Rhodium(II)-Catalyzed Alkynyl Carbene Insertion into N-H Bonds.

The first insertion of an alkynyl carbene into N-H bonds under Rh-catalysis is developed. Alkynyl hydrazone carboxylates are used as donor-acceptor carbene precursors and are exquisitely inserted into the N-H bonds of various amines, amides, and 1,2-diamines. A wide variety of 3-alkynyl 3,4-dihydroquinoxalin-2(1H)-ones and densely functionalized α-alkynyl α-amino esters are obtained in good to excellent yields. Further, chemoselective N-H insertion reactions, mechanistic studies, and various synthetic transformations for obtaining valuable heterocycles are demonstrated.

Doyle-Kirmse Reaction on Alkynyl Hydrazone Carboxylates: Synthesis of 1,4-Allenyne and 1,5-Enyne Thioaryl Carboxylates.

The first Doyle-Kirmse reaction on alkynyl diazoacetates using allyl/propargyl sulfides is reported. The development provides diversified 1,5-enyne and 1,4-allenyne thioaryl carboxylates in good yields under ligand-/additive-free AuCl and Rh2(OAc)4 catalysis, respectively (48 examples, up to 96% yield). The study demonstrated the dual role of allyl sulfide as a ligand and substrate. Also, we have exemplified various synthetic modifications of the products to showcase the utility of different functional groups.

Porous materials as effective chemiresistive gas sensors.

Chemiresistive gas sensors (CGSs) have revolutionized the field of gas sensing by providing a low-power, low-cost, and highly sensitive means of detecting harmful gases. This technology works by measuring changes in the conductivity of materials when they interact with a testing gas. While semiconducting metal oxides and two-dimensional (2D) materials have been used for CGSs, they suffer from poor selectivity to specific analytes in the presence of interfering gases and require high operating temperatures, resulting in high signal-to-noise ratios. However, nanoporous materials have emerged as a promising alternative for CGSs due to their high specific surface area, unsaturated metal actives, and density of three-dimensional inter-connected conductive and pendant functional groups. Porous materials have demonstrated excellent response and recovery times, remarkable selectivity, and the ability to detect gases at extremely low concentrations. Herein, our central emphasis is on all aspects of CGSs, with a primary focus on the use of porous materials. Further, we discuss the basic sensing mechanisms and parameters, different types of popular sensing materials, and the critical explanations of various mechanisms involved throughout the sensing process. We have provided examples of remarkable performance demonstrated by sensors using these materials. In addition to this, we compare the performance of porous materials with traditional metal-oxide semiconductors (MOSs) and 2D materials. Finally, we discussed future aspects, shortcomings, and scope for improvement in sensing performance, including the use of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and porous organic polymers (POPs), as well as their hybrid counterparts. Overall, CGSs using porous materials have the potential to address a wide range of applications, including monitoring water quality, detecting harmful chemicals, improving surveillance, preventing natural disasters, and improving healthcare.

Decomposition of Alkynyl Hydrazones: Synthesis of Allenoates, Dihaloallenoates, and Angularly Fused Tricyclic Azepines.

An unprecedented decomposition of unprotected alkynyl hydrazones is attempted that has provided allenoates, tetrasubstituted α,γ-dihaloallenoates, and functionalized tricyclic azepines. A reaction of alkynyl hydrazones with N-halosuccinimides captures the electrophile in 2-fold that delivers fully substituted dibromo- and diiodoallenoates in good yields. In addition, a DABCO-promoted Wolff-Kishner reduction of hydrazones, followed by isomerization, provides versatile allenoates under mild conditions. In contrast, a similar decomposition with ambiphilic DBU furnishes a completely different tricyclic azepine scaffold in excellent yield and diastereoselectivity.

Synthesis of Tetrasubstituted 1,4-Dicarbonyl (Z)-2,3-Dihaloalkenes via Electrophilic Halogenation of Alkynyl Hydrazones.

A highly practical and stereoselective route to 1,4-dicarbonyl 2,3-dihaloalkenes is presented. The strategy involves bench-stable unprotected alkynyl hydrazones and commercially available N-halosuccinimides that provide γ-oxo-α,ß-(Z)-dihaloenoates in excellent yields with complete Z-selectivity. The protocol also furnishes vicinal dihaloalkenes with two different halogen atoms. Also, a straightforward one-pot synthesis of dihaloenoates from readily accessible 2-oxo-3-butynoate is demonstrated. In addition, potential synthetic transformations of 4-oxo-2,3-dibromoenoates are explored, which include the synthesis of valuable five- and six-membered heterocycles.

SAM C-GAN: a method for removal of face masks from masked faces.

The past years of COVID-19 have attracted researchers to carry out benchmark work in face mask detection. However, the existing work does not focus on the problem of reconstructing the face area behind the mask and completing the face that can be used for face recognition. In order to address this problem, in this work we have proposed a spatial attention module-based conditional generative adversarial network method that can generate plausible images of faces without masks by removing the face masks from the face region. The method proposed in this work utilizes a self-created dataset consisting of faces with three types of face masks for training and testing purposes. With the proposed method, an SSIM value of 0.91231 which is 3.89% higher and a PSNR value of 30.9879 which is 3.17% higher has been obtained as compared to the vanilla C-GAN method.

Synthesis of Alkynyl Hydrazones from Unprotected Hydrazine and Their Reactivity as Diazo Precursors.

Alkynyl hydrazones are synthesized conveniently from 2-oxo-3-butynoates and hydrazine by suppressing the susceptible formation of pyrazoles. The resultant hydrazones are transformed into alkynyl diazoacetates under metal-free and mild oxidative conditions in excellent yields. Further, the alkynyl cyclopropane and propargyl silane carboxylates are synthesized in good yields by developing an unprecedented copper-catalyzed alkynyl carbene transfer reaction.

Acidic graphene organocatalyst for the superior transformation of wastes into high-added-value chemicals.

Our dependence on finite fossil fuels and the insecure energy supply chains have stimulated intensive research for sustainable technologies. Upcycling glycerol, produced from biomass fermentation and as a biodiesel formation byproduct, can substantially contribute in circular carbon economy. Here, we report glycerol's solvent-free and room-temperature conversion to high-added-value chemicals via a reusable graphene catalyst (G-ASA), functionalized with a natural amino acid (taurine). Theoretical studies unveil that the superior performance of the catalyst (surpassing even homogeneous, industrial catalysts) is associated with the dual role of the covalently linked taurine, boosting the catalyst's acidity and affinity for the reactants. Unlike previous catalysts, G-ASA exhibits excellent activity (7508 mmol g-1 h-1) and selectivity (99.9%) for glycerol conversion to solketal, an additive for improving fuels' quality and a precursor of commodity and fine chemicals. Notably, the catalyst is also particularly active in converting oils to biodiesel, demonstrating its general applicability.

A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system.

Law offenders take advantage of face masks to conceal their identities and in the present time of the COVID-19 pandemic wearing face masks is a new norm which makes it a daunting task for the investigation agencies to identify the offenders. To address the issue of detection of people wearing face masks using surveillance cameras, we propose a novel face mask vision system that is based on an improved tiny YOLO v4 object detector. The face masks detection network of the proposed vision system is developed by integrating tiny YOLO v4 with spatial pyramid pooling (SPP) module and additional YOLO detection layer and tested and validated on a self-created face masks detection dataset consisting of more than 50,000 images. The proposed tiny YOLO v4-SPP network achieved a mAP (mean average precision) value of 64.31% on the employed dataset which was 6.6% higher than tiny YOLO v4. Specifically, for detection of the presence of a small object like a face mask on the face region, the proposed tiny YOLO v4-SPP based vision system achieved an AP (average precision) of 84.42% which was 14.05% higher than the original tiny YOLO v4 thus, ensuring that the proposed network is capable of accurate detection of a mask on the face region in real-time surveillance applications where visibility of complete face area is a guideline.

On the performance evaluation of object classification models in low altitude aerial data.

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures.

Postpartum depression burden and associated factors in mothers of infants at an urban primary health center in Delhi, India.

OBJECTIVE: Postpartum depression is a nonpsychotic mental health condition that impairs both the immediate and long-term health of both the mother and her child. MATERIALS AND METHODS: We conducted a cross-sectional study from January to June 2019 at a primary care clinic in Delhi, India, to estimate the burden of postpartum depression in women having an infant child. The Hindi version of the Edinburgh Postnatal Depression Scale was used to screen for the depression in the participants. Data were analyzed with IBM SPSS software version 25. P <0.05 was considered statistically significant. RESULTS: A total of 210 women were screened, and 61 (29%) were detected with postpartum depression. On multivariate analysis, women reporting low and medium levels of perceived social support had significantly higher odds of having postpartum depression. However, depressive symptoms were not associated with the sex and age of the infant or even the sex composition of the women's other children. CONCLUSION: Postpartum depression represents a major public health challenge in India. Regular, mandatory screening for postpartum depression is needed at primary health facilities in resource-constrained settings for an extended period postchildbirth.

The Hindi Version of the Breastfeeding Self-Efficacy Scale-Short Form: Reliability and Validity Assessment.

BACKGROUND: Despite the cultural sanctity and elevation of breastfeeding practices, nearly one in two Indian women nationwide are unable to practice exclusive breastfeeding (EBF). Early identification of mothers at risk of reduced breastfeeding through a suitable instrument can enable targeted interventions for breastfeeding support. OBJECTIVES: We conducted this study with the objectives of translation into Hindi and to psychometrically test the Breastfeeding Self-Efficacy Scale-Short Form (BSES-SF) and to ascertain the sociodemographic and other correlates of breastfeeding self-efficacy. METHODS: The BSES-SF was translated into Hindi using a back and forth translation process to ensure linguistic validity. We enrolled a total of 210 married women who were mothers of infants at an urban primary health center in Delhi, India. RESULTS: The Cronbach's alpha for the Hindi translation of the BSES-SF was 0.87 with all except one correlation coefficient <0.3. We conducted an exploratory factor analysis using principal component analysis that revealed a two-component solution, which explained 47.9% and 16.7% of the total variance, respectively. Mothers perceiving higher social support registered significantly higher mean BSES-SF scores, indicating a greater confidence in their breastfeeding abilities (P = 0.01). However, breastfeeding self-efficacy was unrelated to the mother's age, parity, and education. The women planning to breastfeed partially had lower BSES-SF scores compared to the woman adhering to EBF norms (P < 0.001). CONCLUSION: The Hindi version of the BSES-SF demonstrates good reliability and validity and can also explain previous and planned breastfeeding behavior in mothers of infants.