bluuebunny/crossref_metadata_2025 huggingface dataset:
Determination of Accuracy and Usability of a SLAM Scanner GeoSLAM Zeb Horizon: A Bridge Structure Case Study
Authors: Urban Rudolf , Štroner Martin , Braun Jaroslav , Suk Tomáš , Kovanič Ľudovít , Blistan Peter
Published: June 2024

The presented paper focuses on testing the performance of a SLAM scanner Zeb Horizon by GeoSLAM for the creation of a digital model of a bridge construction. A cloud acquired using a static scanner Leica ScanStation P40 served as a reference. Clouds from both scanners were registered into the same coordinate system using a Trimble S9 HP total station. SLAM scanner acquisition was performed independently in two passes. The data acquired using the SLAM scanner suffered from relatively high noise. Denoising using the MLS (Moving Least Squares) method was performed to reduce noise. An overall comparison of the point clouds was performed on both the original and MLS-smoothed data. In addition, the ICP (Iterative Closest Point) algorithm was also used to evaluate local accuracy. The RMSDs of MLS-denoised data were approximately 0.02 m for both GeoSLAM passes. Subsequently, a more detailed analysis was performed, calculating RMSDs for several profiles of the construction. This analysis revealed that the deviations of SLAM data from the reference data did not exceed 0.03 m in any direction (longitudinal, transverse, elevation) which is, considering the length of the bridge of 133 m, a very good result. These results demonstrate a high applicability of the tested scanner for many applications, such as the creation of digital twins.
Computational Thinking Measurement of CS University Students
Authors: Hijón-Neira Raquel , Pizarro Celeste , French John , Palacios-Alonso Daniel , Çoban Emre
Published: June 2024

The measurement of computational thinking ability among computer science (CS) university students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of computational thinking in CS students. Utilising two key instruments, namely, the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of computational thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and programme specialisations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to the measurement of variations in students’ computational thinking and programming skills by gender, university level, and location. By shedding light on the significance of computational thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.
On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
Authors: Nouhaila Ouyoussef , Hassane Moustabchir , Scutaru Maria Luminita , Jelenschi Liviu
Published: June 2024

This study investigating the accuracy of turbulence model simulations of the exhaust manifold using computational fluid dynamics (CFD) carries significant implications. By modeling and analyzing the flow of emissions, we aim to identify areas of high stress and pressure, minimize the pressure drop, and maximize the flow of exhaust gases. This not only enhances engine performance, reduces emissions, and improves the durability of the manifold but also provides a unique opportunity to predict and analyze the flow and performance of the exhaust manifold, both quantitatively and qualitatively. This paper aims to provide a detailed comparison of five turbulence models that are commonly used in CFD to offer valuable insights into their accuracy and reliability in predicting the flow characteristics of exhaust gases. The results show that the k-kl-ω model showed the highest maximum velocity and the most comprehensive temperature range, efficiently capturing the transitional flow effects. The K-ω STD and SST transition models displayed significantly higher turbulent kinetic energy (TKE) values, indicating their enhanced effectiveness in modeling complex turbulent and transitional flows. Conversely, the Reynolds stress and RNG k-epsilon models displayed lower TKE values, suggesting more subdued turbulence predictions. Despite this, all models exhibited similar pressure drop trends, with a noticeable increase near the midpoint of the manifold. These quantitative findings provide valuable insights into the suitability of different turbulence models for optimizing exhaust manifold design.
Graph Convolutional Spectral Clustering for Electricity Market Data Clustering
Authors: Huang Longda , Shan Maohua , Weng Liguo , Meng Lingyi
Published: June 2024

As the power grid undergoes transformation and the Internet’s influence grows, the electricity market is evolving towards informatization. The expanding scale of the power grid and the increasing complexity of operating conditions have generated a substantial amount of data in the power market. The traditional power marketing model is no longer suitable for the modern power market’s development trend. To tackle this challenge, this study employs random forest and RBF models for processing electricity market data. Additionally, it explores the synergy of graph convolutional network and spectral clustering algorithms to enhance the accuracy and efficiency of data mining, enabling a comprehensive analysis of data features. The experimental results successfully extracted various electricity consumption features. This approach contributes to the informatization efforts of power grid enterprises, enhances power data perception capabilities, and offers reliable support for decision makers.
Emulating Artistic Expressions in Robot Painting: A Stroke-Based Approach
Authors: Wang Zihe , Li Linzhou , Zhang Tan , Liu Tengfei , Li Ming , Wang Zifan , Li Zixiang
Published: June 2024

Representing art using a robotic system is part of artificial intelligence in our lives, especially in the realm of emotional expression. Developing a painting robot involves addressing how to enable the robot to emulate human artistic processes, which often include imprecise techniques or errors akin to those made by human artists. This paper discusses our development of an innovative painting robot utilizing the sim-to-real approach within learning technology. Specifically, this pipeline operates under a deep reinforcement learning (DRL) framework designed to learn drawing strategies from training data derived from real-world settings, aiming for the robot’s proficiency in emulating human artistic expressions. Accordingly, the framework comprises two modules when given a target drawing image: the first module trains in a simulated environment to break down the target image into individual strokes; the second module then learns how to execute these strokes in a real environment. Our experiments have shown that this system can meet our objectives effectively.
Effect of Nitric Acid on the Synthesis and Biological Activity of Silica–Quercetin Hybrid Materials via the Sol-Gel Route
Authors: D’Angelo Antonio , Fiorentino Marika , Viola Veronica , Vertuccio Luigi , Catauro Michelina
Published: June 2024

The sol-gel technique stands out as a valuable method for synthesizing biomaterials and encapsulating bioactive molecules, offering potential for controlled drug release and tissue regeneration in biomedical contexts. This study focused on synthesizing silica (Si)-based hybrid biomaterials containing 5% quercetin (Q5) using two different approaches: one involving nitric acid as a catalyst (SiQ5-HNO3) and the other being acid-free (SiQ5). Structural characterization using Fourier transform infrared (FTIR) and UV-vis spectroscopy revealed oxidation processes compromising the structural integrity of quercetin in both systems. However, it was observed that these oxidation processes led to the formation of oxidized derivatives of quercetin with distinct structures. Additionally, the bioactivity and release kinetics of quercetin from the silica matrices were evaluated, showing that both systems were capable of forming hydroxyapatite, indicating excellent bioactivity. Furthermore, SiQ5 exhibited a higher percentage release of the encapsulated drug at pH 7.4, representing the physiological environment, compared to SiQ5-HNO3, with a drastic reduction in drug release observed at pH 5.0 (cancer environment). Antibacterial efficacy assessment using the Kirby–Bauer test highlighted the greater antibacterial activity of the SiQ5-HNO3 system against all tested strains. Overall, this research aims to advance the development of more effective biomaterials for various biomedical applications, particularly in tissue engineering and infection control.
Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging
Authors: Chen Yi , Jiang Yufei , He Ruonan , Yan Shengxian , Lei Yuyang , Zhang Jing , Cao Hui
Published: June 2024

A new imaging technique called photoacoustic imaging (PAI) combines the advantages of ultrasound imaging and optical absorption to provide structural and functional details of tissues. It has broad application prospects in the accurate diagnosis and treatment monitoring of brain tumours. However, the existing photoacoustic image classification algorithms cannot effectively distinguish benign tumours from malignant tumours. To address this problem, the YoLov8-MedSAM model is proposed in this research to provide precise and adaptable brain tumour identification and detection segmentation. Additionally, it employs convolutional neural networks (CNNs) to classify and identify tumours in order to distinguish between benign and malignant variations in PAI. The experimental results show that the method proposed in this study not only effectively detects and segments brain tumours of various shapes and sizes but also increases the accuracy of brain tumour classification to 97.02%. The method provides richer and more valuable diagnostic information to the clinic and effectively optimizes the diagnosis and treatment strategy of brain tumours.
Using an Uptake Enhancer to Mitigate Nitrogen Leaching While Enhancing Uptake Efficiency
Authors: Somi Zoyolo , Kotzé Elmarie , Van der Watt Elmarie
Published: June 2024

Nitrogen (N) has the most crucial influence on raising agricultural productivity of all other plant nutrients given to crops. However, 50% of the N given to crops is dissipated to the environment globally, resulting in environmental concerns due to leaching. Current research shows that intensive agricultural production systems, which are still used in a large proportion around the world, are prone to N loss. This study aimed to investigate the effect of uptake enhancer applications on N movement in the soil profile based on 10 cm depth intervals, as well as its effects on N uptake and vegetative growth of oats at 4-week intervals over a 16-week period, using sandy soil as a growing medium. Oats were cultivated in a glasshouse setting in polyvinyl chloride (PVC) columns of 60 cm in height. Six treatments were employed at the 3rd leaf growth stage, and each was replicated four times. The experiment had a constructive and a destructive part, which was employed to monitor crop N uptake at four growth stages. Analyses of soil and plant samples were carried out in all the growth stages. The treatments containing the uptake enhancer prevented N from leaching, particularly at the top 20 cm soil depth, with impressive reductions of 194% at 0–10 cm depth and 186% at 10–20 cm depth, during the first 4 weeks after planting. The uptake enhancer also promoted early vegetative growth and crop performance with 15%. In conclusion, the study revealed that employing the uptake enhancer can improve the efficacy of N fertilizer, thereby reducing the application rate of the fertilizer in agroecosystems.
Changes in the Skin Microbiome Following Dermatological Procedures: A Scoping Review
Authors: Ellis Jeremy R. , Powell Eron J. , Tomasovic Luke M. , Marcheskie Rachel L. , Girish Vishruth , Warman Anmol , Sivaloganathan Darshan
Published: June 2024

The skin microbiome consists of bacteria, fungi, viruses, and mites, which play a crucial role in maintaining skin health and immune function. Imbalances in this microbial community, known as dysbiosis, are implicated in various dermatological conditions. While skincare products are known to influence the skin microbiome, the effects of dermatological procedures have not been extensively studied. Here, we perform a scoping review to outline the studies investigating the impacts of dermatological interventions on the skin microbiome. Phototherapy emerged as the most studied intervention, encompassing UV phototherapy, light therapy, laser therapy, and photodynamic therapy. Chemical interventions, such as chemical peels, micropigmentation, and debridement, have comparatively limited studies describing their impacts on the skin microbiome. To date, no studies have been done on a wide variety of common dermatological procedures such as cryotherapy, skin grafts, and dermabrasion, which may have stronger likelihoods of affecting the skin microbiome. This underscores the need for further research on the influences of dermatological procedures, especially chemical and physical interventions, on the skin microbiome. More comprehensive pre-clinical and clinical studies are essential not only for understanding the long-term consequences of these procedures, but also for optimizing patient outcomes in dermatological care.
Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application
Authors: Hu Jiazheng , Zhang Yu , Xu Jianjun , Li Jiajing , Shao Duanzhou , Tan Qichang , Feng Junjie
Published: June 2024

Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons.
A New Perspective to Interpret How the Vestibular Efferent System Correlates the Complexity of Routine Balance Maintenance with Management of Emergency Fall Prevention Strategies
Authors: Longridge Neil S. , Mallinson Arthur I.
Published: June 2024

Bipedalism is unique among mammals. Until modern times, a fall and resulting leg fracture could be fatal. Balance maintenance after a destabilizing event requires instantaneous decision making. The vestibular system plays an essential role in this process, initiating an emergency response. The afferent otolithic neural response is the first directionally oriented information to reach the cortex, and it can then be used to initiate an appropriate protective response. Some vestibular efferent axons feed directly into type I vestibular hair cells. This allows for rapid vestibular feedback via the striated organelle (STO), which has been largely ignored in most texts. We propose that this structure is essential in emergency fall prevention, and also that the system of sensory detection and resultant motor response works by having efferent movement information simultaneously transmitted to the maculae with the movement commands. This results in the otolithic membrane positioning itself precisely for the planned movement, and any error is due to an unexpected external cause. Error is fed back via the vestibular afferent system. The efferent system causes macular otolithic membrane movement through the STO, which occurs simultaneously with the initiating motor command. As a result, no vestibular afferent activity occurs unless an error must be dealt with.
Auditory Sensory Gating: Effects of Noise
Authors: Cheng Fan-Yin , Campbell Julia , Liu Chang
Published: June 2024

Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals’ neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processing in noise, the impact of noise on the neural response during the pre-attentive phase remains unclear. Therefore, this preliminary study aimed to assess the auditory gating response, reflective of the sensory inhibitory stage, to repeated vowel pairs presented in background noise. CAEPs were recorded via high-density EEG in fifteen normal-hearing adults in quiet and noise conditions with low and high informational masking. The difference between the average CAEP peak amplitude evoked by each vowel in the pair was compared across conditions. Scalp maps were generated to observe general cortical inhibitory networks in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception.
Comparative Analysis of Osteoblastic Responses to Titanium and Alumina-Toughened Zirconia Implants: An In Vitro Study
Authors: Saberian Elham , Jenča Andrej , Seyfaddini Rahman , Jenča Andrej , Zare-Zardini Hadi , Petrášová Adriána , Jenčová Janka
Published: June 2024

Introduction: Osteoblastic responses play a crucial role in the success of oral implants. Enhanced proliferation of osteoblast cells is associated with reduced cell mortality and an increase in bone regeneration. This study aims to evaluate the osteoblastic responses following oral implantation. Materials and Methods: Osteoblast stem cells were harvested and subsequently cultivated using cell culture techniques. The osteoblastic phenotype of the extracted cells was confirmed by examining the extracellular matrix. Cell morphogenesis on functionalized biomaterial surfaces was assessed through indirect immunofluorescence staining. The cellular response was investigated in the presence of two types of implant materials: titanium (Ti) and alumina-toughened zirconia (ATZ). Cell viability and apoptosis were quantitatively assessed using MTT assays and flow cytometry, respectively. Results: The survival of osteoblastic lineage cells was moderately reduced post-implantation. Viability in the Ti implant group remained at approximately 86%, while in the ATZ group, it was observed at 75%, which is considered acceptable. Moreover, there was a significant disparity in cell survival between the two implant groups (p < 0.05). Analysis of apoptosis levels at various concentrations revealed that the rate of apoptosis was 3.6% in the control group and 18.5% in the ATZ group, indicating that apoptosis or programmed cell death in the ATZ-treated group had increased nearly four-fold (p < 0.05). Conclusions: The findings of this study indicate a reduction in osteoblastic cell line survival following implant treatment, with titanium implants exhibiting superior performance in terms of cell survival. However, it was also noted that the incidence of apoptosis in osteoblast cells was significantly higher in the presence of zirconium-based implants.
Endothelial Dysfunction and Pre-Existing Cognitive Disorders in Stroke Patients
Authors: Mendyk-Bordet Anne-Marie , Ouk Thavarak , Muhr-Tailleux Anne , Pétrault Maud , Vallez Emmanuelle , Gelé Patrick , Dondaine Thibaut , Labreuche Julien , Deplanque Dominique , Bordet Régis
Published: June 2024

Background: The origin of pre-existing cognitive impairment in stroke patients remains controversial, with a vascular or a degenerative hypothesis. Objective: To determine whether endothelial dysfunction is associated with pre-existing cognitive problems, lesion load and biological anomalies in stroke patients. Methods: Patients originated from the prospective STROKDEM study. The baseline cognitive state, assessed using the IQ-CODE, and risk factors for stroke were recorded at inclusion. Patients with an IQ-CODE score >64 were excluded. Endothelial function was determined 72 h after stroke symptom onset by non-invasive digital measurement of endothelium-dependent flow-mediated dilation and calculation of the reactive hyperemia index (RHI). RHI ≤ 1.67 indicated endothelial dysfunction. Different biomarkers of endothelial dysfunction were analysed in blood or plasma. All patients underwent MRI 72 h after stroke symptom onset. Results: A total of 86 patients were included (52 males; mean age 63.5 ± 11.5 years). Patients with abnormal RHI have hypertension or antihypertensive treatment more often. The baseline IQ-CODE was abnormal in 33 (38.4%) patients, indicating a pre-existing cognitive problem. Baseline IQ-CODE > 48 was observed in 15 patients (28.3%) with normal RHI and in 18 patients (54.6%) with abnormal RHI (p = 0.016). The RHI median was significantly lower in patients with abnormal IQ-CODE. Abnormal RHI was associated with a significantly higher median FAZEKAS score (2.5 vs. 2; p = 0.008), a significantly higher frequency of periventricular lesions (p = 0.015), more white matter lesions (p = 0.007) and a significantly higher cerebral atrophy score (p < 0.001) on MRI. Vascular biomarkers significantly associated with abnormal RHI were MCP-1 (p = 0.009), MIP_1a (p = 0.042), and homocysteinemia (p < 0.05). Conclusions: A vascular mechanism may be responsible for cognitive problems pre-existing stroke. The measurement of endothelial dysfunction after stroke could become an important element of follow-up, providing an indication of the functional and cognitive prognosis of stroke patients.
Exploring the Therapeutic Potential of Gene Therapy in Arrhythmogenic Right Ventricular Cardiomyopathy
Authors: Mundisugih Juan , Ravindran Dhanya , Kizana Eddy
Published: June 2024

Right dominant arrhythmogenic cardiomyopathy, commonly known as Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC), represents a formidable challenge in cardiovascular medicine, as conventional therapies are commonly ineffective in impeding disease progression and the development of end-stage heart failure. Recombinant adeno-associated virus (AAV)-mediated gene therapy presents a promising avenue for targeted therapeutic interventions, potentially revolutionising treatment approaches for ARVC patients. Encouraging results from preclinical studies have sparked optimism about the possibility of curing specific subtypes of ARVC in the near future. This narrative review delves into the dynamic landscape of genetic therapy for ARVC, elucidating its underlying mechanisms and developmental stages, and providing updates on forthcoming trials. Additionally, it examines the hurdles and complexities impeding the successful translation of ARVC genetic therapies into clinical practice. Despite notable scientific advancements, the journey towards implementing genetic therapies for ARVC patients in real-world clinical settings is still in its early phases.
Estimating the Heterogeneous Causal Effects of Parent–Child Relationships among Chinese Children with Oppositional Defiant Symptoms: A Machine Learning Approach
Authors: Zhou Haiyan , Han Fengkai , Chen Ruoxi , Huang Jiajin , Chen Jianhui , Lin Xiuyun
Published: June 2024

Oppositional defiant symptoms are some of the most common developmental symptoms in children and adolescents with and without oppositional defiant disorder. Research has addressed the close association of the parent–child relationship (PCR) with oppositional defiant symptoms. However, it is necessary to further investigate the underlying mechanism for forming targeted intervention strategies. By using a machine learning-based causal forest (CF) model, we investigated the heterogeneous causal effects of the PCR on oppositional defiant symptoms in children in Chinese elementary schools. Based on the PCR improvement in two consecutive years, 423 children were divided into improved and control groups. The assessment of oppositional defiant symptoms (AODS) in the second year was set as the dependent variable. Additionally, several factors based on the multilevel family model and the baseline AODS in the first year were included as covariates. Consistent with expectations, the CF model showed a significant causal effect between the PCR and oppositional defiant symptoms in the samples. Moreover, the causality exhibited heterogeneity. The causal effect was greater in those children with higher baseline AODS, a worse family atmosphere, and lower emotion regulation abilities in themselves or their parents. Conversely, the parenting style played a positive role in causality. These findings enhance our understanding of how the PCR contributes to the development of oppositional defiant symptoms conditioned by factors from a multilevel family system. The heterogeneous causality in the observation data, established using the machine learning approach, could be helpful in forming personalized family-oriented intervention strategies for children with oppositional defiant symptoms.
Analyzing Contingency Estimation for Residential Turnkey Projects in Saudi Arabia: A Neural Network Approach
Authors: Salman Alaa
Published: June 2024

Utilizing a turnkey approach to deliver a construction project entails significant risks from the contractor’s perspective. Essentially, the owner awaits project completion without commitments regarding additional expenditures incurred by the contractor during the project’s duration. This paper specifically focuses on estimating and analyzing the contingency value for residential turnkey projects in Saudi Arabia. The contingency value across the project’s life cycle is estimated using six Artificial Neural Network (ANN) models, which are compared to identify the best-trained network according to project complexity, contingency factor, and contingency impact during the project phases. The output layer provides the contingency factor percentages for each project phase. A 13-story reinforced concrete (RC) residential building established in one of Saudi Arabia’s cities was selected to implement the developed methodology. The contingency estimation, performed using @Risk 7.5 and NeuralTools 7.5, was determined to be 11.34% and was distributed across the five phases of the project’s life cycle: 0.30% for predesign, 0.99% for design, 2.61% for preconstruction, 6.33% for construction, and 1.12% for postconstruction. Furthermore, it was found that the estimated contingency varies based on project complexity, which is 7.20% for low complexity, 8.16% for medium complexity, 9.41% for complicated, and 11.34% for very complicated projects. Historical data and peer review approaches are employed to validate the results, both of which are endorsed by professionals in this field. This paper highlights two main contributions: Firstly, it significantly enhances risk management by facilitating a comprehensive understanding and systematic analysis of risks, thus improving the contractors’ ability to mitigate potential negative impacts on projects. Secondly, it supports more informed decision-making through the use of advanced techniques to estimate and analyze contingency values. These contributions are critical for contractors engaged in Saudi construction projects, particularly those involving residential buildings.
Machine Learning for Pedestrian-Level Wind Comfort Analysis
Authors: Gür Miray , Karadag Ilker
Published: June 2024

(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.
Numerical Integration Study of Penetration and Blasting Damage for Composite Underground Protective Structure with Reinforcement Layers
Authors: Zhu Xingji , Zhao Can , Xu Longjun , Wang Yujin , Lin Shibin , Zhao Guochen
Published: June 2024

In response to the increasing threat of powerful earth-penetrating weapons, underground protective structures typically employ composite structural systems with reinforced steel layers. However, current numerical studies often simplify the entire structural system to plain concrete when assessing damage effects, and penetration and blasting processes are treated separately using a restart method. In this paper, we adopt an integrated simulation approach to analyze the resistance performance of composite protective structures with reinforcement layers. The results reveal significant differences in failure modes between plain concrete and reinforced concrete protective structures. The diameter of the steel bars and the spacing between mesh layers notably impact the penetration and blasting damage. Based on the results of a parameter analysis, we propose a method for optimizing the design of reinforcements in composite underground protective structures. The results of the study show the following: (1) The penetration and blast damage patterns of EPWs on plain concrete and composite protective structures with reinforcing mesh are significantly different. Compared to the plain concrete layer, the composite protection structure can effectively resist the damage of EPWs. (2) With the increase in reinforcement diameter, the decrease in reinforcement mesh spacing, and the increase in reinforcement dosage, the penetration depth gradually decreases; the amount and range of the blast damage also decrease accordingly. (3) Under the condition of the same reinforcement ratio, reducing the number of layers of reinforcement mesh, increasing the diameter of reinforcement, and configuring the reinforcement on the top of the protective structure as much as possible can improve the performance of the protective layer against penetration. At the same time, the reasonable arrangement of the reinforcement mesh can also enhance the ability of the protective structure to resist blasting damage.
Pre- and Post-Operative Quality of Life in Patients with Osteoradionecrosis of the Jaw
Authors: Otto Sven , Shreeja Shreeja , Kakoschke Sara Carina , Albittar Mohammed Michael , Widenhorn Andreas , Kakoschke Tamara Katharina
Published: June 2024

Osteoradionecrosis of the jaw (ORNJ) is a feared complication following radiation therapy performed for oncological treatment of head and neck cancers (HNC). To date, there is no clear evidence regarding the impact of surgical treatment of ORNJ on the quality of life (QoL) of affected patients. However, understanding the significance of the surgical treatment approach and its effects on QoL is an essential factor in the decision-making process for optimal, individualized therapy. In this prospective clinical study, QoL was assessed in relation to health related QoL (HRQoL) and oral health related QoL (OHQoL) before and after surgical treatment of ORNJ using standardized questionnaires (EORTC QLQ-C30, QLQ-HN35, OHIP-14). The overall QoL scores as well as individual domains of the collected scales regarding functional and symptom-related complaints were statistically analyzed. Subgroups concerning age, gender, different risk factors and type of ORNJ therapy were compared using Kruskal Wallis test. In addition, clinical and demographic patient data were collected and analyzed. QoL improvement correlated with the type of surgical ORNJ and the length of hospitalization. Better QoL scores were achieved post-operatively regarding different symptoms like pain, swallowing and mouth opening. Long-term effects of radiation therapy remained visibly restrictive to QoL and worsen over time.
The Anticancer Application of Delivery Systems for Honokiol and Magnolol
Authors: Dominiak Katarzyna , Gostyńska Aleksandra , Szulc Michał , Stawny Maciej
Published: June 2024

Cancer is a leading cause of death worldwide, and the effectiveness of treatment is consistently not at a satisfactory level. This review thoroughly examines the present knowledge and perspectives of honokiol (HON) in cancer therapeutics. The paper synthesizes critical insights into the molecular mechanisms underlying the observed anticancer effects, emphasizing both in vitro and in vivo studies. The effects of HON application, primarily in the common types of cancers, are presented. Because the therapeutic potential of HON may be limited by its physicochemical properties, appropriate delivery systems are sought to overcome this problem. This review discusses the effect of different nanotechnology-based delivery systems on the efficiency of HON. The data presented show that HON exhibits anticancer effects and can be successfully administered to the site of action. Honokiol exerts its anticancer activity through several mechanisms. Moreover, some authors used the combinations of classical anticancer drugs with HON. Such an approach is very interesting and worth further investigation. Understanding HON’s multiple molecular mechanisms would provide valuable insights into how HON might be developed as an effective therapeutic. Therefore, further research is needed to explore its specific applications and optimize its efficacy in diverse cancer types.
Candida auris Updates: Outbreak Evaluation through Molecular Assays and Antifungal Stewardship—A Narrative Review
Authors: Ionescu Silvia , Luchian Ionut , Damian Costin , Goriuc Ancuta , Porumb-Andrese Elena , Popa Cosmin Gabriel , Cobzaru Roxana Gabriela , Ripa Carmen , Ursu Ramona Gabriela
Published: June 2024

Candida auris was reported by the WHO as second to Cryptococcus neoformans, in the list of nineteen fungal priority pathogens, along with two species with a new nomenclature, Nakaseomyces glabrata (Candida glabrata) and Pichia kudriavzevii (Candida krusei). This novel classification was based on antifungal resistance, the number of deaths, evidence-based treatment, access to diagnostics, annual incidence, and complications and sequelae. We assessed which molecular assays have been used to diagnose Candida auris outbreaks in the last five years. Using “Candida auris; outbreak; molecular detection” as keywords, our search in PubMed revealed 32 results, from which we selected 23 original papers published in 2019–2024. The analyzed studies revealed that the detection methods were very different: from the VITEK® 2 System to MALDI TOF (Matrix-Assisted Laser Desorption Ionization–Time of Flight), NGS (Next-Generation Sequencing), WGS (Whole Genome Sequencing), and commercially available real-time PCR (Polymerase Chain Reaction) assays. Moreover, we identified studies that detected antifungal resistance genes (e.g., FKS for echinocandins and ERG11 for azoles). The analyzed outbreaks were from all continents, which confirms the capability of this yeast to spread between humans and to contaminate the environment. It is important that real-time PCR assays were developed for accurate and affordable detection by all laboratories, including the detection of antifungal resistance genes. This will allow the fast and efficient implementation of stewardship programs in hospitals.
Anti-Inflammatory Response of New Postbiotics in TNF-α/IFN-γ-Induced Atopic Dermatitis-like HaCaT Keratinocytes
Authors: Kim Yoo-Kyung , Cho Minji , Kang Dae-Jung
Published: June 2024

This study examines the synergistic interaction between the immunomodulatory functions of lactic acid bacteria postbiotics and the anti-inflammatory properties of Smilax china L. extract through a combined fermentation process. Using atopic dermatitis (AD) as a model, characterized by an immune imbalance that leads to skin inflammation, we developed a fermented product, MB-2006, and compared its effects to those of the heat-killed probiotics Lactobacillus acidophilus (LAC) and Lactobacillus rhamnosus (LRH). Our experiments focused on elucidating the mechanism of action of MB-2006 in AD-like HaCaT keratinocyte cells, particularly its impact on the NF-κB pathway, a pivotal regulator of inflammation. MB-2006 proved more effective in reducing inflammation markers, such as IL-4 and thymic stromal lymphopoietin (TSLP), and in inhibiting NF-κB activation compared to LAC and LRH. Significantly, MB-2006 also reduced the expression of thymus- and activation-regulated chemokine (TARC), highlighting a synergistic effect that enhances its therapeutic potential. These results suggest that the combined fermentation of Smilax china L. extract with lactic acid bacteria enhanced both the anti-inflammatory and immunomodulatory effects, presenting a promising integrative approach to treating conditions like AD. Further studies are needed to validate these results in clinical settings and fully explore the potential of this synergistic fermentation process.
New Compounds from Terrestrial Plants: A Perspective View
Authors: Oliveira Eduarda dos Santos , Kohatsu Cintia Naomi , Hufnagel Mariana Tedesco , Gennaro Victoria Furlanetto , Demarque Daniel Pecoraro
Published: June 2024

In an era where the search for innovative drug leads faces challenges, our study pivots towards exploring the untapped potential of plant-derived compounds, focusing on the period of 2021 to 2022. We assess the classes of compounds these new structures belong to; the plants and families these compounds belong to; and the degree of novelty of the compound compared with already-known structures. The review was conducted following the Preferred Reporting Items for Systematics Reviews and Meta-Analyses (PRISMA) statement checklist for the guided reporting of systematic reviews. A total of 464 articles were selected for the new compounds of natural origin survey. We included 117 complete articles in this review and reported approximately 109 new structures elucidated during the years 2021 and 2022. Many of the compounds showed small structural variations in relation to already-known molecules. For some, however, this small modification was decisive for the biological activity reported, demonstrating the importance of descriptive phytochemical studies.
Detection of Ovarian Cancer Biomarker Lysophosphatidic Acid Using a Label-Free Electrochemical Biosensor
Authors: Ivanova Nataliia , Ahmadi Soha , Chan Edmund , Fournier Léa , Spagnolo Sandro , Thompson Michael
Published: June 2024

Electrochemical biosensors are valued for their sensitivity and selectivity in detecting biological molecules. Having the advantage of generating signals that can be directly or indirectly proportional to the concentration of the target analyte, these biosensors can achieve specificity by utilizing a specific biorecognition surface designed to recognize the target molecule. Electrochemical biosensors have garnered substantial attention, as they can be used to fabricate compact, cost-effective devices, making them promising candidates for point-of-care testing (POCT) devices. This study introduces a label-free electrochemical biosensor employing a gold screen-printed electrode (SPE) to detect lysophosphatidic acid (LPA), a potential early ovarian cancer biomarker. We employed the gelsolin–actin system, previously introduced by our group, in combination with fluorescence spectrometry, as a biorecognition element to detect LPA. By immobilizing a gelsolin–actin complex on an SPE, we were able to quantify changes in current intensity using cyclic voltammetry and differential pulse voltammetry, which was directly proportional to the LPA concentration in the solution. Our results demonstrate the high sensitivity of the developed biosensor for detecting LPA in goat serum, with a limit of detection (LOD) and a limit of quantification (LOQ) of 0.9 µM and 2.76 µM, respectively, highlighting its potential as a promising tool for early-stage diagnosis of ovarian cancer.
Dynamic Resonant-Inductive Wireless Power Transfer System for Automated Guided Vehicles with Reduced Number of Position Sensors
Authors: Stepins Deniss , Sokolovs Aleksandrs , Zakis Janis
Published: June 2024

This paper deals with the position detection of automated guided vehicles (AGVs) in dynamic resonant-inductive wireless power transfer (WPT) systems. A position detection is necessary to activate the correct transmitting coil. One of the simplest and most effective approaches for a position detection method is to use optical or magnetic position sensors for each coil. However, due to needing a high number of sensors, this technique is relatively expensive. Therefore, an AGV position detection technique based on a reduced number of optical or magnetic sensors (by a factor of two) is proposed. The proposed detection technique was verified experimentally by using a scaled-down prototype of the dynamic WPT system. The proposed approach can be easily implemented by uploading a specific program code to a microcontroller. The microcontroller with the code developed by us was used for processing data from AGV position detection sensors, activating a suitable transmitting coil and controlling an inverter of the dynamic WPT system. As shown by the experiments, due to the proposed approach for the position detection of AGVs and activation of transmitting coils, the number of the position detection sensors is reduced by a factor of two, leading to reductions in the overall cost and level of complexity of the dynamic WPT system without degrading its performance.
Linux IoT Malware Variant Classification Using Binary Lifting and Opcode Entropy
Authors: Ramamoorthy Jayanthi , Gupta Khushi , Shashidhar Narasimha K. , Varol Cihan
Published: June 2024

Binary function analysis is fundamental in understanding the behavior and genealogy of malware. The detection, classification, and analysis of Linux IoT malware and its variants present significant challenges due to the wide range of architectures supported by the Linux IoT platform. This study concentrates on static analysis using binary lifting techniques to extract and analyze Intermediate Representation (IR) opcode sequences. We introduce a set of statistical entropy-based features derived from these IR opcode sequences, establishing a practical and straightforward methodology for machine learning classification models. By exclusively analyzing function metadata and opcode entropy, our architecture-agnostic approach not only efficiently detects malware but also classifies its variants with a high degree of accuracy, achieving an F1 score of 97%. The proposed approach offers a robust alternative for enhancing malware detection and variant identification frameworks for IoT devices.
Anti-Resonant Hollow-Core Fibers with High Birefringence and Low Loss for Terahertz Propagation
Authors: Du Yuhang , Zhou Dinghao , Zhang Ruizhe , Zhou Jingkai , Zou Hui
Published: June 2024

A new type of anti-resonant hollow-core fiber for terahertz waveguides is proposed. By introducing central support pillars and an elliptical structure, the fiber achieves high birefringence while maintaining low confinement loss and low material absorption loss. The fiber structure is optimized through simulation using the finite element method. The optimized fiber exhibits a birefringence of up to 1.22 × 10−2 at a frequency of 1 THz, with a confinement loss of 8.34 × 10−6 dB/cm and a material absorption loss of 7.17 × 10−3 dB/cm. Furthermore, when the bending radius of the fiber is greater than 12 cm, the bending loss of the anti-resonant optical fiber at 1 THz is less than 1.36 × 10−4 dB/cm, demonstrating good bending resistance and high practical value. It is expected to play a significant role in optical communication systems.
RN-YOLO: A Small Target Detection Model for Aerial Remote-Sensing Images
Authors: Wang Ke , Zhou Hao , Wu Hao , Yuan Guowu
Published: June 2024

Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target detection model called RN-YOLO (YOLO with RepGhost and NAM), which integrates RepGhost and a normalization-based attention module (NAM) based on YOLOv8. Firstly, NAM is added to the feature extraction network to enhance the capture capabilities for small targets by recalibrating receptive fields and strengthening information flow. Secondly, an efficient RepGhost_C2f structure is employed in the feature fusion network to replace the C2f module, effectively reducing the parameters. Lastly, the WIoU (Wise Intersection over Union) loss function is adopted to mitigate issues such as significant variations in target sizes and difficulty locating small targets, effectively improving the localization accuracy of small targets. The experimental results demonstrate that compared to the YOLOv8s model, the RN-YOLO model reduces the parameter count by 13.9%. Moreover, on the DOTAv1.5, TGRS-HRRSD, and RSOD datasets, the detection accuracy (mAP@.5:.95) of the RN-YOLO model improves by 3.6%, 1.2%, and 2%, respectively, compared to the YOLOv8s model, showcasing its outstanding performance and enhanced capability in detecting small targets.