Integration-based Approach Research Articles

Regional peculiarities of training teachers for education systems of multicultural regions: Substantiation of the forecasting model

Introduction. The authors investigate the problem of how initial teacher education is developing in the context of dynamic ethnocultural processes. The purpose of this study is to identify regional peculiarities of preparing prospective teachers for working in education systems of multicultural regions in order to substantiate a forecasting model of the framework program. Materials and Methods. The research is based on the ideas of system-based and integration-based approaches. The authors use the following methods: content analysis, method of precedents and modeling. Results. The authors presented the following results: studies conducted by Russian and international researchers on the problems of preparing teachers to work in education systems of multicultural regions are reviewed; regional specifics of educational systems of multicultural regions (Voronezh, Samara and Omsk regions) are revealed and the best regional practices of training teachers to solve professional tasks in the conditions of dynamic ethnocultural processes are described; the forecasting model of the framework program for teacher training in the education system of multicultural regions is presented. Conclusions. The study concludes about the forecasting model of the framework program for training teachers who would be able to solve professional tasks in the education system of regions with dynamic ethnocultural processes.

Molecular mechanisms underlying stress vulnerability and resilience in the chronic mild stress model: New insights from mRNA and miRNAs data combining

Stress is a major risk factor for the development of psychiatric disorders, including depression. However, its effects are not the same in all the subjects as only a portion of individuals exposed to stress will eventually develop negative mental outcomes, while others can be considered resilient. However, the biological processes underlying the development of a vulnerable or resilient phenotype are still poor understood. In order to cover this, we here used both transcriptomic and miRNomic based approaches in the ventral hippocampus of control (CON) and rats exposed to the chronic mild stress (CMS) paradigm, which were then divided into vulnerable (VULN) or resilient (RES) animals according to the sucrose consumption test.Transcriptomic analyses in VULN rats, compared to both the group of CON and RES animals, revealed the activation of inflammatory/immune-related pathways, specifically involved in antibodies and cytokine production, and the inhibition of pathways involved in protein synthesis. Conversely, transcriptomic data in RES animals suggested the activation of several pathways involved in neurotransmission.We then performed a mRNA-miRNA integration analysis by using miRComb R package, and we found that the most significant mRNA-miRNA pairs were involved in promoting the inflammatory status in VULN animals and, vice versa, by decreasing it in RES rats. Moreover, in VULN animals, the mRNA-miRNA combining analyses revealed the modulation of the olfactory sensory system, a key biological process that has been already found involved in the etiology of stress related disorders such as depression.Overall, our mRNA-miRNA integration-based approach identified distinct biological processes that are relevant for the development of a vulnerable or resilient phenotype in response to the negative effects of CMS exposure, which could allow the identification of novel targets for prevention or treatment.

Development and validation of systems for genetic manipulation of the Old World tick-borne relapsing fever spirochete, Borrelia duttonii.

Relapsing fever (RF), a vector-borne disease caused by Borrelia spp., is characterized by recurring febrile episodes due to repeated bouts of bacteremia. RF spirochetes can be geographically and phylogenetically divided into two distinct groups; Old World RF Borrelia (found in Africa, Asia, and Europe) and New World RF Borrelia (found in the Americas). While RF is a rarely reported disease in the Americas, RF is prevalent in endemic parts of Africa. Despite phylogenetic differences between Old World and New World RF Borrelia and higher incidence of disease associated with Old World RF spirochete infection, genetic manipulation has only been described in New World RF bacteria. Herein, we report the generation of genetic tools for use in the Old World RF spirochete, Borrelia duttonii. We describe methods for transformation and establish shuttle vector- and integration-based approaches for genetic complementation, creating green fluorescent protein (gfp)-expressing B. duttonii strains as a proof of principle. Allelic exchange mutagenesis was also used to inactivate a homolog of the Borrelia burgdorferi p66 gene, which encodes an important virulence factor, in B. duttonii and demonstrate that this mutant was attenuated in a murine model of RF. Finally, the B. duttonii p66 mutant was complemented using shuttle vector- and cis integration-based approaches. As expected, complemented p66 mutant strains were fully infectious, confirming that P66 is required for optimal mammalian infection. The genetic tools and techniques reported herein represent an important advancement in the study of RF Borrelia that allows for future characterization of virulence determinants and colonization factors important for the enzootic cycle of Old World RF spirochetes.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing “MintTea”, an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies “disease-associated multi-omic modules”, comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species’ metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Exploration of college teacher education path based on cognitive multi-method integration for innovation and entrepreneurship ability cultivation

Abstract In this paper, a cognitive multi-method integration-based approach combined with a genetic algorithm for optimization is used to set the random variables as Bayesian network nodes, and the joint probability distribution is obtained by analyzing the strength of association between the nodes. An independently distributed observation data set with random variables is used to determine the log-likelihood function. After generating the random data, the roulette wheel is used to select the individuals with higher fitness values for transformation, and the excellence of the individuals in the population is improved through cyclic iteration. 20 points improved the educational practice’s 4th test score compared to the pre-training score after the innovation and entrepreneurship training. Therefore, the method of cultivating innovation and entrepreneurial abilities based on cognitive multi-method integration has a positive effect on improving the education level and talent cultivation quality of college teachers.

Graph Neural Pre-training for Recommendation with Side Information

Leveraging the side information associated with entities (i.e., users and items) to enhance recommendation systems has been widely recognized as an essential modeling dimension. Most of the existing approaches address this task by theintegration-based scheme, which incorporates the entity side information by combining the recommendation objective with an extra side information-aware objective. Despite the growing progress made by the existing integration-based approaches, they are largely limited by the potential conflicts between the two objectives. Moreover, the heterogeneous side information among entities is still under-explored in these systems. In this article, we propose a novelpre-training schemeto leverage the entity side information by pre-training entity embeddings using the multi-graph neural network. Instead of jointly training with two objectives, ourpre-training schemefirst pre-trains two representation models under the entity multi/single relational graphs constructed by their side information and then fine-tunes their embeddings under an existing general representation-based recommendation model. Our proposed multi-graph and single-graph neural networks can generate within-entity knowledge-encapsulated embeddings, while capturing the heterogeneity from the entity side information simultaneously, thereby improving the performance of the underlying recommendation model. An extensive evaluation of our pre-training scheme fine-tuned under four general representation-based recommender models, namely, MF, NCF, NGCF, and LightGCN, shows that effectively pre-training embeddings with both the user’s and item’s side information can significantly improve these original models in terms of both effectiveness and stability.

Consistent analysis of differentially expressed genes across 7 cell types in papillary thyroid carcinoma

Single-cell transcriptome sequencing (scRNA-seq) provides a higher resolution of cellular differences than bulk RNA-seq, enabling the dissection of cell-type-specific responses to perturbations in papillary thyroid carcinoma (PTC). However, cellular genomic features are highly heterogeneous and have a large number of genes without any expression signals, which hinders the statistical power to identify differentially expressed genes and may generate many false-positive results. To overcome this challenge, we conducted an integrative analysis on two PTC scRNA-seq datasets and cross-validated consistent differential expression. By combining results from 32 common cell types in the two studies, we identified 31 consistently differentially expressed genes (DEGs) across seven cell types, including B cells, endothelial cells, epithelial cells, monocytes, NK cells, smooth muscle cells, and T cells. Functional enrichment analysis revealed that these genes are important for the adaptive immune response and autoimmune thyroid diseases. The additional disease-free survival analysis also confirmed that these 31 genes significantly affected patient survival time in large scale thyroid cancer cohort. Furthermore, we experimentally validated one of the top consistent DEGs as a potential biomarker gene of PTC epithelial cells, KRT7, which may be a upstream gene for the NF-κB signaling pathway. The result shows that KRT7 may promote thyroid cancer metastasis through the epithelial-mesenchymal transition and NF-κB signaling pathway. In summary, our single-cell transcriptome integration-based approach may provide insights into the important role of NF-κB in the underlying biology of the PTC.

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Enhancing environmental and energy monitoring of residential buildings through IoT

Solutions for monitoring and control of the digital technologies in the residential and tertiary sector are lagging in terms of data exchange and easy deployment. The question arises whether current technologies allow cross-functional integration of data. This paper explores an IoT (Internet of Things) solution in controlling the HVAC system and monitoring of environmental, electric and comfort parameters in a real building. The implemented IoT solution proved to be flexible in terms of data exchange, deployment and debugging time in small and medium-size buildings and thus the IoT integration-based approach to be more efficient. This approach opens new possibilities for a wider set of beneficiaries. As future research, we are considering both extending our solution to other buildings within the university campus premises and on developing predictive analytics services.

Multi-source cross-project software defect prediction based on deep integration

Cross-project defect prediction (CPDP), training a machine learning model by using training data from other projects, has attracted much attention in recent years. This approach provides a feasible way for small-scale or new developed project with insufficient training data to carry out defect prediction. This paper focus on the bottleneck issue of CPDP, poor accuracy, and propose a deep learning model integration-based approach (MTrADL) for CPDP. This paper consists of two main phases. First, the similarity between target project and source projects is measured by the modified maximum mean discrepancy (MMD) and the top K source projects with high similarity to the target project are selected as training data. Second, for the selected training data, this paper use convolutional neural network (CNN) to build the defect predictor. Each selected training data corresponds to one CNN predictor. Then, multiple predictors are integrated to get the final prediction result. To examine the performance of the proposed approach, this paper conduct experiments on 41 datasets of PROMISE and compare our approach with three state-of-the-art baseline approaches: a training data selection model (TDS), a two-stage transfer learning model (TPTL), and the multi-source transfer learning model (MTrA). The experimental results show that the average F1-score of our approach is 0.76. Across the 41 datasets, on average, MTrADL respectively improves these baseline models by 39.8%, 28.50%, and 10% in terms of F1-score.

Ambrosetti\u2013Prodi Type Results for Dirichlet Problems of Fractional Laplacian-Like Operators

We establish Ambrosetti–Prodi type results for viscosity and classical solutions of nonlinear Dirichlet problems for fractional Laplace and comparable operators. In the choice of nonlinearities we consider semi-linear and super-linear growth cases separately. We develop a new technique using a functional integration-based approach, which is more robust in the non-local context than a purely analytic treatment.

Improvement of cutting force and material removal rate for disc milling TC17 blisk tunnels using GRA–RBF–PSO method

Blisk is a key component of new aero-engines. To improve machining efficiency, disc milling is used for roughing blisk tunnels. The multi-objective optimization is employed to optimize disc milling process. In this study, an integration-based approach that used grey relational analysis (GRA) coupled with radial basis function (RBF) neural network and particle swarm optimization (PSO) algorithm is applied to solve the optimization problem. To achieve smaller cutting force and greater material removal rate (MRR), the appropriate cutting speed, feed rate per tooth, and cutting height needed to be determined. A hybrid experiment scheme of three factors–five levels is carried out to generate data sample. Results of verified experiments indicate that GRA–RBF–PSO approach can improve performance better than original GRA.

Simulation data integration-based approach for motion synthesis in virtual maintenance

Immersive and non-immersive simulations have been widely adopted to improve product maintainability design based on virtual maintenance technique. On the one hand, immersive simulation shows that our body actions, especially those of the hand, are suitable for detailed operation simulation. Meanwhile, the problem associated with immersive simulation is hardware capacity as constrained by the environment. On the other hand, non-immersive simulation shows superiority in large-scale movement; however, problems, such as cumbersome operation, time-consuming labor, and inadequate precision, arise in terms of complicated and repeated operations. Considering the preceding advantages and limitations mentioned, this study presents a virtual simulation method by purposefully integrating human motion data from immersive and non-immersive simulations. We first decompose maintenance process based on common operations to determine typical types of maintenance motions. We then analyze the classified motion characters to identify a suitable simulation approach for each type of motion. Subsequently, motion data from immersive and non-immersive simulation are processed by spatial and temporary alignment. These data are further integrated by synchronous or asynchronous cooperative control for their respective simulation requirement. Lastly, we adopt interpolation to further process position and posture angle data and achieve smooth transition between adjacent simulation slices. Three cases are introduced to separately verify the feasibility of synchronous and asynchronous control and smooth transition in the method.

Interventions Targeting Sensory Challenges in Autism Spectrum Disorder: A Systematic Review.

Sensory challenges are common among children with autism spectrum disorder (ASD). To evaluate the effectiveness and safety of interventions targeting sensory challenges in ASD. Databases, including Medline and PsycINFO. Two investigators independently screened studies against predetermined criteria. One investigator extracted data with review by a second. Investigators independently assessed risk of bias and strength of evidence (SOE), or confidence in the estimate of effects. Twenty-four studies, including 20 randomized controlled trials (RCTs), were included. Only 3 studies had low risk of bias. Populations, interventions, and outcomes varied. Limited, short-term studies reported potential positive effects of several approaches in discrete skill domains. Specifically, sensory integration-based approaches improved sensory and motor skills-related measures (low SOE). Environmental enrichment improved nonverbal cognitive skills (low SOE). Studies of auditory integration-based approaches did not improve language (low SOE). Massage improved symptom severity and sensory challenges in studies with likely overlapping participants (low SOE). Music therapy studies evaluated different protocols and outcomes, precluding synthesis (insufficient SOE). Some positive effects were reported for other approaches, but findings were inconsistent (insufficient SOE). Studies were small and short-term, and few fully categorized populations. Some interventions may yield modest short-term (<6 months) improvements in sensory- and ASD symptom severity-related outcomes; the evidence base is small, and the durability of the effects is unclear. Although some therapies may hold promise, substantial needs exist for continuing improvements in methodologic rigor.

An integration-based estimation approach for the determination of slug test parameters under various flow geometries

A simple parameter estimation procedure, designated as integration-based estimation (IBE), was introduced to determine the hydraulic properties of an aquifer using slug test models subjected to certain flow geometries such as radial and spherical flows. The basic idea behind the proposed IBE approach is to link an integration value at pre-defined normalized head levels for field data with that of a theoretical type curve. The IBE method removes the need for the implementation of the classical graphical matching process which would be ineffective to acquire aquifer parameters for non-ideal aquifer conditions. As the second aspect of this study, a new decision tool was suggested to determine the suitable slug test model to be utilized for the site data since diagnosing the flow character properly is of crucial importance for following a convenient analysis procedure. The estimation performance and limitation of the proposed IBE method were tested for several slug test scenarios including radial and spherical flow models with a number of synthetically generated data sets as well as a field application. Results reveal that the IBE together with the identification methodology not only is able to retrieve aquifer parameters as reliable as the existing techniques in the literature but also diagnoses the flow character precisely as demonstrated in this study.

Helicopter Unsteady Maneuver Analysis Using Inverse Flight Dynamics Simulation and Comprehensive Analysis

This paper describes the development of an inverse flight dynamics simulation for helicopters and its application for blade loads prediction using comprehensive analysis for a helicopter undergoing unsteady maneuvers. The inverse simulation is carried out for an unsteady pull-up maneuver and the estimated control angles are used as input to University of Maryland Advanced Rotorcraft Code comprehensive analysis for prediction of blade loads. Inverse simulation is carried out using an integration-based approach using a simplified helicopter dynamics model with rigid blades having only flap degree of freedom, quasi-steady aerodynamics, and Drees inflow model. The predicted control angles and blade loads are compared against the flight-test data for a UH-60A Black Hawk helicopter. The inverse flight dynamics with simple rotor dynamics is able to predict the correct trend of variation of control angle time history for this severe maneuver. The control angle time history estimated using the linear aerodynamics-model-based inverse simulation predicts all three stalls. Advancing blade transonic stall is predicted for the first time using a coupled lifting-line-based analysis. The magnitude of negative peak of stall loads and corresponding structural loads prediction is less satisfactory.

Saddle Point Approximation for Outage Probability Using Cumulant Generating Functions

This letter proposes the use of saddle point approximation (SPA) to evaluate the outage probability of wireless cellular networks. Unlike traditional numerical integration-based approaches, the SPA approach relies on cumulant generating functions (CGFs) and eliminates the need for explicit numerical integration. The approach is generic and can be applied to a wide variety of distributions, given that their CGFs exist. We illustrate the usefulness of SPA on channel fading distributions such as Nakagami-$m$, Nakagami-$q$ (Hoyt), and Rician distributions. Numerical results validate the accuracy of the proposed SPA approach.

A Low-Power Edge Detection Technique for Sensor Wake-Up Applications

A novel low-power edge detection circuit is presented in this work. Upon the arrival of signal falling edge, the proposed design establishes a small voltage difference between the gate and source terminals of a MOS transistor which slightly increases the MOS transistor leakage current. A current integration-based approach is used to robustly sense the current change and subsequently detect the signal falling edge. The design is suitable for ultra-low-power sensor wake-up circuits. Design guidelines for achieving optimal detection sensitivity as well as the implementation of calibration circuits for coping with process variations and mismatches are discussed in the paper. Simulation results are presented to demonstrate the performance of the proposed circuit.

Flexible design of wide‐pipeline‐based WiMAX QC‐LDPC decoder architectures on FPGAs using high‐level synthesis

A novel wide-pipeline low-density parity-check (LDPC) decoder approach for the worldwide interoperability for microwave access (WiMAX) standard (802.16e) is proposed for execution on field-programmable gate arrays (FPGAs), using a high-level synthesis tool to reduce the development effort and design validation time that generates a wide-pipeline architecture. Optimised open computing language (OpenCL)-based kernels are developed and the integration of distinct configurations of single instruction multiple data and compute units to increase the level of parallelism are analysed. The decoding throughput surpasses the minimal requirements of 75 Mbit/s, a key figure of merit that ranks the design with other very large-scale integration-based approaches. Furthermore, extra precision is deployed with 8-bit fixed-point arithmetic, delivering superior bit error rate performance and lower error floor regions.

Design of Satellite Formations in Orbits of High Eccentricity withPerformance Constraints Specified over a Region of Interest

The Magnetospheric Multiscale (MMS) mission requires a formation of four satellites in a nearly regular tetrahedron throughout a Region of Interest (RoI), defined near the apogee of a highly eccentric reference orbit. In this paper, an approach for determining the differential mean orbital element initial conditions for formations in highly eccentric orbits to maximize a Quality Factor (QF) in a RoI is presented. Previous works on the design of formations in high- eccentricity orbits have used numerical integration-based approaches for orbit propagation. We present a fast optimization scheme by using the Gim-Alfriend state transition matrix. Two optimization approaches are presented for the long-term MMS formation design: a single-orbit constrained (SOC) optimization, subject to an along-track drift condition, and a multi-orbit unconstrained (MOU) optimization. A new along-track drift condition is proposed to minimize the along-track drift only in certain portions of the orbit and is shown to produce much more stable long-term behavior of the QF than the previous condition. A verification of the results sing the NASA General Mission Analysis Tool is provided. Under ideal conditions, i.e., without errors in the establishment of the initial conditions, the best formation can satisfy the MMS mission QF requirements for over 80 days without corrective action.