Elucidating the assembly of nanoparticle organic hybrid materials (NOHMs) near an electrode interface with varying potential using neutron reflectivity.

A critical concern regarding electrolyte formulation in an electrochemical environment is the impact of the interaction of the multiple components (i.e., supporting electrolyte or additive) with the electrode surface. Recently, liquid-like neat Nanoparticle Organic Hybrid Materials (NOHMs) have been considered as an electrolyte component to improve the transport of redox-active species to the electrode surface. However, the structure and assembly of the NOHMs near the electrode surface is unknown and could significantly impact the electrode-electrolyte interface. Hence, we have investigated the depth profile of polyetheramine (HPE) polymer and NOHM-I-HPE (nanoparticles with ionically bonded HPE polymer) in deuterated water (D2O) in the presence of two different salts (KHCO3 and ZnCl2) near two different electrode surfaces using neutron reflectometry. Moreover, the depth profile of the NOHM-I-HPE near the electrode surface in a potential has also been studied with in situ reflectivity experiments. Our results indicate that a change in the chemical structure/hydrophilicity of the electrode surface does not significantly impact the ordering of HPE polymer or NOHM-I-HPE near the surface. This study also indicates that the NOHM-I-HPE particles form a clear layer near the electrode surface immediately above an adsorbed layer of free polymer on the electrode surface. The addition of salt does not impact the layering of NOHM-I-HPE, though it does alter the conformation of the polymer grafted to the nanoparticle surface and free polymer sequestered near the surface. Finally, the application of negative potential results in an increased amount of free polymer near the electrode surface. Correlating the depth profile of free polymer and NOHM-I-HPE particles with the electrochemical performance indicates that this assembly of free polymer near the electrode surface in NOHM-I-HPE solutions contributes to the higher current density of the system. Therefore, this holistic study offers insight into the importance of the assembly of NOHM-I-HPE electrolyte and free polymer near the electrode surface in an electrochemical milieu on its performance.

A smart medicine reminder kit with mobile phone calls and some health monitoring features for senior citizens.

The demand for an effective system that combines cutting-edge technologies with medical research to improve healthcare systems has increased with the development of medical technology. The most fundamental form of disease prevention is taking the right medication when needed. With the right care, many fatal diseases can be cured or prevented. Therefore, it is crucial to follow the doctor's recommended drug plan. Healthcare experts now have serious concerns about patients not being able to take their prescribed medications on time, particularly elderly patients. Due to age-related memory loss, people who have been given multiple prescriptions at once over an extended period of time are more likely to forget to take their medication on time or to take the wrong medication. Sometimes, a patient's inability to take the right medication at the right time might have a major impact on their health. Aside from being forgetful, patients, especially the elderly and illiterate, may not be able to read the name stated on medical containers, leading to the consumption of the wrong medication. These errors contribute to non-adherence to pharmaceuticals, which is detrimental to the patient's health. As a result, there is a significant problem that hinders the success of the treatment. The medication reminder system is intended for people who frequently take medications or vitamin supplements in order to handle this. In order to help an elderly person properly take their medication and help the patient have a healthy life, we have created a ground-breaking portable multifunctional medicine reminder kit with phone calls. Other intelligent characteristics of the smart medicine reminder include the capacity to show the time, date, and day in real time, the detection of smoke, the measurement of air humidity and temperature in the room, the measurement of heartbeats per second, the patient's body temperature, and the oxygen saturation level.

The fungal endophyte Metarhizium anisopliae (MetA1) coordinates salt tolerance mechanisms of rice to enhance growth and yield.

The implementation of salt stress mitigation strategies aided by microorganisms has the potential to improve crop growth and yield. The endophytic fungus Metarhizium anisopliae shows the ability to enhance plant growth and mitigate diverse forms of abiotic stress. We examined the functions of M. anisopliae isolate MetA1 (MA) in promoting salinity resistance by investigating several morphological, physiological, biochemical, and yield features in rice plants. In vitro evaluation demonstrated that rice seeds primed with MA enhanced the growth features of rice plants exposed to 4, 8, and 12 dS/m of salinity for 15 days in an agar medium. A pot experiment was carried out to evaluate the growth and development of MA-primed rice seeds after exposing them to similar levels of salinity. Results indicated MA priming in rice improved shoot and root biomass, photosynthetic pigment contents, leaf succulence, and leaf relative water content. It also significantly decreased Na+/K+ ratios in both shoots and roots and the levels of electrolyte leakage, malondialdehyde, and hydrogen peroxide, while significantly increasing proline content in the leaves. The antioxidant enzymes catalase, glutathione S-transferase, ascorbate peroxidase, and peroxidase, as well as the non-enzymatic antioxidants phenol and flavonoids, were significantly enhanced in MA-colonized plants when compared with MA-unprimed plants under salt stress. The MA-mediated restriction of salt accumulation and improvement in physiological and biochemical mechanisms ultimately contributed to the yield improvement in salt-exposed rice plants. Our findings suggest the potential use of the MA seed priming strategy to improve salt tolerance in rice and perhaps in other crop plants.

Role of salicylic acid in improving the yield of two mung bean genotypes under waterlogging stress through the modulation of antioxidant defense and osmoprotectant levels.

Waterlogging (WL) is a major hindrance to the growth and development of leguminous crops, including mung bean. Here, we explored the effect of salicylic acid (SA) pretreatment on growth and yield output of two elite mung bean genotypes (BU Mung bean-4 and BU Mung bean-6) subjected to WL stress. SA pretreatment significantly improved shoot dry weight, individual leaf area, and photosynthetic pigment contents in both genotypes, while those improvements were higher in BU Mung bean-6 when compared with BU Mung bean-4. We also found that SA pretreatment significantly reduced the reactive oxygen species-induced oxidative burden in both BU Mung bean-6 and BU Mung bean-4 by enhancing peroxidase, glutathione S-transferase, catalase, and ascorbate peroxidase activities, as well as total flavonoid contents. SA pretreatment further improved the accumulation of proline and free amino acids in both genotypes, indicating that SA employed these osmoprotectants to enhance osmotic balance. These results were particularly corroborated with the elevated levels of leaf water status and leaf succulence in BU Mung bean-6. SA-mediated improvement in physiological and biochemical mechanisms led to a greater yield-associated feature in BU Mung bean-6 under WL conditions. Collectively, these findings shed light on the positive roles of SA in alleviating WL stress, contributing to yield improvement in mung bean crop.

Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna.

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.

TrIncNet: a lightweight vision transformer network for identification of plant diseases.

In the agricultural sector, identifying plant diseases at their earliest possible stage of infestation still remains a huge challenge with respect to the maximization of crop production and farmers' income. In recent years, advanced computer vision techniques like Vision Transformers (ViTs) are being successfully applied to identify plant diseases automatically. However, the MLP module in existing ViTs is computationally expensive as well as inefficient in extracting promising features from diseased images. Therefore, this study proposes a comparatively lightweight and improved vision transformer network, also known as "TrIncNet" for plant disease identification. In the proposed network, we introduced a modified encoder architecture a.k.a. Trans-Inception block in which the MLP block of existing ViT was replaced by a custom inception block. Additionally, each Trans-Inception block is surrounded by a skip connection, making it much more resistant to the vanishing gradient problem. The applicability of the proposed network for identifying plant diseases was assessed using two plant disease image datasets viz: PlantVillage dataset and Maize disease dataset (contains in-field images of Maize diseases). The comparative performance analysis on both datasets reported that the proposed TrIncNet network outperformed the state-of-the-art CNN architectures viz: VGG-19, GoogLeNet, ResNet-50, Xception, InceptionV3, and MobileNet. Moreover, the experimental results also showed that the proposed network had achieved 5.38% and 2.87% higher testing accuracy than the existing ViT network on both datasets, respectively. Therefore, the lightweight nature and improved prediction performance make the proposed network suitable for being integrated with IoT devices to assist the stakeholders in identifying plant diseases at the field level.

Effect of Metarhizium anisopliae (MetA1) on growth enhancement and antioxidative defense mechanism against Rhizoctonia root rot in okra.

Rhizoctonia solani is an important necrotrophic pathogenic fungus that causes okra root disease and results in severe yield reduction. Many biocontrol agents are being studied with the intent of improving plant growth and defense systems and reducing crop loss by preventing fungal infections. Recently, a member of the Hypocrealean family, Metarhizium anisopliae, has been reported for insect pathogenicity, endophytism, plant growth promotion, and antifungal potentialities. This research investigated the role of M. anisopliae (MetA1) in growth promotion and root disease suppression in okra. The antagonism against R. solani and the plant growth promotion traits of MetA1 were tested in vitro. The effects of endophytic MetA1 on promoting plant growth and disease suppression were assessed in planta. Dual culture and cell-free culture filtrate assays showed antagonistic activity against R. solani by MetA1. Some plant growth promotion traits, such as phosphate solubilization and catalase activity were also exhibited by MetA1. Seed primed with MetA1 increased the shoot, root, leaves, chlorophyll content, and biomass content compared to control okra plants. The plants challenged with R. solani showed the highest hydrogen peroxide (H2O2) and lipid peroxidation (MDA) contents in the leaves of okra. Whereas MetA1 applied plants showed a reduction of H2O2 and MDA by 5.21 and 14.96%, respectively, under pathogen-inoculated conditions by increasing antioxidant enzyme activities, including catalase (CAT), peroxidase (POD), glutathione S-transferase (GST), and ascorbate peroxidase (APX), by 30.11, 10.19, 5.62, and 5.06%, respectively. Moreover, MetA1 increased soluble sugars, carbohydrates, proline, and secondary metabolites, viz., phenol and flavonoid contents in okra resulting in a better osmotic adjustment of diseases infecting plants. MetA1 reduced disease incidence by 58.33% at 15 DAI compared to the R. solani inoculated plant. The results revealed that MetA1 improved plant growth, elevated the plant defense system, and suppressed root diseases caused by R. solani. Thus, MetA1 was found to be an effective candidate for the biological control program.

Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna.

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi-Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi-Uda antenna for the 5G communication system. When considering the antenna's operating frequency, its dimensions are [Formula: see text]. The antenna has an operating frequency of 3.5 GHz, a return loss of [Formula: see text] dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio's simulation and circuit design tools in Agilent ADS software are used to derive the antenna's equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.

Propulsion of Homonuclear Colloidal Chains Based on Orientation Control under Combined Electric and Magnetic Fields.

The remarkable efficiency and dynamics of micromachines in living organisms have inspired researchers to make artificial microrobots for targeted drug delivery, chemical sensing, cargo transport, and waste remediation applications. While several self- and directed-propulsion mechanisms have been discovered, the phoretic force has to be generated via either asymmetric surface functionalization or sophisticated geometric design of microrobots. As a result, many symmetric structures assembled from isotropic colloids are ruled out as viable microrobot possibilities. Here, we propose to utilize orientation control to actuate axially symmetric micro-objects with homogeneous surface properties, such as linear chains assembled from superparamagnetic microspheres. We demonstrate that the fore-and-aft symmetry of a horizontal chain can be broken by tilting it with an angle relative to the substrate under a two-dimensional magnetic field. A superimposed alternating current electric field propels the tilted chains. Our experiments and numerical simulation confirm that the electrohydrodynamic flow along the electrode is unbalanced surrounding the tilted chain, generating hydrodynamic stresses that both propel the chain and reorient it slightly toward the substrate. Our work takes advantage of external fields, where the magnetic field, as a driving wheel and brake, controls chain orientation and direction, while the electric field, as an engine, provides power for locomotion. Without the need to create complex-shaped micromotors with intricate building blocks, our work reveals a propulsion mechanism that breaks the symmetry of hydrodynamic flow by manipulating the orientation of a microscopic object.

Performance Analysis of Linearly Arranged Concentric Circular Antenna Array with Low Sidelobe Level and Beamwidth Using Robust Tapering Technique.

In this research, a novel antenna array named Linearly arranged Concentric Circular Antenna Array (LCCAA) is proposed, concerning lower beamwidth, lower sidelobe level, sharp ability to detect false signals, and impressive SINR performance. The performance of the proposed LCCAA beamformer is compared with geometrically identical existing beamformers using the conventional technique where the LCCAA beamformer shows the lowest beamwidth and sidelobe level (SLL) of 12.50° and -15.17 dB with equal elements accordingly. However, the performance is degraded due to look direction error, for which robust techniques, fixed diagonal loading (FDL), optimal diagonal loading (ODL), and variable diagonal loading (VDL), are applied to all the potential arrays to minimize this problem. Furthermore, the LCCAA beamformer is further simulated to reduce the sidelobe applying tapering techniques where the Hamming window shows the best performance having 17.097 dB less sidelobe level compared to the uniform window. The proposed structure is also analyzed under a robust tapered (VDL-Hamming) method which reduces around 69.92 dB and 48.39 dB more sidelobe level compared to conventional and robust techniques. Analyzing all the performances, it is clear that the proposed LCCAA beamformer is superior and provides the best performance with the proposed robust tapered (VDL-Hamming) technique.

Selection of Lentil (Lens Culinaris (Medik.)) Genotypes Suitable for High-Temperature Conditions Based on Stress Tolerance Indices and Principal Component Analysis.

Legumes, including lentil, are a valuable source of carbohydrates, fiber, protein and vitamins and minerals. Their nutritional characteristics have been associated with a reduction in the incidence of various cancers, HDL cholesterol, type 2 diabetes and heart disease. Among these quality parameters, lectins have been associated with reducing certain forms of cancer, activating innate defense mechanisms and managing obesity. Protease inhibitors such as trypsin and chymotrypsin inhibitors have been demonstrated to reduce the incidence of certain cancers and demonstrate potent anti-inflammatory properties. Angiotensin I-converting enzyme (ACE) inhibitor has been associated with a reduction in hypertension. Therefore, legumes, including lentils, should be part of our daily food intake. However, high temperatures at the terminal stage is a major abiotic constraint leading to a reduction in lentil yield and seed quality. Thus, the selection of heat-tolerant genotypes is essential to identifying the potential for high yields with stable performance. To select lentil genotypes, an experiment was conducted with 60 genotypes including local landraces, advanced breeding lines, commercial varieties and exotic germplasm under stress and non-stress conditions from 2019 to 2020. This study was followed by a subset study involving screening based on a few physicochemical parameters and reproductive traits along with field performances. Different tolerance indices (i.e., stress susceptible index (SSI), relative heat index (RHI), tolerance (TOL), mean productivity (MP), stress tolerance index (STI), geometric mean productivity (GMP), yield index (YI), yield stability index (YSI), heat-resistance index (HRI), modified stress-tolerance index (MSTI), abiotic tolerance index (ATI) and stress susceptibility percentage (SSPI)) were used for the selection of the genotypes along with field performance. Biplot analysis was further performed for choosing the most suitable indices. Based on principal components analysis, the GMP, MP, RRI, STI, YI, YSI, ATI and MSTI indices were identified as the most reliable stress indicators, and these indicators might be used for distinguishing heat-tolerant genotypes. Based on the stress indices, the genotypes BLX 05002-3, BLX 10002-20, LRIL-21-1-1-1-1, LRIL-21-1-1-1-1-6 and BLX 09015 were selected as the most stable and heat-tolerant genotypes. In contrast, the genotypes LG 198, Bagura Local, BLX 0200-08-4, RL-12-178, Maitree, 91517 and BLX 11014-8 were selected as the most heat sensitive. Data also exhibited an average yield reduction of 59% due to heat stress on the lentils. Moreover, eight heat-tolerant (HT) genotypes (BLX 09015, PRECOZ, LRL-21-112-1-1-1-1-6, BLX 05002-3, LR-9-25, BLX 05002-6, BARI Masur-8 and RL-12-181), and two heat-susceptible (HS) genotypes (BLX 12009-6, and LG 198) were selected from the screened genotypes and subjected to further analysis by growing them in the following year under similar conditions to investigate the mechanisms associated with heat tolerance. Comparative studies on reproductive function and physiochemical traits revealed significantly higher pollen viability, proline accumulation, relative water content, chlorophyll concentration and a lower membrane stability index in HT genotypes under heat stress. Therefore, these heat-tolerant genotypes could be used as the parents in the hybridization program for achieving heat-tolerant transgressive segregation.

Biofilm-mediated decolorization, degradation and detoxification of synthetic effluent by novel biofilm-producing bacteria isolated from textile dyeing effluent.

Biofilm-mediated bioremediation of xenobiotic pollutants is an environmental friendly biological technique. In this study, 36 out of 55 bacterial isolates developed biofilms in glass test tubes containing salt-optimized broth plus 2% glycerol (SOBG). Scanning electron microscopy, Fourier transform infrared (FTIR) spectroscopy, and Congo red- and Calcofluor binding results showed biofilm matrices contain proteins, curli, nanocellulose-rich polysaccharides, nucleic acids, lipids, and peptidoglycans. Several functional groups including -OH, N-H, C-H, CO, COO-, -NH2, PO, C-O, and C-C were also predicted. By sequencing, ten novel biofilm-producing bacteria (BPB) were identified, including Exiguobacterium indicum ES31G, Kurthia gibsonii ES43G, Kluyvera cryocrescens ES45G, Cedecea lapagei ES48G, Enterobacter wuhouensis ES49G, Aeromonas caviae ES50G, Lysinibacillus sphaericus ES51G, Acinetobacter haemolyticus ES52G, Enterobacter soli ES53G, and Comamonas aquatica ES54G. The Direct Red (DR) 28 (a carcinogenic and mutagenic dye used in dyeing and biomedical processes) decolorization process was optimized in selected bacterial isolates. Under optimum conditions (SOBG medium, 75 mg L-1 dye, pH 7, 28 °C, microaerophilic condition and within 72 h of incubation), five of the bacteria tested could decolorize 97.8% ± 0.56-99.7% ± 0.45 of DR 28 dye. Azoreductase and laccase enzymes responsible for biodegradation were produced under the optimum condition. UV-Vis spectral analysis revealed that the azo (-NN-) bond peak at 476 nm had almost disappeared in all of the decolorized samples. FTIR data revealed that the foremost characteristic peaks had either partly or entirely vanished or were malformed or stretched. The chemical oxygen demand decreased by 83.3-91.3% in the decolorized samples, while plant probiotic bacterial growth was indistinguishable in the biodegraded metabolites and the original dye. Furthermore, seed germination (%) was higher in the biodegraded metabolites than the parent dye. Thus, examined BPB could provide potential solutions for the bioremediation of industrial dyes in wastewater.

Deep learning-based approach for identification of diseases of maize crop.

In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of 'Inception-v3' network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.

Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents.

Deep eutectic solvents (DESs) are an emerging class of non-aqueous solvents that are potentially scalable, easy to prepare and functionalize for many applications ranging from biomass processing to energy storage technologies. Predictive understanding of the fundamental correlations between local structure and macroscopic properties is needed to exploit the large design space and tunability of DESs for specific applications. Here, we employ a range of computational and experimental techniques that span length-scales from molecular to macroscopic and timescales from picoseconds to seconds to study the evolution of structure and dynamics in model DESs, namely Glyceline and Ethaline, starting from the parent compounds. We show that systematic addition of choline chloride leads to microscopic heterogeneities that alter the primary structural relaxation in glycerol and ethylene glycol and result in new dynamic modes that are strongly correlated to the macroscopic properties of the DES formed.

DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals.

The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world's food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop this models, data pertaining to four different abiotic stress (namely, cold, drought, heat and salinity) responsive proteins of the crops belonging to poaceae family were retrieved from public domain. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models as compared to GeLU activation function, SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) was developed using flask API, along with its mobile app. This server/App will provide researchers a convenient tool, which is rapid and economical in identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.

A lightweight convolutional neural network for recognition of severity stages of maydis leaf blight disease of maize.

Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and overall crop yield. Therefore, it is quite essential to identify the disease at the earliest possible stage to overcome the yield loss. In this study, we created an image database of maize crop, MDSD (Maydis leaf blight Disease Severity Dataset), containing 1,760 digital images of MLB disease, collected from different agricultural fields and categorized into four groups viz. healthy, low, medium and high severity stages. Next, we proposed a lightweight convolutional neural network (CNN) to identify the severity stages of MLB disease. The proposed network is a simple CNN framework augmented with two modified Inception modules, making it a lightweight and efficient multi-scale feature extractor. The proposed network reported approx. 99.13% classification accuracy with the f1-score of 98.97% on the test images of MDSD. Furthermore, the class-wise accuracy levels were 100% for healthy samples, 98% for low severity samples and 99% for the medium and high severity samples. In addition to that, our network significantly outperforms the popular pretrained models, viz. VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile for the MDSD image database. The experimental findings revealed that our proposed lightweight network is excellent in identifying the images of severity stages of MLB disease despite complicated background conditions.

Computational Investigation of Multifaceted Cationic Rearrangement and Stereo- and Regioselectivity in the Formation of Dysideanone's Analogues.

Mechanistic studies of regiodivergent arylations of cycloalkanols to furnish enantioenriched dysideanone's analogues are performed by employing density functional theory (DFT) calculations (B3LYP-D3(SMD)/6-311++G**//B3LYP-D3/6-31+G** level of theory). On the basis of our calculations, remote γ'-C-H arylation is preferred for unsubstituted carbinol 1, an outcome from combined factors like carbocationic stability, less steric hindrance during C-C coupling, and facile dearomatization. Meanwhile, in the presence of dimethyl substituent 1Me, regioselective γ-arylation is favored by 3.4 kcal/mol, and both findings are in agreement with the reported experimental observations. Most importantly, we concur that the barrier associated with the formation of carbocation 6 and its substituted analogues correlates with the C-H arylation outcomes. Furthermore, the ß-arylation route remains unlikely for all the reaction pathways explored in this study.

Conformational Preference of Macrocycles Investigated by Ion-Mobility Mass Spectrometry and Distance Geometry Modeling.

Macrocycles provide intricate shape manifolds that leverage the depth of the modern organic chemistry toolbox. Novel chemistry can be introduced via new bond types and unique torsional angles inaccessible by traditional small molecules and biomolecules. In this work, we investigate the conformational space of a class of biscationic macrocycles in protic and aprotic solvents using a combination of ion-mobility spectrometry mass spectrometry, distance geometry modeling, and quantum mechanical calculations. We identify at least three major conformations of the macrocycles. Two of the conformations are rotational isomers in which the amide (carbonyl amide) N-C bond of the acyl hydrazine can adopt either E- or Z-configuration. The E- and Z-rotational isomers were separately observed in previous X-ray crystallography studies on the same set of macrocycles, but both isomers were never proved to exist for the same molecule. We show that low-dielectric solvents and counterions, such as Cl- or PF6-, appear to stabilize the Z-conformation. Lastly, desolvation of the macrocycles in the absence of bound counterions yields a gas-phase "flat" Z-conformation. Our results suggest that the macrocycles are flexible and behave much like short polypeptides. The combination of ion-mobility spectrometry mass spectrometry and distance geometry modeling provides a versatile and robust approach to unravel fundamental information on the flexible chemical space of macrocycles.