Bandsaw blades are typical band-shaped cutting tools characterized by low stiffness and micro-level cutting depth, resulting in distinct wear mechanisms compared to rigid cutting tools. In this study, the wear curve and wear mechanisms of bandsaw tooth during the bandsawing of Cr12MoV cold-working steel was investigated. The tool life was divided into two stages: a rapid wear stage (Stage I) and a homogeneous wear stage (Stage II). In Stage I, the wear was dominated by chipping although multiple wear mechanisms were found due to the relatively poor manufacturing accuracy compared to rigid cutting tools, which results in remarkable differences among the cutting depths of individual teeth. In Stage II, abrasive and adhesive wear were the primary wear mechanisms instead of chipping, which was related to the microstructure of Cr12MoV. Furthermore, methods for increasing the bandsaw performance were proposed based on the tooth wear mechanisms.

This study presents an advanced Simulated Shearer Machine Cutting Experiment System enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to mirror the operational dynamics of actual mining cutters accurately. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time-domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal-rock identification research, offering significant insights into the nuances of shearer operation.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

To further enhance the intelligence level of coal mining faces and achieve autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. The study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Introduction: The environmentally acquired aerobic spore-forming (EAS-Fs) bacteria that are ubiquitous in nature (e.g. soil) are transient colonisers of the mammalian gastro-intestinal tract. Without regular exposure, their numbers quickly diminish. These species of bacteria have been suggested to be essential to the normal functioning of metabolic and immunogenic health. The modern Western lifestyle restricts exposure to these EAS-Fs, possibly explaining part of the pathogenesis of many Western diseases. To date, the only animal studies that address specific microbiome modelling are based around germ-free animals. We have designed a new animal model that specifically restricts exposure to environmental sources of bacteria. Methodology: A new protocol, termed Super Clean, that involves housing mice in autoclaved individually ventilated cages (IVCs), with autoclaved food / water, and strict ascetic handling practice was first experimentally validated. The quantification of EAS-Fs was assessed by heat-treating faecal samples and measuring colony forming units (CFUs). This was then compared to mice in standard-conditions. Mice were housed in their respective groups from birth until 18 months. Stool samples were taken throughout the experiment to assess for abundance in transiently acquired environmental bacteria. Clinical, biochemical, histological, and gene expression markers were analysed for diabetes, hypercholesterolaemia, obesity, inflammatory bowel disease, and non-alcoholic fatty liver disease (the “diseases of the West”). Results: Our results show that stringent adherence to the Super Clean protocol produces a significantly decreased abundance of aerobic spore-forming Bacillota after 21 days. This microbiomic shift was correlated with significantly increased levels of obesity and impaired glucose metabolism. There was no evidence of colitis, liver disease or hypercholesterolaemia. Conclusion: This new murine model successfully isolates EAS-Fs, and has the potential utility for future research allowing for the investigation into the clinical impact living in relative hygienic conditions.

We revise the definition of graph operations in [WDK2018] and adapt correspondingly the construction of graph term algebras. As a first contribution to a prospective research field Universal Graph Algebra, we generalize some basic concepts and results from algebras to graph algebras. To tackle this generalization task, we revise and reformulate traditional set-theoretic definitions, constructions and proofs in Universal Algebra by means of more category-theoretic concepts and constructions. Especially, we generalize the concept generated subalgebra and prove that all monomorphic homomorphisms between graph algebras are regular. Derived graph operations are the other main topic. After an in depth analysis of terms as representations of derived operations in traditional algebras, we identify three basic mechanisms to construct new graph operations out of given ones: parallel composition, instantiation, and sequential composition. As a counterpart of terms, we introduce graph operation expressions with a structure as close as possible to the structure of terms. We show that the three mechanisms allow us to construct for any graph operation expression a corresponding derived graph operation in any graph algebra.

The aim of this paper is to establish a valid procedure for better understanding all the phenomena associated with the high-speed machining of Glass Fibre Reinforced Plastic (GFRP) composites. Both rectangular and circular specimens were machined at high speeds (up to 50 m/min) in order to understand what occurred for all values of fiber orientation angles during machining operations. An innovative testing methodology was proposed and studied to investigate the phenomenon of burr formation and thus understand how to avoid it during machining operations. To this end, the forces arising during the machining process and the roughness of the resulting surface, were carefully studied and correlated with the cutting angle. Additionally, the cutting surface and chip morphology formed during cutting tests were examined using a high-speed camera. Close correlations are found between the variations of the cutting forces' signals and the trends of the roughness and the morphology of the machined surface.

Constructing new graph from the graph's parameters and related notions in the way that, the study on the new graph and old graph in their parameters could be facilitated. As graph, new graph has some characteristics and results which are related to the structure of this graph. For this purpose, regular graph is considered so the internal relation and external relation on this new graph are studied. The kind of having same number of edges when this number is originated by common number of graphs like maximum degree, minimum degree, domination number, coloring number and clique number, is founded in the word of having regular graph

Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

Aiming at the problems of high cost of manual pulling, low reliability of existing pulling devices, high breaking rate and high leakage rate in the process of cotton stalk reuse after leaving the field in Xinjiang cotton area, a loosening soil and root-cutting cotton stalk pulling the setting machine has been researched and designed, established the root-cutting force model, calculated and optimized the key parameters of the V-shaped root-cutting knife, and determined the range of slide cutting angle, cutting edge angle and soil entry angle. The shoveling process simulation of V-shaped root-cutting knife and root-soil complex was constructed, and the working mechanism of V-shaped root-cutting knife was clarified. In order to verify the reliability and operation performance of the V-shaped root-cutting knife, the slide cutting angle, cutting edge angle and soil entry angle were used as the test factors, the response surface test of three-factors and three-levels was carried out with root-breaking force and mean value of the cutting resistance as the test index. The test results were analyzed by variance analysis, the significant of influencing factors on the root-breaking force are in a descending order as: slide cutting angle, cutting edge angle, soil entry angle; the degree of influence on the mean value of the cutting resistance are as follows: slide cutting angle, soil entry angle, cutting edge angle. In order to make the V-shaped root-cutting knife achieve the optimal working state, the parameters of the test index were optimized, and the optimal design parameters of the V-shaped root-cutting knife were set as follows: the slide cutting angle was 48.3°, cutting edge angle was 43.4°, and soil entry angle was 26.2°. The field uprooting test showed that the average pass rate of root breakage was 94.8% and the average pull-out rate of the cotton stalk was 93.2%. This study provides theoretical guidance for the development of root breaking mechanism of cotton straw harvester.

Graph theory plays significant role in every field of science as well as technology. Every situation can be articulated in terms of suitable graphs by using various approaches of graph theory. Considering the recent pandemic in the world and the precautions taken for prevention of the COVID 19, it is the most appropriate way to exercise the graph models with theoretical as well as practical aspects to control this epidemic. This paper defines the variable set, variable graphs and their types with respect to variable vertex sets and variable edge sets. Depending upon the nature of the pandemic, there are four types of Virus graphs. Virus graph I and III are not so perilous for all living beings. Although, Virus graphs II and IV are extremely hazardous for the harmony of the world. In view of different aspects for expand of pandemic, growth types of virus graphs are divided in 1-1, 1-P and 1-all growth types. The COVID19 initially was in Virus graph-I type, but presently it is in Virus graph-II types. We present the table involving the number of infected people after n days with respect to different values of P and growth rates with I0 = 100. Moreover, the country wise starting dates of stages of the virus graph-I and II are specified. The concept of cut sets is applicable for prevention of COVID19 and the whole world is using the same analogy.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finish. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model's relevance and accuracy were confirmed using F-tests and t-tests. Ultimately, through the utilization of a genetic algorithm, the milling parameters were optimized, resulting in the identification of the most optimal combination of parameters. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

The aerospace industry has made extensive use of titanium alloy material due to its exceptional qualities, which include high strength, low weight, and resistance to corrosion. However, these qualities also pose challenges for the material's processing. This paper examines the Ti6Al4V milling endmills coating. First, an analysis is done on the solubility of Ti and Si elements. It is discovered that W and Co elements are far more soluble in Ti than Si and Zr elements, which can effectively stop element diffusion. Next, the base's composition was planned. It was discovered that when the amount of Al increased, the base's surface roughness increased while its hardness and elastic modulus decreased. The binding force between the substrate and the base was greater at 50: 50 Ti:Al ratio. TiSiN and TiSiN/ZrN functional layer properties are examined. When Zr is added to TiSiN/ZrN coating, it improves the coating's hardness and elastic modulus, increases density, and decreases surface roughness and friction coefficient when compared to TiSiN coating. Lastly, an analysis of the initial and intermediate tool wear was done using the Ti6Al4V milling experiment. It was discovered that adding Zr element could successfully extend the tool's cutting life by preventing adhesive wear.

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human keypoint detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of keypoints. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to re-weight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded data sets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

With recent advancements in network technology and spread of Internet, use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges occur over time. Additionally, owing to the need for efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

Among the various generalizations of persistent topology, the one based on rank functions and leading to indexing-aware functions appears to be particularly suited to catch graph-theoretical properties without the need for a simplicial construction and a homology computation. This paper defines and studies "simple" and "single-vertex" features in directed and undirected graphs, by which several indexing-aware persistence functions are produced, within the scheme of steady and ranging sets. The implementation of the "sink" feature and its application to trust networks provide an example of the ease of use and meaningfulness of the method.

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual and textual features, and hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. Experimental results demonstrate that, compared to the current multimodal state-of-the-art methods, the proposed method achieves significant performance improvements in knowledge graph link prediction tasks. This proves that the proposed method effectively addresses the problem of multimodal feature fusion in knowledge graph link prediction tasks.

In GNSS/IMU integrated navigation systems, factors such as satellite occlusion and non-line-of-sight conditions can lead to degradation of satellite positioning results, Subsequently affecting the overall accuracy of the integrated navigation system. To address this issue and ef-fectively utilize historical pseudorange information from satellites, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints are con-structed using satellite ‘s position from previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint can serve as an alternative solution for individual satellites in case of signal anomalies, ensuring the integrity and continuity of the graph optimi-zation model. Additionally, this paper conducts an analysis of the graph optimization model constructed using these virtual constraints, comparing it with traditional integrated navigation graph optimization model, and analyzes and reconstructs the marginalization process based on these virtual constraints. The experimental results, compared with tightly coupled Kalman fil-tering and the original graph optimization method on a set of real-world data, demonstrate that the introduction of virtual pseudoranges maintains high positioning accuracy. The RMSE error between it and the original graph optimization remains within 5%, affirming the high feasibility of this approach.

Lung cancer remains a formidable global health challenge that necessitates inventive strategies to improve therapeutic outcomes. Conventional treatments, including surgery, chemotherapy, and radiation, have demonstrated limitations in achieving sustained responses. Therefore, the explora-tion of novel approaches encompasses a range of interventions that show promise in enhancing outcomes for patients with lung cancer, particularly those facing advanced or refractory cases. These groundbreaking interventions hold the potential to overcome cancer resistance and offer personalized solutions. Despite the rapid evolution of emerging lung cancer therapies, persistent challenges such as resistance, toxicity, and patient selection underscore the need for continued development. Consequently, the landscape of lung cancer therapy is transforming with the in-troduction of precision medicine, immunotherapy, and innovative therapeutic modalities. Addi-tionally, a multifaceted approach involving combination therapies through the integration of tar-geted agents, immunotherapies, or traditional cytotoxic treatments addresses the heterogeneity of lung cancer while minimizing adverse effects. This review provides a brief overview of the latest emerging therapies that are reshaping the landscape of lung cancer treatment. As these novel treatments progress through clinical trials and integrate into standard care, the potential for more effective, targeted, and personalized lung cancer therapies comes into focus, instilling renewed hope for patients facing challenging diagnoses.

This article deals primarily with the problem of determining the cutting force when machining hardened steels. Secondary issues are focused on the evaluation of surface quality on machined samples and the recommendation of cutting conditions. A wide variety of components are used in engineering, the final heat treatment of which is hardening. These components are usually critical in a particular product. The quality of these components determines the correct functioning of the entire technical equipment and ultimately its service life. In our case, these are the core parts of thrust bearings, specifically the rolling elements. The subject of the experiment is machining these components in the hardened state with cubic boron nitride tools and continuous measurement of the cutting force using a dynamometer. The following evaluation assesses the surface quality by both touch and non-touch methods. A structural equation with appropriate constant and exponents was then constructed from the data obtained using the dynamometer.

The growing threat of antibiotic pollution in water systems demands innovative and sustainable remediation strategies. Nanocellulose-based magnetic metal-organic frameworks (MOFs) represent a cutting-edge solution for the adsorption and removal of antibiotics, leveraging their high porosity, magnetic properties, and environmentally friendly nature. This paper provides an in-depth review of recent advancements in nanocellulose-based MOFs, focusing on their synthesis, adsorption mechanisms, and applications in environmental remediation between 2020 and 2024. By summarizing key studies, we highlight their performance in targeting widely used antibiotics such as tetracyclines and fluoroquinolones. The integration of nanocellulose with MOFs not only enhances adsorption capacity but also offers easy recovery, recyclability, and environmental compatibility. This review explores their adsorption mechanisms, practical applications, and future research directions, contributing to the advancement of scalable and sustainable water treatment technologies.

Although Lov\'{a}sz introduced the graph exponential $G^K$ in 1967, this product has received little attention. This paper focuses on the number of components in $G^K$ when exponent $K$ is a complete graph $K_n$. For a connected bipartite $G$, when $K$ is $K_2$, $G^{K_2}$ has three components; and when $K$ is $K_n$ with $n>2$, $G^{K_n}$ produces $\lceil\frac{n+1}{2}\rceil$ components. A connected $G$ with an odd cycle produces one component in $G^{K_n}$.

In the mold machining process, the cutting tool is worn with machining time, thereby affecting the surface accuracy, leading to poor workpiece dimensions, even fracture. At present, many studies have used multiple sensors to detect the machining conditions of cutting tool and workpiece, including indirect measurement method and direct measurement method. The indirect measurement method, which has been studied widely, mainly uses sensors to capture signals for subsequent data analysis; the direct measurement method mainly analyzes the state of cutting shear zone. Due to the cut-in of cutting tool in the machining process, the workpiece is dislocated rapidly, generating considerable amount of heat, which is transferred to the chips, inducing color change on the surface of chips. Many engineers with machining experience often judge the machining state and tool life according to the chips. The engineers' experience is digitized in this study, and indirect measuring sensors are used to predict the tool life, so as to attain the objective for smart manufacturing, the average percentage error of MAPE using single vibration and voltage eigenvalues as input features is 10%, the voltage signal characteristic values and vibration signal characteristic values are combined. Finally, the chip surface chromaticity eigenvalue is combined with signal characteristic value. The average prediction error of BP-LM method is 7.85%, the average prediction error of GRNN method is 6.59%. Therefore, when the eigenvalue of chip surface chromaticity is added to the prediction result, it can enhance the accuracy of cutting tool wear value prediction more effectively than single sensor signal characteristic value.

In recent years, graph neural networks (GNNs) have become a promising method for analyzing data structured in graph format. By considering connections between entities in a graph, GNNs are able to extract valuable insights. One notable variation of GNN is the graph attention network (GAT), which employs the attention mechanism and has demonstrated promising performance in various applications. However, its ability to incorporate feature information from nodes beyond the immediate neighborhood is limited, leading to degraded performance on heterophilic data. To address this limitation, this thesis proposes a novel attention-based model, namely the Directional Graph Attention Network (DGAT). This model combines the feature-based attention with the global directional information extracted from the graph topology, as inspired by the Directional Graph Network (DGN). A new class of Laplacian matrices is proposed and an existing theoretical result on DGN is extended. This extension bridges a gap in the literature. The experimental results presented in the thesis, based on nine real-world benchmarks and ten synthetic data sets, demonstrate the superiority of the proposed DGAT model compared to the GAT baseline model. Particularly on heterophilic data sets, DGAT showed a notable average increase of approximately 35% in node classification tasks across all heterophilic real-world data sets. In addition, DGAT outperforms GAT by an average margin of around 51% in all ten synthetic data sets with various levels of heterophily

Finite element discretizations of problems in computational physics often rely on hand-generated initial mesh and adaptive mesh refinement (AMR) to preferentially resolve regions containing important features during simulation. We propose Adaptnet, a Graph Neural Networks (GNNs) framework for learning mesh generation and adaptation. The model is composed of two GNNs: the first one, Meshnet, learns mesh parameters commonly used in open-source mesh generators, to generate an initial mesh from a Computer Aid Design (CAD) file; while the second one, Graphnet, learns mesh-based simulations to predict the components of an Hessian-based metric to perform anisotropic mesh adaptation. Our approach is tested on structural (Deforming plate - Linear elasticity) and fluid mechanics (Flow around cylinders - steady-state Stokes) problems. Our results show it can accurately predict the dynamics of the system and adapt the mesh accordingly. The adaptivity of the model supports learning resolution-independent dynamics and can scale to more complex state spaces at test time.

This study re-introduces a protein-free rapid test method for nucleic acids on paper based lateral flow assays utilizing special multichannel nitrocellulose membranes and DNA-Gold conjugates, achieving significantly enhanced sensitivity, easier protocols, reduced time of detection, reduced costs of pro-duction and advanced multiplexing possibilities. It is shown for the first time a protein-free nucleic acid based lateral flow assay (NALFA) with a limit of detection of 1 pmol of DNA. The total production duration of such an assay was successfully reduced from the currently known several days to just a few hours. The simplification and acceleration of the protocol make the method more accessible and practical for various applications. The developed method supports multiplexing, enabling the sim-ultaneous detection of up to six DNA targets. This multiplexing capability is a significant improve-ment over traditional line tests and offers more comprehensive diagnostic potential in a single assay. The approach significantly reduces the run time compared to traditional line tests, which enhances the efficiency of diagnostic procedures. The protein-free aspect of this assay minimizes the prevalent complications of cross-reactivity in immunoassays especially in cases of multiplexing. It is also demonstrated that the NALFA developed in this study is amplification-free and hence does not rely on specialized technicians, nor does it involve labour-intensive steps like DNA extraction and PCR processes. Overall, this study presents a robust, efficient, and highly sensitive platform for DNA or RNA detection, addressing several limitations of current methods documented in the literature. The advancements in sensitivity, cost reduction, production time, and multiplexing capabilities mark a substantial improvement, holding great potential for various applications in diagnostics, forensics, and molecular biology.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Abrasive Water Jet Machining (AWJM) is a non-conventional machining process that, unlike other methods, is deemed superior because of its applicability, versatility, and no heat-affected zones in the target material. The main objective of this paper was to develop a cheap, practical model for a low-pressure water jet cutting machine and provide some empirical test data for cutting delicate materials. Proper design calculations were performed and are included for both the water jet machine and the two-axis numerical control. Finite Element Analysis results validate the structural integrity of beams and columns in AWJM. Computational Fluid Dynamic analysis of the nozzle shows good agreement with the theoretical calculations regarding the pressure and velocity of the water-abrasive mixture exiting the nozzle. Modal analysis in ANSYS was done to mitigate potential failure due to resonance induced by vibrations. Limited testing on various thicknesses of polystyrene foam ranging from 1-5 mm thickness and rubber sheet of 1 mm thickness showed that even low pressure of 25-30 bar was sufficient to produce a kerf without any burr. However, extensive testing to report optimized parameters like nozzle transverse speed, standoff distance, abrasive flow rate, etc for working at low-pressure ranges is still a consideration for future investigation.

The surrounding rock during the coal mine excavation is prone to significant engineering disasters such as considerable deformation and rockbursts. Pressure release can improve the stress field of a deep rock mass and prevent the occurrence of dangers such as roadway collapse and coal and gas outbursts. This paper uses the ANSYS/LS-DYNA finite element software to simulate the crush area and fracture zone of a detonation charge with different diameters under in-situ stress. The stability of the surrounding rock was analyzed based on the impact stress and velocity, and was verified by field tests. The research results show that the blasting load primarily affects the damaged area near the borehole, while the in-situ stress affects the far-field crack propagation. The crack propagates in the direction of high surrounding rock pressure. When the uncoupling index is 1.5, the impact pressure of the 60 mm diameter cartridge is 8 times that of the 20 mm diameter cartridge. The impact speed can reach 2 times that of the 20 mm diameter cartridge. The high-energy event of the roof is transferred to the front of the working face, the distribution is no longer concentrated, and a better blasting pressure relief effect is achieved. The research results can help guide the prevention and control measures of rock bursts and other mining disasters.

The prevalence of cardiac amyloidosis (CA), especially as a cause of heart failure, has significantly increased in recent years. Early detection and accurate assessment of the disease burden are crucial for initiating timely treatment and ensuring precise prognosis. CA primarily results from the infiltration of the myocardium by either immunoglobulin light chain fibrils (AL) or transthyretin fibrils (ATTR), leading to restrictive cardiomyopathy and eventual death if untreated. Over the past decade, advancements in diagnostic imaging and heightened clinical awareness have revealed a substantial presence of CA, particularly ATTR, among the elderly. These diagnostic improvements encompass echocardiography, cardiac computerized tomography scan, magnetic resonance imaging, and radionuclide scintigraphy with bone-avid tracers. Concurrently, significant progress has been made in therapeutic options, with new disease-modifying treatments now available that can dramatically alter the disease trajectory and improve survival rates when administered early. However, despite these advancements, there remains an urgent need for the early and accurate detection of CA to ensure that patients can fully benefit from these emerging therapies.

The article is devoted to the issues of assessing the impact of changes in the delay value in the feed direction, what determines the regenerative nature of self-excitation of the cutting system, on the dynamics of metal processing on metal-cutting machines. It is this interconnected model of the cutting system that allows to identify the main dynamic effects that occur in the cutting system, including the effect of vibrations regeneration when cutting along the track. The article also has a section dedicated to the experiment on a real metalworking machine, on which vibrations of the top of the cutting wedge of the tool were measured and based on these data, a real calculation of the delay time and the magnitude of the change in the area of the cut layer was carried out, due to variations in the real feed. The conducted studies have shown that in addition to the vibrations of the cutting tool in the direction of feeding, the vibration activity of the tool in the direction of cutting plays an essential role in ensuring the regenerative effect. The delay time constant of the operator is formed, which determines the real value of the feed during cutting.

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

Graph databases (GDBs) are becoming increasingly popular across scientific disciplines, being highly suitable to store and connect complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph (KG) databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available GDB technologies. Relevant publications are then categorised according to their domain and application. We detail different approaches and highlight advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardise and harmonise KG creation and the maintenance of integrated resources. Outlining prospects, including the use of GDBs as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of GDBs will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

For a graph G, let P(G, λ) be its chromatic polynomial. Two graphs G and H are said to be chromatically equivalent if P(G,λ) = P(H,λ). A graph is said to be chromatically unique if no other graph shares its chromatic polynomial. In this paper, chromatic polynomial of the necklace graph N_n, for n ≥ 2 has been determined. It is further shown that N₃ is chromatically unique.

We present a Deep Learning generative model specialized to work with graphs with a regular geometry. It is build on a Variational Autoencoder framework and employs Graph convolutional layers in both encoding and decoding phases. We also introduce a pooling technique (ReNN-Pool), used in the encoder, that allows to downsample graph nodes in a spatially uniform and highly interpretable way. In the decoder, a symmetrical un-pooling technique is used to retrieve the original dimensionality of graphs. Performance of the model are tested on the standard Sprite benchmark dataset, a set of 2D images of video game characters, adequately transforming images data into graphs, and on the more realistic use-case of a dataset of cylindrical-shaped graph data that describe the distributions of the energy deposited by a particle beam in a medium.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

The relationship between tissue effects, intensity (I), and voltage (V) of a high-frequency alternative current seems well known. However, the translation of the settings of the electrosurgical unit and their use during surgery seems not to be unanimously understood by all surgeons. This results in many variations of surgical technique and plenty of specific questions asked during live surgery. Above 200 V, the heat of the sparks results in the explosion of superficial cells and a cutting effect. With higher voltages, the associated coagulation is increasing. Voltage and amount of energy (Watt=I*V) are independent since a similar amount of energy can be obtained with a continuous current or an intermittent current half the time and a double voltage. Below 200 V, tissue is heated until the steam from boiling tissue stops the current. Therefore, slower heating with less energy or a larger contact area permits deeper coagulation. The maximal output (W) of the electrosurgical unit results in versatility of use besides being a safety measure. The current intensity increases with the area of contact. Therefore, when I*V exceeds the preset output, V has to drop. A limited maximal output thus permits using the same instrument for non-contact cutting with >200V and sparks and coagulation when the contact area is much larger. In conclusion, the tissue effects of electrosurgery vary with the device’s voltage setting (often indicated as force) and the maximal output. The sur-geon’s choice to cut or coagulate by changing the contact area adds versatility.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

The cutting of difficult-to-machine materials, including their preconditioning, often requires specialised tools and machinery. An alternative processing technology can be cutting materials with a water jet. From the initial stage, the selection of appropriate technological parameters will influence what further finishing treatments will be applied. Vibrations occurring during this process can negatively affect the quality of the cutting edges and surfaces. Therefore, this study analysed vibrations during water-jet cutting with variable technological parameters (speed vfi and pressure pi). To measure the vibrations, a three-axis accelerometer from SEQUOIA was used in water-jet cutting of three materials: aluminum alloy, titanium alloy, and steel. Based on the study, it was found that the maximum value of vibration amplitude reaches the lowest value of vibration acceleration for aluminum alloy (not exceeding 5 m/s2), regardless of the value of pressure during the cutting process. This, taking into account the material properties, indicates that aluminum alloy is the material most susceptible to the cutting process while maintaining a high quality of the cutting surface. Significantly higher values of vibration acceleration amplitude (reaching up to 60 m/s2) during cutting were registered for steel and titanium alloy in all zones and phases of the process (entry zone, cutting zone, and exit zones).

In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.

The aim of this work is to develop a method to produce spherical biopolymer-based aerogel particles, which is capable for scale up in the future. Therefore, jet cutting method is suggested. Amidated pectin and sodium alginate were used as precursor (1–3 wt. % solution) for particle production via jet cutting. Gelation was realized via two methods: internal setting method (using calcium carbonate particles as cross-linker and citric and acidic acid for pH adjustment) and diffusion method (in calcium chloride solutions). Gel particles were subjected to solvent exchange to ethanol and consequent supercritical drying with CO₂. Spherical aerogel particles with narrow particle size distribution in the range of 400 to 1500 µm and with specific surface area of around 500 m²/g could be produced. Overall, it can be concluded that jet cutting method is suitable for aerogel particle production, although the shape of the particles is not perfectly spherical in all cases. However, parameter adjustment might lead to even better shaped particles in further work. Moreover, the biopolymer-based aerogel particles synthesized in this study were tested as humidity absorber in drying units for home appliances, particularly for dishwashers. It could be shown that for several cycles of absorption and desorption of humidity aerogel particles are stable with an absorption capacity of around 20 wt. %.

To guide the design and optimization of King Grass (KG) harvesting machinery, this paper studied the cutting characteristics of KG stalks under different physical properties and mechanical parameters. A cutting device that can achieve different parameter adjustments was designed, and indoor cutting experiments were conducted. The results of the single factor experiment show that with the increase of moisture content and stalk height, the ultimate shear stress (USS) and unit-area cutting energy consumption (UCEC) gradually decrease. The optimal range of blade clearance for cutting KG stalks is 2.0-4.0 mm. The optimal value range for oblique cutting angle is 10 ° -40 °. The reasonable range of skew cutting angle is 10 °-20 °. The optimal range for sliding cutting angle is 20 ° - 40 °. The response surface methodology was used to conduct parameter optimization experiments. The regression models were established for various factors and the USS, as well as the UCEC. The optimal combination of cutting parameters has been determined. Under this parameter setting, the USS is 0.4 MPa and UCEC is 3.5 mJ/mm2. The average relative errors between the predicted and experimental values of USS and UCEC were 6.34% and 4.71%, respectively. This indicates that the model can correct reflect the mathematical relationship between various factors and USS and UCEC. This study provides theoretical support for the design of KG harvesting machinery.

This paper describes a polynomial time algorithm for solving graph isomorphism and automorphism. We introduce a new tree data structure called Walk Length Tree. We show that such tree can be both constructed and compared with another in polynomial time. We prove that graph isomorphism and automorphism can be solved in polynomial time using Walk Length Trees.

Introduced in 1967 by Lov\'{a}sz, graph exponentiation has received little attention over the past several decades. Since graph exponentiation produces large graphs and any graph can be constructed utilizing its adjacency matrix, we provide a four step linear construction algorithm that generates the adjacency matrix for the graph exponential.

The Randić index of a graph G is the sum of (dG(u)dG(v))−1/2 over all edges uv of G, where dG(u) denotes the degree of vertex u in G. In this paper, we investigate a few graph transformations that decrease Randić index of graph. By applying those transformations, we determine the minimum Randić index on tricyclic graphs, and characterize the corresponding extremal graphs.

: Biological networks such as protein interaction networks, gene regulation networks and metabolic pathways are examples of complex networks which are large graphs with small-world and scale-free properties. Analysis of these networks has a profound effect on our understanding the origins of life, health and disease states of organisms, and diagnose diseases to aid the search for remedial processes. In this review, we describe main analysis methods of biological networks using graph theory by first defining main parameters such as clustering coefficient, modularity and centrality. We then survey fundamental graph clustering methods and algorithms followed by the network motif search algorithms with the aim of finding repeating subgraphs in a biological network graph. A frequently appearing subgraph usually conveys a basic function carried out by that small network and discovering such a function provides an insight to the overall function of the organism. Lastly, we review network alignment algorithms that achieve to find similarities between two or more graphs representing biological networks. A conserved subgraph between the biological networks of organisms may mean a common ancestor and finding such relationship may help researchers derive ancestral relationships and predict the future evolution of organisms to enable designing new drugs. We conclude by the current challenging areas of biological network analysis using graph theory and parallel processing for high performance analysis

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

CRISPR/Cas9 has emerged as the predominant method for genome editing due to its cost-effectiveness and broad applicability, playing a crucial role in advancing sustainable practices across various sectors. This systematic review leverages the PRISMA methodology to explore CRISPR/Cas9's impact on environmental protection, thereby supporting the Sustainable Development Goals (SDGs). Analyzing data from the Web of Science, the review found significant growth in related publications, with a 30% increase since 2014, predominantly from the US, China, Germany, and the UK. The study categorizes the scientific developments into three trends: advancements in agriculture, gene editing techniques, and biofuel production. Key discussions include the use of CRISPR/Cas9 in developing fourth-generation biofuels and environmental biosensors, as well as its applications in enhancing genetic resilience and controlling invasive species. These innovations highlight CRISPR/Cas9's potential in promoting sustainable resource management and energy generation, marking a pivotal contribution to ecological conservation and sustainability efforts.

Delve into the dynamic realms of neuroscience, neurology, and psychiatry with this groundbreaking editorial, unveiling the intricate mechanisms of brain function and dysfunction. Explore the latest advancements in molecular biology, genetics, epigenetics, pharmacology, and neuroimaging that are revolutionizing our understanding of neurological and psychiatric disorders. Uncover the challenges and gaps in translating cutting-edge research into practical clinical applications, aiming to enhance the lives of patients and their families. Discover the pivotal role of neuroplasticity in reshaping the brain's adaptability throughout life, alongside the promising potential of non-invasive brain stimulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation. Embark on a journey of innovation and collaboration as researchers and clinicians strive to bridge the gap between bench and bedside, offering hope for improved diagnosis, prevention, and treatment of complex neurological and psychiatric conditions. Join us in this exploration of transformative ideas and novel interventions that hold the key to a brighter future for individuals and society.

This paper defines the two main causes of global warming in the UK and seeks to cut climate change using green hydrogen made from renewable electricity sources. Combustion vehicles powered by fossil gasoline and diesel emit a third of UK climate-warming CO2 while buildings heated by natural gas provide a quarter. First, current UK grid problems are defined: Electricity grid, gas grid and petroleum grid. Then experiments on the private energy community of Keele University allow electrolysis demonstrations producing green hydrogen for buildings and vehicle refueling. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace diesel and gasoline in vehicle transport, while also displacing natural gas for combined heat and power in buildings. In addition, the prospect in 2023 is that profits can now be made all along the green hydrogen supply chain, such that new businesses involved in clean communities can beat the National Grid monopoly and other large dominant fossil grid companies.

Crisis management is essential for businesses to effectively respond to, recover from, and mitigate the effects of unexpected disruptions. This study investigates factors influencing organizational resilience in Himachal Pradesh's pharmaceutical sector (India), focusing on crisis management techniques. The primary goal is to identify key components that support resilience in this industry. Data were collected using structured online and offline questionnaires, yielding 483 valid responses from a sample size of 500 across six pharmaceutical companies. Factor analysis with SPSS software identified three crucial components: "Innovations and Employee Engagement," "Organizational Resources," and "Organizational Culture and Values." Innovations and Employee Engagement" highlight the importance of fostering a creative environment where employees feel empowered to take initiative during crises. "Organizational Resources" underscore the critical role of adequate infrastructure, financial resources, and technological advancements in building resilience. "Organizational Culture and Values" emphasize the importance of shared standards, values, and beliefs in promoting cohesion and unity during adversity. By prioritizing these elements, pharmaceutical companies in Himachal Pradesh can enhance their crisis response capabilities and foster a resilient organizational culture. Implementing these insights through targeted strategies can ensure long-term viability and improve preparedness for future crises.

Given the conceptual confusion between resilience and sustainability in organizations in the literature, the present study reviews the 27-year global literature on resilience and sustainability in organizations to clarify the relationship between the two, and derive a framework as cutting-edge knowledge on how to ensure resilience and sustainability in organizations. Findings uncover five schools of thought: Sustainable Business Strategy, Predictive Analytics, Sustainable Supply Chain, Ecological Resilience and Sustainable Leadership. A cutting-edge framework, comprising sustainability practices, sustainability outputs, sustainability and resilience outcomes, is derived to guide future research and inform practitioners on how to ensure resilience and sustainability in their organizations.

In this work, we demonstrate that cutting diamond crystals with a laser (532 nm wavelength, 0.5 mJ energy, 200 ns pulse duration at 15 kHz) produces a ≲20nm thick surface layer with magnetic order at room temperature. We have measured the magnetic moment with a SQUID magnetometer of six natural and six CVD diamond crystals of different size, nitrogen content and surface orientations. A robust ferromagnetic response at 300 K is observed only for crystals that were cut with the laser along the (100) surface orientation. The magnetic signals are much weaker for the (110) and negligible for the (111) orientations. We attribute the magnetic order to the disordered graphite layer produced by the laser at the diamond surface. The ferromagnetic signal vanished after chemical etching or after moderate temperature annealing. The obtained results indicate that laser treatment of diamond may pave the way to create ferromagnetic spots at its surface.

Recently, there has been a trend towards healthy food. Consumers are looking for products that have health-promoting properties in addition to their taste. Actinidia fruit, apart from being tasty, contains valuable nutrients. They are high in vitamin C, polyphenols, and pectins and low in calories. These pro-health properties allow kiwiberry fruit to qualify in the group of so-called superfoods. All tested cultivars belong to two species of Actinidia, well tolerating conditions of moderate climate with negative temperatures in winter. The cultivars differed significantly in mineral content, color, firmness, weight and content of antioxidant substances. Summer pruning (lightening) increased the content of N, P, K, extract, and NO3 and resulted in darker fruit color. However, it slightly decreased the content of polyphenols. 'Sientiabrskaja' can be recommended for consumption as the cultivar richest in polyphenols and with the highest health-promoting values.

Here we present a fast and highly scalable community structure preserving network module detection that recursively integrates graph sparsification and clustering. Our algorithm, called SparseClust, participated in the most recent DREAM community challenge on disease module identification, an open competition to comprehensively assess module identification methods across a wide range of biological networks.

Combinatoric measures of entropy capture the complexity of a graph, but rely upon the calculation of its independent sets, or collections of non-adjacent vertices. This decomposition of the vertex set is a known NP-Complete problem and for most real world graphs is an inaccessible calculation. Recent work by Dehmer et al. and Tee et al. identified a number of alternative vertex level measures of entropy that do not suffer from this pathological computational complexity. It can be demonstrated that they are still effective at quantifying graph complexity. It is intriguing to consider whether there is a fundamental link between local and global entropy measures. In this paper, we investigate the existence of correlation between vertex level and global measures of entropy, for a narrow subset of random graphs. We use the greedy algorithm approximation for calculating the chromatic information and therefore Körner entropy. We are able to demonstrate close correlation for this subset of graphs and outline how this may arise theoretically.

Advanced persistent threat (APT) attacks are sophisticated and organized attacks commonly motivated by political, financial, and strategic objectives. In order to comprehend their tactics, techniques, and procedures (TTP) and indicators, APT reports are valuable sources. While blue teams typically rely on server logs, firewall rules and user authorizations managed in database tables, attackers have a graph-based mindset. In this work, we propose a framework for discovering and evaluating APTs using graph-based algorithms. Cyber threat intelligence (CTI) was extracted from 40,358 pages of APT reports and transformed into a graph. Centrality, community, and similarity analyses were executed on the graph. As a result, critical and influential APT groups and indicators of compromise (IoC) were discovered. Similar attacks and APT groups were revealed. Analysis results were interpreted to create new strategic CTI that can be utilized in future security operations.

Automation is the key aspect of science and technology field and refers to controlling the systems for efficient output requirements. The development of robotics and similar, technologies in 21st century is a vast topic of research and significant progress has been made on this field. This research study focuses on building a ribbon-cutting robot with a Bluetooth interface for event inaugurations. The entire system hinges on constructing a prototype that is mounted on a robotic car frame with four wheels and an Arduino controller. In this case, ribbons are cut using a nickel wire, and it may be paired with an Android device via Bluetooth or an application. The robot is one of the first steps toward the development of autonomous, cognitive robots and can be used in a variety of events. Efficient temperature monitoring is done with help of LM35 sensor and Bluetooth based communication in order to monitor the conditions and effectively control the actions for which robot is designed. The robot has significantly allowed authors for desired tests on ribbon cutting and final results obtained were very exciting for its desired application target. Ribbon-cutting robots find applications in various events, including inaugurations, exhibitions, and ceremonies. Their precision and novelty add value to such occasions.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

Keywords: Deep mining; Drill bit structural parameters; Fidelity coring; Abaqus simulation and analysis.

Gamma titanium aluminides (γ-TiAl) present an excellent behaviour under high temperature conditions, being a feasible alternative to nickel-based superalloy components in aeroengine sector. However, considered as a difficult to cut material, process cutting parameters require special study to guarantee components quality. In this work, developed drilling mechanistic model is a useful tool in order to predict drilling force (F_z) and torque (T_c) for optimal drilling conditions determination. The model is validated for three types of Gamma-TiAl alloys. Integral hard metal end-drilling tools and different cutting parameters (feeds and cutting speeds) are tested in three different sized holes for each alloy.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

Due to pollution and environmental deterioration, man needed to find other alternatives as energy sources, including biomass. (1) Background: The present research was conducted to evaluate the sugarcane cutting residue; this type of biomass has no use and can be burned to obtain energy. (2) Methods: Through the elaboration of pellets and, in turn, reduce the environmental impact of flora and fauna in crops, for the characterization, the raw material was subjected for the first time to physical treatment such as cleaning, drying, grinding, and sieving so that have the optimal characteristics to be used in the pelleting stage. Then a proximal analysis was conducted based on the ASTM -3172-89 Standard and the determination of the calorific value using the ASTM D-240 Standard. Likewise, the pellets were analyzed in their final stage. This method analyzes the parameters of moisture content, volatile material, ashes, and fixed carbon. (3) Results: It was obtained as a result that for the raw material, the % H=3.543, %MV=86.53, %Cz=6.939, %Cf= 2.987 and Pc= 15.702 KJ/Kg; and for the elaboration of the pellets it was carried out using the mixing proportion, 53% SAR with 47% binder, this was determined using the proximal analysis and the determination of the calorific value, from which the results of % H=5,768 were obtained., %MV=82.313, %Cz=6.376, %Cf= 5.543 and Pc= 15.635 KJ/Kg. (4) Conclusions: In other words, sugarcane cutting residues meet the physicochemical properties to be used as raw material in producing solid biofuel.

We present an Arduino-based automatic robotic system which is used for cutting grass or lawns, mostly healthy grass which needs to cut neatly like in a public park or a private garden. The purpose of this proposed project is to design a programmable automatic pattern design grass cutting robot with solar power which no longer requires time-consuming manual grass-cutting, and that can be operated wirelessly using an Android Smartphone via Bluetooth from a safe distance which is capable of cutting the grass in indeed required shapes and patterns; the cutting blade can also be adjusted to maintain the different length of the grass. The main focus was to design a prototype that can work with a little or no Physical user interaction. The proposed work is accomplished by using an Arduino microcontroller, DC geared Motors, IR obstacle detection sensor, motor shield, relay module, DC battery, solar panel, and Bluetooth module. The grass-cutting robot system can be moved to the location in the lawn remotely where the user wants to cut the grass directly or in desired patterns. The user can press the desired pattern button from the mobile application, and the system will start cutting grass in the similar design such as a circle, spiral, rectangle, and continue pattern. Also, with the assistance of sensors positioned at the front of the vehicle, an automatic barrier detection system is introduced to enhance safety measurements to prevent any risks. IR obstacle detector sensors are used to detect obstacles, if any obstacle is found in front of the robot while traveling; it avoids the barrier by taking a right/right turn or stop automatically appropriately, thereby preventing the collision. Also, the main aim of this project is the formation of a grass cutter that relieves the user from mowing their own grasses and reduces environmental and noise pollution. The proposed system is designed as a lab-scale prototype to experimentally validate the efficiency, accuracy, and affordability of the systems. The experimental results prove that the proposed work has all in one capability (Simple and Pattern based grass cutting with mobile-application, obstacle detection), is very easy to use, and can be easily assembled in a simple hardware circuit. We note that the systems proposed can be implemented on a large scale under real conditions in the future, which will be useful in robotics applications and cutting grass in playing grounds such as cricket, football, and hockey, etc.

Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

Graphs are largely used in computer science to model relations, or associations, among entities the compose complex systems. More recently, they found a broad field of application in bioinformatics and medical informatics supporting modelling, analysis of many systems. The applications span from representing interactions among molecules within cells, to model the functions of the brains. In order to support research, algorithms from graph theory that are able to extract knowledge from network should be coupled to efficient visualisation techniques. Although the importance of such topic, we retain that many challenges should be faced in the futures, such as automated pathway and network layout to better match biologists’ needs, the developing of a standard able to give more and better choices to represent nodes, edges finally, the developing of automated mechanism able to improve the network navigation methods that help users manage large and complex networks with the goal to improve the usability.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.

This paper proposes an interpretable geometry solution based on the formal language set of text and diagram. Geometry problems are solved by machines, which still poses challenges in natural language processing and computer vision. Significant progress promotes existing methods in the extraction of geometric formal languages. However, the neglect of the graph structure information in the formal language and the lack of further refinement of the extracted language set can lead to the poor effect of the theorem prediction and affect the accuracy in problem solving. In this paper, the formal language graph is constructed by the extracted formal language set and used to theorem prediction by graph convolutional network. So as to better extract the relationship set of diagram elements, an improved diagram parser was proposed. The test results indicate that the improved method has good results in solving the problem with interpretability geometry.

The Harmonic Graphs Conjecture states that there exists an asymptotic relation involving the Harmonic Index and the natural logarithm as the order of the graph increases. This conjecture, grounded in the novel context of Prime Graphs, draws upon the Prime Number Theorem and the sum of divisors function to unveil a compelling asymptotic connection. By carefully expanding the definitions of the harmonic index and the sum of divisors function, and leveraging the prime number theorem's approximations, we establish a formula that captures this intricate relationship. This work is an effort to contribute to the advancement of graph theory, introducing a fresh lens through which graph connectivity can be explored. The synthesis of prime numbers and graph properties not only deepens our understanding of structural complexity but also paves the way for innovative research directions.

Many parameters and complex boundaries are involved in the spatial arrangement of an under-ground powerhouse for a hydropower station, which requires reference to many relevant cases and specifications. However, in practical applications, retrieving relevant cases or specifications is difficult, and there is a lack of knowledge of cascading logic among design parameters. For this question, a targeted knowledge graph based on knowledge graph management technology is established to support subsequent applications. This paper proposes a new concept of con-structing a knowledge graph for building information modeling (BIM) underground power-houses of hydropower stations. Firstly, the ontology skeleton of hydropower station spatial ar-rangement design, which represents the knowledge organization structure of the knowledge graph, is reconstructed by carefully analyzing the requirements for intelligent modeling of un-derground powerhouses. A large amount of unstructured data is identified based on optical character recognition (OCR) technology and is divided into words to extract correlation knowledge based on THULAC. In the next step, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in a Neo4j knowledge base to build a knowledge graph. Finally, the knowledge graph is serviced to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of spatial arrangement of an underground powerhouse of pumped storage hydropower stations.

The accurate prediction of molecular properties such as lipophilicity and aqueous solubility are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods like graph-based neural networks (GNNs) have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

This research deals with the processing of wood using a circular saw, which is associated with several risks and factors that affect the wood splitting process. One of them is the wear of the teeth of the saw blade and the change in the size of the cutting forces. The aim of this study is to compare real measured values and theoretical values when processing Norway Spruce wood, using two types of saw blades SK and HSS with a diameter of 600 mm and the number of teeth 54 and 56. Experimental measurements were carried out on a special test device with the possibility of moving the tested wood into the cutting devices and a simple saw blade replacement system. The results of the contributions show significant differences, based on defined factors, which are supported by FEM analysis of cutting forces in the process of cutting wood - when changing the design of the saw blade. The results of the experiment show that the cutting conditions (cutting speed, feed speed) do not have a significant effect on the size of the cutting forces. The theoretical and real stress values for HSS discs ranged from 14 to 22 MPa. Significantly larger differences were recorded for discs with SK, where the real values of the maximum stresses were around 14 MPa and the theoretical ones around 25 MPa. When cutting spruce with a disc with SK slices, the maximum values of the the-oretical cutting forces were in the range of 76 to 94 N and the maximum values of the measured cutting forces were in the range of 40 to 44 N.

The vibrations of a Cartesian cutting machine caused by the pneumatic tool are studied with a sub-system approach. The cutting head is modeled as an equivalent robot arm which is able to mimic the measured resonances, The Cartesian structure is modeled according to the mode superposition approach. The full model is able to predict the variations in the response of the machine to tool excitation that are caused by the motion of the head along the rails of the Cartesian structure. Comparisons with experimental results are made. Finally, the mathematical model is used for assessing the effectiveness of a vibration absorber tuned to main resonance frequency of the cutting head.

In this paper, a metal cutting machine position control have been designed and simulated using Matlab/Simulink Toolbox successfully. The open loop response of the system analysis shows that the system needs performance improvement. Static output feedback and full state feedback H₂ controllers have been used to increase the performance of the system. Comparison of the metal cutting machine position using static output feedback and full state feedback H₂ controllers have been done to track a set point position using step and sine wave input signals and a promising results have been analyzed.

This report gives a comprehensive summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

The performance and lifespan of cutting tools are significantly influenced by their surface quality. The present report highlights recent advances in enhancing the surface characteristics of tungsten carbide and high-speed steel cutting tools using a novel micro-machining technique for polishing and edge-honing. Notably, the main aim is to reduce the surface roughness while maintaining the hardness of the materials at an optimal level. By conducting a thorough analysis, of surfaces obtained with different techniques. It was that micro-machining method effectively decreased the surface roughness of the cutting tools, most effectively of the techniques investigated. Significantly, the surface roughness is reduced from an initial measurement of 400 nm to an impressive value of 60 nm. No significant change in hardness was observed, which guarantees the maintenance of the mechanical properties of the cutting tools. This analysis enhances the comprehension of surface enhancement methodologies for cutting tools through the presentation of these findings. The observed decrease in surface roughness, along with the consistent hardness, exhibits potential for improving tool performance. These enhancements possess the capacity to optimise manufacturing processes, increase tool reliability, and minimise waste generation.

Livestock production in the Cajamarca, La Libertad and Amazonas basins is based on pasture-based feeding with "Cajamarquino ecotype" ryegrass under monoculture or in association with white clover. The objective of this research work was the morphological characterization and evaluation of the productive yield and cutting time of annual ryegrass - Lolium multiflorum L. Six local genotypes of Lolium multiflorum in six locations were characterised, using the multiplicative interaction effect model and additive main effects for the analysis of interaction and variability of the genotypes and locations. Ryegrass “Cajamarquino ecotype” is semi-erect with intermediate growth, with a dark green colouring, 0.8 cm broadleaf, and can reach an average stem length of 46 cm, up to 1.6 cm.day, achieving the growth of the fourth leaf after 28 days under adequate management conditions. Despite the differentiated characteristics, according to the BLASTn evaluation, the ITS1 sequences showed an identification greater than 99.9% similar to Lolium multiflorum L. It was determined that the Cajamarquino ecotype produces from 3.52 to 4.98 Mg.ha.biomass cutting. Likewise, the pasture's quality is better when used at 30 days with 14.84% protein and at 45 days, it achieves 12.75% protein with a biomass of 4.05 Mg.ha.cut. With these morphological and yield characteristics, the "Cajamarca ecotype" ryegrass is a well-adapted species for local livestock farming. These species are adapted to the Sierra Norte area of Peru.

The proposed method deals with the problem of data privacy and sharing when processing personal mobility tracking data. Previous methods concentrate on producing highly detailed data on short-term and restricted areas, e.g. for autonomous driving scenarios. Another possibility consists of city-wide scales and beyond, that are used to predict general traffic flows. The presented approach takes the tracked mobility behavior of individuals to create coherent new mobility data that reflects the long-term mobility behavior of the person, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step with the original data, the area is distributed into a geospatial grid structure (H3 is used here), and the neighborhood relationships between the grids are interpreted as a graph. A feed-forward-autoencoder and a graph encoding-decoding network generate a latent space representation of the area. The original clustered data is associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are top-level patterns for individual mobility behavior. Concrete locations are found and connected within the grids based on the original data. The described method is then applied to a study with 1000 participants from the city of Munich in Germany, and the results are described, showing the application of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result is a sharable dataset on the same abstraction level as the input data, which makes it interesting, particularly for machine learning applications.

The real-time Railway Traffic Management Problem (rtRTMP) is discussed in the field of railway transportation. In this paper, rtRTMP is considered for planning shunting routes based on the use of routing methods on a spatial-temporal graph. An approach and algorithm of Spatial-Temporal Graph formation for rail yard transportation network is proposed. The idea is to construct an undirected loop-free Double Vertex Graph for determining optimal shunting routes consisting of several half-runs for several trains shunting under draw-out tracks capacity constraints. Experimental results on the rail yard simulation model are presented.

The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory fast graph mutations. Existing graph structures offer a hard tradeoff between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these tradeoffs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, as well as state-of-the-art update-friendly graph structures: LLAMA, STINGER, GraphOne, and Teseo. In our evaluation, which is based on popular graph processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates. We also show that both CSR++’s update throughput and analytics performance exceed that of several state-of-the-art graph structures, while maintaining low memory consumption when the workload includes updates.

It is shown how the secondary structure of proteins, musical forms and verses of poems are approximately ruled by universal laws relying on graph coverings. In this direction, one explores the group structure of a variant of the SARS-Cov-2 spike protein and the group structure of apolipoprotein-H, passing from the primary code with amino acids to the secondary structures organizing the foldings. Then one look at the musical forms employed in the classical and contemporary periods. Finally, one investigates in much detail the group structure of a small poem in prose by Charles Baudelaire and that of the Bateau Ivre by Arthur Rimbaud.

Cutting and packing problems have been studied for decades and are of concern for a wide variety of industries to reduce costs and material utilization. This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Procedures that address this problem include linear programming methods and metaheuristics. However, linear programming methods are limited to low-complexity instances, while most metaheuristics require extensive parameter tuning. In this paper, we develop a Petri-net model to construct cutting patterns. Using the reachability tree of the Petri net, the filtered beam search algorithm is implemented to find the best solution. Our algorithm is compared with the Least Lost Algorithm and the Generate & Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal solutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.

With the first case of human immunodeficiency virus (HIV) infection confirmed in US in 1981, the efforts of discovering anti-HIV therapeutics have been continued ever since. Ten years later, the first HIV drug zidovudine (AZT) was developed to inhibit HIV reverse transcriptase. At meantime, scientists were enlightened to discover new drugs of different targets acting on HIV integrase, protease, and host receptors. The advent of combination antiretroviral therapy (cART) is completely a game changer, with high efficiency in suppressing viremia for people with HIV (PWH) by controlling the viral load below the detectable level. On the bright side, the ART treatment has made HIV a chronic infection rather than a fatal disease. However, it cannot eradicate integrated HIV DNA from the host cells, thus the latent viral reservoir has become a lifelong threaten. In this review, we first discuss the scientific history of conventional HIV drug discovery, with more and more anti-HIV agents have been developed to solve drug resistant issue and relieve the side effect. As a complementary therapy, advanced gene editing technologies have been applied to excise HIV provirus from host genome. Within four decades, novel research conducted on HIV treatment and their contributions to eliminate HIV have been altogether summarized in our review.

Adventitious roots play a crucial role inr the nourishment and propagation of arboreal vegetation. In order to shed light on the physiological and biochemical characteristics of the challenging-to-propagate mulberry tree species, an investigation was conducted. This study aimed to compare the responses of various root morphological indicators, endogenous hormones, and oxidase activities in the "Yueshenda 10" fruit mulberry, at different stages of treatment. The ultimate objective was to identify the factors influencing the process of root development. The findings revealed a distinct '/\' pattern in the levels of IAA and JA within the cuttings. Conversely, the changes in ABA, ZR and GA3 exhibited a '/\/' pattern. The fluctuation of IAA/ABA values followed a '\/\' mode, whereas the IAA / ZR values initially increased, followed by a subsequent decrease. The correlation between the initial concentrations of these five endogenous hormones and the rooting rate displayed variations. Notably, IAA demonstrated the strongest association with the rooting rate, exhibiting a positive correlation with both IAA and ZR. Regarding the activity of three antioxidant enzymes (IAAO, POD and PPO), a '/\' trend was observed, wherein the enzyme activity increased under ABT1 treatment. However, the peak activity levels of the enzymes appeared during different periods: germination, induction, and expression, respectively. Overall, the most effective treatment for promoting root development and significantly enhancing the root growth parameters of mulberry was found to be 800mg/L ABT1. Exogenous hormone treatment expedited the synthesis of antioxidant enzymes, thereby shortening the rooting time and facilitating root formation.

The gasification of sugarcane cutting residues (RAC) is a process that occurs in a gasifier where the transformation of this raw material into a solid-state and a gasifying agent with a moderate calorific value occurs, thanks to the application of heat. And under restricted oxygen levels, we can say that there are several styles of gasifiers for air, steam, oxygen, and hydrogen, all of which have a performance that can be analyzed and categorized by their performance to avoid damage to the environment. (1) The objective of this article is based on the mathematical development using simulation of the gasification of cane cutting residues. (2) In the methodology, the simulation of the gasification and CO₂ capture process was developed from the biomass residues of the sugarcane cutting residues; it was carried out as a transformation of the primary fuel into a gas stream whose main components are CO₂ and H₂, which can be separated relatively easily by their concentrations, available pressures and in some cases, their temperatures; (3) According to the kinetic data obtained, the second-order reaction in the transformation and improvement of the process was identified; applying to the optimization of development in the capture of CO₂, contributing to the reduction of greenhouse gases. (4) The gasification simulation process results in a biomass conversion corresponding to 93% of its feed and the formation of volatiles whose molar fraction corresponds to 37% H₂, 12% CH₄, 37% CO and 12 % CO₂.

The data from the extended system was recorded on-going basis in the archives of the monitoring system, and the multipoint system was directly stored in the methane-anemometers, which allows one to perform a comparative analysis of the obtained records from many methane detectors. An important element of the analyses was the simultaneous registration of the methane concentration in the longwall, together with information on operating time, direction of shearer operation and type of work: mining/cleaning. Mining practice shows that in methane coal mines, methane is often the basic limitation for modern and high-efficiency longwall complexes. The paper reports on selected observations and tests carried out in the Cw-4 longwall in seam 364/2 of the Budryk mine belonging to JSW SA. In observation of the methane level in the Cw-4 longwall, additional sensors installed in the end part of the longwall from the side of the gobs and registration of air velocity and methane concentration in the multipoint system built in the selected longwall cross-section were used, regardless of the air parameters recorded in the monitoring system.

This study evaluates the feasibility of stabilizing clay bricks with marble cutting waste (MCW). This waste currently discarded in huge quantities as a sludge resulted from sawing the marble blocks to slabs, grinding and polishing of marble processes to the landfills located around the marble processing factories located in the Shaq El-Thoban industrial zone, Cairo governorate, Egypt causing negative impacts on the environment, health and sustainable development. Experimental investigations were carried out to explore the effect of addition of the MCW in different clay-base mixes at different percentages up to 25% at the expense of the hydrated lime. Cement, hydrated lime and MCW are the three types of solidification agents used, clay and sand were also added in the formulations of the unfired clay brick specimens. Laboratory cylindrical stabilized and compressed specimens were made, and then they were cured in a humidity chamber for 2 and 4 weeks, then after were air dried, tested and evaluated according to the Egyptian code for the building by the stabilized and compressed earth soil (ECBS, 2016). To enhance the durability of the cured specimens, transparent silicon – based paint was used for this purpose. The laboratory results demonstrate high potential usage of MCW based additives up to 15% incorporating HL. In addition, the used paint could be an effective treatment way for the use of stabilized bricks in a wet environment. The use of eco-friendly building materials will be a great contribution for the environmental advantages and suggest a remarkable economical alternative to the fired building units.

Computational Fluid Dynamics (CFD) is revolutionizing the oil and gas industry by offering powerful insights and innovative solutions. This paper explores the cutting-edge applications of CFD in enhancing and innovating within the sector. CFD simulations provide engineers with invaluable insights into fluid behavior, enabling optimized designs and informed decision-making. The software facilitates foresight by predicting system performance, optimizing drilling techniques, and maximizing reservoir management strategies. Moreover, CFD optimizes equipment design, increases operational efficiency, and reduces costs through virtual testing, eliminating the need for costly physical prototypes. The application of CFD aligns with the United Nations Sustainable Development Goals (SDGs), promoting energy efficiency, climate action, responsible production, and sustainable consumption. It addresses flow assurance challenges, enhances safety protocols, and mitigates environmental impacts. CFD is a powerful tool driving innovation, resilience, and sustainability within the oil and gas industry, ensuring a more efficient, safe, and environmentally conscious future. As advancements in CFD methodologies continue, the potential for further enhancements and breakthroughs within the sector becomes even more promising, driving the industry towards a sustainable and prosperous future.

This study documents the conditions associated to white spruce and balsam fir regeneration after partial cutting. Measurements were collected 9 to 30 years after partial cutting in 12 natural fir stands and 5 white spruce plantations. We estimated seed input, measured light reaching the undergrowth, recorded seedlings (<150 cm) and their age on 6 different seedling establishment substrates: mineral soil, moss, rotten wood, litterfall, herbaceous and dead wood. Partial cutting generally favours the establishment and growth of seedlings. The number of fir and spruce seedlings is always greater in natural stands than in plantations, a trend likely associated with the reduced abundance of preferential establishment substrate in the latter. White spruce significantly prefers rotten wood while fir settles on all types of substrates that cover at least 10% of the forest floor. There is a strong relationship between light intensity and the median height of spruce seedlings, but this relationship is non-significant for fir. Seedlings of both species can survive at incident light intensities as low as 3%, but an intensity of 15% or more seems to offer the best growth conditions. The conditions for successful forest regeneration proposed in this study should be applied when the goal is to establish a new stand prior to clear cutting or to convert stand structure.

This study innovates upon the field of dialog-driven query resolution (DDQR) utilizing knowledge graphs (KGs). Traditional methods in DDQR predominantly depend on fully supervised signals that presuppose the existence of perfect logical forms for queries. This reliance on gold logical forms for answer extraction is impractical in diverse real-world applications. When such forms are absent, contemporary methods, which lean on weak supervisory signals or employ heuristic and reinforcement strategies, recast DDQR as a knowledge graph path optimization problem. Despite the non-availability of gold logical forms, the rich conversational context provided by comprehensive dialog histories and domain-specific knowledge can be leveraged to optimize path selection in KGs effectively. We introduce an advanced method, termed CONVEX (CONVersational EXploration), which utilizes contrastive learning for path ranking. CONVEX addresses critical challenges by enabling learning under weak supervision and integrating the conversational context to enhance the representation quality for more effective path discrimination. Extensive evaluations of CONVEX on established benchmarks demonstrate its superiority across various metrics over current methods, notably improving Mean Reciprocal Rank (MRR) and Hit@5 by up to 20 and 36 percentage points respectively.

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graph can introduce user-item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user-item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: 1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and 2) how to merge interaction information from the two graphs while ensuring that user-item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC has strengthened and represented the information of different connecting edges in both graphs, and extracted the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We have conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results.

The study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model, incorporating Graph Masked Autoencoders. This approach integrates textual information with graph connectivity evidence, resulting in a more sophisticated model of citation distribution. By training a deep network using partial data and reconstructing masked connections, the method leverages the inherent characteristics of central connections under network perturbations. Quantitative evaluations demonstrate its remarkable ability to pinpoint trustworthy citations in the analyzed data and raise concerns about potentially unreliable references due to potential manipulation.

Due to the inclusion of both the principles and divination methods, the study of Yijing (Yijing) becomes more complex. The complex data types, diverse semantic relationships, and unclear influencing mechanisms in Yijing pose challenges to its scientific research. Knowledge graph, as an advanced way of organizing knowledge, can provide technical support in exploring the hidden connections and knowledge structure within text resources. This paper proposes a method of constructing a Yijing knowledge graph based on Neo4j. By analyzing the concepts, entities, and various related relationships in Yijing, the method discusses the basic architecture, data model, and implementation steps of constructing the Yijing knowledge graph. It also extracts, integrates, and structures the Yijing knowledge, allowing for inquiries, associations, and reasoning about the knowledge and its various elements. By adopting a top-down approach, the paper reconstructs the fundamental knowledge system of the Yijing, including yin-yang, the five elements, generation and restriction, 64 hexagrams, and six lines(yao). It establishes a knowledge graph pattern layer and a data layer that encompass concepts, attributes, and relationships. With the technical support of the Neo4j platform, the visualization and retrieval of Yijing knowledge are realized, providing new ideas and methods for studying the mechanisms of the Yijing. The proposed method of constructing a Yijing knowledge graph can be expanded to other types of ancient scripture knowledge graph research and can be applied to the field of digital humanities, promoting in-depth cross-disciplinary research and integration.