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Current approaches to breast cancer therapy include neoadjuvant systemic therapy (NST). The efficacy of NST is measured by pathologic complete response (pCR). A patient who attains pCR has significantly enhanced disease-free survival progress. The accurate prediction of pCR in response to a given treatment regimen could increase the likelihood of achieving pCR and prevent toxicities caused by treatments that are not effective. Th early prediction of response to NST can increase the likelihood of survival and help with decisions regarding breast-conserving surgery. An automated NST prediction framework that is able to precisely predict which patient undergoing NST will achieve a pathological complete response (pCR) at an early stage of treatment is needed. Here, we propose an end-to-end efficient multimodal spatiotemporal deep learning framework (deep-NST) framework to predict the outcome of NST prior or at an early stage of treatment. The deep-NST model incorporates imaging data captured at different timestamps of NST regimens, a tumor’s molecular data, and a patient’s demographic data. The efficacy of the proposed work is validated on the publicly available ISPY-1 dataset, in terms of accuracy, area under the curve (AUC), and computational complexity. In addition, seven ablation experiments were carried out to evaluate the impact of each design module in the proposed work. The experimental results show that the proposed framework performs significantly better than other recent methods.

Aim: In the present work, the preparation and catalytic activity of spinel ferrite (MFe2O4; M = Fe, Mn, Co, Cu, Ni) nanoparticles to synthesize 5-hydroxymethylfurfural (HMF) have been discussed. Background: Ferrites possess unique physicochemical properties, including excellent magnetic characteristics, high specific surface area, active surface sites, high chemical stability, tunable shape and size, and easy functionalization. These properties make them essential heterogeneous catalysts in many organic reactions. Objective: This study aims to synthesize a series of transition metal ferrite nanoparticles and use them in the dehydration of carbohydrates for 5-hydroxymethylfurfural (HMF) synthesis. Method: The ferrite nanoparticles were prepared via the co-precipitation method, and PXRD confirmed their phase stability. The surface area and the crystallite size of the nanoparticles were calculated using BET and PXRD, respectively. Result: The easily prepared heterogeneous nanocatalyst showed a significant catalytic performance, and among all spinel ferrites, CuFe2O4 revealed maximum catalytic ability. Conclusion: Being a heterogeneous catalyst and magnetic in nature, ferrite nanoparticles were easily recovered by using an external magnet and reused up to several runs without substantial loss in catalytic activity. Others: HMF was synthesized from fructose in a good yield of 71%.

This work reports the synthesis of semiconductor selenium nanoparticles/micro-rods (Se-NP/MRs) using the bio-fabrication approach and studies its photocatalytic activity under natural sunlight irradiation. Cabbage leaf extract was used as a reducing agent to fabricate Se-NP/MRs. X-ray diffraction patterns shown the crystalline structure with a crystallite size of 28.17 nm, conferring to planes of hexagonal structure. A characteristic sharp resonance peak in Raman spectra at 235 cm−1 endorsed to the Se – Se bond of crystalline Se-NP/MRs. The prominent peak in the FTIR spectra at 621 cm−1 supported the formation of Se-NP/MRs. Scanning electron micrograph revealed a nearly spherical shape with irregular layers in the 35‒95 nm size range. In contrast, the dimension of the micro-rods was observed to be ~ 10 µm in length and 2.5 µm in diameter. The bandgap energy of Se-NP/MRs was analysed to be 2.14 eV using the Tauc plot. The exciting photocatalytic performance of the bio-fabricated Se-NP/MRs revealed that the methylene blue degradation was ~ 86% in 40 min and ~ 97% in 140 min. Kinetic analysis shows that the pseudo-second-order correlation coefficient is well fitted compared to the pseudo-first-order. Therefore, this work opens the door to further exploration of nano-materials/composites with mixed nanoparticles/micro-rods morphology for sustainable applications in the removal of various textile and industrial contaminants.

In this paper, a lightweighted Intensive Feature Extrication Deep Network (ExtriDeNet) is proposed for precise hand gesture recognition (HGR). ExtriDeNet primarily consists of two blocks: Intensive Feature Fusion Block (IFFB) and Intensive Feature Assimilation Block (IFAB). IFFB incorporates two different scaled filters $$3\times 3$$ and $$5 \times 5$$ to capture contextual features of hands, while IFAB is designed by embedding influential features of IFFB with two extreme minute and high-level feature responses from two receptive fields generated by employing $$1\times 1$$ and $$7\times 7$$ sized filters, respectively. The combination of multiscaled filters enriches the network with the most significant features and enhances the learnability of the network. Thus, the proposed ExtriDeNet efficiently defines the distinctive features of different hand gesture classes and achieves high performance as compared to state-of-the-art HGR approaches. The performance of the proposed network is evaluated on the standard datasets: MUGD, Finger Spelling, OUHands, NUS-I, NUS-II and HGR1 for both subject-dependent and subject-independent scheme.

The existence of per- and polyfluoroalkyl substances (PFASs) in water is of serious interest due to their toxic, bioaccumulative, and persistent nature, and adsorption is an effective approach for the PFASs removal. In the present study, we developed a polymeric adsorbent by cross-linking chitosan and β-cyclodextrin using glutaraldehyde (Chi-Glu-β-CD) and evaluated its removal performance for perfluorobutanesulfonate (PFBS) from water. The results indicate that the performance was highly affected by solution pH; under a more acidic condition (e.g., pH 2.0), a higher removal efficiency was detected, and faster adsorption kinetics was observed with the rate constant (k

The micro-expression recognition (MER) has gained high attention in real-world applications: human-computer interaction, depression estimation, virtual reality, etc. However, MER systems still face difficulty in capturing the subtle information with spatial appearance of micro expressions. In this paper, an efficient and robust motion flow-guided MicroNet framework is proposed for MER. The proposed MicroNet consists: motion flow generator (MFGen) and avalanche feature (AFeat) block. The MFGen is introduced to extract the temporal changes at expressive regions by analyzing the motion intensity of pixels among the frames. Further, the AFeat block is designed to capture the spatiotemporal feature using a multi-lateral complementary feature (MCFeat) block. The MCFeat block is proposed to elicit the coarse and deep edge responses from multi-scale receptive fields. Thus, the proposed MicroNet estimates not only momentary variations but also learns affective appearance features of micro expressions. Furthermore, the efficacy of the proposed MicroNet is examined by conducting experiments on two experimental setups using six comprehensive datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC and COMPOSITE, with two validation schemes: leave-one-subject-out and cross-domain to demonstrate the generalization capacity and robustness of the proposed framework. Also, eight ablation experiments have been executed to validate each module’s role in the MicroNet framework.

Microexpressions are hard to spot due to fleeting and involuntary moments of facial muscles. Interpretation of microemotions from video clips is a challenging task. In this article, we propose an affective-motion imaging that cumulates rapid and short-lived variational information of microexpressions into a single response. Moreover, we have proposed an AffectiveNet: Affective-motion feature learning network that can perceive subtle changes and learns the most discriminative dynamic features to describe the emotion classes. The AffectiveNet holds two blocks: MICRoFeat and MFL block. MICRoFeat block conserves the scale-invariant features, which allows network to capture both coarse and tiny edge variations. Whereas, the MFL block learns microlevel dynamic variations from two different intermediate convolutional layers. Effectiveness of the proposed network is tested over four datasets by using two experimental setups: person independent and cross dataset validation. The experimental results of the proposed network outperform the state-of-the-art approaches with significant margin for MER approaches.

Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and finegrained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich survey articles on MER are available by summarizing the datasets, experimental settings, conventional and deep learning methods. In contrast, these studies lack the ability to convey the impact of network design paradigms and experimental setting strategies for DL based MER. Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs. Also, the detailed categorization of available MER frameworks is presented in various aspects of model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted by MER methods is presented. The challenges mentioned earlier and network design strategies may assist the affective computing research community in forge ahead in MER research. Finally, we point out the future directions, research needs and draw our conclusions.

Unlike prevalent facial expressions, micro expressions have subtle, involuntary muscle movements which are short-lived in nature. These minute muscle movements reflect true emotions of a person. Due to the short duration and low intensity, these micro-expressions are very difficult to perceive and interpret correctly. In this paper, we propose the dynamic representation of micro-expressions to preserve facial movement information of a video in a single frame. We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region. The LEARNet refines the salient expression features in accretive manner by incorporating accretion layers (AL) in the network. The response of the AL holds the hybrid feature maps generated by prior laterally connected convolution layers. Moreover, LEARNet architecture incorporates the cross decoupled relationship between convolution layers which helps in preserving the tiny but influential facial muscle change information. The visual responses of the proposed LEARNet depict the effectiveness of the system by preserving both high- and micro-level edge features of facial expression. The effectiveness of the proposed LEARNet is evaluated on four benchmark datasets: CASME-I, CASME-II, CAS(ME)^2 and SMIC. The experimental results after investigation show a significant improvement of 4.03%, 1.90%, 1.79% and 2.82% as compared with ResNet on CASME-I, CASME-II, CAS(ME)^2 and SMIC datasets respectively., Comment: Dynamic imaging, accretion, lateral, micro expression recognition

In this letter, we propose a novel lightweight network HiNet: hybrid inherited feature learning network and its variants: HiNet -ReLU, HiNet -Concat, Large Scale HiNet, 2 stack HiNet and 4 stack HiNet for facial expression recognition. In HiNet, we introduce the hybrid feature (HyFeat) block that follows a two - level hybrid structure to capture the local contextual information of expressive regions. In level -1, HyFeat block uses two different scaled conv filters to preserve the domain knowledge features for expressive regions. Similarly, level -2 captures the enriched features through refine edge variation of the receptive fields by employing another two filters. Thus, HyFeat block allows network to perpetuate prominent features of facial expressions and enhances the discriminability of the HiNet. Moreover, performance of the proposed HiNet is evaluated by conducting person dependent (PD) and person independent (PI) experiments on four datasets: CK+, MUG, AFEW and OULU respectively. Experimental results on CK+ dataset prove the adequacy of the proposed HiNet over its variants in terms of accuracy and computational complexity. Furthermore, the proposed HiNet uses approximately 5 /16, 1/138, 1/144 and 1/31 times less parameters as compared to the MobileNet, VGG -16, VGG - 19 and ResNet, respectively. The experimental and computational complexity analysis demonstrate the significant performance improvement of the proposed method over the state-of-the-art techniques in terms of recognition rate.

Polyacrylic acid hydrogel doped with copper ferrite nanoparticles has been prepared and studied for the removal and degradation of organic dye (methylene blue) from wastewater. Phase purity of nanoparticles was confirmed by powder X-ray diffraction and EDX analysis. Raman spectra showed three bands at Eg, F2g and A1g, attributing the formation of spinel phase with a band gap of 2.12 eV. Hydrogel composite showed an increase in weight by 1771% when soaked in water. Degradation and absorption studies of methylene blue dye solution with copper ferrite nanoparticles as well as hydrogel nanocomposite suggest absorption of dye by hydrogel and degradation with copper ferrite nanoparticles. Such hydrogel nanocomposites could be a promising material for mitigation of water pollution caused by organic dyes discharged by small and medium scale industries.

The emergence of inorganic and organic contaminants has raised great concerns owing to their adverse impact on human health and ecological security. Herein, first time one-pot process was applied for chitosan (CS) functionalization using graphene oxide (GO) and ethylenediaminetetraacetic acid (EDTA) for simultaneous capturing of toxic inorganic (lead (Pb