bluuebunny/crossref_metadata_2025 huggingface dataset:
The Sustainable Development of Bridges in China: Collapse Cause Analysis, Existing Management Dilemmas and Potential Solutions
Authors: Tang Dina , Huang Minshui
Published: February 2024

The construction of sustainable bridge projects has become a global mission and challenge in the 21st century. Unfortunately, there has been a rise in bridge collapse incidents due to various factors in recent years both during the construction and service phases. These incidents have resulted in significant loss of life and property damage, exacerbating the five sustainable development issues faced by bridge engineering: natural, resource, environmental, social, and economic factors. As a result, the prevention and resolution of bridge collapse accidents have garnered attention from professionals, research institutions, and government departments, making it a prominent research area. In line with the sustainable development concept of bridge engineering, this article classifies the causes of bridge collapses into two categories: those occurring during the construction phase and those happening during the service phase; the latter includes lack of inspection, maintenance and management, external natural factors, and human factors. Furthermore, this article thoroughly examines the existing national management framework, identifying the dilemmas that hinder its effectiveness in regulating bridge collapse prevention. Finally, several effective suggestions are proposed for the prevention of bridge collapse incidents. These recommendations aim to motivate governments, project owners, designers, constructors, managers, and users to actively develop and promote high-quality sustainable bridges.
Research on Factors Influencing the Style of Chinese Historic Districts Based on the Mask R-CNN Deep Learning Model
Authors: Luo Shengzhong , Shang Wei , Yan Zerong , Bi Shiyao
Published: February 2024

With the rapid development of Chinese cities, the spatial pattern in historical districts has deteriorated due to a lack of protection measures. Indigenous communities have taken it upon themselves to expand certain spaces around the original residential buildings in order to meet the demands of modern life. However, this expansion has had a negative impact on the stylistic integrity of historical districts, leading to a conflict between cultural heritage and district protection. This study focuses on Tanhualin, a representative historical district in Wuhan. The study divides the district into 10 sub-areas, extracting a total of 4850 street views and selecting 6752 spontaneous spatial samples from them. Utilizing the Mask R-CNN model, the study uses computer-based deep learning to identify, summarize, and categorize the various forms and functions of these spontaneous spaces. The study also analyzes the negative impact of these spaces on the unity of the historical blocks’ interfaces, as well as their positive impact on cultural heritage and the creation of a distinctive atmosphere. Finally, the study proposes reconstruction and renewal strategies from both urban design and architectural design perspectives. These strategies aim to improve the quality of life for indigenous communities, establish a sustainable system for preserving historical imprints, protect the cultural atmosphere of historical blocks, and enhance the adaptability of these blocks in modern cities.
Infiltration Models in EnergyPlus: Empirical Assessment for a Case Study in a Seven-Story Building
Authors: Bastos Porsani Gabriela , Fernández-Vigil Iglesias María , Echeverría Trueba Juan Bautista , Fernández Bandera Carlos
Published: February 2024

The current decarbonization transition to be achieved by 2050 according to the European Council has given great prominence to the use of Digital Twins as tools for energy management. For their correct operation, it is essential to control the uncertainties of the energy models, which lead to differences between the measured and predicted data. One of the key parameters that is most difficult to assess numerically is air leakage. The existent infiltration models available in EnergyPlus were developed to be applied in low-rise residential buildings with fewer than three stories. Therefore, it is common to rely on air leakage equations employing predefined coefficients. This research presents an empirical assessment of the performance of two EnergyPlus air leakage models, the “Effective Leakage Area” and the “Flow Coefficient”, in predicting dynamic infiltration within the attic of a seven-story building. Blower door tests, along with the application of CO2 tracer gas, were conducted to establish coefficients for the models. Then, they were evaluated in three independent periods according to the criteria established in the American Society for Testing Material D5157 Standard. Those models that only used in situ coefficients consistently met the standard across all three periods, demonstrating for both equations their accurate performance and reliability. For the best model derived from tracer gas data, the R2 and NMSE values are 0.94 and 0.019, respectively. In contrast, the model developed using blower door test data and EnergyPlus default values presented a 64% reduction in accuracy compared to the best one. This discrepancy could potentially lead to misleading energy estimates. Although other software options exist for estimating infiltration, this study specifically targets EnergyPlus users. Therefore, these findings offer valuable insights to make more informed decisions when implementing the infiltration models into energy simulations for high-rise buildings using EnergyPlus.
Is the Concept of Zero Waste Possible to Implement in Construction?
Authors: Kapica Bogumiła , Targowski Wojciech , Kulowski Andrzej
Published: February 2024

The scientifically treated problem of reusing building materials appeared in the literature more than 50 years ago. This paper includes an analysis of the characteristics of recycled building materials, which is a prerequisite for rational recycling. The analysis and simulation of building stocks at different scales has led to the conclusion that the most important link in the transformation of the construction industry towards the widespread use of reused materials is the unification of the demolition, storage, and redistribution systems. System solutions based on policy studies for each of the first three phases of a building’s life cycle assessment (LCA), i.e., design, legislation, and construction, were proposed. It was also recognised that the socially widespread habit of reusing building materials requires a properly conducted educational process and legalisation at the state level. This article presents proposals for educational activities along with the definition of the individual stages of the process of recycling building materials. The expected results are the decarbonisation of the building industry and communities, the growth of sustainable systems, and lowering the environmental impacts of buildings.
Study on the Stress Threshold of Preventing Interfacial Fatigue Debonding in Concrete Beams Strengthened with Externally-Bonded FRP Laminates
Authors: Min Xinzhe , Yang Dong , Song Shoutan , Li Xing
Published: February 2024

Externally-bonded FRP laminate is widely used in structural strengthening due to the many advantages of FRP materials. Further enhancement of the strengthening effect can be achieved by inducing prestress into the FRP laminate. However, FRP debonding is still a main issue of this strengthening method, especially the Intermediate Crack-induced debonding (IC debonding). To better understand the impact of FRP debonding on the strengthening effect, a series of parameter analyses were conducted in this study based on the fatigue life prediction model proposed by the authors. The proposed model involves the fatigue damage accumulation of components of the beam, the mutual interaction between each component, and the impact of FRP fatigue debonding. As a result, a stress threshold for preventing FRP fatigue debonding in strengthening the concrete beam was proposed, which aimed to avoid safety hazards caused by IC debonding in practical engineering.
Investigation of Shear Strength Reduction Method in Slope Stability of Reinforced Slopes by Anchor and Nail
Authors: Hosseini SeyedSaber , Astaraki Farshad , Imam Seyed Mohammad Reza , Chalabii Jafar , Movahedi Rad Majid
Published: February 2024

Since the stability of slopes in infrastructures such as road and railroad embankments, excavations, and, in general, earthwork is important, analyzing the stability of these slopes has been one of the main focuses of geotechnical engineers. Although analyzing both reinforced and unreinforced slopes is needed, reinforced slopes require special attention as the reinforcement elements significantly affect the calculations. Hence, the current study’s aim is to find out the differences between obtained safety factors using the Limit Equilibrium Method (LEM) and Shear Strength Reduction Method (SSRM). For this purpose, first, the origin differences in terms of Safety Factor (SF) are theoretically determined according to basic formulas for the aforementioned techniques. Then, to verify the formula, several numerical modelings are carried out using in situ measured geotechnical data to better understand the differences in terms of safety factors. The results indicate that for the reinforced slope with an SF value of higher than 1, the SSRM provides a higher SF in comparison with the other techniques, and the origin of this difference is the definitions of the SF in the different methods.
Data-Driven Decision Support for Equipment Selection and Maintenance Issues for Buildings
Authors: Jiang Fengchang , Xie Haiyan , Inti Sundeep , Issa Raja R. A. , Vanka Venkata Sai Vikas , Yu Ye , Huang Tianyi
Published: February 2024

Equipment costs play a critical role in decision making during design and construction, which requires up-to-date information and data. The design of this study incorporates the inputs from the literature review on the influencing factors of equipment costs and major targeted equipment types to enhance decision support for equipment selection, project construction, and maintenance issues. Two traditional cost estimation methods and five machine-learning methods were compared in this study to identify significant attributes related to the predictions of the costs and residual values of each targeted equipment type. The novelty of this study is that the developed method improves prediction accuracy by establishing a comprehensive and well-structured database framework. A comparison of this method with the existing prediction models reveals that the results and the accuracy of multiple regression analysis are improved in the range of (3% to 33.97%) with the use of a modified decision-tree model combined with support vector machines. The major contribution of this research is the design, implementation, and validation of a machine-learning-based modified decision tree with a support vector machine model for improved accuracy and decision support in construction management. Future research should consider the relationship between geographical variations and value changes.
Modified Constitutive Models and Mechanical Properties of GFRP after High-Temperature Cooling
Authors: Wu Junjie , Zhang Chuntao
Published: February 2024

Many materials are highly sensitive to temperature, and the study of the fire resistance of materials is one of the important research directions, which includes the study of the fire resistance of fiber-reinforced polymer (FRP) composites, but the cooling mode on the change of FRP mechanical properties after high temperature has not been investigated. This study analyzes the mechanical properties of GFRP under various cooling methods after exposure to high temperatures. The tensile strength of GFRP was evaluated through water cooling, firefighting foam cooling, and air cooling within the temperature range of 20–300 °C. Damage modes were investigated at different target temperatures. The results indicate that the tensile strength of air-cooled GFRP is the highest, whereas water cooling yields the lowest retention rate. It indicates that the FRP temperature decreases slowly under air cooling and the better recovery of the damage within the resin matrix, while under water cooling, the damage at the fiber/resin interface is exacerbated because of the high exposed temperature and the water, resulting in a reduction in the strength of GFRP. Between 20 and 150 °C, GFRP essentially recovers its mechanical properties after cooling, with a residual tensile strength factor exceeding 0.9. In the range of 150–250 °C, GFRP exhibits a graded decline in strength. At 300 °C, GFRP loses certain mechanical properties after cooling, with a residual tensile strength factor below 0.1. Furthermore, the analysis of experimental results led to the modification of the Johnson–Cook constitutive model, proposing a model for GFRP under three cooling methods. Additionally, a predictive model for the elastic modulus of GFRP after high-temperature cooling was derived, showing agreement with experimental results.
Innovative and Practical Trends in the Photocatalytic Degradation of Environmental Pollutants
Authors: Ateia Mohamed , Johnson Matthew S. , Czech Bożena
Published: February 2024

This Catalysts Special Issue explores cutting-edge research in the field of photocatalysis, offering a glimpse into the evolving landscape of environmental science and catalysis [...]
Human β-Defensin 3 Inhibition of P. gingivalis LPS-Induced IL-1β Production by BV-2 Microglia through Suppression of Cathepsins B and L
Authors: Inoue Erika , Minatozaki Shiyo , Shimizu Sachi , Miyamoto Sayaka , Jo Misato , Ni Junjun , Tozaki-Saitoh Hidetoshi , Oda Kosuke , Nonaka Saori , Nakanishi Hiroshi
Published: February 2024

Cathepsin B (CatB) is thought to be essential for the induction of Porphyromonas gingivalis lipopolysaccharide (Pg LPS)-induced Alzheimer’s disease-like pathologies in mice, including interleukin-1β (IL-1β) production and cognitive decline. However, little is known about the role of CatB in Pg virulence factor-induced IL-1β production by microglia. We first subjected IL-1β-luciferase reporter BV-2 microglia to inhibitors of Toll-like receptors (TLRs), IκB kinase, and the NLRP3 inflammasome following stimulation with Pg LPS and outer membrane vesicles (OMVs). To clarify the involvement of CatB, we used several known CatB inhibitors, including CA-074Me, ZRLR, and human β-defensin 3 (hBD3). IL-1β production in BV-2 microglia induced by Pg LPS and OMVs was significantly inhibited by the TLR2 inhibitor C29 and the IκB kinase inhibitor wedelolactonne, but not by the NLRPs inhibitor MCC950. Both hBD3 and CA-074Me significantly inhibited Pg LPS-induced IL-1β production in BV-2 microglia. Although CA-074Me also suppressed OMV-induced IL-1β production, hBD3 did not inhibit it. Furthermore, both hBD3 and CA-074Me significantly blocked Pg LPS-induced nuclear NF-κB p65 translocation and IκBα degradation. In contrast, hBD3 and CA-074Me did not block OMV-induced nuclear NF-κB p65 translocation or IκBα degradation. Furthermore, neither ZRLR, a specific CatB inhibitor, nor shRNA-mediated knockdown of CatB expression had any effect on Pg virulence factor-induced IL-1β production. Interestingly, phagocytosis of OMVs by BV-2 microglia induced IL-1β production. Finally, the structural models generated by AlphaFold indicated that hBD3 can bind to the substrate-binding pocket of CatB, and possibly CatL as well. These results suggest that Pg LPS induces CatB/CatL-dependent synthesis and processing of pro-IL-1β without activation of the NLRP3 inflammasome. In contrast, OMVs promote the synthesis and processing of pro-IL-1β through CatB/CatL-independent phagocytic mechanisms. Thus, hBD3 can improve the IL-1β-associated vicious inflammatory cycle induced by microglia through inhibition of CatB/CatL.
Dilated Cardiomyopathy Due to Alimentary Iron Deficiency
Authors: Dewein Leonie , Kresz Andrea , Essers Jochen , Bride Peter , Kaestner Michael , Apitz Christian
Published: February 2024

Dilated cardiomyopathy (DCM) is a severe condition, characterised by left ventricular dilation and systolic dysfunction, necessitating heart transplantation when all other treatment options fail. This case report describes a 2-year-old girl initially presenting with oedema, listlessness, and severe iron deficiency anaemia. She was diagnosed with DCM. Extensive diagnostic workup ruled out other causes, leading to the suspicion of DCM due to alimentary iron deficiency. This was confirmed by the parents’ report that the girl was fed almost exclusively with low-fat cow’s milk. Prompt treatment, including packed red cell transfusion, iron supplementation, and heart failure medications (diuretics, ACE inhibitors, beta blockers, and aldosterone antagonists), resulted in significant improvement in cardiac function within days. This report demonstrates the potential risks of alimentary iron deficiency, the most common cause of microcytic hypochromic anaemia in young children, which might even result in the development of life-threatening cardiac dysfunction in extreme cases.
FDIP—A Fast Diffraction Image Processing Library for X-ray Crystallography Experiments
Authors: Gevorkov Yaroslav , Galchenkova Marina , Mariani Valerio , Barty Anton , White Thomas A. , Chapman Henry N. , Yefanov Oleksandr
Published: February 2024

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses.
Pericranial Muscle Stiffness, Pain Thresholds, and Tenderness during a Treatment Cycle of OnabotulinumtoxinA for Chronic Migraine Prevention
Authors: Dalby Sebastian Worsaae , Hvedstrup Jeppe , Carlsen Louise Ninett , Ashina Sait , Bendtsen Lars , Schytz Henrik Winther
Published: February 2024

Background: Treatment with OnabotulinumtoxinA (BoNT-A) is effective as a preventive treatment for chronic migraine (CM). Preclinical studies suggest that the mechanism of action of BoNT-A in migraine is based on blocking unmyelinated C fibers. We aimed to investigate whether the muscle-relaxing effect of BoNT-A is associated with the preventive mechanism in patients with chronic migraine by measuring the stiffness, pain thresholds, and tenderness of the BoNT-A-applied muscles. Methods: A total of 22 patients with CM who were already in BoNT-A treatment participated in this longitudinal prospective study. Pericranial muscle stiffness was measured using ultrasound shear wave elastography, which measures the speed of shear waves propagating through the muscle. Pressure pain thresholds (PPT) were obtained via algometry, and muscle tenderness was measured via manual palpation. Measurements were made before BoNT-A injections and six weeks after the treatment. The measurements were performed while the muscles were maximally relaxed. The patients also completed daily diaries on headache and neck pain. Results: No change was observed in muscle stiffness (p = 0.737) or pericranial muscle tenderness (p = 0.400). The PPT over the trapezius muscles increased from 250 kPa before treatment to 304 kPa six weeks after treatment (p = 0.027). No change was observed on the temporalis muscles (p = 0.200) nor the non-dominant index finger (p = 0.067). BoNT-A decreased neck pain (p = 0.008) and headache (p = 0.007). Conclusions: The findings suggest that BoNT-A leads to the desensitization of cutaneous and muscle nociceptors in the head and neck regions, whereas muscle relaxation might not be an important part of the anti-migraine effect.
The Impact of a Single Haemodialysis Session on the Retinal Thickness and Optic Nerve Morphology
Authors: Roskal-Wałek Joanna , Gołębiewska Joanna , Mackiewicz Jerzy , Bociek Agnieszka , Wałek Paweł , Biskup Michał , Bołtuć-Dziugieł Kamila , Starzyk Katarzyna , Odrobina Dominik , Wożakowska-Kapłon Beata , Jaroszyński Andrzej
Published: February 2024

Background: The aim of the study was to assess the influence of a single haemodialysis (HD) session on the retinal and optic nerve morphology in end-stage kidney disease (ESKD) patients. Methods: It is a prospective study including only the right eye of 35 chronic kidney disease (CKD) patients subjected to HD. Each patient underwent a full eye examination 30 min before HD (8 a.m.) and 15 min after HD. Optical coherence tomography (OCT) was used to assess the peripapillary retinal nerve fibre layer (pRNFL) thickness, macular nerve fibre layer (mRNFL) thickness, ganglion cell layer with inner plexiform layer thickness (GCL+), GCL++ (mRNFL and GCL+) thickness, total retinal thickness (RT) and total macular volume (TMV). The correlation was tested between such systemic parameters changes as systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), body weight, plasma osmolarity and ocular perfusion pressure (OPP) and ultrafiltration volume with total RT and pRNFL thickness changes during HD. Results: In the results of a single HD session, we could observe a statistically significant increase in the total RT thickness (pre-HD 270.4 ± 19.94 μm, post-HD 272.14 ± 20.11 μm; p = 0.0014), TMV (pre-HD 7.48 ± 0.53 mm3, post-HD 7.52 ± 0.55 mm3; p = 0.0006), total pRNFL thickness (pre-HD 97.46 ± 15.71 μm, post-HD 100.23 ± 14.7 μm; p = 0.0039), total GCL+ thickness (pre-HD 70.11 ± 9.24 μm, post-HD 70.6 ± 9.7 μm; p = 0.0044), and GCL++ thickness (pre-HD 97.46 ± 12.56 μm, post-HD 97.9 ± 12.94 μm; p = 0.0081). We observed a significant correlation between the change in total RT and DBP change, as well as between body weight change and the change in total pRNFL thickness. There was also a correlation between total pRNFL thickness change and the presence of diabetes mellitus. Conclusion: Even a single HD session affects the retinal and pRNFL thickness, which should be taken into account when interpreting the OCT results in patients subjected to HD. The impact of changes after a single HD session on selected parameters requires further assessment in subsequent studies, including long-term observation.
MRI-Based Risk Factors for Adverse Maternal Outcomes in Prophylactic Aortic Balloon Occlusion for Placenta Accreta Spectrum and Placenta Previa
Authors: Tokue Hiroyuki , Ebara Masashi , Yokota Takayuki , Yasui Hiroyuki , Tokue Azusa , Tsushima Yoshito
Published: February 2024

Purpose: We previously reported that T2 dark bands and placental bulges observed in magnetic resonance imaging (MRI) can predict adverse maternal outcomes in patients with placenta accreta spectrum (PAS) and placenta previa undergoing prophylactic balloon occlusion of the internal iliac artery. On the other hand, the risk factors associated with the use of prophylactic aortic balloon occlusion (PABO) have not been sufficiently investigated. This retrospective study aimed to identify MRI-based risk factors associated with adverse maternal outcomes in the context of PABO during a cesarean section (CS) for PAS and placenta previa. Materials and Methods: Ethical approval was obtained for a data analysis of 40 patients diagnosed with PAS and placenta previa undergoing PABO during a CS. Clinical records, MRI features, and procedural details were examined. The inclusion criteria for the massive bleeding group were as follows: an estimated blood loss (EBL) > 2500 mL, packed red blood cell (pRBC) transfusion (>4 units), and the need for a hysterectomy or transcatheter arterial embolization after delivery. The massive and nonmassive bleeding groups were compared. Results: Among the 22 patients, those in the massive bleeding group showed significantly longer operative durations, a higher EBL (p < 0.001), an increased number of pRBC transfusions (p < 0.001), and prolonged postoperative hospital stays (p < 0.05). T2 dark bands on MRI were significant predictors of adverse outcomes (p < 0.05). Conclusion: T2 dark bands on MRI were crucial predictors of adverse maternal outcomes in patients undergoing PABO for PAS or placenta previa during a CS. Recognizing these MRI features proactively indicates the need for effective management strategies during childbirth and emphasizes the importance of further prospective studies to validate and enhance these findings.
Spontaneous Contralateral Adrenal Hemorrhage during Periduodenal Abscess Drain Placement
Authors: Hasan Eusha , Lamba Ashley , Ghosh Abheek , Kocharyan Hakob , Al-Roubaie Mustafa , Yeisley Christopher
Published: February 2024

A spontaneous adrenal hemorrhage is a rare complication occurring in the setting of interventional radiology procedures. Here, we present the case of a 73-year-old male who underwent CT-guided drainage of a periduodenal collection. During the procedure, he developed sudden onset left back pain and hypertension, which revealed a left adrenal gland hemorrhage on CT imaging. Potential mechanisms for this complication include a physiological stress response, procedure-associated hypercoagulability, and direct trauma to the adrenal gland. Moreover, an adrenal hemorrhage should be monitored closely as it can lead to severe clinical consequences requiring treatment with IV analgesics and antihypertensives.
Development of an Artificial Soft Solid Gel Using Gelatin Material for High-Quality Ultrasound Diagnosis
Authors: Kim Minchan , Yoon Kicheol , Lee Sangyun , Shin Mi-Seung , Kim Kwang Gi
Published: February 2024

For ultrasound diagnosis, a gel is applied to the skin. Ultrasound gel serves to block air exposure and match impedance between the skin and the probe, enhancing imaging efficiency. However, if use of the ultrasound gel exceeds a certain period of time, it may dry out and be exposed to air, causing impedance mismatch and reducing imaging resolution. In such cases, the use of a soft, solid gel proves advantageous, as it can be employed for an extended period without succumbing to the drying phenomenon and can be reused after disinfection. Its soft consistency ensures excellent skin adhesion. Our soft solid gel demonstrated approximately 1.2 times better performance than water, silicone, and traditional ultrasound gels. When comparing the dimensions of grayscale, dead zone, vertical, and horizontal regions, the measurements for the traditional ultrasound gel were 93.79 mm, 45.32 mm, 103.13 mm, 83.86 mm, and 83.86 mm, respectively. In contrast, the proposed soft solid gel exhibited dimensions of 105.64 mm, 34.48 mm, 141.1 mm, and 102.8 mm.
Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data
Authors: Sobiecki André , Hadjiiski Lubomir M. , Chan Heang-Ping , Samala Ravi K. , Zhou Chuan , Stojanovska Jadranka , Agarwal Prachi P.
Published: February 2024

The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more likely to have long-standing residual changes in their lungs and may need follow-up imaging. We have developed deep learning neural network models for classifying severe vs. non-severe lung infections in COVID-19 patients on chest radiographs (CXR). A deep learning U-Net model was developed to segment the lungs. Inception-v1 and Inception-v4 models were trained for the classification of severe vs. non-severe COVID-19 infection. Four CXR datasets from multi-country and multi-institutional sources were used to develop and evaluate the models. The combined dataset consisted of 5748 cases and 6193 CXR images with physicians’ severity ratings as reference standard. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. We studied the reproducibility of classification performance using the different combinations of training and validation data sets. We also evaluated the generalizability of the trained deep learning models using both independent internal and external test sets. The Inception-v1 based models achieved AUC ranging between 0.81 ± 0.02 and 0.84 ± 0.0, while the Inception-v4 models achieved AUC in the range of 0.85 ± 0.06 and 0.89 ± 0.01, on the independent test sets, respectively. These results demonstrate the promise of using deep learning models in differentiating COVID-19 patients with severe from non-severe lung infection on chest radiographs.
Molecularly Confirmed Pseudomyogenic Hemangioendothelioma with Unusual EGFL7::FOSB Fusion in the Head and Neck Region of an Older Patient
Authors: Ren Dong , Lou Jerry , Wei Katherine , Ifegwu Ibe
Published: February 2024

Pseudomyogenic hemangioendothelioma (PMHE), a rare vascular neoplasm, was first described in 1992 asa fibroma-like variant of epithelioid sarcoma, and would be termed as epithelioid sarcoma-like hemangioendothelioma a decade later due to its significant histologic overlap with epithelioid sarcoma and diffuse cytokeratin expression. PHME is currently defined as a distinct, potentially intermediate malignant, rarely metastasizing neoplasm with vascular/endothelial differentiation. It is characterized by young age (typically less than 40 years old), extremity location (approximately ~80%), and t(7:19) SERPINE1::FOSB fusion as the most common molecular alteration. Herein, we report a case of a 59-year-old male presenting with multifocal lesions, including in the right temporalis muscle, right frontoparietal calvarium, right pterygoid muscles, and right mandibular condyle. Histologic examination of the right temporal lesion revealed a multinodular biphasic lesion composed of sheets and fascicles of elongated spindle and epithelioid cells infiltrating into the adjacent skeletal muscle. Admixed abundant neutrophilic infiltration is noted; however, areas of necrosis, increased mitosis, nuclear atypia, or rhabdomyoblast-like cells are absent. Immunohistochemical (IHC) staining showed that the tumor cells were diffusely and strongly positive for FOSB, pan-cytokeratin (AE1/AE3), CD31, and ERG. Molecular testing demonstrated a t(9:19) EGFL7::FOSB fusion mRNA. This constellation of morphological, IHC and molecular findings was consistent with a diagnosis of PMHE. This is the first reported case of multifocal PMHE with EGFL7::FOSB fusion in the head and neck area of a patient aged more than 50 years old. Since the differential diagnoses for PMHE includes high-grade malignancies with aggressive clinical behavior, coupled with the rare reports of PMHE in the head and neck region, awareness of this tumor in the head and neck region will avoid the misdiagnosis and overtreatment of this entity.
Different Methods of Scan Alignment in Erosive Tooth Wear Measurements: An In Vitro Study
Authors: Loumprinis Nikolaos , Michou Stavroula , Rahiotis Christos
Published: February 2024

Background: Model alignment in cases of erosive tooth wear can be challenging, and no method has been reported to outweigh the others. Methods: Extracted human teeth were mounted on two models and scanned at different times, from 1 h to 2 weeks, with an intraoral scanner (3Shape TRIOS 4) before and after immersion in Monster® energy drink and tap water. The scans were superimposed (3Shape TRIOS Patient Monitoring, Version 2.2.3.3, 3Shape A/S, Copengagen, Denmark). Best fit, best-fit tooth comparison, reference best fit using fillings, and palatal rugae as reference points were used for alignment. Surface profile differences were calculated in a cross-section view. The nonparametric Bland–Altman and Kruskal–Wallis tests were used. Results: First, statistically significant differences were marked after 4 days of immersion. The measurements obtained after 2 weeks of immersion were statistically significantly different from the measurements obtained at the different time points until 1 week. No statistically significant differences (p < 0.05) were observed among the alignment methods at any time. Conclusion: In comparison to the best-fit model, both palatal rugae and fillings can be used. The best-fit tooth comparison method is a reliable option; however, it should be used with caution in cases of major surface loss.
Differential Privacy Preservation for Continuous Release of Real-Time Location Data
Authors: Mao Lihui , Xu Zhengquan
Published: February 2024

Continuous real-time location data is very important in the big data era, but the privacy issues involved is also a considerable topic. It is not only necessary to protect the location privacy at each release moment, but also have to consider the impact of data correlation. Correlated Laplace Mechanism (CLM) is a sophisticated method to implement differential privacy on correlated time series. This paper aims to solve the key problems of applying CLM in continuous location release. Based on the finding that the location increment is approximately stationary in many scenarios, a location correlation estimation method based on the location increment is proposed to solve the problem of nonstationary location data correlation estimation; an adaptive adjustment model for the CLM filter based on parameter quantization idea (QCLM) as well as its effective implementation named QCLM-Lowpass utilizing the lowpass spectral characteristics of location data series is proposed to solve the problem of output deviations due to the undesired transient response of the CLM filter in time-varying environments. Extensive simulations and real data experiments validate the effectiveness of the proposed approach and show that the privacy scheme based on QCLM-Lowpass can offer a better balance between the ability to resist correlation-based attacks and data availability.
Ref-MEF: Reference-Guided Flexible Gated Image Reconstruction Network for Multi-Exposure Image Fusion
Authors: Huang Yuhui , Zhou Shangbo , Xu Yufen , Chen Yijia , Cao Kai
Published: February 2024

Multi-exposure image fusion (MEF) is a computational approach that amalgamates multiple images, each captured at varying exposure levels, into a singular, high-quality image that faithfully encapsulates the visual information from all the contributing images. Deep learning-based MEF methodologies often confront obstacles due to the inherent inflexibilities of neural network structures, presenting difficulties in dynamically handling an unpredictable amount of exposure inputs. In response to this challenge, we introduce Ref-MEF, a method for color image multi-exposure fusion guided by a reference image designed to deal with an uncertain amount of inputs. We establish a reference-guided exposure correction (REC) module based on channel attention and spatial attention, which can correct input features and enhance pre-extraction features. The exposure-guided feature fusion (EGFF) module combines original image information and uses Gaussian filter weights for feature fusion while keeping the feature dimensions constant. The image reconstruction is completed through a gated context aggregation network (GCAN) and global residual learning GRL. Our refined loss function incorporates gradient fidelity, producing high dynamic range images that are rich in detail and demonstrate superior visual quality. In evaluation metrics focused on image features, our method exhibits significant superiority and leads in holistic assessments as well. It is worth emphasizing that as the number of input images increases, our algorithm exhibits notable computational efficiency.
“Research Mentors Should Support Students of Color by Taking an Extra Step.” Undergraduates’ Reasoning about Race and STEM Research Mentorship
Authors: Vierra Kristin Dee , Beltran Diana R. , Denecker Lindsay , Robnett Rachael D.
Published: February 2024

Racial inequities and the adoption of a colorblind approach in education contribute to a situation wherein many academics lack the personal experience and incentive needed to identify and confront racism within society and institutions. This may be particularly the case in fields related to science, technology, engineering, and math (STEM), which tend to have lower levels of diversity compared to other fields. The current study examines undergraduates’ perceptions of whether STEM research mentors should consider race when mentoring students from marginalized backgrounds. We employ a mixed-methods approach with the goal of uncovering how undergraduate students perceive and rationalize research mentoring practices. Findings reveal that a strong majority of undergraduate students believe that research mentors should take race into account when mentoring students from marginalized backgrounds. During the qualitative coding process, the research team unearthed seven overarching themes that outline undergraduates’ reasoning, both in favor and against STEM research mentors considering race. We conclude by presenting an intervention intended to motivate individuals to redress colorblind ideologies and thus support a culturally sensitive mentoring style.
Local-Global Spatial-Temporal Graph Convolutional Network for Traffic Flow Forecasting
Authors: Zong Xinlu , Chen Zhen , Yu Fan , Wei Siwei
Published: February 2024

Traffic forecasting’s key challenge is to extract dynamic spatial-temporal features within intricate traffic systems. This paper introduces a novel framework for traffic prediction, named Local-Global Spatial-Temporal Graph Convolutional Network (LGSTGCN). The framework consists of three core components. Firstly, a graph attention residual network layer is proposed to capture global spatial dependencies by evaluating traffic mode correlations between different nodes. The context information added in the residual connection can improve the generalization ability of the model. Secondly, a T-GCN module, combining a Graph Convolution Network (GCN) with a Gated Recurrent Unit (GRU), is introduced to capture real-time local spatial-temporal dependencies. Finally, a transformer layer is designed to extract long-term temporal dependence and to identify the sequence characteristics of traffic data through positional encoding. Experiments conducted on four real traffic datasets validate the forecasting performance of the LGSTGCN model. The results demonstrate that LGSTGCN can achieve good performance and be applicable to traffic forecasting tasks.
Driving Behaviour Estimation System Considering the Effect of Road Geometry by Means of Deep NN and Hotelling Transform
Authors: Barreno Felipe , Santos Matilde , Romana Manuel
Published: February 2024

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving.
Precision Data-Driven Collision Localization with a Dedicated Matrix Template for Electric Vehicle Automatic Charging
Authors: Lin Haoyu , Quan Pengkun , Liang Zhuo , Lou Ya’nan , Wei Dongbo , Di Shichun
Published: February 2024

With the increasing maturity of autonomous driving technology and automated valet parking, public awareness of robot-based automatic charging for electric vehicles has gradually increased. The positioning of the charging port for electric vehicles is a prerequisite for achieving automatic charging. The common approach is to use visual methods for charging port positioning. However, due to factors such as external light conditions, humidity, and temperature, the visual system may experience insufficient positioning accuracy, leading to difficulties in executing the charging plug-in task. To address this issue, this paper proposes a data-driven collision localization method based on the vibration signal generated by the contact. During the data collection process, we first introduce a collision point matrix template suitable for automatic charging plug-in. This template covers the entire charging port and supports the acquisition of dense collision vibration data. Using this collision point matrix template, the collision localization problem can be transformed into a classification problem of collision vibration information corresponding to different collision points. Then, the collision vibration data obtained, based on this template, are used to train the collision localization model, which mainly consists of an echo state network (ESN) and support vector machine (SVM). The AUBO-i5 6-DOF articulated robot is employed to test the proposed collision localization method under different joint configurations. The simulated experimental results demonstrate the effectiveness of the proposed collision localization method, showcasing a promising localization accuracy and root mean square error (RMSE).
A Hybrid Model for Prognostic and Health Management of Electronic Devices
Authors: Murgia Alessandro , Harsha Chaitra , Tsiporkova Elena , Nawghane Chinmay , Vandevelde Bart
Published: February 2024

Techniques for prognostic and health management are becoming common in the electronic domain to reduce the cost of failures. Typically, the proposed techniques rely either on physics-based or data-driven models. Only a few studies explored hybrid models to combine the advantages of both models. For this reason, this work investigates the potential of hybrid modeling by presenting a new framework for the diagnostics and prognostics of an electronic system. The methodology is validated on simulation data describing the behavior of a QFN package subject to die delamination. The main results of this work are twofold. First, it is shown that the hybrid model can achieve better performance than the performance obtained by either the physics-based or the data-driven models alone. Second, a baseline is set for the best performance achievable by the hybrid model, allowing us to estimate the remaining useful life of the package.
VF-Mask-Net: A Visual Field Noise Reduction Method Using Neural Networks
Authors: Zhang Zhenyu , Zhu Haogang , Li Lei
Published: February 2024

Visual Field (VF) measurements, crucial for diagnosing and treating glaucoma, often contain noise originating from both the instrument and subjects during the response process. This study proposes a neural network-based denoising method for VF data, obviating the need for ground truth labels or paired measurements. Using a mask-imposed VF as an input for the neural network, while the original VF serves as a training label, we evaluated performance metrics such as the accuracy, precision, and sensitivity of denoised VFs. Orthogonal experiments were also employed to assess the impact of mask number, mask structure, and replacement strategy on model accuracy. This study reveals that mask number, replacement strategy, and their interaction significantly affect the accuracy of the denoising model. Under recommended parameters, VF-Mask-Net effectively enhances the accuracy and precision of VF measurements. Furthermore, in deterioration detection tasks, denoised VFs display heightened sensitivity compared to their pre-denoising counterparts.
Transcriptome Analysis Provides Insights into Korean Pine Tree Aging and Response to Shading
Authors: Ye Zha-Long , Liu Jin-Yi , Feng Jian , Li Wan-Feng
Published: February 2024

Age controls a tree’s responses to environmental cues and shading influences tree growth and physiology. These are basic principles of “Afforestation under canopy”, an approach that is widely used in the regeneration of Korean pine forests. Studying the underlying mechanism helps us to understand tree adaptation and utilize it in forest management. In this study, we investigated the transcriptomic changes in the uppermost main stems of the Korean pine tree (Pinus koraiensis, Sieb. et Zucc.) at different ages (5, 7, 10, 14, and 17 years) and in different growth conditions (open-grown and shade-grown trees) using RNA-Seq. In total, 434,005,837 reads were produced and assembled into 111,786 unigenes. After pairwise comparisons, 568 differentially expressed unigenes (DEUs) were identified. The greatest number of DEUs was identified in the comparison between 5-year-old open-grown trees and 17-year-old shade-grown trees, while no DEUs were identified in 15 pairwise comparisons. Among these 568 DEUs, 45 were assigned to gene ontology (GO) terms associated with response to environmental changes, including “response to stress” (26) and “response to light and temperature” (19); 12 were assigned to GO terms associated with sexual reproduction, such as “sexual reproduction”, “specification of floral organ identity”, “pollen tube guidance”, and “fruit ripening”; 15 were heat shock protein genes and showed decreased expression patterns with age; and one, annotated as Pinus tabuliformis DEFICIENS-AGAMOUS-LIKE 1, showed an increased expression pattern with age, independent of the reproductive state or growth conditions of Korean pine trees. Altogether, these findings not only demonstrate the molecular aspects of the developmental and physiological effects of age and shading on Korean pine trees, but also improve our understanding of the basic principles of “Afforestation under canopy”.