bluuebunny/crossref_metadata_2025 huggingface dataset:
Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach
Authors: Liu Mao-Yi , Li Zheng , Zhang Hang
Published: September 2023

To ensure the safety of buildings, accurate and robust prediction of a reinforced concrete deep beam’s shear capacity is necessary to avoid unpredictable accidents caused by brittle failure. However, the failure mechanism of reinforced concrete deep beams is very complicated, has not been fully elucidated, and cannot be accurately described by simple equations. To solve this issue, machine learning techniques have been utilized and corresponding prediction models have been developed. Nevertheless, these models can only provide deterministic prediction results of the scalar type, and the confidence level is uncertain. Thus, these prediction results cannot be used for the design and assessment of deep beams. Therefore, in this paper, a probabilistic prediction approach of the shear strength of reinforced concrete deep beams is proposed based on the natural gradient boosting algorithm trained on a collected database. A database of 267 deep beam experiments was utilized, with 14 key parameters identified as the inputs related to the beam geometry, material properties, and reinforcement details. The proposed NGBoost model was compared to empirical formulas from design codes and other machine learning methods. The results showed that the NGBoost model achieved higher accuracy in mean shear strength prediction, with an R2 of 0.9045 and an RMSE of 38.8 kN, outperforming existing formulas by over 50%. Additionally, the NGBoost model provided probabilistic predictions of shear strength as probability density functions, enabling reliable confidence intervals. This demonstrated the capability of the data-driven NGBoost approach for robust shear strength evaluation of RC deep beams. Overall, the results illustrated that the proposed probabilistic prediction approach dramatically surpassed the current formulas adopted in design codes and machine learning models in both prediction accuracy and robustness.
First Examples of Reactions of 3-Trimethylsilyl-2-Propynamides and Organic Diselenides: Synthesis of Novel Derivatives of Propynamides
Authors: Andreev Mikhail V. , Potapov Vladimir A. , Musalov Maxim V. , Larina Lyudmila I.
Published: September 2023

First examples of the reactions of 3-trimethylsilyl-2-propynamides with organic diselenides yielding 3-alkylselanyl-2-propenamides and 3-organylselanyl-2-propynamides were realized. The latter compounds were obtained by the Cu-catalyzed reaction of organic diselenides with 4-propioloylmorpholine. The reaction of 3-trimethylsilyl-2-propynamides with dialkyl diselenides in the system NaBH4/H2O/K2CO3/THF proceeded in a regio- and stereoselective fashion, affording 3-alkylselanyl-2-propenamides in 90–94% yields. An unsymmetrical divinyl selenide with the cyclic amide groups and a product, containing two selanyl-2-propenamide moieties and three cyclic amide groups, were synthesized. The Cu-catalyzed allylation reaction of 3-trimethylsilyl-2-propynamides was accompanied with desilylation to yield 3-allyl-2-propynamides.
MicroRNA-30d-5p—A Potential New Therapeutic Target for Prevention of Ischemic Cardiomyopathy after Myocardial Infarction
Authors: Boxhammer Elke , Paar Vera , Wernly Bernhard , Kiss Attila , Mirna Moritz , Aigner Achim , Acar Eylem , Watzinger Simon , Podesser Bruno K. , Zauner Roland , Wally Verena , Ablinger Michael , Hackl Matthias , Hoppe Uta C. , Lichtenauer Michael
Published: September 2023

(1) Background and Objective: MicroRNAs (miRs) are biomarkers for assessing the extent of cardiac remodeling after myocardial infarction (MI) and important predictors of clinical outcome in heart failure. Overexpression of miR-30d-5p appears to have a cardioprotective effect. The aim of the present study was to demonstrate whether miR-30d-5p could be used as a potential therapeutic target to improve post-MI adverse remodeling. (2) Methods and Results: MiR profiling was performed by next-generation sequencing to assess different expression patterns in ischemic vs. healthy myocardium in a rat model of MI. MiR-30d-5p was significantly downregulated (p < 0.001) in ischemic myocardium and was selected as a promising target. A mimic of miR-30d-5p was administered in the treatment group, whereas the control group received non-functional, scrambled siRNA. To measure the effect of miR-30d-5p on infarct area size of the left ventricle, the rats were randomized and treated with miR-30d-5p or scrambled siRNA. Histological planimetry was performed 72 h and 6 weeks after induction of MI. Infarct area was significantly reduced at 72 h and at 6 weeks by using miR-30d-5p (72 h: 22.89 ± 7.66% vs. 35.96 ± 9.27%, p = 0.0136; 6 weeks: 6.93 ± 4.58% vs. 12.48 ± 7.09%, p = 0.0172). To gain insight into infarct healing, scratch assays were used to obtain information on cell migration in human umbilical vein endothelial cells (HUVECs). Gap closure was significantly faster in the mimic-treated cells 20 h post-scratching (12.4% more than the scrambled control after 20 h; p = 0.013). To analyze the anti-apoptotic quality of miR-30d-5p, the ratio between phosphorylated p53 and total p53 was evaluated in human cardiomyocytes using ELISA. Under the influence of the miR-30d-5p mimic, cardiomyocytes demonstrated a decreased pp53/total p53 ratio (0.66 ± 0.08 vs. 0.81 ± 0.17), showing a distinct tendency (p = 0.055) to decrease the apoptosis rate compared to the control group. (3) Conclusion: Using a mimic of miR-30d-5p underlines the cardioprotective effect of miR-30d-5p in MI and could reduce the risk for development of ischemic cardiomyopathy.
Role of the YAP/TAZ-TEAD Transcriptional Complex in the Metabolic Control of TRAIL Sensitivity by the Mevalonate Pathway in Cancer Cells
Authors: El Yousfi Younes , Mora-Molina Rocío , López-Rivas Abelardo , Yerbes Rosario
Published: September 2023

Different studies have reported that inhibiting the mevalonate pathway with statins may increase the sensitivity of cancer cells to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), although the signaling mechanism leading to this sensitization remains largely unknown. We investigated the role of the YAP (Yes-associated protein)/TAZ (transcriptional co-activator with PDZ-binding motif)-TEAD (TEA/ATTS domain) transcriptional complex in the metabolic control of TRAIL sensitivity by the mevalonate pathway. We show that depleting nuclear YAP/TAZ in tumor cells, either via treatment with statins or by silencing YAP/TAZ expression with siRNAs, facilitates the activation of apoptosis by TRAIL. Furthermore, the blockage of TEAD transcriptional activity either pharmacologically or through the ectopic expression of a disruptor of the YAP/TAZ interaction with TEAD transcription factors, overcomes the resistance of tumor cells to the induction of apoptosis by TRAIL. Our results show that the mevalonate pathway controls cellular the FLICE-inhibitory protein (cFLIP) expression in tumor cells. Importantly, inhibiting the YAP/TAZ-TEAD signaling pathway induces cFLIP down-regulation, leading to a marked sensitization of tumor cells to apoptosis induction by TRAIL. Our data suggest that a combined strategy of targeting TEAD activity and selectively activating apoptosis signaling by agonists of apoptotic TRAIL receptors could be explored as a potential therapeutic approach in cancer treatment.
Generation of Human Regulatory Dendritic Cells from Cryopreserved Healthy Donor Cells and Hematopoietic Stem Cell Transplant Recipients
Authors: Scroggins Sabrina M. , Schlueter Annette J.
Published: September 2023

Acute graft versus host disease (GVHD) remains a significant complication following hematopoietic stem cell transplant (HSCT), despite improved human leukocyte antigen (HLA) matching and advances in prophylactic treatment regimens. Previous studies have shown promising results for future regulatory dendritic cell (DCreg) therapies in the amelioration of GVHD. This study evaluates the effects of cryopreservation on the generation of DCreg, the generation of young and older DCreg in serum-free media, and the feasibility of generating DCreg from young and older HSCT patient monocytes. DCregs were generated in X-vivo 15 serum-free media from donor or patient monocytes. This study includes the use of monocytes from young and older healthy, donor, and HSCT patients with varying hematological diseases. Phenotypic differences in cell populations were assessed via flow cytometry while pro-inflammatory and anti-inflammatory cytokine production was evaluated in culture medium. The number of DCreg generated from cryopreserved monocytes of healthy donors was not significantly different from freshly isolated monocytes. DCreg generated from cryopreserved monocytes had comparable levels of co-stimulatory molecule expression, inhibitory molecule expression, and cytokine production as freshly isolated monocytes. Young and older healthy donor monocytes generated similar numbers of DCreg with similar cytokine production and phenotype. Although monocytes from older HSCT patients generated significantly fewer DCreg, DCreg from young and older HSCT patients had comparable phenotypes and cytokine production. Monocytes from young and older myelodysplastic syndrome (MDS) patients generated reduced numbers of DCreg compared to non-MDS-derived DCreg. We demonstrate that the cryopreservation of monocytes from HSCT patients of varying hematological diseases allows for the cost-effective generation of DCreg on an as-needed basis. Although the generation of DCreg from MDS patients requires further assessment, these data support the possibility of in vitro-generated DCreg as a therapy to reduce GVHD-associated morbidity and mortality in young and older HSCT recipients.
Analysis of Free Circulating Messenger Ribonucleic Acids in Serum Samples from Late-Onset Spinal Muscular Atrophy Patients Using nCounter NanoString Technology
Authors: Leo Markus , Schmitt Linda-Isabell , Mairinger Fabian , Roos Andreas , Hansmann Christina , Hezel Stefanie , Skuljec Jelena , Pul Refik , Schara-Schmidt Ulrike , Kleinschnitz Christoph , Hagenacker Tim
Published: September 2023

5q-related Spinal muscular atrophy (SMA) is a hereditary multi-systemic disorder leading to progressive muscle atrophy and weakness caused by the degeneration of spinal motor neurons (MNs) in the ventral horn of the spinal cord. Three SMN-enhancing drugs for SMA treatment are available. However, even if these drugs are highly effective when administrated early, several patients do not benefit sufficiently or remain non-responders, e.g., adults suffering from late-onset SMA and starting their therapy at advanced disease stages characterized by long-standing irreversible loss of MNs. Therefore, it is important to identify additional molecular targets to expand therapeutic strategies for SMA treatment and establish prognostic biomarkers related to the treatment response. Using high-throughput nCounter NanoString technology, we analyzed serum samples of late-onset SMA type 2 and type 3 patients before and six months under nusinersen treatment. Four genes (AMIGO1, CA2, CCL5, TLR2) were significantly altered in their transcript counts in the serum of patients, where differential expression patterns were dependent on SMA subtype and treatment response, assessed with outcome scales. No changes in gene expression were observed six months after nusinersen treatment, compared to healthy controls. These alterations in the transcription of four genes in SMA patients qualified those genes as potential SMN-independent therapeutic targets to complement current SMN-enhancing therapies.
Paediatric COVID-19 Outcomes: Haematology Parameters, Mortality Rates, and Hospitalization Duration
Authors: Alshalani Abdulrahman , Alotaibi Badi A. , Aldali Jehad A. , AlSudais Hamood , Almuqrin Abdulaziz M. , Alshehri Nasser A. , Alamar Nasser B. , Alhejji Mogtba A.
Published: September 2023

The global COVID-19 pandemic has strained healthcare systems around the globe, necessitating extensive research into the variables that affect patient outcomes. This study examines the relationships between key haematology parameters, duration of hospital stay (LOS), and mortality rates in COVID-19 cases in paediatric patients. Researchers analyse relationships between independent variables (COVID-19 status, age, sex) and dependent variables (mortality, LOS, coagulation parameters, WBC count, RBC parameters) using multivariate regression models. Although the R-square values (0.6–3.7%) indicate limited explanatory power, coefficients with statistical significance establish the impact of independent variables on outcomes. Age emerges as a crucial predictor of mortality; the mortality rate decreases by 1.768% per age group. Both COVID-19 status and age have an inverse relationship with length of stay, emphasising the milder hospitalisation of children. Platelet counts decline with age and male gender, potentially revealing the influence of COVID-19 on haematological markers. There are significant correlations between COVID-19 status, age, gender and coagulation measures. Lower prothrombin time and D-dimer concentrations in elder COVID-19 patients are indicative of distinct coagulation profiles. WBC and RBC parameters exhibit correlations with variables: COVID-19-positive patients have lower WBC counts, whereas male COVID-19-positive patients have higher RBC counts. In addition, correlations exist between independent variables and the red cell distribution width, mean corpuscular volume, and mean corpuscular haemoglobin. However, there is no correlation between mean corpuscular haemoglobin concentration and outcomes, indicating complex interactions between haematological markers and outcomes. In essence, this study underlines the importance of age in COVID-19 mortality, provides novel insights into platelet counts, and emphasises the complexity of the relationships between haematological parameters and disease outcomes.
Association between Dietary Behavior and Overweight and Obesity among Chinese Students: A Cross-Sectional Study
Authors: Hong Jia , Gong Qinghai , Gao Hua , Wang Jinghui , Guo Yanbo , Jiang Danjie , Zhang Yan
Published: September 2023

Objectives: To investigate the prevalence of overweight and obesity among Ningbo students and explore the association between students’ dietary behavior and overweight and obesity. Materials and Methods: A total of 7299 students were recruited, including 3755 males and 3544 females. A questionnaire on student health status and influencing factors was used to investigate dietary behavior. Logistic regression analysis investigated the relationship between dietary behavior and overweight and obesity. Age, gender, area, grade, sleep duration, and physical activity were adjusted in the multivariate regression models. Results: This study revealed that the prevalence of overweight and obesity in Ningbo students was 16.14% and 9.88%, respectively. The logistic regression analysis results showed that regular consumption of sugary beverages was associated with a higher risk of being overweight (OR = 1.256, 95% CI: 1.023–1.542, p = 0.029). The research indicated that skipping breakfast was considered a risk factor for obesity (OR = 2.102, 95% CI: 1.087–4.065, p = 0.027). After adjusting for age, gender, area, and grade and continuing to adjust for sleep duration and physical activity, the results showed that consuming fried food at least once a day increased the risk of obesity (OR = 1.494, 95% CI: 1.050–2.125, p = 0.026; OR = 1.516, 95% CI: 1.065–2.158, p = 0.021, respectively). This study found that the frequency of breakfast and the consumption of fried food, fresh vegetables, and fruits were not significantly associated with being overweight (p > 0.05). Conclusions: This study indicated that dietary behavior was related to overweight and obesity among Ningbo students. Further studies and more government support are required to confirm this study’s findings and address the current overweight/obesity problems.
Research on Nano-Titanium Modified Phenolic Resin Coating and Corrosion Resistance
Authors: Zheng Chengwu , Yuan Xingdong , Li Xiaojing , Wang Xuegang , Cui Fadong , Wang Xiaoliang
Published: September 2023

Nano-titanium can be used in the field of anticorrosive coatings due to its excellent corrosion resistance. In this paper, phenolic resin was modified by nano-titanium using a physicochemical method. The nano-titanium-modified phenolic resin was used as a matrix to prepare the anticorrosive coating. The microstructures of the coatings were analyzed by Scanning Electron Microscope (SEM), X-ray spectroscopy (EDS) and Fourier transform infrared spectroscopy (FTIR). Raman and UV spectrum adhesion of the coating was tested by a scratching method. The corrosion behavior was studied by electrochemical workstation and salt spray test. The results showed that the corrosion resistance of pure phenolic resin coating was significantly improved by the nano-titanium-modified phenolic resin. The coating containing 4% titanium nanoparticles exhibited the best corrosion resistance, with the highest impedance and the smallest corrosion current. The coating remained intact after 480 h of salt spray, showing the best salt spray resistance performance.
Microstructure and Corrosion Resistance of Quartz Sand-Modified Enamel-Coated Steel Plates
Authors: Cui Hao , Tang Fujian , Li Bo , Lin Zhibin
Published: September 2023

Coating, as a corrosion protection measure, not only reduces the costs of repairs due to corrosion damage but also saves lives from injuries brought by corroded facilities or equipment. The corrosion behavior of quartz sand-modified enamel (QSME)-coated carbon steel plates was evaluated in a 3.5 wt.% NaCl solution for a period of 30 days using open circuit potential, electrochemical impedance spectroscopy (EIS), and linear polarization resistance. The enamel coating was made by firing enamel slurry to the steel plate at a temperature of around 840 °C. The effect of the size and content of quartz sand on the corrosion resistance is studied, considering four different contents (5 wt.%, 10 wt.%, 20 wt.%, and 30 wt.%) and two different particle sizes (0.38–0.83 mm and 0.83–1.70 mm). The microstructure and phase composition of QSME were characterized with a scanning electron microscope (SEM) and X-ray diffraction (XRD) techniques. SEM images show that the thickness of QSME coating ranges from 430 to 1424 µm depending on the size of quartz sand, and the quartz sand is completely embedded in the enamel matrix. The QSME coating increases the corrosion resistance of uncoated steel plates by approximately 1000 times. The corrosion performance of QSME-coated plates decreases with an increase in quartz sand content, while the effect of the quartz sand size on the corrosion behavior is not significant. The QSME coating can be used to prolong the service lives of civil infrastructures subjected to chloride attack.
Three-Dimensional Simulation of Melt Convection and Oxygen Transport in CZ-Si Crystal Growth with Cusp Magnetic Fields
Authors: Cen Xianrong , Guo Shuangxi
Published: September 2023

The application of magnetic fields has become a standard control technique in the CZ-Si growth industry. To investigate the impact of cusp-shaped magnetic fields (CMF) on heat and mass transfer in the melt, a series of transient three-dimensional simulations were conducted for the growth of a 100 mm CZ-Si crystal with a cylindrical crucible. The turbulent melt motion was modeled using the large eddy simulation (LES) method. Six configurations of CMF with various zero-Gaussian plane (ZGP) positions were examined and numerically compared. The computed results showed that different ZGP positions resulted in distinct types of melt convection, buoyant plumes, and thermal waves. Additionally, it was observed that the studied CMF configurations effectively reduced oxygen dissolution from the crucible wall along with oxygen impurity incorporation into the crystal. These findings demonstrate the potential for precise control of the heat and mass transfer process in CZ-Si growth through the application of suitable CMF.
Flux Growth and Characterization of Bulk InVO4 Crystals
Authors: Voloshyna Olesia , Gorbunov Mikhail V. , Mikhailova Daria , Maljuk Andrey , Seiro Silvia , Büchner Bernd
Published: September 2023

The flux growth of InVO4 bulk single crystals has been explored for the first time. The reported eutectic composition at a ratio of V2O5:InVO4 = 1:1 could not be used as a self-flux since no sign of melting was observed up to 1100 °C. Crystals of InVO4 of typical size 0.5 × 1 × 7 mm3 were obtained using copper pyrovanadate (Cu2V2O7) as a flux, using Pt crucibles. X-ray powder diffraction confirmed the orthorhombic Cmcm structure. Rests of the flux material were observed on the sample surface, with occasional traces of Pt indicating some level of reaction with the crucible. X-ray absorption spectroscopy showed that oxidation states of indium and vanadium ions are +3 and +5, respectively. The size and high quality of the obtained InVO4 crystals makes them excellent candidates for further study of their physical properties.
The Role of MicroRNAs in Breast Cancer and the Challenges of Their Clinical Application
Authors: Muñoz Juan P. , Pérez-Moreno Pablo , Pérez Yasmín , Calaf Gloria M.
Published: September 2023

MicroRNAs (miRNAs) constitute a subclass of non-coding RNAs that exert substantial influence on gene-expression regulation. Their tightly controlled expression plays a pivotal role in various cellular processes, while their dysregulation has been implicated in numerous pathological conditions, including cancer. Among cancers affecting women, breast cancer (BC) is the most prevalent malignant tumor. Extensive investigations have demonstrated distinct expression patterns of miRNAs in normal and malignant breast cells. Consequently, these findings have prompted research efforts towards leveraging miRNAs as diagnostic tools and the development of therapeutic strategies. The aim of this review is to describe the role of miRNAs in BC. We discuss the identification of oncogenic, tumor suppressor and metastatic miRNAs among BC cells, and their impact on tumor progression. We describe the potential of miRNAs as diagnostic and prognostic biomarkers for BC, as well as their role as promising therapeutic targets. Finally, we evaluate the current use of artificial intelligence tools for miRNA analysis and the challenges faced by these new biomedical approaches in its clinical application. The insights presented in this review underscore the promising prospects of utilizing miRNAs as innovative diagnostic, prognostic, and therapeutic tools for the management of BC.
Unveiling the Hidden Power of Uromodulin: A Promising Potential Biomarker for Kidney Diseases
Authors: Thielemans Raïsa , Speeckaert Reinhart , Delrue Charlotte , De Bruyne Sander , Oyaert Matthijs , Speeckaert Marijn M.
Published: September 2023

Uromodulin, also known as Tamm-Horsfall protein, represents the predominant urinary protein in healthy individuals. Over the years, studies have revealed compelling associations between urinary and serum concentrations of uromodulin and various parameters, encompassing kidney function, graft survival, cardiovascular disease, glucose metabolism, and overall mortality. Consequently, there has been a growing interest in uromodulin as a novel and effective biomarker with potential applications in diverse clinical settings. Reduced urinary uromodulin levels have been linked to an elevated risk of acute kidney injury (AKI) following cardiac surgery. In the context of chronic kidney disease (CKD) of different etiologies, urinary uromodulin levels tend to decrease significantly and are strongly correlated with variations in estimated glomerular filtration rate. The presence of uromodulin in the serum, attributable to basolateral epithelial cell leakage in the thick ascending limb, has been observed. This serum uromodulin level is closely associated with kidney function and histological severity, suggesting its potential as a biomarker capable of reflecting disease severity across a spectrum of kidney disorders. The UMOD gene has emerged as a prominent locus linked to kidney function parameters and CKD risk within the general population. Extensive research in multiple disciplines has underscored the biological significance of the top UMOD gene variants, which have also been associated with hypertension and kidney stones, thus highlighting the diverse and significant impact of uromodulin on kidney-related conditions. UMOD gene mutations are implicated in uromodulin-associated kidney disease, while polymorphisms in the UMOD gene show a significant association with CKD. In conclusion, uromodulin holds great promise as an informative biomarker, providing valuable insights into kidney function and disease progression in various clinical scenarios. The identification of UMOD gene variants further strengthens its relevance as a potential target for better understanding kidney-related pathologies and devising novel therapeutic strategies. Future investigations into the roles of uromodulin and regulatory mechanisms are likely to yield even more profound implications for kidney disease diagnosis, risk assessment, and management.
Correction: Zhang, J.; Liu, K. Neural Information Squeezer for Causal Emergence. Entropy 2023, 25, 26
Authors: Zhang Jiang , Liu Kaiwei
Published: September 2023

There was an error in the original publication [...]
A Novel Multi-Step Global Mechanism Scheme for n-Decane Combustion
Authors: Xiong Shaozhuan , Bi Yantian
Published: September 2023

Based on the directed relation graph with error propagation (DRGEP) reduction method, a detailed mechanism consisting of 119 species and 527 reactions for n-decane was simplified. As a result, a skeletal mechanism comprising 32 species and 73 reactions was derived. Subsequently, the quasi-steady state approximation (QSSA) reduction method was employed to further simplify the skeletal mechanism, resulting in a reduced mechanism with 18 species and 14 global reactions. A comparison between the reduced mechanism, skeletal mechanism, and detailed mechanism revealed that the reduced and skeletal mechanisms successfully replicated the combustion characteristics of the detailed mechanism under a range of initial conditions. These models can be credibly incorporated into large-scale combustion simulation, serving as a solid foundation for enhancing computational efficiency.
Artificial Intelligence in Higher Education: A Predictive Model for Academic Performance
Authors: Pacheco-Mendoza Silvia , Guevara Cesar , Mayorga-Albán Amalín , Fernández-Escobar Juan
Published: September 2023

This research work evaluates the use of artificial intelligence and its impact on student’s academic performance at the University of Guayaquil (UG). The objective was to design and implement a predictive model to predict academic performance to anticipate student performance. This research presents a quantitative, non-experimental, projective, and predictive approach. A questionnaire was developed with the factors involved in academic performance, and the criterion of expert judgment was used to validate the questionnaire. The questionnaire and the Google Forms platform were used for data collection. In total, 1100 copies of the questionnaire were distributed, and 1012 responses were received, representing a response rate of 92%. The prediction model was designed in Gretl software, and the model fit was performed considering the mean square error (0.26), the mean absolute error (0.16), and a coefficient of determination of 0.9075. The results show the statistical significance of age, hours, days, and AI-based tools or applications, presenting p-values < 0.001 and positive coefficients close to zero, demonstrating a significant and direct effect on students’ academic performance. It was concluded that it is possible to implement a predictive model with theoretical support to adapt the variables based on artificial intelligence, thus generating an artificial intelligence-based mode.
Promoting Collaborative Learning in Students Soon to Graduate through a Teaching–Learning Model
Authors: Rodriguez-Salvador Marisela , Castillo-Valdez Pedro F.
Published: September 2023

For students to compete in the globalized world, it is essential to be proficient not only in knowledge but also in soft skills. Students should know how to work collaboratively towards a common goal; in this sense, it is recommended for educators to enhance collaborative learning competency, particularly in students in the final part of their studies. Important efforts have been made to study collaborative learning; however, those that focus on collaborative competencies in engineering students close to finishing their studies have not yet been explored. In this context, this research proposes a teaching–learning model named “Colabora” to foster collaborative competencies in students soon to graduate from an industrial engineering program. It includes the development of a progressive final project, the use of a digital platform, and specific assessment tools. An exploratory study was conducted in a private university in Mexico during a semester-long innovation management course. Results show that the students who applied “Colabora” obtained the most knowledge on the subject of innovation management and showed greater collaborative competence. This approach adds value for educators and researchers interested in fostering collaborative competencies useful for future professionals that work in teams pursuing common goals.
Generalization of Split DC Link Voltages Behavior in Three-Phase-Level Converters Operating with Arbitrary Power Factor with Restricted Zero-Sequence Component
Authors: Siton Yarden , Kuperman Alon
Published: September 2023

This article examines the impact of a power factor on the behavior of partial DC link voltages in three-phase three-level AC/DC (or DC/AC) converters operating without additional balancing hardware. We consider the case in which the controller utilizes a bandwidth-restricted (DC in steady state) zero-sequence component to achieve average partial DC link voltage equalization since the injection of high-order zero-sequence components is impossible or forbidden. An assessment of partial split DC-link capacitor voltage behavior (particularly that of ripple magnitudes and phases) is necessary for, e.g., minimizing the values of DC link capacitances and selecting reference voltage values. Previous studies assessed the abovementioned behavior analytically for operation under a unity power factor based on third-harmonic-dominated split partial voltages’ ripple nature. However, it is shown here that deviation from the unity power factor introduces additional (to the third harmonic) non-negligible harmonic content, increasing partial voltage ripple magnitudes and shifting their phase (relative to the mains voltages). As a result, the third-harmonic-only assumption is no longer valid, and it is then nearly impossible to derive corresponding analytical expressions. Consequently, a numerical approach is used in this work to derive a generalized expression of normalized ripple energy as a function of the power factor, which can then easily be utilized for assessments of split DC link voltage behaviors for certain DC link capacitances and reference voltages. Simulations and experimental results validate the proposed methodology applied to a 10 kVA T-type converter prototype.
Image-Fused-Guided Underwater Object Detection Model Based on Improved YOLOv7
Authors: Wang Zhenhua , Zhang Guangshi , Luan Kuifeng , Yi Congqin , Li Mingjie
Published: September 2023

Underwater object detection, as the principal means of underwater environmental sensing, plays a significant part in the marine economic, military, and ecological fields. Due to the degradation problems of underwater images caused by color cast, blurring, and low contrast, we proposed a model for underwater object detection based on YOLO v7. In the presented detection model, an enhanced image branch was constructed to expand the feature extraction branch of YOLOv7, which could mitigate the feature degradation issues existing in the original underwater images. The contextual transfer block was introduced to the enhanced image branch, following the underwater image enhancement module, which could extract the domain features of the enhanced image, and the features of the original images and the enhanced images were fused before being fed into the detector. Focal EIOU was adopted as a new model bounding box regression loss, aiming to alleviate the performance degradation caused by mutual occlusion and overlapping of underwater objects. Taking URPC2020 and UTDAC2020 (Underwater Target Detection Algorithm Competition 2020) datasets as experimental datasets, the performance of our proposed model was compared against with other models, including YOLOF, YOLOv6 v3.0, DETR, Swin Transformer, and InternImage. The results show that our proposed model presents a competitive performance, achieving 80.71% and 86.32% in mAP@0.5 on URPC2020 and UTDAC2020, respectively. Comprehensively, the proposed model is capable of effectively mitigating the problems encountered in the task of object detection in underwater images with degraded features and exhibits great advancement.
Broadband Modelling of Power Transformers for Sweep Frequency Impedance Studies on Winding Short-Circuit Faults
Authors: Liu Yong , Li Cheng , Guo Zhe , Ren Fuqiang , Liu Fei , Fu Yizhe , Zhu Yongsheng , Wang Xiaolei
Published: September 2023

To study sweep frequency impedance (SFI) features of short-circuit (SC) faults easily, this paper proposes a broadband electric circuit model of a transformer winding and solves its three key problems. The first problem is the calculation of lumped-circuit parameters considering frequency-dependent complex anisotropic permeabilities (FDCAPs), which are caused by the physical characteristics, such as skin, proximity, and geometrical effects and anisotropic properties, of the transformer core and winding materials. The other issue is the establishment of the electric circuit model based on the SFI measurement connection mode, the transformer winding parameters, and a double-ladder network (DLN). Another issue is the construction of the state-space model of the electric circuit toward different SFI values to obtain all network branch voltages and currents. The accuracy of the proposed model is assessed by comparing its SFI signatures with those of the simulation model, without considering FDCAPs under healthy winding, and the corresponding physical transformer model during healthy winding and SC faults. It is observed that the SFI results of the proposed model are closer to the experimental measurements, and the model can be effectively used to study the SFI features of SC faults. Moreover, the impacts of different types of SC faults on the SFI data are concluded in this paper.
Secure and Privacy-Preserving Authentication Scheme Using Decentralized Identifier in Metaverse Environment
Authors: Kim Myeonghyun , Oh Jihyeon , Son Seunghwan , Park Yohan , Kim Jungjoon , Park Youngho
Published: September 2023

The metaverse provides a virtual world with many social activities that parallel the real world. As the metaverse attracts more attention, the importance of security and privacy preservation is increasing significantly. In the metaverse, users have the capability to create various avatars, which can be exploited to deceive or threaten others, leading to internal security issues. Additionally, users attempting to access the metaverse are susceptible to various external security threats since they communicate with service providers through public channels. To address these challenges, we propose an authentication scheme using blockchain, a decentralized identifier, and a verifiable credential to enable metaverse users to perform secure identity verification and authentication without disclosing sensitive information to service providers. Furthermore, the proposed approach mitigates privacy concerns associated with the management of personal information by enabling users to prove the necessary identity information independently without relying on service providers. We demonstrate that the proposed scheme is resistant to malicious security attacks and provides privacy preservation by performing security analyses, such as AVISPA simulation, BAN logic, and the real-or-random (ROR) model. We also show that the performance of our proposed scheme is better suited for the metaverse environment by providing greater security and efficiency when compared to competing schemes.
Retrofit of a 600 MW Down-Fired Pulverized-Coal Furnace for Low NOx Emission
Authors: Liu Tao , Wang Sheng , Wei Ziming , Yu Jie
Published: September 2023

Aiming at solving the problem of high NOx emissions of a down-fired boiler, a new combustion system has been proposed by means of the numerical simulation using Ansys Fluent. The coal-lean stream (tertiary air), which was originally mixed with a separated overfired air (SOFA) stream on the front and rear walls of the upper furnace, was relocated to the lower zone of the furnace after retrofit. The secondary-air slots were transformed into a new annular port type, which was injected into the furnace with a down-tilt angle to increase the residence time of the coal stream. Furthermore, the effect of secondary air distribution and velocity of coal stream on performance was studied. After retrofitting the combustion system, the NO emissions were effectively controlled, decreasing from 906 mg Nm−3 to 576 mg Nm−3, but the carbon content of fly ash increased from 2.46% to 5.78%. Aiming at decreasing the carbon content of fly ash, the effect of coal/primary air velocity on the arch was studied. Less carbon content in fly ash can be observed for the lower arch airflow velocity. The results showed that the NO emissions can be controlled below 595 mg Nm−3, and the carbon content of fly ash was reduced to 3.39% when the velocity was decreased to 18 m s−1.
Intelligent Monitoring Platform and Application for Building Energy Using Information Based on Digital Twin
Authors: Li Cui , Lu Ping , Zhu Weiran , Zhu Han , Zhang Xinmin
Published: September 2023

With the development and popularization of the Internet of Things, big data, cloud computing, and other technologies, Digital twin technology (DTT) is increasingly applied to building operation and maintenance (O&M). However, most of the research focuses on building energy consumption, safety, and other management, and relatively little research on the monitoring of building terminal energy using information. The information is closely related to occupants’ behavior, such as air conditioning, lighting, shading, window status information, as well as personnel number and location, and it has a great impact on building energy consumption. Due to different occupants’ behaviors, the level of building energy consumption varies several times or even more. Take an office building as an example. Based on digital twin technology, the framework of building energy using intelligent monitoring is constructed. It mainly includes four parts, namely building physical space, virtual twin space, predictive control simulation engine, and twin big data. For each part, functions are realized through building Information Modelling (BIM), smart sensors, and Internet of Things (IoT) technologies. Based on the standard framework and every function realization method, the DTT can used for building O&M effectually. The application of building an intelligent control system based on the occupants’ characteristics is simulated and analyzed in Designbuilder software 6.1.2. The results show that the digital technology application in building intelligent control systems can realize maximum energy saving for 30%. However, the DTT in building O&M is not widely used now. There is a lot of research to be completed in the future.
New Feature Selection Approach for Photovoltaïc Power Forecasting Using KCDE
Authors: Macaire Jérémy , Zermani Sara , Linguet Laurent
Published: September 2023

Feature selection helps improve the accuracy and computational time of solar forecasting. However, FS is often passed by or conducted with methods that do not suit the solar forecasting issue, such as filter or linear methods. In this study, we propose a wrapper method termed Sequential Forward Selection (SFS), with a Kernel Conditional Density Estimator (KCDE) named SFS-KCDE, as FS to forecast day-ahead regional PV power production in French Guiana. This method was compared to three other FS methods used in earlier studies: the Pearson correlation method, the RReliefF (RRF) method, and SFS using a linear regression. It has been shown that SFS-KCDE outperforms other FS methods, particularly for overcast sky conditions. Moreover, Wrapper methods show better forecasting performance than filter methods and should be used.
Modelling and Estimation in Lithium-Ion Batteries: A Literature Review
Authors: Martí-Florences Miquel , Cecilia Andreu , Costa-Castelló Ramon
Published: September 2023

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.
An Overview of Anaerobic Digestion of Agricultural By-Products and Food Waste for Biomethane Production
Authors: Neri Alessandro , Bernardi Bruno , Zimbalatti Giuseppe , Benalia Souraya
Published: September 2023

In a world facing increasing environmental and energy challenges, anaerobic digestion of agrifood by-products and food waste could contribute to the production of green energy while reducing greenhouse gas emissions into the atmosphere. Anaerobic digestion is a biological process capable of breaking down and stabilising organic matter in the absence of oxygen and converting it into a renewable source of energy, known as biogas. Biomethane production also enables the generation of electricity and produces digestate, a by-product of the digestion process that can be used as a soil conditioner or fertiliser. This review aims to highlight how substrate pretreatment, together with the optimisation of operating parameters, application of additives, recirculation of digestate and frequent feeding, can increase biogas production. An overview of the basics of the anaerobic digestion of agrifood by-products and food waste is provided, including feedstock characteristics (nutrient content, particle size and inhibitory compounds) and process parameters (process configuration, pH, temperature, total and volatile solids, total Kjeldahl nitrogen, ammonium, chemical oxygen demand, carbon/nitrogen ratio, retention time, organic loading rate, etc.). In addition, recent studies in the field of processes, equipment and pretreatments that can significantly improve the anaerobic digestion process of agricultural and food wastes were classified and discussed. Finally, the challenges and future perspectives of biogas production from the agrifood sector are addressed.
Simulation of the Gas Distribution Law and Operational Risk Analysis of a Vertical Gas Tank in a Ventilation State
Authors: Yang Chunli , Wang Yinqing , Liu Yan , Li Xiangchun
Published: September 2023

As a type of airtight equipment, vertical gas tanks are prone to accumulations of toxic and harmful gases due to their poor ventilation and narrow space. This poses many safety hazards. Therefore, we conducted a FLUENT simulation for vertical gas tanks with different diameters. The results indicated that the larger the diameter, the longer the required ventilation time. It was necessary to monitor the gas concentration after ventilation for at least 6 h when the tank diameter was 2.6 m, after ventilation for at least 24 h when the tank diameter was 5.2 m, and after ventilation for at least 80 h when the tank diameter was 7.8 m. To ensure comprehensive monitoring, at least one monitoring point was required to be placed at the upper and lower ends of the vertical gas tank, respectively. Monitoring was initiated after these requirements were reached. A theoretical numerical analysis and an experimental verification analysis were conducted on the simulation results. The variation trend of the simulation value, the theoretical value, and the experimental test value were the same. The measured value of the ventilation duration was greater than the theoretical value of the ventilation duration and the simulation value of the ventilation duration. Therefore, the simulation results and theoretical analysis could be used for a risk analysis of gas tanks. The determination of ventilation characteristics via a simulation of vertical gas tanks has a practical significance when guiding on-site operations.
Robust PLL-Based Grid Synchronization and Frequency Monitoring
Authors: Iov Florin , Zhao Weihao , Kerekes Tamas
Published: September 2023

Nowadays, the penetration of inverter-based energy resources is continuously increasing in low-voltage distribution grids. Their applications cover traditional renewable energy production and energy storage but also new applications such as charging points for electric vehicles, heat pumps, electrolyzers, etc. The power ratings range from a couple of kW to hundreds of kW. Utilities have, in the last few years, reported more challenges regarding power quality in distribution grids, e.g., high harmonic content, high unbalances, large voltage and frequency excursions, etc. Phase-Lock-Loop (PLL) algorithms are typically used for grid synchronization and decoupled control of power converters connected to the grid. Most of the research within PLLs is mainly focusing on grid voltage angle estimation while the byproducts of the algorithms, e.g., frequency and voltage magnitude, are often overlooked. However, both frequency and voltage magnitude estimations are crucial for grid code compliance. Practical considerations for implementation on microcontroller boards of these algorithms are also missing in most of the cases. The present paper proposes a modified PLL algorithm based on a Synchronous Reference Frame that is suitable for both grid synchronization and frequency monitoring, i.e., the estimation of RMS phase voltages and frequencies in highly distorted distribution grids. It also provides the tuning methodology and practical considerations for implementation on commercial DSP boards. The performance of the proposed approach is assessed through simulation studies and laboratory tests under a wide range of operational conditions, showing that the proposed PLL can estimate the grid frequency, for all considered grid events, with an accuracy of less than ±5 mHz, which is a significant improvement on the current state-of-the-art solutions, having an accuracy of at least ±20 mHz or more.