bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing Urban Mobility Resilience: An Exploratory Approach Using Hazard-Based Duration Models
Authors: Jardim Gonçalves Luís A. P. , Ferreira Sara , Ribeiro Paulo J. G.
Published: October 2024

Urban systems are vulnerable to disturbances from both natural and human origins, which can disrupt their normal functioning. Evaluating the resilience of these systems, particularly the main transportation networks and their usage levels, is crucial and innovative for understanding the impacts of such disturbances. Thus, this work aims to assess resilience in urban mobility through the probability of a particular journey using a specific mode: “surviving” through critical travel time. To achieve this, a methodology was developed based on the Weibull model with gamma heterogeneity (hazard-based duration models), which was applied to a medium-sized Portuguese municipality. Eighteen groups representing active populations were set and compared. The results indicated that using the bus and cycling are the most resilient modes of transport, whereas walking is the least resilient. Additionally, a specific group was identified as having lower mobility resilience, making them more vulnerable to disruptions in the transport system. Finally, the findings of this study demonstrate the practical application of this methodology, which relies on travel time to assess resilience and, thus, guide political attention and actions to the less resilient mode. Future research should aim to develop a comprehensive framework that incorporates several variables to fully describe the complex nature of transport systems and their resilience.
L-GraphSAGE: A Graph Neural Network-Based Approach for IoV Application Encrypted Traffic Identification
Authors: Zhang Shihe , Chen Ruidong , Chen Jingxue , Zhu Yukun , Hua Manyuan , Yuan Jiaying , Xu Fenghua
Published: October 2024

Recently, with a crucial role in developing smart transportation systems, the Internet of Vehicles (IoV), with all kinds of in-vehicle devices, has undergone significant advancement for autonomous driving, in-vehicle infotainment, etc. With the development of these IoV devices, the complexity and volume of in-vehicle data flows within information communication have increased dramatically. To adapt these changes to secure and smart transportation, encrypted communication realization, real-time decision-making, traffic management enhancement, and overall transportation efficiency improvement are essential. However, the security of a traffic system under encrypted communication is still inadequate, as attackers can identify in-vehicle devices through fingerprinting attacks, causing potential privacy breaches. Nevertheless, existing IoV traffic application models for encrypted traffic identification are weak and often exhibit poor generalization in some dynamic scenarios, where route switching and TCP congestion occur frequently. In this paper, we propose LineGraph-GraphSAGE (L-GraphSAGE), a graph neural network (GNN) model designed to improve the generalization ability of the IoV application of traffic identification in these dynamic scenarios. L-GraphSAGE utilizes node features, including text attributes, node context information, and node degree, to learn hyperparameters that can be transferred to unknown nodes. Our model demonstrates promising results in both UNSW Sydney public datasets and real-world environments. In public IoV datasets, we achieve an accuracy of 94.23%(↑0.23%). Furthermore, our model achieves an F1 change rate of 0.20%(↑96.92%) in α train, β infer, and 0.60%(↑75.00%) in β train, α infer when evaluated on a dataset consisting of five classes of data collected from real-world environments. These results highlight the effectiveness of our proposed approach in enhancing IoV application identification in dynamic network scenarios.
Charge Transport Characteristics in Doped Organic Semiconductors Using Hall Effect
Authors: Morab Seema , Sundaram Manickam Minakshi , Pivrikas Almantas
Published: October 2024

Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping on the performance and reliability of organic semiconductor devices by accurately modeling these processes. In this work, the number density of charge carriers and Hall voltages are computed for n-type doped semiconductors with two different recombination processes, such as non-Langevin and Langevin-type. The findings reveal that in the Langevin system with β′=1, the number density of charge carriers is almost five and four times lower compared with the non-Langevin system with β′=0.01 for increasing dopant concentrations of Npd = 1 and 3, respectively. The Langevin system also had lower Hall voltages than the steady-state and non-Langevin systems for different magnetic fields with dopants, and the non-Langevin system had nearly identical Hall voltages as the steady-state case. The outcome of the current work provides insights into charge transportation mechanisms in low-mobility doped organic semiconductors with Hall effect measurements to improve device efficiency.
Deep Convolutional Transformer Network for Stock Movement Prediction
Authors: Xie Li , Chen Zhengming , Yu Sheng
Published: October 2024

The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise present in financial data. This study introduces an innovative Deep Convolutional Transformer (DCT) model that amalgamates convolutional neural networks, Transformers, and a multi-head attention mechanism. It features an inception convolutional token embedding architecture alongside separable fully connected layers. Experiments conducted on the NASDAQ, Hang Seng Index (HSI), and Shanghai Stock Exchange Composite (SSEC) employ Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), accuracy, and Matthews Correlation Coefficient (MCC) as evaluation metrics. The findings reveal that the DCT model achieves the highest accuracy of 58.85% on the NASDAQ dataset with a sliding window width of 30 days. In terms of error metrics, it surpasses other models, demonstrating the lowest average prediction error across all datasets for MAE, MSE, and MAPE. Furthermore, the DCT model attains the highest MCC values across all three datasets. These results suggest a promising capability for classifying stock price trends and affirming the DCT model’s superiority in predicting closing prices.
Implementation of a Whisper Architecture-Based Turkish Automatic Speech Recognition (ASR) System and Evaluation of the Effect of Fine-Tuning with a Low-Rank Adaptation (LoRA) Adapter on Its Performance
Authors: Polat Hüseyin , Turan Alp Kaan , Koçak Cemal , Ulaş Hasan Basri
Published: October 2024

This paper focuses on the implementation of the Whisper architecture to create an automatic speech recognition (ASR) system optimized for the Turkish language, which is considered a low-resource language in terms of speech recognition technologies. Whisper is a transformer-based model known for its high performance across numerous languages. However, its performance in Turkish, a language with unique linguistic features and limited labeled data, has yet to be fully explored. To address this, we conducted a series of experiments using five different Turkish speech datasets to assess the model’s baseline performance. Initial evaluations revealed a range of word error rates (WERs) between 4.3% and 14.2%, reflecting the challenges posed by Turkish. To improve these results, we applied the low-rank adaptation (LoRA) technique, which is designed to fine-tune large-scale models efficiently by introducing a reduced set of trainable parameters. After fine-tuning, significant performance improvements were observed, with WER reductions of up to 52.38%. This study demonstrates that fine-tuned Whisper models can be successfully adapted for Turkish, resulting in a robust and accurate end-to-end ASR system. This research highlights the applicability of Whisper in low-resource languages and provides insights into the challenges of and strategies for improving speech recognition performance in Turkish.
A Narrative Review of Identity, Data and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcing
Authors: Bashir Syed Raza , Raza Shaina , Misic Vojislav
Published: October 2024

As digital technology advances, the proliferation of connected devices poses significant challenges and opportunities in mobile crowdsourcing and edge computing. This narrative review focuses on the need for privacy protection in these fields, emphasizing the increasing importance of data security in a data-driven world. Through an analysis of contemporary academic literature, this review provides an understanding of the current trends and privacy concerns in mobile crowdsourcing and edge computing. We present insights and highlight advancements in privacy-preserving techniques, addressing identity, data, and location privacy. This review also discusses the potential directions that can be useful resources for researchers, industry professionals, and policymakers.
Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
Authors: Yu Chunjiang , Sun Yongke , Cao Yong , Liu Lei , Zhou Xiaotao
Published: October 2024

Logs require multiple verifications to ensure accurate volume and quantity measurements. Log end detection is a crucial step in measuring log volume and counting logs. Currently, this task primarily relies on the Mask R-CNN instance segmentation model. However, the Feature Pyramid Network (FPN) in Mask R-CNN may compromise accuracy due to feature redundancy during multi-scale fusion, particularly with small objects. Moreover, counting logs in a single image is challenging due to their large size and stacking. To address the above issues, we propose an improved log segmentation model based on Cascade Mask R-CNN. This method uses ResNet for multi-scale feature extraction and integrates a hierarchical Convolutional Block Attention Module (CBAM) to refine feature weights and enhance object emphasis. Then, a Region Proposal Network (RPN) is employed to generate log segmentation proposals. Finally, combined with Deep SORT, the model tracks log ends in video streams and counts the number of logs in the stack. Experiments demonstrate the effectiveness of our method, achieving an average precision (AP) of 82.3, APs of 75.3 for small, APm of 70.9 for medium, and APl of 86.2 for large objects. These results represent improvements of 1.8%, 3.7%, 2.6%, and 1.4% over Mask R-CNN, respectively. The detection rate reached 98.6%, with a counting accuracy of 95%. Compared to manually measured volumes, our method shows a low error rate of 4.07%.
Seasonal Changes in Salicylic and Jasmonic Acid Levels in Poplar with Differing Stress Responses
Authors: Zhang Linxuan , Wu Xinyu , Tian Chengming , Schneiter Roger
Published: October 2024

Poplars are essential tree species with critical roles in wood production, ecological conservation, environmental protection, and climate change mitigation. Plants of different poplar populations exhibit varying responses to biotic and abiotic stresses, largely driven by phytohormones—key regulators of plant growth and stress responses. Phytohormones control fundamental processes such as cell division, growth, tissue differentiation, organ development, and resistance to environmental challenges such as temperature extremes and drought. In this study, we conducted a quantitative analysis of phytohormone levels in the leaves of two populations of poplar species from the same geographical region over a one-year period, covering the early, mid, and late growth stages. Our results revealed that salicylic acid (SA) and jasmonic acid (JA) concentrations in the stress-resistant poplar (RP) increased significantly during the late growth stage (September), particularly in response to elevated pathogen pressure and fluctuating climatic conditions. In contrast, the susceptible poplar (SP) consistently exhibited lower levels of SA and JA across all growth stages, particularly during the mid-growth stage (characterized by high temperatures and low precipitation) and the late growth stage, when pathogen stress typically intensifies. The limited increase in phytohormone levels in the SP suggests a weaker defense response compared with the RP. These findings demonstrate that climatic factors significantly influence the phytohormonal dynamics in poplars, with the RP exhibiting more robust regulation of SA and JA to mitigate both biotic and abiotic stresses. This study provides a theoretical framework for better understanding the resistance mechanisms in Populus and offers insights for improving stress tolerance in future breeding programs.
The Effects and Contributions of Ecological Factors on Soil Carbon, Water and Nutrient Storages Under Long-Term Vegetation Restoration on the Eastern Loess Plateau
Authors: Xiong Yingnan , Zhang Yufei , Zhang Zhuo , Feng Tianjiao , Wang Ping , Keesstra Saskia
Published: October 2024

Vegetation restoration plays a crucial role in conserving soil and water, as well as rehabilitating ecosystems, by enhancing soil properties and vegetation attributes. The evaluation of the ecological consequences among different vegetation restoration types can be achieved by clarifying the impacts on carbon, water and nutrient storages. In this study, we selected four typical vegetation restoration types (Pinus tabuliformis forest (PTF), Platycladus orientalis forest (POF) and Robinia pseudoacacia forest (RPF) as typical planted forests, and the natural secondary forest (NSF) as the control treatment) in the eastern Loess Plateau of China. The soil properties (at 0–200 cm depth) and vegetation attributes (including arborous, shrubs and herbaceous plants) were measured, as well as calculated soil carbon, water and nutrient storages, with a total of 1600 soil samples and 180 vegetation survey plots. The partial redundancy analysis (pRDA) and correlation analysis were also used to analyze the contributions and relationships among environmental factors, soil eco-hydrology and nutrient supplement services in different forestry ecosystems. The results indicate the following: (1) NSF has the lowest soil bulk density (1.21 ± 0.184 g·cm−3). Soil properties varied significantly at vertical scales, and had obvious surface accumulation. (2) Soil moisture storages were better in natural forests than those in planted forests, with more drastic changes in soil moisture dynamics. (3) The soil carbon, nitrogen, and phosphorus storages significantly differed among four vegetation types, with the highest carbon storages in PTF (207.75 ± 0.674 t·ha−1), the highest nitrogen storages in POF (5.54 t·ha−1), and the highest phosphorus storages in RPF (4.33 t·ha−1), respectively. (4) Soil carbon storages depend primarily on the coupling effect of soil properties and precipitation, while nutrient storage is mainly influenced by soil properties. The results quantify the significant differences in soil water, carbon, and nutrient storage across various vegetation restoration types, and reveal the individual and combined contributions of environmental factors, providing new insights into the mechanisms driving these differences. These findings offer practical guidance for the sustainable management of forest ecosystems and the optimization of ecological restoration strategies on the Loess Plateau.
A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
Authors: Xu Jinlin , Pan Wansu , Tan Haibo , Cheng Longle , Li Xiru , Li Xiaofeng
Published: October 2024

Wireless networks, especially 5G and WiFi networks, have made great strides in increasing network bandwidth and coverage over the past decades. However, the mobility and channel conditions inherent to wireless networks have the potential to impair the performance of traditional Transmission Control Protocol (TCP) congestion control algorithms (CCAs). Google proposed a novel TCP CCA based on Bottleneck Bandwidth and Round-Trip propagation time (BBR), which is capable of achieving high transmission rates and low latency through the estimation of the available bottleneck capacity. Nevertheless, some studies have revealed that BBR exhibits deficiencies in fairness among flows with disparate Round-Trip Times (RTTs) and also displays inter-protocol unfairness. In high-speed wireless networks, ensuring fairness is of paramount importance to guarantee equitable bandwidth allocation among diverse traffic types and to enhance overall network utilization. To address this issue, this paper proposes a BBR–Pacing Gain (BBR–PG) algorithm. By deriving the pacing rate control model, the impact of pacing gain on BBR fairness is revealed. Adjusting the pacing gain according to the RTT can improve BBR’s performance. Simulations and real network experiments have shown that the BBR–PG algorithm retains the throughput advantages of the original BBR algorithm while significantly enhancing fairness. In our simulation experiments, RTT fairness and intra-protocol fairness were improved by 50% and 46%, respectively.
Influence of Wind Direction on Fire Spread on High-Rise Building Facades
Authors: Song Huaitao , Shi Yali , Yao Haowei , Wei Xiaoge , Qin Hengjie , Lou Zhen , Bai Zhenpeng , Li Jingfen , Yu Yueyang
Published: October 2024

In order to study the influence of wind direction on fire spread on building facades, a three-dimensional model of a high-rise building was built with PyroSim software. Numerical simulations were conducted with five wind directions (0°, 45°, 90°, 135° and 180°) and a reference wind speed of 3 m/s. The results show that the fire spread on the building facade was most significant on the leeward side, followed by the upwind condition. The horizontal spread of fire was promoted by crosswinds at 45°, 90° and 135°, while its vertical spread was inhibited. Among these, the inhibitory effect of the 45° crosswind was the greatest. The fire spread area under the leeward condition was the greatest at approximately 341.3 m2, accounting for 17.1% of the facade, and the vertical spread velocity under the condition of no wind was the fastest at 0.075 m/s. In this study, the motion characteristics of facade fire spread under different wind directions were determined, providing supporting evidence to enhance fire safety prevention and control measures in high-rise buildings.
Fishes Associated with a Vulnerable Marine Ecosystem Network in the Central Mediterranean Sea
Authors: Carluccio Angela , Capezzuto Francesca , Maiorano Porzia , Sion Letizia , D’Onghia Gianfranco
Published: October 2024

In order to collect information on ichthyofauna of a deep-sea vulnerable marine ecosystem (VME) network along the Apulian margin (central Mediterranean Sea), two low-impact sampling tools were used in three VMEs characterized by cold-water corals (CWC), namely Bari Canyon (BC), off Monopoli (Mn), and off Santa Maria di Leuca (SML). Using an experimental longline, 53 deployments were carried out between a 314 and 650 m depth for a total of 217 fishing hours, whereas when using the baited lander MEMO (Marine Environment MOnitoring system), 31 deployments were carried out between 427 and 792 m, for a total of 223 h of video recordings. A total of 37 taxa were recorded, comprising 13 Chondrichthyes and 24 Osteichthyes. The similarities in species observed among the VMEs confirm the presence of a network of CWC-VMEs along the Apulian margin, whereas some differences detected are due to the different abundance of some species, such as Galeus melastomus, Helicolenus dactylopterus, and Phycis blennoides. The presence of commercial species, vulnerable/endangered cartilaginous fishes, and large and sexually mature individuals of G. melastomus, H. dactylopterus, and Pagellus bogaraveo in all the VMEs confirms that the network of CWC-VMEs along the Apulian margin can act as a network of refuge areas and an essential fish habitat (EFH) for species threatened by fishing activities.
Effects of Temperature and Salinity on the Growth, Reproduction, and Carotenoid Accumulation in Artemia sinica and Transcriptome Analysis
Authors: Xue Yucai , Jiang Gang , Shu Huang , Wang Weilong , Huang Xuxiong
Published: October 2024

Brine shrimp (Artemia), rich in carotenoids, are widely used in intensive aquaculture to supplement nutrients and enhance the coloration of farmed organisms. This study investigates the growth, reproduction, and carotenoid accumulation in Artemia sinica under varying salinity and temperature conditions. The results showed that temperature and salinity displayed significant interactions with survival, body length, and carotenoid accumulation in the body. The optimal survival and growth conditions of A. sinica (Bohai Sea Gulf) were a temperature range of 25–30 °C and a salinity range of 30–50‰. High temperatures accelerated growth and sexual maturity at the expense of survival rates, while temperatures below 20 °C prevented ovigerous development. Extreme salinity levels negatively affected survival and growth, though high salinity promoted sexual maturity. Carotenoids in A. sinica mainly accumulate as echinenone and canthaxanthin form. Carotenoid accumulation decreased with increased temperature and salinity, and the temperature effect decreased with rising salinity. A. sinica cultivated at a salinity of 10‰ and a temperature of 25 °C exhibits the highest carotenoid content. Transcriptomic analysis revealed that high temperatures primarily affected genes related to stress response and metabolism, while high-salinity regulated genes associated with ion balance and signaling pathways. These findings provide a theoretical basis for enhancing Artemia sinica aquaculture and optimizing cultivation conditions, offering novel insights into nutritional and environmental impacts on brine shrimp biology.
A Review of Comprehensive Guidelines for Computational Fluid Dynamics (CFD) Validation in Solar Chimney Power Plants: Methodology and Manzanares Prototype Case Study
Authors: Fertahi Saïf ed-Dîn , Rehman Shafiqur , Lahrech Khadija , Samaouali Abderrahim , Arbaoui Asmae , Kadiri Imad , Agounoun Rachid
Published: October 2024

This review provides a comprehensive examination of CFD modeling procedures for SCPP, with an emphasis on the detailed methodologies and a case study of the Manzanares prototype in Spain. The introduction delineates the historical context and physical modeling principles of solar chimneys, while highlighting their potential in industrial applications. The governing equations are meticulously discussed, covering assumptions in both 2D and 3D CFD modeling, the continuity and momentum equations, and the selection and accuracy of turbulence models, particularly the k-ε equations. The review also delves into heat transfer modeling, encompassing the energy equation and radiation modeling. Analytical evaluations of turbine pressure drop ratios and performance metrics for power generation efficiency are critically analyzed. The establishment of boundary conditions in solar chimney applications, including sky temperature assessments and distinctions between 2D and 3D boundary conditions, is extensively explored. Mesh generation techniques for both 2D and 3D CFD models are presented, supported by case studies. Parametric studies and experimental investigations are scrutinized to elucidate their impact on the performance of solar chimneys. The temperature–entropy diagram for an idealized Brayton cycle is introduced as a conceptual framework for efficiency analysis. Validation of the CFD codes, both 2D and 3D, against experimental data is performed to ensure model accuracy. The review further examines energy balance approaches in modeling solar chimneys, presenting state-of-the-art CFD results and discussing their implications in both 2D and 3D contexts. The synthesis of these findings culminates in a comprehensive conclusion, offering insights into the future directions and potential advancements in the CFD modeling of solar chimneys. This work aims to serve as a definitive reference for researchers and practitioners in the field, providing a robust foundation for the development and optimization of SCPP technology.
The Physicochemical Properties and Structure of Mung Bean Starch Fermented by Lactobacillus plantarum
Authors: Huang Zhen , Li Yisi , Guo Tian , Xu Li , Yuan Jieyao , Li Zuyin , Yi Cuiping
Published: October 2024

Understanding the relationship between gel formation and the hierarchical structure of mung bean starch fermented by Lactobacillus plantarum has potential value for its green modification and quality improvement. The variations in characteristics, including gelation characteristics, starch chain, and the molecular order degree of mung bean starch fermented by different L. plantarum, were compared. The results show that in the gelation process, starch began to disintegrate at 65 °C, indicating a critical temperature for structural changes. Compared with the control group, although the effects of different L. plantarum sources on mung bean starch varied, notable improvements were observed in water absorption across all groups of fermented starch, along with reduced free water-soluble substances and enhanced anti-expansion ability. This led to the easier formation of gels with higher viscosity, primarily attributed to decreased crystallinity, increased short-chain amylopectin tendency, an elevated amylose content, and enhanced short-range order when microorganisms acted on the crystallization zone. In conclusion, although L. plantarum came from different sources, its action mode on mung bean starch was similar, which could enhance the gel structure.
Impact of Mild Field Drought on the Aroma Profile and Metabolic Pathways of Fresh Tea (Camellia sinensis) Leaves Using HS-GC-IMS and HS-SPME-GC-MS
Authors: Liu Xiaohui , Dong Fabao , Li Yucai , Lu Fu , Wang Botao , Zhou Taicen , Zhao Degang , Huang Mingzheng , Wang Feifei
Published: October 2024

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.
Dietary Pleurotus citrinopileatus Polysaccharide Improves Growth Performance and Meat Quality Associated with Alterations of Gut Microbiota in Arbor Acre Broilers
Authors: Zhou Nannan , Song Xiaoxiao , Wu Changxi , Liang Shuangmin , Yang Liangyu , Ge Changrong , Xiao Zhichao
Published: October 2024

Adding edible fungal polysaccharides to animal diets improves growth performance, meat quality, intestinal health, and immunity without adverse effects. This study aimed to evaluate the impact of Pleurotus citrinopileatus polysaccharide (PCP, including PCP250, PCP500, PCP750, and PCP1000 mg/kg) on the growth performance, meat quality, and microbial composition of Arbor Acre (AA) broilers (total 180) by metabolomics and high-throughput sequencing. The results showed that adding PCP enhanced chicken meat tenderness, redness (a*), and water retention and raised essential amino acids and flavor amino acids (such as umami and sweet amino acids) content. The metabolomics revealed that IMP, creatine, betaine, sarcosine, and taurine were related to improving meat quality in broilers by PCP addition. In addition, amino acid, purine, and lipid metabolism were the main metabolic pathways. Moreover, PCP could regulate muscle metabolism by increasing the relative abundance of Lachnospiraceae and Lactobacillus and the content of short-chain fatty acids (SCFAs). Therefore, PCP may become a promising new dietary supplement in the future, which may improve the yield and quality of broiler chickens.
Characterization of Free and Glycosidically Bound Volatile and Non-Volatile Components of Shiikuwasha (Citrus depressa Hayata) Fruit
Authors: Katherinatama Aldia , Asikin Yonathan , Shimoda Kazuki , Shimomura Momoko , Mitsube Fumimasa , Takara Kensaku , Wada Koji
Published: October 2024

Shiikuwasha, a citrus fruit native to Okinawa, Japan, has various cultivation lines with distinct free volatile and non-volatile components. However, the glycosylated volatiles, which are sources of hidden aromas, remain unknown. This study aimed to characterize the chemical profiles of free and glycosidically bound volatile as well as non-volatile components in the mature fruits of six Shiikuwasha cultivation lines: Ishikunibu, Izumi kugani-like, Kaachi, Kohama, Nakamoto seedless, and Ogimi kugani. Free volatiles were analyzed using solid-phase microextraction–gas chromatography–mass spectrometry. Glycosides were collected via solid-phase extraction and hydrolyzed with β-glucosidase, and the released volatiles were measured. Additionally, the non-volatile components were determined using non-targeted proton nuclear magnetic resonance analysis. Total free and bound volatiles ranged from 457 to 8401 µg/L and from 104 to 548 µg/L, respectively, and the predominant free volatiles found were limonene, γ-terpinene, and p-cymene. Twenty volatiles were released from glycosides, including predominant 1-hexanol and benzyl alcohol, with Kaachi and Ogimi kugani showing higher concentrations. Principal component analysis (PCA) revealed that taste-related compounds like sucrose, citrate, and malate influenced line differentiation. The PCA of the combined data of free and bound volatile and non-volatile components showed flavor component variances across all lines. These findings provide valuable insights into the chemical profiles of Shiikuwasha fruits for fresh consumption and food and beverage processing.
Evaluation of the Physicochemical Attributes of Beef, Chicken, and Pork Muscles Injected with Microbial Proteases for Designing Senior-Friendly Processed Meat Products
Authors: Kim Si-Young , Song Dong-Heon , Chung Wookyung , Choi Hyun-Shik , Han Sung Gu , Kim Hyun-Wook
Published: October 2024

In developed countries, the growing elderly population has increased the demand for senior-friendly processed meat products. This study investigated the effects of four commercial microbial proteases (Alcalase, Flavourzyme, Neutrase, and Protamex) on the general physicochemical attributes of beef top round, chicken breast, and pork loin, which are lean muscle cuts suitable for developing senior-friendly meat products. Muscle samples were injected with microbial protease solutions (0.7% and 1.2% (w/w)), cooked, and used for analysis. The microbial protease injection significantly reduced the hardness of cooked muscles. Despite the evident degradation of the myosin heavy chain in Alcalase treatment, the lowest hardness values were observed in Protamex-treated samples, suggesting that myosin degradation alone does not fully account for tenderness improvement. Unfortunately, microbial protease treatments increased cooking loss in beef and chicken muscles (p < 0.05). The surface color characteristics, including redness and yellowness, remained unaffected by the enzymatic treatments, supporting the practical use of these proteases for meat tenderization without inducing color defects. While microbial proteases demonstrate potential for improving meat tenderness, future research should focus on mitigating cooking loss and ensuring desirable taste and flavor for the commercial production of senior-friendly processed meat products using the microbial proteases.
Detection of Tert-Butylhydroquinone in Edible Oils Using an Electrochemical Sensor Based on a Nickel-Aluminum Layered Double Hydroxide@Carbon Spheres-Derived Carbon Composite
Authors: Zhang Jin , Chen Jingrong , Li Jiejun , Xie Yixi
Published: October 2024

Phenolic antioxidants such as tert-butylhydroquinone (TBHQ) can prolong the shelf life of edible oils by delaying the oxidation process. The excessive use of TBHQ can damage food quality and public health, so it is necessary to develop an efficient TBHQ detection technique. In this work, nickel-aluminum double hydroxide (NiAl-LDH) was grown on glucose carbon spheres (GC), which formed porous carbon nanomaterials (named NiAl-LDH@GC-800) after pyrolysis at 800 °C. The successful synthesis of the material was verified by scanning electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The obtained NiAl-LDH@GC-800 was dopped onto a glass carbon electrode to prepare an electrochemical sensor for TBHQ. The synergistic effect of porous carbon and Ni metal reduced from NiAl-LDH by high-temperature calcination accelerated the electron transfer rate and improved the sensitivity of the sensor. The prepared sensor showed a low limit of detection (LOD) of 8.2 nM, a high sensitivity (4.2 A·M−1), and a good linear range (20~300 µM) in detecting TBHQ. The sensor was also successfully used for TBHQ detection in edible oils, including chili oil, peanut oil, and rapeseed oil.
Development of Cashew and Pistachio Ladders through a Food-Processing Approach
Authors: Shwe Yee Nicki , Ng Hoi Ka , Zeng Jingjing , Bao Jinjing , Campbell Dianne E. , Turner Paul J. , Lee Nanju Alice
Published: October 2024

Following successful oral immunotherapy (OIT) for peanut allergy using boiled peanuts (BOPI trial), this study investigated the potential of wet-thermal-processing-induced allergen modification, specifically soaking and boiling (1–4 h) to reduce the allergenicity of cashew and pistachio allergens. In addition, this study provides a foundation of understanding for developing safer forms of cashew/pistachio administration for application in OIT by gradual exposure to increasing doses of modified allergens with reduced potency as an “allergen ladder”. An SDS-PAGE analysis and an intrinsic-fluorescence spectroscopy revealed altered tertiary structures of the allergens, leading to their denaturation and even degradation. Notably, the reduction in both allergen-specific polyclonal IgG and human-specific IgE (sIgE) binding correlated with the treatment time, with the most significant decrease observed after 4 h of boiling. In contrast, shorter soaking treatments showed negligible effects on the IgE-binding capacity of these nuts, therefore indicating a further need for optimization. These findings indicate that extended boiling effectively reduced the amounts of the highly potent allergenic component Ana o 3 in cashew and Pis v 1 in pistachio, as confirmed by ELISA using polyclonal anti-Ana o 3 antibodies, and an immunoblot showed decreased IgE epitope binding in cashew and pistachio allergens, which further modified their allergenic profiles. This approach shows promise as a viable method for offering a safer therapeutic option for cashew/pistachio allergy.
Does a Fractional-Order Recurrent Neural Network Improve the Identification of Chaotic Dynamics?
Authors: Echenausía-Monroy José Luis , Magallón-García Daniel Alejandro , Ontañón-García Luis Javier , Rivera Rodriguez Raul , Pena Ramirez Jonatan , Álvarez Joaquín
Published: October 2024

This paper presents a quantitative study of the effects of using arbitrary-order operators in Neural Networks. It is based on a Recurrent Wavelet First-Order Neural Network (RWFONN), which can accurately identify several chaotic systems (measured by the mean square error and the coefficient of determination, also known as R-Squared, r2) under a fixed parameter scheme in the neural algorithm. Using fractional operators, we analyze whether the identification capabilities of the RWFONN are improved, and whether it can identify signals from fractional-order chaotic systems. The results presented in this paper show that using a fractional-order Neural Network does not bring significant advantages in the identification process, compared to an integer-order RWFONN. Nevertheless, the neural algorithm (modeled with an integer-order derivative) proved capable of identifying fractional-order dynamical systems, whose behavior ranges from periodic and multi-stable to chaotic oscillations. That is, the performances of the Neural Network model with an integer-order derivative and the fractional-order network are practically identical, making the use of fractional-order RWFONN-type networks meaningless. The results deepen the work previously published by the authors, and contribute to developing structures based on robust and generic neural algorithms to identify more than one chaotic oscillator without retraining the Neural Network.
Artificial Intelligence-Based Segmentation and Classification of Plant Images with Missing Parts and Fractal Dimension Estimation
Authors: Batchuluun Ganbayar , Kim Seung Gu , Kim Jung Soo , Mahmood Tahir , Park Kang Ryoung
Published: October 2024

Existing research on image-based plant classification has demonstrated high performance using artificial intelligence algorithms. However, limited camera viewing angles can cause parts of the plant to be invisible in the acquired images, leading to an inaccurate classification. However, this issue has not been addressed by previous research. Hence, our study aims to introduce a method to improve classification performance by taking these limitations into account; specifically, we incorporated both segmentation and classification networks structured as shallow networks to expedite the processing times. The proposed shallow plant segmentation network (Shal-PSN) performs adversarial learning based on a discriminator network; and a shallow plant classification network (Shal-PCN) with applied residual connections was also implemented. Moreover, the fractal dimension estimation is used in this study for analyzing the segmentation results. Additionally, this study evaluated the performance of the proposed Shal-PSN that achieved the dice scores (DSs) of 87.43% and 85.71% with PlantVillage and open leaf image (OLID-I) open datasets, respectively, in instances where 40–60% of plant parts were missing. Moreover, the results demonstrate that the proposed method increased the classification accuracy from 41.16% to 90.51% in the same instances. Overall, our approach achieved superior performance compared to the existing state-of-the-art classification methods.
Hover Flight Improvement of a Quadrotor Unmanned Aerial Vehicle Using PID Controllers with an Integral Effect Based on the Riemann–Liouville Fractional-Order Operator: A Deterministic Approach
Authors: Delgado-Reyes Gustavo , Valdez-Martínez Jorge Salvador , Guevara-López Pedro , Hernández-Pérez Miguel Angel
Published: October 2024

The hovering flight of a quadrotor Unmanned Aerial Vehicle (UAV) refers to maintaining the aircraft in a fixed position in the air, without lateral, vertical, or rotational movements, using only the vehicle’s control systems to maintain proper balance in all spatial dimensions. Algorithms and control systems have been developed to continuously adjust motor speeds to counteract deviations from the desired position and achieve effective hovering flight. This paper proposes a set of PID controllers with an integral effect based on the Riemann–Liouville fractional-order approach to improve the hovering flight of a quadrotor UAV. This research innovates by introducing a set of fractional-order PID controllers for UAV hover stability, which offer better adaptability to non-linear dynamics and robustness than traditional PID controllers. Also presented is the development of new performance metrics (MSE, BQC-LR), which allow for more comprehensive control system evaluations. A thorough comparative analysis with conventional control methods demonstrates the superior performance of fractional-order control in real-world simulations. The numerical simulation results show the effectiveness of the proposed Fractional Integral Action PID Controller in the control of UAV hovering flight, while comparative analyses against a classical controller emphasize the benefits of the fractional-order approach in terms of control accuracy.
On the Large-x Asymptotic of the Classical Solutions to the Non-Linear Benjamin Equation in Fractional Sobolev Spaces
Authors: Parumasur Nabendra , Aluko Olabisi
Published: October 2024

In this work, we study the large-x asymptotic of classical solutions to the non-linear Benjamin equation modeling propagation of small amplitude internal waves in a two fluid system. In our analysis, we extend known Hs-well-posedness results to the case of the variable-weight Sobolev spaces. The spaces provide a direct control over the asymptotics of classical solutions and their weak derivatives, and permit us to compute the bulk large-x asymptotic of classical solutions explicitly in terms of input data. The asymptotic formula provides a precise description of the qualitative behaviour of classical solutions in weighted spaces and yields a number of weighted persistence and continuation results automatically.
Re-Supplying Autonomous Mobile Parcel Lockers in Last-Mile Distribution
Authors: Hedayati Sajjad , Setak Mostafa , Van Woensel Tom , Demir Emrah
Published: October 2024

This paper investigates a practical last-mile delivery scenario where a fleet of trucks replenishes autonomous mobile parcel lockers (AMPLs) in an urban setting. The lockers move along specified paths within restricted zones to reach customers’ locations. Ensuring seamless coordination between trucks and AMPLs requires the identification of suitable locations to exchange empty or loaded modular lockers. We first introduce a mixed-integer linear programming (MILP) formulation for the investigated problem. The proposed formulation establishes the basis for optimizing meeting point selection and routing decisions. Additionally, the study introduces a cluster-based simulated annealing (CSA) algorithm tailored for addressing larger-scale instances of the studied problem. The CSA algorithm incorporates the K-means clustering method with specialized operators rooted in an extensive neighborhood search, aiming to improve the effectiveness of solution discovery. We generated a new set of benchmark instances to assess the MILP formulation’s efficiency and the proposed metaheuristic algorithm and conducted comprehensive numerical experiments.
The Relationship of Duffy Gene Polymorphism with High-Sensitivity C-Reactive Protein, Mortality, and Cardiovascular Outcomes in Black Individuals
Authors: Ha Edward T. , Haessler Jeffery , Taylor Kent D. , Tuftin Bjoernar , Briggs Matt , Parikh Manish A. , Peterson Stephen J. , Gerszten Robert E. , Wilson James G. , Kelsey Karl , Tahir Usman A. , Seeman Teresa , Rich Stephen S. , Carson April P. , Post Wendy S. , Kooperberg Charles , Rotter Jerome I. , Raffield Laura M. , Auer Paul , Reiner Alex P.
Published: October 2024

Background: Black adults have higher incidence of all-cause mortality and worse cardiovascular disease (CVD) outcomes when compared to other U.S. populations. The Duffy chemokine receptor is not expressed on erythrocytes in a large majority of Black adults, but the clinical implications of this are unclear. Methods: Here, we investigated the relationship of Duffy receptor status, high-sensitivity C-reactive protein (hs-CRP), and mortality and incident CVD events (coronary heart disease, stroke, and heart failure) in self-identified Black members of three contemporary, longitudinal cohort studies (the Women’s Health Initiative, Jackson Heart Study, and Multi-Ethnic Study of Atherosclerosis). Data on 14,358 Black participants (9023 Duffy-null and 5335 Duffy-receptor-positive, as defined using single-nucleotide polymorphism (SNP) rs2814778) were included in this analysis. Results: Duffy null was strongly associated with higher hs-CRP (meta-analysis p = 2.62 × 10−9), but the association was largely attenuated, though still marginally significant (p = 0.005), after conditioning on known CRP locus alleles in linkage disequilibrium with the Duffy gene. In our discovery cohorts, Duffy-null status appeared to be associated with a higher risk of all-cause mortality and incident stroke, though these associations were attenuated and non-significant following adjustment for traditional risk factors including hs-CRP. Moreover, the association of Duffy-null status with mortality could not be replicated in an independent sample of Black adults from the UK Biobank. Conclusions: These findings suggest that the higher levels of hs-CRP found in Duffy-null individuals may be in part independent of CRP alleles known to influence circulating levels of hs-CRP. During the follow-up of this community-based sample of Black participants, Duffy-null status was not associated with mortality or incident CVD events independently of traditional risk factors including hs-CRP.