bluuebunny/crossref_metadata_2025 huggingface dataset:
On Convolved Fibonacci Polynomials
Authors: Abd-Elhameed Waleed Mohamed , Alqubori Omar Mazen , Napoli Anna
Published: December 2024

This work delves deeply into convolved Fibonacci polynomials (CFPs) that are considered generalizations of the standard Fibonacci polynomials. We present new formulas for these polynomials. An expression for the repeated integrals of the CFPs in terms of their original polynomials is given. A new approach is followed to obtain the higher-order derivatives of these polynomials from the repeated integrals formula. The inversion and moment formulas for these polynomials, which we find, are the keys to developing further formulas for these polynomials. The derivatives of the moments of the CFPs in terms of their original polynomials and different symmetric and non-symmetric polynomials are also derived. New product formulas of these polynomials with some polynomials, including the linearization formulas of these polynomials, are also deduced. Some closed forms for definite and weighted definite integrals involving the CFPs are found as consequences of some of the introduced formulas.
Adaptive Variational Modal Decomposition–Dual Attention Mechanism Parallel Residual Network: A Tool Lifetime Prediction Method Based on Adaptive Noise Reduction
Authors: Kang Jing , Wang Taiyong , Li Yi , Wei Ye , Zhang Yaomin , Tian Ying
Published: December 2024

This paper addresses the issue of noise interference and variable working conditions in the production and machining environment, which lead to weak tool life features and reduced prediction accuracy. A tool lifetime prediction method based on AVMD-DAMResNet is proposed. The method first adapts the parameters of the variational modal noise reduction algorithm using an improved sparrow optimization algorithm, and then reconstructs the original vibration signal with noise reduction. Second, the residual module of the deep residual network is enhanced using a two-dimensional attention mechanism. A parallel residual network tool prediction model (DAMResNet) was constructed to optimize the model’s weight allocation to different features, achieving multi-channel and multi-dimensional feature fusion. Finally, the noise-reduced signal was input into the DAMResNet model to accurately predict tool lifetime. The experimental results show that, compared with the original ResNet model, the proposed AVMD-DAMResNet model improves the coefficient of determination (R2) by 5.8%, reduces the root mean square error (RMSE) by 31.2%, and decreases the mean absolute percentage error (MAPE) by 31.4%. These results demonstrate that the AVMD-DAMResNet-based tool lifetime prediction method effectively reduces noise and achieves high prediction accuracy.
Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization
Authors: Kwon Seoyoon , Ji Minsoo , Kim Min , Leung Juliana Y. , Min Baehyun
Published: December 2024

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions.
A Method for Predicting the Timing of Mine Earthquakes Based on Deformation Localization States
Authors: Zhu Chenli , Ding Linlin , Song Yimin , Li Yuda
Published: December 2024

As a prevalent geological hazard in underground engineering, the accurate prediction of mine earthquakes is crucial for ensuring operational safety and enhancing mining efficiency. The deformation localization method effectively predicts the instability of disaster rocks, yet the timing of mine earthquakes remains understudied. This study established a correlation between rock deformation localization and seismic activity within mines through theoretical derivations. A predictive model algorithm for forecasting mine earthquake timing was developed based on Saito’s theory, integrating optics, acoustics, and mathematical modeling theories. The “quiet period” was identified as a significant precursor; thus, the model used the initiation of deformation localization to accurately predict rock failure. Using the model, a coal mine in Inner Mongolia was selected as a case study to predict a historical mining earthquake. The results indicated that the following: (1) Deformation localization and the “quiet period” of microseismic (MS) and acoustic emission (AE) activities were identified as two key pre-cursory indicators. The model utilized the initiation time of deformation localization and the inflection point of the “quiet period” in MS and AE activity as primary parameters. (2) For predicting rock failure times, the earliest prediction time deviates from the actual failure time by 143 s. The accuracy rate of predicted time points falling within a 90% confidence interval of the actual failure times is 100%. The model achieved 60% in forecasting the occurrence times of mine earthquakes. (3) The model’s prediction accuracy improved as the starting time parameter more closely approximated the actual initiation time of deformation localization, with the accuracy increasing from 0% to 100%.
A Simulation Study on Adaptive Assignment Versus Randomizations in Clinical Trials
Authors: Lin Chien-Tai , Li Yun-Wei , Hong Yi-Jun
Published: December 2024

This study investigates a sequential clinical trial comparing two treatments with dichotomous outcomes. We evaluate the effectiveness of five adaptive procedures and three randomization methods for assigning patients to different therapies. The primary objective is to identify an optimal treatment allocation policy that maximizes the proportion of successful outcomes in a trial. By comparing the performance of adaptive and randomized procedures, this research provides valuable insights for enhancing treatment allocation strategies in clinical trials, ultimately aiming to improve the overall success rates of therapeutic interventions.
Dynamic Hierarchical Optimization for Train-to-Train Communication System
Authors: Song Haifeng , Xu Mingxuan , Cheng Yu , Zeng Xiaoqing , Dong Hairong
Published: December 2024

To enhance the operational efficiency of high-speed trains (HSTs), Train-to-Train (T2T) communication has received considerable attention. This paper introduces a T2T cooperative communication model that allows direct information exchange between HSTs, enhancing communication efficiency and system performance. The model incorporates a mix of dynamic and static nodes, and within this framework, we have developed a novel Dynamic Hierarchical Algorithm (DHA) to optimize communication paths. The DHA combines the stability of traditional algorithms with the flexibility of machine learning to adapt to changing network topologies. Furthermore, a communication link quality assessment function is proposed based on stochastic network calculus, which accounts for channel randomness, allowing for a more precise adaptation to the actual channel environment. Simulation results demonstrate that DHA has superior performance in terms of optimization time and effect, particularly in large-scale and highly dynamic network environments. The algorithm’s effectiveness is validated through comparative analysis with traditional and machine learning-based approaches, showing significant improvements in optimization efficiency as the network size and dynamics increase.
Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks
Authors: Zhang Chuangchuang , Liu Siquan , Yang Hongyong , Cui Guanghai , Li Fuliang , Wang Xingwei
Published: December 2024

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.
A Throughput Analysis Using a Non-Saturated Markov Chain Model for LTE-LAA and WLAN Coexistence
Authors: Kim Mun-Suk
Published: December 2024

To address the severe spectrum shortage in mobile networks, the 3rd Generation Partnership Project (3GPP) standardized Long Term Evolution (LTE)-License Assisted Access (LAA) technology. The LTE-LAA system ensures efficient coexistence with other existing unlicensed systems by incorporating listen-before-talk functionality and conducting random backoff operations similar to those in the IEEE 802.11 distributed coordination function. In this paper, we propose an analytical model to calculate the throughput of each system in a scenario where a single LTE-LAA system shares an unlicensed channel with multiple wireless local area network (WLAN) systems. The LTE-LAA system is utilized for supplementary downlink transmission from the LTE-LAA eNodeB (eNB) to LTE-LAA devices. Our proposed analytical model uses a Markov chain to represent the random backoff operations of the LTE-LAA eNB and WLAN nodes under non-saturated traffic conditions and to calculate the impact of the clear channel assessment (CCA) performed by the LTE-LAA eNB. Through numerical results, we demonstrate how the throughput of both the LTE-LAA and WLAN systems is determined by the contention window size and CCA threshold of the LTE-LAA eNB.
Evolution of Liver Resection for Hepatocellular Carcinoma: Change Point Analysis of Textbook Outcome over Twenty Years
Authors: Park Yeshong , Han Ho-Seong , Lim Seung Yeon , Joo Hyelim , Kim Jinju , Kang MeeYoung , Lee Boram , Lee Hae Won , Yoon Yoo-Seok , Cho Jai Young
Published: December 2024

Background and Objectives: The aim of this study was to comprehensively analyze the evolution in textbook outcome (TO) achievement after liver resection for hepatocellular carcinoma (HCC) over two decades at a single tertiary referral center. Materials and Methods: All consecutive liver resections for HCC at Seoul National University Bundang Hospital from 2003 to 2022 were analyzed. The included 1334 patients were divided into four groups by time intervals identified through change point analysis. TO was defined as no intraoperative transfusions, positive margins, major complications, 30-day readmission or mortality, and prolonged length of hospital stay (LOS). Results: Multiple change point analysis identified three change points (2006, 2012, 2017), and patients were divided into four groups. More recent time interval groups were associated with older age (59 vs. 59 vs. 61 vs. 63 years, p < 0.0001) and more comorbidities. Minimally invasive procedures were increasingly performed (open/laparoscopic/robotic 37.0%/63.0%/0%) vs. 43.8%/56.2%/0% vs. 17.1%/82.4%/0.5% vs. 22.9%/75.9%/1.2%, p < 0.0001). TO achievement improved over time (1.9% vs. 18.5% vs. 47.7% vs. 62.5%, p < 0.0001), and LOS was the greatest limiting factor. Conclusions: TO after liver resection improved with advances in minimally invasive techniques and parenchymal sparing procedures, even in older patients with more comorbidities and advanced tumors.
Multimodal Screening for Pulmonary Arterial Hypertension in Systemic Scleroderma: Current Methods and Future Directions
Authors: Dragoi Ioan Teodor , Rezus Ciprian , Burlui Alexandra Maria , Bratoiu Ioana , Rezus Elena
Published: December 2024

Systemic sclerosis (SSc) is an immuno-inflammatory rheumatic disease that can affect both the skin and internal organs through fibrosis. Pulmonary arterial hypertension (PAH) is one of the most severe secondary complications. Structural changes in the vascular bed lead to increased pressures in the pulmonary circulation, severely impacting the right heart and significantly affecting mortality. The gold standard for diagnosing PAH is right heart catheterization (RHC), an invasive method for measuring cardiac pressure. Due to the high risk of complications, procedural difficulties, and significant costs, non-invasive screening for SSc-PAH has garnered significant interest. Echocardiography is likely the most important screening tool, providing structural and functional information about the right heart through measurements that have proven their utility over time. In addition to imagistic investigations, serum biomarkers aid in identifying patients at risk for PAH and can provide prognostic information. Currently, well-known serum biomarkers (NT-proBNP, uric acid) are used in screening; however, in recent years, researchers have highlighted new biomarkers that can enhance diagnostic accuracy for SSc patients. Pulmonary involvement can also be assessed through pulmonary function tests, which, using established thresholds, can provide additional information and help select patients requiring RHC. In conclusion, given the invasiveness of RHC, non-invasive screening methods are particularly important for SSc patients.
Challenges and Innovations in Alveolar Bone Regeneration: A Narrative Review on Materials, Techniques, Clinical Outcomes, and Future Directions
Authors: Marian Diana , Toro Giuseppe , D’Amico Giovanbattista , Trotta Maria Consiglia , D’Amico Michele , Petre Alexandru , Lile Ioana , Hermenean Anca , Fratila Anca
Published: December 2024

This review explores the recent advancements and ongoing challenges in regenerating alveolar bone, which is essential for dental implants and periodontal health. It examines traditional techniques like guided bone regeneration and bone grafting, alongside newer methods such as stem cell therapy, gene therapy, and 3D bioprinting. Each approach is considered for its strengths in supporting bone growth and integration, especially in cases where complex bone defects make regeneration difficult. This review also looks at different biomaterials, from bioactive scaffolds to nanomaterials, assessing how well they encourage cell growth and healing. Personalized treatments, like customized 3D-printed scaffolds, show promise in enhancing bone formation and tissue compatibility. Additionally, signaling molecules, like bone morphogenetic proteins, play a crucial role in guiding the process of bone formation and remodeling. Despite these advances, challenges remain—particularly with severe bone loss and with refining biomaterials for more reliable, long-term outcomes. This review proposes combining advanced materials, regenerative technologies, and personalized approaches to achieve more effective and consistent outcomes in oral and maxillofacial surgery.
Emerging Strategies in Cartilage Repair and Joint Preservation
Authors: Focsa Mircea Adrian , Florescu Sorin , Gogulescu Armand
Published: December 2024

Background and Objectives: Cartilage repair remains a critical challenge in orthopaedic medicine due to the tissue’s limited self-healing ability, contributing to degenerative joint conditions such as osteoarthritis (OA). In response, regenerative medicine has developed advanced therapeutic strategies, including cell-based therapies, gene editing, and bioengineered scaffolds, to promote cartilage regeneration and restore joint function. This narrative review aims to explore the latest developments in cartilage repair techniques, focusing on mesenchymal stem cell (MSC) therapy, gene-based interventions, and biomaterial innovations. It also discusses the impact of patient-specific factors, such as age, defect size, and cost efficiency, on treatment selection and outcomes. Materials and Methods: This review synthesises findings from recent clinical and preclinical studies published within the last five years, retrieved from the PubMed, Scopus, and Web of Science databases. The search targeted key terms such as “cartilage repair”, “stem cell therapy”, “gene editing”, “biomaterials”, and “tissue engineering”. Results: Advances in MSC-based therapies, including autologous chondrocyte implantation (ACI) and platelet-rich plasma (PRP), have demonstrated promising regenerative potential. Gene-editing tools like CRISPR/Cas9 have facilitated targeted cellular modifications, while novel biomaterials such as hydrogels, biodegradable scaffolds, and 3D-printed constructs have improved mechanical support and tissue integration. Additionally, biophysical stimuli like low-intensity pulsed ultrasound (LIPUS) and electromagnetic fields (EMFs) have enhanced chondrogenic differentiation and matrix production. Treatment decisions are influenced by patient age, cartilage defect size, and financial considerations, highlighting the need for personalised and multimodal approaches. Conclusions: Combining regenerative techniques, including cell-based therapies, gene modifications, and advanced scaffolding, offers a promising pathway towards durable cartilage repair and joint preservation. Future research should focus on refining integrated therapeutic protocols, conducting long-term clinical evaluations, and embracing personalised treatment models driven by artificial intelligence and predictive algorithms.
Planar Two-Dimensional Vibration Isolator Based on Compliant Mechanisms
Authors: Zhu Ruizhe , Hu Jinpeng , Huang Long , Zhang Leiyu , Ren Guangan
Published: December 2024

In practical engineering applications, the vibration is often generated in various directions and can be harmful to the engineering equipment. Thus, it is necessary to develop vibration isolators that can reduce vibration in multiple directions. In this paper, we propose a planar two-dimensional vibration isolator based on compliant mechanisms. The proposed mechanism consists of two negative stiffness-compliant modules and two positive stiffness-compliant modules, which leads to the quasi-zero stiffness (QZS) property in the mechanism. The dynamic model is established by using the third-order Taylor expansion and the harmonic balance method. Based on the dynamic model, the influence of different parameters on the displacement transmissibility is discussed, including damping ratio, system stiffness, and excitation amplitude. Finally, we conducted the vibration isolation experiments and obtained the displacement transmissibility of the isolator. The results verify that the proposed isolator has good isolation performance for low-frequency vibration.
Bacterial Multiresistance and Microbial Diversity of Milk Received by a University Hospital Milk Bank
Authors: Zanini Dayane da Silva , Menozzi Benedito Donizete , Teixeira Wanderson Sirley Reis , Fornazari Felipe , Angeluci Gismelli Cristiane , Gaspar Raquel Cuba , Ribeiro Lucas Franco Miranda , Fidelis Carlos Eduardo , Veiga dos Santos Marcos , Pereira Juliano Gonçalves , Langoni Helio
Published: December 2024

Breastfeeding is fundamental for the development and protection of the newborn, and microorganisms present in breast milk are associated with the development of the infant’s intestinal microbiota. However, there are factors that interfere with breastfeeding, resulting in the need to supply donated milk to milk banks for these children. Even though there is a restriction on medications prescribed for pregnant and breastfeeding women, some antimicrobials are accepted, as long as they are used correctly and as they can increase the selection pressure for resistant bacteria. The microorganisms present in breast milk from a human milk bank were evaluated and the resistance of the isolates to antimicrobials was phenotypically characterized. In total, 184 microbial isolates were identified by mass spectrometry, of 12 bacterial genera and 1 yeast genus. There was a high prevalence of bacteria of the genus Staphylococcus, mainly S. epidermidis (33%). Resistance to antimicrobials varied among species, with a higher percentage of isolates resistant to penicillins and macrolides. Multidrug resistance was identified in 12.6% of 143 isolates. Breast milk contains a wide variety of microorganisms, mainly those of the Staphylococcus and Enterobacter genera. There was a high percentage of resistant isolates, and multidrug resistance in Klebsiella oxytoca (66.7%; 4/6) and S. epidermidis (15.0%; 9/60) isolates, which increases the public health concern.
Enhanced Detection of Viable Escherichia coli O157:H7 in Romaine Lettuce Wash Water Using On-Filter Propidium Monoazide-Quantitative PCR
Authors: Chen Zhao
Published: December 2024

Accurate detection of viable Escherichia coli O157:H7 in fresh produce wash water is critical for ensuring food safety and mitigating foodborne illnesses. This study evaluated an optimized on-filter propidium monoazide (PMA)-quantitative PCR (qPCR) method for detecting viable E. coli O157:H7 in romaine lettuce wash water, involving PMA pretreatment on a filter to block DNA amplification from dead cells. The method consistently detected viable cells across chemical oxygen demand (COD) levels of 1000 and 200 mg O2/L, with no significant differences (p > 0.05), indicating its tolerance to organic matter interference. Optimization experiments identified 10 µM PMA with a 10 min exposure time as the most effective pretreatment, achieving efficient inhibition of DNA from dead cells while preserving viable cell integrity. The limit of detection (LOD) was 1.3 CFU/mL, confirming its suitability for detecting low bacterial loads. Performance evaluations revealed that PMA-qPCR was accurate at viable-to-dead cell ratios of 1:10 or higher but became less reliable when dead cells outnumbered viable cells by a factor of 10 or more. The study demonstrates the potential of on-filter PMA-qPCR for routine food safety monitoring protocols in the fresh produce industry, while highlighting the critical role of viable-to-dead cell ratios in ensuring accurate detection, particularly in challenging samples with high dead cell loads.
Identification and Geological Significance of Late Cambrian OIB-Type Volcanic Rocks in the Nailenggeledaban Area, Northern Yili Block
Authors: Xu Da , Cao Ming , Wang Meng , Chen Youxin , Zhao Shaowei , Zhu Shengqiang , Wen Tai , Bao Zhi’an
Published: December 2024

Paleozoic igneous rocks exposed in the northern Yili Block are thought to have resulted from the subduction of the North Tianshan oceanic crust. However, the exact timing of the transition of the northern margin of the Yili Block from a passive to an active continental margin remains unknown. In this paper, the petrological and geochemical features, zircon U-Pb chronology, Lu-Hf isotopes, and Sr-Nd isotopes of volcanic rocks in the Nailenggeledaban area on the northern margin of the Yili Block were studied. Zircon U-Pb dating results show that the crystallization ages of the volcanic rocks in the Nailenggeledaban area on the northern margin of the Yili Block are 491 ± 2 Ma and 500 ± 2 Ma, suggesting they were formed during the Late Cambrian. Geochemical features show that the volcanic rocks are alkaline basalts with rare earth and trace element distribution patterns similar to OIB, although they exhibit some degree of Zr and Hf depletion. The εHf(t) values of alkaline basalts in the Nailenggeledaban area at the northern Yili Block range from −3.48 to −1.00, with a TDM1 age of 1152 to 1263 Ma. The εNd(t) values range from −3.53 to −0.96, with a TDM1 age of 1471 to 2162 Ma. Combined with geochemical data, the alkaline basalt magma in the Nailenggeledaban area on the northern margin of the Yili Block may be derived from the Mesoproterozoic enriched lithospheric mantle. The composition of the mantle source area is potentially garnet lherzolite, and the magma appears to have been either unaffected or only minimally contaminated by crustal materials during the ascending process. On the basis of the research results of the Early Paleozoic tectonic evolution in the northern margin of the Yili Block, this paper proposes that the volcanic rocks in the Nailenggeledaban area, located on the northern margin of the Yili Block, were formed in a back-arc extensional environment resulting from the subduction of the North Tianshan Ocean (or Junggar Ocean) beneath the northern margin of the Yili Block during the Late Cambrian.
Assessment of Heavy Metal Content and Identification of Their Sources in Bottom Sediments and Various Macrophyte Species of the Narew River (Poland)
Authors: Skorbiłowicz Mirosław , Sidoruk Marcin
Published: December 2024

The condition of the aquatic environment, particularly in protected areas of high ecological value such as the Narew River, requires detailed monitoring to identify and minimise the impact of anthropogenic factors on the ecosystem. This study focused on the content of heavy metals in bottom sediments and macrophytes of the Narew River, emphasising the influence of human activities and natural factors on this ecologically valuable ecosystem. Pb, Cr, Zn, Cd, Fe, and Mn concentrations were analysed in sediment samples, and ten macrophyte species were collected at 11 sampling points along the river. A geochemical index (Igeo) and multivariate statistical analyses were employed to identify sources of contamination. The digested samples (sediments and plants) were analysed for Pb, Cr, Cu, Zn, Ni, Cd, Fe, and Mn using flame atomic absorption spectrometry (AAS) on an ICE 3500 Thermo Scientific spectrometer, with a measurement error below 5%, validated against certified reference materials. The study results indicated that most metals, including Ni, Cr, Co, Fe, and Mn, predominantly originate from natural geological processes. In contrast, Zn, Cd, Cu, and Pb were identified as being enriched due to anthropogenic activities. An analysis of macrophytes revealed varied patterns of metal accumulation, which correspond to the bioavailability of metals and their environmental concentrations. Comprehensive statistical analyses provided insights into the predominant sources of metal contamination, closely associated with industrial emissions, agricultural runoff, and transportation activities. The integration of sediment and macrophyte monitoring allowed for a thorough evaluation of the Narew River ecosystem, facilitating the identification of key pollution sources. These findings highlight the critical need for measures to mitigate anthropogenic contributions of heavy metals—particularly from industrial, agricultural, and transportation sectors—to safeguard the Narew River’s unique ecological and natural heritage.
Experimental Investigations on the Dissolution Process of Dolomite by Sulfate-Rich Geothermal Water: A Case Study of the Shuijing Hot Springs in Guizhou Province
Authors: Yang Ke , Zhou Li , Chen Zhengshan , Zhang Wei , Zhou Wenge
Published: December 2024

The dissolution of dolomite can not only provide the chemical components in hot springs but also provide a high-quality reservoir for geothermal resources. However, there is still debate about the main controlling factors and mechanisms of the dissolution process of dolomite. The Shuijing hot springs in Guizhou Province are rich in SO42− and the geothermal reservoir is dolomite, which provides an excellent opportunity to understand the role of SO42− in the dissolution process of dolomite. In this paper, water–rock interaction experiments were conducted at different temperatures to study the effects of SO42−, pH, and CO2 on the dissolution of dolomite from the Shuijing hot springs geothermal reservoir. The results indicate that temperature is a significant factor affecting the chemical composition of hot springs water, with higher temperatures having a more pronounced effect on the dissolution of dolomite. At lower temperatures of 25 °C and 90 °C, the molar ratio of the released Ca2+ and Mg2+ during the dissolution of dolomite in the initial reaction stage generally approaches the Ca/Mg molar ratio of dolomite, exhibiting congruent dissolution. However, at elevated temperatures of 150 °C, the released Ca/Mg molar ratio surpasses the Ca/Mg molar ratio of dolomite, demonstrating an incongruent dissolution characteristic with Ca2+ being preferentially released over Mg2+. Additionally, the relative importance of CO2, SO42− and pH on the dissolution degree of dolomite is CO2 > SO42− > pH = 4 > pH = 7 > pH = 10. The promotion effect of SO42− on dolomite dissolution indicates that the greater the SO42− concentration, the stronger the dissolution of dolomite, and its dissolution ability is enhanced with the increase in temperature. Furthermore, the effect of CO2 on the dissolution of dolomite is stronger than that of SO42−, leading to the oscillating fluctuation trend of the released Ca2+ and Mg2+.
Berthierine-2H1 from Lovozero Alkaline Massif, Kola Peninsula, Russia: First Structure Model for Berthierine and Complexity-Stability Relations
Authors: Krivovichev Sergey V. , Yakovenchuk Victor N. , Goychuk Olga F. , Pakhomovskii Yakov A. , Krivovichev Vladimir G.
Published: December 2024

Berthierine was found in a natrolite vein intersecting volcanogenic-sedimentary rocks on the slope of Mt. Quamdespakh in the upper reaches of the Suolwai River, Lovozero alkaline massif, Kola peninsula, Russian Arctic. The mineral occurs as well-formed translucent pyramidal crystals up to 250 μm in size. The chemical composition determined by electron microprobe analysis corresponds to the empirical formula VI(Fe2+1.99Al0.94Mg0.03Mn0.04)Σ3.00[IV(Si1.15Al0.85)Σ2.00O5] [(OH)3.92O0.08]Σ4.00; the idealized formula is VI(Fe2+2Al)[IV(SiAl)O5](OH)4. The crystal-structure determination (the first detailed crystal-structure characterization of berthierine) shows that the Lovozero mineral is hexagonal, P63cm (a = 5.3903(4), c = 14.0146(10) Å, V = 352.64(6) Å3, R1 = 0.053 for 338 unique observed reflections), and corresponds to the 2H1 polytype of serpentine-group minerals with 1:1 tetrahedral-octahedral layers. The unit cell contains two M3[T2O5](OH)4 layers (M = Fe2+,Al; T = Si,Al) stacked along the c axis. The calculations of information-based structural and topological complexity parameters indicate that berthierine is structurally and topologically simpler than its chlorite-group polymorph chamosite. Since berthierine usually crystallizes metastably in the stability field of chamosite, the complexity analysis is agreement with the Goldsmith rule that states that, in Ostwald sequences of crystallization, metastable phases are simpler and more disordered than their stable counterparts. This observation can be applied to a general case of the metastable formation of serpentine-group minerals prior to the crystallization of chlorites.
Microscopic, Spectroscopic and Chemical Analysis of Emeralds from Habachtal, Austria
Authors: Nikopoulou Maria , Karampelas Stefanos , Hennebois Ugo , Gruss Pierre , Gaillou Eloïse , Fritsch Emmanuel , Herreweghe Annabelle , Papadopoulou Lambrini , Melfos Vasilios , Kantiranis Nikolaos , Delaunay Aurélien
Published: December 2024

Studies on emeralds from Habachtal (Austria) are scarce and non-exhaustive. However, they represent a significant interest to the community, as they have been assumed to be present in historical pieces of jewelry. Along with Egypt, Habachtal is suggested to be one of the main sources of gem-quality emeralds of archaeological significance. In this regard and due to their similar macroscopic look and geological context, it has always been challenging to distinguish Austrian and Egyptian emeralds set in historic jewelry. In this paper, a first comprehensive study of several Habchtal emeralds is presented based on a combination of classic gemology, chemistry and spectroscopy, using non-destructive to micro-destructive methods. Spectroscopic analyses, such as via Raman, FTIR and UV-Vis–NIR spectroscopies, showed that emeralds from Habachtal (Austria) contain Type II H2O molecules with alkalis and they are colored by chromium and iron, similarly to emeralds from Egypt. Under an optical microscope (and identified by Raman spectroscopy), actinolite needle-like inclusions are frequently observed in Austrian emeralds. Other inclusions, such as quartz, plagioclase, albite, phlogopite and pyrite, can also be observed in Austrian samples. Chemical analysis of Austrian emeralds’ trace elements by LA–ICP–MS show similar results to those from Egypt, with the exception of V, K and Rb, which show a lesser content in Austrian emeralds. Thanks to the determination of inclusions combined with a careful examination of specific chemistry, this study shows that Austrian emeralds can be distinguished from their Egyptian counterpart.
Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework
Authors: Zhang Xiangying , Gao Haotian , Qi Yifei , Li Yan , Wang Renxiao
Published: December 2024

As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery.
A Simplified Method for Evaluating Chitin-Binding Activity Applied to YKL-40 (HC-gp39, CHI3L1) and Chitotriosidase
Authors: Suzuki Keita , Suzuki Hidetoshi , Tanaka Ami , Tanaka Miwa , Takase Kairi , Takei Hiromu , Kanaizumi Tomoki , Okawa Kazuaki , Bauer Peter O. , Oyama Fumitaka
Published: December 2024

YKL-40 is structurally similar to chitotriosidase (CHIT1), an active chitinase, but it lacks chitin-degrading activity while retaining chitin-binding capability. Elevated YKL-40 levels are associated with inflammatory diseases and cancers, making it a valuable biomarker. We previously reported that the W69T substitution in YKL-40 significantly reduces its chitin-binding affinity, identifying W69 as a crucial binding site. In this study, we establish a novel chitin-binding affinity evaluation method using a three-step buffer system to assess the binding strength and specificity of chitin-binding proteins and apply it to characterize YKL-40’s binding mechanism. Our findings confirm that YKL-40, through its key residue W69, exhibits highly specific and robust affinity to chitin. Unlike CHIT1, which has both a catalytic domain (CatD) and a chitin-binding domain (CBD) that allow for diverse binding and degradation activities, YKL-40 lacks a CBD and is specialized for specific chitin recognition without degrading it. Comparative analysis with YKL-39, which does not contain a corresponding W69 residue, highlights the unique role of this residue in YKL-40’s chitin-binding activity that is potentially linked to immune and inflammatory responses. Our evaluation method clarifies YKL-40’s binding properties and provides a versatile approach applicable to other chitin-binding proteins.
Interrelation of Natural Polyphenol and Fibrosis in Diabetic Nephropathy
Authors: Ma Ye , Wang Jiakun , Fan Juyue , Jia Huiyang , Li Jinyao
Published: December 2024

Diabetic nephropathy (DN) is a common and serious complication of diabetes mellitus and a major cause of end-stage renal disease (ESRD). Renal fibrosis, which corresponds to excessive deposition of extracellular matrix and leads to scarring, is a characteristic feature of the various progressive stages of DN. It can trigger various pathological processes leading to the activation of autophagy, inflammatory responses and a vicious circle of oxidative stress and inflammation. Although it is known that DN can be alleviated by mechanisms linked to antioxidants, reducing inflammation and improving autophagy, how to improve DN by reducing fibrosis using natural polyphenols needs to be studied further. Nowadays, natural polyphenolic compounds with excellent safety and efficacy are playing an increasingly important role in drug discovery. Therefore, this review reveals the multiple mechanisms associated with fibrosis in DN, as well as the different signaling pathways (including TGF-β/SMAD, mTORC1/p70S6K, JAK/STAT/SOCS and Wnt/β-catenin) and the potential role in the fibrotic niche. In parallel, we summarize the types of polyphenolic compounds and their pharmacodynamic effects, and finally evaluate the use of polyphenols to modulate relevant targets and pathways, providing potential research directions for polyphenols to improve DN. In summary, the problem of long-term monotherapy resistance can be reduced with natural polyphenols, while reducing the incidence of toxic side effects. In addition, potential targets and their inhibitors can be identified through these pathways, offering potential avenues of research for natural polyphenols in the pharmacological treatment of multisite fibrosis.
Ultra-Fast Removal of CBB from Wastewater by Imidazolium Ionic Liquids-Modified Nano-Silica
Authors: Zhang Mengyue , Yang Fan , Wang Nan , Du Jifu , Yan Juntao , Sun Ya , Zhang Manman , Zhao Long
Published: December 2024

The efficient removal of dyes is of significant importance for environmental purification and human health. In this study, a novel material (Si-MPTS-IL) has been synthesized by the immobilization of imidazole ionic liquids (ILs) onto nano-silica using the radiation grafting technique. The adsorption performance of Si-MPTS-IL for Coomassie Brilliant Blue (CBB) removal is studied by a series of static adsorption experiments. It is found that Si-MPTS-IL has ultra-fast adsorption kinetics, reaching equilibrium within 2 min. The adsorption process for CBB conforms to the Langmuir model. In addition, Si-MPTS-IL exhibits a negligible impact on the adsorption efficiency of CBB with the increase in salt concentration. After six cycles of adsorption–desorption, the adsorption efficiency of Si-MPTS-IL remained above 80%, indicating excellent regenerative properties and a promising candidate for the treatment of wastewater containing CBB. A study of the mechanism indicates that the CBB capture by Si-MPTS-IL can be attributed to the synergistic effects of electrostatic interactions and pore filling.
Co-Hydrothermal Carbonization of Goose Feather and Pine Sawdust: A Promising Strategy for Disposal of Sports Waste and the Robust Improvement of the Supercapacitor Characteristics of Pyrolytic Nanoporous Carbon
Authors: Ma Tingyu , Wang Jieni , Han Xiaobo , Zhang Chuanbing , Xu Yahui , Cao Leichang , Zhao Shuguang , Zhang Jinglai , Zhang Shicheng
Published: December 2024

Discarded sports waste faces bottlenecks in application due to inadequate disposal measures, and there is often a neglect of enhancing resource utilization efficiency and minimizing environmental impact. In this study, nanoporous biochar was prepared through co-hydrothermal carbonization (co-HTC) and pyrolytic activation by using mixed goose feathers and heavy-metals-contaminated pine sawdust. Comprehensive characterization demonstrated that the prepared M-3-25 (Biochar derived from mixed feedstocks (25 mg/g Cu in pine sawdust) at 700 °C with activator ratios of 3) possesses a high specific surface area 2501.08 m2 g−1 and abundant heteroatomic (N, O, and Cu), exhibiting an outstanding physicochemical structure and ultrahigh electrochemical performance. Compared to nanocarbon from a single feedstock, M-3-25 showed an ultrahigh capacitance of 587.14 F g−1 at 1 A g−1, high energy density of 42.16 Wh kg−1, and only 8.61% capacitance loss after enduring 10,000 cycles at a current density of 10 A g−1, positioning M-3-25 at the forefront of previously known biomass-derived nanoporous carbon supercapacitors. This research not only introduces a promising countermeasure for the disposal of sports waste but also provides superior biochar electrode materials with robust supercapacitor characteristics.
Advancing Graphene Synthesis: Low-Temperature Growth and Hydrogenation Mechanisms Using Plasma-Enhanced Chemical Vapor Deposition
Authors: Meškinis Šarūnas , Lazauskas Algirdas , Jankauskas Šarūnas , Guobienė Asta , Gudaitis Rimantas
Published: December 2024

This study explores the low-temperature synthesis of graphene using plasma-enhanced chemical vapor deposition (PECVD), emphasizing the optimization of process parameters to achieve controlled growth of pristine and hydrogenated graphene. Graphene films were synthesized at temperatures ranging from 700 °C to as low as 400 °C by varying methane (25–100 sccm) and hydrogen (25–100 sccm) gas flow rates under 10–20 mBar pressures. Raman spectroscopy revealed structural transitions: pristine graphene grown at 700 °C exhibited strong 2D peaks with an I(2D)/I(G) ratio > 2, while hydrogenated graphene synthesized at 500 °C showed increased defect density with an I(D)/I(G) ratio of ~1.5 and reduced I(2D)/I(G) (~0.8). At 400 °C, the material transitioned to a highly hydrogenated amorphous carbon film, confirmed by photoluminescence (PL) in the Raman spectra. Atomic force microscopy (AFM) showed pristine graphene with a root mean square roughness (Rq) of 0.37 nm. By carefully adjusting PECVD synthesis parameters, it was possible to tune the surface roughness of hydrogenated graphene to levels close to that of pristine graphene or to achieve even smoother surfaces. Conductive AFM measurements revealed that hydrogenation could enhance graphene’s contact current under specific conditions. The findings highlight the role of PECVD parameters in tailoring graphene’s structural, morphological, and electronic properties for diverse applications. This work demonstrates a scalable, low-temperature approach to graphene synthesis, offering the potential for energy storage, sensing, and electronic devices requiring customized material properties.
Nuclear Magnetic Resonance (NMR)-Based Lipidomics Reveal the Association of Altered Red Blood Cell (RBC) Membrane Lipidome with the Presence and the Severity of Coronary Artery Stenosis
Authors: Kastani Ioanna A. , Soltani Paraskevi K. , Baltogiannis Giannis G. , Christou Georgios A. , Bairaktari Eleni T. , Kostara Christina E.
Published: December 2024

Coronary heart disease (CHD) is the leading cause of morbidity and mortality worldwide despite significant improvements in diagnostic modalities. Emerging evidence suggests that erythrocytes, or red blood cells (RBCs), are one of the most important contributors to the events implicated in atherosclerosis, although the molecular mechanisms behind it are under investigation. We used NMR-based lipidomic technology to investigate the RBC lipidome in patients with CHD compared to those with normal coronary arteries (NCAs), all angiographically documented, and its correlation with coronary artery stenosis. Targeted and untargeted lipidomic analysis revealed that CHD patients presented significant lipid alterations in the RBC membrane, characterized by higher cholesterol, sphingolipids, saturated and monounsaturated fatty acids, lower phospholipids (glycerophospholipids and ether glycerolipids), and unsaturated and polyunsaturated fatty acids. These aberrations gradually distinguish the three subgroups of patients with mild, moderate, and severe coronary stenosis, potentially indicating their non-negligible involvement in the onset and progression of atherosclerosis. The comprehensive analysis of RBC-membrane-derived lipids with omics approaches could unravel specific lipid abnormalities taking place at the silent subclinical stage of atherosclerosis and could have the potential to identify patients with subtle, but still proatherogenic, abnormalities that may confer a higher risk for the development of CHD.