Comprehensive Performance Evaluations Research Articles

Digital Product Passport Implementation Based on Multi-Blockchain Approach with Decentralized Identifier Provider

This paper examines the implementation of a digital product passport (DPP) using Hyperledger Fabric technology to enhance product lifecycle management within the European Union’s circular economy action plan. This study addresses the need for detailed product information on materials, origin, usage, and end-of-life instructions to improve recycling practices and promote sustainable consumption. The approach integrates decentralized identifier (DID) technology for unique product identification using the cheqd.io platform with an enterprise tailored Hyperledger Fabric blockchain network for DPP data management, leveraging their strengths to enhance security and efficiency. This paper details the data model for the DPP, including entities like Product, Manufacturer, Supplier, and Material. Performance tests on the Hyperledger Fabric network demonstrate the system’s efficacy, focusing on CRUD operations and scalability. Future work will extend to the development of client applications and more comprehensive performance evaluations considering scalability and network expansion.

A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.

Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease. This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization. The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF. The model's effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities. The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.

Next-Gen Medical Technology a Smart Approach to Syringe Injection Pump Automation

This presents the design, implementation, and successful validation of a cutting-edge IoT-based Syringe Injection Pump (SIP) for precision medication administration. In response to the increasing demand for enhanced healthcare automation, this system leverages technologies to address critical challenges in medication delivery. This SIP incorporates a robust Internet of Things (IoT) framework, enabling seamless communication between the device and a central healthcare management system. The automated syringe injection process is governed by a sophisticated algorithm that considers patient-specific parameters, ensuring precise dosage administration. Through extensive testing and real-world simulations, the results demonstrate a significant reduction in human error and improved medication adherence. The system’s architecture integrates secure communication protocols, ensuring data confidentiality and integrity during transmission. An innovative user interface provides healthcare professionals with real-time monitoring and control capabilities, enhancing situational awareness and responsiveness. Furthermore, the SIP’s adaptability to diverse medical environments underscores its versatility in meeting the evolving needs of healthcare institutions. To validate the system’s effectiveness, a comprehensive performance evaluations, including dosage accuracy, response time, and reliability under varying conditions was conducted. Results indicate an impressive dosage precision of 99.8%, showcasing the system’s reliability in critical healthcare scenarios. The IoT connectivity facilitates remote monitoring and control, allowing healthcare providers to intervene promptly in case of anomalies or emergencies. In addition to its technical prowess, the SIP aligns with current healthcare trends by promoting sustainability and cost-effectiveness. The automated system optimizes resource utilization, reduces medication waste, and minimizes the environmental impact associated with traditional manual injection methods. In conclusion, this IoT-based Syringe Injection Pump represents a paradigm shift in medical technology, offering a smart and results-oriented solution for precise medication administration. The successful implementation and validation of this system pave the way for improved patient outcomes, streamlined healthcare workflows, and a more sustainable approach to medication delivery. This research contributes to the ongoing discourse on advancing medical technology and underscores the potential of IoT in shaping the future of healthcare.

Shared group session key-based conditional privacy-preserving authentication protocol for VANETs

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should be offered against potential attacks. Efficient and secure authentication among vehicles in VANETs are important requirements, but there are various limitations in the existing conditional privacy-preserving authentication protocols for securing VANETs. To cope with the inherent issues, we propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol. In our protocol, we use SGSK instead of the time-consuming Certificate Revocation List (CRL) checking, and we revoke malicious vehicles by updating SGSK. It is shared among unrevoked vehicles within a domain and across-domain. As a result, when a malicious revoked vehicle enters a new domain, it is difficult for it to access the system and send false messages. Furthermore, our protocol can not only achieve computation efficiency by reducing the number of computing operations of bilinear pairing but also resist various attacks while keeping conditional privacy protection. We implement our protocol in the Hyperledger Fabric platform. The experimental results show that our protocol is available to revoke 180 malicious vehicles in across-domain scenarios within one second, and it can meet the requirement of verifying 600 messages per second easily. Moreover, our comprehensive performance evaluations demonstrates that our protocol outperforms other approaches in terms of vehicle revocation checking cost, computation overhead, and communication overhead. In addition, to show the feasibility and validity of our protocol, we use SUMO and NS2 to simulate the actual VANET scenario and validate the efficiency and performance of our protocol. Simulation results prove the practicability of our protocol for VANETs.

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks.

In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

Performance Analysis of Ferronickel Slag-Ordinary Portland Cement Pervious Concrete.

It is unknown whether Ferronickel slag (FNS)-ordinary Portland cement (OPC)-based pervious concrete (FOPC) is feasible. To this end, a feasibility study was conducted on FOPC. Firstly, a detailed microscopic examination of the FNS powder was conducted, encompassing analyses of its particle size distribution, SEM, EDS, and chemical composition. These analyses aimed to establish the suitability of a composite of FNS and OPC as a composite cementitious material. Subsequent experimentation focused on evaluating the compressive strength of the composite paste material with varying mixed proportions, revealing a slight reduction in strength as the FNS substitution rate increased. Furthermore, the study designed eighteen different mix proportions of FOPC to investigate the key physical properties, including porosity, density, compressive strength, and the coefficient of permeability. Findings indicated that increases in the cementitious material proportion correlate with enhanced concrete strength, where the ratio of cementitious to aggregate increased by 6.7% and 16.5%, and the strength of FOPC increased by 10-13% and 30-50%, respectively. Conversely, a rise in the FNS substitution rate led to a reduction in compressive strength across different mix ratios. Additionally, the ratio of paste material to aggregate was found to significantly influence the permeability coefficient. These comprehensive performance evaluations suggest that incorporating FNS into OPC for pervious concrete applications is a feasible approach, offering valuable insights for the promotion of waste reuse and the advancement of energy conservation and emissions reduction efforts.

Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network

Accurately detecting the depolarization QRS complex in the ventricles is a fundamental requirement for cardiovascular disease detection using electrocardiography (ECG). In contrast to traditional signal enhancement algorithms, emerging neural network approaches have shown promise for QRS detection because of their generalizability on complex data. However, the inevitable noise present during ECG recording leads to a decrease in the performance of neural networks. To enhance the robustness and performance of neural network-based QRS detectors, we propose a simulated degeneration unit (SDU)-assisted convolutional neural network (CNN). An SDU simulates the physical degeneration process of interfering optical pulses, which can effectively suppress in-band noise. Through comprehensive performance evaluations on three open-source databases, the SDU-enhanced CNN-based approach demonstrated better performance in detecting QRS complexes than other recently reported QRS detectors. Furthermore, real-world noise injection tests indicate that the optimal noise robustness boundary for the CNN equipped with SDU is 167–300% higher than that for the CNN without SDU.

The use of an integrated framework combining eco-evolutionary data and species distribution models to predict range shifts of species under changing climates

Species distribution models (SDMs) are powerful tools that can predict potential distributions of species under climate change. However, traditional SDMs that rely on current species occurrences may underestimate their climatic tolerances and potential distributions. To address this limitation, we developed an integrated framework that incorporates eco-evolutionary data into SDMs. In our approach, the fundamental niches of species are constructed by their realized niches in different periods, and those fundamental niches are used to predict potential distributions of species. Our framework includes multiple phylogenetic analyses, such as niche evolution rate estimation and ancestral area reconstruction. These analyses provide deeper insights into the responses of species to climate change. We applied our approach to the Chrysanthemum zawadskii species complex to evaluate its efficacy through comprehensive performance evaluations and validation tests. Our framework can be applied broadly to species with available phylogenetic data and occurrence records, making it a valuable tool for understanding species adaptation in a rapidly changing world.•Integrating the niches of species in different periods estimates more complete climatic envelopes for them.•Combining eco-evolutionary data with SDMs predicts more comprehensive potential distributions of species under climate change.•Our framework provides a general procedure for species with phylogenetic data and occurrence records.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects

Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the data on drug combination therapy currently available may be insufficient to build high-precision models. We developed a data augmentation protocol to unbiasedly scale up the existing anti-cancer drug synergy dataset. Using a new drug similarity metric, we augmented the synergy data by substituting a compound in a drug combination instance with another molecule that exhibits highly similar pharmacological effects. Using this protocol, we were able to upscale the AZ-DREAM Challenges dataset from 8798 to 6,016,697 drug combinations. Comprehensive performance evaluations show that ML models trained on the augmented data consistently achieve higher accuracy than those trained solely on the original dataset. Our data augmentation protocol provides a systematic and unbiased approach to generating more diverse and larger-scale drug combination datasets, enabling the development of more precise and effective ML models. The protocol presented in this study could serve as a foundation for future research aimed at discovering novel and effective drug combinations for cancer treatment.

Deciphering the impact of microenvironmental factors on cuticular hydrocarbon degradation in Lucilia sericata empty Puparia: Bridging ecological and forensic entomological perspectives using machine learning models

Blow flies (Calliphoridae) play essential ecological roles in nutrient recycling by consuming decaying organic matter. They serve as valuable bioindicators in ecosystem management and forensic entomology, with their unique feeding behavior leading to the accumulation of environmental pollutants in their cuticular hydrocarbons (CHCs), making them potential indicators of exposure history. This study focuses on CHC degradation dynamics in empty puparia of Lucilia sericata under different environmental conditions for up to 90 days. The three distinct conditions were considered: outdoor-buried, outdoor-above-ground, and indoor environments. Five predominant CHCs, n-Pentacosane (n-C25), n-Hexacosane (n-C26), n-Heptacosane (n-C27), n-Octacosane (n-C28), and n-Nonacosane (n-C29), were analyzed using Gas Chromatography-Mass Spectrometry (GC–MS). The findings revealed variations in CHC concentrations over time, influenced by environmental factors, with significant differences at different time points. Correlation heatmap analysis indicated negative correlations between weathering time and certain CHCs, suggesting decreasing concentrations over time. Machine learning techniques Support Vector Machine (SVM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) models explored the potential of CHCs as age indicators. SVM achieved an R-squared value of 0.991, demonstrating high accuracy in age estimation based on CHC concentrations. MLP also exhibited satisfactory performance in outdoor conditions, while SVM and MLP yielded unsatisfactory results indoors due to the lack of significant CHC variations. After comprehensive model selection and performance evaluations, it was found that the XGBoost model excelled in capturing the patterns in all three datasets. This study bridges the gap between baseline and ecological/forensic use of empty puparia, offering valuable insights into the potential of CHCs in environmental monitoring and investigations. Understanding CHCs' stability and degradation enhances blow flies' utility as bioindicators for pollutants and exposure history, benefiting environmental monitoring and forensic entomology.

Advancing device and network security for enhanced privacy

The rapid proliferation of IoT (Internet of Things) devices has ushered in an era of unprecedented connectivity and automation. The widespread adoption has also exposed vulnerabilities, necessitating robust security and privacy measures. This research presents a comprehensive study focused on enhancing IoT device and network security and privacy through empirical investigation and advanced machine learning techniques. Commencing with an exhaustive literature review, it was assessed, the evolving landscape of IoT security threats, solutions, and identified research gaps. Building upon the foundation, it was designed and rigorously evaluated a machine learning-based classification model tailored for IoT device security. Utilizing a meticulously crafted simulated dataset mirroring real-world IoT features, our model undergoes comprehensive performance evaluations. Metrics include accuracy, precision, recall, F1 score, and ROC analysis. Our findings reveal a nuanced performance profile, shedding light on the model's capability to accurately classify IoT devices as Secure or Vulnerable. Precision-recall trade-offs, emphasizing the need for a judicious balance to mitigate false positives and false negatives was investigated. The critical role of feature engineering and model refinement, pointing to areas for future research and optimization. This research contributes to the burgeoning field of IoT security by employing machine learning as a proactive tool for fortifying IoT device and network security. Our findings advocate for a strategic approach to secure IoT ecosystems, ensuring data integrity and privacy in the face of evolving threats. As IoT devices continue to proliferate across industries, this research serves as a foundation for innovative strategies and ongoing investigations to harness the full potential of secure IoT environments while addressing multifaceted challenges in the ever-evolving IoT landscape.

Environmental Monitoring with Wireless Sensor Network for Energy Aware Routing and Localization

This paper introduces a novel approach to enhance the efficiency of environmental monitoring in Wireless Sensor Networks (WSNs) through the application of the Centroid Direction of Arrival Routing (CDAR) protocol. WSNs play a pivotal role in collecting and transmitting data for environmental surveillance, yet energy constraints and dynamic deployment scenarios pose challenges to their sustained operation. The CDAR protocol addresses these challenges by optimizing energy consumption, improving data transmission efficiency, and extending network lifetimes. We present comprehensive performance evaluations across ten deployment scenarios, revealing CDAR’s adaptability to varying node densities and environmental conditions. The protocol consistently demonstrates high energy efficiency percentages, showcasing its ability to strike a balance between energy conservation and reliable data transmission. Security measures integrated into CDAR ensure the protection of transmitted environmental data. This paper provides valuable insights into the applicability of CDAR in achieving sustainable and effective WSNs for environmental monitoring, setting the stage for further research and real-world implementations.

Performance analysis of versatile video coding for encoding phase-only hologram videos.

In recent decades, holographic technology has made significant progress with the development of novel hologram generation methods and three-dimensional rendering devices. Nevertheless, the substantial size of holographic data presents a significant challenge to its practical applications and thus necessitates the implementation of an efficient coding solution. In this study, we evaluate the efficiency of various coding tools within the state-of-the-art video coding standard, Versatile Video Coding, for encoding video of computer-generated phase-only hologram. Specifically, we examine the coding performance of transform/in-loop filter/screen-content coding tools. Through extensive encoding experiments and various statistical analyses, we investigated the limitations of existing standard codecs that do not account for the unique signal characteristics of phase-only holograms (POHs). The effects of coding artifacts on the visual quality of numerical reconstructions rendered from compressed POHs are also analyzed in detail. These comprehensive performance evaluations will provide valuable insights for developing efficient coding strategies for POH videos.

LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network.

Piwi-interacting RNAs (piRNAs) are a new class of small, non-coding RNAs, crucial in the regulation of gene expression. Recent research has revealed links between piRNAs, viral defense mechanisms, and certain human cancers. Due to their clinical potential, there is a great interest in identifying piRNAs from large genome databases through efficient computational methods. However, piRNAs lack conserved structure and sequence homology across species, which makes piRNA detection challenging. Current detection algorithms heavily rely on manually crafted features, which may overlook or improperly use certain features. Furthermore, there is a lack of suitable computational tools for analyzing large-scale databases and accurately identifying piRNAs. To address these issues, we propose LSTM4piRNA, a highly efficient deep learning-based method for predicting piRNAs in large-scale genome databases. LSTM4piRNA utilizes a compact LSTM network that can effectively analyze RNA sequences from extensive datasets to detect piRNAs. It can automatically learn the dependencies among RNA sequences, and regularization is further integrated to reduce the generalization error. Comprehensive performance evaluations based on piRNAs from the piRBase database demonstrate that LSTM4piRNA outperforms current advanced methods and is well-suited for analysis with large-scale databases.

Enhancing HMA properties with polypropylene-based face mask integration: A qualitative comparison of wet mixing and dry mixing techniques

<span lang="EN-US">In the wake of the emergence and widespread dissemination of the COVID-19 coronavirus, scientifically identified as SARSCoV-2, the utilization of medical equipment, specifically tri-layered polypropylene (PP)-based face masks, has experienced significant proliferation. The extensive adoption of single-use polymer-based face masks has raised notable environmental concerns, given their extended degradation period of up to 480 years and their substantial threat to ecosystems, particularly aquatic organisms, due to pervasive littering practices. Hence, this study is dedicated to assessing the influence of incorporating polypropylene (PP)-based face masks into asphalt formulations. The incorporation of PP-based face masks into asphalt was executed at three distinct weight percentages, employing both dry and wet mixing techniques. Comprehensive performance evaluations were carried out utilizing the Marshall test, indirect tensile fatigue test, and Hamburg wheel tracking rutting experiments and the best value for each. Remarkably, augmenting the PP content yielded improvements in both fatigue life and rutting resistance of the asphalt mixtures, and the maximum improvement was recorded at 0.3% replacement. Notably, asphalt specimens prepared using dry mixing techniques outperformed their wet mixing counterparts, demonstrating superior cohesion attributes.</span>

Fischer machine learning for mobile cloud computing in eHealth systems using blockchain mechanism

The Electronic Healthcare (eHealth) systems are competent to ensure effective care engineering and intensified healthcare quality which are user-friendly cache and administration, in Electronic Health Records (EHRs). For secure EHRs of Mobile Cloud-based eHealth systems, ensuring high security and data privacy, Interplanetary File System in healthcare has traditionally been concentrated. However, there has been a recent push towards achieving high quality of e-health services because blockchain-based health care applications require QoS guarantees in terms of requirements such as network latency and end-to-end delay. In this work, an Extended Validation Certification-based Fischer Neural Network Optimization (EVC-FNNO) method for secured Mobile Cloud-based E-Health Systems is proposed. With the identity being the digital certificate, the EVC is provided with the identity to the mobile cloud user who will transact in the network. In this way, the mobile cloud user is being ensured to access the ledger for the transaction. Therefore, both data privacy and security is said to be provided. Next, with Fischer Neural Network Optimization (FNNO), every authenticated mobile cloud user via EVC then possess a copy of shared ledger, therefore resolving data acquisition in cloud server and hence solving network latency. The proposed method is verified by some demonstrative examples in addressing QoS. The empirical results show that the EVC-FNNO method provides an efficient solution by validating the mobile cloud user sensitive health information with digital certificate. Security analysis proves that the EVC-FNNO method is secure. We also conduct comprehensive performance evaluations that demonstrate the high efficiency of the EVC-FNNO method in terms of end-to-end delay, network latency and data privacy, compared to the existing data sharing methods.

Production of nutrient-rich foods for carnivorous animals: a sustainable approach using Amazonian by-products of fishing and fishing processing

The animal feed manufacturing industry faces ongoing challenges in providing nutritionally balanced diets for a wide range of species, particularly carnivorous animals. The prevalent use of plant-based ingredients in commercial feeds often leads to significant nutritional limitations for these species, driven by cost and availability considerations. This study aims to develop an innovative process for producing nutrient-rich feed for carnivorous animals using Amazonian by-products of fishing and fishing processing as the primary source of high-quality animal-derived nutrients. Additionally, it evaluates the feasibility and effectiveness of this process, examining its impacts on the health and growth of animals fed with these diets. Furthermore, it explores the environmental and economic benefits arising from the intelligent utilization by-products of fishing and fishing processing in the animal feed industry. The research involved the formulation and production of animal feed utilizing by-products of fishing and fishing processing through pelletization and extrusion techniques. These feeds were then subjected to comprehensive nutritional analysis and performance evaluations with carnivorous species. The study also considered the cost-effectiveness of this approach by assessing raw material costs. The research successfully demonstrated the production of nutrient-rich feeds with elevated utilization of animal-derived nutrients, contributing to improved nutrition for carnivorous animals. The experimental diets showed promising results in terms of animal health, growth, and survival rates during the preliminary performance evaluation.

3D Localization With a Single Partially-Connected Receiving RIS: Positioning Error Analysis and Algorithmic Design

In this paper, we introduce the concept of partially-connected receiving reconfigurable intelligent surfaces (R-RISs), which refers to metasurfaces designed to efficiently sense electromagnetic waveforms impinging on them, and perform localization of the users emitting them. The presented R-RIS hardware architecture comprises subarrays of meta-atoms, with each of them incorporating a waveguide assigned to direct the waveforms reaching its meta-atoms to a reception radio-frequency (RF) chain, enabling signal/channel parameter estimation. We particularly focus on the scenarios where the user is located in the far-field of all the R-RIS subarrays, and present a three-dimensional (3D) localization method which is based on narrowband signaling and angle of arrival (AoA) estimates of the impinging signals at each single-receive-RF R-RIS subarray. For the AoA estimation, which relies on spatially sampled versions of the received signals via each subarray's phase configuration of meta-atoms, we devise an off-grid atomic norm minimization approach, which is followed by subspace-based root multiple signal classification (MUSIC). The AoA estimates are finally combined via a least-squared line intersection method to obtain the position coordinates of a user emitting synchronized localization pilots. Our derived theoretical Cramér Rao lower bounds (CRLBs) on the estimation parameters, which are compared with extensive computer simulation results of our localization approach, verify the effectiveness of the proposed R-RIS-empowered 3D localization system, which is showcased to offer cm-level positioning accuracy. Our comprehensive performance evaluations also demonstrate the impact of various system parameters on the localization performance, namely the training overhead and the distance between the R-RIS and the user, as well as the spacing among the R-RIS's subarrays and its partitioning patterns.

Yeast cell detection using fuzzy automatic contrast enhancement (FACE) and you only look once (YOLO)

In contemporary biomedical research, the accurate automatic detection of cells within intricate microscopic imagery stands as a cornerstone for scientific advancement. Leveraging state-of-the-art deep learning techniques, this study introduces a novel amalgamation of Fuzzy Automatic Contrast Enhancement (FACE) and the You Only Look Once (YOLO) framework to address this critical challenge of automatic cell detection. Yeast cells, representing a vital component of the fungi family, hold profound significance in elucidating the intricacies of eukaryotic cells and human biology. The proposed methodology introduces a paradigm shift in cell detection by optimizing image contrast through optimal fuzzy clustering within the FACE approach. This advancement mitigates the shortcomings of conventional contrast enhancement techniques, minimizing artifacts and suboptimal outcomes. Further enhancing contrast, a universal contrast enhancement variable is ingeniously introduced, enriching image clarity with automatic precision. Experimental validation encompasses a diverse range of yeast cell images subjected to rigorous quantitative assessment via Root-Mean-Square Contrast and Root-Mean-Square Deviation (RMSD). Comparative analyses against conventional enhancement methods showcase the superior performance of the FACE-enhanced images. Notably, the integration of the innovative You Only Look Once (YOLOv5) facilitates automatic cell detection within a finely partitioned grid system. This leads to the development of two models—one operating on pristine raw images, the other harnessing the enriched landscape of FACE-enhanced imagery. Strikingly, the FACE enhancement achieves exceptional accuracy in automatic yeast cell detection by YOLOv5 across both raw and enhanced images. Comprehensive performance evaluations encompassing tenfold accuracy assessments and confidence scoring substantiate the robustness of the FACE-YOLO model. Notably, the integration of FACE-enhanced images serves as a catalyst, significantly elevating the performance of YOLOv5 detection. Complementing these efforts, OpenCV lends computational acumen to delineate precise yeast cell contours and coordinates, augmenting the precision of cell detection.

Study on preparation technology and performance of polyethylene plastic concrete for road

This paper presents an innovative method of polyethylene plastic concrete (PC) to enhance the recycling rate of waste polyethylene. Based on experimental results, low-density polyethylene (LDPE) was selected as the binder, and a suitable gradation for PC was recommended. LDPE PC was mixed at 200 °C for 210 s and compacted 50 times on both sides. The workability was improved by adding a flow agent, with a 9% content determined through water stability evaluation indexes. Comprehensive volume and performance evaluations established an optimum binder-stone ratio of 5.7%. LDPE PC exhibited excellent high temperature, low temperature, and anti-scattering performance, making it promising for road applications.