Comparative Analysis Research Articles

Remanufactured Waste Tire By-product Valorization: Quantitative–Qualitative Sustainability-based Assessment

Automotive tires are a major waste stream and there are currently numerous options for reusing, recycling, and remanufacturing the waste tire rubber (WTR) and other components. Some competing technologies (retreading, product repurposing, energy recovery, pyrolysis oil, waste-tire crumbing, and devulcanized rubber) are discussed here to demonstrate the broad scope of possible applications and identify the advantages/disadvantages of the processes. Based on the industrial maturity of the processes, this study performed a qualitative–quantitative fuzzy analysis of twelve alternative by-products of WTR, including 1) devulcanized or desulfurized rubber; 2) tiles, mats, and rod barriers; 3) composite materials; 4) bitumen; 5) asphalt; 6) heat energy; 7) crude oil; 8) diesel; 9) char and carbon black; 10) gasification gas; 11) activated adsorption carbon; and 12) lightweight concrete. Quantitative sustainability considerations were applied to weight the technical, economic, environmental, and social sustainability factors according to their importance, while qualitative factors were applied to weight the sustainability criteria of productivity, reliability, efficiency, waste tire size range, remanufacturing time, remanufacturing cost, ergonomics, human development, and employment potential based on insights from the published literature. Comparative literature-based statistical analysis of potential tire resources and conventional waste-tire recycling was performed. The results identified remanufacturing of WTR into devulcanized rubber sheets as the best sustainable business option with the highest sustainability index of 0.628, while lightweight concrete was the worst by-product option with a low sustainability index of 0.012. Other very-high-potential options include producing value-added rubber products and composite materials from the devulcanized rubber. These findings provide valuable insights for stakeholders in the field of waste-tire management and small-to-medium businesses looking to add further value to their WTR products. Furthermore, these findings could guide international research to help focus on the most sustainable use of WTR.

Degradation induced superlubricity on the rough surface of black phosphorus composite

A rough surface composed of asperities was considered as the obstacle for achieving superlubricity in traditional friction theory. Here, microscale superlubricity with the friction coefficient of 0.0086 was observed on a relatively rougher soft surface of black phosphorus (BP) composite due to the degradation of BP with oxygen and moisture via the atomic force microscopy (AFM) experiment equipped with diamond-like carbon (DLC) microsphere probe. Comparative analyses corroborated that the produced phosphorus oxides (oBP) with abundant P=O and P−OH bonds and the local graphitized amorphous carbon film at the sliding interface decreased the possibility of direct contact between original rough peaks and provided an ultra-low shear strength for achieving superlubricity. Furthermore, a certain nanoscale roughness structure on the composite surface facilitated a more reliable superlubricity state, which supplied the larger contact area and locking capillary force to hold and retain the lubricating liquid film formed by the water absorption of oBP. These findings provided fundamental insights into the nature of tribological mechanisms and suggested a new method to achieve ultra-low friction of polymer composite materials in engineering.

Comparative Analysis of Stroke Rates in Arch TEVAR: Total Endovascular Repair Plagued by High Stroke Rates

Comparative Analysis of Stroke Rates in Arch TEVAR: Total Endovascular Repair Plagued by High Stroke Rates

Intelligent intrusion detection through deep autoencoder and stacked long short-term memory

In the realm of network intrusion detection, the escalating complexity and diversity of cyber threats necessitate innovative approaches to enhance detection accuracy. This study introduces an integrated solution leveraging deep learning techniques for improved intrusion detection. The proposed framework consists on a deep autoencoder for feature extraction, and a stacked long short-term memory (LSTM) network ensemble for classification. The deep autoencoder compresses raw network data, extracting salient features and mitigating noise. Subsequently, the stacked LSTM ensemble captures intricate temporal dependencies, correcting anomaly detection precision. Experiments conducted on the UNSW-NB15 dataset, and a benchmark in intrusion detection validate the effectiveness of the approach. The solution achieves an accuracy of 90.59%, with precision, recall, and F1-Score metrics reaching 90.65, 90.59, and 90.57, respectively. Notably, the framework outperforms standalone models and demonstrates the advantage of synergizing deep autoencoder-driven feature extraction with the stacked LSTM ensemble. Furthermore, a binary classification experiment attains an accuracy of about 90.59%, surpassing the multiclass classification and affirming the model's potential for binary threat identification. Comparative analyses highlight the pivotal role of feature extraction, while experimentation illustrates the enhancement achieved by incorporating the synergistic deep autoencoder-Stacked LSTM approach.

Comparative analysis of intermittent and continuous energy restriction in obesity management

An alternative to continuous energy restriction in the medical nutrition treatment of obesity, which has come to the fore in recent times, is called intermittent fasting. The aim of this study is to compare the effects of intermittent energy restriction (IER) and continuous energy restriction (CER) practices on body composition and anthropometric measurements in overweight and first-degree obese individuals. The comparison study was conducted on 34 people aged 19-64 between April and September 2022. They had no health problems, and applied to a private institution providing nutrition consultancy. In the study, the participants were divided into two groups and were asked to maintain the IER or CER diet type for six weeks. When the participants in the two diet types were compared in terms of parameters of anthropometric measurements, body compositions, blood pressure measurements, and initial and final measurements of abdominal fat analysis, no statistically significant difference was found (pα, pβ>0.05). The mean weight loss of the participants in the IER and CER groups was 3.95±1.94 kg and 4.09±1.48 kg, respectively. The weight loss of the participants in the two diet groups was similar. The long-term efficacy and safety of time-restricted feeding in weight loss is still unclear. More comprehensive research is needed on this subject.

Patch in the ritual and daily practice of the Turkic people

The article is devoted to the study of the patch as a stable tradition of the spiritual and material-artistic culture of the Turkic peoples. The patch is considered as a "participant" in ritual and ritual practice and manual artistic craft. The scientific novelty of the results lies in the fact that for the first-time sources on the ritual and everyday practice of the Turkic peoples with the participation of the patch have been systematized. The collected material is summarized and formed as a comprehensive study. The reconstruction of the functions and the role of the patch in the ritual and daily practice of the Turkic peoples was carried out on the basis of a comparative analysis. The general and distinctive features in the artistic and technological techniques of patchwork technique are established and systematized. Ethnographic materials were collected during field research in the period 2019-2021 and introduce a set of factual sources into the history of art of the peoples of Central Asia. The findings can be used in developments in the field of ethnoculturology of the Turkic world.

Comparative Analysis of Machine Learning Algorithms for Alzheimer’s Disease Classification Using EEG Signals and Genetic Information

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Performance Characterization of a Slotted Wind Turbine Airfoil Featuring Passive Blowing

Renewable energy is crucial for a sustainable future, and wind energy holds significant potential as a viable solution to meet global demands. The current study employs high-fidelity Computational Fluid Dynamics (CFD) simulations to investigate the unsteady aerodynamics of an S809 wind turbine airfoil carved with an inclined contracting curved slot at the mid-chord location. The numerical analyses cover a wide range of angles of attack, α = 0°-20°, at a chord-based Reynolds number of Rec = 5×105. The slotted airfoil exhibits superior aerodynamic performance over moderate-to-high angles of attack (α > 6°), with increased lift, reduced drag, and enhanced glide ratio of up to 1.3x, 0.5x, and 3.7x respectively, compared to the baseline. The slot-induced flow control significantly suppresses flow transition and separation across the airfoil surfaces. The attached-flow regime impedes the formation of flow anomalies, resulting in enhanced aerodynamic performance. Comparative analyses reveal significant reductions in velocity fluctuations, Reynolds shear, vorticity, and turbulent kinetic energy, across the slotted airfoil surface and wake region. The aeroacoustic analysis of the slotted airfoil exhibits substantial reduction in the far-field noise over moderate-high angles of attack, with an overall noise level reduction of approximately 14 decibels recorded at α = 17°. Overall, this study highlights the potential of slot-induced flow control as an effective strategy for enhancing the aerodynamic performance of wind turbines.

Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data

The investigation of time series data forecasting is a critical topic within the realms of economics and business. The autoregressive integrated moving average (ARIMA) model has been prevalently utilized, notwithstanding its limitations, which include the necessity for a substantial quantity of data points and the presumption of data linearity. However, with recent developments, the long short-term memory (LSTM) network has emerged as a promising alternative, potentially overcoming these limitations. The objective of this study is to determine an effective approach for managing time series data characterized by volatility and missing values. Evaluation was conducted using RMSE for accuracy assessment, and the execution time measured using the Python Timeit library. The findings indicates that in a dataset comprising 60 data points, the LSTM model (RMSE 0.037618) surpasses the ARIMA model (RMSE 0.062667) in terms of accuracy. However, this trend reverses in a larger dataset of 228 data points, where the ARIMA model demonstrates superior accuracy (RMSE 0.006949) compared to the LSTM model (RMSE 0.036025). In scenarios with missing data, the LSTM model consistently outperforms the ARIMA model, although the accuracy of both models diminishes with an increase in the number of missing values. The ARIMA model significantly outpaces the LSTM model.

Antenatal care coverage and early childhood mortality in Zimbabwe: new interpretations from nationally representative household surveys.

Zimbabwe has implemented universal antenatal care (ANC) policies since 1980 that have significantly contributed to improvements in ANC access and early childhood mortality rates. However, Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), two of Zimbabwe's main sources of health data and evidence, often provide seemingly different estimates of ANC coverage and under-five mortality rates. This creates confusion that can result in disparate policies and practices, with potential negative impacts on mother and child health in Zimbabwe. We conducted a comparability analysis of multiple DHS and MICS datasets to enhance the understanding of point estimates, temporal changes, rural-urban differences and reliability of estimates of ANC coverage and neonatal, infant and under-five mortality rates (NMR, IMR and U5MR, separately) from 2009 to 2019 in Zimbabwe. Our two samples z-tests revealed that both DHS and MICS indicated significant increases in ANC coverage and declines in IMR and U5MR but only from 2009 to 2015. NMR neither increased nor declined from 2009 to 2019. Rural-urban differences were significant for ANC coverage (2009-15 only) but not for NMR, IMR and U5MR. We found that there is a need for more precise DHS and MICS estimates of urban ANC coverage and all estimates of NMR, IMR and U5MR, and that shorter recall periods provide more reliable estimates of ANC coverage in Zimbabwe. Our findings represent new interpretations and clearer insights into progress and gaps around ANC coverage and under-five mortality rates that can inform the development, implementation, monitoring and evaluation of policy and practice responses and further research in Zimbabwe.

Redefining brain tumor segmentation: a cutting-edge convolutional neural networks-transfer learning approach

Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

Điều tra bệnh hại sau thu hoạch trên giống chuối Tiêu hồng (Musa acuminata) và chuối Nam Mỹ (Musa paradisiaca) ở Việt Nam

The survey implemented in the 2021 winter season on the Tieu hong banana cultivar (Musa acuminata) grown in Phu Tho and Hung Yen provinces showed that the most severe diseases were fruit rot and fruit spot with infection rates of 79.1% and 75.5%, respectively. Peel cracking and end rot (cigar rot) were also recorded at less than 5.0%. Fruit spot was the main disease that affected the South-American banana cultivar (Musa paradisiaca) during the 2022 spring-summer season in Gia Lai province. Of the 24 fungal and 37 bacterial strains isolated from the disease samples collected during the investigation and tested for their pathogenicity via artificial infection on healthy bananas, only 17 fungal strains showed the ability of lesion formation of which 4 strains caused typical symptoms of the fruit rot disease. There was not a single bacterial strain ascertained as phytopathogen indicating that fungi were the primary agents of post-harvest diseases on bananas. These four fungal strains were taxonomically characterized based on their morphology (colony and spore), comparative analysis of the ITS sequences of the 18S rDNA gene, and were then identified as Corynespora torulosa B2B, Microdochium colombiense B6, Fusarium circinatum B13F, and Aspergillus niger GL2.1. The ITS sequences of these strains were deposited to GenBank under the accession numbers OP855524, OP855525, OP855526, and OP855527, respectively.

The importance of a semantic approach in understanding the texts of the Holy Quran and Sunnah

The relevance of the study is determined by contemporary ideas of modernisation and radicalisation of Islam, the formation of which is directly linked to the problem of interpreting the sacred texts of the Muslim world. Therefore, the aim of the research work is to determine the veracity of applying linguistic and semantic approaches to the interpretation of the Holy Quran in order to find new ways of regulating the socio-cultural space. The research was conducted on the basis of the semantic, cultural, semiotic and formal-logical approaches, using such methods of scientific knowledge as induction, deduction, linguistic analysis, systematisation, comparison, synthesis and generalisation. In the course of the research work, key narratives of contemporary Islam were analysed in terms of their specific understanding of the Holy Quran. The current state of research into the interpretation of the sacred texts of Islam is reviewed. The specifics of understanding the Holy Quran in the context of its metaphorical constructions are explored. The relationship between the linguistic structure of the Arabic language and the interpretations of the texts of the holy injunctions has been established, on the basis of which two ways of investigating the understanding of the Quran have been proposed. A comparative analysis of the research findings with other research findings in the category of modern interpretation of the Quran and Sunnah has been conducted. The trend towards digitalisation of the sacred texts of Islam, as well as the use of artificial intelligence technologies to interpret the ayahs, has been noted. The practical significance of the research work lies in proposing new methodologies for interpreting sacred texts, which can then be qualitatively applied in practice.

Performance of machine learning models to forecast PM10 levels

Machine learning techniques have garnered considerable attention in modern technologies due to their promising outcomes across various domains. This paper presents the comprehensive methodology of an optimized and efficient forecasting approach for Particulate Matter 10, specifically tailored to predefined locations. The execution of a comparative analysis involving eight models enables the identification of the most suitable model that aligns with the primary research objective. Notably, the test results underscore the superior performance of an ensemble model, which integrates state-of-the-art methodologies, surpassing the performance of the other seven state-of-the-art models. Adopting a case-specific methodology with machine learning techniques contributes to achieving a notably high regression coefficient (R²≈1) across all models. Furthermore, the study underscores the potential for future endeavors in predicting location-specific environmental factors.•This study focused on forecasting PM10 with machine learning models with the consideration of air quality factors and meteorological factors•Ensemble model was developed for the forecasting purposes with higher performance.

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

Financial medicine: A multi-dimensional concept moving towards contextually specific working definitions for use in the South African prehospital setting

IntroductionThe phenomenon that has been described as Financial Medicine has been occurring within the South African Healthcare sector for at least the last decade. Despite the ongoing effect of this phenomenon, there is no organised body of knowledge or formulated working definitions to guide knowledge sharing and theorisation within this research focus area. The practice of Financial Medicine exerts a deleterious effect on the South African prehospital healthcare system, and represents an area in dire need of focused research efforts. Establishing appropriate working definitions and associated taxonomy is an important first step in supporting further research efforts into this aspect of South African prehospital healthcare systems. MethodsA qualitative research methodology following a constructivist grounded theory design was used. Participants voluntarily consented to be enrolled into one-on-one in-depth interviews, and were selected using purposive and theoretical sampling techniques. Data was subjected to validated coding procedures and analysed using the constant comparative analysis approach, analytical diagramming, and supported by researcher theoretical sensitivity. ResultsA working definition for Financial Medicine is provided. Six new terms are introduced, with associated working definitions, namely Financial Medicine Practices, Treatment-based profiteering, Cost-of-care-aversion, Personal-gain-at-patient-expense, Money-racketeering-in-healthcare, and Impoverishing-healthcare-earning. ConclusionThe working definitions and suggested taxonomy presented in this article are the first step in formally conceptualising and theorising the phenomenon of Financial Medicine, in order to support further research and collaboration in this space.

Resource-efficient federated learning over IoAT for rice leaf disease classification

Rice is an important staple food in Asia. It is produced and consumed in large quantities. It contributes to 15% of protein intake and 21% of total per capita energy intake in the region, underscoring its important role as a primary global food source. Conversely, rice plants are heavily affected by bacterial, fungal and other microbial diseases, resulting in reduced plant health and crop yields and posing a major challenge for rice farmers. Manual diagnosis of these diseases is particularly problematic in regions with a shortage of agricultural experts. Farmers with insufficient experience sometimes incorrectly identify these diseases by hand. Recent advances in deep learning (DL) models offer a promising solution through automatic image recognition systems that can be very helpful in accurately identifying these diseases. This manuscript presents a resource-efficient federated learning IoAT (Internet of Agriculture Things) approach for rice leaf disease classification. This approach incorporates two key strategies, namely federated transfer learning and feature extraction, and evaluates their performance on various metrics such as accuracy, loss, precision, recall, AUC, and resource-related parameters such as GPU memory consumption, virtual memory, CPU and GPU process utilization. The dataset used in the study includes 5932 images of rice leaf diseases categorized as bacterial leaf blight, blast, brown spot and tungro. The research investigates the application of federated transfer learning and federated feature extraction techniques to classify rice leaf disease images. It performs a comparative analysis of their performance and resource utilization. EfficientNetB3, with an impressive validation accuracy of 99%, is identified as the base model for the federated learning (FL) environment. Furthermore, we implement FL using both transfer learning and feature extraction methods and compare their results on a number of performance and resource related parameters. It is shown that the federated feature extraction approach has lower GPU memory and process utilization compared to the federated transfer learning approach and is thus more resource efficient. Extracting features before model training results in a lightweight model, which is especially beneficial for FL, IoAT devices that require efficient execution on edge devices. Therefore, the proposed approach is presented as a lightweight, privacy-friendly and resource-efficient solution for rice leaf disease classification.

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

Decoding sarcasm: unveiling nuances in newspaper headlines

This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.