bluuebunny/crossref_metadata_2025 huggingface dataset:
Metabolic Dysfunction-Associated Steatotic Liver Disease Is Associated with Increased Risk of Kidney Cancer: A Nationwide Study
Authors: Oh Juyeon , Kim Beom Kyung , Yoon Jin-Ha , Lee Hyung Ho , Park Heejoo , Lee Jian , Park Youngsun , Yun Byungyoon , Chung Jinsoo
Published: September 2024

Background: This study investigated the association between metabolic dysfunction-associated steatotic liver disease (MASLD) and Kidney Cancer Risk, as the incidence of both diseases gradually increases owing to metabolic health issues. Methods: Participants (aged 20–79) undergoing a national health examination between 2009 and 2010 were monitored for new-onset kidney cancer. The MASLD spectrum was classified as non-MASLD, MASLD, or MASLD with increased alcohol uptake (MetALD). Kidney Cancer Risk associated with the MASLD spectrum was estimated using multivariate Cox proportional hazard models. Age- and sex-stratified analyses were also performed. Results: Among 8,829,510 participants (median follow-up 13.3 years), the proportion of non-MASLD, MASLD, and MetALD was 64.9%, 30.3%, and 4.7%, respectively, with newly developed kidney cancer in 17,555 participants. Kidney cancer was significantly increased with MASLD (adjusted hazard ratio [aHR] 1.51, 95% confidence interval [CI] 1.46–1.56) and MetALD (aHR 1.51, 95% CI 1.42–1.61), compared with the non-MASLD group. Kidney Cancer Risk was the highest among young populations (aHR 1.93, 95% CI 1.77–2.11 for MASLD and aHR 1.91, 95% CI 1.65–2.22 for MetALD), according to stratification analysis. Furthermore, the cumulative relationship between metabolic dysfunction and Kidney Cancer Risk was confirmed across all MASLD spectra. Conclusions: Our study highlights the positive association between MASLD and Kidney Cancer Risk, emphasizing a comprehensive approach to metabolic health. This also serves as a call to devote closer attention to the metabolic health of younger patients.
De-Escalation of Axillary Surgery in Clinically Node-Positive Breast Cancer Patients Treated with Neoadjuvant Therapy: Comparative Long-Term Outcomes of Sentinel Lymph Node Biopsy versus Axillary Lymph Node Dissection
Authors: Tinterri Corrado , Barbieri Erika , Sagona Andrea , Di Maria Grimaldi Simone , Gentile Damiano
Published: September 2024

Backgrounds: This study compares the long-term outcomes of axillary lymph node dissection (ALND) versus sentinel lymph node biopsy (SLNB) in clinically node-positive (cN+) breast cancer (BC) patients treated with neoadjuvant therapy (NAT).Methods: We conducted a retrospective analysis of 322 cN+ BC patients who became clinically node-negative (ycN0) post-NAT. Patients were categorized based on the final type of axillary surgery performed: ALND or SLNB. Recurrence-free survival (RFS), distant disease-free survival (DDFS), overall survival (OS), and breast cancer-specific survival (BCSS) were evaluated and compared between the two groups. Results: Patients in the SLNB group had significantly better 3-, 5-, and 10-year RFS, DDFS, OS, and BCSS compared to those in the ALND group. The SLNB group also had a higher proportion of patients achieving pathologic complete response (pCR). Multivariate analysis identified pCR, ypN0 status, and SLNB as favorable prognostic factors for all survival metrics. Axillary recurrence rates were low for both groups (0.6–2.1%). Conclusions: SLNB may be a safe and effective alternative to ALND for selected cN+ BC patients who convert to ycN0 after NAT. These findings suggest that careful patient selection is crucial, and further research is needed to validate these results in more comparable populations.
Isotopic Composition of C, N, and S as an Indicator of Endometrial Cancer
Authors: Zuzak Tomasz , Bogaczyk Anna , Krata Agnieszka Anna , Kamiński Rafał , Paneth Piotr , Kluz Tomasz
Published: September 2024

Objectives: The metabolic pathway of cancerous tissue differs from healthy tissue, leading to the unique isotopic composition of stable isotopes at their natural abundance. We have studied if these changes can be developed into diagnostic or prognostic tools in the case of endometrial cancer. Methods: Measurements of stable isotope ratios were performed using isotope ratio mass spectrometry for nitrogen, carbon, and sulfur isotopic assessment. Uterine tissue and serum samples were collected from patients and the control group. Results: At a natural abundance, the isotopic compositions of all three of the studied elements of uterus cancerous and healthy tissues are different. However, no correlation of the isotopic composition of the tissues with that of serum was found. Conclusions: Differences in the isotopic composition of the tissues might be a potential prognostic tool. However, the lack of a correlation between the differences in the isotopic composition of the tissues and serum seems to exclude their application as diagnostic biomarkers, which, however, might be possible if a position-specific isotopic analysis is performed.
Isoform-Level Transcriptome Analysis of Peripheral Blood Mononuclear Cells from Breast Cancer Patients Identifies a Disease-Associated RASGEF1A Isoform
Authors: Čelešnik Helena , Gorenjak Mario , Krušič Martina , Crnobrnja Bojana , Sobočan Monika , Takač Iztok , Arko Darja , Potočnik Uroš
Published: September 2024

Background: Breast cancer (BC) comprises multiple subtypes with distinct molecular features, which differ in their interplay with host immunity, prognosis, and treatment. Non-invasive blood analyses can provide valuable insights into systemic immunity during cancer. The aim of this study was to analyze the expression of transcriptional isoforms in peripheral blood mononuclear cells (PBMCs) from BC patients and healthy women to identify potential BC immune biomarkers. Methods: RNA sequencing and isoform-level bioinformatics were performed on PBMCs from 12 triple-negative and 13 luminal A patients. Isoform expression validation by qRT-PCR and clinicopathological correlations were performed in a larger cohort (156 BC patients and 32 healthy women). Results: Transcriptional analyses showed a significant (p < 0.001) decrease in the ENST00000374459 RASGEF1A isoform in PBMCs of BC compared to healthy subjects, indicating disease-related expression changes. The decrease was associated with higher ctDNA and Ki-67 values. Conclusions: The levels of the RASGEF1A transcriptional isoform ENST00000374459 may have the potential to distinguish between BC and healthy subjects. The downregulation of ENST00000374459 in breast cancer is associated with higher proliferation and ctDNA shedding. Specialized bioinformatics analyses such as isoform analyses hold significant promise in the detection of biomarkers, since standard RNA sequencing analyses may overlook specific transcriptional changes that may be disease-associated and biologically important.
Enhancing Inter-AUV Perception: Adaptive 6-DOF Pose Estimation with Synthetic Images for AUV Swarm Sensing
Authors: Wei Qingbo , Yang Yi , Zhou Xingqun , Hu Zhiqiang , Li Yan , Fan Chuanzhi , Zheng Quan , Wang Zhichao
Published: September 2024

The capabilities of AUV mutual perception and localization are crucial for the development of AUV swarm systems. We propose the AUV6D model, a synthetic image-based approach to enhance inter-AUV perception through 6D pose estimation. Due to the challenge of acquiring accurate 6D pose data, a dataset of simulated underwater images with precise pose labels was generated using Unity3D. Mask-CycleGAN technology was introduced to transform these simulated images into realistic synthetic images, addressing the scarcity of available underwater data. Furthermore, the Color Intermediate Domain Mapping strategy is proposed to ensure alignment across different image styles at pixel and feature levels, enhancing the adaptability of the pose estimation model. Additionally, the Salient Keypoint Vector Voting Mechanism was developed to improve the accuracy and robustness of underwater pose estimation, enabling precise localization even in the presence of occlusions. The experimental results demonstrated that our AUV6D model achieved millimeter-level localization precision and pose estimation errors within five degrees, showing exceptional performance in complex underwater environments. Navigation experiments with two AUVs further verified the model’s reliability for mutual 6D pose estimation. This research provides substantial technical support for more complex and precise collaborative operations for AUV swarms in the future.
R-LVIO: Resilient LiDAR-Visual-Inertial Odometry for UAVs in GNSS-denied Environment
Authors: Zhang Bing , Shao Xiangyu , Wang Yankun , Sun Guanghui , Yao Weiran
Published: September 2024

In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%.
Dynamic Target Tracking and Following with UAVs Using Multi-Target Information: Leveraging YOLOv8 and MOT Algorithms
Authors: Ferreira Diogo , Basiri Meysam
Published: September 2024

This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking (MOT) methods in target following scenarios over traditional single-object tracking (SOT) algorithms. The system integrates the real-time object detection model, You Only Look Once (YOLO)v8, with the MOT algorithms BoT-SORT and ByteTrack, extracting multi-target information. It leverages this information to improve redetection capabilities, addressing target misidentifications (ID changes), and partial and full occlusions in dynamic environments. A depth sensing module is incorporated to enhance distance estimation when feasible. A 3D flight control system is proposed for target following, capable of reacting to changes in target speed and direction while maintaining line-of-sight. The system is initially tested in simulation and then deployed in real-world scenarios. Results show precise target tracking and following, resilient to partial and full occlusions in dynamic environments, effectively distinguishing the followed target from bystanders. A comparison between the BoT-SORT and ByteTrack trackers reveals a trade-off between computational efficiency and tracking precision. In overcoming the presented challenges, this work enables new practical applications in the field of vision-based target following from UAVs leveraging multi-target information.
A Green Laboratory Approach to Medical Sample Transportation: Assessing the Carbon Dioxide (CO2) Footprint of Medical Sample Transportation by Drone, Combustion Car, and Electric Car
Authors: Stierlin Noel , Loertscher Fabian , Renz Harald , Risch Lorenz , Risch Martin
Published: September 2024

In response to escalating climate change concerns, this study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across various terrains and weather conditions in Liechtenstein and Switzerland. Through a comparative analysis, we found that combustion cars emit the highest average CO2 at 159.5 g per kilometer (g/km), while electric cars significantly reduce emissions to an average of 3.43 g/km, representing just 2.15% of the emissions from combustion vehicles. Drones emerged as the most environmentally sustainable option, with an average CO2 emission of 0.09 g/km, which is only 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also demonstrated superior transport efficiency, covering routes that were, on average, 17% shorter in flat terrain and 24% shorter in mountainous regions compared to cars. Additionally, drones achieved substantial time savings, ranging from 13% to 80% faster delivery times depending on the terrain and traffic conditions. These findings highlight the potential of drone technology to revolutionize healthcare logistics by significantly reducing carbon footprints, optimizing transport routes, and improving delivery efficiency. Integrating drones into healthcare transportation networks offers a promising pathway toward a more sustainable and resilient healthcare system.
Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission
Authors: Cárdenas-Valdez José Ricardo , Ramírez-Villalobos Ramón , Ramirez-Ubieta Catherine , Inzunza-Gonzalez Everardo
Published: September 2024

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system’s effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.
Is Seeing Believing? A Practitioner’s Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies
Authors: Fan Kun , Subedi Srijana , Yang Gongshun , Lu Xi , Ren Jie , Wu Cen
Published: September 2024

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the “unpenalized loss function + penalty term” formulation for regularization methods and the “likelihood function × shrinkage prior” framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.
Monitoring and Diagnosing Faults in Induction Motors’ Three-Phase Systems Using NARX Neural Network
Authors: Araújo Valbério Gonzaga de , Bissiriou Aziz Oloroun-Shola , Villanueva Juan Moises Mauricio , Villarreal Elmer Rolando Llanos , Salazar Andrés Ortiz , Teixeira Rodrigo de Andrade , Fonsêca Diego Antonio de Moura
Published: September 2024

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.
Enhanced Electrical Tree Resistance of Polypropylene Cable Insulation by Introducing β-Crystals
Authors: Yang Zhuoran , Tong Bin , Wang Han , Zhu Peixuan , Rao Huanyu , Li Zhonglei
Published: September 2024

Polypropylene (PP) is regarded as a recyclable material for high-voltage direct current (HVDC) cable insulation due to its high melting point and electrical resistivity. This work focuses on the effect of the β-nucleating agent content on the electrical tree growth characteristics in isotactic PP (iPP) insulation. The results demonstrate that adding β-nucleating agents promotes the growth of β-crystals while limiting the α-crystal content. The crystallinity improves with the reduction in the average size of spherulites due to the addition of a β-nucleating agent with 0.1 wt% content. Electrical tree experiments show that the electrical tree growth rate declines as the nucleating agent content rises from 0 to 0.1 wt%. Meanwhile, the expansion coefficient increases with higher nucleating agent content. Continuous increases in the nucleate agent content result in the upward growth rate of electrical trees. When the nucleating agent content is below 0.1 wt%, the α–β-crystal interface introduced by the agent suppresses carrier migration and limits impact ionization, leading to the slower growth rate of the electrical tree. Further addition of the nucleate agent induces a β–β-crystal interface with weak coupling in carriers. It is concluded that β-nucleating agent-modified PP with 0.1 wt% content has potential application in HVDC cable insulation.
A Standardized Sky Condition Classification Method for Multiple Timescales and Its Applications in the Solar Industry
Authors: Poddar Shukla , Kay Merlinde , Boland John
Published: September 2024

The deployment of photovoltaic (PV) systems has increased globally to meet renewable energy targets. Intermittent PV power generated due to cloud-induced variability introduces reliability and grid stability issues at higher penetration levels. Variability in power generation can induce voltage fluctuations within the distribution system and cause adverse effects on power quality. Therefore, it is essential to quantify resource variability to mitigate an intermittent power supply. In this study, we propose a new scheme to classify the sky conditions that are based on two common variability metrices: daily clear-sky index and normalized aggregate ramp rates. The daily clear-sky index estimates the cloudiness in the sky, and ramp rates account for the variability introduced in the system generation due to sudden cloud movements. This classification scheme can identify clear-sky, highly variable, low intermittent, high intermittent and overcast days. By performing a Chi-square test on the training and test sets, we obtain Chi-square statistic values greater than 3 with p-value > 0.05. This indicates that the distribution of the training and test clusters are similar, indicating the robustness of the proposed sky classification scheme. We have demonstrated the applicability of the scheme with diverse datasets to show that the proposed classification scheme can be homogenously applied to any dataset globally despite their temporal resolution. Using various case studies, we demonstrate the potential applications of the scheme for understanding resource allocation, site selection, estimating future intermittency due to climate change, and cloud enhancement effects. The proposed sky classification scheme enhances the precision and reliability of solar energy forecasts, optimizing system performance and maximizing energy production efficiency. This improved accuracy is crucial for variability control and planning, ensuring optimal output from PV plants.
Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control
Authors: Liu Yingming , Wang Yi , Wang Xiaodong
Published: September 2024

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.
Transient Active Power in Two-Terminal Networks
Authors: Hawron Konrad , Rozegnał Bartosz , Sułowicz Maciej
Published: September 2024

This article presents the hitherto unknown concept of transient active power in two-terminal networks. This phenomenon occurs when current and voltage signals are not sinusoidal but quasi-sinusoidal—in the case of an approximation of transient state. The modification of Parseval’s formula and the power–immittance relations are presented in this paper. To illustrate the phenomenon, a simulation is included for several types of transient states, and their influence on transient active power waveform is shown. The article also contains a comparison of transient active power and classical active power and highlights situations where it is impossible to use classical theory but where transient active power yields measurable results. In the article, an analysis of an idealized case and measurement data obtained from a modeled voltage sag in a laboratory setup is conducted. The impact of disturbance power on the total power in the case of disturbances that may occur in the real power network is demonstrated.
Experimental Assessment of a Novel Irradiance Sensorless Intelligent Control Scheme for a Standalone Photovoltaic System under Real Climatic Conditions
Authors: Sun Jialan , Fan Jinwei
Published: September 2024

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions.
Wind Farm Prediction of Icing Based on SCADA Data
Authors: Zhang Yujie , Rotea Mario , Kehtarnavaz Nasser
Published: September 2024

In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine.
Participation of Energy Communities in Electricity Markets and Ancillary Services: An Overview of Successful Strategies
Authors: Cruz-De-Jesús Emely , Marano-Marcolini Alejandro , Martínez-Ramos José Luis
Published: September 2024

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators.
Effects of Water Mist on the Initial Evolution of Turbulent Premixed Hydrogen/Air Flame Kernels
Authors: Concetti Riccardo , Hasslberger Josef , Chakraborty Nilanjan , Klein Markus
Published: September 2024

In this study, a series of carrier-phase direct numerical simulations are conducted on spherical expanding premixed hydrogen/air flames with liquid water addition. An Eulerian–Lagrangian approach with two-way coupling is employed to describe the liquid–gas interaction. The impacts of preferential diffusion, the equivalence ratio, water loading, and the initial diameter of the water droplets are examined and analyzed in terms of flame evolution. It is observed that liquid water has the potential to influence flame propagation characteristics by reducing the total burning rate, flame area, and burning rate per unit area, attributed to flame cooling effects. However, these effects become discernible only under conditions where water evaporation is sufficiently intense. For the conditions investigated, the influence of preferential diffusion on flame evolution is found to be more significant than the interaction with liquid water. The results suggest that due to the slow evaporation rate of water, which is a result of its high latent heat of evaporation, the water droplets do not disturb the initial flame kernel growth significantly. This has implications for water injection concepts in internal combustion engines and for explosion mitigation.
PHF8/KDM7B: A Versatile Histone Demethylase and Epigenetic Modifier in Nervous System Disease and Cancers
Authors: Fan Tingyu , Xie Jianlian , Huang Guo , Li Lili , Zeng Xi , Tao Qian
Published: September 2024

Many human diseases, such as malignant tumors and neurological diseases, have a complex pathophysiological etiology, often accompanied by aberrant epigenetic changes including various histone modifications. Plant homologous domain finger protein 8 (PHF8), also known as lysine-specific demethylase 7B (KDM7B), is a critical histone lysine demethylase (KDM) playing an important role in epigenetic modification. Characterized by the zinc finger plant homology domain (PHD) and the Jumonji C (JmjC) domain, PHF8 preferentially binds to H3K4me3 and erases repressive methyl marks, including H3K9me1/2, H3K27me1, and H4K20me1. PHF8 is indispensable for developmental processes and the loss of PHF8 enzyme activity is linked to neurodevelopmental disorders. Moreover, increasing evidence shows that PHF8 is highly expressed in multiple tumors as an oncogenic factor. These findings indicate that studying the role of PHF8 will facilitate the development of novel therapeutic agents by the manipulation of PHF8 demethylation activity. Herein, we summarize the current knowledge of PHF8 about its structure and demethylation activity and its involvement in development and human diseases, with an emphasis on nervous system disorders and cancer. This review will update our understanding of PHF8 and promote the clinical transformation of its predictive and therapeutic value.
Integrating Active and Passive Remote Sensing Data for Forest Age Estimation in Shangri-La City, China
Authors: Cheng Feng , Yang Ruijiao , Wu Junen
Published: September 2024

The accurate mapping of age structure and access to spatially explicit information are essential to optimal planning and policy-making for forest ecosystems, including forest management and sustainable economic development. Specifically, surveying and mapping the age structure of forests is crucial for calculating the carbon sequestration capacity of forest ecosystems. However, spatial heterogeneity and limited accessibility make forest age mapping in mountainous areas challenging. Here, we present a new workflow using ICESat-2 LiDAR data integrated with multisource remote sensing imagery to estimate forest age in Shangri-La, China. Two methods—a climate-driven exponential model and a random forest algorithm—are compared to infer the age structure of the five dominant species in Shangri-La. The climate-driven model, with an R2 of 0.67 and an RMSE of 12.79 years, outperforms the random forest model. The derived wall-to-wall forest age map at 30 m resolution reveals that nearly all forests in Shangri-La are mature or overmature, especially among the high-elevation species Abies fabri (Mast.) Craib and Picea asperata Mast., compared with Pinus yunnanensis Franch., Quercus aquifolioides Rehd. and E.H. Wils. and Pinus densata Mast., where the age structure is more evenly distributed across different elevation ranges. Younger forests are frequently found around human settlements and along the Jinsha River valley, whereas older forests are located in remote and high-elevation areas that are less disturbed. The combined use of active and passive remote sensing data has resulted in substantial improvements in the spatial detail and accuracy of wall-to-wall age mapping, which is expected to be a cost-effective approach for supporting forest management and carbon accounting in this important ecological region. The method developed here can be scaled to other mountain areas both to understand the age patterns and structure of mountain forests and to provide critical information for forestation, reforestation and carbon accounting in surface-to-high mountain areas, which are increasingly crucial for climate mitigation.
Efficient Bio-Based Insulation Panels Produced from Eucalyptus Bark Waste
Authors: Fuentealba Cecilia , Segovia César , Pradena-Miquel Mauricio , César Andrés G.
Published: September 2024

Traditional thermal insulation panels consume large amounts of energy during production and emits pollutants into the environment. To mitigate this impact, the development of bio-based materials is an attractive alternative. In this context, the characteristics of the Eucalyptus fiber bark (EGFB) make it a candidate for insulation applications. However, more knowledge about the manufacturing process and in-service performance is needed. The present study characterized the properties that determine the in-service behavior of the EGFB insulation panel. The assessment involved two different manufacturing processes. The results indicated that the hot plates and the saturated steam injection manufacturing system can produce panels with similar target and bulk density. The thermal conductivity fluctuated between 0.064 and 0.077 W/m·K, which indicated good insulation, and the values obtained for thermal diffusivity (0.10–0.37 m mm2/s) and water vapor permeability (0.032–0.055 m kg/GN s) are comparable with other commercially available panels. To guarantee a good in-service performance, the panels need to be treated with flame retardant and antifungal additive. The good performance of the panel is relevant because bio-based Eucalyptus bark panels generate less CO2 eq and require less energy consumption compared to traditional alternatives, contributing to the sustainability of the forestry and the construction industry.
Contrasting Non-Timber Forest Products’ Case Studies in Underdeveloped Areas in China
Authors: Qiao Qin , Lei Shuo , Zhang Wenting , Shao Guomei , Sun Yong , Han Yongwei
Published: September 2024

Enhancing the sustainability of the non-timber forest products industry has dual significance for both the management of local forest resources and socio-economic development. This paper adopts a systems theory perspective to construct an analytical model for the sustainable development of non-timber forest products, based on a “social-economic-natural” framework. By analyzing case studies of non-timber forest products industry sustainability from four underdeveloped counties in China, the paper derives the following main conclusions and insights: The sustainability of non-timber forest products development models is influenced by factors such as resource endowments and institutional environments and includes both single and composite models. Underdeveloped regions can achieve considerable sustainability in the development of non-timber forest products, but this requires a rational allocation of six key elements—policy, model, stakeholders, natural resources, funding, and technology—to stimulate industry growth. To promote the sustainable development of this industry, optimization should be pursued across five aspects: “policy leadership and top-level design to guide industry development”, “selection of appropriate development models based on local natural endowments and socio-economic foundations”, “large enterprise-driven mechanisms to form multi-stakeholder interest connections”, “focus on product technology research and development, and establishment of technical training mechanisms”, and “market-driven funding to develop product sales markets”.
Sex Identification and Male–Female Differences in Ginkgo Biloba Hybrid F1 Generation Seedlings
Authors: Gao Xiaoge , Hu Yaping , Li Fangdi , Cao Fuliang , Guo Qirong
Published: September 2024

In exploring the male–female differentiation and differences in the seedling stage of the F1 generation of ginkgo hybrids, an early selection test for the cultivation and research of leafy or medicinal ginkgo industry was conducted, which may provide a basis for boosting the precision of the ginkgo industry. The hybrid F1 generation obtained from the cross-mating was used as material for the determination of growth and development, as well as of the physiology and biochemistry of the monocots, and the male and female differential genes were obtained based on the data of SNPs obtained from the GBS sequencing of the hybrid progeny. In the seedling stage of ginkgo hybrid offspring, male plants had a significantly higher nutrient growth capacity than female plants, while the total flavonoid and terpene lactone contents in female plants were higher than those of the male plants. This result can provide a corresponding theoretical basis for the use of ginkgo germplasm resources, which can make full use of the male and female differences in the seedling stage and maximize the benefits of early sex identification.
Socially Acceptable Feed Formulations May Impact the Voluntary Feed Intake and Growth, but Not Robustness of Nile Tilapia (Oreochromis niloticus)
Authors: Mendes Rodrigo , Rema Paulo , Dias Jorge , Gonçalves Ana Teresa , Teodósio Rita , Engrola Sofia , Sánchez-Vázquez Francisco J. , Conceição Luís E. C.
Published: September 2024

Society is becoming more demanding with aquaculture’s environmental footprint and animal wellbeing. In order to potentially mitigate these concerns, feed formulations could be based on eco-efficient (circular economy-driven) or organic ingredients. This study aimed to investigate the growth performance, feed utilization, and health status of juvenile Nile tilapia (Oreochromis niloticus) when fed with such feeds. The growth trial lasted for 8 weeks, and fish had an initial weight of 31.0 ± 0.5 g (mean ± SD). Fish were fed until visual satiation, in quadruplicate, with one of three isonitrogenous and isoenergetic experimental feeds: a commercial-like feed without fishmeal (PD), a diet based on ingredients compatible with organic certification (ORG), or a feed formulated using circular economy-driven subproducts and emergent ingredients (ECO). Fish fed ECO showed a tendency for decreased feed intake, while ORG fish significantly reduced their intake compared to those fed PD. Consequently, fish fed ECO (62.7 ± 5.4 g) exhibited almost half the growth than those fed PD (107.8 ± 6.1 g), while ORG fish almost did not increase their weight (32.7 ± 1.3 g). ECO and ORG diets had a lower digestibility for protein, lipid, and energy when compared to PD. Feed utilization of fish fed ECO or ORG was also lower than those fed PD. From the health-related genes analyzed, only glutathione reductase (gsr) showed statistically significant differences, being more expressed in fish-fed ECO than those fed PD. Thus, even when such novel formulations induced extreme effects on voluntary feed intake, their impact was noted only in fish growth, but not in robustness.
Analysis of Infection and Diffusion Coefficient in an SIR Model by Using Generalized Fractional Derivative
Authors: Alazman Ibtehal , Mishra Manvendra Narayan , Alkahtani Badr Saad , Dubey Ravi Shanker
Published: September 2024

In this article, a diffusion component in an SIR model is introduced, and its impact is analyzed using fractional calculus. We have included the diffusion component in the SIR model. in order to illustrate the variations. Here, we have applied the general fractional derivative to analyze the impact. The Laplace decomposition technique is employed to obtain the numerical outcomes of the model. In order to observe the effect of the diffusion component in the SIR model, graphical solutions are also displayed.
Second Partnership and Dementia Care in a Blended Family: Case Study of a Wicked Problem
Authors: Stefansdottir Olga Asrun , Kaas Merrie J. , Sudmann Tobba
Published: September 2024

Dementia care research has largely ignored the challenges that may emerge from couple and family dynamics, especially about second partnerships in blended families. This paper details the case of a 79-year-old man, Hannes, in his second partnership who tried to handle the complexities of his wife’s dementia care as her children and healthcare providers discounted his role as husband and decision maker. He faced difficult communications with family members and challenges to his role as decision maker by healthcare providers and legal system professionals. This man’s story is explored through multiple interviews and document analyses from 2020 to 2023. This case study uses the concept of the “wicked problem” to frame the potential complexities of dementia care when blended families are involved in decision making. This framework allows us to consider the many facets of family dementia care and how improvements could be made to facilitate role transitions and family decision making.
Inclusive Crisis Communication in a Pandemic Context: A Rapid Review
Authors: Hannes Karin , Thyssen Pieter , Bengough Theresa , Dawson Shoba , Paque Kristel , Talboom Sarah , Tuand Krizia , Vandendriessche Thomas , van de Veerdonk Wessel , Wopereis Daniëlle , Vandamme Anne-Mieke
Published: September 2024

Background: Crisis communication might not reach non-native speakers or persons with low literacy levels, a low socio-economic status, and/or an auditory or visual impairments as easily as it would reach other citizens. The aim of this rapid review was to synthesize the evidence on strategies used to improve inclusive pandemic-related crisis communication in terms of form, channel, and outreach. Methods: After a comprehensive search and a rigorous screening and quality assessment exercise, twelve comparative studies were selected for inclusion in this review. Data were analyzed and represented by means of a structured reporting of available effects using narrative tables. Results: The findings indicate that a higher message frequency (on any channel) may lead to a lower recall rate, audio–visual productions and tailored messages prove to be valuable under certain conditions, and primary healthcare practitioners appear to be the most trusted source of information for most groups of citizens. Trust levels were higher for citizens who were notified in advance of potential exceptions to the rule in the effect of preventive and curative measures promoted. Conclusions: This review contributes to combatting information inequality by providing evidence on how to remove the sensorial, linguistic, cultural, and textual barriers experienced by minorities and other underserved target audiences in COVID-19-related governmental crisis communication in response to the societal, health-related costs of ineffective communication outreach.
Exploring the Connection between Social Housing and Employment: A Scoping Review
Authors: Jansen-van Vuuren Julia , Rijal Hibo , Bobbette Nicole , Lysaght Rosemary , Krupa Terry , Aguilar Daniella
Published: September 2024

Adequate housing is a social determinant of health and well-being, providing stability from which people can engage in important life activities, including self-care and productivity. Social housing is a system-level intervention that aims to provide affordable housing to people vulnerable to experiencing social and economic marginalisation. Given the importance of employment to social-economic status and overall health and well-being, we sought to better understand the available knowledge and research related to employment and living in a social housing environment. We used scoping review methodology to explore peer-reviewed research published between 2012–2022 regarding social housing and employment, identifying 29 relevant articles. Using the Psychology of Working Theory and neighbourhood effects as interpretive theoretical frameworks, we analysed the extracted data. Overall, the results affirmed that social housing residents have low employment rates conceptualised as related to the complex interplay of a range of personal and environmental factors. Most published literature was quantitative and originated from the United States. Policy and research implications are discussed, including the need for more multifaceted, person-centred interventions that support employment and ultimately promote health and quality of life for social housing residents.
Application of Electrical Prospecting Methods for Monitoring the Condition of Earth Dam in the Almaty Region of Kazakhstan
Authors: Assemov Kambar , Akhmetov Yermek , Orazov Dastan
Published: September 2024

This article deals with the issue of diagnostics of the physical condition of earthen dams, taking into account seasonal changes in the water level of hydraulic structures using electrical exploration methods. The simplicity of the method, the accuracy of measurements of geophysical parameters, and the availability of software packages for the processing, interpretation, and visualization were the basis for the choice of method. The method of electrical resistivity and self-potential was chosen. The methodology, technique, technology of field surveys, processing, and geological interpretation of the study results are given. A comparative analysis of the obtained geophysical parameters of seasonal measurements is given. The research results are given in the form of sections of the resistivity model and self-potential isolines.