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No full textNot AvailableRaisins are widely consumed and globally traded dried fruit. Evaluating white-seeded grape varieties for
raisin quality is essential to determine their suitability for commercial raisin production. Among the different
varieties evaluated, Angoor Kalan recorded maximum bunch weight (398.3g), 50 berry weight (830.9g), TSS
(23.1p brix) and raisin recovery (24.8%). The accession Aledo recorded maximum berry diameter and length
(23.0 and 24.0 mm). The performance with respect to biochemical composition pointed out that the variety
Angoor Kalan recorded highest range of phenol (4.3 mg/g), tannin (4.6 mg/g), reducing sugar (182.6 mg/g)
and carbohydrates(365.3 mg/g). However, the maximum protein and proline was recorded in Aledo (52.1 and
15.8 mg/g). It was concluded that, Angoor Kalan performed better for good quality of raisins followed by
Palomina and Aledo.Not Availabl
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No full textNot AvailableIn viticulture, the use of synthetic chemical formulations introduces insecticide residues into harvested grapes and further into processed grape products, posing a safety concern to consumers. This study investigated the fate of ten insecticide residues and their metabolites from vine to wine. A rapid validated multi-residue approach using QuEChERS extraction and LC-MS/MS configuration was employed for targeted analysis in grape, pomace, and wine. The targeted insecticides showed satisfactory mean recoveries (76.03–111.95%) and precision (RSD = 0.75–7.90%) across the three matrices, with a matrix effect ranging from −16.88 to 35.18%, particularly higher in pomace. Preliminary grape washing effectively removed 15.52–61.31% of insecticide residues based on water solubility and systemic nature. Residue dissipation during fermentation ranged from 73.19% to 87.15% with a half-life spanning from 1 to 5.5 days. The mitigation rate was observed at 12.85–26.81% for wine and 17.76–51.55% for pomace, with the highest transfer rate for buprofezin (51.55%) to pomace and fipronil (25.72%) to wine. Calculated processing factors (PF) for final wine ranged from 0.16 to 0.44, correlating strongly with the octanol-water partition ratio of targeted insecticides. The reported PF, calculated hazard quotient (HQ) (0.003–5.800%), and chronic hazard index (cHI) (2.041–10.387%) indicate reduced residue concentrations in wine and no potential chronic risk to consumers, ensuring a lower dietary risk to wine consumers.Not Availabl
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No full textNot AvailableBunch load management is a critical practice in grape cultivation, deeply intertwined with both training
systems and the photosynthesis process. Effective management of bunch load involves strategically regulating the
number and distribution of grape clusters on the vine. This practice is closely linked to the chosen training system,
influencing factors like canopy architecture, sunlight exposure and resource allocation. The photosynthesis process in
grapevines plays a pivotal role in determining the quality attributes of both berries and resulting wines. Bunch load
management directly affects photosynthesis by influencing factors such as leaf-to-fruit ratio, canopy density, and
light penetration. These factors collectively impact sugar accumulation, color development, and overall grapevine
health, subsequently shaping the sensory and chemical qualities of the produced berries and wines. The interplay
between bunch load management, training systems, and photosynthesis is therefore integral to achieving desired berry
and wine quality. A well-balanced approach to bunch load management, tailored to specific training systems,
enhances photosynthetic efficiency, leading to optimal sugar production and flavor development in grapes.
Consequently, the resulting wines showcase enhanced aromas, flavors, and overall character. This abstract underscore
the significance of understanding and implementing effective bunch load management strategies within various
training systems to produce grapes with superior qualities, ultimately contributing to the production of high-quality
wines.Not Availabl
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No full textNot AvailableIn experimental design, -resolvable designs are preferred for their ability to partition blocks into subsets, each containing every treatment times. These designs offer advantages such as orthogonality to management effects, ensuring that such effects do not interfere with treatment assessments. Moreover, they provide protection against the loss of entire blocks, where all treatments are equally affected. Resolvable designs facilitate intra-block analysis of variance, breaking down the block sum of squares into replication and block-within-replication components, aiding in data interpretation. However, -resolvable designs are not always feasible for all experimental parameters. In such cases, nearly -resolvable designs, where blocks can be grouped into sets containing (−1) treatments times, offer a flexible alternative. These design concepts contribute significantly to experimental robustness, orthogonality, and variance analysis in various research contexts. In this article, we have developed a construction method of -resolvable BIB designs for even number of treatments and a general construction method of nearly -resolvable BIB designs.Not Availabl
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No full textNot AvailableTime series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T-test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.Not Availabl
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No full textNot AvailableTwo-level factorial designs are widely used to identify significant effects in agriculture and allied fields.
The occurrence of practical constraints, which can make it necessary to arrange runs in small block size
like block size four, provide a major motivation for work in this area. Investigating the optimal number
of replications needed to be carried out to estimate important factorial effects for factorial experiments
with blocks of size four has attracted considerable interest from researchers. Estimating all main effects
and two-factor interactions in a two-level factorial experiment in blocks setup, obtaining precise
estimates generally requires a significant number of replications. The article presents methodologies
aimed at obtaining efficient block designs for two-level factorial experiments in block size 4, which hold
potential for improving the efficiency factor and effectiveness of experimental investigations.Not Availabl
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No full textNot AvailableProbability estimation plays a pivotal role across diverse domains, particularly in scenarios
where the objective is to select non-repetitive units one at a time, with the option of replacement, from a
predefined set of units. Traditional probability calculations in this scenario pose three challenges: the number
of floating-point operations to be executed is directly proportional to the chosen set size, susceptibility to
floating-point precision errors, and exponential growth in storage needs with increasing number of chosen
units. In this scenario, the presented work aims to develop SPM: a sigmoid function-based model that
estimates probabilities for such problems with a fixed number of calculations (independent of the input
parameter), achieving a constant time complexity algorithm. The research methodology involves generating
probability data points, selecting the optimal sigmoid function, augmenting additional data to enhance
parameter estimation, identifying parameter estimation equations, and evaluating the model. Moreover, the
study’s second objective includes training and comparing six established machine learning-based models
(including Decision Tree, Random Forest, Support Vector, Linear Regression, Nearest Neighbour, and
Artificial Neural Network) against the proposed SPM. The rigorous assessment of the model’s performance,
utilising metrics including RMSE, MAE and r2 across a wide range of scenarios involving varying values
of the total units, affirms the model’s accuracy and resilience. The study findings can improve decision-
making processes in various domains, including statistics, cryptography, machine learning and optimisation,
by offering a faster, more adaptable solution for probability estimation in units’ selection with replacement.Not Availabl
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No full textNot AvailableGrape cultivation, in India, is dependent on a narrow range of table grape varieties, with more than 70 per
cent cultivated area in Maharashtra alone, followed by Karnataka and Tamil Nadu. Therefore, to increase the
scope of area extension under grape cultivation, the suitability of six coloured grape varieties was assessed
for yield, quality and phenological parameters at four locations, i.e., Pune (Maharashtra), Arabhavi (Karnataka),
Rajendranagar (Telangana) and Theni (Tamil Nadu) during 2019-2022. From the study, it was revealed that the
‘Manjari Shyama’ and ‘Red Globe’ yielded the maximum (13.7 kg/vine) as compared to the rest of the varieties.
Variety ‘Manjari Shyama’ was found stable at all four locations for yield, bunch weight, 100-berry weight, berry
diameter, TSS, acidity, sugar: acid ratio and days to veraison with the regression coefficient 1.22, 1.11, 1.01,
1.13, 1.35, 1.44, 0.88 and 0.79, respectively. The yield and bunch weight in ‘Red Globe’ was found to be more
expressive under the favourable environment as compared to ‘Fantasy Seedless’ (5.59 kg/vine), ‘Nanasaheb
Purple Seedless’ (6.13 kg/vine) and ‘Crimson Seedless’ (7.63 kg/vine), which were found lower.Not Availabl
