bluuebunny/crossref_metadata_2025 huggingface dataset:
Environmental Impacts Associated with the Production and Packing of Persian Lemon in Mexico through Life-Cycle Assessment
Authors: Castillo-González Eduardo , De Medina-Salas Lorena , Giraldi-Díaz Mario Rafael , Velásquez-De La Cruz Raúl , Jiménez-Ochoa José Rafael
Published: May 2024

In this study, the environmental impacts associated with the intensive production of Persian lemons are assessed, including the agricultural and packing phases of the fresh fruit. A life-cycle assessment (LCA) tool was used in accordance with the ISO 14040 and 14044 standards and implemented in SimaPro PhD (9.2) software. The life-cycle inventory database was primarily composed of data collected during field visits to local lemon orchards and the main packing company in the region. The functional unit was defined as 1 kg of packed fresh Persian lemons. The selected impact categories were the carbon footprint, water footprint, and energy footprint, and the results obtained for the defined functional unit were 405.8 g CO2 eq, 40.3 L of water, and 5.9 MJ, respectively. The industrial packing phase of the fruits had a greater impact on the carbon and energy footprints, mostly due to the manufacturing of packaging materials and cardboard boxes, followed by the transportation of supplies. Regarding the water footprint, the agricultural phase was identified as the most significant contributor to water consumption, primarily attributed to maintenance operations and the application of agrochemicals.
Evaluation of the Plant Growth Regulator SPGP4 in Agricultural Crops: A Case Study in Oaxaca, México
Authors: López-Castillo Guiee N. , Wong-Villareal Arnoldo , Castellanos-Moguel Judith , Correa Gilberto Vela , Cabrera-Hilerio Sandra L. , Miranda-Arámbula Mariana , Carrasco-Carballo Alan , Sandoval-Ramírez Jesús
Published: May 2024

The search for new plant growth regulators is a cornerstone of agricultural research; however, laboratory studies rarely go on to be evaluated in the field. This is because greater production is required, as well as longer studies. Particularly, brassinosteroids present these difficulties, and although they have been evaluated in crops with good results, their high production cost gives rise to the search for new alternatives. 22-Oxocholestanes such as SPGP4, previously used in silico and in vitro studies, have shown great potential, so their evaluation in crops grown from native seeds from the study region becomes of interest. Based on these data, SPGP4 was evaluated under crop conditions in three agricultural plots located on the Isthmus of Tehuantepec region, Oaxaca, México. The seeds were treated with a 0.5 mg/L aqueous solution of the 22-Oxocholestane compound SPGP4 by imbibition one night before sown. Later, 45 days after sowing, a solution of 0.5 mg/L at a rate of 200 L per hectare was applied. At the production level, the bean harvest showed an increase in the range of 21.0–38.1%, and the corn harvest increased between 22 and 32%. In addition, the latter also demonstrated an increase in biomass production, given the increase in diameter and height observed in the corn plant. This indicates that SPGP4 functions as a regulator of plant growth at the crop level to increase both seed and biomass production.
Advanced Aerospace Materials: Processing, Microstructure, Mechanical Properties and Applications
Authors: Zu Yufei , Pang Huifang , Wu Fan
Published: May 2024

Advanced aerospace alloy deformation processing (contribution 1–3) is investigated in this collection [...]
Roseateles agri sp. nov., a New Species Isolated from Fresh Soil in Uiwang, South Korea
Authors: You Yelim , Kim Jaisoo
Published: May 2024

Two isolates of Roseateles were discovered in soil samples collected from Uiwang-si, Gyeonggi-do, Republic of Korea. These isolates exhibited rod-shaped morphology and were facultatively anaerobic, non-motile, and tested positive for oxidase and catalase. Designated as strains R3-3T and R3-11, their growth was hindered by NaCl concentrations exceeding 0.5%, while their optimal growth conditions were observed at temperatures ranging from 25 °C to 30 °C and pH levels between 7.0 and 9.0. Both strains exhibited positive results for the hydrolysis of Tween 80 and DNA, but tested negative for starch, casein, chitin, and gelatin hydrolysis. Additionally, they assimilated L-Arabinose, D-mannitol, and D-Maltose, while exhibiting negative results for the fermentation of D-glucose, esculin ferric citrate, D-mannose, N-acetyl-glucosamine, potassium gluconate, capric acid, adipic acid, trisodium citrate, and phenylacetic acid. The DNA G+C content of strain R3-3T was measured at 67.5 mol%. Comparative analysis revealed that the average nucleotide identity (ANI) values between R3-3T and the Roseateles type strains ranged from 75.14% to 78.30% while the digital DNA-DNA hybridization (dDDH) values ranged from 20.70% to 22.70%. Consequently, based on comprehensive genomic, chemotaxonomic, phenotypic, and phylogenomic evaluations, the isolated strains have been designated as a new species within the genus Roseateles, named Roseateles agri sp. nov. (with type strain R3-3T = KACC 23678T = NBRC 116681T).
Assessment of Medical Test Overuse and Its Impact on Pediatric Emergency Department Outcomes in Upper Respiratory Tract Infections in a University Hospital in Lithuania
Authors: Nedzinskaite Melita , Karakaite Dagna , Zubrickyte Erika , Jankauskaite Lina
Published: May 2024

Medical overuse poses potential risks to patients and contributes to increasing healthcare costs, pediatric emergency departments (PED) in particular. Often, upper respiratory tract infection (URTI) cases are viral-induced and self-limiting, and they do not require specific investigations or treatment. We conducted a retrospective study from 1 December 2021 to 31 January 2022, thereby aiming to identify the common tests and factors influencing specific diagnostic and treatment decisions for URTI in PED. In total, 307 (74.9%) URTI cases underwent complete blood count (CBC) tests, 312 (76.1%) were subjected to C-reactive protein (CRP) tests, and 110 (26.8%) received urinalysis tests. Patients with a longer duration of fever and a physician‘s suspicion of bacterial infection were more likely to receive CBC, CRP, and/or urinalysis tests (p < 0.05). Moreover, 75.1% of the cases were classified as viral URTIs, 9.8% were bacterial URTIs, and 15.1% were unspecified. Notably, 86 (20.1%) children received antibiotics and antibiotic prescription correlated with age, tonsillitis diagnosis, CRP values higher than 30 mg/L, and a CBC of p < 0.05. Patients triaged in the second or third categories were three times more likely to be observed for 24 h compared to patients with URTI and the fourth triage category (p < 0.05). This study highlights the need for interventions to improve the appropriateness of emergency service utilization, thereby emphasizing the importance of judicious decision making in managing pediatric URTIs.
Assessment of the Nasopalatine Canal Length and Shape Using Cone-Beam Computed Tomography: A Retrospective Morphometric Study
Authors: Soman Cristalle
Published: May 2024

The anatomical position of the nasopalatine canal in the anterior maxilla makes it one of the most important vital structures in the region. Surgical and implant procedures in this area require local anesthesia to be administered. It is, therefore, important to morphologically assess the length and shape of the nasopalatine canal for performing surgical procedures with more accuracy in this area. Cone-Beam Computed tomography scans were scrutinized using inclusion criteria of age 18 years and above, absence of any pathological lesions/fracture/surgery in the nasopalatine area, absence of orthodontic treatment or maxillary jaw correction surgeries, and exclusion criteria including CBCT scans with artifacts or error s in the area of interest, anterior implants, absence of bone diseases, trauma, surgeries, and impactions in the area of interest. A total of 360 scans were analyzed for the length and shape of the nasopalatine canals. The results of the study showed that the mean nasopalatine canal length was 12.51 mm. The hourglass shape of the canal was most common and had the highest representation in both genders, with male 80.62% and female 87.01%. A statistically significant difference (p < 0.001) was noted in nasopalatine canal length between males and females. The study provides insight into the significant association of gender with the canal’s shape and length of the canal. The length of the canal does not influence with age. These parameters are helpful for surgical planning and interventions in the anterior maxillary region.
The Prognostic Role of Serum β-Trace Protein Levels among Patients on Maintenance Hemodialysis
Authors: Huang Po-Yu , Hsu Bang-Gee , Wang Chih-Hsien , Tsai Jen-Pi
Published: May 2024

Cardiovascular (CV) diseases are the most commonly encountered etiology of mortality in patients having kidney failure. β-Trace protein (BTP) is a biomarker of glomerular filtration function as well as a potential predictor of adverse CV outcomes. This study aimed to determine the prognostic value of BTP in patients on chronic hemodialysis (HD). A total of 96 patients undergoing HD were enrolled. Baseline variables were collected, and the patients were tracked for 3 years. Twenty-five patients died at 3 years. Those who experienced mortality were noted to have higher serum concentrations of BTP and a higher incidence of diabetes mellitus (DM). The area under the receiver operating characteristic curve for serum BTP distinguishing mortality from survival was 0.659 (95% confidence interval [CI], 0.555–0.752; p = 0.027). After the adjustment of variables potentially affecting survival rates, BTP levels above the median (adjusted hazard ratio [aHR]: 2.913, 95% CI, 1.256–6.754; p = 0.013), the presence of DM (aHR: 2.474, 95% CI, 1.041–5.875; p = 0.040), and low serum albumin (aHR: 0.298, 95% CI, 0.110–0.806; p = 0.017) independently correlated with survival in HD patients. Serum BTP is a novel biomarker for predicting overall outcomes in HD patients.
Ultra-Processed Foods Consumption and Asthma in the Western Diet
Authors: Frontela-Saseta Carmen , Finlayson Graham , Sánchez-Moya Teresa , Lorenzetti Stefano , López-Nicolás Rubén
Published: May 2024

Obesity is considered an important risk factor for the onset of asthma, playing a key role in enhancing the disease’s severity. However, there is increasing evidence linking not only obesity but also overweight with a higher risk of asthma. Although the correlation between obesity and asthma has already been reported, several aspects are still not fully elucidated, mainly about the inflammatory processes underlying both diseases. It is well known that Western-type calorically rich diets and overfeeding can act as triggers of chronic metabolic inflammation, but few studies have examined associations between ultra-processed foods (UPFs) intake, despite its positive correlation with obesity, and biomarkers of inflammation. In addition to their nutrient composition, UPF may have chemical additives and contaminants from packaging, whose effects on health and food addiction are still under research. In this review, we provide an overview of the current data that identify the associations between the obese asthma phenotype and UPF consumption, highlighting the potential central role played by the intestinal microbiota.
Influence of Cavity Lining on the 3-Year Clinical Outcome of Posterior Composite Restorations: A Randomized Controlled Clinical Trial
Authors: Nguyen Anh Duc , Pütz Natalie , Michaelis Mary , Bitter Kerstin , Gernhardt Christian Ralf
Published: May 2024

The purpose of this randomized, split-mouth-designed controlled and single-blinded clinical study was to evaluate the 3-year clinical performance of Class I and Class II resin composite restorations placed with or without cavity lining with a flowable composite. Fifty patients with treatment needs in two premolars or molars were included. One of the teeth was restored using the nanohybrid composite (Grandio®SO, control group), in the test group a high viscosity flowable composite was additionally applied as a first layer. In both groups, the same self-etch adhesive system was applied. Clinical evaluation after 3 years was carried out using the modified USPHS/Ryge criteria. At the 3-year follow-up the recall rate was 92%. Four restorations failed in the test group (8.7%), three due to the loss of vitality and one after fracture. The control group exhibited a cumulative success rate of 100%, while the test group achieved a success rate of 91.3%. This led to significant differences in the annual failure rate (AFR) between the two groups, with rates of 0% and 2.9% (p < 0.05; Mann–Whitney U-test). After 3 years the cumulative survival rate including all restorations was 95.7%. Statistical analysis revealed significant differences for the parameters: tooth vitality, marginal discoloration, success rate, and AFR. The other parameters exhibited no significant differences. Consequently, the nanohybrid composite demonstrated excellent performance over a 3-year period, whereas the utilization of a flowable composite for the cavity lining did not appear to exert a beneficial influence on clinical outcomes.
Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory
Authors: Prakoso Dino Budi , Windiatmaja Jauzak Hussaini , Mulyanto Agus , Sari Riri Fitri , Nordin Rosdiadee
Published: May 2024

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.
Data-Driven Prediction Model for Analysis of Sensor Data
Authors: Yotov Ognyan , Aleksieva-Petrova Adelina
Published: May 2024

In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset.
Variable Switching Frequency for ZVS over Wide Voltage Range in Dual Active Bridge
Authors: Cárcamo Alberto , Fernandez-Hernandez Alejandro , Gonzalez-Hernando Fernando , Vázquez Aitor , Rodríguez Alberto
Published: May 2024

The Dual Active Bridge (DAB) converter is known for its advantageous characteristics, including bidirectionality, galvanic isolation, and soft-switching operation. However, achieving Zero Voltage Switching (ZVS) across the complete operation range is not guaranteed, particularly through a wide input–output voltage ratio. This paper explores the integration of the switching frequency as a control variable in the DAB converter to ensure ZVS operation across a wide voltage range, employing Single Phase Shift (SPS) modulation. The study evaluated the RMS and reactive currents under variable switching frequency, presenting the advantages of this approach. Moreover, it includes a design-oriented analysis of the ZVS limits and their relationship with the switching frequency, aiming to ensure ZVS at any operating point. Experimental results validated the theoretical analysis, while presenting the main advantages of variable switching frequency implementation.
Assessing Residential Building Energy Efficiency Using Evolutionary Dendritic Neural Regression
Authors: Song Zhenyu , Tang Yajiao , Song Shuangbao , Zhang Bin , Tang Cheng
Published: May 2024

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain.
Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach
Authors: Li Feixiang , Qu Kai , Liu Mingzhe , Li Ning , Sun Tian
Published: May 2024

With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.
Improvement of PBFT Consensus Algorithm Based on Affinity Propagation Clustering in Intellectual Property Transaction Scenarios
Authors: Du Dan , Feng Wenlong , Huang Mengxing , Feng Siling , Wang Jing
Published: May 2024

In response to the problems of random selection of primary nodes, high communication complexity, and low consensus efficiency in the current consensus mechanism for intellectual property transactions, a Practical Byzantine Fault Tolerance (PBFT) consensus algorithm based on the Affinity-Propagation (AP) clustering algorithm, termed AP-PBFT, is proposed. Firstly, the election strategy of the leader node is constructed based on the reputation mechanism; the reward and punishment mechanism is designed to achieve the dynamic adjustment of the reputation value of the nodes in the PBFT consensus process, and the number of votes among the nodes is introduced to determine the node’s reputation value in collaboration with the reward and punishment mechanism to guarantee the precise ordering of the nodes. Secondly, nodes with high reputation values are selected as cluster centers to run the AP clustering algorithm, and clustering groups of knowledge property transaction nodes are constructed based on responsibility and availability. Finally, the three-stage consensus process of the PBFT consensus algorithm is optimized, and the consensus task is decomposed into two layers: the intra-consensus group and the inter-leader node group, reducing the communication complexity of transaction data in the blockchain. Experimental findings indicate a significant performance improvement of the algorithm over the PBFT consensus algorithm in communication complexity, throughput, and consensus efficiency in the simulation environment of multiple types of transactions in intellectual property transactions, including different types of large-scale transaction scenarios, such as purchases, sales, licenses, and transfers.
Technical and Economic Aspects of Environmentally Sustainable Investment in Terms of the EU Taxonomy
Authors: Ciuła Józef , Generowicz Agnieszka , Oleksy-Gębczyk Aneta , Gronba-Chyła Anna , Wiewiórska Iwona , Kwaśnicki Paweł , Herbut Piotr , Koval Viktor
Published: May 2024

Removing impurities that occur in landfill gas, from sulphur and silicon compounds, is crucial for the energetic use of biogas in a cogeneration unit for energy purposes. The aim of this study was to analyse the shaped activated carbon, examining its structure and elemental composition as part of the biogas purification. The qualitative study of the purified landfill gas performed in this study showed a significant overshoot of hydrogen sulphide at 304.1 ppm with respect to the gas engine manufacturer’s requirements, while the calculated hydrogen sulphide reduction efficiency was 24.58%. Examination of the surface of the spent carbon and its pores with a scanning microscope revealed a high level of clotting by sulphur compounds, which prevents proper reduction of this compound and reduces the efficiency of the treatment plant. Replacement of the activated carbon bed with a new one showed a hydrogen sulphide value of 7.5 ppm in the purified gas and a calculated reduction efficiency of 97.9%. The results of the study confirmed that continuous monitoring of the quality of the purified gas is necessary to control the adsorption properties of the activated carbon and can be used for the operation of gas engines in cogeneration units. The landfill gas treatment method described in this paper constitutes an environmentally sustainable project within the meaning of the EU regulation on the establishment of a framework to promote and facilitate this type of investment in terms of its financing and operation. The topic of the work fits into three key areas of broad research and implementation activities. The first, technological, is the transition to a low-carbon, sustainable and resource-efficient closed-loop economy; the second, environmental, pollution prevention and control. The third area is economics and finance in terms of making financial products available designed to reduce climate change and reporting on these activities.
Spatial Graphene Structures with Potential for Hydrogen Storage
Authors: Jastrzębski Krzysztof , Cłapa Marian , Kaczmarek Łukasz , Kaczorowski Witold , Sobczyk-Guzenda Anna , Szymanowski Hieronim , Zawadzki Piotr , Kula Piotr
Published: May 2024

Spatial graphene is a 3D structure of a 2D material that preserves its main features. Its production can be originated from the water solution of graphene oxide (GO). The main steps of the method include the crosslinking of flakes of graphene via treatment with hydrazine, followed by the reduction of the pillared graphene oxide (pGO) with hydrogen overpressure at 700 °C, and further decoration with catalytic metal (palladium). Experimental research achieved the formation of reduced pillared graphene oxide (r:pGO), a porous material with a surface area equal to 340 m2/g. The transition from pGO to r:pGO was associated with a 10-fold increase in pore volume and the further reduction of remaining oxides after the action of hydrazine. The open porosity of this material seems ideal for potential applications in the energy industry (for hydrogen storage, in batteries, or in electrochemical and catalytic processes). The hydrogen sorption potential of the spatial graphene-based material decorated with 6 wt.% of palladium reached 0.36 wt.%, over 10 times more than that of pure metal. The potential of this material for industrial use requires further refining of the elaborated procedure, especially concerning the parameters of substrate materials.
Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers
Authors: Rengifo Johnny , Moreira Jordan , Vaca-Urbano Fernando , Alvarez-Alvarado Manuel S.
Published: May 2024

Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.
Numerical Investigation of Interventions to Mitigate Heat Stress: A Case Study in Dubai
Authors: Korkut Talha Batuhan , Rachid Ahmed
Published: May 2024

Urbanization and changes in microclimate have negative impacts on outdoor thermal comfort, making urban design more important. This study aims to improve outdoor thermal comfort in a local climate zone (LCZ) in Dubai using computational fluid dynamics (CFD) methods. This study evaluates cooling interventions, such as vegetation, architectural, and pavement material, using Reynolds-averaged Navier–Stokes (RANS) equations and the SIMPLE scheme. The results show that a combination of cooling interventions affects the average temperature between 4.44 °C and 6.14 °C. Light-colored ground material has a 5.4 °C cooling effect in the LCZ compared with dark-colored materials. The predicted mean vote (PMV) method is used to compare outdoor thermal comfort and the results show that thermal sensation in the LCZ improves from warm to slightly cool. Lastly, the most effective cooling interventions are, in order, shade structures, trees, chimneys, and bushes.
Optimized Dynamic Vehicle-to-Vehicle Charging for Increased Profit
Authors: Alaskar Shorooq , Younis Mohamed
Published: May 2024

Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier’s profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption.
Uncertainty Quantification of Engineering Parameters for a Nuclear Reactor Loaded with Dispersed Fuel Particles
Authors: Li Yukun , Chen Zhenping , Yang Chao , Huang Guocai , Gao Kekun , Sun Aikou , Liu Chengwei , Wu Zhiqiang
Published: May 2024

Owing to their high intrinsic safety, dispersed fuel particles are an important fuel pattern for fourth-generation nuclear reactors. Due to the unique cladding layers and the random dispersion characteristics, dispersed fuel particles significantly differ from pressurized water reactors regarding operation-induced uncertainty. This study quantitatively analyzed overall uncertainty while considering a random distribution of dispersed fuel particles, material thickness, and fuel enrichment. The results demonstrated that, for all packing fractions, the uncertainty induced by the random dispersion of dispersed fuel particles was below 0.03%. For every packing fraction, the differences between the results obtained by the regular and the random distribution models increased, and then decreased, until reaching its maximum (1.297%) at 15%. Keff decreased as the radius of the UO2 kernel increased; Keff increased as the thickness of the cladding layer increased; the uncertainty of Keff was 1.003% when a random distribution of particles, material thickness, and fuel enrichment were taken into consideration; the uncertainty of the power distribution of reactor core assemblies was maximized (1.495%) at the edge of the reactor core. Quantitative analysis of uncertainty provides references for the optimization of design and safety margin analysis for reactors.
Environmental Design on Site-Specific Energy Solidary Communities around Public High Schools in the Metropolitan Area of Naples (Italy)
Authors: Bosco Roberto , Giacobbe Savino , Losco Salvatore , Mozingo Louise Anna , Valente Renata
Published: May 2024

Renewable energy communities (RECs) around photovoltaic systems on public buildings are optimal solutions to counter energy poverty, ensuring all stakeholders access to cheap, reliable, sustainable, and modern energy systems. As the neighborhood is the minimum suitable unit for the implementation of highly sustainable settlements, this article discusses the potential and criticality of RECs at this scale in southern Italy. Starting with the concept of RECs, this study presents a methodology to size sustainable urban communities around school buildings. It integrates practical energy indicators with those defining performance in vegetation and water management. The impact of these factors is analyzed to identify the ideal community size in terms of energy efficiency, economic value, and social cohesion. An interactive scorecard ranks high school sites suitable for transformation into community hubs, taking into consideration the scale of substation distribution. The findings provide empirically validated operational guidelines and best practices to support the transition to smart, efficient, and socially inclusive communities. At the urban scale, the analysis evaluates different urban morphologies, microclimates, characteristics and density of buildings, and population around each assumed community hub. The study provides valuable guidance to local designers, planners, and administrators for the implementation of sustainable technologies by preparing a map of potential RECs.
Active Fault-Locating Scheme for Hybrid Distribution Line Based on Mutation of Aerial-Mode Injected Pulse
Authors: Jiang Zhuang , Zeng Xiangjun , Liu Feng , Yu Kun , Bi Lanxi , Wang Youpeng
Published: May 2024

Due to the overlap of initial traveling wave signals, the traveling wave propagation process in hybrid distribution lines is complicated to analyze. The most significant challenge posed by the traditional passive traveling wave-locating method for hybrid distribution lines lies in identifying the fault section and distinguishing the reflected wave from the fault point or the hybrid connection points. Based on this approach, with the application of the aerial-mode component of the pulse signal generated at the fault point, a fault-section-identification and fault-locating scheme for hybrid distribution feeders with active pulse injection is proposed. When power in a line is cut after a single-to-line ground (SLG) fault occurs, the same pulse is injected into the three phases from the neutral point of the coupling capacitor bank to construct the zero-mode component, which propagates to the SLG fault three-phase asymmetrical point, producing an aerial-mode component that is reflected back to the first end of the line. With the application of the arrival time of an aerial-mode wavefront, it is simple to locate the SLG fault for arbitrary forms of hybrid lines. The simulation results confirm the feasibility of the fault-locating scheme under different feeders, different fault locations, and fault resistances. The results of the experiments confirm the high practical value of the proposed method.
RoUIE: A Method for Constructing Knowledge Graph of Power Equipment Based on Improved Universal Information Extraction
Authors: Ye Zhenhao , Qi Donglian , Liu Hanlin , Yan Yunfeng , Chen Qihao , Liu Xiayu
Published: May 2024

The current state evaluation of power equipment often focuses solely on changes in electrical quantities while neglecting basic equipment information as well as textual information such as system alerts, operation records, and defect records. Constructing a device-centric knowledge graph by extracting information from multiple sources related to power equipment is a valuable approach to enhance the intelligence level of asset management. Through the collection of pertinent authentic datasets, we have established a dataset for the state evaluation of power equipment, encompassing 35 types of relationships. To better suit the characteristics of concentrated relationship representations and varying lengths in textual descriptions, we propose a generative model called RoUIE, which is a method for constructing a knowledge graph of power equipment based on improved Universal Information Extraction (UIE). This model first utilizes a pre-trained language model based on rotational position encoding as the text encoder in the fine-tuning stage. Subsequently, we innovatively leverage the Distribution Focal Loss (DFL) to replace Binary Cross-Entropy Loss (BCE) as the loss function, further enhancing the model’s extraction performance. The experimental results demonstrate that compared to the UIE model and mainstream joint extraction benchmark models, RoUIE exhibits superior performance on the dataset we constructed. On a general Chinese dataset, the proposed model also outperforms baseline models, showcasing the model’s universal applicability.
Which Provinces Will Be the Beneficiaries of Forestry Carbon Sink Trade? A Study on the Carbon Intensity–Carbon Sink Assessment Model in China
Authors: Liu Changxi , Xia Enjun , Huang Jieping
Published: May 2024

Carbon emissions pose a significant challenge to sustainable development, particularly for China, which is the world’s largest emerging economy and is under pressure to achieve carbon neutrality and reduce emissions amid escalating human activities. The variation in economic development levels and carbon sequestration capacities among its provinces poses a significant hurdle. However, previous research has not adequately examined this dual discrepancy from the perspective of spatial heterogeneity, resulting in a lack of differentiated management of forest carbon sinks across diverse regions. Therefore, to mitigate this discrepancy, this study presents an assessment methodology that analyzes over 100 types of natural and plantation forests using forest age and biomass expansion factors. This study presents a model that can significantly support the efforts of both China and the whole world to achieve carbon neutrality through the improved management of forest carbon sinks. This approach facilitates the assessment of carbon offsets required to meet reduction targets, the development of a provincial framework for carbon intensity and sequestration, and the exploration of their potential for trading markets. Analysis is conducted using MATLAB. Key achievements of this study include the following: (1) The collection of a comprehensive carbon stock dataset for 50 natural and 57 plantation forest types in 31 provinces from 2009 to 2018, highlighting the significant role of new forests in carbon sequestration. (2) The development of a provincial carbon status scoring system that categorizes provinces as carbon-negative, carbon-balancing, or carbon-positive based on local forest sink data and carbon credit demand. (3) The formulation of the carbon intensity–carbon sink assessment (CISA) model, which suggests that provinces with middle- to upper-middle-level economies may have a prolonged need for carbon sink credits during their peak carbon phase. Furthermore, the results show that carbon trading may benefit Guangxi and Yunnan, but may also bring opportunities and risks to Hunan and Hubei. To address regional imbalances, this study advocates tailored policies: carbon-negative and carbon-balancing provinces should enhance carbon sink management, while carbon-positive provinces must focus on energy structure transformation to achieve sustainable development goals.
Stepwise Multidimensional Climate Envelop Modeling of Pitch Pine (Pinus rigida)
Authors: Rumyantseva Olga , Strigul Nikolay
Published: May 2024

Understanding the intricate relationships between climate and vegetation remains a fundamental challenge in contemporary ecology. The ability to anticipate the specific climatic factors affecting different tree species and understand how they respond is crucial for mitigating the impacts of climate change on forested ecosystems. Additionally, quantitatively assessing habitat loss resulting from anthropogenic activities is essential for informed conservation efforts. Our objective is to evaluate the potential distribution of pitch pine (Pinus rigida) in North America and assess the associated habitat loss. To achieve this, we employ a stepwise multidimensional climate envelope modeling approach, comparing two data-intensive models—the Variable Interaction Model (VIM) and the Variable Non-Interaction Model (VNM). These models discern the influence of diverse combinations of climatic characteristics on the distribution of the species. Both VNM and VIM employ Shapley values for factor ranking during construction. VNM assumes independent effects, resulting in a hyperrectangle-shaped climate envelope, while VIM considers interactions, yielding a complex, data-driven multidimensional envelope. Data integration involves mining the US Forest Inventories and climatic data, encompassing 19 parameters. The results unequivocally highlight the superior predictive accuracy of VIM compared to the Variable Non-Interaction Model, VNM. The modeling approach developed in this study has the potential to enhance species distribution models for various tree species in the context of evolving climatic conditions.
Encouraging Invasive Acacia Control Strategies by Repurposing Their Wood Biomass Waste for Pulp and Paper Production
Authors: Neiva Duarte M. , Godinho Maria C. , Simões Rogério M. S. , Gominho Jorge
Published: May 2024

Concerns on the expanding infestation of several Acacia species in the southern Mediterranean European countries have triggered an ever-growing requirement for costly targeted control actions. Valorizing biomass waste produced could help promote and better finance these actions. For that purpose, wood wastes from invasive control actions were tested regarding their pulp and paper potential, aiming to entice cellulose industries to partake in future conservation actions. Wood waste from the five most pervasive Acacia species was studied (Acacia dealbata Link, Acacia longifolia Willd, Acacia mearnsii De Wild, Acacia melanoxylon R.Br, and Acacia saligna Labill) regarding physical and chemical characteristics, and a central composite design was used to optimize alkali charge and reaction temperature on pulping yield and delignification. Bleached kraft pulps were produced with each species’ optimized conditions and for an equitable mixture of all species. Optimized pulp yields (52.6%–53.5%) and pulp polymerization degree (2867–3690) of Acacia species were higher than those of Eucalyptus globulus Labill (used as reference). Optimized bleached pulps were refined and fiber, pulp, and handsheet properties determined. Acacia dealbata and A. longifolia presented high specific wood consumption and lower handsheet strength properties, pointing to overall lower pulping potential, while A. melanoxylon and A. mearnsii characteristics were equal to or higher than those of E. globulus. A. saligna pulp and handsheet characteristics appear more suited for tissue paper. The Acacia mixture achieved acceptable characteristics, enabling the indiscriminate use of Acacia wood regardless of the species. As a shortcoming, the Acacia pulps showed the worst optical characteristics, with brightness dropping substantially with beating (64%–76%) when compared to E. globulus (81%).
Development and Characterization of Fifteen Polymorphic Microsatellite Loci for Rare and Endangered Species within Luciobarbus Heckel, 1843 Genus in the Aral Basin and Their Conservation Application
Authors: Adyrbekova Kamila , Perea Silvia , Doadrio Ignacio
Published: May 2024

Biodiversity conservation entails not only the preservation of specific taxa but also genetic diversity. Despite the crucial role of molecular data in freshwater fish conservation management, there is a scarcity of information regarding the genetic diversity of Luciobarbus Heckel, 1843 (Actinopterygii, Cyprinidae) populations in the Aral system. Therefore, the primary aim of this study was to provide genetic information on two native species of the Luciobarbus genus found in the Aral system: L. conocephalus (Kessler, 1872) and L. brachycephalus (Kessler, 1872). These species, like many others in the Aral system, confront the imminent threat of extinction due to system alterations. However, genetic studies on these species at the nuclear level are challenging because Luciobarbus is an allotetraploid genus. Consequently, genetic investigations thus far have focused mainly on sequencing mitochondrial genes due to their haploid nature. This study has successfully developed fifteen new polymorphic microsatellite loci, which can prove to be valuable for population genetics, conservation, and other pertinent research on these species.
Exploiting the Potential of Powdered Blends of Recovered Sunflower Seed Cake Phenolics and Whey—Development of Sustainable Food Additives
Authors: Michalska-Ciechanowska Anna , Brzezowska Jessica , Lech Krzysztof , Masztalerz Klaudia , Korzeniowska Malgorzata , Zambrowicz Aleksandra , Szoltysik Marek
Published: May 2024

The management of side streams from the food industry, especially oil and dairy by-products, has become an important issue linked to the European Commission’s recommendations for a circular economy. This study aimed to obtain sustainable food additives in the form of soluble-type powders composed of whey and recovered phenolics originating from sunflower seed cake. In order to valorise these di-blend products, the powders were characterised in terms of their physical, chemical, and sensory attributes. Based on the study findings, the addition of sunflower seed cake washouts (SSCWs) to whey (Wh) decreased the dry matter in the feed that affected the viscosity and drying yield. The addition of SSCWs did not have a significant effect on the physical properties of powders, except for colour. By-product management proposed in the study resulted in the production of nutritious and ready-to-use products in powder form with improved functional properties in terms of phenolic compounds and antioxidant capacity. The powders were sensorially appealing with a tangy sourness entwined with a delicate interplay of sweet and salty flavours, which can be easily incorporated into different types of foodstuffs.
Identification of Pheophytin a and Hydroxy Pheophytin a from Rang Chuet (Thunbergia laurifolia Linn.) as Potent NQO-1 Inducers in Liver Cells
Authors: Moeurng Sreylak , Posridee Kakanang , Kamkaew Anyanee , Thaiudom Siwatt , Oonsivilai Anant , Oonsivilai Ratchadaporn
Published: May 2024

Thunbergia laurifolia Linn. (Rang Chuet, RC), a Thai medicinal plant, possesses various bioactive compounds with potential health benefits. This study aimed to identify detoxifying compounds within RC crude extract. RC leaves were extracted using the Soxhlet method with chloroform. Total carotenoids, chlorophylls, extract yield, total phenolic contents (TPCs), and total flavonoid contents (TFCs) were measured. The extract’s composition was analyzed. Cytotoxicity and effects on the detoxification enzyme NQO-1 were assessed in liver cell lines (AML12 and HepG2) using MTT and NQO-1 assays, respectively. Bioactive fractions were identified using fractionation techniques and mass spectrometry (LC-MS). RC extract displayed significant levels of carotenoids (0.375 mg/g), chlorophylls (2.682 mg/g), and favorable yield (15.3%). TPC and TFC were 363.776 mg/g and 112.22 mg/g of extract, respectively. Analysis revealed phenolic acids (gallic acid, caffeic acid), flavonoid (apigenin), chlorophylls (chlorophylls a, b, pheophytin a and b), and lutein. Among the fractions, Fraction 3 (F3) exhibited the highest NQO-1 enzyme activity. F3 contained pheophytin a and hydroxy pheophytin a, confirmed by LC-MS (m/z 871.59+ [M + H]+ and 887.59+ [M + H]+). F3 significantly induced NQO-1 activity in both HepG2 (3.908-fold) and AML12 (1.99-fold) cells. This study identified F3 from RC extract as a promising fraction containing pheophytin a and hydroxy pheophytin a, responsible for inducing the detoxification enzyme NQO-1 in liver cells. These findings suggest RC’s potential for promoting detoxification.