Traditional Model Research Articles

Dynamic localization of the chromosomal passenger complex in trypanosomes is controlled by the orphan kinesins KIN-A and KIN-B.

The chromosomal passenger complex (CPC) is an important regulator of cell division, which shows dynamic subcellular localization throughout mitosis, including kinetochores and the spindle midzone. In traditional model eukaryotes such as yeasts and humans, the CPC consists of the catalytic subunit Aurora B kinase, its activator INCENP, and the localization module proteins Borealin and Survivin. Intriguingly, Aurora B and INCENP as well as their localization pattern are conserved in kinetoplastids, an evolutionarily divergent group of eukaryotes that possess unique kinetochore proteins and lack homologs of Borealin or Survivin. It is not understood how the kinetoplastid CPC assembles nor how it is targeted to its subcellular destinations during the cell cycle. Here, we identify two orphan kinesins, KIN-A and KIN-B, as bona fide CPC proteins in Trypanosoma brucei, the kinetoplastid parasite that causes African sleeping sickness. KIN-A and KIN-B form a scaffold for the assembly of the remaining CPC subunits. We show that the C-terminal unstructured tail of KIN-A interacts with the KKT8 complex at kinetochores, while its N-terminal motor domain promotes CPC translocation to spindle microtubules. Thus, the KIN-A:KIN-B complex constitutes a unique 'two-in-one' CPC localization module, which directs the CPC to kinetochores from S phase until metaphase and to the central spindle in anaphase. Our findings highlight the evolutionary diversity of CPC proteins and raise the possibility that kinesins may have served as the original transport vehicles for Aurora kinases in early eukaryotes.

A FEA-Based Fast AC Steady-State Algorithm for a Voltage-Driven PMSM by a Novel Voltage-Flux-Driven Model

Finite element analysis (FEA) has been used more frequently than ever before in electrical machine design covering different disciplines such as electromagnetics, rotor dynamics, thermodynamics etc. The accuracy and speed of FEA are in high demands, especially when the time-stepping FEA is employed in the electromagnetic design of the machine. In this paper, to accelerate numerical transient in FEA of a voltage-driven permanent magnet synchronous machine (PMSM), a fast AC steady-state algorithm has been proposed, in which the voltage supply is converted into the stator flux linkage and the machine can be regarded as driven by the flux linkage directly. The electromagnetic transient is eliminated by injecting additional voltage signals to compensate the oscillating stator flux linkage in transient and the time-stepping error caused by the backward Euler in FEA. And the additional voltage signals will finally disappear after the steady state is achieved within about a half electrical time period. In addition, the maximum torque per ampere (MTPA) control logic can also be realized by adjusting the initial phase angle of the voltage supply based on the vector diagram. The simulation speed of the proposed method in this paper was compared to other traditional models and the accuracy was verified by experimental results.

The Convergence Analysis of Evolutionary Dynamics for Continuous Action Iterated Dilemma in Information Loss Networks

In this article, we propose a convergence analysis method for evolution dynamics in information loss networks, which overcomes the analytical difficulties caused by complex network relationships. Evolutionary game theory is a widely-used tool for analyzing player behavior in networks, where players typically adopt binary strategies, either cooperation or defection. However, player behavior in real-world scenarios is often multidimensional and complex, and thus a dynamic model of continuous action iterated dilemma (CAID) with continuous strategy is proposed to enrich the strategies of players, allowing them to choose intermediate states between cooperation and defection, providing a more accurate representation of the evolution of cooperation than traditional dynamic models. Meanwhile, the convergence of traditional models is often analyzed using Jacobian matrices, which requires a significant amount of derivation related to the complex network structure, leading to inefficiencies. As such, a new convergence analysis method based on the Lyapunov function has been designed to circumvent these complex calculations. Additionally, as there is often noise present during the transfer of information between players, we further analyze the convergence of dynamic models in information loss networks using the Lyapunov function. Two examples based on the prisoner’s dilemma and snowdrift dilemma on networks are proposed to show the effectiveness of the designed convergence analysis.

Cloud service ranking with an integration of k-means algorithm and decision-making trail and evaluation laboratory approach

The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.

Guava fruit disease identification based on improved convolutional neural network

Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.

Structure-Aware Prototypical Neural Process for Few-Shot Graph Classification.

Graph classification plays an important role in a wide range of applications from biological prediction to social analysis. Traditional graph classification models built on graph kernels are hampered by the challenge of poor generalization as they are heavily dependent on the dedicated design of handcrafted features. Recently, graph neural networks (GNNs) become a new class of tools for analyzing graph data and have achieved promising performance. However, it is necessary to collect a large number of labeled graph data for training an accurate GNN, which is often unaffordable in real-world applications. Therefore, it is an open question to build GNNs under the condition of few-shot learning where only a few labeled graphs are available. In this article, we introduce a new Structure-aware Prototypical Neural Process (SPNP for short) for a few-shot graph classification. Specifically, at the encoding stage, SPNP first employs GNNs to capture graph structure information. Then, SPNP incorporates such structural priors into the latent path and the deterministic path for representing stochastic processes. At the decoding stage, SPNP uses a new prototypical decoder to define a metric space where unseen graphs can be predicted effectively. The proposed decoder, which contains a self-attention mechanism to learn the intraclass dependence between graphs, can enhance the class-level representations, especially for new classes. Furthermore, benefited from such a flexible encoding-decoding architecture, SPNP can directly map the context samples to a predictive distribution without any complicated operations used in previous methods. Extensive experiments demonstrate that SPNP achieves consistent and significant improvements over state-of-the-art methods. Further discussions are provided toward model efficiency and more detailed analysis.

Developing a restaurant recommended system via the Vietnamese food image classification

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.

Training Demand Prediction Models by Decision Error for Two-Stage Lot-Sizing Problems

Demand prediction to support appropriate production decisions is being actively studied. Many prediction models are designed to minimize the prediction error, which is measured by determining the difference between the predicted and ground-truth demand. However, these models ignore the effect of the prediction error on downstream production decisions. This prompted our study, which focuses on demand prediction models for two-stage uncapacitated lot-sizing problems. In this paper, we present a prediction model that minimizes the decision error, which is measured by the optimization objective of lot-sizing problems. Our model mitigates the impact of prediction errors by leveraging the structure of the lot-sizing problems. To enhance the ability to accommodate imperfect data, such as data based on inaccurate information, we subsequently extend the prediction models to distributionally robust versions. We consider the worst-case formulation in the feature space to enhance the robustness of the model to data imperfection. Numerical experiments demonstrate that the proposed prediction models are significantly superior to the traditional prediction methods when the model being trained is misspecified. In addition, the robust extension enables the models to train well on imperfect datasets while requiring less training data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study is motivated by the two-stage lot-sizing problem in manufacturing systems which involves the determination of the time at which (the setup decisions) to produce before the demand is revealed, and the production quantity decisions are adjusted during production processing. Predicting the demand properly while making the set-up decisions helps to decrease the production cost. This paper proposes a novel method that aims to minimize the total production cost to train demand prediction models. Practitioners can deploy the proposed method to train popular prediction models including linear prediction, decision tree, and neural network models. It is shown that the production cost associated with the decisions based on our models is lower than that associated with the traditional prediction models. Furthermore, our method enhances the ability of the model to handle model misspecification and imperfect data. Practitioners can apply our method to overcome the problems caused by imperfect data such as those containing contaminated and inaccurate measurements.

Limiting Temperature Rise in Cochlear Implantation Bone Drilling: A Novel Approach.

In the process of cochlear implantation surgery, it is crucial to develop a method to control the temperature during the drilling of the implant channel since high temperatures can result in damage to bone and nerve tissue. This paper simplified the traditional point heat source temperature rise model and proposed a novel extreme peck drilling model to quantitatively calculate the maximum temperature rise value. It is also innovatively introduced a new method for calculating the best peck drilling duty cycle to strictly control the maximum temperature rise value. Besides, the neural network is trained with virtual data to identify two important thermal parameters in the temperature rise model. In the experiment of epoxy resin and temporal bone, the difference between predicted maximum temperature and actual maximum temperature was less than 1.5 °C, and the error rate was less than 10%. And the error source was analyzed by variational mode decomposition, along with discussion of potential solutions. In the temperature control experiment, the model successfully controlled the maximum temperature rise within 10 °C.For cochlear implantation surgery, we also divide the implantation channel into different stages based on the bone density in CT images to identify thermal parameters and calculate drilling strategies. This method provides a new strategy for accurate and effective control of borehole heat generation. These achievements provide new ideas and directions for research in cochlear implantation surgery and related fields, and are expected to have extensive application in medical practice.

A deep dive into enhancing frequency stability in integrated photovoltaic power grids

Voltage control strategies (VCS) and frequency stability analysis (FSA) are essential for power system reliability, particularly during high-load periods. Stable voltage and frequency levels prevent malfunction, power quality deterioration, and supply interruptions. Grid operators must skillfully manage VCS and FSA control to ensure system stability. Nonlinear loads, especially under transient conditions, significantly affect voltage stability (VS), introducing harmonics, waveform distortion, and stability complexities. Accurate modeling of these nonlinear loads is vital when traditional static load models fall short. Frequency fluctuations from power generation-demand imbalances require vigilant monitoring and regulation. Effective frequency control mechanisms are indispensable for preserving desired frequencies. Using a Western Algeria case study, this paper underscores FSA's significance in integrating photovoltaic (PV) systems into power grids. It addresses challenges from frequency fluctuations due to dynamic ZIP load profiles, emphasizing the importance of FSA for reliable grid operation. The study offers insights and practical approaches to enhance VS, FSA control, and energy management (EM), improving grid reliability and ensuring uninterrupted power supply. We must look into FSA's benefits in integrating PV systems to improve performance and lower grid interruptions. This includes looking into its control mechanisms and feedback systems.

Dynamic Recommendation Based on Graph Diffusion and Ebbinghaus Curve

Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pair-wise link. Besides, different users usually perform differently for their long-term and short-term tastes, leaving traditional sequential models far from personalized. In this article, we propose a novel recommendation model based on Graph Diffusion and Ebbinghaus Curve. Specifically, to explore the underline reasons for different interactions, we explore an underlying sub-graph for each interaction and find important reasoning paths within the sub-graph via a well-designed graph diffusion method. To capture users’ personalized strategies on long-term and short-term tastes, we are inspired by the Ebbinghaus Curve, which can naturally describe users’ memory patterns, and design an effective neural network to process users’ evolving behaviors. We conduct extensive experiments on four real-world datasets and the results further confirm the superiority of our model compared with existing state-of-the-art baselines.

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.

ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation.

Graph convolutional networks (GCNs) have shown success in many graph-based applications as they can combine node features and graph topology to obtain expressive embeddings. While there exist numerous GCN variants, a typical graph convolution layer uses neighborhood aggregation and fully-connected (FC) layers to extract topological and node-wise features, respectively. However, when the receptive field of GCNs becomes larger, the tight coupling between the number of neighborhood aggregation and FC layers can increase the risk of over-fitting. Also, the FC layer between two successive aggregation operations will mix and pollute features in different channels, bringing noise and making node features hard to converge at each channel. In this article, we explore graph convolution without FC layers. We propose scale graph convolution, a new graph convolution using channel-wise scale transformation to extract node features. We provide empirical evidence that our new method has lower over-fitting risk and needs fewer layers to converge. We show from both theoretical and empirical perspectives that models with scale graph convolution have lower computational and memory costs than traditional GCN models. Experimental results on various datasets show that our method can achieve state-of-the-art results, in a cost-effective fashion.

It Is High Time for Serious Women

Abstract This article aims to highlight aspects of female emancipation in the Kingdom of Poland in the second half of the nineteenth century that gave this movement a specific, national character. The political and economic consequences of the January Uprising (1863–1864) hit the landed gentry particularly hard. As a result, many gentry women faced the necessity of earning a living and this forced a change in their way of life and aspirations. The traditional model for elite women did not match the economic challenges, and with this in mind, the Polish press, especially magazines for female readers, aimed to create a new model of gentry womanhood. Most periodicals agreed that women's emancipation was on a certain level necessary. But to what extent? This question has been a subject of dispute. Some columnists postulated to limit women's professional aspirations to manual occupations in order to preserve the domain of prestigious intellectual labor exclusively for men. This conservative movement succeeded only partially, with some upper-class women devoting themselves to handicrafts while others, against conservatives’ wishes, graduated from universities abroad and obtained qualifications for performing previously male professions. The majority of these educated women combined paid labor with social work. They were strongly influenced by the promoted ideal of a “new Polish woman” who was to be hard working, constantly improving her mental faculties, and engaging in activities that benefitted Polish society and the nation. This model contributed significantly to the formation of a new generation of distinguished female activists.

387 Adaptive Priming Meets Innate Killing: TCR-Activated CD8+ T Cells Can Kill Tumor Cells in an MHC-Independent Manner via NKG2D

INTRODUCTION: The accepted paradigm for antitumor immunity relies upon tumor cell kill by CD8+ T cells recognizing cognate antigens presented in the context of target cell major histocompatibility complex class I (MHC-I) molecules. METHODS: We sought to better evaluate the role of MHC-I expression within the tumor microenvironment. RESULTS: T cell-activating immunotherapies, surprisingly, remained effective against glioma and melanoma lines engineered to lack MHC-I expression. Such efficacy proved independent of NK cells and, instead, relies consistently upon CD8+ T cell-mediated cytotoxicity, despite the absence of MHC-I on tumor targets. This cytotoxicity is both antigen- and MHC-agnostic and, instead, is mediated by T cell NKG2D engagement of NKG2D ligands on tumor cells, which are upregulated on MHC-loss variants. Necessarily, tumor cell kill in these instances is antigen-independent, although prior T cell receptor activation is required and can be furnished by myeloid cells or even neighboring MHC-replete tumors cells. In this manner, adaptive priming can beget innate killing. These mechanisms are active in vivo in mice, as well as in vitro in human tumor systems, and are obviated by NKG2D knockout or blockade. Tumor cell killing following T cell NKG2D engagement is Fas-independent and appears to involve granzyme. CONCLUSIONS: Such findings challenge the traditional model of T cell-mediated tumor immunity and likewise identify the NKG2D/NKG2DL axis as a novel therapeutic target for enhancing T cell killing of MHC loss variants.

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup

Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on 𝕏 (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.

Revolutionizing Cardiovascular Health: A Machine Learning Approach for Predictive Analysis and Personalized Intervention in Heart Disease

Abstract: Cardiovascular Diseases (CVDs) continue to be a leading cause of global morbidity and mortality, necessitating innovative approaches for early detection and personalized interventions. This research explores the transformative potential of machine learning (ML) in revolutionizing cardiovascular health. Leveraging advanced predictive analytics, our study employs a comprehensive dataset of cardiovascular parameters, incorporating clinical, genetic, and lifestyle factors. The machine learning model developed demonstrates remarkable accuracy in predicting the risk of heart disease, enabling early identification of individuals susceptible to CVD. This predictive analysis empowers healthcare professionals with a powerful tool for pre-emptive intervention and tailored treatment strategies. By considering individual variations in genetic predispositions, lifestyle choices, and clinical data, our approach moves beyond traditional risk assessment models, paving the way for a more personalized and effective healthcare paradigm. Furthermore, the integration of real-time monitoring devices and continuous data streams enhances the adaptability and responsiveness of our model. This allows for dynamic adjustments in treatment plans, ensuring ongoing optimization based on the evolving health status of each patient. The synergy between machine learning and cardiovascular health not only augments diagnostic precision but also facilitates a proactive healthcare ecosystem that prioritizes preventive measures.

Inventory Optimization Using an AI-Powered Holistic Model for Decision-Support System

Abstract: This study presents a groundbreaking approach to inventory optimization through the implementation of an AIpowered holistic model within a decision-support system. Leveraging machine learning algorithms, specifically Logistic Regression (LR) and Decision Trees, the methodology explores historical sales data, supplier metrics, and market trends during an extensive Exploratory Data Analysis phase. The implementation of an AI-powered holistic model for inventory optimization in a decision-support system presents a transformative approach to managing and enhancing supply chain efficiency. The proposed methodology, integrating machine learning algorithms such as Logistic Regression and Decision Trees, has demonstrated its efficacy in achieving superior results compared to traditional models, as evidenced by higher accuracy, precision, recall, and F1 scores. The comprehensive exploration of historical sales data, supplier metrics, and market trends during the exploratory data analysis phase has facilitated a nuanced understanding of inventory dynamics. The seamless integration of the AI-powered model into the decision-support system has empowered organizations with timely and data-driven insights, fostering more agile and informed decision-making. The seamless integration of the AI-powered model into the decision-support system provides organizations with timely and data-driven insights, fostering agile and informed decisionmaking. Performance evaluation, including accuracy, precision, recall, and F1 scores, reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics. The LR model exhibits commendable precision, recall, and F1 score values of 0.95, 0.97, and 0.98, respectively, while the Decision Tree model demonstrates even higher values, with precision and recall at 0.96 and an exceptionally high F1 score of 0.99. These outcomes underscore the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices. While acknowledging challenges and limitations, this research signifies a crucial advancement in establishing responsive and intelligent decision-support systems for inventory optimization, paving the way for future innovations in supply chain management

Research on the Remaining Life Prediction Method of Rolling Bearings Based on Optimized TPA-LSTM

The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series information in the process of the life prediction of rolling bearings, which leads to a decline in prediction accuracy. Therefore, a method for predicting the remaining useful life (RUL) of rolling bearings based on the combination of temporal pattern attention mechanism (TPA) and LSTM is proposed. The method firstly combines hierarchical clustering and principal component analysis (PCA) to construct a multi-faceted and multi-scale preferred feature set reflecting the degradation information of rolling bearings, then strengthens the information correlation between hidden layers of the LSTM model through TPA and optimates the parameters of the fusion model of TPA and LSTM by using the gazelle optimization algorithm (GOA). Finally, the model is applied to the experimental data set of rolling bearing degradation. The results show that, compared with the traditional model, this method is more suitable for the remaining life prediction of rolling bearings.