Mixing Technique Research Articles

Parallel convolutional SpinalNet: A hybrid deep learning approach for breast cancer detection using mammogram images

ABSTRACT Breast cancer is the foremost cause of mortality among females. Early diagnosis of a disease is necessary to avoid breast cancer by reducing the death rate and offering a better life to the individuals. Therefore, this work proposes a Parallel Convolutional SpinalNet (PConv-SpinalNet) for the efficient detection of breast cancer using mammogram images. At first, the input image is pre-processed using the Gabor filter. The tumour segmentation is conducted using LadderNet. Then, the segmented tumour samples are augmented using Image manipulation, Image erasing, and Image mix techniques. After that, the essential features, like CNN features, Texton, Local Gabor binary patterns (LGBP), scale-invariant feature transform (SIFT), and Local Monotonic Pattern (LMP) with discrete cosine transform (DCT) are extracted in the feature extraction phase. Finally, the detection of breast cancer is performed using PConv-SpinalNet. PConv-SpinalNet is developed by an integration of Parallel Convolutional Neural Networks (PCNN) and SpinalNet. The evaluation results show that PConv-SpinalNet accomplished a superior range of accuracy as 88.5%, True Positive Rate (TPR) as 89.7%, True Negative Rate (TNR) as 90.7%, Positive Predictive Value (PPV) as 91.3%, and Negative Predictive Value (NPV) as 92.5%.

CA2CL: Cluster-Aware Adversarial Contrastive Learning for Pathological Image Analysis.

Pathological diagnosis assists in saving human lives, but such models are annotation hungry and pathological images are notably expensive to annotate. Contrastive learning could be a promising solution that relies only on the unlabeled training data to generate informative representations. However, the majority of current methods in contrastive learning have the following two issues: (1) positive samples produced through random augmentation are less challenging, and (2) false negative pairs problem caused by negative sampling bias. To alleviate the above issues, we propose a novel contrastive learning method called Cluster-Aware Adversarial Contrastive Learning (CA2CL). Specifically, a mixed data augmentation technique is provided to learn more transferable representations by generating more discriminative sample pairs. Furthermore, to mitigate the effects of inherent false negative pairs, we adopt a cluster-aware loss to identify similarities between instances and incorporate them into the process of contrastive learning. Finally, we generate challenging contrastive data pairs by adversarial learning, and adversarially learn robust representations in the representation space without the labeled training data, which aims to maximize the similarity between the augmented sample and the related adversarial sample. Our proposed CA2CL is evaluated on two public datasets: NCT-CRC-HE and PCam for the fine-tuning and linear evaluation tasks and on two other public datasets: GlaS and CARG for the detection and segmentation tasks, respectively. Extensive experimental results demonstrate the superior performance improvement of our method over several Self-supervised learning (SSL) methods and ImageNet pretraining particularly in scenarios with limited data availability for all four tasks. The code and the pre-trained weights are available at https://github.com/junjianli106/CA2CL.

Self-targeted nanosystem for enhanced chemodynamic cancer therapy.

Chemodynamic therapy (CDT) could have a significant potential for advancing cancer treatment via the utilization of Fenton and Fenton-like reactions, which produce toxic reactive species. Nonetheless, the efficacy of CDT is constrained by the limited availability of catalyst ions capable of decomposing pre-existing intracellular H2O2 and generating reactive oxygen species (ROS) necessary to achieve a therapeutic response. To address these limitations, a tailored strategy has been developed to enhance the efficacy of Fenton-like reactions to eradicate selectively cancer cells. This innovative approach involves the utilization of dual metal cations (Zn2+, Fe2+) within zinc nitroprusside (ZnNP) material. Remarkably, this method takes advantage of the acidic conditions prevalent in tumors, thus eliminating the need for external stimuli. Through these advancements, the tailored approach exhibits the potential to specifically target and eliminate cancer cells, overcoming the mentioned limitations. A simple mixing technique was utilized to synthesize ZnNP, which was structurally and morphologically characterized. Furthermore, extensive in vitro investigations were conducted to assess its anti-tumoral mechanism of action. ZnNP exhibits a remarkable capability to increase intracellular H2O2 within cells. This process leads to the generation of various reactive species, including hydroxyl (˙OH) and superoxide (O2˙-) radicals, and peroxynitrite (ONOO-), which act as apoptotic inducers specifically targeting cancer cells. Cellular uptake studies have shown that ZnNP enters the lysosomes, evades degradation, and takes advantage of their acidic pH environment to significantly increase the production of ROS. These findings are further supported by the activation of multiple oxidative genes. Furthermore, the biocompatibility of ZnNP has been demonstrated in ex vivo models using healthy liver cells. Notably, ZnNP exhibited therapeutic effectiveness in high-grade serous ovarian cancer (HGSOC) patient-derived tumor organoids (PDTO), further confirming its potential as a therapeutic agent. The present study highlights the therapeutic potential of ZnNP as a generator of multiple ROS via a Fenton-like reaction. This research offers a promising therapeutic approach for CDT application in combatting HGSOC, a highly aggressive and life-threatening cancer.

Mixed uncertainty analysis in pressure systems inspection applications

Abstract Pressure systems contain hazardous fluids within industrial processes. Inspection plays a vital role in managing the reliability of these safety-critical systems. It allows engineers to identify, characterize and manage degradation of pressure vessels, piping, and associated equipment to prevent failure and the associated consequences on people and the environment. Mixed uncertainty can affect decision-making in at least three distinct aspects of inspection practice: inspection planning, inspection data analysis and integrity assessment. Despite this, the inspection engineering discipline lacks methodologies for handling both aleatory and epistemic uncertainties within analyses, which could be expensively misleading. This paper demonstrates the benefits of applying mixed uncertainty quantification and analysis techniques to pressure vessel inspection and integrity assessment through a worked example, which shows how the epistemic and aleatory uncertainty in inspection data can be represented using an imprecise probability approach. The limitations of empirical data are shown to pose challenges to implementing these techniques in practice, and so practical requirements for a framework for implementing uncertainty analysis methods in inspection are proposed. These include, for example, the ability to generate meaningful yet conservative results from even a limited amount of poor-quality data, while allowing results to be bounded more narrowly as more data is collected, findings from better data are pooled or engineering judgment and assumptions are applied.

Fear of Making Mistakes and its Effect on Pre-Service English Teachers' Oral Communication Skills

This study investigates how pre-service English teachers' oral communication abilities are affected by their anxiety about making mistakes. Data were gathered via surveys, interviews, classroom observations, and visual narratives using an explanatory sequential mixed techniques methodology. The results show that students' confidence and willingness to participate in oral activities are greatly impacted by their fear of making mistakes, which can result in anxiety, mental blocks, and avoidance strategies. Peer and instructor presence was found to be a significant contributing element to this fear, as students feared being mocked or criticized. As a result, they had fewer possibilities for practice and development because their speaking abilities were hindered. The study emphasizes the necessity of a supportive classroom setting where errors are accepted as a necessary learning component. Promoting group projects and scaffolding methods. Promoting teamwork and using scaffolding strategies would help pre-service teachers become more confident and improve their oral communication abilities. The results highlight the significance of addressing anxiety in teacher preparation programs and advance our understanding of emotional barriers in language acquisition.

Characterizing heterogeneity in Alzheimer’s disease progression: a semiparametric model

The progression of Alzheimer’s disease (AD), a leading cause of dementia worldwide, is known for its variability and complexity, challenging the conventional methods of monitoring and predicting disease trajectories. This study introduces a semiparametric modeling approach to analyze longitudinal cognitive and imaging data. We studied two different outcome variables from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database: the Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS13) scores and ventricular volumes . Unlike traditional linear mixed effects models, semiparametric models do not assume a linear AD progression over time. Semiparametric models offer the advantage of capturing the non-linear features of AD progression, such as cognitive decline and neurodegeneration, represented by changes in ADAS13 scores and ventricular enlargement, respectively. By integrating regression splines and mixed modeling techniques, we provide a nuanced understanding of AD progression that captures the heterogeneity of disease trajectories. Our analysis reveals variations in the timing and degree of cognitive decline and neurodegeneration among AD patients, underlining the need for personalized approaches for monitoring and managing AD. This study’s findings contribute to the modeling of AD progression and offer potential implications for interventions and prognostic assessments in clinical and research settings.

Implications of East-West Great Power Competition on Kenya’s Defense and Security Policy (2003 – 2022)

This study sought to investigate the implications of East-West Great Power Competition on Kenya’s defense and security policy between 2003 and 2022. The first section introduces the nature of East-West strategic competition and the second section analyses the implications of East-West strategic competition on Kenya defense and security policies. Four key areas of Kenya’s Defense Policies are analyzed. These are: firstly, policies on military assistance to Kenya, secondly, Kenya’s peace support operation policies, thirdly, Kenya’s policies on military and security allies and fourthly, Kenya’s counter-terrorism policies. The study is anchored on realism and the rational actor model of international relations. Review of related literature was done using various search engines utilizing the keywords of the study. A mixed research design and sampling techniques were used to generate a sample of eighty study participants from a target population that included policy makers in the Ministry of Defense, Ministry of Foreign and Diaspora Affairs, Non-governmental organizations, National Government Officials, and international relations experts. The main methods for gathering data were questionnaires and interviews. This study revealed that, the implications of East-West Strategic competition on Kenya’s Défense policy include rationality in decision making in regards to military assistance the country receives, a conscious choice of possible trading partners during procurement and acquisition of military equipment and arms, selective entry and signing of military pacts on military training and technology transfers. Moreover, the Kenya defense policy is non-aligned in regards to choice of allies. The defense policy further seeks to maintain traditional allies in the West while also courting of new allies in the East. Finally, the defense policy is assertive in fostering international peace and security as well in counter violent extremism. This study recommends Kenya defense policy strategist to maintain the status quo of balancing the East and the West so as to remain neutral in the wake revamped East West rivalry in the Horn and Eastern African region.

Can homegarden commercialization contribute to food security in Sahel? Case study from northern Ghana

Policymakers and development organizations have widely identified homegardening as an activity that can alleviate global pressure on food production and food security in developing countries. In Sub-Saharan Africa, studies on homegardens have examined species diversity and conservation, as well as factors influencing species composition and landscape management. However, little attention has been given to understanding the relationship between food security and commercialization. Therefore, this study examines for the role of commercialization of homegardens and its potential to contribute to food security in northern areas of Ghana. A mixed sampling technique was employed to identify and interview 120 homegardeners. Probit regression analysis revealed that homegarden size, ownership status, and water availability, together with farmer’s age and gender, were statistically significant factors influencing market orientation. Propensity score matching and endogenous treatment regression showed that a higher degree of market orientation of homegarden produce significantly contributes to the food security of farmers. Gender, educational status, and access to input subsidies influenced the food security situation of the homegardeners. Our findings suggest that government and development agencies should promote and support homegardens as a promising farming practice that enhances rural development, food security and overall livelihood resilience in Sahel region, such as northern Ghana.

Invited review: A review of some commonly used meta-analysis methods in dairy science research.

Meta-analyses have become increasingly common, providing meaningful summaries of cumulative knowledge in the dairy science literature. Some of the corresponding meta-analytic techniques have been developed by knowledgeable dairy scientists, some of which predate tractable likelihood-based random or mixed effects model meta-analytic techniques and associated software developed by statisticians. This review compares various meta-analytic techniques on aggregate data (i.e., study-specific treatment effect or slope estimates and their standard errors) generated from simulated data involving regression, completely randomized designs (CRD), and Latin square design scenarios. In all cases, meta-estimates generated from the analysis of individual performance data (IPD), using the same statistical model as that used to simulate the data, were considered to be gold-standard references for meta-estimates derived from various meta-analysis strategies on aggregate data. In all cases, likelihood-based techniques outperformed techniques developed by dairy scientists for meta-estimate proximity to corresponding IPD estimates. An extensive simulation study comparing meta-analytic techniques within a CRD framework indicated that these advantages widen with increasing study heterogeneity in effect sizes, smaller number of experimental replicates (i.e., cows) per treatment per study, and lower within-study variability; nevertheless, the impact of meta-analytic methods on estimated standard errors of these meta-estimates were rather trivial. To best utilize aggregate data from Latin square studies in meta-analyses, a concerted effort is required to recover standard errors of mean differences rather than the standard errors of the means themselves. Perhaps the most compelling reason for choosing likelihood-based methods for meta-analysis is their ability to provide reliable prediction intervals on effect sizes, noting that these intervals are currently under-reported in the dairy science literature. Compared with the reporting of meta-estimates and their standard errors, prediction intervals provide a far more appropriate indication of uncertainty on treatment effects in future studies and should be greater emphasized in extension or outreach efforts. Although R software packages such as metafor are readily available for likelihood-based methods, both SAS and R code for linear mixed models can be readily modified to facilitate these analyses as demonstrated extensively in the supplemental materials of this review.

Effect of initial mixing process and calcination temperature on magnetic properties of La–Ca–Co doped strontium ferrite

This study investigates the significant correlation between preparation techniques, calcination temperatures, and the magnetic properties of La–Ca–Co-doped strontium ferrite. By comparing typical dry mixing and wet mixing processes and investigating the effects of different calcination temperatures, this work reveals detailed insights into how to optimize the magnetic performance of these materials. Results show that dry mixing at 1260 °C produces the largest magnetic energy product (35.4 kJ/m3) when sintered at 1225 °C, with a maximum remanence of 437 mT and coercivity of 384 kA/m. Conversely, wet mixing at 1230 °C produces the optimal magnetic performance as a coercivity of 415 kA/m, remanence of 435 mT and (BH)max of 33.4 kJ m−3 at a sintering temperature of 1215 °C. Wet mixing is better for low-temperature calcination due to reduced particle size, greater specific surface area, and lower energy needs. The dry mixing technique at 1260 °C and the wet mixing process at 1230 °C yield good magnetic characteristics for La–Ca–Co doped strontium ferrite. The importance of calcination temperatures and preparation methods on the magnetic properties of doped ferrite is highlighted in this research.

ENHANCED SOIL PILES WITH PORTLAND CEMENT-RHA-MWCNT MIX FOR DEEP MIXING METHODS IN LOW-RISE BUILDING AND BRIDGE FOUNDATIONS

Soft soil's low strength and high deformation characteristics pose significant challenges in low-rise building and bridge constructions. This research introduces an innovative method to fortify soft soil using Portland cement (PC), rice husk ash (RHA), and multi-walled carbon nanotubes (MWCNTs) as additives for reinforced soil pile (RSP) development through a deep mixing technique. The physico-mechanical characterization result reveals a remarkable 83% (1075 to 1962 kg/m3) increase in average density and a significant 405% (4.14 to 20.92 MPa) enhancement in compressive strength after 28 days of curing, achieved with the addition of only 0.04 wt% MWCNT to the soil-PC-RHA mixture. Furthermore, a large-scale static load test was conducted to assess its translational efficiency in a real-world scenario. The result revealed a comparable compressive strength between the laboratory-scale and large-scale implementation (20.9 MPa vs. 23.4 MPa). This substantial improvement, attributed to the synergistic effects of MWCNT, surpasses the performance of traditional soil-PC-RHA blends in the literature. By leveraging the distinctive attributes of PC, RHA, and MWCNT, this eco-friendly technology offers a promising alternative for high-strength low-rise construction, addressing the inherent limitations of soft soils and presenting transformative potential with far-reaching implications for geotechnical engineering and sustainable infrastructure development.

The Role of Artificial Intelligence in Transforming Public Relations Practices: Insights from the UAE

AI (Artificial Intelligence) has turned out to be a life-changing technology in PR (Public Relations), and in other fields of strategic communications, even though its adoption and applications remain undiscovered. This research therefore addresses the gap by leveraging the (UTAUT) Unified Theory of Acceptance and Use of Technology model to investigate the integration and acceptance of AI among the PR professionals in the UAE (United Arab Emirates). Using a mixed method technique, data was collected via interviews and surveys with 103 PR practitioners, offering extensive insights into aspects that drive and hinder AI (Artificial Intelligence) adoption. The research findings reveal that AI technologies notably improve PR functions by modernizing content creation to encourage robust stakeholder relationships, while enabling real time audience engagements. Key aspects that impact adoption is inclusive of ease of use, the ability to effectively customize messaging, and operational efficiency. Nevertheless, barriers like ethical concerns, limited training, and inefficient regulation implementation policies persist. This study pinpoints the potential of AI to revolutionize PR practices while stressing the need for targeted strategies and policies to address the adoption challenges. Through contribution to the body of understanding on AI in PR, the research offers actionable perceptions for practitioners and policy makers who aim to use AI’s opportunities effectively and responsibly.

Influence of filler mixing technique on the properties of silica-filled natural rubber vulcanizates

Influence of filler mixing technique on the properties of silica-filled natural rubber vulcanizates

Square-shaped Cu2MoS4 loaded on three-dimensional flower-like AgBiS2 to form S-scheme heterojunction as a light-driven photoelectrochemical sensor for efficient detection of serotonin in biological samples.

Square-shaped Cu2MoS4 loaded on three-dimensional flower-like AgBiS2 to form S-scheme heterojunction as a light-driven photoelectrochemical sensor for efficient detection of serotonin in biological samples.

Quantitative assessment of dispersion stability and processing parameters in graphene‐enhanced unsaturated polyester resins: Effect of mixing techniques

Quantitative assessment of dispersion stability and processing parameters in graphene‐enhanced unsaturated polyester resins: Effect of mixing techniques

High Spatiotemporal Resolution Monitoring of Water Body Dynamics in the Tibetan Plateau: An Innovative Method Based on Mixed Pixel Decomposition.

The Tibetan Plateau, known as the "Third Pole" and the "Water Tower of Asia", has experienced significant changes in its surface water due to global warming. Accurately understanding and monitoring the spatiotemporal distribution of surface water is crucial for ecological conservation and the sustainable use of water resources. Among existing satellite data, the MODIS sensor stands out for its long time series and high temporal resolution, which make it advantageous for large-scale water body monitoring. However, its spatial resolution limitations hinder detailed monitoring. To address this, the present study proposes a dynamic endmember selection method based on phenological features, combined with mixed pixel decomposition techniques, to generate monthly water abundance maps of the Tibetan Plateau from 2000 to 2023. These maps precisely depict the interannual and seasonal variations in surface water, with an average accuracy of 95.3%. Compared to existing data products, the water abundance maps developed in this study provide better detail of surface water, while also benefiting from higher temporal resolution, enabling effective capture of dynamic water information. The dynamic monitoring of surface water on the Tibetan Plateau shows a year-on-year increase in water area, with an increasing fluctuation range. The surface water abundance products presented in this study not only provide more detailed information for the fine characterization of surface water but also offer a new technical approach and scientific basis for timely and accurate monitoring of surface water changes on the Tibetan Plateau.

Production and Shielding Effectiveness Features of Chopped Strands Backed-GdMnO3 Composites for 6.5-17.5 GHz Applications.

This research investigates the synthesis and characterization of GdMnO3/chopped strands composites using the conventional oxide mixing technique. A single-phase GdMnO3 compound was successfully formed through sintering at 1350 °C for 20 h. Structural analysis using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) confirmed phase purity and uniform grain morphology. The microwave shielding effectiveness of GdMnO3/chopped strands composites was evaluated within the 6.5-17.5 GHz frequency range using a network analyzer (NA). The GdMnO3/chopped strands composite with a 60-40% weight ratio exhibited superior shielding performance, achieving a minimum shielding effectiveness of -35.61 dB at 6.9 GHz, while the 80-20% composite reached -32.54 dB at 16.74 GHz. Both compositions demonstrated shielding effect values below -10 dB across wide frequency bands, with significant attenuation below -20 dB at various GHz ranges. The study demonstrates that by adjusting the content of the components in the samples, the microwave shielding effect performance of the GdMnO3/chopped strands composites can be easily controlled to meet the requirements of specific frequency bands. These findings highlight the potential of GdMnO3-based composites for tailored microwave shielding applications, particularly in the military, aerospace, and telecommunication industries.

Application of recycled high-density polyethylene for construction of non-structural wattle and daub walls

The evolution of construction techniques has spanned millennia, integrating locally sourced materials with innovative technological advancements. The use of earth in construction demonstrates remarkable versatility, exhibiting plasticity when wet and robust solidity upon drying. The environmental sustainability and abundant availability of earth further reinforce its role in architectural practices. This research explores the integration of recycled thermoplastic technology with traditional earthen construction methods, focusing on non-structural walls combining thermoplastic frameworks and mixed earth techniques. The study places particular emphasis on recycled High-Density Polyethylene (r-HDPE), a material that has garnered acclaim for its role in sustainable development through the reuse of secondary raw materials and its high moldability. The primary function of r-HDPE in this context is as a supplemental framework supporting earthen materials. Utilizing recycling and production methods from Precious Plastic, the research emphasizes the prototyping of r-HDPE structures with diverse configurations, digitally designed to create components ranging from beams to intricate truss networks. The earthen mortar comprises fine and coarse sand, wheat straw, and lime, adhering to traditional mixed earth construction practices. While the materials share structural limitations with conventional mixed earth methods, the potential addition of composites to thermoplastics could improve strength but hinder mechanical recyclability, favoring chemical recycling as an alternative. This study prioritizes recyclability with minimal environmental impact, limiting its scope to mechanical recycling without composite additives. The objective of this study is to achieve a balance between innovative applications of recycled thermoplastics and long-term environmental stewardship, thereby enhancing the synergy between sustainability and construction practices.

Rapid, Highly Sustainable Ring-Opening Polymerization via Resonant Acoustic Mixing.

Reported herein is the first combination of resonant acoustic mixing (RAM) and controlled ring-opening polymerization (ROP) to deliver fully sustainable, end-functionalized, biodegradable polymers via a manufacturing route with a much-reduced environmental impact. This includes the successful use of agriculturally sourced functionalized initiators (terpene alcohols) in ROP synthesis of cyclic esters to generate an array of novel, biodegradable polyesters applicable to numerous biomedical applications, such as drug delivery. Furthermore, RAM was utilized as a novel mixing technique, resulting in a synthetic process that was conducted: (a) with minimal use of toxic, flammable, costly, and environmentally detrimental solvents, (b) in the absence of organometallic catalysts, and (c) with significantly shorter ROP reaction times and temperatures. Consequent comparison with conventional magnetic stirring or sonication-based mixing methods showed that RAM allowed the more facile, kilogram-scale synthesis of polyesters via reactions conducted at room temperature rather than 150 °C and without the need for a metal catalyst. As a proof of concept, the polymers were used to encapsulate bovine serum albumin as a model protein, and its release was measured using an automated, high-throughput protein assay. This study demonstrated that the headgroup chemistry appears to affect the release rate of protein from the polymers.

Stabilising highly expansive soil by using Nano-Clay additive

ABSTRACT This study aims to investigate the efficacy of hydrophilic bentonite Nano-Clay in improving the geotechnical characteristics of expansive soil sourced from the Al-Mushaqar region in Jordan. In this study sonic wave-assisted and manual mixing techniques applied to expansive soil specimens. Subsequently, a comparative analysis was conducted to explain the disparities in outcomes between these two mixing methods. Various proportions of Nano-Clay were introduced into the soil, ranging from 0% to 2.5% by dry weight of the soil. The study focused on evaluating their influence on key parameters, including compaction, Unconfined Compressive Strength (UCS), and Free Swell Index (FSI). The introduction of Nano-Clay into the soil exhibited a noteworthy enhancement in its strength characteristics, accompanied with a reduction in its swelling tendencies. Notably, the application of 0.5% Nano-Clay during sonication led to a remarkable 53.4% augmentation in UCS and a simultaneous 41% reduction in FSI. In contrast, non-sonicated samples treated with 1% Nano-Clay exhibited a notable 35.21% increase in UCS and a substantial 52.4% reduction in FSI. These findings underscore the significant potential of Nano-Clay in fortifying expansive soils, thereby offering promising prospects for geotechnical engineering applications.