bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Impact of Political Patronage Networks on Financial Stability: Lebanon’s 2019 Economic Crisis
Authors: Abou Ltaif Samar , Mihai-Yiannaki Simona
Published: July 2024

Amid Lebanon’s multifaceted economic crisis, this paper explores the intricate dynamics between political patronage networks and financial stability. Grounded in the theoretical frameworks of New Institutional Economics (NIE) and Project Management (PM), the study delves into how entrenched political elites and patronage networks have shaped Lebanon’s financial system, exacerbating vulnerabilities and perpetuating the ongoing crisis. Utilizing qualitative methods including in-depth interviews, document analysis, and case studies, the research illuminates the pivotal role of political actors and their vested interests in economic policies and financial institutions. The findings reveal systemic governance failures, crony capitalism, and institutional decay as underlying causes of Lebanon’s economic stress. In response, the paper proposes a comprehensive framework for governance reform that integrates insights from NIE and PM, emphasizing structured planning, accountability mechanisms, and institutional strengthening. The purpose of this study is not only to contribute to a nuanced understanding of Lebanon’s challenges but also to offer actionable insights for policymakers, academics, and stakeholders to address the root causes of the crisis and pave the way for sustainable economic recovery and revitalization.
AMTT: An End-to-End Anchor-Based Multi-Scale Transformer Tracking Method
Authors: Zheng Yitao , Deng Honggui , Xu Qiguo , Li Ni
Published: July 2024

Most current trackers utilize only the highest-level features to achieve faster tracking performance, making it difficult to achieve accurate tracking of small and low-resolution objects. To address this problem, we propose an end-to-end anchor-based multi-scale transformer tracking (AMTT) approach to improve the tracking performance of the network for objects of different sizes. First, we design a multi-scale feature encoder based on the deformable transformer, which better fuses the multilayer template features and search features through the self-enhancement module and cross-enhancement module to improve the attention of the whole network to objects of different sizes. Then, to reduce the computational overhead of the decoder while further enhancing the multi-scale features, we design a feature focusing block to compress the number of coded features. Finally, we introduce a feature anchor into the traditional decoder and design an anchor-based decoder, which utilizes the feature anchor to guide the decoder to adapt to changes in object scale and achieve more accurate tracking performance. To confirm the effectiveness of our proposed method, we conduct a series of experiments on different datasets such as UAV123, OTB100 and GOT10k. The results show that our adopted method exhibits highly competitive performance compared to the state-of-the-art methods in recent years.
RETRACTED: Cao et al. Energy Efficiency Maximization for Hybrid-Powered 5G Networks with Energy Cooperation. Electronics 2022, 11, 1605
Authors: Cao Yang , Zhong Ye , Peng Xiaofeng , Pan Song
Published: July 2024

The Electronics Editorial Office retracts the article, “Energy Efficiency Maximization for Hybrid Powered 5G Networks with Energy Cooperation” [...]
Influence of Post-Processing Techniques on Random Number Generation Using Chaotic Nanolasers
Authors: Zhao Jing , Liu Guopeng , Li Rongkang , Mu Penghua
Published: July 2024

In this paper, we propose using a chaotic system composed of nanolasers (NLs) as a physical entropy source. Combined with post-processing technologies, this system can produce high-quality physical random number sequences. We investigated the parameter range for achieving time-delay signature (TDS) concealment in the chaotic system. This study demonstrates that NLs exhibit noticeable TDS only under optical feedback. As mutual injection strength between the master NLs (MNLs) increases, the TDS of the MNLs is gradually suppressed until they are completely concealed. Compared to MNLs, the slave NL (SNL) exhibits better TDS suppression performance. Additionally, we investigated the chaotic and highly unpredictable regions of the SNL, demonstrating that high-quality chaotic signals can be produced over a wide range of parameters. Using TDS hidden and highly unpredictable chaotic signals as the source of random entropy, the effects of different post-processing techniques on random number extraction were compared. The results indicate that effective post-processing can enhance the unpredictability of the random sequence. This study successfully utilized NLs for random number generation, showcasing the potential and application prospects of NLs in the field of random numbers.
Research on Line Maintenance Strategies Considering Dynamic Island Partitioning in Distribution Areas under Adverse Weather Conditions
Authors: Chen Hao , Guo Yufeng , Xu Wei , Zhang Linyao , Liu Yifei
Published: July 2024

As global climate change intensifies, extreme weather events are becoming more frequent, with ice disasters posing an increasingly significant threat to the stable operation of power distribution networks. Particularly during power outages for de-icing, multiple power islands may form within a distribution area, increasing the complexity of grid operations. Existing research has not fully considered the comprehensive coordination of stable operation of these power islands and de-icing maintenance schedules. Therefore, for the potential multi-island operation of distribution networks caused by freezing disasters, this paper first establishes a dynamic island partitioning model based on distribution network reconfiguration technology. Secondly, based on the characteristics of the de-icing phase, a de-icing maintenance schedule model is established. Finally, dispatch optimization of the distribution network is coordinated with the line de-icing maintenance schedule. By adjusting the de-icing strategies and network structure, the aim is to minimize the risk of load loss. The relevant case analysis indicates that the collaborative optimization model established in this paper helps power distribution networks to reduce their economic losses when facing adverse weather conditions.
An Email Cyber Threat Intelligence Method Using Domain Ontology and Machine Learning
Authors: Venčkauskas Algimantas , Toldinas Jevgenijus , Morkevičius Nerijus , Sanfilippo Filippo
Published: July 2024

Email is an excellent technique for connecting users at low cost. Spam emails pose the risk of collecting a user’s personal information by fooling them into clicking on a link or engaging in other fraudulent activities. Furthermore, when a spam message is delivered, the user may read the entire message before deciding it is spam and deleting it. Most approaches to email classification proposed by other authors use natural language processing (NLP) methods to analyze the content of email messages. One of the biggest shortcomings of NLP-based methods is their dependence on the language in which a message is written. To construct an effective email cyber threat intelligence (CTI) sharing framework, the privacy of a message’s content must be preserved. This article proposes a novel domain-specific ontology and method for emails that require only the metadata of email messages to be shared to preserve their privacy, making them applicable to solutions for sharing email CTI. To preserve privacy, a new semantic parser was developed for the proposed email domain-specific ontology to populate email metadata and create a dataset. Machine learning algorithms were examined, and experiments were conducted to identify and classify spam messages using the newly created dataset. Feature-ranking algorithms, chi-squared, ANOVA (analysis of variance), and Kruskal–Wallis tests were used. In all experiments, the kernel naïve Bayes model demonstrated acceptable results. The highest accuracy of 92.28% and an F1 score of 95.92% for recognizing spam email messages were obtained using the proposed domain-specific ontology, the newly developed semantic parser, and the created metadata dataset.
A Driving Simulator Study to Examine the Impact of Visual Distraction Duration from In-Vehicle Displays: Driving Performance, Detection Response, and Mental Workload
Authors: Liang Zhoushuo , Wang Yahui , Qian Cheng , Wang Yueyang , Zhao Chengyi , Du Hongjiang , Deng Jie , Li Xiaoqiong , He Yue
Published: July 2024

This research explores the impact of visual distraction duration from multifunctional in-car displays on driver safety. Utilizing a driving simulator and eye-tracking technology, this study involved 35 participants in visual search and car-following tasks, assessing their performance and mental workload across different durations of distraction. The results show that distractions lead to a decrease in driving control and a rise in mental workload, characterized by deteriorated vehicle handling and longer reaction times. With continued exposure to distractions, drivers begin to adapt, indicating a non-linear relationship between the duration of distraction and its consequences. This adaptation points to a threshold beyond which the negative effects of distractions no longer intensify. This work aids in developing safer automotive interfaces by highlighting the effects of larger screen trends on driving behavior and proposing strategies to mitigate distractions. It enriches the discourse on human–machine interaction by offering fresh perspectives on how visual distraction duration from in-car displays influences driving dynamics and cognitive load, thereby enhancing road safety.
Enhancing Wireless Charging for Electric Vehicles: Active Load Impedance Matching and Its Impact on Efficiency, Cost and Size
Authors: Allali Nicolas
Published: July 2024

This paper presents an advanced Wireless Power Transfer (WPT) system for electric vehicles (EVs) featuring Active Load Impedance Matching (ALIM) at the rectification stage. Unlike traditional synchronous rectification, ALIM dynamically adjusts load impedance, optimizing energy transfer efficiency and reducing thermal stresses, system costs, and mass. The system incorporates two circuits optimized for distinct frequency bands: one operates below 10 kHz using standard copper wiring for cost-effectiveness, and the other at 85 kHz, which significantly reduces the mass of the onboard coil and magnetic circuit while ensuring interoperability according to SAE J2954 standard. Our approach enhances charging efficiency across various operating conditions, improves thermal management, and minimizes maintenance costs. Additionally, it enables partial compensation for vehicle misalignment and ground assembly impedance, further boosting efficiency and interoperability. Experimental results demonstrate a notable increase in efficiency and reduction in system mass, confirming the superiority of the ALIM-equipped WPT system over conventional solutions. This paper underscores the potential of ALIM to advance the scalability, efficiency, and economic viability of wireless EV charging technology, promoting broader adoption and sustainability in EV infrastructures. By providing a comprehensive solution that addresses key challenges in wireless charging, our work paves the way for more efficient and cost-effective EV charging systems.
Methods for Magnetic Signature Comparison Evaluation in Vehicle Re-Identification Context
Authors: Balamutas Juozas , Navikas Dangirutis , Markevicius Vytautas , Cepenas Mindaugas , Valinevicius Algimantas , Zilys Mindaugas , Prauzek Michal , Konecny Jaromir , Frivaldsky Michal , Li Zhixiong , Andriukaitis Darius
Published: July 2024

Intelligent transportation systems represent innovative solutions for traffic congestion minimization, mobility improvements and safety enhancement. These systems require various inputs about vehicles and traffic state. Vehicle re-identification systems based on video cameras are most popular; however, more strict privacy policy necessitates depersonalized vehicle re-identification systems. Promising research for depersonalized vehicle re-identification systems involves leveraging the captured unique distortions induced in the Earth’s magnetic field by passing vehicles. Employing anisotropic magneto-resistive sensors embedded in the road surface system captures vehicle magnetic signatures for similarity evaluation. A novel vehicle re-identification algorithm utilizing Euclidean distances and Pearson correlation coefficients is analyzed, and performance is evaluated. Initial processing is applied on registered magnetic signatures, useful features for decision making are extracted, different classification algorithms are applied and prediction accuracy is checked. The results demonstrate the effectiveness of our approach, achieving 97% accuracy in vehicle re-identification for a subset of 300 different vehicles passing the sensor a few times.
A New Photonic Filterless Scheme for the Generation of Frequency 16-Tupling Millimeter Wave Signals Utilizing Cascading Polarization Modulators
Authors: Yang Zufang , Yan Xueyao , Wang Dongfei , Wang Xiangqing , Yang Xiaokun
Published: July 2024

A new photonic scheme is proposed for the generation of a frequency 16-tupling millimeter wave (mm-wave) signal, based on four cascaded polarization modulators (PolMs). Firstly, through the precise control of these four PolMs, odd harmonics can be effectively suppressed sequentially. Next, combined with the modulation index of the PolMs, only the pure ± 8th-order optical harmonics are retained and enter the photodetector to beat the frequency. After multiple rounds of detailed, mathematical formula derivation and computer simulation, the accuracy and advantages of the proposed solution can be demonstrated. As a result, a 160 GHz mm-wave signal is obtained, which is produced from a 10 GHz radio frequency (RF) signal. By properly setting the modulation index and the angle of three phase differences, the optical suppression ratio (OSSR) and the radio frequency suppression ratio (RFSSR) of the acquired signal are 64.21 dB and 60.99 dB, respectively. Further, the impact of the variables on the OSSR and the RFSSR are analyzed and discussed.
Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar–Communication System
Authors: Zhao Yinan , Zhao Zhongqing , Tong Fangqiu , Fan Yu , Feng Xiang
Published: July 2024

In this paper, we try to design the joint waveform and passive beamforming within the context of dual-function radar–communication (DFRC) systems. Focusing on the intricate trade-off between stringent radar beampattern constraints and their desired performance, we introduce a novel manifold idea based on the alternating direction method of multipliers (ADMM) framework. Specifically, our proposed method, named DFRC-MA, could address the challenge of constant modulus waveform design in a multiple-input–multiple-output (MIMO) DFRC system. Firstly, our methodology begins by formulating the reference waveform to achieve an optimal radar beamforming pattern. Subsequently, we define the DFRC optimization problem to mitigate the multi-user interference (MUI) under the constant modulus constraint. Through a series of simulations, we evaluate the efficacy of DFRC-MA, where the integrated waveform designed by DFRC-MA exhibits superior performance over some prevalent ones.
A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning
Authors: Liu Shiqi , Zhou Yuting , Yang Xuemei , Wang Xiaoying , Yin Junping
Published: July 2024

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.
A Browser Fingerprint Authentication Scheme Based on the Browser Cache Side-Channel Technology
Authors: Yan Yiming , Zhao Haiyong , Qu Haipeng
Published: July 2024

Users encounter various threats, such as cross-site scripting attacks and session hijacking, when they perform login operations in the browser. These attacks pose significant risks to the integrity and confidentiality of personal data. The browser fingerprint, as an authentication technique, can effectively enhance user security. However, attackers can bypass browser fingerprint authentication through phishing attacks and other methods, leading to unauthorized logins. To address these issues, we propose a secure browser fingerprint authentication scheme that integrates the data of the browser cache side-channel into the traditional browser fingerprint. Consequently, it enhances the dynamics and non-determinism of the browser fingerprint and improves the anti-attack capabilities of the authentication process. Experimental results demonstrate that this scheme can effectively mitigate phishing attacks and man-in-the-middle attacks, achieving a 95.33% recognition rate for attackers and a 96.17% recall rate for authorized users.
A Zero-Knowledge-Proof-Based Anonymous and Revocable Scheme for Cross-Domain Authentication
Authors: Zhao Xinjian , Xia Fei , Xia Hanning , Mao Yunlong , Chen Shi
Published: July 2024

Authentication is a crucial security service on the Internet. In real-world applications, multiple independent trust domains often exist, with each recognizing only certain identities within their own systems. During cross-domain access, users cannot directly use their original certificates, which presents a cross-domain authentication problem. Traditional centralized schemes typically employ a trusted third party (TTP) to facilitate the transfer of identity trust across domains. These schemes inevitably inherit the vulnerabilities associated with single points of failure. In contrast, blockchain-based decentralized schemes effectively eliminate the potential threats posed by TTPs. However, the openness and transparency of the blockchain also bring new security issues, such as privacy leakage. In this paper, we propose a zk-SNARK-based anonymous scheme on the blockchain for cross-domain authentication. Specifically, our scheme adopts an authorization-then-proof structure, which strikes a delicate balance between anonymity and revocability. We provide theoretical proofs for the security of our scheme and explain how it achieves proactive revocability. Experimental evaluation results demonstrated that our scheme is both secure and efficient, and the revocation could be accomplished by introducing only 64 bytes of on-chain storage with one hash comparison.
Deep Learning Innovations in Video Classification: A Survey on Techniques and Dataset Evaluations
Authors: Mao Makara , Lee Ahyoung , Hong Min
Published: July 2024

Video classification has achieved remarkable success in recent years, driven by advanced deep learning models that automatically categorize video content. This paper provides a comprehensive review of video classification techniques and the datasets used in this field. We summarize key findings from recent research, focusing on network architectures, model evaluation metrics, and parallel processing methods that enhance training speed. Our review includes an in-depth analysis of state-of-the-art deep learning models and hybrid architectures, comparing models to traditional approaches and highlighting their advantages and limitations. Critical challenges such as handling large-scale datasets, improving model robustness, and addressing computational constraints are explored. By evaluating performance metrics, we identify areas where current models excel and where improvements are needed. Additionally, we discuss data augmentation techniques designed to enhance dataset accuracy and address specific challenges in video classification tasks. This survey also examines the evolution of convolutional neural networks (CNNs) in image processing and their adaptation to video classification tasks. We propose future research directions and provide a detailed comparison of existing approaches using the UCF-101 dataset, highlighting progress and ongoing challenges in achieving robust video classification.
Analysis of Energy Loss Characteristics in an Axial-Flow Reactor Coolant Pump Based on Entropy Production Theory
Authors: Li Zhong , Sun Yanna , Gong Weifeng , Ni Dan , Gao Bo
Published: July 2024

As the critical component of a nuclear power plant (NPP), the reactor coolant pump (RCP) will suffer energy losses during operation, which can lead to a series of safety issues and adversely affect the efficiency and stability of the NPP. In this study, the SST k-ω turbulence model is utilized to simulate the internal flow field of an axial-flow reactor coolant pump (RCP) under operating conditions of 0.8QN to 1.2QN. Combined with entropy production theory, the distribution characteristics and hydraulic causes of energy loss within different regions of the RCP are revealed. The research findings are as follows: the total entropy production in the RCP first decreases and then increases during operation; with turbulent entropy production consistently accounting for over 70% of the total, and direct entropy production accounting for less than 10%. The impeller and annular casing are always the main components responsible for hydraulic losses within the pump. As the flow rate increases, the total entropy production in the impeller initially decreases and then increases, accounting for between 34.3% and 51% of the total; with energy losses mainly concentrated on the suction side of the impeller blades. The total entropy production in the annular casing gradually increases under operating conditions ranging from 0.8QN to 1.2QN, accounting for between 20.4% and 50.3% of the total. Rotor-stator interaction (RSI), backflow, and flow separation near the volute tongue are significant causes of energy losses within the annular casing. Optimizing the geometric parameters of the impeller and annular casing is an effective way to reduce flow losses in axial-flow RCPs. The research results can provide a reference for the development of optimization techniques for RCPs.
Capacity Expansion Planning of Hydrogen-Enabled Industrial Energy Systems for Carbon Dioxide Peaking
Authors: Zhang Kai , Dong Xiangxiang , Li Chaofeng , Zhao Yanling , Liu Kun
Published: July 2024

As the main contributor of carbon emissions, the low-carbon transition of the industrial sector is important for achieving the goal of carbon dioxide peaking. Hydrogen-enabled industrial energy systems (HIESs) are a promising way to achieve the low-carbon transition of industrial energy systems, since the hydrogen can be well coordinated with renewable energy sources and satisfy the high and continuous industrial energy demand. In this paper, the long-term capacity expansion planning problem of the HIES is formulated from the perspective of industrial parks, and the targets of carbon dioxide peaking and the gradual decommissioning of existing equipment are considered as constraints. The results show that the targets of carbon dioxide peaking before different years or with different emission reduction targets can be achieved through the developed method, while the economic performance is ensured to some extent. Meanwhile, the overall cost of the strategy based on purchasing emission allowance is three times more than the cost of the strategy obtained by the developed method, while the emissions of the two strategies are same. In addition, long-term carbon reduction policies and optimistic expectations for new energy technologies will help industrial parks build more new energy equipment for clean transformation.
Assessing the Environmental Impact of Biobased Exterior Insulation Panel: A Focus on Carbon Uptake and Embodied Emissions
Authors: Hossain Md Sahadat , Therasme Obste , Crovella Paul , Volk Timothy A.
Published: July 2024

There are millions of older buildings in the colder climate regions of the world where envelope upgrades are needed to improve the indoor quality of buildings, reduce energy costs, and lower greenhouse gas (GHG) emissions. This study assessed the global warming potential (GWP) with and without accounting for CO2 uptake in trees (biogenic carbon) for 20- and 100-year timespans of an exterior insulation panel. The panels consisted of six different materials with three-ply cross-laminated timber (CLT) as its main component. The net GWP100-Uptake impact when explicitly accounting for biogenic CO2 uptake over a 100-year time period was 7.2 kgCO2-eq/m2 which was 92.7% lower than if it was not included (GWP100-Fossil of 98.7 kgCO2-eq/m2). Using a 20-year GWP increased the impact of the GWP fossil by 21.7% and the CO2 uptake scenario by 298%. The major contributor was the energy used for manufacturing panel’s materials (53%), with embodied carbon in bio-products primarily responsible for offsetting emissions. The findings will be helpful for policymakers in setting net-zero carbon emission goals for embodied and operational impacts of building materials.
Optimal Scheduling for Increased Satisfaction of Both Electric Vehicle Users and Grid Fast-Charging Stations by SOR&KANO and MVO in PV-Connected Distribution Network
Authors: Yan Qingyuan , Gao Yang , Xing Ling , Xu Binrui , Li Yanxue , Chen Weili
Published: July 2024

The surge in disordered EV charging demand, driven by the rapid growth in the ownership of electric vehicles (EVs), has highlighted the potential for significant disruptions in photovoltaic (PV)-connected distribution networks (DNs). This escalating demand not only presents challenges in meeting charging requirements to satisfy EV owners and grid fast-charging stations (GFCSs) but also jeopardizes the stable operation of the distribution network. To address these challenges, this study introduces a novel model called SOR&KANO for charging decisions, which focuses on addressing the dual-sided demand of GFCSs and EVs. The proposed model utilizes the salp swarm algorithm-convolutional neural network (SSA-CNN) to predict the PV output and employs Monte Carlo simulation to estimate the charging load of EVs, ensuring accurate PV output prediction and efficient EV distribution. To optimize charging decisions for reserved EVs (REVs) and non-reserved EVs (NREVs), this study applies the multi-verse optimizer (MVO) in conjunction with time-of-use (TOU) tariff guidance. By integrating the SOR&KANO model with the MVO algorithm, this approach enhances satisfaction levels for GFCSs by balancing the charging demand, increasing utilization rates, and improving voltage quality within the DN. Simultaneously, for EVs, the optimized scheduling strategy reduces charging time and costs while addressing concerns related to range anxiety and driver fatigue. The efficacy of the proposed approach is validated through a simulation on a modified IEEE-33 system, confirming the effectiveness of the optimal scheduling methods proposed in this study.
Considering the Tiered Low-Carbon Optimal Dispatching of Multi-Integrated Energy Microgrid with P2G-CCS
Authors: Liu Zixuan , Gao Yao , Li Tingyu , Zhu Ruijin , Kong Dewen , Guo Hao
Published: July 2024

The paper addresses the overlooked interaction between power-to-gas (P2G) devices and carbon capture and storage (CCS) equipment, along with the stepwise carbon trading mechanism in the context of current multi-park integrated energy microgrids (IEMGs). Additionally, it covers the economic and coordinated low-carbon operation issues in multi-park IEMGs under the carbon trading system. It proposes a multi-park IEMG low-carbon operation strategy based on the synchronous Alternating Direction Method of Multipliers (ADMM) algorithm. The algorithm first enables the distribution of cost relationships among multi-park IEMGs. Then, using a method that combines a CCS device with a P2G unit in line with the tiered carbon trading scheme, it expands on the model of single IEMGs managing thermal, electrical, and refrigeration energy. Finally, the comparison of simulation cases proves that the proposed strategy significantly reduces the external energy dependence while keeping the total cost of the users unchanged, and the cost of interaction with the external grid is reduced by 56.64%, the gas cost is reduced by 27.78%, and the carbon emission cost is reduced by 29.54% by joining the stepped carbon trading mechanism.
Technological Prospects of Biochar Derived from Viticulture Waste: Characterization and Application Perspectives
Authors: D’Eusanio Veronica , Lezza Antonio , Anderlini Biagio , Malferrari Daniele , Romagnoli Marcello , Roncaglia Fabrizio
Published: July 2024

The increasing demand for sustainable agricultural practices aimed at reducing carbon dioxide emissions has driven the exploration of converting viticulture residues into biochar. This study investigates the potential technological applications of biochar as a filler for the production of electrically conductive composite materials, suitable to Bipolar Plate (BP) manufacturing. Grape seeds (GSs), defatted grape seeds (DGSs), wood stems (WSs), and whole grape seeds (WGSs) were converted into biochar samples through low-temperature (300 °C) pyrolysis for 3 or 24 h. The composition and thermal stability of biochar were evaluated through thermogravimetric analysis (TG), which provided valuable insights into interpreting the in-plane conductivity (IPC) values of the BP samples. Pyrolyzed GS and DGS biochar samples demonstrated enhanced thermal stability and conferred higher IPC values compared to WS counterparts. This indicates a clear correlation between the formation of carbon-rich structures during pyrolysis and overall electrical conductivity. In contrast, pyrolyzed WGSs produced BP samples with lower IPC values due to the presence of lipids, which were not effectively degraded by the low-temperature pyrolysis.
Study on Lighting Energy Savings by Applying a Daylight-Concentrating Indoor Louver System with LED Dimming Control
Authors: Lee June Hae , Kang Jae-Sik
Published: July 2024

This study analyzed the effect of indoor lighting energy reduction using a daylight-concentrating indoor louver system, which is a renewable energy equipment item. Daylight-concentrating indoor louver systems enhance indoor lighting by directing natural light, entering through windows, into the room via louvers. This study demonstrates significant lighting energy savings through the use of LED-linked dimming control, particularly during the transitional season, achieving an 85.65% reduction in power consumption. In contrast, the winter season showed higher cumulative power consumption due to reduced natural light availability, with a three-day average consumption of 1128.22 W compared to 836.60 W in the transitional season, representing a 25.85% increase. The illuminance distribution analysis revealed that, while winter had higher illuminance at 1 m from the window, the transitional season recorded higher values at 3 m and 5 m, indicating more effective natural light penetration. The solar altitude during the transitional season facilitated even light distribution through daylighting louvers. These findings confirm the substantial energy savings and improved illuminance distribution achieved with daylighting louvers and LED dimming control, with notable efficiency during the transitional season. Consequently, daylight-concentrating indoor louvers are confirmed to be effective in reducing indoor electric lighting energy consumption.
The Effects of High-Pressure Processing Pre-Treatment on Apple Fruit for Juice Production
Authors: Rinaldi Massimiliano , Dhenge Rohini , Rodolfi Margherita , Littardi Paola , Lacey Karen , Cavazza Antonella , Grimaldi Maria , Lolli Veronica , Cirlini Martina , Chiancone Benedetta , Ganino Tommaso
Published: July 2024

One of the most difficult issues in the juice industry is to manufacture juices where processing processes minimise the impact on the native characteristics of the fruits. In this study, high-pressure technology was used on whole apple fruits in order to evaluate the effect on the juice production. Two varieties, cv. Limoncella and cv. Pink Lady, were considered. Preliminarily, the fruits were subjected to different pressures, and histological as well as pomological measurements were taken in order to identify the best treatment condition, which was established to be 600 MPa for 3 min. Juice samples were then characterised by measuring the colour, viscosity, total antioxidant capacity (TAC), and total phenolic content (TPC). The storage colour stability of the juices for both varieties showed not significant L* values between the untreated and pre-treated fruits. Juices obtained from pre-treated fruits had a viscosity significantly higher than that obtained from untreated ones. Interestingly, the TPC of high-pressure processing (HPP) pre-treated juice resulted in being significantly higher compared to the untreated ones. The HPP pre-treatment can be considered as a commercial application to modulate some quality standards for apple juice production.
Hypoglycemic Ability of Sericin-Derived Oligopeptides (SDOs) from Bombyx mori Yellow Silk Cocoons and Their Physiological Effects on Streptozotocin (STZ)-Induced Diabetic Rats
Authors: Tocharus Chainarong , Sutheerawattananonda Manote
Published: July 2024

Patients with diabetes require daily medication to maintain blood sugar levels. Nevertheless, the long-term use of antidiabetics can lose efficacy and cause degeneration in some patients. For long-term diabetes care, integrating natural dietary foods and medicine is being considered. This study investigated the impact of SDOs on blood sugar levels and their physiological effects on diabetic rats. We induced diabetes in male Wistar rats with STZ (50 mg/kg) and then administered an oral glucose tolerance test to determine the SDO dosage comparable to glibenclamide. The rats were divided into nine groups: normal, diabetic, and diabetic with insulin (10 U/kg), glibenclamide (0.6 mg/kg), bovine serum albumin (BSA; 200 mg/kg), soy protein isolate (200 mg/kg), or SDOs (50, 100, and 200 mg/kg). Diabetic rats administered SDOs had a higher body weight and serum insulin but a lower blood sugar than diabetic control rats. Biochemical assays indicated lower AST/SGOT, ALT/SGPT, BUN, and triglycerides but higher HDL in the SDO groups. Immunohistochemistry showed that SDOs reduced damaged islet cells, increased beta-cell size, and improved insulin levels while decreasing alpha cell size and glucagon. The vascular effects of SDOs were like those of normal control treatment and insulin treatment in diabetic rats. SDOs, a yellow silk protein, show potential for long-term diabetes care.
Valorization of Fruit and Vegetables Industry By-Streams for 3D Printing—A Review
Authors: Tyupova Alona , Harasym Joanna
Published: July 2024

An energy supply crisis is impacting all the branches, including the agriculture and food industry. The wise and responsible utilization of plant raw materials already cultivated is becoming a must in the country’s economy. Not only the waste of the resources included but also the environmental challenge are concerns behind the not exploited food production by-streams and leftovers’ valorization. Fruits and vegetables’ out of the market quality “beauty” standards are still valuable sources of nutritious compounds. The conversion of raw materials into edible products can be provided by many techniques, with three-dimensional printing being the most individualized one. The main objective of this review was to summarize the existing efforts for the valorization of fruits and vegetable residuals into edible 3D inks and then 3D printed products. The clustering analysis was used for the separation of certain research approaches in fruit and vegetable wastes exploitation for 3D printing inks’ formulation. As the multilayer deposit technique is strongly dependent on the printing conditions and 3D ink formulation, therefore the tabularized description was included presenting the nozzle diameter, printing speed and other conditions specified.
Effects of Osmotic Dehydration on Mass Transfer of Tender Coconut Kernel
Authors: Wu Sihao , Wang Juntao , Zhang Lin , Liu Sixin , Li Congfa
Published: July 2024

Tender coconut water has been very popular as a natural beverage rich in various electrolytes, amino acids, and vitamins, and hence a large amount of tender coconut kernel is left without efficient utilization. To explore the possibility of making infused tender coconut kernel, we investigated the effects of two osmosis methods, including solid-state osmotic dehydration and liquid-state osmotic dehydration, as well as two osmosis agents such as sorbitol and sucrose, on the mass transfer of coconut kernel under solid-state osmotic dehydration conditions. The results showed that under the conditions of solid-state osmosis using sucrose and liquid-state osmosis using sucrose solution, the water diffusion coefficients were 9.0396 h−1/2 and 2.9940 h−1/2, respectively, with corresponding water mass transfer coefficients of 0.3373 and 0.2452, and the equilibrium water loss rates of 49.04% and 17.31%, respectively, indicating that the mass transfer efficiency of solid-state osmotic dehydration of tender coconut kernel was significantly higher than that of liquid-state osmotic dehydration. Under solid osmosis conditions, the water loss rates using sucrose and sorbitol were 38.64% and 41.95%, respectively, with dry basis yield increments of 61.38% and 71.09%, respectively, demonstrating superior dehydration efficiency of sorbitol over sucrose under solid-state osmosis. This study can provide a reference for the theoretical study of the mass transfer of tender coconut kernel through osmotic dehydration, and also provide technical support for the development and utilization of tender coconut kernel.
Glycogen Quantification and Gender Identification in Di-, Tri-, and Tetraploid Crassostrea gigas Using Portable Near-Infrared Spectroscopy
Authors: Fu Jingjing , Wang Weijun , Sun Youmei , Zhang Yousen , Luo Qihao , Wang Zhongping , Wang Degang , Feng Yanwei , Xu Xiaohui , Cui Cuiju , Sun Guohua , Li Zan , Yang Jianmin
Published: July 2024

Near-infrared spectroscopy (NIR) has become an essential tool for non-destructive analysis in various fields, including aquaculture. This study presents a pioneering application of portable NIR spectrometers to analyze glycogen content in the gonadal tissues of the Pacific oyster (Crassostrea gigas), marking the first instance of developing quantitative models for glycogen in tetraploid C. gigas. The research also provides a comparative analysis with models for diploid and triploid oysters, underscoring the innovative use of portable NIR technology in aquaculture. Two portable NIR spectrometers were employed: the Micro NIR 1700 (908–1676 nm) and the Micro PHAZIR RX (1624–2460 nm). Near-infrared spectra were acquired from the gonadal tissues of diploid, triploid, and tetraploid C. gigas. Quantitative models for glycogen content were developed and validated using cross-validation methods. Additionally, qualitative models for different ploidies and genders were established. For the Micro NIR 1700, the cross-validation correlation coefficients (Rcv) and cross-validation relative predictive errors (RPDcv) for glycogen were 0.949 and 3.191 for diploids, 0.915 and 2.498 for triploids, and 0.902 and 2.310 for tetraploids. The Micro PHAZIR RX achieved Rcv and RPDcv values of 0.781 and 2.240 for diploids, 0.839 and 2.504 for triploids, and 0.717 and 1.851 for tetraploids. The Micro NIR 1700 demonstrated superior quantitative performance, with RPD values exceeding 2, indicating its effectiveness in predicting glycogen content across different ploidy levels. Qualitative models showed a performance index of 91.6 for diploid and 95 for tetraploid genders using the Micro NIR 1700, while the Micro PHAZIR RX achieved correct identification rates of 99.79% and 100% for diploid and tetraploid genders, respectively. However, differentiation of ploidies was less successful with both instruments. This study’s originality lies in establishing the first quantitative models for glycogen content in tetraploid C. gigas using portable NIR spectrometers, highlighting the significant advancements in non-destructive glycogen analysis. The applicability of these findings is substantial for oyster breeding programs focused on enhancing meat quality traits. These models provide a valuable phenotyping tool for selecting oysters with optimal glycogen content, demonstrating the practical utility of portable NIR technology in aquaculture.
Identification, Bioaccessibility, and Antioxidant Properties of Phenolic Compounds in Carob Syrup
Authors: Zannini Melissa , Cattivelli Alice , Nissen Lorenzo , Conte Angela , Gianotti Andrea , Tagliazucchi Davide
Published: July 2024

Carob syrup is a brown, thick syrup produced from carob pulp that can be directly consumed or used as a sweetener, which also finds applications in folk medicinal practices. In this work, the quali–quantitative phenolic profile of five different carob syrups was elucidated before and after in vitro gastro–intestinal digestion. Moreover, the anti-oxidant properties of undigested and digested carob syrups were investigated. A total of 75 phenolic compounds were identified in undigested carob syrups. The most important phenolic compound in all the samples was gallic acid, the concentration of which ranged between 54.28 and 117.73 mg/100 g. Additional compounds belonging to the classes of hydroxybenzoic acids (in particular glycosylated gallic acid derivatives), hydroxycinnamic acids, and flavonoids (especially flavonols) were also identified. During in vitro gastric digestion, gallic acid mono- and di-hexosides were diglycosylated, releasing gallic acid, which was further degraded in ellagic acid through oxidative polymerization in the intestinal phase of the digestion. Ellagic acid was the major compound detected after in vitro gastro–intestinal digestion of carob syrups. With few exceptions, the anti-oxidant properties of carob syrup were preserved even after digestion. Carob syrup can be considered an important source of phenolic compounds with demonstrated positive effects on human health.
Microbial Contamination and Disease Outbreaks Associated with Rockmelons (Cucumis melo): Implications for Public Health Protection
Authors: Rabiee Pouria , Faraz Ayesha , Ajlouni Said , Hussain Malik A.
Published: July 2024

Foodborne illnesses caused by consuming contaminated fresh produce not only pose serious public health risks but also lead to huge economic losses. Rockmelons (cantaloupes) have emerged as a recurrent source of disease outbreaks caused by foodborne pathogens, including Listeria monocytogenes, Salmonella, and Escherichia coli. The most common factor of the outbreaks was the microbial contamination of rockmelons at the farm, and subsequently, the pathogenic bacteria were transferred to the flesh during cutting and processing. One of the deadliest outbreaks occurred in the USA due to L. monocytogenes contamination of rockmelons which caused 33 deaths in 2011. Since then, several guidelines and recommendations have been developed for food safety management to reduce the microbial contamination of melons on farms and post-harvest operations. This article explicitly provides an updated overview of microbiological contamination, disease outbreaks, pathogens prevalence, and mitigation strategies to reduce public health risks due to the consumption of rockmelons.
Fuzzy Differential Subordination for Classes of Admissible Functions Defined by a Class of Operators
Authors: Ali Ekram E. , Vivas-Cortez Miguel , El-Ashwah Rabha M.
Published: July 2024

This paper’s findings are related to geometric function theory (GFT). We employ one of the most recent methods in this area, the fuzzy admissible functions methodology, which is based on fuzzy differential subordination, to produce them. To do this, the relevant fuzzy admissible function classes must first be defined. This work deals with fuzzy differential subordinations, ideas borrowed from fuzzy set theory and applied to complex analysis. This work examines the characteristics of analytic functions and presents a class of operators in the open unit disk Jη,ςκ(a,e,x) for ς>−1,η>0, such that a,e∈R,(e−a)≥0,a>−x. The fuzzy differential subordination results are obtained using (GFT) concepts outside the field of complex analysis because of the operator’s compositional structure, and some relevant classes of admissible functions are studied by utilizing fuzzy differential subordination.
Damage Law and Reasonable Width of Coal Pillar under Gully Area: Linking Fractal Characteristics of Coal Pillar Fractures to Their Stability
Authors: Wu Zhaopeng , Liang Yunpei , Miao Kaijun , Li Qigang , Liu Sichen , Ran Qican , Sun Wanjie , Yin Hualong , Ma Yun
Published: July 2024

The coal pillar is an important structure to control the stability of the roadway surrounding rock and maintain the safety of underground mining activities. An unreasonable design of the coal pillar size can result in the failure of the surrounding rock structure or waste of coal resources. The northern Shaanxi mining area of China belongs to the shallow buried coal seam mining in the gully area, and the gully topography makes the bearing law of the coal pillar and the development law of the internal fracture more complicated. In this study, based on the geological conditions of the Longhua Mine 20202 working face, a PFC2D numerical model was established to study the damage characteristics of coal pillars under the different overlying strata base load ratios in the gentle terrain area and the different gully slope sections in the gully terrain area, and the coal pillar design strategy based on the fractal characteristics of the fractures was proposed to provide a reference for determining the width of the coal pillars in mines under similar geological conditions. The results show that the reliability of the mathematical equation between the overlying strata base load ratio and the fractal dimension of the fractures in the coal pillar is high, the smaller the overlying strata base load ratio is, the greater the damage degree of the coal pillar is, and the width of the coal pillar of 15 m under the condition of the actual overlying strata base load ratio (1.19) is more reasonable. Compared with the gentle terrain area, the damage degree of the coal pillar in the gully terrain area is larger, in which the fractal dimension of the fracture in the coal pillar located below the gully bottom is the smallest, and the coal pillar in the gully terrain should be set as far as possible to make the coal pillar located below the gully bottom, so as to ensure the stability of the coal pillar.