bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Response Postures of Subway Train in Straight Line Collision
Authors: Liu Shuhao , Yu Yiqun , Li Yi , Liu Rongqiang , Zhang Rong
Published: December 2024

A six-car subway train finite element model was developed to investigate possible response postures under impact accidents. Considering the plastic deformation of carriages and weak points of the train front, the simulation included two conditions: train–train collision and collision with an obstacle. Comprehensive response postures were obtained. A one-dimensional dynamic theory model was established to verify the rationality of the FEM model. The influence of impact speed, impact angle, and impact position on the energy consumption and response postures were discussed. The results show that one accident condition is accompanied by a variety of response postures. The main factors of climbing are asynchronism of yaw and pitch motions of adjacent carriages and plastic deformation of carriage ends, which leads to vertical arch. In oblique and front-side collision, the lateral force and the deviation of train front cause rapid derailment, and large lateral movement of the front and lateral buckling happen subsequently.
Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
Authors: Che Yunhao , Chi Enan
Published: December 2024

In evaluating the construction safety of the building in the subway tunnel using the cover-and-cut method, the main objective is to analyze the diaphragm wall, the central pillar, and the roof. This article conducted a blasting vibration test based on the background of the Guiyang Metro Line 3 project and used the FLAC3D software to establish a three-dimensional numerical model. The results showed that the peak particle velocity (PPV) decreased with increasing distance from the blasting center. The PPV measured at the underground diaphragm wall was 1.424 cm/s, while at the bottom of the central pillar it was 1.482 cm/s. The predicted PPV on the roof was up to 1.537 cm/s, which met the safety standards. According to the cloud map of particle vibration velocity and the comprehensive analysis of particle vibration velocity, the degree of impact of artificial structures in the subway tunnel was the central pillar, the underground diaphragm wall, and the roof in order from high to low. After eight blasting operations per day, the vibration velocity trend at the vulnerable point of the central column increases, but it will not exceed the safety standard.
A Review of Supply Chain Resilience: A Network Modeling Perspective
Authors: Ma Chuhan , Zhang Lei , You Liang , Tian Wenjie
Published: December 2024

Against the backdrop of globalization, the complexity of supply chains has been increasing, making supply chain resilience a critical factor in ensuring the stable operation of enterprises, national economies, and international trade. This paper adopts a network modeling perspective to systematically review the theoretical foundations and research progress in supply chain resilience, focusing on the application of network modeling methods. First, the concept of supply chain resilience is defined, and its developmental trajectory is reviewed. Through literature visualization analysis, this study delves into the current state of research on supply chain resilience, addressing challenges and risk management, highlighting the importance of network modeling techniques in this field. Subsequently, it explores supply chain network modeling based on complex networks and agent-based modeling, analyzing their strengths and limitations in simulating the overall evolution of supply chains and the dynamic behavior of individual entities. By integrating network structural characteristics with resilience evaluation methods, this paper suggests potential directions for future research. These include enhancing the description of individual firm behavior, analyzing the dynamics of information networks, and emphasizing task-oriented model design, thereby offering new perspectives and pathways for managing supply chain resilience in a way that can generate significant positive externalities for global economies. This research also indicates that the enhanced resilience of supply chains can produce a multiplier effect, benefiting not only individual firms but also promoting economic stability and growth across multiple countries.
A Practical Prognostics Method Based on Stepwise Linear Approximation of a Nonlinear Degradation Model
Authors: An Dawn
Published: December 2024

Prognostics aims to predict the remaining useful life (RUL) of an in-service system based on its degradation data. Existing methods, such as artificial neural networks (ANNs) and their variations, often face challenges in real-world applications due to their complexity and the lack of sufficient data. In this paper, a practical prognostic method is proposed, based on the stepwise linear approximation of nonlinear degradation behavior, to simplify the prognostic process while significantly reducing computational costs and maintaining high accuracy. The proposed approach is validated using synthetic data generated at different noise levels, with 100 data sets tested at each level, and compared against a typical ANN method. The results demonstrate that the proposed method consistently outperforms the ANN in terms of accuracy and robustness, while remarkably reducing computational time by a factor of 50 to 60, making it a promising solution for real-world applications.
See-Then-Grasp: Object Full 3D Reconstruction via Two-Stage Active Robotic Reconstruction Using Single Manipulator
Authors: Hong Youngtaek , Kim Jonghyeon , Cha Geonho , Kim Eunwoo , Lee Kyungjae
Published: December 2024

In this paper, we propose an active robotic 3D reconstruction methodology for achieving full object 3D reconstruction. Existing robotic 3D reconstruction approaches often struggle to cover the entire view space of the object or reconstruct occluded regions, such as the bottom or back side. To address these limitations, we introduce a two-stage robotic active 3D reconstruction pipeline, named See-Then-Grasp (STG), that employs a robot manipulator for direct interaction with the object. The manipulator moves toward the points with the highest uncertainty, ensuring efficient data acquisition and rapid reconstruction. Our method expands the view space of the object to include the entire perspective, including occluded areas, making the previous fixed view candidate approach time-consuming for identifying uncertain regions. To overcome this, we propose a gradient-based next best view pose optimization method that efficiently identifies uncertain regions, enabling faster and more effective reconstruction. Our method optimizes the camera pose based on an uncertainty function, allowing it to identify the most uncertain regions in a short time. Through experiments with synthetic objects, we demonstrate that our approach effectively addresses the next best view selection problem, achieving significant improvements in computational efficiency while maintaining high-quality 3D reconstruction. Furthermore, we validate our method on a real robot, showing that it enables full 3D reconstruction of real-world objects.
EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
Authors: Kuang Yuan , Tian Shuicheng , Li Hongxia , Yuan Chengwei , Chen Lei
Published: December 2024

In the high-attention-demanding environment of underground coal mines, distraction is a major cause of unsafe behavior and decreased safety performance. Research on the cognitive neural mechanisms and monitoring of distraction in miners is limited. This study used an electroencephalogram (EEG) to examine the correlation between distraction and brain activity in coal miners, aiming to provide an objective method for monitoring distraction in coal miners. Thirty participants completed a simulated hazard recognition task, using the Sustained Attention to Response Task (SART) and noise to induce distraction. Brain activity was recorded and labeled as focused or distracted based on the correctness of the hazard recognition task. EEG features were extracted and selected, and a Random Forest model for distraction identification was constructed based on the selected features. In the focused state, delta power in the temporal region and theta power in the frontal region increased significantly. In the distracted state, alpha power in the temporal and occipital regions and beta power in the occipital and parietal regions increased. The selected EEG features could be used to identify distraction with 84% accuracy. This method can objectively identify distraction in coal miners, highlighting the potential of using EEG for real-time distraction monitoring.
A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
Authors: Rasool M. J. Aashik , Ahmad Shabir , Mardieva Sevara , Akter Sumaiya , Whangbo Taeg Keun
Published: December 2024

In contemporary computer vision, deep learning-based real-time single image super-resolution approaches have gained significant attention for their ability to enhance the resolution of images in real time. These approaches are interconnected with various other computer vision domains, including image segmentation and object detection. Numerous surveys have summarized the state of the image SR domain. However, there is no survey that specifically addresses real-time single image SR on IoT devices. Therefore, in this study, we aim to explore strategies, identify the technical challenges, and outline the future directions of SR research, with a special emphasis on real-time super-resolution techniques. We begin with an overview of the core concepts related to real-time SR, recent challenges, and algorithm classification and delve into potential application scenarios that merit attention. Additionally, we explore the challenges and identify promising research areas related to real-time SR specifically related to IoT devices, highlighting potential advancements, limitations, and opportunities for future innovation in this rapidly evolving field.
Deep Soft Rock Tunnel Perimeter Rock Control Technology and Research
Authors: Liu Gang , Yang Yu
Published: December 2024

With the further development of China’s coal resources, mining operations are constantly transferred to the deep soft rock. As such, the mine roadway is under the action of high geostress, the surrounding rock body engineering properties are poor, the overall strength is low, the traditional support method struggles to meet the needs of safe production, and the surrounding rock control has become a major technical challenge. This paper relies on the actual project, analyzes the destabilization mechanism of the roadway, analyzes the deformation of the peripheral rock of the deep roadway, determines the physical and mechanical parameters of the peripheral rock through indoor tests, establishes numerical analysis model, proposes to adopt the joint support scheme of anchor rods + anchor cables + a 36U-type steel metal bracket + a laying net + a laying mat + filling behind the wall, and monitors the displacement of peripheral rock of the roadway on a regular basis by using the numerical display convergence meter, and then obtains the displacement of the peripheral rock of the roadway after excavation as well as under the influence of the quarrying movement. Under the influence of the roadway perimeter rock displacement, we evaluate the reasonableness of the support program, as well as the safe and effective control of the roadway perimeter rock, to achieve the ideal roadway perimeter rock support and control effect.
Numerical Fit Modeling for Temperature Mitigation in Arid Cities
Authors: Hoback Alan S.
Published: December 2024

The purpose of the study is to develop a general method to predict local temperature changes from mitigating the urban heat island effect using local climate engineering. Specifically, the effects of a plume of calcite particles above cities have been found. Previous modeling work has been carried out with supercomputers, but those models have limited geographies and timelines. The main goal of this work is to produce a method that can be applied more generally and more quickly. This overcomes limited modeling data in arid regions. Arid cities show the most effective use of calcite plumes for local solar radiation management, but those areas have limited data. The new method is to use numerical fit techniques using actual weather data. The default heating and cooling rates are fit to historic rates, and then the radiative properties of the calcite are used to predict the change in the heat transfer rates. Air temperatures at a standard height of 2 m are predicted. The key findings are that the numerical fit gives comparable results to the full supercomputer model, but the numerical fit gives predictions of greater temperature change. This was explained as primarily due to how advection is handled differently by the methods. Adjustments in the methods are discussed so that the effect of advection is included. The conclusion is that numerical fit provides a method that can easily be applied to arid regions.
Integration of Multi-Source Archival Data for 3D Reconstruction of Non-Existent Historical Buildings
Authors: Calka Beata , Jaczewska Paulina , Slowik Justyna
Published: December 2024

The city of Warsaw, Poland, has endured significant damage throughout its history, particularly during World War II. The city was bombed in September 1939, and many buildings were blown up following the Warsaw Ghetto Uprising in May 1943. The Warsaw Uprising in August and September 1944 led to further destruction from artillery bombardments and fires. Even after the surrender and civilian evacuation in October 1944, additional demolitions occurred, leaving almost 90% of Warsaw in ruins. Despite ongoing efforts to rebuild the city, many landmarks could not be fully reconstructed. However, invaluable historical archives preserve remnants of Warsaw’s rich history. To reconstruct 3D models of pre-war buildings in Warsaw, a methodology was developed that integrates cartographic materials, spatial data, and results from tachymetric measurements. Historical maps, terrestrial and aerial photographs, and architectural blueprints from the National Archives in Warsaw were used to propose three distinct approaches to 3D modeling. Notable structures such as the Grand Synagogue, the Kamienica Theater building, and the Tłomackie buildings were selected for 3D modeling. These buildings either were destroyed or endured significant damage during the war. The 3D modeling process involved meticulous processing and calibrating of historical photographs alongside tachymetric surveying for accurate measurements. The proposed methodology showcases the feasibility of recreating 3D renderings of historical edifices, even those lost to time, utilizing archival cartographic data and spatial information from diverse sources. By leveraging cartographic heritage with digital advancements, a unique perspective on Warsaw’s narrative can be gained, enriching the understanding of its past for both residents and professionals such as historians, archivists, and cartographers.
Experimental Study on the Uplift Bearing Capacity of Pre-Drilled Planted Piles in Isolated Stone Strata Based on Transparent Soil Technology
Authors: Liao Wenli , Cai Qipeng , Guo Xiangyu , Lin Hao , Zhou Jiajin , Su Shizhuo
Published: December 2024

The presence of isolated stones in the soil layers of engineering sites has significantly increased. Currently, the existing methods for dealing with isolated stones are inadequate to meet engineering needs. This paper combines pile-planting technology with isolated stones to incorporate them into the load-bearing system, resulting in a new type of pre-drilled composite pile suitable for isolated stone sites. A visualization testing system for pile-soil deformation is developed using Particle Image Velocimetry (PIV) technology and transparent soil, conducting non-intrusive model tests on pile-planting and boulder-capped piles under different uplift load conditions, and comparing the results with a discrete-continuous coupled three-dimensional numerical model analysis. The results indicate that when an isolated stone with a cross-sectional area four times that of the pile exists at the pile tip, the ultimate pullout bearing capacity of the pile increases by a factor of two. Regarding the distribution of internal and external side friction resistances of the core and outer concrete of the piles, the internal friction resistance of piles without isolated stones is approximately 1.47 times that of the external friction resistance and about 0.8 times the ratio of the diameters of the pile and core. For piles with isolated stones at the tip, the internal friction resistance is approximately 1.37 times that of the external friction resistance. Under the ultimate load, the displacement field around the pile without an isolated stone exhibits an “inverted triangular” distribution; the displacement field around the pile with an isolated stone at the tip exhibits a “trapezoidal” distribution. This study investigates the bearing capacity and load transfer mechanisms of the new pre-drilled composite piles in isolated stone engineering sites, and the research findings may provide new solutions for similar construction projects involving rubble reclamation.
Leveraging Non-Covalent Adaptable Networks to Stabilize Drug-Polymer Systems in Supersaturated Solutions: A Computational and Experimental Approach
Authors: Budiman Arif , Fakih Taufik Muhammad , Megantara Sandra , Muchtaridi Muchtaridi , Aulifa Diah Lia
Published: December 2024

This study’s integration of molecular dynamics (MD) simulations with non-covalent adaptable networks (NANs) and corroborative wet lab experiments offers a comprehensive approach to understanding the interactions between ritonavir (RTV) and polymers in supersaturated solutions. This multifaceted study not only explored the stabilization mechanisms facilitated by NANs but also examined the influence of polymer selection on the pharmaceutical properties of RTV, a class III compound known for its slow crystallization rate. This research utilized molecular dynamics simulations to model the intermolecular interactions between RTV and two polymers, Polyvinylpyrrolidone (PVP) K30 and Eudragit L100. These simulations were specifically designed to incorporate the effects of NANs, highlighting their dynamic nature and potential to enhance drug stability and solubility. Simultaneously, wet lab experiments were conducted to measure the nucleation induction times and observe the crystallization behavior of RTV under varying conditions of polymer presence. The experimental data demonstrated a significant extension in nucleation induction time, prolonging the duration from 12 to approximately 64 h when PVP K30 and Eudragit L100 were present. This substantial delay in crystallization was attributed to the strong intermolecular interactions between RTV and the polymers, which were effectively stabilized by the non-covalent bonds within the NANs. These findings were consistently confirmed across both computational and experimental settings, illustrating how NANs can effectively inhibit crystallization and enhance the supersaturation state of RTV. This study successfully demonstrates how the physical and chemical properties of polymers influence the crystallization process of poorly water-soluble drugs such as RTV. Leveraging the synergy between computational simulations and empirical laboratory data, this research provides deep insights into the mechanisms at play, ensuring that drug formulations are optimized for both stability and performance.
Study on the Near-Distance Object-Following Performance of a 4WD Crop Transport Robot: Application of 2D LiDAR and Particle Filter
Authors: Pak Eun-Seong , Kim Byeong-Hun , Lee Kil-Soo , Cha Yong-Chul , Kim Hwa-Young
Published: December 2024

In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications.
Application of Enhanced Weighted Least Squares with Dark Background Image Fusion for Inhomogeneity Noise Removal in Brain Tumor Hyperspectral Images
Authors: Yan Jiayue , Tao Chenglong , Wang Yuan , Du Jian , Qi Meijie , Zhang Zhoufeng , Hu Bingliang
Published: December 2024

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used.
Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
Authors: Papadaki Eugenia , Kakkos Ioannis , Vlamos Panagiotis , Petropoulou Ourania , Miloulis Stavros T. , Palamas Stergios , Vrahatis Aristidis G.
Published: December 2024

The rapid advancement of high-throughput technologies has led to the generation of vast amounts of omics data, including genomics, epigenomics, and metabolomics. Integrating these diverse datasets has become essential for gaining comprehensive insights into complex biological systems and enhancing personalized healthcare solutions. This critical review examines the current state of multi-omics data integration platforms, highlighting both the strengths and limitations of existing tools. By evaluating the latest digital platforms, such as GraphOmics, OmicsAnalyst, and others, the paper explores how they support seamless integration and analysis of omics data in healthcare applications. Special attention is given to their role in clinical decision-making, disease prediction, and personalized medicine, with a focus on their interoperability, scalability, and usability. The review also discusses the challenges these platforms face, such as data complexity, standardization issues, and the need for improved machine learning and AI-based analytics. Finally, the paper proposes directions for future research and development, emphasizing the importance of more advanced, user-friendly, and secure platforms that can better serve comprehensive healthcare needs.
Lombard Sculptures from Saint Sophia of Kijv at the Russian National Museum in Moscow
Authors: Andrea Spiriti
Published: December 2024

A group of Romanesque sculptures today at the Gosudarstvennyj Istoričeskij Muzej in Moscow, coming from the restoration of the Cathedral of Saint Sophia in Kijv, can be related to the commission of Vladimir II Monomak, Grand Prince of Kijv, cultural heir of both his great-grandfather, the grand prince Vladimir I (who had founded the church between 1011 and 1037), and of his grandfather, the Eastern Roman Emperor Constantine IX: It is argued here that, alongside the Byzantine mosaicists certainly present, the sculptures are the work of a group of artists from the Lombardy lakes (also known as Comacine masters), attested in central and eastern Europe through Bavaria, Bohemia, Poland and then arriving in Sweden, active in Kijv between 1113 and 1125. It is probable that their specific origin is from Valchiavenna.
Assessment of Observed and Projected Extreme Droughts in Perú—Case Study: Candarave, Tacna
Authors: Cruz-Baltuano Ana , Huarahuara-Toma Raúl , Silva-Borda Arlette , Chucuya Samuel , Franco-León Pablo , Huayna Germán , Ramos-Fernández Lía , Pino-Vargas Edwin
Published: December 2024

Droughts have always been one of the most dangerous hazards for civilizations, especially when they impact the headwaters of a watershed, as their effects can spread downstream. In this context, observed droughts (1981–2015) and projected droughts (2016–2100) were assessed in Candarave, the headwaters of the Locumba basin. Regarding observed droughts, SPI-3 and SPEI-3 detected seven extreme droughts (1983, 1992, 1996, 1998, 2010, 2011, and 2012), with the most intense occurring in 1992 and 1998. SPI-6 and SPEI-6 identified the same extreme drought events, highlighting 1992 as the most intense. Additionally, it was concluded that the VCI also detected the droughts identified by the SPEI; however, a more detailed analysis of its use is necessary due to the limited availability of suitable satellite images in the area. On the other hand, a high-resolution dataset of climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under the SSP3-7.0 scenario was used to project future droughts. Of the models in that dataset, CanESM5, IPSL–CM6A–LR, and UKESM1–0–LL did not perform well in the study area. SPI and SPEI projected more than ten episodes of extreme drought, indicating that extreme droughts will become more frequent, severe, and intense in the last 30 years of this century.
Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
Authors: Latif Rana Muhammad Amir , He Jinliao
Published: December 2024

Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions.
Divergent Responses of Grassland Productivity to Large-Scale Atmospheric Circulations Across Ecoregions on the Mongolian Plateau
Authors: Jiao Cuicui , Yi Xiaobo , Luo Ji , Wang Ying , Deng Yuanjie , Guo Xiao
Published: December 2024

The Mongolian Plateau grassland (MPG) is critical for ecological conservation and sustainability of regional pastoral economies. Aboveground net primary productivity (ANPP) is a key indicator of grassland health and function, which is highly sensitive to variabilities in large-scale atmospheric circulations, commonly referred to as teleconnections (TCs). In this study, we analyzed the spatial and temporal variations of ANPP and their response to local meteorological and large-scale climatic variabilities across the MPG from 1982 to 2015. Our analysis indicated the following: (1) Throughout the entire study period, ANPP displayed an overall upward trend across nine ecoregions. In the Sayan montane steppe and Sayan alpine meadow ecoregions, ANPP displayed a distinct inflection point in the mid-1990s. In the Ordos Plateau arid steppe ecoregion, ANPP continuously increased without any inflection points. In the six other ecoregions, trends in ANPP exhibited two inflection points, one in the mid-1990s and one in the late-2000s. (2) Precipitation was the principal determinant of ANPP across the entire MPG. Temperature was a secondary yet important factor influencing ANPP variations in the Ordos Plateau arid steppe. Cloud cover affected ANPP in Sukhbaatar and central Dornod, Mongolia. (3) The Atlantic Multidecadal Oscillation affected ANPP by regulating temperature in the Ordos Plateau arid steppe ecoregion, whereas precipitation occurred in the other ecoregions. The Pacific/North America, North Atlantic Oscillation, East Atlantic/Western Russia, and Pacific Decadal Oscillation predominantly affected precipitation patterns in various ecoregions, indicating regional heterogeneities of the effects of TCs on ANPP fluctuations. When considering seasonal variances, winter TCs dominated ANPP variations in the Selenge–Orkhon forest steppe, Daurian forest steppe, and Khangai Mountains alpine meadow ecoregions. Autumn TCs, particularly the Pacific/North America and North Atlantic Oscillation, had a greater impact in arid regions like the Gobi Desert steppe and the Great Lakes Basin desert steppe ecoregions. This study’s findings will enhance the theoretical framework for examining the effects of TCs on grassland ecosystems.
Detection of High Radar Reflectivity Volumes at High Tropospheric Levels in Large Hail Events
Authors: Rigo Tomeu , Farnell Carme
Published: December 2024

Different giant and very large hail events have occurred in Catalonia (NE of the Iberian Peninsula) in the last three years, with stones ranging between 8 and more than 10 cm in diameter. These sizes have not occurred in this area in at least thirty years. This research analyzed all those events with at least one severe hail register (more than 2 cm diameter) in the region for 2013–2023. The present study considered large volumes of high reflectivity in weather radar 3D fields at high tropospheric levels (more than 10 km). The goal was to determine if high reflectivity cores (over 55 dBZ) occurred at those levels before or during the hail-fall. The main question was whether this radar signature could reveal the occurrence of very large or giant hail. The 55 dBZ volumes occurred and were maintained over 10 km between six and sixty minutes in a high percentage of large-hail cases. However, giant hail cases did not present the maximum duration of high reflectivity at high levels as was initially expected.
Properties of Generalized Bronze Fibonacci Sequences and Their Hyperbolic Quaternions
Authors: Özkan Engin , Akkuş Hakan , Özkan Alkan
Published: December 2024

In this study, we establish some properties of Bronze Fibonacci and Bronze Lucas sequences. Then we find the relationships between the roots of the characteristic equation of these sequences with these sequences. What is interesting here is that even though the roots change, equality is still maintained. Also, we derive the special relations between the terms of these sequences. We give the important relations among these sequences, positive and negative index terms, with the sum of the squares of two consecutive terms being related to these sequences. In addition, we present the application of generalized Bronze Fibonacci sequences to hyperbolic quaternions. For these hyperbolic quaternions, we give the summation formulas, generating functions, etc. Moreover, we obtain the Binet formulas in two different ways. The first is in the known classical way and the second is with the help of the sequence’s generating functions. In addition, we calculate the special identities of these hyperbolic quaternions. Furthermore, we examine the relationships between the hyperbolic Bronze Fibonacci and Bronze Lucas quaternions. Finally, the terms of the generalized Bronze Fibonacci sequences are associated with their hyperbolic quaternion values.
The Theory and Applications of Hölder Widths
Authors: Lu Man , Ye Peixin
Published: December 2024

We introduce the Hölder width, which measures the best error performance of some recent nonlinear approximation methods, such as deep neural network approximation. Then, we investigate the relationship between Hölder widths and other widths, showing that some Hölder widths are essentially smaller than n-Kolmogorov widths and linear widths. We also prove that, as the Hölder constants grow with n, the Hölder widths are much smaller than the entropy numbers. The fact that Hölder widths are smaller than the known widths implies that the nonlinear approximation represented by deep neural networks can provide a better approximation order than other existing approximation methods, such as adaptive finite elements and n-term wavelet approximation. In particular, we show that Hölder widths for Sobolev and Besov classes, induced by deep neural networks, are O(n−2s/d) and are much smaller than other known widths and entropy numbers, which are O(n−s/d).
About Stabilization of the Controlled Inverted Pendulum Under Stochastic Perturbations of the Type of Poisson’s Jumps
Authors: Shaikhet Leonid
Published: December 2024

The classical problem of stabilization of the controlled inverted pendulum is considered in the case of stochastic perturbations of the type of Poisson’s jumps. It is supposed that stabilized control depends on the entire trajectory of the pendulum. Linear and nonlinear models of the controlled inverted pendulum are considered, and the stability of the zero and nonzero equilibria is studied. The obtained results are illustrated by examples with numerical simulation of solutions of the equations under consideration.
DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography
Authors: Santarrosa-López Ismael , Alor-Hernández Giner , Bustos-López Maritza , Hernández-Capistrán Jonathan , Sánchez-Morales Laura Nely , Sánchez-Cervantes José Luis , Marín-Vega Humberto
Published: December 2024

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments.
Novel Fast-Setting and Mechanically-Strong Calcium Phosphate Pulp-Capping Cement with Metformin Release to Enhance Dental Pulp Stem Cells
Authors: Alenizy Mohammad , Alhussein Abdullah , Almutairi Nader , Ba-Armah Ibrahim , Ma Tao , Wang Suping , Pilli Nageswara R. , Kane Maureen A. , Weir Michael D. , Schneider Abraham , Xu Hockin H. K.
Published: December 2024

Traditional pulp-capping materials like mineral trioxide aggregate (MTA) offer excellent biocompatibility and sealing, but limitations such as prolonged setting time, low bioactivity, and high costs persist. Metformin, with its potential in craniofacial regeneration, could enhance dentin synthesis by targeting pulp cells. This study aimed to: (1) develop a calcium phosphate cement with chitosan (CPCC) with improved physio-mechanical properties; (2) incorporate metformin (CPCC-Met) to assess release; and (3) evaluate human dental pulp stem cells (hDPSCs) response. CPCC was mixed at different powder-to-liquid ratios to evaluate physio-mechanical properties compared to MTA. The optimized CPCC formulation was loaded with 0, 50, 100, and 150 µg of metformin to measure release and assess hDPSCs attachment and proliferation (1, 4, and 7 d) via live/dead imaging and SEM. One-way ANOVA was used for statistical analysis. Results showed CPCC at a 3.25:1 ratio significantly reduced setting time to 41.5 min versus 123 min for MTA (p < 0.05). Metformin release correlated with concentration, and SEM confirmed the presence of a porous, hydroxyapatite-rich surface. Cell viability was consistently high across groups (>93% at 1 d, >95% at 4 d, ≈98% at 7 d), with no significant differences (p > 0.05). These findings suggest that the novel CPCC-Met demonstrates promise as a fast-setting, cost-effective pulp-capping material, offering metformin delivery to enhance dentin repair.
Delivering Volumetric Hyperthermia to Head and Neck Cancer Patient-Specific Models Using an Ultrasound Spherical Random Phased Array Transducer
Authors: Zubair Muhammad , Uddin Imad , Dickinson Robert , Diederich Chris J.
Published: December 2024

In exploring adjuvant therapies for head and neck cancer, hyperthermia (40–45 °C) has shown efficacy in enhancing chemotherapy and radiation, as well as the delivery of liposomal drugs. Current hyperthermia treatments, however, struggle to reach large deep tumors uniformly and non-invasively. This study investigates the feasibility of delivering targeted uniform hyperthermia deep into the tissue using a non-invasive ultrasound spherical random phased array transducer. Simulations in 3D patient-specific models for thyroid and oropharyngeal cancers assessed the transducer’s proficiency. The transducer consisting of 256 elements randomly positioned on a spherical shell, operated at a frequency of 1 MHz with various phasing schemes and power modulations to analyze 40, 41, and 43 °C isothermal volumes and the penetration depth of the heating volume, along with temperature uniformity within the target area using T10, T50, and T90 temperatures, across different tumor models. Intensity distributions and volumetric temperature contours were calculated to define moderate hyperthermia boundaries. The results indicated the array’s ability to produce controlled heating volumes from 1 to 48 cm3 at 40 °C, 0.35 to 27 cm3 at 41 °C, and 0.1 to 8 cm3 at 43 °C. The heating depths ranged from 7 to 39 mm minimum and 52 to 59 mm maximum, measured from the skin’s inner surface. The transducer, with optimal phasing and water-cooled bolus, confined the heating to the targeted regions effectively. Multifocal sonications also improved the heating homogeneity, reducing the length-to-diameter ratio by 38% when using eight foci versus a single one. This approach shows potential for treating a range of tumors, notably deep-seated and challenging oropharyngeal cancers.
Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images
Authors: Pang Wenbo , Ma Yi , Jiang Huiyan , Yu Qiming
Published: December 2024

Cervical cancer is one of the most prevalent cancers among women, posing a significant threat to their health. Early screening can detect cervical precancerous lesions in a timely manner, thereby enabling the prevention or treatment of the disease. The use of pathological image analysis technology to automatically interpret cells in pathological slices is a hot topic in digital medicine research, as it can reduce the substantial effort required from pathologists to identify cells and can improve diagnostic efficiency and accuracy. Therefore, we propose a cervical cell detection network based on collecting prior knowledge and correcting confusing labels, called PGCC-Net. Specifically, we utilize clinical prior knowledge to break down the detection task into multiple sub-tasks for cell grouping detection, aiming to more effectively learn the specific structure of cells. Subsequently, we merge region proposals from grouping detection to achieve refined detection. In addition, according to the Bethesda system, clinical definitions among various categories of abnormal cervical cells are complex, and their boundaries are ambiguous. Differences in assessment criteria among pathologists result in ambiguously labeled cells, which poses a significant challenge for deep learning networks. To address this issue, we perform a labels correction module with feature similarity by constructing feature centers for typical cells in each category. Then, cells that are easily confused are mapped with these feature centers in order to update cells’ annotations. Accurate cell labeling greatly aids the classification head of the detection network. We conducted experimental validation on a public dataset of 7410 images and a private dataset of 13,526 images. The results indicate that our model outperforms the state-of-the-art cervical cell detection methods.
Synergistic Enzybiotic Effect of a Bacteriophage Endolysin and an Engineered Glucose Oxidase Against Listeria
Authors: Talens-Perales David , Daròs José-Antonio , Polaina Julio , Marín-Navarro Julia
Published: December 2024

Listeria monocytogenes represents one of the main risks for food safety worldwide. Two enzyme-based antimicrobials (enzybiotics) have been combined in a novel treatment against this pathogenic bacterium, resulting in a powerful synergistic effect. One of the enzymes is an endolysin from Listeria phage vB_LmoS_188 with amidase activity (henceforth A10), and the other is an engineered version of glucose oxidase from Aspergillus niger (GOX). Both enzymes, assayed separately against Listeria innocua, showed antibacterial activity at the appropriate doses. The combination of the two enzybiotics resulted in a synergistic effect with a log reduction in viable cells (log N0/N) of 4, whereas, taken separately, the same dose of A10 and GOX caused only 1.2 and 0.2 log reductions, respectively. Flow cytometry and microscopy analyses revealed that A10 treatment alone induced the aggregation of dead cells. L. monocytogenes showed higher resistance to single treatment with GOX or A10 than L. innocua. However, the synergic combination of A10 and GOX resulted in a high lethality of L. monocytogenes with a log N0/N higher than 5 (below the detection limit in our analysis). Altogether, these results represent a novel efficient and eco-friendly antimicrobial treatment against the most lethal food-borne pathogen.
Targeting Invasion: The Role of MMP-2 and MMP-9 Inhibition in Colorectal Cancer Therapy
Authors: Shoari Alireza , Ashja Ardalan Arghavan , Dimesa Alexandra M. , Coban Mathew A.
Published: December 2024

Colorectal cancer (CRC) remains one of the most prevalent and lethal cancers worldwide, prompting ongoing research into innovative therapeutic strategies. This review aims to systematically evaluate the role of gelatinases, specifically MMP-2 and MMP-9, as therapeutic targets in CRC, providing a critical analysis of their potential to improve patient outcomes. Gelatinases, specifically MMP-2 and MMP-9, play critical roles in the processes of tumor growth, invasion, and metastasis. Their expression and activity are significantly elevated in CRC, correlating with poor prognosis and lower survival rates. This review provides a comprehensive overview of the pathophysiological roles of gelatinases in CRC, highlighting their contribution to tumor microenvironment modulation, angiogenesis, and the metastatic cascade. We also critically evaluate recent advancements in the development of gelatinase inhibitors, including small molecule inhibitors, natural compounds, and novel therapeutic approaches like gene silencing techniques. Challenges such as nonspecificity, adverse side effects, and resistance mechanisms are discussed. We explore the potential of gelatinase inhibition in combination therapies, particularly with conventional chemotherapy and emerging targeted treatments, to enhance therapeutic efficacy and overcome resistance. The novelty of this review lies in its integration of recent findings on diverse inhibition strategies with insights into their clinical relevance, offering a roadmap for future research. By addressing the limitations of current approaches and proposing novel strategies, this review underscores the potential of gelatinase inhibitors in CRC prevention and therapy, inspiring further exploration in this promising area of oncological treatment.