bluuebunny/crossref_metadata_2025 huggingface dataset:
Dark energy and dark matter configurations for wormholes and solitionic hierarchies of nonmetric Ricci flows and $$F(R,T,Q,T_{m})$$ gravity
Authors: Bubuianu Laurenţiu , Vacaru Sergiu I. , Veliev Elşen Veli , Zhamysheva Assel
Published: February 2024

We extend the anholonomic frame and connection deformation method, AFCDM, for constructing exact and parametric solutions in general relativity, GR, to geometric flow models and modified gravity theories, MGTs, with nontrivial torsion and nonmetricity fields. Following abstract geometric or variational methods, we can derive corresponding systems of nonmetric gravitational and matter field equations which consist of very sophisticate systems of coupled nonlinear PDEs. Using nonholonomic frames with dyadic spacetime splitting and applying the AFCDM, we prove that such systems of PDEs can be decoupled and integrated in general forms for generic off-diagonal metric structures and generalized affine connections. We generate new classes of quasi-stationary solutions (which do not depend on time like coordinates) and study the physical properties of some physically important examples. Such exact or parametric solutions are determined by nonmetric solitonic distributions and/or ellipsoidal deformations of wormhole hole configurations. It is not possible to describe the thermodynamic properties of such solutions in the framework of the Bekenstein–Hawking paradigm because such metrics do not involve, in general, certain horizons, duality, or holographic configurations. Nevertheless, we can always elaborate on associated Grigori Perelman thermodynamic models elaborated for nonmetric geometric flows. In explicit form, applying the AFCDM, we construct and study the physical implications of new classes of traversable wormhole solutions describing solitonic deformation and dissipation of non-Riemannian geometric objects. Such models with nontrivial gravitational off-diagonal vacuum are important for elaborating models of dark energy and dark matter involving wormhole configurations and solitonic-type structure formation.
A molecular simulation study on the adsorption and separation performance of carbon nanotubes for SO2 in flue gas
Authors: Zhang Jiawei , Qin Dachuan , Liu Siyao , Wang Wei
Published: February 2024

To control the emission of SO2 from flue gas into the atmosphere while considering the capture and collection of greenhouse gas CO2, the adsorption behavior of a binary mixture of SO2 and other gases in flue gas (O2/N2/H2O/CO[Formula: see text] in (10, 10) carbon nanotube (CNT) was simulated using grand canonical Monte Carlo (GCMC) simulation. The adsorption and separation performance of SO2 in CNTs with a diameter range of 0.81–1.63 nm for the five-component mixture gas was also analyzed. The findings suggest that the adsorption and separation of SO2 are primarily influenced by CO2 (reduction of adsorption capacity by about 50%, separation coefficient of SO2/CO2 is lowest) with this effect being more pronounced under high pressure. Meanwhile, it was observed that CNTs with larger pipe diameters exhibit higher SO2 adsorption capacity, but relatively lower SO2/CO2 selectivity and lower stability. On the other hand, CNTs with smaller diameters have relatively lower adsorption capacity for SO2, but exhibit good selectivity and stability (under different pressure) for SO2/CO2. Based on the statistical analysis of SO2 adsorption capacity and SO2/CO2 selectivity, it was determined that (6, 6) CNT with a diameter of 0.81[Formula: see text]nm can exhibit excellent SO2 adsorption and separation performance at atmospheric pressure, while appropriate large diameter CNTs should be selected for flue gas treatment under high pressure.
Adiabatic elimination in the presence of multiphoton transitions in atoms inside a cavity
Authors: Maity Prosenjit
Published: February 2024

Various approaches have been used in the literature for eliminating nonresonant levels in atomic systems and deriving effective Hamiltonians. Important among these are elimination techniques at the level of probability amplitudes, operator techniques to project the dynamics on to the subspace of resonant levels, Green’s function techniques, the James’ effective Hamiltonian approach, etc. None of the previous approaches is suitable for deriving effective Hamiltonians in intracavity situations. However, the James’ approach does work in the case of only two-photon transitions in a cavity. A generalization of the James’ approach works in the case of three-photon transitions in a cavity, but only under Raman-like resonant conditions. Another important approach for adiabatic elimination is based on an adaptation of the Markov approximation well-known in the theory of system–bath interactions. However, this approach has not been shown to work in intracavity situations. In this paper, we present a method of adiabatic elimination for atoms inside cavities in the presence of multiphoton transitions. We work in the Heisenberg picture, and our approach has the advantage that it allows one to derive effective Hamiltonians even when Raman-like resonance conditions do not hold.
Unsteady dynamical analysis of convective hydromagnetic thermal migration of chemically reacting tiny species with dissipation and radiation in an inclined porous plate
Authors: Sademaki L. Joseph , Shamshuddin MD. , Salawu S. O. , Reddy B. Prabhakar
Published: February 2024

The essence of the current examination is to carry out thermofluid parametric sensitivity with time-varying thermal migration of chemically reactive tiny species across an oscillating infinite plate surface. The impact of thermal motile tiny particles under the influence of many other oscillating flow parameters has yet to be investigated; hence the results obtained in this research are novel. Using a suitable non-dimensional variable, the leading PDEs (partial differential equations) are transmuted into dimensionless PDEs, ensuring equations are numerically solved using the MAPLE built-in approach. The numerical values produced in a limited scenario are linked with the outcomes found in the literature to validate the precision of the numerical approach utilized. The fluctuations in the profiles of the velocity, temperature, and concentration, in addition to the wall friction and rate of thermal and solutal transport, are illustrated via graphs and tables due to the modification of the critical parameters. The endmost results of the study concede that increasing permeability quantity and thermal and solutal buoyancy impellers intensify the fluid velocity. In contrast, a converse tendency is perceived with magnetic parameter and also, wall friction acts opposite to the velocity. The fluid temperature attenuated with dilation of the Prandtl number and radiation parameter, whilst a contrary trend was perceived with Eckert number. The increasing thermo-diffusion helps to develop fluid concentration whilst the Schmidt number and chemical reaction displayed opposite trend. Further, we achieved a tremendous conformity between the current findings and genuine results in the literature.
Analytical study and heat transfer analysis of on couple stress flow of hybrid nanofluid over a nonlinear stretching surface
Authors: Rehman Ali , Khun Ma Chau , Tlija Mehdi , Jan Rashid , Inc Mustafa , Hussain Shah
Published: February 2024

This research paper investigates a two-dimensional couple stress flow of hybrid nanofluid (HN) over a nonlinear stretching surface with heat transfer analysis. HNs are well-known for their exceptional heat transfer properties compared to conventional fluids. The mathematical modeling of the problem involves the formulation of basic governing equations, namely, continuity, momentum and energy equations. To simplify the analysis, a similarity transformation technique is employed to convert the dimensional NLPDEs into dimensionless NODEs. Subsequently, the obtained governing equations are analytically solved using the HAM. The investigation explores the impact of several parameters, including magnetic field inclination, slip parameter, couple stress parameter, nanoparticle volume fraction, nonlinear stretching parameter, EN, thermophoresis parameter and PN. This study presents graphical representations of temperature and velocity distribution to visualize the effects of these parameters on the HN flow. Furthermore, different graphs and tables are employed to explain the impact of the factors on SF and NN. Notably, the results indicate that the HN exhibits significantly enhanced heat transfer properties over the base fluid, particularly under the influence of an inclined magnetic field. This research is expected to contribute to the advancement of the field of condensed nanostructure and nanomaterials, opening new avenues for further exploration in heat transfer enhancement applications.
Hidden skyrmion on the spin direction of the Majorana zero mode
Authors: Shang Xin , Tang Huimin
Published: February 2024

The spin information of Majorana zero mode (MZM) has important application value in distinguishing MZM from other zero energy states. We study the spin direction of the Majorana zero mode (MZM) localized in a vortex state in a topological superconductor. We find that the topological number of the magnetic structure of the MZM is zero. However, with an increase in the vortex radius, the magnetic structure of the electron oscillates between the antiskyrmion and skyrmion (starting with the antiskyrmion), while that of the hole oscillates between the skyrmion and antiskyrmion (starting with the skyrmion). The total magnetic structure of the electron is a antiskyrmion (the topological number is −1) while that of the hole is a skyrmion (the topological number is 1). We hope these new features provide more characters for detection and regulation of MZM.
SPC-Indexed Indirect Branch Hardware Cache Redirecting Technique in Binary Translation
Authors: Li Chunqiang , Liu Zhiwei , Shang Yunhai , He Lenian , Yan Xiaolang
Published: February 2024

In the domain of process virtual machine (PVM) binary translation, the difference in address space layout between the guest program and the translated program requires the recalculation of jump instruction targets, resulting in suboptimal execution efficiency. This paper presents a novel method called SPC-Indexed Indirect Branch Hardware Cache Redirecting (SPCIC) technique. SPCIC utilizes specialized branch instruction to represent indirect branches from guest programs while frequently-used target addresses are cached in a customized hardware mapping table. When translating an indirect branch, SPCIC queries the jump target cache first to achieve a fast redirection unless the destination address is not cached. Besides, SPCIC merely falls back to the software-based remapping approach when the query fails, improving the translation efficiency to the greatest extent. SPCIC is implemented on the QEMU platform to accelerate the translation of ARM payloads into RISC-V. Experiments are carried on SPEC2006 to demonstrate the effectiveness of SPCIC for reducing the runtime overhead of indirect branch translation. The experimental results indicate up to 11% average improvement and 35% maximum improvement are obtained on the selected benchmark.
Enhancing Transparency and Trust in Turbofan Engine Classification: Leveraging Explainable AI
Authors: Sharma Jeetesh , Mittal Murari Lal , Soni Gunjan
Published: February 2024

Implementing predictive maintenance is paramount in guaranteeing the dependability and efficiency of intricate industrial systems. Classification approaches have been extensively utilized to detect and anticipate potential malfunctions in acknowledged systems. Numerous machine learning algorithms exhibit effectiveness, yet they encounter interpretability issues, posing a challenge in comprehending the fundamental factors contributing to their predictions. This study investigates the utilization of machine learning algorithms for classification in the domain of predictive maintenance. This research focuses on applying Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms for Turbofan Engine classification into multiple categories. We use Explainable Artificial Intelligence (XAI) methodologies to mitigate the interpretability challenges commonly associated with black-box models. We aim to utilize XAI algorithms on the most effective models to elucidate the essential characteristics and decision-making procedures underlying the classification outcomes. The findings of our experiment indicate that the Random Forest algorithm based on ensemble learning and the XGBoost algorithm based on gradient boosting exhibit superior classification accuracy. Additionally, the utilization of Explainable Artificial Intelligence (XAI) algorithms amplifies the transparency and interpretability of said models, thereby facilitating a more profound comprehension of the determinants that impact the classification results. The results of this study make a valuable contribution to predictive maintenance by demonstrating the efficacy of machine learning algorithms in multiclass classification and emphasizing the significance of explainable artificial intelligence (XAI) in furnishing practical insights for decision-making.
Identification of multi-mineral-species geochemical anomalies using Bayesian maximum entropy and the spectrum separable module-constrained convolutional autoencoder
Authors: Zhao Bo , Deng Dongmin , Zhang Dehui , Tang Kun , Tang Panpan , An Lin
Published: February 2024

In this paper, we present a dual-drive multi-mineral-species anomaly detection system which involves the combined use of Bayesian maximum entropy, a spectral separable module, high-order factor analysis, a geologically constrained loss function, as well as a mixed Gaussian distribution-based thresholding algorithm, attaching to a deep convolutional autoencoder. We have achieved several firsts rarely considered in the existing literature, for example: previous works focus mainly on separating single-mineral-species rather than multi-elemental anomalies, while we have attempted to recognize multi-mineral-species anomalies; previous works pay more attention to data, while we suggest how to discover the ore-related correlations hidden within the input data; previous works fail to integrate soft data in a quantitative fashion, while we have achieved that by capitalizing on Bayesian maximum entropy and the stratigraphic combination entropy. A series of comparative experiments have demonstrated the advantages over other state-of-the-art approaches. Finally, we obtained a mineral-occurrence identification rate (

δ

) ranging from 36.87 to 61.46% v. the anomaly area ranging from 33.75 to 55.38% for each metalliferous anomaly division.
Perceptual Quality-Oriented Rate Allocation via Distillation from End-to-End Image Compression
Authors: Yang Runyu , Liu Dong , Ma Siwei , Wu Feng , Gao Wen
Published: February 2024

Mainstream image/video coding standards, exemplified by the state-of-the-art H.266/VVC, AVS3, and AV1, follow the block-based hybrid coding framework. Due to the block-based framework, encoders designed for these standards are easily optimized for peak signal-to-noise ratio (PSNR) but have difficulties optimizing for the metrics more aligned to perceptual quality, e.g., multi-scale structural similarity (MS-SSIM), since these metrics cannot be accurately evaluated at the small block level. We address this problem by leveraging inspiration from the end-to-end image compression built on deep networks, which is easily optimized through network training for any metric as long as the metric is differentiable. We compared the trained models using the same network structure but different metrics and observed that the models allocate rates in different ratios. We then propose a distillation method to obtain the rate allocation rule from end-to-end image compression models with different metrics and to utilize such a rule in the block-based encoders. We implement the proposed method on the VVC reference software—VTM and the AVS3 reference software—HPM, focusing on intraframe coding. Experimental results show that the proposed method on top of VTM achieves more than 10% BD-rate reduction than the anchor when evaluated with MS-SSIM or LPIPS, which leads to concrete perceptual quality improvement.
ScalaBFS2: A High-performance BFS Accelerator on an HBM-enhanced FPGA Chip
Authors: Li Kexin , Xu Shaoxian , Shao Zhiyuan , Zheng Ran , Liao Xiaofei , Jin Hai
Published: February 2024

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for the Breadth-First Search (BFS) algorithm that demands a high bandwidth for accessing the graph data stored in memory. Different from traditional FPGA-DRAM platforms where memory bandwidth is the precious resource due to the limited DRAM channels, FPGA chips equipped with HBM have much higher memory bandwidths provided by the large quantities of HBM channels, but still a limited amount of logic (LUT, FF, and BRAM/URAM) resources. Therefore, the key to design a high-performance BFS accelerator on an HBM-enhanced FPGA chip is to efficiently use the logic resources to build as many as possible Processing Elements (PEs) and configure them flexibly to obtain as high as possible
effective memory bandwidth
that is useful to the algorithm from the HBM, rather than partially emphasizing the absolute memory bandwidth. To exploit as high as possible effective bandwidth from the HBM, ScalaBFS2 conducts BFS in graphs in a vertex-centric manner and proposes designs, including the independent module (HBM Reader) for memory accessing, multi-layer crossbar, and PEs that implement hybrid mode (i.e., capable of working in both push and pull modes) algorithm processing, to utilize the FPGA logic resources efficiently. Consequently, ScalaBFS2 is able to build up to 128 PEs on the XCU280 FPGA chip (produced with the 16 nm process and configured with two HBM2 stacks) of a Xilinx Alveo U280 board and achieves performance of 56.92 Giga Traversed Edges Per Second (GTEPS) by fully using its 32 HBM memory channels. Compared with the state-of-the-art graph processing system (i.e., ReGraph) built on top of the same board, ScalaBFS2 achieves 2.52x~4.40x performance speedups. Moreover, when compared with Gunrock running on an Nvidia A100 GPU that is produced with the 7 nm process and configured with five HBM2e stacks, ScalaBFS2 achieves 1.34x~2.40x speedups on absolute performance, and 7.35x~13.18x speedups on power efficiency.
Tapestry of Time and Actions: Modeling Human Activity Sequences Using Temporal Point Process Flows
Authors: Gupta Vinayak , Bedathur Srikanta
Published: February 2024

Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. These activities can range from the simplest daily routines, like walking and sitting, to multi-level complex endeavors such as cooking a four-course meal. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature—the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, and next action recommendation. Existing neural network based approaches that learn a continuous-time activity sequence are limited to the presence of only visual data or are designed specifically for a particular task (i.e., limited to next action or goal prediction). In this article, we present
ProActive
, a neural marked temporal point process framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems: next action prediction, sequence goal prediction, and
end-to-end
sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. Moreover, for time-sensitive prediction, we perform an
early
detection of sequence goal via a constrained margin-based optimization procedure. This in turn allows
ProActive
to predict the sequence goal using a limited number of actions. In addition, we propose a novel addition over the
ProActive
model, called
ProActive++
, that can handle variations in the order of actions (i.e., different methods of achieving a given goal). We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of our
ProActive
and
ProActive++
over the state of the art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.
Reductive Segregation During the Synthesis of Sb−SnO2-Supported Iridium Electrocatalysts for the Oxygen Evolution Reaction
Authors: Rajan Ziba S. H. S. , Binninger Tobias , Kooyman Patricia J. , Susac Darija , Mohamed Rhiyaad
Published: February 2024

A metal-organic chemical deposition approach using an Ir(acac)3 precursor was employed to synthesise oxide−supported iridium-based electrocatalysts for the oxygen evolution reaction (OER) in acidic media. Inert and oxidising deposition conditions were explored in two temperature regimes, and the influence of these parameters on the physicochemical and electrochemical properties of catalysts consisting of IrOx supported on Sb–SnO2 (ATO) were studied. Surprisingly, it was found that increasing the deposition temperature up to 620 °C favoured the formation of reduced iridium phases even in the presence of an oxidising reaction environment. This was explained by a thermodynamic preference for reduced phases at high temperatures, as well as the presence of acetylacetonate ligand decomposition products serving as reducing agents. Similarly, the reductive segregation of Sb from the ATO support at high temperatures leads to the loss of conductivity of the support, and the formation of Ir–Sb–Sn alloyed nanoparticles. These processes resulted in a loss of OER performance for the materials prepared under high-temperature conditions. Our findings highlight the importance of carefully selecting the temperature range during the synthesis of oxidic support materials and electrocatalysts for the OER.
Interaction Studies of PVP and CTAB Capped CuO Nanorods with Aldicarb and Chlorpyrifos
Authors: Lakshmi G. B. V. S. , Singh Rahul K. , Punia Yashi , Dhiman Tarun K. , Singh Avinash K. , Solanki Pratima R.
Published: February 2024

Copper oxide uncapped nanorods (UC-CuO), capped with cetyltrimethylammonium bromide (CTAB-CuO), and polyvinyl pyrrolidine (PVP-CuO) were utilized for interaction study of Aldicarb (A.D.) and Chlorpyrifos (C.P.) pesticides. Electron microscopy (FE-SEM & TEM) studies confirmed the nanocrystalline structure and nanorod morphology of UC-CuO, CTAB-CuO, and PVP-CuO. The contact angle study showed the hydrophilic nature of the UC-CuO and PVP-CuO with contact angle of 51° and 57°, respectively. While CTAB-CuO exhibited hydrophobic nature with a contact angle of more than 90°. Interaction study of UC-CuO, CTAB-CuO, and PVP-CuO with A.D. and C.P was conducted using UV–vis absorption study (in the 250–400 nm region). UC-CuO showed the specific detection with A.D., while CTAB-CuO have shown with C.P. without using any bio-recognition elements. PVP-CuO did not show systematic change with both pesticides confirming the capping agent-dependent specific interaction of the pesticides.
Graduate prospects explain undergraduate program standing in university league sports science tables
Authors: Kemi Ole J. , Penpraze Victoria , Scobie Nairn , MacFarlane Niall G.
Published: February 2024

University rankings are used by applicants and stakeholders to judge programs, including undergraduate studies. In the rankings, undergraduate UK programs are compared and contrasted against each other based on how they score for criteria that affect student life and future prospects. Here, we determined the relative influence of those criteria and found that graduate prospects, how students professionally benefit from their study after graduation, is the factor that matters most for the ranking results.
Descriptive Epidemiology and Phylodynamics of the “First Wave” of an Outbreak of Highly Pathogenic Avian Influenza (H5N1 Clade 2.3.4.4b) in British Columbia and the Yukon, Canada, April to September 2022
Authors: Andrew Cassandra L. , Russell Shannon L. , Coombe Michelle , Zlosnik James E. A. , Kuchinski Kevin S. , Caleta Jessica , Fjell Chris , Berhane Yohannes , Bowes Victoria , Redford Tony , Thacker Caeley , Wilson Laurie , Henaff Maud , Harms N. Jane , Jassem Agatha , Giacinti Jolene , Soos Catherine , Prystajecky Natalie , Himsworth Chelsea
Published: February 2024

Highly pathogenic avian influenza (HPAI) is a viral disease that causes significant rates of morbidity and mortality in domestic poultry and wild birds, with occasional spillover into mammals, including humans. Beginning in November 2021, Canada experienced its longest and largest outbreak of HPAI in history. A portion of this outbreak (H5N1, clade 2.3.4.4b) occurred in western Canada, specifically in British Columbia (B.C.) and the Yukon, between April 12 and September 11, 2022, which was classified as the “first wave” in this region. Wild birds and mammals identified through passive surveillance and suspect domestic poultry flocks were screened for avian influenza virus (AIV), typed H5 by qPCR, and positive cases were whole genome sequenced. Descriptive epidemiological and phylodynamic analyses were performed to: (1) understand the taxonomic and geographic extent of wild species involved; and (2) examine the origins and probable transmission networks of HPAI viruses introduced into B.C./Yukon by comparing local viruses with those circulating elsewhere in North America. This outbreak included 21 species of wild birds, 2 species of wild mammals, 4 commercial, and 12 domestic small flock infected premises. Canada geese (Branta canadensis) and bald eagles (Haliaeetus leucocephalus) were the most common wild species detected. We demonstrate that north‐south avian migration via the Pacific Flyway is the probable route of multiple incursions into this region. Phylogenetic analysis of the hemagglutinin (HA) segment revealed that the B.C./Yukon viruses detected formed five distinct genetic clusters which were maintained across the whole genome. Although, the genome segments were predominantly Eurasian in origin, NP and PB2 segments from all samples, as well as NS and PB1 segments from Cluster 3, had North American origins. Overall, we demonstrate the utility of genomic epidemiology to inform HPAI transmission dynamics across Western Canada and discuss potential knowledge gaps that exist in passive surveillance strategies for HPAI.
Optimal Sizing of Hybrid Renewable Energy System Using Two‐Stage Stochastic Programming
Authors: Mohammed Nouralden , Ali M. Montaz
Published: February 2024

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two‐stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three‐block alternating direction method of multipliers (ADMM), a cutting‐edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first‐order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations.
Stability Evaluation of the Goaf Based on Combination Weighting and Cloud Model
Authors: Guo Linning , Hou Kepeng , Sun Huafen , Yang Yong
Published: February 2024

Goaf has become one of the most significant sources of hazard affecting the safety of metal and nonmetal mines. Evaluation of goaf stability is of paramount importance for mine safety production. First, 13 indices such as rock mass structure, geological structure, and goaf volume are selected based on engineering experience and literature review to assess the stability of goaf. These indices are classified according to the characteristics of each factor, and a stability evaluation system for underground mine goaf is constructed. Second, the analytic hierarchy process method based on group decision theory is utilized to calculate the subjective weight of each index. Additionally, the CRITIC method is used to calculate the objective weight of each index. Finally, game theory is used to combine the subjective and objective weights, thereby improving the accuracy of the index weight. The stability grade of the goaf is calculated using the normal cloud model. The FLAC3D numerical simulation is used to analyze the stability of the goaf and verify the accuracy of the model. The abovementioned model is utilized for assessing the stability of the goaf in the Duimenshan mine section. The results indicate that 90% of the goaf area is in a stable or relatively stable condition, while the remaining 10% is unstable. The evaluation outcomes were compared with FLAC3D numerical simulations, highlighting a scientific and reliable method with an accuracy rate of 90%.
miR‐224‐5p Attenuates Allergic Responses in Mice with Allergic Rhinitis by Modulating the Th1/Th2 Response
Authors: Li Yuxiao , An Ran , Wu Mingjin , He Jiayan , He Xiaoguang
Published: February 2024

Background. Allergic rhinitis (AR) is a common chronic respiratory disease that has become a global health problem. miRNAs play an important role in multiple immune and inflammatory diseases, including AR. In this work, the mechanism by which miR‐224‐5p regulates AR in vivo and in vitro was examined. Methods. Human nasal epithelial cells (HNEpCs) were used to establish an AR cell model induced by Der P1, and C57BL/6 mice were used to establish an AR animal model induced by OVA (ovalbumin). RT‐qPCR was used to determine the level of miR‐224‐5p; western blot analysis was used to determine GATA3; ELISA was used to determine the levels of OVA‐specific IgE, IFN‐γ, IL‐4, IL‐5, and IL‐13; flow cytometry was used to determine the differentiation of Th1 and Th2 cells; and HE and PAS staining was used to observe the histopathological alterations in the mouse nasal mucosa and spleen. Results. miR‐224‐5p was downregulated in nasal mucosa from mice with AR and an AR cell model. Overexpressed miR‐224‐5p can improve AR development and attenuate AR symptoms by regulating GATA3‐mediated Th1/Th2 responses. Conclusion. miR‐224‐5p attenuates allergic reactions in mice with AR by regulating the Th1/Th2 response.
Analysis of Factors Affecting Road Transport Accidents of Hazardous Materials Based on PG‐BN
Authors: Li Zewen , Zhang Mengmeng
Published: February 2024

To analyze the factors affecting road accidents involving hazardous materials, the Bayesian network (BN) model was used to fit the accident data. However, considering the possible overfitting phenomenon of the BN model, the model was optimised by combining Pearson’s chi‐squared test and Granger causality test (PG) methods. First, the data of hazardous materials accidents were preprocessed, and the index system of factors affecting hazardous materials road transport was constructed from five dimensions of “people, vehicles, hazmat, roads, and environment”; second, Pearson’s chi‐squared test and the Granger causality test were used to screen the factors affecting hazardous materials road transport accidents and to determine the causal relationship between the factors; finally, the BN model was constructed with accident severity and accident processing time as target nodes, and the results were analyzed and validated. The results show that the overall relative error rate of the model is less than 10% and can be used to explore the risk factors of hazardous materials transport accidents; weather, visibility, lighting, intersection type, road condition, road type, driver condition, vehicle type, etc. are all important factors affecting the severity of hazardous materials transport accidents. The study can serve as a reference for the safety supervision and management of hazardous materials transport enterprises and industrial management departments.
The Macrophage Activator GcMAF‐RF Enhances the Antitumor Effect of Karanahan Technology through Induction of M2–M1 Macrophage Reprogramming
Authors: Ruzanova Vera S. , Kirikovich Svetlana S. , Levites Evgeniy V. , Proskurina Anastasia S. , Dolgova Evgeniya V. , Ritter Genrikh S. , Efremov Yaroslav R. , Dubatolova Tatyana D. , Sysoev Alexander V. , Koleno Danil I. , Ostanin Alexandr A. , Chernykh Elena R. , Bogachev Sergey S.
Published: February 2024

Macrophages are the immune cells of high‐immunological plasticity, which can exert both pro‐ and anti‐inflammatory activity, as well as repolarize their phenotype to the opposite or neutral one. In this regard, M2 macrophages of the tumor‐associated stroma (TAS) are a promising therapeutic target in treating malignant neoplasms. Using FACS assay, we have estimated the CD11b+/Ly‐6G+/Ly‐6C+ fraction of macrophages from the peritoneum and TAS in intact healthy mice and those with developed Lewis carcinoma, both untreated and treated according to Karanahan technology in combination with group‐specific macrophage activator (GcMAF‐RF). As well, the pattern of pro‐ and anti‐inflammatory cytokines mRNA expression in different groups of experimental and tumor‐bearing animals was assessed. It was found that: (i) exposure of intact mice to GcMAF‐RF results in the increased number of CD11b+/Ly‐6C+ peritoneal macrophages and, at the same time, the expression pattern of cytokines in peritoneal macrophages switches from that characteristic of the mixed M1/M2 phenotype to that characteristic of the neutral M0 one; (ii) combination of Karanahan technology and GcMAF‐RF treatment results in M0/M1 repolarization of TAS macrophages; (iii) in tumor‐bearing mice, the response of peritoneal macrophages to such a treatment is associated with the induction of anti‐inflammatory reaction, which is opposite to that in TAS macrophages.
Quercetin as a Promising Antiprotozoan Phytochemical: Current Knowledge and Future Research Avenues
Authors: Memariani Hamed , Memariani Mojtaba , Ghasemian Abdolmajid
Published: February 2024

Despite tremendous advances in the prevention and treatment of infectious diseases, only few antiparasitic drugs have been developed to date. Protozoan infections such as malaria, leishmaniasis, and trypanosomiasis continue to exact an enormous toll on public health worldwide, underscoring the need to discover novel antiprotozoan drugs. Recently, there has been an explosion of research into the antiprotozoan properties of quercetin, one of the most abundant flavonoids in the human diet. In this review, we tried to consolidate the current knowledge on the antiprotozoal effects of quercetin and to provide the most fruitful avenues for future research. Quercetin exerts potent antiprotozoan activity against a broad spectrum of pathogens such as Leishmania spp., Trypanosoma spp., Plasmodium spp., Cryptosporidium spp., Trichomonas spp., and Toxoplasma gondii. In addition to its immunomodulatory roles, quercetin disrupts mitochondrial function, induces apoptotic/necrotic cell death, impairs iron uptake, inhibits multiple enzymes involved in fatty acid synthesis and the glycolytic pathways, suppresses the activity of DNA topoisomerases, and downregulates the expression of various heat shock proteins in these pathogens. In vivo studies also show that quercetin is effective in reducing parasitic loads, histopathological damage, and mortality in animals. Future research should focus on designing effective drug delivery systems to increase the oral bioavailability of quercetin. Incorporating quercetin into various nanocarrier systems would be a promising approach to manage localized cutaneous infections. Nevertheless, clinical trials are needed to validate the efficacy of quercetin in treating various protozoan infections.
Research and Application of Coal Seam Permeability Improvement Technology by Sectional Directional Hydraulic Fracturing
Authors: Wang Feng
Published: February 2024

To address the challenging issue of gas control in the working faces of the Hegang mining area, based on segmented directional hydraulic fracturing technology, ABAQUS simulation software was employed to simulate and analyze the fracturing process of coal‐rock masses under the action of segmented hydraulic fracturing. The study focused on the engineering application research within the mining roadway of the 23# coal seam in the Junde Coal Mine, China. The results indicate that the numerical simulation analysis reveals elliptical patterns in the stress, strain, and fracture width variation of the coal‐rock masses influenced by the fracturing action. The width of the fractures exhibits a periodic variation pattern, initially increasing and then slightly decreasing with the injection of water pressure. Additionally, each successive variation shows a decreasing magnitude. After applying segmented hydraulic fracturing technology, the average gas extraction concentration increased by a maximum of 1.73 times, the average mixed flow rate increased by a maximum of 2.16 times, and the average gas extraction pure quantity increased by a maximum of 3.10 times. Segmented hydraulic fracturing can effectively improve gas extraction efficiency, reduce coal seam gas content, and have a depressurizing effect on coal‐rock layers. The research findings provide new means for safe and efficient coal mining, particularly in enhancing gas extraction efficiency, alleviating strata pressure, and preventing gas disasters.
Ex Vivo Efficacy of SAR442257 Anti-CD38 Trispecific T-cell Engager in Multiple Myeloma Relapsed After Daratumumab and BCMA-targeted Therapies
Authors: Keller Alana L. , Reiman Lauren T. , Perez de Acha Olivia , Parzych Sarah E. , Forsberg Peter A. , Kim Peter S. , Bisht Kamlesh , Wang Hongfang , van de Velde Helgi , Sherbenou Daniel W.
Published: February 2024

T cell–engaging antibodies (TCEs) are showing promising efficacy in relapsed/refractory multiple myeloma, even in patients that relapsed after B-cell maturation antigen (BCMA)-targeted therapy. Patients with multiple myeloma may have compromised T-cell health unaccounted for by preclinical models. Here, we use Myeloma Drug Sensitivity Testing (My-DST) for ex vivo measurement of anti-multiple myeloma cytotoxicity for the trispecific CD38/CD28xCD3 TCE SAR442257 through activation of the patients’ own endogenous T cells to inform clinical development of the compound in multiple myeloma. My-DST incubates primary mononuclear cells in humanized media for 48 hours followed by flow cytometry for multiple myeloma cell viability with or without drug treatment. SAR442257 was tested on 34 samples from patients with multiple myeloma across disease settings. Potential biomarkers, T-cell dependence, and degranulation were assessed. SAR442257 was effective at low dose in My-DST cultures. High ex vivo response rates were observed in primary aspirates taken from patients with multiple myeloma at diagnosis, with modestly reduced response in multiple myeloma recently treated with anti-CD38 mAbs. SAR442257 was highly effective in patients relapsing after BCMA therapy. The CD38/CD28xCD3 trispecific format was substantially more effective than a conventional bispecific CD38/CD3 antibody format and CD38 mAbs. Anti-multiple myeloma cell cytotoxicity was dependent on the presence of endogenous T cells. Surface CD38 expression was the strongest biomarker of TCE response. My-DST is capable of measuring T cell–dependent killing using the multiple myeloma patient's own bone marrow–derived T cells. SAR442257 shows promise for multiple myeloma and may be best suited for patients declared resistant to both CD38 mAbs and BCMA-targeted therapy.

Significance:

This study introduces the use of My-DST to measure and characterize sensitivity to anti-CD38 T-cell engager SAR442257 in primary samples using matched endogenous T cells. Preclinical testing in samples from patients with diverse treatment history supports further testing in post-chimeric antigen receptor T-cell multiple myeloma.
Innovative design and development of attitude determination and control systems for CubeSats with reaction wheels
Authors: Harsha S. Thamizh , Thentral T. M. Thamizh , Ramasamy Palanisamy , Pal Animesh , M. Sabarish , Panda Swastik , Das Indraneela
Published: February 2024

Attitude determination and control systems (ADCS) represent a critical facet of CubeSat missions, orchestrating the precise orientation and stabilization of these small satellites in the space environment. This paper presents a comprehensive design and development of an ADCS tailored for CubeSats, harnessing a reaction wheel system to deliver a cost-effective and dependable solution for small satellite applications. The research begins by elucidating the requisites and specifications for the ADCS and then delves into the design phase, complemented by intricate modelling and simulation employing MATLAB Simulink and the Webots Simulator. The results of this study underscore the exceptional performance of the proposed ADCS configuration, leveraging the reaction wheel model. This system demonstrates an unparalleled capacity to achieve precise and controlled attitude adjustments, well within the defined parameters. Furthermore, this research underscores the pivotal role played by efficient system design, meticulous simulation, and rigorous testing in the triumphant implementation of ADCS, greatly enhancing CubeSat missions and their contributions to the realm of space exploration and technology innovation. This comprehensive approach to the design and testing of an ADCS for CubeSats ensures that these diminutive satellites continue to make significant strides in space missions, paving the way for an exciting future of space research and technology development.
Performance analysis of photovoltaic panel using machine learning method
Authors: Wahile Ganesh S. , Londhe Srikant , Trikal Shivshankar , Kothare Chandrakant , Malwe Prateek D. , Sherje Nitin P. , Kulkarni Prasad D. , Aswalekar Uday , Sonawane Chandrakant , Abdul Zahra Mustak Maher , Kumar Abhinav
Published: February 2024

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.
Automatic segmentation of human ear in the wild
Authors: Lahkar Rahul , Borbora Khurshid Alam
Published: February 2024

Ear biometrics has been a challenging and distinctive research area in recent times. The human ear possesses unique promising attributes that are being used by the researchers to carry out significant improvements in the field of human recognition using ear as a biometric. In order to achieve efficiency on any ear biometric system, the detection and segmentation of the human ear need to be performed precisely. Feeding accurately segmented images to the recognition system will result in higher recognition accuracy. In this paper, we present our work of segmentation of human ears from the images captured in unconstrained environment by employing the U-Net architecture on our own dataset and presented the results of ear segmentation. The U-Net model is also tested on the annotated web ears (AWE) segmentation dataset. We obtained 92.38% accuracy and 79.33% intersection over union (IoU) on the test data on our own dataset and 76.2% IoU on AWE segmentation dataset.
Performance evaluation of adaptive offloading model using hybrid machine learning and statistic prediction
Authors: Byun Siwoo , Jang Seok-Woo , Byun Joonho
Published: February 2024

We introduce fast sensor diagnosis and focuses on intelligent offloading skills to enhance the sensor data screening efficiency. This study proposed the adaptive offloading model based on statistics-based prediction feedback and sensor candidate filtering. For the statistics-based filtering, sliding sensor grids and compounded sensor context were devised. This study also proposed hybrid prediction model using support vector machine (SVM) and k-nearest neighbors (KNN) machine training for the adaptive offloading. Therefore, the sensor information that is highly likely to be the cause of the actual device faults can be selected and transmitted, resulting in improved offloading performance. The test results through Google Colab show that the fault prediction accuracy of proposed models is 95%.
Exploring user satisfaction and improvement opportunities in public charging pile resources
Authors: Xu Licheng , Sani Asmiza A. , Xie Shuai , Shuib Liyana
Published: February 2024

The existing market of public charging pile services for electric vehicle (EV) users has occupied a particular market share. However, instead of solely focusing on pre-planning the construction of charging piles, it is crucial to address the shortcomings of the existing charging pile service and develop effective marketing strategies. This approach can help optimize the utilization of charging pile resources and minimize wastage. In this study, we explore EV users’ comments on the public charging pile service and adopt a natural language pre-training model to classify comments for extracting positive and negative comments. For these two types of comments respectively, we construct the text-to-knowledge to mine the keywords from multiple dimensions. We further excavate the words correlated with the keywords by utilizing dependency parsing to create relational dependency graphs. Taken together, we identify key factors influencing EV user satisfaction or dissatisfaction and uncover the relationships among these factors. These insights provide valuable information for charging pile operators to develop targeted marketing strategies and improvement plans for the existing public charging pile resources, ultimately enhancing the overall user experience.