bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources
Authors: Caban Jacek , Małek Arkadiusz , Šarkan Branislav
Published: March 2024

The ever-growing number of electric vehicles requires increasing amounts of energy to charge their traction batteries. Electric vehicles are the most ecological when the energy for charging them comes from renewable energy sources. Obtaining electricity from renewable sources such as photovoltaic systems is also a way to reduce the operating costs of an electric vehicle. However, to produce cheap electricity from renewable energy sources, you first need to invest in the construction of a photovoltaic system. The article presents a strategic model for charging a fleet of electric vehicles with energy from photovoltaic systems. The model is useful for sizing a planned photovoltaic system to the energy needs of a vehicle fleet. It uses the Metalog family of probability distributions to determine the probability of producing a given amount of energy needed to power electric vehicle chargers. Using the model, it is possible to determine the percentage of energy from photovoltaic systems in the total energy needed to charge a vehicle fleet. The research was carried out on real data from an operating photovoltaic system with a peak power of 50 kWp. The approach presented in the strategic model takes into account the geographical and climatic context related to the location of the photovoltaic system. The model can be used for various renewable energy sources and different sizes of vehicle fleets with different electricity demands to charge their batteries. The presented model can be used to manage the energy produced both at the design stage of the photovoltaic system and during its operation.
Investigating the Potential of a Transparent Xanthan Polymer for Enhanced Oil Recovery: A Comprehensive Study on Properties and Application Efficacy
Authors: Hublik Gerd , Kharrat Riyaz , Dastjerdi Ali Mirzaalian , Ott Holger
Published: March 2024

This study delves into the properties and behavior of xanthan TNCS-ST, a specialized variant designed for enhanced oil recovery (EOR) purposes. A notable aspect of this polymer is its transparency and capability to dissolve in high salt concentrations, notably up to 18% total dissolved solids. Various laboratory methods are employed to assess the polymer’s distinctive traits, including transparency, salt tolerance, and high pyruvylation. These methods encompass preparing xanthan solutions, conducting filtration tests, assessing energy consumption, and measuring rheological properties. The findings highlight the influence of salt concentration on xanthan’s filterability, indicating increased energy requirements for dissolution with higher salt and xanthan concentrations. Additionally, this study observes temperature-dependent viscosity behavior in different solutions and evaluates the shear stability of xanthan. A significant and novel characteristic of TNCS-ST is its high salt tolerance, enabling complete dissolution at elevated salt concentrations, thus facilitating the filterability of the xanthan solution with sufficient time and energy input. Core flooding experiments investigate fluid dynamics within porous rock formations, particularly sandstone and carbonate rocks, while varying salinity. The results underscore the substantial potential of the new xanthan polymer, demonstrating its ability to enhance oil recovery in sandstone and carbonate rock formations significantly. Remarkably, the study achieves a noteworthy 67% incremental recovery in carbonate rock under the high salinity level tested, suggesting promising prospects for advancing enhanced oil recovery applications.
A Three-Phase Multilevel Inverter Synthesized with 31 Levels and Optimal Gating Angles Based on the GA and GWO to Supply a Three-Phase Induction Motor
Authors: Hussein Taha Ahmad , Ishak Dahaman , Tarnini Mohamad
Published: March 2024

A three-phase multilevel inverter (MLI), synthesized with 31 levels in regard to its output voltage, is used to provide the AC supply to a three-phase, squirrel cage induction motor. The gating angles required for the 30 power switches on the MLI are optimized using both the genetic algorithm (GA) and the grey wolf optimizer (GWO), in which the optimal angles are determined through solving the trigonometric equations taken from Fourier analysis to target the minimum total harmonic distortion (THD) at the MLI output. A simulation model and an experimental prototype are developed for performance analysis and validation. The results demonstrate that the MLI is effectively able to produce 31 levels of three-phase AC output voltage, with the THD not exceeding 5% when loaded with a resistive load and a three-phase induction motor. The voltage and current are measured and recorded for different loads and operating conditions, including the amount of energy consumed by the load. The results of the frequency analysis demonstrate that most of the triple harmonics, which can harm the efficiency of the inverter, are cancelled.
An Experimental Investigation of the Effects of Using Hexagonal BN–Water Nanofluids on the Thermal Performance and Pressure Drop of a Concentric Tube Heat Exchanger
Authors: Ziyadanogullari Nese Budak , Percin Suleyman
Published: March 2024

The thermal conductivity of recently produced hexagonal boron nitride (hBN)-containing nanofluids is comparatively higher than their viscosity, indicating that these materials belong to a relatively novel class. In this study, hBN–water nanofluids in stable and dispersed concentrations were used in parallel and counterflow experiments at volumes of 0.01%, 0.1%, and 1%, as well as at various flow rates and Reynolds numbers. When employing hBN–water nanofluid in a counter-flow heat exchanger instead of distilled water, the results showed a 16.7% increase in the overall heat transfer coefficient. The findings also showed that, in comparison to a counter-flow heat exchanger, employing hBN nanofluid as the cold fluid in a parallel-flow heat exchanger produced superior results in terms of an increase in heat transfer. The effects of nanofluid concentration on pressure drops were investigated through experiments conducted in both parallel- and counter-flow conditions.
Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023
Authors: Song Dongran , Tan Xiao , Huang Qian , Wang Li , Dong Mi , Yang Jian , Evgeny Solomin
Published: March 2024

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.
Review of Cell-Balancing Schemes for Electric Vehicle Battery Management Systems
Authors: Ashraf Adnan , Ali Basit , Alsunjury Mothanna S. A. , Goren Hakime , Kilicoglu Halise , Hardan Faysal , Tricoli Pietro
Published: March 2024

The battery pack is at the heart of electric vehicles, and lithium-ion cells are preferred because of their high power density, long life, high energy density, and viability for usage in relatively high and low temperatures. Lithium-ion batteries are negatively affected by overvoltage, undervoltage, thermal runaway, and cell voltage imbalance. The minimisation of cell imbalance is particularly important because it causes uneven power dissipation by each cell and, hence, temperature distribution that adversely impacts the battery lifetime. Several papers in the literature proposed advanced cell-balancing techniques to increase the effectiveness of basic cell-balancing approaches, reduce power losses, and reduce the number of components in balancing circuits. The new developments and optimisations over the last few years have been particularly intense due to the increased interest in battery technologies for several end-use applications. This paper reviews and discusses recent cell-balancing techniques or methods, covering their operating principles and the optimised utilisation of electrical components.
An Adaptive Reclosing Scheme for Cross-Line Faults on Double-Circuit Wind Power Outgoing Lines with Shunt Reactors
Authors: Hu Sen , Xie Chao , Yin Chunya
Published: March 2024

Wind turbines are vulnerable to negative sequence current injection, the conventional automatic reclosing scheme for wind power outgoing lines, since we may inject negative sequence components into the system and reclose it without distinguishing the nature of the fault through rectification. Moreover, reclosing in permanent faults could induce a secondary impact on the system. To solve the above problems, an adaptive reclosing scheme for cross-line faults on double-circuit wind power outgoing lines with shunt reactors is proposed. Firstly, a new tripping strategy and a single-side partial-phase reclosing method are proposed for multiple types of outgoing line faults, while, simultaneously, phase-to-phase coupling loops are established. Secondly, the criteria of fault nature are established based on the fault phase shunt reactor current and fault phase voltage characteristics, and transient faults are rapidly and accurately distinguished from permanent ones according to the criteria. Finally, the theoretical derivation and simulation experiments are conducted on the PSCAD/EMTDC platform to demonstrate that the proposed adaptive reclosing method is applicable to avoid the injection of negative sequence currents into wind turbines. Meanwhile, the success rate of reclosing for wind power outgoing line is significantly improved and the continuity of power transmission on the wind farm(s) is ensured.
Research on Adaptive Cycle Engine Mode Transition Control Method
Authors: Zhao Xingyu , Li Qiuhong , Pang Shuwei , Xue Zhaohui , Deng Daming , Lu Feng
Published: March 2024

Mode transition is an important dynamic process of an adaptive cycle engine (ACE). In order to obtain a smooth mode transition process, the closed-loop controller is designed based on the strong robust augmented linear quadric regulator (ALQR) method, and with the objective of minimizing the thrust fluctuation in the process of mode transition, an open-loop geometrical mechanism control schedules optimization method based on Bézier curves is proposed, so that the closed-loop control and the open-loop control can work in coordination. The simulation results at the subsonic cruise operating point and supersonic cruise operating point show that based on the optimized open-loop geometrical mechanism control schedules and the designed closed-loop ALQR control system, the ACE achieves fast and smooth geometrical mechanisms and engine output transition during the mode transition process with a maximum thrust fluctuation of 2.58%, which is much smaller than that of the traditional linear variation geometric mechanisms with a maximum 4.64% thrust fluctuation, which verifies the effectiveness of the proposed control method.
A Composite Index for Tracking the Evolution towards Energy Transition at Urban Scale: The Turin Case Study
Authors: Desogus Eleonora , Bompard Ettore , Grosso Daniele
Published: March 2024

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin’s environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities.
Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities
Authors: Pettas Vasilis , Cheng Po Wen
Published: March 2024

As the maturity and complexity of wind energy systems increase, the operation of wind turbines in wind farms needs to be adjustable in order to provide flexibility to the grid operators and optimize operations through wind farm control. An important aspect of this is monitoring and managing the structural reliability of the wind turbines in terms of fatigue loading. Additionally, in order to perform optimization, uncertainty analyses, condition monitoring, and other tasks, fast and accurate models of the turbine response are required. To address these challenges, we present the controller tuning and surrogate modeling for a wind turbine that is able to vary its power level in both down-regulation and power-boosting modes, as well as reducing loads with an individual blade control loop. Two methods to derive the setpoints for down-regulation are discussed and implemented. The response of the turbine, in terms of loads, power, and other metrics, for relevant operating conditions and for all control modes is captured by a data-driven surrogate model based on aeroelastic simulations following two regression approaches: a spline-based interpolation and a Gaussian process regression model. The uncertainty of the surrogate models is quantified, showing a good agreement with the simulation with a mean absolute error lower than 4% for all quantities considered. Based on the surrogate model, the aeroelastic response of the entire wind turbine for the different control modes and their combination is analyzed to shed light on the implications of the control strategies on the fatigue loading of the various components.
A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types
Authors: Wu Jinrong , Nguyen Su , Alahakoon Damminda , De Silva Daswin , Mills Nishan , Rathnayaka Prabod , Moraliyage Harsha , Jennings Andrew
Published: March 2024

Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems.
Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response
Authors: Wei Lu , Li Yiyin , Xie Boyu , Xu Ke , Meng Gaojun
Published: March 2024

With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.
Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power
Authors: Zhang Fei , Ren Xiaoying , Yang Guidong , Zhang Shulong , Liu Yongqian
Published: March 2024

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.
Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System
Authors: Yuan Xiaoling , Cao Hao , Chen Zheng , Xu Jieyan , Liu Haoming
Published: March 2024

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.
Preliminary Insight into Ice Melting, Surface Subsidence, and Wellhead Instability during Oil and Gas Extraction in Permafrost Region
Authors: Zhou Xiaohui , Su Yinao , Cheng Yuanfang , Li Qingchao
Published: March 2024

Oil and gas production in permafrost can effectively alleviate energy tensions. However, ice melting around wellbores and the accompanying wellhead instability affect the efficiency and safety of oil and gas development in permafrost. Moreover, the potential oil and gas leakage will damage the environment and the ecology of permafrost. Unfortunately, ice melting, formation subsidence, and wellhead behavior during this process have rarely been investigated in previous studies. In the present work, mechanical properties of permafrost were first experimentally investigated, which provided the basic parameter for subsequent numerical simulation. It was found that the ultimate strength gradually increased with the decreasing temperature, as well as the increasing confining pressure. Meanwhile, although the elastic modulus increased with decreasing temperature, it was less affected by confining pressure. Unlike other parameters, the Poisson’s ratio was hardly affected by temperature and confining pressure. Moreover, both the internal friction angle and the cohesion increased with decreasing temperature, but the influence degree varied within different temperature ranges. Then, ice melting, formation subsidence, and the instability behavior of the wellhead caused by the disturbance of the development operation were numerically explored. The investigation results show that the ice melting range in the reservoir section reached 8.06 m, which is much wider than that in other well sections. Moreover, failure of the cement–permafrost interface, caused by ice melting, resulted in a wellhead sinking of up to 1.350 m. Finally, the insulation effect of the vacuum-insulated casing showed that the temperature drop of the designed vacuum-insulated casing was much lower than that of the ordinary casing. When the fluid temperature within the wellbore was 70 °C, the temperature drop of the designed vacuum-insulated casing was 3.54 °C lower than that of the ordinary casing. This study provides support for maintaining wellhead stability during oil and gas extraction in permafrost for avoiding some environmental disasters (such as oil and gas leakage).
Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves
Authors: Zhang Wenxiu , Rong Li , Xiong Kangning , Zhang Zhenzhen , Chang Huanhuan
Published: March 2024

Karst nature reserve (NR) ecosystems are vulnerable, and the development of regulatory and cultural services industries is still in its infancy. Realization of ecological product value (EPV) is a crucial way to promote the integration of eco-protection and industrial development in nature reserves (NRs). This study calculates EPV using a modified equivalent factor, analyzes the spatial pattern of EPV using the fishnet tool in Arc GIS, and constructs a model for realizing EPV. Finally, the driving factors for the formation of eco-industry are identified using the grey correlation analysis (GCA) method. The results show that (1) the regulatory service products in karst NRs play a decisive role in EPV, and forest ecosystems have significant EPV potential; (2) high-value grids are concentrated in the core and buffer zones of karst NRs and the spatial distribution of EPV in the experimental zone is highly heterogeneous, with high and low-value grids in mixed distribution; (3) the realization model of EPV in karst NRs follows the logic of “preservation, transformation, and appreciation”; (4) human resources (0.813), ecology (0.798), industry (0.693), policy (0.783), the market (0.778), and economy (0.715) are essential drivers for the formation of eco-industry in karst NRs. Overall, karst NRs can promote the realization of EPV through planning and regulation of land use, based on the interaction between internal and external factors, to promote the coordinated development of eco-industry in karst NRs. This study provides a reference for the scientific management of eco-resources and the sustainable development of eco-industry in karst NRs.
Unsteady Multiphase Simulation of Oleo-Pneumatic Shock Absorber Flow
Authors: Sheikh Al-Shabab Ahmed A. , Grenko Bojan , Silva Paulo A. S. F. , Antoniadis Antonis F. , Tsoutsanis Panagiotis , Skote Martin
Published: March 2024

The internal flow in oleo-pneumatic shock absorbers is a complex multiphysics problem combining the interaction between highly unsteady turbulent flow and multiphase mixing, among other effects. The aim is to present a validated simulation methodology that facilitates shock absorber performance prediction by capturing the dominant internal flow physics. This is achieved by simulating a drop test of approximately 1 tonne with an initial contact vertical speed of 2.7 m/s, corresponding to a light jet. The flow field solver is ANSYS Fluent, using an unsteady two-dimensional axisymmetric multiphase setup with a time-varying inlet velocity boundary condition corresponding to the stroke rate of the shock absorber piston. The stroke rate is calculated using a two-equation dynamic system model of the shock absorber under the applied loading. The simulation is validated against experimental measurements of the total force on the shock absorber during the stroke, in addition to standard physical checks. The flow field analysis focuses on multiphase mixing and its influence on the turbulent free shear layer and recirculating flow. A mixing index approach is suggested to facilitate systematically quantifying the mixing process and identifying the distinct stages of the interaction. It is found that gas–oil interaction has a significant impact on the flow development in the shock absorber’s upper chamber, where strong mixing leads to a periodic stream of small gas bubbles being fed into the jet’s shear layer from larger bubbles in recirculation zones, most notably in the corner between the orifice plate and outer shock absorber wall.
Survival of Listeria Strains and Shelf Life Determination of Fresh Blueberries (Vaccinium corymbosum) Treated with Cold Atmospheric Plasma
Authors: Concha-Meyer Anibal A. , González-Esparza Alexandra , Cullen Patrick J. , Veloso Felipe , Favre Mario , Valenzuela Julio C. , Toloza Lorena , Niemira Brendan A.
Published: March 2024

Fresh blueberries are delicate, hand-picked, packaged, and refrigerated fruits vulnerable to spoilage and contamination. Cold atmospheric plasma (CAP) is a promising antimicrobial technology; therefore, this study evaluated the CAP treatment effect on acid-tolerant Listeria innocua and Listeria monocytogenes and evaluated changes in the quality of the treated fruit. Samples were spot-inoculated with pH 5.5 and 6.0 acid-adapted Listeria species. Samples were treated with gliding arc CAP for 15, 30, 45, and 60 s and evaluated after 0, 1, 4, 7, and 11 days of storage at 4 °C and 90% humidity for the following quality parameters: total aerobic counts, yeast and molds, texture, color, soluble solids, pH, and titratable acidity. CAP treatments of 30 s and over demonstrated significant reductions in pathogens under both the resistant strain and pH conditions. Sixty-second CAP achieved a 0.54 Log CFU g−1 reduction in L. monocytogenes (pH 5.5) and 0.28 Log CFU g−1 for L. monocytogenes (pH 6.0). Yeast and mold counts on day 0 showed statistically significant reductions after 30, 45, and 60 s CAP with an average 2.34 Log CFU g−1 reduction when compared to non-CAP treated samples. Quality parameters did not show major significant differences among CAP treatments during shelf life. CAP is an effective antimicrobial treatment that does not significantly affect fruit quality.
Statistical Study of the Bias and Precision for Six Estimation Methods for the Fractal Dimension of Randomly Rough Surfaces
Authors: Flores Alarcón Jorge Luis , Figueroa Carlos Gabriel , Jacobo Víctor Hugo , Velázquez Villegas Fernando , Schouwenaars Rafael
Published: March 2024

The simulation and characterisation of randomly rough surfaces is an important topic in surface science, tribology, geo- and planetary sciences, image analysis and optics. Extensions to general random processes with two continuous variables are straightforward. Several surface generation algorithms are available, and preference for one or another method often depends on the specific scientific field. The same holds for the methods to estimate the fractal dimension D. This work analyses six algorithms for the determination of D as a function of the size of the domain, variance, and the input value for D, using surfaces generated by Fourier filtering techniques and the random midpoint displacement algorithm. Several of the methods to determine fractal dimension are needlessly complex and severely biased, whereas simple and computationally efficient methods produce better results. A fine-tuned analysis of the power spectral density is very precise and shows how the different surface generation algorithms deviate from ideal fractal behaviour. For large datasets defined on equidistant two-dimensional grids, it is clearly the most sensitive and precise method to determine fractal dimension.
Computing Power and Sample Size for the False Discovery Rate in Multiple Applications
Authors: Ni Yonghui , Seffernick Anna Eames , Onar-Thomas Arzu , Pounds Stanley B.
Published: March 2024

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.
Cross-Cultural Validation of the Healthcare Provider’s Practices, Attitudes, Self-Confidence, and Knowledge Regarding Bullying Questionnaire
Authors: Celdrán-Navarro María del Carmen , Jiménez-Ruiz Ismael , Seva-Llor A. Myriam , Moore James R. , Leal-Costa Cesar
Published: March 2024

How can we know the reality of the context of bullying in the field of primary health care? The aim of this study is to obtain a validated and reliable tool that allows measurement of the involvement of primary care professionals in addressing bullying through a systematic content validation process. A cross-cultural validation of the Healthcare Provider’s Practices, Attitudes, Self-Confidence, and Knowledge Regarding Bullying Questionnaire was conducted for the Spanish perspective. This involved linguistic adaptation through translation–back-translation, content validity index (CVI) analysis, construct validity using confirmatory factor analysis (CFA), and internal consistency (Cronbach’s α). The total CVI was 0.95, with individual item scores ≥ 0.78. CFA revealed a good fit for the three subscales, with discrimination indices (item–total correlation within the dimension) > 0.30. Cronbach’s α for each dimension indicated a high level of reliability, with values of 0.735 for attitudes, 0.940 for self-confidence, and 0.895 for knowledge. The questionnaire is valid and reliable for evaluating the knowledge, attitudes, and self-confidence of primary care professionals in Spain regarding bullying. Its validity and reliability guarantee its potential use in other health settings and may lead to better training of professionals and school biopsychosocial health.
Political Leaders’ Communication Strategies during COVID-19 in Highly Infected Countries: A Scoping Review
Authors: Mohamed Nour Magde , Kisa Adnan
Published: March 2024

This scoping review maps communication strategies employed by political leaders in countries that experienced high infection rates during the COVID-19 pandemic. Using the Arksey and O’Malley scoping review framework, this study systematically explored the literature from 2019 to October 2023. The process involved identifying and selecting relevant studies, charting them, and summarizing the data from the 40 articles that met the inclusion criteria. This review identified a diverse array of communication strategies, which highlight the complex nature of crisis communication. These strategies featured the use of social media, science-based policy communication, strategic narrative control, empathy, ideological influences, and storytelling. These six approaches underscore the importance of adaptability and context-specific strategies in political leadership during a health crisis. The findings demonstrate that political communication during the pandemic varied significantly and was influenced by factors such as media platform, political ideology, gender, and non-verbal cues. This review enriches our understanding of crisis communication in political contexts. It emphasizes the necessity of combining traditional and digital media and considering various sociopolitical factors. The insights gained are crucial for enhancing crisis management and public trust, and they set the stage for further research and practical application in crisis communication.
Scutellaria baicalensis Induces Cell Apoptosis and Elicits Mesenchymal–Epithelial Transition to Alleviate Metastatic Hepatocellular Carcinoma via Modulating HSP90β
Authors: Wu Tung-Ho , Lin Tung-Yi , Yang Pei-Ming , Li Wen-Tai , Yeh Chau-Ting , Pan Tai-Long
Published: March 2024

Hepatocellular carcinoma is one of the most common malignant tumors in the world and shows strong metastatic potential. Current medicine for hepatocellular carcinoma therapy is invalid, while Scutellaria baicalensis Georgi exhibits the pharmaceutical potential to treat liver diseases and liver cancer. Herein, we verified the inhibitory properties and the pivotal molecules regimented by Scutellaria baicalensis on advanced hepatocellular carcinoma. At first, the viability of SK-Hep-1 cells was significantly reduced under treatment of Scutellaria baicalensis extract in a dose-dependent manner without affecting the growth of normal hepatocyte. Scutellaria baicalensis extract application could remarkably cause apoptosis of SK-Hep-1 cells through p53/cytochrome C/poly-ADP ribose polymerase cascades and arrest the cell cycle at the G1/S phase by downregulating cyclin-dependent kinases. Meanwhile, administration of Scutellaria baicalensis extract remarkably attenuated the migration capability as well as suppressed matrix metalloproteinase activity of advanced hepatocellular carcinoma cells. The proteome profiles and network analysis particularly implied that exposure to Scutellaria baicalensis extract downregulated the expression of HSP90β, and the clinical stage of hepatocellular carcinoma is also positively correlated with the HSP90β level. Combined treatment of Scutellaria baicalensis extract and HSP90β siRNAs could markedly enhance the ubiquitination activity and the degradation of vimentin to subsequently inhibit the metastatic property of SK-Hep-1 cells. Moreover, application of Scutellaria baicalensis extract and HSP90β siRNAs depleted phosphorylation of AKT, which stimulated the expression of p53 and consecutively triggered cell apoptosis. These findings suggest that HSP90β may be a prospective target for the effective therapy of advanced hepatocellular carcinoma via accelerating apoptosis of hepatocellular carcinoma cells and eliciting mesenchymal–epithelial transition with the administration of Scutellaria baicalensis extract.
Pathophysiology and Main Molecular Mechanisms of Urinary Stone Formation and Recurrence
Authors: Tamborino Flavia , Cicchetti Rossella , Mascitti Marco , Litterio Giulio , Orsini Angelo , Ferretti Simone , Basconi Martina , De Palma Antonio , Ferro Matteo , Marchioni Michele , Schips Luigi
Published: March 2024

Kidney stone disease (KSD) is one of the most common urological diseases. The incidence of kidney stones has increased dramatically in the last few decades. Kidney stones are mineral deposits in the calyces or the pelvis, free or attached to the renal papillae. They contain crystals and organic components, and they are made when urine is supersaturated with minerals. Calcium-containing stones are the most common, with calcium oxalate as the main component of most stones. However, many of these form on a calcium phosphate matrix called Randall’s plaque, which is found on the surface of the kidney papilla. The etiology is multifactorial, and the recurrence rate is as high as 50% within 5 years after the first stone onset. There is a great need for recurrence prevention that requires a better understanding of the mechanisms involved in stone formation to facilitate the development of more effective drugs. This review aims to understand the pathophysiology and the main molecular mechanisms known to date to prevent recurrences, which requires behavioral and nutritional interventions, as well as pharmacological treatments that are specific to the type of stone.
Identification of Key Molecular Pathways and Associated Genes as Targets to Overcome Radiotherapy Resistance Using a Combination of Radiotherapy and Immunotherapy in Glioma Patients
Authors: Zhang Tianqi , Zhang Qiao , He Xinwei , Lu Yuting , Shao Andrew , Sun Xiaoqiang , Shao Yongzhao
Published: March 2024

Recent mechanistic studies have indicated that combinations of radiotherapy (RT) plus immunotherapy (via CSF-1R inhibition) can serve as a strategy to overcome RT resistance and improve the survival of glioma mice. Given the high mortality rate for glioma, including low-grade glioma (LGG) patients, it is of critical importance to investigate the mechanism of the combination of RT and immunotherapy and further translate the mechanism from mouse studies to improve survival of RT-treated human glioma patients. Using the RNA-seq data from a glioma mouse study, 874 differentially expressed genes (DEGs) between the group of RT-treated mice at glioma recurrence and the group of mice with combination treatment (RT plus CSF-1R inhibition) were translated to the human genome to identify significant molecular pathways using the KEGG enrichment analysis. The enrichment analysis yields statistically significant signaling pathways, including the phosphoinositide 3-kinase (PI3K)/AKT pathway, Hippo pathway, and Notch pathway. Within each pathway, a candidate gene set was selected by Cox regression models as genetic biomarkers for resistance to RT and response to the combination of RT plus immunotherapies. Each Cox model is trained using a cohort of 295 RT-treated LGG patients from The Cancer Genome Atlas (TCGA) database and validated using a cohort of 127 RT-treated LGG patients from the Chinese Glioma Genome Atlas (CGGA) database. A four-DEG signature (ITGB8, COL9A3, TGFB2, JAG1) was identified from the significant genes within the three pathways and yielded the area under time-dependent ROC curve AUC = 0.86 for 5-year survival in the validation set, which indicates that the selected DEGs have strong prognostic value and are potential intervention targets for combination therapies. These findings may facilitate future trial designs for developing combination therapies for glioma patients.
Oxidative Stress-Mediated Repression of Virulence Gene Transcription and Biofilm Formation as Antibacterial Action of Cinnamomum burmannii Essential Oil on Staphylococcus aureus
Authors: Shi Lingling , Lin Wei , Cai Yanling , Chen Feng , Zhang Qian , Liang Dongcheng , Xiu Yu , Lin Shanzhi , He Boxiang
Published: March 2024

This work aimed to identify the chemical compounds of Cinnamomum burmannii leaf essential oil (CBLEO) and to unravel the antibacterial mechanism of CBLEO at the molecular level for developing antimicrobials. CBLEO had 37 volatile compounds with abundant borneol (28.40%) and showed good potential to control foodborne pathogens, of which Staphylococcus aureus had the greatest inhibition zone diameter (28.72 mm) with the lowest values of minimum inhibitory concentration (1.0 μg/mL) and bactericidal concentration (2.0 μg/mL). To unravel the antibacterial action of CBLEO on S. aureus, a dynamic exploration of antibacterial growth, material leakage, ROS formation, protein oxidation, cell morphology, and interaction with genome DNA was conducted on S. aureus exposed to CBLEO at different doses (1/2–2×MIC) and times (0–24 h), indicating that CBLEO acts as an inducer for ROS production and the oxidative stress of S. aureus. To highlight the antibacterial action of CBLEO on S. aureus at the molecular level, we performed a comparative association of ROS accumulation with some key virulence-related gene (sigB/agrA/sarA/icaA/cidA/rsbU) transcription, protease production, and biofilm formation in S. aureus subjected to CBLEO at different levels and times, revealing that CBLEO-induced oxidative stress caused transcript suppression of virulence regulators (RsbU and SigB) and its targeted genes, causing a protease level increase destined for the biofilm formation and growth inhibition of S. aureus, which may be a key bactericidal action. Our findings provide valuable information for studying the antibacterial mechanism of essential oil against pathogens.
Advanced Therapy Medicinal Products as Potential Therapeutic Strategy against COVID-19 and Immune-Related Disorders
Authors: Mallis Panagiotis , Michalopoulos Efstathios , Stavropoulos-Giokas Catherine
Published: March 2024

Advanced Therapy Medicinal Products (ATMPs) comprise a heterogenous class of innovative medicinal products, which further require extensive preclinical and clinical assessments before their broader use in the general population [...]
Jozimine A2, a Dimeric Naphthylisoquinoline (NIQ) Alkaloid, Shows In Vitro Cytotoxic Effects against Leukemia Cells through NF-κB Inhibition
Authors: Damiescu Roxana , Yücer Rümeysa , Klauck Sabine M. , Bringmann Gerhard , Efferth Thomas , Dawood Mona
Published: March 2024

Naphthylisoquinoline (NIQ) alkaloids are rising as a promising class of secondary metabolites with pharmaceutical potential. NF-κB has already been recognized as a significant modulator of cancer proliferation and drug resistance. We have previously reported the mechanisms behind the cytotoxic effect of dioncophylline A, an NIQ monomer, in leukemia cells. In the current study, we have investigated the cytotoxic effect of jozimine A2, an NIQ dimer, on leukemia cells in comparison to a second, structurally unsymmetric dimer, michellamine B. To this end, molecular docking was applied to predict the binding affinity of the dimers towards NF-κB, which was then validated through microscale thermophoresis. Next, cytotoxicity assays were performed on CCRF-CEM cells and multidrug-resistant CEM/ADR5000 cells following treatment. Transcriptome analysis uncovered the molecular networks affected by jozimine A2 and identified the cell cycle as one of the major affected processes. Cell death modes were evaluated through flow cytometry, while angiogenesis was measured with the endothelial cell tube formation assay on human umbilical vein endothelial cells (HUVECs). The results indicated that jozimine A2 bound to NF-κB, inhibited its activity and prevented its translocation to the nucleus. In addition, jozimine A2 induced cell death through apoptosis and prevented angiogenesis. Our study describes the cytotoxic effect of jozimine A2 on leukemia cells and explains the interactions with the NF-κB signaling pathway and the anticancer activity.
The Genetic Profile of Large B-Cell Lymphomas Presenting in the Ocular Adnexa
Authors: Vest Stine Dahl , Eriksen Patrick Rene Gerhard , de Groot Fleur A. , de Groen Ruben A. L. , Kleij Anne H. R. , Kirkegaard Marina Knudsen , Kamper Peter , Rasmussen Peter Kristian , von Buchwald Christian , de Nully Brown Peter , Kiilgaard Jens Folke , Vermaat Joost S. P. , Heegaard Steffen
Published: March 2024

To provide insights into targetable oncogenic pathways, this retrospective cohort study investigated the genetic profile of 26 patients with diffuse large B-cell lymphoma, not otherwise specified (DLBCL-NOS), and two patients with high-grade B-cell lymphoma with MYC and BCL2 rearrangements (HGBCL) presenting in the ocular adnexa. Pathogenic variants and copy number variations in 128 B-cell lymphoma-relevant genes were analyzed by targeted next-generation sequencing. Genetic subtypes were determined with the LymphGen algorithm. Primary ocular adnexal DLBCL-NOS constituted 50% (n = 14) and was generally characterized by non-germinal center B-cell origin (non-GCB) (n = 8, 57%), and LymphGen MCD subtype (n = 5, 36%). Primary ocular adnexal DLBCL-NOS presented pathogenic variants in genes involved in NF-κB activation and genes which are recurrently mutated in other extranodal lymphomas of non-GCB origin, including MYD88 (n = 4, 29%), CD79B (n = 3, 21%), PIM1 (n = 3, 21%), and TBL1XR1 (n = 3, 21%). Relapsed DLBCL-NOS presenting in the ocular adnexa (n = 6) were all of non-GCB origin and frequently of MCD subtype (n = 3, 50%), presenting with a similar genetic profile as primary ocular adnexal DLBCL-NOS. These results provide valuable insights into genetic drivers in ocular adnexal DLBCL-NOS, offering potential applications in future precision medicine.