The influence of technology corporations on the development of smart cities can generate corporate-centric models of governance. Thus, it is relevant to examine which governance models help to promote sustainability. Based on a rapid review on the governance of smart cities, this paper aims to evaluate the merits and drawbacks of various governance models adopted by diverse smart city initiatives. It contends that top-down models often prioritize corporate-centric governance structures, potentially neglecting sustainability concerns. Nonetheless, our findings indicate that top-down approaches can mitigate corporate-centric tendencies by integrating citizen participation into their visions and strategies. Hybrid models offer the advantage of governmental leadership but may lean towards a triple-helix model, with limited citizen involvement. Conversely, bottom-up models represent quadruple-helix collaborations with active citizen engagement.

The Transformer architecture, while adept at capturing context through self-attention, falls short in encapsulating complex syntactic structures effectively. Addressing this gap, we introduce the Linguistic Structure through Graphical Interpretation with BERT (LSGIB) approach in Machine Translation (MT) frameworks. Combining the strengths of Graph Attention Network (GAT) and BERT, LSGIB intricately captures syntactic dependencies as explicit knowledge from the source language. This enhances the source language representation and aids in more accurate target language generation. Our empirical analysis leverages gold-standard syntax-annotated sentences and employs a Quality Estimation (QE) model. This approach enables us to assess translation improvements in terms of syntactic accuracy, extending beyond traditional BLEU score metrics. The LSGIB model demonstrates superior translation quality across diverse MT tasks, maintaining robust BLEU scores. Our study delves into the optimal sentence lengths benefiting from LSGIB and identifies which syntactic dependencies are more precisely captured. We observe that GAT's ability to learn specific dependency relations directly influences the translation quality of sentences with those relations. Additionally, we discover that incorporating syntactic structure into BERT's intermediate and lower layers offers a novel approach to modeling linguistic structure in source sentences.

Anesthesia ensures the welfare of the animal, enables safe and effective procedures, and allows accurate data collection [1]. In clinical veterinary practice, proper training and expertise in anesthesia administration and monitoring are essential. Pigs are commonly used in medical and scientific research as models for studying various aspects of human health, physiology, and disease due to their physiological and anatomical similarities to humans [2–4]. Pigs are suitable experimental animals for many surgery techniques because they are similar in size to humans and have a short reproductive cycle. This makes them ideal for research concerning organ transplantation, cardiovascular surgery, and other procedures that require a large animal model. Pigs also have a similar anatomy to humans, which makes them a good choice for studying diseases and developing new treatments [5]. Sedation and premedication should be administered at the lowest dose to be effective with predictable results and reduced adverse effects, to ensure the safety of both the animal and the team involved in the procedure, with a fast onset and optimizing the induction and maintenance of anesthesia. The goal of induction is to achieve a safe and effective level of anesthesia that ensures the patient's safety and facilitates the research. Most of the time, inhalation anesthesia with endotracheal intubation is the ideal choice for maintenance., allowing efficient anesthetic management. The difficulties related to the endotracheal intubation of pigs can be overcome by knowing the anatomical peculiarities related to the species and the multiple methods cited in the literature. Effective analgesia tailored to the specific procedure, the pig's condition, and individual responses to medications should complete the maintenance and recovery protocols, reducing perioperative complications.

In this article, we give a brief review of the origin of the neutrino mass in some interesting non-linear supersymmetric models with R-symmetry. These models are able to address and solve the most important problems of the particle physics and provide mechanisms for neutrino mass generation and their mixing parameters in agreement with the current experimental data. Their prediction could be experimentally tested in the near future by collider experiments.

Virtual validation of radar sensor models is becoming increasingly important for the safety validation of adf. Therefore, methods for quantitative comparison of radar measurements in the context of model validation need to be developed. This paper presents a novel methodology for accessing and quantifying validation measurements of radar sensor models. This method uses edf and the so-called dvm to effectively quantify deviations between distributions. By applying this metric, the study measures the consistency, reproducibility and repeatability of radar sensor measurements. Different interfaces and different levels of detail are investigated. By comparing the radar signals from real world experiments where different objects are present, valuable insights are gained into the performance of the sensor. In particular, the research extends to assessing the impact of varying rain intensities on the measurement results, providing a comprehensive understanding of the sensor’s behaviour under these conditions. This holistic approach significantly advances the evaluation of radar sensor capabilities and enables the quantification of the maximum required quality of radar simulation models.

Diseases of the lung account for more than 5 million deaths worldwide and are a burden to healthcare. Improving clinical outcomes including mortality and quality of life involves a holistic understanding the etiopathogenesis, which can be provided by multi-omics integration of lung data. An enhanced understanding of large comprehensive datasets provides opportunities to mine those datasets for features that contribute to prevention and amelioration of disease. In this review, we evaluate lung disease models including animal models, organoids and single cell lines as mechanisms to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided a deeper insight into pathogenesis that has resulted in improved preventive and therapeutic interventions.

With the rising frequency of pathogen spillover worldwide, wildlife disease dynamics have received increased attention. There are many possible pathway a pathogen can invade and spread through a host population, and the assumed transmission model used to capture disease propagation can influence predictions of pathogen net reproductive success (R0), determining the outbreak dynamics. We synthesize a comprehensive overview of these models and overarching implications, using bovine Tuberculosis (Mycobacterium bovis) as a case study. We unify sub-models from the disease ecology literature and clarify the biological motivation behind these models and resulting ecological dynamics. We warn readers of pitfalls regarding the relative orders of the transmission parameters and reiterate that the contact rate determines the transmission model and thus defines key dynamical properties of an outbreak. Transmission in wildlife is linked to ecosystem and human health, and host community structure can mediate pathogen spread. We link these models with disease-biodiversity theories, by considering the role of host diversity in disease transmission, contributing to the debate on the effect of biodiversity and on disease outbreak potential. We decompose the various mechanisms of transmission in a stepwise process, and provide the reader a guide for modelling pathogens in both single-host and multi-host systems.

Over the past decade, research in the field of Deep Learning has brought about novel improvements in image generation and feature learning; one such example being a Generative Adversarial Network. However, these improvements have been coupled with an increasing demand on mathematical literacy and previous knowledge in the field. Therefore, in this literature review, I seek to introduce Generative Adversarial Networks (GANs) to a broader audience by explaining their background and intuition at a more foundational level. I begin by discussing the mathematical background of this architecture, specifically topics in linear algebra and probability theory. I then proceed to introduce GANs in a more theoretical framework, along with some of the literature on GANs, including their architectural improvements and image-generation capabilities. Finally, I cover state-of-the-art image generation through style-based methods, as well as their implications on society.

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the information infrastructure that connects modeling and measurement efforts, and propose a roadmap that accelerates production of new knowledge. We propose that community cyberinfrastructure tools can help mend the divisions between empirical research and modeling, and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, not just a clique of ‘modelers’. This roadmap focuses on five key opportunities for community tools: the underlying backbone to community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is key to meeting the pressing needs of science and society in the 21st century.

This paper models and estimates the volatility of nonfinancial, innovative and hi-tech focused stock index, the Nasdaq-100, using univariate symmetric and asymmetric GARCH models. We employ GARCH, EGARCH and GJR-GARCH using daily data over the period January 4, 2000 through March 19, 2019. We find that the volatility shocks on the index returns are quite persistent. Furthermore, our findings show that the index has leverage effect, and the impact of shocks is asymmetric, whereby the impacts of negative shocks on volatility are higher than those of positive shocks of the same magnitude.

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

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.

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.

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.

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.

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.

In 2022, prostate cancer (PCa) is estimated to be the most commonly diagnosed cancer in men in the United States – almost 270,000 American men are estimated to be diagnosed with PCa in 2022 [1]. This review compares and contrasts in vivo models of PCa with regards to the altered genes, signaling pathways, and stages of tumor progression associated with each model. The main type of model included in this review are genetically engineered mouse models, which include conditional and constitutive knockout model. 2D cell lines, 3D organoids and spheroids, xenografts and allografts, and patient derived models are also included. The major applications, advantages and disadvantages, and ease of use and cost are unique to each type of model, but they all make it easier to translate the tumor progression that is seen in the mouse prostate to the human prostate. Although both human and mouse prostates are androgen-dependent, the fact that the native, genetically unaltered prostate in mice cannot give rise to carcinoma is an especially critical component of PCa models. Thanks to the similarities between the mouse and human genome, our knowledge of PCa has been expanded, and will continue to do so, through models of PCa.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from 34.5% and 30.9% of the state where standard surveys predicted the highest likelihood of occurrence of the respective vectors. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs, of the convenience collections, frequently were associated with adjacency to at least one SDM or errors in geocoding algorithms that failed to correctly locate convenience samples. These geocoding errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for vector survey data used in spatial models.

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.

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 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.

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.

: 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.

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.

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.

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.

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.

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.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

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. .

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 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.

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 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.

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.

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%).

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.

This note first summarizes the history of the manuscript "On a Continuum Model for Avalanche Flow and Its Simplified Variants" by Grigorian and Ostroumov―published in the Special Issue on snow avalanche dynamics of Geosciences―since the early 1990s and explains the guiding principles in editing it for publication. The changes are then detailed and some explanatory notes given for the benefit of readers who are not familiar with the early Russian work on snow avalanche dynamics. Finally, the editor's personal views as to why he still considers this paper of relevance for avalanche dynamics research today are presented in brief essays on key aspects of the paper, namely the role of simple and complex models in avalanche research and mitigation work, the status and possible applications of Grigorian's stress-limited friction law, and non-monotonicity of the dynamics of the Grigorian–Ostroumov model in the friction coefficient. A comparison of the erosion model proposed by those authors with two other models suggests to enhance it with an additional equation for the balance of tangential momentum across the shock front. A preliminary analysis indicates that continuous scouring entrainment is possible only in a restricted parameter range and that there is a second erosion regime with delayed entrainment.

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.

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.

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.

Hydrocephalus is caused by an overproduction of cerebrospinal fluid (CSF), a blockage of fluid circulation, or improper reabsorption. CSF accumulation in the brain’s ventricles causes ventriculomegaly and brain cell damage. Hydrocephalus can be caused by brain trauma, hemorrhage, infection, tumors, or genetic mutations. Currently, there is no cure for hydrocephalus. Treatments like shunting and endoscopic third ventriculostomies are used, but unfortunately, these techniques require brain surgery and have high failure rates. To advance the development of hydrocephalus treatments, physiologically relevant pre-clinical models are crucial. This review covers some of the current animal and cell culture methods used to study hydrocephalus. The choroid plexus epithelium (CPe) is thought to be the major producer of CSF in the brain. It is a polarized epithelium that regulates ion and water movement from a fenestrated capillary exudate to the ventricles. Despite decades of research, control of electrolyte movement in the CPe is still not fully understood. This review discusses important transporters on the CPe and how some of these could be potential targets for hydrocephalus treatment.

Lung cancer remains a pervasive global health challenge, necessitating precise and efficient classification methods to inform optimal treatment strategies. In this study, we investigate the potential of machine learning algorithms in developing a computer-aided diagnostic tool for early and accurate lung cancer classification, leveraging essential protein biomarkers as predictive features. Our investigation reveals the superiority of Deep Neural Networks (DNNs) in lung cancer classification, achieving an impressive accuracy of 96.91%. This highlights the transformative potential of DNNs in facilitating real-world clinical applications, offering clinicians a powerful tool for accurate diagnosis and prognosis. Additionally, we analyze the performance of Ensemble Methods, including Voting (91.75%) and Bagging (93.81%), as viable alternatives to DNNs. These ensemble techniques demonstrate robust performance, further underlining their utility in lung cancer classification tasks. Furthermore, our study underscores the critical role of hyperparameter tuning, particularly in adjusting parameters such as min child weights and learning rates, in mitigating overfitting and enhancing the generalizability of diverse machine learning models. For instance, Support Vector Machines (SVM) exhibit varying accuracies ranging from 74.23% to 95.88% across different hyperparameter configurations, emphasizing the importance of hyperparameter optimization in maximizing model performance. In summary, our comprehensive comparative analysis sheds light on the efficacy of different machine learning approaches in lung cancer classification. By leveraging advanced algorithms and optimizing model parameters, researchers and clinicians can harness the power of machine learning to advance early detection and personalized treatment strategies for lung cancer patients.

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.

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 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.

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.

Heritage bridges constitute an integral feature of the urban landscape in numerous cities. However, it is common for these structures to surpass their life cycle, rendering them ill-equipped to withstand the dynamic demands of users and extreme events, particularly hydrological occurrences. This research presents a methodology for the assessment of heritage riverine bridges, with a focus on the Grau Bridge in Peru as a case study. The investigation commences with an exhaustive literature review, complemented by a historical examination, followed by a preliminary diagnosis. Subsequently, hydrological and hydraulic studies are conducted, encompassing drone surveys of the riverbed and the bridge, soil analyses, and the application of 1D and 2D models in HEC-RAS. The outcomes of this comprehensive analysis reveal the high vulnerability of the Grau Bridge. Finally, strategic recommendations for its conservation are recommended.

The use of mathematical models of physiological systems in medicine has allowed the development of diagnostic, treatment, and medical educational tools, but their application for predictive, preventive, and personalized purposes is restricted by their complexity. Although there are strategies that reduce the complexity of applying models by fitting techniques, they focus on a single instant of time, neglecting the effect of the system's temporal evolution. The aim of this work is to propose a dynamic fitting strategy of physiological models with large number of parameters and a constrained amount of experimental data, focused on obtaining better predictions based on the system's temporal trend and useful to predict future states. It was applied in a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects under aerobic exercise were used for fitting and validation. The model predictions obtained at steady-state using the proposed strategy and the nominal values of the parameters were compared. The best results corresponded mostly to the proposed strategy, mainly regarding the overall prediction error. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

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.

In the present paper, we investigate the impact of the timescale factor on the quality of life index behavior on specific time intervals characterized by the presence of socio-economic, political, and/or health severe movements such as pandemics and crises. We essentially aim to show that effectively the quality of life measuring based on a single index in the existing studies may be described more adequately by a variable index due to the social, political, economic, and also healthy environment. The variability discovered is expressed by the existence and the estimation of a multi-index instead of a single one relatively to many factors. Our focus is mainly on the effect of the COVID-19 pandemic on the quality of life. Our model is applied empirically to a sample corresponding to Saudi Arabia as a case of study during the period from January 1990 to December 2021 as the main period affected by the COVID-19 pandemic. The sample is based on social media conversations and texts discussing and describing the satisfaction with the quality of life. The study confirms effectively that the role of the timescale factor is more described when considering a multi-index rather than measurement on the whole time interval.

Arrhythmogenic cardiomyopathy (AC) is a hereditary cardiac disorder characterized by the gradual replacement of cardiomyocytes with fibrous and adipose tissue, leading to ventricular wall thinning, chamber dilation, arrhythmias, and sudden cardiac death. Despite advances in treatment, disease management remains challenging. Animal models, particularly mice and zebrafish, have become invaluable tools for understanding AC's pathophysiology and testing potential therapies. Mice models, although useful for scientific research, cannot fully replicate the complexity of the human AC. However, they have provided valuable insights into gene involvement, signalling pathways, and disease progression. Zebrafish offer a promising alternative to mammalian models, despite the phylogenetic distance, due to their economic and genetic advantages. By combining animal models with in vitro studies, researchers can comprehensively understand AC, paving the way for more effective treatments and interventions for patients and improving their quality of life and prognosis.

The scattering eigenstates of any Hamiltonian of electrons, coupled through a two-body force and moving on a one-dimensional lattice, are shown to be Bethe's wave-functions. The energy of the groundstate is compared with values, obtained previously for the Hubbard Hamiltonian by means of Bethe's wave-function and within the framework of the correlated Fermi gas. The same analysis is applied to electrons, interacting on neighbouring sites. The significance of those various groundstates is assessed with help of thermodynamics.

Blood-based biomarkers for early detection of recurrence have a significant clinical impact on the improved management of breast cancer patients, as cancer recurrence results in poor prognosis. This study aimed to investigate if the measurement of blood endocan in breast cancer patients with high ESM1 expression could be an effective tool to detect postoperative recurrence compared with existing tumor markers. Blood was collected before and after the tumor resection from the mouse models of breast cancer, and endocan levels were measured while visualizing metastatic recurrence with noninvasive luminescence imaging. In clinical settings, blood was withdrawn from 16 breast cancer patients before and after the tumor resection, and the effect of lumpectomy on blood endocan level was evaluated. Additionally, the blood endocan from 20 patients diagnosed with postoperative recurrence was measured, and their positivity rate for endocan was compared with that for serum carcinoembryonic antigen (CEA) or cancer antigen 15-3 (CA15-3). Our preclinical and clinical experiments revealed that blood endocan levels reflected tumor burden. Furthermore, over 60% of patients suffering from postoperative recurrence who tested negative for CEA or CA15-3 were positive for endocan. Our results support the clinical significance of endocan in breast cancer patients for detecting breast cancer recurrence.

The construction business environment is becoming competitive and intense, and Business Models (BMs) are receiving considerable attention as potential sources of sustainable survival and growth. Converse to the linear economy model that industry follows, the Circular Economy (CE) has the potential to create a sustainable construction industry, while creating a sustainable business environment in construction organisations. Nevertheless, in traditional BMs, value is often concentrated on the financial value for the stakeholders; in Circular Economy Business Models (CEBMs), value is seen more broadly by considering a comprehensive range of value chain partners, the environment, and the society. Hence, implementing CE in the construction industry requires reforms in each value chain. Thus, the aim of this paper is to develop a conceptual model for construction organisations' adoption of CEBMs for their organisations. To accomplish the aim, the objectives of this paper are twofold: (i) to define the concept of CEBMs, and (ii) to map the Conceptual CEBMC (Circular Economy Business Model Canvas) as applicable in the construction organisational context. This research adopted a systematic document review, including a content analysis of selected papers. The study contributes significantly by proposing a CEBMC, in which construction organisations could successfully transform their businesses from linear to circular environments.

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

This article describes the methodology used to identify the mathematical model that describes the correlations between the input parameters of an experiment and the parameters being followed. As a technological process, the aerodynamic separation was chosen, respectively, the behavior of a solid particle within an ascending vertical airflow. The experimental data obtained were used to identify two parameters, the average linear velocity, and the angular velocity, and through the Table Curve 3D program was developed a mathematical model which describes the dependence between the input parameters (the shape and size of the solid particle and the velocity of the airflow) and the monitored parameters. In order to determine a single mathematical equation that describes as accurately as possible the correlation between the input variables and those obtained, a pyramid-type analysis was designed. The determination of the mathematical equation started from the number of equations generated by the Table Curve 3D program, then the equations with a correlation coefficient greater than 0.85 were chosen, and finally, the common equations were identified. Respecting the working methodology was identified one equation which has for the average linear velocity a correlation coefficient r2 between 0.88-0.99 and 0.86-0.99 for the angular velocity.

A series of experiments were conducted to measure and quantify the yield, dry matter content, sugars content and nitrates content of grass intended for ensilement. These experiments took place in the East Midlands of Ireland during the Spring, Summer and Autumn of 2019. A bespoke sensor rig was constructed; included in this rig was a hyperspectral radiometer that measured a broad spectrum of reflected natural light from a circular spot approximately 1.2 metres in area. Grass inside a 50cm square quadrat was manually collected from the centre of the circular spot for ground truth estimation of the grass qualities. Up to 25 spots were recorded and sampled each day. The radiometer readings for each spot were automatically recorded onto a laptop that controlled the sensor rig, and ground truth measurements were made either on site or within 24 hours in a wet chemistry laboratory. The collected data was used to build Partial Least Squares Regression (PLSR) predictive models of grass qualities from the hyperspectral dataset and it was found that substantial relationships exist between the spectral reflectance from the grass and yield (r2 = 0.62), dry matter % (r2 = 0.54), sugar content (r2 = 0.54) and nitrates (r2 = 0.50). This shows that hyperspectral reflectance data contains substantial information about key grass qualities and can form part of a broader holistic data driven approach to provide accurate and rapid predictions to farmers, agronomists and agricultural contractors.

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

Architecture is more than just buildings. Its associated production and reception pro­cesses take place through a variety of different media. Among those media, the model is of special significance: because architecture, like almost every science or art, works with models as representa­tionally or theoretically simplified images mediating between the abstract and the reality. The properties that characterise models give them a special significance in archi­tecture—both in the abstract, as well as in the concrete. The following article sketches out the history of the architectural model as a medium in a short tour d’horizon. A special focus is placed on showing the versatility of the model—for design and presentation and as an artefact, teaching resource and research medium. It transmits a specific form of knowledge which can be replaced by no other medium.

In this paper we develop a forecasting algorithm for recurrent patterns in consumer demand. We study this problem in two different settings: pull and push models. We discuss several features of the algorithm concerning sampling, periodic approximation, denoising and forecasting.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.

The focus of this paper is on analyzing the role and the choice of parameters in sociophysics diffusion models, by leveraging the potentialities of sociophysics from a mathematical programming perspective. We first present a generalised version of Galam’s opinion diffusion model (see, e.g. , ). For a given selection of the coefficients in our model, this proposal yields the original Galam’s model. The generalised model suggests guidelines for possible alternative selection of its parameters that allow to foster diffusion. Examples of the parameters selection process as steered by numerical optimisation, taking into account various objectives, are provided.

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

Hyperacusis, i.e., an increased sensitivity to sounds, is described in several neurodevelopmental disorders (NDDs), including Fragile X Syndrome (FXS). The mechanisms underlying hyperacusis in FXS are still largely unknown and effective therapies are lacking. Big conductance calci-um-activated potassium (BKCa) channels have been proposed as a therapeutic target to treat several behavioral disturbances in FXS preclinical models, but their role in mediating their au-ditory alterations has not been specifically addressed. Furthermore, studies on the acoustic phe-notypes of FXS animal models have mostly focused on central rather than peripheral auditory pathways. Here we provided an extensive characterization of the peripheral auditory phenotype of the Fmr1-knockout (KO) mouse model of FXS at adulthood. We also assessed whether the acute administration of Chlorzoxazone, a BKCa agonist, could rescue the auditory abnormalities of mutant mice. Fmr1-KO mice both at 3 and 6 months showed a hyperacusis-like startle pheno-type with paradoxically reduced auditory brainstem responses associated with a loss of ribbon synapses in the inner hair cells (IHCs). BKCa expression was markedly reduced in the IHCs of KO mice, but only at 6 months, when Chlorzoxazone rescued mutant auditory dysfunction. Our findings highlight the age-dependent and progressive contribution of peripheral mechanisms and BKCa channels to hyperacusis in FXS, suggesting a novel therapeutic target to treat auditory dysfunction in NDDs.

The incorporation of renewable energy systems in the world energy system has been steadily increasing during the last years. In terms of the building sector, the usual consumers are becoming increasingly prosumers, and the trend is that communities of energy, whose households share produced electricity, will increase in number in the future. Another observed tendency is that the aggregator (the entity that manages the community) trades the net community energy in public energy markets. To accomplish economically good transactions, accurate and reliable forecasts of the day-ahead net energy community must be available. These can be obtained using an ensemble of multi-step shallow artificial neural networks, with prediction intervals obtained by the covariance algorithm. Using real data obtained by a small energy community of four houses located in the South region of Portugal, one can verify that the deterministic and probabilistic performance of the proposed approach is at least similar, typically better than using complex, deep models.

Accurate assessment and projections of extreme climate events requires the use of climate datasets with no or minimal error. This study uses quantile mapping bias correction (QMBC) method to correct the bias of five Regional Climate Models (RCMs) from the latest output of Rossby Climate Model Center (RCA4) over Kenya, East Africa. The outputs were validated using various scalar metrics such as Root Mean Square Difference (RMSD), Mean Absolute Error (MAE) and mean Bias. The study found that the QMBC algorithm demonstrate varying performance among the models in the study domain. The results show that most of the models exhibit significant improvement after corrections at seasonal and annual timescales. Specifically, the European community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict exemplary improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model show little improvement across various timescales (i.e. March-April-May (MAM) and October-November-December (OND)). The projections forced with bias corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data prior to performing any regional impact analysis. The corrected models can be used for projections of drought and flood extreme events over the study area. This study analysis is crucial from the sustainable planning for adaptation and mitigation of climate change and disaster risk reduction perspective.

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.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

. This paper features an analysis of the relative effectiveness of a variety of methods of modelling Realised Volatility (RV), namely: the use of Gegenbaur processes in Auto-Regressive Moving Average format, GARMA, as opposed to Heterogenous Auto-Regressive HAR models and simple rules of thumb. The analysis is applied to two data sets that feature the RV of the S&P500 index, as sampled at 5 minute intervals, provided by the Oxford Man RV database. The GARMA model does perform slightly better than the HAR model, but both models are matched by a simple rule of thumb regression model based on the application of lags of squared, cubed and quartic, demeaned daily returns.

The emerging industry of offshore wind turbines mounted on floating bases has garnered significant attention from both academia and industry. The desire to understand the complex physics of these floating structures has led to the development of numerical and physical modelling techniques. While physical testing has traditionally been employed, there is a growing focus on cost-effective and accurate high-fidelity numerical modelling as a potential alternative or supplement. However, commonly used numerical engineering tools in the offshore industry are considered mid- to low-fidelity and may lack the desired precision for floating offshore wind turbines (FOWTs). Given the complexity of these simulation codes, it is crucial to validate their accuracy. To address this, the International Energy Agency (IEA) Wind Technology Collaboration Programme initiated various research endeavors, including the Offshore Code Comparison Collaboration (OC3), Offshore Code Comparison Collaboration Continuation (OC4), Offshore Code Comparison Collaboration Continuation with Correlation (OC5), and the recent Offshore Code Comparison Collaboration Continued with Correlation and Uncertainty (OC6) projects. This study offers a comprehensive survey of the simulation tools available for FOWTs which were part of OC projects, focusing particularly on horizontal axis wind turbines (HAWTs) and highlighting their capabilities and fundamental theories.

Model animals are employed in experiments as substitutes for human tissues and fluids, particularly when accessing particular human samples (such as cerebrospinal fluid, brain, ocular tissues, etc.) poses significant challenges or is ethically constrained. Nonhuman primates are frequently regarded as superior animal models for investigating human ophthalmological diseases. However, despite this recognition, the metabolomic composition of ocular tissues in non-human primates remains predominantly unexplored. In this work, we present a dataset on metabolite concentrations in serum and ocular tissues, including aqueous humor (AH), vitreous humor (VH), and lens, in two Macaque species: crab-eating macaque (Macaca fascicularis) and rhesus macaque (Macaca mulatta). A total of 99 compounds were quantified in 45 samples, shedding light on the previously unknown metabolomic profiles of primate eye tissues.

Soccer is type of sport that carries a high risk of injury. Injury is not only cause in the unlucky soccer carrier and also team performance as well as financial effects can be worse since soccer is a team-based game. The duration of recovery from a soccer injury typically relies on its type and severity. Therefore, we conduct this research in order to predict the probability of players injury type using machine learning technologies in this paper. Furthermore, we compare different machine learning models to find the best fit model. Supervised classification machine learning models are applied in this paper. We gathered information about 54 professional soccer players who are playing in the top five European leagues based on their career history.

Molnupiravir, a prodrug known for its broad antiviral activity, demonstrated efficacy in animal models of Covid-19, prompting clinical trials where initial results indicated a significant effect against the disease. However, subsequent clinical studies did not confirm these findings, leading to the rejection of molnupiravir for permanent market authorization in many countries. This report critically assessed 19 studies published in 17 reports that investigated the efficacy of molnupiravir in animal models of Covid-19 with the purpose of determining how well the design of these models informed human studies. We found that the administered doses of molnupiravir in most studies involving animal Covid-19 models were disproportionately higher than the dose rec-ommended for human use. Specifically, when adjusted for body surface area, half of the doses of molnupiravir used in the animal studies were more than twice as high as the human dose. Addi-tionally, the drug was frequently given prophylactically or shortly after SARS-CoV-2 inoculation in these models, in contrast to clinical trials where such timing is not consistently achieved. Furthermore, the recommended five-day treatment duration for humans was exceeded in several animal studies. Collectively, we suggest that these design elements in the reported animal studies contributed to a bias favoring molnupiravir, and thus inflated expectations for its efficacy against Covid-19. Addressing these elements may offer avenues to enhance the clinical efficacy of mol-nupiravir for treatment of Covid-19 that include dose increment, early treatment, and admin-istration by inhalation along with use of molnupiravir in antiviral combination therapy.

Neural networks models are mostly represented by oriented graphs where only the components, constitutive elements of the graph, are transcribed into mathematical xpression. Indeed, accurate knowledge of the full expression of the model is required in certain situations such as selecting among several reference models, the one that best fits the available data or comparing the explanatory and predictive performance of an established model with respect to some reference models. In this paper, we establish a formalism of the mathematical expression for multilayer perceptron neural network in a general framework, MLP-p-n-q, with p, n and q natural integers and show its restriction to cases where one has a hidden layer and multivariate outputs (MLP-p-1-q), and then a single output (MLP-p-1-1). Then, we give some specific cases of the most commonly used models. An application case is presented in the context of solving a nonlinear regression problem.

Olive Anthracnose (OA) is the most important fungal disease of olive fruits worldwide. In the context of integrated pest management, the development of predictive models could be used for early diagnosis and control. In the current study, a dataset representing 58 cases (6 locations with 12 olive cultivars) was used to study the relationship between ΟΑ incidence (OAI) and 35 heterogeneous variables, including orchard characteristics, olive fruit parameters, foliar and soil nutrients, soil parameters and soil texture classes. The Random Forest-Recursive Feature Elimination with Cross Validation (RF-RFECV) feature selection method identified Location, Water Content, P, Ca, Mg, Exchangeable Mg, Trace Zn, Trace Cu as possible new indicators associated with OAI. Six different classification algorithms, namely Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), were developed for predicting conditions leading to OAI >0% and 10%. Hyperparameter optimization using grid search was used to optimize the parameters of the models and finally the best parameters were applied to predict the OAI. The final models were evaluated in terms of several standard metrics, such as accuracy, sensitivity, specificity and ROC AUC score. Findings suggested that GB performance was superior compared to the other models for the prediction of the occurrence of OA disease (OAI>0%) with an accuracy of 86.7%, a sensitivity of 100%, a specificity of 75% and a ROC-AUC score of 93%, while for the prediction of the spread of the disease (OAI>10%), DT stood out with an accuracy of 86.7%, a sensitivity of 81.8%, a specificity of 100% and a ROC-AUC score of 91%. RF classifier performed very well in both cases, with an accuracy of 80%, a sensitivity of 85.7%, a specificity of 75% and a ROC-AUC score of 93% for the prediction of the occurrence of the disease (OAI>0%), and an accuracy of 86.7%, a sensitivity of 90.9%, a specificity of 75% and a ROC-AUC score of 84% for the prediction of the spread of the disease (OAI>10%).

The goal of this paper is to review the relevant literature on autism questionnaires, models, analytic tools, and subgrouping, focusing on the opportunities and limitations. We examined how the size and nature of the database and the number and type of parameters measured determine the use of analytic tools and the expected type of results. To support our position about the examined aspects, we rely on various parts of the reviewed articles. We emphasize that the individual results in each article can only be interpreted in the light of the methods that brought about the conclusions. The reviewed literature suggests a heterogeneous palette in autism subtypes instead of distinct, well-characterized subgroups, or a single-dimensional continuous spectrum.

With increasing constraints on energy and resource markets and the non-decreasing trend in energy demand, the need for relevant clean energy generation and storage solutions is growing and is gradually reaching the individual home. But small-scale energy storage is still an expensive investment in 2022 and the risk/reward ratio is not yet attractive enough for individual homeowners. One solution is for homeowners not to store excess clean energy individually but to produce hydrogen for mutual use. In this paper a collective production of hydrogen for a daily filling of a bus is considered. Following our previous work on the subject, the investigation consists of finding an optimal buy/sell rule to the grid, and the use of the energy with an additional objective: mobility. The dominant technique in the energy community is reinforcement learning, which is however difficult to use when the learning data is limited as in our study. We chose a less data-intensive and yet technically well-documented approach. Our results show that rulebooks, different but more interesting than the usual robust rule, exist and can be cost-effective. But they require fine-tuning as to not deteriorate system performance. In some cases, it is worth missing the H2 production requirement in exchange for higher economic performance.

This paper presents the results of the experimental research and numerical analysis of three reinforced concrete deep beams with openings, designed by the Strut-and-Tie method according to the EN 1992-1-1 recommendations. All tested specimens were made in full size, with the same geometric characteristics and quality of the materials. The specimens, constructed as simply supported beams, were loaded with two concentrated forces and were tested for bending until failure. Each specimen was reinforced with different reinforcement layout determined by parameter variation within the Strut-and-Tie method. Based on the results of experimental research, it was concluded that the behavior of loaded members was in agreement with the proposed forms of the Strut-and-Tie models that were used for their design.

Increasing rates of infection by antibiotic resistant bacteria have led to a resurgence of interest in bacteriophage (phage) therapy. Several phage therapy studies in animals and humans have been completed over the last two decades. We conducted a systematic review of safety and toxicity data associated with phage therapy in both animals and humans reported in English-language publications from 2008 – 2021. Overall, 69 publications met our eligibility criteria including 20 animal studies, 35 clinical case reports or case series, and 14 clinical trials. After summarizing safety and toxicity data from these publications, we discuss potential approaches to optimizing safety and toxicity monitoring with the therapeutic use of phage moving forward. In our systematic review of the literature, we found few, but no serious, adverse events associated with phage therapy. Comprehensive and standardized reporting of potential toxicities associated with phage therapy has generally been lacking in the published literature. Structured safety and tolerability endpoints are necessary when phages are administered as anti-infective therapeutics.

We study the Gaudin models with gl(1|1) symmetry that are twisted by a diagonal matrix and defined on tensor products of polynomial evaluation gl(1|1)[t]-modules, extending all the results of to the twisted case. Namely, we give an explicit description of the algebra of Hamiltonians (Gaudin Hamiltonians) acting on tensor products of polynomial evaluation gl(1|1)[t]-modules and show that there exists a bijection between common eigenvectors (up to proportionality) of the algebra of Hamiltonians and monic divisors of an explicit polynomial written in terms of the highest weights and evaluation parameters. In particular, our result implies that each common eigenspace of the algebra of Hamiltonians has dimension one. We also give dimensions of the generalized eigenspaces.

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. Thus, this investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of ten euro area stock indices from 4th January 2016 to 31th December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with large and middle financial centres while the second wave had a significant impact only on stock market volatility of Belgium.

Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, proposed by Cox. et al. , Chen and Cox , and Özen and Şahin , which we consider in this study, differ in terms of the duration of the jumps - transitory or permanent, frequency of the jumps, and size of the jumps. To illustrate the effect of jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter , Renshaw and Haberman and the Cairns-Blake-Dowd models. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also analyse the impact of mortality jump models on catastrophe bond pricing.

This study investigated the trends of registered Death and Birth in Nigeria using Generalized Linear Models. Annual data on Death and Birth was collected from National Population Commission for the period of 2004 to 2017. The Natural increase calculated revealed a positive trend in the natural increase in Nigeria from 2004 to 2017. Evidence from summary statistics revealed some level of over dispersion (variance > mean). This study explored Poisson Regression Models and Negative Binomial Regression Models using two links (identity and log). The results revealed a positive increase in registration of birth and death rates in Nigeria and among the competing the models, Negative Binomial regression model with identity link emerged as the best model for modeling birth and death rates registration in Nigeria. Data on numbers of deaths and causes of death are essential if countries are to determine priorities, formulate and monitor policies for public health care as well as other government policies that may be based on such data

In the context of the energy transition, the integration of land use considerations into energy planning is increasingly vital, especially in scenarios where land availability is a constraint. This study addresses the challenge of incorporating land-use aspects into Energy System Optimization Models (ESOMs), with a focus on the unique context of Pantelleria Island. It aims to bridge the gap in methodologies for renewable energy potential assessment and model integration, considering the critical role of land pricing and availability. It combines geospatial data aggregation with model adaptation to include detailed land use aspects. Findings highlight the substantial impact of land costs on renewable energy planning, with land pricing significantly altering model outcomes. This research offers key insights for sustainable energy planning and underscores the importance of considering land use in energy transition strategies.

In this paper it will be considered three deterministic models for the study of the interaction between the human immune system and a virus: the logistic model, the Gompertz model, and the generalized logistic model (or Richards model). A qualitative analysis of this models based on dynamical systems theory will be performed, by studying the local behavior of the equilibria and obtaining local dynamics properties from the linear stability point of view. Also, we will compare this models in order to understanding which is more appropriate to models the interaction between the human immune system and a virus. It will be obtained some natural medical interpretations which are available for all three models and can be useful to the medical community.

Throughout the Covid19 period, families were forced to stay indoors, adapting to online school-ing, remote work, and virtual social engagements, inevitably altering the dynamics within households. There is a notable increase in mental health challenges in terms of anxiety and de-pression in children and adolescents. This study intends to explore the psychosocial effects of the Covid-19 pandemic in Italian families adopting self- and proxy-report questionnaires on anxiety, anger, and health-related quality of life. The results showed that approximately 20% obtained a clinical anxiety score and only 10% obtained a clinical anger score. There was found a difference in the perception of the quality of life reported by the child and that perceived by the parent. The stepwise regression model showed that total anxiety scores were predicted by sex, quality of life scores from the parents' self-report version and total anger score. Another stepwise regression model identified physiological and social anxiety as the best predictors that impact the quality of life. Parental well-being actively influences the well-being of children, so it is fundamental to implement preventive programmes and promote child well-being providing parents the most adequate support possible.

This paper addresses the Hubble tension in cosmology by exploring dynamic dark energy models, specifically focusing on quintessence scalar fields. We develop a comprehensive theoretical framework for these models, examining various potential forms and their implications for the universe's evolution. We elaborate on the effects of dynamic dark energy on cosmological perturbations, analyzing its influence on the Cosmic Microwave Background (CMB) anisotropies and the formation of large-scale structures. By integrating the theoretical models with observational data, including Type Ia supernovae, CMB observations, and large-scale structure surveys, we investigate the potential of dynamic dark energy to resolve the discrepancies in measurements of the Hubble constant. Our findings suggest that dynamic dark energy models resolve the Hubble tension and have broader implications for our understanding of cosmic evolution and the universe's fate. This study aims to contribute to the ongoing discourse in cosmology, providing a new perspective on dark energy and its role in the expanding cosmos.

In situations of uncertainty, scenarios serve as input for evidence-based decision making. However, past experience shows that not all scenarios are treated equally, and we hypothise that only those based ion a world view shared by decision makers are perceived as credible and receive full attention. While intuitively plausible, this hypothesis has not been analysed by quantitative correlation analyses, so instead of drawing on quantitative data the paper analyses the archetypical scenarios developed in the ALARM project to substantiate the plausibility by a comparative analysis of world views, value systems and policy orientations. Shock scenarios are identified as a means to explore the possibility space of future developments beyond the linear developments models and most scenario storylines suggest. The analysis shows that the typical scenarios are based on mutually exclusive assumptions. In conclusion, a comparison of storyline and empirical data can reveal misperceptions, policy failures and the need to rethink world views as a necessary step to open up to new challenges. Deeply held beliefs will make this a transition unlikely to happen without severs crises, if not dedicated efforts to reveal the role of world views for scenarios and policies are undertaken.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

The Saudi Vision 2030 is critical to boosting national development, notably in the construction industry. The construction industry is getting a lot of attention in a variety of ways, the most visible of which is the promotion of best practices, including project management office (PMO) applications. Hail province, like others, is experiencing project delivery issues. Thus, the purpose of this research is to explore the suitable PMO model for Hail public construction projects. A literature review included the drive factors for establishing PMO in construction firms, as well as PMO models and typologies that describe functions. Therefore, those drive factors and PMO models were subjected to a focus group to explore their suitability in the context of Hail. The research findings reveal that the Enterprise PMO is the main for Hail public construction pro-jects, followed respectively by the Program Management Office model and the Project Management Center of Excellence (PMCOE). This method of selection a PMO model is driving construction companies and related businesses to change in order to improve the sector while also ad-dressing construction project delivery challenges in Hail province.

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.

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.

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.

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.