The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces their evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

The lack of a Dengue vaccine and pharmacological antiviral drugs has made Vector Control Programs the primary resource in the fight against arboviral diseases. Although Integrated Management and Control programs implement intense control measures for adult, pupal, larval, and breeding sites during outbreaks, there is a lack of studies to understand the role of the vector egg stage in disease dynamics. This study aimed to assess the dry season quiescent Aedes aegypti and Aedes albopictus egg populations in houses and backyards in the tropical city of Tapachula, southern Mexico. Two hundred fifty ovitraps were placed in 125 homes in the Las Americas neighborhood. A total of 7,290 eggs were collected from 211 (84.4%) ovitraps, with 4,031 (71.1%) identified as Ae—aegypti, and 1,636 (28.8%) as albopictus, respectively. Eighty-three larval sites were sampled with desiccated eggs during the dry season; most of them were described as trash-like because larvicides are only used for larger containers of 5-10 L. Evolutionary characteristics for the two species include rapid and delayed hatching of eggs, ambient-regulated quiescence, the ability of the embryo to survive for a more extended period intra-seasonally, and the sticking of egg walls to container walls. This demands urgent operational research to achieve successful egg-proof larval container methods.

In this report we describe the case of a healthy, young, athletic woman who developed acute lymphoblastic leukaemia (ALL)/lymphoblastic lymphoma (LBL) after receiving the second dose of the Pfizer/BioNTech modified mRNA (modRNA) COVID-19 genetic vaccine (marketed as Comirnaty^®). The first dose of the genetic vaccine did not appear to illicit any noticeable side effects, but within 24 hours of the second dose the patient suffered widespread and intensifying bone pain, fever, vomiting, and general malaise. Due to the persistence of the symptoms, the patient underwent a series of tests and examinations including a full laboratory workup, a consult with a clinical immunologist and rheumatologist, a Positron Emission Tomography (PET) imaging, as well as an osteomedullary biopsy. These together led to a definitive diagnosis of ALL. A time interval of 16 weeks from the second vaccination to the diagnosis of cancer was noted. Several similar cases with identical pathology which developed after the modRNA COVID-19 vaccination, are described in case reports in the scientific literature. The massive and indiscriminate use of genetic vaccines to fight COVID-19 is raising serious concerns about their safety and about the technology platform as a whole for this purpose. Growing evidence is accumulating regarding the biodistribution and persistence of the modRNA which can reach, thanks to the lipid nanoparticles, a multitude of tissues and organs of the body, including the bone marrow and other blood-forming organs and tissues. Moreover, there is evidence that the modRNA vaccines display a particular tropism for the bone marrow, influencing the immune system at multiple levels and being able to trigger not only autoimmune-based pathologies, but also neoplastic mechanisms. The aim of this article is to assess, on the basis of the available scientific literature, the risk of developing haematopoietic cancers after modRNA vaccination, and to investigate the potential genetic mechanisms involved in the pathogenesis of disease.

The article addresses the problem of course development in virtual teaching and learning spaces by teachers to support blended learning in the Ecuadorian unified baccalaureate. For this purpose, a documentary analysis of 465 papers selected by a search in Scopus and Google Scholar about blended learning and 145 papers selected by a search in Scopus and Google Scholar about development of virtual courses was carried out. The definition of the first concept is qualitatively different from the previous ones by analysing it as a form of organisation and assessing the configuration of face-to-face and distance learning depending on the educational objective. The second concept defined presents the unrepeatable and unique character of each course given by the configuration of its digital resources, the characteristics of these resources and the new qualities that are conferred on them. Subsequently, based on them, the methodology that contributed to the development of courses in virtual spaces as a support to blended learning is proposed. Unlike other methodologies, the proposal explicitly states its conceptual apparatus and the necessary requirements for its introduction in an educational organization. It incorporates important actions that do not appear in other methodologies such as cross-cutting processes including quality assurance with its corresponding metrics, maintenance, recall and evaluation of previous actions.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

Cardiogenic shock (CS) is part of a clinical syndrome consisting of acute left ventricular failure causing severe hypotension leading to inadequate organ and tissue perfusion. The most com-monly used devices to support patients affected by CS are Intra-Aortic Balloon Pump (IABP), Impella 2.5 pump and Extracorporeal Membrane Oxygenation. The aim of this study is the com-parison between Impella and IABP using CARDIOSIM© software simulator of the cardiovascular system. The results of the simulations included baseline conditions from a virtual patient in CS followed by IABP assistance in synchronized mode with different driving and vacuum pressures. Subsequently, the same baseline conditions were supported by the Impella 2.5 with different ro-tational speeds. The percentage variation with respect to baseline conditions was calculated for hemodynamic and energetic variables during IABP and Impella assistance. Impella pump driven with a rotational speed of 50000 rpm increased the total flow by 4.36% with a reduction in left ventricular end diastolic volume (LVEDV) by ≅15% to ≅30%. A reduction in left ventricular end systolic volume (LVESV) by ≅10% to ≅18% (≅12% to ≅33%) was observed with IABP (Impella) as-sistance. Taking into account the limitations of a simulation setting, this study highlights that assistance with the Impella device leads to higher reduction in LVESV, LVEDV, left ventricular external work and left atrial pressure-volume loop area compared to IABP support.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

The Covid-19 pandemic caused a shift in teaching practice towards blended learning for many Higher Education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs and facilitate learning. Integration of these technologies caused numerous challenges for practitioners and often provided mixed results. This paper is an attempt to summarise and extend pre-Covid pedagogical research to leverage digital immersive technologies for blended teaching in the post-pandemic era. Focus is given towards the evolution of Virtual Learning Environments through elements of immersive audio-visual technologies, which are shown to be effective when coupled in a blended approach. It is both a review of these methodologies and a case study of the I-Ulysses: Virtual Learning Environment as a point of comparison for evaluating the review.

Critically ill patients with COVID-19 may develop serious respiratory difficulties, causing a significant reduction in blood oxygen saturation pressure. Physical Ventilation of the lungs is one of the main methods to help critically ill patients through the acute phase of infection. During extreme situations when dysfunctional lungs are filled with sticky sputum in the alveolus or when there are simply not enough ventilators to match the need, the mortality rate can be dramatically increased. Here, we propose an intravenous injection method that may increase and maintain the blood oxygen pressure at normal levels. The intravenous (IV) infusion contains oxygen nanobubbles in physiological saline solution (ONPS), in which the dissolved oxygen content can be 2-6 times higher than the normal oxygen solubility in pure water. This makes it possible to oxygenate blood with a small limited volume of IV fluid without the risk of gaseous bubble formation in blood vessels.

Interactive Planning Support Systems (PSS) implemented on a maptable are deemed suitable to support participatory planning processes. Through their interactive nature and user-friendly interface they are supposed to facilitate exchange of knowledge between stakeholders, consensus building among them, group learning processes, and thereby strengthen participation. We analyze in this systematic review, based on 16 case studies using interactive PSS, how such PSS have contributed to the goal of strengthening stakeholder participation. Results show that tools and applications have become more sophisticated in recent years and the goals of the studies changed from collaboratively designing interventions to observing and understanding how the application of such tools contributes to improved plan outcomes and group based learning. However, many case studies lack a proper framework and operationalization for investigating the impacts of the tools and applications on participation. Consequently, impacts on participation are assessed rather incidentally based on implicit assumptions and often no distinction is made between the different aspects of participation. In conclusion, further theoretical studies conceptualizing impacts of interactive PSS on participation are needed as well as empirical studies testing these impacts in real world case contexts with various groups of stakeholders.

Numerical modelling is becoming a major tool for supporting environmental studies at different scales, thanks to the capability of up-to-date codes in reproducing the natural behaviour in a quite reliable manner. In evaluating the habitat diversity of anthropized rivers, however, many issues are rising because of the intrinsic complexity of the processes involved. Using a reach of the Po River in Italy as a case study, the present works aims to provide an estimate of the changes of the Eco-Environmental Diversity as a response to different constant flow discharges. The goals are achieved by means of two solvers of the iRIC suite, applied in sequence to firstly simulate the fluvial hydrodynamics and subsequently provide an estimate of the habitat conditions. Despite the simplifications intrinsically present in the models and the ones introduced for practical purposes, the results pointed out that the reduction of the flow discharge recently observed can threat the overall biological status of the river. Because of the modelling uncertainties, on the other side, these preliminary outcomes show the need for more research, both in terms of data acquisition and numerical schematization, for adequately evaluate the effects of transient hydrology on the river ecosystems. Moreover, additional field surveys are necessary to calibrate and validate the used model for having sufficiently reliable estimates.

Deep learning-based handwriting personality prediction uses neural networks (CNNs) to consider and predict personality characters from handwritten text to improve applications such as adaptive technology and automated data access. This method requires high computational resources and widely classified data, and its accuracy may suffer from signature variation and overfitting. This study introduces a novel optimizeddeep learning for Hand Written Personality Prediction. First the input data is collected from givendataset and an enhanced Gaussian filter is used in pre-processing to improve image quality by reducing noise and increasing contrast.After pre-processing, features are extracted by PCA-based normalized GIST, which standardizes the features to improve image representation.These extracted features are generated using an adaptive horse herd optimization algorithm to select the most important features. Based on selected features Improved Generative Adversarial Networks with artificial hummingbird optimization (IGAN_AHb) enables fast and efficient convergence of GANs. This reduce the mode collapse and ensures training with consistently fine-tuned parameters.The selected features are considered by characters such as openness, conscientiousness, extroversion, agreeableness and neuroticism, and finally classified using the IGAN_AHb classifier. In the result section, the proposed model is compared with various models with the matrix precision, recall, F1-score. The proposed model attained the value of 97.3% of precision and a recall rate of 96%, respectively. By comparing with other models, the proposed model attained highest values.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

A pattern of NK cell heterogeneity in each individual determines proliferative and functional responses of NK cells to activating stimuli. Obtaining the progeny of a single cell by cloning original population is one of the ways to study the NK cell heterogeneity. In this work, we used single cell sorting into a plate and stimulation by IL-2 and gene-modified K562 feeder cells expressing membrane-bound IL-21 (K562-mbIL21) that led to generation of phenotypically confirmed and functionally active NK cell clones. We applied two models of clone cultivation, which differently affected their phenotype, lifespan and functional activity. The first model, which included weekly restimulation of clones with K562-mbIL21 and IL-2, resulted in the generation of relatively short-lived (5-7 weeks) clones of highly activated NK cells. HLA-DR expression in the expanded NK cells correlated strongly with IFN-γ production. The second model, in which NK cells were restimulated mainly with IL-2 alone, produced long-lived clones (8-14 weeks) that expanded up to 10⁷ cells with lower ability to produce IFN-γ. Our method is applicable for studying variability in phenotype, proliferative and functional activity of the certain NK cell progeny in response to the stimulation, which may help in selecting NK cells best suited for clinical use.

The complex and interconnected challenges of climate change, water stress, disasters, and health crises have far-reaching implications for sustainable development and global sustainability agendas, such as the Sustainable Development Goals (SDGs). However, one critical issue that has been overlooked is the nexus between climate change impacts and mental health (CCMH). Recognizing and addressing the negative emotions associated with this global phenomenon is essential to fostering a holistic approach to climate action planning and building long-term resilience. In this assessment, we present a set of narratives to argue that CCMH research requires a collaborative, transdisciplinary approach that integrates socio-economic and socio-cultural complexities. For this assessment, we used a case study approach to elucidate that the mental health impacts of climate change are unequally distributed, disproportionately affecting vulnerable groups based on age, gender, race, and socioeconomic status. The assessment presented in this study concluded that adequate mental health support programs are limited due to sociocultural stigmas and limited socioeconomic resources in some regions. Existing climate-related mental health services mechanisms lack coordination and specific action plans, leaving affected populations underserved. Unlike traditional understandings of the climate-health nexus, this research calls for experts from multiple fields to work together and for enhanced attention to and investment in CCMH research to bridge the gap between scientific knowledge and practical solutions. Such solutions will lead to scalable and lasting change as communities can implement research findings to support those in need and enhance disaster resilience. Furthermore, by collectively recognizing the climate and mental health nexus, global commitments such as the SDGs and the Paris Declaration can advance awareness and action in climate-related mental health, ultimately promoting a healthier relationship between humanity and nature.

While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.

Objectives: To develop a risk prediction model for the identification of features involved in the prediction of erectile dysfunction (ED) at 12 months following COVID-19 recovery. Methods: We performed an observational prospective multicentre study. Participants were classified according to their history of COVID-19: I) Patients with a past history of COVID-19; and II) Patients without a previous microbiological diagnosis of COVID-19. A total of 361 patients (Past history of COVID-19, n=166; No past history of COVID-19, n=195) were assessed from January 2022 to March 2023. Patients with a past history of COVID-19 were assessed at 12 months following COVID-19 recovery. The primary outcome measure was ED, assessed through the 5-item International Index of Erectile Function (IIEF-5). Data concerning epidemiologic variables, comorbidities and active treatment were also collected. We performed a binary logistic regression to develop a risk predictive model. Among the models developed, we selected the one with the higher Area Under the Curve (AUC). Results: The median age was 55 years in both groups. The ED prevalence was 55.9% in patients with past history of COVID-19 and 44.1% in those with no past history of COVID-19. The best predictive model developed for ED comprised 40 variables and had an AUC of 0.8. Conclusions: We developed a regression model for the prediction of ED 12 months after COVID-19 recovery. The application of our predictive tool in a community setting could eventually prevent the adverse effects of ED on cardiovascular health, and the associated unfavourable economic impact.

In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.

Febrile neutropenia (FN) is a major concern in patients undergoing chemotherapy for diffuse large B-cell lymphoma (DLBCL); however, the overall risk of FN is difficult to assess. This study aimed to develop a model for predicting the occurrence of FN in patients with DLBCL. In this multicenter, retrospective, observational analysis, a multivariate logistic regression model was used to analyze the association between FN incidence and pretreatment clinical factors. We included adult inpatients and outpatients (aged ≥ 18 years) diagnosed with DLBCL who were treated with chemotherapy. The study examined 246 patients. Considering FN occurring during the first cycle of chemotherapy as the primary outcome, a predictive model with a total score of 5 points was constructed as follows: 1 point each for viral hepatitis, extranodal involvement, and a high level of soluble interleukin-2 receptor and 2 points for lymphopenia. The area under the receiver operating characteristic curve of this model was 0.844 (95% confidence interval: 0.777–0.911). Our predictive model can assess the risk of FN before patients with DLBCL start chemotherapy, leading to better outcomes.

In this study, we developed a model that can predict whether patients with cerebral arteriovenous malformation (AVM) will be cured 36 months after intervention by means of stereotactic radiosurgery (SRS), and identified the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 92.16%, sensitivity of 92.86%, specificity of 88.89%, precision of 97.50%, and AUC of 97.62%, which shows that ML models are suitable for use for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AMV would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

This article describes the leadership and organizational elements that have made the Seven Mountains Mandate (7MM), a very adaptable, simple to explain, and feasible to implement message and portable practice, which can start at a local level to be expanded progressively to regional and national dimensions, one of the main doctrines of the Apostolic Restoration Movement (ARM). A new classification of apostolic networks is proposed in this article and a partial map of some of the networks that participate in the expansion of the 7MM is presented. Additionally, by using definitions from network leadership theory, it is shown how different types of leaders that function as horizontal connectors in apostolic networks, such as conveners, catalysts, mobilizers, facilitators, weavers, provocateurs, illuminators, hosts, and curators contribute to the diffusion of the 7MM by promoting its global widening, deepening, and lengthening (WDL). The article first describes the evolution of the church from denominations to networks, and how the ARM adopted the network organizational structure that serves as a channel for the diffusion of the 7MM. Cases from the USA, Guatemala, Zambia, and Venezuela are used as examples to demonstrate how the 7MM expansion or widening (W), its contextualization and adaption or deepening (D), and its sustainability, reproduction, and evolution, or lengthening (L), occur globally based on the network nature of the ARM and the network leadership enacted in the process.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Background/Objectives Recurrent shoulder instability following Bankart lesion repair often necessitates surgical revision. Identified risk factors include young age, glenoid bone loss, and off-track Hill-Sachs lesions. This systematic review aims to understand failure rates of arthroscopic revision Bankart repair and if these aforementioned factors influence its success. Methods Following PRISMA guidelines, this systematic review, registered on PROSPERO, included twenty-four articles. Two independent reviewers assessed eligibility across three databases, focusing on recurrent instability as the primary endpoint, while also noting functional measures, adverse events, revision operations, and return-to-sport rates when available. Results Key surgical techniques for recurrent instability post-Bankart repair were identified, with revision arthroscopic Bankart being the most common (685/1032). Despite generally endorsed effectiveness, recurrence rates have increased over the past four years. Comparative analysis revealed significantly lower recurrence for open Latarjet compared to revision arthroscopic Bankart (p

Background: Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP based Swiss COPD cohort, and develop a statistical model for predicting exacerbation. Methods: In a questionnaire-based COPD cohort, demographic and medical data were recorded for 24 months. Data set was split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and the overall performance was validated. A nomogram was created to facilitate the clinical use of the model. Results: Of the 229 COPD patients analyzed, 77% of patients had no exacerbation during the follow-up. The best subset in the training dataset found that lower forced expiratory volume, high scores on the MRC dyspnoea scale, exacerbation history, being on combination therapy of LABA+ICS or LAMA+LABA at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) was 0.75 for one or more exacerbations. Calibration was accurate (predicted 0.34 exacerbations vs observed 0.28 exacerbations). Conclusion: Nomograms built from these models can assist clinicians in the decision-making process of their COPD care.

Recurrent hypoglycaemia, a common side-effect of insulin therapy in the treatment of type 1 diabetes, induces impaired glucose-sensing. Better understanding of how astrocytes, important non-neuronal cells in the brain, function in low glucose environments may improve our understanding of recurrent hypoglycaemia-induced defective counterregulation. Astrocytes contribute to glutamatergic signalling, which is required for hypoglycaemia counterregulation and is impaired by recurrent insulin-induced hypoglcyaemia. This study examined the glutamate response of astrocytes when challenged with acute and recurrent low glucose (RLG) exposure. The metabolic responses of cortical (CRTAS) and hypothalamic (HTAS) primary rat astrocytes were measured in acute and recurrent low glucose using extracellular flux analyses. RLG caused mitochondrial adaptations in both HTAS and CRTAS, many of which were attenuated by glutamate exposure during low glucose treatments. We observed an increase in capacity of HTAS to metabolise glutamine after RLG exposure. Demonstrating astrocytic heterogeneity in the response to LG, CRTAS increased cellular acidification, a marker of glycolysis in LG, whereas this decreased in HTAS. The directional change in intracellular Ca2+ levels of each cell type, correlated with the change in extracellular acidification rate (ECAR) during LG. Further examination of glutamate-induced Ca2+ responses in low glucose treated CRTAS and HTAS identified sub-populations of glucose-excited- and glucose-inhibited-like cells with differing responses to glutamate. Lastly, release of the gliotransmitter ATP by HTAS was elevated by RLG, both with and without concurrent glutamate exposure. Therefore, hypothalamic astrocytes adapt to RLG by increasing glutamate uptake and oxidation in a manner that attenuates RLG-induced mitochondrial adaptations.

Keywords: Аrtificial neural network, electric drive system, database, stability, training algorithm, technological mechanisms.

Deep learning neural networks require an immense amount of computation, especially in the training phase of the network when networks with multiple layers of intermediate neurons need to be built. In this paper, we will focus on the PSO algorithm with the aim of significantly accelerating the DLNN training phase by taking advantage of the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimization method whose goal is to iteratively improve the solution to a (usually complex) problem by attempting to approximate a given objective. However, parallelizing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented , both designed for a distributed execution environment. The synchronous parallel PSO implementation ensures consistency at the cost of potential idle time due to global synchronization, while the asynchronous parallel PSO approach improves execution time by reducing the need for global synchronization, making it more suitable for large datasets and distributed environments such as Apache Spark. Both variants of PSO have been implemented to distribute the computational load supported by this algorithm –due to the costly fitness evaluation and updating of particle positions– across the different Spark cluster executor nodes to effectively achieve coarse-grained parallelism, resulting in a significant performance increase over current sequential variants of PSO.

Background/Objectives: Home and community care (HCC) nurses experienced increased occupational challenges during the COVID-19 pandemic including increased workloads, job stressors and occupational risks like virus exposure. The objective was to elucidate what factors influenced nurses’ decisions to stay in their role, take a temporary leave, or exit HCC during the COVID-19 pandemic. Methods: A secondary analysis of data collected using a cross-sectional online open survey distributed among HCC Registered Practical Nurses across Ontario between June and September 2022 was conducted. The factors contributing to nurses’ decision to remain in HCC, temporarily leave, or exit the sector were evaluated using multinomial logistic regression (p < 0.05). Results: Of the 664 participants, 54% (n=357) stayed in the HCC sector, 30% (n=199) temporarily left, and 16% (n=108) exited the sector. Nurses with greater years of experience working in HCC and those who avoided infection were more likely to stay in their role in HCC, which may reflect strong relationships with long-term clients, opportunity and accumulated experience to increase income, and maintained good health. Nurses with higher levels of emotional intelligence were more likely to take leaves and exit HCC, suggesting that stepping away may have been a strategy to safeguard themselves. Conclusions: HCC leadership should prioritize the development of solutions to support nurses in the HCC workforce, including those with fewer years’ experience. This may promote nurses’ participation in the sector, particularly during times of heightened occupational challenges and crises like COVID-19.

Introduction: Papatasi fever, or three-day fever, is a viral disease transmitted by a species of female sandfly called Phlebotomus papatasi that has symptoms similar to influenza. Robat Karim County, due to the high abundance of Sandfly and climate change in recent years, provides a suitable environment for the growth and reproduction of Sandfly, especially Phlebotomus papatasi Therefore, the aim of this study was molecular monitoring of Papatasi fever virus in Sandfly of Robat Karim County, Tehran province. Materials and methods: In 7 selected areas in Robat Karim County, 679 Sandfly using sampling methods including traps and aspirators were caught and 500 samples were randomly selected according to the fishing location and transferred to the laboratory of the Medical Entomology Department. After fixing Sandfly on the slide surface, identification and percentage of frequency of sandfly species were determined and then by Real-time PCR method, molecular examination of Papatasi virus infection was performed at Tehran Pasteur Institute. Findings: Out of 679 samples taken, 310 (45.65%) belonged to males and 369 (54.35%) belonged to females. 7 species including 3 species of Sandfly of the genus Phlebotomus and 4 species of the genus Sergentomyia were identified among the samples, the highest percentage of catch belonged to Phlebotomus sergenti (28.57%). Of the 500 samples sent to the laboratory for testing for phleboviruses by Real-time PCR, no positive cases of Papatasi virus infection were observed in them. Conclusion: Robat Karim is one of the endemic areas of infectious diseases such as leishmaniasis. Due to climate change in this region that conditions for the growth and reproduction of Sandfly, especially Phlebotomus papatasi as a Vector of sandfly fever and since the sandfly fever disease is not vaccinated therefore, preventive measures to control and reduce diseases transmitted by Sandfly are important.

Therapeutic efficacy of retroviral replicating vector (RRV)-mediated prodrug activator gene therapy has been demonstrated in a variety of tumor models, but clinical investigation of this approach has so far been restricted to glioma and gastrointestinal malignancies. In the present study, we evaluated replication kinetics, transduction efficiency, and therapeutic efficacy of RRV in experimental models of lung cancer. RRV delivering GFP as a reporter gene showed rapid viral replication in a panel of lung cancer cells in vitro, as well as robust intratumoral replication and high levels of tumor transduction in subcutaneous and orthotopic pleural dissemination models of lung cancer in vivo. Toca 511 (vocimagene amiretrorepvec), a clinical-stage RRV encoding optimized yeast cytosine deaminase (yCD) which converts the prodrug 5-fluorocytosine (5-FC) to the active drug 5-fluorouracil (5-FU), showed potent cytotoxicity in lung cancer cells upon exposure to 5-FC prodrug. In vivo, Toca 511 achieved significant tumor growth inhibition following 5-FC treatment in subcutaneous and orthotopic pleural dissemination models of lung cancer in both immunodeficient and immunocompetent hosts, resulting in significantly increased overall survival. This study demonstrates that RRV can serve as highly efficient vehicles for gene delivery to lung cancer, and indicates the translational potential of RRV-mediated prodrug activator gene therapy with Toca 511/5-FC as a novel therapeutic strategy for pulmonary malignancies.

Ixodes scapularis is the major vector of Lyme disease in the eastern United States. Each active life stage (larva, nymph, and adult) takes a blood meal either for developing and molting to the next stage (larvae and nymphs) or for oviposition (adult females). This protein-rich blood meal is the only food taken by Ixodes ticks and therefore blood digestion is very important for tick survival. Most studies on blood digestion in ticks have shown that the initial stages of digestion are carried out by cathepsin proteases within acidic digestive cells. However, most of these studies have focused on partially engorged ticks. In other hematophagous arthropods, the serine proteases play an important role in blood protein degradation. In this study, we determined transcript expression of four I. scapularis serine proteases with previously characterized roles in blood digestion. RNA interference was used for functional analysis and a trypsin-benzoyl-D, L-arginine 4-nitoanilide assay was used to measure active trypsin levels. An in vitro hemoglobinolytic assay was performed with or without serine protease inhibitor. Our data suggest that trypsin levels increase significantly after blood feeding and peaked in larvae, nymphs, and adults at 3, 1, and 1 day post host detachment, respectively. The knockdown of three previously identified serine proteases by RNAi negatively impacted blood intake, survival, fecundity, levels of active trypsin in the gut and resulted in lower hemoglobin degradation in vitro. A trypsin inhibitor, PMSF, blocked the action of trypsin in the gut extract resulting in 65% lower hemoglobin degradation. We provide evidence of the serine proteases as digestive enzymes in fully engorged, replete females. Our data also demonstrated that in addition to blood digestion, these serine proteases might have a role in blood feeding success in I. scapularis.

In this present study, we conducted untargeted metabolic profiling using Gas Chromatography-Mass Spectrometry (GC-MS) analysis of ascidian Didemnum bistratum to assess the chemical constituents by searching in NIST library with promising biological properties against anti-bacterial and Zika virus vector mosquitocidal Properties. Metabolites, steroids and fatty acids are abundant in crude compounds of ascidian D. bistratum and showed potential zone growth inhibition against bacterial strains Kluyvera ascorbate (10 mm). The active crude compounds of D. bistratum exhibited prominent larvicidal activity against the Zika vector mosquitoes of Aedes aegypti and Cluex quinquefasciatus (LC₅₀ values of 0.4436 to 2.23 mg/mL). The findings of this study provide a first evidence of the biological properties exhibited by D. bistratum extracts, thus increasing the knowledge about the Zika virus vector mosquitocidal properties of ascidian. Overall, ascidian D. bistratum are promising and biocontrol or eco-friendly tool against A. aegypti and C. quinquefasciatus with prospective toxicity against non-target organisms.

The monitoring of maternal and child health, using wearable devices made with wireless sensor technologies, is expected to reduce maternal and child death rates. Wireless sensor technologies have been used in wireless sensor networks to enable the acquisition of data for monitoring machines, smart cities, transportation, asset tracking, and tracking of human activity. Applications based on wireless body area network (WBAN) have been used in healthcare for measuring and monitoring of patient health and activity through integration with wearable devices. Wireless sensors used in WBAN can be cost-effective, enable remote availability, and can be integrated with electronic health record (EHR) management systems. Interoperability of WBAN sensor data with other linked data has the potential to improve health for all, including maternal and child health through the improvement of data access, data quality and healthcare access. This paper presents a survey of the state-of-the-art techniques for managing WBAN sensor data interoperability. The findings in this study will provide reliable support to enable policymakers and health care providers to take action to enhance the use of e-health to improve maternal-child health and reduce the mortality rates of women and children.

The paper provides and comments on the results of studies of the effect of sandstone-based abrasives and quartz sand alone on the wear of martensitic surfaces of wear-resistant steels. The wear process was examined on a ring-on-ring test rig seeking to determine the mass decrement parameter which characterised wear. The tests were conducted for three sandstone varieties: Carboniferous, Permian, and Cretaceous, and they made it possible to determine that the most intense process of deterioration of wear-resistant steels took place in the presence of quartz sand grain, while less intense wear was observed in the case of sandstone-based abrasives. The mass decrement values established in the presence of the sandstones in question did not differ significantly between individual sandstone varieties. Based on a surface damage analysis, the basic damage mechanism was found to be micro-scratching, however, with regard to the sandstones examined, it was also determined that individual grains could be pressed into surface irregularities, as well as that films of soft hematite cement developed in the Permian sandstone and that inclusions of carbonaceous matter were formed in the Carboniferous sandstone. With reference to the wear process observations, a wear model was described for the surface of the steels examined in the presence of sandstone-based abrasives.

Graph neural networks and graph convolutional networks are effective at representing complex relationships. We attempt to apply them to sports analytics by reviewing existing applications in sports. These methodologies leverage the inherent graph structure of sports data, capturing interactions among players, teams, and game events. By systematically examining various studies, we highlight how GNNs and GCNs can enhance performance prediction, player evaluation, and tactical analysis in sports. Our review emphasizes the unique advantages these models offer over traditional analytical approaches, particularly in their ability to account for the dynamic and interconnected nature of sports activities. We conclude by identifying potential areas for future research and application, suggesting that the integration of advanced graph-based techniques could significantly advance the field of sports analytics.

In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects’ boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and we develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combine different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask which is more suitable for combining transformers in an ensemble. In our extensive experimental evaluation, the proposed ensembles exhibit state-of-the-art performance.

Recent progress in microfabrication technique allowed the rapid development of neural implants. They are getting categorized as effective tools for clinical practice, especially to treat traumatic and neurodegenerative disorders. Microelectrode arrays already have been used in numerous neural interface devices. Basically, almost all neural implants have been developed based on BCI (Brain Computer Interface) system. When BCI system falls under invasive technique, it is referred as BMI or Brain Machine Interface. BMIs hold promises for neurorehabilitation of motor and sensory function, cognitive state evaluation and treatment of neurological chaos. A directed overview of the field of neural implants is discussed in this article. The aim of this review is to give a brief introduction of neural prosthetics as well as their exciting applications in treating neurological disorders and a deep discussion on their functionality are mentioned. BCI system and their different types, their functionality, their pros and cons, how other neural implants developed, and their present status have been covered. Different possibilities and possible future of deep brain stimulation (DBS), Neuralink, motor and sensory neural prosthetics are further discussed.

The DNA Damage Response (DDR) network and the mitogen-activated protein kinase (MAPK) signaling pathway are crucial mechanisms for the survival of all living beings. An accumulating body of evidence suggests that there is a crosstalk between these two systems, thus favoring the appropriate functioning of multi-cellular organisms. On the other hand, aberrations within these mechanisms are thought to play a vital role in the onset and progression of several diseases, including cancer, as well as in the emergence of drug resistance. Here, we provide an overview of the current knowledge regarding alterations in the DDR machinery and the MAPK signaling pathway, as well as abnormalities in the DDR-MAPK functional crosstalk in multiple myeloma, the second most common hematologic malignancy. We also present the latest advances in the development of anti-myeloma drugs targeting crucial DDR- and MAPK-associated molecular components. These data could potentially be exploited to discover new therapeutic targets and effective biomarkers, as well as for the design of novel clinical trials. Interestingly, they might provide a new approach to increase the efficacy of anti-myeloma therapy by combining drugs targeting the DDR network and the MAPK signaling pathway.

Adverse medical events not only harm patients and families, but also cause significant negative impact on healthcare providers, with potential to compromise future professional functioning. These “second victims” may need organizational support and rehabilitation to return to functionality. We analyzed the validity of an adapted tool, the Second Victim Experience and Support Tool (SVEST), to a population in Israel, H-SVEST. The H-SVEST was completed by 172 nurse participants working in a variety of patient care settings. The H-SVEST was assessed for content validity, internal consistency, and construct validity with confirmatory factor analysis (CFA). The CFA run on the initial model with 10 factors and 36 items did not meet criteria for goodness of fit. After removing five items based on their low factor loadings and the correlation, The model fit significantly improved with acceptable CFI, TLI, RMSEA, and SRMR; χ2(389) =626.939, p<.001; χ2/df=1.61; TLI=0.901; CFI=0.917; RMSEA [90% CI]=0.060 [0.051, 0.068]; SRMR=0.064. The final version included 31 items and 10 factors with Cronbach α values ranging from 0.66 to 0.94. The H-SVEST demonstrates robust psychometric properties and valuable insights into the second victim experience in the Israeli context. Comparative analysis with other versions highlights potential cultural influences and areas for further investigation. Implementing this tool and developing evidence-based interventions based on its results can significantly improve well-being and resilience of healthcare providers in Israel and other countries with diverse cultural populations. This study was not registered.

Social responsibility is imperative for its important function in the growth of societies and organizations. However, the research on corporate social responsibility (OSR) has predominantly focused on the actions of organizations. Not much has been discussed regarding the employee's behaviours in promoting the development of social responsibility within organizations. CI is known for its significant role in the organization's various outcomes, Although a conceptualization and some empirical support have been provided for its relationship with the behaviour of employees, However, the effect of CI on employee socially responsible behaviours has not yet been investigated. In this article, following OC and OI theories, we improve the research model which can describe mechanisms underpinning through which CI, OI and POS support actions for employees' social responsibility. Data was collected from members of service professionals in Saudi Arabia and examined using Structural Equation Modeling- Partial Least Squares. The findings indicated that CI actions and activities develop the skills that motivate employees to support social responsibility inside and outside the organization. Moreover, our research paper explained the mechanism under which CI activities reshape employees' socially responsible manners. The current study is among the first to look at how to modify the socially responsible behaviour of employees across organizations, which can be a starting point for transforming those employees into a sustainable society.

The research of tangible creativity support tools (T-CSTs) has been increasing in recent years. However, there is a lack of review that interprets what T-CSTs trends must be considered and how challenging opinions should be broken. Hence, a focused and critical review of existing studies is necessary. This paper analyzed 86 studies related to T-CSTs following the PRISMA protocol. First, the research tendencies were extracted and provided a basic knowledge construction. Then, we extracted primary research questions, categorized the adopted design and technological solutions, and identified the researchers’ hypotheses-validation. Last, we proposed research gap, challenge and potential opportunities for future researchers and designers. This review will benefit people in the fields of tangible interaction, design studies and education.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been extensively investigated in developmental biology and pathology. More than a half-century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. EMT and MET effects represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during developmental process and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

In this study, tree selection/plantation DSS (Decision Support Systems) were reviewed and assessed based on essential objectives in the available literature. We reviewed existing DSS using multiple data sources and available online resources such as web interfaces. We compared the existing DSS, focusing primarily on five primary objectives in this study, which DSS may address when it comes to the tree selection, including a) Climate resilience, b) Infrastructure/Space Optimization, c) Agroforestry, d) Ecosystem services, e) Urban sustainability. Climate resilience of tree species and urban sustainability is addressed relatively less in existing systems, which can be holistically internalized in future DSS tools Based on this review, DNN (Deep Neural Networks) is recommended to address achieving trade-offs between complex objectives such as maximizing ecosystem services, climate resilience of tree species, maintaining Agroforestry, and other benefits.

This research aims to thoroughly understand the delicate interplay between stakeholders' perspectives, biometric data protection policies, and the deployment of face recognition technologies in Indonesia's dynamic digital world. Adopting a qualitative methodology, the study conducts Focus Group Discussions (FGDs) with various stakeholders, including government agencies, technology developers, regulatory agencies, civil society groups, and data subjects as end-users. Face-to-face group discussions facilitate an in-depth exploration of participants' perspectives, yielding rich qualitative data that reveals nuanced insights into the delicate balance between technological innovation, ethical considerations, and socioeconomic repercussions associated with implementing face recognition technology. The research comprehensively explains the difficulties and opportunities associated with responsible technological progress and biometric data security. The findings from the FGDs influence the development of strategies that effectively balance technical advancement with individual rights, privacy, and social well-being within the growing digital landscape of Indonesia. The regulatory function of the government seeks to bridge the gap between public expectations and technological advancements. Collaboration between academic institutions, government agencies, the private sector, and data subjects as end-users emerges as a crucial element, fostering an all-encompassing strategy integrating research, law, technology deployment, and user empowerment. This holistic approach is necessary to ensure that biometric data and facial recognition technology are utilized responsibly, thereby laying the way for Indonesia's technologically sophisticated and ethically aware digital future.

Accidents at mining enterprises, including tailing dumps, pose significant threats to human lives, structures, and the environment. This study focuses on designing, constructing, and operating tailing dumps in Siberia's northern region. To ensure safety and minimize environmental impacts, comprehensive scientific monitoring and research were conducted at all stages, including design, construction, operation, and disposal. The aim was to create a uniform mass within the dam body, requiring understanding of the tailings' structure and technological characteristics during placement. Parameters like particle size, distribution, density, and moisture content were considered to assess tailings' physical and geometric properties. Estimated monitoring was introduced as a permanent model to quickly assess the stability of hydrotechnical constructions. This involved monitoring changes in exploitation properties, structure height, beach length, and water levels. A controlled inwashing technology for subsequent dam layers was developed. Complex research facilitated the formulation of an estimated monitoring methodology and an algorithm for tailing dam formation. Practical application demonstrated high reliability and confirmed load-bearing capacity, allowing for the forecast of dam stability and safe execution. Findings led to alterations in work techniques, ensuring safe and efficient operation of tailing dams.

Enzyme immobilization is known to enhance polypeptide resistance towards environmental changes, particularly in terms of their stability. The Y509E mutant of β-xylosidase from Geobacil-lus stearothermophilus (XynB2Y509E) (which also bears xylanase activity) has been immobilized in chitosan spheres through either entrapment or covalent bond formation methods. In the immo-bilization by entrapment, besides observing a total loss of xylanase activity, it was found that chitosan concentration affects the process efficiency. Additionally, the maturation time of the spheres and the alkaline pH used to precipitate the spheres affect the β-xylosidase activity of the enzyme. In covalent bond immobilization, it was found that the pH during the modification with glutaraldehyde, as well as the subsequent protein binding, were key factors in preserving the dual enzymatic activity. The activity expressed after immobilization by covalent bonding was 23% higher compared to the activity expressed following entrapment immobilization, with values of 122.3 and 99.4 U. g-1, respectively. Both biocatalysts showed increased thermal and pH stability, along with an improved storage capacity, as they retained 40% and 88% of their activi-ty after being stored at 4 C for two months. Moreover, XynB2Y509E immobilized by covalent binding also exhibited outstanding reusability, retaining a 92% of activity after ten cycles of re-use.

Background: Our study aimed to assess the differences between domestic and international students in terms of social support, vital exhaustion, and depression during the time of COVID-19. Methods: The online cross-sectional survey was conducted via Google Forms® at three time intervals during the pandemic. Results: Respectively 1320, 246, and 139 students completed our questionnaires in the different time intervals. International students reported significantly lower values of perceived social support. Women reached higher scores regarding vital exhaustion in both samples. Concerning depression, international female students had higher values than their male counterparts but the difference diminished with time. No differences could be found in the comparison of depression between domestic female and male students. Significant correlations were found between depression, perceived social support, and vital exhaustion. Discussion: International students perceive diminished social support just when they need more. Decreased levels of perceived social support may contribute to the development of their psychological problems.

Phlebotomus (Paraphlebotomus) sergenti Parrot, 1917, the proven vector of Leishmania tropica Wright, 1903, the causative agent of anthroponotic cutaneous leishmaniasis, is widely distributed in Morocco. Previous works using molecular markers (ITS2 rDNA and Cyt b mtDNA) have hy-pothesized the existence of multiple closely related populations of sandfly species (cryptic species) that would exhibit distinct vectorial capacities. In this work, descriptive characteristics of wings (size and shape of the right and left wings) were measured in samples collected from fourteen sta-tions in central Morocco. These analyses support the existence of distinct P. sergenti populations, enlightening significant phenotypic variations of P. sergenti’s wings, regarding their size and shape, depending on geographic origin. In addition, geomorphometric analyses of wing’s length, centroid size, alpha, and beta distances allowed clear discrimination of P. sergenti sub-populations. These data pinpoint the adaptative ability of P. sergenti to local environmental conditions. Additional studies are now required to further shed light on the genetic structure of P. sergenti populations in Morocco.

The confinement experienced due to the COVID-19 pandemic has prompted a rethink of the teaching-learning process to which teachers have responded without planning and instead using their resources. This study aims to analyze the relationships between work-family interactions, technostress, and perceived organizational support in teachers during the confinement period in Spain that began in March 2020. An online survey was administered to 640 pre-school, primary, and secondary school teachers. Positive reciprocal work-family interactions and their relationship with organizational support were found, with differences according to gender, with women showing a more negative perception of the impact on the family. There were no marked levels of technostress in the overall sample, although higher levels of perceived ineffectiveness and skepticism were found in teachers aged 46 years or older. Teachers in private and subsidized schools showed a higher level of perceived support than those in public schools. There is a need to continue this work to verify the values of these dimensions in other contexts and to apply institutional measures and public policies to improve these indicators in this group.

The formula Chong-Lou-Yao-Fang (CLYF) is an herbal medicinal formulation developed by the indigenous Naxi people for treating liver cancer. This study is to reveal the biological activity, potential targets, and molecular mechanisms of CLYF for cancer treatment through network pharmacology, molecular docking and in vitro experiments. 35 key compounds were screened from 176 active ingredients in CLYF, which played a vital role in the treatment of cancer, 20 core targets were filtered through protein–protein interaction (PPI) network analysis. The enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that PI3K-Akt, MAPK, hepatitis B, hepatitis C might be effective in liver cancer treatment. Mo-lecular docking revealed stable binding between EGFR, TP53, and AKT1 with active ingredients. Experiments with cell cultures confirmed that CLYF-A suppressed the proliferation of HepG2 cells, which is consistent with the network pharmacology approach undertaken and the predictions of molecular docking. CLYF-A also induced significant apoptosis and cell cycle arrest in HepG2 cells, which was associated with the loss of mitochondrial membrane potential. This study explored the biological activity, potential targets, and molecular mechanisms of CLYF for cancer treatment. Our results may provide a scientific basis for the clinical use of CLYF for cancer treatment and have important implications for developing pharmaceutical preparations, which also need more phar-macological experiments.

Pancreatic ductal adenocarcinoma (PDAC) represents an extremely aggressive type of cancer due in part to early invasion and metastasis, which in turn involves epithelial-mesenchymal transition (EMT) of the tumor cells. Prompted by the discovery that two PDAC-derived tumor cell lines of the quasi-mesenchymal subtype (PANC-1, MIA PaCa-2) exhibit neuroendocrine differentiation (NED), we asked whether NED is associated with EMT. Using quantitative real-time PCR and immunoblot analysis, we initially verified endogenous expression of various NED markers, i.e., chromogranin A (CHGA), synaptophysin (SYP), somatostatin receptor 2 (SSTR2) and SSTR5 in PANC-1 and MIA PaCa-2 cells. In HPDE6c7 normal pancreatic duct epithelial cells and in BxPC-3, another PDAC cell line with an epithelial phenotype, the expression of CHGA, SYP and neuron-specific enolase 2 (NSE) was either undetectable or much lower than in PANC-1 and MIA PaCa-2 cells. Parental cultures of PANC-1 cells exhibit EM plasticity (EMP) and harbor clonal subpopulations with different EMT phenotypes [Ungefroren et al 2022]. Monitoring the NED state of mesenchymal and epithelial PANC-1 subclones showed that the NE phenotype is more pronounced in M-type clones when compared to E-type ones. Inducing EMT in parental cultures of PANC-1 cells by treatment with transforming growth factor (TGF)-β1 repressed epithelial genes and co-induced mesenchymal and NED genes, except for SSTR5. Surprisingly, treatment with bone morphogenetic protein (BMP)-7 differentially affected gene expression in PANC-1 and MIA PaCa-2 cells. It synergised with TGF-β1 in the induction of vimentin, SNAIL, SSTR2 and NSE but antagonised it in the regulation of CHGA and SSTR5. From these data, we conclude that in EMT of PDAC cells mesenchymal and NED markers are co-regulated, and that mesenchymal-epithelial transition (MET) is associated with a loss of both the mesenchymal and NED phenotypes. Analysing NED in another tumor type, small cell carcinoma of the ovary hypercalcemic type (SCCOHT), revealed that two model cell lines of this disease (SCCOHT-1, BIN-67) do express CDH1, SNAI1, VIM, CHGA, SYP, ENO2 and SSTR2, but that in contrast to BMP-7 none of these genes was transcriptionally regulated by TGF-β1. We conclude that in PDAC-derived tumor cells, NED is closely linked to EMT and TGF-β signaling, which may have implications for the therapeutic use of TGF-β inhibitors in PDAC management.

Internet of Things (IoT) is what we have as a great breakthrough in 5G network. Although 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6 addressing which is the foundation of the Network Slicing for the 5G core network. Comparing to other related papers, we provide in depth information on how the Internet of Things (IoT) is going to affect our lifes and how this technology is handled as Internet of Everything (IoE) in 6G network. Finally, a full reference is made to the 6G network with its challenges comparing to the 5G network.

The prognosis of patients with recurrent or metastatic of the head and neck squamous-cell carcinoma (HNSCC) refractory to platinum-based chemotherapy is severe and, consequently, the identification of therapeutic options for this category of patients is a priority. Nivolumab, an anti- programmed cell death protein 1 (anti-PD-1) monoclonal antibody, has been approved for the treatment of recurrent or metastatic HNSCC after platinum-based therapy progressing. We present the early experience of two academic center including diagnostic, clinical, biological, therapeutic and outcomes characteristics of head and neck cancer (HNC) patients treated with Nivolumab. The purpose of the study is to identify certain peculiarities and to report them compared to the data from the literature in order to generate some hypotheses that coud constitute criteria for the selection of cases with maximum benefit to immunotherapy. Analyzing the data obtained from 18 patients treated in Emergency County Hospital Craiova, "Saint Nectarie" Oncological Hospital Craiova and Euroclinic Oncological Center Iasi January 2020 and March 2023 it could be hypothesized that lower nadir values of neutrophil to lymphocyte ratio (<3.5) and the adition of intensive regimens of chemotherapy and radiotherapy could justify a favorable response in terms of progression free survival (PFS) during immunotherapy with Nivolumab. To our knowledge, the study included the first two cases of second primary malignancy (SPM) in the head neck region treated with Nivolumab. The reported data support a possible benefit of the sequential administration of radiotherapy, immunotherapy for SPM cases. Higher positive dynamic of neutrophil to lymphocyt ratio (NLR) could be associated with worst outcome during immunotherapy. All study observations including the role of infections and antibiotic treatment as well as the possible biomarker value of NLR variation during immunotherapy should be investigated in multicenter clinical trials.

This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

In this study, we introduce an innovative framework for multimodal learning that leverages enhanced fusion gate units within gated neural network architectures. The proposed Fusion Gate Unit (FGU) serves as a pivotal component in neural network designs, aiming to derive a comprehensive intermediate representation by amalgamating data from diverse modalities. The FGU is adept at determining the extent to which each modality influences the unit's activation through the utilization of multiplicative gating mechanisms. We conducted evaluations on a multilabel genre classification task for movies, utilizing both plot summaries and poster images as input modalities. The results demonstrate that the FGU significantly elevates the macro F-score compared to single-modality approaches and surpasses existing fusion techniques, including mixture of experts models. Additionally, we present the MM-IMDb dataset alongside this publication, which, to our knowledge, represents the most extensive publicly accessible multimodal dataset for movie genre prediction to date. This dataset is expected to facilitate further research and development in the field of multimodal information processing.

Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.

Image denoising has been one of the important problems in the field of computer vision, and it has a wide range of practical value in many applications, such as medical image processing, image enhancement, and computational photography. Traditional image denoising methods are usually based on hand-designed features and filters, but these methods perform poorly under complex noise and image structures. In recent years, the rapid development of neural network technology has revolutionized the image-denoising task. This paper introduces the knowledge about neural networks and image denoising, explores the impact of neural networks on image denoising, and how is it possible to denoise images by neural networks. It also summarises other image-denoising methods and finally points out the challenges and problems faced by image-denoising at present. Some possible new development directions are proposed to provide new solutions for image-denoising researchers and to promote the development of the field.

Numerous approaches address over-fitting in neural networks: by imposing a penalty on the parameters of the network (L1, L2, etc); by changing the network stochastically (drop-out, Gaussian noise, etc.); or by transforming the input data (batch normalization, etc.). In contrast, we aim to ensure that a minimum amount of supporting evidence is present when fitting the model parameters to the training data. This, at the single neuron level, is equivalent to ensuring that both sides of the separating hyperplane (for a standard artificial neuron) have a minimum number of data points — noting that these points need not belong to the same class for the inner layers. We firstly benchmark the results of this approach on the standard Fashion-MINST dataset, comparing it to various regularization techniques. Interestingly, we note that by nudging each neuron to divide, at least in part, its input data, the resulting networks make use of each neuron, avoiding a hyperplane completely on one side of its input data (which is equivalent to a constant into the next layers). To illustrate this point, we study the prevalence of saturated nodes throughout training, showing that neurons are activated more frequently and earlier in training when using this regularization approach. A direct consequence of the improved neuron activation is that deep networks are now easier to train. This is crucially important when the network topology is not known a priori and fitting often remains stuck in a suboptimal local minima. We demonstrate this property by training a network of increasing depth (and constant width): most regularization approaches will result in increasingly frequent training failures (over different random seeds) whilst the proposed evidence-based regularization significantly outperforms in its ability to train deep networks.

Recent biotechnological innovations make feasible the new paradigm of creating biological neural circuits de novo. With advances in protein, cell and tissue engineering techniques, as well as cellular reprogramming methods, we are entering an era where the construction of neural circuits can open completely new ways for studying nervous systems and for treating nervous system disorders. I explore here three technologies, namely cellular engraftment, neuronal reprogramming and transsynaptic molecule engineering, and delineate how they are being used in a variety of basic research and translational medicine contexts. In basic neuroscience, neural circuit construction methods are enabling ways to study causality in neural development (e.g. neural precursor differentiation and migration) and circuit function (e.g. excitation/inhibition balance, neural population dynamics). In translational neuroscience, they are providing opportunities for the targeted correction of circuit malfunction in brain disorders, both psychiatric (e.g. schizophrenia) and neurological (e.g. Parkinson’s, Huntington’s and Alzheimer’s disease, as well as epilepsy). I discuss the challenges that these methods currently face, such as targeting specificity and cell survival, and outline future paths and opportunities to realize the full potential of technologies for creating new biological neural circuits.

Neural decoding from spiking activity is an essential tool for understanding the information encoded in population neurons, especially in applications like brain-computer interface (BCI). Various quantitative methods have been proposed and have shown superiorities under different scenarios respectively. From the machine learning perspective, the decoding task is to map the high-dimensional spatial &amp; temporal neuronal activity to the low-dimensional physical quantities (e.g., velocity, position). Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. Recently, the Transformer-based models are widely used in processing natural languages and images due to its superior performances in handling long-range and global dependencies. Hence, in this work we examine the potential applications of Transformers in neural decoding and introduce two Transformer-based models. Besides adapting the Transformer to neuronal data, we also propose a data augmentation method for overcoming the data shortage issue. We test our models on three experimental datasets and their performances are comparable to the previous state-of-the-art (SOTA) RNN-based methods. In addition, Transformer-based models show increased decoding performances when the input sequences are longer, while LSTM-based models deteriorate quickly. Our research suggests that Transformer-based models are important additions to the existing neural decoding solutions, especially for large datasets with long temporal dependencies.

Activation Functions are crucial parts of the Deep Learning Artificial Neural Networks. From the Biological point of view, a neuron is just a node with many inputs and one output. A neural network consists of many interconnected neurons. It is a “simple” device that receives data at the input and provides a response. The function of neurons is to process and transmit information; the neuron is the basic unit in the nervous system. Carly Vandergriendt (2018) stated the human brain at birth consists of an estimated 100 billion Neurons. The ability of a machine to mimic human intelligence is called Machine Learning. Deep Learning Artificial Neural Networks was designed to work like a human brain with the aid of arbitrary choice of Non-linear Activation Functions. Currently, there is no rule of thumb on the choice of Activation Functions, “Try out different things and see what combinations lead to the best performance”, however, sincerely; the choice of Activation Functions should not be Trial and error. Jamilu (2019) proposed that Activation Functions shall be emanated from AI-ML-Purified Data Set and its choice shall satisfy Jameel’s ANNAF Stochastic and or Deterministic Criterion. The objectives of this paper are to propose instances where Deep Learning Artificial Neural Networks are SUPERINTELLIGENT. Using Jameel’s ANNAF Stochastic and or Deterministic Criterion, the paper proposed four classes where Deep Learning Artificial Neural Networks are Superintelligent namely; Stochastic Superintelligent, Deterministic Superintelligent, and Stochastic-Deterministic 1^st and 2^nd Levels Superintelligence. Also, a Normal Probabilistic-Deterministic case was proposed.

Maternal and child nutrition has been a critical component of health, sustainable development, and progress in low- and middle-income countries (LMIC). While a decrement in maternal mortality is an important indicator, simply surviving pregnancy and childbirth does not imply better maternal health. One of the fundamental obligations of nations under international human rights law is to enable mothers and teenage girls to endure pregnancy and delivery as an aspect of their enjoyment of reproductive and sexual health and rights and live a dignified life. The aim of this study was to discover the correlation between the Maternal Observation and Motivation (MOM) program and m-Health support for maternal and newborn health. A Comparative study was done among 109 pregnant mothers (study group-94; control group-102 mothers) with not less than 20 weeks of gestation. Maternal outcomes such as Hb, weight gain and newborn results like birth weight and crown- heel length was obtained on the baseline, 28 and 36 weeks of gestation. Other secondary data collected were abortion, stillbirth, low birth weight, major congenital malformations, twin or triplet pregnancies, physical activity and maternal wellbeing. The MOM intervention included initial face to face education, three in-person visits and eight virtual health coaching by WhatsApp. The baseline data on Hb of the mothers show that 31(32.98%) vs 27(28.72%) of the study and control group had anaemia, which improved to 27.66% and 14.98% among study group mothers at 28 and 36 weeks of gestation (p<0.001). The weight gain (p< 0.001), level of physical activity (p< 0.001), and maternal wellbeing (p< 0.01) also had significant differences after the Intervention. Even after controlling for potentially confounding variables, the maternal food practices regression model revealed that birth weight was directly correlated with consumption of milk (p 0.001), fruits (p 0.01), and green vegetables (p 0.05).As per the physical activity and maternal wellbeing regression model, the birth weight and crown heel length were strongly related with the physical activity and maternal wellbeing of mothers at 36 weeks of gestation (p <0.05). Combining the MOM intervention with standard antenatal care is a safe and effective way to improve maternal welfare while upholding pregnant mothers' human rights.

Human space exploration is evolving, with private and government organizations working to develop the new generation of astronauts and technology for future colonization of the Moon and other extreme locations. The coming decade is promising, which is reflected in a number of emerging habitats to simulate analog missions around the world. Analog missions have always been at the forefront of advancing knowledge and now this method is widely used by space enthusiasts and professionals. The Analog Astronaut Training Center is designed to simulate one of the major challenges of human space exploration, namely isolation with limited resources, and focuses on collecting data based on various parameters such as environment, physiology and psychology. The environmental monitoring of the habitat conditions is remotely controlled, allowing the Mission Control Center to receive the mission data in real time. The data obtained, together with physiological and psychological inputs, allow monitoring of human adaptations to limited resources and sustainable living in extreme conditions. This paper presents an overview of the sensors used and the data collected during organized analogue simulations, which help to optimize the design of future life-support systems.

A recent reanalysis of publicly available data claimed that the carbohydrate-insulin model was supported by results from a randomized crossover trial comparing low carbohydrate (LC) versus low fat (LF) diets. The reanalysis also claimed to have invalidated the primary outcome of the trial, namely the within-participant differences in ad libitum energy intake when they consumed LC vs LF diets. Here, we demonstrate that both claims are untrue. The reanalysis was fatally flawed in several respects, with the most egregious errors related to ignoring the within-participant, repeated-measures structure of the data and treating data timepoints as independent. This resulted in almost all statistical comparisons being incorrect and reporting wildly optimistic p-values. Furthermore, the authors failed to engage sufficiently with prior work on the same trail data and portrayed their reanalysis as being more novel than it was. The reanalysis also failed to disclose the possibility of bias and succumbed to common statistical errors that falsely led the authors to interpret the data as supporting the carbohydrate-insulin model of obesity.

The amendment or removal of superfluous government support policies is typically difficult, yet in the ever more important debate on low-carbon (i.e. clean) marine energy policy under the international law of climate action, the law of the sea, and international investment protection, there are additional dimensions of legal or economic peril. Coastal states enact policies subsidising clean energy investments, such as offshore wind energy generation, in their exclusive economic zones or continental shelves. Investors are attracted to the prospect that policies granting subsidies for ostensibly new industries are sufficiently durable. Are such subsidy policies salient or stale? In principle, the purpose of regulatory policy is the promotion of social welfare, and hence there is an optimal incidence, magnitude, and duration of the subsidy, in essence, an ideal strategy for starting, altering, or exiting such policy. We aim to introduce the concept of optimisation to the design and implementation of regulatory policy in this context. Our contribution is to offer three maxims of optimal clean marine energy law and policy: the efficiency and equity of alternative regulatory arrangements; the continuous optimisation of such arrangements; and the recognition of linguistic entanglements in the law. We test these maxims against the case of clean marine energy policy on offshore wind energy generation. One legal implication for international investment protection is that coastal states should establish a policy exit clause in their investment contracts. Our analysis of policy optimisation is generalisable across policies supporting the transition to sustainable energy forms.

Decision makers have to work on resolving multiple forms of problems within their organizations. The problems can be structured, semi-structured, or unstructured. Handling these issues requires intensive time dedication and resource allocation. Looking at the perspective of strategic decision making in Saudi Arabia, current operations are lacking in various sectors. This indulges an immediate need to introduce proper systems and highlight the loopholes that would allow the leaders make informed decisions aided by a support system. For this reason, the researchers set out to work on two research questions. The first question focused on how decision makers ensured accuracy of the decisions they are making. It would allow to identify why such system is important that should also guarantee consistency and accuracy. The second question asked about the proper process that would govern and control the outcome of decision. Considering the above questions, this research aimed at identifying the ideal framework, and whether the proposal developed would fit into the Saudi organizations. A case study in health sector has been reviewed containing various models across the world. Following this, interviews with five decision makers in their organization were conducted to perform the qualitative research. The researcher identified that most organizations lacked systems to ensure their decisions were accurate. The research also concluded that the tools and elements presented in the proposed informed decision framework would fit into most Saudi organizations to eliminate the identified problem, and especially due to the inclusion of the non-digital sources of information as part to the decision process. Furthermore, the analysis was conducted and discussion was determined stating validations of the study.

In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

Nonporous corundum powder, known as an abrasive material in the industry, was functionalized covalently with protein binders to isolate and enrich specific proteins from complex matrices. The materials based on corundum were characterized by TEM, ESEM, BET, DLS, and zeta potential measurements. The strong Al-O-P bonds between the corundum surface and amino phosphonic acids are used to introduce functional groups for further conjugations. The common crosslinker glutaraldehyde was compared with a hyperbranched polyglycerol (PG) of around 10 kDa. The latter is oxidized with periodate to generate aldehyde groups that can covalently react with the amines of the surface and the amino groups from the protein via a reductive amination process. The amount of bound protein was quantified via aromatic amino acid analysis (AAAA). This work shows that oxidized polyglycerol can be used as an alternative to glutaraldehyde. With polyglycerol, more of the model protein bovine serum albumin (BSA) could be attached to the surface under the same conditions, and lower nonspecific binding (NSB) was observed. As a proof of concept, IgG was extracted with protein A from crude human plasma. The purity of the product was examined by SDS-PAGE. A binding capacity of 1.8 mg IgG per g of corundum powder was achieved. The advantages of corundum are the very low price, extremely high physical and chemical stability, pressure resistance, favorable binding kinetics, and flexible application.

During operation, the support bracket is the main part to withstand the axial loads of the pumped storage unit. Moreover, the effects of axial loads including the hydraulic thrust of runner flow and the weight of runner body may cause the support bracket deformation and fatigue damage. For the safe and stable operation, the simulation of the axial force and the structural analysis of the support bracket of a pumped storage unit was carried out in this paper. The CFD simulation result has revealed the variation rule of the axial force in different operating conditions. Using ANSYS Mechanical, the static stresses and deformation of support bracket with axial loads were calculated. The results release the location and variations of maximum stress and maximum deformation caused by the axial loads. By comparing the predicted maximum axial force with the admission force calculated by the structural analysis, it is found that the axial force of the researched machine is within the safe range. This study provides the reference for the safety and stable operation of the pumped storage unit.

Laser powder bed fusion (L-PBF) is a type of additive manufacturing technology that processes metal powders into a component. Support structures are an essential part of the L- PBF process as they transfer the laser-induced heat during and shortly after the process to the substrate, sustaining positional accuracy of downward facing surfaces of the component. Since the use of support structures is inevitable, optimized designs for them are crucial in realizing more sustainable production process. In a serial production setup, reducing the lead time and cost of a non-value-added process step like support structure removal is of significance when improving the overall business case and competitiveness.The goal of this study was to verify the applicability of lattice-based support structures for L-PBF. To achieve this, different lattice types as support structures were designed. They were tested, compared and verified for a Siemens gas turbine component. The results showed that the generated lattice-based support structures could be suitable for L-PBF. The supports had to be designed appropriately such that they could preserve the geometry of the part. Furthermore, they had to have a short fabrication time and to be removable easily, preferably without machining or sawing.

As a universal pathogen leading to neonatal defects and transplant failure, Human cytomegalovirus (HCMV) has strict species specificity that the inability to using this virus in animals has hampered its pathogenesis study. However, the mechanism of cross-species barrier remains elusive that no non-human cell model has been established to fill this knowledge gap. We observed that primary dermis fibroblasts (TSDF) isolated from the Chinese tree shrew (Tupaia belangeri chinensis), a small laboratory animal with close affinity to primates, were permissive to HCMV replication. In TSDF infected with GFP-expressing HCMV, the green fluorescence and cytopathic effect were observed and the expression of 3 kinetic genes and replication of viral genome were detected. The cell-free viruses produced in TSDF reached 103 pfu/mL at 96 hpi, which were 10-fold lower than in primary human foreskin fibroblasts. Our results demonstrated that TSDF supported low level of lytic replication of HCMV. The TSDF model provides a useful platform for the mechanism study of species barrier of HCMV.

Tidal stream energy is a low carbon energy source. Tidal stream turbines operate in a turbulent environment, and the effect of the structure between the turbine and seabed on this environment is not fully understood. An experimental study using 1:72 scale models based on a commercial turbine design was carried out to study the support structure influence on turbulence intensity around turbine blades. The study was conducted using the wave-current tank at LABIMA, University of Florence. A realistic flow environment (ambient turbulent intensity = 11%) was established. Turbulence intensity was measured upstream and downstream of a turbine mounted on two different support structures (one resembling a commercial design, the other the same with an additional vertical element), in order to quantify any variation in turbulence and performance between the support structures. Turbine drive power was used to calculate power generation. Acoustic Doppler Velocimetry was used to record and calculate upstream and downstream turbulence intensity. In otherwise identical conditions, performance variation of only 4% was observed between two support structures. Turbulent intensity at 1, 3 and 5 blade diameters, both upstream and downstream, showed variation up to 21% between the two cases. The additional turbulent structures generated by the additional element of the second support structure appears to cause this effect, and the upstream propagation of turbulent intensity is believed to be permitted by surface waves. This result is significant for the prediction of turbine array performance.

Although significant efforts have been made to combat the spread of vector-borne diseases (VBDs), they still account for more than 17% of all infectious diseases. According to the World Health Organization (WHO), there were 216 million estimated cases in 2016, which is a 9.3% decrease from the estimated cases reported one decade earlier. It is known that the built environment, through features such as openings, can propagate the spread of malaria. There have been some significant efforts directed at addressing this risk. This notwithstanding, there are some knowledge gaps that have resulted in a missed opportunity for synergistically tackling the problem of vectors through leveraging design decisions made by built environment professionals. This work assesses the extent to which design decisions in the built environment can have a positive impact on the efforts directed at mitigating the risk of malaria based on selected cases from East Africa. Secondary data derived from relevant urban health journals as well as repositories curated by leading health agencies such as WHO were synthesized and analyzed using a web of causation approach. The outcome of the analysis is a schema of primary and secondary source (risk) factors. The use of the web of causation approach revealed the existing factor-to-factor interactions that could have a reinforcing effect. This information was used to identify the critical linkages and interdependencies across different factors. The outcome of the analysis was mapped against risk factors that can be linked to decisions made during the six primary phases of the construction life cycle: preliminary phase, conceptual design, detailed design, construction, facilities management, and end of life/disuse. The findings of the research have established that 1) there is, in fact, a built environment–related opportunity that can be leveraged to advance the impact of malaria mitigation effort; 2) cross-disciplinary synergies are critical to managing the interdependencies and complexity of malaria risk factors that have a reinforcing effect; and 3) a knowledge-management framework that serves as a decision support tool would be valuable for sharing data under a push-and-pull mechanism, in which data shared in real time can address the timeliness of mitigating the spread of malaria at the earliest stages for the greatest impact. Based on the findings, a conceptual architecture for a decision support framework has been proposed. This will be developed into a knowledge-management platform in subsequent efforts.

The literature on substance use usually extracts conclusions from data with correlational methods. Our study shows the usefulness of complementing ordered logistic regression (OLR) and fuzzy set qualitative comparative analysis (fsQCA) to assess factors inducing cannabis consumption in a sample of 1,935 teenagers. OLR showed a significant influence of gender (odd ratio (OR) =0.383, p<0.0001), parental monitoring (OR=0.587, p=0.0201); religiousness (OR=0.476, p=0.006); parental tolerance to substance use (OR=42.01, p<0.0001) and having close peers that consume substances (OR=5.60, p<0.0001). FsQCA has allowed fitting linkages between factors from a complementary perspective. (1) Coverage (cov) and consistency (cons) attained by solutions explaining use (cons=0.808, cov=0.357) are clearly lower than by recipes of non-use (cons=0.952, cov=0.869) (2) The interaction of gender, a tolerant family to use and the attitude toward substances by peers is very consistent to explain cannabis use. (3) The most important recipe explaining resistance to cannabis is simply parental disagreement with substance consumption (cons=0.956, cov=0.861) (4) Factors as gender, religiosity, parental monitoring and age show also a relevant impact on attitude toward cannabis use. However, whereas some of them impact symmetrically on use and non-use this does not follow in factors such as parental monitoring or age.

Introduction: Urinary tract infections (UTIs) are a significant global health issue, especially among women, with growing concerns related to antibiotic resistance and adverse effects. The Uromune® vaccine, a sublingual bacterial lysate, represents a promising therapeutic alternative by enhancing immune responses against uropathogens. Methods: This retrospective study, conducted at Hospital Universitari de Sant Joan de Reus from January 2017 to August 2022, evaluated the effectiveness of Uromune® in treating recurrent UTIs (rUTIs). Patients received personalized autovaccines administered as two sublingual puffs daily for three months. Clinical, microbiological, and demographic data were analyzed to assess treatment outcomes and identify recurrence-associated factors. Results: Forty-nine patients (mean age, 61 years, and 59.2% women) were included in the study. Uromune® treatment decreased UTI episodes, from 3.73 ± 0.97 the year before to 0.98 ± 1.36 (P &lt; 0.001) the year after its administration. The number of patients who suffered three or more episodes per year dropped from 43 (87.7%) before the intervention to 7 (14.3%) afterward. The maximum effectiveness of the autovaccine was observed three months post-administration, with 44 patients not experiencing any UTI episodes. Regression analysis identified having had a urostomy, chronic kidney disease, and being immunosuppressed as predictors of recurrence. Conclusion: The Uromune® autovaccine effectively reduced the frequency of rUTIs and associated hospitalizations, offering substantial relief to patients. These findings support the integration of Uromune® into routine clinical practice as a viable alternative to conventional antibiotic treatments.

(1) Background: The aim of this study is to investigate the diagnostic-therapeutic problem of pelvic recurrence of rectum cancer, highlighting current surgical standards and the possible role of the minimally invasive approach. This retrospective analysis of our surgical case study wishes to enter this debate while suggesting a possible line of action, based on the site of recurrence; (2) Methods: We examined, retrospectively, all the patients diagnosed, between 2008 and 2018, with cancer of the rectum at the "Pietro Valdoni" Department of Surgery and monitored their follow-up for 5 years. The sample consisted of 368 patients with rectal neoplasm, 136 females and 232 males, with an average age of 65.8 (ranging from 37 to 86); (3) Results: In 103 of the cases, the neoplasm was located in the upper rectum (28%), in 119 cases in the middle rectum (32.3%), in 102 cases in the lower rectum (27.7%), in 31 cases (8.4%) at the level of the right/sigma junction and in 13 cases it was not possible to define the site with certainty (3.5%); (4) Conclusions: The discussion re-mains open as to which approach is best at surgical level, whether laparoscopic, robotic or open-air. Our experience informs us that the most dangerous site remains on the anastomotic site.

Background and Objectives: The aim of this study is to investigate the diagnostic-therapeutic prob-lem of pelvic recurrence of rectum cancer, highlighting current surgical standards and the possi-ble role of the minimally invasive approach. This retrospective analysis of our surgical case study wishes to enter this debate while suggesting a possible line of action, based on the site of recur-rence. Materials and Methods: We examined, retrospectively, all the patients diagnosed, between 2008 and 2018, with cancer of the rectum at the "Pietro Valdoni" Department of Surgery and monitored their follow-up for 5 years. The sample consisted of 368 patients with rectal neoplasm, 136 females and 232 males, with an average age of 65.8 (ranging from 37 to 86). In 103 of the cases, the neo-plasm was located in the upper rectum (28%), in 119 cases in the middle rectum (32.3%), in 102 cases in the lower rectum (27.7%), in 31 cases (8.4%) at the level of the right/sigma junction and in 13 cases it was not possible to define the site with certainty (3.5%). Results: We examined all patients diagnosed with cancer of the rectum between 2008 and 2018 at the "Pietro Valdoni" Department of Surgery retrospectively and monitored their follow-up for 5 years. The sample consists of 368 patients with rectal neoplasm, 136 females and 232 males. Conclusions: The discussion remains open as to which approach is best at surgical level, whether laparoscopic, robotic or open-air. Our experience informs us that the most dangerous site remains on the anastomotic site.

This review explores the complex interrelationships between emotion, attention, and behavior, drawing upon empirical evidence from psychological, neuroscientific, and clinical studies. It delves into the neural mechanisms that underlie these interactions, with a focus on key brain regions such as the prefrontal cortex and amygdala. Clinical cases involving stroke and attention-deficit/hyperactivity disorder (ADHD) are examined to illustrate the real-world implications of these cognitive interactions. The review also discusses the impact of effective emotion regulation strategies and the role of attention as a cognitive mechanism that influences both emotion and behavior. Neuroimaging studies, particularly functional MRI, are highlighted for their role in providing a deeper understanding of these intricate relationships. The review concludes by emphasizing the importance of this triadic relationship in adaptive functioning and its broader implications for mental health, interpersonal relationships, and quality of life.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Oleuropein (OLE), a main constituent of olive, displays a pleiotropic beneficial dynamic in health and disease, based mainly on its antioxidant and hypolipidemic properties, and its capacity to protect myocardium during ischemia. Furthermore, OLE activates peroxisome proliferator-activated receptor (PPARα) in neurons and astrocytes, providing neuroprotection against noxious biological reactions that are induced following cerebral ischemia. Current study investigated the effect of OLE in the regulation of various neural plasticity indices, emphasizing the role of PPARα. For this purpose, 129/Sv wild-type (WT) and Pparα-null mice were treated with OLE for three weeks. The present findings revealed that chronic treatment with OLE up-regulated the brain-derived neurotrophic factor (BDNF) and its receptor TrkB in the prefrontal cortex (PFC) of mice via activation of ERK1/2, AKT and PKA/CREB signaling pathways. No similar effects were observed in the hippocampus. The OLE-induced effects on BDNF and TrkB appear to be mediated by PPARα, because no similar alterations were observed in the PFC of Pparα-null mice. Notably, OLE did not affect the neurotrophic factors NT3 and NT4/5 in both brain tissues. Fenofibrate though, a selective PPARα agonist, up-regulated BDNF and NT3 in the PFC of mice, whereas the drug induced NT4/5 in both brain sites tested. Interestingly, OLE provided neuroprotection in differentiated human SH-SY5Y cells against β-amyloid and H2O2 toxicity independently from PPARα activation. In conclusion, OLE and similar drugs acting either as PPARα agonists or via PPARα independent mechanisms could improve synaptic function/plasticity mainly in the PFC and to a lesser extent in the hippocampus, thus beneficially affecting cognitive functions.

Adult neural stem and progenitor cells (NSPCs) contribute to learning, memory, maintenance of homeostasis, energy metabolism and many other essential processes. They are highly heterogeneous populations that require input from a regionally distinct microenvironment including a mix of neurons, oligodendrocytes, astrocytes, ependymal cells, NG2+ glia, vasculature, cerebrospinal fluid (CSF), and others. The diversity of NSPCs is present in all three major parts of the CNS, i.e., the brain, spinal cord, and retina. Intrinsic and extrinsic signals, e.g., neurotrophic and growth factors, master transcription factors, and mechanical properties of the extracellular matrix (ECM), collectively regulate activities and characteristics of NSPCs: quiescence/survival, proliferation, migration, differentiation, and integration. This review discusses the heterogeneous NSPC populations in the normal physiology and highlights their potentials and roles in injured/diseased states for regenerative medicine.

The industry is increasingly relying on fish for toxicity assessment. However, current guidelines for toxicity assessment focus on teratogenicity and mortality. From an ecotoxicological point of view, however, these endpoints may not reflect the “full picture” of possible deleterious effects that can nonetheless result in decreased fitness and/or inability to adapt to a changing environment, affecting whole populations. Therefore, assessing sublethal effects add relevant data covering different aspects of toxicity at different levels of analysis. Impacts of toxicants on neurobehavioral function have the potential to affect many different life-history traits, and are easier to assess in the laboratory than in the wild. We propose that carefully-controlled laboratory experiments on different behavioral domains – including anxiety, aggression, and exploration – can increase our understanding of the ecotoxicological impacts of contaminants, since these domains are related to traits such as defense, sociality, and reproduction, directly impacting life-history traits. The effects of selected contaminants on these tests are reviewed, focusing on larval and adult zebrafish, showing that these behavioral domains are highly sensitive to small concentrations of these substances. These strategies suggest a way forward on ecotoxicological research using fish.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

Emergency departments (EDs) are critical in urgent care, where accurate triage optimizes patient flow and resource allocation. Manual triage faces challenges due to increasing volume and com-plexity. This study compares logistic regression, gradient boosting, neural network, and random forest models in predicting Emergency Severity Index (ESI) triage levels for non-traumatic pa-tients at Lampang Hospital's ED. Using data from January 1, 2023, to April 30, 2024, we ana-lyzed 45,245 complete records. The gradient boosting model achieved the highest accuracy (0.81), significantly outperforming logistic regression (accuracy 0.64). Pain scale, sex, and mean arterial pressure were key predictors. This is the first comparison of these models for ESI triage in a Thai hospital, highlighting their potential to improve triage accuracy and efficiency. Implementing these models could enhance patient outcomes and resource management in ED.

In this paper, we propose an efficient numerical method to solve the diffusive logistic model with a free boundary which is often used to simulate the spreading of the new or invasive species. The boundary movement is tracked by the level-set method, where the Hamilton-Jacobi weighted essentially nonoscillatory (HJ-WENO) scheme is utilized to capture the boundary curve embedded by the Cartesian grids via the embedded boundary method. Then the radial basis function-finite difference (RBF-FD) method is adopted to the spatial discretization and the IMEX scheme is considered for time integration. A variety of numerical examples are carried out to demonstrate the evolution of diffusive logistic model in different initial boundaries.

Generalized distributions have been studied a lot recently because of their flexibility and relia-bility in modeling lifetime data. The two-parameter exponentially-modified logistic distribution is a new flexible modified distribution that was introduced recently. It is regarded as a strong competitor for widely used classical symmetrical and non-symmetrical distributions such as normal, logistic, lognormal, and log-logistic. In this study, the unknown parameters of the dis-tribution are estimated using the maximum likelihood method. The Grey Wolf Optimization al-gorithm, which is a new meta-heuristic algorithm, is applied in order to solve the nonlinear like-lihood equations of the study model. The performance of the Grey Wolf Optimization method is compared to that of the other meta-heuristic algorithms used in this study, which include the Whale Optimization Algorithm, the Sine Cosine Algorithm, and the Particle Swarm Optimiza-tion Algorithm. The efficiencies of maximum likelihood estimates for all algorithms are com-pared via an extensive Monte-Carlo simulation study. The likelihood estimates for the location α and scale β parameters of the exponentially-modified logistic distribution developed with the Grey Wolf Optimization algorithm are the most efficient among others, according to simulation findings. Four real datasets are analyzed to show the flexibility of this distribution.

Aedes scapularis is a neotropical mosquito known to transmit pathogens of medical and veterinary importance. Its recent establishment in southeastern Florida has potential public health implications. We used an ecological niche modeling approach to predict the abiotic environmental suitability for Ae. scapularis across much of the Americas and Caribbean Islands. Georeferenced occurrence data obtained from the Global Biodiversity Inventory Facility and recent collection records of Ae. scapularis from southern Florida served as input for model calibration. Environmental layers included bioclimatic variables provided in 2000 to 2010 average Modern Era Retrospective-analysis for Research and Applications climatic (MERRAclim) data. Models were run in the software program Maxent. Isothermality values found often in costal environments contributed strongest to model performance. Model projections suggested areas predicted suitable for Ae. scapularis across portions of the Amazon Basin, the Yucatán Peninsula, the Florida Peninsula, and multiple Caribbean Islands. Additionally, model predictions suggested connectivity of highly suitable or relatively suitable environments spanning the United States Gulf Coast, which may facilitate geographic expansion of this species. At least sixteen Florida counties were predicted highly suitable for Ae. scapularis, suggesting vigilance is needed by vector control and public health agencies to recognize further spread of this vector.

We developed a honey bee RNA-virus vector based on the genome of a picorna-like Deformed wing virus (DWV), the main viral pathogen of the honey bee (Apis mellifera). To test the potential of DWV to be utilized as a vector, the 717 nt sequence coding for the enhanced green fluorescent protein (eGFP), flanked by the peptides targeted by viral protease, was inserted into an infectious cDNA clone of DWV in-frame between the leader protein and the virus structural protein VP2 genes. The in vitro RNA transcripts from egfp-tagged DWV cDNA clones were infectious when injected into honey bee pupae. Stable DWV particles containing genomic RNA of the recovered DWV with egfp inserts were produced, as evidenced by cesium chloride density gradient centrifugation. These particles were infectious to honey bee pupae when injected intra-abdominally. Fluorescent microscopy showed GFP expression in the infected cells and Western blot analysis demonstrated accumulation of free eGFP rather than its fusions with DWV LP and/or VP2 proteins. Analysis of the progeny egfp-tagged DWV showed gradual accumulation of genome deletions for egfp, providing estimates for the rate of loss of a non-essential gene an insect RNA virus genome during natural infection.

Minimal plasmids play an essential role in many intermediate steps in molecular biology. They can for example be used to assemble building blocks in synthetic biology or be used as intermediate cloning plasmids that are ideal for PCR-based mutagenesis methods. A small backbone also opens up for additional unique restriction enzyme cloning sites. Here we describe the generation of pICOz, a 1185 bp fully functional high-copy cloning plasmid with an extended multiple cloning site (MCS). To our knowledge, this is the smallest high-copy cloning vector ever described.

In this article, one-dimensional viscous quantum hydrodynamical model of semiconductor devices is numerically investigated. The model treats the propagation of electrons in a semiconductor device as the flow of a charged compressible fluid. It plays an important role in predicting the behavior of electron flow in semiconductor devices. The nonlinear viscous quantum hydrodynamic models contain Euler-type equations for density and current, viscous and quantum correction terms, and a Poisson equation for electrostatic potential. Due to high nonlinearity of model equations, numerical solution techniques are applied to obtain their solutions.. The proposed numerical scheme is a splitting scheme based on the kinetic flux-vector splitting (KFVS) method for the hyperbolic step, and a semi-implicit Runge-Kutta method for the relaxation step. The KFVS method is based on the direct splitting of macroscopic flux functions of the system on the cell interfaces. The second order accuracy of the scheme is achieved by using MUSCL-type initial reconstruction and Runge-Kutta time stepping method. Several case studies are considered. For validation, the results of current scheme are compared with those obtained from the splitting scheme based on the NT central scheme. The effects of various parameters such as device length, viscosities, different doping and voltage are analyzed. The accuracy, efficiency and simplicity of the proposed KFVS scheme validates its generic applicability to the given model equations.

Perceived peer support is a critical factor in the academic adjustment of higher vocational college students. While previous studies have explored this relationship, few studies have delved into the underlying mechanisms. This study surveyed 9782 students from 35 higher vocational colleges in 13 provinces and cities in China to explore the relationship and mechanisms between perceived peer support and academic adjustment. Drawing on Conservation of Resources theory and Social Support Resource theory, the findings revealed that perceived peer support not only directly predicted the academic adjustment of higher vocational college students, but also indirectly through the independent mediating effect of academic hope and professional identity, as well as the chain-mediating effect of academic hope to professional identity. Additionally, students from vocational high schools showed a more significant increase in academic hope due to perceived peer support compared to those from general high schools, highlighting the moderating effect of educational background. The study provided a new theoretical perspective for understanding the complex mechanisms of academic adjustment in students and offers valuable guidance for educational practice, emphasizing the importance of fostering perceived peer support, academic hope, and professional identity in higher vocational education.

The IDS serves as a security system that maintains constant surveillance over network traffic and host systems in order to identify any security breaches or potentially concerning activities. Recently. the rise in cyber-attacks has driven the necessity for the development of automated and intelligent network intrusion detection systems. These systems are designed to learn the typical patterns of network traffic, allowing them to identify any deviations from normal behaviour, which can be classified as anomalous or malicious. Machine learning methods are widely used to exhibit a satisfactory effectiveness in detecting malicious payloads in the network traffic. While the volume of the data generated from IDS is increasing exponentially results in the emergence of substantial security risks, it highlighted the imperative to strengthen network security. The performance of traditional machine learning methods depends on the dataset and the data balance distribution in it. while most of IDS datasets suffer from unbalancing, this limits the performance of the machine learning method used in the system and results in missed detections and false alarms in the conventional IDSs. To address this issue, this paper presents a new model-based Generative Adversarial Network (GAN) called TDCGAN to enhance the detection rate of less of minor class in the dataset while maintaining efficiency. The proposed model consists of one generator and three discriminators with an election layer at the end of architecture. The UGR’16 data set is used for evaluation purposes. In order to demonstrate the efficacy of our proposed model, various machine learning algorithms have been utilized for comparison. The experimental findings have determined that TDCGAN presents an efficient resolution for addressing imbalanced intrusion detection and surpasses the performance of other oversampling machine learning methods.

This paper outlines a tool developed with the purpose of creating a simple configurable emulated network environment that can be used in cyber defence exercises. Research has been conducted into the various related subject areas: cyber defence exercises, network threats, network emulation, network traffic replay, network topologies, and common network services. From this research a requirements specification was produced to encapsulate the features required to create this tool. A network, containing many of the aspects researched, was designed and implemented using Netkit-NG to act as a blueprint for the tool and to further knowledge in the construction of an emulated network. Following this the tool was developed and tested to ensure requirements were met.

Post-acute COVID-19 vaccination syndrome (PACVS) is distinguished from the normal post-vaccination state by altered receptor antibodies (Semmler et al. Vaccines 2023, 11, 1642). Here, we explore the symptoms registry and standard serum markers acquired in the above study to delineate the disease phenotype of PACVS. Symptoms reported by the participants overlapped with established complex dysautonomia syndromes (ME/CFS, POTS, MCAS, SFN). However, many participants were qualified for several (131/191) or none (31/191) of these syndromes. Unbiased clustering (modified Jaccard index) revealed a prevalent cross-cohort symptomatology of malaise and chronic fatigue (&gt; 80% of cases). Overlapping clusters of (i) peripheral nerve dysfunction, dysesthesia, motor weakness, pain, vasomotor dysfunction, (ii) cardiovascular impairment, and (iii) cognitive impairment, headache, optic, oculo-motoric and acoustic dysfunctions were also frequently represented. Conspicuous serum markers encompassed increases in interleukins 6 and 8 and low fT3 levels (&gt; 80%), IgG-subclass imbalances and impaired iron-storage (&gt; 50%), and increases in sNFL (&gt; 30%). Serum markers were not correlated to specific symptoms or symptom clusters. A single and unique cause (i. e. SARS-CoV-2 vaccination) is improbable to trigger several distinct etiologies. Therefore, the symptoms and serological features observed in our study cohort probably represent facets of a single complex syndrome.

In this paper, we suggest an expansion of the Planck distribution function derived from the Boltzmann principle. Thereby, we consider expanding Planck's law with a new distribution function, and discuss fine structure constant. Furthermore, using ideas applied to the expansion of the Planck distribution function, we show that Von Koch's inequality can be derived without using the Riemann Hypothesis, that is, the Riemann Hypothesis is true, and that the abc conjecture is not true. Furthermore, we define a generalization of Entropy and discuss that Entropy is relevant to dynamical systems described by logistic function models, such as the growth of bacteria and populations.

The revolutionary idea of complementary intelligence involves a unique strategy in which artificial intelligence (AI) works with human cognitive abilities embodied by natural intelligence (NI), creating a harmonious partnership instead of engaging in a competitive relationship. This innovative concept can potentially revolutionize how we approach scientific research and discovery. This cutting-edge process capitalizes on pooling the vast information from various distinct and autonomous sources to generate forecasts that frequently outshine human perspectives by significant margins, leading to groundbreaking advancements in scientific development based on hybrid intelligence. Multilayer networks extend traditional network theory to adjacent layers linked through copula nodes. Artificial intelligence, with its retrieval capacity to collect and interpret big data, sharply increases the number of nodes and their linking interpretation. This process, amplified by multilayer networks, dramatically improves the scalability of the networked ecosystem. Multilayer networks can represent NI and AI in complementary hyperplanes, connecting them with copula nodes and edge properties. Thus, it is possible to measure the interaction of NI and AI, assessing their scalable value co-creation properties. This study examines the interaction of NI and AI with an innovative and multidisciplinary approach. A theoretical framework of these related concepts precedes practical insights for possible applications in future research.

The synchronization of complex networks is believed to be closely related to network topology. How to study synchronization of real network without clear topology and parameters? In this Letter, a synchronized stability boundary equation is derived that is system- and disturbance-independent and applicable to arbitrarily coupled grids. The results imply that spontaneous synchronization in a network may be network independent. These conclusions provide new research paths for network synchronization and analyze the synchronization stability of grids in a unified way.