Dual Features Research Articles

Porosity controlled soya protein isolate-polyethylene oxide multifunctional dual membranes as smart wound dressings

Multifunctional membranes S7P0.7, S7P3.0, and dual membranes composed of soya protein isolate (SPI) and polyethylene oxide (PEO) were produced for wound dressing applications. The internal structure of the membranes was confirmed by scanning electron microscopy (SEM) to be homogeneous and coarser with a porous-like network. S7P3.0 showed the tensile strength of 0.78 ± 0.04 MPa. In the absence of antibiotics, the dual membrane (combination of S7P0.7 and S7P3.0) exhibited potential antibacterial activity against Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) bacteria. Hemolysis quantitative data presented in the image demonstrates that all samples exhibited hemolysis levels below 5 %. Dual membrane showed 77.93 ± 9.5 % blood uptake which reflects its absorption capacity. The combination of S7P0.7 and S7P3.0 influenced the dual membrane's antibacterial, biocompatibility, and good hemolytic potentials. The dual membranes' promising histology features after implantation suggest they could be used as wound dressings.

Phagocytosis-initiated tumor hybrid cells acquire a c-Myc-mediated quasi-polarization state for immunoevasion and distant dissemination

While macrophage phagocytosis is an immune defense mechanism against invading cellular organisms, cancer cells expressing the CD47 ligand send forward signals to repel this engulfment. Here we report that the reverse signaling using CD47 as a receptor additionally enhances a pro-survival function of prostate cancer cells under phagocytic attack. Although low CD47-expressing cancer cells still allow phagocytosis, the reverse signaling delays the process, leading to incomplete digestion of the entrapped cells and subsequent tumor hybrid cell (THC) formation. Viable THCs acquire c-Myc from parental cancer cells to upregulate both M1- and M2-like macrophage polarization genes. Consequently, THCs imitating dual macrophage features can confound immunosurveillance, gaining survival advantage in the host. Furthermore, these cells intrinsically express low levels of androgen receptor and its targets, resembling an adenocarcinoma-immune subtype of metastatic castration-resistant prostate cancer. Therefore, phagocytosis-generated THCs may represent a potential target for treating the disease.

A dual approach to agency problems

This paper presents a dual approach to the standard model of moral hazard. We formulate the dual of the principal–agent problem under the assumption that the incentive constraint can be replaced by a local constraint (the first-order approach), to examine whether the relaxed agency problem yields a candidate solution. The dual formulation generates a convex conjugate, which transforms the agent’s utility from compensation into a dual functional. The dual problem features a simple convex structure, which enables us to perform a comprehensive analysis for the agency problem. We derive novel and more tractable conditions for existence and uniqueness of a solution to the problem with the dual elements. Furthermore, the approach to the dual problem provides illuminating insights into the previous nonexistence results.

ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition

Electroencephalography (EEG) emotion recognition is an important task for brain–computer interfaces. The time, frequency, and spatial domains of EEG signals have been widely studied. However, these methods often ignore the spatial and temporal correlations in dual modules, resulting in insufficient emotional representations. In this paper, a dual module EEG emotion recognition method based on an improved capsule network and residual Long-Short Term Memory (ResLSTM) is proposed. Using an improved capsule network as the spatial module is more advantageous in learning specific EEG spatial representations. The ResLSTM of the temporal module inherits the information flow from the upper spatial module and conducts complementary learning of the spatiotemporal dual module features through residual connections, thus obtaining more discriminative EEG features and ultimately boosting the classification capabilities of the model. The average accuracy of arousal, valence, and dominance on the DEAP dataset reached 98.06%, 97.94%, and 98.15%, respectively. The DREAMER dataset’s average accuracy of arousal, valence, and dominance reached 94.97%, 94.71%, and 94.96%, respectively. The results of our experiments indicate that our method outperforms state-of-the-art approaches.

Automatic Screening System to Distinguish Benign/Malignant Breast-Cancer Histology Images Using Optimized Deep and Handcrafted Features

Breast Cancer (BC) has been increasing in incidence among women for a variety of reasons, and prompt detection and management are essential to reducing mortality rates. In the context of clinical-level breast cancer screening, the needle biopsy sample is used to generate Breast Histology Images (BHIs), which will then be used to confirm the results. Using a novel Deep-Learning Plan (DLP), the proposed work identifies BHI accurately and confirms the severity of BC by confirming its severity. As part of the proposed DLP implementation, four phases are involved: (i) the collection and enhancement of images, (ii) the extraction of features, (iii) the reduction of features and their integration, and (iv) binary classification and validation. The purpose of this study is to optimize deep features and machine features using particle swarm algorithms. To evaluate the performance of the proposed scheme, we compare the results obtained using individual deep features, dual deep features, and hybrid features. Using the hybrid image features in the classifier, this study has determined that ResNet18 with k-nearest neighbor provides superior classification accuracy (> 94%).

Revamped System Performance of Grid-Interactive Hybrid DFIG-PV System Using WSO Based Dual Layer MRFKMP Adaptive Control

Due to the stochastic characteristicsof wind, keeping high-quality power into the grid-dependent hybrid wind energy systems is important for effective energy utilization, especially when there are non-linear loads involved. In this paper, a dual layer random features kernel mean p-power (MRFKMP) adaptive control for the grid interfaced converter of a DFIG-PV based hybrid energy system is implemented for the purpose of maintaining power balances and quality power at both the source and grid ends while operating at unity power factor. The introduced control extracts the weight of stator and load current components with finer convergence and lower computational complexity, excluding complex reference transformations. The dual layer adaptive control improves grid side converter functionality by improving power quality, interruption of loads, taking into account reactive power and harmonic remediation under steady-state and dynamic scenarios.In the secondary step, to achieve optimal DFIG-PV control response, the stability of the DC-link, which is themain coupling element for power transfer between DFIG, solar PV, and battery energy storage (BES), is reinforced by tuning the DC link voltage controller with the white shark optimization (WSO) algorithm. Under the worst-case scenario of large step wind change, the primacy of the envisaged WSO algorithm for tuning DC-link PI controller has been evidenced. It is discovered that WSO tuning positively affects the settling time and oscillatory response of the DC-link voltage not just at the outset, but also throughout the subjugated dynamic wind speedchange.Power flow to the grid is stable due to optimized DC-link voltage performance. WSO parameter tuning improved efficiency is validated using MATLAB simulation results and compared to GA and PSO techniques in this study.

Named Entity Recognition in Power Marketing Domain Based on Whole Word Masking and Dual Feature Extraction

With the aim of solving the current problems of low utilization of entity features, multiple meanings of a word, and poor recognition of specialized terms in the Chinese power marketing domain named entity recognition (PMDNER), this study proposes a Chinese power marketing named entity recognition method based on whole word masking and joint extraction of dual features. Firstly, word vectorization of the electricity text data is performed using the RoBERTa pre-training model; then, it is fed into the constructed dual feature extraction neural network (DFENN) to acquire the local and global features of text in a parallel manner and fuse them. The output of the RoBERTa layer is used as the auxiliary classification layer, the output of the DFENN layer is used as the master classification layer, and the output of the two layers is dynamically combined through the attention mechanism to weight the outputs of the two layers so as to fuse new features, which are input into the conditional random field (CRF) layer to obtain the most reasonable label sequence. A focal loss function is used in the training process to alleviate the problem of uneven sample distribution. The experimental results show that the method achieved an F1 value of 88.58% on the constructed named entity recognition dataset in the power marketing domain, which is a significant improvement in performance compared with the existing methods.

Synaptic Mechanisms of Delay Eyeblink Classical Conditioning: AMPAR Trafficking and Gene Regulation in an In Vitro Model.

An in vitro model of delay eyeblink classical conditioning was developed to investigate synaptic plasticity mechanisms underlying acquisition of associative learning. This was achieved by replacing real stimuli, such as an airpuff and tone, with patterned stimulation of the cranial nerves using an isolated brainstem preparation from turtle. Here, our primary findings regarding cellular and molecular mechanisms for learning acquisition using this unique approach are reviewed. The neural correlate of the in vitro eyeblink response is a replica of the actual behavior, and features of conditioned responses (CRs) resemble those observed in behavioral studies. Importantly, it was shown that acquisition of CRs did not require the intact cerebellum, but the appropriate timing did. Studies of synaptic mechanisms indicate that conditioning involves two stages of AMPA receptor (AMPAR) trafficking. Initially, GluA1-containing AMPARs are targeted to synapses followed later by replacement by GluA4 subunits that support CR expression. This two-stage process is regulated by specific signal transduction cascades involving PKA and PKC and is guided by distinct protein chaperones. The expression of the brain-derived neurotrophic factor (BDNF) protein is central to AMPAR trafficking and conditioning. BDNF gene expression is regulated by coordinated epigenetic mechanisms involving DNA methylation/demethylation and chromatin modifications that control access of promoters to transcription factors. Finally, a hypothesis is proposed that learning genes like BDNF are poised by dual chromatin features that allow rapid activation or repression in response to environmental stimuli. These in vitro studies have advanced our understanding of the cellular and molecular mechanisms that underlie associative learning.

Story of Man’s Creation for Shelley’s Frankenstein:

This study is an attempt to examine ecofeminism in the patriarchal society of Frankenstein of Shelley. At first glance, the novel is a manifestation of the patriarchal world of Shelley because the male characters are present, and almost all of the women in the novel either kill or die. But it can be perceived that Shelley’s female characters are present in their own ways because they are present in nature, as a matter of fact, the female character’s absence and passivity are the emblems of their overwhelming presence so much that even the monster also has some feminine features, and he is used to being passive, because when he appears, it is either dark or moonlight, both of them have feminine attributes. The narrations of novel events are almost described in the sea, which is also the female symbol. Hence, Shelley tries to increase the women’s presence in her own ways in her novel through natural features. The presentation of natural elements influences Monster so much that he also reveals the feminine, goddess features. It can be concluded that the research method of this study is to find Shelley’s ecofeminism attributes, the actual presence of women, and their virtual absence due to ecofeminism, because Shelley has used their own specific ways to show that women are very present in her patriarchal society so much that even all of the male characters have dual features and the feminine features are not absent but they are very present in nature and characters of the novel. Therefore, women’s absence is Shelley’s method to reveal their absolute presence.

Three-dimensional (3D) printing of oral dental films (ODFs) using blended Compactcel® polymers through semi-solid extrusion (SSE) bioprinter

This study aimed to prototype oral dental films (ODFs) loaded with diclofenac sodium (DS) using two different grades of CompactCel® polymers through a semi-solid extrusion (SSE) bioprinter. This three-dimensional (3D) printed ODFs were developed for the treatment of toothaches with immediate and sustained release features. Two different grades of CompactCel® polymers, CompactCel® P 002.02 SR and CompactCel® P Clear 194.04 SIL, with sustained and immediate release features, respectively, were explored in this study. A blend of CompactCel® polymers was found to be capable of forming hydrogels with the addition of dibutyl phthalate (DBP) as a plasticizer to improve the foldability/flexibility of the developed ODFs. ODFs were 3D printed using an SSE bioprinter by varying the amount of DBP. All 3D bio-printed ODFs were analyzed systematically in terms of in vitro physicochemical characteristics, including drug content, drug release, and release kinetics models. The in vitro release graph of DS from ODFs showed an initial burst release of around 50–70% in 20 min followed by the sustained release of up to 150 min for all the formulations. The prototype ODFs showed dual drug delivery features in terms of initial fast release followed by sustained release. Thus, these ideal biomaterial combinations were explored for the first time to establish not only their pharmaceutical 3D bioprinting capabilities but also their potential for drug delivery applications.

Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.

Advancements in Layered Double Hydroxide-Based Chemotherapeutic Nanosystems for Cancer Treatment

Globally, Cancer therapy is still a major issue. With the latest developments in nanotechnology, layered double hydroxide (LDH)-based nanosystems are receiving special attention due to their pH dependent biodegradability, superior biocompatibility, easy surface modification, anion exchange capacity and high chemical stability, giving rise to great potential for cancer therapy. By intercalating inorganic, organic, or bio molecules into their lamellar lattice, new hybrid materials with dual or multi-functional features, including anticancer capabilities, can be developed from layered double hydroxide (LDH). Despite the fact that outstanding research has been published, few review papers address these essential and promising discoveries to stimulate the ongoing development of LDH-based nanosystems in the field of cancer therapy. This paper therefore study focuses on the most recent advancements in LDH-based chemotherapeutic nanosystems for cancer treatment. The information utilized in this review was gathered from studies that have previously been published and were retrieved from several Journal outlets. These reports dealt with the usage of layered doubled hydroxide-based chemotherapeutic nanosystems for the treatment of cancer. The research demonstrates that layered double hydroxides may be utilized to develop single or composite nanosystems that distribute therapeutic components precisely without causing cumulative damage for the field of nanomedicine.

Integrated DWT-DHT Feature Set for ABC Optimized SVM-Based PQ Classifier

The power quality (PQ) literature contains abundant methodologies for the classification of PQ disturbances that show the significance of the problem and the necessity to design a reliable method that will accurately and quickly identify the type of disturbance. However, most of the available methods are not suitable for real-time applications as they are sensitive to noise, require more memory, computationally complex, and are also time-consuming. This work suggests an approach using Artificial Bee Colony Algorithm (ABC) for concurrent feature and classifier parameters selection to reduce execution time and enhance classification accuracy. An integrated feature set consisting of features extracted using two signal processing techniques namely Discrete Wavelet Transform (DWT) and Discrete Hilbert Transform (DHT) is proposed for clear discrimination of disturbances in contrast to the conventional single feature extraction tool commonly employed. To authenticate the effectiveness of the proposed dual set features, this method has been tested on both simulated and real-time PQ data and is found to perform better than methods using a single signal processing tool.

Dual emotion based fake news detection: A deep attention-weight update approach

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.

Dual fluorescence and RTP features of carbazole-cyclic triimidazole derivatives: The fluorophores’ connectivity does matter

Organic room temperature phosphorescent (ORTP) materials with stimuli responsive behaviour are extremely desirable for various applications. Two derivatives of cyclic triimidazole (TT) functionalized through a different connectivity with carbazole (Cz) are investigated. The two compounds possess quite different emissive behaviour associated with both molecular reasons and aggregation modes. In TT-(N)-Cz, having a C–N bond connecting Cz to the triimidazole unit, the twisted molecular conformation results in the absence of electronic communication between the two moieties. It crystallizes in two polymorphs (TT-(N)-CzM and TT-(N)-CzT) showing structure dependent dual fluorescence and dual phosphorescence, both of molecular and aggregate origin with aggregation induced fluorescence component highly sensitive to grinding. In TT-(C)-Cz, having the two moieties connected through a C–C bond, the much-reduced twisting allows higher electronic communication resulting in a single red shifted fluorescence. Moreover, crystals of TT-(C)-Cz display long lived components of molecular and supramolecular origin.

Variation in nonadiabaticity and dual fluorescence causality by extension with aromatic rings in tropolone

Photophysical aspects of tropolone upon extension with aromatic rings are studied theoretically. Tropolone possesses three energetically close lying singlet excited states (S 1 (1 π π ∗ ), S 2 (2 π π ∗ ) and S 3 (1 n π ∗ )), resulting in a rapid internal conversion among these states upon photoexcitation. The excited-state energetics and decay dynamics of benzotropolone are similar to the tropolone molecule. On the other hand, upon extending the tropolone with naphthalene moiety (naphthatropolone), the molecule exhibits relatively well-separated electronic states, thus displaying significantly slower photorelaxation compared to the tropolone molecule. Both benzotropolone and naphthatropolone exhibit the dual emission feature, which is absent in the parent tropolone due to the symmetrical double-well potential of S 1 . • Loss of near-degeneracy upon ring extension in tropolone. • Competing nonadiabatic relaxation and ESIPT. • O-H vibrations facilitates rapid internal conversion. • Dual emission features in extended tropolones.

Genesis and Geodynamic Significance of Chromitites from the Fuchuan Ophiolite, Southern China, as Evidenced by Trace Element Fingerprints of Chromite, Olivine and Pyroxene

AbstractThe Fuchuan ophiolite is located in the northeasternmost segment of the Neoproterozoic Jiangnan orogen and consists mainly of harzburgites, with minor dunites, pyroxenite and gabbro veins and dykes. In order to investigate the genesis and tectonic setting of the Fuchuan ophiolite and chromitites, in situ analyses of unaltered chromites and silicates were carried out. Trace element analyses of unaltered chromites from the Fuchuan chromitites indicate the parental magma is of mid‐ocean ridge basalt (MORB)‐like origin, with the Ti/Fe3+#–Ga/Fe3+# diagram of chromites showing that the chromitites are a result of melt/rock interaction of MORB melts with mantle peridotites, and that the Fuchuan harzburgites present the dual features of MORB and supra‐subduction zone peridotites (SSZP). Trace and rare earth element (REE) analyses of olivines and orthopyroxenes from the Fuchuan harzburgites hint at the possibility of mantle metasomatism influenced by SSZ‐subducted fluids. Finally, integrating with previous study, the Fuchuan ophiolite and chromitites might have been formed in a back‐arc spreading ridge between the Yangtze and Cathaysia blocks during the Neoproterozoic.

A large-scale point cloud semantic segmentation network via local dual features and global correlations

For large-scale point cloud semantic segmentation, the relationships between long-range neighbourhoods are as important as short-range features. The current methods focus on aggregating the fine-grained geometric structures of local regions, the long-range features are usually ignored. To this end, we propose a novel framework named LG-Net that can efficiently learn local dual features and global correlations from point clouds. We adopt random sampling to decrease point density, thereby processing large-scale point cloud efficiently. Specifically, we propose a dual feature complementary (DFC) module that makes geometric and semantic features complementary to preserve local information. We utilize positional relations to capture geometric details, and aggregate semantic features according to the similarity of neighbours. The global correlation mining (GCM) module simply shares an attention map for all 3D points to learn long-range contextual information. We conducted extensive experiments on public datasets, including indoor scenes (S3DIS, ScanNet) and outdoor scenes (SemanticKITTI, SensatUrban). Experimental results demonstrate that our method outperforms the state-of-the-art approaches.

Smart green CQD@SiO2 hybrid coated optical fiber manifesting dual versatile absorptive and MIP features towards epinephrine detection.

For the first time, in this study, a novel optical fiber biosensor is proposed and developed via coating only one smart functional layer of silica-supported carbon dots realizing the concepts of both lossy mode resonance (LMR) and molecularly imprinted polymer (MIP) for epinephrine detection. The carbon quantum dots (CQDs) are prepared using a green synthesis method and then treated with a molecularly imprinted polymer (MIP) strategy. Under ultrasonic irradiation, a SiO2 shell was stabilized on the surface of the CQDs to graft and to provide the LMR/MIP functional layer onto the curved optical fiber surface. Accurate structural and morphological characterization confirmed the carbon quantum dot agents and also the SiO2 supporting shells on the optical fiber, while spectroscopic analysis confirms the formation of the imprinted polymer and desirable absorbance characteristics. The experimental and numerical sensing studies revealed that the proposed sensing probe allows the rapid adsorption/desorption of epinephrine to the sensing films and highly permeable coating for studying the influence of effective parameters. Under the optimal experimental conditions, the sensitivity of the proposed LMR-based optical fiber sensor is reported to be 0.37 nm μM-1 with a correlation coefficient of 0.99. So, sensitive detection of epinephrine at a low concentration can be guaranteed with a 0.72 mM LOD.

Luminescent alkynylplatinum(II) terpyridine-containing conjugated polymers: synthesis, characterization and photophysical studies

A series of alkynylplatinum(II) terpyridine complexes and alkynylplatinum(II) terpyridine-containing conjugated polymers with different polymer backbones has been synthesized, and their spectroscopic properties and Förster resonance energy transfer (FRET) processes has been investigated. The platinum(II)-containing polymers exhibit dual emissive features with emission maxima at ca. 416-465 nm and ca. 671-673 nm, which are assigned to be originated from singlet intraligand (1IL) excited states from the polymer backbone and triplet metal-metal-to-ligand charge transfer (3MMLCT) excited states from the platinum(II) pendants, respectively. The Förster radii (R0) of the platinum(II)-containing conjugated polymers have been determined, and their distinctive thermo-responsive luminescence changes have also been observed. The present work has demonstrated the utilization of “click” reaction for the preparation of platinum(II)-containing conjugated polymers, which show unique photophysical and spectroscopic properties. Through the judicious design, this type of platinum(II)-containing polymer is found to be sensitive to temperature, resulting in ratiometric emission changes. This study has provided valuable insights into the preparation of metal-containing polymeric systems for different applications.