bluuebunny/crossref_metadata_2025 huggingface dataset:
RNA sequencing analysis of sexual dimorphism in Japanese quail
Authors: Park Sinwoo , Kim Jaeryeong , Lee Jinbaek , Jung Sungyoon , Pack Seung Pil , Lee Jin Hyup , Yoon Kyungheon , Woo Seung Je , Han Jae Yong , Seo Minseok
Published: July 2024

Introduction

Japanese quail are of significant economic value, providing protein nutrition to humans through their reproductive activity; however, sexual dimorphism in this species remains relatively unexplored compared with other model species.

Method

A total of 114 RNA sequencing datasets (18 and 96 samples for quail and chicken, respectively) were collected from existing studies to gain a comprehensive understanding of sexual dimorphism in quail. Cross-species integrated analyses were performed with transcriptome data from evolutionarily close chickens to identify sex-biased genes in the embryonic, adult brain, and gonadal tissues.

Results

Our findings indicate that the expression patterns of genes involved in sex-determination mechanisms during embryonic development, as well as those of most sex-biased genes in the adult brain and gonads, are identical between quails and chickens. Similar to most birds with a ZW sex determination system, quails lacked global dosage compensation for the Z chromosome, resulting in directional outcomes that supported the hypothesis that sex is determined by the individual dosage of Z-chromosomal genes, including long non-coding RNAs located in the male hypermethylated region. Furthermore, genes, such as WNT4 and VIP, reversed their sex-biased patterns at different points in embryonic development and/or in different adult tissues, suggesting a potential hurdle in breeding and transgenic experiments involving avian sex-related traits.

Discussion

The findings of this study are expected to enhance our understanding of sexual dimorphism in birds and subsequently facilitate insights into the field of breeding and transgenesis of sex-related traits that economically benefit humans.
From fasting to fat reshaping: exploring the molecular pathways of intermittent fasting-induced adipose tissue remodeling
Authors: Vo Nathaniel , Zhang Qiwei , Sung Hoon-Ki
Published: July 2024

Obesity, characterised by excessive fat accumulation, is a complex chronic condition that results from dysfunctional adipose tissue expansion due to prolonged calorie surplus. This leads to rapid adipocyte enlargement that exceeds the support capacity of the surrounding neurovascular network, resulting in increased hypoxia, inflammation, and insulin resistance. Intermittent fasting (IF), a dietary regimen that cycles between periods of fasting and eating, has emerged as an effective strategy to combat obesity and improve metabolic homeostasis by promoting healthy adipose tissue remodeling. However, the precise molecular and cellular mechanisms behind the metabolic improvements and remodeling of white adipose tissue (WAT) driven by IF remain elusive. This review aims to summarise and discuss the relationship between IF and adipose tissue remodeling and explore the potential mechanisms through which IF induces alterations in WAT. This includes several key structural changes, including angiogenesis and sympathetic innervation of WAT. We will also discuss the involvement of key signalling pathways, such as PI3K, SIRT, mTOR, and AMPK, which potentially play a crucial role in IF-mediated metabolic adaptations.
Experimental Investigation of Runback Water Flow Behavior on Aero-Engine Rotating Spinners with Different Wettabilities
Authors: Ma Kuiyuan , Lin Guiping , Jin Haichuan , Shen Xiaobin , Bu Xueqin
Published: July 2024

The accumulation of ice on the aero-engine inlet compromises engine safety. Traditional hot air anti-icing systems, which utilize bleed air, require substantial energy, decreasing engine performance and increasing emissions. Superhydrophobic materials have shown potential in reducing energy consumption when combined with these systems. Research indicates that superhydrophobic surfaces on stationary components significantly reduce anti-icing energy consumption by altering runback water flow behavior. However, for rotating aero-engine components, the effectiveness of superhydrophobic surfaces and the influence of surface wettability on runback water flow remain unclear due to centrifugal and Coriolis forces. This study investigates the runback water flow behavior on aero-engine rotating spinner surfaces with varying wettabilities in a straight-flow spray wind tunnel. The results demonstrated that centrifugal force reduces the amount of runback water on the rotating spinner compared to the stationary surface, forming rivulet flows deflected opposite to the direction of rotation. Furthermore, wettability significantly affects the flow characteristics of runback water on rotating surfaces. As the contact angle increases, the liquid water on the rotating spinner transitions from continuous film flow to rivulet and bead-like flows. Notably, the superhydrophobic surface prevents water adhesion, indicating its potential for anti-icing on rotating components. In addition, the interaction between rotational speed and surface wettability enhances the effects, with both increased rotational speed and larger contact angles contributing to higher liquid water flow velocities, promoting the rapid formation and detachment of rivulet and bead-like flows.
Comparative Study on Mechanical Response in Rigid Pavement Structures of Static and Dynamic Finite Element Models
Authors: Meng Qiao , Zhong Ke , Li Yuchun , Sun Mingzhi
Published: July 2024

The safety of airport runways is important to guarantee aircraft taking-off, landing, and taxiing, and the comparison of the mechanical response of pavement structures under dynamic and static loading by LS-DYNA has rarely been studied. The purpose of this work is to separate two analysis methods to investigate the mechanical response of rigid airport pavements. Firstly, a tire–road coupling model of an airfield was established to evaluate the suitability of dynamic and static analyses. Then, the effects of landing pitch angles, sinking speeds, and tire pressures on the effective stress, effective strain, and z-displacement of the runway were investigated for both dynamic and static analysis. Finally, the significance of influence factors was analyzed by regression analysis in Statistical Product and Service Solutions (SPSS). The results indicated that the effective stress, effective strain, and z-displacement of the runway increased with a decrease in the landing pitch angle, which also increased with an increase in the sinking speed and tire pressure. It was demonstrated that the difference in pavement mechanical response between dynamic and static analyses progressively widened at high tire pressure and sinking speed. In other words, the static analysis method can be adopted to assess the dynamic mechanical behavior when the landing pitch angle is large and the tire pressure is small. Among the various factors of mechanical response, the effect of tire pressure was the most obvious, followed by sinking speed and landing pitch angle. The work proposes a new approach to understanding the mechanical behavior of runways under complicated and varied conditions, evaluates the applicability of the dynamic and static mechanical analysis methods, identifies key factors in the dynamic and static mechanical analysis of rigid runways, and provides technical support for improving and maintaining the impact resistance of pavement facilities.
Case Study of Along-Track Separation Maintenance of Distributed Synthetic Aperture Radar Systems in Low Earth Orbits
Authors: D’Errico Marco
Published: July 2024

Parasitic SAR formation can be flown at low altitude using smaller satellites and adding potential to conventional SAR mission From the orbital point of view, the main issue is related to the differential aerodynamic drag, which rapidly disrupts the formation. In this ambit, this paper proposes a case study of an along-track distributed parasitic receiver flying in formation with PLATiNO-1. Formation maintenance is the core contribution, highlighting how the active control of both altitude and in-plane anomalies leads to an unfeasible ΔV. Then, the active control of the altitude around the nominal value, which naturally controls anomaly shift, is proposed, modeled, and applied to the presented case study. It is shown that the annual ΔV can be reduced to the m/s range.
Immunohistochemical Expression of PTEN in Canine Gliomas
Authors: Molín Jéssica , José-López Roberto , Ramírez Gustavo A. , Pumarola Martí
Published: July 2024

Phosphatase and tensin homolog (PTEN) is a critical tumor suppressor gene with a vital role in regulating cell proliferation, migration, and survival. The loss of PTEN function, either by genetic alterations or decreased protein expression, is frequent in human gliomas and has been correlated with tumor progression, grade, therapeutic resistance, and decreased overall survival in patients with glioma. While different genetic mutations in PTEN gene have been occasionally reported in canine gliomas, no alterations in protein expression have been reported. This study investigates the immunohistochemical expression of PTEN in canine gliomas to evaluate possible alterations, as those reported in human gliomas. Immunohistochemical PTEN expression and pattern distribution were analyzed in 37 spontaneous canine gliomas. Among gliomas, 52.6% cases showed high PTEN expression and 48.6% displayed reduced (13.5%) or highly reduced (35.1%) immunopositivity. Most oligodendrogliomas showed high expression (73.7%), while the majority of astrocytomas (69.2%) showed a reduced or highly reduced expression. A reduced PTEN expression was mostly associated with a heterogeneous loss of PTEN immunopositivity. These observations are in line with those reported in human gliomas and provide a rationale for future studies regarding abnormalities in PTEN expression and PI3K/Akt/mTor pathway in canine gliomas, to evaluate its prognostic and therapeutic implications.
Pig Weight Estimation Method Based on a Framework Combining Mask R-CNN and Ensemble Regression Model
Authors: Jiang Sheng , Zhang Guoxu , Shen Zhencai , Zhong Ping , Tan Junyan , Liu Jianfeng
Published: July 2024

Using computer vision technology to estimate pig live weight is an important method to realize pig welfare. But there are two key issues that affect pigs’ weight estimation: one is the uneven illumination, which leads to unclear contour extraction of pigs, and the other is the bending of the pig body, which leads to incorrect pig body information. For the first one, Mask R-CNN was used to extract the contour of the pig, and the obtained mask image was converted into a binary image from which we were able to obtain a more accurate contour image. For the second one, the body length, hip width and the distance from the camera to the pig back were corrected by XGBoost and actual measured information. Then we analyzed the rationality of the extracted features. Three feature combination strategies were used to predict pig weight. In total, 1505 back images of 39 pigs obtained using Azure kinect DK were used in the numerical experiments. The highest prediction accuracy is XGBoost, with an MAE of 0.389, RMSE of 0.576, MAPE of 0.318% and R2 of 0.995. We also recommend using the Mask R-CNN + RFR method because it has fairly high precision in each strategy. The experimental results show that our proposed method has excellent performance in live weight estimation of pigs.
Predicting Body Weight in Pre-Weaned Holstein–Friesian Calves Using Morphometric Measurements
Authors: Silva Flávio G. , Carreira Emanuel , Ramalho Joana M. , Correia Tomás , Meira Marília , Conceição Cristina , Silva Severiano R. , Pereira Alfredo M. F. , Cerqueira Joaquim L.
Published: July 2024

Regularly weighing calves helps to assess the efficiency of the rearing period and contributes to animal welfare by allowing more precise feeding and medication application in dairy farming, but many farmers do not weigh their calves regularly. Improving the feasibility of this process is, therefore, important. The use of morphometric measurements has been used to estimate the weight of cattle. However, many studies have focused on adult animals or used a wide age range. As calves experience allometric tissue growth, specific models for certain ranges might be more accurate. Therefore, the aim of this work was to develop a weight estimation model specific for pre-weaned Holstein–Friesian calves using morphometric measurements and to compare the model with another equation previously validated for the same breed with young and adult animals. From four dairy farms, 237 measurements of body weight, heart girth, abdominal girth, hip height, withers height, and body length were taken from Holstein–Friesian male and female calves. Linear and non-linear regression analysis was used to test the relationship between body weight and morphometric measurements, with age, sex, and farm as possible explanatory variables. Selected models were compared with goodness of fit and agreement tests. The final model was able to accurately predict body weight (R2 = 0.96) with a mean difference of −1.4 ± 3.24 kg. Differences in the relationship between body weight and morphometric traits were observed between farms, but not between males and females. The genetics of the animal population at farm level may be responsible for this variability and further studies are needed to understand this variability and improve weight prediction models. The developed model was able to perform better in the agreement tests than the previously validated model for Holstein–Friesian animals, suggesting that different equations should be used depending on the growth phase the animal is in. In addition, a web application has been developed to facilitate the use of the developed model by farmers. This avoids the use of calibrated weight bands, which are usually calibrated for a broader age range or for beef cattle.
Buzzing through Data: Advancing Bee Species Identification with Machine Learning
Authors: Ratnayake Ashan Milinda Bandara , Yasin Hartini Mohd , Naim Abdul Ghani , Abas Pg Emeroylariffion
Published: July 2024

Given the vast diversity of bee species and the limited availability of taxonomy experts, bee species identification has become increasingly important, especially with the rise of apiculture practice. This review systematically explores the application of machine learning (ML) techniques in bee species determination, shedding light on the transformative potential of ML in entomology. Conducting a keyword-based search in the Scopus and Web of Science databases with manual screening resulted in 26 relevant publications. Focusing on shallow and deep learning studies, our analysis reveals a significant inclination towards deep learning, particularly post-2020, underscoring its ability to handle complex, high-dimensional data for accurate species identification. Most studies have utilized images of stationary bees for the determination task, despite the high computational demands from image processing, with fewer studies utilizing the sound and movement of the bees. This emerging field faces challenges in terms of dataset scarcity with limited geographical coverage. Additionally, research predominantly focuses on honeybees, with stingless bees receiving less attention, despite their economic potential. This review encapsulates the state of ML applications in bee species determination. It also emphasizes the growing research interest and technological advancements, aiming to inspire future explorations that bridge the gap between computational science and biodiversity conservation.
Examining Hybrid Nanofluid Flow Dynamics in the Conical Gap between a Rotating Disk and Cone Surface: An Artificial Neural Network Approach
Authors: Barakat Julien Moussa H. , Al Barakeh Zaher , Ghandour Raymond
Published: July 2024

To comprehend the thermal regulation within the conical gap between a disk and a cone (TRHNF-DC) for hybrid nanofluid flow, this research introduces a novel application of computationally intelligent heuristics utilizing backpropagated Levenberg–Marquardt neural networks (LM-NNs). A unique hybrid nanoliquid comprising aluminum oxide, Al2O3, nanoparticles and copper, Cu, nanoparticles is specifically addressed. Through the application of similarity transformations, the mathematical model formulated in terms of partial differential equations (PDEs) is converted into ordinary differential equations (ODEs). The BVP4C method is employed to generate a dataset encompassing various TRHNF-DC scenarios by varying magnetic parameters and nanoparticles. Subsequently, the intelligent LM-NN solver is trained, tested, and validated to ascertain the TRHNF-DC solution under diverse conditions. The accuracy of the LM-NN approach in solving the TRHNF-DC model is verified through different analyses, demonstrating a high level of accuracy, with discrepancies ranging from 10−10 to 10−8 when compared with standard solutions. The efficacy of the framework is further underscored by the close agreement of recommended outcomes with reference solutions, thereby validating its integrity.
Fault Characterization for AC/DC Distribution Networks Considering the Control Strategy of Photovoltaic and Energy Storage Battery
Authors: Yuan Yubo , Li Juan , Lyu Pengpeng , Qian Zhonghao , Jiang Yunlong , Wang Jiaming
Published: July 2024

In order to cope with the failure of existing fault analysis schemes for AC/DC distribution networks with a high proportion of distributed generations, this paper proposes a fault characteristic analysis method for AC/DC distribution networks that considers the influence of distributed generation control strategies. Firstly, a transient model for the AC/DC distribution network connected to distributed generations is built. Then, the fault characteristics of the AC/DC distribution network in different stages, such as the capacitor discharge stage, inductive renewal stage, and steady state stage, is analyzed. Finally, detailed simulation analysis is conducted using PSCAD/EMTDC to validate the effectiveness of the developed scheme by the superior approximation performance between simulated curves and calculated curves.
Breakthrough COVID-19 Infections after Booster SARS-CoV-2 Vaccination in a Greek Cohort of People Living with HIV during the Delta and Omicron Waves
Authors: Protopapas Konstantinos , Thomas Konstantinos , Moschopoulos Charalampos D. , Oktapoda Eirini , Marousi Eirini , Marselou Eirini , Stamoulis Nikiforos , Filis Christos , Kazakou Pinelopi , Oikonomopoulou Chrysanthi , Zampetas Georgios , Efstratiadou Ourania , Chavatza Katerina , Kavatha Dimitra , Antoniadou Anastasia , Papadopoulos Antonios
Published: July 2024

Introduction: Currently approved SARS-CoV-2 vaccines have been proven effective in protecting against severe COVID-19; however, they show variable efficacy against symptomatic infection and disease transmission. We studied the breakthrough COVID-19 infection (BTI) after booster vaccination against SARS-CoV-2 in people living with HIV (PWH). Methods: This was a retrospective, single-center, descriptive cohort study involving PWH, who were followed in the HIV Clinic of “Attikon” University Hospital in Athens, Greece. A BTI was defined as a case of laboratory-confirmed COVID-19 occurring at least 14 days after the third (booster) vaccine dose. Results: We studied 733 PWH [males: 89%, mean age: 45.2 ± 11.3 years, mean BMI: 26.1 ± 4.1, HIV stage at diagnosis (CDC classification): A/B/C = 80/9/11%, MSM: 72.6%] with well-controlled HIV infection. At least one comorbidity was recorded in 54% of cases. A history of ≥1 vaccination was reported by 90%, with 75% having been vaccinated with ≥3 vaccines. Four hundred and two (55%) PWH had a history of COVID-19 and 302 (41.2%) had a BTI, with only 15 (3.7%) needing hospitalization. Only one patient was admitted to the ICU, and no death was reported. Regarding BTI after booster dose, increased age (OR = 0.97, 95% CI: 0.96–0.99, per 1-year increase), and COVID-19 infection prior to booster dose (OR = 0.38, 95% CI: 0.21–0.68) were associated with a lower likelihood for BTI, whereas higher BMI (OR = 1.04, 95% CI: 1.01–1.08) and MSM as a mode of HIV transmission were associated with increased risk (OR = 2.59, 95% CI: 1.47–4.56). The incidence rate of total COVID-19 and BTI followed the epidemic curve of the general population, with the highest incidence recorded in June 2022. Conclusions: A significant proportion of PWH with well-controlled HIV infection experienced a BTI, with the majority of them having mild infection. These data, which include the period of Omicron variant predominance, confirm the importance of vaccination in the protection against severe COVID-19.
Preclinical Models of Hepatocellular Carcinoma: Current Utility, Limitations, and Challenges
Authors: Cigliano Antonio , Liao Weiting , Deiana Giovanni A. , Rizzo Davide , Chen Xin , Calvisi Diego F.
Published: July 2024

Hepatocellular carcinoma (HCC), the predominant primary liver tumor, remains one of the most lethal cancers worldwide, despite the advances in therapy in recent years. In addition to the traditional chemically and dietary-induced HCC models, a broad spectrum of novel preclinical tools have been generated following the advent of transgenic, transposon, organoid, and in silico technologies to overcome this gloomy scenario. These models have become rapidly robust preclinical instruments to unravel the molecular pathogenesis of liver cancer and establish new therapeutic approaches against this deadly disease. The present review article aims to summarize and discuss the commonly used preclinical models for HCC, evaluating their strengths and weaknesses.
Optimal Operation of an Industrial Microgrid within a Renewable Energy Community: A Case Study of a Greentech Company
Authors: Fresia Matteo , Robbiano Tommaso , Caliano Martina , Delfino Federico , Bracco Stefano
Published: July 2024

The integration of renewable energy sources in the European power system is one of the main goals set by the European Union. In order to ease this integration, in recent years, Renewable Energy Communities (RECs) have been introduced that aim to increase the exploitation of renewable energy at the local level. This paper presents an Energy Management System (EMS) for an industrial microgrid owned and operated by a greentech company located in the north of Italy. The company is a member of an REC. The microgrid is made of interconnected busbars, integrating photovoltaic power plants, a fleet of electric vehicles, including company cars and delivery trucks supporting Vehicle-to-Grid (V2G), dedicated charging stations, and a centralized battery energy storage system. The industrial site includes two warehouses, an office building, and a connection to the external medium-voltage network. The EMS is designed to optimize the operation of the microgrid and minimize the operating costs related to the sale and purchase of energy from the external network. Furthermore, as the company is a member of an REC, the EMS must try to follow a desired power exchange profile with the grid, suggested by the REC manager, with the purpose of maximizing the energy that is shared within the community and incentivized. The results demonstrate that, when minimizing only costs, local self-consumption is favored, leading to a Self-Sufficiency Rate (SSR) of 65.37%. On the other hand, when only the adherence to the REC manager’s desired power exchange profile is considered in the objective function, the SSR decreases to 56.43%, net operating costs increase, and the energy shared within the REC is maximized.
Impact of Air-Cathodes on Operational Stability of Single-Chamber Microbial Fuel Cell Biosensors for Wastewater Monitoring
Authors: Salvian Anna , Farkas Daniel , Ramírez-Moreno Marina , Avignone Rossa Claudio , Varcoe John R. , Gadkari Siddharth
Published: July 2024

The increasing global water pollution leads to the need for urgent development of rapid and accurate water quality monitoring methods. Microbial fuel cells (MFCs) have emerged as real-time biosensors for biochemical oxygen demand (BOD), but they grapple with several challenges, including issues related to reproducibility, operational stability, and cost-effectiveness. These challenges are substantially shaped by the selection of an appropriate air-breathing cathode. Previous studies indicated a critical influence of the cathode on both the enduring electrochemical performance of MFCs and the taxonomic diversity at the electroactive anode. However, the effect of different gas diffusion electrodes (GDE) on 3D-printed single-chamber MFCs for BOD biosensing application and its effect on the bioelectroactive anode was not investigated before. Our study focuses on comparing GDE cathode materials to enhance MFC performance for precise and rapid BOD analysis in wastewater. We examined for over 120 days two Pt-coated air-breathing cathodes with distinct carbonaceous gas diffusion layers (GDLs) and catalyst layers (CLs): cost-effective carbon paper (CP) with hand-coated CL and more expensive woven carbon cloth (CC) with CL pre-applied by the supplier. The results show significant differences in electrochemical characteristics and anodic biofilm composition between MFCs with CP and CC GDE cathodes. CP-MFCs exhibited lower sensitivity (16.6 C L mg−1 m−2) and a narrower dynamic range (25 to 600 mg L−1), attributed to biofouling-related degradation of the GDE. In contrast, CC-MFCs demonstrated superior performance with higher sensitivity (37.6 C L mg−1 m−2) and a broader dynamic range (25 to 800 mg L−1). In conclusion, our study underscores the pivotal role of cathode selection in 3D-printed MFC biosensors, influencing anodic biofilm enrichment time and overall BOD assessment performance. We recommend the use of cost-effective CP GDL with hand-coated CL for short-term MFC biosensor applications, while advocating for CC GDL supplied with CL as the preferred choice for long-term sensing implementations with enduring reliability.
Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California
Authors: Oliveira Santos Victor , Marinho Felipe Pinto , Costa Rocha Paulo Alexandre , Thé Jesse Van Griensven , Gharabaghi Bahram
Published: July 2024

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m2 and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows.
Magnetocaloric Refrigeration in the Context of Sustainability: A Review of Thermodynamic Bases, the State of the Art, and Future Prospects
Authors: Lucia Umberto , Grisolia Giulia
Published: July 2024

At present, one of the main challenges that industry faces is its impact on global warming, linked to the greenhouse effect and ozone hole problems. These two environmental issues have not yet been solved completely and, concerning the industrial cold sector, countries are making various decisions on refrigerants. Magnetic refrigeration potentially represents a less impactful refrigeration technology. In this review, the physical basis of magnetic refrigeration is analysed, in order to propose this technology for industrial use.
Integration of Electric Vehicle Power Supply Systems—Case Study Analysis of the Impact on a Selected Urban Network in Türkiye
Authors: Lewicki Wojciech , Coban Hasan Huseyin , Wróbel Jacek
Published: July 2024

Undoubtedly, the transition to electromobility with several million new, efficient charging points will have consequences for the energy industry, and in particular for network operators of the distribution infrastructure. At the same time, in the coming years the energy landscape will change into a system in which an increase in decentralized systems based on renewable energy sources will take over the leading function. This transformation process will further increase the complexity and overall pressure for change in energy systems over the next decade. In order to be able to ensure the energy supply and the reliable system operation of the grids in the future as well, communicative networking of generators, storage systems, electrical consumers and grid equipment is indispensable. This study aims to investigate the consequences of including electric vehicles in Istanbul’s power system using a unit commitment simulation model. The presented considerations analyze how uncertain and managed charging strategies affect the power system in terms of operating costs and renewable resources. The presented simulations indicate that, in economic terms, the use of a managed charging strategy saves 2.3%, reducing the total cost from USD 66.71 million to USD 65.18 million. The recipients of the presented research are both the demand and supply sides of the future energy transformation based on the idea of synergy of electromobility and renewable energy sources within the framework of the smart city idea.
Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions
Authors: Delgado-Currín Raúl R. , Calderón-Muñoz Williams R. , Elicer-Cortés J. C.
Published: July 2024

The optimal performance of a hydroelectric power plant depends on accurate monitoring and well-functioning sensors for data acquisition. This study proposes the use of artificial neural networks (ANNs) to estimate the Pelton turbine shaft power of a 10 kW micro-hydropower plant. In the event of a failure of the sensor measuring the torque and/or rotational speed of the Pelton turbine shaft, the synthetic turbine shaft power data generated by the ANN will allow the turbine output power to be determined. The experimental data were obtained by varying the operating conditions of the micro-hydropower plant, including the variation of the input power to the electric generator and the variation of the injector opening. These changes consequently affected the flow rate and the pressure head at the turbine inlet. The use of artificial neural networks (ANNs) was deemed appropriate due to their ability to model complex relationships between input and output variables. The ANN structure comprised five input variables, fifteen neurons in a hidden layer and an output variable estimating the Pelton turbine power. During the training phase, algorithms such as Levenberg–Marquardt (L–M), Scaled Conjugate Gradient (SCG) and Bayesian were employed. The results indicated an error of 0.39% with L–M and 7% with SCG, with the latter under high-flow and -energy consumption conditions. This study demonstrates the effectiveness of artificial neural networks (ANNs) trained with the Levenberg–Marquardt (L–M) algorithm in estimating turbine shaft power. This contributes to improved performance and decision making in the event of a torque sensor failure.
Effects of Substituting Tenebrio molitor and Elodea nuttallii as Feed on Growth, Flesh Quality and Intestinal Microbiota of Red Swamp Crayfish (Procambarus clarkii)
Authors: Li Muyan , Lv Weiwei , Zhao Yifan , Huang Weiwei , Yuan Quan , Yang Hang , Wang Aimin , Zhou Wenzong , Li Mingyou
Published: July 2024

This study aimed to evaluate the impact of substituting a portion of feed with Tenebrio molitor (TM) and Elodea nuttallii (EN) on crayfish culture. A total of 270 crayfish (5.1 ± 0.4 g) were fed three different diet combinations (A: 100% feed; B: 80% feed + 10% TM + 10% EN; C: 75% feed + 15% TM + 10% EN) for 12 weeks. The findings demonstrated that group C had an important beneficial impact on the growth performance of crayfish. This was evidenced by a rise in digestive enzyme activity (trypsin, lipase, and cellulase) in the intestinal and hepatopancreas, as well as an upregulation in the expression of growth-related genes (ghsr, igfbp7, mhc, mlc1, mef2, and pax7) in the muscle. Furthermore, the assessment of the flesh quality of crayfish muscle in group C was conducted. The findings indicated a significant increase (p < 0.05) in the energy value (moisture, crude protein, and crude lipid) within the muscle. The levels of delicious amino acids (Glu, Ala, Ser, Gly, and Tyr) and polyunsaturated fatty acids (ARA, DHA) were enhanced, resulting in an improved nutritional profile and flavor of the muscle while maintaining the Σn-3/Σn-6 ratio. The remodeling of the intestinal microbiota (abundance of Proteobacteria and ratio of Firmicutes/Bacteroidota bacteria) also revealed improved growth performance. Additional research is necessary to ascertain whether excessive use of TM or EN feed substitution can have negative effects on crayfish culture.
Developing Effective Radio Frequency Vacuum Drying Processes for Moutan Cortex: Effect on Moisture Migration, Drying Kinetics, Physicochemical Quality, and Microstructure
Authors: Zang Zepeng , Wan Fangxin , Jia Haiwen , Ma Guojun , Xu Yanrui , Zhao Qiaozhu , Wu Bowen , Lu Hongyang , Huang Xiaopeng
Published: July 2024

This study aims to maximize the post-harvest quality of Moutan Cortex and reduce energy consumption. Radio frequency vacuum (RFV) technology was used to dehydrate Moutan Cortex in this study to investigate the effects of different drying temperatures, plate spacing, and vacuum degree on the drying kinetics, physicochemical quality, and microstructure of Moutan Cortex. The results showed that RFV drying shortened the dehydration time of the Moutan Cortex by 10.71–28.57% and increased the drying rate by 15.79–54.39% compared to hot-air drying. The best color (∆E = 6.08 ± 0.28, BI = 26.97 ± 0.98) and relatively high retention of polysaccharides, total phenolics, total flavonoids, antioxidant properties, paeonol, gallic acid, paeoniflorin, and benzoylpaeoniflorin contents were observed in the dried products of Moutan Cortex at a drying temperature of 50 °C, spacing of 90 mm, and vacuum of 0.025 MPa. Analyzing the microstructure, it was found that RFV drying could effectively inhibit the shrinkage and collapse of the cellular structure, and a regular and loose honeycomb pore structure appeared inside the samples, which contributed to the rapid migration of the internal moisture. This study can provide a theoretical reference basis for the selection and application of industrialized processing methods of high-quality Moutan Cortex.
Impact of Sourdough from a Commercial Starter Culture on Quality Characteristics and Shelf Life of Gluten-Free Rice Breads Supplemented with Chickpea Flour
Authors: Keramari Sevasti , Nouska Chrysanthi , Hatzikamari Magdalini , Biliaderis Costas G. , Lazaridou Athina
Published: July 2024

This study aimed to develop a novel gluten-free bread using a rice/chickpea flour-based sourdough, fermented by a commercial starter culture, to improve the quality characteristics and shelf life of this product. The effects of sourdough incorporation, chickpea flour content (6.5 and 10.0%), and added water level (80–110%) on batter rheology and bread quality were investigated; bread textural characteristics upon storage (0–2 days) were also monitored. The level of added water was the primary factor influencing batter rheology, as evaluated by the back extrusion test. Sourdough incorporation decreased the pH and increased the acidity of batters and breads. The inclusion of sourdough, the water level, and the storage time affected the moisture and texture parameters of the bread crumb. Sourdough incorporation into bread formulations decreased crumb hardness and staling rate and increased loaf specific volume. Moreover, intermediate water (90 and 100%) and high chickpea (10%) levels in the batters increased loaf specific volumes and crust redness, respectively. Sensory analysis revealed that sourdough-enriched breads were preferred by the assessors concerning general appearance and crumb texture. Overall, bread formulations with the incorporation of sourdough, at a 90% level of added water in the batter mixtures, exhibited the most desirable characteristics according to both instrumental and sensory analyses.
Preparation and Performance Evaluation of Polysaccharide–Iron Complex of Eucommia ulmoides
Authors: Liu Mengpei , Wang Yan , Wang Rong , Zong Wei , Zhang Lihua , Wang Lu
Published: July 2024

An innovative iron supplement crucial for treating iron-deficiency anemia was developed in this study. Polysaccharide was extracted from Eucommia ulmoides leaves using a microwave-assisted hot water method, and subsequently, the polysaccharide–iron complex was synthesized through co-thermal synthesis with FeCl3. The physicochemical properties, structure, and thermal stability of the complex were analyzed using FE-SEM, SEC-MALLS, FT-IR, XRD, and DSC techniques. Furthermore, the antioxidant activity of the polysaccharide–iron complex was evaluated through an experiment in vitro. The results revealed that the polysaccharide–iron complex had an iron content of 6.1% and an average particle size of 860.4 nm. The microstructure analysis indicated that the polysaccharide–iron complex possessed a flaky morphology with smooth and compact surfaces. Moreover, the formation of the Fe3+ complex did not alter the structural framework of the polysaccharide; instead, it enhanced the polysaccharide’s thermal stability. Compared to traditional iron supplements, the E. ulmoides-derived polysaccharide–iron complex demonstrated significant antioxidant activity. Therefore, this novel compound exhibits significant potential as a viable iron supplement.
Laser-Induced Breakdown Spectroscopy–Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma
Authors: Chen Feng , Zhang Mengsheng , Huang Weihua , Sattar Harse , Guo Lianbo
Published: July 2024

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model’s robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications.
Spectral Galerkin Methods for Riesz Space-Fractional Convection–Diffusion Equations
Authors: Zhang Xinxia , Wang Jihan , Wu Zhongshu , Tang Zheyi , Zeng Xiaoyan
Published: July 2024

This paper applies the spectral Galerkin method to numerically solve Riesz space-fractional convection–diffusion equations. Firstly, spectral Galerkin algorithms were developed for one-dimensional Riesz space-fractional convection–diffusion equations. The equations were solved by discretizing in space using the Galerkin–Legendre spectral approaches and in time using the Crank–Nicolson Leap-Frog (CNLF) scheme. In addition, the stability and convergence of semi-discrete and fully discrete schemes were analyzed. Secondly, we established a fully discrete form for the two-dimensional case with an additional complementary term on the left and then obtained the stability and convergence results for it. Finally, numerical simulations were performed, and the results demonstrate the effectiveness of our numerical methods.
Constraining Geogenic Sources of Boron Impacting Groundwater and Wells in the Newark Basin, USA
Authors: Rddad Larbi , Spayd Steven
Published: July 2024

The Newark Basin comprises Late Triassic and Early Jurassic fluvio-lacustrine rocks (Stockton, Lockatong, Passaic, Feltville, Towaco, and Boonton Formations) and Early Jurassic diabase intrusions and basalt lava flows. Boron concentrations in private well water samples range up to 18,000 μg/L, exceeding the U.S. Environmental Protection Agency Health Advisory of 2000 μg/L for children and 5000 μg/L for adults. Boron was analyzed in minerals, rocks, and water samples using FUS-ICPMS, LA-ICP-MS, and MC ICP-MS, respectively. Boron concentrations reach up to 121 ppm in sandstone of the Passaic Formation, 42 ppm in black shale of the Lockatong Formation, 31.2 ppm in sandstone of the Stockton Formation, and 36 ppm in diabase. The δ11B isotopic values of groundwater range from 16.7 to 32.7‰, which fall within those of the diabase intrusion (25 to 31‰). Geostatistical analysis using Principal Component Analysis (PCA) reveals that boron is associated with clay minerals in black shales and with Na-bearing minerals (possibly feldspar and evaporite minerals) in sandstones. The PCA also shows that boron is not associated with any major phases in diabase intrusion, and is likely remobilized from the surrounding rocks by the intrusion-related late hydrothermal fluids and subsequently incorporated into diabase. Calcite veins found within the Triassic rock formations exhibit relatively elevated concentrations ranging from 6.3 to 97.3 ppm and may contain micro-inclusions rich in boron. Based on the available data, it is suggested that the primary sources of boron contaminating groundwater in the area are clay minerals in black shales, Na-bearing minerals in sandstone, diabase intrusion-related hydrothermal fluids, and a contribution from calcite veins.
Use of Chipless Radio Frequency Identification Technology for Smart Food Packaging: An Economic Analysis for an Australian Seafood Industry
Authors: Fathi Parya , Bhattacharya Mita , Bhattacharya Sankar , Karmakar Nemai
Published: July 2024

Effective monitoring of perishable food products has become increasingly important for ensuring quality, enabling smart packaging to be a key consideration for food companies. Among the promising technologies available for transforming packaging into intelligent packaging, chipless radio frequency identification (RFID) sensors stand out. Despite the high initial implementation costs associated with chipless RFID technology, the potential benefits could outweigh the costs if electrical challenges can be overcome. We examine various economic methods to analyze the economic benefits of chipless RFID technology, evaluating the benefits of using this technology for the quality monitoring of seafood products of an Australian seafood producer, Tassal. The analysis considers three primary business drivers, viz. quality monitoring, operational efficiency, and tracking and tracing, using net present value and return on investment as the key indicators to assess the feasibility of implementing the technology. Based on sensitivity analysis, we suggest chipless RFID technology is currently best suited for large firms facing significant quality monitoring and operational efficiency challenges. However, as the cost of chipless RFID sensors decreases with further development, this technology may become a more viable option for small businesses in the future.
Maneuver Planning for Multiple Pursuit Intelligent Surface Vehicles in a Sequence of Zero-Sum Pursuit–Evasion Games
Authors: Hong Le , Cui Weicheng , Chen Hao , Song Changhui , Li Weikun
Published: July 2024

Unmanned surface pursuit is a complex and challenging engineering problem, especially when conducted by multiple intelligent surface vehicles (ISVs). To enhance the pursuit performance and facilitate strategic interaction during the target pursuit, this paper proposes a novel game theory-based maneuver planning method for pursuit ISVs. Firstly, a specific two-player zero-sum pursuit–evasion game (ZSPEG)-based target-pursuit model is formed. To ensure the vehicles reach a quick consensus, a target-guided relay-pursuit mechanism and the corresponding pursuit payoffs are designed. Meanwhile, under the fictitious play framework, the behavioral pattern and the strategies of the target could be fictitiously learned. Furthermore, mixed-strategy Nash equilibrium (MNE) is employed to determine the motions for the vehicles, the value of which is the best response in the proposed ZSPEG model. Finally, simulations verify the effectiveness of the above methods in multi-ISV surface pursuit.
Fishing Eco-Efficiency of Ports in Northwest Spain
Authors: Antelo Luis T. , Franco-Uría Amaya
Published: July 2024

Fishing is an essential economic activity and source of livelihood for millions of people worldwide. However, overfishing and unsustainable practices have led to a decline in fish populations and the degradation of marine ecosystems. Moreover, fishing activities can contribute to climate change through the emission of greenhouse gases (e.g., carbon dioxide and methane) from fishing vessels and seafood transportation. To mitigate the environmental impacts of fishing activities, sustainable fishing practices must be implemented to minimize the negative impacts of fishing on the environment while maintaining the productivity and diversity of fish populations and ecosystems. These practices include using selective fishing gear, avoiding fishing in vulnerable habitats, implementing fishery management plans, and reducing the carbon footprint of the fishing industry. To this end, and as a first step in defining efficient and effective measures towards the sustainability of capture fishing activity, an analysis of the environmental sustainability of the Galician fishing sector, one of the main European regions in this field, is presented in this work. An ecosystem-based indicator (ecological footprint, calculated by adding the so-called fishing ground footprint and the carbon footprint) was employed to quantify the main impacts of capture fishing during extractive activity. The catch composition and fuel consumption of the fleet based on the vessels’ power, and economic benefits, were the parameters used in this analysis. The results showed that ports with larger vessels and fleets seem to be more eco-efficient than those concentrating smaller vessels in targeting lower trophic level species.