bluuebunny/crossref_metadata_2025 huggingface dataset:
Standardized Extraction of Air Traffic Control Hazard Features Based on Expert Knowledge
Authors: Tan Xianghua , Cai Zhipeng , Quan Zhibin , Zeng Weili
Published: January 2025

Air traffic control (ATC) hazard feature extraction is a key information retrieval task for air traffic hazard records. While text-based feature extraction ranks term importance based solely on statistical results, we aim to use external knowledge to extract features that meet the definition of hazards. This paper proposes a feature extraction method based on expert knowledge to define hazard features and construct a hazard analysis framework. We illustrate the model training process using communication navigation and surveillance (CNS) data, which includes candidate feature generation, feature vectorization, and cluster-based standardization. The correct structure of terms in hazard records, the vector distribution of candidate features, and the clustering effect of different methods are briefly explored. The algorithm refines and accumulates expert knowledge through iteration. The experiment results demonstrate that the dataset obtained after specific linguistic processing based on expert knowledge could extract more informative candidate features to construct analysis context by k-means. The proposed model outperformed four comparative algorithms in accuracy, reaching 82% and 86% in the air traffic control operation (ATCO) dataset and the CNS dataset, respectively. Additionally, the information-rich hazard features support safety management departments’ decision-making, reducing the cost of investigating hidden hazards.
Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning
Authors: He Luyan , Duanmu Lingjian , Guo Li , Qin Yang , Shi Bowen , Liang Lin , Chen Weiwei
Published: January 2025

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM2.5 concentration. The RSM relevance prediction model (MET-PM2.5) established a correlation equation between meteorological factors and PM2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM2.5 value remained below 75 μg/m3 when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities.
Brucella ceti in Common Dolphins (Delphinus delphis) in Portugal—Characterization of First Isolates
Authors: Cavaco Sandra , Grilo Miguel L. , Dias Ricardo , Nunes Mónica , Pascoal Pedro , Pereira Marcelo , Fogaça Catarina , Costa Ana Beatriz , Pardal Sofia , Ferreira Ana Cristina
Published: January 2025

This study investigates Brucella ceti infection in marine mammals stranded along the Lisbon and Tagus Valley coast between 2022 and mid-2024, marking the first report of Brucella presence in Portuguese waters. Out of 59 examined marine mammals, B. ceti was isolated in three common dolphins (5.1%), a prevalence rate consistent with previous studies from other coastlines. PCR-based detection indicated a higher infection rate (23.7%), suggesting an underestimation of the prevalence of B. ceti infection in this population. Multi-locus Sequence Typing (MLST) and Multiple-Locus Variable-Number Tandem-Repeat Analysis (MLVA) revealed distinct genetic profiles and close relationships to B. ceti strains from the Atlantic, supporting the hypothesis of specific host-adapted lineages in dolphins. Virulence genes, including those for host interaction (bspE, btpB) and intracellular survival (virB7, vceA), were consistent across isolates, highlighting the pathogenic potential. Additionally, antimicrobial resistance (AMR) genes, such as mprF and efflux proteins (bepC-G), were also identified. These findings underscore the need for further research and surveillance to understand B. ceti transmission, host range, and impacts on Atlantic cetaceans, as well as to develop effective diagnostic and management strategies to mitigate infection risks in marine environments.
Influence of High Eimeria tenella Immunization Dosages on Total Oocyst Output and Specific Antibodies Recognition Response in Hybrid Pullets (Gallus gallus)—A Pilot Study
Authors: Juarez-Estrada Marco A. , Tellez-Isaias Guillermo , Petrone-Garcia Víctor M. , Gayosso-Vazquez Amanda , Hernandez-Velasco Xochitl , Alonso-Morales Rogelio A.
Published: January 2025

Background: Two high primary-immunization doses of a wild-type E. tenella strain were assessed in healthy pullets (5K versus 10K sporulated oocysts/bird) to understand the effects of coccidia infection. Methods: Acquired immunity was evaluated following primary immunization and two booster doses with the homologous strain. Total oocyst shedding, clinical signs, and viability of every bird/group after each immunization/booster were recorded. Indirect ELISA measured the time course of humoral responses from each immunization group against sporozoite and second-generation merozoite of E. tenella. Antigen pattern recognition on these two asexual zoite stages of E. tenella was analyzed using Western blotting with antibodies from each immunization program. Afterwards, antigen recognition of specific life-cycle stages was performed using individual pullet serums from the best immunization program. Results: A primary-immunization dose of 1 × 104 oocysts/bird reduced the oocyst output; however, all pullets exhibited severe clinical signs and low specific antibodies titers, with decreased polypeptide recognition on both E. tenella asexual zoite stages. In contrast, immunization with 5 × 103 oocysts/bird yielded the best outcomes regarding increased oocyst collection and early development of sterilizing immunity. After the first booster dosage, this group’s antisera revealed a strong pattern of specific antigen recognition on the two assayed E. tenella life-cycle stages. Conclusions: The E. tenella-specific antibodies from the 5 × 103 oocysts/bird immunization program can aid in passive immunization trials and further research to identify B-cell immunoprotective antigens, which could help in the development of a genetically modified anticoccidial vaccine.
Resveratrol Protects Photoreceptors in Mouse Models of Retinal Degeneration
Authors: Li Shujuan , Ma Hongwei , Ding Xi-Qin
Published: January 2025

Photoreceptor/retinal degeneration is the major cause of blindness. Induced and inherited mouse models of retinal degeneration are valuable tools for investigating disease mechanisms and developing therapeutic interventions. This study investigated the potential of the antioxidant resveratrol to relieve photoreceptor degeneration using mouse models. Clinical studies have shown a potential association between thyroid hormone (TH) signaling and age-related retinal degeneration. Excessive TH signaling induces oxidative stress/damage and photoreceptor death in mice. C57BL/6 (rod-dominant) and Nrl−/− (cone-dominant) mice at postnatal day 30 (P30) received triiodothyronine (T3) via drinking water (20 µg/mL) with or without concomitant treatment with resveratrol via drinking water (120 µg/mL) for 30 days, followed by evaluation of photoreceptor degeneration, oxidative damage, and retinal stress responses. In experiments using Leber congenital amaurosis model mice, mother Rpe65−/− and Rpe65−/−/Nrl−/− mice received resveratrol via drinking water (120 µg/mL) for 20 days and 10–13 days, respectively, beginning on the day when the pups were at P5, and pups were then evaluated for cone degeneration. Treatment with resveratrol significantly diminished the photoreceptor degeneration induced by T3 and preserved photoreceptors in Rpe65-deficient mice, manifested as preserved retinal morphology/outer nuclear layer thickness, increased cone density, reduced photoreceptor oxidative stress/damage and apoptosis, reduced upregulation of genes involved in cell death/inflammatory responses, and reduced macroglial cell activation. These findings demonstrate the role of oxidative stress in photoreceptor degeneration, associated with TH signaling and Rpe65 deficiency, and support the therapeutic potential of resveratrol/antioxidants in the management of retinal degeneration.
Review of Robotic Prostheses Manufactured with 3D Printing: Advances, Challenges, and Future Perspectives
Authors: Salazar Maxwell , Portero Paola , Zambrano Marcelo , Rosero Ricardo
Published: January 2025

Three-dimensional printing has significantly transformed the design and manufacture of robotic prostheses, making these devices more accessible, customized, and functional. This paper examines the historical evolution of prosthetic technology, tracing its development from rudimentary mechanical devices to the integration of advanced technologies, such as 3D printing. This innovation has enabled the production of prostheses at lower costs while enhancing their adaptability and performance. The review highlights how 3D printing has driven a disruptive shift in prosthetic customization, and how emerging technologies—including smart materials and artificial intelligence—have expanded the capabilities of prosthetic devices, offering more adaptive and natural movements. However, challenges persist, particularly regarding the need for standardization and infrastructural expansion to ensure equitable access to these technologies. Future research into novel materials and manufacturing techniques holds the potential to further improve the functionality, affordability, and accessibility of prosthetic devices. In conclusion, while 3D printing has marked a significant milestone in the evolution of robotic prosthetics, overcoming existing challenges is essential to realize its global impact and benefits fully.
Furniture Wood Waste Management Towards the Circular Economy
Authors: Pinho Giusilene Costa de Souza , Calmon João Luiz , Medeiros Diego Lima , Pinho Clarisse
Published: January 2025

Wood waste (WW) from the furniture industry is a material that deserves special attention, given that it is still relatively unexplored in manufacturing industries. Therefore, this study aims to elaborate guidelines for decision-making in the furniture industry’s wood waste, focusing on the north–central region of the state of Espírito Santo, Brazil, as a spatial outline of the research. The SWOT matrix is the main methodology for this stage of analysis, based on the previous study of Life Cycle Assessments (LCAs), the calculation of the Material Circularity Index (MCI), and the field research conducted in the industries. The obtained results show promising recycling scenarios over energy recovery, and the studied guidelines indicate this path. The three basic guidelines include discouraging the disposal of wood waste potentially usable as raw material into landfills; encouraging the use of wood waste based on the concept of industrial symbiosis; and promoting the creation of a market for the use of WW as a raw material in new products. Finally, with the proposed guidelines, it will be possible to contribute to the decision-making process on the management of wood waste from the furniture industry, reduce the amount of wood waste discarded in landfills, and increase the recycling rate. All of this being covered by a market created and maintained for the adequate management of wood waste. Furthermore, despite focusing on a Brazilian region for analysis, this paper intends to contribute to future research in the field, to be conducted in other locations.
Spatio-Temporal Evolution and Influencing Factors of Supply–Demand Coupling and Coordination in Civil Aviation Passenger Transport
Authors: Yang Yanling , Hu Minghua , Yin Jianan , Wu Wei
Published: January 2025

The interaction between supply and demand of civil aviation passenger transport serves as an important reference for airport planning, transportation structure optimization and dynamic matching of supply and demand. Based on the panel data of 31 province-level administrative divisions (excluding Hong Kong, Macao, and Taiwan) in China spanning from 2004 to 2019, this study employs the entropy-weighted TOPSIS method to evaluate the supply level and demand level of civil aviation passenger transport. On this foundation, this study uses the modified coupling coordination degree model to measure the coupling coordination degree of civil aviation passenger transport supply and demand, further analyzes the spatio-temporal evolution characteristics of coupling coordination degree and employs the double fixed-effect model to investigate the influencing factors. The results indicate that China’s civil aviation passenger supply, demand and supply and demand coupling coordination degree are increasing year by year. The level of provincial supply and demand coupling coordination has risen to the upper-middle level, and has shown significant spatial differences. Economically developed regions demonstrate a higher level of coordination. Economic development, urbanization rate and the degree of opening to the outside world have a positive impact on the coupling and coordinated development.
In Silico Identification of Banana High-Confidence MicroRNA Binding Sites Targeting Banana Streak GF Virus
Authors: Ashraf Muhammad Aleem , Ali Babar , Fareed Maryam , Sardar Ahsan , Saeed Eisha , Islam Samaa , Bano Shaher , Yu Naitong
Published: January 2025

Banana streak GF virus (BSGFV) is the extremely dangerous monopartite badnavirus (genus, Badnavirus; family, Caulimoviridae) of banana (Musa acuminata AAA Group) that imposes a serious threat to global banana production. The BSGFV causes a devastating pandemic in banana crops, transmitted by deadly insect pest mealybug vectors and replicated through an RNA intermediate. The BSGFV is a reverse-transcribing DNA virus that has a monopartite open circular double-stranded DNA (dsDNA) genome with a length of 7325 bp. RNA interference (RNAi) is a natural mechanism that has revolutionized the target gene regulation of various organisms to combat virus infection. The current study aims to locate the potential target binding sites of banana-encoded microRNAs (mac-miRNAs) on the BSGFV-dsDNA-encoded mRNAs based on three algorithms, RNA22, RNAhybrid and TAPIR. Mature banana (2n = 3x = 33) miRNAs (n = 32) were selected and hybridized to the BSGFV genome (MN296502). Among the 32 targeted mature locus-derived mac-miRNAs investigated, two banana mac-miRNA homologs (mac-miR162a and mac-miR172b) were identified as promising naturally occurring biomolecules to have binding affinity at nucleotide positions 5502 and 9 of the BSGFV genome. The in silico banana-genome-encoded mac-miRNA/mbg-miRNA-regulatory network was developed with the BSGFV—ORFs using Circos software (version 0.69-9) to identify potential therapeutic target proteins. Therefore, the current work provides useful biological material and opens a new range of opportunities for generating BSGFV-resistant banana plants through the genetic manipulation of the selected miRNAs.
A Systematic Review of Indoor Environmental Quality in Passenger Transport Vehicles of Tropical and Subtropical Regions
Authors: Ogundiran John Omomoluwa , Nyembwe Jean-Paul Kapuya Bulaba , Ogundiran James , Ribeiro Anabela Salgueiro Narciso , Gameiro da Silva Manuel
Published: January 2025

This systematic literature review (SLR) focuses on indoor environmental quality (IEQ) in passenger transport vehicles within tropical and subtropical regions. It specifically examines indoor air quality (IAQ), thermal comfort (TC), acoustic comfort (AC), and visual comfort (VC) of passenger vehicle cabins (PVCs) in auto rickshaws, sedans, trucks, bus rapid transits (BRTs), buses, trains, trams, metro systems, aircraft and ferries of tropical and subtropical regions. The SLR used the PRISMA approach to identify and review scientific studies between 2000 and 2024 on the IEQ of PVCs in the tropics. Studies reviewed were found in SCOPUS, Web of Science, Science Direct, and EBSCO databases including relevant citation references. Findings reveal a significant geographical imbalance in research, with most studies concentrated in tropical Asia (78.2%), while sub-Saharan Africa (8.2%), South America (11.8%), and Oceania (1.8%) are considerably underrepresented. In 113 studies, most addressed IAQ and TC but limited attention to AC and VC. Moreover, fewer studies have jointly addressed all the IEQ parameters, highlighting the need for a more comprehensive approach to IEQ for tropical PVCs. Several studies alluded to in-cabin commuter risk linked to PM2.5, PM10, carbon monoxide (CO), and volatile organic compounds (VOCs). These risks are exacerbated by traffic hotspots, poor ventilation, ambient pollution, overcrowding, and poor vehicle conditions. Additionally, thermal discomfort is compounded by extreme heat loads, inefficient HVAC systems, and high vehicle occupancy. Common gaps include a paucity of IEQ studies and inadequate IEQ regulations or adapted standards in developing tropics. Infrastructural and regulatory deficiencies have been identified, along with strategies for mitigation. Recommendations are for more holistic IEQ studies in the tropics, including exposure studies for emerging gaps in new indoor pollutants, integration of AI and IoT for sustainable ventilation strategies, and development of effective regulatory frameworks considering region-specific conditions. Finally, Policymakers are encouraged to establish localized IEQ standards, enforce regulations, and prioritize upgrades to transport infrastructure. The SLR findings emphasize the urgent need for targeted interventions in developing tropical regions to address disparities in IEQ, ensuring healthier and more sustainable transport environments that could be replicated across transport systems worldwide.
Noncanonical Third-Order Advanced Differential Equations of Unstable Type: Oscillation and Property B via Canonical Transform
Authors: Rani Bose , Chatzarakis George E. , Thandapani Ethiraju
Published: January 2025

In this paper, without assuming any extra conditions, the third-order unstable noncaonical advanced differential equation is changed into a canonical form that reduces the number of classes of nonoscillatory solutions into two instead of four. Using comparison and integral averaging method, new sufficient conditions are obtained so that all solutions to have Property B or oscillate. Specific numerical examples are provided to demonstrate the importance and the significance of the main results.
Existence and Asymptotic Estimates of the Maximal and Minimal Solutions for a Coupled Tempered Fractional Differential System with Different Orders
Authors: Chen Peng , Zhang Xinguang , Li Lishuang , Jiang Yongsheng , Wu Yonghong
Published: January 2025

In this paper, we focus on the existence and asymptotic estimates of the maximal and minimal solutions for a coupled tempered fractional differential system with different orders. By introducing an order reduction technique and some new growth conditions, we establish some new results on the existence of positive extremal solutions for the tempered fractional differential system, meanwhile, we also obtain the asymptotic estimate of the positive extreme solution by an iterative technique, which possesses a sharp asymptotic estimate. In particular, the iterative sequences converging to maximal and minimal solutions starting from two known initial values are easy to compute. Moreover, the weight function ℏi is allowed to have an infinite number of singular points in [0,1].
Rogue Waves in the Nonlinear Schrödinger, Kadomtsev–Petviashvili, Lakshmanan–Porsezian–Daniel and Hirota Equations
Authors: Gaillard Pierre
Published: January 2025

We give some of our results over the past few years about rogue waves concerning some partial differential equations, such as the focusing nonlinear Schrödinger equation (NLS), the Kadomtsev–Petviashvili equation (KPI), the Lakshmanan–Porsezian–Daniel equation (LPD) and the Hirota equation (H). For the NLS and KP equations, we give different types of representations of the solutions, in terms of Fredholm determinants, Wronskians and degenerate determinants of order 2N. These solutions are called solutions of order N. In the case of the NLS equation, the solutions, explicitly constructed, appear as deformations of the Peregrine breathers PN as the last one can be obtained when all parameters are equal to zero. At order N, these solutions are the product of a ratio of two polynomials of degree N(N+1) in x and t by an exponential depending on time t and depending on 2N−2 real parameters: they are called quasi-rational solutions. For the KPI equation, we explicitly obtain solutions at order N depending on 2N−2 real parameters. We present different examples of rogue waves for the LPD and Hirota equations.
Some New Geometric State-Space Properties of the Classical Linear Time-Optimal Control Problem with One Input and Real Non-Positive Eigenvalues of the System Following from Pontryagin’s Maximum Principle
Authors: Penev Borislav G.
Published: January 2025

This purely theoretical study considers two new geometric state-space properties of the classical linear time-optimal control problem with one input and real non-positive eigenvalues of the system, with constraints only on the control input and without constraints on the state-space variables, following from Pontryagin’s maximum principle. These properties complement the well-known facts from the maximum principle about the number of switchings of the control function and the character of the optimal phase trajectories of the system leading it to the state-space origin. They lay the foundation of a new method for synthesizing the time-optimal control without the need to describe the switching hyper-surfaces. The new technique is demonstrated on two examples. The so-called “axes initialization” and the synthesis technique are illustrated on the double integrator system in its entirety. The second one is on a hypothetical seventh-order system.
Asymptotic Stability of the Magnetohydrodynamic Flows with Temperature-Dependent Transport Coefficients
Authors: Zhang Mingyu
Published: January 2025

The objective of this paper is to analyze the asymptotic stability of global strong solutions to the boundary value problem of the compressible magnetohydrodynamic (MHD) equations for the ideal polytropic gas in which the viscosity λ and heat conductivity κ depend on temperature, i.e., λ=θα and κ=θβ with α,β∈[0,+∞). Both the global-in-time existence and uniqueness of strong solutions are obtained under certain assumptions on the parameter α and initial data. Moreover, based on accurate uniform-in-time estimates, we show that the global large solutions decay exponentially in time to the equilibrium states. Compared with the existing results, the initial data could be large if α is small and the growth exponent β can be arbitrarily large.
Li-Ion Mobility and Solvation Structures in Concentrated Poly(ethylene carbonate) Electrolytes: A Molecular Dynamics Simulation Study
Authors: Tan Wei , Kimura Kento , Tominaga Yoichi
Published: January 2025

With the rapid global increase in the use of digital devices and electric vehicles, solid polymer electrolytes (SPEs) have emerged as promising candidates for all-solid-state batteries. They are expected to resolve safety concerns and overcome the limitations of energy density and charging speed associated with traditional Li-ion batteries with liquid electrolytes. However, a limited understanding of ionic conduction mechanisms remains a significant barrier to their further development and practical application. In this study, we employed molecular dynamics simulations using the COMPASS II force field under NPT/NVT ensembles at 298 K to investigate the static and dynamic properties of poly(ethylene carbonate) (PEC) electrolytes at various salt concentrations. Key analyses included the radial distribution function, solvation free energy, and mean-square displacement (MSD) of individual Li cations. Based on their MSD data, Li cations were categorized into “faster” or “slower” groups, corresponding to conductivity levels above or below the average in each model. Our findings reveal that, at higher concentrations, a smaller fraction of faster Li cations contributes disproportionately more than slower Li cations to the overall mobility, highlighting that targeted manipulation of solvation structures could enhance ion transport efficiency in highly concentrated SPEs. Additionally, changes in coordination number and solvation free energy for both faster and slower Li cations suggest the existence of three different solvation patterns as salt concentration increases. These insights provide a deeper understanding of ionic transport and solvation structures in PEC electrolytes, with potential implications for the design of more efficient all-solid-state batteries.
Advancements in Ocular Neuro-Prosthetics: Bridging Neuroscience and Information and Communication Technology for Vision Restoration
Authors: Giansanti Daniele
Published: January 2025

Background: Neuroprosthetics for vision restoration have advanced significantly, incorporating technologies like retinal implants, cortical implants, and non-invasive stimulation methods. These advancements hold the potential to tackle major challenges in visual prosthetics, such as enhancing functionality, improving biocompatibility, and enabling real-time object recognition. Aim: The aim of this review overview is to provide a comprehensive analysis of the latest advancements in ocular neuroprostheses. Methods: A narrative review was conducted, focusing on the latest developments in visual neuroprosthetics. Comprehensive searches were carried out on Google Scholar, PubMed, and Scopus using specific keywords. A specific narrative checklist was applied, alongside a tailored quality assessment methodology, to evaluate the quality of the studies included. A total of sixteen relevant studies from the past three years were included in the review. Results and discussion: The integration of artificial retinas, cortical implants, high technology-enabled prosthetics, gene therapies, nanotechnology, and bioprinting has shown significant promise in enhancing the quality and functionality of vision restoration systems, offering potential to address complex visual impairments and improve independence and mobility for individuals with blindness. These innovations appear to have the potential to transform healthcare systems in the future by enabling more efficient and personalized therapies and prosthetic devices. However, challenges such as energy efficiency, scalability, and the neural dynamics of vision restoration persist, requiring continued interdisciplinary collaboration to refine these technologies, overcome ethical and regulatory hurdles, and ensure their effectiveness in real-world applications. Conclusions: While visual neuroprosthetics have made remarkable progress, addressing challenges related to energy consumption and regulatory and ethical concerns will be crucial for ensuring that neuroprosthetic devices can effectively meet the needs of individuals with visual impairments.
A Pilot Field Evaluation of Dietary Ginger Zingiber officinale Effects on Immunity, Blood Metabolic Profile, and Disease Resistance in Labeo rohita Under Semi-Intensive Farming
Authors: Rawat Priya , Kaur Vaneet Inder , Tyagi Anuj , Norouzitallab Parisa , Baruah Kartik
Published: January 2025

A feeding trial was conducted for 120 days in a pilot field condition to evaluate ginger powder (GP; Zingiber officinale) as a feed supplement for Labeo rohita to improve immunity, blood metabolic profile, and resistance to Aeromonas hydrophila bacterial infection. The study was conducted following a complete randomized design in outdoor cemented tanks (20 m2) with a stocking density of 30 fish (average weight: 20.5 g) per tank. L. rohita fingerlings were divided into five groups and fed a diet with no GP supplementation (control diet; C) or a diet supplemented with 5 g (GP5), 10 g (GP10), 15 g (GP15), or 20 g (GP20) of GP per kg of the control diet. The results showed that fish well-being, determined by measuring the condition factor, remained higher than 1.0 for all the experimental groups. The condition factors of all the groups were not significantly different between the groups, suggesting GP had no negative impact on overall fish health. Immune responses significantly improved in the groups fed with diets supplemented with GP at doses of 10 to 15 g per kg of their diet, as seen in respiratory burst activity, total immunoglobulins, and lysozyme activity at 60 and 120 days. Blood metabolic variables, particularly the albumin level, tended to show some level of improvement in the GP15-fed group. However, the effect was not significant. Total protein and globulin levels and the albumin/globulin ratio were not significantly affected by dietary GP. Importantly, fish fed the GP15 diet showed the highest resistance to the A. hydrophila challenge. In conclusion, ginger powder holds promise as a health-promoting nutraceutical for farmed L. rohita, contributing to their sustainable farming.
A Narrative Review: Relationship Between Glycemic Variability and Emerging Complications of Diabetes Mellitus
Authors: Wang Xinxin , Cao Yanli
Published: January 2025

A growing body of evidence emphasizes the role of glycemic variability (GV) in the development of conventional diabetes-related complications. Furthermore, advancements in diabetes management and increased life expectancy have led to the emergence of new complications, such as cancer, liver disease, fractures, infections, and cognitive dysfunction. GV is considered to exacerbate oxidative stress and inflammation, acting as a major mechanism underlying these complications. However, few reviews have synthesized the association between GV and these emerging complications or examined their underlying mechanisms. Hence, this narrative review provides a comprehensive discussion of the burden, risks, and mechanisms of GV in these complications, offering further evidence supporting GV as a potential therapeutic target for diabetes management.
Linear and Non-Linear Optimal Control Methods to Determine the Best Chemotherapy Schedule for Most Effectively Inhibiting Tumor Growth
Authors: Liliopoulos Sotirios G. , Stavrakakis George S. , Dimas Konstantinos S.
Published: January 2025

Background/Objectives: Cancer is a dynamic and complex disease that remains largely untreated despite major advances in oncology and treatment. In this context, we aimed here to investigate optimal control techniques in the management of tumor growth inhibition, with a particular focus on cancer chemotherapy treatment strategies. Methods: Using both linear autoregressive with exogenous inputs (ARX) and advanced non-linear tumor growth inhibition (TGI) modeling approaches, we investigated various single-agent treatment protocols, including continuous, periodic, and intermittent chemotherapy schedules. By integrating advanced mathematical modeling with optimal control theory and methods, namely the Linear Quadratic Regulator (LQR) and the “pseudo-linear” state-space equivalent representation and suboptimal control of a non-linear dynamic system known as the State-Dependent Riccati Equation (SDRE) approach, this work explores and evaluates successfully, more effective chemotherapy treatment strategies at the computer simulation level, using real preclinical data which increases the expectation to be applied in the clinical practice of oncology. Results: The integration of these methods provides insights into how different drug administration schedules may affect tumor response at the preclinical level. This work uses mathematical modeling to evaluate the efficacy of various periodic and intermittent chemotherapy treatment strategies, with a focus on optimizing drug doses while minimizing the potential side effects of chemotherapy due to the administration of less effective chemotherapeutic doses. Conclusions: The treatment scenarios tested in this study could effectively stop tumor growth or even lead to tumor regression to a negligible or near-zero size. This approach highlights the importance of computational tools for more effective treatment strategies in chemotherapy and offers a promising direction for future research and more efficient clinical applications in oncology as part of a more individualized approach.
Performance Portrait Method: Robust Design of Predictive Integral Controller
Authors: Huba Mikulas , Bistak Pavol , Skrinarova Jarmila , Vrancic Damir
Published: January 2025

The performance portrait method (PPM) can be characterized as a systematized digitalized version of the trial and error method—probably the most popular and very often used method of engineering work. Its digitization required the expansion of performance measures used to evaluate the step responses of dynamic systems. Based on process modeling, PPM also contributed to the classification of models describing linear and non-linear dynamic processes so that they approximate their dynamics using the smallest possible number of numerical parameters. From most bio-inspired procedures of artificial intelligence and optimization used for the design of automatic controllers, PPM is distinguished by the possibility of repeated application of once generated performance portraits (PPs). These represent information about the process obtained by evaluating the performance of setpoint and disturbance step responses for all relevant values of the determining loop parameters organized into a grid. It can be supported by the implementation of parallel calculations with optimized decomposition in the high-performance computing (HPC) cloud. The wide applicability of PPM ranges from verification of analytically calculated optimal settings achieved by various approaches to controller design, to the analysis as well as optimal and robust setting of controllers for processes where other known control design methods fail. One such situation is illustrated by an example of predictive integrating (PrI) controller design for processes with a dominant time-delayed sensor dynamics, representing a counterpart of proportional-integrating (PI) controllers, the most frequently used solutions in practice. PrI controllers can be considered as a generalization of the disturbance–response feedback—the oldest known method for the design of dead-time compensators by Reswick. In applications with dominant dead-time and loop time constants located in the feedback (sensors), as those, e.g., met in magnetoencephalography (MEG), it makes it possible to significantly improve the control performance. PPM shows that, despite the absence of effective analytical control design methods for such situations, it is possible to obtain high-quality optimal solutions for processes that require working with uncertain models specified by interval parameters, while achieving invariance to changes in uncertain parameters.
Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture
Authors: Wu Chin-Cheng , Tsai Cheng-Wei , Wu Fei-En , Chiang Chi-Hsuan , Chiou Jin-Chern
Published: January 2025

Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition. The model extracted gait features using pressure-sensitive mats, focusing on foot pressure distribution and foot size during walking. Preprocessing techniques, including region of interest (ROI) selection, feature image extraction, and data horizontal flipping, were utilized to establish a CNN model that assessed gait recognition accuracy under two conditions: without carried items and carrying a 500 g object. To extend the application of the CNN to open set recognition for unauthorized personnel, the proposed convolutional neural network-autoencoder (CNN-AE) architecture compressed the average foot pressure map into a 64-dimensional feature vector and facilitated identity determination based on the distances between these vectors. Among 60 participants, 48 were classified as authorized individuals and 12 as unauthorized. Under the condition of not carrying an object, an accuracy of 91.218%, precision of 93.676%, recall of 90.369%, and an F1-Score of 91.993% were achieved, indicating that the model successfully identified most actual positives. However, when carrying a 500 g object, the accuracy was 85.648%, precision was 94.459%, recall was 84.423%, and the F1-Score was 89.603%.
Metal Nanocluster-Based Biosensors for DNA Detection
Authors: He Ran , Wang Sheng , Ju Feiye , Huang Zhao , Gao Yuan , Zhang Jing , He Nongyue , Nie Libo
Published: January 2025

The early detection of genetic diseases is a critical need in modern medicine, underscoring the importance of developing deoxyribonucleic acid (DNA) biosensors. In recent years, metal nanoclusters (MNCs) have demonstrated significant potential as biosensors for DNA detection due to their ultra-small size, excellent photostability, bright photoluminescence, low toxicity and other outstanding properties. This review firstly discusses the characteristics of MNCs, which are effective in the early diagnosis of DNA diseases. Subsequently, different synthesis methods of MNCs are introduced. In the following section, DNA sensors based on different types of MNCs and their respective detection mechanisms are discussed in detail. Finally, the opportunities and challenges faced by DNA sensors based on MNCs are analyzed.
Dual Biomarker Strategies for Liquid Biopsy: Integrating Circulating Tumor Cells and Circulating Tumor DNA for Enhanced Tumor Monitoring
Authors: Moon Ga Young , Dalkiran Basak , Park Hyun Sung , Shin Dongjun , Son Chaeyeon , Choi Jung Hyun , Bang Seha , Lee Hosu , Doh Il , Kim Dong Hyung , Jeong Woo-jin , Bu Jiyoon
Published: January 2025

The liquid biopsy has gained significant attention in cancer diagnostics, with circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) being recognized as key biomarkers for tumor detection and monitoring. However, each biomarker possesses inherent limitations that restrict its standalone clinical utility, such as the rarity and heterogeneity of CTCs and the variable sensitivity and specificity of ctDNA assays. This highlights the necessity of integrating both biomarkers to maximize diagnostic and prognostic potential, offering a more comprehensive understanding of the tumor biology and therapeutic response. In this review, we summarize clinical studies that have explored the combined analysis of CTCs and ctDNA as biomarkers, providing insights into their synergistic value in diverse tumor types. Specifically, this paper examines the individual advantages and limitations of CTCs and ctDNA, details the findings of combined biomarker studies across various cancers, highlights the benefits of dual biomarker approaches over single-biomarker strategies, and discusses future prospects for advancing personalized oncology through liquid biopsies. By offering a comprehensive overview of clinical studies combining CTCs and ctDNA, this review serves as a guideline for researchers and clinicians aiming to enhance biomarker-based strategies in oncology and informs biosensor design for improved biomarker detection.
Can ChatGPT AI Replace or Contribute to Experts’ Diagnosis for Renovation Measures Identification?
Authors: Hidalgo-Betanzos Juan Maria , Prol-Godoy Irati , Terés-Zubiaga Jon , Briones-Llorente Raúl , Martín-Garín Alexander
Published: January 2025

Building energy renovations demand expertise from professionals to guide processes, including diagnostics, project planning, interventions, and maintenance. The emergence of open-access AI, like ChatGPT in November 2022, offers new possibilities for improving these processes by assisting or potentially replacing human experts. This study explores the effectiveness of ChatGPT in diagnosing energy renovation measures. Initial assessments involve basic queries to the AI, followed by the inclusion of additional data and secondary questions to gauge its full diagnostic potential. An existing building case from the literature is given to the AI to define the best energy renovation measures. Expert evaluations and comparisons with research-backed solutions assess the AI’s performance using different degrees of questioning details over 60 repetitions. The results indicate that ChatGPT can provide valuable insights and generate comprehensive lists of feasible measures and preliminary cost calculations and payback, but, in general, it lacks depth and quality without specialized input and preparation. A significant quality improvement was found between the tests with 2023 and 2024 AI versions. Open-access AI proves capable of enhancing renovation diagnostics but remains a complement rather than a replacement for building renovation expert judgment. This research underscores the potential of mainstream AI to democratize access to knowledge, albeit with limitations tied to its dependence on quality inputs and contextual expertise.
Collagen Injections for Rotator Cuff Diseases: A Systematic Review
Authors: Aicale Rocco , Savarese Eugenio , Mottola Rosita , Corrado Bruno , Sirico Felice , Pellegrino Raffaello , Donati Danilo , Tedeschi Roberto , Ruosi Luca , Tarantino Domiziano
Published: January 2025

Background: Because of its anatomy and function, the rotator cuff (RC) is vulnerable to considerable morbidity. The prevalence of RC diseases (RCDs) among the general population is 5–39%, reaching over 30% in patients older than 60. The aim of the present systematic review is to investigate the effects of the use of collagen injections in the treatment of RCDs. Methods: A systematic search of scientific electronic databases (such as PubMed, Scopus and Web of Science) was performed up to November 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two independent authors conducted the search and assessed the articles. The inter-rater reliability for the quality assessment was measured using Cohen’s kappa coefficient, while the Modified Coleman Methodology Score (CMS) was applied to evaluate the methodological quality of the articles included in this systematic review. Results: A total of eight articles were included, with the overall quality of the included articles being evaluated as fair. Despite the use of different types of collagen and injection protocols, as well as the different scores applied, each included study showed clinically relevant improvements. However, given the high degree of heterogeneity of the included studies, we cannot draw conclusions regarding which type of collagen and injection protocol are best for RCD treatment. Discussion: Collagen administration for RCDs seems to be effective at reducing pain and improving function, as well as the tendon structure, especially in partial tears and RC tendinopathy. High-quality, prospective studies with long-term follow-up are necessary to validate the findings of the articles included in this systematic review.
A Review of Vessel Time of Arrival Prediction on Waterway Networks: Current Trends, Open Issues, and Future Directions
Authors: Noman Abdullah Al , Heuermann Aaron , Wiesner Stefan , Thoben Klaus-Dieter
Published: January 2025

With the vast majority of global trade volume and value reliant on maritime transport, accurate prediction of vessel estimated time of arrival (ETA) is crucial for optimizing supply chain efficiency and managing logistical complexities in port operations. This review paper systematically examines the current state of research and practices in the field of vessel ETA prediction, highlighting significant trends, methodologies, and technologies. It explores various approaches, including classical methods, machine learning and deep learning algorithms, and hybrid methods, developed to enhance the accuracy and reliability of vessel travel time and arrival time predictions. Additionally, this paper categorizes key influencing factors and metrics, and identifies open issues and challenges within current prediction models. Concluding with proposed future research directions aimed at addressing the identified gaps and leveraging technological advancements, this review emphasizes the importance of fostering innovation in maritime ETA prediction systems, particularly within the framework of Intelligent Transportation Systems (ITSs) and maritime logistics. By applying a systematic literature review (SLR) methodology and conducting an in-depth evaluation, the results provide a comprehensive overview of vessel ETA prediction for researchers, practitioners, and policy makers involved in maritime transport and logistics, and offer insights into the potential for improved efficiency, safety, and environmental sustainability in waterway networks.
Redefining Event Detection and Information Dissemination: Lessons from X (Twitter) Data Streams and Beyond
Authors: Srivastava Harshit , Sankar Ravi
Published: January 2025

X (formerly known as Twitter), Reddit, and other social media forums have dramatically changed the way society interacts with live events in this day and age. The huge amount of data generated by these platforms presents challenges, especially in terms of processing speed and the complexity of finding meaningful patterns and events. These data streams are generated in multiple formats, with constant updating, and are real-time in nature; thus, they require sophisticated algorithms capable of dynamic event detection in this dynamic environment. Event detection techniques have recently achieved substantial development, but most research carried out so far evaluates only single methods, not comparing the overall performance of these methods across multiple platforms and types of data. With that view, this paper represents a deep investigation of complex state-of-the-art event detection algorithms specifically customized for streams of data from X. We review various current techniques based on a thorough comparative performance test and point to problems inherently related to the detection of patterns in high-velocity streams with noise. We introduce some novelty to this research area, supported by appropriate robust experimental frameworks, to performed comparisons quantitatively and qualitatively. We provide insight into how those algorithms perform under varying conditions by defining a set of clear, measurable metrics. Our findings contribute new knowledge that will help inform future research into the improvement of event detection systems for dynamic data streams and enhance their capabilities for real-time and actionable insights. This paper will go a step further than the present knowledge of event detection and discuss how algorithms can be adapted and refined in view of the emerging demands imposed by data streams.