bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Diversity and Genome-Wide Association Study of Total Phenolics, Flavonoids, and Antioxidant Properties in Potatoes (Solanum tuberosum L.)
Authors: Rasheed Haroon , Deng Bowen , Ahmad Daraz , Bao Jinsong
Published: November 2024

Genetic diversity of nutritional quality traits is crucial for potato breeding efforts to develop better varieties for the diverse market demands. In this study, the genetic diversity of 104 potato genotypes was estimated based on nutritional quality traits such as color parameters, total phenolic content, total flavonoid content, 2,2-Diphenyl-1-picrylhydrazyl (DPPH), and 2,2-azino-bis-(3-ethylbezothiazoline-6-sulphonic acid) radical scavenging potential across two environments. The results indicated that environment II, Hangzhou 2020, exhibited higher bioactive compounds and antioxidant properties than environment I, Hangzhou 2019. The colored potato accessions exhibited higher levels of total phenolic content, total flavonoid content, DPPH, and ABTS activities than the white potato accessions, indicating the superiority of the colored to white potato accessions. The genome sequencing identified 1,101,368 high-quality single-nucleotide polymorphisms (SNPs), and 141,656 insertion/deletions (Indels). A population structure analysis revealed that genotypes can be divided into two subpopulations. Genome-wide association studies (GWAS) identified 128 significant SNPs associated with potato’s color, total phenolic content, total flavonoid content, and antioxidant properties. Thus, the study provides new opportunities for strategic breeding and marker-assisted selection of ideal varieties and favorable alleles to enhance bioactive compounds and health-beneficial properties.
Genomic Insights into the Role of cAMP in Carotenoid Biosynthesis: Enhancing β-Carotene Production in Escherichia coli via cyaA Deletion
Authors: Kwon Soon-Jae , Park Chan Bae , Lee Pyung Cheon
Published: November 2024

The gamma-ray-induced random mutagenesis of an engineered β-carotene-producing Escherichia coli XL1-Blue resulted in the variant Ajou 45, which exhibits significantly enhanced β-carotene production. The whole-genome sequencing of Ajou 45 identified 55 mutations, notably including a reduction in the copy number of cyaA, encoding adenylate cyclase, a key enzyme regulating intracellular cyclic AMP (cAMP) levels. While the parental XL1-Blue strain harbors two copies of cyaA, Ajou 45 retains only one, potentially leading to reduced intracellular cAMP concentrations. This reduction may alleviate catabolite repression and redirect metabolic flux toward the β-carotene biosynthesis pathway. To validate this mechanistic insight, a targeted cyaA knockout was engineered in XL1-Blue, and its β-carotene production and growth phenotypes were compared with those of Ajou 45 and XL1-Blue. The findings demonstrated that a reduced cyaA copy number substantially enhances β-carotene biosynthesis by modulating cAMP-mediated regulatory networks. This study highlights the efficacy of integrating random mutagenesis with integrative genomic analysis for microbial strain engineering and presents a novel strategy for enhancing carotenoid production in E. coli.
Special Issue “Machine Learning and Bioinformatics in Human Health and Disease”—Chances and Challenges
Authors: Leiherer Andreas
Published: November 2024

Machine learning (ML) and bioinformatics are catalyzing a new era in biomedical research, enabling unprecedented insights into the complex systems that govern human health and disease [...]
In the Beginning: Let Hydration Be Coded in Proteins for Manifestation and Modulation by Salts and Adenosine Triphosphate
Authors: Song Jianxing
Published: November 2024

Water exists in the beginning and hydrates all matter. Life emerged in water, requiring three essential components in compartmentalized spaces: (1) universal energy sources driving biochemical reactions and processes, (2) molecules that store, encode, and transmit information, and (3) functional players carrying out biological activities and structural organization. Phosphorus has been selected to create adenosine triphosphate (ATP) as the universal energy currency, nucleic acids for genetic information storage and transmission, and phospholipids for cellular compartmentalization. Meanwhile, proteins composed of 20 α-amino acids have evolved into extremely diverse three-dimensional forms, including folded domains, intrinsically disordered regions (IDRs), and membrane-bound forms, to fulfill functional and structural roles. This review examines several unique findings: (1) insoluble proteins, including membrane proteins, can become solubilized in unsalted water, while folded cytosolic proteins can acquire membrane-inserting capacity; (2) Hofmeister salts affect protein stability by targeting hydration; (3) ATP biphasically modulates liquid–liquid phase separation (LLPS) of IDRs; (4) ATP antagonizes crowding-induced protein destabilization; and (5) ATP and triphosphates have the highest efficiency in inducing protein folding. These findings imply the following: (1) hydration might be encoded in protein sequences, central to manifestation and modulation of protein structures, dynamics, and functionalities; (2) phosphate anions have a unique capacity in enhancing μs-ms protein dynamics, likely through ionic state exchanges in the hydration shell, underpinning ATP, polyphosphate, and nucleic acids as molecular chaperones for protein folding; and (3) ATP, by linking triphosphate with adenosine, has acquired the capacity to spacetime-specifically release energy and modulate protein hydration, thus possessing myriad energy-dependent and -independent functions. In light of the success of AlphaFolds in accurately predicting protein structures by neural networks that store information as distributed patterns across nodes, a fundamental question arises: Could cellular networks also handle information similarly but with more intricate coding, diverse topological architectures, and spacetime-specific ATP energy supply in membrane-compartmentalized aqueous environments?
Synthetic Microbial Community Isolated from Intercropping System Enhances P Uptake in Rice
Authors: Ma Huimin , Zhang Hongcheng , Zheng Congcong , Liu Zonghui , Wang Jing , Tian Ping , Wu Zhihai , Zhang Hualiang
Published: November 2024

Changes in root traits and rhizosphere microbiome are important ways to optimize plant phosphorus (P) efficiency and promote multifunctionality in intercropping. However, whether and how synthetic microbial communities isolated from polyculture systems can facilitate plant growth and P uptake are still largely unknown. A field experiment was first carried out to assess the rice yield and P uptake in the rice/soybean intercropping systems, and a synthetic microbial community (SynCom) isolated from intercropped rice was then constructed to elucidate the potential mechanisms of growth-promoting effects on rice growth and P uptake in a series of pot experiments. Our results showed that the yield and P uptake of intercropped rice were lower than those of rice grown in monoculture. However, bacterial networks in the rice rhizosphere were more stable in polyculture, exhibiting more hub nodes and greater modularity compared to the rice monoculture. A bacterial synthetic community (SynCom) composed of four bacterial strains (Variovorax paradoxus, Novosphingobium subterraneum, Hydrogenophaga pseudoflava, Acidovorax sp.) significantly enhanced the biomass and P uptake of potted rice plants. These growth-promoting effects are underpinned by multiple pathways, including the direct activation of soil available P, increased root surface area and root tip number, reduced root diameter, and promotion of root-to-shoot P translocation through up-regulation of Pi transporter genes (OsPht1;1, OsPht1;2, OsPht1;4, OsPht1;6). This study highlights the potential of harnessing synthetic microbial communities to enhance nutrient acquisition and improve crop production.
Metal Halide Perovskites for Applications in Biomimetic Devices
Authors: Chu Wending , Su Lei
Published: November 2024

Metal halide perovskites have demonstrated exceptional multifunctionality, finding applications in photovoltaics, light-emitting devices and sensors, which has stimulated intense research interest. Recently, their integration into biomimetic devices has emerged as a promising frontier, exploiting the unique optoelectronic properties of perovskites to mimic biological functions. This review provides a comprehensive analysis of recent advances in the use of metal halide perovskites for biomimetic applications, focusing on their role in different device configurations and fabrication techniques. We elucidate the mechanisms that drive their performance and demonstrate their potential as versatile materials for high performance biomimetic devices. By exploring the intricate interplay between material properties and device functionality, we highlight the transformative potential of metal halide perovskites in creating more efficient, adaptable and biologically inspired technologies. Finally, we discuss future research directions to maximise their application scope, with the aim of bridging materials science and bioengineering for innovative device development.
Interdisciplinary Fascia Therapy: A Proof-of-Concept Pilot Study for a New Myofascial Approach for Chronic Low Back Pain
Authors: Gordon Christopher M. , Dugan Victoria , Hörmann Christina , Montoya Pedro
Published: November 2024

Background/Objectives: Chronic low back pain (CLBP) is highly prevalent and relevant in all medical fields. This study evaluated the safety and effectiveness of interdisciplinary fascia therapy (IFT) for CLBP, focusing on its potential to reduce pain intensity, disability, and regulate autonomic nervous system (ANS) activity. Methods: Nine participants with CLBP each underwent nine sessions of IFT, twice weekly. The intervention involved a 12-grip manual sequence targeting deep paravertebral myofascial structures, complemented by heart rate variability (HRV) biofeedback training twice daily for 15 min. Pain and quality-of-life measures were assessed using the Brief Pain Inventory (BPI) questionnaire at baseline, mid-treatment (4th session), and post-treatment (9th session). HRV metrics were monitored with a 24 h ECG Holter device before and after the treatment period. Statistical analyses included paired t-tests, Wilcoxon signed-rank tests, and Cohen’s d for effect size. Results: Significant reductions in pain levels were observed across all subjective BPI measures, including momentary, strongest, minimal, and average pain scores (p < 0.001), with 83% and 87% reductions in pain intensity and disability, respectively. Quality-of-life indicators such as mood, sleep, and enjoyment of life showed significant improvements (p < 0.001). While only one HRV metric (rMSSD) achieved statistical significance, other HRV measures indicated medium to large effect sizes, suggesting favorable trends in ANS regulation. Conclusions: IFT demonstrated significant effects on subjective BPI pain reduction and quality of life, alongside potential regulatory impacts on ANS activity in individuals with CLBP. These results support the use of IFT as an effective intervention for pain management in CLBP and ANS regulation, meriting further exploration.
Is There a Pediatric Role for the General Thoracic Surgeon in a General Hospital? A 22-Year Single-Center Experience
Authors: Petrella Francesco , Degiovanni Sara , Raveglia Federico , Cara Andrea , Cassina Enrico Mario , Graziano Francesca , Libretti Lidia , Pirondini Emanuele , Vaquer Sara , Tuoro Antonio , Foti Giuseppe , Moretto Alessandra , Cattoni Alessandro , Biondi Andrea , Balduzzi Adriana
Published: November 2024

Background: Although general thoracic surgery is usually focused on adult patients, there are some settings of pediatric diseases which can benefit from thoracic surgical procedures. In this study, we retrospectively reviewed the contribution of general thoracic surgeons to pediatric patients in a high-volume hospital. Methods: From September 2002 to August 2024, 8897 consecutive patients were operated on; among them, 202 patients (2.2%) were younger than 18. Age, sex, operatory setting, side, indications, procedures, procedure duration, and perioperative mortality were collected for each patient. Results: Among the 202 patients younger than 18, 27 (13.3%) were 0–6 yo; 13 (6.4%) were 6–12 yo; 162 (80.1%) were 12–18 yo. In the first group, metapneumonic pleural effusion was the most frequent indication (44.4%) and chest drain the most frequent procedure (51.8%). No perioperative mortality was reported. In the second group, metapneumonic pleural effusion was the most frequent indication (30.7%) and chest drain the most frequent procedure (46.1%). No perioperative mortality was reported. In the third group, pneumothorax was the most frequent indication (41.3%) and bullectomy the most frequent procedure; (41.3%); one intraoperative death (0.4%) was reported in a case of major trauma. Conclusions: A general thoracic surgeon can effectively contribute to the surgical care of pediatric patients; in younger patients (<12 yo) urgent procedures related to infections are the most commonly performed; on the other hand, in patients aged between 12 and 18, elective procedures are more commonly performed, these being sympathectomy for hyperhidrosis and bullectomy for pneumothorax the most frequent.
Health-Related Quality of Life with Iatrogenic Inferior Alveolar Nerve Injuries Treated with Photobiomodulation: A Comparative Study
Authors: de Abreu João Mendes , Nunes Tiago , Almiro Pedro A. , Figueiredo José , Corte-Real Ana
Published: November 2024

Background/Objectives: Photobiomodulation therapy (PBM) creates a biostimulatory or modulatory effect, promoting tissue regeneration and improving patients’ health-related quality of life (HRQoL). PBM has shown promise as an effective treatment and management strategy for peripheral nerve injuries, including inferior alveolar nerve (IAN) damage. This study aims to assess the impact of PBM on HRQoL in patients with iatrogenic IAN injuries. Methods: A prospective study was implemented to investigate the research question. PBM treatments were administered weekly, with patient discharge contingent upon either complete recovery or stabilization of associated signs and symptoms. HRQoL was assessed using the EQ-5D-5L questionnaire at the initial and final appointments. Results: The study included 71 participants, divided into 3 groups based on etiology: inferior third molar surgery, mandibular orthognathic surgery, and dental implant surgery. The results showed a widespread reduction of symptoms along with a statistically significant improvement (p < 0.001) across four of the five dimensions of the EQ-5D-5L questionnaire, as well as in patients’ perceived health levels in both Groups I and II. Group III patients also demonstrated a notable improvement; however, due to the small sample size, statistical analysis was not conducted for this group. Conclusions: PBM demonstrated a comparable ability to enhance HRQoL and alleviate symptoms in patients with IAN injuries within different etiologies. These findings underscore the effectiveness of the protocol used in this study and highlight potential avenues for expanding research in this field.
Computerised Attention Functions Training Versus Computerised Executive Functions Training for Children with Attention Deficit/Hyperactivity Disorder: A Randomised Controlled Trial
Authors: Trinczer Inbar Lucia , Shalev Lilach
Published: November 2024

Background: Attention deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder characterised by deficits in attention, hyperactivity, and impulsivity. Current treatments, such as stimulant medication and behavioural therapy, ameliorate symptoms but do not address the core cognitive dysfunctions. This study aimed to investigate the effects of two computerised neurocognitive training programs, attention functions training and executive functions training, in children with ADHD. Methods: Eighty children with ADHD (ages 8–13) were randomly assigned to one of three groups: Attention functions training (AFT), targeting sustained, selective-spatial, orienting, and executive attention; executive functions training (EFT), focusing on working memory, cognitive flexibility, and problem solving; or a passive control group. Training sessions were administered in small groups twice a week for nine weeks. Participants underwent comprehensive assessments of attention (Continuous Performance Test, Conjunctive Visual Search Task), executive functions (Corsi Block-Tapping Tasks), nonverbal reasoning (Raven’s Colored Progressive Matrices), parent-rated behavioural symptoms, and arithmetic performance at baseline, post-intervention, and follow-up. Results: The AFT group demonstrated significant improvements in sustained and selective-spatial attention, nonverbal reasoning, inattentive symptoms, and arithmetic performance, and most improvements persisted at follow-up. The EFT group showed gains in nonverbal reasoning and inattentive symptoms, although no improvements were documented in working memory or in parent ratings of executive functions. Conclusions: The AFT program that addressed core attentional functions in children with ADHD produced robust cognitive and behavioural benefits, whereas the EFT program yielded behavioural benefits and a limited improvement in executive functions. Future research should explore different training protocols for broader gains in executive functions. These findings support the potential of theory-driven, structured neurocognitive training targeting basic cognitive functions as an effective small-group intervention for ADHD.
Combining Neural Networks and Genetic Algorithms to Understand Composition–Microstructure–Property Relationships in Additively Manufactured Metals
Authors: Patel Sooraj , Nathani Anvesh , Poozesh Amin , Xu Shuozhi , Kazempoor Pejman , Ghamarian Iman
Published: November 2024

Additive manufacturing (AM) has revolutionized the production of complex metallic components by enabling the direct fabrication of intricate geometries from 3D model data. Despite its advantages in reducing material waste and customization of mechanical properties, AM faces challenges related to microstructural heterogeneity and mechanical property variability. This review highlights the structure–property relationships in additively manufactured metals, emphasizing how heterogeneous microstructure influences yield strength and fracture toughness. Phenomenological equations are provided based on the integration of neural networks and genetic algorithm-based models to predict mechanical properties from composition and microstructural features. We also outline key considerations such as acquiring high-fidelity datasets and understanding mathematical correlations within the data needed to formulate phenomenological equations.
An Offline Reinforcement Learning Approach for Path Following of an Unmanned Surface Vehicle
Authors: Zhou Zexing , Bao Tao , Ding Jun , Chen Yihong , Jiang Zhengyi , Zhang Bo
Published: November 2024

Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests.
The Dynamic Characteristics of the Water Entry of a Lifeboat
Authors: Yi Wenxin , Liu Hui , Zou Jiahe , Lu Conghong , Luo Wenqing , Zhou Bo
Published: November 2024

The International Convention for the Safety of Life at Sea (SOLAS) stipulates that all ships must be equipped with lifesaving devices. The freefall lifeboat has the advantages of simple operation, fast release speed, and good safety performance, so it is widely used. The interaction between the hull and the water body of the freefall lifeboat during the water entry process is a complex fluid–structure interaction process that has great influence on the motion characteristics and structural force of the lifeboat. In order to improve the safety of lifeboats used in the lifesaving process, this paper establishes a 3D, full-scale model of a lifeboat and the fluid area, uses the ALE method to deal with the fluid–structure interaction problem, and numerically simulates the water entry of a lifeboat. Key information such as the hull motion trajectory, motion speed, and impact load are obtained, and three typical modes of lifeboat movement are summarized. At the same time, the influence of different skid angles, skid heights, and skid lengths on the lifeboat launch process is explored. The results show that increasing the angle, height, and length of the skid to a certain extent is conducive to the rapid escape of the lifeboat from a danger zone. The research results of this paper can provide a reference for the design of lifesaving systems for offshore floating facilities such as ships, which is of great significance for ensuring the safety of marine personnel.
Machine Learning-Based Classification of Malnutrition Using Histological Biomarkers of Fish Intestine: Preliminary Data
Authors: Oliveira Joana , Barata Marisa , Soares Florbela , Pousão-Ferreira Pedro , Oliva-Teles Aires , Couto Ana
Published: November 2024

The gut is the first organ to contact food, and it is often the target of nutrition studies performed on aquaculture fish. Histological analysis reveals morphological changes in fish intestines caused by ingredients in formulated feeds. However, this type of analysis is mainly based on a semi-quantitative approach, often restricted to specialized researchers, and may provide inconsistent results between studies. This study addresses these limitations by combining semi-quantitative and quantitative features to characterize the anterior, intermediate, and distal sections of the intestine of meagre (Argyrosomus regius) subjected to different nutritional status. Collected data were used to build machine learning models, select the most accurate ones, and identify key features for predicting malnutrition. Logistic regression, support vector machines, and ensemble stacking performed best across all intestinal sections. Combining semi-quantitative and quantitative features yielded the best predictions, with villi number, density and area, and goblet cell count being the most crucial for the classification task. When considering the distal intestine alone, semi-quantitative features outperformed quantitative ones. The intermediate section of the intestine showed the best model accuracy, indicating higher sensitivity to nutritional changes. These results demonstrate the potential of machine learning models to streamline histomorphological analyses to evaluate nutritional status, making them more accessible and standard across users.
One-Year Functional Evaluation of Patient After CD34+ Stem Cell Treatment for Hip Osteoarthritis
Authors: Stefańska-Szachoń Eleonora , Koszela Kamil , Woldańska-Okońska Marta
Published: November 2024

Background/Objectives: The etiology and causes of osteoarthritis are still being studied at the cellular and molecular level by many scientists around the world. With advances in knowledge, technology, and the need for complexity, new therapeutic approaches—such as restorative medicine—are being developed to protect the patient from endoprosthesis implantation, aiming to simultaneously restore and maintain physical and psychosocial function. The purpose of this study was to evaluate the effectiveness of physiotherapy after the implantation of CD34+ stem cells into the hip joints of patients with osteoarthrosis. Methods: The study included 71 patients, previously diagnosed with HOA (hip osteoarthritis) and undergoing CD34+ stem cell therapy followed by rehabilitation. The twelve-month prospective follow-up included 23 women and 48 men. Participants were asked to provide information, regarding the quality of their daily functioning, according to the Harris Questionnaire at four time points. Results: In all evaluations and groups of patients, i.e., women and men, and rehabilitated and non-rehabilitated, a significantly high improvement was noted at 3 months after the administration of CD34+ stem cells, while afterwards there was a slight decrease in the value of the results obtained at 6 months after the procedure, which improved at further stages of the study. The value of functioning on the Harris scale did not differ significantly between genders, although there was a trend of better functioning in men after one year, while it was higher in rehabilitation subjects. Conclusions: Patients’ daily functioning was better in the rehabilitation group, as reflected by the Harris scale. The absence of adverse symptoms and the small differences in physiotherapy results between the rehabilitated and non-rehabilitated groups testify to the high effectiveness of the stem cell therapy method.
A Comprehensive Review of UAV-UGV Collaboration: Advancements and Challenges
Authors: Munasinghe Isuru , Perera Asanka , Deo Ravinesh C.
Published: November 2024

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration.
Influence of Temperature on the Damping Properties of Selected Viscoelastic Materials
Authors: Witek Lucjan , Łabuński Piotr
Published: November 2024

The paper presents results of experimental investigations of the influence of temperature on the effectiveness of passive vibration isolation. Two types of viscoelastic materials (butyl rubber and bituminous material) were tested. In the performed vibration analysis, the Oberst beam made out of aluminum alloy with a damping material in a Free Layer Damping (FLD) configuration was used. The experimental modal analysis was performed using the Unholtz-Dickie UDCO TA-250 vibration system. To investigate the influence of temperature on the effectiveness of passive vibration isolation, an isothermal cooling chamber (using Peltier cells) was designed and constructed. The tests were carried out in a wide frequency range from 40 Hz to 4000 Hz, at a constant sweep rate, in a temperature range from −2 °C to 22 °C. Miniature piezoelectric acceleration sensors were used to determine the acceleration of the beam and the exciter head. The analysis of accelerations of both the object and the shaker head allowed for the determination of a Frequency Response Function (FRF) for the beam. The course of FRF was used to determine the resonance frequencies and the vibration amplitudes of the beam damped with bituminous material and butyl rubber at various temperatures. The loss factor η, calculated for each resonance using the generalized half-power method (n-dB method), was used as an indicator of damping intensity. The research results presented in this work (important from scientific point of view) also have utilitarian significance and can be used in the design of more quiet and comfortable motor vehicles, railway wagons and aircraft structures.
Dynamic Testing of Materials for Galvanising Pot Roll Bearings with Improved Performance
Authors: Alparone Giovanni Paolo , Sullivan James , Mills Christopher , Edy James , Penney David
Published: November 2024

Galvanising pot roll bearings are subjected to severe deterioration due to the corrosion of the bearing materials in liquid Zn, resulting in maintenance stops that can cost thousands of pounds per hour in downtime. Dynamic wear testing in molten Zn-Al and Zn-Al-Mg was conducted to assess the corrosion and wear resistance of three material pairs using a bespoke testing rig. The materials investigated in this study were Wallex6TM coated with WC-Co, stainless steel 316L coated with Al2O3, and as-received Wallex6TM and Wallex4TM alloys. It was found that only the Al2O3 coating remained unreactive in Zn alloy, whereas the materials containing Co were corroded, as evidenced by the formation of intermetallic compounds containing Al-Co-Zn-Fe. The results also highlighted that the dissolution of the Co matrix and diffusion of Zn and Al from the bath occurred in Wallex6TM and Wallex4TM. However, the diffusion of Zn into the WallexTM alloys was reduced by approximately 60% in the Zn-Al-Mg bath compared to Zn-Al. The wear scars were analysed to determine the wear coefficient of the worn specimens. Out of the three material couplings investigated in this study, minimal wear damage in both Zn-Al and Zn-Al-Mg was only obtained by pairing Wallex6TM with Al2O3 coatings.
Multi-Objective NSGA-II Optimization of Single- and Dual-Fluid ORC–VCC Systems Using Butane and Isobutane
Authors: Witanowski Łukasz
Published: November 2024

The urgent need for environmentally sustainable cooling technologies, driven by global regulatory constraints, has intensified the search for natural refrigerants with low global warming potential. This study evaluates the potential of natural refrigerants, specifically butane and isobutane, in advanced single- and dual-fluid Organic Rankine Cycle–Vapor Compression Cycle (ORC–VCC) systems to enhance energy efficiency and environmental sustainability. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within a multi-objective framework, the optimization maximizes key performance metrics such as coefficient of performance (COP) and cooling power, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method enables a refined ranking of optimal solutions. Findings reveal that the isobutane (ORC)–butane (VCC) dual-fluid configuration achieves the highest overall COP of 0.447 and a cooling capacity of 35.517 kW, surpassing the reference fluid R1233zd, which attains a COP of 0.374 and a cooling capacity of 30.361 kW. Isobutane-based configurations consistently deliver higher COP and cooling capacities than R1233zd, highlighting isobutane’s suitability for applications demanding high energy efficiency. Pressure analysis revealed that R1233zd exhibits the highest pressure ratio of 4.10, necessitating more complex compressor designs. In contrast, isobutane configurations offer favorable pressure ratios and similar pressure parameters in both single and dual setups, simplifying compressor design requirements. This research provides valuable guidance for developing sustainable ORC–VCC systems by combining effective fluid selection and advanced multi-objective optimization techniques to meet both environmental and operational criteria.
Considering the Bottom Edge Cutting Effect of the Carbon Fiber Reinforced Polymer Milling Force Prediction Model and Optimization of Machining Parameters
Authors: Zhang Yiwei , Yan Mengke , Lai Yushu , Wang Guixing , Yang Yifan
Published: November 2024

The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of the bottom edge. In this paper, the prediction of milling force in CFRP milling processes is taken as the research object. By analyzing the milling mechanism and considering the end milling cutter’s bottom cutting edge, the prediction model of milling force was established. Based on the experimental data and simulation data of milling force, the milling force coefficient was obtained by inverse calculation. Subsequently, the predicted cutting force was compared with the experimental cutting force, showing a maximum error of 14.5%, which is within a reasonable range, and the correctness of the model was verified. Furthermore, combined with the delamination damage and the milling force prediction model, a multi-objective optimization model of milling parameters was established, and the genetic algorithm was used to solve the model. The unidirectional carbon fiber plate with a fiber direction angle of 45° was selected as the optimization example. The minimum delamination damage was obtained under the cutting conditions of a spindle speed of 4903.1569 r/min, feed rate per tooth of 0.01 mm/z, and an axial depth of cut of 0.5 mm, and the experimental verification was carried out. The feasibility of the genetic algorithm in CFRP milling parameter optimization modeling was also verified.
Maglev Derived Systems: An Interoperable Freight Vehicle Application Focused on Minimal Modifications to the Rail Infrastructure and Vehicles
Authors: Felez Jesus , Vaquero-Serrano Miguel A. , Liu William Z. , Casanueva Carlos , Schultz-Wildelau Michael , Coquery Gerard , Proietti Pietro
Published: November 2024

Magnetic levitation (maglev) offers unique opportunities for guided transport; however, only a few existing maglev systems have demonstrated their potential benefits. This paper explores the potential of maglev-derived systems (MDS) in conventional rail, focusing on the use of linear motors to enhance freight operations. Such traction boosters provide additional propulsion capabilities by reducing the train consist’s dependence on wheel–rail adhesion and improving performance without needing an additional locomotive. The study analyses the Gothenburg–Borås railway in Sweden, a single-track, mixed-traffic line with limited capacity and slow speeds, where installing linear motors on uphill sections would allow freight trains to match the performance of passenger trains, even under challenging adhesion conditions. Target speed profiles were precomputed using dynamic programming, while a model predictive control algorithm determined the optimal train state and control trajectories. The results show that freight trains can achieve desired speeds but at the cost of increased energy consumption. A system-level cost–benefit analysis reveals a positive impact with a positive benefit-to-cost ratio. Although energy consumption increases, the time savings and reduced CO2 emissions from shifting goods from road to rail demonstrate substantial economic and environmental benefits, improving the efficiency and sustainability of rail freight traffic.
Discrete Dynamic Berth Allocation Optimization in Container Terminal Based on Deep Q-Network
Authors: Wang Peng , Li Jie , Cao Xiaohua
Published: November 2024

Effective berth allocation in container terminals is crucial for optimizing port operations, given the limited space and the increasing volume of container traffic. This study addresses the discrete dynamic berth allocation problem (DDBAP) under uncertain ship arrival times and varying load capacities. A novel deep Q-network (DQN)-based model is proposed, leveraging a custom state space, rule-based actions, and an optimized reward function to dynamically allocate berths and schedule vessel arrivals. Comparative experiments were conducted with traditional algorithms, including ant colony optimization (ACO), parallel ant colony optimization (PACO), and ant colony optimization combined with genetic algorithm (ACOGA). The results show that DQN outperforms these methods significantly, achieving superior efficiency and effectiveness, particularly under high variability in ship arrivals and load conditions. Specifically, the DQN model reduced the total waiting time of vessels by 58.3% compared to ACO (262.85 h), by 57.9% compared to PACO (259.5 h), and by 57.4% compared to ACOGA (257.4 h), with a total waiting time of 109.45 h. Despite its impressive performance, DQN requires substantial computational power during the training phase and is sensitive to data quality. These findings underscore the potential of reinforcement learning to optimize berth allocation under dynamic conditions. Future work will explore multi-agent reinforcement learning (MARL) and real-time adaptive mechanisms to further enhance the robustness and scalability of the model.
Reducing Waiting Times to Improve Patient Satisfaction: A Hybrid Strategy for Decision Support Management
Authors: Morales Jenny , Silva-Aravena Fabián , Saez Paula
Published: November 2024

Patient satisfaction and operational efficiency are critical in healthcare. Long waiting times negatively affect patient experience and hospital performance. Addressing these issues requires accurate system time predictions and actionable strategies. This paper presents a hybrid framework combining predictive modeling and optimization to reduce system times and enhance satisfaction, focusing on registration, vitals, and doctor consultation. We evaluated three predictive models: multiple linear regression (MLR), log-transformed regression (LTMLR), and artificial neural networks (ANN). The MLR model had the best performance, with an R2 of 0.93, an MAE of 7.29 min, and an RMSE of 9.57 min. MLR was chosen for optimization due to its accuracy and efficiency, making it ideal for implementation. The hybrid framework combines the MLR model with a simulation-based optimization system to reduce waiting and processing times, considering resource constraints like staff and patient load. Simulating various scenarios, the framework identifies key bottlenecks and allocates resources effectively. Reducing registration and doctor consultation wait times were identified as primary areas for improvement. Efficiency factors were applied to optimize waiting and processing times. These factors include increasing staff during peak hours, improving workflows, and automating tasks. As a result, registration wait time decreased by 15%, vitals by 20%, and doctor consultation by 25%. Processing times improved by 10–15%, leading to an average reduction of 22.5 min in total system time. This paper introduces a hybrid decision support system that integrates predictive analytics with operational improvements. By combining the MLR model with simulation, healthcare managers can predict patient times and test strategies in a risk-free, simulated environment. This approach allows real-time decision-making and scenario exploration without disrupting operations. This methodology highlights how reducing waiting times has a direct impact on patient satisfaction and hospital operational efficiency, offering an applicable solution that does not require significant structural changes. The results are practical and implementable in resource-constrained healthcare environments, allowing for optimized staff management and patient flow.
Lottery Rank-Pruning Adaptation Parameter Efficient Fine-Tuning
Authors: Kim Juhyeong , Kim Gyunyeop , Kang Sangwoo
Published: November 2024

Recent studies on parameter-efficient fine-tuning (PEFT) have introduced effective and efficient methods for fine-tuning large language models (LLMs) on downstream tasks using fewer parameters than required by full fine-tuning. Low-rank decomposition adaptation (LoRA) significantly reduces the parameter count to 0.03% of that in full fine-tuning, maintaining satisfactory performance when training only two low-rank parameters. However, limitations remain due to the lack of task-specific parameters involved in training. To mitigate these issues, we propose the Lottery Rank-Pruning Adaptation (LoRPA) method, which utilizes the Lottery Ticket Hypothesis to prune less significant parameters based on their magnitudes following initial training. Initially, LoRPA trains with a relatively large rank size and then applies pruning to enhance performance in subsequent training with fewer parameters. We conducted experiments to compare LoRPA with LoRA baselines, including a setting with a relatively large rank size. Experimental results on the GLUE dataset with RoBERTa demonstrate that LoRPA achieves comparable results on the base scale while outperforming LoRA with various rank sizes by 0.04% to 0.74% on a large scale across multiple tasks. Additionally, on generative summarization tasks using BART-base on the CNN/DailyMail and XSum datasets, LoRPA outperformed LoRA at the standard rank size and other PEFT methods in most of the metrics. These results validate the efficacy of lottery pruning for LoRA in downstream natural-language understanding and generation tasks.
Discovery of Exact Equations for Integer Sequences
Authors: Gec Boštjan , Džeroski Sašo , Todorovski Ljupčo
Published: November 2024

Equation discovery, also known as symbolic regression, is the field of machine learning that studies algorithms for discovering quantitative laws, expressed as closed-form equations or formulas, in collections of observed data. The latter is expected to come from measurements of physical systems and, therefore, noisy, moving the focus of equation discovery algorithms towards discovering approximate equations. These loosely match the noisy observed data, rendering them inappropriate for applications in mathematics. In this article, we introduce Diofantos, an algorithm for discovering equations in the ring of integers that exactly match the training data. Diofantos is based on a reformulation of the equation discovery task into the task of solving linear Diophantine equations. We empirically evaluate the performance of Diofantos on reconstructing known equations for more than 27,000 sequences from the online encyclopedia of integer sequences, OEIS. Diofantos successfully reconstructs more than 90% of these equations and clearly outperforms SINDy, a state-of-the-art method for discovering approximate equations, that achieves a reconstruction rate of less than 70%.
Research on Topological Characteristics of Spatial Network Based on Complex Network Theory and Its Applications
Authors: Su Siqin , Liu Jintao , Wang Yongqi , Dong Keqiang
Published: November 2024

The spatial economic network serves as a pivotal methodology in spatial economics research. This paper endeavors to integrate complex network theory with the gravity model to establish a directed and unweighted spatial network model. Building upon this foundation, we introduce two distinct types of indicator systems designed to systematically analyze both the overall structure of the network and the importance of individual nodes within it. Furthermore, we employ the Theil index and Moran index to investigate the dynamic evolution of the network structure across both temporal and spatial dimensions. To explore the spillover effects inherent in this network, this article introduces relevant basic elements, uses spatial econometric models, and combines indicators that can reflect the characteristics of the network structure to comprehensively evaluate the multiple factors that affect changes in network structure. The above research methods are applied to the spatial economic network composed of 31 provinces and cities in Chinese Mainland, and the results show that the economic network presents a core periphery structure and that the spatial distribution of economic links is uneven. We further apply the research method to the civil aviation transportation field in the Chinese Mainland, as well as analyze the overall structure and dynamic changes of the civil aviation transportation network.
Improved Quantization Method of Coupled Circuits in Charge Discrete Space
Authors: Ma Jin-Ying , Zhao Weiran , Wang Weilin , Yan Zhan-Yuan
Published: November 2024

A quantum theory of mesoscopic circuits, based on the discreteness of electric charges, was recently proposed. However, it is not applied widely, mainly because of the difficulty of the mathematical solution to the finite-difference Schrödinger equation. In this paper, we propose an improved perturbation method to calculate Schrödinger equations of the inductance and capacity coupling mesoscopic circuit. With a unitary transformation, the finite differential Schrödinger equation of the system is divided into two equations in generalized momentum representation. The concrete value of the parameter in circuits is important to solving the equation. Both the perturbation method suitable case and the improved perturbation method suitable case, the wave functions, and energy level of the system are achieved. As an application, the current fluctuations are calculated and discussed. The improved mathematical method would be helpful for the popularization of the quantum circuits theory.
A Lucknolide Derivative Induces Mitochondrial ROS-Mediated G2/M Arrest and Apoptotic Cell Death in B16F10 Mouse Melanoma Cells
Authors: Lee Jae Hyeop , Choi Byeoung-Kyu , Kim Minsoo , Shin Hee Jae , Park Sun Joo
Published: November 2024

Melanoma is an aggressive skin cancer with a high risk of cancer-related deaths, and inducing apoptosis in melanoma cells is a promising therapeutic strategy. This study investigates the anti-tumor potential of a novel lucknolide derivative LA-UC as a therapeutic candidate for melanoma. Lucknolide A (LA), a tricyclic ketal-lactone metabolite isolated from marine-derived Streptomyces sp., was chemically modified by introducing a 10-undecenoyl group to synthesize LA-UC. LA-UC preferentially inhibited the proliferation of melanoma cells, including B16F10, while exerting minimal effects on normal melanocytes or other tumor cell types, indicating the selective action of LA-UC against melanoma cells. LA-UC decreased G2/M checkpoint proteins, including cyclin B1 and Cdc2, while activating caspase-3 and caspase-9, resulting in G2/M cell cycle arrest and inducing apoptotic cell death in B16F10 cells. The addition of a pan-caspase inhibitor confirmed the caspase-dependent mechanism of LA-UC-induced cell death. Additionally, LA-UC elevated mitochondrial ROS levels, leading to mitochondrial membrane disruption, upregulation of pro-apoptotic proteins, and DNA damage in melanoma cells. The ROS scavenger N-acetylcysteine reduced LA-UC-induced mitochondrial ROS accumulation, mitochondrial membrane disruption, DNA damage, and apoptosis. Collectively, these findings suggest that LA-UC induces G2/M cell cycle arrest and caspase-dependent apoptosis in B16F10 cells through excessive mitochondrial ROS generation, membrane impairment, and DNA damage, highlighting its potential as a promising therapeutic candidate for melanoma treatment.