bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Dietary Cold-Pressed Hempseed Cake Supplemented with Tomato Waste on Laying Hen Performance and Egg Yolk Lipid Profile and Antioxidant Status Before and After Storage
Authors: Mierlita Daniel , Daraban Stelian , Teușdea Alin Cristian , Stanciu Alina Stefania
Published: November 2024

A ten-week study was conducted to investigate the effects of the dietary inclusion of HSC as a source of PUFAs together with a natural source of antioxidants such as DT or DTP on the performance of laying hens and egg quality, as well as the lipid profile, antioxidant content and oxidative stability of egg yolk before and after storage. A total of 96 laying hens (aged between 28 and 37 weeks) were divided into three groups of 32 birds each (eight replicate cages, four birds per cage) and were assigned randomly the following dietary treatments: a standard corn–soybean meal diet (C), a diet containing 20% hempseed cake and 4% dried whole tomato (HT) and a diet containing 20% hempseed cake and 4% dried tomato pomace (HTP). The incorporation of HSC and tomato waste (DT and DTP) into the diet of hens caused increases in feed intake (p < 0.05) and yolk color score without changing egg production, egg weight or feed conversion ratio (FCR). The HT and HTP diets ensured a lower content (p < 0.05) of cholesterol in the egg yolk but a higher content (p < 0.001) of n-3 FAs, especially of ALA (α-linolenic acid, 18:3n-3), EPA (eicosapentaenoic acid, 20:5n-3) and DHA (docosahexaenoic acid, 22:6n-3), which allowed for an increase in the hypo-/hypercholesterolemic FA ratio and a reduction in the n-6/n-3 FA ratio and TI (thrombogenicity index) value compared to those of C eggs. In addition, the inclusion of DT or DTP in the diet of laying hens increased the antioxidant content in the yolk, which led to an increase in the oxidative stability of the yolk lipids, evidenced by a decrease in the concentrations of MDA (malondialdehyde) in fresh eggs and those stored for 30 days at 4 °C. DTP exerted the most desirable effects because the egg yolk had the most intense color, the highest contents of n-3 FAs and natural antioxidants and the best oxidative stability of yolk lipids before and after egg storage. It was concluded that the simultaneous enrichment of the diet of laying hens with n-3 FAs and natural antioxidants (carotenoids, tocopherols and phenols), in addition to improving the color of the yolk, leads to eggs with a higher content of n-3 FAs and antioxidants, improved antioxidant activity and better PUFA stability during egg storage.
Reply to Hockenhull et al. Comment on “Ssuna et al. Animal Welfare Guidelines for International Development Organisations in the Global South. Animals 2024, 14, 2012”
Authors: Ssuna Paul , Siegmund Karin , Crump Andrew
Published: November 2024

We thank Hockenhull et al [...]
Cooled Multifunctional Platforms to Alleviate Heat Stress in Broiler Chickens: Effects on Performance, Carcass and Meat Quality Traits
Authors: Khalfi Bassem , Buyse Kobe , Khan Imad , Carvalho Camila Lopes , Soster Patricia , Antonissen Gunther , Tuyttens Frank André Maurice
Published: November 2024

Platforms have been shown to be a suitable environmental enrichment for broiler chickens, accommodating their motivation to roost and rest at an elevated position. In order to increase the animal welfare benefits, we designed prototype elevated platforms with additional functionalities: a local cooling system, a sheltered area underneath the platform and collection trays underneath the platform that prevent manure from falling on the litter. This study assessed the effects of these multifunctional platforms during thermoneutral and heat stress conditions on two key determinants of their commercial uptake potential, namely production performance, carcass and meat quality. In each of the three experimental rounds, 560 one-day-old male chicks (Ross 308) were equally assigned to four pens and reared for 43 days. The barn was divided into two compartments (thermoneutral and heat), each containing one enriched and one barren pen. To induce heat stress, the ambient temperature was increased to 32 °C during 6 h/day when the birds were 29–40 d old. The platforms did not compromise broiler performance. Heat stress negatively impacted body weight (p = 0.008), average daily gain (p = 0.009) and feed intake (p < 0.001) and improved the feed conversion ratio (p = 0.026). The platforms reduced mortality rate under heat stress but not under thermoneutral conditions (heat × enrichment p = 0.025), likely due to the cooling functionality. No major effects of the platforms on overall carcass and meat quality were observed, except for a reduction in the risk of breast muscle myopathies (p < 0.001), which could enhance consumer acceptance and improve profitability. To conclude, the use of cooled platforms under the conditions of this experiment mitigated the adverse effects of heat stress on mortality, reduced the risk of breast muscle myopathies and did not compromise any other production or carcass and meat quality parameters.
A Comprehensive Analysis of the ceRNA Network and Hub Genes in Avian Leukosis Virus Subgroup J and Infectious Bursal Disease Virus Superinfection
Authors: Chen Sheng , Xu Huijuan , Pan Tingxi , Nie Yu , Zhang Xinheng , Chen Feng , Xie Qingmei , Chen Weiguo
Published: November 2024

In the realm of poultry production, viral superinfections pose significant challenges, causing substantial economic losses worldwide. Among these, avian leukosis virus subgroup J (ALV-J) and infectious bursal disease virus (IBDV) are particularly concerning as they frequently lead to superinfections in chicken, further exacerbating production losses and health complications. Our previous research delved into the pathogenicity and immunosuppressive effects of these superinfections through in vitro and in vivo analyses. Yet, the underlying key genes and pathways governing this phenomenon remained elusive. In this study, we randomly selected three chickens at 21 days post infection from each treatment group (ALV-J, IBDV, ALV-J+IBDV, and control group) to collect the bursa of Fabricius samples for full transcriptome analysis. Utilizing these data, we constructed a comprehensive circRNA/lncRNA-miRNA-mRNA network which elucidated both synergistic and specific activations during the superinfection. Notably, three pivotal genes (FILIP1L, DCX, and MYPN) were pinpointed in datasets reflecting synergistic activations. Conversely, four other genes (STAP, HKR6, XKR4, and TLR5) emerged in datasets associated with specific activations. Further exploration revealed diverse significant GO terms and pathways associated with both synergistic and distinct activation processes. These ceRNA network and core genes potentially wield substantial influence over the synergistic or specific activation of tumorigenesis and pathogenesis induced by ALV-J and IBDV. These findings could help develop targeted therapies and improve disease control in poultry, reducing economic losses.
Adsorption of Heavy Metals from Low Concentration Solutions onto Dried Chlamydomonas reinhardtii
Authors: Genduso Maria Giuseppina , Guagliano Marianna , Finocchio Elisabetta , Cristiani Cinzia , Dotelli Giovanni , Santomauro Giulia
Published: November 2024

Heavy metals, such as Pb(II), Cr(III), and Cd(II), are frequently discharged into the environment, resulting in a threat for the health of living organisms. Microalgae represent an ecological method for the removal of these pollutants. The capture capability of active and dried Chlamydomonas reinhardtii towards both mono-ionic and multi-ionic solutions is evaluated to study the bioremediation in low-polluted wastewaters. Several characterization techniques have been performed to study morphology, composition, and the interaction between the metals and the microalgal functional groups of the wall. The effect of the operating conditions, such as contact time and pH, is assessed; an increased contact time of 4 days did not influence the adsorption yields of Pb and Cr, but it had a negative effect on Cd adsorption, while the impact of pH was mainly affecting the chemical speciation, rather than the adsorption capability. The microalga showed a larger affinity for Pb and Cd, respectively, 80 and 76% (w/w) of adsorption, while only the 65% (w/w) of the total Cr was adsorbed, suggesting a preferential biosorption.
Unilateral Drop Jump Kinetics and Kinematics Differentiate Patellofemoral Pain Patients from Healthy Controls
Authors: Deysel Germari , van Aswegen Mariaan , Kramer Mark
Published: November 2024

The diagnosis of patellofemoral pain (PFP) remains imprecise due to the variable sensitivity and specificity scores of most clinical tests. More functionally relevant assessments and metrics are needed to provide better diagnostic insights. A total of 33 participants (control: n = 17 [M/F = 9/7]; PFP: n = 18 [M/F = 2/14]) volunteered for this study. The control group followed no intervention program, whereas the PFP group completed a joint-specific intervention program at a clinical practice for 8 weeks. At 0- and 8-week isometric knee extensor torques, unilateral drop jumping metrics, and anterior knee pain scale (AKPS) scores were collected and analysed. Statistically significant between-group differences were evident for the reactive strength index (RSI) (Mdiff = 0.05 m/s, p = 0.032) and ground contact time (GCT) (Mdiff = 0.28 s, p = 0.011) but not for peak force (p = 0.390), jump height (p = 0.721), impulse (p = 0.740), or peak torque (p = 0.359). While controlling for gender as a confounder, the RSI and GCT showed diagnostic utility for differentiating PFP from healthy controls (AUC = 0.83–0.87, sensitivity = 0.85–0.91, and specificity = 0.59–0.72). The AKPS score demonstrated moderate positive correlations with jump metrics (impulse: r = 0.58, 95% confidence interval (CI) [0.11, 0.83]; RSI: r = 0.50, 95% CI [0.01, 0.80]), accounting for 25–34% of performance variability. The unilateral drop jump provides useful metrics for differentiating those with PFP from their healthy counterparts. The RSI and GCT may serve as diagnostic metrics in evaluating PFP patients and can be coupled with the AKPS to track performance and rehabilitation progress in PFP patients.
Special Issue on “Advances and Applications in Computational Fluid Dynamics”
Authors: Ahn Joon
Published: November 2024

Advances in computational fluid dynamics (CFD) are essential for understanding the complex behaviors of fluids in various conditions [...]
The Cytotoxicity and Antioxidant Potentials of the Endophytic Fungus Xylaria sp. KET18 Associated with Keteleeria evelyniana Mast.
Authors: Pham Ngoc Son , Le Thi Thanh Xuan , Pham Quynh Anh , Vu Thi Hanh Nguyen , Quach Ngoc Tung , Nguyen Thi Thanh Loi , Do Thi Thao , Do Hoang Anh , Tran Hong Quang , Chu Ky-Son , Pham Anh Tuan , Chu Hoang Ha , Phi Quyet-Tien
Published: November 2024

Fungal endophytes colonizing plant tissues are considered a reservoir of secondary metabolites that exhibit diverse bioactivities applicable in biomedicine. This study aims to reveal for the first time endophytic fungi associated with Keteleeria evelyniana as a potential source of bioactive compounds. A total of 24 fungal endophytes were successfully isolated from K. evelyniana and classified into 10 genera: Aspergillus, Penicillium, Fusarium, Xylaria, Talaromyces, Nodulisporium, Apiospora, Neopestalotiopsis, Diaporthe, and Hypoxylon. Screening for antimicrobial activity revealed that 8 out of 24 ethyl acetate extracts inhibited antimicrobial activity against at least one tested pathogen. Among them, Xylaria sp. KET18 showed the most potent antimicrobial activity with inhibition diameters ranging from 16.5 to 21.5 mm. In addition, the KET18 extract showed the most significant cytotoxic effects against A549 (IC50 = 18.8 ± 3.1 µg/mL) and MCF7 cell lines (IC50 = 24.1 ± 2.5 µg/mL). The KET18 extract showed moderate antioxidant activity against hydroxyl and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals. The chemical characterization and structural elucidation indicated the presence of four bioactive compounds that have not been found in fungi, including methyl pyroglutamate, prunetin, macrolactin A, and macrolactin F. These findings demonstrated that K. evelyniana is a host of endophytic fungi with antimicrobial, anticancer, and antioxidant potential.
Machine Learning for Child Oral Health: A Scoping Review
Authors: Mohajeri Amir , Schlaud Samantha , Spector Sydnee , Hung Man
Published: November 2024

Background: Machine learning (ML) has potential to assist dental professionals with diagnosing and predicting outcomes of oral health. Tooth decay in children is the most common chronic childhood disease and it can be prevented by early detection. We aim to provide a map of the current evidence on ML in child oral health and provide insight for future research. Methods: A scoping review used databases like Medline, Web of Science, EBSCO Dentistry & Oral Science Source, Cochrane Library, and Scopus. Included studies assessed ML models for diagnoses, prediction, or management of oral health in children (0–9 years). Data extraction included publication year, location, age, sample size, disease, study type, and ML algorithms. Results: a total of 29 studies were included, mainly in pediatric dentistry and dental public health. Study designs comprised cross-sectional (34.5%), cohort (31.0%), case-control (20.7%), clinical trials (10.3%), and descriptive surveys (3.5%). The majority of studies were from high-income (69.0%) and upper middle-income countries (27.6%), with a small representation from low middle-income countries (3.4%). ML focused on predicting and diagnosing oral health issues such as caries progression and risk, with neural networks predominantly tested alongside emerging techniques like random forest, regression, and gradient boosting. Conclusions: ML algorithms hold promise in improving dental diagnosis and prediction accuracy, benefiting dental professionals, including pediatric and general dentists, in enhancing proficiency and reducing clinical errors.
Strategies to Extend the Shelf Life of Fresh and Minimally Processed Fruit and Vegetables with Edible Coatings and Modified Atmosphere Packaging
Authors: Zdulski Jan Aleksander , Rutkowski Krzysztof P. , Konopacka Dorota
Published: November 2024

Edible coatings are an innovative solution for food producers and suppliers looking for healthy alternatives that would be appealing to consumers seeking for ready-made products in fresh form. Low-processed horticulture products are expected to appear in more and more convenient form, ready to eat and completely safe. The application of edible surface films contributes to the extension of shelf life while maintaining strict hygiene standards. Properly selected coating components are not only able to slow down the decomposition processes but also inhibit microbial growth, thus preventing quality losses and eliminating any potential threats to health. The additional use of modified atmosphere packaging (MAP) can significantly increase the shelf life of fruit and vegetables both fresh and minimally processed. However, single treatments (edible coatings and MAP) are limited in maintaining the quality and shelf life of fresh and fresh-cut fruit and vegetables. Little is known about the synergistic effect of MAP and edible coatings, and more research is necessary. The synergic action means combined action—first applying the edible coating, then packing in MAP.
Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
Authors: Dionis-Ros Alejandro , Vila-Francés Joan , Magdalena-Benedito Rafael , Mateo Fernando , Serrano-López Antonio J.
Published: November 2024

In this article, we propose the detection of crowd anomalies through the extraction of information in the form of time series in video format using a multimodal approach. Through pattern recognition algorithms and segmentation, informative measures of the number of people and image occupancy are extracted at regular intervals, which are then analyzed to obtain trends and anomalous behaviors. Specifically, through temporal decomposition and residual analysis, intervals or specific situations of unusual behaviors are identified, which can be used in decision-making and the improvement of actions in sectors related to human movement such as tourism or security. This methodology introduces a novel, privacy-focused approach by analyzing anonymized metrics rather than tracking or recognizing individuals, setting a new standard for ethical crowd monitoring. Applied to the webcam of Turisme Comunitat Valenciana in the town of Morella (Comunitat Valenciana, Spain), this approach has shown excellent results, correctly detecting specific anomalous situations and unusual overall increases during the previous weekend and during the October 2023 festivities. These results have been obtained while preserving the confidentiality of individuals at all times by using measures that maximize anonymity, without trajectory recording or person recognition.
A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
Authors: Mohammed Medina Ayta , De-Pablos-Heredero Carmen , Montes Botella José Luis
Published: November 2024

Blockchain technology and its business applications have attracted considerable scholarly interest, leading to a surge in academic studies. While this wealth of research is beneficial, it also poses challenges in identifying the most relevant publications. Despite the availability of survey articles, research on this topic remains fragmented and concentrates on specific industrial sectors. This review addresses this gap by providing a detailed literature analysis, highlighting key themes, recent advancements, the benefits of blockchain adoption for businesses, and associated challenges. This study employs a multi-method literature review approach called bibliometric systematic literature review (B-SLR), combining bibliometric analysis with systematic literature review (SLR) techniques. This review critically examines studies of blockchain adoption in modern business from 2017 to 2023. Our findings reveal a decline in academic publications on blockchain for businesses since 2023, along with a shift in core themes from traditional supply chains to exploring blockchain’s role in environmentally sustainable supply chains, such as reverse logistics, green supply chains, and the circular economy. Additionally, there is an emerging focus on the role of blockchain in virtual environments, such as the metaverse and digital twins. Drawing from our analysis, we also present a theoretical framework and highlight ten crucial areas for future research.
Seismic Resilience of CRC- vs. RC-Reinforced Buildings: A Long-Term Evaluation
Authors: Maidi Moab , Lifshitz Sherzer Gili , Shufrin Igor , Gal Erez
Published: November 2024

Corrosion-induced degradation in concrete and reinforced concrete (RC) structures, often initiated within the first few decades of their lifespan, significantly challenges seismic resistance. While existing research tools can assess performance, they fall short in predicting changes in seismic resistance resulting from alterations in the core properties of RC structures. To bridge this gap, we introduce a numerical seismic resistance prediction method (SRPM) specifically designed to predict changes in the seismic resistance of structures, including those reinforced with carbon-fiber-reinforced polymer (CFRP), known for its non-corrosive properties. This study utilizes classical models to estimate corrosiveness and employs these models alongside section strength predictions to gauge durability. The nonlinear static pushover analysis (POA) model is implemented utilizing SAP-2000 and Response-2000 software. A comparative analysis between steel-reinforced and carbon-fiber-reinforced polymer concrete (CRC) structures reveals distinct differences in their seismic resistance over time. Notably, steel-reinforced structures experience a significant decrease in their ability to dissipate seismic energy, losing 54.4% of their capacity after 170 years. In contrast, CFRP-reinforced structures exhibit a much slower degradation rate, with only 25.5% reduction over the same period. The discrepancy demonstrates CFRP’s superior durability and ability to maintain structural integrity in the face of seismic stresses.
Fermented Cashew Nut Cheese Alternative Supplemented with Chondrus crispus and Porphyra sp.
Authors: Campos Bruno M. , Moreira-Leite Bruno , Salgado Abigail , Ramalho Edgar , Marmelo Isa , Malfeito-Ferreira Manuel , Sousa Paulo , Diniz Mário S. , Mata Paulina
Published: November 2024

This study is aimed at the development of a fermented cashew nut cheese alternative supplemented with Chondrus crispus and Porphyra sp. and the evaluation of the impact of seaweed supplementation through analysis of physicochemical, microbiological, and organoleptic properties of the developed food products. The total lipid content decreased with the supplementation with seaweeds. Crude protein content also slightly decreased, while elemental analysis showed that mineral and trace element (Ca, K, Mg, Na, Fe, I, Se, and Zn) content increased when C. crispus was added to the paste. The analyses of color and textural (TPA) attributes showed that these were significantly influenced by adding seaweeds to the cashew paste. Generally, the microbiological results comply with the different European guidelines for assessing the microbiological safety of ready-to-eat foods placed on the market, except for aerobic mesophilic bacteria and marine agar counts. Flash Profile analysis allowed for distinguishing sample attributes, showing an increased flavor complexity of the plant-based cheese alternatives supplemented with seaweeds. Overall, the study indicates that seaweed enrichment mainly influenced the physicochemical and sensory characteristics of plant-based cheese alternatives.
Speech Signal Analysis in Patients with Parkinson’s Disease, Taking into Account Phonation, Articulation, and Prosody of Speech
Authors: Majda-Zdancewicz Ewelina , Potulska-Chromik Anna , Nojszewska Monika , Kostera-Pruszczyk Anna
Published: November 2024

This study involved performing tests to detect Parkinson’s disease (PD) based on voice changes, including speech phonation, articulation, and prosody, in patients with PD using different types of speech signal. For this purpose, during the first stage of the investigation, three separately modeled PD diagnosis systems using different types of speech signal characteristics were defined. The classification results were obtained when the SVM method was applied compared to the k-nearest neighbors method applying 1-nn in general. The tests were carried out within the database of patient voice recordings collected in the Department of Neurology at the Medical University of Warsaw. The second stage of the research was the selection of descriptors. The SFFS (sequential floating forward) method was applied together with the k-nn and SVM classifier. These subsets were used to create a new system based on a descriptor loose integration. Within the experiments conducted, general diagnosis results lead to improved classifier performance only in certain cases. This prompted the authors to conduct the last experimental research stage—selection at the feature fusion stage. Feature evaluation ranking methods (Relief, Fisher Score, F-tests, Chi-square) were applied for this purpose. With 10-fold validation, the k-nn method achieved an recognition rate of 92.2% with 91.1% sensitivity and 93.3% specificity.
Recent Advancements in Guided Ultrasonic Waves for Structural Health Monitoring of Composite Structures
Authors: Tanveer Mohad , Elahi Muhammad Umar , Jung Jaehyun , Azad Muhammad Muzammil , Khalid Salman , Kim Heung Soo
Published: November 2024

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of laminated composite structures. Their favorable strength-to-weight ratio renders them ideal for the automotive, marine, and aerospace industries. Among various non-destructive testing (NDT) methods, ultrasonic techniques have emerged as robust tools for detecting and characterizing internal flaws in composites, including delaminations, matrix cracks, and fiber breakages. This review concentrates on recent developments in ultrasonic NDT techniques for the SHM of laminated composite structures, with a special focus on guided wave methods. We delve into the fundamental principles of ultrasonic testing in composites and review cutting-edge techniques such as phased array ultrasonics, laser ultrasonics, and nonlinear ultrasonic methods. The review also discusses emerging trends in data analysis, particularly the integration of machine learning and artificial intelligence for enhanced defect detection and characterization through guided waves. This review outlines the current and anticipated trends in ultrasonic NDT for SHM in composites, aiming to aid researchers and practitioners in developing more effective monitoring strategies for laminated composite structures.
Collaborative Optimization of Container Liner Slot Allocation and Empty Container Repositioning Based on Online Booking Platform
Authors: Wang Wenmin , Yang Zaili , Diao Cuijie , Jin Zhihong
Published: November 2024

The shipping market is unpredictable and volatile due to some uncontrollable factors such as epidemic, conflicts and natural disasters. There is always an imperfect match between the supply capacity of liner companies and the actual demand of the market, which leads to a waste of slot resources and/or unsatisfied customer demand. Furthermore, the trade off between empty container transportation and laden container transportation is the crucial problem of strategic importance for liner companies. To deal with the above problem, this paper aims to develop a new solution to the collaborative optimization problem of container slot allocation and empty container repositioning by exploring the resource allocation, storage, and repositioning methods collaboratively. An online booking platform is introduced in this paper, and no-shows and customer preferences are considered in the analysis. An innovative integer programming model is established based on an online booking mode and a delivery-postponed strategy. A new branch-and-cut algorithm is then proposed to solve the problem. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and algorithm. The experimental results show that collaborative optimization can remarkably enhance the revenue of liner companies along with increasing the utilization of slot resources.
Innovative Learning in a Digital Forensics Laboratory: Tools and Techniques for Data Recovery
Authors: Cruz Carlos
Published: November 2024

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods.
Box–Behnken Design-Based Optimization of Extraction Parameters of Phenolics, Antioxidant Activity, and In Vitro Bioactive and Cytotoxic Properties of Rhus typhina Fruits
Authors: Cocîrlea Maria Denisa , Simionescu Natalia , Călin Teodora , Gatea Florentina , Badea Georgiana Ileana , Vamanu Emanuel , Oancea Simona
Published: November 2024

Rhus typhina, an invasive plant species, contains valuable compounds that can be utilized in various fields. The main aim of this paper was to find the optimal conditions for extracting high amounts of bioactive compounds from R. typhina fruits using ultrasound-assisted and bead-beating techniques under different parameters (solvent concentration, solvent/solid ratio, extraction time, bead size, and material). A Box–Behnken design was applied for ultrasound-assisted extraction. The following process parameters were found to be optimal: 20/1 solvent/solid ratio (v/w), 61.51% aqueous ethanol, 10 min extraction time, with a composite desirability of 0.7719. The HPLC profile indicates that p-coumaric acid was the most abundant phenolic compound found in the BBE extract. The BBE extract was subjected to in vitro biological tests. The results indicate a high antimicrobial activity on Streptococcus pyogenes (20 mm inhibition zone) and Salmonella enterica (12 mm inhibition zone). A hemolysis rate of 19.85% was found at an extract concentration of 1000 µg/mL on sheep erythrocytes. We report for the first time the protective role of the extract on cell viability of human gingival fibroblasts, but also a weak antiproliferative effect on the HepG2 human liver cancer cell line. Overall, we conclude that R. typhina fruits are rich in bioactive compounds that can be recovered using proper extraction conditions. Further research is required to understand and valorize their biological potential.
Rewiring for Victory: Neuro-Athletic Training Enhances Flexibility, Serve Speed, and Upper Limb Performance in Elite Volleyball Players—A Randomized Controlled Trial
Authors: Soylu Caglar , Altundag Emre
Published: November 2024

This randomized controlled trial investigated the effects of neuro-athletic training (NAT) on flexibility, spike speed, and upper extremity stability in elite volleyball players. Thirty professional male athletes aged 18–23 years old (mean age of 19.5 ± 1.77 years old in the NAT group and 19.8 ± 1.87 years old in the control group) participated, with 26 completing this study. The participants were randomly assigned into an NAT intervention group or a control group continuing traditional training. Both groups trained three days per week for eight weeks, with the NAT program targeting neuromuscular adaptations while maintaining equal total training durations. Flexibility was assessed using the Sit and Reach Test, spike speed was evaluated using the Pocket Radar Ball Coach, and upper extremity stability was measured using the Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST). The NAT group demonstrated significant improvements across all performance metrics. Flexibility increased significantly (p = 0.040; Cohen’s d = 0.845), indicating improved range of motion and musculoskeletal adaptability. Spike speed showed a highly significant improvement (p < 0.001; Cohen’s d = 1.503), reflecting enhanced neuromuscular coordination and power. Similarly, upper extremity stability exhibited substantial gains (p = 0.002; Cohen’s d = 1.152), highlighting improved shoulder stability and motor control. In contrast, the control group did not show statistically significant changes in their flexibility (p = 0.236; Cohen’s d = 0.045), spike speed (p = 0.197; Cohen’s d = 0.682), or upper extremity stability (p = 0.193; Cohen’s d = 0.184). Between-group comparisons confirmed the superiority of the NAT intervention, with significant differences across all metrics (p-values ranging from 0.040 to <0.001) and effect sizes spanning from moderate to large (Cohen’s d = 0.845–1.503). These findings demonstrate the effectiveness of NAT in enhancing volleyball-specific performance metrics, emphasizing its potential to target neuromuscular adaptations for improved flexibility, power, and stability. Future studies should explore the long-term effects of NAT and its applicability across various sports disciplines.
Transmission Spectroscopy Along the Transit of Venus: A Proxy for Exoplanets Atmospheric Characterization
Authors: Branco Alexandre , Machado Pedro , Demangeon Olivier , Azevedo Silva Tomás , Jaeggli Sarah A. , Widemann Thomas , Tanga Paolo
Published: November 2024

We present an analysis of high-resolution, near-infrared (NIR) spectra relative to the solar transit of Venus of 5–6 June 2012, as observed with the Facility Infrared Spectropolarimeter (FIRS) at the Dunn Solar Telescope in New Mexico. These observations offer the unique opportunity to probe the upper layers (above ∼84 km in altitude) of a thick, CO2-dominated atmosphere with the transmission spectroscopy technique—a proxy for future studies of highly-irradiated atmospheres of Earth-sized exoplanets. We were able to directly observe absorption lines from the two most abundant CO2 isotopologues, and from the main isotopologue of CO in the retrieved spectrum of Venus. Furthermore, we performed a cross-correlation analysis of the transmission spectrum using transmission templates generated with petitRADTRANS. With the cross-correlation technique, it was possible to confirm detections of both CO2 isotopologues and CO. Additionally, we retrieved a tentative cross-correlation signal for O3 on Venus. We demonstrate the feasibility of high-resolution, ground-based observations to study the chemical inventory of planetary atmospheres, employing techniques commonly used in exoplanet characterization.
Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China
Authors: He Zhenfang , Guo Qingchun
Published: November 2024

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m3. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control.
Characterizing Affine Vector Fields on Pseudo-Riemannian Manifolds
Authors: Alshehri Norah , Guediri Mohammed
Published: November 2024

We study the characteristics of affine vector fields on pseudo-Riemannian manifolds and provide tensorial formulas that characterize these vector fields. Using our approach, we present a straightforward proof that any affine vector field on a compact Riemannian manifold is a Killing vector field. Moreover, we establish several necessary and sufficient conditions for an affine vector field on a Riemannian manifold to be classified as a Killing or parallel vector field.
Fuzzy Decision Tree Based on Fuzzy Rough Sets and Z-Number Rules
Authors: Zhu Boya , Wang Jingqian , Zhang Xiaohong
Published: November 2024

The decision tree algorithm is widely used in various classification problems due to its ease of implementation and strong interpretability. However, information in the real world often has uncertainty and partial reliability, which poses challenges for classification tasks. To address this issue, this paper proposes a fuzzy decision tree based on fuzzy rough sets and Z-numbers, aimed at enhancing the decision tree’s ability to handle fuzzy and uncertain information. In the aspect of rule extraction, we combine the fuzzy rough set model to propose a fuzzy confidence based on lower approximation as a metric for attribute selection, effectively addressing the role of imprecise knowledge in classification. In terms of the tree structure, the concept of Z-numbers is introduced, specifically focusing on the fuzzy constraint reliability B, making the information representation more aligned with human evaluation habits, as well as using Z-number rules to replace traditional fuzzy rules in constructing the fuzzy decision tree. Furthermore, as generating Z-numbers still presents certain challenges, this paper also establishes a method for reasonably generating Z-numbers in situations with limited information, utilizing the generated fuzzy constraint reliability B to adjust fuzzy numbers A. Finally, the proposed decision tree algorithm is experimentally compared with other classifiers, and the results indicate that this algorithm demonstrates higher classification accuracy and a more concise tree structure when handling datasets containing fuzzy and uncertain factors. This research enriches the existing research on fuzzy decision trees and shows greater potential in solving practical problems.
Horadam–Lucas Cubes
Authors: Tan Elif , Podrug Luka , Iršič Chenoweth Vesna
Published: November 2024

In this paper, we introduce a novel class of graphs referred to as the Horadam–Lucas cubes. This class extends the concept of Lucas cubes and retains numerous desirable properties associated with them. Horadam–Lucas cubes can also be viewed as a companion graph family of the Horadam cubes, in a similar way the Lucas cubes relate to Fibonacci cubes or the Lucas-run graphs relate to Fibonacci-run graphs. As special cases, they also give rise to new graph families, such as Pell–Lucas cubes and Jacobsthal–Lucas cubes. We derive the several metric and enumerative properties of these cubes, including their diameter, periphery, radius, fundamental decomposition, number of edges, cube polynomials, and generating function of the cube polynomials.
Manganese-Coordinated Cellulose Based-Separator for Efficient and Reliable Zn-Ion Transport
Authors: Cheng Jiazhe , Wang Kai , Ning Xiaoyu , Zhang Jichao , Jia Hao , Tawiah Benjamin , Jiang Shouxiang
Published: November 2024

Aqueous zinc-ion batteries (AZIBs) are increasingly being acknowledged as a promising candidate to safely power large-scale energy storage systems and portable devices. However, the development of effective separator materials remains a significant challenge due to issues such as harmful dendrite growth on zinc (Zn) anodes and parasitic side reactions in aqueous electrolytes. To address this challenge, we synthesize a manganese-coordinated cellulose nanofibril (Mn-CNF)-based separator for high-performance AZIBs. This separator affords enhanced ion transport channel, a large number of hydroxyl groups, and exceptional mechanical properties, with a tensile strength of 2.8 MPa and superior ionic conductivity of 5.14 mS·cm−1. These attributes collectively enhance Zn-ion transport, minimize nucleation overpotential for Zn, and accelerate the Zn deposition kinetics, thus significantly outperforming the untreated CNF separators. Consequently, the Zn||MnO2 battery with the Mn-CNF separator shows a marked improvement in the galvanostatic rate performance and cycling stability by effectively accelerating and optimizing Zn-ion transport. This study offers valuable insights into the development of efficient and reliable separators for advanced electrochemical energy storage technologies.
Personality Traits Estimation Based on Job Interview Video Analysis: Importance of Human Nonverbal Cues Detection
Authors: Kassab Kenan , Kashevnik Alexey
Published: November 2024

In this research, we delve into the analysis of non-verbal cues and their impact on evaluating job performance estimation and hireability by analyzing video interviews. We study a variety of non-verbal cues, which can be extracted from video interviews and can provide a framework that utilizes the extracted features, and we combine them with personality traits to estimate sales abilities. Experimenting on the (Human Face Video Dataset for Personality Traits Detection) VPTD dataset, we proved the importance of smiling as a valid indicator for estimating extraversion and sales abilities. We also examined the role of head movements (represented by the rotation angles, roll, pitch, and yaw) since they play a crucial role in evaluating personality traits in general and extraversion and neuroticism in particular. The testing results show how these non-verbal cues can be used as assisting features in the proposed approach to provide a valid, reliable, and accurate estimation of sales abilities and job performance.
Exploring IoT and Blockchain: A Comprehensive Survey on Security, Integration Strategies, Applications and Future Research Directions
Authors: Obaidat Muath A. , Rawashdeh Majdi , Alja’afreh Mohammad , Abouali Meryem , Thakur Kutub , Karime Ali
Published: November 2024

The rise of the Internet of Things (IoT) has driven significant advancements across sectors such as urbanization, manufacturing, and healthcare, all of which are focused on enhancing quality of life and stimulating the global economy. This survey offers an in-depth analysis of the integration of blockchain technology with IoT, addressing aspects such as architectural alignment, applications, security, limitations, scalability, and latency. Moreover, this survey focuses on security, integration techniques, and future research directions. The primary contributions of this review include a taxonomy of security concerns specific to IoT, an analysis of integration methods, and insights into consensus mechanisms suitable for resource-constrained environments. These findings highlight the unique challenges and opportunities in IoT–blockchain integration, providing a foundation for advancing secure and scalable IoT applications. By exploring consensus mechanisms and resource-constrained deployments, this paper provides a framework for developing secure and efficient IoT applications utilizing blockchain technology and providing a basis for future research and practical applications. In addition, this survey investigates innovative trends, including AI-driven blockchain for IoT.
Selection and Characterization of Antibodies Recognizing Unnatural Base Pairs
Authors: Lillo Antonietta M. , Velappan Nileena , Wu Ruilian , Bolding Madeline R.
Published: November 2024

Background: Introducing unnatural base pairs into a natural, double-stranded DNA construct is a powerful tool within synthetic biology. Accordingly, the ability to detect these unnatural base pairs has many applications, including the study and detection of semisynthetic organisms. Objective and Methods: The work described here aimed to select human antibodies for the specific recognition of Hirao’s base pair dDs–dPn in various natural DNA contexts by using a combination of phage and yeast display technologies. Results: We selected a total of six antibodies in yeast-displayed scFv format, and further tested three of these antibodies in soluble form as minibodies and IgGs. We also describe an assay that can be used to detect plasmids containing dDs–dPn pair. Conclusions: Our antibodies did not afford the desired specificity or sensitivity for detection of a single unnatural base pair among thousands of natural. However, our data indicate not only that such detection is possible but also that these antibodies may be candidates for further affinity and specificity maturation.
Comparative Analysis of Intestinal Microbiota in Wild, Domesticated, and Cultured Gymnocypris potanini firmispinatus
Authors: Ma Baoshan , Zhang Jiaqi , Li Dapeng , Chu Zhipeng , Liu Jieya , Jin Jiali , Zhong Liqiao
Published: November 2024

The impact of the living environments on the intestinal microbiota of Gymnocypris potanini firmispinatus was studied by analyzing intestinal microbiota diversity, composition, and potential function among wild, domesticated, and cultured groups. The results showed that the fish living in the wild environment exhibited the highest alpha diversity of intestinal microbiota. Intestinal microbial communities in the three groups clustered by living environment, with domesticated and cultured groups showing higher similarity. The dominant phylum in the wild group was Proteobacteria, whereas Fusobacteria and Proteobacteria were the predominate bacteria in the domesticated and cultured groups. RsaHF231, Actinobacteria, Fusobacteria, Cyanobacteria, and Firmicutes were the five key bacteria differentially expressed among the groups. Functional prediction revealed significant variation in the L-cysteine degradation III pathway (PWY-5329) between wild and domesticated groups, and in the vitamin E biosynthesis pathway (PWY-1422) between wild and cultured groups (p < 0.05). These findings highlight the differences in microbiota communities between wild and farmed fish, which are essential for enhancing the cultivation of G. p. firmispinatus and for the formulation of specific probiotics and functional feeds.
Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency
Authors: Hew Yaling , Kutuk Duygu , Duzcu Tuba , Ergun Yagmur , Basar Murat
Published: November 2024

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement in reproductive medicine. AI technologies, such as neural networks, deep learning, and machine learning, promise to enhance quality control (QC) and quality assurance (QA) through increased accuracy, consistency, and operational efficiency. This comprehensive review examines the effects of AI on IVF laboratories, focusing on its role in automating processes such as embryo and sperm selection, optimizing clinical outcomes, and reducing human error. AI’s data analysis and pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans and increasing success rates in fertility treatments. However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human–machine interface in clinical decision-making. Through an in-depth examination of current case studies, advancements, and future directions, this manuscript highlights how AI can revolutionize IVF by standardizing processes, improving patient outcomes, and advancing the precision of reproductive medicine. It underscores the necessity of ongoing research and ethical oversight to ensure fair and transparent applications in this sensitive field, assuring the responsible use of AI in reproductive medicine.