bluuebunny/crossref_metadata_2025 huggingface dataset:
iPSC-Derived Cardiomyocytes as a Disease Model to Understand the Biology of Congenital Heart Defects
Authors: Pushpan Chithra K. , Kumar Subramanyan Ram
Published: August 2024

The discovery of human pluripotent stem cells (hiPSCs) and advances in DNA editing techniques have opened opportunities for personalized cell-based therapies for a wide spectrum of diseases. It has gained importance as a valuable tool to investigate genetic and functional variations in congenital heart defects (CHDs), enabling the customization of treatment strategies. The ability to understand the disease process specific to the individual patient of interest provides this technology with a significant advantage over generic animal models. However, its utility as a disease-in-a-dish model requires identifying effective and efficient differentiation protocols that accurately reproduce disease traits. Currently, iPSC-related research relies heavily on the quality of cells and the properties of the differentiation technique In this review, we discuss the utility of iPSCs in bench CHD research, the molecular pathways involved in the differentiation of cardiomyocytes, and their applications in CHD disease modeling, therapeutics, and drug application.
TRIM44, a Novel Prognostic Marker, Supports the Survival of Proteasome-Resistant Multiple Myeloma Cells
Authors: Vu Trung , Wang Yuqin , Fowler Annaliese , Simieou Anton , McCarty Nami
Published: August 2024

TRIM44, a tripartite motif (TRIM) family member, is pivotal in linking the ubiquitin-proteasome system (UPS) to autophagy in multiple myeloma (MM). However, its prognostic impact and therapeutic potential remain underexplored. Here, we report that TRIM44 overexpression is associated with poor prognosis in a Multiple Myeloma Research Foundation (MMRF) cohort of 858 patients, persisting across primary and recurrent MM cases. TRIM44 expression notably increases in advanced MM stages, indicating its potential role in disease progression. Single-cell RNA sequencing across MM stages showed significant TRIM44 upregulation in smoldering MM (SMM) and MM compared to normal bone marrow, especially in patients with t(4;14) cytogenetic abnormalities. This analysis further identified high TRIM44 expression as predictive of lower responsiveness to proteasome inhibitor (PI) treatments, underscoring its critical function in the unfolded protein response (UPR) in TRIM44-high MM cells. Our findings also demonstrate that TRIM44 facilitates SQSTM1 oligomerization under oxidative stress, essential for its phosphorylation and subsequent autophagic degradation. This process supports the survival of PI-resistant MM cells by activating the NRF2 pathway, which is crucial for oxidative stress response and, potentially, other chemotherapy-induced stressors. Additionally, TRIM44 counters the TRIM21-mediated suppression of the antioxidant response, enhancing MM cell survival under oxidative stress. Collectively, our discoveries highlight TRIM44’s significant role in MM progression and resistance to therapy, suggesting its potential value as a therapeutic target.
A Comprehensive Review of Protein Biomarkers for Invasive Lung Cancer
Authors: Mezentsev Alexandre , Durymanov Mikhail , Makarov Vladimir A.
Published: August 2024

Invasion and metastasis are important hallmarks of lung cancer, and affect patients’ survival. Early diagnostics of metastatic potential are important for treatment management. Recent findings suggest that the transition to an invasive phenotype causes changes in the expression of 700–800 genes. In this context, the biomarkers restricted to the specific type of cancer, like lung cancer, are often overlooked. Some well-known protein biomarkers correlate with the progression of the disease and the immunogenicity of the tumor. Most of these biomarkers are not exclusive to lung cancer because of their significant role in tumorigenesis. The dysregulation of others does not necessarily indicate cell invasiveness, as they play an active role in cell division. Clinical studies of lung cancer use protein biomarkers to assess the invasiveness of cancer cells for therapeutic purposes. However, there is still a need to discover new biomarkers for lung cancer. In the future, minimally invasive techniques, such as blood or saliva analyses, may be sufficient for this purpose. Many researchers suggest unconventional biomarkers, like circulating nucleic acids, exosomal proteins, and autoantibodies. This review paper aims to discuss the advantages and limitations of protein biomarkers of invasiveness in lung cancer, to assess their prognostic value, and propose novel biomarker candidates.
Developing Vaccines in Pancreatic Adenocarcinoma: Trials and Tribulations
Authors: Phan Thuy , Fan Darrell , Melstrom Laleh G.
Published: August 2024

Pancreatic adenocarcinoma represents one of the most challenging malignancies to treat, with dismal survival rates despite advances in therapeutic modalities. Immunotherapy, particularly vaccines, has emerged as a promising strategy to harness the body’s immune system in combating this aggressive cancer. This abstract reviews the trials and tribulations encountered in the development of vaccines targeting pancreatic adenocarcinoma. Key challenges include the immunosuppressive tumor microenvironment, the heterogeneity of tumor antigens, and a limited understanding of immune evasion mechanisms employed by pancreatic cancer cells. Various vaccine platforms, including peptide-based, dendritic cell-based, and viral vector-based vaccines, have been explored in preclinical and clinical settings. However, translating promising results from preclinical models to clinical efficacy has proven elusive. In recent years, mRNA vaccines have emerged as a promising immunotherapeutic strategy in the fight against various cancers, including pancreatic adenocarcinoma. We will discuss the potential applications, opportunities, and challenges associated with mRNA vaccines in pancreatic cancer treatment.
Does Prior Experience Matter? Intention to Undergo Cervical Cancer Screening among Rural Women in South-Central Ethiopia
Authors: Ketema Bezawit , Addissie Adamu , Negash Sarah , Kantelhardt Eva Johanna , Kaba Mirgissa
Published: August 2024

Early screening for cervical cancer has substantially reduced the morbidity and mortality attributed to it. This study aimed to assess factors that affect the intention to undergo cervical cancer screening among rural women attending primary healthcare facilities in south-central Ethiopia. A health-facility-based, cross-sectional study design was employed for which the calculated required sample size was 427. An interviewer-administered structured questionnaire was adapted from previously published research and used to collect data. Statistical Package for Social Sciences (SPSS) version 27 was used for the statistical analysis. A logistic regression model was used to determine the factors that influenced the women’s intention to undergo cervical cancer screening. A total of 420 women participated in this study, with a response rate of 98%. The mean score from the questionnaire that was used to assess the women’s intention to undergo cervical cancer screening was 10.25 (SD ± 2.34; min 3, max 15). The absence of previous screening experience (AOR: 0.498; 95% CI 0.27–0.92) and high degree of perceived behavioural control (AOR, 0.823; 95% CI 0.728–0.930) were significantly negatively associated with women’s intention to undergo cervical cancer screening. Previous screening experience and perceived behavioural control significantly influenced the intention to undergo cervical cancer screening. Women in rural areas could, therefore, benefit from awareness-creation programmes that focus on these factors.
Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?
Authors: Toniolo Alessandro , Agostini Elena , Ceccato Filippo , Tizianel Irene , Cabrelle Giulio , Lupi Amalia , Pepe Alessia , Campi Cristina , Quaia Emilio , Crimì Filippo
Published: August 2024

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91). A total of 136 texture parameters were extracted in the unenhanced phase for each volume of interest (VOI). Random Forest was used in the training and validation cohorts to test the accuracy of CT textural features and cortisol-related comorbidities in identifying MACS patients. Twelve parameters were retained in the Random Forest radiomic model, and in the validation cohort, a high specificity (81%) and positive predictive value (74%) were achieved. Notably, if the clinical data were added to the model, the results did not differ. Radiomic analysis of adrenal incidentalomas, in unenhanced CT scans, could screen with a good specificity those patients who will need a further endocrinological evaluation for mild autonomous cortisol secretion, regardless of the clinical information about the cortisol-related comorbidities.
Quality of Life Determinants in Patients with Metastatic Prostate Cancer: Insights from a Cross-Sectional Questionnaire-Based Study
Authors: Mittal Chetanya , Gupta Hardik , Nagpal Chitrakshi , Sahoo Ranjit K. , Sharma Aparna , Gangadharaiah Bharat B. , Tansir Ghazal , Panaiyadiyan Sridhar , Shamim Shamim A. , Kaushal Seema , Das Chandan J. , Haresh Kunhi P. , Seth Amlesh , Nayak Brusabhanu , Batra Atul
Published: August 2024

Introduction: Prostate cancer is one of the most prevalent malignancies affecting men globally, with a significant impact on health-related quality of life (HRQOL). With the recent therapeutic advancements and improvements in survival, there is a need to understand the determinants of HRQOL in metastatic prostate cancer patients to optimize treatment strategies for quality of life as the number of survivors increases. The aim of this study was to identify clinical variables that affect HRQOL and its domains in patients with metastatic prostate cancer. Methods: We conducted a cross-sectional questionnaire-based study in patients diagnosed with metastatic prostate cancer at a tertiary cancer center in India. Baseline clinical features, treatment details, and completed Functional Assessment of Cancer Therapy—Prostate (FACT-P), composed of FACT-general (FACT-G) and prostate cancer-specific concerns subscale (PCS) and FACT-P Trial Outcome Index (FACT-P TOI) questionnaires, were collected. The mean total, as well as individual domain scores, were calculated. Additionally, these were stratified by the current treatment being received by patients. Linear regression was used to identify independent factors affecting HRQOL in these patients. Results: Of the 106 enrolled patients, 84 completed the FACT-P questionnaire and were included in the analysis. The median age was 66 years, and at the time of assessment, 3 patients (3.6%) were receiving androgen deprivation therapy only, 53 patients (63.1%) were on ADT + androgen receptor-targeted agents (ARTAs), and 18 patients (21.4%) patients received ADT + chemotherapy. The mean (±standard deviation) of the FACT-P TOI score was 70.33 (±15.16); the PCS subscale was the most affected, followed by functional well-being. Patients on chemotherapy scored significantly higher on PCS, but the composite scores were not significantly different. Univariable regression identified obesity (body mass index > 25 kg/m2) and duration of first-line treatment as significant predictors of better HRQOL; however, obesity was the only independent predictor in multivariable analysis (β = 8.2; 95% confidence interval, 1.2 to 15.0; p = 0.022). Obesity also independently predicted a better FACT-P and its physical well-being domain score and PCS. Conclusion: Prostate cancer patients experience impaired QoL, especially in the prostate cancer-specific and functional well-being domains. Lower BMI is an independent predictor of poor QoL, and this requires efforts to assess the impact of strategies to manage the nutritional status of patients with metastatic disease on QoL outcomes.
Complete Blood Count-Based Biomarkers as Predictors of Clinical Outcomes in Advanced Non-Small Cell Lung Cancer Patients with PD-L1 < 50% Treated with First-Line Chemoimmunotherapy
Authors: Putzu Carlo , Serra Riccardo , Campus Rachele , Fadda Giovanni Maria , Sini Claudio , Marongiu Andrea , Ginesu Giorgio Carlo , Fois Alessandro Giuseppe , Palmieri Giuseppe , Zinellu Angelo , Cossu Antonio , Paliogiannis Panagiotis
Published: August 2024

Background: The aim of the study was to investigate a series of complete blood cell count-based biomarkers of systemic inflammation as predictors of clinical outcomes in patients who underwent first-line chemoimmunotherapy for advanced NSCLC. Methods: Consecutive patients with pathologically diagnosed stage III/IV NSCLC and PD-L1 < 50% who underwent first-line chemoimmunotherapy were retrospectively enrolled. The clinical outcomes used for biomarker evaluation were Objective Response Rate (ORR) and Overall Survival (OS). Results: Non-responders had significantly higher values of neutrophil to lymphocyte ratio (NLR, median: 5.36; IQR: 2.78–10.82 vs. 3.31; IQR: 2.15–4.12, p = 0.019), neutrophil to monocyte ratio (NMR, median: 14.00; IQR: 8.82–21.20 vs. 9.20; IQR: 7.45–11.20, p = 0.013), and systemic inflammation index (SII, median: 1395; IQR: 929–3334 vs. 945; IQR: 552–1373, p = 0.025), but only NLR and NMR remained independently associated with clinical response in multivariate logistic regression. In the univariate analysis, white blood cells (OR:1.2202; 95% CI: 1.0339–1.4400, p = 0.019), neutrophils (OR:1.2916; 95% CI: 1.0692–1.5604, p = 0.008), NLR (OR:1.3601: 95% CI: 1.0949–1.6896, p = 0.005) and NMR (OR:1.2159; 95% CI: 1.00396–1.4221, p = 0.015) were significantly associated with survival; Cox regression models confirmed that neutrophils, NLR, and MLR were independently associated with survival; NLR, at a cut-off value of 4.0, showed the better AUC (0.749) in predicting OS. Conclusions: Baseline complete blood cell count biomarkers, especially the NLR, can predict clinical outcomes in patients with advanced NSCLC treated with first-line chemoimmunotherapy.
Diagnosis of Pleural Mesothelioma: Is Everything Solved at the Present Time?
Authors: Roca Elisa , Aujayeb Avinash , Astoul Philippe
Published: August 2024

Ranked high in worldwide growing health issues, pleural diseases affect approximately one million people globally per year and are often correlated with a poor prognosis. Among these pleural diseases, malignant pleural mesothelioma (PM), a neoplastic disease mainly due to asbestos exposure, still remains a diagnostic challenge. Timely diagnosis is imperative to define the most suitable therapeutic approach for the patient, but the choice of diagnostic modalities depends on operator experience and local facilities while bearing in mind the yield of each diagnostic procedure. Since the analysis of pleural fluid cytology is not sufficient in differentiating historical features in PM, histopathological and morphological features obtained via tissue biopsies are fundamental. The quality of biopsy samples is crucial and often requires highly qualified expertise. Since adequate tissue biopsy is essential, medical or video-assisted thoracoscopy (MT or VATS) is proposed as the most suitable approach, with the former being a physician-led procedure. Indeed, MT is the diagnostic gold standard for malignant pleural pathologies. Moreover, this medical or surgical approach can allow diagnostic and therapeutic procedures: it provides the possibility of video-assisted biopsies, the drainage of high volumes of pleural fluid and the administration of sterile calibrated talcum powder under visual control in order to achieve pleurodesis, placement of indwelling pleural catheters if required and in a near future potential intrapleural therapy. In this context, dedicated diagnostic pathways remain a crucial need, especially to quickly and properly diagnose PM. Lastly, the interdisciplinary approach and multidisciplinary collaboration should always be implemented in order to direct the patient to the best customised diagnostic and therapeutic pathway. At the present time, the diagnosis of PM remains an unsolved problem despite MDT (multidisciplinary team) meetings, mainly because of the lack of standardised diagnostic work-up. This review aims to provide an overview of diagnostic procedures in order to propose a clear strategy.
Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images
Authors: Horasan Alparslan , Güneş Ali
Published: August 2024

Prostate cancer remains a leading cause of mortality among men globally, necessitating advancements in diagnostic methodologies to improve detection and treatment outcomes. Magnetic Resonance Imaging has emerged as a crucial technique for the detection of prostate cancer, with current research focusing on the integration of deep learning frameworks to refine this diagnostic process. This study employs a comprehensive approach using multiple deep learning models, including a three-dimensional (3D) Convolutional Neural Network, a Residual Network, and an Inception Network to enhance the accuracy and robustness of prostate cancer detection. By leveraging the complementary strengths of these models through an ensemble method and soft voting technique, the study aims to achieve superior diagnostic performance. The proposed methodology demonstrates state-of-the-art results, with the ensemble model achieving an overall accuracy of 91.3%, a sensitivity of 90.2%, a specificity of 92.1%, a precision of 89.8%, and an F1 score of 90.0% when applied to MRI images from the SPIE-AAPM-NCI PROSTATEx dataset. Evaluation of the models involved meticulous pre-processing, data augmentation, and the use of advanced deep-learning architectures to analyze the whole MRI slices and volumes. The findings highlight the potential of using an ensemble approach to significantly improve prostate cancer diagnostics, offering a robust and precise tool for clinical applications.
Nonlinear Economic State Equilibria via van der Waals Modeling
Authors: Hongler Max-Olivier , Gallay Olivier , Hashemi Fariba
Published: August 2024

The renowned van der Waals (VDW) state equation quantifies the equilibrium relationship between the pressure P, volume V, and temperature kBT of a real gas. We assign new variable interpretations adapted to the economic context: P→Y, representing price; V→X, representing demand; and kBT→κ, representing income, to describe an economic state equilibrium. With this reinterpretation, the price elasticity of demand (PED) and the income elasticity of demand (YED) are non-constant factors and may exhibit a singularity of the cusp-catastrophe type. Within this economic framework, the counterpart of VDW liquid–gas phase transition illustrates a substitution mechanism where one product or service is replaced by an alternative substitute. The conceptual relevance of this reinterpretation is discussed qualitatively and quantitatively via several illustrations ranging from transport (carpooling), medical context (generic versus original medication), and empirical data drawn from the electricity market in Germany.
Study on the Single-Event Burnout Effect Mechanism of SiC MOSFETs Induced by Heavy Ions
Authors: Liu Cuicui , Guo Gang , Shi Huilin , Zhang Zheng , Li Futang , Zhang Yanwen , Han Jinhua
Published: August 2024

As a prominent focus in high-voltage power devices, SiC MOSFETs have broad application prospects in the aerospace field. Due to the unique characteristics of the space radiation environment, the reliability of SiC MOSFETs concerning single-event effects (SEEs) has garnered widespread attention. In this study, we employed accelerator-heavy ion irradiation experiments to study the degradation characteristics for SEEs of 1.2 kV SiC MOSFETs under different bias voltages and temperature conditions. The experimental results indicate that when the drain-source voltage (VDS) exceeds 300 V, the device leakage current increases sharply, and even single-event burnout (SEB) occurs. Furthermore, a negative gate bias (VGS) can make SEB more likely via gate damage and Poole–Frenkel emission (PF), reducing the VDS threshold of the device. The radiation degradation behavior of SiC MOSFETs at different temperatures was compared and analyzed, showing that although high temperatures can increase the safe operating voltage of VDS, they can also cause more severe latent gate damage. Through an in-depth analysis of the experimental data, the physical mechanism by which heavy ion irradiation causes gate leakage in SiC MOSFETs was explored. These research findings provide an essential basis for the reliable design of SiC MOSFETs in aerospace applications.
DFS-DETR: Detailed-Feature-Sensitive Detector for Small Object Detection in Aerial Images Using Transformer
Authors: Cao Xinyu , Wang Hanwei , Wang Xiong , Hu Bin
Published: August 2024

Object detection in aerial images plays a crucial role across diverse domains such as agriculture, environmental monitoring, and security. Aerial images present several challenges, including dense small objects, intricate backgrounds, and occlusions, necessitating robust detection algorithms. This paper addresses the critical need for accurate and efficient object detection in aerial images using a Transformer-based approach enhanced with specialized methodologies, termed DFS-DETR. The core framework leverages RT-DETR-R18, integrating the Cross Stage Partial Reparam Dilation-wise Residual Module (CSP-RDRM) to optimize feature extraction. Additionally, the introduction of the Detail-Sensitive Pyramid Network (DSPN) enhances sensitivity to local features, complemented by the Dynamic Scale Sequence Feature-Fusion Module (DSSFFM) for comprehensive multi-scale information integration. Moreover, Multi-Attention Add (MAA) is utilized to refine feature processing, which enhances the model’s capacity for understanding and representation by integrating various attention mechanisms. To improve bounding box regression, the model employs MPDIoU with normalized Wasserstein distance, which accelerates convergence. Evaluation across the VisDrone2019, AI-TOD, and NWPU VHR-10 datasets demonstrates significant improvements in the mean average precision (mAP) values: 24.1%, 24.0%, and 65.0%, respectively, surpassing RT-DETR-R18 by 2.3%, 4.8%, and 7.0%, respectively. Furthermore, the proposed method achieves real-time inference speeds. This approach can be deployed on drones to perform real-time ground detection.
Machine Vision-Based Method for Reconstructing the Vehicle Coordinate System in End-of-Line ADAS Calibration
Authors: Ding Zhigang , Jiang Jingjing , Zheng Jishi , Kong Linghua
Published: August 2024

To ensure the accuracy and reliability of Advanced Driver Assistance Systems (ADAS), it is essential to perform offline calibration before the vehicles leave the factory. This paper proposes a method for reconstructing the vehicle coordinate system based on machine vision, which can be applied to the offline calibration of ADAS. Firstly, this study explains the preliminary preparations, such as the selection of feature points and the choice of camera model, combining actual application scenarios and testing requirements. Subsequently, the YOLO model is trained to identify and obtain feature regions, and feature point coordinates are extracted from these regions using template matching and ellipse fitting. Finally, a validation experiment is designed to evaluate the accuracy of this method using metrics such as the vehicle’s lateral and longitudinal offset distances and yaw angle. Experimental results show that, compared to traditional vehicle alignment platforms, this method improves reconstruction accuracy while reducing costs.
CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network
Authors: Zhang Wenna , Xue Kailiang , Yao Aiqin , Sun Yunqiang
Published: August 2024

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.
Review of Computational Fluid Dynamics in the Design of Floating Offshore Wind Turbines
Authors: Haider Rizwan , Li Xin , Shi Wei , Lin Zaibin , Xiao Qing , Zhao Haisheng
Published: August 2024

The growing interest in renewable energy solutions for sustainable development has significantly advanced the design and analysis of floating offshore wind turbines (FOWTs). Modeling FOWTs presents challenges due to the considerable coupling between the turbine’s aerodynamics and the floating platform’s hydrodynamics. This review paper highlights the critical role of computational fluid dynamics (CFD) in enhancing the design and performance evaluation of FOWTs. It thoroughly evaluates various CFD approaches, including uncoupled, partially coupled, and fully coupled models, to address the intricate interactions between aerodynamics, hydrodynamics, and structural dynamics within FOWTs. Additionally, this paper reviews a range of software tools for FOWT numerical analysis. The research emphasizes the need to focus on the coupled aero-hydro-elastic models of FOWTs, especially in response to expanding rotor diameters. Further research should focus on developing nonlinear eddy viscosity models, refining grid techniques, and enhancing simulations for realistic sea states and wake interactions in floating wind farms. The research aims to familiarize new researchers with essential aspects of CFD simulations for FOWTs and to provide recommendations for addressing challenges.
Multi-Variable Optimization to Enhance the Performance of a Cantilevered Piezoelectric Harvester
Authors: Alzuwayer Bashar , Almokmesh Saad F. , Alhashem Abdulwahab , Alotaibi Talal
Published: August 2024

Over the past decade, various researchers have shown the practicality of extracting electrical energy from vibrating structures. The primary objective of this research was to optimize the parameters of a basic cantilever harvester to maximize the energy production of electricity from surrounding mechanical energy. A distributed parameter model and its modal solution were utilized to determine the design variables via a parametric investigation. It is important to ensure sufficient power generation when there is a wide range of frequencies being excited, without any specific frequency being emphasized. The cost function for the optimization problem was defined as the average power within a specific frequency range, considering geometric and physical restrictions. It was discovered that different parameters’ values can be utilized to generate varying maximum power levels for different frequency ranges. Furthermore, a comparative analysis was performed between the finite element method (FEM) using ANSYS and the current harvesting model. The comparison demonstrated a high level of concordance, enabling the utilization of the finite element method (FEM) for subsequent investigation.
A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids
Authors: Mohammad Zaheri Damoon , Nazerian Salmani Shahrzad , Shahnia Farhad , Wang Hai , Su Xiangjing
Published: August 2024

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.
Transport Fleet Electrification Development Conditions—Perspective of Transport, Shipping, and Logistics Industry in Poland
Authors: Raźniewska Marta , Wronka Anna
Published: August 2024

The development of electric vehicle fleets is an important element of today’s economic, social, and ecological development. This multidimensional sustainable process, although not easy, generates many tangible benefits for various stakeholders, ranging from environmental to financial and competence issues. Despite the fact that the phenomenon of transformation toward rational energy management is gaining momentum on a global scale, there is a significant disproportion in terms of development levels depending on the origin of the economy. The aim of this research article is to identify the key factors affecting the development of fleet electrification in the transport, shipping, and logistics (TSL) sector in Poland. Based on a literature review, a fleet development framework was developed using a PESTEL (political, economic, social, technological, environmental, and legal) analysis and evaluated by TSL companies. According to the conclusions drawn, the most important stimulants are economic factors and technological factors, which limit the development of electrified transport fleets in the TSL industry. Based on this, the authors propose various solutions to improve economic profitability and technological conditions. In addition, it was found that the attitudes of the decision makers at transport companies and cooperation within the TSL sector are also important.
A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model
Authors: Zhang Bing , Zhu Hongbo , Song Weidong , Zhu Jianjun , Dai Jiguang , Zhang Jichao , Li Chengjin
Published: August 2024

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.
The Effectiveness of Tannin on the Amount of Damage to Forest Trees and Stands Caused by Red Deer in the Western Carpathians
Authors: Štefanec Marek , Hlaváč Pavol , Sedmák Róbert , Lebocký Tibor , Ivan Mojmír , Kropil Rudolf
Published: August 2024

Damage caused by cloven-hoofed game and wild animals is a serious problem in Slovakia and neighboring countries. In 2022, the damage to forests in Slovakia was estimated at a level of almost EUR 2.9 million. Despite several implemented measures, the situation is so severe that successfully restoring some trees in some heavily attacked stands is impossible. This paper deals with the preventive elimination of damage caused by red deer in forest stands by the biological method of applying tannin-based mineral licks. Specifically, licks containing tannin obtained from the edible chestnut were used for testing. Several positive effects are attributed to tannins in developing livestock and animal fitness status. To date, however, the question of using tannins as biological agents for the possible protection of forest stands has not been addressed. Therefore, red deer damage was assessed at two localities with similar natural conditions in the central part of the Western Carpathians to explore such a possibility. After the initial sample measurement of the bark-stripping damage caused by the red deer in the autumn, mineral licks with tannin were placed in one selected study area over the winter. The levels of and differences in forest damage by deer between areas with the application of licks with tannin and without tannin were detected in spring of the following year. In the location treated with tannin, the number of damaged sample plots decreased, and the mean plot damage did not increase (no statistically significant differences were registered in mean plot damage between autumn and spring). Meanwhile, in the control location, the total amount of bark-stripped area doubled, mainly due to a statistically significant increase in mean plot damage between autumn and spring. Based on this, we conclude that tannin had a significant preventive effect and considerably reduced the extent of bark stripping. Such promising results indicate that tannin licks can effectively mitigate the problem of bark-stripping damage in areas heavily attacked by red deer.
Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
Authors: Qi Zhibo , Du Lei , Huo Ru , Huang Tao
Published: August 2024

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.
Enrichment of White Chocolate with Microencapsulated β-Carotene: Impact on Quality Characteristics and β-Carotene Stability during Storage
Authors: Drosou Christina , Krokida Magdalini
Published: August 2024

This study developed functional white chocolate enriched with free (WC-F) and encapsulated β-carotene using whey protein isolate (WPI) and pullulan (PUL) blends through spray drying (WC-SP), freeze drying (WC-LP), and coaxial electrospinning (WC-EL). The thermal properties, rheological properties, hardness, and color of the chocolates were evaluated, and the stability of β-carotene was monitored over 4 months at 25 °C. No significant differences were found in melting profile temperatures among samples; however, WC-LP and WC-EL exhibited higher melting energies (30.88 J/g and 16.00 J/g) compared to the control (12.42 J/g). WC-F and WC-SP showed rheological behaviors similar to those of the control, while WC-LP and WC-EL displayed altered flow characteristics. Hardness was unaffected in WC-F and WC-SP (7.77 N/mm2 and 9.36 N/mm2), increased slightly in WC-LP (10.28 N/mm2), and decreased significantly in WC-EL (5.89 N/mm2). Over storage, melting point, rheological parameters, and hardness increased slightly, while color parameters decreased. β-carotene degradation followed a first-order reaction model, with degradation rate constants (k) of 0.0066 day−1 for WC-SP, 0.0094 day−1 for WC-LP, and 0.0080 day−1 for WC-EL, compared to 0.0164 day−1 for WC-F. WC-SP provided the best β-carotene retention, extending the half-life period by 2 times compared to WC-F (126.04 days vs. 61.95 days). Practical implications: The findings suggest that WC-SP, with its superior β-carotene stability, is particularly suitable for the development of functional confectionery products with extended shelf life, offering potential benefits in industrial applications where product stability is crucial. Future research directions: Further studies could explore the incorporation of additional bioactive compounds in white chocolate using similar encapsulation methods, as well as consumer acceptance and sensory evaluation of these enriched products.
Comparative Epidemiological and Clinical Outcomes on COVID-19 and Seasonal Influenza Hospitalized Patients during 2023
Authors: Vlase Constantin-Marinel , Stuparu Cretu Mariana , Vasile Mihaela-Camelia , Popovici George-Cosmin , Arbune Manuela
Published: August 2024

COVID-19 and influenza are highly contagious respiratory viral diseases and priority global public health concerns. We conducted a retrospective observational study of COVID-19 and/or influenza hospitalized cases, during 2023. We identified 170 influenza cases, 150 COVID-19 cases and 3 co-infections. Overall, 29.10% of patients had at least one COVID-19 vaccine dose and 4.6% received the seasonal Flu vaccine. The demographic data found older patients in the COVID-19 group and a higher index of the comorbidities, mainly due to chronic heart diseases, hypertension, and diabetes. Fever, chills, and rhinorrhea were more frequently related to influenza, while cough was prevalent in COVID-19. Antibiotics were more used in influenza than COVID-19, either pre-hospital or in-hospital. The mortality rate within the first 30 days from the onset of the respiratory infection was higher in influenza compared to COVID-19. We concluded that the COVID-19 clinical picture in hospitalized patients is changing to influenza-like symptoms. The evolution is variable, related to chronic comorbidities, but influenza had more frequent severe forms. All through 2023, due to poor vaccination rates, COVID-19 and influenza have continued to cause numerous hospitalizations, and a new strategy for efficient vaccinations is required.
Lipid Metabolism Disorders as Diagnostic Biosignatures in Sepsis
Authors: Birner Charlotte , Mester Patricia , Liebisch Gerhard , Höring Marcus , Schmid Stephan , Müller Martina , Pavel Vlad , Buechler Christa
Published: August 2024

Critical illness causes disturbances in lipid metabolism. Here, we investigated the levels of apolipoprotein A-IV (apoA-IV), a regulator of triglyceride and cholesterol metabolism, in human sepsis. ApoA-IV (analyzed in 156 patients with systemic inflammatory response syndrome (SIRS)/sepsis) and cholesteryl ester (CE) (analyzed in 121 of these patients) were lower in patients compared to 43 healthy controls. In contrast, triglyceride (TG) levels were elevated in patients. ApoA-IV levels in plasma of the patients did not correlate with these lipids. Patients with SIRS, sepsis or septic shock had comparable apoA-IV, TG, CE and free cholesterol (FC) levels. Patients on dialysis had significantly lower CE levels, whereas apoA-IV levels did not change much. CE levels were elevated in patients with viral sepsis due to SARS-CoV-2 infection in comparison to SIRS/sepsis patients not infected by this virus. CE levels correlated negatively with procalcitonin, interleukin-6 and bilirubin, while TGs were positively associated with bilirubin and C-reactive protein. ApoA-IV, TG, CE and FC levels were not associated with bacterial infection or survival. In conclusion, this analysis suggests that CE levels decline in sepsis-related renal failure and also shows that plasma apoA-IV and CE levels are early biomarkers of sepsis.
Bloodstream Infections Due to Wild-Type Pseudomonas aeruginosa: Carbapenems and Ceftazidime/Avibactam Prescription Rate and Impact on Outcomes
Authors: Pallotto Carlo , Tommasi Andrea , Svizzeretto Elisabetta , Genga Giovanni , Gamboni Giulia , Gidari Anna , Francisci Daniela
Published: August 2024

Background. Pseudomonas aeruginosa is one of the major concerns among bacterial diseases even when it shows a wild-type susceptibility pattern. In 2020, EUCAST reconsidered antibiogram interpretation shifting “I” from “intermediate” to “sensible, increased exposure” with possible significant impact on antibiotic prescription. The aim of this study was to evaluate mortality in patients with P. aeruginosa bloodstream infections treated with antipseudomonal penicillins or cephalosporins vs. carbapenems and ceftazidime/avibactam. Methods. This is a retrospective observational study. All the patients with a bloodstream infection due to P. aeruginosa admitted to our hospital were enrolled. Exclusion criteria were as follows: extremely critical conditions, age <18 years, pregnancy, isolation of a strain non-susceptible to piperacillin/tazobactam and antipseudomonal cephalosporins. Patients were divided into group A (treatment with carbapenems or ceftazidime/tazobactam) and group B (treatment with antipseudomonal penicillin or cephalosporins). Results. We enrolled 77 patients, 56 and 21 in groups A and B, respectively. The two groups were homogeneous for age, sex, and biochemical and clinical characteristics at admission. All-cause in-hospital mortality was 17/56 (30.4%) and 3/21 (14.3%) in groups A and B, respectively (p > 0.1). In group A, in-hospital BSI-related mortality was 23.2% (13/56), while it was 14.3% (3/21) in group B (p > 0.1). After multivariate analysis, only the PITT score represented a risk factor for BSI-related mortality (OR 2.917, 95% CI 1.381–6.163). Conclusions. Both all-cause and BSI-related mortality were comparable between the two groups. Treatment with carbapenem or ceftazidime/avibactam did not represent a protective factor for mortality in wild-type P. aeruginosa BSI.
Time-Use Sequences: A Mixed-Methods Study Exploring How, When, and Where Spatiotemporal Patterns of Everyday Routines Can Strengthen Public Health Interventions
Authors: Barber Brittany V. , Kephart George , Vallis Michael , Matthews Stephen A. , Martin-Misener Ruth , Rainham Daniel G.
Published: August 2024

Background: Behavior change interventions are critical for the secondary prevention of cardiovascular disease and for reducing the risk of a repeat event or mortality. However, the effectiveness of behavior change interventions is challenged by a lack of spatiotemporal contexts, limiting our understanding of factors that influence the timing and location in which day-to-day activities occur and the maintenance of behavior change. This study explored how behavior change interventions could incorporate spatiotemporal contexts of patient activities for modifying behaviors. Methods: A mixed-methods approach with adapted geo-ethnography techniques was used to solicit detailed descriptions of patients’ day-to-day routines, including where, when, and how patients spend time. Data were gathered from patients in one cardiac intervention program in Nova Scotia, Canada, from June to September 2021. Results: A total of 29 individuals (19 men and 10 women) between the ages of 45 and 81 and referred to the program after a cardiac event participated. The results show three key findings: (1) most patients exceeded the minimum guidelines of 30 min of daily physical activity but were sedentary for long periods of time, (2) patient time-use patterns are heterogenous and unique to contexts of individual space-time activity paths, and (3) time-use patterns reveal when, where, and how patients spend significant portions of time and opportunities for adapting patients’ day-to-day health activities. Conclusions: This study demonstrates the potential for interventions to integrate tools for collecting and communicating spatial and temporal contexts of patient routines, such as the types of activities that characterize how patients spend significant portions of time and identification of when, where, and how to encourage health-promoting changes in routine activities. Time-use patterns provide insight for tailoring behavior change interventions so that clinic-based settings are generalizable to the contexts of where, when, and how patient routines could be adapted to mitigate cardiovascular risk factors.