bluuebunny/crossref_metadata_2025 huggingface dataset:
Glycemic Control and the Heart: The Tale of Diabetic Cardiomyopathy Continues
Authors: Longo Miriam , Scappaticcio Lorenzo , Cirillo Paolo , Maio Antonietta , Carotenuto Raffaela , Maiorino Maria Ida , Bellastella Giuseppe , Esposito Katherine
Published: February 2022

Cardiovascular diseases are the leading cause of death in people with diabetes. Diabetic cardiomyopathy (DC) is an important complication of diabetes and represents a distinct subtype of heart failure that occurs in absence of cardiovascular diseases. Chronic hyperglycemia and hyperinsulinemia along with insulin resistance and inflammatory milieu are the main mechanisms involved in the pathophysiology of DC. Changes in lifestyle favoring healthy dietary patterns and physical activity, combined with more innovative anti-diabetes therapies, are the current treatment strategies to safeguard the cardiovascular system. This review aims at providing an updated comprehensive overview of clinical, pathogenetic, and molecular aspects of DC, with a focus on the effects of anti-hyperglycemic drugs on the prevention of pump dysfunction and consequently on cardiovascular health in type 2 diabetes.
Role of Urinary Kidney Stress Biomarkers for Early Recognition of Subclinical Acute Kidney Injury in Critically Ill COVID-19 Patients
Authors: Casas-Aparicio Gustavo , Alvarado-de la Barrera Claudia , Escamilla-Illescas David , León-Rodríguez Isabel , Del Río-Estrada Perla Mariana , Calderón-Dávila Natalia , González-Navarro Mauricio , Olmedo-Ocampo Rossana , Castillejos-López Manuel , Figueroa-Hernández Liliana , Peralta-Prado Amy , Luna-Villalobos Yara , Piten-Isidro Elvira , Fernández-Campos Paola , Ávila-Ríos Santiago
Published: February 2022

A high proportion of critically ill patients with COVID-19 develop acute kidney injury (AKI) and die. The early recognition of subclinical AKI could contribute to AKI prevention. Therefore, this study was aimed at exploring the role of the urinary biomarkers NGAL and [TIMP-2] × [IGFBP7] for the early detection of AKI in this population. This prospective, longitudinal cohort study included critically ill COVID-19 patients without AKI at study entry. Urine samples were collected on admission to critical care areas for determination of NGAL and [TIMP-2] × [IGFBP7] concentrations. The demographic information, comorbidities, clinical, and laboratory data were recorded. The study outcomes were the development of AKI and mortality during hospitalization. Of the 51 individuals that were studied, 25 developed AKI during hospitalization (49%). Of those, 12 had persistent AKI (23.5%). The risk factors for AKI were male gender (HR = 7.57, 95% CI: 1.28–44.8; p = 0.026) and [TIMP-2] × [IGFBP7] ≥ 0.2 (ng/mL)2/1000 (HR = 7.23, 95% CI: 0.99–52.4; p = 0.050). Mortality during hospitalization was significantly higher in the group with AKI than in the group without AKI (p = 0.004). Persistent AKI was a risk factor for mortality (HR = 7.42, 95% CI: 1.04–53.04; p = 0.046). AKI was frequent in critically ill COVID-19 patients. The combination of [TIMP-2] × [IGFBP7] together with clinical information, were useful for the identification of subclinical AKI in critically ill COVID-19 patients. The role of additional biomarkers and their possible combinations for detection of AKI in ritically ill COVID-19 patients remains to be explored in large clinical trials.
APR-246—The Mutant TP53 Reactivator—Increases the Effectiveness of Berberine and Modified Berberines to Inhibit the Proliferation of Pancreatic Cancer Cells
Authors: McCubrey James Andrew , Abrams Stephen L. , Steelman Linda S. , Cocco Lucio , Ratti Stefano , Martelli Alberto M. , Lombardi Paolo , Gizak Agnieszka , Duda Przemysław
Published: February 2022

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer. In ~75% of PDAC, the tumor suppressor TP53 gene is mutated. Novel approaches to treat cancer involve compounds called mutant TP53 reactivators. They interact with mutant TP53 proteins and restore some of their growth suppressive properties, but they may also interact with other proteins, e.g., TP63 and TP73. We examined the ability of the TP53 reactivator APR-246 to interact with eleven modified berberine compounds (NAX compounds) in the presence and absence of WT-TP53 in two PDAC cell lines: the MIA-PaCa-2, which has gain of function (GOF) TP53 mutations on both alleles, and PANC-28, which lacks expression of the WT TP53 protein. Our results indicate the TP53 reactivator-induced increase in therapeutic potential of many modified berberines.
The War Is on: The Immune System against Glioblastoma—How Can NK Cells Drive This Battle?
Authors: da Silva Lucas Henrique Rodrigues , Catharino Luana Correia Croda , da Silva Viviane Jennifer , Evangelista Gabriela Coeli Menezes , Barbuto José Alexandre Marzagão
Published: February 2022

Natural killer (NK) cells are innate lymphocytes that play an important role in immunosurveillance, acting alongside other immune cells in the response against various types of malignant tumors and the prevention of metastasis. Since their discovery in the 1970s, they have been thoroughly studied for their capacity to kill neoplastic cells without the need for previous sensitization, executing rapid and robust cytotoxic activity, but also helper functions. In agreement with this, NK cells are being exploited in many ways to treat cancer. The broad arsenal of NK-based therapies includes adoptive transfer of in vitro expanded and activated cells, genetically engineered cells to contain chimeric antigen receptors (CAR-NKs), in vivo stimulation of NK cells (by cytokine therapy, checkpoint blockade therapies, etc.), and tumor-specific antibody-guided NK cells, among others. In this article, we review pivotal aspects of NK cells’ biology and their contribution to immune responses against tumors, as well as providing a wide perspective on the many antineoplastic strategies using NK cells. Finally, we also discuss those approaches that have the potential to control glioblastoma—a disease that, currently, causes inevitable death, usually in a short time after diagnosis.
Effects of a Novel Selective Peroxisome Proliferator-Activated Receptor α Modulator, Pemafibrate, on Metabolic Parameters: A Retrospective Longitudinal Study
Authors: Yanai Hidekatsu , Katsuyama Hisayuki , Hakoshima Mariko
Published: February 2022

The modulation of peroxisome proliferator-activated receptors (PPARs), the superfamily of steroid–thyroid–retinoid nuclear receptors, is expected to induce an amazing crosstalk between energy-demanding organs. Here, we aimed to study the effects of the novel selective PPARα modulator, pemafibrate, on metabolic parameters in patients with dyslipidemia. We retrospectively studied patients who had taken pemafibrate and compared metabolic parameters at baseline with the data at 3, 6 and 12 months after the start of pemafibrate. Serum triglyceride significantly decreased and high-density lipoprotein-cholesterol significantly increased at 3, 6 and 12 months after the start of pemafibrate. Serum aspartate aminotransferase levels significantly decreased at 3 and 6 after the start of pemafibrate as compared with baseline. Serum alanine aminotransferase and gamma-glutamyl transferase significantly decreased and albumin significantly increased after 3, 6 and 12 months. HbA1c levels significantly decreased after 3 months. Further, serum uric acid significantly decreased after 12 months. Such metabolic favorable changes due to pemafibrate were significantly correlated with changes in serum lipids. In conclusion, we observed a significant improvement of liver function, HbA1c and serum uric acid along with an amelioration of dyslipidemia after the start of pemafibrate.
Deep Learning Architecture Reduction for fMRI Data
Authors: Alvarez-Gonzalez Ruben , Mendez-Vazquez Andres
Published: February 2022

In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer’s features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter’s effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.
Interdisciplinary Approach to Biological and Health Implications in Selected Professional Competences
Authors: Kostrzewa-Nowak Dorota , Nowak Robert , Kubaszewska Joanna , Gos Waldemar
Published: February 2022

Everyday life’s hygiene and professional realities, especially in economically developed countries, indicate the need to modify the standards of pro-health programs as well as modern hygiene and work ergonomics programs. These observations are based on the problem of premature death caused by civilization diseases. The biological mechanisms associated with financial risk susceptibility are well described, but there is little data explaining the biological basis of neuroaccounting. Therefore, the aim of the study was to present relationships between personality traits, cognitive competences and biological factors shaping behavioral conditions in a multidisciplinary aspect. This critical review paper is an attempt to compile biological and psychological factors influencing the development of professional competences, especially decent in the area of accounting and finance. We analyzed existing literature from wide range of scientific disciplines (including economics, psychology, behavioral genetics) to create background to pursuit multidisciplinary research models in the field of neuroaccounting. This would help in pointing the best genetically based behavioral profile of future successful financial and accounting specialists.
Combination of Histone Deacetylase Inhibitor Panobinostat (LBH589) with β-Catenin Inhibitor Tegavivint (BC2059) Exerts Significant Anti-Myeloma Activity Both In Vitro and In Vivo
Authors: Savvidou Ioanna , Khong Tiffany , Whish Sophie , Carmichael Irena , Sepehrizadeh Tara , Mithraprabhu Sridurga , Horrigan Stephen K. , de Veer Michael , Spencer Andrew
Published: February 2022

Over the last three decades changes in the treatment paradigm for newly diagnosed multiple myeloma (MM) have led to a significant increase in overall survival. Despite this, the majority of patients relapse after one or more lines of treatment while acquiring resistance to available therapies. Panobinostat, a pan-histone deacetylase inhibitor, was approved by the FDA in 2015 for patients with relapsed MM but how to incorporate panobinostat most effectively into everyday practice remains unclear. Dysregulation of the Wnt canonical pathway, and its key mediator β-catenin, has been shown to be important for the evolution of MM and the acquisition of drug resistance, making it a potentially attractive therapeutic target. Despite concerns regarding the safety of Wnt pathway inhibitors, we have recently shown that the β-catenin inhibitor Tegavivint is deliverable and effective in in vivo models of MM. In this study we show that the combination of low concentrations of panobinostat and Tegavivint have significant in vitro and in vivo anti-MM effects including in the context of proteasome inhibitor resistance, by targeting both aerobic glycolysis and mitochondrial respiration and the down-regulation of down-stream β-catenin targets including myc, cyclinD1, and cyclinD2. The significant anti-MM effect of this novel combination warrants further evaluation for the treatment of MM patients with relapsed and/or refractory MM.
Correlation between Tissue Cellularity and Metabolism Represented by Diffusion-Weighted Imaging (DWI) and 18F-FDG PET/MRI in Head and Neck Cancer (HNC)
Authors: Freihat Omar , Zoltán Tóth , Pinter Tamas , Kedves András , Sipos Dávid , Repa Imre , Kovács Árpád , Zsolt Cselik
Published: February 2022

Background: This study aimed to assess the association of 18F-Fluorodeoxyglucose positron-emission-tomography (18F-FDG/PET) and DWI imaging parameters from a primary tumor and their correlations with clinicopathological factors. Methods: We retrospectively analyzed primary tumors in 71 patients with proven HNC. Primary tumor radiological parameters: DWI and FDG, as well as pathological characteristics were analyzed. Spearman correlation coefficient was used to assess the correlation between DWI and FDG parameters, ANOVA or Kruskal–Wallis, independent sample t-test, Mann–Whitney test, and multiple regression were performed on the clinicopathological features that may affect the 18F- FDG and apparent-diffusion coefficient (ADC) of the tumor. Results: No significant correlations were observed between DWI and any of the 18F-FDG parameters (p > 0.05). SUVmax correlated with N-stages (p = 0.023), TLG and MTV correlated with T-stages (p = 0.006 and p = 0.001), and ADC correlated with tumor grades (p = 0.05). SUVmax was able to differentiate between N+ and N− groups (p = 0.004). Conclusions: Our results revealed a non-significant correlation between the FDG-PET and ADC-MR parameters. FDG-PET-based glucose metabolic and DWI-MR-derived cellularity data may represent different biological aspects of HNC.
Clinical Validity of Circulating Tumor DNA as Prognostic and Predictive Marker for Personalized Colorectal Cancer Patient Management
Authors: Hallermayr Ariane , Steinke-Lange Verena , Vogelsang Holger , Rentsch Markus , de Wit Maike , Haberl Christopher , Holinski-Feder Elke , Pickl Julia M. A.
Published: February 2022

Circulating tumor DNA (ctDNA) is a promising liquid biopsy (LB) marker to support clinical decisions in precision medicine. For implementation into routine clinical practice, clinicians need precise ctDNA level cutoffs for reporting residual disease and monitoring tumor burden changes during therapy. We clinically validated the limit of blank (LOB) and the limit of quantification (LOQ) of assays for the clinically most relevant somatic variants BRAF p.V600E and KRAS p.G12/p.G13 in colorectal cancer (CRC) in a study cohort encompassing a total of 212 plasma samples. We prove that residual disease detection using the LOB as a clinically verified cutoff for ctDNA positivity is in concordance with clinical evidence of metastasis or recurrence. We further show that tumor burden changes during chemotherapy and the course of disease are correctly predicted using the LOQ as a cutoff for quantitative ctDNA changes. The high potential of LB using ctDNA for accurately predicting the course of disease was proven by direct comparison to the routinely used carcinoembryonic antigen (CEA) as well as the circulating free DNA (cfDNA) concentration. Our results show that LB using validated ctDNA assays outperforms CEA and cfDNA for residual disease detection and the prediction of tumor burden changes.
Biomarker Development in Cardiology: Reviewing the Past to Inform the Future
Authors: Kott Katharine A. , Bishop Michael , Yang Christina H. J. , Plasto Toby M. , Cheng Daniel C. , Kaplan Adam I. , Cullen Louise , Celermajer David S. , Meikle Peter J. , Vernon Stephen T. , Figtree Gemma A.
Published: February 2022

Cardiac biomarkers have become pivotal to the clinical practice of cardiology, but there remains much to discover that could benefit cardiology patients. We review the discovery of key protein biomarkers in the fields of acute coronary syndrome, heart failure, and atherosclerosis, giving an overview of the populations they were studied in and the statistics that were used to validate them. We review statistical approaches that are currently in use to assess new biomarkers and overview a framework for biomarker discovery and evaluation that could be incorporated into clinical trials to evaluate cardiovascular outcomes in the future.
Acknowledgment to Reviewers of COVID in 2021
Authors:   COVID Editorial Office
Published: February 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Acknowledgment to Reviewers of Crops in 2021
Authors:   Crops Editorial Office
Published: February 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm
Authors: Sturm Bart , Creytens David , Smits Jan , Ooms Ariadne H. A. G. , Eijken Erik , Kurpershoek Eline , Küsters-Vandevelde Heidi V. N. , Wauters Carla , Blokx Willeke A. M. , van der Laak Jeroen A. W. M.
Published: February 2022

An increasing number of pathology laboratories are now fully digitised, using whole slide imaging (WSI) for routine diagnostics. WSI paves the road to use artificial intelligence (AI) that will play an increasing role in computer-aided diagnosis (CAD). In melanocytic skin lesions, the presence of a dermal mitosis may be an important clue for an intermediate or a malignant lesion and may indicate worse prognosis. In this study a mitosis algorithm primarily developed for breast carcinoma is applied to melanocytic skin lesions. This study aimed to assess whether the algorithm could be used in diagnosing melanocytic lesions, and to study the added value in diagnosing melanocytic lesions in a practical setting. WSI’s of a set of hematoxylin and eosin (H&E) stained slides of 99 melanocytic lesions (35 nevi, 4 intermediate melanocytic lesions, and 60 malignant melanomas, including 10 nevoid melanomas), for which a consensus diagnosis was reached by three academic pathologists, were subjected to a mitosis algorithm based on AI. Two academic and six general pathologists specialized in dermatopathology examined the WSI cases two times, first without mitosis annotations and after a washout period of at least 2 months with mitosis annotations based on the algorithm. The algorithm indicated true mitosis in lesional cells, i.e., melanocytes, and non-lesional cells, i.e., mainly keratinocytes and inflammatory cells. A high number of false positive mitosis was indicated as well, comprising melanin pigment, sebaceous glands nuclei, and spindle cell nuclei such as stromal cells and neuroid differentiated melanocytes. All but one pathologist reported more often a dermal mitosis with the mitosis algorithm, which on a regular basis, was incorrectly attributed to mitoses from mainly inflammatory cells. The overall concordance of the pathologists with the consensus diagnosis for all cases excluding nevoid melanoma (n = 89) appeared to be comparable with and without the use of AI (89% vs. 90%). However, the concordance increased by using AI in nevoid melanoma cases (n = 10) (75% vs. 68%). This study showed that in general cases, pathologists perform similarly with the aid of a mitosis algorithm developed primarily for breast cancer. In nevoid melanoma cases, pathologists perform better with the algorithm. From this study, it can be learned that pathologists need to be aware of potential pitfalls using CAD on H&E slides, e.g., misinterpreting dermal mitoses in non-melanotic cells.
Specifically Regional Cerebral Hypoperfusion in a Case of Highly Suspected Sporadic Creutzfeldt-Jakob Disease on 99mTc-ECD SPECT/CT with Easy Z-Score Imaging System Analysis
Authors: Chen Shun-Chieh , Shen Daniel Hueng-Yuan , Chan Hung-Yen , Yang Ming-Hui , Chan Hung-Pin
Published: February 2022

Sporadic Creutzfeldt-Jakob disease (sCJD) is a rapidly-progressive dementing illness, the challenge of diagnosis during life. We presented a 78-year-old woman reported stupor, right facial palsy, and fluctuations in consciousness. 99mTc-ECD brain SPECT/CT with eZIS analysis revealed significant decreased regional cerebral blood flow mainly in specific regions of Alzheimer’s disease as the published article reported with involving frontal region. Brain DWI MRI increased signal intensities corresponding to similar location of 99mTc-ECD brain SPECT/CT. In this case, we reported the pattern of decreased rCBF may correlate to rapidly progressive dementia and associated neurodegenerative symptoms of the highly suspected sCJD patient.
Analysis of the Structure and Dynamics of European Flight Networks
Authors: Milazzo Matteo , Musciotto Federico , Miccichè Salvatore , Mantegna Rosario N.
Published: February 2022

We analyze structure and dynamics of flight networks of 50 airlines active in the European airspace in 2017. Our analysis shows that the concentration of the degree of nodes of different flight networks of airlines is markedly heterogeneous among airlines reflecting heterogeneity of the airline business models. We obtain an unsupervised classification of airlines by performing a hierarchical clustering that uses a correlation coefficient computed between the average occurrence profiles of 4-motifs of airline networks as similarity measure. The hierarchical tree is highly informative with respect to properties of the different airlines (for example, the number of main hubs, airline participation to intercontinental flights, regional coverage, nature of commercial, cargo, leisure or rental airline). The 4-motif patterns are therefore distinctive of each airline and reflect information about the main determinants of different airlines. This information is different from what can be found looking at the overlap of directed links.
An Energy-Saving Scheduling Algorithm for Multipath TCP in Wireless Networks
Authors: Dong Pingping , Shen Rongcheng , Li Yajing , Nie Cheng , Xie Jingyun , Gao Kai , Zhang Lianming
Published: February 2022

Multipath TCP (MPTCP) allows the system to use available paths through multiple network interfaces. This aggregation of bandwidth increases the throughput of the network but also consumes more energy to maintain connections between multiple interfaces. Existing algorithms tend to shift traffic from the high energy path to the low energy path to save energy. However, when the performance of the less energy-intensive path deteriorates, continued use of that path will reduce throughput. To alleviate this issue and reduce energy consumption while ensuring network throughput, this paper proposes an energy-saving scheduling system named ES-MPTCP, which can be combined with the path management module to manage subflows, thereby determining the priority of subflows and which subflows to be used for data transmission. The ES-MPTCP algorithm designs a target optimization function based on path throughput and energy consumption to find different sets of suitable subflows for different applications to reduce energy consumption and guarantee throughput. The extensive experimental results based on the Linux real platform demonstrate that the ES-MPTCP algorithm has the best performance, which can improve throughput by 13.6%, reduce energy consumption by 16.2% and increase the energy efficiency up to 23%.
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
Authors: Dhiman Poonam , Kukreja Vinay , Manoharan Poongodi , Kaur Amandeep , Kamruzzaman M. M. , Dhaou Imed Ben , Iwendi Celestine
Published: February 2022

Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.
A Novel Path Voting Algorithm for Surface Crack Detection
Authors: Yu Jianwei , Chen Zhipeng , Xiong Zhiming
Published: February 2022

Path voting is a widely used technique for line structure detection in images. Traditional path voting, based on minimal-path, is performed to track paths based on how seeds grow. The former requires to set a starting point and an end point. Thus, the performance of minimal-path path voting depends on the initialization. However, high-quality initialization often requires human interaction, which limits its applications in practice. In this paper, a fully automatic path voting method has been proposed and applied for crack detection. The proposed path voting is performed to segment images, which partitions an image patch along the potential crack path and integrates the path to form a crack probability map. After path voting, crack seeds are sampled and modeled into a graph, and the edge weights are assigned using an attraction field algorithm. Finally, cracks are extracted by using spanning tree and tree pruning algorithms. Experimental results demonstrate that the proposed path voting approach can effectively infer the cracks from 2D optic images and 3D depth images.
Analysis and Design of the Energy Storage Requirement of Hybrid Modular Multilevel Converters Using Numerical Integration and Iterative Solution
Authors: Wang Kailun , Song Qiang , Xu Shukai
Published: February 2022

Increasing the modulation index by utilizing the negative voltage states of full-bridge submodules (FBSMs) can greatly reduce capacitor usage of modular multilevel converters (MMCs), thereby optimizing the cost and volume. The hybrid MMC is composed of half-bridge submodules (HBSMs) and FBSMs, and the capacitor voltages of the two types of submodules (SMs) have different shapes as long as negative voltage states exist. This condition greatly complicates the analysis and design of the energy storage requirement of the hybrid MMC, which utilizes the negative voltage states of FBSMs to boost the AC voltage. A numerical calculation method for solving the capacitor voltages and designing the capacitances of FBSMs and HBSMs is proposed in order to accurately determine the minimum energy storage requirement considering the difference between the energy variations in FBSMs and HBSMs. In the numerical calculation, the energy storage and voltage of the arm are decomposed into FBSM and HBSM parts. According to the physical switching process, the output voltages of FBSM and HBSM parts are determined separately. The one-cycle waveforms of the capacitor voltages are then obtained by numerical integration of the power flows in FBSM and HBSM parts. An iterative solution procedure and the termination criterion that can ensure the accuracy of the obtained one-cycle waveforms are also proposed. Using the numerical integration and iterative solution procedure as the kernel algorithm, the proposed method can accurately analyze the capacitor voltages of the FBSMs and HBSMs and determine the minimum energy storage requirement of the hybrid MMC. Furthermore, the proposed method is applicable for various operating working conditions and various proportions of FBSMs. The simulation results verify the feasibility and accuracy of the analysis and design method.
Simulation Research on a Cogeneration System of Low-Concentration Photovoltaic/Thermal Coupled with Air-Source Heat Pump
Authors: Ai Dengxin , Xu Ke , Zhang Heng , Chen Tianheng , Wang Guilin
Published: February 2022

In this paper, a low-concentration photovoltaic/thermal (LCPV/T) coupled with air-source heat pump (AHP) system is proposed which fully utilizes the heat generated by LCPV/T and improves the performance of the AHP. The system is built and investigated in the Transient System Simulation Program (TRNSYS) and an experimental room model is established to verify the feasibility of the system. The performance of the system is researched from the perspective of energy and exergy, and the system performance with LCPV/T and without LCPV/T is compared. Finally, the influence of the variation of key parameters of the system is studied. The results indicated that on the coldest day, the electrical efficiency of LCPV/T reached 10% which was equal to the electrical exergy efficiency. The maximum thermal efficiency was 31.88% while thermal exergy efficiency was 2.7%. The maximum coefficient of performance (COP) of AHP was 3.3, and the thermal exergy efficiency was 47%. The indoor temperature was maintained at about 20 °C in the heating season. When LCPV/T was adopted, the COP and thermal exergy efficiency of the AHP was generally higher than those without LCPV/T. In conclusion, the utilization of LCPV/T has a positive impact on the performance of the AHP.
Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation
Authors: Bogensperger Alexander J. , Fabel Yann , Ferstl Joachim
Published: February 2022

Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the “supply and demand ratio” (SDR) and “mid-market rate pricing” (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model’s initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility.
Multi-Objective Optimization of Parameters of Channels with Staggered Frustum of a Cone Based on Response Surface Methodology
Authors: Zhao Zhen , Xu Liang , Gao Jianmin , Xi Lei , Ruan Qicheng , Li Yunlong
Published: February 2022

In this study, Response Surface Methodology (RSM) and multi-objective genetic algorithm were used to obtain optimum parameters of the channels with frustum of a cone with better flow and heat transfer performance. Central composite face-centered design (CCF) was applied to the experimental design of the channel parameters, and on this basis, the response surface models were constructed. The sensitivity of the channel parameters was analyzed by Sobol’s method. The multi-objective optimization of the channel parameters was carried out with the goal of achieving maximum Nusselt number ratio (Nu/Nu0) and minimum friction coefficient ratio (f/f0). The results show that the root mean square errors (RSME) of the fitted response surface models are less than 0.25 and the determination coefficients (R2) are greater than 0.93; the models have high accuracy. Sobol’s method can quantitatively analyze the influence of the channel parameters on flow and heat transfer performance of the channels. When the response is Nu/Nu0, from high to low, the total sensitivity indexes of the channel parameters are frustum of a cone angle (α), Reynolds number (Re), spanwise spacing ratio (Z2/D), and streamwise spacing ratio (Z1/D). When the response is f/f0, the total sensitivity indexes of the channel parameters from high to low are Re, Z1/D, α and Z2/D. Four optimization channels are selected from the Pareto solution set obtained by multi-objective optimization. Compared with the reference channel, the Nu/Nu0 of the optimized channels is increased by 21.36% on average, and the f/f0 is reduced by 9.16% on average.
Optimal Eco-Compensation for Forest-Based Carbon Sequestration Programs: A Case Study of Larch Carbon Sink Plantations in Gansu, Northwest China
Authors: Chang Wei-Yew , Li Zhuolun , Lu Kefei , Chang Sun J.
Published: February 2022

This study investigated the potential financial benefits that private forest famers can derive when participating in a larch carbon sink plantation project in the northwestern Chinese province of Gansu. A decision matrix was developed to help forest farmers justify participation in forest carbon sink projects relative to the traditional land-use goal of timber production under various carbon trading prices, site conditions, and contract terms. The results showed that when the carbon trading price is at the theoretically optimal carbon price (CNY 110/tCO2e, equivalent to USD 17/tCO2e), Chinese business entities are willing to pay for forest carbon credits under the current global carbon emissions level, and forest farmers who participate in a 25-year forest carbon sink plantation project on high-productivity sites would generate the greatest financial benefit compared with the net income from pure timber production forests. Thus, the government does not need to provide a carbon sink subsidy for participating tree farmers. However, at the current average carbon trading price (CNY 19.8/tCO2e or USD 3/tCO2e) in the domestic market, a minimum additional subsidy of CNY 735/ha (USD 113/ha) is required upfront to motivate forest farmers to convert timber production forests into carbon sink forests. The results of this study can help policymakers and forest managers formulate optimal eco-compensation strategies for enrollment in forest-based carbon sequestration programs.
Identifying Potential Planting Sites for Three Non-Native Plants to Be Used for Soil Rehabilitation in the Tula Watershed
Authors: Buendía-Espinoza Julio César , Martínez-Ochoa Elisa del Carmen , Díaz-Aguilar Irma , Cahuich-Damián Jesús Eduardo , Zamora-Elizalde Mayra Clementina
Published: February 2022

The Tula watershed in Mexico, located in a semiarid and sub-humid climate zone, is experiencing intensive population growth, the expansion of mining concessions for construction materials, and agricultural and urban development, resulting in the degradation of soils and vegetation and a greater demand on natural resources. The aims of this study were to evaluate the survival rates and identify potential habitats within the Tula watershed for planting three non-native forage species (Atriplex canescens, Cynodon dactylon, and Leucaena collinsii) using the Kaplan-Meier estimator and the MaxEnt model with the purpose of rehabilitating degraded soils via agroforestry systems. There were 19 edaphoclimatic variables used and the occurrences of three species, obtained from the GBIF, MEXU, and SNIB databases. The models generated with MaxEnt were very accurate (area under the curve [AUC] ≥ 0.7). The species Atriplex canescens and Cynodon dactylon showed areas of potential planting sites (>0.4) and high survival rates (80% and 92%, respectively). The species Leucaena collinsii presented areas with lower potential planting (<0.4) but registered the greater survival rate (100%). The results provide a solid basis to evaluate the survival rates of forage species within potential planting sites in the Tula watershed using agroforestry systems to rehabilitate degraded soils.
Exploration of Mechanical Properties of Enset–Sisal Hybrid Polymer Composite
Authors: Bekele Abera E. , Lemu Hirpa G. , Jiru Moera G.
Published: February 2022

Enset and sisal fibers are among the most widely used reinforcement to fabricate natural fiber-based composite materials. Hand lay-up techniques were employed in this study to fabricate enset–sisal (E/S) hybrid fiber composite with volume ratios of 100/0, 75/25, 50/50, 25/75, and 0/100 and constant polyester resin. The tensile, flexural, impact strength, water absorption and morphological properties of the fabricated composite were investigated experimentally. The effects of hybridization to volume ratio were determined and the results show that hybrid composites excel in mechanical properties, compared with single composites. For better mechanical properties, the enset fiber has been hybridized with sisal fiber. Tensile and flexural strengths were enhanced by 47.3% and 41.03%, respectively, at 50/50 E/S volume ratio compared with 100/0 E/S composite. The impact strength of sisal fiber composite was improved by adding enset fiber in the composites. The inherent benefits and limitations of these two fibers were balanced out by each other in a positive way. While sisal fiber helped the composite intermesh of tensile, flexural, and reduction of water absorption, enset ensured impact strength. Morphological analysis was carried out in order to observe the fracture behavior and fiber pull-out of the samples by means of scanning electron microscopy.
Acknowledgment to Reviewers of Hydrology in 2021
Authors:   Hydrology Editorial Office
Published: February 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Attitudes and Practices Regarding Research among Romanian Medical Undergraduate Students
Authors: Pop Andreea Iulia , Lotrean Lucia Maria , Buzoianu Anca Dana , Suciu Soimita Mihaela , Florea Mira
Published: February 2022

Objectives: This study focuses on the assessment of attitudes and practices regarding research among undergraduate medical students from Cluj-Napoca, Romania. Material and Methods: A cross-sectional study was performed thorough anonymous questionnaires (May–June 2018) among 510 third- and fifth-year students of Iuliu Hatieganu University of Medicine and Pharmacy from Cluj-Napoca, Romania. Results: More than 60% of the third- and fifth-year students declared that they were interested and willing to perform research during medical studies, while more than two-thirds were interested in doing so after graduation. In total, 6% of third-year students and 31% of fifth-year students declared that they had prepared a scientific presentation for a medical congress at least once. Around 9% of the third-year students contributed to the writing of a scientific article and participated in research projects. Among fifth-year students, one-quarter were involved in writing scientific papers, and 21% participated in research projects. Conclusions: To the best of our knowledge, this study assesses, for the first time in Romania, the perspectives and behaviors of medical undergraduate students with regard to involvement in research activities. The results show that Romanian medical students value opportunities for conducting research, which encourages institutional initiatives that support their involvement in curricular and extracurricular research activities.
Conflict Sources and Management in the ICU Setting before and during COVID-19: A Scoping Review of the Literature
Authors: Czyż-Szypenbejl Katarzyna , Mędrzycka-Dąbrowska Wioletta , Falcó-Pegueroles Anna , Lange Sandra
Published: February 2022

Introduction. Conflicts are an inherent part of work within any organisation. They can arise between members of an interdisciplinary team (or between teams representing different departments), between patients and team members/family members, and patients’ families and team members. Various conflict situations among employees may occur, therefore it is very important to identify their causes and take preventive or targeted corrective measures. The aim of this study was to review the available literature concerning conflicts arising in ICUs—their types, methods of expression as well as their management and mitigation. In addition, we reviewed the available literature on the impact of the pandemic on the ICU environment caring for COVID-19 patients. Methods. The databases were searched. Single key words or their combinations using AND or OR operators were entered. Eventually, 15 articles were included in our review, which included two identical papers. Results. Conflicts occurred occasionally or rarely; researchers describing ethical conflicts demonstrated a moderate level of exposure to conflicts. The pandemic created many challenges and ethical dilemmas that are a source of ethical conflict. Conclusions. As conflict by nature remains inevitable, adequate procedures in conflict management should be developed and the leadership of managing personnel should be reinforced, because team members frequently expect guidance from their supervisors. The importance of training in interpersonal communication and crisis situation management in healthcare should therefore be emphasised.
Attitude of Thai Dental Practitioners towards the Use of Botulinum Toxin in Dentistry
Authors: Laorpipat Sasi , Fuangtharnthip Pornpoj , Yuma Suraphong , Tantipoj Chanita
Published: February 2022

This study aims to investigate the attitude of Thai dentists towards the use of botulinum toxin (BTX) in dentistry and the associated factors. An online survey was conducted using a semi-structured questionnaire consisting of four parts: demographic data, background knowledge, attitude, and an open-ended question for further suggestions related to BTX usage in dental patients. Multivariate logistic regression was used to analyze factors that affect the decision to use BTX in dentistry, and a content analysis approach was used to describe open-ended suggestion data. We received 444 responses from currently practicing dentists throughout Thailand. Roughly 80% of the participants agreed to the use of BTX in their patients. Most participants were aware that BTX could be used for facial esthetic repairs and bruxism treatment but unaware of other therapeutic benefits. Despite impressively positive attitudes towards BTX use, only 5.9% of the participants had the experience of using BTX in their dental patients. The limit on BTX use is mainly due to the lack of knowledge of related laws and educational resources. In summary, official training courses should be established to promote the safe and legal use of BTX in dentistry in Thailand.
Factors Involved in the Food Choices of Diners in a Kibbutz Communal Dining Room Buffet: A Qualitative Study
Authors: Katz-Shufan Ofira , Simon-Tuval Tzahit , Shahar Danit R. , Feder-Bubis Paula
Published: February 2022

Improving nutrition improves health outcomes. Eating in a catering system may provide an environment for promoting healthy dietary choices. To map the factors that shape the food choices of diners who routinely eat in catering systems, we collected and analyzed qualitative data about diners’ perceptions of their food choices in communal dining rooms in three kibbutzim in Israel. From May to July 2014, we conducted in-depth, semi-structured, face-to-face interviews with 13 diners who ate at least three lunches per week in the kibbutz’s dining room. Data analysis followed thematic analysis principles. Two categories of themes emerged from the interviews. In the personal context category, the themes identified were eating as a task and attempts to control one’s eating. In the contextual aspects of eating in the catering system category, themes identified were eating in the dining room as a default, the characteristics of the food served, routine, and personal versus public aspects. The sub-theme of the diners’ freedom of choice emerged in the two categories of themes. Diners’ wishes of maintaining their freedom of choice may be an important contribution to the debate of whether catering systems should provide only healthy foods, which may jeopardize diners’ freedom of choice.
Chronic Pain and Its Association with Depressive Symptoms and Renal Function in Hypertensive Patients
Authors: Kalaitzidis Rigas G. , Theofilis Panagiotis , Touchantzidou Kalliopi , Vordoni Aikaterini , Siamopoulos Kostas C. , Skapinakis Petros
Published: February 2022

Chronic pain is a common concern and is considered to be one of the major problems in patients with chronic physical disorders. We studied the effect of pain in patients with hypertension with or without chronic kidney disease (CKD) and the association between pain and symptoms of depression. The study involved 158 hypertensive individuals (59.5% male, mean age 55 years), of whom 47 (29.8%) had CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2). Pain was assessed with the pain/discomfort domain of the EuroQol-5 D, while depressive symptoms were assessed with the depression module of the Patient health questionnaire (PHQ-9). The prevalence of chronic pain in our sample was 44.3%. Women exhibited chronic pain more often compared to men (57.1% vs. 42.9%, p < 0.001). The presence of CKD was not significantly associated with a higher prevalence of chronic pain among hypertensive patients. Depressive symptoms were significantly associated with the presence of chronic pain. These findings were confirmed in the logistic regression analysis. Chronic pain is common in hypertensive individuals and the association with depression warrants further investigation and may have practical implications in managing these patients.