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Objectives: This study aims to validate the Palliative and Complex Chronic Pediatric Patients QoL Inventory (PACOPED QL), a new quality-of-life (QoL) assessment tool for pediatric palliative patients with complex chronic conditions. The goal is to create a comprehensive and inclusive instrument tailored to this unique population, addressing the gap in existing tools that do not meet these specific needs., Methods: The validation process included a literature review and consultations with experts. A pilot study refined the items, followed by a cross-sectional study involving pediatric palliative patients and their caregivers. Statistical analyses, such as Cronbach's alpha for internal consistency and exploratory factor analysis for structural validity, were utilized., Results: The PACOPED QL, comprising 50 items across 8 domains and 6 subdomains, demonstrated strong reliability with Cronbach's alpha and Guttman split-half reliability both exceeding .9. Validity assessments confirmed its suitability for children with complex illnesses. The tool was refined through expert consultations and pilot testing, reducing items from an initial 85 to a final 50, ensuring relevance and clarity., Significance of Results: The PACOPED QL shows strong reliability and validity in assessing QoL in pediatric palliative patients. Its comprehensive structure makes it a promising tool for clinical practice and research, addressing a critical need for a tailored assessment in this population. The instrument's robust psychometric properties indicate its potential utility in improving the QoL assessment and care for children with life-threatening illnesses. Further studies are encouraged to confirm its effectiveness across various settings.

The quark-gluon plasma analysis relies on the heavy quark potential, which is influenced by the anisotropic plasma parameter ( ξ ) , temperature (t), and baryonic chemical potential (μ). Employing the generalized fractional derivative Nikiforov-Uvarov (GFD-NU) method, we solved the topologically-fractional Schrödinger equation. Two scenarios were explored: the classical model (α = β = 1) and the fractional model (α, β < 1). This allowed us to obtain the binding energy of charmonium ( c c ¯ ) and bottomonium ( b b ¯ ) in the 1p state. The presence of the topological defect leads to a splitting between the np and nd states. While increasing the temperature reduces the binding energy, increasing the anisotropic parameter has the opposite effect. Compared to the classical model, the fractional model yields lower binding energies. Additionally, the binding energy further decreases with increasing topological defect parameter, and the influence of the baryonic chemical potential is negligible. We also obtained the wave function for the p-state of charmonium and bottomonium. Here, increasing the anisotropic parameter shifts the wave function to higher values. Moreover, the wave function is lower in the fractional model compared to the classical model. Increasing the topological defect parameter again increases the wave function, while the baryonic chemical potential has no discernible effect., Competing Interests: Competing interests: The authors declare no competing interests. Ethical approval: The authors declare that they comply with ethical standards regarding the content of this paper., (© 2025. The Author(s).)

Smart healthcare systems are gaining increased practicality and utility, driven by continuous advancements in artificial intelligence technologies, cloud and fog computing, and the Internet of Things (IoT). However, despite these transformative developments, challenges persist within IoT devices, encompassing computational constraints, storage limitations, and attack vulnerability. These attacks target sensitive health information, compromise data integrity, and pose obstacles to the overall resilience of the healthcare sector. To address these vulnerabilities, Network-based Intrusion Detection Systems (NIDSs) are crucial in fortifying smart healthcare networks and ensuring secure use of IoMT-based applications by mitigating security risks. Thus, this article proposes a novel Secure and Authenticated Federated Learning-based NIDS framework using Blockchain (SA-FLIDS) for fog-IoMT-enabled smart healthcare systems. Our research aims to improve data privacy and reduce communication costs. Furthermore, we also address weaknesses in decentralized learning systems, like Sybil and Model Poisoning attacks. We leverage the blockchain-based Self-Sovereign Identity (SSI) model to handle client authentication and secure communication. Additionally, we use the Trimmed Mean method to aggregate data. This helps reduce the effect of unusual or malicious inputs when creating the overall model. Our approach is evaluated on real IoT traffic datasets such as CICIoT2023 and EdgeIIoTset. It demonstrates exceptional robustness against adversarial attacks. These findings underscore the potential of our technique to improve the security of IoMT-based healthcare applications., Competing Interests: Leandros Maglaras is an Academic Editor for PeerJ., (© 2024 Bensaid et al.)

The generalization of BVPs always covers a wide range of equations. Our choice in this research is the generalization of Caputo-type fractional discrete differential equations that include two or more fractional q -integrals. We analyze the existence and uniqueness of solutions to the multi-point nonlinear BVPs base on fixed point theory, including fixed point theorem of Banach, Leray-nonlinear Schauder's alternative, and Leray-degree Schauder's theory. Finally, several examples are presented to demonstrate accuracy of our results., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

In recent years, diffusion models have made significant progress in accelerating magnetic resonance imaging. Nevertheless, it still has inherent limitations, such as prolonged iteration times and sluggish convergence rates. In this work, we present a novel generalized map generation model based on mean-reverting SDE, called GM-SDE, to alleviate these shortcomings. Notably, the core idea of GM-SDE is optimizing the initial values of the iterative algorithm. Specifically, the training process of GM-SDE diffuses the original k-space data to an intermediary degraded state with fixed Gaussian noise, while the reconstruction process generates the data by reversing this process. Based on the generalized map, three variants of GM-SDE are proposed to learn k-space data with different structural characteristics to improve the effectiveness of model training. GM-SDE also exhibits flexibility, as it can be integrated with traditional constraints, thereby further enhancing its overall performance. Experimental results showed that the proposed method can reduce reconstruction time and deliver excellent image reconstruction capabilities compared to the complete diffusion-based method., (© 2024 John Wiley & Sons Ltd.)

Background and Objective: Cancer's complex and multifaceted nature makes it challenging to identify unique molecular and pathophysiological signatures, thereby hindering the development of effective therapies. This paper presents a novel fractal-fractional cancer model to study the complex interplay among stem cells, effectors cells, and tumor cells in the presence and absence of chemotherapy. The cancer model with effective treatment through chemotherapy drugs is considered and discussed in detail., Methods: The numerical method for the fractal-fractional cancer model with a generalized Mittag-Leffler kernel is presented. The Routh-Hurwitz stability criteria are applied to confirm the local asymptotically stability of an endemic equilibrium point of the cancer model without treatment and with effective treatment under some conditions. The existence and uniqueness criteria of the fractal-fractional cancer model are derived. Furthermore, the stability analysis of the fractal-fractional cancer model is performed., Results: The temporal concentration pattern of stem cells, effectors cells, tumor cells, and chemotherapy drugs are procured. The usage of chemotherapy drugs kills the tumor cells or decreases their density over time and as a consequence takes a longer time to reach to equilibrium point. The decay rate of stem cells and tumor cells plays a crucial role in cancer dynamics. The notable role of fractal dimensions along with fractional order is observed in capturing the cancer cell concentration., Conclusion: A dynamic analysis of the fractal-fractional cancer model is demonstrated to examine the impact of chemotherapy drugs with a generalized Mittag-Leffler kernel. The model possesses three equilibrium points among them two correspond to the cancer model without treatment namely the tumor-free equilibrium point and endemic equilibrium point. One additional endemic equilibrium point exists in the case of effective treatment through chemotherapy drugs. The Routh-Hurwitz stability criteria are applied to confirm the local asymptotically stability of an endemic equilibrium point of the cancer model with and without treatment under some conditions. The chemotherapy drug plays a crucial role in controlling the growth of tumor cells. The fractal-fractional operator provided robustness to study cancer dynamics by the inclusion of memory and heterogeneity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this paper, we analyze the role of fear in a three-species non-delayed ecological model that examines the interactions among susceptible prey, infectious (diseased) prey, and predators within a food web. The prey population grows in a logistic manner until it achieves a carrying capacity, reflecting common population dynamics in the absence of predators. Diseased prey is assumed to transmit infection to healthful prey by the use of a Holling type II reaction. Predators, alternatively, are modeled to consume their prey using Beddington-DeAngelis and Crowley-Martin response features. This evaluation specializes in ensuring the non-negativity of solutions, practical constraints on population dynamics, and long-term stability of the system. Each biologically possible equilibrium point is tested to understand the environmental stable states. Local stability is assessed through eigenvalue analysis, while global stability of positive equilibria is evaluated by the use of Lyapunov features to determine the overall stability of the model. Furthermore, Hopf bifurcation is explored primarily based on infection rate ɛ . Numerical simulations are carried out to validate the theoretical effects and offer practical insights into the model behaviour under specific conditions., (Creative Commons Attribution license.)

Background and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson's disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.

In this paper, we investigate the generalized Langevin-Sturm-Liouville differential problems involving Caputo-Atangana-Baleanu fractional derivatives of higher orders with respect to another positive, increasing function denoted by ρ. The fixed point theorems in the framework of Kransnoselskii and Banach are utilized to discuss the existence and uniqueness of the results. In addition, the stability criteria of Ulam-Hyers, generalize Ulam-Hyers, Ulam-Hyers-Rassias, and generalize Ulam-Hyers-Rassias are investigated by non-linear analysis besides fractional calculus. Finally, illustrative examples are reinforced by tables and graphics to describe the main achievements., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Thabet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in transient antibody response against the spike protein. The individual immune status at the time of vaccination influences the response. Using mathematical models of antibody decay, we determined the dynamics of serum immunoglobulin G (IgG) and serum immunoglobulin A (IgA) over time. Data fitting to longitudinal IgG and IgA titers was used to quantify differences in antibody magnitude and antibody duration among infection-naïve and infection-positive vaccinees. We found that prior infections result in more durable serum IgG and serum IgA responses, with prior symptomatic infections resulting in the most durable serum IgG response and prior asymptomatic infections resulting in the most durable serum IgA response. These findings can guide vaccine boosting schedules., Competing Interests: Declaration of competing interest We declare no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The last few years have seen a surge of interest from field ecologists and evolutionary biologists to study neoplasia and cancer in wildlife. This contributes to the One Health Approach, which investigates health issues at the intersection of people, wild and domestic animals, together with their changing environments. Nonetheless, the emerging field of wildlife cancer is currently constrained by methodological limitations in detecting cancer using non-invasive sampling. In addition, the suspected differential susceptibility and resistance of species to cancer often make the choice of a unique model species difficult for field biologists. Here, we provide an overview of the importance of pursuing the study of cancer in non-model organisms and we review the currently available methods to detect, measure and quantify cancer in the wild, as well as the methodological limitations to be overcome to develop novel approaches inspired by diagnostic techniques used in human medicine. The methodology we propose here will help understand and hopefully fight this major disease by generating general knowledge about cancer, variation in its rates, tumour-suppressor mechanisms across species as well as its link to life history and physiological characters. Moreover, this is expected to provide key information about cancer in wildlife, which is a top priority due to the accelerated anthropogenic change in the past decades that might favour cancer progression in wild populations., (© 2024 The Author(s). Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Background and Objective: Hepatitis virus infections are affecting millions of people worldwide, causing death, disability, and considerable expenditure. Chronic infection with hepatitis C virus (HCV) can cause severe public health problems because of their high prevalence and poor long-term clinical outcomes. Thus a fractional-order epidemic model of the hepatitis C virus involving partial immunity under the influence of memory effect to know the transmission patterns and prevalence of HCV infection is studied. Investigating the transmission dynamics of HCV makes the issue more interesting. The HCV epidemic model and worldwide dynamics are examined in this study. Calculate the basic reproduction number for the HCV model using the next-generation matrix technique. We determine the model's global dynamics using reproduction numbers, the Lyapunov functional approach, and the Routh-Hurwitz criterion. The model's reproduction number shows how the disease progresses., Methods: A fractional differential equation model of HCV infection has been created. Maximum relevant parameters, such as fractional power, HCV transmission rate, reproduction number, etc., influencing the dynamic process, have been incorporated. The model's numerical solutions are obtained using the fractional Adams method. Finally, numerical simulations support the theoretical conclusions, showing the great agreement between the two., Results: In the fractional-order HCV infection model, the memory effect, which is not seen in the classical model, was shown on graphs so that disease dynamics and vector compartments could be seen. We found that the fractional-order HCV infection model has more stages of freedom than regular derivatives. Fractional-order derivations, which may be the best and most reliable, explained bodily approaches better than classical order., Conclusion: The current study modeled and analyzed a fractional-order HCV infection model. The current approach results in a much better understanding of HCV transmission in a population, which leads to important insights into its spread and control, such as better treatment dosage for different age groups, identifying the best control measure, improving health, prolonging life, reducing the risk of HCV transmission, and effectively increasing the quality of life of HCV patients. The creation of a fractional-order HCV infection model, which provides a better understanding of HCV transmission dynamics and leads to significant insights for better treatment dosages, identification of optimal control measures, and ultimately improvement of the quality of life for HCV patients, is the study's major outcome., Competing Interests: Declaration of competing interest No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all listed authors for publication., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Vitamin D3 is a crucial fat-soluble pro-hormone essential for bolstering bone health and fortifying immune responses within the human body. Orodispersible films (ODFs) serve as a noteworthy formulation strategically designed to enhance the rapid dissolution of vitamin D, thereby facilitating efficient absorption in patients. This innovative approach not only streamlines the assimilation process but also plays a pivotal role in optimizing patient compliance and therapeutic outcomes. The judicious utilization of such advancements underscores a paradigm shift in clinical strategies aimed at harnessing the full potential of vitamin D for improved patient well-being. This study aims to examine the vitamin D3 ODF structure using spectroscopic techniques to analyze interactions with excipients like mannitol. Fourier-transform infrared spectroscopy (FTIR) and ultraviolet-visible (UV-Vis) spectroscopy were utilized to assess molecular composition, intermolecular bonding, and vitamin D3 stability. Understanding these interactions is essential for optimizing ODF formulation, ensuring stability, enhancing bioavailability, and facilitating efficient production. Furthermore, this study involves a translational approach to interpreting chemical properties to develop an administration protocol for ODFs, aiming to maximize absorption and minimize waste. In conclusion, understanding the characterized chemical properties is pivotal for translating them into effective self-administration modalities for Vitamin D films.

Contact tracing played a crucial role in minimizing the onward dissemination of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the recent pandemic. Previous studies had also shown the effectiveness of preventive measures such as mask-wearing, physical distancing, and exposure duration in reducing SARS-CoV-2 transmission. However, there is still a lack of understanding regarding the impact of various exposure settings on the spread of SARS-CoV-2 within the community, as well as the most effective preventive measures, considering the preventive measures adherence in different daily scenarios. We aimed to evaluate the effect of individual protective measures and exposure settings on the community transmission of SARS-CoV-2. Additionally, we aimed to investigate the interaction between different exposure settings and preventive measures in relation to such SARS-CoV-2 transmission. Routine SARS-CoV-2 contact tracing information was supplemented with additional data on individual measures and exposure settings collected from index patients and their close contacts. We used a case-control study design, where close contacts with a positive test for SARS-CoV-2 were classified as cases, and those with negative results classified as controls. We used the data collected from the case-control study to construct a Bayesian network (BN). BNs enable predictions for new scenarios when hypothetical information is introduced, making them particularly valuable in epidemiological studies. Our results showed that ventilation and time of exposure were the main factors for SARS-CoV-2 transmission. In long time exposure, ventilation was the most effective factor in reducing SARS-CoV-2, while masks and physical distance had on the other hand a minimal effect in this ventilation spaces. However, face masks and physical distance did reduce the risk in enclosed and unventilated spaces. Distance did not reduce the risk of infection when close contacts wore a mask. Home exposure presented a higher risk of SARS-CoV-2 transmission, and any preventive measures posed a similar risk across all exposure settings analyzed. Bayesian network analysis can assist decision-makers in refining public health campaigns, prioritizing resources for individuals at higher risk, and offering personalized guidance on specific protective measures tailored to different settings or environments., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Fuster-Parra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The main objective of this work is to use convolutional neural networks (CNN) to improve the performance in previous works on their baseline for pollen grain classification, by improving the performance of the following eight popular architectures: InceptionV3, VGG16, VGG19, ResNet50, NASNet, Xception, DenseNet201 and InceptionResNetV2, which are benchmarks on several classification tasks, like on the ImageNet dataset. We use a well-known annotated public image dataset for the Brazilian savanna, called POLLEN73S, composed of 2523 images. Holdout cross-validation is the name of the method used in this work. The experiments carried out showed that DenseNet201 and ResNet50 outperform the other CNNs tested, achieving results of 97.217% and 94.257%, respectively, in terms of accuracy, higher than the existing results, with a difference of 1.517% and 0.257%, respectively. VGG19 is the architecture with the lowest performance, achieving a result of 89.463%.

This study presents a new mathematical framework for analyzing the behavior of semiconductor elastic materials subjected to an external magnetic field. The framework encompasses the interaction between plasma, thermal, and elastic waves. A novel, fully coupled mathematical model that describes the plasma thermoelastic behavior of semiconductor materials is derived. Our new model is applied to obtain the solution to Danilovskaya's problem, which is formed from an isotropic homogeneous semiconductor material. The Laplace transform is utilized to get the solution in the frequency domain using a direct approach. Numerical methods are employed to calculate the inverse Laplace transform, enabling the determination of the solution in the physical domain. Graphical representations are utilized to depict the numerical outcomes of many physical fields, including temperature, stress, displacement, chemical potential, carrier density, and current carrier distributions. These representations are generated for different values of time and depth of the semiconductor material. Ultimately, we receive a comparison between our model and several earlier fundamental models, which is then graphically represented., (© 2024. The Author(s).)

To improve the efficiency of multi-coil data compression and recover the compressed image reversibly, increasing the possibility of applying the proposed method to medical scenarios. A deep learning algorithm is employed for MR coil compression in the presented work. The approach introduces a variable augmentation network for invertible coil compression (VAN-ICC). This network utilizes the inherent reversibility of normalizing flow-based models. The aim is to enhance the readability of the sentence and clearly convey the key components of the algorithm. By applying the variable augmentation technology to image/k-space variables from multi-coils, VAN-ICC trains the invertible network by finding an invertible and bijective function, which can map the original data to the compressed counterpart and vice versa. Experiments conducted on both fully-sampled and under-sampled data verified the effectiveness and flexibility of VAN-ICC. Quantitative and qualitative comparisons with traditional non-deep learning-based approaches demonstrated that VAN-ICC carries much higher compression effects. The proposed method trains the invertible network by finding an invertible and bijective function, which improves the defects of traditional coil compression method by utilizing inherent reversibility of normalizing flow-based models. In addition, the application of variable augmentation technology ensures the implementation of reversible networks. In short, VAN-ICC offered a competitive advantage over other traditional coil compression algorithms., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Antarctic silverfish Pleuragramma antarcticum is the most abundant pelagic fish in the High Antarctic shelf waters of the Southern Ocean, where it plays a pivotal role in the trophic web as the major link between lower and higher trophic levels. Despite the ecological importance of this species, knowledge about its role in the biogeochemical cycle is poor. We determine the seasonal contribution of Antarctic silverfish to carbon flux in terms of faeces and eggs, from samples collected in the Ross Sea. We find that eggs and faeces production generate a flux accounting for 41% of annual POC flux and that the variability of this flux is modulated by spawning strategy. This study shows the important role of this organism as a vector for carbon flux. Since Antarctic silverfish are strongly dependent on sea-ice, they might be especially sensitive to climatic changes. Our results suggest that a potential decrease in the biomass of this organism is likely to impact marine biogeochemical cycles, and this should be factored in when assessing Southern Ocean carbon budget., (© 2024. The Author(s).)

The accumulation of tau fibrils is associated with neurodegenerative diseases, which are collectively termed tauopathies. Cryo-EM studies have shown that the packed fibril core of tau adopts distinct structures in different tauopathies, such as Alzheimer's disease, corticobasal degeneration, and progressive supranuclear palsy. A subset of tauopathies are linked to missense mutations in the tau protein, but it is not clear whether these mutations impact the structure of tau fibrils. To answer this question, we developed a high-throughput protein purification platform and purified a panel of 37 tau variants using the full-length 0N4R splice isoform. Each of these variants was used to create fibrils in vitro , and their relative structures were studied using a high-throughput protease sensitivity platform. We find that a subset of the disease-associated mutations form fibrils that resemble wild-type tau, while others are strikingly different. The impact of mutations on tau structure was not clearly associated with either the location of the mutation or the relative kinetics of fibril assembly, suggesting that tau mutations alter the packed core structures through a complex molecular mechanism. Together, these studies show that single-point mutations can impact the assembly of tau into fibrils, providing insight into its association with pathology and disease.

Social interactions affect physiological and pathological processes, yet their direct impact in peripheral tissues remains elusive. Recently we showed that disruption of pair bonds in monogamous Peromyscus californicus promotes lung tumorigenesis, pointing to a direct effect of bonding status in the periphery (Naderi et al., 2021). Here we show that lung transcriptomes of tumor-free Peromyscus are altered in a manner that depends on pair bonding and superseding the impact of genetic relevance between siblings. Pathways affected involve response to hypoxia and heart development. These effects are consistent with the profile of the serum proteome of bonded and bond-disrupted Peromyscus and were extended to lung cancer cells cultured in vitro, with sera from animals that differ in bonding experiences. In this setting, the species' origin of serum (deer mouse vs FBS) is the most potent discriminator of RNA expression profiles, followed by bonding status. By analyzing the transcriptomes of lung cancer cells exposed to deer mouse sera, an expression signature was developed that discriminates cells according to the history of social interactions and possesses prognostic significance when applied to primary human lung cancers. The results suggest that present and past social experiences modulate the expression profile of peripheral tissues such as the lungs, in a manner that impacts physiological processes and may affect disease outcomes. Furthermore, they show that besides the direct effects of the hormones that regulate bonding behavior, physiological changes influencing oxygen metabolism may contribute to the adverse effects of bond disruption., (© 2023. The Author(s).)

Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its ability to reduce scan time. Nevertheless, the reconstruction problem remains a thorny issue due to its ill posed nature. Recently, diffusion models, especially score-based generative models, have demonstrated great potential in terms of algorithmic robustness and flexibility of utilization. Moreover, a unified framework through the variance exploding stochastic differential equation is proposed to enable new sampling methods and further extend the capabilities of score-based generative models. Therefore, by taking advantage of the unified framework, we propose a k-space and image dual-domain collaborative universal generative model (DD-UGM), which combines the score-based prior with a low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrate the noise reduction and detail preservation abilities of the proposed method. Moreover, DD-UGM can reconstruct data of different frames by only training a single frame image, which reflects the flexibility of the proposed model., (© 2023 John Wiley & Sons, Ltd.)

Deep learning based parallel imaging (PI) has made great progress in recent years to accelerate MRI. Nevertheless, it still has some limitations: for example, the robustness and flexibility of existing methods are greatly deficient. In this work, we propose a method to explore the k-space domain learning via robust generative modeling for flexible calibrationless PI reconstruction, coined the weighted k-space generative model (WKGM). Specifically, WKGM is a generalized k-space domain model, where the k-space weighting technology and high-dimensional space augmentation design are efficiently incorporated for score-based generative model training, resulting in good and robust reconstructions. In addition, WKGM is flexible and thus can be synergistically combined with various traditional k-space PI models, which can make full use of the correlation between multi-coil data and realize calibrationless PI. Even though our model was trained on only 500 images, experimental results with varying sampling patterns and acceleration factors demonstrate that WKGM can attain state-of-the-art reconstruction results with the well learned k-space generative prior., (© 2023 John Wiley & Sons Ltd.)

This study aims to assess the safety profile of COVID-19 vaccines (mRNA and viral vector vaccines) in teenagers and young adults, as compared to Influenza and HPV vaccines, and to early data from Monkeypox vaccination in United States., Methods: We downloaded data from the Vaccine Adverse Event Reporting System (VAERS) and collected the following Serious Adverse Events (SAEs) reported for COVID-19, Influenza, HPV and Monkeypox vaccines: deaths, life-threatening illnesses, disabilities, hospitalizations. We restricted our analysis to the age groups 12-17 and 18-49, and to the periods December 2020 to July 2022 for COVID-19 vaccines, 2010-2019 for Influenza vaccines, 2006-2019 for HPV vaccines, June 1, 2022 to November 15, 2022 for Monkeypox vaccine. Rates were calculated in each age and sex group, based on an estimation of the number of administered doses., Results: Among adolescents the total number of reported SAEs per million doses for, respectively, COVID-19, Influenza and HPV vaccines were 60.73, 2.96, 14.62. Among young adults the reported SAEs rates for, respectively, COVID-19, Influenza, Monkeypox vaccines were 101.91, 5.35, 11.14. Overall, the rates of reported SAEs were significantly higher for COVID-19, resulting in a rate 19.60-fold higher than Influenza vaccines (95% C.I. 18.80-20.44), 4.15-fold higher than HPV vaccines (95% C.I. 3.91-4.41) and 7.89-fold higher than Monkeypox vaccine (95% C.I. 3.95-15.78). Similar trends were observed in teenagers and young adults with higher Relative Risks for male adolescents., Conclusion: The study identified a risk of SAEs following COVID-19 vaccination which was markedly higher compared to Influenza vaccination and substantially higher compared to HPV vaccination, both for teenagers and young adults, with an increased risk for the male adolescents group. Initial, early data for Monkeypox vaccination point to significantly lower rates of reported SAEs compared to those for COVID-19 vaccines. In conclusion these results stress the need of further studies to explore the bases for the above differences and the importance of accurate harm-benefit analyses, especially for adolescent males, to inform the COVID-19 vaccination campaign., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cappelletti-Montano, Demuru, Laconi and Musio.)

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned from or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Equipped with parallel and sequential orders, experimental comparisons with the state-of-the-arts demonstrated that they involve less error in reconstruction accuracy and are more stable under different acceleration factors., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Since the outbreak of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012 in the Middle East, we have proposed a deterministic theoretical model to understand its transmission between individuals and MERS-CoV reservoirs such as camels. We aim to calculate the basic reproduction number ($ \mathcal{R}&#95;{0} $) of the model to examine its airborne transmission. By applying stability theory, we can analyze and visualize the local and global features of the model to determine its stability. We also study the sensitivity of $ \mathcal{R}&#95;{0} $ to determine the impact of each parameter on the transmission of the disease. Our model is designed with optimal control in mind to minimize the number of infected individuals while keeping intervention costs low. The model includes time-dependent control variables such as supportive care, the use of surgical masks, government campaigns promoting the importance of masks, and treatment. To support our analytical work, we present numerical simulation results for the proposed model.

In this paper, we discuss the use of multi-criteria analysis for investment alternatives as a rational, transparent, and systematic approach that reveals the decision-making process during a study of influences and relationships in complex organizational systems. It is shown that this approach considers not only quantitative but also qualitative influences, statistical and individual properties of the object, and expert objective evaluation. We define the criteria for evaluating startup investment prerogatives, which are organized in thematic clusters (types of potential). To compare the investment alternatives, Saaty's hierarchy method is used. As an example, the analysis of three startups is carried out based on the phase mechanism and Saaty's analytic hierarchy process to identify investment appeal of startups according to their specific features. As a result, it is possible to diversify the risks of an investor through the allocation of resources between several projects, in accordance with the received vector of global priorities.

SiC detectors based on a Schottky junction represent useful devices to characterize fast laser-generated plasmas. High-intensity fs lasers have been used to irradiate thin foils and to characterize the produced accelerated electrons and ions in the target normal sheath acceleration (TNSA) regime, detecting their emission in the forward direction and at different angles with respect to the normal to the target surface. The electrons' energies have been measured using relativistic relationships applied to their velocity measured by SiC detectors in the time-of-flight (TOF) approach. In view of their high energy resolution, high energy gap, low leakage current, and high response velocity, SiC detectors reveal UV and X-rays, electrons, and ions emitted from the generated laser plasma. The electron and ion emissions can be characterized by energy through the measure of the particle velocities with a limitation at electron relativistic energies since they proceed at a velocity near that of the speed of light and overlap the plasma photon detection. The crucial discrimination between electrons and protons, which are the fastest ions emitted from the plasma, can be well resolved using SiC diodes. Such detectors enable the monitoring of the high ion acceleration obtained using high laser contrast and the absence of ion acceleration using low laser contrast, as presented and discussed.

The dynamics of basic and applied research at university and industry have steadily changed since the Eighties, with the private sector reducing its investments in science and universities experiencing significant remodelling in the governance of their funding. While studies have focussed on documenting these changes in industry, less attention has been paid to observe the trajectories of basic and applied research in universities. This work contributes to fill this gap by looking at the evolution of publicly funded research that has been patented by universities between 1978 and 2015. First, we adopt a critical perspective of the basic versus applied dichotomy and identify patents according to three typologies of research: basic, mission-oriented, and applied research. Second, we describe the evolution of these three typologies in universities compared to industry. Our results show that over the years, patents from academic research that was publicly funded have become more oriented towards pure basic research, with mission-oriented basic research and pure applied research decreasing from the late 1990s. These results complement and extend the literature on basic and applied research dynamics in the private sector. By introducing mission-oriented research as a type of basic research with consideration of use, the work problematises the basic and applied research dichotomy and provides insights into the evolution of academic research focus, offering a more complex picture of how university research contributes to industry and broader social value creation., Competing Interests: Conflict of interestThe authors have no competing interests to declare that are relevant to the content of this article., (© The Author(s) 2023.)

Thin sheets of ultra-high molecular weight polyethylene (UHMWPE), both in pristine form and containing carbon nanotubes (CNTs) or Fe 2 O 3 nanoparticles (NPs) at different concentrations, were prepared. The CNT and Fe 2 O 3 NP weight percentages used ranged from 0.01% to 1%. The presence of CNTs and Fe 2 O 3 NPs in UHMWPE was confirmed by transmission and scanning electron microscopy and by energy dispersive X-ray spectroscopy analysis (EDS). The effects of the embedded nanostructures on the UHMWPE samples were studied using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and UV-Vis absorption spectroscopy. The ATR-FTIR spectra show the characteristic features of the UHMWPE, CNTs, and Fe 2 O 3 . Concerning the optical properties, regardless of the type of embedded nanostructures, an increase in the optical absorption was observed. The allowed direct optical energy gap value was determined from the optical absorption spectra: in both cases, it decreases with increasing CNT or Fe 2 O 3 NP concentrations. The obtained results will be presented and discussed.

Background: It is known that people with prediabetes increase their risk of developing type 2 diabetes (T2D), which constitutes a global public health concern, and it is associated with other diseases such as cardiovascular disease., Methods: This study aimed to determine those factors with high influence in the development of T2D once prediabetes has been diagnosed, through a Bayesian network (BN), which can help to prevent T2D. Furthermore, the set of features with the strongest influences on T2D can be determined through the Markov blanket . A BN model for T2D was built from a dataset composed of 12 relevant features of the T2D domain, determining the dependencies and conditional independencies from empirical data in a multivariate context. The structure and parameters were learned with the bnlearn package in R language introducing prior knowledge. The Markov blanket was considered to find those features (variables) which increase the risk of T2D., Results: The BN model established the different relationships among features (variables). Through inference, a high estimated probability value of T2D was obtained when the body mass index (BMI) was instantiated to obesity value, the glycosylated hemoglobin (HbA1c) to more than 6 value, the fatty liver index (FLI) to more than 60 value, physical activity (PA) to no state, and age to 48-62 state. The features increasing T2D in specific states (warning factors) were ranked., Conclusion: The feasibility of BNs in epidemiological studies is shown, in particular, when data from T2D risk factors are considered. BNs allow us to order the features which influence the most the development of T2D. The proposed BN model might be used as a general tool for prevention, that is, to improve the prognosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fuster-Parra, Yañez, López-González, Aguiló and Bennasar-Veny.)

In this paper, we construct the SV1V2EIR model to reveal the impact of two-dose vaccination on COVID-19 by using Caputo fractional derivative. The feasibility region of the proposed model and equilibrium points is derived. The basic reproduction number of the model is derived by using the next-generation matrix method. The local and global stability analysis is performed for both the disease-free and endemic equilibrium states. The present model is validated using real data reported for COVID-19 cumulative cases for the Republic of India from 1 January 2022 to 30 April 2022. Next, we conduct the sensitivity analysis to examine the effects of model parameters that affect the basic reproduction number. The Laplace Adomian decomposition method (LADM) is implemented to obtain an approximate solution. Finally, the graphical results are presented to examine the impact of the first dose of vaccine, the second dose of vaccine, disease transmission rate, and Caputo fractional derivatives to support our theoretical results.

Introduction: Intimate partner violence (IPV) is the most common form of violence against women. Pregnant women are also not exempted from the menace of IPV which has dire consequences for both the mother and child. There is an established link between HIV and IPV and both have a synergistic effect. This study is aimed at comparing the prevalence, pattern, and determinants of IPV among pregnant women living with HIV and HIV-negative pregnant women attending antenatal clinics in Oyo state. Methodology: This is a descriptive cross-sectional study carried out among women attending antenatal clinics in Oyo state using a multistage sampling technique. The study spanned through March and September 2019. The data collection was conducted using a semi-structured questionnaire and the analysis was done using Statistical Package for Social Sciences version 22. The pattern and prevalence of IPV were measured using the Composite Abuse Scale, a 30-item validated interviewer-administered research instrument. It measured 4 dimensions of abuse: physical, emotional, severe, combined, and sexual harassment. A preliminary cut-off score of 7 was used to divide respondents into the presence or absence of IPV. A Chi-square test was used to test for an association between IPV and socio-demographic characteristics and a logistic regression was used at the multivariate level to identify the determinants of IPV. The P -value was set at <.05. Results: Out of the 240 booked pregnant women, 44.2% of HIV-negative respondents and 47.5% of women living with HIV reported being abused in the index pregnancy. Severe combined abuse was the most common type of abuse, 110 (75.1%), followed by emotional abuse, 70 (40.2%), physical abuse, 68 (39.3%), and sexual harassment, 67 (38.1%). Respondents living with HIV reported suffering more physical abuse than their HIV-negative counterparts. Occupation of respondents and duration of marriage determinants of IPV among HIV-positive participants are statistically significant while the duration of marriage was not statistically significant for IPV among HIV-negative respondents. Conclusion: This study recorded a high prevalence of IPV among pregnant women living with HIV and HIV-negative pregnant women with a slight increase in the group living with HIV. It is therefore recommended that IPV screening programs and intervention strategies should be developed for every pregnant woman, irrespective of their HIV status.

Malaria is a vector-borne disease that poses major health challenges globally, with the highest burden in children less than 5 years old. Prevention and treatment have been the main interventions measures until the recent groundbreaking highly recommended malaria vaccine by WHO for children below five. A two-group malaria model structured by age with vaccination of individuals aged below 5 years old is formulated and theoretically analyzed. The disease-free equilibrium is globally asymptotically stable when the disease-induced death rate in both human groups is zero. Descarte's rule of signs is used to discuss the possible existence of multiple endemic equilibria. By construction, mathematical models inherit the loss of information that could make prediction of model outcomes imprecise. Thus, a global sensitivity analysis of the basic reproduction number and the vaccination class as response functions using Latin-Hypercube Sampling in combination with partial rank correlation coefficient are graphically depicted. As expected, the most sensitive parameters are related to children under 5 years old. Through the application of optimal control theory, the best combination of interventions measures to mitigate the spread of malaria is investigated. Simulations results show that concurrently applying the three intervention measures, namely: personal protection, treatment, and vaccination of childreen under-five is the best strategy for fighting against malaria epidemic in a community, relative to using either single or any dual combination of intervention(s) at a time., Competing Interests: Conflict of InterestNone., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

We propose and analyze a new class of three dimensional space models that describes infectious diseases caused by viruses such as hepatitis B virus (HBV) and hepatitis C virus (HCV). This work constructs a Reaction-Diffusion-Ordinary Differential Equation model of virus dynamics, including absorption effect, cell proliferation, time delay, and a generalized incidence rate function. By constructing suitable Lyapunov functionals, we show that the model has threshold dynamics: if the basic reproduction number R 0 ( τ ) ≤ 1 , then the uninfected equilibrium is globally asymptotically stable, whereas if R 0 ( τ ) > 1 , and under certain conditions, the infected equilibrium is globally asymptotically stable. This precedes a careful study of local asymptotic stability. We pay particular attention to prove boundedness, positivity, existence and uniqueness of the solution to the obtained initial and boundary value problem. Finally, we perform some numerical simulations to illustrate the theoretical results obtained in one-dimensional space. Our results improve and generalize some known results in the framework of virus dynamics., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2022.)

In this paper, a fractional-order coinfection model for the transmission dynamics of COVID-19 and tuberculosis is presented. The positivity and boundedness of the proposed coinfection model are derived. The equilibria and basic reproduction number of the COVID-19 sub-model, Tuberculosis sub-model, and COVID-19 and Tuberculosis coinfection model are derived. The local and global stability of both the COVID-19 and Tuberculosis sub-models are discussed. The equilibria of the coinfection model are locally asymptotically stable under certain conditions. Later, the impact of COVID-19 on TB and TB on COVID-19 is analyzed. Finally, the numerical simulation is carried out to assess the effect of various biological parameters in the transmission dynamics of COVID-19 and Tuberculosis coinfection., Competing Interests: Conflict of interestThe authors have no competing interests to declare., (© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate-related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio-temporal skill of short-term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter-tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts., Competing Interests: CONFLICT OF INTEREST The authors declare no conflicts of interest.

In this paper, we consider one of the key problems in modular arithmetic. It is known that scaling in the residue number system (RNS) is a rather complicated non-modular procedure, which requires expensive and complex operations at each iteration. Hence, it is time consuming and needs too much hardware for implementation. We propose a novel approach to power-of-two scaling based on the Chinese Remainder Theorem (CRT) and rank form of the number representation in RNS. By using minimal redundancy of residue code, we optimize and speed up the rank calculation and parity determination of divisible integers in each iteration. The proposed enhancements make the power-of-two scaling simpler and faster than the currently known methods. After calculating the rank of the initial number, each iteration of modular scaling by two is performed in one modular clock cycle. The computational complexity of the proposed method of scaling by a constant Sl=2l associated with both required modular addition operations and lookup tables is estimeted as k and 2k+1, respectively, where k equals the number of primary non-redundant RNS moduli. The time complexity is log2k+l modular clock cycles.

Background: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem., Objective: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region., Methods: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM)., Results: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278)., Conclusion: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach., (© 2022 Shoko C et al.)