bluuebunny/crossref_metadata_2025 huggingface dataset:
RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network
Authors: Shen Jiaping , Zhou Haiting , Jin Muda , Jin Zhongping , Wang Qiang , Mu Yanchun , Hong Zhiming
Published: February 2025

Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. In addition, introducing FOA enables the model to dynamically adjust the hidden layers and thresholds while optimizing the optimal path, thereby finding the best solution. This article conducts ablation experiments on the model using the acceleration life dataset of IEEE PHM 2012 rolling bearings. The experimental results show that the FOA-CNN-LSTM model proposed in this paper significantly outperforms other comparative methods in RUL prediction accuracy and stability, verifying its effectiveness and innovation in dealing with complex degradation processes. This method helps to take preventive measures before faults occur, thereby reducing economic losses and having important practical significance for predicting the remaining life of rolling bearings.
The Influence of Alkali-Resistant MiniBars™ on the Mechanical Properties of Geopolymer Composites
Authors: Furtos Gabriel , Sarosi Codruta , Moldovan Marioara , Korniejenko Kinga , Łach Michał , Ungureanu Viorel , Miller Leonard , Nováková Iveta
Published: February 2025

Geopolymer concrete reinforced with MiniBars™ could be an eco-friendly, innovative, durable, high-strength material substitute for common Portland cement in buildings. AR glass fiber MiniBars™ composites (AR MiniBars™) (ReforceTech AS, Royken, Norway) 60 mm in length were utilized to strengthen the geopolymer matrix for the fabrication of unidirectional geopolymer composites reinforced by AR MiniBars™ (AR MiniBars™ FRBCs). New AR MiniBars™ FRBCs were fabricated by adding different amounts of AR MiniBars™ (0, 12.5, 25, 50, 75 vol.%) into the fly ash geopolymer paste. Geopolymers were obtained by combining fly ash powder with Na2SiO3/NaOH in a ratio of 2.5:1, which served as an alkaline activator. AR MiniBars™ FRBCs were cured for 48 h at 70 °C and tested for different mechanical properties. Fly ash, AR MiniBars™, and AR MiniBars™ FRBC were evaluated by optical microscopy and SEM. The addition of AR MiniBars™ increased the mechanical properties of AR MiniBars™ FRBCs. The mechanical properties of AR MiniBars™ FRBCs were heightened compared to the geopolymer without AR MiniBars™; the flexural strength was 18.80–30.71 times greater, the flexural modulus 4.07–5.25 times greater, the tensile strength 3.49–8.27 times greater, the force load at upper yield tensile strength 3.6–7.72 times greater, and the compressive strength for cubic samples 2.75–3.61 times greater. The fractured surfaces and sections of AR MiniBars™ FRBCs were inspected by SEM and optical microscopy analyses, and even though there was no chemical adhesion, we achieved a good micromechanical adhesion of the geopolymer to AR MiniBars™. These results obtained encouraged us to propose AR MiniBars™ FRBCs for application in construction.
Assessment of Externally Prestressed Beams with FRP Rebars Considering Bond–Slip Effects
Authors: Li Zhangxiang , Chen Bo , Wang Xueliang , Lou Tiejiong
Published: February 2025

This paper presents detailed numerical modeling of externally prestressed concrete (EPC) beams with fiber-reinforced polymer (FRP) rebars. Particular attention is paid to the bond–slip interactions between FRP rebars and concrete. A refined 3D finite element model (FEM) incorporating a script describing the bond–slip of FRP rebars and concrete is developed in ABAQUS. The model effectiveness, rooted in the interface behavior between FRP rebars and concrete, is comprehensively assessed using experimental data. A comprehensive investigation has been conducted using FEM on the mechanical behavior of carbon fiber-reinforced polymer (CFRP) tendon–EPC beams with FRP rebars. Due to the bond–slip effect, FRP rebars in EPC beams exhibit a distinct phenomenon of stress degradation. This suggests that the traditional method based on plane cross-sectional assumptions is no longer suitable for the engineering design of EPC beams with FRP rebars. Moreover, the study assesses several models including typical design codes for their accuracy in predicting the elevation of ultimate stress in external tendons. It is demonstrated that some of the design codes are overly conservative when estimating the tendon stress in EPC beams with FRP rebars.
Effect of Laser Parameters on Surface Morphology and Material Removal Mechanism of Ablation Grooves in CFRP Composites Using Finite Element Simulations
Authors: Song Juan , Wang Bangfu , Jiang Qingyang , Hao Xiaohong
Published: February 2025

Carbon fiber resin matrix composites (CFRP) are widely recognized for their exceptional properties such as high temperature resistance and high strength, making them indispensable in aerospace, automotive, and medical applications. Despite their growing use, precision machining of CFRP remains challenging. Traditional mechanical machining methods often lead to severe tool wear, matrix damage, fiber pullout, delamination, and chipping. In contrast, nanosecond pulsed laser machining has garnered significant attention due to its high precision, minimal heat-affected zone (HAZ), and versatility in processing various materials. In this study, a finite element model was developed to account for the anisotropic heat transfer and non-homogeneous properties of CFRP, enabling accurate simulation of laser machining processes. The study analyzed the influence of laser parameters on machining quality and revealed the ablation mechanism and HAZ evolution under varying laser conditions. Notably, it was observed that the thermal conductivity along the carbon fiber’s axial direction is higher than in the radial direction, resulting in an elliptical ablation pattern after laser irradiation. Additionally, the effects of the laser power, pulse frequency, and scanning speed on the depth and width of grooves were investigated through finite element simulations and validation experiments. A heat accumulation effect between laser pulses was observed, where resin matrix material around the grooves was removed once the accumulated heat exceeded the resin’s pyrolysis temperature. In addition, if there is too much laser power or too small a laser scanning speed, the fiber will undergo severe ablation removal, which will form serious thermal damage and a heat-affected zone. Gradually increasing the laser power or decreasing the scanning speed led to deeper and wider grooves, with an inverted triangular morphology. Moreover, the selection of different parameters had a significant effect on the ablation morphology, heat-affected zone, and the contour parameters of the grooves. This research contributes to understanding the laser–CFRP interaction mechanism and offers insights for optimizing laser processing parameters to improve material processing accuracy and efficiency, further expanding the potential applications of laser technology in composite material machining.
Magnesium Silicate Honeycomb Structure on Attapulgite Clay Composite by Self-Template Method for Adsorption of Methylene Blue
Authors: Kyawoo Tin , Wei Tiefeng , Karim Naveed , Jiang Chao , Ahmed Saeed , Li Huiyu , Feng Yongjun
Published: February 2025

A series of porous silica materials coated with honeycomb-like magnesium silicate were prepared under hydrothermal conditions, using natural one-dimensional porous attapulgite as the template and a silicon source with different Mg/Si ratios, by adjusting the amount of MgCl2⋅6H2O and the attapulgite precursor and regulating pH. The influence of the Mg/Si ratios was carefully investigated on morphology, pore structure, and related adsorption actions toward methylene blue. The Langmuir isotherm and pseudo-second-order kinetic models were used to explain the adsorption behavior of methylene blue. The synthesized composite with the lowest magnesium content displayed the highest removal capability of 166.67 mg/g for methylene blue, with a zeta potential of −18.18 mV, a specific surface area of 310.4 m2/g, and an average pore size of 3.7 nm. The removal result was the synergetic adsorption between porous magnesium silicate grown on the surface and the rest of the silica, further indicating that the attapulgite is available as a silicon source and a rod-shaped template for magnesium silicate.
The Quality of the Herbal Product Obtained in the Pressure Agglomeration Process
Authors: Urszula Sadowska , Andrzej Żabiński , Ewelina Kukiełka , Aneta Kopeć , Krzysztof Mudryk
Published: February 2025

The aim of the conducted research was to evaluate the impact of the pressure agglomeration process of lemon balm (Melissa officinalis L.) on the mechanical properties of the obtained product, its antioxidant capacity, and total polyphenol content. Two fractions of lemon balm were isolated with particle sizes of 0.5–2.5 mm and 2.5–5.0 mm. The isolated fractions were compacted using a Fritz Heckert EU 20 hydraulic press, applying compaction pressures of 100, 150, and 200 MPa. A closed die was used, with 2 g of the plant sample introduced each time. The mechanical properties of the obtained product were determined through an abrasion test and diameter test (Brazilian method) using the MTS Insight 2 testing machine. The total polyphenol content and antioxidant activity were measured using the ABTS method, both directly after product preparation and after a 6-month storage period. The compaction of lemon balm resulted in an increase in total polyphenol content and antioxidant properties compared to the unpressed raw material. The obtained product displayed favorable mechanical properties, as confirmed by the conducted mechanical tests. Regardless of the applied herb fraction, an agglomeration pressure of 200 MPa is particularly recommended.
Study on the Micro-Surfacing Properties of SBR Modified Asphalt Emulsion with Reclaimed Asphalt Pavement
Authors: Yu Di , Luo Junchi , Lang Huan , Hua Fang , Yang Yongyong , Xie Meng
Published: February 2025

With the updates and differences in the usage of reclaimed asphalt pavement (RAP) separation technology, the production of fine-particle RAP exceeds their usage, resulting in an excess of fine-particle RAP. How to apply this excess RAP on a large scale in micro-surfacing technology has become a challenge. This study aims to investigate the advantages and disadvantages of incorporating RAP into micro-surfacing. To this end, a mix design process for RAP-containing micro-surfacing, based on the current gradation design procedure and existing research findings, is proposed. The study examines the influence of six different RAP contents, as well as the effects of SBR emulsified asphalt, added water, and RAP on the micro-surfacing mix design. Subsequently, the effects of RAP content on the pavement performance of micro-surfacing are evaluated through rutting deformation rate, wet wheel abrasion, and British pendulum tests. Finally, an economic analysis from a construction perspective is conducted. The results indicate that the optimized mix design process meets specific usage requirements and is effective for RAP-containing micro-surfacing. The mix design results show that the addition of RAP reduces the asphalt demand and mixing time of slurry mixtures. Increasing the amount of added water can meet mixing requirements, but it leads to a reduction in early strength. As the RAP content increases, skid resistance improves, with a maximum increase of 14.9%; the rutting deformation rate increases, and this is the main factor limiting the RAP content, restricting it to no more than 40%; water damage resistance shows an initial increase followed by a decrease, but this does not affect the RAP content. Therefore, the maximum RAP content is limited to 40% without the addition of other additives, mainly due to the phenomenon of weak agglomeration in RAP. Finally, cost calculations show that incorporating 40% RAP can save approximately 17% of the construction costs.
Condition Monitoring of Electric Machines: Modern Frameworks and Data-Driven Methodologies
Authors: Doorsamy Wesley
Published: February 2025

Electrical machines are at the centre of most engineering processes, with rotating electrical machines, in particular, becoming increasingly important in recent history due to their growing applications in electric vehicles and renewable energy. Although the landscape of condition monitoring in electrical machines has evolved over the past 50 years, the intensification of engineering efforts towards sustainability, reliability, and efficiency, coupled with breakthroughs in computing, has prompted a data-driven paradigm shift. This paper explores the evolution of condition monitoring of rotating electrical machines in the context of maintenance strategy, focusing on the emergence of this data-driven paradigm. Due to the broad and varying nature of condition monitoring practices, a framework is also offered here, along with other essential terms of reference, to provide a concise overview of recent developments and to highlight the modern challenges and opportunities within this area. The paper is purposefully written as a tutorial-style overview for the benefit of practising engineers and researchers who are new to the field or not familiar with the wider intricacies of modern condition monitoring systems.
The Use of Common Bean and Mesquite Pods Flours as Partial Substitute of Semolina, Impact of Their Proteins and Polysaccharides in the Physical, Chemical, and Microstructural Characteristics of Spaghetti Pasta
Authors: Pérez-Lozano Alejandro , Gallegos-Infante José-Alberto , Chaírez-Ramírez Manuel Humberto , Rocha-Guzmán Nuria-Elizabeth , Moreno-Jiménez Martha Rocío , Ochoa-Martínez Luz-Araceli , Fierro Ignacio Villanueva , Castañeda Verónica Loera , Medina-Torres Luis
Published: February 2025

Spaghetti pasta is a popular food; different ingredients than wheat have been explored to increase their nutritional value, the use of mesquite flour with pea protein remains unexplored. This study aimed to evaluate the impact of substituting semolina with mesquite pod flour and pea protein isolate on the techno-functional properties of spaghetti. Spaghetti was prepared using semolina hydrated to 35–40%, (15 cm strands), dried at 50 or 60 °C until 7–8% moisture. Semolina was substituted (0–30%), with pea protein isolate (PPI) (0–20%) and mesquite flour (0–25%). Guar and xanthan gum were added (0–1%). Proximate analysis, trypsin inhibitors, culinary properties, water absorption, texture profile, color, soluble protein, protein, starch digestibility, Raman and confocal microscopy were performed. The legume incorporation increased the protein content and digestibility of the pasta. Although the culinary properties were affected by legume substitution, levels of 75–85% substitution yielded acceptable results. Spaghetti containing PPI and mesquite flour, dried at 60 °C, showed similar secondary protein structure compared to the control. However, mesquite flour notably altered the color of the pasta. The combination of PPI, mesquite flour, and hydrocolloids improved protein availability while reducing available starch and enhancing the nutritional quality of the spaghetti.
An Organic–Inorganic Hybrid Semiconducting Quantum Spin Liquid Candidate: (BEDT-TTF)3[Cu2(μ-C2O4)3·CH3CH2OH·1.2H2O]
Authors: Zhang Bin , Zhang Yan , Wang Dongwei , Wang Zheming , Chang Guangcai , Gao Zengqiang , Guo Yanjun , Liu Fen , Zhao Zhijuan , Zhang Xiaoyu , Qu Baolong , Xu Peng , Wang Jiaou , Dong Fenliang , Liang Tongling , Sun Yang , Yang Deliang , Li Qiaolian , Luo Xiaofei , Feng Rongjuan , Liu Mei , Zhang Xueying
Published: February 2025

The organic–inorganic hybrid (BEDT-TTF)3[Cu2(μ-C2O4)3·CH3CH2OH·1.2H2O] (I) was obtained using the electrocrystallization method. It comprises a θ21-phase organic donor layer and a two-dimensional inorganic antiferromagnetic honeycomb lattice. Cu(II) is octahedrally coordinated by three bisbidenetate oxalates, exhibiting Jahn–Teller distortion. CH3CH2OH and H2O molecules are located within the cavities of the honeycomb lattice. The total formal charge of the three donor molecules was assigned to be +2 based on the bond lengths in the TTF core, which corresponded to the Raman spectra. It is a semiconductor with σrt = 0.04 S/cm and Eα = 40 meV. No long-range ordering was observed above 2 K from zero-field cooling/field cooling magnetization, as confirmed by specific heat measurements. The spin frustration with f > 10 from the antiferromagnetic copper-oxalate-framework was observed. It is a candidate quantum spin liquid.
Improved Mixture Cure Model Using Machine Learning Approaches
Authors: Wang Huina , Feng Tian , Liang Baosheng
Published: February 2025

The mixture cure model has been widely used in medicine, public health, and bioinformatics. The traditional mixture cure model has limitations in model flexibility and handling complex structured data and big data. In recent years, some improved new methods have been developed. Through a literature review and numerical studies, this article discusses the advantages and disadvantages of the progressions of mixture cure models incorporating machine learning techniques such as SVMs for model improvements. Machine learning algorithms have advantages in model flexibility and computation. When combined with mixture cure models, they can effectively improve the performance of mixture cure models, distinguish between susceptible and non-susceptible individuals, and accurately predict the influencing factors and their magnitude of incidence and latency.
Distributed Observer-Based Adaptive Formation-Containment Tracking for Multi-Agent Systems on Directed Graphs
Authors: Zhang Qi , Yan Ke-Xing , Xiao Bo , Han Tao
Published: February 2025

This article investigates the issue of observer-based adaptive time-varying formation-containment tracking (TVFCT) for multi-agent systems (MASs) with bounded unknown input in directed graphs. By applying estimated statuses of neighboring agents, two observer-based adaptive TVFCT control algorithms are deployed for MASs with multiple tracking leaders and one tracking leader, respectively. Notably, the proposed control algorithms remain independent and do not rely on the comprehensive global information of the entire communication network. Furthermore, the algebraic Riccati inequalities and the Lyapunov theorem provide the bases for assessing the achievement of TVFCT in MASs within directed graphs. Lastly, simulation results are provided to validate the theoretical conclusions.
An Efficient Iterative Scheme for Approximating the Fixed Point of a Function Endowed with Condition (Bγ,μ) Applied for Solving Infectious Disease Models
Authors: Okeke Godwin Amechi , Udo Akanimo Victor , Alqahtani Rubayyi T.
Published: February 2025

The purpose of this paper is to construct a new fixed-point iterative scheme, called the Picard-like iterative scheme, for approximating the fixed point of a mapping that satisfies condition (Bγ,μ) in the setting of a uniformly convex Banach space. We prove that this novel iterative scheme converges faster than some existing iterative schemes in the literature. Moreover, G-stability and almost G-stability results are proven. Furthermore, we apply our results for approximating the solution of an integral equation that models the spread of some infectious diseases. Similarly, we also applied the results for approximating the solution of the boundary value problem by embedding Green’s function in our novel method. Our results extend and generalize other existing results in the literature.
Verification of Detectability for Time Labeled Petri Net Systems with Unobservable Transitions
Authors: Qin Tao , Li Zhiwu
Published: February 2025

We investigate the detectability verification problem of time-dependent systems modeled by time labeled Petri nets that are a typical time-dependent model of many computer-integrated systems in modern society, characterized by networked connections. In a time labeled Petri net, the detectability proposed in this paper characterizes the current state of a time-dependent system that can be uniquely determined after a finite number of observations within a given time instant. Moreover, we further define strong and weak detectability in a time labeled Petri net system. To verify strong and weak detectability, we excogitate a label-based state class graph, which is not required to enumerate all states of a system, to compute the states for a given real-time observation. Based on the proposed label-based state class graph, an information structure called a timed state observer is formulated to verify strong and weak detectability. The proposed verification technique is advantageous and is effectively applied to an intelligent garage system, since the enumeration of all states of the time-dependent system is not required.
IPM-AgriGPT: A Large Language Model for Pest and Disease Management with a G-EA Framework and Agricultural Contextual Reasoning
Authors: Zhang Yuqin , Fan Qijie , Chen Xuan , Li Min , Zhao Zeying , Li Fuzhong , Guo Leifeng
Published: February 2025

Traditional pest and disease management methods are inefficient, relying on agricultural experts or static resources, making it difficult to respond quickly to large-scale outbreaks and meet local needs. Although deep learning technologies have been applied in pest and disease management, challenges remain, such as the dependence on large amounts of manually labeled data and the limitations of dynamic reasoning. To address these challenges, this study proposes IPM-AgriGPT (Integrated Pest Management—Agricultural Generative Pre-Trained Transformer), a Chinese large language model specifically designed for pest and disease knowledge. The proposed Generation-Evaluation Adversarial (G-EA) framework is used to generate high-quality question–answer corpora and combined with Agricultural Contextual Reasoning Chain-of-Thought Distillation (ACR-CoTD) and low-rank adaptation (LoRA) techniques further optimizes the base model to build IPM-AgriGPT. During the evaluation phase, this study designed a specialized benchmark for the agricultural pest and disease domain, comprehensively assessing the performance of IPM-AgriGPT in pest management tasks. Experimental results show that IPM-AgriGPT achieved excellent evaluation scores in multiple tasks, demonstrating its great potential in agricultural intelligence and pest management.
TensorTrack: Tensor Decomposition for Video Object Tracking
Authors: Gu Yuntao , Zhao Pengfei , Cheng Lan , Guo Yuanjun , Wang Haikuan , Ding Wenjun , Liu Yu
Published: February 2025

Video Object Tracking (VOT) is a critical task in computer vision. While Siamese-based and Transformer-based trackers are widely used in VOT, they struggle to perform well on the OTB100 benchmark due to the lack of dedicated training sets. This challenge highlights the difficulty of effectively generalizing to unknown data. To address this issue, this paper proposes an innovative method that utilizes tensor decomposition, an underexplored concept in object-tracking research. By applying L1-norm tensor decomposition, video sequences are represented as four-mode tensors, and a real-time background subtraction algorithm is introduced, allowing for effective modeling of the target–background relationship and adaptation to environmental changes, leading to accurate and robust tracking. Additionally, the paper integrates an improved multi-kernel correlation filter into a single frame, locating and tracking the target by comparing the correlation between the target template and the input image. To further enhance localization precision and robustness, the paper also incorporates Tucker2 decomposition to integrate appearance and motion patterns, generating composite heatmaps. The method is evaluated on the OTB100 benchmark dataset, showing significant improvements in both performance and speed compared to traditional methods. Experimental results demonstrate that the proposed method achieves a 15.8% improvement in AUC and a ten-fold increase in speed compared to typical deep learning-based methods, providing an efficient and accurate real-time tracking solution, particularly in scenarios with similar target–background characteristics, high-speed motion, and limited target movement.
SSTMNet: Spectral-Spatio-Temporal and Multiscale Deep Network for EEG-Based Motor Imagery Classification
Authors: Alotaibi Albandari , Hussain Muhammad , Aboalsamh Hatim
Published: February 2025

Motor impairment is a critical health issue that restricts disabled people from living their lives normally and with comfort. Detecting motor imagery (MI) in electroencephalography (EEG) signals can make their lives easier. There has been a lot of work on detecting two or four different MI movements, which include bilateral, contralateral, and unilateral upper limb movements. However, there is little research on the challenging problem of detecting more than four motor imagery tasks and unilateral lower limb movements. As a solution to this problem, a spectral-spatio-temporal multiscale network (SSTMNet) has been introduced to detect six imagery tasks. It first performs a spectral analysis of an EEG trial and attends to the salient brain waves (rhythms) using an attention mechanism. Then, the temporal dependency across the entire EEG trial is worked out using a temporal dependency block, resulting in spectral-spatio-temporal features, which are passed to a multiscale block to learn multiscale spectral-–spatio-temporal features. Finally, these features are deeply analyzed by a sequential block to extract high-level features, which are used to detect an MI task. In addition, to deal with the small dataset problem for each MI task, the researchers introduce a data augmentation technique based on Fourier transform, which generates new EEG trials from EEG signals belonging to the same class in the frequency domain, with the idea that the coefficients of the same frequencies must be fused, ensuring label-preserving trials. SSTMNet is thoroughly evaluated on a public-domain benchmark dataset; it achieves an accuracy of 77.52% and an F1-score of 56.19%. t-SNE plots, confusion matrices, and ROC curves are presented, which show the effectiveness of SSTMNet. Furthermore, when it is trained on augmented data generated by the proposed data augmentation method, it results in a better performance, which validates the effectiveness of the proposed technique. The results indicate that its performance is comparable with the state-of-the-art methods. An analysis of the features learned by the model reveals that the block architectural design aids the model in distinguishing between multi-imagery tasks.
A New Multidimensional Computerized Testing Approach: On-the-Fly Assembled Multistage Adaptive Testing Based on Multidimensional Item Response Theory
Authors: Li Jingwen , Sun Jianan , Shao Mingyu , Lai Yinghui , Chen Chen
Published: February 2025

Unidimensional on-the-fly assembled multistage adaptive testing (OMST), a flexible testing method, integrates the strengths of the adaptive test assembly of computerized adaptive testing (CAT) and the modular test administration of multistage adaptive testing (MST). Since numerous latent trait structures in practical applications are inherently multidimensional, extending the realm from unidimensional to multidimensional is necessary. Multidimensional item response theory (MIRT), a branch of mathematical and statistical latent variable modeling research, has an important position in the international testing field. Based on MIRT, this study proposes an approach of multidimensional OMST (OMST-M), and on-the-fly automated test assembly algorithms are proposed based on point estimation and confidence ellipsoid, respectively. OMST-M can effectively and flexibly measure multidimensional latent traits through stage-by-stage adaptive testing. The simulation results indicated that under different settings of latent trait structures, module lengths, and module contents, the OMST-M approach demonstrated good performance in terms of ability estimation accuracy and item exposure control. The empirical research revealed that the OMST-M approach was comparable to both multidimensional MST and CAT in ability estimation accuracy and exhibited remarkable flexibility in adjusting the length and content across its test stages. In summary, the proposed OMST-M features relatively high measurement accuracy, efficiency, convenient implementation, and practical feasibility.
Macro-Scale Temporal Attenuation for Electoral Forecasting: A Retrospective Study on Recent Elections
Authors: Topîrceanu Alexandru
Published: February 2025

Forecasting election outcomes is a complex scientific challenge with notable societal implications. Existing approaches often combine statistical analysis, machine learning, and economic indicators. However, research in network science has emphasized the importance of temporal factors in the dissemination of opinions. This study presents a macro-scale temporal attenuation (TA) model, which integrates micro-scale opinion dynamics and temporal epidemic theories to enhance forecasting accuracy using pre-election poll data. The findings suggest that the timing of opinion polls significantly influences opinion fluctuations, particularly as election dates approach. Opinion “pulse” is modeled as a temporal function that increases with new poll inputs and declines during stable periods. Two practical variants of the TA model, ETA and PTA, were tested on datasets from ten elections held between 2020 and 2024 around the world. The results indicate that the TA model outperformed several statistical methods, ARIMA models, and best pollster predictions (BPPs) in six out of ten elections. The two TA implementations achieved an average forecasting error of 6.92–6.95 percentage points across all datasets, compared to 7.65 points for BPP and 14.42 points for other statistical methods, demonstrating a performance improvement of 10–83%. Additionally, the TA methods maintained robust performance even with limited poll availability. As global pre-election survey data become more accessible, the TA model is expected to serve as a valuable complement to advanced election-forecasting techniques.
Integrated Macrogenomics and Metabolomics Analysis of the Effect of Sea Cucumber Ovum Hydrolysates on Dextran Sodium Sulfate-Induced Colitis
Authors: Gong Shunmin , Sun Liqin , Sun Yongjun , Ju Wenming , Wang Gongming , Zhang Jian , Fu Xuejun , Lu Chang , Zhang Yu , Song Wenkui , Li Mingbo , Sun Leilei
Published: February 2025

Inflammatory bowel disease remains a significant challenge in clinical settings. This study investigated the therapeutic potential of sea cucumber ovum hydrolysates (SCH) in a dextran sulfate sodium (DSS)-induced colitis mouse model. SCH, defined by its elevated stability and solubility, with a molecular weight below 1000 Da, significantly alleviated DSS-induced colitis, as evidenced by enhanced splenic index, reduced colonic damage, and diminished serum pro-inflammatory cytokines. Furthermore, macrogenomic analysis demonstrated that SCH increased beneficial gut microbes and decreased pro-inflammatory bacteria. Furthermore, metabolomic analysis of colonic tissues identified elevated levels of anti-inflammatory metabolites, such as Phenyllactate, 2-Hydroxyglutarate, and L-Aspartic acid, in colitis mice after oral administration of SCH. In conclusion, SCH represents a promising candidate for the treatment of colitis.
An Evaluation of Neuron-Specific Enolase as a Biomarker of Neurological Impact in Pacemaker-Implanted Patients with Atrial High-Rate Episodes: An Observational Study from Turkey
Authors: Cinar Ahmet , Gedikli Omer , Uyanik Muhammet , Avci Bahattin , Terzi Ozlem
Published: February 2025

Background and Objectives: An atrial high-rate episode (AHRE) is defined according to the European Society of Cardiology (ESC) guidelines as a heart rate of ≥175 bpm lasting at least 5 min. This study aimed to evaluate whether neuron-specific enolase (NSE) levels, an indicator of neurological impact, could serve as a surrogate biomarker for silent neurological ischemia in patients with atrial high-rate episodes (AHREs). Materials and Methods: Patients with AHRE detected in a pacemaker analysis and a control group without any arrhythmias were included. Patients with AHRE were divided into subgroups according to AHRE duration—Group 1: AHRE < 5 min, Group 2: AHRE ≥ 5 min–<1 h, Group 3: AHRE ≥ 1 h–<24 h, Group 4: AHRE ≥ 24 h. Neuron-specific enolase (NSE) levels were measured using a double-antibody enzyme-linked immunosorbent assay (ELISA) with a sensitivity of 0.05 ng/mL. Imaging techniques were not employed in this study, and NSE was used as an indirect measure of potential neurological impact. Results: There were 160 patients, including 80 (50.0%) in the AHRE group and 80 (50.0%) in the control group. According to AHRE duration, there were 24 (30.0%) patients in Group 1, 33 (41.2%) in Group 2, 19 (23.8%) in Group 3, and 4 (5.0%) in Group 4. Patients with AHRE had statistically significant differences in age, sPAP, transmitral E/A ratio, and NSE levels. The mean NSE levels of all groups were significantly different (p < 0.001). A correlation analysis in patients with AHRE showed a very strong positive correlation between AHRE duration and NSE values as well as correlations with age, virtual CHA2DS2-VASc score, and LA diameter. NSE levels were positively correlated with AHRE duration and LA diameter. AHRE duration was an independent predictor of elevated NSE levels. Conclusions: It was shown that AHRE is associated with silent neurological ischemia and that NSE levels can be used to demonstrate these neurological effects. Future studies can contribute to the development of more effective treatment strategies based on these findings by investigating the neurological effects of AHRE in more detail.
Metabolomics Analysis Reveals Characteristic Functional Components in Pigeon Eggs
Authors: Zhang Rui , Chang Lingling , Shen Xinyue , Tang Qingping , Mu Chunyu , Fu Shengyong , Bu Zhu
Published: February 2025

We aimed to identify the characteristic functional components of pigeon eggs and the differences among pigeon, chicken, and quail eggs. We analyzed the metabolite profiles of three kinds of eggs using an untargeted metabolomics-based approach to better understand the differences in metabolites among pigeon, chicken, and quail eggs. Then, we quantitatively validated the differences in abundance of partial metabolites through a targeted metabolomics-based approach. A total of 692 metabolites were identified in the three types of eggs. A total of 263 significantly differentially abundant metabolites were found between pigeon eggs and chicken eggs, and 263 significantly differentially abundant metabolites were found between pigeon eggs and quail eggs. The metabolites that were significantly more abundant in pigeon eggs than in other eggs were mainly lipids, lipid-like molecules, nucleosides, nucleotides, and their analogues. We identified the eight metabolites that were significantly greater in abundance in pigeon eggs than in chicken eggs and quail eggs and quantitatively validated the differences in abundance of these metabolites. Our study demonstrates that there are more functional components in pigeon eggs than chicken eggs and quail eggs, especially for the prevention and treatment of various disordered glucose and lipid metabolism-related diseases. The discovery of these differentially abundant metabolites paves the way for further research on the unique nutritional functions of pigeon eggs and the further utilization of pigeon egg products.
All-Optical Single-Longitudinal-Mode Forward Brillouin Microwave Oscillator with an Unbalanced Fiber Mach–Zehnder Interferometer
Authors: Fang Xinyue , He Wenjun , Wang Wen , Liu Yi , You Yajun , Yan Qing , Hou Yafei , Wu Zepeng , Yu Lei , Yan Songquan , Li Mingxing , He Jian , Chou Xiujian
Published: February 2025

An all-optical single-longitudinal-mode (SLM) forward Brillouin microwave oscillator (FB-MO) with an unbalanced Fiber Mach–Zehnder interferometer (UF-MZI) for microwave photonics (MWP) generation is proposed and experimentally investigated. UF-MZI consists of an optical coupler (OC), a polarization controller (PC), and two asymmetric length arms with 5 km and 500 m single-mode fibers (SMFs), which implements two unbalanced length feedback rings that are connected to one another. One long-length ring with a forward Brillouin gain cooperates with the other short-length ring to maintain a spectral Vernier effect and improve the effective free spectral range (FSR). By contrast with traditional optoelectronic oscillators (OEOs), this design does not require any photoelectric conversion devices and additional modulation, avoids external electromagnetic interference, and side-mode suppression and linewidth are favorable. Experimental results reveal that the 3-dB linewidth of the all-optical SLM FB-MO with UF-MZI is about 140 Hz. The acoustic-mode and side-mode suppression ratios are 26 dB and 31 dB. Within 60 min of the stability experiment, the power and frequency stability fluctuation were ±1 dB and ±100 Hz. Thanks to its long main ring cavity length, our all-optical SLM FB-MO with UF-MZI maintains good phase-noise performance. The measurement shows that a phase noise as low as −120 dBc/Hz at an offset frequency of 100 kHz is achieved. This SLM MWP generation technology holds great potential for applications in radar monitoring and wireless communication systems.
Visceral Leishmaniasis in a 25-Year-Old Female Kidney Transplant Recipient from a Non-Endemic Region: A Case Report from Romania
Authors: Mihu Alin Gabriel , Patiu Mariana , Dima Delia Monica , Oatis Daniela Adriana , Cismaru Cristina Mihaela , Lighezan Rodica , Olariu Tudor Rares
Published: February 2025

Visceral leishmaniasis is a rare parasitic infection in non-endemic regions such as Romania. We report the case of a 25-year-old female kidney transplant recipient from Cluj County, Romania, who developed persistent bicytopenia with anemia and thrombocytopenia. Despite no history of travel outside Cluj County and being the only organ recipient from the same donor to experience signs and symptoms, she was diagnosed with visceral leishmaniasis. The second bone marrow aspirate performed revealed Leishmania amastigotes. She was quickly sent to Victor Babes Infectious Disease Hospital in Bucharest for additional tests and treatment. The kidney function of the patient was maintained. This case highlights the importance of considering leishmaniasis in immunosuppressed patients presenting with unexplained cytopenia, even in non-endemic regions. This is the first documented case of visceral leishmaniasis in a kidney transplant recipient in Romania. The present report could serve as a foundation for future educational programs targeted toward informing both healthcare providers and patients about the risks, diagnosis, and management of leishmaniasis in immunosuppressed individuals in non-endemic regions.
Comparative Toxicity of Micro, Nano, and Leachate Fractions of Three Rubber Materials to Freshwater Species: Zebrafish and Daphnia
Authors: Jackson Miranda E. , Harper Bryan J. , Garcia-Jaramillo Manuel , Harper Stacey L.
Published: February 2025

Rubber materials enter aquatic environments by stormwater runoff via sources such as playground mulch, athletic fields, and roadway surfaces. Tire rubbers are considered plastics as they comprise a substantial portion of synthetic polymers. Rubber particles are complex and variable depending on the type, source, and age of rubber. In this study, zebrafish embryos and daphnids were exposed to nano-scale or micro-scale particles, or leachate from recycled rubber (RR), crumb rubber (CR), and cryo-milled tire tread (CMTT). Zebrafish embryos were evaluated for lethal and sub-lethal effects over a 120 h exposure, while daphnids were tested over a 48 h period. Nano-scale RR, CR, and CMTT particles elicited a hatch delay in zebrafish embryos with similar EC50 values (1.3 × 109–1.4 × 109 particles/mL). Micro-scale particles did not elicit any significant effects in developing zebrafish. Nano-scale particles of all rubber materials significantly increased hatch delay compared to leachate, suggesting an adverse nanoparticle effect unexplained by chemical leaching alone, indicating tire particle-specific effects. Daphnia RR micro- and nanoparticle exposures resulted in mortality, with LC50 values of 9.8 × 105 microparticles/mL and 5.0 × 108 nanoparticles/mL, respectively. Leachate exposures did not elicit significant Daphnia mortality. Sublethal micro- and nano-TP exposures significantly decreased microalgae ingestion by Daphnia after 24 h. The effects of tire-derived exposures observed pose a risk to aquatic organism survival at environmentally relevant concentrations.
Study on the Reaction Kinetics of Sulfur Mustard, Nitrogen Mustard and Their Chosen Analogues with Sodium Ethoxide
Authors: Kozon Klaudia , Nawała Jakub , Sura Paweł , Popiel Stanisław
Published: February 2025

The course and kinetics of the reactions of sulfur mustard, nitrogen mustard and their selected analogues with sodium ethoxide were studied using a gas chromatograph coupled with a mass spectrometer. 2-chloroethyl ethyl sulfide (CEES), a monofunctional analogue of sulfur mustard (HD), bis(2-chloroethyl) ether (BCEE), an oxygen analogue of sulfur mustard, and bis(2-chloroethyl)amine, an analogue of nitrogen mustard HN-3, in which one hydrogen atom remains unsubstituted with a chloroethyl group, were used as imitators of mustards. For the study, the last mentioned compound was given the acronym HN-0. The research included checking how the form of sodium ethoxide influences the reaction rate. Two solutions were used: sodium ethoxide solution obtained by dissolving a commercially available compound in crystalline form and ethoxide solution obtained by dissolving sodium in ethanol. Additionally, the extent to which diethylenetriamine (DETA) accelerates the reactions of the studied compounds with sodium ethoxide was checked. The decontamination reactions were carried out in an anhydrous environment at a constant temperature of 25.0 °C. The rate of the mustard decontamination reaction increased significantly in systems containing DETA. Therefore, this amine can be used as a catalyst for this reaction. DETA has the most significant effect on the rate of the reaction of sodium ethoxide with CEES. The effect of the EtONa form was tested in the decontamination reaction of HD, revealing that both forms are equally effective, with only minor differences in reaction rates. Freshly synthesised sodium ethoxide reacts with HD 1.24 times faster. The study also assessed whether selected non-CWA compounds can be successfully used in studies as mustard imitators. Nitrogen mustard and bis(2-chloroethyl)amine reactions proceed according to the same mechanism—nucleophilic substitution. Bis(2-chloroethyl)amine reacts slightly faster than HN-3, both in solution with and without the addition of a catalyst. Sulfur mustard (HD) and CEES with sodium ethoxide and DETA undergo an elimination reaction, while BCEE undergoes a substitution reaction, which proceeds much slower. The observed differences disqualify BCEE as a sulfur mustard imitator. HD and CEES react with sodium ethoxide and DETA so quickly that the exact kinetic parameters under the developed experimental conditions could not be determined.
Identifying Inhibitor-SARS-CoV2-3CLpro Binding Mechanism Through Molecular Docking, GaMD Simulations, Correlation Network Analysis and MM-GBSA Calculations
Authors: Chen Jianzhong , Wang Jian , Yang Wanchun , Zhao Lu , Xu Xiaoyan
Published: February 2025

The main protease of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known as 3CLpro, is crucial in the virus’s life cycle and plays a pivotal role in COVID-19. Understanding how small molecules inhibit 3CLpro’s activity is vital for developing anti-COVID-19 therapeutics. To this end, we employed Gaussian accelerated molecular dynamics (GaMD) simulations to enhance the sampling of 3CLpro conformations and conducted correlation network analysis (CNA) to explore the interactions between different structural domains. Our findings indicate that a CNA-identified node in domain II of 3CLpro acts as a conduit, transferring conformational changes from the catalytic regions in domains I and II, triggered by the binding of inhibitors (7YY, 7XB, and Y6G), to domain III, thereby modulating 3CLpro’s activity. Normal mode analysis (NMA) and principal component analysis (PCA) revealed that inhibitor binding affects the structural flexibility and collective movements of the catalytic sites and domain III, influencing 3CLpro’s function. The binding free energies, predicted by both MM-GBSA and QM/MM-GBSA methods, showed a high correlation with experimental data, validating the reliability of our analyses. Furthermore, residues L27, H41, C44, S46, M49, N142, G143, S144, C145, H163, H164, M165, and E166, identified through residue-based free energy decomposition, present promising targets for the design of anti-COVID-19 drugs and could facilitate the development of clinically effective 3CLpro inhibitors.
Enhanced Hydrogen-Rich Syngas Production Through In-Situ Heavy Oil Gasification Process Using Nanoscale Nickel Catalyst
Authors: Wang Tiantian , Zhao Renbao , Yang Ying , Ren Haitao , Lv Wentao , Xu Han , Liu Jiyang
Published: February 2025

With the increasing demand for clean energy, in-situ hydrogen production from hydrocarbon reservoirs has attracted increasing attention. In this work, a nanoscale nickel catalyst was prepared using the water-in-oil (w/o) microemulsion method and applied in the in-situ generation of hydrogen-rich syngas from heavy oil reservoirs. The activation energy (Ea) of the gasification reactions significantly decreased with the addition of the nickel catalyst. The catalytic effect was monitored through remarkable increases in the peak temperature values for both the low-temperature oxidation (LTO) and high-temperature oxidation (HTO) processes, and the two peaks also shifted to lower-temperature regions. Additionally, the catalyst exhibited excellent activity and selectivity during the reaction process, and therefore the highest production rate of hydrogen-rich syngas of 20.07%, combined with the peak hydrogen concentration of 5.00%, was obtained in the presence of the catalyst and water. The substantial rate of hydrogen conversion from heavy oil was calculated to be 397.87 mL/g. The preliminary results obtained in this work show that this method is a significant improvement, and the catalyst-assisted method is believed to have great potential for underground fossil fuel conversion in the future.