bluuebunny/crossref_metadata_2025 huggingface dataset:
Differential Diagnosis of Tuberculosis and Sarcoidosis by Immunological Features Using Machine Learning
Authors: Osipov Nikolay , Kudryavtsev Igor , Spelnikov Dmitry , Rubinstein Artem , Belyaeva Ekaterina , Kulpina Anastasia , Kudlay Dmitry , Starshinova Anna
Published: September 2024

Despite the achievements of modern medicine, tuberculosis remains one of the leading causes of mortality globally. The difficulties in differential diagnosis have particular relevance in the case of suspicion of tuberculosis with other granulomatous diseases. The most similar clinical and radiologic changes are sarcoidosis. The aim of this study is to apply mathematical modeling to determine diagnostically significant immunological parameters and an algorithm for the differential diagnosis of tuberculosis and sarcoidosis. Materials and methods: The serum samples of patients with sarcoidosis (SD) (n = 29), patients with pulmonary tuberculosis (TB) (n = 32) and the control group (n = 31) (healthy subjects) collected from 2017 to 2022 (the average age 43.4 ± 5.3 years) were examined. Circulating ‘polarized’ T-helper cell subsets were analyzed by multicolor flow cytometry. A symbolic regression method was used to find general mathematical relations between cell concentrations and diagnosis. The parameters of the selected model were finally fitted through multi-objective optimization applied to two conflicting indices: sensitivity to sarcoidosis and sensitivity to tuberculosis. Results: The difference in Bm2 and CD5−CD27− concentrations was found to be more significant for the differential diagnosis of sarcoidosis and tuberculosis than any individual concentrations: the combined feature Bm2 − [CD5−CD27−] differentiates sarcoidosis and tuberculosis with p < 0.00001 and AUC = 0.823. An algorithm for differential diagnosis was developed. It is based on the linear model with two variables: the first variable is the difference Bm2 − [CD5−CD27−] mentioned above, and the second is the naïve-Tregs concentration. The algorithm uses the model twice and returns “dubious” in 26.7% of cases for patients with sarcoidosis and in 16.1% of cases for patients with tuberculosis. For the remaining patients with one of these two diagnoses, its sensitivity to sarcoidosis is 90.5%, and its sensitivity to tuberculosis is 88.5%. Conclusions: A simple algorithm was developed that can distinguish, by certain immunological features, the cases in which sarcoidosis is likely to be present instead of tuberculosis. Such cases may be further investigated to rule out tuberculosis conclusively. The mathematical model underlying the algorithm is based on the analysis of “naive” T-regulatory cells and “naive” B-cells. This may be a promising approach for differential diagnosis between pulmonary sarcoidosis and pulmonary tuberculosis. The findings may be useful in the absence of clear differential diagnostic criteria between pulmonary tuberculosis and sarcoidosis.
Classification of Alzheimer’s Disease and Frontotemporal Dementia Using Electroencephalography to Quantify Communication between Electrode Pairs
Authors: Ma Yuan , Bland Jeffrey Keith Spaneas , Fujinami Tsutomu
Published: September 2024

Accurate diagnosis of dementia subtypes is crucial for optimizing treatment planning and enhancing caregiving strategies. To date, the accuracy of classifying Alzheimer’s disease (AD) and frontotemporal dementia (FTD) using electroencephalogram (EEG) data has been lower than that of distinguishing individuals with these diseases from healthy elderly controls (HCs). This limitation has impeded the feasibility of a cost-effective differential diagnosis for the two subtypes in clinical settings. This study addressed this issue by quantifying communication between electrode pairs in EEG data, along with demographic information, as features to train machine learning (support vector machine) models. Our focus was on refining the feature set specifically for AD-FTD classification. Using our initial feature set, we achieved classification accuracies of 76.9% for AD-HC, 90.4% for FTD-HC, and 91.5% for AD-FTD. Notably, feature importance analyses revealed that the features influencing AD-HC classification are unnecessary for distinguishing between AD and FTD. Eliminating these unnecessary features improved the classification accuracy of AD-FTD to 96.6%. We concluded that communication between electrode pairs specifically involved in the neurological pathology of FTD, but not AD, enables highly accurate EEG-based AD-FTD classification.
Influence of Continuous Rotation and Optimal Torque Reverse Kinematics on the Cyclic Fatigue Strength of Endodontic NiTi Clockwise Cutting Rotary Instruments
Authors: Martins Jorge N. R. , Silva Emmanuel J. N. L. , Marques Duarte , Versiani Marco A.
Published: September 2024

Objectives: The objective of the present study was to evaluate the cyclic fatigue strength of clockwise cutting rotary endodontic instruments when subjected to two different kinematics: continuous clockwise rotation and clockwise reciprocation movement under optimum torque reverse (OTR) motion. Methods: New ProTaper Next X1 (n = 20) and X2 (n = 20) instruments were randomly divided into two subgroups (n = 10) based on kinematics (continuous rotation or OTR). The specimens were tested using a custom-made device with a non-tapered stainless-steel artificial canal measuring 19 mm in length, featuring a 6 mm radius and an 86-degree curvature. All instruments were tested with a lubricant at room temperature until a fracture occurred. The time to fracture and the length of the separated fragment were recorded. Subsequently, the fractured instruments were inspected under a scanning electron microscope for signs of cyclic fatigue failure, plastic deformation, and/or crack propagation. The subgroup comparisons for time to fracture and instrument length were performed using the independent samples t-test, with the level of statistical significance set at 0.05. Results: When using OTR movement, the ProTaper Next X1 increased the time to fracture from 52.9 to 125.8 s (p < 0.001), while the ProTaper Next X2 increased from 45.4 to 66.0 s (p < 0.001). No subgroup exhibited plastic deformations, but both showed dimpling marks indicative of cyclic fatigue as the primary mode of failure. Additionally, OTR movement resulted in more metal alloy microcracks. Conclusions: The use of OTR motion extended the lifespan of the tested instruments and resulted in a higher number of metal microcracks. This suggests that OTR motion helped to distribute the mechanical stress more evenly across the instrument, thereby relieving localized tension. As a result, it delayed the formation of a single catastrophic crack, enhancing the overall performance of the instruments during the experimental procedures.
Barriers and Facilitators to Inclusive Education for Learners Who Are Deafblind: A Scoping Review
Authors: Masuku Khetsiwe Phumelele , Khumalo Gift , Moroe Nomfundo
Published: September 2024

The case of Hellen Keller presents a great example of what a learner who is deafblind can achieve if provided with the appropriate educational accommodation in terms of content, environment, learning approach, instructional strategies, and teaching methodologies. However, access to education is not the reality for most learners who are deafblind due to their unique educational needs, owing to their dual sensory impairment. There has been an assertion that learners who are deafblind may not thrive in inclusive educational spaces, but there is a paucity of research to support or dispute this assertion. This scoping review explored what is documented in the literature on the barriers and facilitators to inclusive education for learners who are deafblind. A scoping review methodology using Arksey and O’Malley’s framework was employed. A search was conducted using six databases, including Science Direct, PubMed, Scopus Medline, ProQuest and gray literature, to identify publications focusing on the barriers and facilitators to inclusive education for learners who are deafblind. The findings from the scoping review suggest that the factors that influence the inclusion of learners who are deafblind in inclusive educational settings include the availability of educational settings; accessibility to the physical and social environment, and assistive devices; acceptability by peers and educators and the preparedness of educators; and the adaptability of classroom strategies. For learners who are deafblind to thrive in inclusive educational settings, transformational attitudes and intentional and specific strategies from multiple stakeholders are necessary. Therefore, the educational system, schools, peers and communities should be deliberate in putting in place specialized support and resources to accommodate the diverse needs of learners who are deafblind in all phases of their schooling life.
Efficient Fabric Classification and Object Detection Using YOLOv10
Authors: Mao Makara , Lee Ahyoung , Hong Min
Published: September 2024

The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized a specialized, annotated dataset featuring diverse textile samples, including cotton, hanbok, cotton yarn-dyed, and cotton blend plain fabrics, to detect the torn path in fabric. The YOLOv10 model was selected for its superior performance, leveraging advancements in deep learning architecture and applying data augmentation techniques to improve adaptability and generalization to the various textile patterns and textures. Through comprehensive experiments, we demonstrate the effectiveness of YOLOv10, which achieved an accuracy of 85.6% and outperformed previous YOLO variants in both precision and processing speed. Specifically, YOLOv10 showed a 2.4% improvement over YOLOv9, 1.8% over YOLOv8, 6.8% over YOLOv7, 5.6% over YOLOv6, and 6.2% over YOLOv5. These results underscore the significant potential of YOLOv10 in automating fabric detection processes, thereby enhancing operational efficiency and productivity in textile manufacturing and retail.
Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review
Authors: Moon Juhyun , Ju Byeong-Kwon
Published: September 2024

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.
Skin Lesion Segmentation through Generative Adversarial Networks with Global and Local Semantic Feature Awareness
Authors: Zou Ruyao , Zhang Jiahao , Wu Yongfei
Published: September 2024

The accurate segmentation of skin lesions plays an important role in the diagnosis and treatment of skin cancers. However, skin lesion areas are rich in details and local features, including the appearance, size, shape, texture, etc., which pose challenges for the accurate localization and segmentation of the target area. Unfortunately, the consecutive pooling and stride convolutional operations in existing convolutional neural network (CNN)-based solutions lead to the loss of some spatial information and thus constrain the accuracy of lesion region segmentation. In addition, using only the traditional loss function in CNN cannot ensure that the model is adequately trained. In this study, a generative adversarial network is proposed, with global and local semantic feature awareness (GLSFA-GAN) for skin lesion segmentation based on adversarial training. Specifically, in the generator, a multi-scale localized feature fusion module and an effective channel-attention module are designed to acquire the multi-scale local detailed information of the skin lesion area. In addition, a global context extraction module in the bottleneck between the encoder and decoder of the generator is used to capture more global semantic features and spatial information about the lesion. After that, we use an adversarial training strategy to make the discriminator discern the generated labels and the segmentation prediction maps, which assists the generator in yielding more accurate segmentation maps. Our proposed model was trained and validated on three public skin lesion challenge datasets involving the ISIC2017, ISIC2018, and HAM10000, and the experimental results confirm that our proposed method provides a superior segmentation performance and outperforms several comparative methods.
Online Learning Strategy Induction through Partially Observable Markov Decision Process-Based Cognitive Experience Model
Authors: Gao Huifan , Ma Biyang
Published: September 2024

Inducing learning strategies is a crucial component of intelligent tutoring systems. Previous research has predominantly focused on the induction of offline learning strategies. Although the existing offline learning strategy induction methods can also be used for real-time updates of learning strategies, their update efficiency is not high, making it difficult to capture the characteristics exhibited by learners during the learning process in a timely manner. With the superior performance of the Partially Observable Markov Decision Process (POMDP), this paper proposes a POMDP-based cognitive experience model, which can be quickly updated during interactions and enables the real-time induction of learning strategies by weighting the learning experiences of different learners. Experimental results demonstrate that the learning strategies induced by PCEM are more personalized and exhibit superior performance.
DCNFYOLO: Dual-Convolution Network and Feature Fusion for High-Precision Smoke Detection
Authors: Chen Xin , Liu Xuzhao , Liu Bing , Zhu Yaolin
Published: September 2024

Fast, real-time, and accurate detection of smoke characteristics in the early stage of a fire is crucial for reducing fire losses. Existing smoke detection methods mainly rely on traditional algorithms and smoke sensors, and these approaches have limitations in false detection rates, accuracy, and real-time performance. Therefore, a novel DCNFYOLO network for smoke detection is proposed in this paper. Firstly, Switchable Atrous Convolution (SAConv) is introduced in the YOLOv5 backbone network to enhance the fusion extraction of smoke features by the Convolutional Neural Network (CNN). Secondly, both Distribution Shifts Convolution (DSConv) operator and Efficient Channel Attention (ECA) mechanisms are considered in the neck part to reduce the computational load of the model, and better capture the relationship between channels to improve the detection performance. Finally, to make low-quality examples less harmful to the gradients, the Wise-IoU (WIoU) loss function in the prediction part is used for reducing the competitiveness of high-quality anchor frames during model training, allowing the model to converge more quickly and stably. Experimental results show that the DCNFYOLO network can achieve a remarkable detection accuracy of 96.6%, which has a substantial improvement of 7.7% compared with the original YOLOv5 network performance, thereby validating the effectiveness of the proposed network.
Direction of Arrival Estimation Based on DNN and CNN
Authors: Cao Wu , Ren Wen , Zhang Zhenyu , Huang Weiqiang , Zou Jun , Liu Guangzu
Published: September 2024

The accuracy of Direction of Arrival (DOA) estimation primarily depends on the precision of the data. When the receiver uses a low-precision analog-to-digital converter (ADC), traditional DOA estimation algorithms exhibit poor accuracy. To face the challenge of multi-target DOA estimation in scenarios with low-precision ADC quantized sampling, this paper proposes a novel DOA estimation algorithm for quantized signals based on classification problems. A deep learning network was constructed using Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), divided into the quantized signal recovery framework and the DOA estimation framework. The DNN network is utilized to recover signals that have undergone low-precision quantization, while the CNN network addresses the classification problem to estimate the DOA from received data with an unknown number of signal sources. A comprehensive analysis of the impact of signal-to-noise ratio (SNR), the number of array elements, and the number of quantization bits on the proposed algorithm was conducted. Simulation results indicate that the proposed algorithm exhibits superior DOA estimation performance in low-precision scenarios, characterized by reduced computational complexity, thereby facilitating real-time DOA estimation.
A Prediction Model for Pressure and Temperature in Geothermal Drilling Based on Physics-Informed Neural Networks
Authors: Yuan Yin , Li Weiqing , Bian Lihan , Lei Junkai
Published: September 2024

With the global expansion of geothermal energy, accurate prediction of pressure and temperature during drilling has become essential for ensuring the safety and efficiency of geothermal wells. Traditional numerical methods, however, often struggle to handle complex wellbore environments due to their high data demands and limited computational accuracy. To address these challenges, this paper introduces an innovative predictive model based on Physics-Informed Neural Networks (PINNs). By integrating physical laws with deep learning, the model theoretically surpasses the limitations of conventional methods. Trained on pressure and temperature data from a geothermal well in the Xiong’an area, the model demonstrates exceptional accuracy and robustness. Additionally, the model was rigorously tested under extreme wellbore conditions, showcasing its strong generalization capabilities. The findings suggest that PINNs offer a highly advantageous solution for geothermal drilling, with significant potential for practical engineering applications.
A Stealthy Communication Model for Protecting Aggregated Results Integrity in Federated Learning
Authors: Li Lu , Sun Xuan , Shi Ning , Ci Xiaotian , Liang Chen
Published: September 2024

Given how quickly artificial intelligence technology is developing, federated learning (FL) has emerged to enable effective model training while protecting data privacy. However, when using homomorphic encryption (HE) techniques for privacy protection, FL faces challenges related to the integrity of HE ciphertexts. In the HE-based privacy-preserving FL framework, the public disclosure of the public key and the homomorphic additive property of the HE algorithm pose serious threats to the integrity of the ciphertext of FL’s aggregated results. For the first time, this paper employs covert communication by embedding the hash value of the aggregated result ciphertext received by the client into the ciphertext of local model parameters using the lossless homomorphic additive property of the Paillier algorithm. When the server receives the ciphertext of the local model parameters, it can extract and verify the hash value to determine whether the ciphertext of the FL’s aggregated results has been tampered with. We also used chaotic sequences to select the embedding positions, further enhancing the concealment of the scheme. The experimental findings demonstrate that the suggested plan passed the Welch’s t-test, the K–L divergence test, and the K–S test. These findings confirm that ciphertexts containing covert information are statistically indistinguishable from normal ciphertexts, thereby affirming the proposed scheme’s effectiveness in safeguarding the integrity of the FL’s aggregated ciphertext results. The channel capacity of this scheme can reach up to 512 bits per round, which is higher compared to other FL-based covert channels.
Natural Language Inference with Transformer Ensembles and Explainability Techniques
Authors: Perikos Isidoros , Souli Spyro
Published: September 2024

Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means to illustrate the decision-making procedure of its methods. First, we investigate the performance and generalization capabilities of several transformer-based models, including BERT, ALBERT, RoBERTa, and DeBERTa, across widely used datasets like SNLI, GLUE Benchmark, and ANLI. Then, we employ stacking-ensemble techniques to leverage the strengths of multiple models and improve inference performance. Experimental results demonstrate significant improvements of the ensemble models in inference tasks, highlighting the effectiveness of stacking. Specifically, our best-performing ensemble models surpassed the best-performing individual transformer by 5.31% in accuracy on MNLI-m and MNLI-mm tasks. After that, we implement LIME and SHAP explainability techniques to shed light on the decision-making of the transformer models, indicating how specific words and contextual information are utilized in the transformer inferences procedures. The results indicate that the model properly leverages contextual information and individual words to make decisions but, in some cases, find difficulties in inference scenarios with metaphorical connections which require deeper inferential reasoning.
Maximum Likelihood Sequence Estimation Optimum Receiver Design with Channel Identification Based on Zero Distribution
Authors: Wang Peng , Xiang Xin , Dong Pengyu , Liang Yuan
Published: September 2024

In this paper, a novel maximum likelihood sequence estimation (MLSE) optimum receiver design is proposed to reduce the complexity of the conventional optimum receiver. We first propose a multipath channel classification method according to the zero distribution of the channel impulse response; that is, the multipath channel is divided into a minimum phase channel and a non-minimum phase channel. Then, an MLSE optimum receiver design with channel identification based on zero distribution is also proposed. In this design, the distribution of zero can be obtained directly by calculating the Z transform of the channel. Thus, the complexity is reduced. The number of the multiplication reduced is N(2N+1). In addition, 2N+1 memory units storing the autocorrelation sequence are reduced. The simulation results indicate that this channel classification method effectively represents the characteristics of the channel. Moreover, the MLSE optimum receiver proposed performs similarly and is less complex than the conventional one. The performance is greatly improved compared with the minimum mean square error (MMSE) equalizer.
Simple Voltage Balancing Control of Four-Level Inverter
Authors: Su Shi , Xie Qingyang , Wang Mengyuan , Wang Yu , Chen Jianfei , Hu Zhikun
Published: September 2024

Multilevel inverters with improved voltage quality are widely used in applications such as motor control and electric vehicles. The four-level active neutral point clamped (4L-ANPC) inverter effectively meets the demands for high power density and low device voltage stress. However, balancing the capacitor voltage and reducing its low-frequency voltage fluctuation are critical challenges that need to be addressed. To address these challenges, this paper proposes a “variable reference + zero-sequence injection” method that requires only three reference voltage signals to determine the injected zero-sequence components. Particularly, the expression of the midpoint current, regarding the modulation index and phase current amplitude, is theoretically derived. This reveals the fundamental connection between the zero-sequence voltage signal and the midpoint current, providing a theoretical foundation for the zero-sequence injection method in four-level inverters. Subsequently, a simulation model and an experimental platform of the 4L-ANPC inverter were developed to compare and analyze the waveforms of the upper and lower capacitor voltages, phase currents, and line voltages under different modulation methods. Additionally, the upper and lower capacitor voltage waveforms were examined for various modulation indices. The results indicate that as the modulation index increases, the low-frequency voltage fluctuation in the upper and lower capacitor voltages also rises. At a modulation index of 0.95, the “variable reference + zero-sequence injection” method effectively suppresses the fluctuation in the upper and lower capacitor voltages to be no more than 1 V. These experimental findings validate the effectiveness of the proposed method.
Analysis of Influence of Grid-Following and Grid-Forming Static Var Generators on High-Frequency Resonance in Doubly Fed Induction Generator-Based Wind Farms
Authors: Sun Yong , Wu Hongbin , Song Xiaozhe , Zhang Haifeng , Zhang Yifu , Chen Jikai , Liu Hongpeng
Published: September 2024

In Doubly Fed Induction Generator (DFIG)-based wind farms with Static Var Generators (SVGs), high-frequency resonance will be more like to occur when an unloaded cable is put into operation, which will threaten the stable operation of the wind farm. To address this issue, the influence of power outer loops on the impedance of grid-connected inverters is considered. Based on harmonic linearization, theoretical models for the sequence impedances of DFIGs, Grid-following (GFL) SVGs, and Grid-forming (GFM) SVGs are established. The correctness of the three models is verified by impedance scanning using the frequency sweep method. Through a comparative analysis of these sequence impedances, it is found that unlike the GFM SVG (which exhibits inductive impedance), the GFL SVG exhibits capacitive impedance in the high-frequency band, which leads to negative damping characteristics in the high-frequency band for the wind farm system with the grid-following SVG; thereby, the risk of high-frequency resonance also increases accordingly. On the contrary, GFM control adopted by SVGs can effectively eliminate the negative damping region in the high-frequency band for wind farms to suppress high-frequency resonance. Meanwhile, for grid-forming SVGs, the parameter variations in power synchronous loops have no significant impact on the suppressing effect of high-frequency resonance for wind farms. Finally, an electromagnetic simulation model for a DFIG-based wind farm system with an SVG is established using the StarSim-HIL (hardware-in-the-loop) experiment platform, and the simulation results validate the correctness of the theoretical analysis.
A Hybrid Approach to Modeling Heart Rate Response for Personalized Fitness Recommendations Using Wearable Data
Authors: Kayange Hyston , Mun Jonghyeok , Park Yohan , Choi Jongsun , Choi Jaeyoung
Published: September 2024

Heart rate (HR) is a key indicator of fitness and cardiovascular health, and accurate HR monitoring and prediction are essential for enhancing personalized fitness experiences. The rise of wearable technology has significantly improved the ability to track personal health, including HR metrics. Accurate modeling of HR response during workouts is crucial for providing effective fitness recommendations, which help users achieve their goals while maintaining safe workout intensities. Although several HR monitoring and prediction models have been developed for personalized fitness recommendations, many remain impractical for real-world applications, and the domain of personalization in fitness applications still lacks sufficient research and innovation. This paper presents a hybrid approach to modeling HR response to workout intensity for personalized fitness recommendations. The proposed approach integrates a physiological model using Dynamic Bayesian Networks (DBNs) to capture heart rate dynamics during workout sessions. DBNs, combined with Long Short-Term Memory (LSTM) networks, model the evolution of HR over time based on workout intensity and individual fitness characteristics. The DBN parameters are dynamically derived from flexible neural networks that account for each user’s personalized health state, enabling the prediction of a full HR profile for each workout, while incorporating factors such as workout history and environmental factors. An adaptive feature selection module further enhances the model’s performance by focusing on relevant data and ensuring responsiveness to new data. We validated the proposed approach on the FitRec dataset, and experimental results show that our model can accurately predict HR responses to workout intensity in future sessions, achieving an average mean absolute error of 5.2 BPM per workout—significantly improving upon existing models. In addition to HR prediction, the model provides real-time fitness personalized recommendations based on individual’s observed workout intensity to an exercise. These findings demonstrate the model’s effectiveness in delivering precise, user personalized heart response to exercise with potential applications in fitness apps for personalized training and health monitoring.
Methods for Enhancing Electrolysis for Hydrogen Production: The Benefits of Applying Magnetic Fields
Authors: Binns Michael
Published: September 2024

The electrolysis of water is one of the most promising ways of producing green hydrogen. This produces hydrogen using electricity and does not generate additional carbon dioxide like the more conventional reforming of fossil fuels. However, making electrolysis competitive with conventional methods for hydrogen production is a challenge because of the cost of electricity and because of inefficiencies and costs in electrolysis systems. Initially this review looks at the basic design of water electrolysis and asks where energy is lost. Then, a selection of the latest results in the area of magnetic field-enhanced water electrolysis are examined and discussed, in particular focusing on the empirical results of magnetic field-assisted electrolysis with the aim of comparing findings and identifying limitations of current studies such that recommendations can be made for advanced design of hydrogen producing electrolysis systems.
The Evolving Role of FSC Certification in Croatia: From Market Pressures to Sustainable Practices
Authors: Klarić Kristina , Klarić Miljenko , Josipović Slađana , Tafro Azra
Published: September 2024

Forests are crucial in sustaining life on Earth and are at the core of global sustainability efforts, to which Forest Stewardship Council (FSC) certification makes a significant contribution. Changes in motivations, benefits, and challenges associated with FSC certification among Croatian companies, particularly in light of recent market fluctuations and global sustainability trends, have been examined in this study. The research was conducted in 2015 and 2023 using a survey method among FSC chain of custody (CoC) certificate holders in Croatia. A statistically significant decline in the proportion of companies citing customer demands and company image as the main reasons for FSC certification was observed, while environmental awareness became a more prominent motivation. Additionally, although key benefits such as retaining existing customers and attracting new ones remain important, a significant decline in the proportion of companies reporting increased profits as a benefit was noted. Challenges related to extensive documentation and frequent changes in standards also significantly increased. This study suggests that FSC certification remains crucial for maintaining competitiveness in the Croatian wood industry, especially as environmental concerns gain importance. Given that FSC standards help enforce European sustainability regulations, FSC certification is important for ensuring long-term resilience and success in an unstable market environment.
Farmers’ Perception of Shelter Forests and the Influencing Factors in Windy Desert Areas of the Hexi Corridor, China
Authors: Zhang Yuzhong , Zhu Gaofeng , Zhang Yang , Wu Jingjing , Gao Zhiqi , Su Yonghong , Xu Cong , Fan Haochen
Published: September 2024

Shelter forest systems in the sandy areas mainly comprise farmland shelter forests (FSF) and windbreak and sand-fixing forests (WSF). Through a questionnaire survey of farmers in the oasis–desert transition zone of the windy desert areas of the Hexi Corridor in China, a perception assessment model of farmers’ perception of the status quo and ecosystem service function of shelter forests was constructed, and the willingness of farmers to pay for shelter forest construction and protection was measured. Influencing factors for the farmers’ perception of shelter forests were analyzed by a multiple regression analysis. The results showed problems with pests and diseases all present in FSF and WSF, the destruction of farmland in FSF, and water scarcity in WSF. Farmers evaluated the ecological role of shelter forests as the most obvious in windbreak and sand fixation; intermediate in the four ecological roles of leisure and recreation, agricultural production, a sense of locality, and climate regulation; and the smallest in soil improvement. Nearly 95% of farmers are willing to donate funds to shelter forest construction and protection, and the average amount the farmers were willing to pay was CNY 54.30 per year. Willingness to pay, annual household income, gender, environmental experience, age, and interview area have significant effects on farmers’ perception of shelter forests. Finally, in this paper, we recommend increasing motivation through government financial support and training for farmers to address pests and diseases, water security, and the destruction of farmland in shelter forests to ensure healthy and sustainable growth.
Estimation of the Aboveground Biomass of Forests in Complex Mountainous Areas Using Regression Kriging
Authors: Luo Yining , Yan Lihui , Zhou Zhongfa , Huang Denghong , Cai Lu , Du Shuanglong , Yang Yue , Huang Youyan , Li Qianxia
Published: September 2024

The forest area in China’s plateaus and mountainous regions accounts for as much as 43% of the country’s total forest area. Accurately estimating the aboveground biomass (AGB) in these plateau and mountain forests is significant for global carbon sink assessment and climate change. However, the complexity of the natural environment poses significant challenges to the accurate estimation of forests’ aboveground biomass (AGB), and the accuracy of both AGB estimation and spatial mapping needs further improvement. This study utilized support vector regression, backpropagation neural networks, and random forests to predict trends in AGB and establish an optimal original model for forest AGB estimation. Further calibration was performed using regression kriging on the optimal model. The results indicated that (1) random forests achieved the highest coefficient of determination (R2 for cypress = 0.63, R2 for fir = 0.66, R2 for cryptomeria = 0.64, and R2 for mixed forest = 0.54), showing greater potential in predicting AGB in complex mountainous mixed forests; (2) the residual kriging method significantly improved the estimation accuracy, increasing the R2 values of the original RF model by 25%, 24%, and 22%, and improving the accuracy of mixed plot estimates from 54% to 81%; and (3) the residual kriging method effectively addressed the underestimation of high values and overestimation of low values in AGB estimates, broadening the range of AGB values and allowing for a more detailed spatial distribution of forests’ aboveground biomass.
FmRbohH Mediates ROS Generation and Enhances Pollen Tube Growth in Fraxinus mandshurica
Authors: Jakada Bello Hassan , Wang Shuqi , Yang Shun , Wu Ying , Huang Zerui , Liu Yunping , Lan Xingguo
Published: September 2024

Flowering plants require normal pollen germination and growth to be fertilized, but studies on the mechanism regulating pollen tube growth in Fraxinus mandshurica are limited. Here, we used transcriptomic data to study the oxidative phosphorylation pathway during pollen tube growth in Fraxinus mandshurica. Our study identified 8,734 differentially expressed genes during the stages S1 to S3 of pollen tube growth. Significant enrichment of the oxidative phosphorylation pathway, amino acid synthesis, protein processing in the ER, carbon metabolism, pyruvate metabolism, citrate cycle (TCA cycle), and glycolysis/gluconeogenesis were examined using the Kyoto Encyclopedia of Genes and Genomes, and 58 genes linked to ROS synthesis and scavenging during the S1–S3 stages were identified. Also, H2DCFDA staining confirmed ROS formation in the pollen and the pollen tubes, and treatment with copper (II) chloride (CuCl2) and diphenyleneiodonium (DPI) was shown to reduce ROS in the pollen tube. Reduction in ROS content caused decreased pollen germination and pollen tube length. Furthermore, FmRbohH (respiratory burst oxidase homolog H) expression was detected in the pollen and pollen tube, and an antisense oligodeoxynucleotide assay demonstrated reduced ROS and pollen tube growth in Fraxinus mandshurica. This study shed more light on the RbohH gene functions during pollen tube growth.
Characterizing the Contribution of Strain Specificity to the Microbiota Structure and Metabolites of Muqu and Fresh High-Temperature Daqu
Authors: Zhang Yi , Zhang Zhu , Huang Jun , Zhou Rongqing , Tang Qiuxiang , Jin Yao
Published: September 2024

In this study, the differences in physicochemical properties, microbial community structure, and metabolic characteristics between various fortified Muqu and their corresponding high-temperature Daqu (HTD) were investigated using multiphase detection methods. The results demonstrated that the physicochemical properties, community structure, dominant bacterial composition, and metabolic components varied significantly among the different types of fortified HTD. The differences between HTDs became more pronounced when fortified HTD was used as Muqu. Compared to HTD, Muqu exhibited a more complex volatile profile, while HTD contained higher levels of characteristic non-volatile components. The cultivable bacteria count in Muqu was significantly higher than that in HTD, while the cultivable fungi count was slightly lower than that in HTD. The fungal profiles in HTD were primarily associated with starch hydrolysis and ethanol synthesis, while bacterial activity was more prominent in Muqu. Additionally, pyrazine synthesis was mainly attributed to fungi in Muqu and bacteria in HTD. Source Tracker analysis indicated that 8.11% of the bacteria and 26.76% of the fungi originated from Muqu. This study provides a theoretical foundation for the controlled production of HTD, contributing to improvements in its quality and consistency.
The Impact of pH on Fouling and Related Physicochemical Properties of Skim Milk Concentrate during Heat Treatment Using a Laboratory-Scale Fouling Rig
Authors: Murphy Tara R. , Finnegan Eoin W. , Tarapata Justyna , O’Callaghan Tom F. , O’Mahony James A.
Published: September 2024

The objective of this study was to investigate the effect of pH (6.1, 6.3, 6.5, and 6.7) on heat-induced changes in concentrated skim milk as related to fouling in heat exchangers. Skim milk (30%, w/w, total solids) was recirculated in a laboratory-scale fouling rig at an initial target temperature of 85 °C for 90 min to simulate thermal processing and preheating of evaporated liquid concentrate feeds in dairy processing. This study investigated key changes in relevant physicochemical properties, such as viscosity, particle size, and sedimentation, as major contributors to fouling at lower pHs (6.1 and 6.3). Additionally, protein aggregation and calcium phosphate precipitation were identified as significant contributors to fouling deposits. Possible strategies to mitigate fouling were determined, including optimizing pH and adjusting heat treatment parameters to minimize protein denaturation and mineral deposition. The findings indicate that carefully controlling pH and processing parameters can greatly enhance the efficiency of milk concentration by evaporation and tailor finished product quality. Moreover, this study showed that monitoring of CIP solutions for protein content and turbidity provides valuable information on the intensity of fouling and the efficiency of cleaning.
Color Matters: A Study Exploring the Influence of Packaging Colors on University Students’ Perceptions and Willingness to Pay for Organic Pasta
Authors: Nagy László Bendegúz , Temesi Ágoston
Published: September 2024

The organic food market’s rapid expansion necessitates an understanding of factors influencing consumer behavior. This paper investigates the impact of packaging colors on perceptions and willingness to pay (WTP) for organic foods, utilizing an experimental auction among university students. Drawing on previous research, we explore how colors influence perceived healthiness, premiumness, trust, and sustainability. The results indicate nuanced responses to different colors, emphasizing the need for businesses to adopt tailored packaging strategies. White and green dominate organic food packaging, aligning with associations of freshness and health. However, the study uncovers varied consumer responses, suggesting a more intricate relationship between color, trust, premiumness, and healthiness perceptions. Demographic factors such as age, gender, income, and residence areas influence WTP for organic foods with different colors, emphasizing the importance of diverse consumer segments in marketing strategies. Trust and perceived premiumness significantly influence WTP, highlighting their pivotal role in consumer valuation. The results highlight that green packaging builds trust among non-organic buyers, while organic buyers are influenced by a broader range of colors that emphasize premiumness and healthiness. The study concludes that businesses in the organic food market should carefully consider color choices in branding and packaging to effectively communicate product qualities and align with consumer values.
From Waste to Value: Fish Protein Hydrolysates as a Technological and Functional Ingredient in Human Nutrition
Authors: Honrado Adrián , Miguel Marta , Ardila Paula , Beltrán José Antonio , Calanche Juan B.
Published: September 2024

Fish provides a low-caloric content, polyunsaturated fatty acids, many essential trace elements and is also a rich source of protein, ranging from 10% to 25%. Therefore, the production of FPH (fish protein hydrolysates) is of great interest, as the resulting products exhibit a variety of important bioactive and technological properties, making them potential ingredients for new functional foods and supplements. The aim of this review was to compile and analyze information on enzymatic hydrolysates, with particular emphasis on those derived from fish by-products, as a potential ingredient in human nutrition. Their nutritional characteristics, food safety aspects, bioactive properties, technological attributes, key influencing factors, and applications in food products were evaluated. The findings revealed that these properties are influenced by several factors, such as the raw material, enzymes used, degree of hydrolysis, and the molecular weight of the peptides, which need to be considered as a whole. In conclusion, the gathered information suggests that it is possible to obtain high-value products through enzymatic hydrolysis, even when using fish by-products. However, although numerous studies focused on FPH derived from fish muscle, research on by-products remains limited. Further investigation is needed to determine whether the behavior of FPH from by-products differs from that of muscle-derived FPH.
A Simulation-Based Approach for Evaluating the Effectiveness of Robotic Automation Systems in HMR Product Loading
Authors: Baek Seunghoon , Oh Seung Eel , Lee Seung Hyun , Kwon Ki Hyun
Published: September 2024

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.
Impact of Microencapsulation on Ocimum gratissimum L. Essential Oil: Antimicrobial, Antioxidant Activities, and Chemical Composition
Authors: Granados Angela Del Pilar Flores , Duarte Marta Cristina Teixeira , Noguera Nathan Hargreaves , Lima Dyana Carla , Rodrigues Rodney Alexandre Ferreira
Published: September 2024

Ocimum gratissimum (OG) is a species rich in essential oils (EO), which is known for its antimicrobial and antioxidant properties. This study aimed to encapsulate the essential oil of Ocimum gratissimum (OGE), determine its chemical composition, and evaluate its antioxidant and antimicrobial activities against six pathogenic bacteria, comparing it with the free essential oil (OGF). The EO was extracted by hydrodistillation using a Clevenger-type apparatus, and an oil-in-water emulsion was prepared using a combination of biopolymers: maltodextrin (MA), cashew gum (CG), and inulin (IN). The chemical profile was identified using gas chromatography–mass spectrometry (GC–MS). Antioxidant activity was assessed using the Oxygen Radical Absorbance Capacity with fluorescein (ORAC-FL) method, while the Minimum Inhibitory Concentrations (MIC) and Minimum Bactericidal Concentrations (MBC) were determined by the microdilution method. Microparticles were formed using the spray-drying method, achieving an encapsulation efficiency of 45.2%. The analysis identified eugenol as the main compound both before and after microencapsulation. The OGE microparticles demonstrated high inhibitory and bactericidal effects against S. aureus, S. choleraesuis, and E. coli, with MIC values of 500 µg·mL−1 and MBC values of 1000 µg·mL−1, as well as antioxidant activity of 1914.0 µmol-TE·g−1. Therefore, it can be inferred that the EO of OG maintained its antimicrobial and antioxidant effects even after microencapsulation by spray-drying, making it a promising natural ingredient.