As a manifestation of technological innovation achievements, patents reflect the frontier of technological development in the field. This paper constructs a multi-entity patent collaboration network and analyzes its evolution patterns to provide a reference for innovation organizations to grasp the general direction of innovation development and select partners. Based on the data of collaboration patents of two and three organizations in China's new energy vehicles (NEV) industry from 2008 to 2021, this paper constructs higher-order interaction collaboration innovation networks in different periods to analyze the spatial evolution of networks and patent cooperation activities. The results show that the generalized degree distribution of nodes and edges conforms to the power-law distribution. The two-dimensional simplex networks gradually form triangle groups centered on the core nodes, and the importance of key edges is beginning to emerge. Key organizations and partners, including State Grid Corporation of China, Tsinghua University, and China Electric Power Research Institute, will largely determine the direction for the future development of cooperation innovation. The study also reveals that Beijing, Jiangsu, Zhejiang, Guangzhou, and Shanghai are in central positions in the cooperative innovation network, based on their cooperation breadth and depth measures. Additionally, key links formed by Beijing as hubs are not only the center of information exchange but also function as bridges that connect to other regions.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

Background: Leishmaniasis is a significant global public health issue that is caused by parasites from Leishmania genus. With limited treatment options and rising drug resistance, there is a pressing need for new therapeutic approaches. Molecular chaperones, particularly Hsp90, play a crucial role in parasite biology and are emerging as promising targets for drug development. Objective: This study evaluates the efficacy of 17-DMAG in treating BALB/c mice from cutane-ous leishmaniasis through in vitro and in vivo approaches. Materials and Methods: We assessed 17-DMAG cytotoxic effect on bone marrow-derived mac-rophages (BMMΦ) and its effects against L. braziliensis promastigotes and intracellular amastigotes. Additionally, we tested the compound's efficacy in BALB/c mice infected with L. braziliensis via intraperitoneal administration to evaluate the reduction of lesion size and de-crease of parasite load in the ears and lymph nodes of infected animals. Results: 17-DMAG showed selective toxicity [selective index=432) towards Leishmania amastigotes, causing minimal damage to host cells. The treatment significantly reduced lesion sizes in mice and results in parasite clearance from ears and lymph nodes. It also diminished in-flammatory responses and reduced the release of pro-inflammatory cytokines (IL-6, IFN-g, TNF) and the regulatory cytokine, IL-10, underscoring its dual leishmanicidal and anti-inflammatory properties. Conclusion: Our findings confirm the potential of 17-DMAG as a viable treatment for cutaneous leishmaniasis and support further research into its mechanisms and potential applications against other infectious diseases.

In a recent paper we have a shown that the flattening of galactic rotation curves can be explained by retardation. However, this will rely on a temporal change of galactic mass. In our previous work we have kept only second order terms of the retardation time in our analysis, while higher terms in the Taylor expansion where not considered. Here we consider analysis to all orders and show that indeed a second order analysis will suffice, and higher order terms can be neglected.

The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group opinion discussion among its peers. Then, the discussion’s result influences all participants’ opinions. Since an agent is also the peer of its peers, the agent actually participates in multiple group discussions. We assume the agent’s opinion update is the average over multiple group discussions. The opinion dynamics rules can be arbitrary in each discussion. In this work, we experiment with two discussion rules: centralized and decentralized. We show that the centralized rule is equivalent to the classic bounded confidence model. The decentralized rule, however, can promote opinion consensus. In need of modeling specific real-life scenarios, the higher-order bounded confidence is convenient to combine with other higher-order dynamics, from the contagion process to evolutionary dynamics.

In this paper we study the initial boundary value problem for logarithmic Higher Order Wave equation. Introducing the Logarithmic Sobolev inequality and using the combination of Galerkin method, we consider the theorem of existence of a global weak solution to problem for the initial boundary value problem of the logarithmic wave equation. By constructing an appropriate Lyapunov function, we obtain the decay estimates of energy for logarithmic Higher Order Wave equation. The proof of the main theorem is given.

We present a case of adult-onset systemic chronic active EBV disease (CAEBV) in a 40-year-old woman with chronic HBV hepatitis. Initial symptoms resembled a viral illness, progressing to recurrent fever, transaminitis, and anasarca. Investigations revealed high-level EBV viremia and an abnormal T-cell population in liver and bone marrow, indicative of CAEBV. Liver biopsy showed CD3+ T-cells lacking TCRbeta and displaying dim/negative CD5, with elevated EBV-infected T-cells. Next-generation sequencing identified rare variants in CREBBP, SPEN, TP73, and PLCG2, suggesting potential contributions to disease pathogenesis. This case underscores diagnostic challenges and management complexities of adult-onset CAEBV, particularly with underlying chronic HBV infection. Genomic profiling offers crucial insights into the molecular landscape of rare lymphoid malignancies, highlighting the importance of personalized treatment strategies. The distinct immunophenotypic features underscore the heterogeneity in EBV-associated T-cell LPDs, urging further research for optimized clinical management

In this paper, we derive multifarious relationships among the two types of higher order q-Daehee polynomials and p-adic gamma function via Mahler theorem. Also, we compute some weighted p-adic q-integrals of the derivative of p-adic gamma function related to the Stirling numbers of the both kinds and the q-Bernoulli polynomials of order k.

Based on the demographic shift and the related challenges resulting from the growing number of elderly and persons with chronic diseases, the idea of smart home that supports its inhabitants in the daily life, gains importance. The purpose of this paper was to examine in a prototypic Ambient Assisted Living environment if users after interaction with different health-supporting applications intend to use such in the future. Two experimental studies exemplary show possible applications of home-integrated technology that can support, assist and accompany the target group in different contexts, and examine to what extent participants are willing to future use such sophisticated technology at home. The results show that people in general, but especially the old and chronically ill ones are quite fascinated of health-supporting ambient technology and the majority intends to use such ambient assistance in the future (study I). Moreover, serious games for healthcare are shown as a hedonic use of technology in smart homes that have a great potential to retain or improve the physical health, mobility and the overall well-being of the inhabitants (study II). The article provides two examples of ambient technology to leverage the demographic change and presents important user factors for facilitating high user acceptance.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman & Williams, 2015; Gebhardt & Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

Tuberculosis (TB) remains a global health threat, necessitating innovative strategies for control and prevention. This comprehensive review explores the Mycobacterium tuberculosis Lysine Exporter (LysE) gene, unveiling its multifaceted roles and potential uses in controlling and preventing tuberculosis (TB). As a pivotal player in eliminating excess L-lysine and L-arginine, LysE contributes to the survival and virulence of M. tuberculosis. This review synthesizes findings from different electronic databases and includes 13 studies focused on the LysE of M. tuberculosis. The research unveils that LysE can be a potential drug target, a diagnostic marker for TB, and a promising candidate for vaccine development. The absence of LysE in the widely used BCG vaccine underscores its uniqueness and positions it as a novel area for TB prevention. In conclusion, this review underscores the significance of LysE in TB pathogenesis and its potential as a drug target, diagnostic marker, and vaccine candidate. The multifaceted nature of LysE positions it at the forefront of innovative approaches to combat TB, calling for sustained research efforts to harness its full potential in the global fight against this infectious disease.

Plato defined a quality as “some degree of perfection” [49]. Studies on quality have unveiled the internal and external quality factors [17] that significantly impact product quality [4]. Internal factors are invisible to the end customer and are dependent on overall work organisation by the development team [17]. This work organisation is called “the process” [11],[33]. In this article, we will discuss how to address the drone-dedicated software development process to support system verification and ensure product quality by supporting quality characteristics and therefore increase software development awareness and control. We will discuss the process's influence on overall work activities and relationships between those activities by addressing the software development process utilised in the drone-dedicated GNSS-denied navigation system development project. The result was observable after three weeks of starting the software development. It was focused on better team awareness, faster task completion, lower number of development mistakes, and better and faster change response than we observed in other projects that didn’t follow the predefined process.

In the former mining district of La Carolina (southern Spain), waste materials were accumulated without first waterproofing the substrate. Additionally, these mining dams were abandoned without any type of restoration, conditions that present a significant risk of contaminating the surrounding soils and waters. Prior to carry out the necessary works for isolating these residues, morphological characterization of the contact with the base substrate is needed. For that purpose, in recent years, different indirect study techniques have been used, among which electrical to-mography stands out. In this work, a less common geophysical technique, magnetometry, both in its magnetic and gra-diometric modalities, is used as an alternative method to analyse the geometry and structure of a mining dam. The results obtained with this methodology are compared with the results provided by electrical techniques, as well as with direct data from mechanical surveys and aerial photo-graphs prior to waste deposition. The magnetic anomaly curves obtained with geometric models generated with the Mag2dc program were also analysed. This comparative study shows the use-fulness of magnetic prospecting in characterizing the base geometry of these mining structures and therefore the interest in its application to environmental characterization studies.

The clinical features of increasing muscle weakening are shared by a diverse set of illnesses known as muscular dystrophies. In early childhood, males with Duchenne muscular dystrophy (DMD) exhibit proximal muscle weakening and calf enlargement, making it the most frequent X-linked disease of muscular dystrophy in children. Development of motor skills is typically delayed, and wheelchair confinement eventually leads to early death from heart or lung problems. In terms of function, ambulation, quality of life, and life expectancy, treatment approaches like corticosteroid therapy and intermittent positive pressure breathing have improved. This is a case report of a 6-year-old kid who had early ambulatory stage Duchenne Muscular Dystrophy and complained to the neuro-physiotherapy department about frequent falls, walking difficulties, and dyspnea when walking. Other grievances included being unable to get off the ground and having trouble ascending stairs. He had physiotherapy and dynamic neuromuscular stabilization for his complaints. The main goals of treatment were educating the family, respiratory training, preventing ankle plantar flexors, maintaining strength, balance and gait training.

This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 10^5. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.

This paper aims to investigate the global asymptotic stability of following system of higher order difference equations with quadratic terms: x_{n+1}=A+B((y_{n})/(y_{n-m}²)),y_{n+1}=A+B((x_{n})/(x_{n-m}²)) where A and B are positive numbers and the initial values are positive numbers. We also study the rate of convergence and oscillation behaviour of the solutions of related system.

In this paper, we investigate the dynamics of following higher order difference equation x_{n+1}=A+B((x_{n})/(x_{n-m}²)) with A,B and initial conditions are positive numbers, and m∈{2,3,⋯}. Especially we study the boundedness, periodicity, semi-cycles, global asymptotically stability and rate of convergence of solutions of related higher order difference equations.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Higher-order topological insulators (HOTIs) are recently discovered topological phases, possessing symmetry-protected corner states with fractional charges. An unexpected connection between these states and the seemingly unrelated phenomenon of bound states in the continuum (BICs) was recently unveiled. When nonlinearity is added to a HOTI system, a number of fundamentally important questions arise. For example, how does nonlinearity couple higher-order topological BICs with the rest of the system, including continuum states? In fact, thus far BICs in nonlinear HOTIs have remained unexplored. Here, we unveil the interplay of nonlinearity, higher-order topology, and BICs in a photonic platform. We observe topological corner states which are also BICs in a laser-written second-order topological lattice, and further demonstrate their nonlinear coupling with edge (but not bulk) modes under the proper action of both self-focusing and defocusing nonlinearities. Theoretically, we calculate the eigenvalue spectrum and analog of the Zak phase in the nonlinear regime, illustrating that a topological BIC can be actively tuned by nonlinearity in such an HOTI. Our studies are applicable to other nonlinear HOTI systems, with promising applications in emerging topology-driven devices.

In this paper we investigate the global asymptotic stability of following system ofhigher order difference equations with quadratic terms:xn+1=A+Byn/yn−m^2, yn+1=A+Bxn/xn−m^2, where A and B are positive numbers and the initial values are positive numbers.We also study the boundedness, rate of convergence and oscillation behaviour of thesolutions of related system.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Biopolymer gels have gained tremendous potential for therapeutic applications due to their biocompatibility, biodegradability, and ability to adsorb and bind biological fluids, making them attractive for drug delivery and therapy. The versatility of biopolymer gels in theranostic backgrounds, with a focus on integrating imaging features and facilitating real-time monitoring of drug delivery. Different methods of delivery are explored imaging agents into biopolymer gels, including encapsulation, surface functionalization, nanoparticle encapsulation and layer-by-layer assembly techniques. Integration of Agents and Real-Time Monitoring Drug Delivery We summarize the synthesis methods, general properties, and mechanisms of biopolymer gels functionality, demonstrating their broad applications as multimodal systems for imaging-based therapeutics. These techniques not only enable multiple imaging but also provide signal enhancement and imaging a targeted facilitates, increasing the diagnostic accuracy and therapeutic efficacy. In addition, current techniques for incorporating imaging agents into biopolymer gels are discussed, and their role in precise drug delivery and monitoring.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

Parasomnias are a group of sleep disorders characterized by abnormal and unpleasant motor, verbal or behavioral events that occur during sleep or during transitions between wake and sleep states. They disrupt sleep and can have a detrimental impact on the individual experiencing them. Our goal was to identify types of parasomnias and their prevalence in the post-secondary student population and to explore students' coping strategies for parasomnias they found distressing. Seventy-seven post-secondary students completed the 21-item Munich Parasomnia Screening (MUPS) frequency scale. They also rated, on a 10-point scale, how disturbing each parasomnia was. Not only did 92% percent of students report at least one parasomnia, but our results also indicate that the vast majority of students experienced more than one parasomnia. This led us to investigate the likelihood of the co-occurrence of different parasomnias. With respect to the level of subjectively experienced distress, the most prevalent parasomnias were not always the more disturbing ones. Coded open-ended responses about what students do about the disturbing parasomnias indicate that grounding strategies and physical manipulation of one’s body were the most common, although most participants indicated that in spite of distress, they do nothing to cope. In conclusion, our study found a strikingly high prevalence of parasomnias in this sample of young adults and a lack of knowledge about effective means of dealing with these.

Background. Gene rearrangements affecting KMT2A are frequent in acute myeloid leukemia (AML) and are often associated with a poor prognosis. KMT2A gene fusions are often detected by chromosome banding analysis and confirmed by fluorescence in-situ hybridization. However, small intragenic insertions, termed KMT2A partial tandem duplication (PTD), are particularly challenging to detect using standard molecular and cytogenetic approaches. Methods. We have validated the use of a custom hybrid capture based next-generation sequencing (NGS) panel for comprehensive profiling of AML patients seen at our institution. This NGS panel targets the entire consensus coding DNA sequence of KMT2A; to deduce the presence of a PTD we used the relative ratio of KMT2A exons coverage. We sought to corroborate the KMT2A-PTD NGS results using (1) multiplex-ligation probe amplification (MLPA) and (2) optical genome mapping (OGM). Results. We analyzed 932 AML cases and identified 41 individuals harboring a KMT2A-PTD. MLPA, NGS and OGM confirmed the presence of a KMT2A-PTD in 22 of the cases analyzed where orthogonal testing was possible. Two false positive PTD calls by NGS could be explained by the presence of cryptic structural variants impacting KMT2A and interfering with PTD analysis. OGM revealed the nature of these previously undetected gene rearrangements in KMT2A, while MLPA yielded in-conclusive results. MLPA analysis for KMT2A-PTD is limited to exon 4, whereas NGS and OGM resolved KMT2A-PTD sizes and copy number levels. Conclusion. KMT2A-PTDs are complex gene rearrangements that cannot be fully ascertained using a single genomic platform. MLPA, NGS panels and OGM are complementary technologies applied in standard of care testing for AML patients. MLPA and NGS panels are designed for targeted copy number analysis; however, our results show that integration of concurrent genomic alterations is needed for accurate KMT2A-PTD identification. Unbalanced chromosomal rearrangements overlapping with KMT2A can interfere with the diagnostic sensitivity and specificity of copy number based KMT2A-PTD detection methodologies.

In the paper, by virtue of the Fa`a di Bruno formula, some properties of the Bell polynomials of the second kind, and the inversion formulas of binomial numbers and the Stirling numbers of the first and second kinds, the authors simplify meaningfully and significantly coefficients in two families of ordinary differential equations associated with higher order Frobenius–Euler numbers.

Aim: To investigate the application of anterior and posterior corneal higher order aberrations (HOAs) in detecting keratoconus (KC) and suspect keratoconus (SKC). Method: This is a retrospective, case-control study which evaluated non-ectatic (normal) eyes, SKC eyes, and KC eyes. The Sirius Scheimpfug (CSO, Italy) analyzer was used to measure HOAs of the anterior and posterior corneal surfaces. Sensitivity, specificity and area under receiver operating characteristic curve (AUC) were calculated. Results: Two-hundred and twenty eyes were included in the analysis (normal n = 108, SKC n= 42, KC n= 70). Receiver operating characteristic (ROC) curve analysis revealed a high predictive ability for anterior corneal HOAs parameters: Root mean square (RMS) total corneal HOAs, RMS trefoil and RMS coma to detect keratoconus (AUC > 0.9 for all). RMS Coma (3, ±1) derived from the anterior corneal surface was the parameter with the highest ability to discriminate between suspect keratoconus and normal eyes (AUC = 0.922; cutoff > 0.2). All posterior corneal HOAs parameters were insufficient in discriminating between SKC and normal eyes (AUC < 0.8 for all). In contrast, their ability to detect KC was excellent with AUC of > 0.9 for all except RMS spherical aberrations (AUC = 0.846). Conclusion: Anterior and posterior corneal higher order aberrations can differentiate between keratoconus and normal eyes, with a high level of certainty. In suspect keratoconus disease however, only anterior corneal HOAs, and in particular coma-like aberrations, are of value. Corneal aberrometry may be of value in screening for keratoconus in populations with a high prevalence of the disease.

The Work Ability Index (WAI) is the most widely used questionnaire for the self-assessment of working ability. Because of its different applications, shorter versions, and widespread use in healthcare activities, it is worthwhile assessing its characteristics. The WAI was distributed online among the workers of a healthcare company; the results were compared with data contained in the workers' personal health records and with absence registers. 340 out of 575 workers (59.1%) participated; 6.5% of them reported poor work ability. Exploratory factor analysis indicated that the one-factor version best described the characteristics of the WAI. The scores of the complete WAI, the shorter form without the list of diseases, and the minimal one-item version (WAS) had equal distribution and were significantly correlated. The WAI score was inversely related to age and significantly lower in women than in men, but it was higher in night workers than in colleagues due to the probable effect of selective factors. The WAI score was also correlated with absenteeism, but no differences were found between males and females in the average number of absences, suggesting that cultural or emotional factors influence the self-rating of the WAI. Workers tended to overreport illnesses in the online survey compared to data collected during occupational health checks. Musculoskeletal disorders were the most frequently reported illnesses (53%). Psychiatric illnesses affected 21% of workers and had the greatest impact on work ability. Multilevel ergonomic and human factor intervention seems to be needed to recover the working capacity of healthcare workers.

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

The European Green Deal poses a two-pronged challenge for the automotive industry: migrating to solutions based in light structures, requiring light weight concepts and light materials, while at the same time avoiding dependence towards the importation of these advanced materials. Aluminium alloys are lightweight and cost-effective materials that can successfully cover the requirements of many structural applications, however their production requires from bauxite and other Critical Raw Materials (CRM), such as Si and Mg. Aluminium alloys are fully recyclable, but scrap is usually contaminated and its use is related to an increment of impurities, tramp element and undesired inclusions. Traditionally, the use of secondary alloys has been restricted to low performance applications. The present work analyses the effect that the use of scrap has in the quantity of inclusions present in the alloy and in other properties relevant for the material processing. The study was carried out for common alloys belonging to 3 of the most common aluminium processes used in the car manufacturing industry: High Pressure Die Casting (HPDC) (EN AC-43500), extrusion (6063) and sheet metal forming (5754 and 6181). Reference alloy were mixed with different level of scrap (0, 20, 40, 60, 80 and 100%) trying to keep the chemical composition as unaffected as possible. The inclusions level of the alloy was characterized with the Prefil Foot-printer® test. In addition, the obtained materials, after being cast in an open mould, were subjected to metallographic characterization. Relevant properties to assess the formability quality of the alloys for the corresponding transforming process, were obtained: flowability test for HPDC alloy and high temperature compression for extrusion alloys. The results obtained suggest that the number of inclusions present in the melt highly increase with the amount of scrap used to produce the alloy. These inclusions are also related with an important lost of flowability, but do not have a noticeable impact on microstructure.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

In the paper, by virtue of the Faà di Bruno formula, some properties of the Bell polynomials of the second kind, and an inversion formula for the Stirling numbers of the first and second kinds, the authors establish meaningfully and significantly two identities which simplify coefficients in a family of ordinary differential equations associated with higher order Bernoulli numbers of the second kind.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

The conserved cyanobacterial protein PipX is part of a complex interaction network with regulators involved in essential processes that include metabolic homeostasis or ribosome assembly. Because PipX interactions depend on the relative levels of their different partners and of the effector molecules binding to them, in vivo studies are required to understand the physiological significance and contribution of environmental factors to regulation of PipX complexes. Here we have used the NanoBiT complementation system to analyse regulation of complex formation in Synechococcus elongatus between PipX and each of its two best characterized partners, PII and NtcA. Our results confirm previous in vitro analysis on regulation of PipX-PII and PipX-NtcA complexes by 2-oxoglutarate and on regulation of PipX-PII by the ATP/ADP ratio, showing the disruption of PipX-NtcA complexes by increased levels of ADP-bound PII in Synechococcus elongatus. The demonstration of a positive role of PII on PipX-NtcA complexes under the initial response to nitrogen starvation or the impact of a PipX point mutation on the activity of PipX-PII and PipX-NtcA reporters are further indications of the sensitivity of the system. This study reveals additional regulatory complexities in the PipX interaction network, opening the way to future research in cyanobacteria.

(1) Background: Multiple alterations of cellular metabolism have been documented in experimental studies of autosomal dominant polycystic kidney disease (ADPKD) and are thought to contribute to its pathogenesis. (2) Methods: To elucidate the molecular pathways and transcriptional regulators associated with the metabolic changes of renal cysts in ADPKD, we compared global gene expression data from human PKD1 renal cysts, minimally cystic tissues (MCT) from the same patients, and healthy human kidney cortical tissue samples. (3) Results: We found PKD1 renal cysts displayed the Warburg effect with gene pathway changes favoring increased cellular glucose uptake and lactate production, instead of pyruvate oxidation. Additionally, mitochondrial energy metabolism was globally depressed, associated with downregulation of gene pathways related to fatty acid oxidation (FAO), branched-chain amino acid (BCAA) degradation, the Krebs cycle, and oxidative phosphorylation (OXPHOS) in renal cysts. Activation of mTORC1 and its two target proto-oncogenes, HIF-1α and MYC, was predicted to drive the expression of multiple genes involved in the observed metabolic reprogramming (e.g., GLUT3, HK1/HK2, ALDOA, ENO2, PKM, LDHA/LDHB, MCT4, PDHA1, PDK1/3, MPC1/2, CPT2, BCAT1, NAMPT); indeed, their predicted expression patterns were confirmed by our data. Conversely, we found AMPK inhibition was predicted in renal cysts. AMPK inhibition was associated with decreased expression of PGC-1α, a transcriptional coactivator for transcription factors PPARα, ERRα, and ERRγ, all of which play a critical role in regulating oxidative metabolism and mitochondrial biogenesis. (4) Conclusions: These data provide a comprehensive map of metabolic pathway reprogramming in ADPKD and highlight nodes of regulation that may serve as targets for therapeutic intervention.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

Recent studies supported a clinical association between Parkinson’s Disease (PD) and periodontitis. Hence, investigating possible protein interactions between these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins for PD and periodontitis. Genes of interest were collected via GWAS database. Then, we conducted a protein interaction analysis using STRING database, with a highest confidence cut-off of 0.9. Our protein network casted a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted giving the limitations of this approach.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

Coronavirus disease 2019 (COVID-19) is a high contagious respiratory infectious disease that has afiected millions of people worldwide. Initially, basic public health measures were implemented to control specially the spread of such virus. However, vaccination has emerged as a highly efiective strategy in combating COVID-19 and reducing its transmission. Several efiective and safe vaccines have been approved to prevent the inadvertent spread of the disease. In this study, a modeling approach is used to analyze the impact of these vaccines on the dynamics of COVID-19. By applying a higher-order numerical method, the model is solved based on reported cases in Pakistan. A spectral method is employed for the numerical solution, and the model is simulated to assess the efiects of vaccination. The Next-generation method is used to calculate the threshold quantity, known as R0, which indicates the potential for disease transmission. The study also includes a detailed stability analysis, examining the invariance properties of the model solution and discussing equilibrium points and their stability in disease-free and endemic states. Furthermore, the study presents graphical representations of the influence of special parameters on the dynamics of the pandemic.

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

This study aimed to evaluate the effects of Blazepod technology when incorporated with various training regimens on the essential elements of physical fitness in volleyball, such as agility, speed, visual response time, and vertical leap. Thirty-six participants (age :17.33±0.48 y, height: 183.78±6.34 cm, weight: 77.14±5.16 kg) were in-volved in this study. In randomized design, three equal groups with two experimental groups and one control group were involved in the study over a period of six weeks: (GE1) used Blazepod technology to workout at a low level; (GE2) used Blazepod technology to workout at a higher intensity; (CG) did not employ Blazepod technology, but it adhered to the same fundamental training curriculum. We conducted agility, speed, reaction time, and vertical jump tests before, midway and after the study to determine optimal training volume, intensity, and frequency. A significant main effect of time (P0.01), and time×group interaction (P>0.001) were found for all tests. Post hoc test showed that the two experimental groups gadget significantly improved in all performance variables (P

In the ever-evolving landscape of higher education, the shift to hybrid teaching approaches has introduced novel challenges and opportunities for both educators and students. This qualitative study, grounded in a robust theoretical framework, combines in-depth literature reviews, informal interviews, observations, and thematic analysis to explore the multifaceted dynamics of hybrid language learning within a College of Arts and Sciences. By investigating the experiences of twenty students (referred to as 'S-Part 1' to 'S-Part 20'), the research delves into the core theme of 'Teacher-Student Interaction in Higher Education Institutions' and unpacks nine additional themes: 'Adaptability and Resilience,' 'Flexibility and Autonomy,' 'Connectivity Challenges,' 'Feelings of Isolation,' 'Technology Competence,' 'Personalized Learning,' 'Resource Accessibility,' 'Support Mechanisms,' and 'Positive Learning Experiences.' In this study, these themes encapsulate the diverse experiences and challenges encountered by teachers and college students as they navigate the evolving educational landscape. The insights derived from the study are underpinned by a robust theoretical framework and complemented by existing scholarly literature. As we delve into these themes, it becomes evident that they shed light on not only the intricacies of hybrid language learning but also the broader educational context, offering valuable guidance for educators, institutions, students, and researchers. Based on the ample results, recommendations are presented for school administrators, teachers, students, and future researchers to enhance the quality of hybrid teaching and learning, aligning it with the demands of the post-academic era. Overall, this study contributes to the ongoing dialogue on innovative pedagogical strategies and responsive educational environments, underlining the significance of teacher-student interaction in the new normal of higher education.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

Data-Flow and Higher-Order Mutation are white-box testing techniques. To our knowledge, no work has been proposed to compare data flow and higher-order mutation. This paper compares all def-uses data-flow and second-order mutation criteria. This compassion investigates the subsumption relation between these two criteria and evaluates the effectiveness of test data developed for each. To compare the two criteria, a set of test data satisfying each criterion is generated, which is used to explore whether one criterion subsumes the other criterion and assess the effectiveness of the test set that was developed for one methodology in terms of the other. The results showed that the mean mutation coverage ratio of the all du-pairs adequate test cover is 80.9%, and the mean data flow coverage ratio of the 2nd-order mutant adequate test cover is 98.7%. Consequently, 2nd-order mutation “ProbSubsumes” the all du-pairs data flow. The failure detection efficiency of the mutation (98%) is significantly better than the failure detection efficiency of data flow (86%). Consequently, 2nd-order mutation testing is “ProbBetter” than all du-pairs data flow testing. In contrast, the size of the test suite of 2nd-order mutation is more significant than the size of the test suite of all du-pairs.

African scientific research faces formidable challenges, particularly with limited access to state-of-the-art measurement instruments. The high cost associated with these devices presents a significant barrier for regional research laboratories, impeding their ability to conduct sophisticated experiments and gather precise data. This predicament not only hampers the individual laboratories but also has broader implications for the African scientific community and the advancement of knowledge in developing nations—the financial cost barrier considerably impacts the research quality of these laboratories. Reflection on technical and economical solutions needs to be quickly found to help these countries advance their research. In civil Engineering, the thermal conductivity property is the most important measurement for characterising heat transfer in construction materials. Existing devices (i.e., conductometers) in a laboratory are expensive (approximately 30,000 euros) and unavailable for some African laboratories. The study proposes a new and affordable device to evaluate thermal conductivity in construction materials. The method involves establishing a thermal flux between a heat source ( from the Joule effect provided by steel wool where a current is circulating) and a cold source (generated by ice cube) under steady-state conditions. The development of the cylindrical prototype is based on the comparative flux-meter method outlined in the measuring protocol of the ASTM E1225 standard document. Experiments were conducted on four distinct materials (polystyrene, wood, agglomerated wood, and rigid foam). The results indicate a correct correlation between the experimental values obtained from the newly developed prototype and the reference values found in the literature. For example, concerning the experimental polystyrene study, the detailed case analysis reveals a good correlation, with a deviation of only 4%. The percent error found falls within the acceptable range indicated by the standards recommendations of the ASTM E1225 standard, i.e., within 5% acceptable error.

With the rapid growth of large language models (LLMs), there is increasing demand for memory and computation in LLMs. Recent efforts on post-training pruning of LLMs aim to reduce the model size and computation requirements, yet the performance is still sub-optimal. In this paper, we present a plug-and-play solution for post-training pruning of LLMs. The proposed solution has two innovative components: 1) Relative Importance and Activations (RIA), a new pruning metric that jointly considers the weight and activations efficiently on LLMs; and 2) Channel Permutation, a new approach to maximally preserve important weights under N:M sparsity. The two proposed components can be readily combined to further enhance the N:M semi-structured pruning of LLMs. Our empirical experiments show that RIA alone can already surpass all existing post-training pruning methods on prevalent LLMs, e.g., LLaMA ranging from 7B to 65B. Furthermore, N:M semi-structured pruning with channel permutation can even outperform the original LLaMA2-70B on zero-shot tasks, together with practical speed-up on specific hardware. Our code is available at: https://github.com/biomedical-cybernetics/Relative-importance-and-activation-pruning

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

Nonlinear electrodynamics is known as the generalizations of Maxwell electrodynamics at strong fields and presents interesting features such as curing the classical divergences present in the linear theory when coupled to general relativity. In this paper, we consider the asymptotically flat Reissner-Nordstr\"om black hole solution with higher-order magnetic correction in Einstein-nonlinear-Maxwell fields. We study the effect of the magnetic charge parameters on the black hole, viz. weak deflection angle of photons and massive particles using Gauss-bonnet theorem. Moreover, we apply the Keeton-Petters formalism to confirm our results of the weak deflection angle. Apart from vacuum, their influence in the presence of different media such as plasma and dark matter are probed as well. Finally, We examine the black hole shadow cast using the null-geodesics method and investigate its spherically in-falling thin accretion disk. Our inferences show how the magnetic charge parameter $p$ affects the other physical quantities; so, we impose some constraints on this parameter using the observations from the Event Horizon Telescope.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Adult neurogenesis involves generation of functional neurons from neural progenitor cells, which have the potential to complement and restore damaged neurons and neural circuits. Therefore, the development of drugs that stimulate neurogenesis represents a promising strategy in stem cell therapy and neural regeneration, greatly facilitating the reconstruction of neural circuits in cases of neurodegeneration and brain injury. Our study reveals that compound A5, previously designed and synthesized by our team, exhibits remarkable neuritogenic activities, effectively inducing neurogenesis in neural stem/progenitor cells (NSPCs). Subsequently, transcriptome analysis using high-throughput Illumina RNA-seq technology was performed to further elucidate the underlying molecular mechanisms by which compound A5 promotes neurogenesis. Notably, comparative transcriptome analysis showed that the up-regulated genes were mainly associated with neurogenesis and the down-regulated genes were mainly concerned with cell cycle progression. Furthermore, we confirmed that compound A5 significantly affected the expression of transcription factors related to neurogenesis and cell cycle regulatory proteins. Collectively, these findings identify a new compound with neurogenic activity and may provide insights into drug discovery for neural repair and regeneration.

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh-Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as 2- and 3-body interactions in three homogenous and two non-homogenous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

A field experiment was conducted for determination of crop coefficient (KC) and water stress coefficient (Ks) for wheat crop under different salinity levels, during 2015-16. Complete randomized block design of five treatments were considered, i.e., 0.51 dS/m (fresh water) as a control treatment and other four saline water treatments (4, 6, 8 and 10 dS/m), for S1, S2, S3 and S4 with three replications. The results revealed that the water consumed by plants during the different crop growth stages follows the order of FW>S1>S2>S3>S4 salinity levels. According to the obtained results, the calculated values of crop coefficients significantly differed from those suggested by FAO No.56 for the crops. The Ks values clearly differ from one stage to another because the salt stress causes both osmotic stress, due to a decrease in the soil water potential, and ionic stress which the average values of water stress coefficient (Ks) follows this order; FW(1.0)=S1(1.0)>S2(1.0)>S3(0.93)>S4(0.82). Overall, it was found the differences are attributed primarily to specific cultivar, the changes in local climatic conditions and seasonal differences in crop growth patterns. Thus, further studies are essential to determine the crop coefficient values under different variables, to make the best management practice (BMP) in agriculture.

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Embracing innovative digital technologies within educational systems is essential for overcoming challenges in modern learning environments, especially in the post-COVID-19 era. Teaching sustainability management in higher education institutions with innovative digital tools plays a fundamental role in the transition toward sustainable societies. Suitable game design elements play a significant role in facilitating sustainable learning. This study explored the effectiveness of incorporating business simulation games with project-based learning (PBL) in a flipped classroom setting. This approach was adopted within the context of a university cross-border e-commerce course to prepare students for acquiring 21st -century skills as higher-order thinking skills in a rapidly changing educational landscape. A quasi-experimental method was employed, involving a total of 60 university students from China's Zhejiang Province. Participants completed an online questionnaire designed to assess their learning engagement across three dimensions (cognitive, emotional, and behavioral) as well as their higher-order thinking skills (problem-solving, critical thinking, and creativity). The results show that the business simulation games combined with flipped classroom learning had a significantly positive impact on students’ learning outcomes, enhancing their problem-solving, critical thinking, and creative capabilities. Importantly, this approach also improved student engagement and promoted sustainable practices by applying real-life scenarios in an interactive environment. We conclude that business simulation games integrated with project-based learning (PBL) in flipped classroom settings represent a valuable educational approach. This approach not only enhances learning engagement but also fosters the development of higher-order thinking skills, encouraging students to adopt sustainable learning practices.

.Most of the accident cases at work are caused by unsafe behaviour. Scholars have thus extensively researched factors of safe behavior.Safety leadership is stated as the most effective factor in safety behaviour. Besides, safety knowledge and safety attitudes are also found to influence safety behaviour. The purpose of this paper is to investigate the nature of the relationship between safety leadership, safety knowledge, safety attitude, and safety behavior, which has been found to be limited or rare in previous research.A self-administered questionnaire was applied and was distributed randomly to 140 SME manufacturing workers and they were analysed using the structural equation modelling partial least squares (PLS-SEM). The results prove that safety leadership has a significant effect on safety knowledge, safety attitude, and safety behaviour, with safety knowledge and safety attitude mediating the safety leadership-safety behaviour relationship. Consequently, the results provide a more in-depth view of the relationship between safety leadership and safety knowledge-attitude-behaviour via adapting social-cognitive theory and social learning theory in solving safety issues in SME. This study also provides a starting point for researchers to better understand how effective safety leadership is provided by supervisors in terms of improving worker safety and safety knowledge-attitude-behaviour.

The literature offers limited information on the effect of obesity on the rate of force development, a critical parameter for mobility in older adults. The objectives of this study were; to explore the influence of obesity on RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. 42 older adults were classified into two groups: the control group (CG, n=22; age=81.13±4.02 years; Body mass index (BMI)=25.13±3.35 kg/m²), and the obese group (OG, n=20; age=77.71±2.95 years; BMI=34.46±3.25 kg/m²). Walking speed (m/s) was measured using the 10-meter walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force – time curve in two phases: from the onset of the contraction to 50 ms (RFD50-100) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

Abstract: Patients with Cleft Lip Palate (CLP) require comprehensive treatment, so the people who take care of them must constantly provide care and support, along with continuous care and support from their caregivers, often extending until youth. The purpose was to compare the Quality of Life (QoL) of caregivers of children with CLP with those without the condition. A cross-sectional paired study was conducted at the Dental Clinic of the Escuela Nacional de Estudios Superiores (ENES León), National Autonomous University of Mexico (UNAM), from May to December 2021. Primary caregivers of patients with CLP (n=70) were included as well as those of the patients without CLP (n=70) of the same institution. Sample size was calculated considering an OR=2.5, establishing a convenience selection. Caregivers answered the WHOQoL Bref instrument to assess QoL. Of the total sample analyzed, 88.6% of the caregivers were female (p>0.05). Caregivers of patients with CLP reported poor QoL (64.8%), compared to caregivers in the control group (p

The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, forecasting potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI networks, first in a sensitivity purpose (confidence cut-off of 0.7), and then with a highest confidence cut-off (0.9). Genes over-representation was inspected in the final network. As a result, we foresee a prospective protein network of interaction between stroke and periodontitis. Inflammation, pro-coagulant/pro-thrombotic state and ultimately atheroma plaque rupture is the main biological mechanism derived from the network. These pilot results may pave the way to future molecular and therapeutic studies to further comprehend the mechanisms between these two conditions.

Glutathione peroxidases (GPxs) form a broad family of antioxidant proteins essential for maintaining redox homeostasis in eukaryotic cells. In this study, we used an integrative approach that combines bioinformatics, molecular biology, and biochemistry to investigate the role of GPxs in reactive oxygen species detoxification in the unicellular eukaryotic model organism Tetrahymena thermophila. Both phylogenetic and mechanistic empirical model analyses provided indications about the evolutionary relationships among the GPXs of Tetrahymena and the orthologous enzymes of phylogenetically related species. In-silico gene characterization and text mining were used to predict the functional relationships between GPxs and other physiologically-relevant processes. The GPx genes contain conserved transcriptional regulatory elements in the promoter region, which suggest that transcription is under tight control of specialized signaling pathways. The bioinformatic findings were next experimentally validated by studying the time course of copper (Cu)-dependent regulation of gene transcription and enzymatic activity. Results emphasize the role of GPxs in the detoxification pathways that, by complex regulation of Cu-dependent GPx gene expression, enables Tetrahymena to survive in high Cu concentrations and the associated redox environment.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Microfluidic systems offer precise control over physiological, biochemical, and mechanical stimuli in cell culture, allowing in vitro emulation of tissue or tumor microenvironments. This study aims to advance our understanding of tumor biology and contribute to personalized therapy development. Overcoming challenges in cell culture, including cell density and microfluidic device properties, is essential for this purpose. We designed a microfluidic system to facilitate the interaction of diverse cell lineages, incorporating human brain microvascular endothelial cells (HBEC5i), glioblastoma multiforme cells (U87MG), and astrocytes (ScienceCells 1800). Post-fabrication, we assessed the system's functionality and systematically evaluated cell inoculation conditions. Photographic acquisitions comprehensively captured cell culture and their interactions. The device successfully enabled assessment of cellular proliferation, invasion, and migration, supporting the growth of monolayers and tumor organoids. Importantly, each channel demonstrated independence in study processes while allowing cellular intercommunication, both physically and biochemically. In conclusion, the functional microfluidic device facilitated the study of cell migration, tumor invasion, and organoid growth, faithfully replicating biological features observed in a tumor microenvironment. This research contributes to advancing our understanding of tumor biology for the development of personalized therapies.

Salmonella Typhimurium is a typical Gram-Negative bacterium associated with food-borne illness threatening public health. This pathogen is reported to have caused millions of infections and thousands of hospitalizations yearly in the United States alone. Salmonella Typhimurium has a sophisticated array of virulent factors, including its flagella, which contributes significantly to its pathogenicity. Cannabidiol (CBD), the non-psychoactive component of the cannabis plant, has attracted the interest of many researchers recently because of its antimicrobial properties. With the growing interest in exploring CBD’s antimicrobial properties, we, in this study, have delved into unravelling how prolonged exposure of Salmonella Typhimurium to CBD influences the expression of the flgA gene. We hypothesized that the exposure of Salmonella Typhimurium to CBD may result in flgA upregulation, causing the bacteria to withstand various stresses and potentially enhance its survival rates and pathogenicity. We have employed several assays, such as mRNA expression, motility assays, pH induction assays, nutrient starvation interventions, and biofilm analysis, to explore the influence of CBD on the pathophysiology of Salmonella Typhimurium. Our findings reveal that CBD-resistant strains exhibit enhanced resilience to extreme pH levels and motility and survive poorly under nutrient starvation, with more robust biofilm formation compared to the susceptible strains. The results contribute to the broader comprehension of CBD resistance mechanisms in bacteria. This work provides insight into the potential therapeutic interventions for controlling CBD-resistant bacterial infections. This study also underscores the essence of exploring novel alternative antimicrobial agents to tackle the ever-persistence of bacterial antibiotic resistance.

Polycentric governance, as conceptualized by the Nobel Laureate Elinor Ostrom, has been widely celebrated as a promising approach to fostering inclusive innovation ecosystems in the Global South. By emphasizing the coordination of multiple decision-making centers and the engagement of diverse stakeholders, polycentric systems are believed to enhance the inclusivity and responsiveness of innovation processes, crucial for addressing societal challenges and promoting equitable development. However, this article argues that the theoretical and practical application of polycentric governance may not adequately address the inherent power structures and complexities within inclusive innovation ecosystems. Drawing on critical analysis, empirical evidence, and complementary theoretical perspectives, this paper interrogates the limitations of polycentric governance and proposes a more nuanced, multidimensional framework for inclusive innovation governance in the context of the Global South.

Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

A comprehensive study of the interactions of human serum albumin (HSA) and α-1-acid glycoprotein (AAG) with two isoquinoline alkaloids: allocryptopine (ACP) and protopine (PP) was performed. The UV-Vis spectroscopy, molecular docking, competitive binding assays, and circular dichroism (CD) spectroscopy were used for the investigations. The results showed that ACP and PP form spontaneous and stable complexes with HSA and AAG, with ACP displaying a stronger affinity towards both proteins. Molecular docking studies showed also revealed the preferential binding of ACP and PP to specific sites within HSA, with site 2 (IIIA) being identified as the favored location for both alkaloids. This was supported by competitive binding assays using markers specific to HSA's drug binding sites. Similarly, for AAG, a decrease in fluorescence intensity upon addition of the alkaloids to AAG/ Quinaldine Red (QR) complexes indicated the replacement of the marker by the alkaloids, with ACP showing a greater extent of replacement than PP. CD spectroscopy showed that the proteins’ structures remained largely unchanged suggesting that the formation of complexes did not significantly perturb the overall spatial configuration of these macromolecules. These findings are crucial for advancing the knowledge on the natural products-protein interactions and the future design of isoquinoline alkaloid-based therapeutics.

Despite global efforts to improve vaccination coverage, the number of zero dose and under immunized children has increased in the African continent, particularly in Nigeria where more than 2.3 million unvaccinated children currently reside, making it the country with highest number of unvaccinated children on the African continent. It is thus imperative to identity barriers and facilitators of immunization in Nigeria with a view to formulate and design targeted interventions that can effectively bridge the immunization gap in Nigeria. In this review, we systematically mapped and summarized existing literature and data on key barriers to vaccine uptake, and facilitators influencing immunization practices in Nigeria. The electronic databases were systematically searched and articles that reported determinants of routine immunization uptake in Nigeria from inception of the databases up till October 2023 were included. The eligible articles were evaluated using a set of pre-defined criteria and the extracted data were analysed and visualized. This scoping review identified barriers spanning logistical, socio-economic, cultural, and healthcare system-related aspects, highlighting the need for a multi-pronged approach to address these challenges. While considerable progress has been made over the years, persistent barriers like distance between area of residence of caregivers and immunization centers, vaccine shortages, poor interpersonal relationship between healthcare workers and caregivers and cultural/religious influences persist. Lastly, collaborative efforts among stakeholders and communities remain paramount in ensuring improved routine immunization coverage across the Nigeria’s diverse socio-cultural landscape.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which yields best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, thus incorporating, as particular cases, the results previously predicted by the second-order predictive modeling methodology 2nd-BERRU-PM, and vastly generalizing the results produced by extant data assimilation and data adjustment procedures. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC). The application of the 4th-BERRU-PM methodology to energy systems is illustrated by means of the above-mentioned OECD/NEA reactor physics benchmark, which is modeled using the neutron transport Boltzmann equation involving 21976 imprecisely known parameters, the solution of which is representative of “large-scale computations.” The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

The emergence of prosumers, who actively participate in designing and producing goods, has generated interest in domestically manufactured products. Factors such as innovation, design methods, component reuse, prototyping, and digital fabrication empower prosumer designers to realize their ideas and enhance products to meet their specific design requirements. This article presents a case that combines bioinspired design methods with prosumer characteristics from the perspective of a designer who uses biological research results acting as a prosumer. By analyzing prosumer needs, bioinspired design, and domestic fabrication requirements, designers define the methods that best suit their possibilities. As a result of using biological information, the geometry of Samara seeds, propeller sizing, wind tunnel tests, and subsequent improvements, the custom design of a bioinspired microgenerator has been refined to the final design of a bicycle flashlight. The aim is to popularize a working approach that facilitates the interaction of design methods and reinforces domestic manufacturing processes. Implementing bioinspired design methods within the prosumer context offers a methodological alternative to transform the current economic model, promoting systemic sustainability and the development of proprietary technologies.

In the trend of transforming existing systems and services into non-face-to-face models, the concert industry is also showing movements toward transitioning to virtual formats. Physical concerts in the real world require venues that can accommodate hundreds to tens of thousands of spectators, but non-face-to-face methods that can accommodate large audiences face various limitations. Moreover, to elevate the satisfaction level of virtual concert attendees to that of real-world concerts, it's important to implement interaction between performers and audiences. Modern metaverse platforms apply cutting-edge network technologies to accommodate numerous users within a single channel. Many researchers are adopting network technologies such as SDN (Software Defined Networking) and CDN (Content Delivery Network) to set up a virtual concert that can accommodate large audiences. In this paper, we propose a network framework to be designed for the composition of virtual concerts. In particular, we separate a channel dedicated to interaction in order to provide an immersive experience of exchanging interactions between performers and audiences. As massive audiences transmitting interaction data to the performer in a 1:N format can lead to problems with acceptance and latency, this study introduces a concept of a channel form called 'Zone' and proposes an interaction data channel network framework that does not compromise immersion. The proposed framework supports tasks for effectively transmitting interaction data using network technologies for metaverse platforms like XDN and clustering algorithms like fuzzy c-means. We also suggest a CDN-based architecture that can ensure low latency for performers to transmit interaction data to the audience.

In the famous medieval magical manual called Picatrix, the unknown author describes the phenomenon of magic with the term nigromantia. As is well known, the original meaning of the Greek term necromantia has a more concise meaning. It is used for a special kind of divination, i.e., divination through the parts of a cadaver and the conjured spirit of the dead. Seemingly in the Picatrix, no necromantic ritual can be found; moreover, the author stresses that his main goal is pious, i.e., to find the path leading to the ultimate source of the universe, the one and only God. In my paper, I show that on some pages of the Picatrix, there are traces of divinatory practices that may be connected to the original meaning of the term nigromantia. In the third book of the manual, descriptions of some interesting rituals attributed to the pagan Sabeans of Harran and their teacher, the god Hermes can be found. During the practices, the magician involved conjured spirits of the heavenly bodies and powers as well. Because of this, by looking closely at and analyzing the given text, it is possible to piece together a complex web of necromantic and demonic divinatory rituals.

Cancer is one of the most dreaded illnesses of the 20th century, cancer is continuing to spread and is becoming more common in the 21 st century.As of right now, breast cancer seems to be the most common cancer worldwide and the leading cause of cancer-related fatalities.In present study we used different network pharmacology tools for identification of target that involved in breast cancer that network pharmacology tool used target identification and validation process in drug discovery.In present study we used UniprotKB as text mining tool for identification of breast cancer gene in that we got the 2,658 gene in that we take the 165 in study the we done the GO and KEGG enrichment analysis in that we take pathways related to the breast cancer further we used STRING for determination protein-protein interaction in that we got the interaction between NDUFA1 with NDUFA3 and EREG,MYO1B with ACTN4 and XRCC3 with XRCC2.The interaction network was drawn and the network was analyzed by Cytoscape.

This project designed a set of network security mechanisms for cloud applications in VANETs. At present, cloud computing is one of the government’s development priorities in the industry. We divide cloud into public, private, and hybrid clouds in this project. Vehicles or passengers can access road conditions or public transport information through the public cloud; public transportation can access current driving records and users can access relevant enterprise information on the private cloud and hybrid cloud is a combination of public and private clouds. Both cloud computing or VANETs require network and information security. At present, many researches on VANETs security only focus on message communication, and neglect information storage. Researches on cloud computing security only focus on information protection, and neglect user privacy and anonymity. This project designed a set of network and information security mechanisms in line with the requirements of Confidentiality, Authentication, Non-repudiation, Conditional Anonymity, and Conditional Untraceability. This project primarily needs to achieve the following: 1.an authentication mechanism to verify the identity of each other between the passenger and the vehicle and to verify the identity with Single Sign-On; 2. vehicle or user privacy and anonymity, which need to be able to replace the anonymous ID and related parameters for the vehicle or the user; 3. a private communication mechanism, which enables any vehicle or user to communicate privately; and 4. an information security encryption method, which can encrypt the information on a cloud server to avoid unauthorized access by internal personnel or hackers.

In the present study, a mathematical model of non-steady partial differential equation from the process of oxygen mass transport in the human pulmonary circulation is proposed. Mathematical modelling of this kind of problems lead to a non-steady partial differential equation and for its numerical simulation, we have used finite differences. The aim of the process is the exact numerical analysis of the study, wherein consistency, stability and convergence is proposed. The necessity of doing the process is that, we would like to increase the order of numerical solution to a higher order scheme. An increment in the order of numerical solution makes the numerical simulation more accurate, also makes the numerical simulation being more complicated. In addition, the process of numerical analysis of the study in this order of solution needs more research work.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

Abstract. This paper proposes a Second Order Terminal Sliding Mode (2TSM) approach to the trajectory tracking of Differential Drive Mobile Robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the inner most loop, tracks the robot’s velocity quantities; while a kinematic controller, at the outer most loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated and finite time convergence is guaranteed by the second order TSM manifold which involves higher order derivatives of the state variables, resulting in inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

Computational simulation using mathematical models is a useful method for understanding the complex behavior of a living system. The majority of studies using mathematical models to reveal biological mechanisms uses one of the two main approaches: the bottom-up or the top-down approach. When we aim to analyze a large-scale network, such as a comprehensive knowledge-integrated model of a target phenomenon, for example a whole-cell model, the variation of analyses is limited to particular kind of analysis because of the size and complexity of the model. To analyze a large-scale regulatory network of neural differentiation, we developed a hybrid method that combines both approaches. To construct a mathematical model, we extracted network motifs, subgraph structures that recur more often in a metabolic network or gene regulatory network than in a random network, from a large-scale regulatory network, detected regulatory motifs among them, and combined these motifs. We confirmed that the model reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale regulatory network is controlled by a combination of four feedback loops, including a large loop which has not been focused on. The model extracted by our hybrid method has the potential to reveal the critical mechanisms of neural differentiation. The hybrid method is applicable to other biological events.

Student-centered active learning (SCAL) shifts the focus from the teacher to the student. Implementing SCAL requires the development of new forms of assessment beyond memorization and comprehension. This paper aims to demonstrate the effectiveness of SCAL by analyzing student performance at different cognitive levels. In flipped classrooms, students completed tasks with varying cognitive demands. The tests measured knowledge (level 1), comprehension (level 2), application (level 3) and analysis (level 4). This approach to assessment assesses not only the acquisition of content, but also skills that are critical to a health science student's future career. The results of expository learning were compared with student-centered collaborative learning in the Anatomy and Embryology I course over three academic years. Student opinions were collected through 326 anonymous Wooclap® surveys. No differences in performance were found for low cognitive level questions. However, SCAL significantly improved performance on higher cognitive level questions that required problem solving and application of knowledge. Despite the benefits of SCAL, it requires more effort from both teachers and students. The current assessment system, which measures the quality of teaching, penalizes this effort and requires teachers to believe strongly in the effectiveness of SCAL.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Energy poverty in the Global South generally focuses on the lack of basic access to modern fuels and energy carriers. Impoverished people rely primarily on traditional biomass as a source of energy. This situation of basic resource deprivation, in which almost one third of the world's population lives, masks other relevant features of the problem. Current assessments of energy poverty in impoverished areas and the mitigation strategies being implemented derive from the development agenda and, with variations in detail and scope, highlight electricity connections and access to clean cooking fuels as guarantors of progress. However, a comprehensive understanding of energy poverty requires going beyond basic access, building on the interactions between the supply of energy sources and carriers, the provision of energy services and its impact on decent living conditions. Several studies in the last decade have attempted to construct an assessment framework centered on energy services to deal with these interactions. This work discusses the relevant dimensions in the framework (supply, services and impact in wellbeing), reviews the multidisciplinary work available in each aspect, presents a range of proposed taxonomies, and discusses the different issues. A detailed framework is proposed for the integrated assessment of the supply of energy carriers and energy equipment, the provision of relevant energy services, and the improvements obtained in living conditions.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

Hepatitis C virus (HCV) is a major public health threat associated with serious clinical consequences worldwide. The WHO reports an estimated 58 million people globally living with chronic hepatitis C virus (HCV) infection, with 1.5 million new cases and over 350,000 deaths annually due to HCV-related diseases. Despite the existence of multiple diagnostic methods, the accessibility of diagnosis and treatment is often hindered across the continent by limited resources, insufficient healthcare infrastructure, and low public awareness of Hepatitis C virus Hepatitis C virus infection is a major public health concern in African countries, which have the highest prevalence rates of HCV in the world (1–26%). Egypt, African country which had the highest prevalence of Hepatitis C virus infection in the world. Its national hepatitis C elimination initiative emerges as an exemplary case of success an having screened over 60 million individuals, and treated more than 4 million, thereby markedly diminishing both the prevalence and incidence of the disease. The strategies employed by Egypt offer critical insights and adaptable frameworks for other African Countries facing analogous challenges. The Aim of this Review is to highlight the triumphs and hurdles of Egypt's approach, while also providing strategic guidance for African Countries to accelerate the journey towards the eradication of hepatitis C.

Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding duration, or dwell time, of TCR–pMHC interactions correlates with downstream signaling efficacy, with strong agonists exhibiting longer lifetimes compared to weak agonists. Traditional surface plasmon resonance (SPR) methods quantify 3D affinity but lack cellular context and fail to account for factors like membrane fluctuations. In the recent years, single-molecule Förster resonance energy transfer (smFRET) has been applied to measure 2D binding kinetics of TCR–pMHC interactions in a cellular context. Here, we introduce a rigorous mathematical model based on survival analysis to determine exponentially distributed receptor–ligand interaction lifetimes, verified through simulated data. Additionally, we developed a comprehensive analysis pipeline to extract interaction lifetimes from raw microscopy images, demonstrating the model’s accuracy and robustness across multiple TCR–pMHC pairs. Our new software suite automates data processing to enhance throughput and reduce bias. This methodology provides a refined tool for investigating T cell activation mechanisms, offering insights into immune response modulation.

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

This paper, from the perspective of Mode 3 of knowledge production, takes the important carrier of knowledge, scientific research papers, as the research object. The mainstream databases (SCIE and SSCI) are chosen as the data sources. Firstly, bibliometric methods are applied to analyze the cooperation status among the main bodies of paper production, including authors and their affiliated institutions, countries, and disciplines; secondly, the Analytic Hierarchy Process (AHP) is used to construct a comprehensive evaluation model for the state of research cooperation in carbon neutrality. Based on the evaluation model, the cooperation characteristics of different disciplinary fields and the relationship between paper cooperation status and high-impact research papers are comparatively analyzed. At the same time, an analysis is conducted on the support from the government, the sponsorship from enterprises, and the participation of the public in the process of paper production. Finally, some suggestions are put forward on how to build an innovative ecosystem for carbon neutrality research from the perspective of Mode 3 of knowledge production.

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

This study used established biomarkers of death due to ischemic stroke (IS) and performed network, enrichment, and annotation analysis. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell surface interactions, and the proteoglycan-integrin-extra cellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, VEGFA, proteoglycan-integrin-ECM complex, NFKB/RELA and SP1.

Isolation, quarantine, and contact tracing were some of the most critical and commonly employed non-pharmaceutical interventions to control the COVID-19 pandemic. However, the effectiveness of these strategies after their implementation depends upon many factors such as the country's population, availability of sufficient resources to diagnose and effectively isolate the infected individuals, and feasibility, and acceptability of isolation and quarantine policy by the public. This narrative review aims to appraise the current evidence on the correct implementation strategies and shortcomings made in the isolation and quarantine strategies during the COVID-19 pandemic. PubMed, SCOPUS, EMBASE, and Web of Science citation index with the following keywords were searched: Pandemic, COVID-19, Coronavirus, Quarantine, and Isolation. The results showed that quarantine and isolation of COVID-19 patients were more effective than late adoption (5-fold versus 2.6-fold)’. Around 90% of outbreaks could have been managed if 80% of contacts were monitored, traced, and isolated within 4 days. For each new case of COVID-19, around thirty-six individuals should have been traced. When the percentage of individuals without symptoms is greater than 30%, isolation and contact tracing alone cannot contain the infection. The delay between the onset of symptoms and isolation is the most important factor determining whether an outbreak is controllable. The ideal day for testing should start on the 6th of the quarantine. However, this evidence is mostly based on mathematical modeling studies, and evidence for its effectiveness in real scenarios is required. Symptom-based isolation strategy should not be considered the sole criterion for considering isolation.

For many engineering applications it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches length scale parameter have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modelling as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior.

The period of the COVID-19 pandemic resulted in a rapid development of innovative education methods, in particular using e-learning and remote work tools. The effects of this development are also post-pandemic changes in formal teaching regulations, in particular in higher education, allowing classes and lectures to be carried out with Blended Learning or Flipped Classroom methods. This paper provides information about a case study of implementing ICT technologies with elements of the mentioned methods in one of the subjects at a technical university. The implemented tools and solutions in the field of e-learning and interactivity are presented, along with information regarding their perception by students during two academic years in the post-COVID-19 period. The analysis of results of these information and student evaluations indicates their generally positive approach to such innovations in the educational path, and at the same time shows significant challenges for teachers to increase the attractiveness and effectiveness of the teaching process and the development of practical, technical skills of students.

Abstract — Game development using technology was started in 1950 around the first video game created Tennis for Two by Higginbotham as a fun diversion, to show the power of technology. The advanced development of Artificial intelligence (AI), has turned out to be the heart of game development technology and AI has revolutionized gaming and brought so many features to life.in reality, it isn't easy to give a definition and meaning to it even though games are becoming a part of human survival. It is difficult to define the real meaning of a game but it is part of human survival as it usually refers to the cleverness of the way the human being involvement in the characters of the game. As a part of game development, many technologies were used to create a game for the players with creativity, complex design, variety of levels, and aspects for the player to use choices effectively and efficiently to interact with the character in the game and enhance the experience to the next levels. This paper initially gives the importance of game development and need for the technology usage to enhance the current state of games. Then analyzes the history and present state of Artificial intelligence in game development. Finally, it explains the highlights of potential changes in game development and the impact and influence of artificial intelligence and machine learning models in the future development of games.