The wine business relies heavily on wine quality certification. The excellence of New Zealand Pinot noir wines is well-known worldwide. Our major goal in this research is to predict wine quality by ...generating synthetic data and construct a machine learning model based on this synthetic data and available experimental data collected from different and diverse regions across New Zealand. We utilised 18 Pinot noir wine samples with 54 different characteristics (7 physiochemical and 47 chemical features). We generated 1381 samples from 12 original samples using the SMOTE method, and six samples were preserved for model testing. The findings were compared using four distinct feature selection approaches. Important attributes (referred as essential variables) that were shown to be relevant in at least three feature selection methods were utilised to predict wine quality. Seven machine learning algorithms were trained and tested on a holdout original sample. Adaptive Boosting (AdaBoost) classifier showed 100% accuracy when trained and evaluated without feature selection, with feature selection (XGB), and with essential variables (features found important in at least three feature selection methods). In the presence of essential variables, the Random Forest (RF) classifier performance was increased.
Wine research has as its core components the disciplines of sensory analysis, viticulture, and oenology. Wine quality is an important concept for each of these disciplines, as well as for both wine ...producers and consumers. Any technique that could help producers to understand the nature of wine quality and how consumers perceive it, will help them to design even more effective marketing strategies. However, predicting a wine’s quality presents wine science modelling with a real challenge. We used sample data from Pinot noir wines from different regions of New Zealand to develop a mathematical model that can predict wine quality, and applied dimensional analysis with the Buckingham Pi theorem to determine the mathematical relationship among different chemical and physiochemical compounds. This mathematical model used perceived wine quality indices investigated by wine experts and industry professionals. Afterwards, machine learning algorithms are applied to validate the relevant sensory and chemical concepts. Judgments of wine intrinsic attributes, including overall quality, were made by wine professionals to two sets of 18 Pinot noir wines from New Zealand. This study develops a conceptual and mathematical framework to predict wine quality, and then validated these using a large dataset with machine learning approaches. It is worth noting that the predicted wine quality indices are in good agreement with the wine experts’ perceived quality ratings.
•Sexual dimorphism using unfused hyoid bone through discriminant analysis.•Feature selection through discriminant analysis.•Machine learning algorithms used to improve accuracy.•Results evaluated by ...precision, recall, sensitivity, specificity, area under curve.
Depending on the metric and non-metric skeletal features of various bones, forensic experts proposed diverse sex identification methods. The main focus of the present study is to calculate sexual dimorphism in human unfused or disarticulated hyoid bone and compared it with studies conducted by different researchers. For this study, 293 unfused hyoid bones were accumulated and investigated from 173 male and 120 female cadavers of the northwest Indian population from the age of 15 to 80 years. Initially, discriminant analysis was performed on the dataset to predict sex and to get an idea for the crucial variables for sexual dimorphism. Later, significant variables predicted by the discriminant analysis were used for machine learning approaches to improve accuracy for sex determination. The standard scaler method is used for pre-processing of the data before machine learning analysis and to prevent overfitting and underfitting, 70 % of the whole dataset was utilized in the training of the model and the remaining data were used for testing the model. According to the discriminant analysis, body length (BL) and body height (BH) were found to be highly significant for the sex determination and predicted sex with 75.1 % accuracy. However, implementation of machine learning approaches such as the XG Boost classifier increased the accuracy to 83 % with sensitivity, and specificity scores of 0.81 and 0.84, respectively. Moreover, the ROC-AUC score achieved by the XG Boost classifier is 0.89; indicating machine learning investigation can improve the sex determination accuracy up to the appropriate standard.
The present paper reports the indentation of integral transform technique for a semi-infinite initially stressed elastic medium under the action of an axi-symmetric flat-ended circular cylindrical ...punch pressing the medium normally. The incremental deformation theory is used to solve the problems for Neo-Hookean solid. The distribution of incremental stress and strain is obtained by using the Hankel’s transformation. The effects of the punch have been studied numerically and presented in various forms of curves. The plane punch indentation has its broad applications in the field of Engineering Mechanics. There are so many firing and launching pads, which use the Neo-Hookean solid as buffer and bear the punch during the action of machines. Thus the present problem has a lot of applications to find the effect of punch on machines.
Purpose The primary aim of this paper is to present a novel design approach for a ring voltage-controlled oscillator (VCO) suitable for L-band applications, whose oscillation frequency is less ...sensitive to power supply variations. In a few decades, with the advancement of modern wireless communication equipment, there has been an increasing demand for low-power and robust communication systems for longer battery life. A sudden drop in power significantly affects the performance of the VCO. Supply insensitive circuit design is the backbone of uninterrupted VCO performance. Because of their important roles in a variety of applications, VCOs and phase locked loops (PLLs) have been the subject of significant research for decades. For a few decades, the VCO has been one of the major components used to provide a local frequency signal to the PLL. Design/methodology/approach First, this paper chose to present recent developments on implemented techniques of ring VCO design for various applications. A complementary metal oxide semiconductor (CMOS)-based supply compensation technique is presented, which aims to reduce the change in oscillation frequency with the supply. The proposed circuit is designed and simulated on Cadence Virtuoso in 0.18 µm CMOS process under 1.8 V power supply. Active differential configuration with a cross-coupled NMOS structure is designed, which eliminates losses and negates supply noise. The proposed VCO is designed for excellent performance in many areas, including the L-band microwave frequency range, supply sensitivity, occupied area, power consumption and phase noise. Findings This work provides the complete design aspect of a novel ring VCO design for the L-band frequency range, low phase noise, low occupied area and low power applications. The maximum value of the supply sensitivity for the proposed ring VCO is 1.31, which is achieved by changing the VDD by ±0.5%. A tuning frequency range of 1.47–1.81 GHz is achieved, which falls within the L-band frequency range. This frequency range is achieved by varying the control voltage from 0.0 to 0.8 V, which shows that the proposed ring VCO is also suitable for low voltage regions. The total power consumed by the proposed ring VCO is 14.70 mW, a remarkably low value using this large transistor count. The achievable value of phase noise is −88.76 dBc/Hz @ 1 MHz offset frequency, which is a relatively small value. The performance of the proposed ring VCO is also evaluated by the figure of merit, achieving −163.13 dBc/Hz, which assures the specificity of the proposed design. The process and temperature variation simulations also validate the proposed design. The proposed oscillator occupied an extremely small area of only 0.00019 mm 2 compared to contemporary designs. Originality/value The proposed CMOS-based supply compensation method is a unique design with the size and other parameters of the components used. All the data and results obtained show its originality in comparison with other designs. The obtained results are preserved to the fullest extent.
Parkinson's disease (PD) is primarily characterized by midbrain dopamine depletion. Dopamine acts through dopamine receptors (D1 to D5) to regulate locomotion, motivation, pleasure, attention, ...cognitive functions and formation of newborn neurons, all of which are likely to be impaired in PD. Reduced hippocampal neurogenesis associated with dopamine depletion has been demonstrated in patients with PD. However, the precise mechanism to regulate multiple steps of adult hippocampal neurogenesis by dopamine receptor(s) is still unknown. In this study, we tested whether pharmacological agonism and antagonism of dopamine D1 and D2 receptor regulate nonmotor symptoms, neural stem cell (NSC) proliferation and fate specification and explored the cellular mechanism(s) underlying dopamine receptor (D1 and D2) mediated adult hippocampal neurogenesis in rat model of PD-like phenotypes. We found that single unilateral intra-medial forebrain bundle administration of 6-hydroxydopamine (6-OHDA) reduced D1 receptor level in the hippocampus. Pharmacological agonism of D1 receptor exerts anxiolytic and antidepressant-like effects as well as enhanced NSC proliferation, long-term survival and neuronal differentiation by positively regulating Wnt/β-catenin signaling pathway in hippocampus in PD rats. shRNA lentivirus mediated knockdown of Axin-2, a negative regulator of Wnt/β-catenin signaling potentially attenuated D1 receptor antagonist induced anxiety and depression-like phenotypes and impairment in adult hippocampal neurogenesis in PD rats. Our results suggest that improved nonmotor symptoms and hippocampal neurogenesis in PD rats controlled by D1-like receptors that involve the activation of Wnt/β-catenin signaling.
•D1 receptor agonism attenuates 6-OHDA induced anxiety and depression-like phenotypes.•D1 receptor stimulation positively regulates long-term survival of NSCs in 6-OHDA induced rat model of PD-like phenotypes.•D1 receptor regulates hippocampal neurogenesis by the activation of Wnt signaling pathway in PD rats.
Operating reserves (OR), alongside other ancillary services, are essential for mitigating the increasing uncertainty in generation from Intermittent Renewable Energy Sources (IRES). Traditional OR ...are determined based on rules of thumb, such as reliability considerations (N−1 or N−k), and they neglect the uncertainty between dispatch intervals. Load–generation imbalances lead to excessive reliance on regulation reserves and out-of-merit dispatch. Highlighting this, recent literature has focused on developing models for dynamic reserve quantification. While the models significantly enhance the traditional static reserve quantification, they heavily rely on probabilistic approaches. Despite their effectiveness, probabilistic approaches are computationally complex and necessitate precise historical data. Furthermore, most studies do not consider the correlation between load and IRES when determining reserves, leading to inaccurate estimation and impacting the system’s economics. Affine arithmetic-based models emerge as a promising solution, offering the ability to estimate correlated uncertainties with reduced computational complexity. This paper contributes to developing affine arithmetic models for OR quantification, considering IRES and load correlated uncertainties. Two distinct approaches to reserve quantification are explored: (1) deterministic scheduling with exogenous OR quantification, and (2) affine arithmetic-based scheduling framework to quantify and allocate OR endogenously. A comparative analysis is conducted with probabilistic scenario-based method and interval arithmetic scheduling. The effectiveness of the models is analyzed on the Great Britain test system with 40% renewable integration. Numerical results highlight that around 99% of the probabilistic net-load scenarios are within the net-load bounds generated by the proposed methodology. The reserve requirement is minimized by approximately 8% during peak hours and 35% during off-peak hours with correlated uncertainty. Furthermore, AA-based approach achieved a 15% reduction in total operating cost during the considered operational time-frame, compared to interval arithmetic optimization.
•A multi-objective optimization model for reserve quantification and allocation.•Affine arithmetic based correlated uncertainties of renewable power and load.•Exogenous and endogenous methods for reserve assessment are presented.•Trade-offs between computational time and cost are deciding factors.
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