bluuebunny/crossref_metadata_2025 huggingface dataset:
Protective Effects of a Combined Herbal Medicine against Amyotrophic Lateral Sclerosis-Associated Inflammation and Oxidative Stress
Authors: Yang Eun Jin
Published: June 2024

Despite extensive studies to identify effective curative drugs for amyotrophic lateral sclerosis (ALS), only riluzole and edaravone have been approved by the Food and Drug Administration. However, these drugs only delay disease progression and exhibit adverse effects, necessitating the development of more effective drugs. Herbal medicines are effective against incurable diseases with various pathogenic factors owing to their low toxicity and presence of multiple components, which target multiple organs. Therefore, we aimed to investigate whether a combined herbal medicine (CHM), comprising Gastrodia elata, Cnidium officinale Makino, and Ostericum koreanum, affects muscle function and motor neuron death in an animal model of ALS. We treated 8-week-old hSOD1G93A mice with 1 mg/g CHM, administered orally once daily for 6 weeks. Muscle function was measured via a footprint test. Biochemical analyses, including immunoblotting, western blotting, and immunohistochemistry, of the muscles (tibialis anterior and gastrocnemius) and spinal cord of hSOD1G93A mice were performed. The CHM treatment improved movement and reduced motor neuron loss in the mouse spinal cord. It also enhanced anti-inflammatory and anti-oxidant activities and regulated autophagy in the mouse muscles and spinal cord. These findings suggest that CHM has multi-active components that effectively target muscles and the spinal cord, delaying disease progression.
SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering
Authors: Wang Jian , Zhao Jiale , Cheng Ting
Published: June 2024

The structural and cognitive functions of the brain undergo significant changes throughout an individual’s lifetime. The analysis of EEG background waves based on age groups will help reveal the correlation between human cognitive development ability and their age, and provide a new perspective for a deeper understanding of neurodegenerative diseases. Unfortunately, the available literature shows that, in recent years, the analysis of EEG signal background waves at different age groups has been extremely rare. To address the vacuum of this research, this paper introduces an innovative semi-supervised graph attention autoencoder method, SGAAE-AC, an age-based clustering method based on EEG background wave analysis. This method utilizes feedback from the labels generated by age-based clustering to guide the encoder in generating more accurate EEG graph embeddings. Furthermore, by adopting multi-objective optimization techniques, the accuracy and interpretability of EEG signal clustering are significantly improved. Our experimental outcomes elucidate the relationship and impact between human age and EEG background waves from perspectives such as comprehensive EEG spectral activity and frequency band attention, thereby uncovering the patterns of EEG background wave activity as they evolve with age.
The Aerodynamic Mechanisms of the Formation Flight of Migratory Birds: A Narrative Review
Authors: Beaumont Fabien , Murer Sébastien , Bogard Fabien , Polidori Guillaume
Published: June 2024

Recently, significant advances have marked the scientific knowledge of the formation flight of migratory birds. Both experimental and theoretical research have played a central role in understanding the aerodynamic mechanisms associated with this behavior, laying the groundwork for future investigations into the benefits of group formation. These studies have specifically demonstrated the energy savings achieved by birds adopting this practice. Technological evolution, in turn, has opened new perspectives, allowing an in-depth experimental understanding of the flight behaviors of birds in formation, ranging from their energy saving to sophisticated aerodynamic strategies. Studies converge towards an increasing recognition of the complexity and variability of the mechanisms governing formation flight in different avian species. Recent progress in computer modeling has generated impressive visual representations of V-formation flight, prompting several hypotheses about its functions and mechanisms. However, the challenge persists in the ability to test these hypotheses. In conclusion, a multidisciplinary approach involving biologists, engineers, mathematicians, computer scientists, and physicists is essential to unravel the mysteries of the aerodynamics of V-formation flight in migratory birds. This literature review aims to compile studies addressing aerodynamic questions related to the formation flight of migratory birds, exploring various aspects from aerodynamic modeling to energy saving and formation optimization.
Algorithms for Computer-Based Calculation of Individual Strand Tensioning in the Stay Cables of Cable-Stayed Bridges
Authors: Spasojević Šurdilović Marija , Živković Srđan , Turnić Dragana
Published: June 2024

This paper introduces algorithms for computer-aided calculation, based on a proposed computational model for stay cables, decomposed from a bridge’s structural system. These algorithms determine the necessary tension forces and corresponding deformations in the cables for individual strand tensioning using lightweight hydraulic jacks. Two tensioning methods are discussed: the first involves single-cycle tensioning with varying force intensities, while the second employs multiple cycles by applying forces of constant intensity until achieving an equalization of forces in all strands of the cable. The efficiency of the proposed procedures is demonstrated through a numerical example.
Establishing a Generic Geographic Information Collection Platform for Heterogeneous Data
Authors: Liu Songcao , Li Bozhao , Chen Yuqiao , Cai Zhongliang
Published: June 2024

Geographic information collection platforms are widely used for acquiring geographic information. However, existing geographic information collection platforms have limited adaptability and configurability, negatively affecting their usability. They do not support complete field collection workflows or capture data with complex nested structures. To address these limitations, this paper proposes a generic geographic information collection platform based on a comprehensive XML schema definition and a corresponding XML toolkit. This platform includes professional and non-professional versions of collection software, as well as a management system. Users can configure controls and define nested tables within this platform to collect heterogeneous and complex nested data. Moreover, the platform supports functions such as task assignment, local deployment servers, multitasking parallelism, and summary statistics of heterogeneous data, ensuring complete workflow support for field data collection. The platform has been applied in agriculture, forestry, and related fields. This paper uses the agricultural industry structure survey as a case study. Practical applications and our case study show that this platform can reduce software development costs, lower user knowledge prerequisites, and fulfill 95% of geographic information collection scenarios.
Fixed-Point Results of Generalized (ϕ,Ψ)-Contractive Mappings in Partially Ordered Controlled Metric Spaces with an Application to a System of Integral Equations
Authors: Akram Mohammad , Alshaikey Salha , Ishtiaq Umar , Farhan Muhammad , Argyros Ioannis K. , Regmi Samundra
Published: June 2024

In this manuscript, we prove numerous results concerning fixed points, common fixed points, coincidence points, coupled coincidence points, and coupled common fixed points for (ϕ,Ψ)-contractive mappings in the framework of partially ordered controlled metric spaces. Our findings introduce a novel perspective on this mathematical context, and we illustrate the uniqueness of our findings through various explanatory examples. Also, we apply the main result to find the existence and uniqueness of the solution of the system of integral equations as an application.
Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow
Authors: Alqahtani Mohammed H. , Almutairi Sulaiman Z. , Shaheen Abdullah M. , Ginidi Ahmed R.
Published: June 2024

Multi-Dimensional Optimal Power Flow (MDOPF) is a fundamental task in power systems engineering aimed at optimizing the operation of electrical networks while considering various constraints such as power generation, transmission, and distribution. The mathematical model of MDOPF involves formulating it as a non-linear, non-convex optimization problem aimed at minimizing specific objective functions while adhering to equality and inequality constraints. The objective function typically includes terms representing the Fuel Cost (FC), Entire Network Losses (ENL), and Entire Emissions (EE), while the constraints encompass power balance equations, generator operating limits, and network constraints, such as line flow limits and voltage limits. This paper presents an innovative Improved Kepler Optimization Technique (IKOT) for solving MDOPF problems. The IKOT builds upon the traditional KOT and incorporates enhanced local escaping mechanisms to overcome local optima traps and improve convergence speed. The mathematical model of the IKOT algorithm involves defining a population of candidate solutions (individuals) represented as vectors in a high-dimensional search space. Each individual corresponds to a potential solution to the MDOPF problem, and the algorithm iteratively refines these solutions to converge towards the optimal solution. The key innovation of the IKOT lies in its enhanced local escaping mechanisms, which enable it to explore the search space more effectively and avoid premature convergence to suboptimal solutions. Experimental results on standard IEEE test systems demonstrate the effectiveness of the proposed IKOT in solving MDOPF problems. The proposed IKOT obtained the FC, EE, and ENL of USD 41,666.963/h, 1.039 Ton/h, and 9.087 MW, respectively, in comparison with the KOT, which achieved USD 41,677.349/h, 1.048 Ton/h, 11.277 MW, respectively. In comparison to the base scenario, the IKOT achieved a reduction percentage of 18.85%, 58.89%, and 64.13%, respectively, for the three scenarios. The IKOT consistently outperformed the original KOT and other state-of-the-art metaheuristic optimization algorithms in terms of solution quality, convergence speed, and robustness.
Three-Dimensional Melted Electrowriting Drug Coating Fibers for the Prevention of Device-Associated Infections: A Pilot Study
Authors: Martins Joana P. , da Silva Elisabete T. , Fernandes António A. , Costa de Oliveira Sofia
Published: June 2024

Medical device-related infections (DRIs), especially prevalent among critically ill patients, impose significant health and economic burdens and are mainly caused by bacteria. Severe infections often necessitate device removal when antibiotic therapy is inefficient, delaying recovery. To tackle this issue, PCL drug-eluting coated meshes were explored, and they were printed via melt electrowriting (MEW). These meshes were coated with gentamicin sulfate (GS) and tetracycline hydrochloride (TCH) and underwent FTIR analysis to confirm drug integration. Antimicrobial activity was assessed via agar diffusion assays and biofilm formation assays against bacterial strains: Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 43300, and Staphylococcus epidermidis ATCC 35984. FTIR analysis evidenced the presence of the drugs in the meshes. TCH displayed broad-spectrum antimicrobial activity against all strains, whereas GS was effective against all except S. aureus. These findings indicate the potential of cost-effective ultra-fine drug coating fibers for medical device applications, offering infection prevention during implantation. This preliminary study demonstrates the feasibility of producing drug-eluting fibers for DRI prevention through a non-toxic, fast, and cost-efficient technique, paving the way for enhanced patient care and reduced healthcare costs.
Heart Rate Responses of Post-Menopausal Women to Zumba Gold® Classes
Authors: Delextrat Anne , Shaw Clare Dorothy , Solera-Sanchez Alba
Published: June 2024

This study aimed to describe the heart rate (HR) responses of post-menopausal women during Zumba Gold® classes and to investigate the effects of body fat on HR responses. Twenty-three post-menopausal women (68.8 ± 7.2 years old; 160.0 ± 5.2 cm; 66.9 ± 11.1 kg, 36.0 ± 9.9% body fat) participated. Baseline testing assessed participants’ anthropometric and fitness characteristics. Then, HR measurements were taken during four of their regular Zumba Gold® classes, and average HR (HRmean), as well as time spent in different HR intensity categories, was calculated. Linear regressions and t-tests were performed to analyse the data. The average HR during Zumba Gold® classes was 70.2% of maximum HR. Women with lower body fat achieved a significantly higher HRmean and spent less time at light to very light intensity and more time at moderate intensity compared to those with higher body fat. Body fat percentage and age were identified as determinants of time spent at moderate intensity. These findings suggest that Zumba Gold® can be an effective exercise option for post-menopausal women aiming to meet the recommended daily exercise guidelines. Understanding the HR responses during Zumba Gold® classes can aid in the development of safe and effective exercise prescriptions for this population.
Bioengineered Mesenchymal Stem/Stromal Cells in Anti-Cancer Therapy: Current Trends and Future Prospects
Authors: Gil-Chinchilla Jesús I. , Zapata Agustín G. , Moraleda Jose M. , García-Bernal David
Published: June 2024

Mesenchymal stem/stromal cells (MSCs) are one of the most widely used cell types in advanced therapies due to their therapeutic potential in the regulation of tissue repair and homeostasis, and immune modulation. However, their use in cancer therapy is controversial: they can inhibit cancer cell proliferation, but also potentially promote tumour growth by supporting angiogenesis, modulation of the immune milieu and increasing cancer stem cell invasiveness. This opposite behaviour highlights the need for careful and nuanced use of MSCs in cancer treatment. To optimize their anti-cancer effects, diverse strategies have bioengineered MSCs to enhance their tumour targeting and therapeutic properties or to deliver anti-cancer drugs. In this review, we highlight the advanced uses of MSCs in cancer therapy, particularly as carriers of targeted treatments due to their natural tumour-homing capabilities. We also discuss the potential of MSC-derived extracellular vesicles to improve the efficiency of drug or molecule delivery to cancer cells. Ongoing clinical trials are evaluating the therapeutic potential of these cells and setting the stage for future advances in MSC-based cancer treatment. It is critical to identify the broad and potent applications of bioengineered MSCs in solid tumour targeting and anti-cancer agent delivery to position them as effective therapeutics in the evolving field of cancer therapy.
Chronic Lymphocytic Leukemia (CLL)-Derived Extracellular Vesicles Educate Endothelial Cells to Become IL-6-Producing, CLL-Supportive Cells
Authors: Uziel Orit , Lipshtein Lian , Sarsor Zinab , Beery Einat , Bogen Shaked , Lahav Meir , Regev Alon , Kliminski Vitali , Sharan Roded , Gervits Asia , Signorini Lorenzo Federico , Shimony Shai , Raanani Pia , Rozovski Uri
Published: June 2024

We hypothesized that via extracellular vesicles (EVs), chronic lymphocytic leukemia (CLL) cells turn endothelial cells into CLL-supportive cells. To test this, we treated vein-derived (HUVECs) and artery-derived (HAOECs) endothelial cells with EVs isolated from the peripheral blood of 45 treatment-naïve patients. Endothelial cells took up CLL-EVs in a dose- and time-dependent manner. To test whether CLL-EVs turn endothelial cells into IL-6-producing cells, we exposed them to CLL-EVs and found a 50% increase in IL-6 levels. Subsequently, we filtered out the endothelial cells and added CLL cells to this IL-6-enriched medium. After 15 min, STAT3 became phosphorylated, and there was a 40% decrease in apoptosis rate, indicating that IL-6 activated the STAT3-dependent anti-apoptotic pathway. Phospho-proteomics analysis of CLL-EV-exposed endothelial cells revealed 23 phospho-proteins that were upregulated, and network analysis unraveled the central role of phospho-β-catenin. We transfected HUVECs with a β-catenin-containing plasmid and found by ELISA a 30% increase in the levels of IL-6 in the culture medium. By chromatin immunoprecipitation assay, we observed an increased binding of three transcription factors to the IL-6 promoter. Importantly, patients with CLL possess significantly higher levels of peripheral blood IL-6 compared to normal individuals, suggesting that the inducers of endothelial IL-6 are the neoplastic EVs derived from the CLL cells versus those of healthy people. Taken together, we found that CLL cells communicate with endothelial cells through EVs that they release. Once they are taken up by endothelial cells, they turn them into IL-6-producing cells.
Genomic Profiling for Predictive Treatment Strategies in Fibrotic Interstitial Lung Disease
Authors: Perrotta Fabio , Sanduzzi Zamparelli Stefano , D’Agnano Vito , Montella Antonia , Fomez Ramona , Pagliaro Raffaella , Schiattarella Angela , Cazzola Mario , Bianco Andrea , Mariniello Domenica Francesca
Published: June 2024

Idiopathic pulmonary fibrosis (IPF) has traditionally been considered the archetype of progressive fibrotic interstitial lung diseases (f-ILDs), but several other f-ILDs can also manifest a progressive phenotype. Integrating genomic signatures into clinical practice for f-ILD patients may help to identify patients predisposed to a progressive phenotype. In addition to the risk of progressive pulmonary fibrosis, there is a growing body of literature examining how pharmacogenomics influences treatment response, particularly regarding the efficacy and safety profiles of antifibrotic and immunomodulatory agents. In this narrative review, we discuss current studies in IPF and other forms of pulmonary fibrosis, including systemic autoimmune disorders associated ILDs, sarcoidosis and hypersensitivity pneumonitis. We also provide insights into the future direction of research in this complex field.
Bridging Nature and Engineering: Protein-Derived Materials for Bio-Inspired Applications
Authors: Nawaz Taufiq , Gu Liping , Gibbons Jaimie , Hu Zhong , Zhou Ruanbao
Published: June 2024

The sophisticated, elegant protein-polymers designed by nature can serve as inspiration to redesign and biomanufacture protein-based materials using synthetic biology. Historically, petro-based polymeric materials have dominated industrial activities, consequently transforming our way of living. While this benefits humans, the fabrication and disposal of these materials causes environmental sustainability challenges. Fortunately, protein-based biopolymers can compete with and potentially surpass the performance of petro-based polymers because they can be biologically produced and degraded in an environmentally friendly fashion. This paper reviews four groups of protein-based polymers, including fibrous proteins (collagen, silk fibroin, fibrillin, and keratin), elastomeric proteins (elastin, resilin, and wheat glutenin), adhesive/matrix proteins (spongin and conchiolin), and cyanophycin. We discuss the connection between protein sequence, structure, function, and biomimetic applications. Protein engineering techniques, such as directed evolution and rational design, can be used to improve the functionality of natural protein-based materials. For example, the inclusion of specific protein domains, particularly those observed in structural proteins, such as silk and collagen, enables the creation of novel biomimetic materials with exceptional mechanical properties and adaptability. This review also discusses recent advancements in the production and application of new protein-based materials through the approach of synthetic biology combined biomimetics, providing insight for future research and development of cutting-edge bio-inspired products. Protein-based polymers that utilize nature’s designs as a base, then modified by advancements at the intersection of biology and engineering, may provide mankind with more sustainable products.
The Persistent Paradox of Rapid Eye Movement Sleep (REMS): Brain Waves and Dreaming
Authors: Pagel J. F.
Published: June 2024

The original conceptualization of REM sleep as paradoxical sleep was based on its EEG resembling wakefulness and its association with dreaming. Over time, the concept of paradox was expanded to include various associations with REM sleep, such as dream exclusivity, high recall, and pathophysiology. However, none of these associations are unique to REM sleep; they can also occur in other sleep states. Today, after more than fifty years of focused research, two aspects of REMS clearly retain paradoxical exclusivity. Despite the persistent contention that the EEG of human REMS consists of wake-like, low-voltage, non-synchronous electrical discharges, REMS is based on and defined by the intracranial electrical presence of 5–8 Hz. theta, which has always been the marker of REMS in other animals. The wake-like EEG used to define REMS on human polysomnography is secondary to a generalized absence of electrophysiological waveforms because the strong waves of intracranial theta do not propagate to scalp electrodes placed outside the skull. It is a persistent paradox that the theta frequency is restricted to a cyclical intracranial dynamic that does not extend beyond the lining of the brain. REMS has a persistent association with narratively long and salient dream reports. However, the extension of this finding to equate REMS with dreaming led to a foundational error in neuroscientific logic. Major theories and clinical approaches were built upon this belief despite clear evidence that dreaming is reported throughout sleep in definingly different physiologic and phenomenological forms. Few studies have addressed the differences between the dreams reported from the different stages of sleep so that today, the most paradoxical aspect of REMS dreaming may be how little the state has actually been studied. An assessment of the differences in dreaming between sleep stages could provide valuable insights into how dreaming relates to the underlying brain activity and physiological processes occurring during each stage. The brain waves and dreams of REMS persist as being paradoxically unique and different from waking and the other states of sleep consciousness.
Chef Dalle: Transforming Cooking with Multi-Model Multimodal AI
Authors: Hannon Brendan , Kumar Yulia , Li J. Jenny , Morreale Patricia
Published: June 2024

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system’s adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.
Advancing Skin Cancer Prediction Using Ensemble Models
Authors: Natha Priya , RajaRajeswari Pothuraju
Published: June 2024

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach.
Impact of the COVID-19 Pandemic on Thyroid Cancer Surgery
Authors: Lee Max L. , Megwalu Uchechukwu C. , Finegersh Andrey , Noel Julia E. , Chen Michelle M.
Published: June 2024

The COVID-19 pandemic caused major disruptions to healthcare services in 2020, delaying cancer diagnosis and treatment. While early-stage thyroid cancer often progresses slowly, it is crucial to determine whether treatment delays associated with the pandemic have impacted the clinical presentation and management of advanced-stage thyroid cancer. The purpose of our study was to determine the impact of the early COVID-19 pandemic on thyroid cancer presentation and treatment times. Utilizing the National Cancer Database, chi-squared tests and regression analyses were performed to compare patient demographic and clinical characteristics over time for 56,011 patients diagnosed with primary thyroid cancer who were treated at the Commission on Cancer-accredited sites in 2019 and 2020. We found that thyroid cancer diagnoses decreased between 2019 and 2020, with the biggest drop among patients with cT1 disease relative to other T stages. We also found that patients diagnosed with thyroid cancer in 2020 had similar treatment times to patients diagnosed in 2019, as measured by both the time between diagnosis and start of treatment and the time between surgery and start of radioactive iodine therapy. Overall, our study suggests that resources during the pandemic were allocated to patients with advanced thyroid disease, despite a decrease in diagnoses.
Entropy Fluctuations and Correlations in Compressible Turbulent Plane Channel Flow
Authors: Gerolymos G. A. , Vallet I.
Published: June 2024

The thermodynamic turbulence structure of compressible aerodynamic flows is often characterised by the correlation coefficient of entropy with pressure or temperature. We study entropy fluctuations s′ and their correlations with the fluctuations of the other thermodynamic variables in compressible turbulent plane channel flow using dns data. We investigate the influence of the hcb (Huang–Coleman–Bradshaw) friction Reynolds number (100⪅Reτ★⪅1000) and of the centreline Mach number (0.3⪅M¯CLx⪅2.5) on the magnitude and location of the peak of the root-mean-square srms′. The complete series expansions of s′ with respect to the fluctuations of the basic thermodynamic variables (pressure p, density ρ and temperature T) are calculated for the general case of variable heat-capacity cp(T) thermodynamics. The correlation coefficients of s′ with the fluctuations of the basic thermodynamic quantities (cs′p′, cs′ρ′, cs′T′), for varying (Reτ★,M¯CLx), are studied. Insight on these correlations is provided by considering the probability density function (pdf) of s′ and its joint pdfs with the other thermodynamic variables.
Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm
Authors: Wu Yang , Qian Chonghui , Huang Hengjun
Published: June 2024

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction.
Cable Conduit Defect Recognition Algorithm Based on Improved YOLOv8
Authors: Kong Fanfang , Zhang Yi , Zhan Lulin , He Yuling , Zheng Hai , Dai Derui
Published: June 2024

The underground cable conduit system, a vital component of urban power transmission and distribution infrastructure, faces challenges in maintenance and residue detection. Traditional detection methods, such as Closed-Circuit Television (CCTV), rely heavily on the expertise and prior experience of professional inspectors, leading to time-consuming and subjective results acquisition. To address these issues and automate defect detection in underground cable conduits, this paper proposes a defect recognition algorithm based on an enhanced YOLOv8 model. Firstly, we replace the Spatial Pyramid Pooling (SPPF) module in the original model with the Atrous Spatial Pyramid Pooling (ASPP) module to capture multi-scale defect features effectively. Secondly, to enhance feature representation and reduce noise interference, we integrate the Convolutional Block Attention Module (CBAM) into the detection head. Finally, we enhance the YOLOv8 backbone network by replacing the C2f module with the base module of ShuffleNet V2, reducing the number of model parameters and optimizing the model efficiency. Experimental results demonstrate the efficacy of the proposed algorithm in recognizing pipe misalignment and residual foreign objects. The precision and mean average precision (mAP) reach 96.2% and 97.6%, respectively, representing improvements over the original YOLOv8 model. This study significantly improves the capability of capturing and characterizing defect characteristics, thereby enhancing the maintenance efficiency and accuracy of underground cable conduit systems.
Automatic Classification of Defective Solar Panels in Electroluminescence Images Based on Random Connection Network
Authors: Xu Weiyue , Shi Yinhao , Yang Ruxue , Ye Bo , Qiang Hao
Published: June 2024

Solar energy is an important renewable energy source, and the efficiency of solar panels is crucial. However, tiny cracks and dark spots, defects of panels, can significantly affect power generation performance. To solve the defect identification problem of solar panels, an intelligent electroluminescence (EL) image classification method based on a random network (RandomNet50) is proposed. The randomly connected network module is designed by combining dropout and feature reuse strategies. Feature reuse in random networks optimizes the network structure and improves the feature utilization efficiency. The network model uses geometric transformation and the deep convolution generative adversarial network (DCGAN) method to enhance few-shot EL images (400) with different states. The comparison experiment shows that the RandomNet50 has a good classification effect on the enhanced images. The accuracy of the CIFAR-10/EL dataset (96.15%/88.23%) is better than the residual and dense networks. The method has high classification accuracy and provides strong technical support in the field of solar cells.
Compact Asymmetric T-Feed Closed-Slot Antennas for 2.4/5/6 GHz WiFi-7 MIMO Laptops
Authors: Cheng Shao-Hung , Chen Shu-Chuan , Chen Yu-Jen , Liu Chiang-Lung
Published: June 2024

In this paper, we introduce a closed-slot WiFi-7 multi-input multi-output (MIMO) system equipped with six antennas, designed specifically for laptop integration. Positioned near the lower edge of the laptop’s display ground plane, the antennas are placed 2.5 mm from the hinge and 8 mm from the left side of the ground plane. Each antenna features a 44 × 2 mm2 closed-slot structure with a simple T-shaped feeder that stimulates half-wavelength and full-wavelength resonances via feed-in coupling. The six antennas are aligned in parallel, spaced 4 mm apart, forming a compact array without the need for additional isolation components. This setup supports dual-band functionality across both 2400–2500 MHz and 5150–7125 MHz WiFi-7 bands. Performance assessments indicate a minimum of 10 dB isolation between the antennas and envelope correlation coefficients (ECC) of the radiation patterns being below 0.04. Furthermore, the antenna array’s radiation efficiency was measured to be over 64%.
Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning
Authors: Yin Yongfeng , Wang Zhetao , Zheng Lili , Su Qingran , Guo Yang
Published: June 2024

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.
Improved YOLOV5 Angle Embossed Character Recognition by Multiscale Residual Attention with Selectable Clustering
Authors: Ying Shenshun , Fang Jianhai , Tang Shaozhang , Bao Wenzhi
Published: June 2024

In the intelligentization process of power transmission towers, automated identification of stamped characters is crucial. Currently, manual methods are predominantly used, which are time-consuming, labor-intensive, and prone to errors. For small-sized characters that are incomplete, connected, and irregular in shape, existing OCR technologies also struggle to achieve satisfactory recognition results. Thus, an approach utilizing an improved deep neural network model to enhance the recognition performance of stamped characters is proposed. Based on the backbone network of YOLOv5, a multi-scale residual attention encoding mechanism is introduced during the upsampling process to enhance the weights of small and incomplete character targets. Additionally, a selectable clustering minimum iteration center module is introduced to optimize the selection of clustering centers and integrate multi-scale information, thereby reducing random errors. Experimental verification shows that the improved model significantly reduces the instability caused by random selection of clustering centers during the clustering process, accelerates the convergence of small target recognition, achieves a recognition accuracy of 97.6% and a detection speed of 43 milliseconds on the task of stamped character recognition, and significantly outperforms existing Fast-CNN, YOLOv5, and YOLOv6 models in terms of performance, effectively enhancing the precision and efficiency of automatic identification.
Interpretability and Transparency of Machine Learning in File Fragment Analysis with Explainable Artificial Intelligence
Authors: Jinad Razaq , Islam ABM , Shashidhar Narasimha
Published: June 2024

Machine learning models are increasingly being used across diverse fields, including file fragment classification. As these models become more prevalent, it is crucial to understand and interpret their decision-making processes to ensure accountability, transparency, and trust. This research investigates the interpretability of four machine learning models used for file fragment classification through the lens of Explainable Artificial Intelligence (XAI) techniques. Specifically, we employ two prominent XAI methods, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to shed light on the black-box nature of four machine learning models used for file fragment classification. By conducting a detailed analysis of the SHAP and LIME explanations, we demonstrate the effectiveness of these techniques in improving the interpretability of the models’ decision-making processes. Our analysis reveals that these XAI techniques effectively identify key features influencing each model’s predictions. The results also showed features that were critical to predicting specific classes. The ability to interpret and validate the decisions made by machine learning models in file fragment classification can enhance trust in these models and inform improvements for better accuracy and reliability. Our research highlights the importance of XAI techniques in promoting transparency and accountability in the application of machine learning models across diverse domains.
CRT-Based Clock Synchronization for Millimeter-Wave Communication with Asymmetric Propagation Delays
Authors: Tan Shufeng , Wang Qifei , Wan Ziwei , Luo Fei
Published: June 2024

The rapid advancement of millimeter-wave communication technology has presented new challenges for time synchronization, driven by the need for high-speed and low-latency data transmission. Ethernet synchronization technologies, such as Precise Time Protocol (PTP), have emerged to overcome the limitations of point-to-point architecture and enable precise synchronization across multiple devices. A key drawback of conventional PTP is its reliance on symmetric packet exchange delays, which may not hold in real-world scenarios where there is relative mobility between master and slave nodes, leading to asymmetric propagation delays and clock offset errors. To address this issue, a novel clock synchronization protocol that integrates Chinese Remainder Theory (CRT) has been proposed. This protocol enhances conventional PTP by incorporating distance estimation based on CRT using multi-carrier phase measurement. By combining a coarse estimation of one-way propagation delay from conventional PTP with a fine estimation of remainder distance from CRT, the protocol accurately determines the distance between master and slave nodes, reducing motion errors and enhancing clock synchronization accuracy. Simulation results indicate that the Root Mean Square Error (RMSE) of distance estimation remains below 10−5 m, with a corresponding motion time error of approximately 0.01 picosecond.
Joint Optimization of User Scheduling, Flight Path and Power Allocation in a UAV Secure Communication System
Authors: Liu Lipei , Yao Rugui , Xie Yi , Fan Ye , Zuo Xiaoya , Xu Juan , Zhong Ruikang , Zheng Shijie , Li Xudong
Published: June 2024

In the future network, the data traffic increases rapidly and users’ communication service needs are diversified. The application of unmanned aerial vehicles (UAVs) in communication effectively relieves the pressure of ground communication equipment. However, the UAV-assisted communication also faces many challenges. Due to the broadcast and open characteristics of the UAV communication channel in the wireless network, it is easily subject to eavesdropping, so secure and efficient communication is hard to guarantee. In order to improve the security of the UAV communication system, this paper studies the secure communication system of a UAV as a base station to assist wireless network communication. In this system, the UAV, as an airborne mobile base station, transmits confidential information to multiple users on the ground, and there is an eavesdropper around the users. The optimization problem of joint user communication scheduling, UAV flight path, and power allocation is established to maximize the minimum secrecy rate of the system. Considering the constraints of user service quality, UAV flight path, and transmit power, the optimization problem is a nonconvex problem and the solution complexity is extremely high. Therefore, we propose an efficient iterative algorithm, jointly considering the block coordinate descent method and the successive convex approximation technique. Simulation results verify the correctness of the theoretical analysis, and further show that this scheme significantly improves the minimum secrecy rate of the system compared with other benchmark schemes.
Switched Capacitor Inverter with Reduced Inrush Current and High Boosting Gain
Authors: Choudhary Ankita , Singh Ashutosh Kumar , Mandal Rajib Kumar , Saha Akshay Kumar
Published: June 2024

This article describes a 17-level switched-capacitor-based eight-times-boosting gain inverter. The inverter is made up of a DC power source, thirteen switches, three diodes, and three capacitors. The inverter produces seventeen steps during each cycle and crosses the zero line two times in one complete cycle. The proposed inverter has its polarity change mechanism; it is not necessary to use an H-bridge. Three self-balancing capacitors make up this construction. The capacitors automatically balance voltage by connecting in series/parallel to the input voltage source. Logic gates can generate gate pulses with the phase disposition pulse-width modulation technique, which helps to preserve capacitor voltage balance at the same time. The proposed structure was compared to recent papers, analyzing factors including voltage gain, DC sources, semiconductor devices, cost function, and TSV. The proposed configuration offers cost effectiveness and fewer semiconductor devices for providing a 17-level output with sufficient voltage gain. Also, to reduce the capacitor inrush current, soft charging is used. Additionally, the proposed structure’s power losses were examined, confirming its efficiency. Finally, an experimental prototype was tested to analyze and validate the suggested structure’s performance under various situations. Results show the proposed structure performs well under steady and dynamic situations.
Analysis of the Efficiency of Burning Briquettes from Agricultural and Industrial Residues in a Layer
Authors: Nikiforov Alexandr , Prikhodko Evgeniy , Kinzhibekova Akmaral , Karmanov Amangeldy , Alexiou Ivanova Tatiana
Published: June 2024

The combustion of briquettes made from organic and industrial residues in small boilers requires researchers to consider the characteristics of this type of fuel and methods of its combustion. For the efficient combustion of fuel briquettes, a layered combustion method with the ability to regulate the supply of combustion air is better suited. The purpose of this research is to study the thermal technical conditions of briquetted fuel combustion. In order to carry this out, a stand was created, which made it possible to determine the combustion efficiency of this type of fuel. Two types of briquettes were studied: one with 30% sunflower husks and 70% leaves, and one with and 70% sunflower husks and 30% coke breeze. The combustion results of the briquettes show that heat loss from chemical under-burning was no more than 6.25%. To determine the temperature distribution in the fuel layer, a model of unsteady heat transfer in a fixed layer was used. A calculation of the temperature fields in the layer of burned fuel briquettes was carried out, which showed that the most favorable conditions for burning briquettes were created with a layer about 15–20 cm thick for both burned briquette options. The temperature was in the range of 450–750 °C, which on the one hand corresponds to experimental data and on the other hand provides a combustion regime that occurs with a relatively low loss to the environment. This installation and mathematical model will help future studies based on the processes of other types of organic waste combustion with a grate system.