bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-Bit Quantization Low-Latency Voltage Sense Amplifier Applied in RRAM Computing-in-Memory Macro Circuits
Authors: Hu Wei , Zhang Hangze , Wei Rongshan , Chen Qunchao
Published: January 2024

Conventional sense amplifiers limit the performance of current RRAM computing-in-memory (CIM) macro circuits, resulting in high latency and energy consumption. This paper introduces a multi-bit quantization technology low-latency voltage sense amplifier (MQL-VSA). Firstly, the multi-bit quantization technology enhances circuit quantization efficiency, reducing the number of operational states in conventional VSA. Secondly, by simplifying the sequential logic circuits in conventional VSA, the complexity of sequential control signals is reduced, further diminishing readout latency. Experimental results demonstrate that the MQL-VSA achieves a 1.40-times decrease in readout latency and a 1.28-times reduction in power consumption compared to conventional VSA. Additionally, an 8-bit input, 8-bit weight, 14-bit output macro circuit utilizing MQL-VSA exhibited a 1.11times latency reduction and 1.04-times energy savings.
Operation and Coordinated Energy Management in Multi-Microgrids for Improved and Resilient Distributed Energy Resource Integration in Power Systems
Authors: Aghmadi Ahmed , Mohammed Osama A.
Published: January 2024

Multi-microgrids (MMGs) revolutionize integrating and managing diverse distributed energy resources (DERs), significantly enhancing the overall efficiency of energy systems. Unlike traditional power systems, MMGs comprise interconnected microgrids that operate independently or collaboratively. This innovative concept adeptly addresses challenges posed by pulsed load effects, capitalizing on the cooperative nature of interconnected microgrids. A coordinated MMG system effectively redistributes and shares the impact of pulsed loads, mitigating voltage fluctuations and ensuring sustained system stability. The proposed cooperative MMG scheme optimizes power distribution and load prioritization, facilitating the seamless allocation of surplus energy from neighboring microgrids to meet sudden surges in demand. This study focuses on DC standalone multi-microgrid systems, showcasing their inherent adaptability, resilience, and operational efficiency in managing pulse, variable, and unpredictable generation deficits. Several experiments on a laboratory-scale DC multi-microgrid validate the system’s robust performance. Notably, transient current fluctuations during pulse loads are promptly stabilized through the effective collaboration of microgrids. Variable load experiments reveal distinct behaviors, shedding light on the profound influence of control strategies. This research reveals the transformative potential of MMGs in addressing energy challenges, with a particular focus on DC standalone multi-microgrid systems. The findings underscore the adaptability and resilience of the proposed cooperative scheme, marking a significant stride in the evolution of modern power systems.
MalOSDF: An Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning
Authors: Guo Wenjie , Xue Jingfeng , Meng Wenheng , Han Weijie , Liu Zishu , Wang Yong , Li Zhongjun
Published: January 2024

The evolution of malware poses significant challenges to the security of cyberspace. Machine learning-based approaches have demonstrated significant potential in the field of malware detection. However, such methods are partially limited, such as having tremendous feature space, data inequality, and high cost of labeling. In response to these aforementioned bottlenecks, this paper presents an Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning (MalOSDF). Inspired by traditional code slicing technology, this paper proposes a feature engineering method based on opcode slice for malware detection to better capture malware characteristics. To address the challenges of high expert costs and unbalanced sample distribution, this paper proposes the SSEAL (Semi-supervised Ensemble Active Learning) algorithm. Specifically, the semi-supervised learning module reduces data labeling costs, the active learning module enables knowledge mining from informative samples, and the ensemble learning module ensures model reliability. Furthermore, five experiments are conducted using the Kaggle dataset and DataWhale to validate the proposed framework. The experimental results demonstrate that our method effectively represents malware features. Additionally, SSEAL achieves its intended goal by training the model with only 13.4% of available data.
Modeling Temporal Lobe Epilepsy during Music Large-Scale Form Perception Using the Impulse Pattern Formulation (IPF) Brain Model
Authors: Bader Rolf
Published: January 2024

Musical large-scale form is investigated using an electronic dance music piece fed into a Finite-Difference Time-Domain physical model of the cochlea, which again is input into an Impulse Pattern Formulation (IPF) Brain model. In previous studies, experimental EEG data showed an enhanced correlation between brain synchronization and the musical piece’s amplitude and fractal correlation dimension, representing musical tension and expectancy time points within the large-scale form of musical pieces. This is also in good agreement with a FitzHugh–Nagumo oscillator model.However, this model cannot display temporal developments in large-scale forms. The IPF Brain model shows a high correlation between cochlea input and brain synchronization at the gamma band range around 50 Hz, and also a strong negative correlation with low frequencies, associated with musical rhythm, during time frames with low cochlea input amplitudes. Such a high synchronization corresponds to temporal lobe epilepsy, often associated with creativity or spirituality. Therefore, the IPF Brain model results suggest that these conscious states occur at times of low external input at low frequencies, where isochronous musical rhythms are present.
Steady-State Temperature-Sensitive Electrical Parameters’ Characteristics of GaN HEMT Power Devices
Authors: Wang Kaihong , Zhu Yidi , Zhao Hao , Zhao Ruixue , Zhu Binxin
Published: January 2024

Gallium nitride high-electron-mobility transistor (GaN HEMT) power devices are favored in various scenarios due to their high-power density and efficiency. However, with the significant increase in the heat flux density, the junction temperature of GaN HEMT has become a crucial factor in device reliability. Since the junction temperature monitoring technology for GaN HEMT based on temperature-sensitive electrical parameters (TSEPs) is still in the exploratory stage, the TSEPs’ characteristics of GaN HEMT have not been definitively established. In this paper, for the common steady-state TSEPs of GaN HEMT, the variation rules of the saturation voltage with low current injection, threshold voltage, and body-like diode voltage drop with temperature are investigated. The influences on the three TSEPs’ characteristics are considered, and their stability is discussed. Through experimental comparison, it is found that the saturation voltage with low current injection retains favorable temperature-sensitive characteristics, which has potential application value in junction temperature measurement. However, the threshold voltage as a TSEP for certain GaN HEMT is not ideal in terms of linearity and stability.
MobileNet-Based Architecture for Distracted Human Driver Detection of Autonomous Cars
Authors: Abbass Mahmoud Abdelkader Bashery , Ban Yuseok
Published: January 2024

Distracted human driver detection is an important feature that should be included in most levels of autonomous cars, because most of these are still under development. Hereby, this paper proposes an architecture to perform this task in a fast and accurate way, with a full declaration of its details. The proposed architecture is mainly based on the MobileNet transfer learning model as a backbone feature extractor, then the extracted features are averaged by using a global average pooling layer, and then the outputs are fed into a combination of fully connected layers to identify the driver case. Also, the stochastic gradient descent (SGD) is selected as an optimizer, and the categorical cross-entropy is the loss function through the training process. This architecture is performed on the State-Farm dataset after performing data augmentation by using shifting, rotation, and zooming. The architecture can achieve a validation accuracy of 89.63%, a validation recall of 88.8%, a validation precision of 90.7%, a validation f1-score of 89.8%, a validation loss of 0.3652, and a prediction time of about 0.01 seconds per image. The conclusion demonstrates the efficiency of the proposed architecture with respect to most of the related work.
Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System
Authors: Tierielnyk Sergii , Lukovtsev Valery
Published: January 2024

The problems associated with the greenhouse effect have increased the desire to limit carbon dioxide (CO2) emissions into the atmosphere, including emissions produced by shipboard electrical power systems. This has led to a further search for solutions in this area, such as shipboard hybrid electric power systems (SHEPSs). These systems do not yet have a wide application compared with traditional shipboard electrical power systems for several reasons, including the lack of studies establishing the fault tolerance of such systems. Therefore, in this work, problems related to generated power quality deterioration under large disturbances are studied. To achieve the fault-free operation of SHEPS, an emergency prevention control (EPC) system based on controlled parameter forecasting, along with a system structure and operation algorithm, is developed. The goals of improving electrical power quality are achieved by increasing the control efficiency of the power system’s generating sources. To validate the feasibility of the proposed control system, a computer simulation was carried out after developing a mathematical model of the SHEPS under study. The results of the study show that the use of the proposed EPC system will improve power quality when the controlled parameters are within acceptable limits. At the same time, further research is needed, as the problem of false control action as a consequence of EPC system hardware or software faults remains unstudied.
The Economic and Social Dimension of Energy Transformation in the Face of the Energy Crisis: The Case of Poland
Authors: Piwowar Arkadiusz , Dzikuć Maciej
Published: January 2024

Energy transformation in Poland, including the need to accelerate the shift away from hard coal and lignite in the power system, has been taking place in the face of the energy crisis. Ambitious climate and economic goals force the development of renewable energy sources, but they require legislative changes for the benefit of socially vulnerable consumers. This is important from the point of view of counteracting energy poverty. Energy transformation is not only a change in the fuel used, but most of all, the path and basis of low-carbon development that changes many socio-economic systems. The aim of this study is to link the development and environmental challenges in the studied subject matter with social challenges. The analyses show that it is necessary to strengthen the use of local energy potential and resources, and to activate local communities. The construction of nuclear power plants is also crucial. The results of the presented theoretical and empirical studies can be the basis for a revision of the energy policy in Poland in the area of the social dimension of the energy sector.
A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM
Authors: Chen Yin , Tang Zhenli , Weng Xiaofeng , He Min , Zhou Sheng , Liu Ziqiang , Jin Tao
Published: January 2024

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.
Analysis of the Sand Erosion Effect and Wear Mechanism of Wind Turbine Blade Coating
Authors: Wang Jian , Gao Jin , Zhang Yong , Cui Hongmei
Published: January 2024

The wind–sand climate prevalent in the central and western regions of Inner Mongolia results in significant damage to wind turbine blade coatings due to sand erosion. This not only leads to a decline in power generation but also poses safety risks. This study replicated the wind–sand environment of Alashan and numerically simulated the erosion and wear process of the blade coatings of a 1.5 MW horizontal axis wind turbine under rotational conditions using the DPM model. Additionally, erosion tests were conducted on the operating wind rotor in a wind tunnel. The simulation results demonstrate that sand particle trajectories in the rotating domain are influenced by vortex, incoming wind speed, and sand particle size. For small-sized sand particles, variations in wind speed do not substantially alter the number of particles in contact with the wind turbine blades. However, alterations in the momentum of these particles lead to changes in the impact force on the coating surface. Conversely, the change of wind speed will not only alter the number of large-size sand particles in contact with the wind rotor but also modify the impact force on the coating surface. Furthermore, after impacting the blade, small sand particles continue to move along an approximate helical trajectory with the airflow, while large-size sand particles swiftly rebound. Through statistical analysis of erosion pits on the blade surface after the erosion experiments, it was observed that, in comparison among the leading edge, windward side, trailing edge, and leeward side, the leading edge presents the greatest number of erosion pits, whereas the leeward side has the fewest. Along the spanwise direction, the 0.7R-blade tip segment exhibits the highest count, while the blade root-0.3R section displays the fewest number of pits. The wear morphology of the blade coating was observed from the blade root to tip. The leading edge coating exhibits a range from shallow pits to coating flaking and deeper gouge pits. On the windward side, the coating displays wear patterns varying from tiny cutting pits to cutting marks, and then to gouge pits and coating flaking. Erosion morphology of the trailing edge evolves from only minor scratches to spalling pits, further deepening and enlarging. These research findings provide a basis for the study of zoning-adapted coating materials for wind turbine blades in wind–sand environments.
Modelling and Experimental Characterisation of a Water-to-Air Thermoelectric Heat Pump with Thermal Energy Storage
Authors: Zhou Kaixuan , Zhu Shunmin , Wang Yaodong , Roskilly Anthony Paul
Published: January 2024

Nowadays, increasing the penetration of renewable heat technologies is an important approach to minimise global primary energy use and reduce CO2 emissions for a sustainable future. Thermoelectric heat pumps, which have some unique characteristics in comparison with conventional vapour compression heat pumps, can be integrated with solar thermal energy storage to form a promising renewable heat technology. However, currently, a reliable numerical model for TeHPs suitable for building energy simulation is lacking and the benefits achievable for a TeHP thanks to the integration with heat storage are unclear. To solve these issues, in this work, an experimental apparatus consisting of a water-to-air TeHP unit with a heat storage tank is modelled and tested for the first time, under the scenarios with thermal energy storage and without thermal energy storage, respectively. The results found that the developed numerical model could well predict the output performance of the TeHP unit, with deviations within 12%. Additionally, the output performance of the TeHP unit when combined with a heat storage tank is better than that of the TeHP unit without heat storage, in terms of the maximum temperature achieved in the testing box, the temperature response speed of the testing box, and the coefficient of performance (COP) of the TeHP unit. This work not only paves the way for the following building-integrated simulations of TeHP units, but also provides guidance for the design of the integrated systems that include TeHPs and thermal energy storage.
Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm
Authors: Li Bin , Li Haoran , Liang Zhencheng , Bai Xiaoqing
Published: January 2024

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).
Study on the Mechanism Effect of Bending Loads on the Decay-like Degradation of Composite Insulator GFRP Core Rod
Authors: Yi Xiaoyu , Ding Likun , Liu Hongliang , Zhang Jiaxin , Liu Jie , Li Zhaohui , Zhang Yuming , Wang Ping , Geng Jianghai
Published: January 2024

This paper investigates the deterioration of, and the abnormal temperature rise in, the GFRP core rod material of compact V-string composite insulators subjected to prolonged alternating flexural loading under wind-induced stresses. The axial stress on the GFRP (Glass Fiber Reinforced Plastic) core rod, resulting from transverse wind loads, is a focal point of examination. By establishing a stress model and damage model, the paper simulates and computes the evolution of damage in the outer arc material of composite insulator core rods subjected to alternating flexural loads. Additionally, a multi-factor coupled aging platform is set up, integrating humidity, heat, and mechanical stress, to simulate the crazing deterioration process of composite insulators under alternating flexural loads. Experimental results reveal that during 400,000 alternating load cycles, the core rod underwent stages of surface damage, damage increasing, fatigue embrittlement, matrix hydrolysis, and fiber fracture. Simultaneously, the silicone rubber sheath on the outer side of the composite insulator’s bending arc develops cracks over aging time, creating pathways for moisture ingress into the interface and core rod. The dielectric constant and dielectric loss factor of the aging region of the core rod increase to varying degrees compared to the non-aging part. Moreover, the degree of abnormal heating of the samples intensifies with the duration of aging experiments. These findings underscore the significance of understanding the aging and decay-like fracture process of compact line V-string composite insulators. They provide crucial insights for future research aimed at enhancing the material properties of composite insulator core rods.
Adaptive Position Control for Two-Mass Drives with Nonlinear Flexible Joints
Authors: Jastrzębski Marcin , Kabziński Jacek , Mosiołek Przemysław
Published: January 2024

We consider a two-mass drive with a flexible joint with a nonlinear characteristic of the transmitted torque as a function of the torsion angle. We propose a new, nonlinear, adaptive position-tracking controller, taking this nonlinearity of stiffness into account. The derivation of the controller is based on nonlinear adaptive control theory, incorporates several non-standard mathematical techniques and provides a proof of the uniform ultimate boundedness of tracking errors. As the result, we present a controller that solves the position tracking problem, attenuates dangerous tortional oscillations in the shaft and operates correctly in the presence of unknown torques acting on both sides of the joint, even if all plant parameters are unknown. We demonstrate experimentally that using some materials indeed introduces a nonlinear characteristic of the joint. We prove via real plant experiments that the proposed control algorithm is easily implementable with a DSP controller in real-world applications.
Effects of Different Levels of Physical Damage Combined with Fungal Induction on Agarwood Formation
Authors: Zhao Weiwei , Song Xiaochen , Zhou Zaizhi , Liu Gaofeng , Zhang Qingqing , Pang Shengjiang
Published: January 2024

As wild Aquilaria sinensis resources are exhausted and protected, China has established a huge number of plantations of Aquilaria trees and developed artificial induction techniques. However, the current output and quality of artificial induction technology have not yet met the expected results. It has been found that high-oil-containing agarwood may contain particular fungal stains associated with agarwood production. To enhance the quality of agarwood, we recovered and characterized three highly active fungi from high-oil-containing agarwood and inoculated them onto A. sinensis trees using two traditional physical methods. The results showed that fungi extracted from high-oil-containing agarwood can effectively increase the yield and quality of agarwood. During the agarwood formation process, parenchyma cells, xylem rays, and axial parenchyma cells in the xylem gradually undergo apoptosis, thereby promoting the expansion of the color range of agarwood. Nine months after the treatment, the alcohol-soluble extract content in agarwood reached the standard specified in the Chinese Pharmacopoeia (10%), and the proportions of sesquiterpenes and chromones in each treatment were 55.82%, 58.31%, 62.65%, 70.97%, and 13.71%, respectively. These results indicate that fungal induction has a positive impact on the quality of agarwood. In addition, compared to drilling and fungus combined induction, “burning holes and fungi” combined induction demonstrates better results and can further improve the yield and quality of agarwood.
Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest
Authors: Nikolaychuk Olga , Pestova Julia , Yurin Aleksandr
Published: January 2024

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, “fire” and “no fire”, were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting.
Soil Carbon and Nitrogen Pools and Their Storage Characteristics under Different Vegetation Restoration Types on the Loess Plateau of Longzhong, China
Authors: Xie Mingjun , Yuan Jianyu , Liu Shuainan , Xu Guorong , Lu Yanhua , Yan Lijuan , Li Guang
Published: January 2024

Soil carbon and nitrogen pools are crucial for maintaining the balance of carbon and nitrogen cycling in ecosystems and also for reducing the impacts of global climate change. However, current research lacks an understanding of the effects of long-term vegetation restoration on soil carbon and nitrogen pools and their storage in vulnerable ecosystems. Therefore, we studied the characteristics of soil carbon (soil organic carbon, microbial biomass carbon, dissolved organic carbon) and nitrogen pools (total nitrogen, ammonium nitrogen, nitrate nitrogen) and their storage under four types of vegetation restoration (Stipa bungeana Trin., SB; Caragana korshinskii Kom., CK; Xanthoceras sorbifolia Bunge., XS; Picea asperata Mast., PA) in the Longzhong Loess Plateau area. We found that the carbon and nitrogen pools in the 0–40 cm soil layer under the XS and PA vegetation restoration types were higher compared to those under the SB and CK vegetation, and the values of soil ammonium–nitrogen ratios ranged from 0.72 to 0.83 under different vegetation types. Carbon and nitrogen interactions were stronger in the 0–40 cm soil under PA vegetation, which had significantly higher soil carbon (49.06 t·ha−1) and nitrogen (1.78 t·ha−1) storage than did the other vegetation types. We also found that soil carbon and nitrogen stores differed among different types of vegetation restoration. These elements were mainly distributed in soils from 0 to 20 cm depth, where the carbon and nitrogen pools in soils from 0 to 10 cm exceeded those in the lower layers. Furthermore, we discovered that redundancy analysis (RDA) supported by soil enzyme activity and physical properties significantly explained the variation in soil carbon and nitrogen triggered by vegetation restoration. According to this research, the stability and transformation of soil carbon and nitrogen pools in the region can be influenced by various forms of vegetation restoration. Additionally, the findings highlight that forest vegetation restoration can be a successful strategy for effectively sequestering soil carbon and nitrogen within the Longzhong Loess Plateau area.
Performance of Recombinant Komagataella phaffii in Plant-Based Meat Flavor Compound-Leghemoglobin (LegH) Production through Fed-Batch Fermentations
Authors: Suleiko Arturs , Dubencovs Konstantins , Kazaks Andris , Suleiko Anastasija , Daugavietis Janis Edmunds , Didrihsone Elina , Liepins Janis , Bolmanis Emils , Grigs Oskars , Vanags Juris
Published: January 2024

Soy leghemoglobin (LegH) has been gaining interest over the last years as an efficient flavor and aroma compound in plant-based meat substitutes. Hence, in the following article, we demonstrate the methods for LegH production using a recombinant Komagataella phaffii strain. Multiple fed-batch fermentation with an alternative to a BSM medium, where glucose was used as the main carbon source, was implemented and the growth kinetics, e.g., a maximal specific biomass growth of 0.239 g·g−1·h−1, a biomass yield from the substrate of 0.298 g·g−1, and a maximal specific substrate consumption rate of 0.81 g·g−1·h−1 were identified. Leghemoglobin production resulted in a yield of 0.513 mg·gDCW−1, while the highest biomass density achieved in this study was 121.80 gDCW·L−1. The applied medium that showed potential for additional optimization studies, which, in contrast to BSM, made it possible to separate pH control from nitrogen supply, does not affect medium turbidity measurements and does not induce metabolite synthesis during yeast biomass growth.
Upstream Bioprocesses to Biomass-Based Platform Chemicals and Derivatives
Authors: Ladero Galán Miguel
Published: January 2024

Over the past few decades, the need for new, more accessible and renewable raw materials has become evident [...]
Silica Scaling Inhibition in Water Treatment Process Using Fibrous Al2O3-Nylon 6 Adsorbents
Authors: Phan Ngan Thi Thu , Sato Minehiko , Kobayashi Takaomi
Published: January 2024

This study describes a novel approach using fibrous Al2O3-Nylon 6 composites to induce inhibition behavior in silica scaling systems. The composite fibers were fabricated with a wet-spinning process using the coagulation of a methanolic Nylon-CaCl2 solution with Al2O3 powder after immersing the thread-like solution in water. The mesoporous nylon fibers composed of Al2O3 powders ranging from 10 to 30 wt% loading demonstrated superior adsorption capabilities to silica in water, behaving with the Freundlich model and exhibiting effective multilayer adsorption onto the Al2O3 sites embedded in the fiber. Furthermore, the composite fibers inhibited silica scaling, even at high concentrations, due to a substantially efficient reduction in soluble silica when the composite fiber was present in the system. The utilization of 15 g of composite fibers resulted in a rapid drop to approximately 30 mg/L within the initial 10 h, which is a considerable improvement compared to the 300 mg/L observed in the fiber-free control sample. Notably, the presence of an elevated fiber content exceeding 7.5 g demonstrated the complete inhibition of silica precipitation. An analysis of the pore volume using nitrogen adsorption experiments before and after silica adsorption showed that silica adsorption resulted in a significant decrease in mesoporous properties at the alumina sites. This indicated an efficient adsorption of silica onto the alumina site, effectively removing silica from the system.
Fire-Induced Vegetation Dynamics: An In-Depth Discourse on Revealing Ecological Transformations of the Mahaban and Surrounding Forests
Authors: Israr Azra , Khan Shujaul Mulk , Abdullah Abdullah , Ejaz Ujala , Jehangir Sadia , Ahmad Zeeshan , Hashem Abeer , Avila-Quezada Graciela Dolores , Abd_Allah Elsayed Fathi
Published: January 2024

Since the Palaeozoic era, fire as a potent driver of environmental changes, has dramatically shaped the terrestrial ecosystems. Fire affects soil structure and composition, which in turn affects the floral diversity of an area. This research work aims to examine the impact of fire on vegetation and the physicochemical nature of the soil in fire-affected and fire-free sites across the Mahaban and the surrounding forests, Swabi District, Khyber Pakhtunkhwa, Pakistan. Quadrat quantitative ecological techniques were used for vegetation sampling in fire-free and fire-affected sites. In total, 219 plant species belonging to 173 genera and 70 families were recorded. Among the 219 plant species, 173 species were recorded from fire-free sites and the remaining 122 species were from fire-affected sites. The incidence of fire results in elevated organic matter, nitrogen, phosphorus, and lower calcium carbonate concentrations in the soil. The greatest species richness and evenness were observed across the fire-free sites. Our study concludes that the influence of edaphic and topographic factors on species richness varies between fire-affected and fire-free sites. Fire has significantly altered the nutrient availability in the studied region, and this is confirmed by soil analysis and vegetation research. It is suggested that further research in the field of fire ecology can produce valuable insights.
Food Retail Resilience Pre-, during, and Post-COVID-19: A Bibliometric Analysis and Research Agenda
Authors: Pop Rebeka-Anna , Dabija Dan-Cristian , Pocol Cristina Bianca
Published: January 2024

This paper aims to conduct a bibliometric mapping and systematic review of the food retail industry’s resilience strategy in the context of COVID-19. Specifically, we aim to identify relevant research gaps in the industry during the pre-, during, and post-pandemic periods and highlight the differences between B2B and B2C sectors. We analyzed articles in the Scopus database from 2019 to 2022 using the PRISMA method for article selection, resulting in a total of 69 articles. We employed a VOS viewer for bibliometric mapping. Our analysis revealed that most studies focused on the impact of COVID-19, with only a few examining the pre- and post-pandemic periods critically. In the B2C context, we identified two major topics: changes in purchasing and consumption behavior, and food waste and safety. In the B2B sector, the two most recurrent subjects were retailers’ strategies and supply chain management. This study provides valuable insights for policymakers by exploring industry trends and for scholars by highlighting future research agendas based on the identified topics.
Characterization of the Chemical Composition and Biological Activities of Bog Bilberry (Vaccinium uliginosum L.) Leaf Extracts Obtained via Various Extraction Techniques
Authors: Ștefănescu Bianca Eugenia , Socaci Sonia Ancuța , Fărcaș Anca Corina , Nemeș Silvia Amalia , Teleky Bernadette Emőke , Martău Gheorghe Adrian , Călinoiu Lavinia Florina , Mitrea Laura , Ranga Floricuța , Grigoroaea Dan , Vodnar Dan Cristian , Socaciu Carmen
Published: January 2024

This investigation aimed to assess the chemical composition and biological activities of bog bilberry (Vaccinium uliginosum L.) leaves. Hydroethanolic extracts were obtained using four extraction techniques: one conventional (CE) and three alternative methods; ultrasound (UAE), microwave (MAE) and high-pressure (HPE) extractions. Spectrophotometric analysis was conducted to determine their chemical content, including the total phenolic content (TPC) and total flavonoid content (TFC). Furthermore, their antioxidative and antimicrobial properties were evaluated. HPLC (high performance liquid chromatography) analysis identified and quantified 17 phenolic compounds, with chlorogenic acid being the predominant compound, with the lowest level (37.36 ± 0.06 mg/g) for the bog bilberry leaf extract obtained by CE and the highest levels (e.g., HPE = 44.47 ± 0.08 mg/g) for the bog bilberry leaf extracts obtained by the alternative methods. Extracts obtained by HPE, UAE and MAE presented TPC values (135.75 ± 2.86 mg GAE/g; 130.52 ± 1.99 mg GAE/g; 119.23 ± 1.79 mg GAE/g) higher than those obtained by the CE method (113.07 ± 0.98 mg GAE/g). Regarding the TFC values, similar to TPC, the highest levels were registered in the extracts obtained by alternative methods (HPE = 43.16 ± 0.12 mg QE/g; MAE = 39.79 ± 0.41 mg QE/g and UAE = 33.89 ± 0.35 mg QE/g), while the CE extract registered the lowest level, 31.47 ± 0.28 mg QE/g. In the case of DPPH (1,1-diphenyl-2-picrylhydrazyl) antioxidant activity, the extracts from HPE, UAE and MAE exhibited the strongest radical scavenging capacities of 71.14%, 63.13% and 60.84%, respectively, whereas the CE extract registered only 55.37%. According to Microbiology Reader LogPhase 600 (BioTek), a common MIC value of 8.88 mg/mL was registered for all types of extracts against Staphylococcus aureus (Gram-positive bacteria) and Salmonella enterica (Gram-negative bacteria). Moreover, the alternative extraction methods (UAE, HPE) effectively inhibited the growth of Candida parapsilosis, in comparison to the lack of inhibition from the CE method. This study provides valuable insights into bog bilberry leaf extracts, reporting a comprehensive evaluation of their chemical composition and associated biological activities, with alternative extraction methods presenting greater potential for the recovery of phenolic compounds with increased biological activities than the conventional method.
Development and Characterization of High-Energy Protein Bars with Enhanced Antioxidant, Chemical, Nutritional, Physical, and Sensory Properties
Authors: AlJaloudi Rawan , Al-Dabbas Maher M. , Hamad Hani J. , Amara Rawan A. , Al-Bashabsheh Zaher , Abughoush Mahmoud , Choudhury Imranul H. , Al-Nawasrah Bha’a Aldin , Iqbal Sehar
Published: January 2024

Protein-rich energy bars are known as an excellent nutritional supplement for athletes that help to build and repair connective tissues. The study is, therefore, aimed at developing high-protein bars using lupine seeds, wheat germ, and selected dried fruits including raisins, dates, apricots, and cranberries. Different formulations (F1, F2, F3, and F4) were performed at different ratios of ingredients to produce high-protein bars and compared them with a control bar made of whey-protein concentrate and oat flakes. For this purpose, a proximate analysis, total phenol content, total flavonoid content, DPPH radical scavenging activity, water content, nutritional, and sensory analysis was performed to evaluate the results. The proximate analysis of the produced protein bars showed a significantly higher protein content (22 ± 2) and total phenolic activity (57 ± 33) in formulation group 4 as compared to the other groups. Furthermore, the least water activity content was found in formulation group 1 (1 ± 0.0) when compared with the control group (1 ± 0.0). The results from the sensory evaluation revealed that T3 had the highest average scores in overall consumer acceptability. Our study found that total phenolic, flavonoid, and fiber content were significantly higher in the prepared protein bars indicating prospective health benefits when compared to the control group. Overall, the study demonstrates that high-protein bars using functional ingredients like dried fruit can provide enriched nutritionally valuable food options for consumers.
Effects of Endogenous Antioxidants in Camellia Oil on the Formation of 2-Monochloropropane-1, 3-diol Esters and 3-Monochloropropane-1,2-diol Esters during Thermal Processing
Authors: Liu Shanshan , Shen Mingyue , Xie Jianhua , Liu Bohan , Li Chang
Published: January 2024

2-Monochloropropane-1, 3-diol (2-MCPD) esters and 3-monochloropropane-1,2-diol (3-MCPD) esters, a class of substances potentially harmful to human health, are usually formed during the refining of vegetable oils under high temperature. The effects of endogenous antioxidants in vegetable oils on the formation of 2- and 3-MCPD esters is still unknown. In this study, the effects of endogenous antioxidants (α-tocopherol, stigmasterol and squalene) on the formation of 2- and 3-MCPD esters in model thermal processing of camellia oil were investigated. The possible formation mechanism of 2- and 3-MCPD esters was also studied through the monitoring of acyloxonium ions, the intermediate ions of 2- and 3-MCPD esters formation, and free radicals by employing infrared spectra and electron paramagnetic resonance (EPR), respectively. The results indicated that the addition of α-tocopherol had either promoting or inhibiting effects on the formation of 2- and 3-MCPD esters, depending on the amount added. Stigmasterol inhibited the formation of 3-MCPD ester and 2-MCPD ester at low concentrations, while promoting their formation at high concentrations. Squalene exhibited a promotional effect on the formation of 3-MCPD ester and 2-MCPD ester, with an increased promotion effect as the amount of squalene added increased. The EPR results suggested that CCl3•, Lipid alkoxyl, N3• and SO3• formed during the processing of camellia oil, which may further mediate the formation of chlorpropanol esters. This study also inferred that squalene promotes the participation of the free radical in chlorpropanol ester formation.
Effect of Protein Content on Heat Stability of Reconstituted Milk Protein Concentrate under Controlled Shearing
Authors: Mediwaththe Anushka , Huppertz Thom , Chandrapala Jayani , Vasiljevic Todor
Published: January 2024

Milk protein concentrates (MPCs) possess significant potential for diverse applications in the food industry. However, their heat stability may be a limitation to achieving optimal functional performance. Shearing, an inherent process in food manufacturing, can also influence the functionality of proteins. The aim of this research was to examine the heat stability of reconstituted MPCs prepared at two protein concentrations (4% and 8% w/w protein) when subjected to varying levels of shearing (100, 1000, or 1500 s−1) during heating at 90 °C for 5 min or 121 °C for 2.6 min. While the impact of shear was relatively minor at 4% protein, it was more pronounced in 8% protein MPC suspensions, leading to a considerable decline in heat stability. An increase in protein concentration to 8% amplified protein interactions, intensified by shearing. This, in turn, resulted in comparatively higher aggregation at elevated temperatures and subsequently reduced the heat stability of the reconstituted MPCs.
The Potential of Using Cochayuyo (Durvillaea incurvata) Extract Obtained by Ultrasound-Assisted Extraction to Fight against Aging-Related Diseases
Authors: Muñoz-Molina Nicolás , Parada Javier , Simirgiotis Mario , Montecinos-González Romina
Published: January 2024

The world’s population is in a demographical transition, with an increase in the number of older adults and prevalence of diseases related to aging. This study evaluated in vitro the potential of using Durvillaea incurvata extract (extracted using ultrasound-assisted extraction) to inhibit key enzymes associated with the development of age-related diseases. Our results show that an extract extracted via ultrasound-assisted extracted, as well as an extract conventional extracted from Durvillaea incurvata, presented antidiabetes potential by exhibiting inhibitory activity against α-glucosidase (91.8 ± 1.0% and 93.8 ± 0.3%, respectively, at 500 µg/mL) and α-amylase (42.2 ± 1.4% and 61.9 ± 0.9%, respectively, at 1500 µg/mL) enzymes related to starch digestion and postprandial glycemic response. Also, the extracts showed inhibitory activity against the enzymes acetylcholinesterase (51.5% and 50.8%, respectively, at 500 µg/mL) and butyrylcholinesterase (32.8% and 34.4%, respectively, at 0.5 mg/mL), the biomarkers associated with Alzheimer’s disease, and angiotensin-converting enzyme (98.7 ± 7.4% and 93.0 ± 3.4%, respectively, at 2.0 mg/mL), which is key in the regulation of vascular tone and blood pressure and helps to prevent the development of hypertension. In conclusion, the extract of Durvillaea incurvata obtained from ultrasound-assisted extraction has the potential to prevent the development of age-related pathologies such as diabetes, Alzheimer’s disease, and hypertension.
Physicochemical Characterization of Pectic Polysaccharides from Rose Essential Oil Industry By-Products
Authors: Slavov Anton , Chalova Vesela
Published: January 2024

The rose essential oil industry generates large quantities of solid byproducts yearly. These by-products, usually discarded, could yield valuable substances, such as pectic polysaccharides, widely used in the food industry as jelling agents. Seven industrial by-products were investigated as a source of pectic polysaccharides: four samples resulted from the treatment of Rosa damascena, two from Rosa alba, and one from Rosa centifolia. Three by-products were from steam-water distillation, two from CO2-supercritical extraction, and two after extraction with hexane and 1,1,1,2-tetrafluoroethane. The by-products were pretreated with 70% ethanol and extracted with 0.1 M HCl. The highest polysaccharide yield was observed for 1,1,1,2-tetrafluoroethane-extracted (RD_F) Rosa damascena by-products (13.98 ± 0.14%), followed by hexane (RD_X) and CO2-extracted (RD_CO2) Rosa damascena (12.68 ± 0.11 and 12.66 ± 0.10%, respectively). The polysaccharides were middle-methoxylated pectins, except RD_F and RD_X, having 26.68 ± 1.14 and 31.39 ± 1.39 mol % degree of methoxylation (low-methoxyl pectins). The polysaccharides had molecular masses in the 2.3–2.6 × 104 Da range. The rheological studies suggested RD_F formed a strong high-sucrose gel, while the others yielded weak gels. RD_F and RD_X formed strong Ca2+-mediated gels, comparable with commercial low-methoxylated citrus pectin. This study suggests that rose oil industry by-products could be successfully valorized and yield pectic polysaccharides with gelling properties, comparable with commercial citrus pectins.
Development of Gluten-Free Bread Production Technology with Enhanced Nutritional Value in the Context of Kazakhstan
Authors: Utarova Nazira , Kakimov Mukhtarbek , Gajdzik Bożena , Wolniak Radosław , Nurtayeva Ainur , Yeraliyeva Saule , Bembenek Michał
Published: January 2024

This research aims to enhance the nutritional value of gluten-free bread by incorporating a diverse range of components, including additives with beneficial effects on human health, e.g., dietary fibers. The research was focused on improving the texture, taste, and nutritional content of gluten-free products by creating new recipes and including novel biological additives. The goal was to develop gluten-free bread with less than 3 ppm gluten content that can be eaten by people suffering from gluten sensitivity. The physical and chemical properties of gluten-free rice, corn, green buckwheat, chickpea, amaranth, and plantain flours were examined to understand their unique characteristics and the possibility of their mixing combination to achieve the desired results. Initially, nine recipes were prepared, and in survey research, four baking recipes were selected and tested. The composition of amino acids in the prepared gluten-free bread was determined. The variant made of corn, green buckwheat flour with plantain was found to be top-rated. Changes in the nutritional content of the new product were analyzed, and general regulations and nutritional values were identified. Experimental baking processes were carried out, leading to the successful formulation of gluten-free bread containing corn, green buckwheat, and plantain flour in a ratio of 40:40:20, meeting gluten-free requirements and demonstrating improved nutritional properties, as well as consumption properties, confirmed by surveys conducted on a group of consumers.