bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hierarchical Multi-Task Learning Framework for Semantic Annotation in Tabular Data
Authors: Wu Jie , Hou Mengshu
Published: August 2024

To optimize the utilization and analysis of tables, it is essential to recognize and understand their semantics comprehensively. This requirement is especially critical given that many tables lack explicit annotations, necessitating the identification of column types and inter-column relationships. Such identification can significantly augment data quality, streamline data integration, and support data analysis and mining. Current table annotation models often address each subtask independently, which may result in the neglect of constraints and contextual information, causing relational ambiguities and inference errors. To address this issue, we propose a unified multi-task learning framework capable of concurrently handling multiple tasks within a single model, including column named entity recognition, column type identification, and inter-column relationship detection. By integrating these tasks, the framework exploits their interrelations, facilitating the exchange of shallow features and the sharing of representations. Their cooperation enables each task to leverage insights from the others, thereby improving the performance of individual subtasks and enhancing the model’s overall generalization capabilities. Notably, our model is designed to employ only the internal information of tabular data, avoiding reliance on external context or knowledge graphs. This design ensures robust performance even with limited input information. Extensive experiments demonstrate the superior performance of our model across various tasks, validating the effectiveness of unified multi-task learning framework in the recognition and comprehension of table semantics.
Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning
Authors: Zheng Zijie , Zhang Zhijun , Li Zhenzhang , Yu Qiuda , Jiang Ya
Published: August 2024

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.
Diagnostics on Power Electronics Converters by Means of Autoregressive Modelling
Authors: Diversi Roberto , Sandrolini Leonardo , Simonazzi Mattia , Speciale Nicolò , Mariscotti Andrea
Published: August 2024

Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful and this work shows a novel approach based on the analysis of spectra of an autoregressive (AR) model to recognize a wide range of faulty devices, including incipient faults, when deviations from nominal parameters begin to manifest. AR modeling provides cleaner and easier to interpret spectra, where only the salient features remain, and they are more sensitive to variations in the corresponding time domain waveforms. A log spectral distance is calculated that successfully separates healthy and faulty states of the feeding single-phase inverter, even in challenging scenarios of poor signal-to-noise ratio.
Multimodal Machine Translation Based on Enhanced Knowledge Distillation and Feature Fusion
Authors: Tian Erlin , Zhu Zengchao , Liu Fangmei , Li Zuhe , Gu Ran , Zhao Shuai
Published: August 2024

Existing research on multimodal machine translation (MMT) has typically enhanced bilingual translation by introducing additional alignment visual information. However, picture form requirements in multimodal datasets pose important constraints on the development of MMT because this requires a form of alignment between image, source text, and target text. This limitation is especially compounded by the fact that the inference phase, when aligning images, is not directly available in a conventional neural machine translation (NMT) setup. Therefore, we propose an innovative MMT framework called the DSKP-MMT model, which supports machine translation by enhancing knowledge distillation and feature refinement methods in the absence of images. Our model first generates multimodal features from the source text. Then, the purified features are obtained through the multimodal feature generator and knowledge distillation module. The features generated through image feature enhancement are subsequently further purified. Finally, the image–text fusion features are generated as input in the transformer-based machine translation reasoning task. In the Multi30K dataset test, the DSKP-MMT model has achieved a BLEU of 40.42 and a METEOR of 58.15, showing its ability to improve translation effectiveness and facilitating utterance communication.
A Survey on Maximum Ratio Combination: Applications, Evaluation and Future Directions
Authors: Feng Xiao , Tian Feng , Wang Junfeng , Zhou Mingzhang , Li Dingzhao , Sun Haixin , Song Ruiping
Published: August 2024

With the rapid development of wireless communications, the occupation of time and frequency resources becomes more crowded. The exploitation of space resources is necessary and the diversity combining techniques have substantial applications. Diversity combining achieves great diversity gains and improves the ability to combat multipath fading, among which the maximum ratio combining (MRC) performs as the optimal linear combining approach. However, MRC suffers from detrimental factors such as channel fading and no Gaussian noise in practical scenarios. In this paper, we focus on a comprehensive investigation of MRC. Starting from the MRC principle and system model, we summarize typical scenarios and analyze the channel fading statistics. For the influential factors, we further review related literature on channel correlation, cochannel interference (CCI) and impulsive noise. Major performance criteria and performance bounds are derived and compared. MRC confronts new developing challenges and the major development directions are reviewed. The paper finally discusses recent works and open problems for MRC applications and development. Emerging techniques such as artificial intelligence provide novel solutions for MRC performance improvements. The paper aims to present a summarized insight to assist readers in clarifying the analyzed methodology of MRC, so as to motivate new technology integration and extensive applications of advanced communication systems.
An Efficient Motion Adjustment Method for a Dual-Arm Transfer Robot Based on a Two-Level Neural Network and a Greedy Algorithm
Authors: Chen Mengqian , Liu Qiming , Wang Kai , Yang Zhiqiang , Guo Shijie
Published: August 2024

As the manipulation object of a patient transfer robot is a human, which can be considered a complex and time-varying system, motion adjustment of a patient transfer robot is inevitable and essential for ensuring patient safety and comfort. This paper proposes a motion adjustment method based on a two-level deep neural network (DNN) and a greedy algorithm. First, a dataset including information about human posture and contact forces is collected by experiment. Then, the DNN, which is used to estimate contact force, is established and trained with the collected datasets. Furthermore, the adjustment is conducted by comparing the estimated contact force of the next state and the real contact force of the current state by a greedy algorithm. To assess the validity, first, we employed the DNN to estimate contact force and obtained the accuracy and speed of 84% and 30 ms, respectively (implemented with an affordable processing unit). Then, we applied the greedy algorithm to a dual-arm transfer robot and found that the motion adjustment could reduce the contact force and improve human comfort efficiently; these validated the effectiveness of our proposal and provided a new approach to adjust the posture of the care receiver for improving their comfort through reducing the contact force between human and robot.
Streamlined Deep Learning Models for Move Prediction in Go-Game
Authors: Lin Ying-Chih , Huang Yu-Chen
Published: August 2024

Due to the complexity of search space and move evaluation, the game of Go has been a long-standing challenge for artificial intelligence (AI) to achieve a high level of proficiency. It was not until DeepMind proposed the deep neural network and tree search algorithm AlphaGo in 2014 that an efficient learning algorithm was developed, marking a significant milestone in AI technology. In light of the key technologies in AI Computer Go, this work examines move prediction across different Go rankings and sophisticatedly develops two deep learning models by combining and extending the feature extraction methods of AlphaGo. Specifically, effective modules for neural networks are proposed to guide learning through complicated Go situations based on the Inception module in GoogLeNet and the Convolutional Block Attention Module (CBAM). Subsequently, the two models are combined by ensemble learning to improve generalization, and these streamlined models significantly reduce the number of model parameters to the scale of one hundred thousand. Experimental results show that our models achieve prediction accuracies of 46.9% and 50.8% on two different Go datasets, outperforming conventional models by significant margins. This work not only advances AI development in the Go-game but also offers an innovative approach to related studies.
Stereo Matching Method with Cost Volume Collaborative Filtering
Authors: Wu Wenhuan , Xu Xi , Wang Wenshu , Zhang Haokun
Published: August 2024

Aiming at the problem of matching ambiguity and low disparity accuracy at the object boundary in stereo matching, a novel stereo matching algorithm with cost volume collaborative filtering is proposed. Firstly, for each pixel, two support windows are built, namely a local cross- support window as well as a global support window for the whole image. Secondly, a new adaptive weighted guide filter with a cross-support window as a kernel window is derived, and it is used to locally filter the cost volume. In addition, a minimum spanning tree is constructed in the whole image window, and then the minimum spanning tree filter is used to globally filter the cost volume. The collaborative filtering of cost volume is realized by fusing the filtering results of the local filter and global filter, so that each pixel can not only receive the support of the neighboring pixels in the local adaptive window, but can also receive the effective support of other pixels in the whole image, thus effectively eliminating the matching ambiguity in different texture regions while maintaining the disparity edges. The experimental results show that the average matching error rate of our method on the Middlebury stereo images is 3.17%. Compared with the other state-of-the-art methods, our method has higher robustness and matching accuracy, the generated disparity maps are smoother, and the disparity edges are better preserved.
Special Issue: Recent Advances in Intelligent Vehicular Networks and Communications
Authors: Ren Zhiyuan , Chen Chen
Published: August 2024

Over the last few decades, research on intelligent vehicular networks and communications has grown significantly due to the increasing demand for advanced vehicular technologies and the need for enhanced road safety, traffic management, and efficient transportation systems [...]
Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National-Level Carbon Productivity Indicator
Authors: Lee Jong Hyo , Kang Hong Yoon , Hwang Yong Woo
Published: August 2024

Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicator metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity that, when utilized with current methodologies, is more usable to interpret productivity indicators within the context of varying industrial compositions across OECD countries. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to the unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.
A Study on the Thermal Physical Property Changes in Formation Rocks during Rapid Preheating of SAGD
Authors: Tian Jie , Huang Shiwen , Dong Mingda , Yan Wende , Qi Zhilin
Published: August 2024

The incorporation and application of SAGD rapid preheating technology effectively solve the problem of the long preheating cycle in the SAGD steam cycle. The thermal properties of reservoir rocks are an important factor affecting the heat transfer law governing their formation during the rapid preheating process of SAGD. During the rapid preheating process of SAGD, the expansion of the reservoir and the steam cycle process will cause changes in the pore permeability, oil-water saturation, and temperature of the reservoir rocks, which will inevitably lead to differences in the changes that occur in the thermal properties of the reservoir rocks, compared to those under the influence of a single factor. In this study, experiments were conducted to determine the thermal properties of reservoir rocks under the combined influence of pore permeability, oil-water saturation, and temperature, quantitatively characterizing the changes in the thermal properties of reservoir rocks. Using the orthogonal method to design and carry out experiments for determining the thermal properties of reservoir rocks, the main factors affecting the thermal properties of reservoir rocks and the significance of each factor’s impact on the thermal properties of reservoir rocks were determined through intuitive analysis and variance analysis of the experimental results. Finally, a regression equation that can characterize changes in the thermal properties of reservoir rocks under the influence of multiple factors was obtained through multiple nonlinear regressions of the experimental results.
Linear Active Disturbance Rejection Control for Flexible Excitation System of Pumped Storage Units
Authors: Zhao Bo , Zheng Jiandong , Qin Jun , Wang Dan , Li Jiayao , Cheng Xinyu , Jia Sisi
Published: August 2024

The role of pumped storage in global energy structure transformation is becoming increasingly prominent. This article introduces a flexible excitation system based on fully controlled device converters into pumped storage units (PSUs). It can address the issues of insufficient excitation capacity and limited stability associated with traditional thyristor excitation systems. The study focuses on linear active disturbance rejection control (LADRC) for the flexible excitation control system of pumped storage units and utilizes intelligent optimization algorithms to optimize the controller parameters. This addresses the inherent problem of traditional PID controllers, which are unable to alleviate the trade-off between response speed and overshoot. At the same time, the robustness and anti-interference of the control system are improved, effectively enhancing the performance of the pumped storage flexible excitation control system. Simulation verifies the feasibility and superiority of the proposed method.
An Efficiency Evaluation and Driving Effect Analysis of the Green Transformation of the Thermal Power Industrial Chain: Evidence Based on Impacts and Challenges in China
Authors: Zhu Hui , Bian Yijie , Ren Fangrong , Liu Xiaoyan
Published: August 2024

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs.
Study on the Multiphase Flow Behavior in Jet Pump Drainage and Natural Gas Hydrate Production Wells with Combined Depressurization and Thermal Stimulation Method
Authors: Ping Xiaolin , Zhang Jiqun , Han Guoqing , Chang Junhua , Wang Hongliang
Published: August 2024

Natural gas hydrate (NGH) trials have been performed successfully with different development methods and gas recovery drainage technologies. Multiphase flow in a wellbore and the drainage of natural gas hydrate are two important parts for its whole extraction process. Additionally, the choice of the drainage method is linked to the development method, making the drainage of NGH more complex. Jet pump drainage is usable for NGH production wells with the combined depressurization and thermal stimulation method. The objective of this study is to shed more light on the multiphase flow behavior in jet pump drainage and NGH production wells and put forward suggestions for adjusting heat injection parameters. The mechanism of jet pump drainage recovery technology for NGH wells was analyzed and its applicability to NGH development by the combined depressurization and thermal stimulation method was demonstrated. In addition, multiphase flow models of tubing and annulus were established, respectively, for the phenomenon of the countercurrent flow of heat exchange in the process of jet pump drainage and gas production, and the corresponding multiphase flow laws were derived. On the basis of these studies, sensitivity analysis and the optimization of thermal stimulation parameters were conducted. It is demonstrated that jet pump drainage gas recovery technology is feasible for the development of onshore NGH with the combined depressurization and thermal stimulation method. The laws of multiphase flow in the tubing and annulus of jet pump drainage and NGH production wells were disclosed in this study. Numerical simulation results show that the temperature and pressure profiles along the wellbore of jet pump drainage and NGH production wells during the drainage recovery process are affected by injection conditions. Increasing injection rate and injection temperature can both improve the effect of heat injection and reduce the hydrate reformation risk in the bottom of the annulus. This study offers a theoretical basis and technical support for production optimization and hydrate prevention and control in the wellbore of jet pump drainage and NGH production wells.
Investigation of Dead Time Losses in Inverter Switching Leg Operation: GaN FET vs. MOSFET Comparison
Authors: Barba Vincenzo , Musumeci Salvatore , Stella Fausto , Mandrile Fabio , Palma Marco
Published: August 2024

This paper investigates the commutation transients of MOSFET and GaN FET devices in motor drive applications during hard-switching and soft-switching commutations at dead time operation. This study compares the switching behaviors of MOSFETs and GaN FETs, focusing on their performance during dead time in inverter legs for voltage source inverters. Experimental tests at various phase current levels reveal distinct switching characteristics and energy dissipation patterns. A validated simulation model estimates the experimental energy exchanged and dissipated during switching transients. The results demonstrate that GaN FETs exhibit lower overall losses at shorter dead times compared to MOSFETs, despite higher reverse conduction voltage drops. The study provides a quantitative framework for selecting optimal dead times to minimize energy losses, enhancing the efficiency of GaN FET-based inverters in low-voltage motor drive applications. Finally, a dead time optimization strategy is proposed and described.
Can Ecological Protection Affect High-Quality Forestry Development?—A Case Study of China
Authors: Ma Longbo , Fan Jixiang , Wang Qian , Zhao Rong
Published: August 2024

As the global ecological environment faces serious challenges, ecological protection has attracted global attention. As a basic industry of the national economy, China’s total forestry output value is 8.04 trillion RMB in 2022. This study aims to assess the impact of ecological protection on high-quality forestry development and find its mechanism by using provincial panel data from 2010 to 2021 in China. The variables of environmental regulation and urbanization level were introduced. The benchmark regression model, mediation effect model and threshold effect model were employed for empirical analysis. The results show that: (1) The estimated ecological protection coefficient is 0.146. The ecological protection level significantly promotes high-quality forestry development; (2) The effect is more significant in the western region and the area with higher forest coverage. The estimated coefficients are 1.392 and 0.745. It is less affected by the marginal effect of the ecological protection level in the east; (3) The estimated environmental regulation coefficient is −0.021. Ecological protection promotes high-quality forestry development by reducing environmental regulations; (4) The impact increases with the level of urbanization. The p-value of the double threshold is 0.073. Therefore, policy recommendations are proposed to strengthen ecological protection and promote high-quality forestry development.
Native and TMT Chestnut Extractives as Hydrophobic and Photostabylizing Additives for Wood Surfaces
Authors: Cetera Paola , Gindl-Altmutter Wolfgang , D’Auria Maurizio , Turkulin Hrvoje , Todaro Luigi
Published: August 2024

Wood extractives have proven strong anti-oxidative properties which may be used to mitigate surface deterioration caused by photolytic effects and free radical formations. An interesting challenge regarding wood extractives is understanding how they behave in terms of treating natural wood surfaces to reduce anti-oxidative processes that arise from exposure to the main environmental factors. In this study, the possible efficacy of chestnut (Castanea sativa Mill.) extractives derived from native (CH) and thermally modified wood (TMT CH), as a means of protecting against UV exposure in poplar (Populus spp.) and spruce (Picea abies Karst.) wood, was evaluated. Chestnut wood was first thermally modified at 180 °C for 3 h, and the extractives were obtained by the accelerated solvent extraction technique (ASE) and subsequently used to treat wood surfaces. Samples were immersed in extractive solutions and exposed to artificial UV-weathering exposure, then contact angle and colour stability were monitored during the process. An FTIR analysis of the photo-degradation process of poplar and spruce surfaces was also executed. Extractives of TMT chestnut changed the total colour variation in both poplar and spruce wood. A much darker colour compared to the extractives of native wood was observed and an increase in ∆E* from 9.75 to 30.76 and 6.24 to 22.97 in poplar and spruce was calculated. The stability of the colour depended both on the surface wood and the type of extractive. The initial contact angle remained almost unchanged in the poplar wood surface and only slightly increased in spruce regardless of whether they were treated with extractives from native or TMT chestnut wood. A strong reduction in contact angle after the accelerated UV exposure test was observed, especially in spruce treated with CH extractives. FTIR analysis confirmed the lower levels of chemical degradation of surfaces observed by colorimetry, where TMT CH extractives formed more stable chemical bonds than native extractives. The comparative analysis in this study clarified the complex relationships between the effects of high-temperature modification of wood and the potential protective role of TMT extractives on some wood surfaces.
How Is It Covered?—A Global Perspective on Teaching Themes and Perceived Gaps and Availability of Resources in University Forestry Education
Authors: Wilenius Konsta , Rekola Mika , Nevgi Anne , Sandström Niclas
Published: August 2024

Forest education has evolved significantly in recent decades due to global phenomena such as climate change and globalization, which are transforming the skill sets and competencies that should be provided by university-level education on forests. This evolution has placed pressure on educational institutions to adapt and meet these emerging needs. Previous research has identified gaps in the coverage of specific teaching topics within forest education, yet assessing the global state of education has been challenging because of the absence of comparable data. To address this gap, the Global Forest Education Project was initiated in 2020. The project developed a survey aimed at evaluating the state of forest education across all levels worldwide. In this study, we used data from this survey and conducted an analysis focusing on the perceived adequacy of teaching content and the availability of educational resources at the university level. The study revealed global concerns regarding the comprehensive coverage of the various teaching themes. Although there was some variation in coverage across different regions, the trends appeared to be consistent globally. The perceived availability of resources for education varied significantly by region, including factors that likely impede the ability of educational programs to modernize and meet new educational requirements. These findings indicate that there is a need to reevaluate and improve the coverage of many of the teaching themes in forest education. Moreover, it is evident that education providers in certain regions require increased funding to ensure sufficient resources to address these needs efficiently.
Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors
Authors: Tefera Minyechil Alehegn , Dehnaw Amare Mulatie , Manie Yibeltal Chanie , Yao Cheng-Kai , Bogale Shegaw Demessie , Peng Peng-Chun
Published: August 2024

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.
Production of Nanofibers by Electrospinning as Carriers of Agrochemical
Authors: Colín-Orozco Julia , Colín-Orozco Elena , Valdivia-Barrientos Ricardo
Published: August 2024

Agrochemicals can now be protected from harsh environments like pH, light, temperature, and more with the help of a drug-loading system. This has allowed the creation of targeted and continuous release functions for pesticides and fertilizers, as well as the precise application, reduction, and efficiency of agrochemicals. All of these benefits have been made possible by the recent advancements in the field of nanomaterials. A simple procedure known as electrospinning can be used to create nanofibers from natural and synthetic polymers. Nanofibers have come to be recognized as one of the sustainable routes with enormous applicability in different fields. In agriculture, a promising strategy may entail plant protection and growth through the encapsulating of numerous bio-active molecules as pesticides and fertilizers for intelligent administration at the desired places. Owing to their permeability, tiny dimensions, and large surface area, nanofibers can regulate the rate at which agrochemicals are released. This slows down the rate at which the fertilizer dissolves and permits the release of coated fertilizer gradually over time, which is more effectively absorbed by plant roots, as well as the efficiency of pesticides. Thus, modern agriculture requires products and formulations that are more efficient and environmentally friendly than traditional agrochemicals. In addition to highlighting the significance and originality of using nanofibers and offering a brief explanation of the electrospinning technology, the review article’s main goal is to provide a thorough summary of the research leading to breakthroughs in the nanoencapsulation of fertilizers and pesticides.
Blue Economy Financing Solutions for the Fisheries and Aquaculture Sectors of Caribbean Island States
Authors: Bennett Michael , March Antaya , Failler Pierre
Published: August 2024

This study reviews various financing solutions available for fisheries and aquaculture development in Caribbean small island developing states (SIDS) and Barbados, Grenada, and St. Vincent and the Grenadines. Previously identified financing needs within the fisheries and aquaculture sectors have been matched with the most suitable financing mechanisms. However, the use of blue levies is recommended and applicable in almost every scenario, as they allow these sectors to drive their own development in financing research and conservation projects to their own benefit. The use of “blue tokens” with sufficiently low repayment coupons allows development projects to gather public support for fisheries, thereby increasing the likelihood of the project being successful through community buy-in. The possibility of natural capital being traded as public equities as “Natural Asset Companies” provides the opportunity for development projects to fund themselves. The review concludes that natural capital can be leveraged as the base through which public-private partnerships (PPPs) can facilitate optimal delivery of ecosystem services, benefit multiple stakeholders, and provide numerous development opportunities. An enabling environment for debt and lending with low-interest loan repayments is also applicable to almost every scenario, as it facilitates access to capital finance for infrastructure development and the acquisition of increasingly sustainable fishing equipment. Steps towards generating an enabling environment for financing fisheries and aquaculture in the Caribbean region are also discussed. The establishment of dedicated financing institutions, PPPs, and sufficient data reporting infrastructure for the fisheries and aquaculture industry are essential for driving development in these sectors. Likely, the largest limiting factor in financing Caribbean fisheries and aquaculture industries is a lack of awareness of the range of finance and financing mechanisms available to stakeholders, as well as an enabling environment for financing blue Economy sectors. This review is thus intended to aid financing institutions, Blue Economy developers, and specifically Caribbean fisheries and aquaculture stakeholders and Caribbean governments by raising awareness of the financing mechanisms available, encourage the incorporation of their use in the fisheries and aquaculture industries in the Caribbean, and encourage policymakers to create an enabling environment for financing development in these crucial sectors.
Effects of Salinity on Growth, Digestive Enzyme Activity, and Antioxidant Capacity of Spotbanded Scat (Selenotoca multifasciata) Juveniles
Authors: Liu Jianyi , Ai Tongxi , Yang Jun , Shang Meijuan , Jiang Keji , Yin Yane , Gao Lei , Jiang Wei , Zhao Na , Ju Jianfeng , Qin Bo
Published: August 2024

As a euryhaline fish species that inhabits estuarine and coastal regions, the spotbanded scat (Selenotoca multifasciata) experiences growth influences during its larval stage due to variations in salinity. Here, we evaluated salinity required by early-stage spotbanded scat juveniles to achieve the highest growth performance, digestive enzyme activity, survival, and antioxidant capacity. We reared spotbanded scat juveniles (all 0.50 ± 0.05 g) in 0–35‰ salinity gradients for 50 days and recorded their survival rate every 10 days. After 50 experimental days, we measured morphological data, stomach and intestinal digestive enzyme activities, and liver antioxidant enzyme activities and malondialdehyde contents. In general, 5–15‰ salinity led to 100% survival. The 5‰ salinity group demonstrated the highest values for the following measures: final wet body weight; weight gain rate; specific growth rate; growth percentage; average daily gain; stomach amylase and lipase specific activities; and intestinal amylase, lipase, trypsin, and pepsin specific activities. However, stomach trypsin and pepsin activities did not demonstrate significant between-group differences (all p > 0.05). The 25‰ salinity group demonstrated the highest liver superoxide dismutase and glutathione peroxidase activities and malondialdehyde content. Finally, the 0‰ salinity group demonstrated the highest liver catalase activity. Thus, spotbanded scat juveniles demonstrate the highest survival rates, growth performance, and digestive enzyme activity at 5‰ salinity and the strongest oxidative stress responses at 25‰ salinity.
A Survey on Potentially Beneficial and Hazardous Bioactive Compounds in Cocoa Powder Samples Sourced from the European Market
Authors: Esposito Luigi , Perillo Matteo , Di Mattia Carla Daniela , Scroccarello Annalisa , Della Pelle Flavio , Compagnone Dario , Sacchetti Giampiero , Mastrocola Dino , Martuscelli Maria
Published: August 2024

Cocoa (Theobroma cacao, L.) represents an important market that gained relevance and became an esteemed commodity thanks to cocoa powder, chocolate, and other related products. This work analyzed 59 cocoa powder samples from the European market. Three distinct subgroups were identified: organic or conventional, alkalized or not alkalized, and raw or roasted processing. The impact of the technological process on their pH, color, and compositional traits, as well as their content of biogenic amines and salsolinol, was evaluated. The phenolic fraction was also investigated through both common and emerging methods. The results depict that the influence of the agronomical practices (organic/conventional) did not significantly (p < 0.05) affect the composition of the cocoa powders; similarly, the roasting process was not a determinant of the compounds traced. On the other hand, the alkalinization process greatly impacted color and pH, no matter the cocoa’s provenience or obtention or other processes, also resulting in reducing the phenolic fraction of the treated samples. Principal component analysis confirmed that the alkali process acts on pH, color, and phenolic composition but not on the content of other bioactive molecules (biogenic amines and salsolinol). All the samples were safe, while the alkalized powders saw a great reduction in beneficial biocompounds. A novel strategy could be to emphasize on the label whether cocoa powder is non-alkalized to meet the demand for more beneficial products.
Impact of Mechanical and Manual Peeling on the Volatile Profile of White Pepper (Piper nigrum L.)
Authors: Zhang Yuan , Yu Peiyao , Wei Lijiao , Zhang Bing , Shen Dezhan , Zhao Zhenhua , Guo Xinbo
Published: August 2024

Mechanical peeling is more efficient and environmentally friendly compared to manual peeling. However, comparative studies on the quality of mechanically peeled pepper and manually peeled pepper are limited. This study utilized GC-MS to investigate the effects of immersion, steaming, and peeling machinery speed on the volatile composition of white pepper. A total of thirteen monoterpenes and seven sesquiterpenes were detected, with 3-carene, D-limonene, and sabinene being the most abundant monoterpenoids and β-caryophyllene, δ-elemene, and α-copaene being the most abundant sesquiterpenes. The total volatiles increased with longer steaming times and higher peeling machinery speeds. Compared to manual peeling or steaming followed by mechanical peeling, the volatile content of pepper was higher when using mechanical peeling alone. Additionally, relative odor activity values showed that 3-carene and D-limonene were the main contributors to flavor, with 3-carene, β-caryophyllene, and α-copaene being key volatiles responsible for flavor distinctions. This research aims to provide theoretical support for developing a superior and environmentally friendly mechanical method to replace manual labor.
Role of Quinoa (Chenopodium quinoa Willd) and Chickpea (Cicer arietinum L.) Ratio in Physicochemical Stability and Microbiological Quality of Fermented Plant-Based Beverages during Storage
Authors: Hurtado-Murillo John , Franco Wendy , Contardo Ingrid
Published: August 2024

Three different fermented plant-based beverages were prepared and stored for a long period (50 days) to assess the effect of the quinoa-to-chickpea ratio on physicochemical stability and microbiological quality. Physicochemical stability was evaluated based on pH, acidity, Brix degrees, water-holding capacity (WHC), viscosity, and viscoelasticity. At the end of the long-term storage period, the pH, acidity, and WHC remained stable. During the entire storage period, the beverages maintained good bacterial, fungal, and lactic acid bacteria (LAB) counts. Quinoa and chickpea flour ratios of 50% showed a higher viscosity (18 Pa.s) and WHC (65%) during short-term storage (0–30 d), indicating that the presence of chickpea flour had a positive effect on these parameters, possibly because chickpea starch contains higher amounts of amylose and long-branch chain amylopectin, which impacts the retrogradation pattern under acidic and refrigerated conditions. However, at the end of storage (50 days), the same blend had a higher acidity, lower viscosity (0.78 Pa.s), and lower LAB counts (~1 × 108 CFU/mL), indicating that the increase in chickpea flour had an adverse long-term effect on these parameters. These results suggest that although different ratios of plant sources can improve the physical aspects, they need to be incorporated in a balanced manner to avoid negative effects on both short- and long-term storage, owing to the incorporation of different types of starches and proteins affecting the stability of the system.
Liver-Targeted Nanoparticles Loaded with Cannabidiol Based on Redox Response for Effective Alleviation of Acute Liver Injury
Authors: Zhang Xuan , Yi Xiangzhou , Gao Xia , Li Yongcheng , Shen Xuanri
Published: August 2024

The purpose of this work was to construct liver-targeted nanoparticles based on the redox response to effectively deliver cannabidiol (CBD) for the prevention of acute liver injury (ALI). CBD-loaded nanoparticles (CBD NPs) with a particle size of 126.5 ± 1.56 nm were prepared using the polymer DA-PP-LA obtained by grafting pullulan polysaccharide with deoxycholic acid (DA) and α-lipoic acid (α-LA). CBD NPs showed typical redox-response release behavior. Interestingly, CBD NPs exhibited admirable liver targeting ability, significantly accumulated in the liver, and effectively promoted the internalization of CBD in liver cells, thus effectively reducing the H2O2-induced oxidative damage of HepG2 cells and avoiding apoptosis. More importantly, CBD NPs effectively prevented CCl4-induced ALI by protecting liver function, ameliorating oxidative stress levels, inhibiting the production of inflammatory factors, and protecting the liver from histological damage. This study provides a promising strategy for achieving targeted delivery of CBD NPs in the liver, thereby effectively preventing ALI.
Tribomechanical Properties of PVA/Nomex® Composite Hydrogels for Articular Cartilage Repair
Authors: Santos Francisco , Marto-Costa Carolina , Branco Ana Catarina , Oliveira Andreia Sofia , Galhano dos Santos Rui , Salema-Oom Madalena , Diaz Roberto Leonardo , Williams Sophie , Colaço Rogério , Figueiredo-Pina Célio , Serro Ana Paula
Published: August 2024

Due to the increasing prevalence of articular cartilage diseases and limitations faced by current therapeutic methodologies, there is an unmet need for new materials to replace damaged cartilage. In this work, poly(vinyl alcohol) (PVA) hydrogels were reinforced with different amounts of Nomex® (known for its high mechanical toughness, flexibility, and resilience) and sterilized by gamma irradiation. Samples were studied concerning morphology, chemical structure, thermal behavior, water content, wettability, mechanical properties, and rheological and tribological behavior. Overall, it was found that the incorporation of aramid nanostructures improved the hydrogel’s mechanical performance, likely due to the reinforcement’s intrinsic strength and hydrogen bonding to PVA chains. Additionally, the sterilization of the materials also led to superior mechanical properties, possibly related to the increased crosslinking density through the hydrogen bonding caused by the irradiation. The water content, wettability, and tribological performance of PVA hydrogels were not compromised by either the reinforcement or the sterilization process. The best-performing composite, containing 1.5% wt. of Nomex®, did not induce cytotoxicity in human chondrocytes. Plugs of this hydrogel were inserted in porcine femoral heads and tested in an anatomical hip simulator. No significant changes were observed in the hydrogel or cartilage, demonstrating the material’s potential to be used in cartilage replacement.