be an epistemic notion. In fact, the PBR theorem implies an ontic notion of time. Indeed, if the quantum state is a physical property, then quantum state reduction is a physical process involving information destruction. In this case, quantum probabilities are intrinsic probabilities without epistemic origin, and they generate a genuinely new sequence of events. This novelty introduced by quantum probabilities can be interpreted as time.

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can integrate seamlessly into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms, using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual-semantic gap, steering the model towards enhanced performance in zero-shot instance retrieval. This work not only benchmarks the cognition alignment of AI, but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.}