bluuebunny/crossref_metadata_2025 huggingface dataset:
AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles
Authors: Domenteanu Adrian , Cotfas Liviu-Adrian , Diaconu Paul , Tudor George-Aurelian , Delcea Camelia
Published: January 2025

The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, artificial intelligence (AI), machine learning (ML), and deep learning (DL), revealing a significant annual growth rate of 56.4% in research activity. Key findings include the identification of influential journals, authors, countries, and collaborative networks that have driven advancements in this domain. This study highlights emerging trends, such as the integration of renewable energy sources, vehicle-to-grid (V2G) schemes, and the application of AI in EV battery optimization, charging infrastructure, and energy consumption prediction. The analysis also uncovers challenges in addressing information security concerns. By reviewing the top-cited papers, this research underlines the transformative potential of AI-driven solutions in enhancing EV performance and scalability. The results of this study can be useful for practitioners, academics, and policymakers.
Using the Retrieval-Augmented Generation to Improve the Question-Answering System in Human Health Risk Assessment: The Development and Application
Authors: Meng Wenjun , Li Yuzhe , Chen Lili , Dong Zhaomin
Published: January 2025

While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed a framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, we created both a Naive RAG and an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation of the 300 question–answer pairs in individual research subfields demonstrated that the Advanced RAG outperformed traditional LLMs (including ChatGPT and ChatGLM) and Naive RAG. Finally, we integrated the developed module for a single subfield to launch a multi-knowledge base question answering system. Our study represents a novel application of RAG technology and LLMs to optimize knowledge retrieval methods in human health risk assessment.
Power Grid Faults Diagnosis Based on Improved Synchrosqueezing Wavelet Transform and ConvNeXt-v2 Network
Authors: Liu Zhizhong , Zhao Zhuo , Huang Guangyu , Wang Fei , Wang Peng , Liang Jiayue
Published: January 2025

The increasing demand on electrical power consumption all over the world makes the need for stable and reliable electrical power grids is indispensable. Meanwhile, power grid fault diagnosis based on fault recording data is an important technology to ensure the normal operation of the power grid. Despite the fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides, such as fuzzy characteristics of complex fault samples with small inter-class differences and large intra-class differences in different topology structures of distribution networks. To tackle the above issues, this work proposes a power grid fault diagnosis method based on an improved Synchrosqueezing Wavelet Transform (SWT) and ConvNeXt-v2 network (named PGFDSC). Firstly, PGFDSC extracts fault features from the fault recording data with an improved SWT method, and outputs the vector signal to enhance the instantaneous frequency. Then, PGFDSC inputs the extracted feature vectors into the improved ConvNeXt-v2 network for power grid faults recognition. The improved ConvNeXt-v2 network is a self-supervised learning model with the advantages of fast speed and high accuracy, which can effectively solve the problem of inaccurate judgment caused by the high dimensionality of data samples. Finally, extensive experiments were conducted and the experimental results show that PGFDSC improves the accuracy of fault diagnosis by two percentage points compared to other baseline models.
Recent Developments in Bidirectional DC-DC Converter Topologies, Control Strategies, and Applications in Photovoltaic Power Generation Systems: A Comparative Review and Analysis
Authors: Tuluhong Ayiguzhali , Xu Zhisen , Chang Qingpu , Song Tengfei
Published: January 2025

As an important piece of equipment in photovoltaic power generation systems, the bidirectional DC-DC converter plays a vital role in improving the conversion efficiency of photovoltaic power generation system. The energy transfer in PV systems heavily relies on efficient bidirectional DC-DC converters. To ensure stable operation, converters with high reliability and power density are required. This paper introduces the basic principles and topologies of bidirectional DC-DC converters and provides a comparative analysis. And it examines the characteristics of the converters’ control schemes and switching strategies, summarizes the existing research findings and current issues. Finally, it looks ahead to the future development trends of bidirectional DC-DC converters in PV systems.
Statistical Analysis of the Sum of Double Random Variables for Security Applications in RIS-Assisted NOMA Networks with a Direct Link
Authors: Nguyen Sang-Quang , Tran Phuong T. , Minh Bui Vu , Duy Tran Trung , Le Anh-Tu , Rejfek Lubos , Tu Lam-Thanh
Published: January 2025

Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS’s phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users.
A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
Authors: Liu Baoju , Wei Xiangqian , Hu Haifeng , Yu Peng , Shi Lei
Published: January 2025

To meet the ever-increasing strict transmission requirements of services in the Energy Internet (EI), reliable routing algorithms for service are necessary. Most of the existing routing algorithms in the Internet Protocol (IP) layer concentrate on service requirements and network topology features while neglecting spectrum resource utilization in the optical transport layer. The status of spectrum resources in the optical transport layer also affects the availability of the routing path. However, there are few studies that combined service transmission requirements and network structure with spectrum resources of the link. In light of this, it is more practical to design routing algorithms integrated with the IP layer and the optical layer. There are three main innovations as follows: (1) The indicator of path satisfaction is proposed meanwhile the system model and service model are constructed. (2) Searching routing paths for services is abstracted into a constrained optimization problem. The optimal objective is to maximize path satisfaction. At the same time, various service requirements, such as end-to-end latency and bandwidth, should be satisfied. (3) To reduce computational complexity, a heuristic path satisfaction-based service-aware routing algorithm (PSSRA) is designed to resolve it. Extensive experiments are carried out with varied service requests on different network topologies. The final results demonstrate that the proposed algorithm outperforms the existing algorithms regarding the service blocking ratio and service distribution fairness index.
Effects of Two-Stage Injection on Combustion and Particulate Emissions of a Direct Injection Spark-Ignition Engine Fueled with Methanol–Gasoline Blends
Authors: Zhang Miaomiao , Cao Jianbin
Published: January 2025

Methanol is widely recognized as a promising alternative fuel for achieving carbon neutrality in internal combustion engines. Its use in direct injection spark-ignition (DISI) engines, either as pure methanol or blended fuels, has demonstrated improvements in thermal efficiency and reductions in certain gaseous pollutants. However, due to the complex influencing factors and the great harm to human health, its particulate emissions need to be further explored and controlled, which is also an inevitable requirement for the development of energy conservation and carbon reduction in internal combustion engines. This study explores the effects of two-stage injection strategies combined with fuel blending on the combustion characteristics, stability, and particulate emissions of DISI engines. By testing four methanol blending ratios and four injection ratios, the presented study identifies that M20 fuel with an 8:2 injection ratio achieves optimal combustion performance, stability, and increased indicated mean effective pressure. Furthermore, under low methanol blending ratios, the 8:2 injection ratio can reduce particulate number concentrations by approximately 20%. These findings suggest that a well-designed two-stage injection strategy combined with methanol–gasoline blends can effectively control particulate emissions while maintaining the power, efficiency, and combustion stability of DISI engines.
Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems
Authors: Reese Lisanne , Rettig Arne , Jauch Clemens , Domin Richard Johannes , Karshüning Tom
Published: January 2025

Due to the energy transition, the future electric power system will face further challenges that affect the functionality of the electricity grid and therefore the security of supply. For this reason, this article examines the future frequency stabilisation in a 100% renewable electric power system. A focus is set on the provision of inertia and frequency containment reserve. Today, the frequency stabilisation in most power systems is based on synchronous generators. By using grid-forming frequency converters, a large potential of alternative frequency stabilisation reserves can be tapped. Consequently, frequency stabilisation is not a problem of existing capacities but whether and how these are utilised. Therefore, in this paper, a collaborative approach to realise frequency stabilisation is proposed. By distributing the required inertia and frequency containment reserve across all technologies that are able to provide it, the relative contribution of each individual provider is low. To cover the need for frequency containment reserve, each capable technology would have to provide less than 1% of its rated power. The inertia demand can be covered by the available capacities at a coverage ratio of 171% (excluding wind power) to 217% (all capacities). As a result, it is proposed that provision of frequency stabilisation is made mandatory for all capable technologies. The joint provision distributes the burden of frequency stabilisation across many participants and hence increases redundancy. It ensures the stability of future electricity grids, and at the same time, it reduces the technological and economic effort. The findings are presented for the example of the German electricity grid.
Tilt–Roll Heliostats and Non-Flat Heliostat Field Topographies for Compact, Energy-Dense Rooftop-Scale and Urban Central Receiver Solar Thermal Systems for Sustainable Industrial Process Heat
Authors: Freeman Joshua , Sampath Walajabad , Achuthan Krishnashree
Published: January 2025

Industrial process heat typically requires large amounts of fossil fuels. Solar energy, while abundant and free, has low energy density, and so large collector areas are needed to meet thermal needs. Land costs in developed areas are often prohibitively high, making rooftop-based concentrating solar power (CSP) attractive. However, limited rooftop space and the low energy density of solar power are usually insufficient to meet a facility’s demands. Maximizing annual CSP energy generation within a bounded rooftop space is necessary to mitigate fossil fuel consumption. This is a different optimization objective than minimizing the Levelized Cost of Energy (LCOE) in typical open-land, utility-scale heliostat layout optimization. Innovative designs are necessary, such as compact, energy-dense central receiver systems with non-flat heliostat field topographies that use spatially efficient Tilt–Roll heliostats or multi-rooftop and multi-height distributed urban systems. A novel ray-tracing simulation tool was developed to evaluate these unique scenarios. For compact systems, optimized annual energy production occurred with maximum heliostat spatial density, and the best non-flat heliostat field topography found is a shallow section of a parabolic cylinder with an East–West focal axis, yielding a 10% optical energy improvement. Tightly packed Tilt–Roll heliostats showed a double improvement in optical energy at the receiver compared to Azimuth–Elevation heliostats.
High-Order Engineering Fastest Controller and Its Application in Thermal Power Units
Authors: Shi Shangyao , Li Jun , Huo Yijia , Li Ruiqi , Chen Pengyun
Published: January 2025

In the domain of industrial process control, the ubiquitous proportional–integral–derivative (PID) control paradigm, while foundational, is deemed insufficient amidst evolving complexities. In alignment with China’s strategic “dual-carbon” targets, extant thermal power installations are mandated to facilitate profound peak load navigation and expedited frequency modulation services. The incumbent PID control schema is found wanting in this regard, precipitating the imperative for an innovative process control technology to supplant the conventional PID regimen. Power system engineers have consequently devised the engineering fastest controller (EFC), which has adeptly succeeded PID control in nascent applications, thereby meeting the stringent control exigencies for deep peak regulation and agile frequency modulation. Employing rigorous theoretical analysis and sophisticated simulation experiments, this investigation meticulously compares the performance attributes of high-order controllers (HOCs) with the EFC. The empirical findings underscore the EFC’s pronounced superiority over PI, PID, and SOC in regulatory performance enhancements by 122.2%, 88.0%, and 77.3%, respectively, and in mitigating disturbances by 140.0%, 80.9%, and 54.5%, respectively. This study culminates in the assertion that the EFC represents a paradigmatic advancement in industrial control technology, not only manifesting pronounced performance benefits but also furnishing a robust theoretical scaffolding that transcends the performance zeniths of traditional PID and HOC technologies.
Natural Regeneration, Genetic Diversity, and Provenance of Introduced Fagus sylvatica L. Stands in Latvia
Authors: Ruņģis Dainis Edgars , Danusevičius Darius , Kembrytė-Ilčiukienė Rūta , Jansone Līga , Kempf Marta , Jansons Āris
Published: January 2025

European beech (Fagus sylvatica L.) has a wide distribution range through Central and Western Europe, and is tolerant to a range of environmental factors and shade. Due to the high wood quality and growth rate, there is increasing interest in the cultivation of this species in Northern Europe, and European beech is appropriate for closer-to-nature forestry management practices, where shade-tolerant species are essential. Latvia is located to the north of the natural range of European beech, but stands have been successfully established in the 19th century, using reproductive material of unknown origin. This study investigated the natural regeneration, genetic diversity, population structure, and provenance of Latvian F. sylvatica populations from two areas in western Latvia—Skede and Kaleti. Parent–offspring analyses did not identify a decrease in genetic diversity parameters in the naturally regenerated offspring, indicating that natural regeneration has not decreased the genetic viability of these populations. The three stands located in Skede were genetically very similar (pairwise Fst values ranged from 0.004 to 0.007). The Kaleti stand was more differentiated from the Skede stands (pairwise Fst values with the Skede stands ranged from 0.047 to 0.051), and the genetic diversity was low (He = 0.638). This is probably a result of the use of reproductive material collected from a very limited number of individuals to establish this stand, which also prevented the determination of the origin of this material. The Skede stand was compared to German, Polish, and Swedish F. sylvatica populations, and a Bayesian clustering analysis indicated that the most likely provenance of the Skede stand in Latvia was from southern Germany.
Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China
Authors: Zhang Huayong , Zhou Yue , Ji Xiande , Wang Zhongyu , Liu Zhao
Published: January 2025

Betula albosinensis serves as an important constructive and afforestation tree species in mountainous areas. Its suitable habitat and habitat quality are highly vulnerable to the climate. However, few studies have centered on the shrinkage, expansion, and habitat fragmentation of B. albosinensis forests under climate change. In this study, the Random Forest model was employed to predict current and future trends of shrinking and expanding of B. albosinensis, while a composite landscape index was utilized to evaluate the habitat fragmentation in the highly suitable habitats of B. albosinensis. The results indicated that suitable habitats for B. albosinensis were primarily concentrated in the vicinities of the Qinling, Qilian, and Hengduan Mountains, situated in western China. The most influential factor affecting the distribution of B. albosinensis was temperature seasonality (Bio4). In future scenarios, the center of distribution of B. albosinensis was projected to shift towards the west and higher altitudes. The total suitable habitats of B. albosinensis were anticipated to expand under the scenarios of SSP370 and SSP585 in the 2090s, while they were expected to contract under the remaining scenarios. Although these results indicated that the suitable areas of habitat for B. albosinensis were relatively intact on the whole, fragmentation increased with climate change, with the highest degree of fragmentation observed under the SSP585 scenario in the 2090s. The findings of this study provide a foundation for the protection of montane vegetation, the maintenance of montane biodiversity, and the evaluation of species’ habitat fragmentation.
Monitoring Forest Disturbances and Associated Driving Forces in Guangdong Province Using Long-Term Landsat Time Series Images
Authors: Qiu Lin , Chang Zhongbing , Luo Xiaomei , Chen Songjia , Jiang Jun , Lei Li
Published: January 2025

Research on monitoring forest disturbances and analyzing its driving factors is crucial for the sustainable management of forest ecosystems. To quantitatively identify the spatial distribution and dynamic changes of forest disturbance and its driving factors in Guangdong Province from 1990 to 2019, the long-term Landsat time series imagery and the LandTrendr change detection algorithm were utilized. The impact of forest disturbances on four types of landscape fragmentation (attrition, perforation, shrinkage, and subdivision) was analyzed using the Forman index. The Geodetector model was used to analyze the driving factors of forest disturbance from human activity and the natural environment. The results showed that the LandTrendr algorithm achieved a Kappa coefficient of 0.79, with an overall accuracy of approximately 82.59%. The findings indicate a consistent increase in shrinkage patches, both in quantity and area. Spatially, the centroids of forest fragmentation processes exhibited a clear inland migration trend, reflecting the growing ecological pressures faced by inland forest ecosystems. Furthermore, interactions among driving factors, particularly between population density and economic factors, significantly amplified their combined impacts. The correlation between forest disturbances and socio-economic factors revealed distinct regional variations, highlighting significant differences in forest disturbance dynamics across cities with varying levels of economic development. This study provides critical insights into the spatiotemporal dynamics of forest disturbances under rapid urbanization and economic development. It lays the groundwork for sustainable forest management strategies in Guangdong Province and may contribute to global discussions on managing forest ecosystems during periods of rapid socio-economic transformation.
Dynamic Key Replacement Mechanism for Lightweight Internet of Things Microcontrollers to Resist Side-Channel Attacks
Authors: Kuo Chung-Wei , Wei Wei , Lin Chun-Chang , Hong Yu-Yi , Liu Jia-Ruei , Tsai Kuo-Yu
Published: January 2025

5G technology and IoT devices are improving efficiency and quality of life across many sectors. IoT devices are often used in open environments where they handle sensitive data. This makes them vulnerable to side-channel attacks (SCAs), where attackers can intercept and analyze the electromagnetic signals emitted by microcontroller units (MCUs) to expose encryption keys and compromise sensitive data. To address this critical vulnerability, this study proposes a novel dynamic key replacement mechanism specifically designed for lightweight IoT microcontrollers. The mechanism integrates Moving Target Defense (MTD) with a lightweight Diffie–Hellman (D-H) key exchange protocol and AES-128 encryption to provide robust protection against SCAs. Unlike traditional approaches, the proposed mechanism dynamically updates encryption keys during each cryptographic cycle, effectively mitigating the risk of key reuse—a primary vulnerability exploited in SCAs. The lightweight D-H key exchange ensures that even resource-constrained IoT devices can securely perform key exchanges without significant computational overhead. Experimental results demonstrate the practicality and security of the proposed mechanism, achieving key updates with minimal time overhead, ranging from 12 to 50 milliseconds per encryption transmission. Moreover, the approach shows strong resilience against template attacks, with only two out of sixteen AES-128 subkeys compromised after 20,000 attack attempts—a notable improvement over existing countermeasures. The key innovation of this study lies in the seamless integration of MTD with lightweight cryptographic protocols, striking a balance between security and performance. This dynamic key replacement mechanism offers an effective, scalable, and resource-efficient solution for IoT applications, particularly in scenarios that demand robust protection against SCAs and low-latency performance.
Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
Authors: Bayu Teguh Indra , Huang Yung-Fa , Chen Jeang-Kuo , Hsieh Cheng-Hsiung , Kristianto Budhi , Christianto Erwien , Suharyadi Suharyadi
Published: January 2025

The use of a static modulation coding scheme (MCS), such as 7, and resource keep probability (Prk) value, such as 0.8, was proven to be insufficient to achieve the best packet reception ratio (PRR) performance. Various adaptation techniques have been used in the following years. This work introduces a novel optimization algorithm approach called the fuzzy inference reinforcement learning (FIRL) sequence for adaptive parameter configuration in cellular vehicle-to-everything (C-V2X) mode-4 communication networks. This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. The FIRL sequence generates adaptive configuration parameters for Prk and MCS index values each time the Long-Term Evolution (LTE) packet is generated. Simulation results demonstrate the effectiveness of this optimization algorithm approach, achieving up to a 169.83% improvement in performance compared to static baseline parameters.
Vaporization Dynamics of a Volatile Liquid Jet on a Heated Bubbling Fluidized Bed
Authors: Mitra Subhasish , Evans Geoffrey M.
Published: January 2025

In this paper, droplet vaporization dynamics in a heated bubbling fluidized bed was studied. A volatile hydrocarbon liquid jet comprising acetone was injected into a hot bubbling fluidized bed of Geldart A-type glass ballotini particles heated at 150 °C, well above the saturation temperature of acetone (56 °C). Intense interactions were observed among the evaporating droplets and hot particles during contact with the re-suspension of particles due to a release of vapour. A non-intrusive schlieren imaging method was used to track the hot air and vapour mixture plume in the freeboard region of the bed and the acetone vapour fraction therein was mapped. The jet vaporization dynamics in the bubbling fluidized bed was modelled in a Eulerian–Lagrangian CFD (computational fluid dynamics) modelling framework involving heat and mass transfer sub models. The CFD model indicated a dispersion of the vapour plume from the evaporating droplets which was qualitatively compared with the schlieren images. Further, the CFD simulation predicted a significant reduction (~60 °C) in the local bed temperature at the point of the jet injection, which was indirectly confirmed in an experiment by the presence of particle agglomerates.
Isolation and Evaluation of Rhizopus arrhizus Strains from Traditional Rice Wine Starters (Jiuqu): Enzyme Activities, Antioxidant Capacity, and Flavour Compounds
Authors: Wan Bo , Tian Tian , Xiong Ying , Wang Siqi , Luo Xinyu , Liao Weifang , Liu Pulin , Miao Lihong , Gao Ruijie
Published: January 2025

Seventy-eight autochthonous strains of Rhizopus arrhizus were isolated from rice wine starter samples across twenty-nine regions in China to evaluate their potential in traditional rice wine fermentation. Strains were assessed for enzyme activity, antioxidant properties, amino acid production, and volatile flavour compounds. Significant variation in enzyme activities was observed, with acidic protease activity ranging from 280 to 1023 U/g, amylase from 557 to 1681 U/g, and esterase from 370 to 2949 U/g. Strains W17 and W42 exhibited the highest enzyme activities and antioxidant capacities, with a total phenolic content of 828 mg/L, total flavonoids of 215 μg/L, and an ABTS scavenging rate of 96.3%. They also produced high levels of glutamic acid (up to 3083 mg/L), enhancing the flavour profile. Histamine levels were low, ranging from 8 to 205 μg/L, ensuring product safety. Analysis of volatile compounds identified 80 substances, including 16 key aroma-active compounds, contributing to a complex flavour profile. These results provide a basis for selecting R. arrhizus strains to optimise rice wine fermentation, addressing market demand for diverse and functional products.
Comparing the Structural and Physicochemical Properties of Highland Barley β-Glucan from Different Sources: A Focus on Color
Authors: Yu Ping , Kang Xuemin , Liu Pengfei , Wu Zhengzong , Cheng Yue , Cui Bo , Gao Wei
Published: January 2025

Herein, β-glucan (BG) was extracted from different colored varieties of highland barley (HB, Hordeum vulgare), defined as BBG, WBG, and LBG depending on the colors of black, white, and blue and their molecular structure and physicochemical properties were investigated through a series of technical methods. The high-performance anion-exchange chromatography (HPAEC) results indicated the extracted BBG, LBG, and WBG mainly comprised glucose regardless of color. The molecular weight (Mw) of BBG, LBG, and WBG were 55.87 kDa, 65.19 kDa, and 81.59 kDa, respectively. 4-Glc(p), 3-Glc(p), and t-Glc(p) accounted for a larger proportion (>90%) of the total methylated residues according to gas chromatography–mass spectrometry (GC-MS) analysis. Additionally, Fourier transform infrared (FT-IR) spectroscopy revealed that the β-linkage of LBG had a greater capacity to develop stronger hydrogen bonds, due to the absence of 3,4-Glc(p). Among them, LBG had a low particle size distribution and a high shear viscosity, showing obvious round aggregates on its surface. Meanwhile, BBG presented a high peak viscosity (PV) and thermal stability. Based on the differences in their molecular structure, it could be concluded that there were different physicochemical properties among BBG, LBG, and WBG.
A Sustainable Approach: Repurposing Red Beetroot Peels for Innovative Meringue Products
Authors: Constantin Oana Emilia , Stoica Florina , Lazăr (Mistrianu) Silvia , Andronoiu Doina Georgeta , Turturică Mihaela , Stănciuc Nicoleta , Rațu Roxana Nicoleta , Croitoru Constantin , Râpeanu Gabriela
Published: January 2025

With the increasing global demand for sustainable and eco-friendly food items, it is imperative to investigate alternate sources of natural pigments. The red beetroot (Beta vulgaris L.) is a traditional food in many countries and a rich bioactive compound known for its beneficial properties. Beetroot peel, a by-product of beetroot food processing, is often discarded, contributing to environmental damage. This research explores the potential of beetroot peel (BP) powder as a natural pigment in food products and its functional benefits. The study focuses on incorporating BP powder into meringues, aiming to create a value-added product with enhanced properties, particularly antioxidant activity. Various amounts of BP powder (4–10%) were added to meringue formulations, and the effects on the resulting meringues’ physicochemical properties, sensory qualities, and phytochemical profiles were assessed during 21 days of storage. The research revealed that BP powder, besides its function as a natural colorant and the pleasing pink hue it imparts to meringues, also enhances antioxidant activity due to its high phenolic concentration. BP powder was also incorporated to enhance the meringues’ overall sensory characteristics, improving their flavor and texture. The research findings indicate that BP has the potential to be used as a natural food ingredient to promote human health, resource-use efficiency, and a circular economy.
Changes in Microbial Safety and Quality of High-Pressure Processed Camel Milk
Authors: Osaili Tareq M. , Dhanasekaran Dinesh Kumar , Hasan Fayeza , Obaid Reyad S. , Al-Nabulsi Anas A. , Olaimat Amin N. , Ismail Leila Cheikh , Alkalbani Nadia , Ayyash Mutamed , Bamigbade Gafar Babatunde , Holley Richard , Cheema Adan Shahzadi , Bani Odeh Wael Ahmad , Mohd Khalid Abdulla , Kamal Ayesha Khalid Haji
Published: January 2025

High-pressure processing (HPP) is used as a non-thermal approach for controlling microbial viability. The purposes of this study were to (i) establish the decimal reduction times (D-values) for pathogenic bacteria during 350 MPa HPP treatment,; (ii) evaluate the impact of 350 MPa HPP on total plate count (TPC), yeasts and molds (YM), and lactic acid bacteria (LAB) in camel milk; (iii) investigate the behavior of several spoilage-causing bacteria during storage at 4 °C and 10 °C for up to 10 d post-HPP treatment; and (iv) assess the effect of HPP on the protein degradation of camel milk. The D-values for L. monocytogenes, E. coli O157:H7, and Salmonella spp. were 3.77 ± 0.36 min, 1.48 ± 0.08 min, and 2.10 ± 0.13 min, respectively. The HPP treatment decreased pathogenic microorganisms by up to 2 to 3 log cfu/mL (depending on treatment conditions). However, HPP reduced TPC, YM, and LAB by <1 log cfu/mL, regardless of the length of pressure exposure. HPP treatment, even at extended holding times, did not significantly alter either the proteolytic activity or casein micelle structure in camel milk. This study highlights HPP as a promising non-thermal technique for enhancing the microbiological safety of camel milk.
Risk Perception, Communication and Behaviour Towards Food Safety Issues
Authors: da Cunha Diogo Thimoteo
Published: January 2025

“In spite of over 100 years of research and millions of dollars spent, food safety remains a worldwide public health issue (Christoper Griffith cited in Yiannas (2009) [...]
Characterization and In Vitro Digestion Kinetics of Purified Pulse Starches: Implications on Bread Formulation
Authors: Sangokunle Oluwatoyin O. , Corwin Sarah G. , Hamaker Bruce R.
Published: January 2025

This study investigated the contribution of pulse starches (PSs) to the slowly digestible starch (SDS) properties observed in pulses. Purified pulse starches from 17 commonly consumed pulses were examined, focusing on their digestion kinetics using a pancreatic alpha-amylase (PAA) and rat intestinal acetone powder (RIAP) mixture. Chickpea starch, exhibiting a slow digestibility profile, was incorporated as an ingredient to confer slow digestibility to refined wheat flour bread. Our findings reveal that some PSs exhibited low digestibility when gelatinized (100 °C, 30 min) and retrograded (7 days, 4 °C). Rapid retrogradation was observed in starch from chickpeas, lentils, field peas, adzuki beans, navy beans, large lima beans, and great northern beans. The incorporation of chickpea starch into fortified bread significantly improved its slow digestibility properties. This study reveals the potential of pulse starch as a promising functional ingredient for baked products, related to the faster retrogradation of many pulse-sourced starches. These findings contribute valuable insights into the slow digestibility attributes of pulse starches for developing food products with enhanced nutritional profiles.
Effect of High-Pressure Processing on Color, Texture and Volatile Profile During Sardine Refrigeration
Authors: Ismaiel Lama , Nartea Ancuta , Fanesi Benedetta , Lucci Paolo , Pacetti Deborah , Jaeger Henry , Schottroff Felix
Published: January 2025

Extending sardine shelf life while maintaining their quality is challenging even with non-thermal technologies like high-pressure processing (HPP). This study examines the effects of HPP at 400 and 600 MPa for holding times of 1, 2.5, 5, and 10 min on fresh sardines during 14 days of cold storage. Physicochemical attributes, including texture, color, and volatile organic profiles, were assessed. Increasing both pressure and holding times resulted in increased levels of hardness, chewiness, and L* during storage. HPP-treated samples maintained lower a* values compared to the control ones by the end of the experiment. The volatile profile of HPP samples was significantly affected compared to control samples, which developed exclusively volatile oxidation compounds (hexanal and 2,4-hexadienal) by the end of the storage. Volatile groups such as aldehyde and ketone were slightly impacted by both storage and HPP treatments (i.e., pressure and holding time). Ketone levels were consistently lower in all treated samples, ranging from 25.3% to 33.6% at 400 MPa and 600 MPa, respectively, compared to the control samples, which had a ketone level of 40.5% on day 14. These findings indicate the potential of HPP in prolonging shelf life and preserving quality in the sardine market.
In Vitro and In Ovo Evaluation of Oenothera biennis L. Oil as an Alternative Preservative for Oil-Based Products
Authors: Fecker Ramona , Avram Ștefana , Cocan Ileana , Alexa Ersilia , Bora Larisa , Minda Daliana , Magyari-Pavel Ioana Zinuca , Dehelean Cristina Adriana , Danciu Corina
Published: January 2025

There is a growing need for safer alternatives to synthetic additives commonly used in lipophilic carriers for products such as foods, pharmaceuticals, personal care items, and cosmetics. Natural antioxidants, which prevent lipid peroxidation while providing additional health benefits, offer a promising solution. Evening primrose oil, a rich source of antioxidant compounds with numerous biological benefits, emerges as a potential natural preservative for oil-based products. Our study evaluates a combination of sunflower oil, a widely used cold-pressed oil, with evening primrose oil for potential applications in various fields such as cosmetic, pharmaceutical, or food manufacturing. Various methods were applied to assess oxidative stability by calculating the peroxide value, the p-anisidine value, and the total oxidation value, while biological safety was evaluated using the chick embryo’s chorioallantoic membrane and histological analysis. The findings highlight that evening primrose oil, with its balanced effects on epithelial tissues and vascularization, as well as its strong anti-lipid peroxidation properties, is a suitable alternative to synthetic preservatives when used in combination with cold-pressed oils. This proposed oil combination, emphasizing the safety and beneficial properties of evening primrose oil, shows significant potential for applications in the pharmaceutical industry, dermatology, cosmetology, and food manufacturing.
Recent Advances in Metal–Organic Framework-Based Anticancer Hydrogels
Authors: Kush Preeti , Singh Ranjit , Kumar Parveen
Published: January 2025

Cancer is the second leading cause of death globally and the estimated number of new cancer cases and deaths will be ∼30.2 million and 16.3 million, respectively, by 2040. These numbers cause massive, physical, emotional, and financial burdens to society and the healthcare system that lead to further research for a better and more effective therapeutic strategy to manage cancer. Metal–organic frameworks (MOFs) are promising alternative approaches for efficient drug delivery and cancer theranostics owing to their unique properties and the direct transportation of drugs into cells followed by controlled release, but they suffer from certain limitations like rigidity, poor dispersibility, fragility, aggregation probability, and limited surface accessibility. Therefore, MOFs were conjugated with polymeric hydrogels, leading to the formation of MOF-based hydrogels with abundant absorption sites, flexibility, and excellent mechanical properties. This review briefly describes the different strategies used for the synthesis and characterization of MOF-based hydrogels. Further, we place special emphasis on the recent advances in MOF-based hydrogels used to manage different cancers. Finally, we conclude the challenges and future perspectives of MOF-based hydrogels. We believe that this review will help researchers to develop more MOF-based hydrogels with augmented anticancer effects, enabling the effective management of cancer even without adverse effects.
Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA
Authors: Rimal Prawin , Paul Shamrat Kumar , Panday Shailesh Kumar , Alexov Emil
Published: January 2025

Background/Objectives: Predicting the effects of protein and DNA mutations on the binding free energy of protein–DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein–DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones. Methods: This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation. Results: The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method’s rapid execution time enables genome-scale predictions. Conclusions: The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein–DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.
Gene Therapy and Diabetes: A Narrative Review of Recent Advances and the Role of Multidisciplinary Healthcare Teams
Authors: Khartabil Nadia , Avoundjian Ani
Published: January 2025

Introduction: Gene therapy has emerged as a promising frontier in the management of diabetes, offering innovative approaches to address both type 1 and type 2 diabetes. This narrative review examines the advancements in gene therapy applications, focusing on both animal and human studies, and includes a total of 11 studies in adherence to PRISMA guidelines. These studies utilize various viral vectors, such as adeno-associated virus (AAV) and lentivirus, to deliver genes that regulate insulin production and enhance angiogenesis. This review aims to synthesize recent advancements in gene therapy for both type 1 and type 2 diabetes and its complications, and to explore the evolving role of pharmacists in this emerging field. Methods: A comprehensive search was conducted to identify relevant studies on gene therapy for diabetes. Databases such as PubMed, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, and Google Scholar were queried using keywords such as “Diabetes”, “gene therapy”, “Type 1 diabetes”, and “Type 2 diabetes”. Both animal and human studies were included to provide a broad perspective on the advancements in this field. Results: Animal model studies have shown promising results, including sustained insulin production, improved glucose homeostasis, and enhanced wound healing. Human studies, though fewer in number, have reported significant advancements. Patients with diabetic neuropathy treated with plasmid VEGF and recombinant adeno-associated virus (rAAV) showed improvements in neuropathic symptoms and glycemic control. Other studies involving intramuscular injections of VM202 and bicistronic VEGF165/HGF plasmid have reported pain reduction, improved healing of ischemic lesions, and increased angiogenesis. Conclusions: Despite these encouraging results, limitations such as small sample sizes, short follow-up periods, and the necessity for more extensive clinical trials persist. Diabetes is a metabolic syndrome that requires the collaboration of a multidisciplinary team to assist in several aspects of implementing successful gene therapy. Several healthcare providers and policy makers may play a crucial role in patient education, counseling, and the management of gene therapy treatments.
Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS
Authors: Lemenkova Polina
Published: January 2025

This article presents the application of novel cartographic methods of vegetation mapping with a case study of the Rif Mountains, northern Morocco. The study area is notable for varied geomorphology and diverse landscapes. The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. This approach provides a platform for processing spatiotemporal data and satellite image analysis. The objective is to determine the robustness of the “DecisionTreeClassifier” and “ExtraTreesClassifier” classification algorithms. The time series of satellite images covering northern Morocco consists of six Landsat scenes for 2023 with a bimonthly time interval. Land cover maps are produced based on the processed, classified, and analyzed images. The results demonstrated seasonal changes in vegetation and land cover types. The validation was performed using a land cover dataset from the Food and Agriculture Organization (FAO). This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time.
Forward Modeling Simulations to Validate Changes in Electrical Resistivity Tomography Monitoring Data for a Slope with Complex Geology
Authors: Hojat Azadeh , Zanzi Luigi , Tresoldi Greta , Loke Meng Heng
Published: January 2025

The electrical resistivity tomography (ERT) method has been increasingly integrated with hydrogeological risk mitigation strategies to monitor the internal conditions and the stability of natural and artificial slopes. In this paper, we discuss a case study in which numerical simulations were essential to validate the interpretation of the resistivity images obtained from an ERT monitoring system installed on a critical slope in Italy. An initial analysis of the monitoring data after rainfall events in the study site showed that the resistivity values were decreased only in the central zone along the ERT line, but they were increased in the two sides of the profile. Opposite behaviors were observed during the drying processes following the rainfall events. Core samples show complex geology at the study site, which might justify uneven responses of the different subsurface bodies to meteorological events. However, we decided to investigate the possible inversion artifacts resulting from the individual inversion of the tomographic sections. Forward modeling simulations on simplified time-lapse models of the study site were performed to explore this problem and to compare the individual and time-lapse inversions. Synthetic tests confirmed the nature of these unexpected behaviors and assessed the absolute necessity of a time-lapse approach for a correct inversion of monitoring data in the presence of a complex geological model such as the one of this case study. By applying the time-lapse inversion approach to the real data, the inversion artifact problem was substantially solved, arriving after the proper calibration of the inversion parameters, mainly the time-lapse damping factor and the spatial and temporal roughness constraints, to a reduction in the inversion artifacts to less than 5%.
Tales of Their Times
Authors: Þorsteinsdóttir Rósa
Published: January 2025

The aim of the article is to examine how the fairytales of storytellers from different times agree with the idea that their attitudes towards life appear in the fairytales that they tell and to consider whether they construct their fairytales so that they reflect the tensions and conflicts in their own times. This is achieved by looking into the tales of a woman storyteller in the nineteenth century when organized collecting began in Iceland, and three storytellers‘ repertoires from the twentieth century, when fairytales still belonged to the living oral tradition were tape-recorded. The survey is concluded by examining three recent plays involving fairytales which the author herself attended.