Contrast Features Research Articles

ECCT: Efficient Contrastive Clustering via Pseudo-Siamese Vision Transformer and Multi-view Augmentation

Image clustering aims to divide a set of unlabeled images into multiple clusters. Recently, clustering methods based on contrastive learning have attracted much attention due to their ability to learn discriminative feature representations. Nevertheless, existing clustering algorithms face challenges in capturing global information and preserving semantic continuity. Additionally, these methods often exhibit relatively singular feature distributions, limiting the full potential of contrastive learning in clustering. These problems can have a negative impact on the performance of image clustering. To address the above problems, we propose a deep clustering framework termed Efficient Contrastive Clustering via Pseudo-Siamese Vision Transformer and Multi-view Augmentation (ECCT). The core idea is to introduce Vision Transformer (ViT) to provide the global view, and improve it with Hilbert Patch Embedding (HPE) module to construct a new ViT branch. Finally, we fuse the features extracted from the two ViT branches to obtain both global view and semantic coherence. In addition, we employ multi-view random aggressive augmentation to broaden the feature distribution, enabling the model to learn more comprehensive and richer contrastive features. Our results on five datasets demonstrate that ECCT outperforms previous clustering methods. In particular, the ARI metric of ECCT on the STL-10 (ImageNet-Dogs) dataset is 0.852 (0.424), which is 10.3% (4.8%) higher than the best baseline.

CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation.

Some cognitive research has discovered that humans accomplish event segmentation as a side effect of event anticipation. Inspired by this discovery, we propose a simple yet effective end-to-end self-supervised learning framework for event segmentation/boundary detection. Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundaries by reconstruction errors. This is consistent with the fact that humans spot new events by leveraging the deviation between their prediction and what is perceived. Thanks to their heterogeneity in semantics, the frames at boundaries are difficult to be reconstructed (generally with large reconstruction errors), which is favorable for event boundary detection. In addition, since the reconstruction occurs on the semantic feature level instead of the pixel level, we develop a temporal contrastive feature embedding (TCFE) module to learn the semantic visual representation for frame feature reconstruction (FFR). This procedure is like humans building up experiences with "long-term memory." The goal of our work is to segment generic events rather than localize some specific ones. We focus on achieving accurate event boundaries. As a result, we adopt the F1 score (Precision/Recall) as our primary evaluation metric for a fair comparison with previous approaches. Meanwhile, we also calculate the conventional frame-based mean over frames (MoF) and intersection over union (IoU) metric. We thoroughly benchmark our work on four publicly available datasets and demonstrate much better results. The source code is available at https://github.com/wang3702/CoSeg.

Rethinking inter-firm dynamics from a small firm perspective: the case for inter-organizational bullying

PurposeThis paper aims to explain how the dynamics of inter-firm relations between small and large firms can, in the case of some behaviours, be interpreted as inter-organizational bullying.Design/methodology/approachThis paper draws on a qualitative approach adopting the critical incident method to explore the subjective experiences of 13 individual managers and owners of small service businesses in dealing with the representatives/executives of the large corporations they serviced. The method facilitated an investigation of the significant occurrences identified by the small-firm respondents about the undue advantage taken by the large firms. This was found to be more than simple occasional opportunistic or unfair business practices perpetrated by representatives of the large firms but, instead, involved bullying.FindingsThe results revealed that large corporations actively, though covertly, sought to take advantage of their small service providers by resorting to bullying practices. Intimidation, opportunism, use of deceitful or unfair business practices, as well as abuse of power, were manifestations of inter-organizational bullying committed by the large and powerful corporations. The contrasting characteristics of size, access to resources, economic and market power were identified as strong impediments against building effective ethical relational exchanges between the large corporations and their small service providers.Research limitations/implicationsThe study's findings provide valuable insights into the root causes and consequences of inter-organizational bullying. However, it is crucial to interpret these results in the context of this specific study. It is worth nothing that these findings primarily represent the self-perception of inter-organizational bullying among small service providers and may not capture other viewpoints or aspects of the industrial sector. Replicating this study in different sectors could enhance the generalizability of the conclusions drawn.Practical implicationsThis analysis is valuable in understanding what constitutes the phenomenon referred to as inter-organizational bullying. It also assists to understand the conditions when large firms exhibit such behaviours and their implications on the well-being of relevant stakeholders.Social implicationsFirstly, the business partners should maintain a healthy relationship if they want to avoid incidents of bullying, which can harm the performance of the relationship. In doing so, they need to reduce the level of uncertainty in their business relationships through the transparent information exchange, formulating commonly agreeable contracts and enhancing communication procedures. They also need to put aside their self-interest, but rather strive for achieving results that will be beneficial to both parties.Originality/valueThis exploratory study offers a novel and unexplored way of theorizing inter-organizational bullying, as well as uncovering its antecedents and impacts on the welfare of small businesses, particularly small service providers.

Hard sample mining-enabled supervised contrastive feature learning for wind turbine pitch system fault diagnosis

Abstract The presence of multiple failure severities in the wind turbine pitch system due to the long-term wear and tear poses challenges in accurately classifying the system’s health condition, thus increasing maintenance costs or damage risks. This paper proposes a novel method based on hard sample mining (HSM)-enabled supervised contrastive learning to address this problem. The proposed method leverages the powerful feature extraction capabilities of supervised contrastive learning to extract discriminative features from highly imbalanced data. Additionally, the method incorporates a cosine similarity-based HSM framework that constructs hard sample pairs within mini-batches during both the representation learning and classifier training phases, thereby improving the diagnostic performance of the model for hard samples. The proposed method achieves macro G-mean of 0.9991 and 0.9971 on two real datasets containing data on wind turbine pitch system cog belt fractures. These results indicate significantly superior fault diagnosis performance compared to existing methods, highlighting its potential for enhancing the reliability and efficiency of fault diagnosis in wind turbine pitch systems.

Conveyor CVD to high-quality and productivity of large-area graphene and its potentiality

The mass production of high-quality graphene is required for industrial application as a future electronic material. However, the chemical vapor deposition (CVD) systems previously studied for graphene production face bottlenecks in terms of quality, speed, and reproducibility. Herein, we report a novel conveyor CVD system that enables rapid graphene synthesis using liquid precursors. Pristine and nitrogen-doped graphene samples of a size comparable to a smartphone (15 cm × 5 cm) are successfully synthesized at temperatures of 900, 950, and 1000 °C using butane and pyridine, respectively. Raman spectroscopy allows optimization of the rapid-synthesis conditions to achieve uniformity and high quality. By conducting compositional analysis via X-ray photoelectron spectroscopy as well as electrical characterization, it is confirmed that graphene synthesis and nitrogen doping degree can be adjusted by varying the synthesis conditions. Testing the corresponding graphene samples as gas-sensor channels for NH3 and NO2 and evaluating their response characteristics show that the gas sensors exhibit polar characteristics in terms of gas adsorption and desorption depending on the type of gas, with contrasting characteristics depending on the presence or absence of nitrogen doping; nitrogen-doped graphene exhibits superior gas-sensing sensitivity and response speed compared with pristine graphene.

Neuromyelitis optica spectrum disorder: Exploring the diverse clinical manifestations and the need for further exploration.

Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune disorder characterized by inflammatory assaults on the central nervous system (CNS), particularly on the optic nerves and spinal cord. In recent years, a wider range of clinical manifestations of this complex disease have been observed, emphasizing the importance of gaining a more profound understanding beyond optic neuritis (ON) and transverse myelitis (TM). This study explores the many clinical symptoms of NMOSD, including common and uncommon presentations. Distinctive aspects of ON, TM, and diencephalic/brainstem syndromes are examined, highlighting their unique characteristics in contrast to conditions such as multiple sclerosis. We also discuss extra-CNS involvement, such as unusual signs, including muscle involvement, retinal injury, auditory impairment, and rhinological symptoms. Our study intends to highlight the wide range and complexity of NMOSD presentations, emphasizing the significance of identifying unusual symptoms for precise diagnosis and prompt management. The specific processes that contribute to the varied clinical presentation of NMOSD are not well understood despite existing information. This emphasizes the necessity for more study to clarify the mechanisms that cause different symptoms and discover new treatment targets for this complex autoimmune disorder. It is essential to acknowledge the complex and varied clinical manifestations of NMOSD to enhance diagnosis, treatment, and patient results. By enhancing our comprehension of the fundamental processes and investigating innovative therapeutic approaches, we may aim to enhance the quality of life for persons impacted by this illness.

Features of ore-forming magma and fluid revealed by apatite and zircon geochemistry: A case study from the Huanggang skarn Fe-Sn deposit, NE China

The large-scale Huanggang Skarn Fe-Sn deposit is located in the southern part of the Great Xing’an Range (SGXR), NE China. The causative granitic rocks in the Huanggang ore district are characterized by consistently high SiO2 contents, low EuN/EuN* (0.02–0.24) and Sr/Y (0.2–1.9). The pre-ore granite porphyry and syn-ore syenogranite have been dated through zircon and monazite U-Pb dating at 144.6 ± 1.4 Ma and 138.6 ± 1.3 to 135.9 ± 0.8 Ma, respectively. The U-Pb dating of garnet from ore-associated skarn reveals that the mineralization was happened at 136.5 ± 1.3 Ma, which is consistent with the emplacement age of the ore-related syenogranite.Zircon geochemistry shows that the pre-ore granite porphyry has relatively low oxygen fugacity (average zircon Ce4+/Ce3+ = 14.74), whereas the syn-ore syenogranite is more oxidized (average zircon Ce4+/Ce3+ = 178.9). We suggest that the elevation of oxygen fugacity was most likely caused by degassing. The textural features indicate that apatite crystals from the pre-ore granite porphyry are magmatic in origin. In contrast, textural and chemical characteristics of apatite grains from syn-ore syenogranite reveal that they have been modified by hydrothermal fluids in different degrees. The apatite in both pre- and syn-ore granites display relatively high F and low Cl contents, and very low SO3 concentrations (i.e., <0.032 wt%), indicate extremely low content of S in the causative magma. Apatite from syn-ore syenogranite display more strong and variable enrichment of LREE relative to HREE compared to those from pre-ore granite porphyry. The extensive present of new REE mineral inclusions (monazite and xenotime) in the altered apatite suggests that the concentrations of Ca and Na must be low in the ore-forming fluid. Apatite from syn-ore syenogranite have higher concentrations of Mo, W, Zn, Sn, and Be than those from pre-ore granite porphyry.Combined with zircon and apatite geochemistry, and whole rock components of the ore-related granites in the Huanggang deposit, we propose that the ore-forming magma has undergone long-term evolution under relatively reduced conditions with extensive fractional crystallization of plagioclase. The reduced and S-poor evolved magma could have promoted the enrichment of Sn and prevented Fe from precipitating as sulfide. This study also reveals that the compositional characteristics of ore-forming fluid, such as the enrichment of ore-forming materials, could be revealed by altered apatite geochemistry.

Deciphering the Feature Representation of Deep Neural Networks for High-Performance AI.

AI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.

Enhancing Intercultural Business English Communication Factors Evaluation System Using the Termite Life Cycle Optimization Algorithm and Dynamically Stabilized Recurrent Neural Network

In today's globalized business environment, effective intercultural communication in English is paramount for successful collaboration among professionals from diverse backgrounds. To enhance the accuracy of the evaluation system, enhancing intercultural business English communication factors evaluation system using the termite life cycle optimization algorithm and dynamically stabilized recurrent neural network (IBEC–DSRNN–TLCOA) is proposed in this manuscript. The input image is captured from mobile camera. Then the input images are preprocessed using intelligent weight decreasing firefly–particle filtering (IWDFPF) to remove noise and enhance the input images. Afterwards, the preprocessed image is fed to the entropy-founded spatial fuzzy C-means clustering approach for segmenting the image. Then the contrast, correlation, energy and homogeneousness features are extracted by using force-invariant improved feature extraction technique. The extracted features are given to dynamically stabilized recurrent neural network (DSRNN) to image target detection and English description generation. Termite life cycle optimization algorithm (TLCOA) is employed to enhance the weight parameters of DSRNN. The proposed IBEC–DSRNN–TLCOA method is implemented. The proposed IBEC–DSRNN–TLCOA method provides 32.53%, 31.86%, and 35.72% higher accuracy; 35.58%, 32.16%, and 37.72% higher F-measure when compared with the existing methods, such as exploration of intelligent translation with evaluation systems for business English (IBEC–RCNN), E-learning engagement with convolution neural networks on business education (IBEC–CNN), and deep neural network-based research on scoring business English oral training (IBEC–DNN), respectively.

Computed tomography-based contrast features for distinguishing extra-gastrointestinal stromal tumors from intra-abdominal fibromatosis.

This study aims to define the computed tomography (CT) criteria that distinguish extra-gastrointestinal stromal tumors (eGISTs) from intra-abdominal fibromatosis (IAF). Retrospective analysis was conducted on CT images obtained from 31 pathologically confirmed cases, including 17 cases of eGISTs and 14 of IAF. Various parameters [e.g., lesion location, contour characteristics, border delineation, enhancement patterns, presence of intralesional necrosis, vessels, air, fat, and hemorrhage, the long diameter (LD), LD/short diameter (SD) ratio, and volume (LD × SD × height diameter)] were meticulously evaluated. In addition, the degree of enhancement during arterial and portal venous phases and the lesion-to-aorta CT attenuation ratio during both phases were quantified. Statistical analysis was performed using Fisher's exact test, the Student's t-test, and the receiver operating characteristic curve to identify significant CT criteria. Sensitivity and specificity assessments were conducted for single and combined CT criteria. Significant differentiators between eGISTs and IAF include non-mesenteric localization, irregular contour, well-defined borders, heterogeneous enhancement, presence of intralesional necrosis and vessels, and absence of intralesional fat, with LD exceeding 9.6 cm, an LD/SD ratio >1.22, and volume surpassing 603.3 cm3 (P < 0.05). A combination of seven or more of these criteria yielded a specificity of 100%. Ten distinct CT criteria have been identified to distinguish eGISTs from IAF, notably encompassing non-mesenteric localization, irregular contour, well-defined borders, heterogeneous enhancement, presence of intralesional necrosis and vessels, absence of intralesional fat, LD >9.6 cm, an LD/SD ratio >1.22, and volume surpassing 603.3 cm3. The current findings establish CT criteria to distinguish eGISTs from IAF in a clinical setting.

Wealth/Poverty Opposition in English and Kazakh: A Comparative Study

This research employs linguistic concept modeling to scrutinize the semantic variations between notions of wealth and poverty in English and Kazakh, aiming to discern their contrasting characteristics. The corpora in English and Kazakh encompassing varied genres, styles, and thematic content were examined through word frequency and co-occurrence to elucidate divergent contextual frames for the terms in both languages, unveiling cultural nuances and cognitive associations. The results indicate that in English, wealth is majorly associated with financial prosperity, real estate ownership, and the availability of resources for a comfortable lifestyle. Conversely, in Kazakh-language writings, the concept of wealth extends to encompass elements like family connections and devotion to the homeland, reflecting cultural priorities and lifestyles. A comparable distinction is evident when examining the contextual usage of the term poverty. The findings underscore the significance of exploring linguistic concepts within distinct cultural and cognitive frameworks, opening avenues for further comparative linguistic research.

Reservoir heterogeneities due to diagenesis in Jurassic Samana Suk Formation Kahi section Nizampur Basin North West Himalayas Pakistan

The porosity and permeability determine the reservoir quality of sedimentary rocks, which is fundamentally influenced by both depositional and diagenetic processes. The Jurassic carbonates are targeted in many regions for oil and gas exploration. The current study is carried out to elaborate on the diagenetic alterations and their effect on reservoir properties. A thick outcrop of the Jurassic Samana Suk Formation is studied in the Kahi section of Nizampur Basin Northwest Himalayas, Pakistan, to study and relate the trend of lithological variations and depositional settings. Field investigation revealed that the Samana Suk Formation is extensively distributed in the area and primarily made up of interbedded limestone and dolomite units. The original unaltered limestone has a thick-bedded and oolitic to bioclastic nature. Different types of dolomites have been recognized based on colour contrast and sedimentary features. Moreover, saddle dolomite cement, calcite cementation, and mechanical and chemical compaction have also been observed. The petrographic studies show different types of diagenetic alterations that affected the Samana Suk Formation, including micritization, bioturbation, mechanical and chemical compaction in the form of fractures and stylolites, various calcite cementation, which includes the various cement types that range from isopachous, blocky, granular equant, fibrous, and dog tooth cementation, along with dissolution that occurred in different diagenetic realms. Pyritization was rarely observed. Moreover, different phases of dolomites were identified, ranging in size and shape, i.e., finely crystalline to coarse crystalline and planar euhedral to non-planar anhedral. The stable oxygen isotope values of these dolomites show depletion from original marine signatures and suggest burial-related fault-controlled dolomitization events. Overall, diagenetic processes like dissolution, fracturing and dolomitization increased the reservoir potential. On the contrary, the overburden and cement precipitation result in a decrease in the reservoir properties.

Scene‐content‐sensitive real‐time adaptive foveated rendering

AbstractIn recent years, techniques for accelerating rendering by exploiting the limitations of the human visual system have become increasingly prevalent. The foveated rendering method significantly reduces the computational requirements during rendering by reducing image quality in peripheral regions. In this paper, we propose a scene‐content‐sensitive real‐time adaptive foveated rendering method. First, we pre‐render the three‐dimensional (3D) scene at a low resolution. Then, we utilize the low‐resolution pre‐rendered image as input to extract edge, local contrast, and color features. Subsequently, we generate a screen‐space region division map based on the gaze point position. Next, we calculate the visual importance of each 16 × 16 pixel tile based on edge, local contrast, color, and screen‐space region. We then map the visual importance to the shading rate to generate a shading rate control map for the current frame. Finally, we complete the rendering of the current frame based on variable rate shading technology. Experimental results demonstrate that our method effectively enhances the visual quality of images near the foveal region while generating high quality foveal region images. Furthermore, our method can significantly improve performance compared to per‐pixel shading method and existing scene‐content‐based foveated rendering methods.

Multi-class plaque segmentation in Intravascular Ultrasound via inter-frame feature fusion and contrast feature extraction

Intravascular Ultrasound (IVUS) is an effective aid in diagnosing cardiovascular diseases, with plaque segmentation in IVUS images being a pivotal step in assessing vascular plaque risk. However, IVUS plaque segmentation encounters two major challenges: (1) the variability in plaque shapes makes it difficult to locate plaques accurately, and (2) distinguishing plaque types is challenging due to the similarity in visual features among different types. To tackle these challenges, we propose a novel framework for IVUS plaque segmentation. Our approach incorporates a segmentation network featuring a dual feature attention(DFA) module and a cross feature fusion(CFF) module. Additionally, we introduce contrastive learning to enhance plaque sensitivity and extract contrast features within our network. We evaluate our method using a private IVUS dataset, achieving the state-of-the-art performance with a Dice score of 84.79% and a 14.17 HD value. Our approach represents significant progress in the field of multi-class IVUS plaque segmentation, making it a more effective tool to assist doctors in diagnosing vascular plaques.

Feature-level contrastive learning for full-reference light field image quality assessment

Light field (LF), as a new visual information representation for immersive experiences in virtual reality and augmented reality, has attracted extensive attention. It is of significance to establish the light field image quality assessment (LF-IQA) that conforms to the characteristics of human visual perception. Due to the spatial–angular inter-twined imaging mechanism, the quality evaluation for LF images faces more challenges than ordinary 2D/3D IQA. This paper proposes a feature-level contrastive learning based full-reference LF-IQA with a divide-and-conquer learning strategy. It first employs the cross-attention mechanism to learn the local contrast features related to the reference features from three subsets of distorted sub-aperture images (SAIs). Then a Transformer-based feature fusion is used to predict the overall perceptual quality. The evaluation experiments are carried out on three commonly used LF-IQA datasets: Win5-LID, NBU-LF1.0, and SHU. The experimental results demonstrate that our model outperforms the existing state-of-the-art LF-IQA methods.

P-313 Additional tubal flushing with ethiodized oil during transvaginal hydrolaparoscopy; feasibility and live birth rate, a pilot study in subfertile women

Abstract Study question Is tubal flushing with ethiodized oil during transvaginal hydrolaparoscopy (THL) feasible and what is the live birth rate within 6 months after THL? Summary answer Additional flushing with ethiodized oil during THL is feasible and safe with a live birth rate of 32.0% compared to 23.4% using solely water-based-contrast (p = 0.25). What is known already The use of ethiodized oil during hysterosalpingography (HSG), an alternative method to test tubal patency, has shown to have a positive effect on the live birth rate, compared to the use of water-based contrast (methylene blue). The benefit of THL over HSG as a tubal testing method is the possibility to also explore tubo-ovarian structures and the pouch of Douglas. Currently, only water-based media are used for tubal flushing during THL. Study design, size, duration A single-center cohort study at Máxima MC, the Netherlands, including 50 subfertile women who underwent THL and in whom there was at least unilateral tubal patency for methylene blue. Outcomes of interest included feasibility, safety and 6- months live birth rate. Our data were compared with a historical cohort of 111 subfertile women who underwent THL using solely water-based contrast (van Kessel MA. et al., 2021). Univariate – and multivariate logistic regression analysis were performed. Participants/materials, setting, methods Women with an iodine allergy and/or manifest thyroid dysfunction were excluded. Participants received additional tubal flushing with ethiodized oil (Lipiodol® Ultra Fluid). Main outcomes were appearance of oil and cell/mucus debris at the tubal fimbriae, pain scores (Visual Analogue Scale, VAS) and acceptability scores (0-10, completely unacceptable-acceptable). Secondary outcomes included; amount of ethiodized oil used, thyroid function four weeks afterwards, adverse events and live birth rate within 6 months after THL. Main results and the role of chance In 48 of the 50 THL procedures there was bilateral patency to methylene blue, ethiodized oil appeared from the tubal fimbriae bilateral in 77% (37/48), unilateral in 17% (8/48), while it did not appear in 6% (3/48). In two procedures there was unilateral patency to methylene blue. Ethiodized oil appeared unilateral in one and did not appear during the other THL. The amount of ethiodized oil used was 5 mL [3.4–6.3). The median reported pain scores on the VAS were 2.9 [IQR 1.0–5.0] after flushing with methylene blue and 3.0 [IQR 1.0–5.0] after ethiodized oil. The acceptability of the procedure was rated as 10 out of 10 [IQR 8–10]. Four weeks after the procedure 14% (6/42) had developed subclinical hypothyroidism, one woman had overt hypothyroidism (FT4 slightly decreased to 11.0 pmol/L, TSH 5.60 mU/L) and one woman had isolated hypothyroxinemia (FT4 11.0 pmol/L, TSH 3.40 mU/L). One adverse event, persistent bleeding at the trocar insertion site was reported, for which a diagnostic laparoscopy was performed. The live birth rate within 6 months after a THL was 32% (16/50), compared to 23% (26/111) in our historical cohort using solely water-based contrast (OR = 1.54, 95% CI 0.74–3.22, P = 0.25). Limitations, reasons for caution As this is a pilot study without a power analysis, the study is presumably underpowered to show an effect on the live birth rate. Also, the control group was historical. Wider implications of the findings By proving feasibility of using ethiodized oil during THL we encourage further research on its effect on pregnancy rates. Furthermore, the direct observation of tubal flushing with ethiodized oil during THL enables further investigation of the contrast characteristics, which are presumably responsible for its positive effect on live birth rate. Trial registration number NL8696

Green synthesis of quercetin-loaded magneto-liposomes and their assessment of antioxidant efficacy, hyperthermia and MRI contrast features

Recent years has drawn attention in the preparation of precise theragnostic agents to treat various types of cancers. The current study describes the green synthesis of quercetin encapsulated magneto-liposomes and their subsequent evaluation of anti-cancer efficacy. Punica granatum L peel extract as a stabilizer has been used for the synthesis of Fe3O4 nanoparticles and the resultant exhibited a magnetic susceptibility of 51 emu/g. Quercetin encapsulated magneto-liposomes obtained via thin film hydration technique displayed a spherical size ⁓ 100 nm. DPPH and TBARS assays used for the assessment of quercetin antioxidant behaviour revealed a concentration-dependent upsurge in scavenging activity and a concurrent reduction in lipid peroxidation. Magnetic hyperthermia studies revealed superior heating efficiency (∼57.2 ± 3.8 W/gFe) of the magneto-liposomes under a biologically safe exposure condition of the oscillating magnetic field. The magnetic properties of Fe3O4 nanoparticles have been comprehensively examined through hyperthermia and in vitro magnetic resonance imaging (MRI) studies. In vitro studies performed in MG-63 osteosarcoma cells indicated the reduced viability of cancer cells ∼45 % by the quercetin encapsulated magneto-liposomes. These nanocarriers also exhibited a very minimal cytotoxicity on non-cancerous HEK-293 cells. The results from the overall investigation unveiled the potential of quercetin encapsulated magneto-liposomes in cancer theranosis.

Using the set of informative features of a binding object to construct a decision function by the system of technical vision when localizing mobile robots

The object of this study is the process of constructing a decision function by the optical-electronic system of technical vision under the conditions of the influence of obstacles on the current image, which is formed in the process of localization of a mobile robot. The paper reports the results of solving the problem of constructing a decision function when reducing the signal-to-noise ratio of the current image by using a set of informative features for the selection of the binding object, namely, brightness, contrast, and its area. The selection of the binding object is proposed to be carried out by choosing the appropriate values of the quantization thresholds of the current image for the selected informative features, taking into account the signal-to-noise ratio, which provides the necessary probability of object selection. The dependence of the object selection probability on the selected values of the quantization thresholds was established. The use of the results could enable the construction of a unimodal decision function when localizing mobile robots on imaging surfaces with weakly pronounced brightness and contrast characteristics of objects, as well as with their small geometric dimensions. By modeling, the probability of forming a decision function was estimated depending on the degree of noise of the current images. It is shown that the application of the proposed approach allows selection of objects with a probability ranging from 0.78 to 0.99 for the values of the signal-to-noise ratio of images formed by the technical vision system under real conditions. The method to construct a decision function under the influence of interference could be implemented in information processing algorithms used in optical-electronic technical vision systems for the navigation of unmanned aerial vehicles

Magmatic to solid-state evolution of a shallow emplaced agpaitic tinguaite (the Suc de Sara dyke, Velay volcanic province, France): implications for peralkaline melt segregation and extraction in ascending magmas

Abstract. In the last decades the mush model has been generalized to the complete trans-crustal magmatic system in which differentiation would be driven by segregation and extraction of trapped melts from crystal-rich mushes. Melt extraction processes involved are porous flow and strain localization, the latter being regarded as the main process acting during transfer through dykes and necks along which high differential stresses are acting on. We combine structural measurements together with petrological analyses and textural observations to constrain the model of emplacement and finally emphasize how shear deformation and strain localization structures promoted the residual melt segregation that occurred in a shallow silica-undersaturated peralkaline intrusion (Suc de Sara, Velay volcanic province, French Massif Central). In this study, we demonstrate that segregation and subsequent extraction of the CO2-rich residual melt occurred during magma ascent and final emplacement of the Suc de Sara tinguaite. Contrasting features of shear deformation between the margins that exhibited different permeabilities highlight that melt segregation started by compaction as a loose packing of emerging microlites and continued with melt filling of an anastomosed C/C′ band network developing in the crystal-rich mush subjected to high shear strain. Subsequent melt extraction throughout the country rock was controlled by the permeability of the hanging wall. Along the western hanging wall of the intrusion, extraction of the residual melt was prevented by the 15 cm thick chilled margin. In contrast, segregated melt circulated through the highly porous and permeable eastern margin, causing the fenitization of the country rock.

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

ABSTRACT This study quantitatively investigated reservoir-effects on drought propagation in two semi-arid river basins: India’s Tapi basin with the Ukai reservoir and Uzbekistan’s Chirchik basin with the Charvak reservoir. Meteorological drought (MD) is analyzed using the Standardized Precipitation Index (SPI) and hydrological drought (HD) using the Standardized Streamflow Index (SSI) for the duration from 1980 to 2004. Both the river basins, especially in upstream reservoir areas, exhibited a notable correlation between HD and MD. Reservoir operation was observed to reduce the downstream MD–HD correlation at shorter SPI timescales. Hit-score-based evaluations indicated that reservoir operation has induced changes in the drought propagation patterns for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the drought propagation time (DPT), with distinct influences from monsoon (Tapi) and snow-melting (Chirchik). The average DPT (average DPT over 12 months) for the reservoir-influenced part of the Tapi (∼ six months) and Chirchik (∼ nine months) basins is higher than that of the natural parts of both basins (Tapi: ∼ four months; Chirchik: ∼ six months) as a result of the natural and anthropogenic storage influence.