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Document Image Quality Assessment: A Survey
No full textThe rapid emergence of new portable capturing technologies has significantly increased the number and diversity of document images acquired for business and personal applications. The performance of document image processing systems and applications depends directly on the quality of the document images captured. Therefore, estimating the document\u27s image quality is an essential step in the early stages of the document analysis pipeline. This article surveys research on Document Image Quality Assessment (DIQA). We first provide a detailed analysis of both subjective and objective DIQA methods. Subjective methods, including ratings and pair-wise comparison-based approaches, are based on human opinions. Objective methods are based on quantitative measurements, including document modeling and human perception-based methods. Second, we summarize the types and sources of document degradations and techniques used to model degradations. In addition, we thoroughly review two standard measures to characterize document image quality: Optical Character Recognition (OCR)-based and objective human perception-based. Finally, we outline open challenges regarding developing DIQA methods and provide insightful discussion and future research directions for this problem. This survey will become an essential resource for the document analysis research community and serve as a basis for future research
DRAMS: Double-RIS assisted multihop routing scheme for device-to-device communication
No full textReconfigurable intelligent surfaces (RISs) is a promising solution for enhancing the performance of multihop wireless communication networks. In this paper, we propose a double-RIS assisted multihop routing scheme for a device-to-device (D2D) communication network. Specifically, the scheme is dependent on the already deployed RISs and users in the surroundings. Besides the RISs, the emphasis of this work is to make more use of the existing intermediate users (IUs), which can act as relays. Hence, the density of RIS deployment in the surroundings can be reduced, which leads to the avoidance of resource wastage. However, we cannot solely depend on the IUs because this implies complete dependence on their availability for relaying and as a result, the aspect of reliability in terms of delay-constrained information transfer cannot be guaranteed. Moreover, the IUs are considered capable of energy harvesting and as a result, they do not waste their own energy in the process of volunteering to act as a relay for other users. Numerical results demonstrate the advantage of the proposed scheme over some existing approaches and lastly, useful insights related to the scheme design are also drawn, where we characterize the maximum acceptable delay at each hop under different set-ups
Eisenia fetida-driven vermitechnology for the eco-friendly transformation of steel waste slag into organic amendment: An insight through microbial diversity and multi-model approach
No full textThe processing of steel waste slag from the black metallurgical sector seriously threatened the ecology. To counter these dangers, appropriate detoxification methods were required. Vermitechnology was one such strategy that could successfully convert this industrial waste into nutrient-rich products suitable for use in agriculture. This research primarily focuses on employing vermitechnology for the transformation of waste steel slag into vermicompost and to determine changes in microbial composition, nutrient cycling, and metal detoxification facilitated by earthworms (Eisenia fetida). Earthworm populations in steel waste vermibeds (sw-vermibeds) increased by 2.87–3.07 folds. T1(SW + CD-1:1) comparatively showed increased levels of nutrients such as nitrogen, phosphorus, and potassium. Microbial and enzymatic parameters were more pronounced in treatment T1. The findings of phospholipid fatty acid (PLFA) diversity demonstrate microbial diversity and fatty acid composition. Based on PLFA Sobol Sensitivity Analysis (SSA), PUFA and cyclo were the most sensitive inputs to the presence of heavy metal (HMs) concentrations in SW. In accordance with Taylor-based modelling, R-tree, and Mars were the most trusted regression models for predicting HMs toxicity on microbes. The bioavailable metal fractions of HMs (Fe, Ni, Cd, Cu, Pb, and Cr) decreased by 61–83%. The correlation was performed for 0 and 90 days for metal microbial interactions r (0 days), [BSR vs Fe, Cd, Cu, Ni = −0.99, −0.82, −0.43, −0.99] and r (90 days), [FDA vs Fe, Cu, Ni = −0.97, −0.47, −0.95]. Overall, the results indicated that T1(1:1 SW + CD) provided more favorable conditions for the development of microbes and Eisenia fetida. This research presents a new perspective to the world community on the transformation of harmful steel waste slag into advantageous biological resources by introducing a novel method of employing Eisenia fetida to remediate hazardous steel waste slag
Electrophoresis of hydrophobic and polarizable liquid droplets in hydrogel medium
No full textThe emulsion droplet-hydrogel composite is a new class of biomaterial which finds potential application in several fields, including chemical, biological, food and pharmaceutical industries, as well as to controlled release drug in targeted location, etc. Besides, the electric field response of polarizable emulsion droplets quenched in hydrogel medium offers several Lab-on-a-Chip applications. Note that the gel electrophoresis of such droplets is advantageous compared to free-flow electrophoresis. Given the importance, a general theory of electrophoresis applicable for any hydrophobic and polarizable liquid droplets quenched in hydrogel medium is developed. Such a theory is also applicable for electric field response of emulsion droplets quenched in a hydrogel medium. The mathematical model is based on conservation principles of mass and momentum considering the hydrogel skeleton as soft polymeric material saturated with electrolytic solution. Within the Debye-Hückel electrostatic framework, we have derived a closed form analytical expression for electrophoretic mobility of hydrophobic and polarizable liquid droplets embedded in hydrogel skeleton. The derived general expression is further approximated with negligible error and the corresponding approximate form is found to be very convenient and can be easily programmed on a personal computer. Additionally we further deduce several closed form analytical expressions derived under various limits. Thus, the newly derived analytical results may be useful to the experimentalists to analyze and interpret their observations
Enhancing Contrastive Clustering with Negative Pair-guided Regularization
No full textContrastive Learning (CL) aims to create effective embedding for input data by minimizing the distance between positive pairs, i.e., different augmentations or views of the same sample. To avoid degeneracy, CL also employs auxiliary loss to maximize the discrepancy between negative pairs formed with views of distinct samples. As a self-supervised learning strategy, CL inherently attempts to cluster input data into natural groups. However, the often improper trade-off between the attractive and repulsive forces, respectively induced by positive and negative pairs, can lead to deformed clustering, particularly when the number of clusters k is unknown. To address this, we propose NRCC, a CL-based deep clustering framework that generates cluster-friendly embeddings. NRCC repurposes Stochastic Gradient Hamiltonian Monte Carlo sampling as an approximately invariant data augmentation, to curate hard negative pairs that judiciously enhance and balance the two adversarial forces through a regularizer. By preserving the cluster structure in the CL embedding, NRCC retains local density landscapes in lower dimensions through neighborhood-conserving projections. This enables the application of mode-seeking clustering algorithms, typically hindered by high-dimensional CL feature spaces, to achieve exceptional accuracy without needing a predetermined k. NRCC’s superiority is demonstrated across various datasets with different scales and cluster structures, outperforming 20 state-of-the-art methods
Erratum: Correction: Nutritional status of tribal and non-tribal school-going children in rural Bangladesh: A comparative study (BMC public health (2024) 24 1 DOI: 10.1186/s12889-024-20487-9)
No full textFragQC: An efficient quantum error reduction technique using quantum circuit fragmentation
No full textQuantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity and also the classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study of different classical and quantum approaches to graph partitioning for finding such a cut. We present FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase in fidelity of 13.38% compared to direct execution without cutting the circuit, and 7.88% over the state-of-the-art ILP-based method for the benchmark circuits. The code for FragQC is available at https://github.com/arnavdas88/FragQC
GrI-CNN: Granulated Deep Learning Model With Interpretable Architecture for Remote Sensing Image Classification
No full textConvolutional neural networks (CNNs) are highly effective deep learning architectures for remote sensing (RS) image classification. However, the interpretability of CNN architecture remains challenging for further performance improvement. To address this issue, we propose an end-to-end interpretable CNN architecture called granulated interpretable CNN (GrI-CNN) within the granular computing (GrC) framework. The GrI-CNN uses fuzzy and rough sets to make each architecture component functionally interpretable. Fuzzy sets perform class-dependent (CD) granulation of the input feature space, while rough sets granulate the information with operations, such as reduct for dimension reduction, functional dependency (FD) of samples for the optimal selection of filters, and weighted membership of granules. The decision layer of GrI-CNN measures the roughness of overlapping granules, encodes the domain knowledge, and initializes the weights using weighted membership and roughness measures. We combined two classification networks at the decision layer to achieve the best possible performance: FD-based interpretable-extreme learning machine (I-ELM) and knowledge-encoded evolving granular neural network (e-GNN). E-GNN is a kind of GNN in which the shape and size of granules evolve based on the user\u27s needs. Thus, GrI-CNN uses only the required weight parameters and reduces computational time. We have demonstrated the superiority of GrI-CNN over similar state-of-the-art models for classifying multispectral and hyperspectral RS images
GSH-pH dual-responsive engineered codoped Prussian blue multimodal theranostic nanoplatform induces TP53 deregulated apoptotic death of MDA-MB-231 with enhanced T1 - T2W MRI
No full textThough chemotherapy is an effective clinical treatment, individual drugs hardly achieve precise controlled release, causing unavoidable severe side effects due to off-targeting in the absence of any receptor of triple negative breast cancer (TNBC). Fortunately, the emergence of on-demand drug-release nanoparticles allows potential alternatives to overcome the limitation. In this work, a dual-responsive multimodal theranostic targeted smart nanoplatform has been prepared by employing mesoporous silica (mSiO2)-coated Dy3+, Gd3+-codoped Prussian blue nanocubes (PBNCs) with CytC as a gatekeeper to seal pores of mSiO2 via disulfide and boronate ester bonds as intermediate linkers for intracellular high glutathione, and acidic pH responsive drug release. Hyaluronic acid has been used as a targeting motif, facilitating the uptake of our synthesized nanoplatform, wherein a cumulative drug-release profile demonstrated that the nanoplatform exhibits very low sustained drug release at pH 7.0 (∼20% in 25 h), while the release gets accelerated at pH 5.0 and 8.0 mM GSH (∼60% in 25 h), realizing the “trigger release” of drug. The nanoplatform possesses excellent biocompatibility to HEK 293 cells, while it has high cytotoxicity (∼67%) toward TNBC (IC50 = 35.8 μM), ascribed to synergistic chemo-phototherapeutic effect. An in silico analysis, followed by immunocytochemical studies illustrate that the down-regulated TP53-BAX/BCL2 and upregulated CASP3 CYTS networks initiate an apoptotic cell-death mechanism. In addition, the nanoplatform exhibits potency as a dual-mode MRI contrast agent with high relaxivity (r1 and r2) values of ∼7.84 and ∼29.3 mM-1 s-1, which will be highly facilitating for the diagnosis and tracking of TNBC management for personalized medicine