bluuebunny/crossref_metadata_2025 huggingface dataset:
Alcohol Consumption and Breast and Ovarian Cancer Development: Molecular Pathways and Mechanisms
Authors: Fanfarillo Francesca , Caronti Brunella , Lucarelli Marco , Francati Silvia , Tarani Luigi , Ceccanti Mauro , Piccioni Maria Grazia , Verdone Loredana , Caserta Micaela , Venditti Sabrina , Ferraguti Giampiero , Fiore Marco
Published: December 2024

Alcohol consumption has been consistently linked to an increased risk of several cancers, including breast and ovarian cancer. Despite substantial evidence supporting this association, the precise mechanisms underlying alcohol’s contribution to cancer pathogenesis remain incompletely understood. This narrative review focuses on the key current literature on the biological pathways through which alcohol may influence the development of breast and ovarian cancer. Key mechanisms discussed include the modulation of estrogen levels, the generation of reactive oxygen species, the production of acetaldehyde, the promotion of chronic inflammation, and the induction of epigenetic changes. Alcohol’s impact on estrogenic signaling, particularly in the regulation of estrogen and progesterone, is explored in the context of hormone-dependent cancers. Additionally, the role of alcohol-induced DNA damage, mutagenesis, and immune system modulation in tumor initiation and progression is examined. Overall, this review emphasizes the importance of alcohol as a modifiable risk factor for breast and ovarian cancer and highlights the need for further research to clarify its role in cancer biology.
Advancements in Cellular Imaging: Expanding Horizons with Innovative Dyes and Techniques
Authors: Oak Payal M. , Mali Akash S.
Published: December 2024

Advancements in cellular imaging have significantly enhanced our understanding of membrane potential and Ca2⁺ dynamics, which are crucial for various cellular processes. Voltage-sensitive dyes (VSDs) are pivotal in this field, enabling non-invasive, high-resolution visualization of electrical activity in cells. This review discusses the various types of VSDs, including electrochromic, Förster Resonance Energy Transfer (FRET)-based, and Photoinduced Electron Transfer (PeT)-based dyes. VSDs are essential tools for studying mitochondrial activity and neuronal function and are frequently used in conjunction with Ca2⁺ indicators to elucidate the complex relationship between membrane potential and Ca2⁺ fluxes. The development of novel dyes with improved photostability and reduced toxicity continues to expand the potential of VSDs in biomedical research. This review underscores the importance of VSDs in advancing our understanding of cellular bioenergetics, signaling, and disease mechanisms.
Effects of Energy Consumption, Agricultural Trade, and Productivity on Carbon Emissions in Nigeria: A Quantile Regression Approach
Authors: Edoja Prosper E. , Aye Goodness C. , Gupta Rangan
Published: December 2024

The focus of this investigation was to examine the effects of energy consumption, agricultural commerce, and productivity on CO2 emissions in Nigeria using quantile regression. Time series data from 1960 to 2021 were used. The findings revealed that the impact of agricultural raw materials imports (AGRIMs) and exports on carbon footprints is positive. There is a prevalence of a set of notable percentile differences in the conditional distribution of the variables on CO2 emissions. Initially, the coefficient of energy consumption (EnCons) was high, but constantly nosedived from the 25th quantile until it reached the 90th quantile when it picked up again, and the same was true in the case of AGRIM. Thus, a 1% increase in agricultural imports will bring about 0.0047—a significant unit increase in CO2 emissions in Nigeria from the 0.382946 coefficient in the 10th quantile to the 0.264392 coefficient in the 50th quantile, and thereafter, the effects become insignificant. Profound significant variance across disparate percentiles in the conditional spread of AGRIM, food production index (FPI), CPI, and FDI was found. It further showed that the effects of the regressors on carbon emissions differ over the quantiles. Overall, AGRIM and EnCons have positive and significant effects on carbon emission. However, the agricultural raw material export has significant negative effects on CO2 emissions as the movement (transportation) of goods within a country prior to export involves a huge level of carbon release. This study provides recommendations and policy implications.
Study of Ventilation Strategies in a Passenger Aircraft Cabin Using Numerical Simulation
Authors: Khader S. M. Abdul , Corda John Valerian , Mathias Kevin Amith , Shenoy Gowrava , bin Ahmad Kamarul Arifin , Barboza Augustine V. , Kamath Sevagur Ganesh , Zuber Mohammad
Published: December 2024

Aircraft cabins have high occupant densities and may introduce the risk of COVID-19 contamination. In this study, a segment of a Boeing 767 aircraft cabin with a mixing type of air distribution system was investigated for COVID-19 deposition. A section of a Boeing 737-300 cabin, featuring four rows with 28 box-shaped mannequins, was used for simulation. Conditioned air entered through ceiling inlets and exited near the floor, simulating a mixed air distribution system. Cough droplets were modeled using the Discrete Phase Model from two locations: the centre seat in the second row and the window seat in the fourth row. These droplets had a mean diameter of 90 µm, an exhalation velocity of 11.5 m/s and a flow rate of 8.5 L/s. A high-quality polyhedral mesh of about 7.5 million elements was created, with a skewness of 0.65 and an orthogonality of 0.3. The SIMPLE algorithm and a second-order upwind finite volume method were used to model airflow and droplet dynamics. It was found that the ceiling accounted for the maximum concentration followed by the seats. The concentration of deposits was almost 50% more when the source was at window as compared to the centre seat. The Covid particles resided for longer duration when the source was at the centre of the cabin than when it was located near the widow.
Community-Based Conservation Strategies for Wild Edible Plants in Turkana County, Kenya
Authors: Oduor Francis , Kaindi Dasel Mulwa , Abong George , Thuita Faith , Termote Céline
Published: December 2024

In arid Turkana County, over 90% of the population is food insecure, and wild edible plants (WEPs) provide 12–30% of dietary intake. However, climate change and overexploitation threaten these crucial resources. This study employed sequential qualitative methods to investigate community perceptions, conservation priorities for WEPs, barriers, and necessary actions in Turkana. It combined participatory community workshops and expert validation interviews. The research revealed critical threats to WEP availability, including climate change, shifting cultural practices, and a lack of natural regeneration. Key conservation barriers included intergenerational knowledge gaps, inadequate policy implementation, and conflicts between immediate needs and long-term conservation goals. In developing conservation plans, the stakeholders identified and prioritized WEP species based on food value, medicinal properties, cultural significance, utility, and drought resistance. The co-developed conservation strategy emphasized both in situ protection measures, such as community awareness programs and local policy enforcement mechanisms, and restoration actions that include planting prioritized WEPs in home gardens and community spaces. Collaborative roles for communities, non-governmental organizations, researchers, and government actors were identified to provide training, resources, and technical support. This strategy also emphasizes the need for incentivization through food/cash-for-work programs and small business grants to promote alternative livelihoods. The strategies align with some of the most-utilized conservation frameworks and principles, and present new ideas such as integrating indigenous knowledge. Expert validation confirmed the feasibility of proposed actions, highlighting the importance of multi-stakeholder approaches. This study contributes to expanding our knowledge base on community-based conservation and provides insights for policymakers, emphasizing WEPs’ critical role in food security, cultural preservation, and ecological resilience. The findings could serve as a model for similar initiatives in other arid regions facing comparable challenges.
Impact of Metabolic States on SARS-CoV-2 Vaccine Responses in Mouse Models of Obesity and Diabetes
Authors: Smith Olivia A. , Fujimoto Brent , Wong Teri Ann S. , To Albert , Odo Troy , Ball Aquena , Haun Brien K. , Muramatsu Hiromi , Tam Ying K , Pardi Norbert , Lehrer Axel T.
Published: December 2024

The emergence of SARS-CoV-2 has resulted in a significant impact on public health, particularly for individuals with underlying health conditions such as obesity and diabetes. While vaccination efforts have played a crucial role in reducing hospitalizations, it remains unclear whether the effectiveness of these vaccines varies among different population groups. In this study, we investigated the immune responses generated by various SARS-CoV-2 vaccine platforms in mouse models with obesity and diabetes, focusing on both cell-mediated and humoral immune responses. Our findings revealed diminished immune responses in diabetic and obese mice compared to healthy counterparts. After vaccination with adjuvanted subunit or mRNA lipid nanoparticle (LNP) vaccines, both humoral and cell-mediated responses were significantly reduced in diabetic mice. Obese mice also exhibited decreased immunogenicity, albeit to a lesser extent. However, it should be noted that mRNA vaccines demonstrated strong neutralizing responses across all metabolic states, while adjuvanted subunit vaccines elicited higher antibody avidity in mice with type 2 diabetes (T2D) and obesity compared to healthy mice. These results suggest that the impaired humoral and cell-mediated responses observed in altered metabolic states may be linked to chronic inflammation associated with obesity and suboptimal glycemic control in diabetes. Understanding the impact of these metabolic disturbances on vaccine immunogenicity is crucial for developing optimized vaccines that can effectively enhance immune responses and provide long-lasting protection against SARS-CoV-2, even in individuals with obesity and diabetes. By contributing these findings, we support efforts to improve vaccine efficacy in populations affected by metabolic disorders, advancing effective immunization against SARS-CoV-2.
A Longitudinal Multimodal Imaging Study in Patients with Temporo-Insular Diffuse Low-Grade Tumors: How the Inferior Fronto-Occipital Fasciculus Provides Information on Cognitive Outcomes
Authors: Tomasino Barbara , Baiano Cinzia , Ricciardi Giuseppe Kenneth , Maieron Marta , Romano Andrea , Guarracino Ilaria , Isola Miriam , De Martino Maria , D’Agostini Serena , Bagatto Daniele , Somma Teresa , Skrap Miran , Ius Tamara
Published: December 2024

Background: Tractography allows the in vivo study of subcortical white matter, and it is a potential tool for providing predictive indices on post-operative outcomes. We aim at establishing whether there is a relation between cognitive outcome and the status of the inferior fronto-occipital fasciculus’s (IFOF’s) microstructure. Methods: The longitudinal neuropsychological data of thirty young (median age: 35 years) patients operated on for DLGG in the left temporo-insular cortex along with pre-surgery tractography data were processed. Results: A degraded integrity of the left (vs. right) IFOF (lower fractional anisotropy and length, p < 0.001; higher mean and axial diffusivity, p < 0.01) was found, with lower microstructural variables in the infiltration (vs. dislocation) group. Significant decreases immediately post-surgery vs. pre-surgery mainly occurred in lexico-semantics (p < 0.001), with significant improvements at follow-up in all the tests (p < 0.01 to p < 0.001), despite values in the range of 44% to 47.82% of patients with below cut-off scores regarding naming verbs and making visual lexical decisions. The status of left and right IFOFs is predictive of a decrease in immediate post-surgery performance for several tests (p < 0.05); similarly, it is predictive of better recovery in the follow-up performance for naming nouns, naming verbs, making phonological fluency lexical decisions, and the token test (p < −0.05). For the ROC analysis, a significant result was obtained for the verb-naming test, with a cut-off of 79%. Conclusions: This study supports the role of the predictive value of pre-operative tractography for assessing the immediate post-operative result and at follow-up the risk of developing a cognitive deficit.
Let It Grow: The Role of Growth Factors in Managing Chemotherapy-Induced Cytopenia
Authors: Alyamany Ruah , Alnughmush Ahmed , Alzahrani Hazzaa , Alfayez Mansour
Published: December 2024

Chemotherapy-induced cytopenia (CIC) is characterized by neutropenia, anemia, and thrombocytopenia, which are common and serious complications in cancer treatment. These conditions affect approximately 60% of patients undergoing chemotherapy and can significantly impact quality of life, treatment continuity, and overall survival. The use of growth factors, including granulocyte colony-stimulating factors (GCSFs), erythropoietin-stimulating agents (ESAs), and thrombopoietin receptor agonists (TPO-RAs), has emerged as a promising strategy for managing CIC. However, the use of these growth factors must be approached with caution. This review provides an overview of the mechanisms, efficacy, and safety of growth factors in the management of CIC. Additionally, we discuss predictive markers for treatment response, potential risks, and highlight areas for future research.
The Real-World Clinical Outcomes of Heavily Pretreated HER2+ and HER2-Low Metastatic Breast Cancer Patients Treated with Trastuzumab Deruxtecan at a Single Centre
Authors: Lazaratos Anna-Maria , Dankner Matthew , Hamouda Aalya , Labidi Soumaya , Cohen Victor , Panasci Lawrence , Friedmann Jennifer E. , Patenaude François , Ferrario Cristiano , Basik Mark , Rose April A. N. , Fallah Parvaneh
Published: December 2024

Background: Trastuzumab deruxtecan (TDXd) is an antibody–drug conjugate that has demonstrated impressive activity in randomized controlled clinical trials in the context of patients with HER2-amplified and HER2-low metastatic breast cancer. We aimed to review the activity and adverse event profile of TDXd in heavily pretreated breast cancer patients in real practice. Methods: We describe a single-center retrospective case series of metastatic breast cancer patients who were treated with TDXd. The outcomes of interest were the overall response rate, overall survival, progression-free survival and grade 4–5 adverse events. Objective responses and PFS were assessed in accordance with RECIST 1.1 criteria. Results: We identified 38 patients treated with TDXd. Of these, 15 patients had classically defined HER2-positive (HER2+) breast cancer, 4 of whom had active central nervous system (CNS) metastases. A total of 23 patients had HER2-low breast cancer, 2 of whom had active CNS disease. Of the 33 patients evaluable for response, 21 (63%) patients had a response to treatment, including three (9%) complete responses. Outcomes were similar between patients with a HER2+ and HER2-low status, as well as in patients with or without CNS metastases. No patients experienced grade 4 or 5 toxicities, and four of thirty-eight patients (10.5%) experienced pneumonitis (two patients with grade 3 pneumonitis, one patient with grade 2 and one patient with grade 1), resulting in TDXd discontinuation for three patients (with steroid administration in two patients). Conclusions: TDXd demonstrates impressive activity with manageable adverse event profiles in this heavily pretreated population that includes patients with active CNS metastases.
Detection of UAV GPS Spoofing Attacks Using a Stacked Ensemble Method
Authors: Ma Ting , Zhang Xiaofeng , Miao Zhexin
Published: December 2024

Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method based on stacked ensemble learning that combines convolutional neural network (CNN) and extreme gradient boosting (XGBoost) to detect spoofing signals in the GPS data received by UAVs. First, we applied the synthetic minority oversampling (SMOTE) technique to the dataset to address the issue of class imbalance. Then, we used a CNN model to extract high-level features, combined with the original features as input for the stacked model. The stacked model employs XGBoost as the base learner, which is optimized through five-fold cross-validation, and utilizes logistic regression for the final prediction. Furthermore, we incorporated magnetic field data to enhance the system’s robustness, thereby further improving the accuracy and reliability of GPS spoofing attack detection. Experimental results indicate that the proposed model achieved a high accuracy of 99.79% in detecting GPS spoofing attacks, demonstrating its potential effectiveness in enhancing UAV security.
Sliding Mode Control Approach for Vision-Based High-Precision Unmanned Aerial Vehicle Landing System Under Disturbances
Authors: Wu Hao , Wang Wei , Wang Tong , Suzuki Satoshi
Published: December 2024

Unmanned aerial vehicles (UAVs) face significant challenges when landing on moving targets due to disturbances, such as wind, that affect landing precision. This study develops a system that leverages global navigation satellite system (GNSS) signals and UAV visual data to enable real-time precision landings, and incorporates a sliding mode controller (SMC) to mitigate external disturbances throughout the landing process. To this end, a reference-model-based SMC is proposed, which defines reference values for each state to enhance the steadiness and safety of the velocity control system, thereby improving velocity state tracking and accuracy. The stability of the proposed controller is demonstrated using the Lyapunov method and comparing its performance against other controllers, including backstepping, linear-quadratic regulator (LQR), and proportional–integral–derivative (PID). The experimental results reveal a 75% reduction in maximum velocity tracking error and an 80% reduction in maximum landing error with the proposed controller. Finally, extensive real-flight tests confirm the stability and feasibility of the system.
Investigation into UAV Applications for Environmental Ice Detection and De-Icing Technology
Authors: Li Qingying , Chai Zhijie , Yao Rao , Bai Tian , Zhao Huanyu
Published: December 2024

In cold environments, ice formation poses significant risks to infrastructure such as transportation systems and power transmission. Yet, traditional de-icing methods are often time-consuming, hazardous, and inefficient. In this regard, unmanned aerial vehicles (UAVs) have shown great potential in environmental ice detection and de-icing applications. This study comprehensively reviews the application of UAVs in ice detection and de-icing operations in external environments, emphasizing their potential to replace traditional manual methods. Firstly, the latest developments in UAV-based external ice detection technology are examined, with a focus on the unique capabilities of sensors such as multispectral cameras, infrared imagers, and LiDAR in capturing specific ice features. Subsequently, the implementation and effectiveness of chemical, mechanical, and thermal de-icing methods delivered via UAV platforms are evaluated, focusing on their operational efficiency and adaptability. In addition, key operational requirements are reviewed, including environmental adaptability, mission planning and execution, and command transmission, as well as system design and manufacturing. Finally, the practical challenges involved in deploying UAVs under complex weather conditions are examined and solutions are proposed. These are aimed at promoting future research and ultimately driving the adoption of UAV technology in de-icing applications.
Influence of Biochar Feedstocks on Nitrate Adsorption Capacity
Authors: Eissa Riad , Jeyakumar Lordwin , McKenzie David B. , Wu Jianghua
Published: December 2024

The demand for intensive agriculture to boost food and crop production has increased. High nitrogen (N) fertilizer use is crucial for increasing agricultural productivity but often leads to significant nitrate losses, posing risks to surface and groundwater quality. This study examines the role of biochar as a soil amendment to enhance nutrient retention and mitigate nitrate leaching. By improving nitrogen efficiency, biochar offers a sustainable strategy to reduce the environmental impacts of intensive agriculture while maintaining soil fertility. An incubation study investigated four biochar feedstocks: spruce bark biochar at 550 °C (SB550), hardwood biochar (75% sugar maple) at 500 °C (HW500), sawdust (fir/spruce) biochar at 427 °C (FS427), and softwood biochar at 500 °C (SW500), to identify the most effective nitrate adsorbent. Scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR) were employed to analyze biochar morphology and surface functional groups. Adsorption isotherms were modeled using the Langmuir and Freundlich equations. The results indicated that surface functional groups, such as aromatic C=C stretching and bending, aromatic C–H bending, and phenolic O–H bending, play crucial roles in enhancing electrostatic attraction and, consequently, the nitrate adsorption capacity of biochar. The equilibrium adsorption data from this study fit well with both the Langmuir and Freundlich isotherm models. Among the four biochar types tested, SB550 exhibited the highest nitrate adsorption capacity, with a maximum of 184 mg/g. The adsorption data showed excellent conformity to the Langmuir and Freundlich models, with correlation coefficients (R2) exceeding 0.987 for all biochar types. These findings highlight the high accuracy of these models in predicting nitrate adsorption capacities.
An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model
Authors: Lenk Andreas , Vogt Marcus , Herrmann Christoph
Published: December 2024

The increasing share of renewable energies within energy systems leads to an increase in complexity. The growing complexity is due to the diversity of technologies, ongoing technological innovations, and fluctuating electricity production. To continue to ensure a secure, economical, and needs-based energy supply, additional information is needed to efficiently control these systems. This impacts public and industrial supply systems, such as vehicle factories. This paper examines the influencing factors and the applicability of the Temporal Fusion Transformer (TFT) model for the weekly energy demand forecast at an automobile production site. Seven different TFT models were trained for the weekly forecast of energy demand. Six models predicted the energy demand for electricity, heat, and natural gas. Three models used a rolling day-ahead forecast, and three models predicted the entire week in one step. In the seventh model, the rolling day-ahead forecast was used again, with the three target values being predicted in the same model. The analysis of the models shows that the rolling day-ahead forecasting method with a MAPE of 13% already delivers good results in predicting the electrical energy demand. The prediction accuracy achieved is sufficient to use the model outcomes as a basis for weekly operational planning and energy demand reporting. However, further improvements are still required for use in automated control of the energy system to reduce energy procurement costs. The models for forecasting heat and natural gas demands still show too high deviations, with a MAPE of 62% for heat demand and a MAPE of 39% for natural gas demand. To accurately predict these demands, further factors must be identified to explain the demand.
Underestimation of Wave Energy from ERA5 Datasets: Back Analysis and Calibration in the Central Tyrrhenian Sea
Authors: Afolabi Lateef Adesola , Russo Sara , Lo Re Carlo , Ludeno Giovanni , Nardone Gabriele , Vicinanza Diego , Contestabile Pasquale
Published: December 2024

Characterizing wave climate is crucial for coastal and offshore engineering applications. Reanalysis models, such as ERA5, are increasingly used due to their efficiency and lower costs compared to in situ measurements. However, their accuracy has not been thoroughly examined. This study addresses this gap by calibrating wave data from the ERA5 dataset with the available years of measurements from wave buoys in the Central Mediterranean Sea, specifically near Ponza, Cetraro, and Civitavecchia. A calibration approach was developed to adjust ERA5 wave data by aligning the model predictions closely with the co-located wave buoy observations. Results indicate that ERA5 systematically underestimates significant wave heights and periods, leading to an underestimation of wave power by up to 42% compared to buoy data. Calibration improved alignment between ERA5 and buoy measurements, enhancing wave energy representation and increasing estimated wave power by 35–48% annually. These findings underscore the importance of calibrating reanalysis datasets like ERA5 with in situ data to accurately assess wave energy potential, particularly in regions where model data may not fully capture local wave conditions. The outcomes provide valuable insights for wave energy projects in the Central Tyrrhenian Sea and similar semi-enclosed seas.
Linear Quadratic Gaussian Integral Control for Secondary Voltage Regulation
Authors: Chiodo Elio , Di Palma Pasquale , Fantauzzi Maurizio , Lauria Davide , Mottola Fabio , Villacci Domenico
Published: December 2024

In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances.
Physics-Informed Neural Networks for Heat Pump Load Prediction
Authors: Chifu Viorica Rozina , Cioara Tudor , Pop Cristina Bianca , Anghel Ionut , Pelle Andrei
Published: December 2024

Heat pumps are promising solutions for managing the increasing heating demand of residential houses, reducing the environmental impact when used with renewable energy. Accurate heat load predictions allow the heat pump to operate at the most efficient settings, maintaining comfortable temperatures while reducing excess energy use and lowering operating costs. Data-driven prediction solutions may have difficulty capturing the dynamics and nonlinearities of the thermodynamics involved. The physics-informed models combine the monitored observed data with theoretical knowledge of heat pumps and directly integrate physical constraints, allowing for better generalization and reducing the dependence on large volumes of data. However, they require detailed knowledge of the system topology and refrigerant parameters, which increases the model complexity. Therefore, in this paper, we propose a physics-informed neural network for predicting the heat load of heat pumps that integrates thermodynamics directly into the loss function of the neural network. We model the heat load as a function of the input variables, including the inlet temperature, outlet temperature, and water flow rate. We integrate the function during model training to reduce the model complexity. Our approach increases the accuracy of the predictions compared with data-driven models and generates prediction results that are consistent with the actual physical behavior of the heat pump. The results show superior prediction accuracy, with a 7.49% reduction in the RMSE and a 6.49% decrease in the MAPE, while the R2 value shows an increase of 0.02%.
Fault Gouge Permeability Under Confined Conditions: An Investigation for CO2 Storage Applications
Authors: Sekerbayeva Aigerim , Mortazavi Ali , Hazlett Randy D. , Bohloli Bahman
Published: December 2024

This investigation provides an in-depth experimental analysis of the prepared artificial fault gouge material on permeability characteristics as a function of the confining pressures and injection flow rate pertinent to both CO2 storage and subsurface fluid flow that addresses an ultimate challenge in CO2 storage. The purpose of the research is to gain a better understanding of the role of fault gouge material in structuring fluid flow patterns within geological media and improving the safety and efficiency of subsurface storage systems. In order to ensure the reproducibility of the experimental program, fault gouge material that resembled the size distribution and material type observed in the field and reported within the literature was purposefully designed and prepared. A set of core-flooding experiments were conducted to evaluate the relationships between permeability, confining pressure, and fluid flow rates. The subsequently obtained results showed that lower permeability is always the result of increasing confining pressure, highlighting the significance of fault gouge material for controlling fluid flow in fractured rock formations. These conclusions provide novel insights and can be applicable in practice when evaluating the integrity of CO2 storage sites, which calls for knowledge of permeability behavior under high-stress conditions.
Intensification of Energy Production in the Anaerobic Digestion Process of Sewage Sludge Using Enzymatic Disintegration
Authors: Macherzyński Bartłomiej , Popowska-Nowak Elżbieta , Włodarczyk-Makuła Maria , Bień Beata , Wszelaka-Rylik Małgorzata
Published: December 2024

This paper presents results of a study on changes in the physical and chemical properties of papain-disintegrated sewage sludge subjected to methane fermentation and the effect of enzymes on the colony number of Escherichia coli, Salmonella spp. and Legionella spp. Fermentation studies were conducted in five batches, in which papain was introduced in amounts representing 0, 1, 2, 3 and 4% (w/w) by weight of the sludge dry matter content. The use of enzymatic disintegration allowed us to intensify the decomposition of organic compounds in the digestion process up to 31% and increase biogas production up to 41% compared to control sludge. At the same time, an almost complete reduction (99–100%) in the colony number of E. coli and Salmonella spp. could be achieved. Therefore, enzymatic disintegration can be competitive with physical or chemical disintegration methods.
Carbon Footprint of Electricity Produced in the Russian Federation
Authors: Shirinkina Ekaterina , Mozzhegorova Yuliya , Ilinykh Galina , Korotaev Vladimir
Published: December 2024

Energy generation makes a significant contribution to greenhouse gas emission. The carbon footprint of electricity significantly affects the total carbon footprint of a wide variety of products, which is especially relevant for energy-intensive industries (aluminum, platinum, carbon fiber-reinforced plastics, etc.) and hydrogen energy. The carbon footprint of aluminum, produced in Russia is 8.0–15.0 kg CO2-eq./kg. It is lower than the actual carbon footprint of aluminum produced in other countries due to the lower carbon intensity of Russian grid electricity in comparison with the world average. The carbon footprint of hydrogen, produced by photovoltaic modules with electricity consumption from the Russian national electricity grid is 16.6 kg CO2-eq./kg, while the world average carbon footprint of photovoltaic hydrogen is 18.1 kg CO2-eq./kg. The average carbon footprint of electricity generated and consumed in Russia ranges from 310 to 634 g CO2-eq./kWh. This paper analyzes methodological approaches to determining grid emission factors for Russian electricity. It has been established that different principles of spatial division of the Russian energy system can be used to determine grid emission factors (national average grid emission factor, grid emission factors for the integrated energy system, grid emission factors for price and non-price zones of the wholesale electricity market).
Fuzzy Neural Network Applications in Biomass Gasification and Pyrolysis for Biofuel Production: A Review
Authors: Bukhtoyarov Vladimir , Tynchenko Vadim , Bashmur Kirill , Kolenchukov Oleg , Kukartsev Vladislav , Malashin Ivan
Published: December 2024

The increasing demand for sustainable energy has spurred interest in biofuels as a renewable alternative to fossil fuels. Biomass gasification and pyrolysis are two prominent thermochemical conversion processes for biofuel production. While these processes are effective, they are often influenced by complex, nonlinear, and uncertain factors, making optimization and prediction challenging. This study highlights the application of fuzzy neural networks (FNNs)—a hybrid approach that integrates the strengths of fuzzy logic and neural networks—as a novel tool to address these challenges. Unlike traditional optimization methods, FNNs offer enhanced adaptability and accuracy in modeling nonlinear systems, making them uniquely suited for biomass conversion processes. This review not only highlights the ability of FNNs to optimize and predict the performance of gasification and pyrolysis processes but also identifies their role in advancing decision-making frameworks. Key challenges, benefits, and future research opportunities are also explored, showcasing the transformative potential of FNNs in biofuel production.
Polyhydroxyalkanoates (PHAs): Mechanistic Insights and Contributions to Sustainable Practices
Authors: Pradhan Sushobhan , Khan Mohd Tauhid , Moholkar Vijayanand S.
Published: December 2024

A polymer is a long-chain molecule formed by linking numerous simpler repeating chemical units, known as monomers, with identical structures. Over the past two centuries, there has been a significant increase in the global production and use of petrochemical-based plastics. This surge has led to widespread ecological imbalances, affecting air quality, terrestrial and marine ecosystems, food chains, and plant life. Consequently, the excessive use of such polymers has created challenges in solid waste management, with methods like bio- or photo-degradation, incineration, landfilling, and recycling proving to be time-consuming and laborious. Therefore, there is a growing urgency for biodegradable polymers due to increasing demand. Biodegradable polymers consist of interconnected monomers with unstable links in the backbone, facilitated by various functional groups. Throughout the degradation process of these polymers, numerous biologically acceptable molecules are produced. This study examines the significance of biopolymers over petroleum-based counterparts, offering a detailed analysis. It is noteworthy that within the spectrum of biodegradable polymers, polyhydroxyalkanoates (PHAs) emerge as exceptionally promising candidates for substituting petroleum-derived polymers, owing to their remarkable physical attributes. Therefore, this study provides a systematic overview of PHAs, including their classification, historical background, methods of production, potential challenges to commercialization, and diverse applications.
Improving Jelly Nutrient Profile with Bioactive Compounds from Pine (Pinus sylvestris L.) Extracts
Authors: Szanto Lidia Gizella , Marc Romina Alina , Mureşan Andruța Elena , Mureșan Crina Carmen , Puşcaş Andreea , Ranga Floricuța , Fetea Florinela , Moraru Paula Ioana , Filip Miuța , Muste Sevastița
Published: December 2024

This study aimed to enhance the nutritional value of jellies by fortification with polyphenol extracts derived from Pinus sylvestris L. shoots at various maturation stages. Pinus sylvestris L., a coniferous species, is widely used in traditional medicine and functional foods due to its antioxidant, anti-inflammatory, and antimicrobial properties. Its needles, bark, and shoots are commonly used to extract bioactive compounds such as phenolic acids and flavonoids. In the current study, extracts were derived from young shoots collected directly from natural forest environments and processed using a decoction method to preserve bioactive compounds. The novel jelly formulations were prepared using pine shoots harvested at three maturity stages: stage I (4 cm), stage II (8 cm), and stage III (12 cm). All determinations were conducted both on the pure decoction extracts and the jelly samples to ensure a comprehensive analysis. High-performance liquid chromatography coupled with electrospray ionization mass spectrometry (HPLC-ESI-MS) allowed the identification of eight phenolic acids and six flavonoids in the samples. Significant differences were observed between the pine shoot extracts and jellies at different development stages. Notably, stage II exhibited optimal polyphenol content (312.2 mg GAE/100 g), DPPH free radical scavenging activity (94.9%), dry matter content (79.5%), and acidity (0.79% citric acid/g). A similar pattern emerged in the jelly samples (jelly2 (pine decoction stage II) > jelly1 (pine decoction stage I) > jelly3 (pine decoction stage III)). All extracts demonstrated antioxidant potential in DPPH free radical quenching assays. FTIR analysis evaluated structural changes in phenolic compounds during jelly formulation, focusing on key absorption bands at 1600 cm−1 (C=C stretching) and 3336 cm−1 (-OH stretching) using a Shimadzu IR Prestige-21 spectrophotometer. Compared to extracts, jellies showed diminished band intensities, indicating thermal degradation of phenolic compounds during processing. This aligns with observed reductions in antioxidant capacity and phenolic content, suggesting partial destabilization of these bioactive compounds. However, their integration into the jelly matrix highlights the potential for functional applications. The textural attributes of jellies were also assessed, and differences were attributed to the changes in acidity and moisture content of the pine shoots during maturation. Pine shoot extracts at specific maturation stages are valuable sources of antioxidant and polyphenol compounds and were successfully employed in functional applications belonging to the food or nutraceutical industry.
Assessing Sustainability in Urban Forests: A Case Analysis of Atatürk Urban Forest (Bursa)
Authors: Ender Altay Elvan , Pirselimoğlu Batman Zeynep
Published: December 2024

Urban forests, as part of the green infrastructure systems in cities, are also important components of natural systems. To ensure the sustainability of urban forests, ecological, social, and economic dimensions must be addressed holistically. In this context, qualitative and quantitative evaluations conducted in Atatürk Urban Forest have revealed significant findings regarding the socio-cultural, economic, and ecological sustainability of the urban forest. Atatürk Urban Forest covers an area of 150 hectares and constitutes important urban green infrastructure for Bursa. Sustainability indicators were defined within this study, and an approach for calculating sustainability performance values was developed. In this approach, 32 sustainability indicators were calculated based on parameters such as area size, distance, volume, shape, and the number of facilities. As a result of the calculations, Atatürk Urban Forest’s sustainability performance value was determined to be 187.76 (62.58%). However, this value indicates that there are certain shortcomings in terms of sustainability. Addressing these shortcomings will enhance the quality of sustainability indicators, and Atatürk Urban Forest will play a significant role as a sustainable urban green infrastructure.
Effect of Enhanced Antimicrobial Fish Gelatin and Lactic Acid Coating on the Shelf Life of Fresh and Previously Frozen Shrimp
Authors: Songy Hunter , Parraga Katheryn , Xu Wenqing , Watts Evelyn
Published: December 2024

Gelatin coatings have been shown to successfully slow the oxidation of fresh foods. Furthermore, organic acids have proven effective in the inhibition of bacteria and the extension of product shelf life. The purpose of this study was to observe the effects of gelatin and lactic acid treatment combinations on fresh and previously frozen (thawed) wild-caught shrimp. The samples were separated into four treatment groups: control (C), gelatin coating (G), lactic acid followed by gelatin coating (L), and gelatin infused with lactic acid coating (LG). Half of each group was stored at refrigeration temperature (≤4 °C), while the other half was frozen (≤−18 °C) and thawed for the thawed study. Physical, chemical, and microbiological changes were observed in refrigeration storage for 8 days. Results showed that fresh and thawed shrimp exhibited darkening in color over time. Treatments with lactic acid were more blue than yellow. Treated samples developed less oxidation than the control. Psychrophilic counts for samples L and LG remained below the 7 log CFU/g threshold throughout the study. In fresh shrimp samples, the addition of just a gelatin coating was able to increase shelf life by three days, while the addition of lactic acid successfully lowered microbial counts and extended the shelf life by six days. The freeze–thaw cycle negatively affected the extension of shelf life in the G group.
Rarefied Nozzle Flow Computation Using the Viscosity-Based Direct Simulation Monte Carlo Method
Authors: Mopuru Deepa Raj , Dongari Nishanth , Payyavula Srihari
Published: December 2024

Micro-nozzles are essential for enabling precise satellite attitude control and orbital maneuvers. Accurate prediction of performance parameters, including thrust and specific impulse, is critical, necessitating careful design of these nozzles. Given the high Knudsen numbers associated with micro-nozzle flows, rarefied gas dynamics often dominate, and conventional computational fluid dynamics (CFD) methods fail to capture accurate flow expansion behavior. The Direct Simulation Monte Carlo (DSMC) method, developed by Bird, is widely used for modeling rarefied flows; however, it has been primarily implemented on platforms like OpenFOAM and FORTRAN, with limited exploration in MATLAB. This study presents the development of a viscosity-based DSMC (μDSMC) simulation framework in MATLAB for analyzing rarefied gas expansion through micro-nozzles. Key boundary conditions, including upstream and downstream pressure conditions and thermal wall treatments with diffuse reflection, are incorporated into the code. The μDSMC results are validated against traditional DSMC outcomes, showing strong agreement. Grid convergence studies indicate that the radial grid size must be less than one-third of the mean free path, with a more relaxed requirement on axial grid size. Flow characteristics within micro-nozzles are evaluated across varying ambient pressures and gas species in terms of the back pressure ratio, effective exit flow ratio, and exit flow velocity. Studies indicated that a minimum back pressure ratio is required, beyond which the effective nozzle flow expansion is achieved. Parametric analysis further suggests that gases with lower molecular weights are preferable for achieving optimal expansion in micro-nozzles under low ambient pressures.
Influence of Heat- and Cold-Stressed Raw Milk on the Stability of UHT Milk
Authors: Li Nan , Yang Zhigang , Yuan Zhiyu , Zhen Zizhu , Xie Xinna , Zhu Danqing , Lu Gang , Zhao Feng , Qu Bo , Qi Bingli , Jiang Yujun , Zhao Qianyu , Man Chaoxin
Published: December 2024

This study investigated the variations and alterations in the concentrations of plasmin system components in raw and UHT (ultra-high-temperature) milk under cold stress (WCT ≤ −25 °C), heat stress (THI ≥ 80), and normal (THI < 70 and WCT ≥ −10 °C) circumstances. The findings indicated elevated amounts of plasmin system components in cold-stressed raw milk. While storing UHT milk at 25 °C, the concentrations and activity of plasmin in the milk exhibited an initial increase followed by a decrease, peaking around the 30th day. The maximum plasmin level and activity in cold-stressed milk were 607.86 μg/L and 15.99 U/L, respectively, with the beginning of gelation occurring around day 60. The higher activity of plasmin in cold-stressed milk led to the poorer stability and sensory assessment of the milk. However, heat-stressed milk is not such a problem for UHT milk as cold-stressed milk. The findings indicate shortcomings in the quality of cold-stressed milk and its adverse effects on the stability of UHT milk, underscoring the necessity of preventing cold stress in the herd and refraining from utilizing cold-stressed milk as a raw material for UHT production.
Antimicrobial Peptides from Porcine Blood Cruor Hydrolysates as a Promising Source of Antifungal Activity
Authors: García-Vela Sara , Cournoyer Aurore , Sánchez-Reinoso Zain , Bazinet Laurent
Published: December 2024

Porcine blood, a significant byproduct of the pork industry, represents a potential source of antimicrobial peptides (AMPs). AMPs offer a promising alternative to chemical antimicrobials, which can be used as natural preservatives in the food industry. AMPs can exhibit both antibacterial and/or antifungal properties, thus improving food safety and addressing the growing concern of antibiotic and antifungal resistance. The objective of this study was to evaluate the antimicrobial activity of potential AMPs previously identified from porcine cruor hydrolysates. To this end, a total of sixteen peptides were chemically synthesized and their antimicrobial activities (antibacterial, anti-mold, and anti-yeast) were evaluated using microtitration and agar well diffusion methods against a wide range of microorganisms. Five new peptide sequences demonstrated antifungal activity, with Pep5 (FQKVVAGVANALAHKYH), an alpha-helix peptide, exhibiting the most promising results. Pep5 demonstrated efficacy against nine of the eleven fungal isolates, exhibiting low minimum inhibitory concentrations (MICs) and a fungicidal effect against key spoilage fungi (Rhodotorula mucilaginosa, Debaryomyces hansenii, Candida guilliermondii, Paecilomyces spp., Eurotium rubrum, Mucor racemosus, Aspergillus versicolor, Penicillium commune, and P. chrysogenum). These findings illustrate the potential of porcine blood hydrolysates as a source of AMPs, particularly antifungal peptides, which are less known and less studied than the antibacterial ones. Among the tested sequences, Pep5 exhibited the most promising characteristics, including broad-spectrum activity, low MICs, and a fungicidal effect. It is, therefore, a promising candidate for further research and for potential applications in the porcine industry and beyond.