bluuebunny/crossref_metadata_2025 huggingface dataset:
Discrepant Semantic Diffusion Boosts Transfer Learning Robustness
Authors: Gao Yajun , Bai Shihao , Zhao Xiaowei , Gong Ruihao , Wu Yan , Ma Yuqing
Published: December 2023

Transfer learning could improve the robustness and generalization of the model, reducing potential privacy and security risks. It operates by fine-tuning a pre-trained model on downstream datasets. This process not only enhances the model’s capacity to acquire generalizable features but also ensures an effective alignment between upstream and downstream knowledge domains. Transfer learning can effectively speed up the model convergence when adapting to novel tasks, thereby leading to the efficient conservation of both data and computational resources. However, existing methods often neglect the discrepant downstream–upstream connections. Instead, they rigidly preserve the upstream information without an adequate regularization of the downstream semantic discrepancy. Consequently, this results in weak generalization, issues with collapsed classification, and an overall inferior performance. The main reason lies in the collapsed downstream–upstream connection due to the mismatched semantic granularity. Therefore, we propose a discrepant semantic diffusion method for transfer learning, which could adjust the mismatched semantic granularity and alleviate the collapsed classification problem to improve the transfer learning performance. Specifically, the proposed framework consists of a Prior-Guided Diffusion for pre-training and a discrepant diffusion for fine-tuning. Firstly, the Prior-Guided Diffusion aims to empower the pre-trained model with the semantic-diffusion ability. This is achieved through a semantic prior, which consequently provides a more robust pre-trained model for downstream classification. Secondly, the discrepant diffusion focuses on encouraging semantic diffusion. Its design intends to avoid the unwanted semantic centralization, which often causes the collapsed classification. Furthermore, it is constrained by the semantic discrepancy, serving to elevate the downstream discrimination capabilities. Extensive experiments on eight prevalent downstream classification datasets confirm that our method can outperform a number of state-of-the-art approaches, especially for fine-grained datasets or datasets dissimilar to upstream data (e.g., 3.75% improvement for Cars dataset and 1.79% improvement for SUN dataset under the few-shot setting with 15% data). Furthermore, the experiments of data sparsity caused by privacy protection successfully validate our proposed method’s effectiveness in the field of artificial intelligence security.
Vehicle Simulation Algorithm for Observations with Variable Dimensions Based on Deep Reinforcement Learning
Authors: Liu Yunzhuo , Zhang Ruoning , Zhou Shijie
Published: December 2023

Vehicle simulation algorithms play a crucial role in enhancing traffic efficiency and safety by predicting and evaluating vehicle behavior in various traffic scenarios. Recently, vehicle simulation algorithms based on reinforcement learning have demonstrated excellent performance in practical tasks due to their ability to exhibit superior performance with zero-shot learning. However, these algorithms face challenges in field adaptation problems when deployed in task sets with variable-dimensional observations, primarily due to the inherent limitations of neural network models. In this paper, we propose a neural network structure accommodating variations in specific dimensions to enhance existing reinforcement learning methods. Building upon this, a scene-compatible vehicle simulation algorithm is designed. We conducted experiments on multiple tasks and scenarios using the Highway-Env traffic environment simulator. The results of our experiments demonstrate that the algorithm can successfully operate on all tasks using a neural network model with fixed shape, even with variable-dimensional observations. Our model exhibits no degradation in simulation performance when compared to the baseline algorithm.
DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI
Authors: Xia Zhiqiang , Fan Yusi , Li Kewei , Wang Yueying , Huang Lan , Zhou Fengfeng
Published: December 2023

Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings.
Application of Artificial Intelligence Techniques to Detect Fake News: A Review
Authors: Berrondo-Otermin Maialen , Sarasa-Cabezuelo Antonio
Published: December 2023

With the rapid growth of social media platforms and online news consumption, the proliferation of fake news has emerged as a pressing concern. Detecting and combating fake news has become crucial in ensuring the accuracy and reliability of information disseminated through social media. Machine learning plays a crucial role in fake news detection due to its ability to analyze large amounts of data and identify patterns and trends that are indicative of misinformation. Fake news detection involves analyzing various types of data, such as textual or media content, social context, and network structure. Machine learning techniques enable automated and scalable detection of fake news, which is essential given the vast volume of information shared on social media platforms. Overall, machine learning provides a powerful tool for detecting and preventing the spread of fake news on social media. This review article provides an extensive analysis of recent advancements in fake news detection. The chosen articles cover a wide range of approaches, including data mining, deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based techniques.
A Fast Mismatch Calibration Method Based on Frequency Domain Orthogonal Decomposition for Time-Interleaved Analog-to-Digital Converters
Authors: Sun Lin , Lang Lili , Zhong Wei , Liu Haijing , Dong Yemin
Published: December 2023

This paper proposes a fully digital background calibration method for time-interleaved analog-to-digital converter (TIADC) mismatches. The method analyzes the frequency and phase of spurious signals caused by three types of mismatches in TIADCs in the frequency domain. By utilizing the Hilbert transform and frequency shifting, orthogonal basis signals located at the mismatch frequencies can be constructed. The calibration of mismatches is achieved by linearly combining the orthogonal basis signals with the estimated coefficients and subtracting them from the original signal. The estimation of coefficients is determined by evaluating the correlation between the linear combination of orthogonal basis signals and the calibrated signal. Furthermore, an exponential moving average (EMA) and least mean square (LMS) algorithm are introduced to expedite the coefficient estimation process. The entire calibration process converges in merely 600 samples, significantly improving the convergence speed. By monitoring the amplitude of the input signal and adjusting the LMS step, the algorithm is functional under different amplitude signals, enhancing the robustness. An off-chip calibration is conducted based on a commercial 14-bit, 8-channel, 2.4GSPS TIADC. Results indicate that all spurious signals are suppressed below 80 dB, and the convergence rate is consistent with the simulation.
MCFP-YOLO Animal Species Detector for Embedded Systems
Authors: Ibraheam Mai , Li Kin Fun , Gebali Fayez
Published: December 2023

Advances in deep learning have led to the development of various animal species detection models suited for different environments. Building on this, our research introduces a detection model that efficiently handles both batch and real-time processing. It achieves this by integrating a motion-based frame selection algorithm and a two-stage pipelining–dataflow hybrid parallel processing approach. These modifications significantly reduced the processing delay and power consumption of the proposed MCFP-YOLO detector, particularly on embedded systems with limited resources, without trading off the accuracy of our animal species detection system. For field applications, the proposed MCFP-YOLO model was deployed and tested on two embedded devices: the RP4B and the Jetson Nano. While the Jetson Nano provided faster processing, the RP4B was selected due to its lower power consumption and a balanced cost–performance ratio, making it particularly suitable for extended use in remote areas.
Investigation of the Polyphenol Recovery of Overripe Banana Peel Extract Utilizing Cloud Point Extraction
Authors: Athanasiadis Vassilis , Chatzimitakos Theodoros , Mantiniotou Martha , Kalompatsios Dimitrios , Bozinou Eleni , Lalas Stavros I.
Published: December 2023

Consuming fruits and vegetables necessitates discarding the inedible parts, which raises issues such as waste management and contamination of the environment. Numerous studies have been conducted in recent years in an effort to identify alternatives that reduce the negative effects of food and agricultural waste. This study aims to investigate the polyphenol recovery and the antioxidant capacity of overripe banana peel through cloud point extraction (CPE), which is a green method. The optimal conditions of the CPE were three steps of CPE with 10% w/v lecithin, which was used as surfactant, pH 7, and a salt concentration of 15% at 45 °C for 20 min. The total polyphenol content (TPC) was determined to be 541.25 mg GAE/L, the total flavonoid content (TFC) was determined to be 226.38 mg RtE/L, and FRAP and DPPH assays were found to be 2.52 and 2.91 mmol AAE/L, respectively. According to the results, the antioxidant compounds from banana peels can effectively be extracted with the proposed CPE procedure. The as-prepared extracts can potentially be used as food additives to improve human well-being and even as feed additives for a similar purpose.
Forest Insect Outbreak Dynamics: Fractal Properties, Viscous Fingers, and Holographic Principle
Authors: Soukhovolsky Vladislav , Kovalev Anton , Tarasova Olga , Ivanova Yulia
Published: December 2023

During the growth of a forest insect outbreak epicenter, there are processes that involve the formation and expansion of the primary epicenter of forest damage, as well as secondary epicenters—both connected and unconnected to the primary one. This study characterizes outbreak epicenters in terms of their fractal dimensions and “viscous finger” parameters at the epicenter boundary, highlighting their significance in the context of forest insect management. Local outbreak epicenters were found to be characterized by fractal dimension D = 1.4–1.5, and the boundaries of the epicenters were described using the “viscous finger” model. Proposed models were constructed and validated using remote sensing data obtained from MODIS and Sentinel-2 satellites at epicenter sites and boundaries during the outbreak of the Siberian silk moth Dendrolimus sibiricus Tschetverikov from 2014 to 2020 in the Krasnoyarsk region of Russia. The study revealed that the frequency of the mode spectrum of one-stage spatial series of “viscous fingers” corresponds with the data on the development of the outbreak foci area.
Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
Authors: Adeke James Msughter , Liu Guangjie , Zhao Junjie , Wu Nannan , Bashir Hafsat Muhammad
Published: December 2023

Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.
Influence of Turbidity on Foraging Behaviour in Three-Spined Sticklebacks (Gasterosteus aculeatus)
Authors: Lange Jensen Lasse , Bjørn Thomas , Hein Korsgaard Andreas , Pertoldi Cino , Madsen Niels
Published: December 2023

Anthropogenic activities increase turbidity in coastal marine environments globally, and turbidity is particularly caused by eutrophication. Turbidity is a measurement of the scattering and absorption of light by suspended matter in water. An increase in turbidity influences visual predators and affects community structures and whole ecosystems. The three-spined stickleback (Gasterosteus aculeatus) is a widespread species in the northern hemispheric Pacific and Atlantic oceans. It is a visual predator and, therefore, a very well-suited species for studying the effects of increasing turbidity on foraging behaviour and activity. Sticklebacks used for this study were from an aquarium in the North Sea Oceanarium. They have been in the aquarium for around two months and were originally collected in a highly eutrophicated marine fjord system. They were individually placed in an observation aquarium, fed with krill, given 10 min to forage, and observed by video cameras. The video films were analysed to study stickleback predation behaviour. Experiments were repeated with four different turbidity treatments, ranging from a mean of 0.034 up to 10 NTU (nephelometric turbidity unit). Bentonite clay was used as a turbidity-increasing substance. A statistically significant difference in foraging behaviour and activity between the turbidity treatments was observed. The test subjects were found to lunge less for prey and had a higher feeding latency with increasing turbidity. Additionally, they were less active with increasing turbidity. The behavioural instability estimated as a variation in feeding latency increased with increasing turbidity but decreased at the highest turbidity value. Our study indicates an effect of turbidity-increasing events on the behaviour of the three-spined stickleback and potentially also other similar visual predators.
Effects of Increased Temperature on Brain and Sensory Development in the Port Jackson Shark (Heterodontus portusjacksoni)
Authors: Peele Emily E. , Huveneers Charlie , Brown Culum , Gervais Connor R. , Yopak Kara E.
Published: December 2023

Morphological differences in the peripheral (sensory) and central (brain) nervous system may confer sensory and/or behavioral variation in elasmobranchs, both across taxa and throughout ontogeny. Over the last century, sea surface temperatures have increased over 0.5 °C and are predicted to rise 1–4 °C by the year 2100, potentially affecting species’ physiological performance negatively. As the nervous system of fishes grows continually throughout their lives, it may be highly plastic in response to environmental changes. This study examined the effects of increased rearing temperature on nervous system development in Port Jackson sharks (Heterodontus portusjacksoni). Egg cases (n = 21) were collected from Gulf St. Vincent (Adelaide, SA) and placed into either ambient (17.6 °C) or 3 °C above ambient seawater conditions through hatching and reared for up to five months post-hatch. Relative volumes of the eyes and nose (olfactory rosette) were quantified using magnetic resonance imaging, and relative brain size and size of major brain regions were compared between the two treatment groups. The size of the olfactory bulbs and tegmentum varied significantly between the treatment groups, which suggest differences in primary, secondary, or tertiary sensory processing and/or motor functions at elevated temperatures. While studies on acute responses to environmental conditions cannot inform true adaptation across broad timescales, understanding the effects of increased temperature on the brain phenotype can aid in predicting how elasmobranchs may fare in response to changing ocean conditions.
Flight Dynamic Characteristics of Wide-Body Aircraft with Wind Gust and Turbulence
Authors: Mehmood Kashif , Ali Shah Syed Irtiza , Ali Shams Taimur , Mumtaz Qadri Muhammad Nafees , Khan Tariq Amin , Kukulka David
Published: December 2023

In this research, a wide-body aircraft was analyzed with critical monitoring of its states, a function of several control inputs (wind gust, turbulence, and microburst). The aerodynamic and stability coefficients of a Boeing 747-200 were obtained from previously published works and 6- DOF equations were formulated. Simulations were conducted for various control inputs to determine the aircraft’s free response, as well as the forced response. In order to understand the nature of the atmosphere, three different models were incorporated, including (i) the Dryden Model, (ii) wind gust, and (iii) microburst. The aircraft was found to be stable in the longitudinal and lateral flight modes, with trim conditions agreeing with published data. For a vertical wind gust of −10 ft/s, the AoA and pitch rate were observed to oscillate sinusoidally and became stable with new trim conditions. These states were found to regain trim conditions once the gust was removed. In the case of 3D gust, it was found that the longitudinal modes achieved a new trim condition through Phugoid oscillations, whereas the lateral modes underwent short-period oscillations. For the case of turbulence, random fluctuations were observed for trim conditions with no unstable behavior. When considering the microburst case, it was found that the aircraft initially gained altitude in the region of the headwind; this was followed by a sharp descent under the influence of a vertical velocity component.
Antarctic Soil Yeasts with Fermentative Capacity and Potential for the Wine Industry
Authors: Navarro Laura , Gil i Cortiella Mariona , Gutiérrez-Moraga Ana , Calisto Nancy , Ubeda Cristina , Corsini Gino
Published: December 2023

Low fermentation temperatures are usually employed to obtain high-quality wines. This is especially interesting for white wine production since it prevents the loss of volatile compounds and a browning appearance; however, available fermentative yeasts do not usually tolerate low temperatures. Therefore, an interesting place to find new yeasts with cryotolerance is the Antarctic continent. From soil samples collected in Antarctica, 125 yeasts were isolated, of which 25 exhibited fermentative activity at 10 °C. After a fingerprinting assay, we classified the candidates into nine isotypes and sequenced internal transcribed spacer regions for their identification. These yeasts were identified as part of the Mrakia genus. Sugar and alcohol tolerance tests showed that some of these Antarctic soil yeasts were able to grow up to 9% alcohol, and 25% sugar was reached; however, they exhibited longer latency periods compared to the control Saccharomyces cerevisiae. The optimal growing temperature for the isolated Antarctic yeasts was between 10 °C and 15 °C. A comprehensive analysis of the results obtained showed that the isolates 10M3-1, 4M3-6, and 4B1-35 could be good candidates for fermentation purposes due to their alcohol, sugar tolerance, and growth features. Our results prove that it is possible to isolate fermentative yeasts from Antarctic soil with promising characteristics for their potential use in the wine production industry.
Utilization of Hyperspectral Imaging with Chemometrics to Assess Beef Maturity
Authors: Haughey Simon A. , Montgomery Holly , Moser Bernadette , Logan Natasha , Elliott Christopher T.
Published: December 2023

There is a growing demand from consumers for more assurance in premium food products such as beef and especially steak. The quality of beef steak is primarily dictated by the maturation which ultimately influences its taste and flavor. These enhanced qualities have resulted in steak becoming a premium product that consumers are willing to pay a premium price for. A challenge, however, is analyzing the maturity of beef by traditional analytical techniques. Hyperspectral imaging (HSI) is a methodology that is gaining traction mainly due to miniaturization, improved optics, and software. In this study, HSI was applied to wet aged beef supplied at various stages of maturity, with spectral data generated using a portable hyperspectral camera. Two trials were conducted over a five-month period: (i) proof of principle and (ii) a bespoke sampling trial for the industry. With the support of industry participation, all samples were sourced from a highly reputable UK/Ireland supplier. To enhance data interpretation, the spectral data collected were combined with multivariate analysis. A range of chemometric models were generated using unsupervised and supervised methods to determine the maturity of the beef, and external validation was performed. The external validation showed good accuracy for “unknown samples” tested against the model set and ranged from 74 to 100% for the different stages of maturity (20, 30, and 40 days old). This study demonstrated that HSI can detect different maturity timepoints for beef samples, which could play an important role in solving some of the challenges that the industry faces with ensuring the authenticity of their products. This is the first time that portable HSI has been coupled with chemometric modeling for assessing the maturity of beef, and it can serve as a model for other food authenticity and quality applications.
Research on the Impact of Consumer Experience Satisfaction on Green Food Repurchase Intention
Authors: Wang Jing , Xu Shiwei , Zhang Siyuan , Sun Chen , Wu Linhai
Published: December 2023

With the continuous improvement in people’s living standards and the change in consumption concept, green food is favored by more and more consumers. Consumer repurchase behavior is a necessary condition to activate the market, expand the consumption scale and stabilize the continuous growth of the market. Repurchase intention is the most direct factor affecting consumers’ green food repurchase intention. Therefore, it is necessary to study consumers green food repurchase intentions. This study collects data from 303 consumer surveys on green food consumption to explore the impact of consumer satisfaction with consumption experience on green food repurchase intention and further explore the mechanisms and influence boundaries. The results show that (1) consumer experience satisfaction positively affects green food repurchase intention; (2) consumer experience satisfaction can improve consumers’ green food repurchase intention through consumer perceptions of social value, green self-efficacy and warm glow; (3) the higher the degree of consumer inertia, the stronger the influence of green self-efficacy and warm glow on consumers’ green food repurchase intention; and (4) the higher the degree of consumer subjective norms, the stronger the influence of consumer perceived social value, green self-efficacy and warm glow on the consumer’s green food repurchase intention. This study provides a new perspective and theoretical framework for promoting consumers’ green food repurchase intention, and it may have certain theoretical significance and practical impact on green food market growth, sustainable carrying of the ecological environment and high-quality development of agriculture.
Effects of Fermentation with Tetragenococcus halophilus and Zygosaccharomyces rouxii on the Volatile Profiles of Soybean Protein Hydrolysates
Authors: Cao Chenchen , Waterhouse Geoffrey I. N. , Sun Weizheng , Zhao Mouming , Sun-Waterhouse Dongxiao , Su Guowan
Published: December 2023

The effects of fermentation with lactic acid bacteria (LAB) and yeast on the aroma of samples were analyzed in this work. The volatile features of different soybean hydrolysates were investigated using both GC-MS and GC-IMS. Only 47 volatile flavor compounds (VFCs) were detected when using GC-IMS, while a combination of GC-MS and GC-IMS resulted in the identification of 150 compounds. LAB-yeast fermentation could significantly increase the diversity and concentrations of VFCs (p < 0.05), including alcohols, acids, esters, and sulfurs, while reduce the contents of aldehydes and ketones. Hierarchical clustering and orthogonal partial least squares analyses confirmed the impact of fermentation on the VFCs of the hydrolysates. Seven compounds were identified as significant compounds distinguishing the aromas of different groups. The partial least squares regression analysis of the 25 key VFCs (ROAV > 1) and sensory results revealed that the treatment groups positively correlated with aromatic, caramel, sour, overall aroma, and most of the key VFCs. In summary, fermentation effectively reduced the fatty and bean-like flavors of soybean hydrolysates, enhancing the overall flavor quality, with sequential inoculation proving to be more effective than simultaneous inoculation. These findings provided a theoretical basis for improving and assessing the flavor of soybean protein hydrolysates.
Effect of Molecular Weight on the Structural and Emulsifying Characteristics of Bovine Bone Protein Hydrolysate
Authors: Zhu Yaodi , Hong Niancheng , Zhao Lijun , Liu Shengnan , Zhang Jie , Li Miaoyun , Ma Yangyang , Liang Dong , Zhao Gaiming
Published: December 2023

The emulsifying capacity of bovine bone protein extracted using high-pressure hot water (HBBP) has been determined to be good. Nevertheless, given that HBBP is a blend of peptides with a broad range of molecular weights, the distinction in emulsifying capacity between polypeptide components with high and low molecular weights is unclear. Therefore, in this study, HBBP was separated into three molecular weight components of 10–30 kDa (HBBP 1), 5–10 kDa (HBBP 2), and <5 kDa (HBBP 3) via ultrafiltration, and the differences in their structures and emulsifying properties were investigated. The polypeptide with the highest molecular weight displayed the lowest endogenous fluorescence intensity, the least solubility in an aqueous solution, and the highest surface hydrophobicity index. Analysis using laser confocal Raman spectroscopy showed that with an increase in polypeptide molecular weight, the α-helix and β-sheet contents in the secondary structure of the polypeptide molecule increased significantly. Particle size, rheological characteristics, and laser confocal microscopy were used to characterize the emulsion made from peptides of various molecular weights. High-molecular-weight peptides were able to provide a more robust spatial repulsion and thicker interfacial coating in the emulsion, which would make the emulsion more stable. The above results showed that the high-molecular-weight polypeptide in HBBP effectively improved the emulsion stability when forming an emulsion. This study increased the rate at which bovine bone was utilized and provided a theoretical foundation for the use of bovine bone protein as an emulsifier in the food sector.
Modeling Textural Properties of Cooked Germinated Brown Rice Using the near-Infrared Spectra of Whole Grain
Authors: Kaewsorn Kannapot , Phanomsophon Thitima , Maichoon Pisut , Pokhrel Dharma Raj , Pornchaloempong Pimpen , Krusong Warawut , Sirisomboon Panmanas , Tanaka Munehiro , Kojima Takayuki
Published: December 2023

If a non-destructive and rapid technique to determine the textural properties of cooked germinated brown rice (GBR) was developed, it would hold immense potential for the enhancement of the quality control process in large-scale commercial rice production. We combined the Fourier transform near-infrared (NIR) spectral data of uncooked whole grain GBR with partial least squares (PLS) regression and an artificial neural network (ANN) for an evaluation of the textural properties of cooked germinated brown rice (GBR); in addition, data separation and spectral pretreatment methods were investigated. The ANN was outperformed in the evaluation of hardness by a back extrusion test of cooked GBR using the smoothing combined with the standard normal variate pretreated NIR spectra of 188 whole grain samples in the range of 4000–12,500 cm−1. The calibration sample set was separated from the prediction set by the Kennard–Stone method. The best ANN model for hardness, toughness, and adhesiveness provided R2, r2, RMSEC, RMSEP, Bias, and RPD values of 1.00, 0.94, 0.10 N, 0.77 N, 0.02 N, and 4.3; 1.00, 0.92, 1.40 Nmm, 9.98 Nmm, 1.6 Nmm, and 3.5; and 0.97, 0.91, 1.35 Nmm, 2.63 Nmm, −0.08 Nmm, and 3.4, respectively. The PLS regression of the 64-sample KDML GBR group and the 64-sample GBR group of various varieties provided the optimized models for the hardness of the former and the toughness of the latter. The hardness model was developed by using 5446.3–7506 and 4242.9–4605.4 cm−1, which included the amylose vibration band at 6834.0 cm−1, while the toughness model was from 6094.3 to 9403.8 cm−1 and included the 6834.0 and 8316.0 cm−1 vibration bands of amylose, which influenced the texture of the cooked rice. The PLS regression models for hardness and toughness had the r2 values of 0.85 and 0.82 and the RPDs of 2.9 and 2.4, respectively. The ANN model for the hardness, toughness, and adhesiveness of cooked GBR could be implemented for practical use in GBR production factories for product formulation and quality assurance and for further updating using more samples and several brands to obtain the robust models.
Line Laser Scanning Combined with Machine Learning for Fish Head Cutting Position Identification
Authors: Zhang Xu , Gong Ze , Liang Xinyu , Sun Weichen , Ma Junxiao , Wang Huihui
Published: December 2023

Fish head cutting is one of the most important processes during fish pre-processing. At present, the identification of cutting positions mainly depends on manual experience, which cannot meet the requirements of large-scale production lines. In this paper, a fast and contactless identification method of cutting position was carried out by using a constructed line laser data acquisition system. The fish surface data were collected by a linear laser scanning sensor, and Principal Component Analysis (PCA) was used to reduce the dimensions of the dorsal and abdominal boundary data. Based on the dimension data, Least Squares Support Vector Machines (LS-SVMs), Particle Swarm Optimization-Back Propagation (PSO-BP) networks, and Long and Short Term Memory (LSTM) neural networks were applied for fish head cutting position identification model establishment. According to the results, the LSTM model was considered to be the best prediction model with a determination coefficient (R2) value, root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD) of 0.9480, 0.2957, 0.1933, and 3.1426, respectively. This study demonstrated the reliability of combining line laser scanning techniques with machine learning using LSTM to identify the fish head cutting position accurately and quickly. It can provide a theoretical reference for the development of intelligent processing and intelligent cutting equipment for fish.
Preparation of a Dual-Functional Active Film Based on Bilayer Hydrogel and Red Cabbage Anthocyanin for Maintaining and Monitoring Pork Freshness
Authors: Huang Xiaowei , Zhao Wanying , Li Zhihua , Zhang Ning , Wang Sheng , Shi Jiyong , Zhai Xiaodong , Zhang Junjun , Shen Tingting
Published: December 2023

In this study, a composite film was created with the dual goal of prolonging pork shelf life and showing freshness. Hydrogel materials as solid base films were selected from gelatin (G), sodium alginate (SA) and carboxymethyl cellulose (CMC) based on their antioxidant activity, water vapor permeability, mechanical properties, as well as their stability, antimicrobial activity, and freshness, which indicates effectiveness when combined with anthocyanins. Furthermore, the effects of several concentrations of red cabbage anthocyanin (R) (3%, 6%, 12%, and 24%) on freshness indicators and bacteriostasis were investigated. The antimicrobial activity of the composite films was evaluated against Escherichia coli, Bacillus subtilis, and Staphylococcus aureus. Likewise, the freshness indicates effectiveness was evaluated for NH3. Considering the mechanical properties, antibacterial ability, freshness indicator effect, and stability of the composite film, CS film combined with 12% R was selected to prepare a dual-functional intelligent film for pork freshness indicator and preservation. By thoroughly investigating the effect of composite film on pork conservation and combining with it KNN, the discriminative model of pork freshness grade was established and the recognition rate of the prediction set was up to 93.3%. These results indicated that CSR film can be used for the creation of active food packaging materials.
Therapeutic Use of Palmitoylethanolamide as an Anti-Inflammatory and Immunomodulator
Authors: Sá Maria Clara Inácio de , Castor Marina Gomes Miranda
Published: December 2023

Palmitoylethanolamine (PEA) is an endocannabinoid-like compound first encountered within the lipid fractions of specific foods and has intrigued researchers since the 1950s due to its therapeutic effects. This survey aims to explore the therapeutic promise held by PEA as an anti-inflammatory and immunomodulatory agent. The therapeutic impact of PEA reverberates across diverse physiological systems, such as the central nervous system, gastrointestinal tract, vascular network, and the digestive and respiratory system. Additionally, it is effective in pain management and reducing inflammation and immune responses. These attributes have fostered collaborations targeting conditions such as Alzheimer’s disease, multiple sclerosis, cerebral ischemia, neuroinflammation, general inflammation, pain, coagulopathy, steatohepatitis, and acute lung injury. PEA operates both independently and in synergy with other compounds, like paracetamol, luteolin, and oxymetazoline. This efficacy stems from its interactions with pivotal targets, including PPARα, PPAR-δ, PPAR-γ, CB1, CB2, GPR55, and TRPV1. Additionally, PEA exerts a direct influence on the inflammatory cascade, orchestrating precise adjustments in immune responses. Numerous animal studies have elucidated the inherent potential of PEA. Nevertheless, the imperative of reinforcing clinical investigation is evident. This review notably underscores the pivotal necessity for methodologically rigorous clinical trials to definitively establish the translational efficacy of PEA in ameliorating diverse inflammatory pathologies within the human milieu.
Integrative Physiological and Transcriptome Analysis Reveals the Mechanism of Cd Tolerance in Sinapis alba
Authors: Cai Mengxian , Yang Tinghai , Fang Shiting , Ye Lvlan , Gu Lei , Wang Hongcheng , Du Xuye , Zhu Bin , Zeng Tuo , Peng Tao
Published: December 2023

Recently, pollution caused by the heavy metal Cd has seriously affected the environment and agricultural crops. While Sinapis alba is known for its edible and medicinal value, its tolerance to Cd and molecular response mechanism remain unknown. This study aimed to analyze the tolerance of S. alba to Cd and investigate its molecular response mechanism through transcriptomic and physiological indicators. To achieve this, S. alba seedlings were treated with different concentrations of CdCl2 (0.25 mmol/L, 0.5 mmol/L, and 1.0 mmol/L) for three days. Based on seedling performance, S. alba exhibited some tolerance to a low concentration of Cd stress (0.25 mmol/L CdCl2) and a strong Cd accumulation ability in its roots. The activities and contents of several antioxidant enzymes generally exhibited an increase under the treatment of 0.25 mmol/L CdCl2 but decreased under the treatment of higher CdCl2 concentrations. In particular, the proline (Pro) content was extremely elevated under the 0.25 and 0.5 mmol/L CdCl2 treatments but sharply declined under the 1.0 mmol/L CdCl2 treatment, suggesting that Pro is involved in the tolerance of S. alba to low concentration of Cd stress. In addition, RNA sequencing was utilized to analyze the gene expression profiles of S. alba exposed to Cd (under the treatment of 0.25 mmol/L CdCl2). The results indicate that roots were more susceptible to disturbance from Cd stress, as evidenced by the detection of 542 differentially expressed genes (DEGs) in roots compared to only 37 DEGs in leaves. GO and KEGG analyses found that the DEGs induced by Cd stress were primarily enriched in metabolic pathways, plant hormone signal transduction, and the biosynthesis of secondary metabolites. The key pathway hub genes were mainly associated with intracellular ion transport and cell wall synthesis. These findings suggest that S. alba is tolerant to a degree of Cd stress, but is also susceptible to the toxic effects of Cd. Furthermore, these results provide a theoretical basis for understanding Cd tolerance in S. alba.
CFHTF2 Is Needed for Vegetative Growth, Conidial Morphogenesis and the Osmotic Stress Response in the Tea Plant Anthracnose (Colletotrichum fructicola)
Authors: Zhang Chengkang , Zhou Ziwen , Guo Tianlong , Huang Xin , Peng Chengbin , Lin Zhideng , Chen Meixia , Liu Wei
Published: December 2023

Tea is an important cash crop worldwide, and its nutritional value has led to its high economic benefits. Tea anthracnose is a common disease of tea plants that seriously affects food safety and yield and has a far-reaching impact on the sustainable development of the tea industry. In this study, phenotypic analysis and pathogenicity analysis were performed on knockout and complement strains of HTF2—the transcriptional regulator of tea anthracnose homeobox—and the pathogenic mechanism of these strains was explored via RNA-seq. The MoHox1 gene sequence of the rice blast fungus was indexed, and the anthracnose genome was searched for CfHTF2. Evolutionary analysis recently reported the affinity of HTF2 for C. fructicola and C. higginsianum. The loss of CfHTF2 slowed the vegetative growth and spore-producing capacity of C. fructicola and weakened its resistance and pathogenesis to adverse conditions. The transcriptome sequencing of wild-type N425 and CfHTF2 deletion mutants was performed, and a total of 3144 differentially expressed genes (DEGs) were obtained, 1594 of which were upregulated and 1550 of which were downregulated. GO and KEGG enrichment analyses of DEGs mainly focused on signaling pathways such as the biosynthesis of secondary metabolites. In conclusion, this study lays a foundation for further study of the pathogenic mechanism of tea anthracnose and provides a molecular basis for the analysis of the pathogenic molecular mechanism of CfHTF2.
Near Real-Time Detection and Moment Tensor Inversion of the 11 May 2022, Dharchula Earthquake
Authors: Kumar Pankaj , Silwal Vipul , Mahanta Rinku , Maurya Vipin Kumar , Kamal  , Sharma Mukat Lal , Ammani Ambikapathy
Published: December 2023

On 11 May 2022, an earthquake of Mw 5.2 occurred in the Dharchula region of Uttarakhand Himalayas, India. The Uttarakhand State Earthquake Early Warning System (UEEWS) detected and transmitted the warning within 11.61 s from the origin time, taking only 4.26 s for processing, location, and magnitude estimation and warning dissemination. The complete analysis was performed using three seconds of waveforms. Using the initial earthquake parameters provided by the UEEWS, moment tensor inversion was performed using the broadband seismometers network installed in northern India. The moment tensor (MT) inversion was performed for the event using both the body waves and the surface waves. The first motion polarity was used along with waveform data to enhance the solution’s stability. This paper discusses the importance of real-time event detection and efforts towards real-time MT source inversion of earthquakes in the Uttarakhand Himalayas. Relocation of two past earthquakes near Dharchula is also undertaken in this study. The outcome of this study provides insights into mitigating seismic hazards, understanding earthquake source mechanisms, and enhancing knowledge of local fault structures.
Uranium Isotope Characterization in Volcanic Deposits in a High Natural Background Radiation Area, Mamuju, Indonesia
Authors: Rosianna Ilsa , Nugraha Eka Djatnika , Tazoe Hirofumi , Syaeful Heri , Muhammad Adi Gunawan , Sukadana I Gde , Indrastomo Frederikus Dian , Ngadenin  , Pratiwi Fadiah , Sumaryanto Agus , Sucipta  , Pratama Hendra Adhi , Mustika Deni , Nirwani Leli , Nurokhim  , Omori Yasutaka , Hosoda Masahiro , Akata Naofumi , Tokonami Shinji
Published: December 2023

Mamuju is an area of high natural radiation in Indonesia with high natural radiation levels (average 613 nSv h−1). Mamuju is anomalous due to its high average 238U and 232Th concentrations of 22,882 and 33,549 Bq kg−1, respectively, in laterite and rock. High natural radionuclide concentrations of 238U, 232Th, and 40K have also been reported in soil samples from several locations in Mamuju, including Botteng, Northern Botteng, Takandeang, Ahu, and Taan. High radiation levels are related to radioactive mineral occurrences in the Adang volcanic complex, comprised of phonolitoid and foiditoid lithologies. According to the International Atomic Energy Agency (IAEA), uranium deposits can be classified into several types, among them a volcanic-related deposits, which include three sub-types: stratabound, structure-bound, and volcano-sedimentary deposits. This study aims to characterize volcanic rock deposit sub-types in the Mamuju area based on uranium radioisotope measurements. The uranium isotopes were measured using a tandem quadrupole inductively coupled plasma mass spectrometer combined with chemical separation by extraction chromatography using UTEVA resin. The analytical results for the 234U/238U ratios are used to determine the formation characteristics of minerals in each deposit sub-type based on mineral formation age, post-formation processes, and disturbances that affected the formation processes. Based on geochronological calculations using 234U/238U mineralization age, the deposits in the Mamuju area are 0.914–1.11 million years old and are classified as recent mineralization. These data have important implications for tracing uranium source rocks in the Mamuju area and may explain the anomalously high radiation levels in the Mamuju area.
Relationship between Acceleration in a Sit-To-Stand Movement and Physical Function in Older Adults
Authors: Tateoka Korin , Tsuji Taishi , Shoji Takuro , Tokunaga Satoshi , Okura Tomohiro
Published: December 2023

Acceleration parameters in sit-to-stand (STS) movements are useful for measuring lower-limb function in older adults. The purpose of this study was to examine the relationship between acceleration in STS movements and physical function and the test-retest reliability of acceleration parameters in older adults. We performed cross-sectional analyses on 244 older adults including 107 men (mean age: 77.4 ± 4.7) and 137 women (mean age: 75.6 ± 5.3). Four acceleration parameters were measured in STS movements: maximum acceleration (MA), maximum velocity (MV), maximum power (MP), and stand-up time (ST). Good intraclass correlation coefficients (ICC > 0.70) were observed for all parameters. For the acceleration parameters, MA, MV, and MP were relatively strongly associated with the 5-time STS test (men: r = −0.36~−0.47; women: r = −0.37~−0.45) and the timed up and go test (men: r = −0.39~0.47, women: r = −0.43~−0.51): MP was also strongly associated with grip strength (men: r = 0.48, women: r = 0.43). All acceleration parameters were poorer in participants reporting mobility limitations than in those reporting no mobility limitations. These findings support the usefulness of sensor-based STS measurement. The system is expected to be useful in various settings where care prevention is addressed.
Colorectal Cancer Liver Metastasis—State-of-the-Art and Future Perspectives
Authors: Ruivo Ana , Oliveira Rui Caetano , Silva-Vaz Pedro , Tralhão José Guilherme
Published: December 2023

The current management of colorectal cancer liver metastasis (CRCLM) patients involves a multidisciplinary approach, with surgical resection remaining the primary curative option. The advances in liver surgery have improved outcomes, enabling more patients to undergo surgery successfully. In addition, the development of imaging software has improved the preoperative planning and patient selection for surgery and other interventions. Systemic therapies, such as targeted therapies and immunotherapies, have enhanced the chances of complete resection. Targeted agents, in combination with chemotherapy, have shown efficacy in downstaging tumors and increasing resectability. The algorithm approach for these patients continues to evolve, driven by a deeper understanding of the underlying biology. Personalized medicine, guided by molecular profiling and the potential of liquid biopsies in this field, may lead to more tailored treatment strategies. A greater understanding of the immune microenvironment in CRLM may unlock the potential for immune checkpoint inhibitors and novel immunotherapies to become more prominent in the treatment landscape. This review explores the current state-of-the-art treatment of CRCLM and discusses promising future perspectives.