Growth of Rural Retailing in India with Reference to Kolhapur District

The aim of the study is to identify the growth of  rural retailing  in India with reference to Kolhapur district. A decade ago, the rural market was more unstructured and was not a prioritized target location for corporate. There were no innovative approach and advertisement campaigns. A distribution system did exist, but was feeble. Illiteracy and lack of technology were the other factors leading to the poor reach of merchandise and lower level of awareness amongst villagers. Gradually, corporate realized that there was saturation, stiff competition and clutter in the urban market, and a demand was building up in rural areas. Seeing the vast potential of 70% of Indians living in rural areas, they started focusing on these unexplored, high-potential areas. In India totally there are 5, 70,000 villages and nearly 60 percent of the rural income comes from agriculture. As a result, retail outlets have sprung up in practically all the villages that store merchandises of various brands and categories. To attract the customers, rural retailing requires separate retailing approach for the retailing mix elements include, Merchandise, Cost, Location and Advertisements which could be formulated after studying the market carefully. Merchandise itself might require modifications due to different nature of population, pricing have to be carefully designed since rural consumers unlike their urban counterparts spend less on consumer merchandise, location have to be decided for easy accessibility and promoting the merchandise to encourage the sales. Retailing Strategy is affected by various factors like Type of merchandise (Durable or non-durable), profile of target market, and facilities available for using retailing mix etc. Keywords: Retailing approach, Rural Retailers, Merchandise, Cost, Location, and Advertisement. DOI: 10.7176/EJBM/12-13-05 Publication date:May 31st 202

Pathogenic and Molecular Characterization of Fusarium oxysporum f.sp. ciceri Causing Chickpea Wilt through ISSR Markers

In the present investigation the pathogenic and genetic variability was assayed, amongst the seven isolates of Fusarium oxysporum f.sp. ciceri (Foc) collected from different agro-climatic zones of Maharashtra State, India. The isolates of Fusarium oxysporum f.sp.ciceri were confirmed by SCAR marker which yielded 1.5 KD band. The pathogenicity of each isolate was confirmed using the wilt susceptible chickpea genotype JG-62. On the basis of pathogenic ability the isolates were grouped as highly pathogenic (FOC-2, FOC-5, FOC-6), strongly pathogenic (FOC-1,FOC-3) and moderately pathogenic (FOC-4,FOC-7). Eight Inter Simple Sequence Repeats primers (ISSRs) were used to determine the genetic variability in seven isolates Fusarium oxysporum f.sp. ciceri. The seven primers produced 80 scorable bands. Off 80 bands, 73 bands were polymorphic and average level of polymorphism was 91.25 per cent. In UPGMA analysis, Foc-1 (Wardha) was found to have higher value of similarity coefficient (0.8375) whereas Foc-2 (Lonar) was found to have lower value of similarity coefficient (0.4625). The isolates of Fusarium oxysporum f.sp.ciceri were grouped into two major clusters. First group, cluster-A includes isolates belonging to Wardha, Washim and Lonar. Second group, cluster-B includes Nashik, Ahmednagar, Rahuri and Pune. It shows that Foc-1 (Wardha) have higher value of similarity coefficient with Foc-3 (Washim) whereas Foc-2 (Lonar) have lower value of similarity coefficient with Foc-5 (Ahmednagar).The similarity matrix indicated that seven isolates of Fusarium oxysporum f.sp.ciceri exhibited in between 46-84 per cent similarity coefficient

Subclasses of Analytic and Multivalent Functions Defined by Extended Derivative Operator of Ruscheweyh’s Type

By means of certain extended derivative operator of Ruscheweyh’s type, we introduce and investigate two new subclasses of p-valently analytic functions of complex order. The various results obtained here for each of these subclasses included coefficient estimate, distortion theorem, radius of starlikeness, convexity and closure theorem. Keywords & Phrases: - Multivalent function, coefficient estimate, distortion theorem, radius of starlikeness, differential operator

Study of Municipal Solid Waste Management using Biogas Projects (Spl. Ref. to Mailhem Ikos Environment)

The fast growing urbanization affects the acute problem of Solid Waste Management. The per capita waste generation in India has 150 Million Ton (Per Day). In India out of total Maharashtra state has maximum of solid waste generation. Such situation has created a stress on infrastructure, environment, human health & budgetary resources. The total quantity of per day waste generation is 1600 to 1700 metric tons in Pune Municipal Corporation. Thus it is necessary to make the proper management of solid waste. i.e. collection  of waste, transportation, segregation, storage & waste reduction at source processing & disposal. This study emphasizes on the assessment of detail process of solid waste management by using Bio Energy Projects: Mailhem Ikos process plant

Assessment of the Knowledge Level of Pomegranate Production Technologies in Maharashtra, India

China is the world's top fruit grower, with India ranking in second. Maharashtra, Tamil Nadu, Karnataka, Andhra Pradesh, Gujrat, Bihar, and Uttar Pradesh are the main fruit-growing states in India. The present study was conducted in Maharashtra with the specific objective of determining “Knowledge Level of the Pomegranate Growers”. Nashik, Sholapur and Ahmednagar districts were purposively selected for the study, as they are some of the maximum pomegranates growing districts in Maharashtra state. A Total of 180 pomegranate growers were selected from six talukas of these districts. Descriptive statistics was used for data analysis. The study revealed that more than four fifth (83.34 %) of the pomegranate growers had medium level of knowledge about pomegranate production technologies, followed by 12.77 per cent and 03.89 per cent of the pomegranate growers having low and high level of knowledge, respectively

STUDY OF POLLEN MORPHOLOGY OF SOME ORNAMENTAL PLANTS

Pollen morphology was investigated in ten common ornamental plant species: Hibiscus rosa-sinensis Linn, Rosa indica L.., Ixora coccinea L., Sphagneticola trilobata (L.) Pruski, Catharanthus roseus (L.) G. Don, Portulaca oleraceae L., Talinum paniculatum (Jacq.) Gaertn., Clitoria ternatea L., Zephyranthes candida (Lindl.) Herb., and Polianthes tuberosa L. Observations were made using photographic documentation and standard palynological references. The study recorded morphological traits including pollen size, shape, aperture type, and surface ornamentation. Pollen size ranged from small (~18 µm) in Sphagneticola to very large (>135 µm) in Hibiscus. Most dicot families (Rosaceae, Apocynaceae, Rubiaceae, Fabaceae, Asteraceae, Talinaceae, Malvaceae, Portulacaceae) exhibited tricolporate or colporate (three-aperture) pollen, whereas monocots (Amaryllidaceae, Asparagaceae) displayed monosulcate or disulcate pollen types. Surface ornamentation showed notable variation, such as spiny/echinate exine in Hibiscus and perforate or reticulate patterns in other taxa. These variations reflected taxonomic relationships and possible adaptations to different pollination syndromes. The findings highlight the value of pollen morphological characteristics as diagnostic tools in plant identification and as indicators in evolutionary and systematic studies

Global-Local Self-Attention-Based Long Short-Term Memory with Optimization Algorithm for Speaker Identification

Speaker identification (SI) involves recognizing a speaker from a group of unknown speakers, while speaker verification (SV) determines if a given voice sample belongs to a particular person. The main drawbacks of SI are session variability, noise in the background, and insufficient information. To mitigate the limitations mentioned above, this research proposes Global Local Self-Attention (GLSA) based Long Short-Term Memory (LSTM) with Exponential Neighborhood – Grey Wolf Optimization (EN-GWO) method for effective speaker identification using TIMIT and VoxCeleb 1 datasets. The GLSA is incorporated in LSTM, which focuses on the required data, and the hyperparameters are tuned using the EN-GWO, which enhances speaker identification performance. The GLSA-LSTM with EN-GWO method acquires an accuracy of 99.36% on the TIMIT dataset, and an accuracy of 93.45% on the VoxCeleb 1 datasets, while compared to SincNet and Generative Adversarial Network (SincGAN) and Hybrid Neural Network – Support Vector Machine (NN-SVM). ABSTRAK: Pengenalpastian pembicara (Speaker Identification, SI) melibatkan pengenalan pembicara daripada kumpulan pembicara yang tidak dikenali, manakala pengesahan pembicara (Speaker Verification, SV) menentukan sama ada sampel suara tertentu milik seseorang individu. Kekurangan utama dalam SI ialah variasi sesi, bunyi latar belakang, dan maklumat yang tidak mencukupi. Untuk mengatasi kekangan tersebut, kajian ini mencadangkan kaedah Global Local Self-Attention (GLSA) berasaskan Long Short-Term Memory (LSTM) dengan Pengoptimuman Grey Wolf Jiranan Eksponen (EN-GWO) bagi pengenalpastian pembicara yang berkesan menggunakan set data TIMIT dan VoxCeleb 1. GLSA digabungkan dalam LSTM yang memberi tumpuan pada data yang diperlukan, manakala parameter hiper ditala menggunakan EN-GWO untuk meningkatkan prestasi pengenalpastian pembicara. Kaedah GLSA-LSTM dengan EN-GWO mencapai ketepatan 99.36% pada dataset TIMIT dan ketepatan 93.45% pada dataset VoxCeleb 1, berbanding dengan SincNet dan Generative Adversarial Network (SincGAN) serta Hybrid Neural Network – Support Vector Machine (NN-SVM)

Lucy Richardson and Mean Modified Wiener Filter for Construction of Super-Resolution Image

The ultimate goal of the Super-Resolution (SR) technique is to generate the High-Resolution (HR) image by combining the corresponding images with Low-Resolution (LR), which is utilized for different applications such as surveillance, remote sensing, medical diagnosis, etc. The original HR image may be corrupted due to various causes such as warping, blurring, and noise addition. SR image reconstruction methods are frequently plagued by obtrusive restorative artifacts such as noise, stair casing effect, and blurring. Thus, striking a balance between smoothness and edge retention is never easy. By enhancing the visual information and autonomous machine perception, this work presented research to improve the effectiveness of SR image reconstruction The reference image is obtained from DIV2K and BSD 100 dataset, these reference LR image is converted as composed LR image using the proposed Lucy Richardson and Modified Mean Wiener (LR-MMWF) Filters. The possessed LR image is provided as input for the stage of bicubic interpolation. Afterward, the initial HR image is obtained as output from the interpolation stage which is given as input for the SR model consisting of fidelity term to decrease residual between the projected HR image and detected LR image. At last, a model based on Bilateral Total Variation (BTV) prior is utilized to improve the stability of the HR image by refining the quality of the image. The results obtained from the performance analysis show that the proposed LR-MMW filter attained better PSNR and Structural Similarity (SSIM) than the existing filters. The results obtained from the experiments show that the proposed LR-MMW filter achieved better performance and provides a higher PSNR value of 31.65dB whereas the Filter-Net and 1D,2D CNN filter achieved PSNR values of 28.95dB and 31.63dB respectively

Retinopathy Disease Detection and Classification Using a Coordinate Attention Module-Based Convolutional Neural Network with Leaky Rectified Linear Unit

The detection of Diabetic Retinopathy (DR) is an emergent research topic in recent decades, where DR is a primary cause of vision loss in humans. The existing techniques have limitations such as neuron death issues, vanishing gradient, and output offset. To overcome these issues, this paper proposes a Deep Learning (DL)-based technique for early and accurate DR detection. The Coordinate Attention Module (CAM) based Convolutional Neural Network (CNN) with Leaky Rectified Linear Unit (LReLU) is proposed for early and accurate detection of DR. The MESSIDOR dataset is preprocessed through the median filter to eliminate noise, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) is utilized to increase the contrast level in an input image. The preprocessed images are given to Mayfly Optimization Algorithm-based Region Growing (MOARG) for image segmentation. Then, the features are extracted using ResNet50 and SqueezeNet, which extract deep learning features. The extracted features are given to CAM-based CNN with LReLU to detect DR, which overcomes the dead issues of neurons and minimizes the probability of inactive neurons. The proposed model achieves better results on the MESSIDOR datasets on the metrics of accuracy, precision, recall, specificity, f1-score, and Area Under Curve (AUC) values of about 99.72%, 99.46%, 99.25%, 99.61%, 99.37% and 99.14%, correspondingly, proving to be superior to the existing method, Capsule Network and Hybrid Adaptive DL based DR (HADL-DR). ABSTRAK: Pengesanan Retinopati Diabetik (DR) merupakan topik penyelidikan yang semakin mendapat perhatian dalam dekad-dekad kebelakangan ini, di mana DR merupakan punca utama kehilangan penglihatan pada manusia. Teknik sedia ada mempunyai beberapa kekangan seperti isu kematian neuron, vanishing gradient, dan output offset. Untuk mengatasi isu-isu ini, kertas ini mencadangkan teknik berasaskan Pembelajaran Mendalam (DL) untuk pengesanan awal dan tepat bagi DR. Modul Coordinate Attention Module (CAM) berasaskan Convolutional Neural Network (CNN) dengan Leaky Rectified Linear Unit (LReLU) dicadangkan untuk pengesanan awal dan tepat bagi DR. Dataset MESSIDOR diproses melalui penapis median yang digunakan untuk menghapuskan hingar, dan Contrast-Limited Adaptive Histogram Equalization (CLAHE) digunakan untuk meningkatkan tahap kontras pada imej input. Imej yang telah diproses diberikan kepada Algoritma Pengoptimuman Mayfly berasaskan Region Growing (MOARG) untuk segmentasi imej. Kemudian, ciri-ciri diekstrak menggunakan ResNet50 dan SqueezeNet yang mengekstrak ciri-ciri pembelajaran mendalam. Ciri-ciri yang diekstrak ini diberikan kepada CNN berasaskan CAM dengan LReLU untuk pengesanan DR, yang mengatasi isu kematian neuron dan meminimumkan kebarangkalian neuron tidak aktif. Model yang dicadangkan mencapai keputusan yang lebih baik pada dataset MESSIDOR berdasarkan metrik ketepatan, ketepatan, panggilan semula, kekhususan, skor f1, dan nilai Kawasan di Bawah Lengkung (AUC) iaitu sekitar 99.72%, 99.46%, 99.25%, 99.61%, 99.37% dan 99.14%, masing-masing, membuktikan keunggulannya berbanding kaedah sedia ada, Capsule Network dan Hybrid Adaptive DL berasaskan DR (HADL-DR)

A Review on RP-HPLC Method Development and Validation for Bulk Dosage Form

Chromatography, although primarily a separation technique, is mostly employed in chemical analysis in which High-performance liquid chromatography (HPLC) is an extremely versatile technique where analytes are separated by passage through a column packed with micro meter-sized particles. Now a day reversed-phase chromatography is the most commonly used separation technique in HPLC. The reasons for this include the simplicity, versatility, and scope of the reversed-phase method as it is able to handle compounds of a diverse polarity and molecular mass. Reversed phase chromatography has found both analytical and preparative applications in the area of biochemical separation and purification. Molecules that possess some degree of hydrophobic character, such as proteins, peptides and nucleic acids, can be separated by reversed phase chromatography with excellent recovery and resolution. This review covers the importance of RP-HPLC in analytical method development and their strategies along with brief knowledge of critical chromatographic parameters need to be optimized for an efficient method development