Traditional Methods Research Articles

The Therapeutic Potential of Targeting Tumor Microenvironment and Modulation of Immunotherapy in Gastrointestinal Cancer.

Immunotherapy, as a novel treatment approach for various disorders, including cancers, is designed to either stimulate or suppress the immune system with high speci-ficity. The recent achievements of this therapy in clinical trials are set to transform tradi-tional treatment methods. Furthermore, it holds promise for enhancing the survival rates of patients suffering from both metastatic cancers and primary stages. Gastrointestinal Cancers (GI) account for 26% of global incidence and 35% of worldwide deaths. Treat-ment can be carried out using targeted immunotherapy in these cancers. If the tiers are superior, improvement could require more enterprise. On account that the function of immunotherapy in GI has been so promising, solely in sufferers with severe metastatic levels, within the literature, the immune checkpoint inhibitors in cancer immunotherapy of GI cancers, chimeric antigen receptor T-cell (vehicle-T), modulators of the tumor mi-croenvironment, and drug resistance mechanisms in immunotherapy as an effective treatment approach to GI cancers along with colon, pancreas, gastric, and esophageal cancers have been addressed. This review provides an overview of FDA-approved im-munotherapy drugs and ongoing preclinical developments. Additionally, we offer in-sights into the future of immunotherapy for GI cancer patients, addressing the associated challenges.

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ~5 and ~25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

A Study of Hierarchical Risk Parity in Portfolio Construction

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.</span></p>

Development of a Marmoset Apparatus for Automated Pulling (MarmoAAP) to Study Cooperative Behaviors.

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

Here to stay? The rapid evolution of the temporary staffing market in Iceland

In recent years there has been a significant shortage of workers in Iceland. The traditional method of arranging temporary work, through direct contracts between employees and employers, has not sufficed. Moreover, there is a skills mismatch that compounds the shortage of workers as the sectors that have grown most rapidly in recent years mainly employ unskilled labor. This study examined the historical background of temporary work in Iceland, recent developments and in particular the growing importance of temporary staffing agencies, as well as the economic rationale for temporary staffing agencies, and the segmented labor market in Iceland. The study employs expert opinion approach, together with content and statistical analysis. Experts placed strongest emphasis on temporary workforce dependency on economic conditions, closely followed by sector triggered temporary workforce fluctuation. Socio-legal infrastructure for temporary workforce had the third strongest emphasis with other themes being less emphasized. These dramatic changes to the Icelandic labor market have undoubtedly had a significant impact on Icelandic society but there is surprisingly little research available into this. The efforts of unions and the Federation of Employees have helped to push through legislation on temporary staffing agencies and the rights of foreign workers, however, as long as there is economic rationale for their operation and a legal and regulatory framework that accommodates them the agencies can be expected to continue bringing temporary staff to Iceland. The impact on the labor market and society thus seems likely to be permanent.

Sia-m7G: Predicting m7G Sites through the Siamese Neural Network with an Attention Mechanism

Background: The chemical modification of RNA plays a crucial role in many biological processes. N7-methylguanosine (m7G), being one of the most important epigenetic modifications, plays an important role in gene expression, processing metabolism, and protein synthesis. Detecting the exact location of m7G sites in the transcriptome is key to understanding their relevant mechanism in gene expression. On the basis of experimentally validated data, several machine learning or deep learning tools have been designed to identify internal m7G sites and have shown advantages over traditional experimental methods in terms of speed, cost-effectiveness and robustness. Aims: In this study, we aim to develop a computational model to help predict the exact location of m7G sites in humans. Objective: Simple and advanced encoding methods and deep learning networks are designed to achieve excellent m7G prediction efficiently. Methods: Three types of feature extractions and six classification algorithms were tested to identify m7G sites. Our final model, named Sia-m7G, adopts one-hot encoding and a delicate Siamese neural network with an attention mechanism. In addition, multiple 10-fold cross-validation tests were conducted to evaluate our predictor. Results: Sia-m7G achieved the highest sensitivity, specificity and accuracy on 10-fold crossvalidation tests compared with the other six m7G predictors. Nucleotide preference and model visualization analyses were conducted to strengthen the interpretability of Sia-m7G and provide a further understanding of m7G site fragments in genomic sequences. Conclusion: Sia-m7G has significant advantages over other classifiers and predictors, which proves the superiority of the Siamese neural network algorithm in identifying m7G sites.

Hydrogenation Influences the Created Giant Dielectric Behaviors of (Bi+W) Codoped Anatase TiO2

Introductions: TiO2 nanoparticles ceramic (NPs) codoped with Bi and W ions have been synthesized by a hydrothermal technique. A portion of the prepared ceramic was posthydrogenated. Ceramic NPs were characterized by traditional methods. Crystalline structures and optical properties were investigated using X-Ray diffraction (XRD) and diffuse reflection spectroscopy, respectively. Methods: The present work has focused on the creation of a colossal (giant) dielectric permittivity (GP) behavior with the TiO2 host NCs through the Bi/W codoping to construct electronic core/shell structures. In addition, the influence of post-hydrogenation on the created GP was also examined. Results: It was found a high permittivity of 3.69×104 at 1 kHz, which was reduced to 3.29×104 by the hydrogenation of the sample. This is attributed to the densification of the itinerant electrons by the effect of the catalytic power of the doping W5+ ions to dissociate the adsorbed H2. Conclusion: The present values of GP are much higher than the permittivity of the pure TiO2 and the Bi-doped TiO2 ceramic, which was attributed to the construction of core/shell electronics structures. As a result, the doping process has been studied in detail in relation to scientific expectations.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Multiagent Trust Region Policy Optimization.

We extend trust region policy optimization (TRPO) to cooperative multiagent reinforcement learning (MARL) for partially observable Markov games (POMGs). We show that the policy update rule in TRPO can be equivalently transformed into a distributed consensus optimization for networked agents when the agents' observation is sufficient. By using a local convexification and trust-region method, we propose a fully decentralized MARL algorithm based on a distributed alternating direction method of multipliers (ADMM). During training, agents only share local policy ratios with neighbors via a peer-to-peer communication network. Compared with traditional centralized training methods in MARL, the proposed algorithm does not need a control center to collect global information, such as global state, collective reward, or shared policy and value network parameters. Experiments on two cooperative environments demonstrate the effectiveness of the proposed method.

Determination of Quercetin in Iraqi Green Walnut Husk via Green Ionic Liquid-Based Digestion Extraction and Gel Filtration Procedures-Spectrophotometry and Microfluidic Paper-Based Analytical Platform Detection

في هذه الدراسة تم استخلاص ،عزل وتقدير الكيورستين الموجود في قشور الجوز العراقي باستخدام طريقة جديدة تعتمد على الاستخلاص بطريقة الهضم المبنية على استخدام السوائل الايونية و طريقة الترشيح بالجل للفصل المقترنة مع المطيافية الضوئية للتقدير ومن ثم مقارنتها مع تقنية جديدة تسمى تقنية السوائل المايكروية الورقية مستهدفة بناء طريقة اقتصادية، صديقة للبيئة وخضراء. ولقد تم اختيار 1 بيوتيل-3-مثيل ايمادزوليم كلورايد كمذيب اخضر بديل للمذيبات التقليدية بسبب خصائصة الذوبانية .ولقد اظهرت النتائج ان 100 ملغرام من القشورتحتاج الى 15 مل من المذيب و30 دقيقة من الهضم عند درجة75°C كظروف مثلى لاعطاء افضل ناتج للمستخلص بطريقة الهضم مقارنه مع طرق الاستخلاص الاخرى .تحت هذه الظروف المثلى تم عزل الكيورستين باستخدام طريقة الترشيح بالجل وتم التاكد من المركب المعزول باستخدام تقنية كروموتوغرافيا السائل عالية الاداء و اشعة فورية تحت الحمراء .تم تقدير الكيورستين المعزول من المستخلص لونيا ( طريقة كلوريد الالمنيوم) باستخدام طريقتي المطيافية الضوئية و وتقنية السوائل المايكروية الورقية معطيا الخطية لمنحنيات المعايرة لكلتا الطريقيتين بين 10-100 مليغرام / لتر مع معامل ارتباط0.9369 و 0.7917 لكلتا الطريقتين. ومن ثم تم تقدير الفعالية المضادة للاكسدة باستخجام طريقة قوة الاختزال للمركب المعزول مظهرة فعالية مضادة للاكسدة جيدة و بين تحليل التباين (ANOVA) عدم وجود فرق كبير بين تقنية المطيافية الضوئية وتقنية السوائل المايكروية الورقية. كخلاصة يمكن اعتبار المذيب الايوني BMIMCLمذيبا امنا ومناسبا لاستخلاص المركبات النباتية ذات الخواص الطبية وكذلك فان تقنية السوائل المايكروية الورقية اثبتت مقدرتها على التحليل بكفاءة مساوية الى طرق التحليل التقليدية بالاضافة الى خصائصها الخضراء.

Recent Advances in Transition Metal Catalyzed Synthesis of C3-Substitution-free 2-Oxindole Derivatives

Abstract: 2-Oxindole unit is one of the most important scaffolds found in several alkaloids, natural products, antitumor agents, pharmaceutically important compounds, etc. Molecules containing the 2- oxindole moiety were first isolated from the cat claw plant, widely distributed in the Amazon jungle. It has now been demonstrated that these molecules are present in a wide range of chemicals derived from plant sources. The capacity of 2-oxindole to be altered by various chemical groups to provide unique biological activities can be attributed to its function as a chemical framework for creating and developing biological medications. Since the development of its first synthetic methodology, several research groups have developed protocols for producing 2-oxindole core and its bioactive derivatives. These include the traditional method and the transition/non-transition metal-catalyzed pathway for the synthesis of C3-non-substituted/C3-mono-substituted/C3-di-substituted core. Among those, C3-substitution-free 2-oxindole core synthesis is quite a challenging task, as C3-centre is very reactive. Syntheses of C3-substitution-free 2-oxindole cores have been less explored compared to other substituted 2-oxindole derivatives. In this review article, we have mainly focused on showcasing the transition metal-catalyzed synthetic methodology for the synthesis of 2-oxindoles with no substitution at C3-centre.

Stacking-Kcr: A Stacking Model for Predicting the Crotonylation Sites of Lysine by Fusing Serial and Automatic Encoder

Background: Protein lysine crotonylation (Kcr), a newly discovered important posttranslational modification (PTM), is typically localized at the transcription start site and regulates gene expression, which is associated with a variety of pathological conditions such as developmental defects and malignant transformation. Objective: Identifying Kcr sites is advantageous for the discovery of its biological mechanism and the development of new drugs for related diseases. However, traditional experimental methods for identifying Kcr sites are expensive and inefficient, necessitating the development of new computational techniques. Methods: In this work, to accurately identify Kcr sites, we propose a model for ensemble learning called Stacking-Kcr. Firstly, extract features from sequence information, physicochemical properties, and sequence fragment similarity. Then, the two characteristics of sequence information and physicochemical properties are fused using automatic encoder and serial, respectively. Finally, the fused two features and sequence fragment similarity features are then respectively input into the four base classifiers, a meta classifier is constructed using the first level prediction results, and the final forecasting results are obtained. Results: The five-fold cross-validation of this model has achieved an accuracy of 0.828 and an AUC of 0.910. This shows that the Stacking-Kcr method has obvious advantages over traditional machine learning methods. On independent test sets, Stacking-Kcr achieved an accuracy of 84.89% and an AUC of 92.21%, which was higher than 1.7% and 0.8% of other state-of-the-art tools. Additionally, we trained Stacking-Kcr on the phosphorylation site, and the result is superior to the current model. Conclusion: These outcomes are additional evidence that Stacking-Kcr has strong application potential and generalization performance.

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation.

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

Judgment Prediction Based on Tensor Decomposition With Optimized Neural Networks.

In the field of smart justice, handling legal cases through artificial intelligence technology is a research hotspot. Traditional judgment prediction methods are mainly based on feature models and classification algorithms. The former is difficult to describe cases from multiple angles and capture the correlation information between different case modules, while requires a wealth of legal expertise and manual labeling. The latter is unable to accurately extract the most useful information from case documents and produce fine-grained predictions. This article proposes a judgment prediction method based on tensor decomposition with optimized neural networks, which consists of OTenr, GTend, and RnEla. OTenr represents cases as normalized tensors. GTend decomposes normalized tensors into core tensors using the guidance tensor. RnEla intervenes in a case modeling process in GTend by optimizing the guidance tensor, so that core tensors represent tensor structural and elemental information, which is most conducive to improving the accuracy of judgment prediction. RnEla consists of the similarity correlation Bi-LSTM and optimized Elastic-Net regression. RnEla takes the similarity between cases as an important factor for judgment prediction. Experimental results on real legal case dataset show that the accuracy of our method is higher than that of the previous judgment prediction methods.

PSMA PET/MR is a New Imaging Option for Identifying Glioma Recurrence and Predicting Prognosis.

Glioma is characterized by a high recurrence rate, while the results of the traditional imaging methods (including magnetic resonance imaging, MRI) to distinguish recurrence from treatment-related changes (TRCs) are poor. Prostate-specific membrane antigen (PSMA) (US10815200B2, Deutsches Krebsforschungszentrum, German Cancer Research Center) is a type II transmembrane glycoprotein overexpressed in glioma vascular endothelium, and it is a promising target for imaging and therapy. The study aimed to assess the performance of PSMA positron emission tomography/ magnetic resonance (PET/MR) for diagnosing recurrence and predicting prognosis in glioma patients. Patients suspected of glioma recurrence who underwent 18F-PSMA-1007 PET/MR were prospectively enrolled. Eight metabolic parameters and fifteen texture features of the lesion were extracted from PSMA PET/MR. The ability of PSMA PET/MR to diagnose glioma recurrence was investigated and compared with conventional MRI. The diagnostic agreement was assessed using Cohen κ scores and the predictive parameters of PSMA PET/MR were obtained. Kaplan-Meier method and Cox proportional hazard model were used to analyze recurrence- free survival (RFS) and overall survival (OS). Finally, the expression of PSMA was analyzed by immunohistochemistry (IHC). Nineteen patients with a mean age of 48.11±15.72 were assessed. The maximum tumorto- parotid ratio (TPRmax) and texture features extracted from PET and T1-weighted contrast enhancement (T1-CE) MR showed differences between recurrence and TRCs (all p <0.05). PSMA PET/MR and conventional MRI exhibited comparable power in diagnosing recurrence with specificity and PPV of 100%. The interobserver concordance was fair between the two modalities (κ = 0.542, p = 0.072). The optimal cutoffs of metabolic parameters, including standardized uptake value (SUV, SUVmax, SUVmean, and SUVpeak) and TPRmax for predicting recurrence were 3.35, 1.73, 1.99, and 0.17 respectively, with the area under the curve (AUC) ranging from 0.767 to 0.817 (all p <0.05). In grade 4 glioblastoma (GBM) patients, SUVmax, SUVmean, SUVpeak, TBRmax, TBRmean, and TPRmax showed improved performance of AUC (0.833-0.867, p <0.05). Patients with SUVmax, SUVmean, or SUVpeak more than the cutoff value had significantly shorter RFS (all p <0.05). In addition, patients with SUVmean, SUVpeak, or TPRmax more than the cutoff value had significantly shorter OS (all p <0.05). PSMA expression of glioma vascular endothelium was observed in ten (10/11, 90.9%) patients with moderate-to-high levels in all GBM cases (n = 6/6, 100%). This primitive study shows multiparameter PSMA PET/MR to be useful in identifying glioma (especially GBM) recurrence by providing excellent tumor background comparison, tumor heterogeneity, recurrence prediction and prognosis information, although it did not improve the diagnostic performance compared to conventional MRI. Further and larger studies are required to define its potential clinical application in this setting.

One-Stage Shifted Laplacian Refining for Multiple Kernel Clustering.

Graph learning can effectively characterize the similarity structure of sample pairs, hence multiple kernel clustering based on graph learning (MKC-GL) achieves promising results on nonlinear clustering tasks. However, previous methods confine to a "three-stage" scheme, that is, affinity graph learning, Laplacian construction, and clustering indicator extracting, which results in the information distortion in the step alternating. Meanwhile, the energy of Laplacian reconstruction and the necessary cluster information cannot be preserved simultaneously. To address these problems, we propose a one-stage shifted Laplacian refining (OSLR) method for multiple kernel clustering (MKC), where using the "one-stage" scheme focuses on Laplacian learning rather than traditional graph learning. Concretely, our method treats each kernel matrix as an affinity graph rather than ordinary data and constructs its corresponding Laplacian matrix in advance. Compared to the traditional Laplacian methods, we transform each Laplacian to an approximately shifted Laplacian (ASL) for refining a consensus Laplacian. Then, we project the consensus Laplacian onto a Fantope space to ensure that reconstruction information and clustering information concentrate on larger eigenvalues. Theoretically, our OSLR reduces the memory complexity and computation complexity to O(n) and O(n2) , respectively. Moreover, experimental results have shown that it outperforms state-of-the-art MKC methods on multiple benchmark datasets.

Plant Adaptation and Tolerance to Heat Stress: Advance Approaches and Future Aspects.

Heat stress impacts plant growth at all phases of development, although the particular threshold for heat tolerance varies significantly across different developmental stages. During seed germination, elevated temperatures can either impede or completely halt the process, contingent upon the plant type and the severity of the stress. During advanced stages, high temperatures can have a negative impact on photosynthesis, respiration, water balance, and membrane integrity. Additionally, they can also influence the levels of hormones and primary and secondary metabolites. In addition, during the growth and development of plants, there is an increased expression of various heat shock proteins, as well as other proteins related to stress, and the generation of reactive oxygen species (ROS). These are significant plant responses to heat stress. Plants employ several strategies to deal with heat stress, such as maintaining the stability of their cell membranes, removing harmful reactive oxygen species (ROS), producing antioxidants, accumulating and adjusting compatible solutes, activating mitogen-activated protein kinase (MAPK) and calcium-dependent protein kinase (CDPK) cascades, and, crucially, signaling through chaperones and activating transcription. These molecular-level systems boost the ability of plants to flourish in heat stress. Potential genetic methods to enhance plant heat stress resistance encompass old and modern molecular breeding techniques and transgenic approaches, all of which rely on a comprehensive comprehension of these systems. Although several plants exhibit enhanced heat tolerance through traditional breeding methods, the effectiveness of genetic transformation techniques has been somewhat restricted. The latter results from the current constraints in our understanding and access to genes that have known impacts on plant heat stress tolerance. However, these challenges may be overcome in the future. Besides genetic methods, crops' heat tolerance can be improved through the pre-treatment of plants with various environmental challenges or the external application of osmoprotectants such as glycine betaine and proline. Thermotolerance is achieved through an active process in which plants allocate significant energy to maintain their structure and function to avoid damage induced by heat stress. The practice of nanoparticles has been shown to upgrade both the standard and the quantity of produce when crops are under heat stress. This review provides information on the effects of heat stress on plants and explores the importance of nanoparticles, transgenics, and genomic techniques in reducing the negative consequences of heat stress. Furthermore, it explores how plants might adapt to heat stress by modifying their biochemical, physiological, and molecular reactions.

Machine-Learning Metacomputing for Materials Science Data

Abstract Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine-learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency to “over fit” to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine-learning model in order to process an input dataset. In addition, a unique “cross-training” methodology is used to incorporate underlying physics or multiphysics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing “gaps” in a multiphysics dataset, improving the output of electron back-scatter detection crystallographic measurements, removing spurious artifacts from X-ray microtomography data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering.

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare.

Computer-aided diagnosis (CAD) has always been an important research topic for applying artificial intelligence in smart healthcare. Sufficient medical data are one of the most critical factors in CAD research. However, medical data are usually obtained in chronological order and cannot be collected all at once, which poses difficulties for the application of deep learning technology in the medical field. The traditional batch learning method consumes considerable time and space resources for real-time medical data, and the incremental learning method often leads to catastrophic forgetting. To solve these problems, we propose a real-time medical data processing method based on federated learning. We divide the process into the model stage and the exemplar stage. In the model stage, we use the federated learning method to fuse the old and new models to mitigate the catastrophic forgetting problem of the new model. In the exemplar stage, we use the most representative exemplars selected from the old data to help the new model review the old knowledge, which further mitigates the catastrophic forgetting problem of the new model. We use this method to conduct experiments on a simulated medical real-time data stream. The experimental results show that our method can learn a disease diagnosis model from a continuous medical real-time data stream. As the amount of data increases, the performance of the disease diagnosis model continues to improve, and the catastrophic forgetting problem has been effectively mitigated. Compared with the traditional batch learning method, our method can significantly save time and space resources.