Local Part Research Articles

Principle and Application for Rumination Computing Algorithms

To fully analyze, mine, and utilize the information and knowledge implied in problem resolving use cases, this paper proposed the autonomous learning method based on machine inducting, hypothesis formulating, and result verifying, which was similar to the biological process of cows ruminating, called rumination computing. Firstly, after inducting and summarizing over 1080 mathematic application problem, the system architecture and general algorithm for humanoid automatic resolving mathematic application problems were represented, which typically included functional modules such as commonsense knowledge base, domain knowledge base, and local knowledge base, preprocessing, word segmentation and part of speech tagging, semantic framework matching, global semantic analyzing, thinking mechanism implementing, etc. Secondly, after the use case solutions were approved, three typical rumination computing modes, including vocabulary sequence, semantic relationship, and computing action, were introduced based on the correct results, resolving steps, and basic rumination actions. The rumination computing step plan was formulated, new knowledge was obtained from the commonsense and results verification, so the continuous autonomous learning loop for machine thinking was formed. Detailed explanations were provided for the three core algorithms implemented (rumination framework algorithm, rumination semantic algorithm, rumination action algorithm). Then, by specific mathematic application problem humanoid resolving user cases, the above three types of rumination computing modes were illumined.

An Algorithm for Creating a Synaptic Cleft Digital Phantom Suitable for Further Numerical Modeling

One of the most significant applications of mathematical numerical methods in biology is the theoretical description of the convectional reaction–diffusion of chemical compounds. Initial biological objects must be appropriately mimicked by digital domains that are suitable for further use in computational modeling. In the present study, an algorithm for the creation of a digital phantom describing a local part of nervous tissue—namely, a synaptic contact—is established. All essential elements of the synapse are determined using a set of consistent Boolean operations within the COMSOL Multiphysics software 6.1. The formalization of the algorithm involves a sequence of procedures and logical operations applied to a combination of 3D Voronoi diagrams, an experimentally defined inner synapse area, and a simple ellipsoid under different sets of biological parameters. The obtained digital phantom is universal and may be applied to different types of neuronal synapses. The clear separation of the designed domains reveals that the boundary’s conditions and internal flux dysconnectivity functions can be set up explicitly. Digital domains corresponding to the parts of a synapse are appropriate for further application of the derived numeric meshes, with various capacities of the included elements. Thus, the obtained digital phantom can be effectively used for further modeling of the convectional reaction–diffusion of chemical compounds in nervous tissue.

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing highdimensional microimage data. We propose a novel finegrained classification model, SCKansformer, for bone marrow blood cells, which addresses these challenges and enhances classification accuracy and efficiency. The model integrates the Kansformer Encoder, SCConv Encoder, and Global-Local Attention Encoder. The Kansformer Encoder replaces the traditional MLP layer with the KAN, improving nonlinear feature representation and interpretability. The SCConv Encoder, with its Spatial and Channel Reconstruction Units, enhances feature representation and reduces redundancy. The Global-Local Attention Encoder combines Multi-head Self-Attention with a Local Part module to capture both global and local features. We validated our model using the Bone Marrow Blood Cell FineGrained Classification Dataset (BMCD-FGCD), comprising over 10,000 samples and nearly 40 classifications, developed with a partner hospital. Comparative experiments on our private dataset, as well as the publicly available PBC and ALL-IDB datasets, demonstrate that SCKansformer outperforms both typical and advanced microcell classification methods across all datasets.

Classification of solid and liquid structures using a deep neural network unveils origin of dynamical heterogeneities in supercooled liquids

As the temperature decreases, the dynamics of supercooled liquids significantly slow down and become increasingly heterogeneous in space. Many previous studies have found that static structures also become heterogeneous and are spatially correlated with the dynamical heterogeneity. However, there are still debates on whether the dynamical heterogeneity is controlled by the structures, and which structural order parameters should be used to describe the structural heterogeneities (if exist) in amorphous systems. The appropriate order parameter depends on the specific details of the system and needs to be determined for each system. To address this difficulty, here, we use a machine-learning-based method that was trained solely by the static structures. This method combines convolutional neural networks and gradient-weighted class activation mapping, providing interpretable characteristic structures, which can quantify the degrees of liquid-like and solid-like structures in every local part of the system. We apply this method to a canonical glass-forming system and demonstrate that particles in the liquid-like structures are mobile, while those in the solid-like structures are immobile. The present work develops a novel approach to accurately characterize amorphous structures, which will be particularly useful for systems where appropriate structural order parameters have not yet been identified.

Study of the mechanism of local thermal sensation affecting overall thermal sensation in Xizang people under asymmetrical dressing

Asymmetrical dressing behavior on the plateau, known as the third pole of the world, leads to varying thermal perception in different body parts. Research on how local parts characterize the overall thermal sensation of the Xizang people has attracted widespread attention. This study used the stepwise regression method to identify which local body parts significantly affect overall thermal sensation and the model for local and overall thermal sensation in the Xizang people under asymmetrical dressing was established. Moreover, the model was optimized considering overall thermal difference and thermal asymmetry degree between the sleeve-on and sleeve-off sides. The results showed asymmetrical dressing angle (ADA) affected the local thermal sensation, resulting in a distribution of “left hot and right cold” for the shoulder, arm, chest, and back. The local thermal sensation range of the Xizang people expands by 1 scale when the ADA increases by every 29°. In cold environments, the peak of thermal comfort percentage at 15° ADA increases by 3.1%/°C. The model elucidated the relationship between local and overall thermal sensation under asymmetrical dressing, with a high fit of 0.97. This study provides insights into the influence mechanism of local and overall thermal sensation of the Xizang people.

Calculating the local and overall view factors of a multi-segment human model

In non-uniform environments, such as spaces with radiant ceilings or floors, or with hot or cold windows, view factors (VFs) of a person towards these surfaces are needed for calculating the heat exchanges between the person and the surroundings. Multi-body segmented human models can simulate the heat exchanges of individual body parts with the environment. Simulating heat transfer at the local body parts is important for assessing comfort in non-uniform environments. These models require VFs for each individual body part. Accurately determining these VFs is time-consuming, often requiring methods such as ray-tracing techniques. A fast calculation method to accurately determine the view factor (VF) for each segment has yet to be investigated. This study employed a 16-segment human model into various architectural radiation scenarios. The surface-to-surface model in computational fluid dynamics was utilized to simulate VFs for the 16 body segments in relation to radiant surfaces at different locations. Through a series of simulations, comprehensive formulas for VFs were derived for each individual at various positions within a room, encompassing 16 body segments and computing parameters for 24 building surfaces. The accuracy of these general formulas was validated by comparing the whole-body VF with results from several available studies. Additionally, this study developed a visualization tool for calculating VF and mean radiant temperatures with different room space dimension and embedded radiant surfaces. The users can use this online tool to easily calculate VFs and mean radiant temperatures for each body parts, and for the whole-body. The tool offers valuable insights for predicting thermal comfort in asymmetric radiant environments and facilitating control over building environments.

Hierarchical Organ-Aware Total-Body Standard-Dose PET Reconstruction From Low-Dose PET and CT Images.

Positron emission tomography (PET) is an important functional imaging technology in early disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably increases the exposure risk to patients. To reduce dosage, a lower dose tracer is often used and injected into patients. However, this often leads to low-quality PET images. In this article, we propose a learning-based method to reconstruct total-body standard-dose PET (SPET) images from low-dose PET (LPET) images and corresponding total-body computed tomography (CT) images. Different from previous works focusing only on a certain part of human body, our framework can hierarchically reconstruct total-body SPET images, considering varying shapes and intensity distributions of different body parts. Specifically, we first use one global total-body network to coarsely reconstruct total-body SPET images. Then, four local networks are designed to finely reconstruct head-neck, thorax, abdomen-pelvic, and leg parts of human body. Moreover, to enhance each local network learning for the respective local body part, we design an organ-aware network with a residual organ-aware dynamic convolution (RO-DC) module by dynamically adapting organ masks as additional inputs. Extensive experiments on 65 samples collected from uEXPLORER PET/CT system demonstrate that our hierarchical framework can consistently improve the performance of all body parts, especially for total-body PET images with PSNR of 30.6 dB, outperforming the state-of-the-art methods in SPET image reconstruction.

INS/UWB multi-sensor integrated localization scheme in indoor LOS/NLOS environment based on MEF-GPDAF

Localization technology is crucial for indoor robot navigation. Wireless sensor network (WSN) serves as a significant part in indoor localization. However, the intricate indoor conditions make signal propagation susceptible to interference from obstructions, resulting in a decrease in positioning accuracy. Inertial Navigation System (INS) operates as a self-governing navigational setup undisturbed by indoor environment, and its location determinations remain unaltered by non-line-of-sight (NLOS) conditions. This research presents a combined positioning method of Ultra-wide band (UWB) and INS, which inherits advantages of the two subsystems and elevates the positioning system performance . On the basis of INS autonomous positioning, the maximum entropy fuzzy generalized probability data association filter (MEF-GPDAF) is used to modify the INS positioning results by combining the position of beacon nodes. When using UWB to locate the mobile node, a NLOS mitigation algorithm is applied to lessen the influence of NLOS propagation. Simulation experiment results demonstrate that, when compared to existing algorithm, MEF-GPDAF exhibits superior positioning precision.Meanwhile, in the real environment, the average error of the algorithm of MEF-GPDAF was measured to be 0.4961 m, which improved its accuracy by 32.88 % compared with the latest algorithm.

Evolutions of hydrodynamics and sediment transport pattern in the Qiantang Estuary (China) in response to multidecadal embankment constructions

Since the late 1960s, large-scale coastal embankments have been constructed along the Qiantang Estuary (QE, China) covering both the Qiantang River (QR) and the Hangzhou Bay (HZB) to reduce the tidal prism and thus stabilize the channel. While most previous studies have reported their relevant impacts on hydrodynamic and sediment transport in the HZB, a more comprehensive investigation covering the whole QR + HZB system remains lacking, so as a quantitative analysis of the tidal prism and the channel stability. Therefore, numerical case studies have been conducted to examine evolutions of hydrodynamics and sediment transport of the QR + HZB system in response to different coastal embankments (i.e., no embankment as in 1958; QR embankment as in 1984; QR embankment + part of HZB embankment as in 2004; both QR and HZB embankment as in 2016). It is shown that the flood tidal asymmetry in the QR and the tidal range in the HZB are significantly increased, implying an intensified tidal distortion and thus a strengthened river role after embankments. While this intensification does not change the direction (from HZB to QR) of the net sediment transport, all of the sediment exchange rates [e.g., the landward (during flood tides) and seaward (during ebb tides) sediment transport volumes, as well as the net sediment transport rate between them] exhibits obvious decreasing trends. In addition, strong deposition is observed in the Jianshan (JS) reach, which has led to further increase of the underwater giant sandbar. The constructions of embankments have facilitated the initial goal of reducing tidal prism in most part of the QR + HZB system, except some local parts of the HZB, where increased tidal prism can be observed as the effect of the enlarged tidal range overwhelms that of embankments. Moreover, the channel stability was greatly improved as the mainstream has been mostly converged to the reach centerline and the width-depth ratio of the channel has been considerably reduced. The present investigation of the QE regulation could be indicative for the management of other estuaries in the world.

The 3D plan analysis under progressive collapse for RC buildings through demand capacity ratio (DC) for three levels of damage

The progressive collapse presents a sudden load may cause initial failure in local parts at the frame which could lead to sequence reactions in the elements of the structure. A building of seven storeys was chosen to designed under gravity load according ACI 318 Code. This building is analyzed under progressive collapse condition for two columns damage cases; edge damage, corner damage. The building needs to be loaded as specified according (GSA) General Services Administration requirements through linear static analysis. The results of linear analysis show the variation in ((DC)) demand capacity ratio for the columns. The aspects of damage action in this study regarding section size and damage cases with 100% damage column loss (full damage), 60% damage (0.6 section size), 80% damage (0.8 section size). A GSA guideline to typical building with (DC) values bigger than 1.5 refer to critical potential damage conditions at the columns of the frame. It can be seen of the results the effect of size reduction for the building. This effect is clear to be decreased along distance from any plan. The((DC)) for columns which exceeded 1.5 value which show risky damage condition could actually happened that present serious threat possibility specially for nominated columns C22, C29 and C23 at plans A and B which show middle columns at lower position of the building.

Multitype view of knowledge contrastive learning for recommendation

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability to aggregate diverse knowledge and integrate topologies, while contrastive learning seeks supervisory signals from the model data. The combination of GNNs and contrastive learning can improve recommendations. However, thoughtless or incomplete contrastive learning settings limit the effectiveness of GNNs-based recommender systems in learning knowledge from knowledge and interaction graphs. To better exploit the valuable information within knowledge graphs, we propose a novel multitype view of knowledge contrastive learning for recommendations (MVKC) model.The MVKC model generates hierarchical views and augmented views in two modules, performing cross-hierarchical-view and cross-augmented-view contrastive learning and mining graph features in a self-supervised manner. The hierarchical views consist of global and local parts at multiple levels, while the augmented views are fused from the augmented knowledge graph and augmented interaction graph in our augmented processing. These features allow the MVKC model to alleviate the sparsity of user–item interaction graphs, suppress knowledge graph noise, and filter long-tail entities, which has been proven extremely important for a recommendation. The MVKC model also has strong anti-interference ability and robustness, which is crucial for a well-established model. Our experiments with three public datasets demonstrate that the MVKC model outperforms current state-of-the-art methods.

DOTTs-Database of Therapeutic Plants against Tuberculosis: Rationalizing traditional medicine for therapeutic applications

IntroductionMycobacterium tuberculosis, a highly resistant superbug causing tuberculosis disease, contributes to one of the leading causes of death worldwide. Improved drug regimens and therapeutics have been extensively studied, but still, none is proven to be fully effective in eliminating the pathogen or its drug-resistant forms. Therefore, alternative and novel therapeutic approaches to address this problem are the need of the hour. Traditional medicine can be vital in managing tuberculosis via codified (Ayurveda, Siddha, Unani) and uncodified systems (folk medicine). In recent years, the utilization of medicinal plants for TB therapy has garnered considerable attention. Identifying underexplored plants utilized as a remedy for tuberculosis can aid in developing future treatment strategies. The current study aims to assemble the ethnobotanical literature that provides insight into the undocumented tribal/folk medicinal practices against tuberculosis in one unified platform. MethodsThe medicinal plants with therapeutic properties against tuberculosis were identified manually by screening published literature. The data is represented to the users using a user-friendly web interface developed via the PHP-HTML platform using Bootstrap Cascading Style Sheet (CSS) theme, and the database is managed by MySQL Database Management System (DBMS). ResultsDatabase of Therapeutic Plants against Tuberculosis describes the medicinal plants used traditionally in different forms against tuberculosis by various indigenous tribes and communities in India. A total of 100 plants with their local names, taxonomical classification (order and family), tribe, plant part, and formulations, as well as the anti-tubercular attributes, are provided in the database. Additional information about the plants can be retrieved from the external links provided. ConclusionThe DOTTs database provides the ethnobotanical and ethnopharmacological data of 100 Indian medicinal plants. This database will help the researchers acquire information about the plants that could be beneficial for further evaluation and discovery of potential leads in TB drug discovery. With proper investigation and advancement, it can be anticipated that TB will no longer be a serious public health risk.

Design of locally cooled step-less fan for industrial use using Arduino programming

Under the background of the development of domestic and foreign manufacturing industries and the increasing demand for industrial products at home and abroad, and under the premise of ensuring and even further reducing the manufacturing cost of manufacturers, how to effectively detect and cool the local temperature of industrial parts is a problem worth studying. After analyzing the advantages and disadvantages of traditional manual control and gear adjustment to adjust the fan, the design will be programmed using Arduino. The fan combines infrared sensor technology to detect the presence and position of parts. A temperature sensor is also set up to detect local temperatures in real-time. A fan with automatic step-less speed change according to temperature is obtained. This fan is used for heat dissipation control of local parts in industrial production, thereby reducing the cost of manual control. It further improves control accuracy and efficiency, improves product quality, and ensures production safety.

Observer-based output feedback control for switched T–S fuzzy systems with local nonlinear segments by using past output measurements

This paper researches the observer-based exponential stabilization problem for switched Takagi–Sugeno (T–S) fuzzy systems with unmeasurable premise variables and local nonlinearities. The local nonlinear parts that satisfy the incremental quadratic constraint exist in the systems and observers, which have the merit of being able to contain many common nonlinearities in a unified framework. Because the premise variables are not measurable, the performance of the observers in estimation is critical. Different from existing observer-based control strategies, the strategy of this paper not only depends on the current outputs of the systems but also relates to the past outputs of the systems which can provide a way to improve performance. In order to tackle the difficulties caused by introducing the past outputs and design a set of non-fragile fuzzy controllers to stabilize the systems, a new augmented state vector is constructed. Then, the conditions for designing the observer-based controller are derived by using auxiliary scalars, projection Lemma and S-procedure. Finally, the effectiveness of the control strategy obtained in this paper is proved by two examples.

Loop correction and resummation of vertex functions for a self interacting scalar field in the de Sitter spacetime

We consider a massless and minimally coupled self interacting quantum scalar field theory in the inflationary de Sitter background of dimension four. The self interaction potential is taken to be either quartic, λϕ4/4!, or quartic plus cubic, λϕ4/4!+βϕ3/3! (λ>0). We compute the four and three point vertex functions up to two loop. The purely local or partly local part of these renormalised loop corrected vertex functions grow unboundedly after sufficient number of de Sitter e-foldings, due to the appearances of secular logarithms. We focus on the purely local part of the vertex functions and attempt a resummation of them in terms of the dynamically generated mass of the scalar field at late times. Such local logarithms have sub-leading powers compared to the non-local leading ones which can be resummed via the stochastic formalism. The variation of these vertex functions are investigated with respect to the tree level couplings numerically. Since neither the secular effect, nor the dynamical generation of field mass is possible in the Minkowski spacetime, the above phenomenon has no flat spacetime analogue. We have also compared our result with the ones that could be found via the recently proposed renormalisation group techniques. All these results suggest that at late times the value of the non-perturbative vertex function should be less than the tree level coupling.

SAT-based Decision Tree Learning for Large Data Sets

Decision trees of low depth are beneficial for understanding and interpreting the data they represent. Unfortunately, finding a decision tree of lowest complexity (depth or size) that correctly represents given data is NP-hard. Hence known algorithms either (i) utilize heuristics that do not minimize the depth or (ii) are exact but scale only to small or medium-sized instances. We propose a new hybrid approach to decision tree learning, combining heuristic and exact methods in a novel way. More specifically, we employ SAT encodings repeatedly to local parts of a decision tree provided by a standard heuristic, leading to an overall reduction in complexity. This allows us to scale the power of exact SAT-based methods to comparatively very large data sets. We evaluate our new approach experimentally on a range of real-world instances that contain up to several thousand samples. In almost all cases, our method successfully decreases the complexity of the initial decision tree; often, the decrease is significant.

Brain Cognition-Inspired Dual-Pathway CNN Architecture for Image Classification.

Inspired by the global-local information processing mechanism in the human visual system, we propose a novel convolutional neural network (CNN) architecture named cognition-inspired network (CogNet) that consists of a global pathway, a local pathway, and a top-down modulator. We first use a common CNN block to form the local pathway that aims to extract fine local features of the input image. Then, we use a transformer encoder to form the global pathway to capture global structural and contextual information among local parts in the input image. Finally, we construct the learnable top-down modulator where fine local features of the local pathway are modulated by global representations of the global pathway. For ease of use, we encapsulate the dual-pathway computation and modulation process into a building block, called the global-local block (GL block), and a CogNet of any depth can be constructed by stacking a necessary number of GL blocks one after another. Extensive experimental evaluations have revealed that the proposed CogNets have achieved the state-of-the-art performance accuracies on all the six benchmark datasets and are very effective for overcoming the "texture bias" and the "semantic confusion" problems faced by many CNN models.

Asymptotically compatible energy of variable-step fractional BDF2 scheme for the time-fractional Cahn–Hilliard model

Abstract A novel discrete gradient structure of the variable-step fractional BDF2 formula approximating the Caputo fractional derivative of order $\alpha \in (0,1)$ is constructed by a local-nonlocal splitting technique, that is, the fractional BDF2 formula is split into a local part analogue to the two-step backward differentiation formula (BDF2) of the first derivative and a nonlocal part analogue to the L1-type formula of the Caputo derivative. Then a local discrete energy dissipation law of the variable-step fractional BDF2 implicit scheme is established for the time-fractional Cahn–Hilliard model under a weak step-ratio constraint $0.3960\le \tau _{k}/\tau _{k-1}<r^{*}(\alpha )$, where $\tau _{k}$ is the $k$th time-step size and $r^{*}(\alpha )\ge 4.660$ for $\alpha \in (0,1)$. The present result provides a practical answer to the open problem in [SINUM, 57: 218-237, Remark 6] and significantly relaxes the severe step-ratio restriction [Math. Comp., 90: 19–40, Theorem 3.2]. More interestingly, the discrete energy and the corresponding energy dissipation law are asymptotically compatible with the associated discrete energy and the energy dissipation law of the variable-step BDF2 method for the classical Cahn–Hilliard equation, respectively. To the best of our knowledge, such type energy dissipation law is established at the first time for the variable-step L2 type formula of Caputo’s derivative. Numerical examples with an adaptive stepping procedure are provided to demonstrate the accuracy and the effectiveness of our proposed method.

Ensuring the functioning of society in activities of local governments under the legal regime of martial law

The scientific article is devoted to the study of the activities of local self-government bodies during the period of martial law in ensuring the functioning of society, characterization of the concept, competence and features of local self-government bodies, and types of local selfgovernment bodies. The article examines the scientific and regulatory approaches to defining the most important functions of local authorities, in particular, ensuring the constitutional rights and security of citizens. Local self-government during the period of martial law faces many challenges on a daily basis. That is why this topic is extremely relevant and requires scientific substantiation and research. The decentralization reform has laid the foundation for the activities of competent local governments. However, the unjustified military aggression has changed the usual rhythm of work of regional and district councils, as well as local authorities. The peculiarity of this legal regime is the creation of temporary state bodies, including military authorities. They operate in parallel with autonomous local governments. However, there are cases when the military administration can assume the powers of local self-government bodies if the local council does not meet on time. Local self-government in Ukraine is a right guaranteed and enforced by the state and a territorial community – a voluntary association of residents of a village, villages or several villages, cities – to address local issues within the limits determined by the current legislation, may be formed independently or subordinated to officials and self-government bodies. Local self-government is designed to strengthen the foundations of the constitutional order of Ukraine, ensure the implementation of constitutional principles, human and civil rights, create conditions for meeting the vital needs and legitimate interests of citizens, and develop local democracy. During the legal regime of martial law, military authorities exercise the powers of local state authorities and part of local self-government bodies, as defined by the current legislation. Therefore, the role of local state authorities and local self-government bodies in ensuring the quality functioning of society is very important.

Successful Foreign Direct Investment Through the Development of Parts Supply Industries in the Host Country: A Study of India's Automobile Manufacturing Sector

Several large multinationals and a few local assemblers dominate the Indian automobile industry. The largest multinational, Maruti Suzuki, entered the Indian automobile industry with a policy of developing local automotive parts manufacturers to achieve high local content ratio. This study seeks to determine the extent to which foreign direct investment (FDI) in host countries contributes to the growth of automobile parts manufacturers. We use district‐level panel data from 2000 to 2008 to assess the linkage between the production of major automobile assemblers and the development of automotive parts suppliers located in the same and adjacent districts to those assemblers. Our regression results show the heterogeneity of the linkages, suggesting that the formation of industrial clusters by local parts suppliers and the expansion of their production capacity are likely to be key determinants of the success of FDI in the Indian automobile industry, rather than relying on in‐house parts production or imports.