Integration Of Networks Research Articles

The landscape of chemokine and cytokine is associated with the distinct clinical status of leprosy patients and their respective household contacts

IntroductionLeprosy, a chronic infectious disease, is closely linked to the host immune response. According to the WHO, leprosy patients (L) and household contacts (HHC) are classified into subgroups: paucibacillary (PB) and multibacillary (MB), witch reflect the degree of infection in patients and the level of exposure of their contacts. The main goal of this study was to: i) establish a comprehensive overview of soluble mediator signatures of PBMCs upon in vitro antigen-specific stimuli and ii) identify whether the chemokine (CH) and cytokine (CY) signatures were associated with distinct clinical manifestations in (L) and immune response profiles in (HHC).MethodsLong-term PBMC cultures were carried out and supernatants collected for 12 CH and CY analisys by Cytometric Beads Array.Results and discussionThe CH and CY analysis, using continuous variable modeling, demonstrated that PBMCs from both L and HHC exhibited high levels of TNF upon M. leprae-stimuli. While lower production of IFN-γ were observed for L, low levels of CXCL8 was found for HHC. Soluble mediator signatures, analyzed using categorical variables, revealed that while high levels of TNF were observed for L, high levels of IFN-γ appeared as a hallmark of HHC. Overall, these analyses demonstrated that CXCL8, IFN-γ, and TNF were key markers differentiating L from HHC and endemic control (EC), especially considering the categorical analysis of the soluble mediator signatures. Data further demonstrated that higher levels of IFN-γ and lower levels CXCL8 was features associated with HHC(MB), whereas high levels of TNF were observed in both L subgroups. Moreover, data from integrative networks, based on correlation amongst soluble mediators, revealed that in M. leprae-stimuli, the number of correlations was lower in HHC(MB) compared to HHC(PB), but higher in L(MB) compared to L(PB). It was noted that the number of correlations decreased in the following order: EC > L > HHC. Our findings contribute to additional immunological features associated with L and HHC, witch can be useful complementary diagnostic/prognostic tools for classification of L and HHC, providing insights to enrich the research agenda about the hypothesis that HHC should be closely monitored as they may present a subclinical infection.

Locus coeruleus signal intensity and emotion regulation in agitation in Alzheimer’s disease

Abstract Hyperphosphorylated tau accumulation is seen in the noradrenergic locus coeruleus from the earliest stages of Alzheimer’s disease onwards and has been associated with symptoms of agitation. It is hypothesized that compensatory locus coeruleus-noradrenaline system overactivity and impaired emotion regulation could underlie agitation propensity, but to our knowledge this has not previously been investigated. A better understanding of the neurobiological underpinnings of agitation would help the development of targeted prevention and treatment strategies. Using a sample of individuals with amnestic mild cognitive impairment and probable mild Alzheimer’s disease dementia from the German Center for Neurodegenerative Diseases (DZNE)-Longitudinal Cognitive Impairment and Dementia (DELCODE) study cohort (N=309, aged 67-96 years, 51% female), we assessed cross-sectional relationships between a latent factor representing the functional integrity of an affect-related executive regulation network and agitation point prevalence and severity scores. In a subsample of individuals with locus coeruleus MRI imaging data (N=37, aged 68-93 years, 49% female), we also investigated preliminary associations between locus coeruleus MRI contrast ratios (a measure of structural integrity, whole or divided into rostral, middle, and caudal thirds) and individual affect-related regulation network factor scores and agitation measures. Regression models controlled for effects of age and clinical disease severity and, for models including resting state functional MRI connectivity variables, grey matter volume and education years. Agitation point prevalence showed a positive relationship with a latent factor representing the functional integrity (and a negative relationship with a corresponding structural measure) of the affect-related executive regulation network. Locus coeruleus MRI contrast ratios were positively associated with agitation severity (but only for the rostral third, in N=13) and negatively associated with the functional affect-related executive regulation latent factor scores. Resting-state functional connectivity between a medial prefrontal cortex region and left amygdala was related to locus coeruleus MRI contrast ratios. These findings implicate the involvement of locus coeruleus integrity and emotion dysregulation in agitation in Alzheimer’s disease and support the presence of potential compensatory processes. At the neural level, there may be a dissociation between mechanisms underlying agitation risk per se and symptom severity. Further studies are needed to replicate and extend these findings, incorporating longitudinal designs, measures of autonomic function and non-linear modeling approaches to explore potential causal and context-dependent relationships across Alzheimer’s disease stages.

Integrated CNN‐LSTM for Photovoltaic Power Prediction based on Spatio‐Temporal Feature Fusion

ABSTRACTThe accurate prediction of the output power of each power plant is crucial for effective resource deployment. This paper proposes a convolutional neural network‐long short‐term memory (CNN‐LSTM) network integration model based on spatio‐temporal feature fusion. Firstly, the temporal correlation of the PV features of the target power plant and the spatial correlation between the PV power of the target power plant and the PV power of the neighboring power plants are computed. The features are then fused according to the strength of the correlation, which allows the features to be combined with spatial and temporal attributes, which promotes faster and more effective training of the model. Subsequently, an integrated network architecture comprising three individual models, CNN, LSTM, and CNN‐LSTM, is designed. The SENet attention mechanism is utilized to add non‐linear integration weights to the outputs of the individual models. Due to the variability of different neural networks, the prediction results of the integrated model are often higher than the best‐performing individual model. Additionally, we designed different case studies to compare model performance under sunny, rainy, and cloudy conditions. Extensive simulation experiments demonstrate the effectiveness of our proposed integrated approach. When the prediction interval is set to 5 min, the RMSE loss of the integrated model on the test set is reduced by 13.5%, 6.9%, and 5.1% compared to the CNN, LSTM, and CNN‐LSTM models included in the ensemble, respectively.

Investigation of a Cogeneration System Combining a Solid Oxide Fuel Cell and the Organic Rankine Cycle: Parametric Analysis and Multi-Objective Optimization

A novel solid oxide fuel cell (SOFC)-based cogeneration system is proposed here, integrating an organic Rankine cycle for waste heat recovery. Technical–economic and parametric analyses are conducted, and a multi-objective optimization is carried out. The results reveal that the net electrical efficiency, investment cost, and payback time are 56.6%, USD 2,408,256, and 3.27 years, respectively. The parametric analysis indicates that the current density should be limited between 0.3 A/cm2 and 0.9 A/cm2, and the stack temperature should be controlled between 675 °C and 875 °C. After the operational optimization of ηele-CostTCI, the investment cost and the net electrical efficiency are obtained as USD 2,164,742 and 62.1%. After the ηele-PBT optimization, the payback period and the net electrical efficiency are 3.22 years and 58.9%. The heat transfer network optimization achieves the highest efficiency and reduces the cold utilities by 43 kW, but three additional heat exchangers should be added to the system. This research provides practical reference and pragmatic guidance for the integration, analysis, operation, and heat transfer network optimization of SOFC-based cogeneration systems.

Full-Scale Assessment of the “5GT System” for Tracking and Monitoring of Multimodal Dry Containers

A novel tracking and monitoring system for ISO 668 dry containers was realized by the ESA-funded “5G SENSOR@SEA” project, integrating 5G cellular technologies for massive Internet of Things with a GEO satellite-optimized backhauling link. The scope is the development of monitoring and tracking new services for multimodal container shipping. With the cooperation of four industrial partners and a telecommunication research center, the so-called “5GT System” was designed, developed, tested and validated up to field trials. Several modules of the system were designed, built and finally installed on the ship and in the teleport: the container tracking devices placed on the containers, the NB-IoT cellular network with optimized satellite backhauling, the Ku-band satellite terminals and the maritime service platform based on the OneM2M standard. The field trial conducted during the intercontinental liner voyage of a container ship showed primary technical achievements, including fair switching between terrestrial and satellite networks, reduction in packet loss in the open sea scenario and seamless integration of the BLE mesh network over the container tracking devices as NB-IoT/BLE LE Mesh gateways.

Selection of Grid Road Networks Based on Raster Data

In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.

Prediction and Optimization of Surface Roughness and Cutting Forces in Turning Process Using ANN, SHAP Analysis, and Hybrid MCDM Method

As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters.

Boundary Integral Neural Networks for Acoustic Radiation Prediction from Noisy Boundary Data

Boundary Integral Neural Networks for Acoustic Radiation Prediction from Noisy Boundary Data

Pre-calculated space-ground integrated structure (P-SGIS) against link cut set (LCS) failures in space-ground integrated optical networks

Pre-calculated space-ground integrated structure (P-SGIS) against link cut set (LCS) failures in space-ground integrated optical networks

CCDC69 maintains genome integrity by regulating KIF2C/MCAK depolymerase activity and the stability of the chromosomal passenger complex

Accurate genome inheritance during cell division relies on a complex chromosome segregation mechanism. This process occurs once all the kinetochores of sister chromatids are attached to microtubules emanating from the opposite poles of the mitotic spindle. To control the precision of this mechanism, the Chromosome Passenger Complex (CPC) actively identifies and corrects improper microtubule attachments. The depolymerase activity of the kinesin KIF2C/MCAK at the kinetochores is involved in this process. CCDC69 is a poorly characterized protein, primarily identified as a regulator of central spindle assembly, whose overexpression prompts rapid microtubule depolymerization. Here, we show that CCDC69 is a cell-cycle regulated protein belonging to the Microtubule-associated Tumor Suppressor (MTUS) superfamily, and even slight deregulation of its expression induces severe early mitotic phenotypes. Myristoylation anchors CCDC69 at the plasma membrane, thus protecting microtubule network integrity. We found that CCDC69 microtubule depolymerization activity relies on KIF2C, with a fraction of CCDC69 localizing to the centromere. Importantly, we demonstrated that CCDC69 regulates the stability of the CPC by safeguarding its members from degradation during mitosis. In summary, our findings underscore CCDC69’s essential role as a mitotic regulator, which is crucial for maintaining the fidelity of chromosome segregation.

Evolution and Challenges in 5G Telecommunications: A Technical Overview of Network Architecture and Performance Optimization

Abstract-The transition from 4G to 5G telecommunications networks represents a significant leap in wireless technology, marked by substantial improvements in speed, capacity, and latency. This paper explores the technical aspects of 5G network architecture, its key components, and the challenges faced in the deployment of 5G networks. Through an in-depth examination of technologies such as millimeter-wave (mmWave) communication, Massive MIMO (Multiple Input, Multiple Output), and network slicing, the paper provides a comprehensive overview of how these innovations contribute to the enhanced performance characteristics of 5G. Additionally, the paper discusses the challenges associated with 5G rollouts, including spectrum management, interference mitigation, and the integration of legacy networks. Finally, the paper compares LTE and 5G technologies, highlighting the performance improvements and new capabilities that 5G brings to telecommunications. Keywords- 5G Network Architecture, Latency Reduction, Network Slicing, Ultra-Reliable Low- Latency Communication (URLLC), IoT, Network management

Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network.

The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin-gap calculations, including the singlet-triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems.

Integration of Neural Networks and First-Principles Model for Optimizing l-Lactide Branched Polymerization.

Addressing the growing demand for sustainable materials, this research paves the way for the efficient consumption and sustainable production of branched polylactide (PLA). A novel hybrid modeling approach combines first-principles (FP) model with artificial neural network (ANN) for ring-opening polymerization (ROP). The hybrid ANN, trained with FP model data, demonstrated optimal performance with a hidden layer of 20 neurons, achieving a root mean square error (RMSE) of 0.004 and a regression coefficient (R2) of 0.99. The hybrid model accurately predicted key polymer properties, including average molecular weights (Mn and Mw), polydispersity index (PDI), degree of branching (DB), monomer conversion, and polymerization time. Validation was performed on various branched PLA compositions (PLLH80, PLLH94, and PLLH97). Multiobjective optimization (MOO) using NSGA-II showed strong agreement between FP model and hybrid ANN across six case studies, highlighting their effectiveness in predicting polymerization outcomes.

The Impact of Entrepreneurship Spaces in Higher Vocational Colleges on Students’ Entrepreneurial Intentions-Taking Creativity as the Mediating Variable

This journal article examines the influence of entrepreneurship spaces, also known as maker spaces, in higher vocational colleges on students' entrepreneurial intentions. It specifically focuses on the mediating role of creativity in this relationship. The study reviews existing literature on the significance of entrepreneurship spaces in fostering entrepreneurial intentions and emphasizes the importance of creativity as a precursor to entrepreneurial thinking. Additionally, it discusses the role of maker spaces in shaping students' attitudes towards entrepreneurship and their willingness to engage in entrepreneurial activities. The findings of the study indicate that maker spaces have a positive impact on students' entrepreneurial intentions, and creativity plays a crucial role in mediating this relationship. Maker spaces provide students with resources, mentorship, and a supportive community, which fosters a pro-entrepreneurial mindset and enhances self-efficacy in entrepreneurship. These spaces also simulate real-world entrepreneurial experiences, reducing barriers to entry and increasing students' willingness to pursue entrepreneurial endeavours. Creativity is identified as a key mechanism through which entrepreneurship spaces influence students' entrepreneurial thinking. Maker spaces stimulate innovative thinking, problem-solving, and idea generation, creating a creative environment that fuels entrepreneurial intentions. Therefore, fostering creativity within these spaces is crucial for nurturing entrepreneurial intentions among students and preparing them for the evolving business landscape. The article concludes by emphasizing the importance of understanding the interplay between entrepreneurship spaces, creativity, and entrepreneurial intentions for designing effective entrepreneurship education programs and optimizing the impact of maker spaces in higher vocational colleges. It suggests that future research should explore contextual variations, long-term effects, curriculum integration, inclusivity, interdisciplinary collaboration, mentorship and support networks, measurement and assessment tools, collaborative research, and policy implications of entrepreneurship spaces in higher vocational colleges.

Networked control systems induced delay modeling and stability analysis using Markov-regime-switching GARCH model

This article focuses on the stability analysis of networked control systems (NCSs). It is well-known that network-induced delays pose many control challenges due to their non-uniform and multifractal nature. With the growing integration of communication networks in control systems, the impact of time-varying and random delays on system performance becomes critical. Hence, it is essential to study their impact on the system’s stability. There are several network-induced delay modeling methods, resulting in constant, random, and probabilistic random delays, which provide the most realistic representation according to recent studies. This article introduces a new network-induced delay modeling method based on the Markov-regime-switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) process, which captures the intricate temporal dependence of random network delays. The resulting model offers a more accurate depiction of delay volatility behavior, accommodating sudden shifts between volatility regimes. Simulation results demonstrate that MRS GARCH-type modeling enables a more realistic representation of delay dynamics. We also conduct a stability analysis of NCSs modeled with the proposed method using linear matrix inequalities LMIs criteria derived from the Lyapunov-Krasovskii theorem. The simulation results show less conservatism than conventional models and provide a larger maximum allowable upper delay bound MAUDB.

Optimal operation of hydrogen-based multi-energy microgrid integrating water network and transportation sector

To address the rising demand for hydrogen energy and its reliance with the water sector, this study presents an optimal scheduling framework for a multi-energy microgrid (MEMG) that integrates electric, thermal, water, and hydrogen energy networks. To this end, a mixed-integer linear programming (MILP) model is formulated to minimize both operational costs and emissions. A bi-variate piecewise McCormick envelope technique is utilized to manage the non-linear constraints associated with the water network. The model also incorporates the transportation sector, including electric and hydrogen vehicles (EVs, HVs), with vehicle-to-grid (V2G) technology, and models their associated uncertainties using Monte Carlo simulation (MCS). Additionally, the sale of oxygen as a by-product of the hydrogenation process is also considered. The case study shows significant economic and environmental benefits, with a 29.66% cost reduction and 22.26% emissions decrease from water network integration. Oxygen sales further reduce costs by 14.19%, and V2G technology contributes an additional 2.35% cost and 6.01% emissions reduction. The proposed linear approximation method achieved superior performance, with a root mean square error (RMSE) of 0.72 and a relative error of 2.132%.

Associations between parental psychopathology and youth functional emotion regulation brain networks

Parental mental health is associated with children’s emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children’s functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings’ connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = -0.05-0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (βext = -0.05; βtot = -0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.

Effects of wheat cultivar, bran concentration and endoxylanase on the thermomechanical, viscoelastic and microstructural properties of whole wheat flour dough

Globally, it is challenging to promote the market of whole wheat product due to its undesirable product quality issues. To overcome this remarkable challenge, this research was to work on the constructive strategies for mitigating the negative impact of wheat bran. As such, the objective of this research was to investigate how the wheat cultivar, bran concentration and endoxylanase affected dough thermomechanical properties, viscoelasticity and microstructure by a combined use of mixolab, rheometry, scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). A cultivar-dependent discrimination for the bran tolerance was observed to suggest the suitable wheat cultivars for whole wheat manufacturing. Bran addition positively affected dough mechanical and viscoelastic properties, i.e., larger dough mechanical weakening (C2) and gel strength, and it negatively affected starch gelatinization (C3) and retrogradation (C5). However, the main conclusions are below. The endoxylanase was incorporated to invert the bran impact on the above properties of dough. Endoxylanase was also used to develop a better integrity of gluten network, larger coverage of starch granules and even distribution of gas cells. Outcomes from this work will provide a practical guidance for improving the high-fibre product quality and promoting its market growth.

Hybrid similarity based feature selection and Cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

Plant disease classification using novel integration of deep learning CNN and graph convolutional networks

Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.