Deep Research Research Articles

Proposals for Improving Legal Regulation of the Institute of Authorized Economic Operator

The article discusses current issues related to the development of the institution of an authorized economic operator (hereinafter — AEO) in Russia. The study is devoted to the analysis of the regulatory framework, and also analyzes the current state of affairs. the purpose of the article is to identify problems that require discussion and deeper research in order to improve the regulation of the AEO institution; formulating conceptual proposals for its development and involving in this institute companies that really need simplifications when carrying out customs operations for conducting foreign trade activities. This study is based on the methodological approaches of domestic scientists to the problems of development of economic institutions. When conducting the study, the method of comparative analysis of legislation regulating the AEO institution was used; An analysis of scientific literature and existing data from open AEO Registers was carried out. Methods of scientific knowledge, system analysis (deduction, induction, analysis, synthesis), graphic and statistical methods, systems approach were used; methods of monographic, statistical and comparative analysis, generalization and interviews were used. results and scientific novelty of the study. The work focuses on the study of the terms underlying the AEO institution. According to the author, the term should more accurately reflect the purpose and essence of its creation. The legal regulation of the AEO institution requires a new rethinking, with prospects for the development of the institution, as laid down in the standards of the World Customs Organization, from the understanding of AEOs as national companies that have a number of simplifications when performing customs operations, to the understanding of AEOs as organizations — links in the supply chain of products between AEOs of other countries, provided they all meet trade and safety standards. A set of proposals for the development of the AEO Institute has been formed. Conclusions. The proposals concern clarification of terminology and proposals for improving the regulatory framework, in particular the development of simplifications. The federal customs services of the EAEU countries may use the provisions of this article when developing legal regulation for the development of the AEO institution. The degree of elaboration varies from a conceptual idea to the proposal of specific amendments to articles of laws.

Feature Extraction and Classification of Gray-Scale Images of Brain Tumor using Deep Learning

Deep Learning using CNN plays a paramount role for the classification methods applied on medical image data. With a crucial role in accurate diagnosis, treatment planning and patient management for medical and healthcare systems, CNNs won accolades in the Deep Learning research. As simple the learning model so precise are the results for decision making. The proposed Sequential model of CNN is built with Parametric ReLU with the values aligned to geometric mean, attains a specific goal of tumor classification. The additional support of ground-truth aid in deciding the shape and severity of tumor in the Grayscale MRI of brain tumor. The simple Sequential model, although a minimal version has proved achieved significant classification goals using the GMP-ReLU. Comparative results with variants of ReLU have been charted in this article standing with the proof of consistent classification model with parametric-ReLU. The proposed design is conducted on images from Kaggle and a model is trained (classifier is built), which can be considered as ideal filter for all the benchmark images. The accuracy of proposed design is considerably improved compared to normal ReLU up to 89.214%.

A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations

An emerging trend in deep learning research focuses on the applications of graph neural networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning frameworks operate on graphs wherein each edge connects two nodes. Inspired by the data connectivity in the finite element method, we present a method to construct a hypergraph by connecting the nodes by elements rather than edges. A hypergraph message-passing network is defined on such a node-element hypergraph that mimics the calculation process of local stiffness matrices. We term this method a finite element-inspired hypergraph neural network, in short FEIH(ϕ)-GNN. We further equip the proposed network with rotation equivariance, and explore its capability for modeling unsteady fluid flow systems. The effectiveness of the network is demonstrated on two common benchmark problems, namely the fluid flow around a circular cylinder and airfoil configurations. Stabilized and accurate temporal roll-out predictions can be obtained using the ϕ-GNN framework within the interpolation Reynolds number range. The network is also able to extrapolate moderately towards higher Reynolds number domain out of the training range.

Application of Infrared Thermography in the Rehabilitation of Patients in Veterinary Medicine.

Infrared Thermography (IRT) has become an assistance tool in medicine and is used to noninvasively evaluate heat elimination during and after inflammatory processes or during the recovery period. However, its application in veterinary patients undergoing physiotherapy is a field that requires deep research. This review aims to analyze the application of IRT in the monitoring of animal physiotherapy, using the thermal changes that are present in patients undergoing gait or lameness issues (e.g., inflammation, pain, increased local temperature) as a neurobiological basis. Rehabilitation techniques such as acupuncture, physical therapies, thermotherapy, photo-biomodulation, and electrostimulation have been reported to have an anti-inflammatory effect that decreases the amount of local heat production, which is heat that can be recorded with IRT. Therefore, IRT could be used as a complementary tool to evaluate the effectiveness of the therapy, and it is suggested that further studies evaluate the accuracy, sensibility, and sensitivity of IRT.

Automating hyperparameter optimization in geophysics with Optuna: A comparative study

AbstractDeep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two geophysics‐related deep learning examples where a grid search and Optuna framework (an automated optimization approach) were used for hyperparameter optimization. We consider two geophysical problems related to denoising seismic traces and the inversion of seismic traces for velocity information. In both cases, models created based on Optuna hyperparameter optimization were able to perform better than those created through grid search. The most significant advantage of Optuna, however, is having quantifiable results to justify the choice of a neural network architecture, depth and other hyperparameters rather than relying on inefficient methods of exploring the hyperparameter space such as a trial‐and‐error or grid search. This study aims to stimulate further exploration and adoption of these frameworks, pushing the boundaries of current deep learning based geophysical problem‐solving methodologies towards full automation.

CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation

Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients, limiting the utilization of deep learning models trained on multi-sequence data. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition. State-of-the-art methods tackling this problem are based on convolutional neural networks (CNN) which usually suffer from spectral biases, resulting in poor reconstruction of high-frequency fine details. In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation. The proposed model uses a multi-layer perceptron (MLP) instead of a CNN as the decoder for pixel-to-pixel mapping. Hence, each target image is represented as a neural field that is conditioned on the source image via shift modulation with a learned latent code. Experiments on BraTS 2018 and an in-house clinical dataset of vestibular schwannoma patients showed that the proposed method outperformed state-of-the-art methods for multi-sequence MRI translation both visually and quantitatively. Moreover, we conducted spectral analysis, showing that CoNeS was able to overcome the spectral bias issue common in conventional CNN models. To further evaluate the usage of synthesized images in clinical downstream tasks, we tested a segmentation network using the synthesized images at inference. The results showed that CoNeS improved the segmentation performance when some MRI sequences were missing and outperformed other synthesis models. We concluded that neural fields are a promising technique for multi-sequence MRI translation.

A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G-guided generative multilevel network for training 3D neural networks with poorly sampled MR input data. The author suggest using super-resolution reconstruction (SRR) and modified sparse sampling to address these issues. Image-based Wasserstein GANs retain k-space data sparsity. Wasserstein Generative Adversarial Networks (WGANs) store and represent picture space knowledge. The method obtains null-valued k-space data and repairs fill gaps in the dataset to preserve data integrity. The proposed reconstruction method processes raw data samples and is able to perform subspace synchronization, deblurring, denoising, motion estimation, and super-resolution image production. The suggested algorithm uses different preprocessing methods to deblur and denoise datasets. Preliminary trials contextualize and speed up assessments. Results indicate that reconstructed pictures have better high-frequency features than sophisticated multi-frame techniques. This is supported by rising PSNR, MAE, and IEM measurements. A k-space correction block improves GAN network refinement learning in the suggested method. This block improves the network’s ability to avoid unnecessary data, speeding reconstruction. A k-space correction module can limit the generator’s output to critical lines, allowing the reconstruction of only missing lines. This improves convergence and speeds rebuilding. This study shows that this strategy reduces aliasing artifacts better than contemporaneous and noniterative methods.

Achieving efficient and stable blue organic light-emitting diodes via suppressing triplet–polaron annihilation processes

It is still challenging to achieve blue OLEDs with sufficient color purity, lifetime and efficiency for applications. Thermally activated delayed fluorescence (TADF) materials become the most promising. However, the internal mechanism of the efficiency roll-off and long-term stability degradation still needs deep research. In this work, the energy transfer and exciton quenching processes in TADF-based blue OLEDs were analyzed under steady-state conditions and their relative contributions to efficiency roll-off were quantified. Furthermore, the degradation mechanism in the TADF-based blue OLEDs was investigated both theoretically and experimentally by a comprehensive exciton quenching model. It was demonstrated that suppressing the triplet–polaron annihilation processes and reducing the accumulation of triplet excitons through reasonable energy transfer can decrease the effect of exciton quenching, thereby significantly reduce the efficiency roll-off and improve device lifetime. It can be seen that the external quantum efficiency (EQE) of the resulting blue OLEDs was enhanced to 30.3 % with CIE coordinates of (0.16, 0.25) and kept 20.9 % at the luminance of 1000 cd/m2. More importantly, the device lifetime was also significantly enhanced, which is over three times longer than that of the unoptimized devices due to the suppress of triplet-polaron annihilation and triplet accumulation.

Multiview Deep Anomaly Detection: A Systematic Exploration.

Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.

A review of deep learning-based text sentiment analysis research

The study of textual sentiments is a growing subfield of natural language processing. Research on deep learning-based text sentiment analysis approaches has received a lot of interest as machine learning technology has advanced. There are a variety of approaches to analyzing text for sentiment, but they may be broken down into three broad categories: those that rely on neural networks, those that introduce an attention mechanism, and those that rely on pre-trained models. In this paper, the author uses a literature review approach and CNKI as the search engine to examine previous studies on deep learning-based text sentiment analysis methods and models and to categorize the evolution of this field so as to aid in the development of similar studies in the future.

Ni(Co) Laterite Deposits of Southeast Asia: A Review and Perspective

Southeast Asia has great potential for mineral exploration, and this region is well-known to host huge economic ore deposits located in complex tectonic terranes. Amongst these ore deposits, the Ni(Co) laterite deposits are mainly distributed in Indonesia, the Philippines, and Myanmar. There are two main types of Ni(Co) laterite deposits consisting of hydrous Mg silicate (or garnierite) and oxide ores, with limited development of clay silicate type. These deposits are influenced and controlled by the lithology of ultramafic bedrock, topography, climate, weathering, structures, and tectonic environment. The degree of bedrock serpentinization has an important influence on the grade of Ni laterite ore. Given the growing demand of modern society for Ni(Co) ore resources, deep research should be focused on a better understanding of the genesis of this laterite deposit and geological features of Ni(Co) ore, as well as its exploration applications in southeastern Asia. Improving current research and exploration methods by means of cutting-edge technologies can enhance the understanding of the Ni(Co) enrichment mechanism in weathered laterite and lead to the discovery of new deposits in Southeast Asia. Ni(Co) laterite deposits from this region, especially Indonesia and the Philippines, have the potential to be a source of scandium, rare earth elements, and platinum group elements.

Pengaruh Kompensasi terhadap Kinerja Karyawan dalam Perusahaan

Compensation is a key element in human resource management that has a significant impact on employee performance in the company. Fair pay and financial motivation have long been topics of deep research in a variety of disciplines, including management, economics, and psychology. Compensation has a role in helping employees perform better. Employee performance will be influenced by compensation, so to ensure that workers work professionally, organizations must consult with them to decide on compensation levels. Business effectiveness and efficiency will be influenced by the quality of human resources, which will be determined by success in determining appropriate compensation for employees. This journal uses the library research or literature review method as the main basis in the process of writing scientific articles or journals. The reference sources used in this article were obtained through Google Scholar, a platform that facilitates access to various sources of scientific literature. In this way, we can ensure that this scientific article is supported by quality and relevant sources. The implications of this research indicate that companies should consider these factors in the planning and implementation of their human resource policies to achieve optimal employee performance and ensure employee well-being. Thus, these aspects can serve as guidelines in improving human resource management in organizations.

Genomics and tumor microenvironment of breast mucoepidermoid carcinoma based on whole-exome and RNA sequencing

Mammary mucoepidermoid carcinoma (MEC) is a rare entity. The molecular characteristics of breast MEC have not been fully investigated due to its rarity. We performed a retrospective study among 1000 patients with breast carcinomas and identified four cases of breast MEC. Clinical and demographic data were collected. Immunohistochemistry panels which were used to diagnose salivary gland MEC and breast carcinomas were also performed. MAML2 rearrangements were detected by FISH and fusion partners were identified by RNA sequencing. Whole-exome sequencing (WES) was used to reveal the genomes of these four breast MEC. Then, the biological functions and features of breast MEC were further compared with those of invasive breast carcinomas and salivary gland MEC.According to Ellis and Auclair’s methods, these four breast MEC could be classified as low-grade breast MEC. All the patients were alive, and disease-free survival (PFS) ranged from 20 months to 67 months. Among these four breast MEC, two cases were triple-negative, and the other two cases were found to be ER positive, with one also showing HER2 equivocal by immunohistochemical staining, but no amplification in FISH. FISH analysis confirmed the presence of the MAML2 translocation in three of four tumors, and CRTC1-MAML2 fusion was confirmed in two of them by RNA-sequencing. The average coverage size of WES for the tumor mutation burden estimation was 32 Mb. MUC4, RP1L1 and QRICH2 mutations were identified in at least three tumors, and these mutation also existed in breast invasive carcinoma databases (TCGA, Cell 2015; TCGA, Nature 2012). The results showed that there were many genes in breast MEC overlapping with the breast invasive carcinoma databases mentioned above, range from 5 to 63 genes (median:21 genes). Next, we assessed immune cell infiltration levels in these tumors. In all these tumors, M2 macrophages and plasma cell were in the high infiltration group. Our breast MEC showed different results from the salivary gland MEC, whose plasma cells were in the low infiltration group. Overall, we first analyzed the genomics and tumor microenvironment of breast mucoepidermoid carcinoma and proposed our hypothesis that although MECs arising in the breast resemble their salivary gland counterparts phenotypically, our findings indicate that breast MECs probably resemble invasive breast carcinomas at the genetic level and immune cell infiltration levels. More cases and in deep research need to be done to further understand this rare carcinoma.

Ecological thinking in regenerative architecture: Relevance of abduction in ecoLogic Studio’s Deep Green research project

This article explores the connotations of regenerative architecture in the context of the Anthropocene, emphasizing the role of abduction on human cognition and its potential impact on innovation, especially when applied to a bio-cybernetic design method. The DeepGreen research project, developed by ecoLogicStudio in collaboration with the University of Innsbruck and the Bartlett UCL, serves as an exemplification of this method. The project combines biological and artificial intelligence to create an urban ecological infrastructure that integrates architectural and ecological systems. Centering on the DeepGreen project, this study discusses the creative potential and ecological embodiment of bio-cybernetic design protocols. It focuses on the mechanism of abduction in triggering innovative design solutions from a logical perspective, aligning with the principles of mind ecology at a cognitive level. The analysis suggests an innovative and ecological design thinking mode, asserting that regenerative architecture can encompass not only material aspects but also informational and spiritual dimensions. This approach ultimately promotes a holistic and harmonious urban environment. Furthermore, this research posits that the bio-cybernetic stance and the morphogenetic approach share commonalities with the holistic conception of nature and the ecological view in ancient Chinese Daoist philosophy. This contribution can potentially offer a contemporary interpretation of the Daoist principles of “Nature and Humans in One Unity (天人合一)” and “The Dao Imitates the Laws of Nature (道法自然).” Such an interpretation is deemed beneficial for addressing today’s concerns related to holistic, balanced, and harmonious urbanism. Henceforth, the study endeavors to facilitate discourse between Eastern and Western trends of thought, culminating in a cohesive yet heterogeneous regenerative approach.

Research on Multimodal Fusion of Temporal Electronic Medical Records.

The surge in deep learning-driven EMR research has centered on harnessing diverse data forms. Yet, the amalgamation of diverse modalities within time series data remains an underexplored realm. This study probes a multimodal fusion approach, merging temporal and non-temporal clinical notes along with tabular data. We leveraged data from 1271 myocardial infarction and 6450 stroke inpatients at a Beijing tertiary hospital. Our dataset encompassed static, and time series note data, coupled with static and time series table data. The temporal data underwent a preprocessing phase, padding to a 30-day interval, and segmenting into 3-day sub-sequences. These were fed into a long short-term memory (LSTM) network for sub-sequence representation. Multimodal attention gates were implemented for both static and temporal subsequence representations, culminating in fused representations. An attention-backtracking module was introduced for the latter, adept at capturing enduring dependencies in temporal fused representations. The concatenated results were channeled into an LSTM to yield the ultimate fused representation. Initially, two note modalities were designated as primary modes, and subsequently, the proposed fusion model was compared with comparative models including recent models such as Crossformer. The proposed model consistently exhibited superior predictive prowess in both tasks. Removing the attention-backtracking module led to performance decline. The proposed model consistently shows excellent predictive capabilities in both tasks. The proposed method not only effectively integrates data from the four modalities, but also has a good understanding of how to handle irregular time series data and lengthy clinical texts. An effective method is provided, which is expected to be more widely used in multimodal medical data representation.

LOGISTICS CLUSTERS AND THEIR POTENTIAL FOR ECONOMIC DEVELOPMENT

Supply chain resilience in the face of shocks has arisen as a policy issue due to the multilateral trading system lowering trade barriers. Innovations in transportation technology created intelligent transport systems and production fragmentation of global supply chains. However, climate shocks and the COVID-19 pandemic restructured supplies in the global markets. At the same time, geopolitical conflict and tensions emerged with Russia’s invasion of Ukraine, increased China’s military activity, introduced additional tension, and created difficulties for the supply chains. On the other hand, the current industrial policy, aiming at national economic growth, is motivated by objectives different from increasing company-level productivity or spillovers between sectors. The concern primarily revolves around supply chain resilience, the fear of weaponizing exports, supporting technological advancement, and the need for policymakers to introduce greater control over economic activity in anticipation of potential shocks. In cross-border supply chains, some governments seek to coordinate their industrial policies with the most significant partners without implementing measures at the national level (Bown, 2023). Overlaying other considerations is the existential threat of climate change, a significant driver behind many modern industrial policy initiatives. That is leading to the rebirth of the idea of logistic clusters uniting different partners in size with foreign trade-oriented activities, aiming at efficiency as well as resiliency of the logistics chains. Based mainly on deep secondary research, this paper addresses the rebirth of cluster ideas in international transport and logistic activities.

Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models

Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, datasets, and benchmarks specifically designed to tackle mathematical problems, reporting impressive achievements in disparate fields such as automated theorem proving, numerical integration, and the discovery of new conjectures or matrix multiplication algorithms. However, despite this notable success it is still unclear whether deep learning models possess an elementary understanding of quantities and numbers. This survey critically examines the recent literature, concluding that even state-of-the-art architectures and large language models often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.

Вклад профессора Р.Ю. Рахматуллина в развитие российского исламоведения (памяти друга)

The article describes the contribution of a prominent Russian philosopher professor Rafael Rakh-matullin to the development of domestic Islamic studies. His unusual entry into the academic sphere and teaching activities in higher education, his personal participation in one of the Sufi tariqas (brotherhoods) over a period of several years allowed Rafael Rakhmatullin to develop and leave a rich creative legacy on the problems of Islamic studies: both from the point of view of reli-gious studies, and from the standpoint of a deep theological research into the most important prob-lems of modern Islamic doctrine, while preserving the secular philosophical discourse of analysis.

The present situation, issues and improved schemes of localizing poetry therapy in China

Poetry therapy is a psychological intervention that uses poetic language as the core of treatment and has developed into a more mature psychotherapeutic system in Western countries. With a wave of research, the localization of poetry therapy in China has achieved some success in terms of macro-theory, meso-practice and micro-empirical evidence. However, the existing researches still exhibit following issues which mainly refer to the inadequate deep integration and comparison researches. These two issues are respectively caused by the incomplete mechanism of Western poetry therapy and researchers’ insufficient attention on the adaptation of therapeutic content in the process of localization. Consequently, a double-loop model of the mechanism of poetry therapy based on the transformation of metaphorical meaning could help to alleviate the issue of inadequate integrated research. Meanwhile, a followed therapeutic content adjustment which based on treatment culture comparison could benefit to alleviate the issue of inadequate comparative research. These improved schemes could contribute to advancing the localization of poetry therapy in China and provide a reference for how contemporary psychotherapy research can utilize the traditional culture.

A Systematic Review on Deep Learning in Education: Concepts, Factors, Models and Measurements

Deep learning, a cognitive process involving goal alignment, advanced problem-solving, and rigorous knowledge application, lacks a unified framework despite its wide application in education. This gap poses challenges for researchers, educators, and policymakers. A meticulous review of ten pertinent articles published between 2019 and 2023, aimed to define the concept, identify influencing factors, examine research models, and scrutinize measurement methods. Results show that while "deep learning" is commonly used, a clear, unified definition is absent. Influencing factors are proposed based on diverse theoretical frameworks, including individual and environmental elements. Commonly referenced models are Biggs' 3P model and Bloom's taxonomy. Data collection predominantly employs surveys, interviews, and experiments, with a bias towards quantitative analysis, supplemented by qualitative methods. It's worth mentioning that there is a growing trend in the use of mixed methods. This study provides valuable insights for the effective implementation of empirical deep learning research. It provides practical references bridging theory and practice for education researchers.