bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative and Qualitative Response of Fodder Maize to Use of Bulk and Nano-fertilizers in North Western Plains of India
Authors: Kashyap Suryakanta , Kumar Rakesh , Ram Hardev , Kumar Ashwani , Basak Nirmalendu , Sheoran Parvender , Bhatacharjee Subhradip , Biswal Bisworanjita , Ali Ghous , Kumar Birendra , Bhakuni Kanika , Hindoriya Phool Singh , Birbal  , Min Doohong
Published: July 2023

Optimizing nutrient management strategies is crucial for enhancing the growth, yield, and nutritional quality of fodder maize (Zea mays) while minimizing environmental impacts. This study investigated the effects of innovative nitrogen (N) and zinc (Zn) management approaches on fodder maize production. Different combinations of nitrogen fertilizers, including conventional urea and nano-urea, were applied in conjunction with targeted foliar sprays of zinc sulfate (ZnSO4) and nano-zinc. The experiment was carried out in a Factorial Randomized Block design with four nitrogen management strategies (control N, 100% recommended nitrogen dose RDN through urea, 50% RDN through urea + two sprays of nano-urea at six and ten leaves stages, and 33.33% RDN through urea + two sprays of nano-urea at six and ten leaves stage) and four zinc management strategies (control Zn, soil application of ZnSO4, foliar application of ZnSO4, and foliar application of nano-Zn) which were replicated thrice. The study revealed that applying 50% of RDN through urea, along with dual foliar sprays of nano-urea, achieved comparable productivity to the 100% RDN through urea only. Among the Zn managements, both foliar and soil applications of conventional ZnSO4 recorded similar green and dry fodder yields, although foliar application of ZnSO4 was observed to be superior in terms of qualitative attributes. Maize subjected to the integrated nitrogen and zinc management strategy exhibited elevated protein content and reduced fiber fractions. These findings highlight the potential of nano-urea and foliar zinc application in enhancing both productivity and nutritional quality, while reducing dependence on conventional chemical fertilizers.
Effects of Graphene on Yield, Grain Quality, 2-AP Biosynthesis and Antioxidant Systems of Fragrant Rice
Authors: Tong Shaojie , Cui Baoling , Zhang Xuechan , Wei Jianjiao , Pan Shenggang , Mo Zhaowen , Tian Hua , Duan Meiyang , Tang Xiangru
Published: July 2023

The application of nanotechnology in agricultural neighborhoods is rapidly developing with the aim of promoting growth and enhancing crop tolerance to environmental stresses. However, there are fewer reports on the application of graphene nanoparticles in practical production, especially in fragrant rice. In early-season and late-season pot experiments conducted in 2022, the effects of graphene on the yield, grain quality, 2-acetyl-1-pyrroline (2-AP) and antioxidant systems of two fragrant rice cultivars (19× and Meixiangzhan) were examined at concentrations of 9 g/hm2, 18 g/hm2 and 27 g/hm2. The results showed that graphene T1 treatment at 9 g/hm2 significantly increased the activity of PDH and P5CS, promoted the synthesis of proline and P5C and significantly increased the 2-AP content of the grains of the two fragrant rice cultivars by 10.33–39.88% and 22.05–65.76%, respectively, in both growing seasons. Meanwhile, the lower concentration of T1 treatment (9 g/hm2) increased the grains per panicle and 1000-grain weight, enhancing the grain yield of both fragrant rice cultivars. The T1 treatment (9 g/hm2) had significant effects on the appearance and nutritional quality of both fragrant rice cultivars. It increased the head rice rate and protein content of the grains while also increasing the amylose content of 19× and reducing the chalkiness degree of 19×. Conversely, the T1 treatment reduced the amylose content and increased the chalkiness degree of Meixiangzhan. In addition, the low concentration of T1 treatment significantly increased the POD and SOD activities, increased the content of photosynthetic pigments and decreased the content of MDA in the leaves. However, 18 g/hm2 and 27 g/hm2 had slightly negative effects on yield, grain quality and fragrance biosynthesis in both fragrant rice cultivars. Furthermore, the results of structural equation modeling showed that antioxidant enzymes had a significant, positive effect on the grain’s 2-AP content and 2-AP synthesis-related enzyme activity, and photosynthetic pigments had a significant, positive effect on yield and grain appearance quality, while rice appearance quality and nutritional quality had significant, positive effects on yield. Overall, this study showed that suitable concentrations of graphene have good potential for use in fragrant rice production, but additional attention should be paid to the concentration of graphene application.
A Narrative Review of the Herbal Preparation of Ayurvedic, Traditional Chinese, and Kampō Medicines Applied as Radioprotectors
Authors: Ibáñez Blanca , Melero Ana , Montoro Alegría , Merino-Torres Juan F. , Soriano Jose M. , San Onofre Nadia
Published: July 2023

In recent years, there has been growing scientific interest in the search for natural radioprotectors that can be used to mitigate the effects of radiation on patients, healthcare personnel, and even for space travel. This narrative review covers the past fifty years and focuses on herbal preparations of Ayurvedic, Traditional Chinese, and Kampō Medicines that have the potential to reduce or eliminate the harmful effects of radiation. Our findings highlight ten herbal preparations, namely Abana, Amalakyadi Churna, Amritaprasham, Brahma, Bu-zhong-yi-qi-tang (BZYQT), Chyavanaprasha, Cystone, Geriforte, Mentat, and Triphala, which have demonstrated potential radioprotective effects. This review examines their composition, properties, and possible mechanisms of action in relation to their radioprotective properties. Exploring the ethnobotany of traditional Asian medicine is particularly interesting as it may lead to the discovery of new active compounds with radioprotective properties.
Direct Ink Writing of Alginate–Gelatin Hydrogel: An Optimization of Ink Property Design and Printing Process Efficacy
Authors: Kaliampakou Christina , Lagopati Nefeli , Charitidis Costas A.
Published: July 2023

Direct Ink Writing (DIW), which is widely used for developing functional 3D scaffolds that have robust structural integrity for the growth of target tissues/cells, has emerged as an appealing method for biomedical applications. The production of 3D structures involves three separate but interconnected stages (material development, printing process, and post-printing treatment), whose effectiveness is influenced by several factors that therefore make it challenging to optimize the entire procedure. By studying the material processability and leveling the printing settings, this study proposes a three-step method to enhance the ink property design and the printer’s performance. The recommended approach is focused on the thorough study of alginate–gelatin hydrogel properties, which is a commonly used ink in biomedical applications, due to its natural origin through marine flora, as well as the development process parameters and their intercorrelations. Principal Component Analysis in comparison with K-means clustering was applied to reveal material properties that are highly correlated with additive manufacturing (AM) processability, and Taguchi’s Design of Experiments (DOE) determined the printing settings (primary and secondary) for achieving optimum printing accuracy. PCA results were affirmed by K-means clustering and showed that viscosity, m, G′ and G″ govern blends’ printing behavior while application of DOE led to 85% pore area printability.
The Impact of the Log-Sawing Patterns on the Quantitative and Qualitative Yield of Beech Timber (Fagus sylvatica L.)
Authors: Vilkovský Peter , Klement Ivan , Vilkovská Tatiana
Published: July 2023

The cost of raw materials has a major role in the wood industry. From this point of view, it is necessary to pay attention to the correct log-sawing pattern, which will optimize the sawing process and increase the quantity and quality of the material as well. For measurements, we used beech wood (Fagus sylvatica L.). Beech wood (Fagus sylvatica L.) is the most common wood species in the Slovak Republic; the total amount in forests is 34.8%. Beech wood has a high frequency of defects, such as red false heartwood, reaction wood (i.e., tension wood), dote, cracks, and so on. This research aimed to analyze the effect of the log-sawing patterns of through-and-through sawing, cant sawing, and quarter sawing on the resulting quantitative–qualitative yields of beech timber. The results showed that the highest quantitative yield was observed with the cant sawing pattern, at 84%, which was, on average, 17.3% higher compared to other types of cutting. It was found that quarter sawing resulted in a higher qualitative yield compared to through-and-through sawing or cant sawing (V5 = 62.69% and V6 = 47.86%).
Energy Management Capability in the Reconfigurable Distribution Networks with Distributed Generation for Minimization of Energy Loss
Authors: Jiang Minmin , Zhang Yunfeng
Published: July 2023

One of the approaches to improve operation indices, such as power loss and reliability, is to smoothen voltage profiles. Energy management of generation units and network reconfiguration are two methods to achieve this. Hence, the latter is presented in this paper in a distribution with distributed generation units. To this end, an objective function is formulated aiming to minimize power loss and enhance the operating situation of the network. This problem is subject to various constraints and limits such as AC power flow equations, operation limits, planning model, and operation model of distributed generations. It is an integer nonlinear optimization problem and is solved using the crow search algorithm and the optimal solution is obtained. Two major innovations of the study include modeling generation units’ operation and network configuration mathematical expression of operation indicators. The method is applied to a test system and results demonstrate the high performance of this approach in improving the operation of the network. Finding the solution in less time with a satisfying standard deviation are two advantages of the proposed algorithm. Adopting this method, the network operator can reduce power loss through proper management of distributed generation power and optimal scheduling of switches. Moreover, the obtained voltage profile is more desirable in comparison to that of power flow studies.
The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification
Authors: Al-Mashhadani Zubaidah , Bayat Nasrin , Kadhim Ibrahim F. , Choudhury Renoa , Park Joon-Hyuk
Published: July 2023

Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.
Application of Drone LiDAR Survey for Evaluation of a Long-Term Consolidation Settlement of Large Land Reclamation
Authors: Lee Joonghee , Jo Hyeonjeong , Oh Jaehong
Published: July 2023

Drone surveys are gaining popularity for many construction applications, including in the fields of civil engineering, such as road construction, earthwork, structure monitoring, and coastal topography analysis. Drone surveying has a high potential for periodical long-term ground settlement measurement in the field of geotechnical engineering. Traditionally, manual measurement has been performed for limited points with controlled surface measurement points, but drone surveying may enable automated and periodical measurement for a wide and remote site. However, the accuracy of the elevation measurement and the surface settlement prediction has not been investigated, and the use of drone surveying has thus been limited. Therefore, an experiment was carried out to apply drone LiDAR (Light Detection and Ranging) surveying for soft ground settlement measurement at a large land reclamation site showing a very large settlement up to 10 m. Periodic drone LiDAR surveying was conducted, and the data were processed with direct georeferencing and with outlier removals (such as trees and construction vehicles) in order to generate a clean surface point cloud. We then compared the processed elevation data with ground control data to check the vertical accuracy and to predict the settlement as well as for consolidation. The drone LiDAR survey showed 13 cm, 42.9 cm, and 6.23% differences in RMSE (Root Mean Square Error) in terms of vertical accuracy, predicted long-term settlement, and consolidation, respectively. The drone LiDAR accuracy seems very useful for monitoring settlement over a large and remote land reclamation site of soft ground, showing settlement up to several meters where, without a surface measurement, installment is limited.
A Novel Weld-Seam Defect Detection Algorithm Based on the S-YOLO Model
Authors: Zhang Yi , Ni Qingjian
Published: July 2023

Detecting small targets and handling target occlusion and overlap are critical challenges in weld defect detection. In this paper, we propose the S-YOLO model, a novel weld defect detection method based on the YOLOv8-nano model and several mathematical techniques, specifically tailored to address these issues. Our approach includes several key contributions. Firstly, we introduce omni-dimensional dynamic convolution, which is sensitive to small targets, for improved feature extraction. Secondly, the NAM attention mechanism enhances feature representation in the region of interest. NAM computes the channel-wise and spatial-wise attention weights by matrix multiplications and element-wise operations, and then applies them to the feature maps. Additionally, we replace the SPPF module with a context augmentation module to improve feature map resolution and quality. To minimize information loss, we utilize Carafe upsampling instead of the conventional upsampling operations. Furthermore, we use a loss function that combines IoU, binary cross-entropy, and focal loss to improve bounding box regression and object classification. We use stochastic gradient descent (SGD) with momentum and weight decay to update the parameters of our model. Through rigorous experimental validation, our S-YOLO model demonstrates outstanding accuracy and efficiency in weld defect detection. It effectively tackles the challenges of small target detection, target occlusion, and target overlap. Notably, the proposed model achieves an impressive 8.9% improvement in mean Average Precision (mAP) compared to the native model.
Additive Manufacturing Using Agriculturally Derived Biowastes: A Systematic Literature Review
Authors: Rahman Al Mazedur , Rahman Taieba Tuba , Pei Zhijian , Ufodike Chukwuzubelu Okenwa , Lee Jaesung , Elwany Alaa
Published: July 2023

Agriculturally derived biowastes can be transformed into a diverse range of materials, including powders, fibers, and filaments, which can be used in additive manufacturing methods. This review study reports a study that analyzes the existing literature on the development of novel materials from agriculturally derived biowastes for additive manufacturing methods. A review was conducted of 57 selected publications since 2016 covering various agriculturally derived biowastes, different additive manufacturing methods, and potential large-scale applications of additive manufacturing using these materials. Wood, fish, and algal cultivation wastes were also included in the broader category of agriculturally derived biowastes. Further research and development are required to optimize the use of agriculturally derived biowastes for additive manufacturing, particularly with regard to material innovation, improving print quality and mechanical properties, as well as exploring large-scale industrial applications.
The Workaholism–Technostress Interplay: Initial Evidence on Their Mutual Relationship
Authors: Buono Carmela , Farnese Maria Luisa , Spagnoli Paola
Published: July 2023

During the pandemic, the occurrence of extreme working conditions (e.g., the sudden shift to remote work, isolation, and the slowdown of the work processes) exacerbated several phenomena, such as increased workaholism and stress due to technological devices; that is, technostress. Literature on the onset of these phenomena during the pandemic highlighted a possible interplay among them; however, there is still a dearth of knowledge about the direction of the relationship between workaholism and technostress. The present study assessed the relationship between workaholism and technostress through a two-wave cross-lagged study using path analysis in SEM (Structural Equation Modeling). The study was conducted in Italy during the pandemic, and a total of 113 Italian employees completed the online survey at each wave. Results showed that workaholism at Time 1 was a significant predictor of technostress at Time 2 (β = 0.25, p = 0.049), while the reversed causation was not supported (β = 0.08, p = 0.22). These findings may help employees and organizations to better understand the phenomena of technostress and workaholism and develop strategies to prevent the consequences of excessive and compulsive work and to improve the balanced use of technology for their daily activities.
Fundamental Design Concepts of a Modular Pier System Using Ultra-High-Performance Concrete for Solving Construction Errors
Authors: Lim Kwangmo , Kang Jaeyoon , Ryu Gumsung , Koh Kyungtaek , Kim Kyongchul
Published: July 2023

Concrete structures in marine environments are prone to deterioration and damage due to chloride ion penetration, freezing and thawing, and chemical erosion. Ultra-high-performance concrete (UHPC) mixed with steel fibers has been proposed as a solution to enhance the durability and mechanical properties of concrete in marine environments. Although several studies have been conducted in this regard, they have yet to focus on addressing errors that may be caused during the construction of offshore piers. Therefore, this study proposes a modular system to control horizontal and vertical errors during construction using a new connecting core type. UHPC with a fiber content of 0.75% was considered the optimum mix proportion because this met the tensile and compressive strength requirements and the chloride attack resistibility requirements of marine structures. The structural performance of a specimen constructed using modular technology was evaluated. The results of the lateral load resistance experiments showed minimal deformation in the girder and pier. Additionally, both the precast and cast-in-place types met the criterion of load resistance. This study contributes to the advancement of construction technology in marine environments by considering both material performance and construction conditions.
Synthesis of Palladium Nanoparticles Supported over Fused Graphene-like Material for Hydrogen Evolution Reaction
Authors: Quach Qui , Biehler Erik , Abdel-Fattah Tarek M.
Published: July 2023

The search for a clean abundant energy source brought hydrogen gas into the limelight; however, the explosive nature of the gas brings up issues with its storage. A way to mitigate this danger is through the storing of hydrogen in a hydrogen feedstock material, which contains a large percentage of its weight as hydrogen. Sodium borohydride is a feedstock material that gained a lot of attention as it readily reacts with water to release hydrogen. This study explored a novel composite composed of palladium nanoparticles supported on a sugar-derived fused graphene-like material support (PdFGLM) for its ability to catalyze the reaction of sodium borohydride in water. Transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) were used to characterize and determine the size and shape of the catalyst used in this study. The XRD study detected the presence of palladium nanoparticles, and the EDS date confirmed the presence of 3% palladium nanoparticles. The TEM result shows the palladium nanoparticles of 5.5 nm incorporated to the graphene-like material layers. The composite contained approximately 3% palladium. In the hydrogenation reactions, it was observed that optimal reaction conditions included lower pHs, increased temperatures, and increased dosages of sodium borohydride. The reaction had the greatest hydrogen generation rate of 0.0392 mL min−1 mgcat−1 at pH 6. The catalyst was tested multiple times in succession and was discovered to increase the volume of hydrogen produced, with later trials indicating the catalyst becomes more activated with multiple uses. The activation energy of the reaction as catalyzed by PdFGLM was found to be 45.1 kJ mol−1, which is comparable to other catalysts for this reaction. This study indicates that this catalyst material has potential as a sustainable material for the generation of hydrogen.
Determination of Dipyridamole Using a MIP-Modified Disposable Pencil Graphite Electrode
Authors: Preda Daniel , Jinga Maria Lorena , David Iulia Gabriela , Radu Gabriel Lucian
Published: July 2023

A new method for the determination of the antiplatelet drug dipyridamole (DIP) in pharmaceuticals using a molecularly imprinted polymer (MIP)-modified pencil graphite electrode (PGE) is proposed. The modified electrode was prepared simply and rapidly by electropolymerization of caffeic acid (CA) in the presence of DIP and subsequent DIP extraction with ethanol, resulting in a cost-effective, eco-friendly disposable modified electrode (MIP_PGE). Several working conditions (monomer and template concentration, number of voltametric cycles, scan rate extraction time, and solvent) for the MIP_PGE preparation were optimized. The differential pulse voltammetric (DPV) oxidation signal of DIP obtained at MIP_PGE was 28% higher than that recorded at bare PGE. Cyclic voltammetry emphasized DIP irreversible, pH-dependent, diffusion-controlled oxidation at MIP_PGE. Differential pulse and adsorptive stripping voltammetry at MIP_PGE in phosphate buffer solution pH = 7.00 were applied for the drug quantitative determination in the range of 1.00 × 10−7–1.00 × 10−5 and 1.00 × 10−8–5.00 × 10−7 mol/L DIP, respectively. The obtained limits of detection were at the tens nanomolar level.
Recent Advances in Cyanobacterial Cytotoxin Biosensors Focused on Cylindrospermopsin
Authors: Kwon Yein , Yoon Yejin , Jang Moonbong , Kang Sunggu , Park Chulhwan , Lee Taek
Published: July 2023

Cylindrospermopsin (CYN) is a freshwater algal toxin produced during the proliferation of harmful cyanobacteria, known as cyanobacterial algal blooms (cyano-HABs). Recently, the effects of global warming have facilitated the growth of cyano-HABs, leading to their worldwide occurrence and an increase in toxin-related damage to aquatic ecosystems. CYN is known to exhibit strong cell toxicity upon ingestion, inhibiting protein synthesis and glutathione production and, ultimately, leading to cell death. In addition to cell toxicity, CYN exhibits skin toxicity, genotoxicity, and hepatotoxicity. It can also affect other organs, such as the kidneys (causing tubular necrosis), thymus (causing atrophy), and heart (causing pericardial and myocardial hemorrhage). The standard method used for CYN detection to date, enzyme-linked immunosorbent assay (ELISA), has several drawbacks: it is complex, time-consuming, and requires trained researchers. Recently, biosensors have been shown to offer numerous advantages, such as their simplicity, portability, and rapidity, making them suitable for onsite applications. Consequently, recent studies have actively explored the latest biosensor-based technologies for CYN detection. This review discusses the recent advances in CYN detection platforms that utilize several types of biosensors.
Exploring the Risk Factors and Possible Role of Calcium in Infective Endocarditis
Authors: Kuo Yen-Shou , Tsai Yuan-Ming
Published: July 2023

Infective endocarditis (IE) is a severe and potentially life-threatening infection that affects the endocardium, the inner lining of the heart chambers and valves. Although rare, it is a potentially fatal condition, with an incidence of 3–10 cases per 100,000 people per year in developed countries and a mortality rate of up to 30% within 30 days. Early identification and diagnosis are critical for improving outcomes. The diagnosis of IE typically involves a combination of biomarkers, blood cultures, and echocardiography. However, currently, there are no specific biomarkers for the early detection of IE. Given the lack of specific biomarkers for IE, serum calcium levels have been suggested to play a unique role in IE. There have been few articles on the correlation between serum calcium and IE, suggesting that patients with endocarditis and lower levels of serum calcium may have a poorer prognosis. Therefore, in this article, we focus on biomarkers of endocarditis and discuss the evidence showing serum calcium as a potential indicator of IE.
Updates on Triple-Negative Breast Cancer in Type 2 Diabetes Mellitus Patients: From Risk Factors to Diagnosis, Biomarkers and Therapy
Authors: Matou-Nasri Sabine , Aldawood Maram , Alanazi Fatimah , Khan Abdul Latif
Published: July 2023

Triple-negative breast cancer (TNBC) is usually the most malignant and aggressive mammary epithelial tumor characterized by the lack of expression for estrogen receptors and progesterone receptors, and the absence of epidermal growth factor receptor (HER)2 amplification. Corresponding to 15–20% of all breast cancers and well-known by its poor clinical outcome, this negative receptor expression deprives TNBC from targeted therapy and makes its management therapeutically challenging. Type 2 diabetes mellitus (T2DM) is the most common ageing metabolic disorder due to insulin deficiency or resistance resulting in hyperglycemia, hyperinsulinemia, and hyperlipidemia. Due to metabolic and hormonal imbalances, there are many interplays between both chronic disorders leading to increased risk of breast cancer, especially TNBC, diagnosed in T2DM patients. The purpose of this review is to provide up-to-date information related to epidemiology and clinicopathological features, risk factors, diagnosis, biomarkers, and current therapy/clinical trials for TNBC patients with T2DM compared to non-diabetic counterparts. Thus, in-depth investigation of the diabetic complications on TNBC onset, development, and progression and the discovery of biomarkers would improve TNBC management through early diagnosis, tailoring therapy for a better outcome of T2DM patients diagnosed with TNBC.
Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm
Authors: Ahmad Ahmad Ayid , Polat Huseyin
Published: July 2023

Heart disease is one of the most known and deadly diseases in the world, and many people lose their lives from this disease every year. Early detection of this disease is vital to save people’s lives. Machine Learning (ML), an artificial intelligence technology, is one of the most convenient, fastest, and low-cost ways to detect disease. In this study, we aim to obtain an ML model that can predict heart disease with the highest possible performance using the Cleveland heart disease dataset. The features in the dataset used to train the model and the selection of the ML algorithm have a significant impact on the performance of the model. To avoid overfitting (due to the curse of dimensionality) due to the large number of features in the Cleveland dataset, the dataset was reduced to a lower dimensional subspace using the Jellyfish optimization algorithm. The Jellyfish algorithm has a high convergence speed and is flexible to find the best features. The models obtained by training the feature-selected dataset with different ML algorithms were tested, and their performances were compared. The highest performance was obtained for the SVM classifier model trained on the dataset with the Jellyfish algorithm, with Sensitivity, Specificity, Accuracy, and Area Under Curve of 98.56%, 98.37%, 98.47%, and 94.48%, respectively. The results show that the combination of the Jellyfish optimization algorithm and SVM classifier has the highest performance for use in heart disease prediction.
Possible Use of Linear Echobronchoscope for Diagnosis of Peripheral Pulmonary Nodules
Authors: Zuccatosta Lina , Gonnelli Francesca , Gasparini Gianmarco , Duro Arianna , Barbisan Francesca , Goteri Gaia , Veronesi Giulia , Trisolini Rocco , Gasparini Stefano
Published: July 2023

Echobronchoscope-guided transbronchial needle aspiration (EBUS-TBNA) is mainly used as the transbronchial approach to hilar/mediastinal lymph nodes or lesions, for diagnostic or staging purposes. Moreover, the role of linear EBUS-TBNA as a diagnostic tool for central intrapulmonary lesions adjacent to the trachea or the major bronchi is also well established. However, since the tip of the ultrasound probe at the distal end of the echobronchoscope is very thin, it can be wedged through smaller peripheral bronchi, reaching the distal parenchyma and allowing for peripheral pulmonary lesion sampling. The main aim of this retrospective study was to evaluate the diagnostic yield and the safety of EBUS-TBNA in the diagnosis of pulmonary peripheral nodules. The database of the Interventional Pulmonology Unit of Azienda Ospedaliero-Universitaria delle Marche (Ancona, Italy) was evaluated to identify peripheral pulmonary nodules approached by EBUS-TBNA. Thirty patients with a single peripheral pulmonary nodule located peripherally to the subsegmental bronchi of the lower lobes and adjacent to a small bronchus greater than 3 mm in diameter were included in this study. The nodule was visible using endoscopic ultrasound in 28 patients and the diagnosis was obtained via EBUS-TBNA in 26 cases (12 adenocarcinoma, 5 typical carcinoid tumors, 4 hamartoma and 5 metastatic lesions). The diagnostic yield was 86.6% for all 30 patients and 92.8% if only the 28 patients in which the lesion was visualized via echobronchoscopy were considered. No relevant adverse events were observed. We conclude that EBUS-TBNA may be an effective and safe option to sample pulmonary peripheral nodules in selected patients with lower lobe peripheral pulmonary lesions adjacent to small bronchi greater than 3 mm in diameter and reachable with the EBUS-TBNA probe.
Adverse Events Related to Direct-To-Consumer Sequential Aligners—A Study of the MAUDE Database
Authors: Belgal Priyanka , Mhay Sahil , Patel Vrunda , Nalliah Romesh P.
Published: July 2023

Background—Direct-to-consumer (DTC) sequential aligners promote “teeth straightening” at a low cost and with added patient convenience. DTC sequential aligners have risen in popularity among the general public and sparked debate among dental professionals. Dental professionals argue that using these aligners without an in-person diagnosis and treatment planning protocol set by a licensed dentist or orthodontist may lead to adverse effects on teeth and surrounding structures. The objective of this study is to describe adverse clinical events associated with the use of DTC sequential aligners as reported in the Food and Drug Administration’s Manufacturer and User Facility Device Experience (FDA MAUDE) database. Methods—We searched the MAUDE database from 1 January 2010 to 31 December 2020 for the product code of ‘NXC’ (sequential aligners). The year, type of adverse event, reporter occupation, and event description were noted. Results—651 reports associated with sequential aligners were found, of which 104 were related to DTC sequential aligners. Fifty-four adverse events were reported in 2019. From the event description, 41.3% comprised bite problems, 29.8% comprised orofacial pain, and 26.6% of patients had some form of periodontal sequelae. Furthermore, 69.2% of the patients followed up after an adverse event with a dentist not associated with DTC aligners. Conclusions—The use of DTC sequential aligners without dental supervision has led to oral health problems, as documented in the MAUDE database. Commonly reported adverse events include bite problems, pain, sensitivity, and periodontal disease, and some adverse events are irreversible.
Optimal Deployment Strategy for Reconfigurable Intelligent Surface under LoSD via Joint Active and Passive Beamforming
Authors: Zhao Ke , Song Zhiqun , Xiong Jun
Published: July 2023

A reconfigurable intelligent surface (RIS) is a new and revolutionizing technology to achieve spectrum-efficient (SE) and energy-efficient (EE) wireless networks. In this paper, we study an optimal deployment strategy of RIS in a line-of-sight domain (LoSD) based on an actual deployment scenario, which jointly considers path loss, transmit power and the energy efficiency of the system. Furthermore, we aim to minimize the transmit power via jointly optimizing its transmit beamforming and the reflect phase shifts of RIS, subject to the quality-of-service (QoS) constraint, namely, the signal-to-noise ratio (SNR) constraint at the user. However, this optimization problem is non-convex with intricately coupled variables. To tackle this challenge, we first apply proper transformation on the QoS constraint and then propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that compared to a conventional endpoint deployment strategy that simply deploys RIS at the transceiver ends, our proposed LoSD deployment strategy significantly reduces the transmit power by optimizing the available LoS links when a single RIS is relayed. The impact of the number of reflect elements on the system EE is also unveiled.
A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain
Authors: Salehin Imrus , Kang Dae-Ki
Published: July 2023

Dropout is one of the most popular regularization methods in the scholarly domain for preventing a neural network model from overfitting in the training phase. Developing an effective dropout regularization technique that complies with the model architecture is crucial in deep learning-related tasks because various neural network architectures have been proposed, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and they have exhibited reasonable performance in their specialized areas. In this paper, we provide a comprehensive and novel review of the state-of-the-art (SOTA) in dropout regularization. We explain various dropout methods, from standard random dropout to AutoDrop dropout (from the original to the advanced), and also discuss their performance and experimental capabilities. This paper provides a summary of the latest research on various dropout regularization techniques for achieving improved performance through “Internal Structure Changes”, “Data Augmentation”, and “Input Information”. We can see that proper regularization with respect to structural constraints of network architecture is a critical factor to facilitate overfitting avoidance. We discuss the strengths and limitations of the methods presented in this work, which can serve as valuable references for future research and the development of new approaches. We also pay attention to the scholarly domain in the discussion in order to meet the overwhelming increase of scientific research outcomes by providing an analysis of several important academic scholarly issues of neural networks.
Introducing Novel Correction Methods to Calculate Sedimentary Basin Overpressure and Its Application in Predicting Pressure Value and Origin
Authors: Xu Zeyang , Liu Zhen , Zhao Jingzhou , He Yangyang , Ma Xinruo , Li Jun , Zhao Zhen
Published: July 2023

Overpressure is an important phenomenon observed in sedimentary basins. To enhance the precision of identifying the origin of overpressure and predicting it accurately, this paper presents four practical methods for analyzing formation overpressure, taking into account various factors, such as burial history, rock properties, mechanical models and data quality. By illustrating the application examples in typical basins, this paper demonstrates the practicality and potential of each model. Based on the analysis of organic matter content and denudation, our understanding of the origin of overpressure in the first member of the Cretaceous Qingshankou Formation in Songliao Basin has been revised. A novel model based on the static equilibrium equation was established, which not only demonstrates theoretical rationality but also exhibits practical advantages in the Junggar Basin and Southeast Hainan Basin. The incorporation of lateral loading in the correction model and the utilization of colored inversion in seismic velocity processing model have significantly enhanced pressure prediction accuracy in Junggar Basin and Southeast Hainan Basin, respectively. In conclusion, these correction models demonstrate a high level of reliability and possess significant potential for widespread adoption.
Syngas Production from Protective Face Masks through Pyrolysis/Steam Gasification
Authors: Kiminaitė Ieva , González-Arias Judith , Striūgas Nerijus , Eimontas Justas , Seemann Martin
Published: July 2023

The COVID-19 pandemic has caused a heavy expansion of plastic pollution due to the extensive use of personal protective equipment (PPE) worldwide. To avoid problems related to the entrance of these wastes into the environment, proper management of the disposal is required. Here, the steam gasification/pyrolysis technique offers a reliable solution for the utilization of such wastes via chemical recycling into value-added products. The aim was to estimate the effect of thermo-chemical conversion temperature and steam-to-carbon ratio on the distribution of gaseous products obtained during non-catalytic steam gasification of 3-ply face masks and KN95 respirators in a fluidized bed reactor. Experimental results have revealed that the process temperature has a major influence on the composition of gases evolved. The production of syngas was significantly induced by temperature elevation from 700 °C to 800 °C. The highest molar concentration of H2 gases synthesized from both types of face masks was estimated at 800 °C with the steam-to-carbon ratio varying from 0 to 2. A similar trend of production was also determined for CO gases. Therefore, investigated thermochemical conversion process is a feasible route for the conversion of used face masks to valuable a product such as syngas.
Analytical Approach for Sharp Corner Reconstruction in the Kernel Free Boundary Integral Method during Magnetostatic Analysis for Inductor Design
Authors: Jin Zichao , Cao Yue , Li Shuwang , Ying Wenjun , Krishnamurthy Mahesh
Published: July 2023

It is very important to perform magnetostatic analysis accurately and efficiently when it comes to multi-objective optimization of designs of electromagnetic devices, particularly for inductors, transformers, and electric motors. A kernel free boundary integral method (KFBIM) was studied for analyzing 2D magnetostatic problems. Although KFBIM is accurate and computationally efficient, sharp corners can be a major problem for KFBIM. In this paper, an inverse discrete Fourier transform (DFT) based geometry reconstruction is explored to overcome this challenge for smoothening sharp corners. A toroidal inductor core with an airgap (C-core) is used to show the effectiveness of the proposed approach for addressing the sharp corner problem. A numerical example demonstrates that the method works for the variable coefficient PDE. In addition, magnetostatic analysis for homogeneous and nonhomogeneous material is presented for the reconstructed geometry, and results carried out using KFBIM are compared with the results of FEM analysis for the original geometry to show the differences and the potential of the proposed method.
A Comprehensive Review of the Distinctive Tendencies of the Diffusion of E-Mobility in Central Europe
Authors: Horváth Gábor , Bai Attila , Szegedi Sándor , Lázár István , Máthé Csongor , Huzsvai László , Zakar Máté , Gabnai Zoltán , Tóth Tamás
Published: July 2023

The study introduces the current situation of electric vehicle technologies, the possibilities and possible directions of their market, as well as the economic and environmental aspects in the eastern region of the EU, with special regard to Hungary. Our calculations show that despite the higher costs of ownership, in the case of 7 years of use (among Hungarian conditions), the total cost of the Battery Electric Vehicles (BEV) is lower than that of the Internal Combustion Engine Vehicles (ICEV) in each of the four segments analysed. The purchasing power of the population in the eastern regions of the EU is clearly lower compared to the western regions, yet people in this region spend beyond their financial means on motorization. Despite its unfavourable position, Hungary is in a particularly advantageous position compared to other countries in the region per vehicle and per capita in terms of the spread of BEVs. In the long run, even taking higher prices into account, the use of electric cars, in general, has significant advantages from an economic point of view; lower maintenance costs and fuel prices (especially with increased use) make EVs more cost-effective overall.
Characterization of Flow Parameters in Shale Nano-Porous Media Using Pore Network Model: A Field Example from Shale Oil Reservoir in Songliao Basin, China
Authors: Wang Qingzhen , Jia Zhihao , Cheng Linsong , Li Binhui , Jia Pin , Lan Yubo , Dong Dapeng , Qu Fangchun
Published: July 2023

The pore-throat radius of the shale oil reservoir is extremely small, and it is difficult to accurately obtain the absolute permeability and oil–water two-phase relative permeability of the actual oil reservoir through conventional core experiments. However, these parameters are very important for reservoir numerical simulation. In this paper, a method for characterizing flow parameters based on a pore network model that considers differential pressure flow and diffusion flow is proposed. Firstly, a digital core was reconstructed using focused ion beam scanning electron microscopy (FIB-SEM) from the Gulong shale reservoir in the Songliao Basin, China, and a pore network model was extracted. Secondly, quasi-static single-phase flow and two-phase flow equations considering diffusion were established in the pore network model. Finally, pore-throat parameters, absolute permeability, and oil–water two-phase permeability curves were calculated, respectively. The results show that the pore-throat distribution of the Gulong shale reservoir is mainly concentrated in the nanometer scale; the mean pore radius is 87 nm, the mean throat radius is 41 nm, and the mean coordination number is 3.97. The calculated permeability considering diffusion is 0.000124 mD, which is approximately twice the permeability calculated without considering diffusion. The irreducible water saturation of the Gulong shale reservoir is approximately 0.4, and the residual oil saturation is approximately 0.35. The method proposed in this paper can provide an important approach for characterizing the flow parameters of similar shale oil reservoirs.
Combustion and Emission of Castor Biofuel Blends in a Single-Cylinder Diesel Engine
Authors: Zheng Fangyuan , Cho Haengmuk
Published: July 2023

Fossil fuels confront the problem of strategic resource depletion since they have been continuously utilized for more than 200 years and cause serious damages to the ecological environment of the planet. In this work, the transesterification of castor plant oil was utilized to make biodiesel, and castor biodiesel’s physicochemical qualities were assessed. On a single-cylinder, four-stroke, water-cooled agricultural diesel engine, an experimental study was conducted to compare and analyze the engine performance and emission characteristics of diesel and biodiesel blends in various amounts. The B20, B40, B60, and B80 biodiesel blends were evaluated at different engine speeds (1200, 1400, 1600, and 1800 rpm) with a constant engine load (50%). According to the experimental findings, the brake thermal efficiency (BTE) declines as the engine speed rises, and the biodiesel fuel blend has a lower brake thermal efficiency (BTE) than diesel fuel because of its higher density and viscosity and lower calorific value. The amount of gasoline required to create power increases as the speed does, and the brake-specific fuel consumption (BSFC) trend is upward. Due to their low calorific value and high viscosity properties, biodiesel blends have a greater brake-specific fuel consumption (BSFC) than diesel. The fuel’s exhaust gas temperature (EGT) has an upward trend with an increased rotational speed. The biodiesel blend’s high cetane number shortens the ignition delay and lowers the exhaust gas temperature (EGT) compared to diesel. A fuel with oxygen added, biodiesel enhances combustion, increases the combustion temperature, speeds up the oxidation process, and lowers carbon monoxide (CO) and hydrocarbon emissions. B80 produces the lowest carbon monoxide and hydrocarbon emissions at 1800 rpm, at 0.33%, and 30 ppm, respectively. On the other hand, increased carbon dioxide (CO2) emissions result from a high oxygen concentration. In addition, compared to diesel fuel, biodiesel’s greater combustion temperature causes the creation of increased nitrogen oxide (NOx) emissions. According to the research findings, a castor biodiesel fuel blend is an excellent alternative fuel for engines since it can be utilized directly without modifying the current engine construction and has good engine and exhaust emission performance.
Wind-Induced Responses of Corroded Angle-Steel Transmission Tower
Authors: Zhang Ligang , Ren Yuan , Zhou Xing , Shen Guohui , Tu Zhibin , Yao Jianfeng
Published: July 2023

Corroded transmission towers, whose load-bearing capacities are lowered, are suffering from wind-induced damage. By simulating the member corrosion through section thinning, the structural dynamic finite element model of an angle-steel transmission tower with a corrosion depth of 0 mm to 1.4 mm was established using ANSYS/LS-DYNA software. The incremental dynamic analysis method was used to study the collapse mode of the tower under different degrees of corrosion, as well as the effects of corrosion on structural self-vibration characteristics, wind-induced member stress, and tower-top displacement. The results show that with the corrosion deepening, the first three vibration frequencies decrease, and the torsional mode becomes the first mode. When the corrosion depth is smaller, the overall structure failure occurs under strong wind. The failure starts at the most unfavorable oblique rod between the upper and middle cross arm, then expands to the rods under the lower cross arm. When the corrosion depth is larger, the overall structure failure occurs restrictedly between the upper and middle cross arm. Internal axial force and bending moment redistribution happen when corrosion deepens. Under the critical failure state, the oblique rods are always at a high stress level, while the stresses of the main rods decrease. Corrosion has a stronger impact on component strength and stability than on structural stiffness.