bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Microalgae to Convert Brewery Carbon Gas Emissions into Valuable Bioproducts
Authors: Silkina Alla , Emran Mohamed A. , Turner Simon , Tang Kam W.
Published: December 2024

The brewing industry is a major part of the agri-food sector, but its fermentation processes contribute significantly to global CO2 emissions, exacerbating the greenhouse gas crisis. Achieving net-zero emissions requires innovative solutions, and this study explored one such solution by using microalgae to capture CO2 from a brewery while simultaneously generating valuable bioproducts. Two microalgae species, Tetradesmus obliquus and Limnospira maxima, were cultivated in a 1000 L raceway and a 400 L tubular photobioreactor, both powered by the brewery’s CO2 waste gas. The specific growth rates reached 0.3 in the raceway and 0.4–0.5 in the photobioreactor for both species. Notably, L. maxima showed higher productivity, achieving up to 0.80 g L−1 day−1 in the photobioreactor and 0.5 g L−1 day−1 in the raceway. Operating across 300 brewing days per year, a single module (1400 L) of this system could reduce a brewery’s CO2 emissions by 29%. These low-maintenance systems are modular, allowing for easy scaling and operation. The harvested biomass was nutritionally valuable; L. maxima contained up to 55% protein and 3% phycocyanin, while T. obliquus was rich in carbohydrates (36%) and lipids (12%), levels suitable for feeds and fertilizers. A cost-benefit analysis suggests that coupling CO2 removal with bioproduct generation supports a sustainable circular economy while offering financial returns.
Polynitrobenzene Derivatives, Containing -CF3, -OCF3, and -O(CF2)nO- Functional Groups, as Candidates for Perspective Fluorinated High-Energy Materials: Theoretical Study
Authors: Tamuliene Jelena , Sarlauskas Jonas
Published: December 2024

We performed a theoretical investigation of the fluorinated compounds’ morphology and stability. The research was conducted using the widely adopted DFT approach, specifically the B3LYP method and the cc-pVTZ basis set, aiming to design high-energy materials that exhibit low sensitivity, toxicity, instability, and reduced proneness to decomposition or degradation over a short period. In the paper, we presented the investigation results for the compounds whose total energy is the lowest. Their thermal and chemical stability was evaluated based on stability indicators such as cohesion, chemical hardness, and softness. The oxygen–fluorine balance is assessed to determine the sensitivity of these advanced materials. The density, detonation pressure, and velocity of the selected conformers were theoretically obtained to reveal the influence of -CF3, -OCF3, and cyclic -O(CF2)nO- fragments on the energetic properties of nitroaromatics as well as their stability and resistance to shock stimuli. The results enable the prediction of advanced energetic materials that achieve a favorable balance between power and stability. Based on the results achieved, we put forward CF3N2, OCF3N2, C2F6N2, 1CF2N2/O2CF2N2, and 2CF4N2/O2C2F4N2 for practical usage because these compounds possess greater stability compared to tetryl and better explosive properties than TNT.
The Impacts of Reservoir Heterogeneities on the CO2-Enhanced Oil Recovery Process—A Case Study of Daqingzijing Block in Jilin Oilfield, China
Authors: Li Zetang , Xu Tianfu , Tian Hailong , Pan Ruosheng
Published: December 2024

With the exploitation of oilfields, the oil production efficiency of traditional water flooding has been very low, and CO2-enhanced oil recovery (EOR) has become an inevitable trend of development. CO2-EOR is affected by many factors, among which the heterogeneity of reservoirs is one of the main influencing factors. In order to understand the impact of different reservoir conditions on the production of oil from CO2 and the reasons behind it, and on the basis of researching the heterogeneity of reservoir porosity and permeability and its influence on the CO2-enhanced oil recovery process, this study has altogether established three different reservoir characteristics for comparative analysis. Under the homogeneous and heterogeneous porosity and permeability conditions of a reservoir, the displacement characteristics during a CO2–oil displacement process were analyzed. The layered heterogenous model had the best oil displacement effect, with its oil displacement amount reaching 8.46 × 104 kg, while the homogeneous model and the spatially heterogenous model had lower values; they were 1.51 × 104 and 1.42 × 104, respectively. The results indicate that the heterogeneous conditions overall improved the flooding effect of CO2. Under the same injection volume and other reservoir conditions, the cumulative oil flooding effect of the layered heterogenous model was the best compared to the homogeneous and spatially heterogeneous models. Good permeability promotes the accumulation of oil, leading to a higher saturation of the oleic phase. This work provides an in-depth analysis of the effect of the non-uniform distribution of formation permeability on CO2-enhanced oil recovery and can help to improve carbon sequestration efficiency and oil recovery in CO2–oil recovery projects.
New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting
Authors: Manandhar Prajowal , Rafiq Hasan , Rodriguez-Ubinas Edwin , Palpanas Themis
Published: December 2024

Data mining is vital for smart grids because it enhances overall grid efficiency, enabling the analysis of large volumes of data, the optimization of energy distribution, the identification of patterns, and demand forecasting. Several performance metrics, such as the MAPE and RMSE, have been created to assess these forecasts. This paper presents new performance metrics called Evaluation Metrics for Performance Quantification (EMPQ), designed to evaluate forecasting models in a more comprehensive and detailed manner. These metrics fill the gap left by established metrics by assessing the likelihood of over- and under-forecasting. The proposed metrics quantify forecast bias through maximum and minimum deviation percentages, assessing the proximity of predicted values to actual consumption and differentiating between over- and under-forecasts. The effectiveness of these metrics is demonstrated through a comparative analysis of short-term load forecasting for residential customers in Dubai. This study was based on high-resolution smart meter data, weather data, and voluntary survey data of household characteristics, which permitted the subdivision of the customers into several groups. The new metrics were demonstrated on the Prophet, Random Forest (RF), and Long Short-term Memory (LSTM) models. EMPQ help to determine that the LSTM model exhibited a superior performance with a maximum deviation of approximately 10% for day-ahead and 20% for week-ahead forecasts in the “AC-included” category, outperforming the Prophet model, which had deviation rates of approximately 44% and 42%, respectively. EMPQ also help to determine that the RF excelled over LSTM for the ‘bedroom-number’ subcategory. The findings highlight the value of the proposed metrics in assessing model performance across diverse subcategories. This study demonstrates the value of tailored forecasting models for accurate load prediction and underscores the importance of enhanced performance metrics in informing model selection and supporting energy management strategies.
A Novel Synchronization Strategy for Distributed Energy Resources in Weak Grids Using Remote Strong Grid Sensing
Authors: Zhang Runfan , Chand Shyamal S. , Hredzak Branislav , Bie Zhaohong
Published: December 2024

This paper proposes a novel strategy for the current injection-based control of distributed energy resources connected to weak grids via a voltage source converter. The current injection controller is no longer synchronized with the point of common coupling but with the strong grid point voltage. The strong grid synchronization control strategy improves the output dynamics of the voltage source converter and recovery after faults in weak grids. The phase difference between the voltage source converter and the strong grid voltages caused by the long power lines does not affect the power control. Furthermore, a time delay-compensation method is proposed which tolerates the communication time delay introduced by the transmission of the synchronization signal from the strong grid point. The performance of the proposed control strategy is verified in detail using MATLAB 2023b simulations and real-time digital simulations on a medium voltage model and also validated in an experiment on a low-voltage grid-feeding inverter setup.
State-of-the-Art and Advancement of Charging Infrastructure in Electric Mobility: An Integrated Review
Authors: Waseem Mohammad , Sreeshobha Eniganti , Shashidhar Reddy Kotha , Donateo Teresa
Published: December 2024

Electric mobility is attracting significant attention in the current era due to its environmental benefits, sustainable transportation options, and the absence of carbon emissions. However, challenges such as the high price of batteries, inefficient charging techniques, and compatibility linking the charging station with electric vehicles (EVs) must be addressed. This article reviews advancements and identifies challenges in charging infrastructure for electric mobility. This study incorporates and analyzes an integrated review of approximately 223 research articles. Current research trends and states of charging infrastructure are prepared as per the Web of Science (WoS) database from 2013 to 2023. In light of recent extensions in wireless power transfer technology, including capacitive, inductive, and magnetic gear topology, are presented to advance the charging infrastructure. Different charging tactics based on power source, such as level-1 AC, level-2 AC, level-3 DC fast, and level-3 DC ultra-rapid charging, related to charging infrastructure are addressed. The vehicle-to-grid (V2G) integration methodology is addressed to construct a smart city by presenting the transfer of power and related data through linkage and moving systems. The exploration of artificial intelligence, global connectivity of electric vehicles (EVs), sun-to-vehicle (S2V), and vehicle-to-everything (V2X) techniques with EVs is conducted to enhance and progress the charging infrastructure. Key barriers associated with charging infrastructure are identified.
Control of Three-Phase Two-Level Inverters: A Stochastic LPV Model Approach
Authors: Luo Wensheng , Zhang Ruifang , Zhang Jianwen , Wu Ligang , Vazquez Sergio , Franquelo Leopoldo G.
Published: December 2024

This paper proposes a stochastic linear parameter-varying (LPV) model approach to design a state feedback controller for three-phase, two-level inverters. To deal with the parameter changes, stochastic noise, and delays faced by the inverter, it is modeled as a stochastic LPV system with time delay. Stability analysis and control synthesis are conducted for the LPV system. With parameter-dependent Lyapunov functionals, a condition of sufficient stability for asymptotical mean-square stability is obtained. In addition, the slack matrix technique is employed to improve the feasibility and reduce the conservatism of the conditions. The obtained theoretical results are applied to the three-phase, two-level inverter, whose currents are treated as state variables and are controlled to reach the equilibrium point. The simulation results validate the effectiveness of the proposed theories and demonstrate the advantages of using the slack matrix.
Image Processing Technique for Enhanced Combustion Efficiency of Wood Pellets
Authors: Gasperini Thomas , Pizzi Andrea , Olivi Lucia , Toscano Giuseppe , Ilari Alessio , Duca Daniele
Published: December 2024

The combustion efficiency of wood pellets is partly affected by their average length. The ISO 17829 standard defines the methodology for assessing the average length of sample pellets, but the method does not always lead to representative data. Furthermore, a standard analysis is time-consuming as it requires manual measurement of the pellets using a caliper. This paper, whilst evaluating the effect of pellet length on combustion efficiency, proposes a pending-patented dimensional image processing method (DIP) for assessing pellet length. DIP allows the dimensional data of grouped and stacked pellets to be obtained by exploiting the shadows produced by pellets when exposed to a light source, assuming that different-sized pellets produce different shadows. Thus, the proposed method allows for the extraction of dimensional information from non-distinct objects, overcoming the reliance of classical image processing methods on object distance for effective segmentation. Combustion tests, carried out using pellets varying only in length, confirmed the influence of length on combustion efficiency. Shorter pellets, compared to longer ones, significantly reduced CO emissions by up to 94% (mg/MJ). However, they exhibited a higher fuel mass consumption rate (kg/h), with an increase of up to 22.8% compared to the longest sample. In addition, longer pellets produced fewer but larger shadows than shorter ones. Further studies are needed to correlate the number and size of shadows with samples’ average length so that DIP could be implemented in stoves and programmed to communicate with the control unit and automatically optimize the setting in order to improve combustion efficiency.
One-Part Alkali-Activated Binder Produced from Tungsten-Molybdenum (W-Mo) Tailings
Authors: Lazorenko Georgy , Wang Yanshuai , Fedotov Alexandr , Kasprzhitskii Anton
Published: December 2024

This study explores the feasibility of preparing a one-part alkali-activated binder produced from tungsten-molybdenum (W-Mo) tailings with sodium metasilicate (SM). A series of alkali-activated mortar samples were prepared, and the effects of the water/binder (W/B) ratio and mixture proportion on mechanical properties were investigated. Additionally, the microstructure and composition of the alkali-activated W-Mo tailings were characterized by using a combination of scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy techniques. Optimal results were achieved with a W/B ratio of 0.35 and a formulation containing 20% by weight of SM. Under these conditions, the cured samples exhibited an unconfined compressive strength of 11.2 MPa and a bulk density of 1726 kg/m3 after 28 days. The findings show the potential to advance tungsten-molybdenum mine waste upcycling and contribute to the production of environmentally sustainable building materials.
Advances in Concrete Demolition Technologies: A Review of Conventional and Emerging Methods for Sustainable Waste Management
Authors: Mohammadi Mohsen , Mohammad Saman Mostafa , Roshanbin Mehdi , Lomboy Gilson R. , Abubakri Shahriar
Published: December 2024

The demolishing of concrete structures such as bridges, tunnels, buildings, and pavements has become a common activity due to reasons such as renovation, rehabilitation, retrofitting, or simply ending the service life of these structures. This upsurge has brought major challenges in managing construction demolition waste (CDW). Traditional demolition techniques are often characterized by high environmental impacts, inefficiency in waste management, and safety concerns. This paper critically reviews traditional and emerging concrete structure demolition technologies in terms of efficiency, safety, environmental impact, waste minimization, and material recyclability. A detailed review of manual demolition, mechanical demolition, implosion, and relatively new techniques such as static blasting, diamond wire sawing, soundless chemical demolition agents, hydro demolition, electrical discharge technology, demolition robots, and microwave heating is conducted. The key findings of this paper are that various alternative technologies have significant advantages over their traditional counterparts by offering minimum environmental pollution, improvements in on-site safety, and a possibility for materials to be reused and recycled. For instance, hydro demolition and diamond wire sawing are very efficient and accurate, meaning that actual waste management is highly improved. This paper underlines that the choice of demolition methods adapted to project needs is crucial for the development of sustainable CDW management. Such findings are useful to practitioners and policymakers who have to make fully informed decisions to promote environmental sustainability and resource conservation goals.
On the Game-Based Approach to Optimal Design
Authors: Kobelev Vladimir
Published: December 2024

A game problem of structural design is defined as a problem of playing against external circumstances. There are two classes of players, namely the “ordinal” and “cardinal” players. The ordinal players, designated as the “operator” and “nature”, endeavor to, respectively, minimize or maximize the payoff function, operating within the constraints of limited resources. The fundamental premise of this study is that the action of player “nature” is a priori unknown. Statistical decision theory addresses decision-making scenarios where these probabilities, whether or not they are known, must be considered. The solution to the substratum game is expressed as a value of the game “against nature”. The structural optimization extension of the game considers the value of the game “against nature” as the function of certain parameters. Thus, the value of the game is contingent upon the design parameters. The cardinal players, “designers”, choose the design parameters. There are two formulations of optimization. For the single cardinal player, the pursuit of the maximum and minimum values of the game reduces the problem of optimal design. In the second formulation, there are multiple cardinal players with conflicting objectives. Accordingly, the superstratum game emerges, which addresses the interests of the superstratum players. Finally, the optimal design problems for games with closed forms are presented. The game formulations could be applied for optimal design with uncertain loading, considering “nature” as the source of uncertainty.
Identification of Superoxide Dismutase (SOD) Gene Family in Ginkgo (Ginkgo biloba L.) and Role of GbSOD8 in Response to Salt Stress
Authors: Song Yuxuan , Xie Xinyao , Wang Yuehan , Gao Weijie , Huang Hao , Cao Fuliang , Yang Xiaoming
Published: December 2024

Superoxide dismutase (SOD), a critical enzyme within the plant antioxidant defense system, serves as the primary shield against reactive oxygen species (ROS) under adverse environmental conditions. However, the characterization of GbSODs in ginkgo remains incomplete. In our study, a total of eight GbSODs were identified, which were unevenly distributed across 10 chromosomes and predominantly localized on the plasma membrane. Our phylogenetic analysis revealed that GbSODs from ginkgo and other species form three distinct groups, each characterized by specific binding domains and supported by relatively high bootstrap values. Our promoter analysis indicated that all GbSODs contained multiple cis-elements related to various abiotic stress responses. Our systemic analysis revealed that only one pair of GbSODs underwent segmental duplications. The transcriptome analysis showed the variable expression of GbSODs across different tissues, which were significantly influenced by drought, ultraviolet (UV), and salt stress, with GbSOD8 showing a particularly notable induction. Transgenic plants that overexpressed GbSOD8 exhibited enhanced antioxidant enzyme activities, elevated proline levels, and reduced malondialdehyde (MDA) content, collectively contributing to their improved salt tolerance compared to wild-type plants. Our findings expand the comprehensive understanding of the GbSOD gene family and provide a solid foundation for further elucidating the molecular mechanisms of GbSODs under salt stress.
TR-SNP v1.0: A Desktop Tool to Link Tree Ring Width with Annual Aboveground Biomass Increment
Authors: Chen Yizhao , Lin Zhongyi , Shi Zhixin , Li Yang
Published: December 2024

The past couple of decades have witnessed an increasing application of tree ring observations to assess forest carbon (C) balance and its historical dynamics. To address the growing need for understanding long-term forest C sequestration dynamics through tree rings, we developed a new desktop tool (TR-SNP v1.0) that estimates the annual aboveground biomass increment (AABI) of trees from tree ring width (TRW). Users can easily process and convert TRW into AABI using either the built-in dataset or by uploading local TRW data. TR-SNP offers methods for correcting potential bias from unmeasured initial core width, converting TRW to diameter at breast height (DBH), and estimating AABI using species-specific allometric relationships. We provide examples from specific sites to demonstrate how TR-SNP functions and its potential for identifying bias sources of AABI estimation. We anticipate that TR-SNP will streamline the analysis of tree ring data and advance our understanding of forest biomass increment dynamics.
Elevational Gradients of Soil Nematode Communities in Subtropical Forest Ecosystems
Authors: Ding Kexin , Qiang Zhenyu , Hu Zhengkun , Cheng Saisai , Sun Ruibo , Fang Heng , Zhang Zhen , Ma Chao
Published: December 2024

Soil biodiversity plays a critical role in supporting multiple ecosystem functions. As some of the most diverse and abundant metazoans on the Earth, soil nematode communities exhibit changes along environmental gradients, but the ways in which the abundance and diversity of nematode communities vary along elevational gradients remain poorly understood. Taking advantage of an investigation on Huangshan Mountain, Southeast China, with elevation ranging from 500 to 1200 m, we assessed the abundance and diversity of soil nematodes, as well as the soil physicochemical properties, across subtropical forest ecosystems. Nematode communities were analyzed at the genus level, and the α-diversity was calculated as the genus richness, while the β-diversity was based on the Bray–Curtis dissimilarity. The results showed that, among the top 20 nematode genera ranked by absolute abundance, most genera, such as Eucephalobus, Prismatolaimus, Filenchus, and Rotylenchulus, reached their peak abundance at the highest elevation (1000 m). Additionally, the abundances of Oriverutus, Tylenchus, Criconema, and Tripyla exhibited a positive correlation with the elevation. Moreover, the abundance and α-diversity of the total nematodes and each trophic group of nematodes increased linearly with the elevation, likely due to increased soil moisture at higher elevation. In contrast, the β-diversity of the total nematodes, bacterivores, and herbivores decreased with increasing elevation, indicating the importance of stochastic processes in shaping community assembly at high altitudes. This pattern suggests that as the elevation increases, the nematode communities become more homogeneous in structure. Taken together, our study’s findings demonstrate the divergent responses of nematodes’ α- and β-diversity to an elevation gradient, highlighting the importance of the soil nematode diversity in maintaining ecosystem functions such as nutrient cycling and food web stability in mountainous regions. These results emphasize the need to incorporate the below-ground biodiversity into conservation strategies, particularly in the face of environmental changes driven by climate and human activities.
Estimation of Forest Phenology’s Relationship with Age-Class Structure in Northeast China’s Temperate Deciduous Forests
Authors: Zuo Xiaoqing , Xu Kaijian , Yu Wanwan , Zhao Ping , Liu Huaipeng , Jiang Hailan , Ding Anxin , Li Yi
Published: December 2024

Phenological changes in forests directly influence the spatiotemporal dynamics of carbon fixation and the carbon and water cycles in terrestrial ecosystems. Previous studies have shown that variations in biological factors (e.g., canopy height, leaf area, water use efficiency) can increase uncertainty in forest phenology, and these variations are closely linked to tree species and forest age-class structure. However, the interaction mechanisms through which tree species and forest age-class structure influence phenological changes remain insufficiently explored. In this study, phenological changes and their interactions and response mechanisms to different dominant tree species and forest age-class structures were analyzed via Sentinel-2 normalized difference vegetation index (NDVI) time series data from 2020 and 2021 across 480 typical deciduous forest plots in northeastern China. The results were as follows: (1) There were significant differences (p < 0.05) in the intra-annual phenological responses of temperate deciduous landscapes to the interaction between tree species and forest age-class structure. (2) The indirect effect of forest age-class structure through tree species on phenology exceeded the indirect effect of tree species through forest age-class structure, with a difference of 30.77%–35.09%. (3) When the dominant tree species and forest age-class structure were not distinguished, phenological differences in temperate forests ranged from 3 to 41 days and 2 to 23 days, respectively. This study highlights the differential impacts of key biological factors and their interactions on regional forest phenology, offering valuable insights into how these factors influence forest landscapes and providing a theoretical basis for improving forest management strategies.
Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
Authors: Alshdadi Abdulrahman A. , Almazroi Abdulwahab Ali , Ayub Nasir , Lytras Miltiadis D. , Alsolami Eesa , Alsubaei Faisal S.
Published: December 2024

The increasing threat of Distributed DDoS attacks necessitates robust, big data-driven methods to detect and mitigate complex Network and Transport Layer (NTL) attacks. This paper proposes EffiGRU-GhostNet, a deep-learning ensemble model for high-accuracy DDoS detection with minimal resource consumption. EffiGRU-GhostNet integrates Gated Recurrent Units (GRU) with the GhostNet architecture, optimized through Principal Component Analysis with Locality Preserving Projections (PCA-LLP) to handle large-scale data effectively. Our ensemble was tested on IoT-23, APA-DDoS, and additional datasets created from popular DDoS attack tools. Simulations demonstrate a recognition rate of 98.99% on IoT-23 with a 0.11% false positive rate and 99.05% accuracy with a 0.01% error on APA-DDoS, outperforming SVM, ANN-GWO, GRU-RNN, CNN, LSTM, and DBN baselines. Statistical validation through Wilcoxon and Spearman’s tests further verifies EffiGRU-GhostNet’s effectiveness across datasets, with a Wilcoxon F-statistic of 7.632 (p = 0.022) and a Spearman correlation of 0.822 (p = 0.005). This study demonstrates that EffiGRU-GhostNet is a reliable, scalable solution for dynamic DDoS detection, advancing the field of big data-driven cybersecurity.
Assessment and Validation of a Computer Vision Algorithm for Wildfire Rate of Spread Estimation
Authors: Ameri Ehsan , Awayan Kyle , Cobian-Iñiguez Jeanette
Published: December 2024

As wildfire activity increases worldwide, developing effective methods for estimating how fast it can spread is critical. This study aimed to develop and validate a computer vision algorithm for fire spread estimation. Using visual flame data from laboratory experiments on excelsior and pine needle fuel beds, we explored fire spread predictions for two types of experiments. In the first, the experiments were conducted in an environment where the flame was maintained visually undisturbed while in the second, real-world scenarios were simulated with visual obstructions. Algorithm performance evaluation was conducted by computing the index of agreement and normalized root mean square deviation (NRMSD) error. Results show that the algorithm estimates fire spread well in pristine visual environments with varying accuracy depending on the fuel type. For instance, the index of agreement between the rate of spread values estimated by the algorithm and the measured values is 0.56 for excelsior fuel beds and 0.51 for pine needle fuel beds. For visual obstructions, varying impacts on the rate of spread predictions were observed. Adding an orange background behind the flame had the least effect on algorithm performance (IAmedian = 0.45), followed by placing a Y-shape element resembling a branch (IAmedian = 0.31) and adding an LED light near the flame (IAmedian = 0.30).
Large Eddy Simulation (LES) of Hydrogen Jet Flames and Finite Element Analysis of Thermal Barrier Coating
Authors: Davidy Alon
Published: December 2024

A jet flame occurs when the release of flammable gas or liquid ignites, resulting in a long, intense, and highly directional flame. This type of fire is commonly associated with industrial incidents involving pipelines, storage tanks, and other pressurized equipment. Jet fires are a significant concern in the oil and gas industry due to the handling and processing of large volumes of flammable hydrocarbons under pressure. The new computational method presented here includes several aspects of hydrogen jet flame accidents and their mitigation: the CFD simulation of a hydrogen jet flame using the HyRAM code and Fire Dynamics Simulator (FDS) software 5.0 using a large eddy simulation (LES) turbulence model; the calculation of the gaseous mixture’s thermo-physical properties using the GASEQ thermochemical code; the calculation of convective and radiative heat fluxes using empirical correlation; and a heat transfer simulation on the pipe thermal barrier coating (TBC) using COMSOL Multiphysics software 4.2a during the heating phase. This method developed for the ceramic blanket was validated successfully against the previous experimental results obtained by Gravit et al. It was shown that a jet fire’s maximum temperature obtained using FDS software was similar to that obtained using GASEQ thermochemical software 0.79 and HyRAM software. The TBC’s surface temperature reached 1945 °C. The stainless steel’s maximal temperature reached 165.5 °C. There was a slight decrease in UTS at this temperature.
A Chamfered Anchor Impeller Design for Enhanced Efficiency in Agitating Viscoplastic Fluids
Authors: Benmoussa Amine , Páscoa José C.
Published: December 2024

In industrial mixing processes, impeller design, rotational speed, and mixing conditions play a crucial role in determining process efficiency, product quality, and energy consumption. Optimizing the performance of stirring systems for non-Newtonian fluids is essential for achieving better results. This study examines the hydrodynamic and thermal performance of stirring systems for viscoplastic fluids, utilizing close-clearance anchor impellers with chamfered angles of 22.5°, 45°, and 67.5° in cylindrical, flat-bottom and unbaffled vessels. Through a comprehensive comparative analysis between standard and chamfered impeller designs, the study evaluates their efficacy in overcoming yield stress, enhancing flow dynamics, and improving thermal homogeneity. The effects of Reynolds number and yield stress on the hydrodynamic and thermal states are analyzed. The results indicate that the 67.5° chamfered impeller significantly improves flow distribution and minimizes dead zones, particularly in critical areas between the anchor blades and vessel walls, where mixing stagnation typically occurs. It also enhances vertical mixing by promoting a broader shear spread along the vessel height and a more uniform temperature distribution. These insights contribute to the development of more efficient agitation systems, applicable across various industries handling complex fluids.
Health Benefits of Dairy Products’ Consumption—Consumer Point of View
Authors: Jakubowska Dominika , Dąbrowska Aneta Zofia , Staniewska Katarzyna , Kiełczewska Katarzyna , Przybyłowicz Katarzyna E. , Żulewska Justyna , Łobacz Adriana
Published: December 2024

The aim of this study was to identify and analyse consumer perceptions regarding the health benefits of different dairy products in Poland. This study examines the consumption frequency of selected dairy products in Poland and the health benefits which consumers associate with their regular consumption. It also explores how demographic factors, such as age, gender, and consumption frequency, influence these perceptions and identifies which dairy products are the most linked to specific health benefits. This study involved a quantitative survey of a representative sample of 2009 Polish consumers aged 19–30 and 66–75 years. This study revealed that the vast majority of the consumers recognise the health benefits of dairy products, particularly in areas such as better bone health, enhanced immune function, and improved digestion. The benefits associated with the regular consumption of milk, natural fresh cheeses, and natural fermented milk beverages were most frequently recognised. The respondents’ age had no significant effect on their perceptions of the health benefits of the selected dairy products. A statistically significant effect of gender was found only for the perceived benefits of consuming flavoured, fermented milk beverages. The frequency of consumption had a significant effect on the respondents’ perceptions of the benefits of all the studied groups of dairy products. In general, the consumers had positive attitudes towards the dairy products and believed in their potential health benefits. These findings have important implications for policy-makers. They underline the need for targeted public health campaigns to promote the consumption of dairy products as part of a balanced diet, emphasizing their specific health benefits. Such efforts could be especially effective if tailored to demographic factors such as gender and dietary habits.
Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach
Authors: Saubhagya Shanthi , Tilakaratne Chandima , Lakraj Pemantha , Mammadov Musa
Published: December 2024

Rainfall forecasting, especially extreme rainfall forecasting, is one of crucial tasks in weather forecasting since it has direct impact on accompanying devastating events such as flash floods and fast-moving landslides. However, obtaining rainfall forecasts with high accuracy, especially for extreme rainfall occurrences, is a challenging task. This study focuses on developing a forecasting model which is capable of forecasting rainfall, including extreme rainfall values. The rainfall forecasting was achieved through sequence learning capability of the Long Short-Term Memory (LSTM) method. The identification of the optimal set of features for the LSTM model was conducted using Random Forest and Granger Causality tests. Then, that best set of features was fed into Stacked LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM models to obtain three days-ahead forecasts of rainfall with the input of the past fourteen days-values of selected features. Out of the three models, the best model was taken through post hoc residual analysis and extra validation approaches. This entire approach was illustrated utilizing rainfall and weather-related measurements obtained from the gauging station located in the city of Ratnapura, Sri Lanka. Originally, twenty-three features were collected including relative humidity, ssunshine hours, and mean sea level pressure. The performances of the three models were compared using RMSE. The Bidirectional LSTM model outperformed the other methods (RMSE < 5 mm and MAE < 3 mm) and this model has the capability to forecast extreme rainfall values with high accuracy.
Numerical Investigation of Water Transport and Effective Electrical Conductivity in Perforation of Gas Diffusion Layer Using Lattice Boltzmann Method
Authors: Cho Jae Yong , Lee Hee Min , Nasir Bashir Muhammad , Lee Joon Sang
Published: December 2024

In polymer electrolyte membrane fuel cells, the gas diffusion layer (GDL) is composed of porous media and serves a critical role as a mass transport layer, facilitating reactant gas diffusion, removal of water generated in the catalyst layer, and electron transport. Artificial spacings known as perforations can be introduced to improve water management within this mass transport system. However, the impact of these perforations on the effective electrical conductivity has not been adequately studied. This study employs numerical methods to investigate water management and effective electrical conductivity in the presence of perforations, aiming to provide indicators for optimal design. The pseudopotential lattice Boltzmann method is utilized, which is particularly advantageous for modeling two-phase flow and electron transport in complex geometries. Using this numerical approach, we analyze water penetration in GDL structures and effective electrical conductivity based on electric potential fields focusing on geometric parameters such as the perforation size. Our results demonstrate a relationship between water management efficiency and effective electrical conductivity, suggesting the existence of an optimal perforation diameter. Moreover, when there is a water-induced penetration pattern due to the perforated structure, both the effective electrical conductivity and water management are enhanced at a lower porosity of the GDL structure.
Management of Risk Factors in the Rental Car Market
Authors: Jarašūnienė Aldona , Batarlienė Nijolė , Šidlauskis Benediktas
Published: December 2024

The car rental sector is a dynamic and rapidly growing business sector, which is important for both the development of the automotive industry and consumer mobility needs. In the rental car market, risk management becomes an essential factor determining the success and long-term growth of business operations. Risk factors inherent in this sector, such as the technical condition of cars, customer behavior, economic conditions and the legal environment, require a structured and efficient management that would allow companies to make the most of the available opportunities and to minimize threats. The purpose of this article is to examine the risk management of car rental and evaluate the impact of a 5–10-year-old car rental model with reverse logistics on risk reduction and business efficiency. The article examines the methods of the risk management process, presenting the methods of risk identification, assessment and reduction, as well as the possibilities of applying reverse logistics in the car rental sector. The results of the applied expert method of quantitative research and the conducted questionnaire survey revealed the factors that are most important for a company that renews its fleet of vehicles. These are the price, operating costs, sustainability, and environmental friendliness, with sustainability being the top priority. Companies can benefit from the research results when making decisions about the renewal and optimization of their vehicle fleet. It was concluded that implementing reverse logistics in the car rental sector will increase company profits and reduce pollution.
Preliminary Study on Cooperative Route Planning Reinforcement Learning with a Focus on Avoiding Intersection Congestion
Authors: Katagiri Shintaro , Miwa Tomio , Nishijima Kosuke
Published: December 2024

Intersection control systems have been actively studied in recent years as they could potentially replace traffic signals via the utilization of the communication and automatic driving capabilities of connected and autonomous vehicles (CAVs). In these studies, conflicting travel trajectories at intersections that could cause accidents and delays were safely and efficiently avoided by controlling the vehicle’s speed. However, routing approaches for avoiding conflicts at intersections have only been discussed in a few studies. To investigate the feasibility of avoiding intersection conflicts through network-level route allocation, we propose a cooperative route allocation model using reinforcement learning than can model the relationship between the complex traffic environment and optimal route solutions. Models aimed at decreasing the total travel time and those with high delay importance owing to conflicts in travel times were trained and verified under multiple traffic conditions. The results indicate that our model effectively allocates vehicles to their optimal routes, reducing the number of intersection conflicts and decreasing the average travel time by up to approximately 40 s compared to random allocation, demonstrating the potential of reinforcement learning for cooperative route allocation in the management of multiple vehicles.
Enhancement of Water Uptake in Composite Superabsorbents Based on Carboxymethyl Cellulose Through Porogen Incorporation and Lyophilization
Authors: Lavlinskaya Maria S. , Sorokin Andrey V.
Published: December 2024

Carboxymethyl cellulose sodium salt (CMC)-based superabsorbents are promising materials for the development of agricultural matrices aimed at water management and slow-release fertilizer production. However, an increase in the CMC content tends to reduce their water-absorbing capacity. This study aims to develop a cost-effective method for producing eco-friendly superabsorbents with enhanced water-absorbing capacity by incorporating a porogen and employing lyophilization. Superabsorbents containing 10 wt% CMC (CMC-SAPs) were synthesized via free radical polymerization with the addition of 0, 5, or 10 wt% ammonium carbonate as a porogen, followed by lyophilization. The synthesized CMC-SAPs were characterized using Fourier-transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, differential scanning calorimetry, and X-ray diffraction. The results revealed that CMC-SAPs prepared with the incorporation of a porogen and/or subjected to lyophilization exhibited well-developed surfaces featuring macropores and cavities. Incorporating 5 wt% ammonium carbonate as a porogen, followed by lyophilization, increased the equilibrium swelling ratio to 61%. This improvement was attributed to the enhanced surface morphology of the modified CMC-SAPs, which facilitated water molecule diffusion into the SAP matrix, as confirmed by open porosity measurements. This hypothesis was further supported by the diffusion coefficient values, which were higher for porogen-containing and lyophilized SAPs compared to unmodified samples. Moreover, the CMC-SAPs demonstrated good reusability. Thus, the combination of porogen incorporation and subsequent lyophilization represents a promising approach for enhancing the water uptake capacity of CMC-based composite superabsorbents for sustainable agricultural applications.
Physiological and Transcriptomic Dynamics in Mulberry: Insights into Species-Specific Responses to Midday Depression
Authors: Li Yong , Huang Jin , Song Fangyuan , Guo Zhiyue , Deng Wen
Published: December 2024

Background/Objective: The midday depression of photosynthesis, a physiological phenomenon driven by environmental stress, impacts plant productivity. This study aims to elucidate the molecular and physiological responses underlying midday depression in two mulberry species, Ewu No. 1 (Ew1) and Husan No. 32 (H32), to better understand their species-specific stress adaptation mechanisms. Methods: RNA-seq analysis was conducted on leaf samples collected at three time points (10:00 a.m., 12:00 p.m., and 4:00 p.m.), identifying 22,630 differentially expressed genes (DEGs). A comparative Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed to reveal the involvement of key metabolic and signaling pathways in stress responses. Results: Ew1 displayed enhanced stress tolerance by upregulating genes involved in energy management, water conservation, and photosynthetic processes, maintaining higher photosynthetic rates under midday stress. In contrast, H32 adopted a more conservative response, downregulating genes related to photosynthesis and metabolism, favoring survival at the expense of productivity. The KEGG analysis highlighted starch and sucrose metabolism and plant hormone signaling as critical pathways contributing to these species-specific responses. Conclusions: Ew1’s adaptive molecular strategies make it more suitable for environments with variable light and temperature conditions, while H32’s conservative approach may limit its productivity. These findings provide valuable insights for breeding programs aimed at improving stress tolerance and photosynthetic efficiency in mulberry and other crops, particularly under fluctuating environmental conditions.
Evidence for the Transcription of a Satellite DNA Widely Found in Frogs
Authors: Pompeo Jennifer Nunes , Gatto Kaleb Pretto , Baldo Diego , Lourenço Luciana Bolsoni
Published: December 2024

Background: The satellite DNA (satDNA) PcP190 has been identified in multiple frog species from seven phylogenetically distant families within Hyloidea, indicating its broad distribution. This satDNA consists of repeats of approximately 190 bp and exhibits a highly conserved region (CR) of 120 bp, which is similar to the transcribed region of 5S ribosomal DNA (rDNA), and a hypervariable region (HR) that varies in size and nucleotide composition among and within species. Here, to improve our understanding of PcP190 satDNA, we searched for evidence of its transcription in the available transcriptomes of Rhinella marina (Bufonidae) and Engystomops pustulosus (Leptodactylidae), two phylogenetically distantly related species. Methods: We first characterized the 5S rDNA and PcP190 sequences in these species by searching for them in available genome assemblies. Next, we used the PcP190 (CR and HR) and 5S rDNA sequences of each species as queries to search for these sequences in RNA-seq libraries. Results: We identified two types of 5S rDNA in each analyzed species, with a new type found in E. pustulosus. Our results also revealed a novel type of PcP190 sequence in R. marina and a new subtype of PcP-1 in E. pustulosus. Transcriptome analyses confirmed the expected transcription of the 5S rRNA gene and showed transcription of both the CR and HR of the PcP190 satDNA in both species and in different tissues. Conclusions: As the entire repeat of this satDNA is susceptible to transcription, the high variability observed in the HR cannot be attributed to transcriptional activity confined to the CR.
Spatial Variations of Physical Characteristics of Soil and Their Role in Creating a Model of a Geogenic Radon Hazard Index (GRHI) in the Kuznetsk Coal Basin
Authors: Leshukov Timofey , Legoshchin Konstantin , Savkina Maria , Baranova Elizaveta , Avdeev Kirill , Larionov Aleksey
Published: December 2024

Geographic patterns determine geogenic radon factors that, changing over the territory, form spatial structures of different scales associated with regional and local variations. The study of these structures is important for assessing the possibility of using limited data to predict geogenic radon potential. Our research focuses on the study of the physical properties of soils (moisture, soil density, porosity and void ratio) in the Kuznetsk coal basin. Their variations are studied using statistical methods, a variogram cloud and spatial autocorrelation of data. Soil moisture and porosity have the greatest variability in space and with depth. We conclude that the assessment of geogenic radon predictors requires consideration of the variation coefficient and autocorrelation indices at different scales. Based on the variability of humidity and the fairly homogeneous nature of the studied soils (loams), to assess the radon hazard, it is necessary to study the influence of climatic conditions, since the permeability of the environment for radon will be determined by soil moisture. With the predominance of substantially clayey soils, it is necessary to study the content of 226Ra in the upper horizons, since it is assumed that radon is predominantly diffusely transferred, in which its role is dominant.