bluuebunny/crossref_metadata_2025 huggingface dataset:
Anticancer Therapies Based on Oxidative Damage: Lycium barbarum Inhibits the Proliferation of MCF-7 Cells by Activating Pyroptosis through Endoplasmic Reticulum Stress
Authors: Miranda Maria Rosaria , Basilicata Manuela Giovanna , Vestuto Vincenzo , Aquino Giovanna , Marino Pasquale , Salviati Emanuela , Ciaglia Tania , Domínguez-Rodríguez Gloria , Moltedo Ornella , Campiglia Pietro , Pepe Giacomo , Manfra Michele
Published: June 2024

Lycium barbarum, commonly recognized as goji berry or wolfberry, is highly appreciated not only for its organoleptic and nutritional properties but also as an important source of bioactive compounds such as polysaccharides, carotenoids, phenolics, and various other non-nutritive compounds. These constituents give it a multitude of health benefits, including antioxidant, anti-inflammatory, and anticancer properties. However, the precise biochemical mechanisms responsible for its anticancer effects remain unclear, and the comprehensive composition of goji berry extracts is often insufficiently explored. This study aimed to investigate the biochemical pathways modulated in breast cancer cells by an ethanolic extract of Lycium barbarum fruit (LBE). Following metabolomic profiling using UHPLC-HRMS/MS, we assessed the antitumoral properties of LBE on different breast cancer cell lines. This investigation revealed that LBE exhibited cytotoxic effects, inducing a pro-oxidant effect that triggered pyroptosis activation through endoplasmic reticulum (ER) stress and subsequent activation of the P-IRE1α/XBP1/NLRP3 axis in MCF-7 cells. In addition, LBE did not display cytotoxicity toward healthy human cells but demonstrated antioxidant properties by neutralizing ROS generated by doxorubicin. These findings underscore the potential of LBE as a highly promising natural extract in cancer therapy.
The Study of Patterns and Mechanisms of Continuous Laser Ablation of Carbon Steel Rust Layers in Multi-Medium Environments
Authors: Cui Kunyu , Luo Jian , Xu Kefeng , Ling Lei , Cheng Riping
Published: June 2024

A new multi-scenario, low-cost, high-efficiency, medium-assisted continuous laser cleaning of corrosion layers was developed. By comparing the roughness and cleaning depth of rust layers cleaned under conditions of liquid-assisted, solid-assisted, and mixed solid–liquid-assisted laser cleaning, simultaneously establishing a three-dimensional finite element model to study the variations during the cleaning process, and conducting a comparative analysis of the results of both, the cleaning mechanism is elucidated. The experimental results indicate that under conditions of water-assisted cleaning, the depth of rust layer increases initially and then decreases with varying water layer heights. The maximum cleaning depth is achieved at a water layer height of 0.1 mm, while the optimal surface roughness occurs at a water layer height of 0.2 mm, indicating a change in cleaning mechanism. The cleaning pattern with SiO2 activator assistance follows a similar trend to a water medium, reaching maximum cleaning depth at 0.1 mm height, with a slight improvement in surface roughness compared to water-assisted cleaning. Finally, solid–liquid mixing can achieve cleaning completion and improve surface roughness under conditions where water-assisted cleaning alone fails to reach a clean state. Therefore, the active agent can be used for laser cleaning to promote the cleaning process, and solid–liquid mixing to assist the laser cleaning can be a theoretical guide for the field of laser cleaning.
Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
Authors: Piotrowska Agnieszka
Published: June 2024

The Transmission Control Protocol (TCP) serves as a cornerstone mechanism for implementing Congestion Control (CC) across the Internet. Designing a solution that provides high bandwidth utilization and mitigates the phenomenon of bufferbloat across a spectrum of diverse scenarios poses a considerable challenge. The introduction of Bottleneck Bandwidth and Round Trip propagation time (BBR) in 2016 marked a significant shift in congestion control methodology. Its improved performance and adaptability contributed to the initial acclaim and widespread interest that it received.. Unlike most currently used CCs, it operates around Kleinrock’s optimal point, thus offering high throughput even in lossy networks while preventing buffer saturation. Unfortunately, it quickly became evident that BBR was unable to fairly share bandwidth with flows characterized by different path delays, as well as loss-based CCs. In response, Google recently introduced a third iteration to address these shortcomings. This study explores the performance of BBRv3 across a wide range of scenarios, thereby considering different buffer sizes and paths with varying Round Trip Times (RTTs), and it evaluates its superiority over its predecessors. Through extensive simulations, this work assesses whether BBRv3 can finally play fair with other bandwidth contenders, which is a critical consideration given the widespread deployment of BBR. The framework is publicly available to facilitate additional validation and ensure the reproducibility of the study’s findings. The results indicate that while BBRv3 demonstrates enhanced fairness towards loss-based CC algorithms, it struggles when competing against other BBR flows, especially in multi-RTT networks, thus falling short even when compared to the initial version.
A Framework to Quantify the Quality of Source Code Obfuscation
Authors: Jin Hongjoo , Lee Jiwon , Yang Sumin , Kim Kijoong , Lee Dong Hoon
Published: June 2024

Malicious reverse engineering of software has served as a valuable technique for attackers to infringe upon and steal intellectual property. We can employ obfuscation techniques to protect against such attackers as useful tools to safeguard software. Applying obfuscation techniques to source code can prevent malicious attackers from reverse engineering a program. However, the ambiguity surrounding the protective efficacy of these source code obfuscation tools and techniques presents challenges for users in evaluating and comparing the varying degrees of protection provided. This paper addresses these issues and presents a methodology to quantify the effect of source code obfuscation. Our proposed method is based on three main types of data: (1) the control flow graph, (2) the program path, and (3) the performance overhead added to the process—all of which are derived from a program analysis conducted by human experts and automated tools. For the first time, we have implemented a tool that can quantitatively evaluate the quality of obfuscation techniques. Then, to validate the effectiveness of the implemented framework, we conducted experiments using four widely recognized commercial and open-source obfuscation tools. Our experimental findings, based on quantitative values related to obfuscation techniques, demonstrate that our proposed framework effectively assesses obfuscation quality.
Novel Prognostic Methodology of Bootstrap Forest and Hyperbolic Tangent Boosted Neural Network for Aircraft System
Authors: Fu Shuai , Avdelidis Nicolas P.
Published: June 2024

Complex aviation systems’ integrity deteriorates over time due to operational factors; hence, the ability to forecast component remaining useful life (RUL) is vital to their optimal operation. Data-driven prognostic models are essential for system RUL prediction. These models benefit run-to-failure datasets the most. Thus, significant factors that could affect systematic integrity must be examined to quantify the operational component of RUL. To expand predictive approaches, the authors of this research developed a novel method for calculating the RUL of a group of aircraft engines using the N-CMAPSS dataset, which provides simulated degradation trajectories under real flight conditions. They offered bootstrap trees and hyperbolic tangent NtanH(3)Boost(20) neural networks as prognostic alternatives. The hyperbolic tangent boosted neural network uses damage propagation modelling based on earlier research and adds two accuracy levels. The suggested neural network architecture activates with the hyperbolic tangent function. This extension links the deterioration process to its operating history, improving degradation modelling. During validation, models accurately predicted observed flight cycles with 95–97% accuracy. We can use this work to combine prognostic approaches to extend the lifespan of critical aircraft systems and assist maintenance approaches in reducing operational and environmental hazards, all while maintaining normal operation. The proposed methodology yields promising results, making it suitable for adoption due to its relevance to prognostic difficulties.
Cytotoxicity and Antioxidant Defences in Euplotes aediculatus Exposed to Single and Binary Mixtures of Heavy Metals and Nanoparticles
Authors: Varatharajan Govindhasamay R. , Calisi Antonio , Kumar Santosh , Bharti Daizy , Dondero Francesco , La Terza Antonietta
Published: June 2024

The aim of this study was to analyse the cytotoxicity of heavy metals (HMs) and nanoparticles (NPs) on populations of the ciliated protist Euplotes aediculatus. We used ecotoxicological tests, antioxidant assays, and the MixTOX tool in Microsoft® Excel to evaluate the toxic effect of HMs and NPs in single and binary mixtures on E. aediculatus and to detect the type of interaction between them. Based on our results, the order of toxicity was Cu > Cd >> Zn (1 h and 24 h) for HMs and ZnO > CuO >> TiO2 >> SiO2 (1 h) and CuO > ZnO >> TiO2 >> SiO2 (24 h) for NPs. The interaction between metals in binary mixtures was predominantly synergistic at low doses and antagonistic at high doses. The type of interaction depende on the metals present and their respective concentrations. Furthermore, both HMs and NPs were shown to trigger effective antioxidant responses in E. aediculatus. Our research highlights the importance of considering the combined effects of HMs and NP exposure and their potency in risk assessment.
Assessment of Postbiotic, Mundticin-like Substance EM 41/3 Application in Broiler Rabbits
Authors: Lauková Andrea , Chrastinová Ľubica , Focková Valentína , Plachá Iveta , Bino Eva , Grešáková Ľubomíra , Formelová Zuzana , Žitňan Rudolf , Belzecki Grzegorz , Miltko Renata , Pogány Simonová Monika
Published: June 2024

Nowadays, the use of probiotics, postbiotics, parabiotics and other beneficial natural substances dominate health status sustainability in animals. Mundticin-like substance EM 41/3 (MLS) is a thermo-stable substance of proteinaceous character—a bacteriocin (postbiotic) with a broad antimicrobial (inhibitory) spectrum produced by the horse-derived strain Enterococcus mundtii EM 41/3. The aim of this study was to find if this new one bacteriocin can have an effect and/or a similar effect in rabbits as was achieved previously by using enterocins detected and characterized in our laboratory. In addition, these tests have been conducted to secure the health of rabbits as food-derived animals. Moreover, the aim of this study was also to test the effect of MLS EM 41/3 in broiler rabbits as an animal model before its use in horses. The parameters tested were analyzed by validated methods. Administration of MLS EM 41/3 lead to a significant increase in the non-specific immunity parameter (phagocytic activity, p < 0.05), while microbiota in feces, caecum and appendix were not negatively influenced. However, the total enterococcal count was significantly increased in the EG compared to the CG (p < 0.001). Application of MLS EM 41/3 did not have a negative impact on blood biochemistry. No oxidative stress was noted. However, higher growth parameters were noted and also hydrolytic activity was increased after MLS EM 41/3 application. Finally, to check physico-chemical parameters, no negative influence on rabbit meat quality was noted. Postbiotic MLS EM 41/3 administration seems to be a contributing factor in the feeding strategy for animal health status sustainability.
BERTIVITS: The Posterior Encoder Fusion of Pre-Trained Models and Residual Skip Connections for End-to-End Speech Synthesis
Authors: Wang Zirui , Song Minqi , Zhou Dongbo
Published: June 2024

Enhancing the naturalness and rhythmicity of generated audio in end-to-end speech synthesis is crucial. The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on text and spectrum data from the training set. Particularly, the posterior encoder struggles with mid- and high-frequency feature extraction, impacting waveform reconstruction. Existing efforts mainly focus on prior encoder enhancements or alignment algorithms, neglecting improvements to spectrum feature extraction. In response, we propose BERTIVITS, a novel model integrating BERT into VITS. Our model features a redesigned posterior encoder with residual connections and utilizes pre-trained models to enhance spectrum feature extraction. Compared to VITS, BERTIVITS shows significant subjective MOS score improvements (0.16 in English, 0.36 in Chinese) and objective Mel-Cepstral coefficient reductions (0.52 in English, 0.49 in Chinese). BERTIVITS is tailored for single-speaker scenarios, improving speech synthesis technology for applications like post-class tutoring or telephone customer service.
IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
Authors: Wang Huiqing , Wang Huajun , Wu Linfeng
Published: June 2024

In recent years, classification and identification of Earth’s surface materials has been a challenging research topic in the field of earth science and remote sensing (RS). Although deep learning techniques have achieved some results in remote sensing image classification, there are still some challenges for multimodal remote sensing data classification, such as information redundancy between multimodal remote sensing images. In this paper, we propose a multimodal remote sensing data classification method IFF-Net based on irregular feature fusion, called IFF-Net. The IFF-Net architecture utilizes weight-shared residual blocks for feature extraction while maintaining the independent batch normalization (BN) layer. During the training phase, the redundancy of the current channel is determined by evaluating the judgement factor of the BN layer. If this judgment factor falls below a predefined threshold, it indicates that the current channel information is redundant and should be substituted with another channel. Sparse constraints are imposed on some of the judgment factors in order to remove extra channels and enhance generalization. Furthermore, a module for feature normalization and calibration has been devised to leverage the spatial interdependence of multimodal features in order to achieve improved discrimination. Two standard datasets are used in the experiments to validate the effectiveness of the proposed method. The experimental results show that the IFF-NET method proposed in this paper exhibits significantly superior performance compared to the state-of-the-art methods.
SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
Authors: Trecca Eleonora M. C. , Lonigro Antonio , Ciavarella Domenico , Caponio Vito Carlo Alberto , Patruno Stefano , Cassano Lazzaro , Cassano Michele
Published: June 2024

Background: SnoreLab (Version 5.3, Reviva Softworks Ltd., London, UK) is featured as the number one mobile application (app) for measuring snoring intensity and duration. The aim of this study was to conduct a comparative analysis between the results derived from SnoreLab and polysomnography (PSG). Methods: Male and female patients between 18 and 75 years of age, seeking medical counseling for suspected obstructive sleep apnea syndrome (OSAS), were considered eligible to participate in this study. Exclusion criteria were psychological or neurological disorders, drug or alcohol abuse, and inability to follow instructions. All patients underwent one overnight in-hospital PSG with simultaneous snoring recording using the SnoreLab app. Results: Nineteen patients (15 men, 4 women) aged 50.9 ± 10.5 years were included. The overall cohort exhibited a Snore Score of 24.8 ± 22.2 alongside an AHI of 15.1 ± 17.0, indicating moderate OSAS. Interestingly, elevated Snore Scores were observed in both the simple snoring (30.7 ± 19.2) and severe OSAS group (35.2 ± 21.4) (Kruskal–Wallis p-value = 0.176). The analysis of the Spearman’s test did not reveal a statistically significant correlation between PSG parameters and the Snore Score. Conclusions: While SnoreLab records snoring, it is not designed for screening sleep apnea. Although SnoreLab may not replace PSG for use in diagnosis, it could serve as a complementary tool for monitoring snoring and to improve the interaction between patients and clinicians when integrated into a suitable clinical assessment.
Solid-State Fermentation Initiated by Pleurotus ostreatus of a Cottonseed Cake and Lathyrus clymenum Pericarp Mixture: Impact on Nutritional Profile and Gossypol Content
Authors: Eliopoulos Christos , Langousi Ioanna , Kougia Eleni , Saxami Georgia , Markou Giorgos , Haroutounian Serkos A. , Arapoglou Dimitrios
Published: June 2024

Solid-State fermentation (SSF) is a valuable process used for the enhancement of the nutritional profile of agro-industrial by-products. The main objective of the present study concerns the exploitation of a mixture consisting of Cottonseed Cake (CSC) and Lathyrus clymenum pericarp (LCP) at a ratio of 80–20% w/w, which was utilized as substrate for the initiated by Pleurotus ostreatus SSF process. The final goal is the improvement of their nutritional value and the parallel reduction in their gossypol content. The obtained results revealed a statistically significant increase (p < 0.05) in protein content by 34.91%, while 1,3-1,6 β-glucans exceeded a 5-fold statistically significant increment (p < 0.05) at Day 11. Furthermore, lignin was reduced significantly (−26.71%) at Day 11. Free gossypol’s presence was lowered by 12.45%, while SSF presented a profound effect concerning the total gossypol level since the latter underwent a statistically significant reduction (p < 0.05) that exceeded 9-fold at Day 11. The study herein highlights SSF’s efficiency as a potential means to reduce free and total gossypol content with a parallel upgrade of its nutritional value. The fermentation outcome reveals its potential as a feed supplement and contributes to the reduction in the environmental footprint within the framework of a circular economy.
Comparison between the Dynamic Responses of Steel Buildings with Medium and Deep Columns under Several Seismic Intensities
Authors: Valenzuela-Beltrán Federico , Llanes-Tizoc Mario D. , Bojorquez Eden , Bojorquez Juan , Leal-Graciano J. M. , Baca Victor , Chavez Robespierre , Reyes-Salazar Alfredo
Published: June 2024

Structural engineers often use deep columns in high seismic areas to reduce drifts, yet this somehow contradicts what is stated in some tests in the sense that even though deep columns may satisfy current seismic provisions, they can suffer premature twisting; this is an indication that a lot of research is needed in this area. Numerical and experimental studies have been conducted to estimate the response of steel buildings with medium and deep columns under the action of static and cyclic loading; however, studies accounting for the dynamic characteristics of buildings and strong motions are not common. In addition, responses in terms of local parameters have not been considered either. In this study, the nonlinear seismic responses of steel buildings with perimeter moment-resisting frames and medium (W14) columns are numerically calculated and compared to those of similar steel buildings with equivalent deep columns in terms of cost (W27 and larger). Low-, mid-, and high-rise steel building models with different dynamic characteristics, as well as several strong motions with different frequency contents, are considered. Results indicate that the drifts of the models with medium columns may be up to 140% greater than those of the models with deep columns. Significant reductions are also observed for top displacements, normalized interstory shears, and combined normalized axial loads and bending moments. Hence, the seismic demands of the buildings with deep columns may be much smaller than those of the buildings with medium columns and, therefore, the buildings with deep columns exhibit a superior behavior, which results in more economical designs. The reduction is greater for the case of low- and mid-rise buildings than for high-rise buildings. One of the reasons for this is that as medium columns are replaced by deep columns, the stiffness and the strength increase, which are lower in the tallest model.
Evaluation of Bolt Corrosion Degree Based on Non-Destructive Testing and Neural Network
Authors: Han Guang , Lv Shuangcheng , Tao Zhigang , Sun Xiaoyun , Du Bowen
Published: June 2024

Anchor bolt corrosion is a complex and dynamic system, and the prediction and identification of its corrosion degree are of significant importance for engineering safety. Currently, non-destructive testing using ultrasonic guided waves can be employed for its detection. Building upon the analysis of anchor bolt corrosion mechanisms, this paper proposes a method for evaluating the corrosion degree of anchor bolts based on multi-scale convolutional neural networks (MS-CNNs) that address the multi-mode propagation and dispersion effects of ultrasonic guided wave signals in non-destructive testing. Electrochemical experiments were conducted to simulate anchor bolt corrosion, and ultrasonic guided wave non-destructive testing was performed every 12 h to obtain waveform data. An MS-CNN was then utilized to accurately diagnose the corrosion degree of the anchor bolts. The test results demonstrate that this method effectively detects and diagnoses the extent of anchor bolt corrosion, facilitating timely troubleshooting and preventing potential safety accidents.
Finite Element Analysis of Prefabricated Semi-Rigid Concrete Beam–Column Joint with Steel Connections
Authors: Xiao Yong , Yu Min , Liu Wei
Published: June 2024

This paper introduces a novel type of prefabricated semi-rigid concrete beam–column joint, aiming to examine its load-carrying capacity and seismic performance in comparison with a traditional cast-in-place joint. This study utilized the ABAQUS 2020 software to establish finite element models for both types of joints and conducted finite element analysis under low circumferential reciprocating displacement loads. When comparing the energy dissipation capacity, ductility, ultimate load-carrying capacity, stress mechanism, and damage mode, a comprehensive evaluation of the two types of joints was performed. Furthermore, this study investigated the impacts of various factors such as the axial compression ratio, concrete strength, reinforcement strength, and connector strength on the ultimate load-carrying capacity, ductility, and energy dissipation performance of the joints. Based on the findings, the newly combined joint exhibited a substantial 31.7% increase in its ultimate load-carrying capacity, along with a notable 7.23% enhancement in ductility and an improved energy dissipation capacity when compared with the cast-in-place joint. As a result, it can be concluded that the seismic performance of the new joint surpasses that of cast-in-place joints. Additionally, this study examined the impact of modifying relevant parameters on the seismic performance of the new prefabricated semi-rigid concrete beam–column joint.
Design of a New Supersonic Shock Wave Generator and Application in Power Generation
Authors: Hu Ming-Sen , Hsu Uzu-Kuei
Published: June 2024

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a shock wave monitoring system. It could measure the velocity of the generated shock waves at about Mach 3–5, and the output pressure exceeded 900 kg/cm2 (more than 100 times the input pressure). Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which could generate high-pressure and high-speed blast waves and could be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then rotate the magnetic energy generator for power generation. This paper used tank pressure, output pressure, gas flow, rotation speed, voltage, and current detected by the system to conduct power generation performance analysis. When the minimum rotation speed was set to 1500 rpm and three bulbs were turned on as loads, the system could generate an average voltage of 36.64 V and an average current of 211.01 mA as output (power about 7731.41 mW).
Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing
Authors: Ruelas-Santoyo Edgar Augusto , Ríos-Lira Armando Javier , Pantoja-Pacheco Yaquelin Verenice , Jiménez-García José Alfredo , Hernández-González Salvador , Cruz-Domínguez Oscar
Published: June 2024

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.
A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller
Authors: López-Muñoz Raúl , Molina-Pérez Daniel , Vega-Alvarado Eduardo , Duran-Medina Pino , Maya-Rodriguez Mario C.
Published: June 2024

This work presents a methodology to solve optimization problems with dynamic-size solution vectors containing continuous and integer variables. It is achieved by reformulating the original problem through a bilevel optimization approach and implementing metaheuristic techniques to solve it. In the selected case study, the optimization problem corresponds to tuning a neuro-fuzzy controller (NFC) that operates in a biodiesel production system for controlling temperature. The NFC performs well and is especially robust to disturbances, but due to its complexity, it is difficult to determine the best set of parameters for its use. This has led to biased searches based on criteria such as the experiences of designers. With the proposed method, it was possible to obtain a tuning that—when implemented in a simulation—led to results that surpassed those documented in the literature. Finally, the proposal offers flexibility for implementation with other controllers that have similar architectures and can be integrated into various other plants or processes.
The Synergy Model of Quality Tools and Methods and Its Influence on Process Performance and Improvement
Authors: Wittenberger Gabriel , Teplická Katarína
Published: June 2024

Implementing quality tools and methods creates a basic foundation for innovations, sustainability, optimization, and competitiveness in the era of Industry 4.0 and Quality 4.0. This paper aimed to investigate the use of quality tools and methods in the 24 divisions of a mother manufacturing company without the influence of external factors such as geographical location (America, Africa, Asia, and Europe). It was important for the mother manufacturing company to implement a uniform process standard for innovation and performance. Research methods focused on using the Kanban card, Ishikawa diagram, affinity diagram, Flowchart, 5S, OPL, layout, and Pareto analysis. It was determined in this research that the synergy (combination) of quality tools and methods in divisions improves the process performance. This hypothesis was confirmed by the results of implementing quality tools in processes within divisions. A top result was the new innovative model of synergy of the quality tools and methods for divisions of the parent company thus filling a gap in the scientific field. This model created the basis for the uniform process standard in all divisions. The results brought improvements in the processes such as material input inspection, spare parts production, production process, and product packaging. This model could be a proactive instrument for process innovation.
Resonant Acoustic Metamaterials
Authors: Iannace Gino , Amadasi Giovanni , Bevilacqua Antonella , Cairoli Maria , Trematerra Amelia
Published: June 2024

Acoustic applications of metamaterials have rapidly developed over the past few decades. The sound attenuation provided by metamaterials is due to the interaction between soundwaves and scatterers organized into a reticular grid, with a peak attenuation at a specific frequency band that is highly dependent on the scatterers’ diameter and reticular geometric organization of installation. In this article, the scatterer types chosen for the experiments are represented by a 2D shape, which are cylindrical solid-wood bars of 15 mm diameter and empty cylindrical bars of 20 mm diameter. Acoustic measurements were conducted in a semi-anechoic chamber to identify the specific frequency at which the highest insertion loss (IL) was registered. A second experiment was conducted by creating holes of 5 mm diameter on the external surface of the empty bars; in this way, it registered a higher sound attenuation. In particular, the resonant system characterized with holes, in combination with the attenuation given by 2D scatterer metamaterials, increased the sound attenuation for the frequency range between 1 kHz and 10 kHz.
Microbial Biopolymers: From Production to Environmental Applications—A Review
Authors: Sharma Mohit , Tellili Nihed , Kacem Imen , Rouissi Tarek
Published: June 2024

Industrial evolution and agricultural expansion, explained by continuing population growth, have rendered enormous problems for the world throughout the past few decades, primarily because of waste generation. To reduce environmental impact and dependence on fossil fuels, scientists have explored replacing synthetic polymers with environmentally friendly and sustainable alternatives in many emergent applications. In this regard, microbial biopolymers have gained special attention. Many biopolymers originating from various strains of bacteria, fungi, and algae have been reported and their possible applications have increased rapidly. This review focuses on the wide range of microbial biopolymers, their characteristics, and factors influencing their production. The present study also describes the environmental applications of microbial biopolymers. The use of these biopolymers is very attractive as a value-added and sustainable approach to wastewater treatment. By acting as adsorbents, coagulants, and flocculants as well as filters in membrane processes, microbial biopolymers shine as promising solutions beyond conventional methods. They can be integrated into various stages of the treatment process, further enhancing the efficiency of wastewater treatment methods. Microbial biopolymer applications in bioremediation and soil stabilization are also reviewed. Several studies have demonstrated the strong potential of biopolymers in soil improvement due to their ability to minimize permeability, eliminate heavy metals, stabilize soil, and limit erosion. Challenges related to scaling up and the downstream processing of microbial biopolymers, as well as its future perspectives in environmental applications, are also discussed.
Effects of Short-Rest Interval Time on Resisted Sprint Performance and Sprint Mechanical Variables in Elite Youth Soccer Players
Authors: Jung Daum , Hong Junggi
Published: June 2024

This study explored the impact of short rest intervals on resisted sprint training in elite youth soccer players, specifically targeting enhanced initial-phase explosive acceleration without altering sprint mechanics. Fifteen U19 soccer players participated in a randomized crossover design trial, executing two sprint conditions: RST2M (6 sprints of 20 m resisted sprints with 2 min rest intervals) and RST40S (6 sprints of 20 m resisted sprints with 40 s rest intervals), both under a load equivalent to 30% of sprint velocity decrement using a resistance device. To gauge neuromuscular fatigue, countermovement jumps were performed before and after each session, and the fatigue index along with sprint decrement percentage were calculated. Interestingly, the results indicated no significant differences in sprint performance or mechanical variables between RST2M and RST40S, suggesting that the duration of rest intervals did not affect the outcomes. Horizontal resistance appeared to mitigate compensatory patterns typically induced by fatigue in short rest periods, maintaining effective joint movement and hip extensor recruitment necessary for producing horizontal ground forces. These findings propose a novel training strategy that could simultaneously enhance sprint mechanics during initial accelerations and repeated sprint abilities for elite youth soccer players—a methodology not previously employed
Puzzle Pattern, a Systematic Approach to Multiple Behavioral Inheritance Implementation in Object-Oriented Programming
Authors: Fallucchi Francesca , Gozzi Manuel
Published: June 2024

Object-oriented programming (OOP) has long been a dominant paradigm in software development, but it is not without its challenges. One major issue is the problem of tight coupling between objects, which can hinder flexibility and make it difficult to modify or extend code. Additionally, the complexity of managing inheritance hierarchies can lead to rigid and fragile designs, making it hard to maintain and evolve the software over time. This paper introduces a software development pattern that seeks to offer a renewed approach to writing code in object-oriented (OO) environments. Addressing some of the limitations of the traditional approach, the Puzzle Pattern focuses on extreme modularity, favoring writing code exclusively in building blocks that do not possess a state (e.g., Java interfaces that support concrete methods definitions in interfaces starting from version 8). Concrete classes are subsequently assembled through the implementation of those interfaces, reducing coupling and introducing a new level of flexibility and adaptability in software construction. The highlighted pattern offers significant benefits in software development, promoting extreme modularity through interface-based coding, enhancing adaptability via multiple inheritance, and upholding the SOLID principles, though it may pose challenges such as complexity and a learning curve for teams.
An Accurate Recognition Method for Landslides Based on a Semi-Supervised Generative Adversarial Network: A Case Study in Lanzhou City
Authors: Lu Wenjuan , Zhao Zhan’ao , Mao Xi , Cheng Yao
Published: June 2024

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou.
Application of a Novel Weighted Essentially Non-Oscillatory Scheme for Reynolds-Averaged Stress Model and Reynolds-Averaged Stress Model/Large Eddy Simulation (RANS/LES) Coupled Simulations in Turbomachinery Flows
Authors: Wang Hao , Zhong Dongdong , Zhang Shuo , Wu Xingshuang , Ge Ning
Published: June 2024

In numerical simulations, achieving high accuracy without significantly increasing computational cost is often a challenge. To address this issue, this paper proposes an improved finite volume Weighted Essentially Non-Oscillatory (WENO) scheme for structured grids. By employing a single-point quadrature rule to perform flux integration on the control volume faces, this scheme is designed for use in NUAA-Turbo three-dimensional fluid solvers based on structured grids, utilizing RANS and RANS/LES coupling to simulate turbomachinery flows. Firstly, the new WENO scheme is validated against classical numerical test cases to evaluate its stability and reliability in handling discontinuities, double Mach reflection problems, and Rayleigh–Taylor (RT) instability. Compared to the original scheme, this improved finite-volume WENO scheme demonstrates better stability near discontinuities and more effectively resolves flow features at the same grid resolution. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, calculations using RANS are performed and the results obtained with WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupling simulations of turbine wake and film cooling. The results indicate that the improved finite-volume WENO scheme provides better stability and accuracy in engineering applications. For instance, the average error in calculating compressor efficiency characteristics is reduced from 0.76% to 0.05%, the error in turbine vane pressure distribution compared to the experimental values is within 1%, and the error in film cooling efficiency centerline distribution compared to the experimental values is within 3%. Additionally, the qualitative results of turbine wake and film cooling show that even with a small number of grid points, more detailed flow physics can be captured, thereby reducing computational costs in aerodynamic applications.
Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
Authors: Shen Jiankang , Xu Qinghua , Gao Mingyang , Ning Jicai , Jiang Xiaopeng , Gao Meng
Published: June 2024

Pine wood nematode disease, commonly referred to as pine wilt, poses a grave threat to forest health, leading to profound ecological and economic impacts. Originating from the pine wood nematode, this disease not only causes the demise of pine trees but also casts a long shadow over the entire forest ecosystem. The accurate identification of infected trees stands as a pivotal initial step in developing effective prevention and control measures for pine wilt. Nevertheless, existing identification methods face challenges in precisely determining the disease status of individual pine trees, impeding early detection and efficient intervention. In this study, we leverage the capabilities of unmanned aerial vehicle (UAV) remote sensing technology and integrate the VGG classical small convolutional kernel network with U-Net to detect diseased pine trees. This cutting-edge approach captures the spatial and characteristic intricacies of infected trees, converting them into high-dimensional features through multiple convolutions within the VGG network. This method significantly reduces the parameter count while enhancing the sensing range. The results obtained from our validation set are remarkably promising, achieving a Mean Intersection over Union (MIoU) of 81.62%, a Mean Pixel Accuracy (MPA) of 85.13%, an Accuracy of 99.13%, and an F1 Score of 88.50%. These figures surpass those obtained using other methods such as ResNet50 and DeepLab v3+. The methodology presented in this research facilitates rapid and accurate monitoring of pine trees infected with nematodes, offering invaluable technical assistance in the prevention and management of pine wilt disease.
Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
Authors: Ma Xiaoxuan , Liu Chao
Published: June 2024

The split delivery vehicle routing problem (SDVRP) is a classic combinatorial optimization problem, which is usually solved using a heuristic algorithm. The ant colony optimization algorithm is an excellent heuristic algorithm that has been successfully applied to solve various practical problems, and it has achieved good results. However, in the existing ant colony optimization algorithms, there are issues with weak targeting of different customer selection strategies, difficulty in balancing convergence speed and global search ability, and a predisposition to become trapped in local optima. To solve these problems, this paper proposes an improved ant colony algorithm (IACA). First, in terms of customer point selection, the initial customer and noninitial customer selection strategies are proposed for different customers, and the adaptive selection threshold is designed. Second, in terms of pheromone processing, an initial pheromone distribution method based on a greedy strategy, a pheromone backtracking mechanism, and an adaptive pheromone volatile factor are proposed. Finally, based on the 2-opt local search method, vehicle path self-search and intervehicle path search are proposed to further improve the quality of the solution. This paper tests the performance of the IACA on datasets of different scales. The experimental results show that compared with the clustering algorithm, artificial bee colony algorithm, particle swarm optimization algorithm, traditional ant colony algorithm, and other algorithms, the IACA can achieve more competitive results. Specifically, compared to the path length calculated by other algorithms, the path length calculated by IACA decreased by an average of 1.58%, 4.28%, and 3.64% in small, medium, and large-scale tests, respectively.
Fixed Time Synchronization of Stochastic Takagi–Sugeno Fuzzy Recurrent Neural Networks with Distributed Delay under Feedback and Adaptive Controls
Authors: Niu Yiran , Xu Xiaofeng , Liu Ming
Published: June 2024

In this paper, the stochastic Takagi–Sugeno fuzzy recurrent neural networks (STSFRNNS) with distributed delay is established based on the Takagi–Sugeno (TS) model and the fixed time synchronization problem is investigated. In order to synchronize the networks, we design two kinds of controllers: a feedback controller and an adaptive controller. Then, we obtain the synchronization criteria in a fixed time by combining the Lyapunov method and the related inequality theory of the stochastic differential equation and calculate the stabilization time for the STSFRNNS. In addition, to verify the authenticity of the theoretical results, we use MATLABR2023A to carry out numerical simulation.
Automated Heart Rate Detection in Seismocardiograms Using Electrocardiogram-Based Algorithms—A Feasibility Study
Authors: Pustozerov Evgenii , Kulau Ulf , Albrecht Urs-Vito
Published: June 2024

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved.