bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Construction of a Blockchain-Based Industrial Product Full Life Cycle Information Traceability System
Authors: Xiao Leifeng , Sun Wenlei , Chang Saike , Lu Cheng , Jiang Renben
Published: May 2024

The application of blockchain technology in industrial product quality traceability is analyzed to construct a new model of product quality traceability that is mainly based on blockchain technology and supplemented by an identity system. The blockchain-enabled overall technical architecture of an industrial product quality traceability system is explored, and a blockchain-based industrial product full life cycle information traceability system is constructed. First, the weights of the information indicators of different links of the industrial equipment information traceability system were calculated using the EAHP hierarchical analysis method. The manufacturing link had the largest weight, with a value of 18.8%. Second, the system’s functional module design is based on the weights. We designed and developed the industrial product information traceability platform based on the hybrid blockchain chain structure of private chain + alliance chain. Finally, a manufacturing enterprise in the Xinjiang region is taken as the research object, query validation is carried out for the products produced by the enterprise, and the average query time of the system is measured to be 65.376 ms. It can meet the traceability needs of consumers and enterprise users. The research can provide theoretical support and reference for the whole life cycle information traceability of industrial products.
Influence of Non-Ionic Surfactant and Silver on the Photocatalytic Activity of TiO2 Films for Degradation of Dyes in Distilled and Tap Water
Authors: Ivanova Dobrina , Mladenova Elisaveta , Kaneva Nina
Published: May 2024

This study describes the impact of surfactant molecular weights (PEG 2000 and PEG 4000) on the photocatalytic activity of TiO2 films, deposited via dip-coating from a PEG-stabilized suspension and silver-functionalized photo-fixation of Ag+ under UV illumination. The photocatalytic activity of pure and Ag/TiO2 films is assessed in the aqueous-phase degradation of Malachite green and Methylene blue in distilled and tap water under UV and visible illumination. The results indicate a positive effect of both the higher-molecular-weight non-ionic surfactant and Ag-functionalization yield higher photocatalytic efficiency. Notably, films photo-fixed with 10−2 M Ag+ show the highest degradation percentages in all experimental conditions. A direct correlation between the concentration of Ag+ ions and the enhancement of the photocatalytic activity is revealed: pure TiO2 < Ag, 10−4/TiO2 < Ag, 10−3/TiO2 < Ag, 10−2/TiO2. Flame atomic absorption spectrometry is used to study the Ag+ leeching from the Ag/TiO2 films. The structural properties of the nanostructures are investigated through scanning electron microscopy, Brunauer–Emmett–Teller analysis, energy-dispersive X-ray spectroscopy, and X-ray diffraction. Additionally, after three cycles of operation, Ag, 10−2/TiO2 (PEG 4000) films can maintain their photocatalytic activity, suggesting a potential application in the treatment of dye wastewater.
Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
Authors: Büker Aykut , Hanilçi Cemal
Published: May 2024

Determining whether an audio signal is single compressed (SC) or double compressed (DC) is a crucial task in audio forensics, as it is closely linked to the integrity of the recording. In this paper, we propose the utilization of phase spectrum-based features for detecting DC narrowband and wideband adaptive multi-rate (AMR-NB and AMR-WB) speech. To the best of our knowledge, phase spectrum features have not been previously explored for DC audio detection. In addition to introducing phase spectrum features, we propose a novel parallel LSTM system that simultaneously learns the most representative features from both the magnitude and phase spectrum of the speech signal and integrates both sets of information to further enhance its performance. Analyses demonstrate significant differences between the phase spectra of SC and DC speech signals, suggesting their potential as representative features for DC AMR speech detection. The proposed phase spectrum features are found to perform as well as magnitude spectrum features for the AMR-NB codec, while outperforming the magnitude spectrum in detecting AMR-WB speech. The proposed phase spectrum features yield 8% performance improvement in terms of true positive rate over the magnitude spectrogram features. The proposed parallel LSTM system further improves DC AMR-WB speech detection.
Bond Properties of Glass-Fiber-Reinforced Polymer Hybrid Rebar in Reinforced Concrete with Respect to Bond Length
Authors: Kim Seungwon , Kim Janghwan , Park Cheolwoo
Published: May 2024

Preventing rebar corrosion in reinforced concrete (RC) structures has been actively researched worldwide. One of the most powerful solutions is the use of fiber-reinforced polymer (FRP) rebars. However, there are limitations in the mechanical design and construction of FRP rebars because their tensile characteristics are extremely different from those of conventional rebars and they have a different modulus of elasticity. FRP rebars are relatively cost-efficient when fabricated with glass fibers, but they are still costly compared to conventional rebars. Therefore, hybrid rebars fabricated by covering conventional rebars with glass FRP (GFRP) materials were developed in this study. GFRP hybrid rebars have increased durability in marine environments while maintaining the same mechanical properties as conventional rebars. As the difference in rebar diameter of the bonded area decreased, the tensile strength of the concrete increased. As a result, pull-out failure or tensile failure caused by the yielding of the rebars occurred in small-diameter rebars. The experimental results showed that the maximum load for the D13 deformed steel bar was 52.2 kN at a bond length of 50 mm and 76.1 kN at 100 mm, while for the D19 deformed steel bar, it was 65.3 kN at 50 mm and 103.7 kN at 100 mm. The bond properties of hybrid GFRB rebars were found to be lower than those of deformed steel bars. These properties were improved greatly by increasing the thickness of the GFRP materials on the surface of the deformed steel bars, highlighting a path toward high-performance, corrosion-resistant concrete.
Ransomware Detection Model Based on Adaptive Graph Neural Network Learning
Authors: Li Jun , Yang Gengyu , Shao Yanhua
Published: May 2024

Ransomware is a type of malicious software that encrypts or locks user files and demands a high ransom. It has become a major threat to cyberspace security, especially as it continues to be developed and updated at exponential rates. Ransomware detection technology has become a focus of research on information security risk detection methods. However, current ransomware detection techniques have high false positive and false negative rates, and traditional methods ignore global word co-occurrence and correlation information between key node steps in the entire process. This poses a significant challenge for accurately identifying and detecting ransomware. We propose a ransomware detection model based on co-occurrence information adaptive diffusion learning using a Text Graph Convolutional Network (ADC-TextGCN). Specifically, ADC-TextGCN first assign self-weights to word nodes based on sensitive API call functions and preserve co-occurrence information using Point Mutual Information Theory (COIR-PMI); then our model automatically learn the optimal neighborhood through an Adaptive Diffusion Convolution (ADC) strategy, thereby improving the ability to aggregate long-distance node information across layers and enhancing the network’s ability to represent ransomware behavior. Experimental results show that our method achieves an accuracy of over 96.6% in ransomware detection, proving its effectiveness and superiority compared to traditional methods based on CNN and RNN in ransomware detection.
Investigating the Effects of Virtual Reality-Based Training on Balance Ability and Balance Confidence in Older Individuals
Authors: Wilson Oshin , Arnold Nicole , Thompson Lara A.
Published: May 2024

Each year, over 25% of adults aged sixty-five years old or older suffer a fall, and three million are treated for fall-related injuries due to lack of balance. Here, we aimed to investigate how virtual reality (VR)-based training affects balance performance and confidence in older adults. To accomplish this goal, we studied 21 healthy, older individuals between 60 and 85 years old, both pre- and post-training (6 weeks of training, twice per week (or 12 sessions)). The VR group donned an Oculus VR headset and consisted of nine participants (aged 75.9 ± 3.7 years old), while the control group (aged 75.1 ± 6.7 years old) performed training without a headset and consisted of eight participants that completed our study. To assess balance ability, we utilized the Balance Error Scoring System (BESS) and the Timed Up and Go (TUG) test. To assess balance confidence, we implemented the Activities-Specific Balance Confidence (ABC) Scale and, to assess fear of falling, the Tinetti Falls Efficacy Scale (FES). Further, we assessed depression (via the Geriatric Depression Scale (GDS)) and cognitive ability (via the Mini-Mental State Examination (MMSE)). The post-training results showed improvements in balance ability for both the VR and control groups, as well as changes in the relationship between balance confidence and balance ability for the VR group only. Further, improvements in cognitive ability were seen in the control group. This study is an indication that older individuals’ balance ability may benefit from several weeks of targeted training.
AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
Authors: Chen Yuming , Jiao Tianzhe , Song Jie , He Guangyu , Jin Zhu
Published: May 2024

In recent years, with the rapid development of medicine, pathology, toxicology, and neuroscience technology, animal behavior research has become essential in modern life science research. However, the current mainstream commercial animal behavior recognition tools only provide a single behavior recognition method, limiting the expansion of algorithms and how researchers interact with experimental data. To address this issue, we propose an AI-enabled, highly usable platform for analyzing experimental animal behavior, which aims to provide better flexibility, scalability, and interactivity to make the platform more usable. Researchers can flexibly select or extend different behavior recognition algorithms for automated recognition of animal behaviors or experience more convenient human-computer interaction through natural language descriptions only. A case study at a medical laboratory where the platform was used to evaluate behavioral differences between sick and healthy animals demonstrated the high usability of the platform.
Improving Tomato Fruit Volatiles through Organic Instead of Inorganic Nutrient Solution by Precision Fertilization
Authors: Li Youli , Han Xiaobei , Li Si , Shi Rongchao , Xu Jiu , Zhao Qian , Liu Tianxiang , Guo Wenzhong
Published: May 2024

This study investigated the effects of irrigation with a fully inorganic nutrient solution (control; NNNN) and an organic instead of an inorganic nutrient solution (OIINS) at the flowering–fruit setting (ONNN), fruit expanding (NONN), color turning (NNON), and harvest (NNNO) stages of the first spike on the type and content of tomato fruit volatiles to provide a theoretical basis for tomato aroma improvement and high-quality cultivation. Compared with the control (NNNN), the results showed that all OIINS-related treatments decreased the number of fruit volatiles and increased the relative content of common volatile compounds, characteristic effect compounds, aldehydes, and cis-3-hexenal. In particular, the relative order of performance of the OIINS-related treatments was NNNO > NNON > ONNN > NONN in terms of the relative content of characteristic compounds. For all treatments, the relative cis-3-hexenal and trans-2-hexenal percentages were 20.99–51.49% and 20.22–27.81%, respectively. Moreover, hexanal was only detected in tomato fruits under the NNNN and NNNO treatments. The effects of irrigation with OIINS on tomato fruit volatiles were related to the fruit developmental stage. At the mature stage, the organic nutrient solution was conducive to the accumulation of characteristic compounds and improved the fruit aroma quality.
Activated Carbon from Paper Waste as Potential Adsorbents for Methylene Blue and Hexavalent Chromium
Authors: Mabalane Koketso , Thabede Patience Mapule , Shooto Ntaote David
Published: May 2024

Environmental pollution is a significant problem and is increasing gradually as more and more harmful pollutants are being released into water bodies and the environment. Water pollutants are dangerous and pose a threat to all living organisms and the ecosystem. Paper waste is one of the most widespread and largest wastes in the world. This research aims to address two important problems simultaneously: the reduction in solid waste in the environment using activated carbon from paper waste as potential adsorbents and the removal of harmful contaminants from water. Carbon from paper waste was activated with H2O2, HNO3, and KMnO4 for the adsorption of Cr(VI) and MB. SEM, EDX, FTIR, Raman, and BET were used to determine the properties of the materials. The surface morphology of the materials consisted of amorphous particles. EDX shows that all activated samples have a higher content of (O) than carbon paper waste. Adsorption studies showed that there was a stronger interaction between the pollutants and the adsorbent at a higher initial concentration (200 mg/L) than at the lower initial concentrations. The contact time data show that uptake increases when the interaction time between the contaminant and the adsorbent is increased. The tests for the pH of the solution show that the adsorption of Cr(VI) decreases when the pH is gradually increased, whereas the adsorption of MB increases when the pH of the solution is increased. The results fit better with the Freundlich isotherm and PSO models. The temperature studies show that the enthalpy was positive, indicating that the uptake process is endothermic. The Gibbs free energy values were all negative, indicating that adsorption between the adsorbents and the pollutants was favored. After four consecutive cycles, all the samples retained more than 60% of their uptake capability.
Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
Authors: Wang Soohyun , Kim Byoungkug , Kang Jiheon , Eom Doo-Seop
Published: May 2024

This paper focuses on improving automated approaches to glaucoma diagnosis, a severe disease that leads to gradually narrowing vision and potentially blindness due to optic nerve damage occurring without the patient’s awareness. Early diagnosis is crucial. By utilizing advanced deep learning technologies and robust image processing capabilities, this study employed four types of input data (retina fundus image, region of interest (ROI), vascular region of interest (VROI), and color palette images) to reflect structural issues. We addressed the issue of data imbalance with a modified loss function and proposed an ensemble model based on the vision large language model (VLLM), which improved the accuracy of glaucoma classification. The results showed that the models developed for each dataset achieved 1% to 10% higher accuracy and 8% to 29% improved sensitivity compared to conventional single-image analysis. On the REFUGE dataset, we achieved a high accuracy of 0.9875 and a sensitivity of 0.9. Particularly in the ORIGA dataset, which is challenging in terms of achieving high accuracy, we confirmed a significant increase, with an 11% improvement in accuracy and a 29% increase in sensitivity. This research can significantly contribute to the early detection and management of glaucoma, indicating potential clinical applications. These advancements will not only further the development of glaucoma diagnostic technologies but also play a vital role in improving patients’ quality of life.
Transcranial Doppler Ultrasound and Transesophageal Echocardiography for Intraoperative Diagnosis and Monitoring of Patent Foramen Ovale in Non-Cardiac Surgery
Authors: Bianchini Amedeo , Vitale Giovanni , Romano Stefano , Sbaraini Zernini Irene , Galeotti Lorenzo , Cescon Matteo , Ravaioli Matteo , Siniscalchi Antonio
Published: May 2024

Background: perioperative stroke is one of the major complications after surgery. Patent foramen ovale (PFO) increases the risk of stroke in non-cardiac surgery by right-to-left shunt related to intraoperative hemodynamic alterations, leading to paradoxical embolism. Transesophageal echocardiography is the best tool for obtaining anatomical confirmation of PFO and essential details such as the PFO measure and the degree and direction of the shunt. Despite this, preoperative PFO screening is not routinely performed. Methods and results: we described the features of ten consecutive patients undergoing major abdominal surgery at the Abdominal Organ Transplant Intensive Care Unit, IRCCS Sant’Orsola, Bologna, Italy, who were screened for PFO using a PFO diagnostic and monitoring standardized intraoperative protocol by transesophageal echocardiography and transcranial color Doppler ultrasound. Finally, we highlighted the neurological and respiratory outcomes, the course and the management of three patients with intracardiac and extracardiac shunts. Conclusions: identifying an unknown PFO by a TCCD-TEE approach allowed the intraoperative monitoring of the shunt direction. It prevents the risk of complications secondary to paradoxical embolism in non-cardiac high-embolic-risk surgery.
Enhancing ToF Sensor Precision Using 3D Models and Simulation for Vision Inspection in Industrial Mobile Robots
Authors: Yang Changmo , Kang Jiheon , Eom Doo-Seop
Published: May 2024

In recent industrial settings, time-of-flight (ToF) cameras have become essential tools in various applications. These cameras provide high-performance 3D measurements without relying on ambient lighting; however, their performance can degrade due to environmental factors such as temperature, humidity, and distance to the target. This study proposes a novel method to enhance the pixel-level sensing accuracy of ToF cameras by obtaining precise depth data labels in real-world environments. By synchronizing 3D simulations with the actual ToF sensor viewpoints, accurate depth values were acquired and utilized to train AI algorithms, thereby improving ToF depth accuracy. This method was validated in industrial environments such as automobile manufacturing, where the introduction of 3D vision systems improved inspection accuracy compared to traditional 2D systems. Additionally, it was confirmed that ToF depth data can be used to correct positional errors in mobile robot manipulators. Experimental results showed that AI-based preprocessing effectively reduced noise and increased the precision of depth data compared to conventional methods. Consequently, ToF camera performance was enhanced, expanding their potential applications in industrial robotics and automated quality inspection. Future research will focus on developing real-time synchronization technology between ToF sensor data and simulation environments, as well as expanding the AI training dataset to achieve even higher accuracy.
Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
Authors: Wang Yazhou , Wang Zhen , Wang Gang , Xu Huike
Published: May 2024

This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were R2 = 0.9828, RMSE = 0.0029, and MAPE = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears.
Geometrical and Mechanical Modeling of Polymeric Multi-Ply Yarns
Authors: Pires da Costa Lucas , Moscatelli Marco , Caracino Paola , Novati Giorgio , Comi Claudia
Published: May 2024

This work aims to describe and predict the complex mechanical behavior of polymeric cords used as reinforcements in tires. Starting from the observed microstructure of the cords and from macroscopic experimental tests performed on single-ply yarns, a comprehensive geometric and mechanical model is developed. The real geometry of the cord is replaced by an equivalent three-dimensional continuum of a cylindrical shape, with a properly defined non-isotropic inelastic constitutive behavior. The three-dimensional viscoelastic and viscoplastic material model developed by the authors for rayon fibers is employed for this purpose. The actual directions of filaments inside the cord are computed by an analytical model, accounting for the twist in the yarns and in the filaments inside each yarn. Such directions, relevant to points of the cord cross-section, are then averaged along the pitch of the cord to obtain mean directions which represent the virtual reinforcement directions to be used in the equivalent cylindrical-shaped model. This analysis strategy is implemented in a finite element procedure. For rayon cords, the developed simulation tool (fed with appropriate parameters) gives numerical results that compare well with the corresponding experimental results. This approach could be effectively utilized in the analysis of cord-reinforced rubber composites.
Multi-Stage Metallogenesis and Fluid Evolution of the Hongtoushan Cu-Zn Volcanogenic Massive Sulfide Deposit, Liaoning Province, China: Constraints from Sulfur Isotopes, Trace Elements, and Fluid Inclusions
Authors: You Xinwei , Wang Ende , Fu Jianfei , Men Yekai
Published: May 2024

The Hongtoushan Cu-Zn volcanogenic massive sulfide (VMS) deposit, located in the Hunbei granite–greenstone terrane of the North China Craton, has undergone a complex, multi-stage metallogenic evolution. The deposit comprises three main types of massive ores: Type-1 ores, characterized by a sulfide matrix enclosing granular quartz and dark mineral aggregates; Type-2 ores, distinguished by large pyrite and pyrrhotite porphyroblasts and a small amount of gangue minerals; and Type-3 ores, mainly distributed in the contact zone between the ore body and gneiss, featuring remobilized chalcopyrite and sphalerite filling the cracks of pyrite. The metallogenic process of the Hongtoushan deposit is divided into three main stages: (1) an early mineralization stage forming Type-1 massive ores; (2) a metamorphic recrystallization stage resulting in Type-2 massive ores with distinct textural features; and (3) a late-stage mineralization event producing Type-3 massive ores enriched in Cu, Zn, and other metals. This study integrates sulfur isotope, trace elements, and fluid inclusion data to constrain the sources of ore-forming materials, fluid evolution and metallogenic processes of the deposit. Sulfur isotope analyses of sulfide samples yield δ34S values ranging from −0.7 to 4.2 (mean: 1.8 ± 1.5, 1σ), suggesting a predominant magmatic sulfur source with possible contributions from Archean seawater. Trace element analyses of pyrite grains from different ore types reveal a depletion of rare earth elements, Cu, and Zn in Type-2 massive ores due to metamorphic recrystallization, and a subsequent re-enrichment of these elements in Type-3 massive ores. Fluid inclusion studies allowed for identifying three types of ore-forming fluids: Type-1 (avg. Th: 222.9; salinity: 6.74 wt.% NaCl eqv.), Type-2 (avg. Th: 185.72; salinity: 16.56 wt.% NaCl eqv.), and Type-3 (avg. Th: 184.81; salinity: 16.22 wt.% NaCl eqv.), representing a complex evolution involving cooling, water–rock interaction and fluid mixing. This multi-disciplinary study reveals the interplay of magmatic, hydrothermal and metamorphic processes in the formation of the Hongtoushan VMS deposit, providing new insights into the fluid evolution and metallogenic mechanisms of similar deposits in ancient granite–greenstone terranes.
Application of Direct Shear Test to Analysis of the Rate of Soil Improvement with Polyester Fibres
Authors: Nguyen Giang , Masarovičová Soňa , Gago Filip , Grzybowska-Pietras Joanna
Published: May 2024

When improving soil shear strength using various materials, determination of the improvement rate is a key issue and can be carried out using a direct shear test (DST). However, many standards for DST require only three specimens in the test and do not deal with test result uncertainty. In this study, shear strength parameters of clay of intermediate plasticity (CI) and sandy clays (CS1, CS2) improved with the addition of polyester fibres of 70 mm in length in amounts of 0.5%, 1.0%, and 1.5% of dry soil mass were obtained using DST with a shear box of size 0.3 m × 0.3 m × 0.08 m. The results show that using fibres provides significant improvement and the number of tested specimens (three or four) in DST has a significant impact on the obtained values of shear strength parameters. It is not recommended to carry out DST with only three specimens. The analysis of uncertainty shows that covariance between correlated input quantities (normal stresses and shear stresses) has a negligible influence on result uncertainty. The worst-case estimated uncertainties are very high and should not be applied. Analysis of the state of the fibre surface before and after shearing using scanning electron microscopy (SEM) shows that suitable fibre scratch resistance may be the reason for the large improvement.
Fast-M Adversarial Training Algorithm for Deep Neural Networks
Authors: Ma Yu , An Dou , Gu Zhixiang , Lin Jie , Liu Weiyu
Published: May 2024

Although deep neural networks have been successfully applied in many fields, research studies show that neural network models are easily disrupted by small malicious inputs, greatly reducing their performance. Such disruptions are known as adversarial attacks. To reduce the impact of adversarial attacks on models, researchers have proposed adversarial training methods. However, compared with standard training, adversarial training results in additional computational overhead and training time. To improve the training effect without significantly increasing the training time, an improved Fast-M adversarial training algorithm based on the fast adversarial training algorithm is proposed in this paper. Extensive comparative experiments are conducted with the MNIST, CIFAR10, and CIFAR100 datasets. The results show that the Fast-M algorithm achieves the same training effect as the commonly used projected gradient descent (PGD) adversarial training method, with a training time that is only one-third of that of PGD and a performance comparable to that of fast adversarial training, demonstrating the proposed algorithm’s effectiveness.
Enhancing Urban Public Safety through UAS Integration: A Comprehensive Hazard Analysis with the STAMP/STPA Framework
Authors: Go Eutteum , Jeon Hee-Chang , Lee Jong-Sup , Lim Joong-Yeon
Published: May 2024

Rapid urbanization in developing countries poses challenges such as rising crime rates and resource scarcity. Unmanned Aircraft Systems (UAS) offer a promising solution to enhance public safety, but their integration requires addressing specific challenges. This study employs the Systems-Theoretic Accident Model and Processes (STAMP) and System-Theoretic Process Analysis (STPA) methodologies to identify potential hazards and requirements for integrating UAS into public safety systems in urban environments. The research objectives include identifying hazards and challenges, developing safety requirements and guidelines, and proposing strategies for efficient infrastructure investment. The proposed framework, based on STAMP/STPA, includes additional steps to consider early-stage systems and maintain stakeholder traceability. A risk matrix approach is utilized to prioritize risk mitigation measures for cost-effectiveness. The findings of this study provide valuable insights for policymakers and urban planners in developing countries seeking to harness the potential of UAS technology for enhancing public safety while addressing the unique challenges posed by rapid urbanization.
Australian Native Lemongrass (Cymbopogon ambiguus A. Camus): An Underestimated Herbal Plant
Authors: Zhou Yuntao , Akter Saleha , Phan Anh Dao Thi , Bobasa Eshetu Mulisa , Seididamyeh Maral , Sivakumar Dharini , Sultanbawa Yasmina
Published: May 2024

Lemongrass (genus Cymbopogon) is commonly used in foods, beverages, cosmetics, pharmaceuticals, and material science. Cymbopogon ambiguus A. Camus, the Australian Native Lemongrass, is a lesser-known member of the genus Cymbopogon, and research on this plant is scarce. Australian Indigenous people use the stalks and leaves of C. ambiguus as teas. Dried chopped leaves are also used as herbs in cooking. The aim of this study was to determine the proximate composition and bioactive properties of Australian native lemongrass (C. ambiguus). Antimicrobial capacity was carried out using the well diffusion method, antioxidant capacity by the FRAP method, and antidiabetic capacity by using the α-glucosidase inhibitory activity assay. The results obtained in the current study were compared with previously published literature on lemongrass (C. citratus). The results showed that C. ambiguus has lower fat and protein content and lower antioxidant and antimicrobial capacities than C. citratus, but it is very rich in fibre (67.55%) and has strong α-glucosidase inhibitory capacity. The total phenolic and total flavonoid content determined in the aqueous extract of C. ambiguus are also notable. The results of the present study showed that Australian native lemongrass has promising bioactive potential to be used as an alternative native herbal tea.
Trichoderma: Population Structure and Genetic Diversity of Species with High Potential for Biocontrol and Biofertilizer Applications
Authors: Ismaiel Adnan , Lakshman Dilip K. , Jambhulkar Prashant P. , Roberts Daniel P.
Published: May 2024

Certain Trichoderma isolates provide biofertilizer, biocontrol, and other plant-beneficial activities while inhabiting the soil or internal plant tissue, and their use in agricultural systems can contribute to sustainable food production. It is thought that colonization of soil or internal plant tissue is fundamental for biocontrol and biofertilizer applications. Our collective analyses of prior surveys, where the tef1α sequence was almost exclusively used to identify Trichoderma species, showed that isolates from the Harzianum complex clade, the T. asperellum/T. asperelloides group, T. virens, T. hamatum, and T. atroviride were prevalent in soil and/or as endophytes. Population structure and genetic diversity based on the genetic markers tef1α, rpb2, and ITS were investigated, and new lineages with statistical bootstrap support within T. atroviride, T. asperellum, T. hamatum, and T. virens populations were found. The nearest relatives of some of these species were also revealed. Choosing isolates from among more than 500 known Trichoderma species for use in non-targeted evaluation screens for biocontrol or biofertilizer applications is time-consuming and expensive. Preferentially selecting isolates from T. atroviride, T. asperellum/T. asperelloides, T. hamatum, the T. harzianum complex clade, T. virens, and possibly nearest relatives may speed the identification of candidates for commercialization due to the demonstrated ability of these species to successfully inhabit the soil and internal plant tissue. To our knowledge, this is the first report where dominant soil and endophytic Trichoderma species were identified from past survey data and population structure and genetic diversity analyses conducted.
Description of Limb Anomalies Resulting from Molt Irregularities in Ammothea hilgendorfi (Pycnogonida: Ammotheidae)
Authors: Flandroit Antoine , Simon Louis , Caulier Guillaume
Published: May 2024

Limb anomalies are widespread and diversified in arthropods. From trilobites to insects, they range from the loss to the addition or fusion of legs and may appear congenitally or be induced experimentally (e.g., amputation or injury). Basal chelicerates pycnogonids, or sea spiders, also show deformities. Despite being understudied compared to other arthropods, quite a high diversity of limb malformations has been reported in the literature. The present study reports the leg anomalies of two adult females Ammothea hilgendorfi (Böhm, 1879) observed with duplicated leg podomeres. Both individuals were described ethologically and morphologically. Although the current knowledge on pycnogonids is limited, the anomaly is likely due to a problem in the molting process; the specimens were unable to totally remove their old exuviae, which then stacked after the proximal leg segments. The second specimen also showed other leg deformities, hinting at a problem during the molting process itself. The discussion emphasizes that understanding how pycnogonids normally molt would not only help our understanding of how the abnormal patterns appeared but also put pycnogonids into perspective with other arthropods, a phylum in which they have a key taxonomic position.
Sensitivity Analysis of Modelled Air Pollutant Distribution around Buildings under Different Meteorological Conditions
Authors: Petrov Anton , Georgieva Emilia , Hristova Elena
Published: May 2024

The distribution of air pollutants in urban areas is significantly influenced by the presence of various geometric structures, including buildings, bridges, and tunnels. In built-up environments, meteorological conditions may influence the accumulation or dispersion of air pollutants in specific zones. This study examines the impact of wind and atmospheric stability on the dispersion of air pollutants around an apartment building situated in close proximity to a busy boulevard in a residential district of Sofia, Bulgaria. A series of dispersion simulations were conducted using the Graz Lagrangian Model (GRAL v.22.09) for a range of meteorological conditions, defined as combinations of the direction and velocity of the approaching flow, and of stability conditions within the study area of 1 × 1 km, with a horizontal resolution of 2 m. The resulting spatial distribution revealed the presence of hotspots and strong gradients in the concentration field. A simulation with meteorological data was also conducted, which was aligned with a campaign to monitor vehicular traffic. The sensitivity tests indicate that GRAL is capable of reproducing high-resolution pollutant fields, accounting for building effects at relatively low computational costs. This makes the model potentially attractive for city-wide simulations as well as for air pollution exposure estimation.
Cloud Top Height Retrieval from FY-4A Data: A Residual Module and Genetic Algorithm Approach
Authors: Li Tao , Chen Niantai , Tao Fa , Hu Shuzhen , Xue Jianjun , Han Rui , Wu Di
Published: May 2024

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper.
Applicability Study of Euler–Lagrange Integration Scheme in Constructing Small-Scale Atmospheric Dynamics Models
Authors: Wei Xiangqian , Liu Yi , Guo Jun , Chang Xinyu , Li Haochuan
Published: May 2024

The atmospheric flow field and weather processes exhibit complex and variable characteristics at small scales, involving interactions between terrain features and atmospheric physics. To investigate the mechanisms of these process further, this study employs a Lagrangian particle motion model combined with a Euler background field approach to construct a small-scale atmospheric flow field model. The model streamlines the modeling process by combining the benefits of the Lagrangian dynamics model and the Eulerian integration scheme. To verify the effectiveness of the Euler–Lagrange hybrid model, experiments using the Fluent wind field model were conducted for comparison. The results show that both models have their advantages in handling terrain-induced wind fields. The Fluent model excels in simulating the general characteristics of wind fields under specific terrain, while the Euler–Lagrange hybrid model is better at capturing the upstream and downstream disturbances of the terrain on the atmospheric flow field. These findings provide powerful tools for in-depth diagnostic analysis of atmospheric flow simulation and convective precipitation processes. Notably, the Euler–Lagrange hybrid model demonstrates excellent computational efficiency, with an average computation time of approximately 2 s per time step in a Python environment, enabling rapid simulation of 40 time steps within approximately 90 s.
New Nonlinear Retarded Integral Inequalities and Their Applications to Nonlinear Retarded Integro-Differential Equations
Authors: Samar Mahvish , Zhu Xinzhong , Shakoor Abdul , Osman Mawia
Published: May 2024

The purpose of this article is to present some new nonlinear retarded integral inequalities which can be utilized to study the existence, stability, boundedness, uniqueness, and asymptotic behavior of solutions of nonlinear retarded integro-differential equations, and these inequalities can be used in the symmetrical properties of functions. These inequalities also generalize some former famous inequalities in the literature. Two examples as applications will be provided to demonstrate the strength of our inequalities in estimating the boundedness and global existence of the solution to initial value problems for nonlinear integro-differential equations and differential equations which can be seen in graphs. This research work will ensure opening new opportunities for studying nonlinear dynamic inequalities on a time-scale structure of a varying nature.
Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles
Authors: Shan Chunlai , Chin Cheng Siong , Mohan Venkateshkumar , Zhang Caizhi
Published: May 2024

Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.
Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering
Authors: Margaris Dionisis , Sgardelis Kiriakos , Spiliotopoulos Dimitris , Vassilakis Costas
Published: May 2024

Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys.
Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation
Authors: Singh Vishal Kumar , Jiménez del Val Ioscani , Glassey Jarka , Kavousi Fatemeh
Published: May 2024

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of “Industry 4.0” concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.
Assistive Communication Devices in Rett Syndrome: A Case Report and Narrative Review
Authors: Lee Justin , Lee Jessica , Abu-Daoud Mouath , Al-Ajlouni Yazan A.
Published: May 2024

Rett syndrome (RTT) is a severe neurodevelopmental disorder primarily affecting females, characterized by developmental regression and significant communication impairments. Despite the critical role of communication in maintaining quality of life, the long-term efficacy and optimal utilization of augmentative and assistive communication (AAC) devices in RTT remain underexplored. This study evaluates the impact of AAC devices on communication outcomes and quality of life in individuals with RTT through a case report and narrative review. We conducted a comprehensive literature review and thematic analysis categorizing technologies into communication aids, mobility aids, educational tools, and daily living aids, assessing their effectiveness and challenges. The results show that AAC technologies, including eye-tracking devices, speech-generating devices, and adapted computers, enhance communication, cognitive development, and quality of life for individuals with RTT. The case report of Patient E, utilizing the Tobii Dynavox device, highlights the transformative impact of AAC devices despite challenges in device complexity and therapist training. AAC devices are indispensable for supporting individuals with RTT, though challenges persist related to accessibility, device complexity, and therapist training. Future research should focus on longitudinal studies to assess the sustained impact of AAC technologies and explore family-centered approaches to AAC integration.
Causality between Sex Hormones and Bone Mineral Density in Childhood: Age- and Tanner-Stage-Matched Sex Hormone Level May Be an Early Indicator of Pediatric Bone Fragility
Authors: Kim Sung Eun , Kim Seulki , Kim Shin-Hee , Cho Won Kyoung , Cho Kyoung Soon , Jung Min Ho , Ahn Moon Bae
Published: May 2024

This study aimed to investigate the impact of hypogonadism on bone mineral density (BMD) in children and adolescents with chronic diseases to determine the relationship between sex hormones and BMD. This retrospective study included 672 children and adolescents with chronic diseases such as hemato-oncologic, rheumatoid, gastrointestinal, and endocrinologic diseases. The relationship between the sex- and Tanner-stage-matched Z-scores for sex hormones and the sex- and age-matched lumbar spine BMD (LSBMD) Z-scores was evaluated. Adjustments were made for confounders such as underlying diseases, age at diagnosis, and age- and sex-matched body mass index Z-scores. Patients had a mean LSBMD Z-score of −0.55 ± 1.31. In the multivariate regression analysis, male testosterone showed a positive association with the LSBMD Z-score (p < 0.001), whereas female estradiol, luteinizing hormone, and follicular-stimulating hormone showed no significant association with the LSBMD Z-scores. In the male group, the testosterone level was associated with LSBMD Z-scores > −1.0 (p < 0.001), > −2.0 (p < 0.001), and > −3.0 (p = 0.002), while the estradiol level was associated with LSBMD Z-scores > −2.0 (p = 0.001) and > −3.0 (p = 0.002) in the female group. In conclusion, sex hormones are associated with BMD in children and adolescents with chronic diseases. Therefore, various measures may be necessary to predict future skeletal problems and improve bone health in these patients.