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Context: Soy is the main source of phytoestrogens, which has long been used as traditional food. One major subtype of phytoestrogens includes isoflavones and they are scientifically validated for their beneficial actions on many hormone-dependent conditions.Objective: The present study examines the effect of soy isoflavones on letrozole-induced polycystic ovary syndrome (PCOS) rat model.Materials and methods: PCOS was induced in Sprague–Dawley rats with of 1 mg/kg letrozole, p.o. once daily for 21 consecutive days. Soy isoflavones (50 and 100 mg/kg) was administered for 14 days after PCOS induction. Physical parameters (body weight, oestrous cycle determination, ovary and uterus weight) metabolic parameters (oral glucose tolerance test, total cholesterol), steroidal hormone profile (testosterone and 17β-oestradiol), steroidogenic enzymes (3β-hydroxy steroid dehydrogenase (HSD) and 17β-HSD), oxidative stress and histopathology of ovary were studied.Results: Soy isoflavones (100 mg/kg) treatment significantly altered the letrozole-induced PCOS symptoms as observed by decreased body weight gain (p < 0.05), percentage diestrous phase (p < 0.001), testosterone (p < 0.001), 3β-HSD (p < 0.01) and 17β-HSD (p < 0.001) enzyme activity and oxidative stress. Histological results reveal that soy isoflavones treatment in PCOS rats resulted in well-developed antral follicles and normal granulosa cell layer in rat ovary.Discussion: Treatment with soy isoflavones exerts beneficial effects in PCOS rats (with decreased aromatase activity) which might be due to their ability to decrease testosterone concentration in the peripheral blood.Conclusion: Analysis of physical, biochemical and histological evidences shows that soy isoflavones may be beneficial in PCOS.

THz antennas, which function at high speeds, frequencies, and data rates, were developed in response to the increased need for high-speed communication equipment. In this work, a MIMO antenna operating between 2.25 and 2.85 THz is built and optimised with partial ground. The designed antenna possesses H-shaped slots and circular rings over the patch to enhance the antenna performance. The proposed antenna states the isolation loss value of 50 dB across the operating frequency with a bandwidth of 0.6 THz. In the manuscript, two antennas were designed, the first one having only circular slots and the second one, in addition to the circular slots over the patch, also including H-slots. The antenna has the highest gain value of 8.9 dBi in the design. Optimising the design is performed using parametric optimisation and geometrical parameters. The suggested antenna measures 50 x 50 x 100 µm². The suggested antenna can be used for high-speed communications because of its high gain and operating frequency applicability. Antenna having a low Error Correlation Coefficient (ECC) value of 0.08, a high Diversity Gain (DG) value with minimum mutual coupling < -25dB, an optimum Total Active Reflection Coefficient (TARC) value of -55dB and a Mean Effective Gain (MEG) value of 8.5dB. These antennae also operate across biomedical imaging applications, wireless network applications, beam scanning applications, and satellite communication applications with reflection coefficient values < -25dB.This study supports UN SDG 9: Industry, Innovation and Infrastructure by advancing sustainable THz communication technologies, and contributes to SDG 3: Good Health and Well-Being through its biomedical imaging applications.

The multi criteria decision analysis (MCDA) approach was used to optimise Abrasive waterjet (AWJ) pocket milling process parameters to examine the machinability of Ti-6Al-4 V alloy and the experiments were done by the L 32 Taguchi array. Abrasive mesh (AM), waterjet pressure (WP), and traverse speed (TS) were chosen for this study, and material removal rate (MRR), depth of cut (DC), undercut (UC), and surface roughness (SR) were studied. Furthermore, the multiparametric ANOVA was used to determine the statistical significance of milling parameters, and the spatial pattern of output response parameter values was statistically assessed to support process parameter selection. Finally, regression models were developed based on milling parameters and output. To estimate the optimum level of input parameters for multi-objective optimization criteria on milling, particle swarm optimization (PSO), moth-flame optimization (MFO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) have been combined with the combinative distance-based assessment (CODAS) methodology and multi-objective optimization on the basis of ratio analysis (MOORA). Weights for output were assigned using the objective-based entropy weight-age approach. The entropy weights for DC, UC, SR, and MRR were 0.3255, 0.2312, 0.1837, and 0.2596, respectively. The MOORA method predicts the best ideal parameters and based on the assessment scores MFO algorithm outperformed the others in maximising DC and MRR while reducing UC and SRR.

This study aimed to see how different treatments affect the low-stress mechanical properties of micro-denier polyester/cotton (MDP/C—65/35) fabrics. This blend was chosen for the study because it is the most popular blend used in polyester/cotton blended material. The results of fabric properties treated with sericin revealed that fabrics treated with sericin and glutaraldehyde as a cross-linking agent had higher bending rigidity, regardless of how it was tested. Concerning the blend fabrics, it was noticed that there was deterioration in tensile resilience following sericin treatment. Shear rigidity, accompanied by shear hysteresis, showed an increase in sericin-treated fabrics. Compression properties were affected by the treatment, and in general, the fabric suffered deterioration in those the samples were hard. Surface properties such as coefficient of friction, mean deviation of friction and mean deviation of surface contour were found to be higher than those of the control and sericin-treated fabrics in a few cases.

The need for precise manufacturing in aerospace, medical, and automotive industries requires an investigation of upscale drilling methods that can achieve small-diameter deep holes with exceptional accuracy. Abrasive Waterjet Drilling (AWJD) has developed as a promising technology due to its distinctive blend of precision and adaptability. Despite several advantages, overcutting is the fundamental obstacle restricting the widespread use of AWJD. The novelty of this research is to investigate the impact of process parameters, specifically water pressure, standoff distance, and abrasive mass flow rate, on the top, bottom, and depth-averaged radial overcut developed during the deep hole drilling of stainless steel 316L material. The deep hole drilling experiments have been conducted utilizing Taguchi’s (L 16 ) orthogonal array by adjusting the drilling settings. The statistical significance of specific drilling parameters and second-order quadratic models for the responses have been established by analysis of variance. Additionally, to mitigate the impact of overcut and improve the drilling quality necessary for diverse sectors such as automotive, biomedical, and oil and gas, a metaheuristic optimization method, specifically the Grasshopper Optimization Algorithm (GHO), has been utilized. Thereafter, the effectiveness of the suggested algorithm has been validated using quality measures, namely hyper-volume and spacing by comparing it to the approaches of whale optimization, harmony search, and multiverse optimization algorithms. The comparison shows that the GHO algorithm outperformed the others. The GHO algorithm identified the optimal process parameters for AWJD as water pressure 305.36 MPa, standoff distance 1.00 mm, and mass flow rate 600 g/min. The anticipated values for the top, bottom, and depth-averaged radial overcut, according to the optimal parameters, are 1.19 mm, 0.64 mm, and 1.53 mm, respectively. Furthermore, a validation test has been conducted to verify the efficacy of the GHO algorithm. The validation test showed top, bottom, and depth-averaged radial overcut values of 1.17 mm, 0.66 mm, and 1.49 mm, with percentage deviations of 1.71%, 3.03%, and 2.68%, respectively, with the GHO algorithm. The surface quality of the drilled holes has been examined through a Scanning Electron Microscope (SEM). The SEM images have been obtained at magnifications of 12X and 500X of the drilled hole surface using optimum parameters, demonstrating smooth and uniform surfaces at the top, middle, and bottom of the drilled hole.

The production of granite waste from stone industry processes poses environmental challenges, particularly in urban areas where waste accumulation leads to large landfills. This non-biodegradable waste contributes to pollution and ecological degradation. To address this issue, this research investigates the potential use of granite waste as a filler in geopolymer concrete paver blocks, aiming to enhance sustainability and performance in construction materials. The experimental study focuses on incorporating granite waste with fly ash (FA) and ground granulated blast furnace slag (GGBS) as binders, activated by Na 2 SiO 3 and NaOH. Two mix proportions (M1 and M2) are examined, with binder contents of 440 kg/m 3 and 403 kg/m 3 , respectively. Granite waste is introduced at varying percentages of 10%, 15%, and 20%, alongside a control mix without waste inclusion. Ambient curing conditions are employed. Mechanical properties (compressive strength, tensile splitting strength, flexural strength), durability (carbonation, chloride penetration, water absorption), and microstructure (XRD, FTIR) are evaluated. Results show that at 15% granite waste inclusion, there is a significant improvement in compressive strength (10.71% for M1, 11.20% for M2) compared to control mixes. Enhanced mechanical properties are attributed to dense particle packing and reduced voids in the concrete matrix. Additionally, geopolymer concrete with 15% granite waste exhibits reduced chloride penetration, carbonation depth, water absorption, and higher ultrasonic pulse velocity, indicating improved durability.

This paper presents the design, simulation and implementation of microstrip bandpass filter having SRR structures on radiating side which resonates at tri-frequencies (2.4,4.5 and 5.2 GHz) which are used for WLAN applications. Filter parameters like return loss, insertion loss, group delay, phase delay and bandwidth are improved by the implementation of SRR structures on the center of the transmission line. To optimize the dimension of the SRR unit cell and filter parametric analysis are performed with ANSYS HFSS. The proposed filter with 3 SRR unit cell having the minimum transmission coefficient of -0.7dB, -1.27dB, and -2.1dB across 2.4,4.5 and 5.2GHz, reflection coefficient of -22.7dB, -23dB, and -16.5dB across 2.4,4.5 and 5.2GHz, group delay value of 1.52ns,1.38ns and 0.58ns across 2.4,4.5 and 5.2GHz with minimum nonlinear phase response and the quality factor value of 14.1,18.7,13.7 across 2.4,4.5 and 5.2GHz. The proposed filter was designed with the order of 5 using Chebyshev type 1 approximation technique having a ripple factor value of 0.01dB. Surface current distributions of the proposed filter are also evaluated. Fabricated microstrip bandpass filter results are measured and validated using ANRITSU-MS2037C combinational analyser.

The key point is to design and implementation of the full adder which provides high-speed, Power efficiency and leas area with good voltage swing”. Where the term ‘Novel’ indicates that If something is so new, genuine and original that it had never been seen, used or even thought of before, call it is considered as ‘novel’ and ‘Ultra low power’ indicates that with the minimal amount of system power is enough for performing the respective operation of its own. In this article, a new High Performance and low power full adder utilizing a distinctive design \"Mixed Logic Design\" is recommended in implementation. The mixed - logic design combines Modified Gate diffusion input (MGDI) Transmission Gate Logic (TGL), Static CMOS logic, Pass transistor logic(PTL )and various logics which requires the recommended circuit. Full adder is a digital circuit which performs the sum of bits. In many PC’s and various kinds of microprocessors, adders are utilized in the ALU. The traditional Complementary Metal Oxide Semiconductor (CMOS) Full adder consisting of 28-Transistors and is built on a traditional Complementary Metal Oxide Semiconductor structure. GDI technique is low power and high-speed design technique where it takes 10 T. Gate Diffusion Input is one of the circuit design logics which occupies less area, simulates with high speed and power-efficient technique. It entails less count of transistors as correlated to traditional Complementary Metal Oxide Semiconductor technology. But the disadvantage with Gate Diffusion Input technique is that it provides an output having poor logic swing after simulation. The Modified-Gate diffusion input (MGDI ) technique rectifies this issue by implementing FA with 8T. But we are implementing another alternative “Mixed logic design” (combining the GDI, CMOS, TGL, etc logics ) and designing the Circuit with least count of transistors and compare with other unique logics which helps them to simulate the circuit in a power-efficient way and time delay.

Recognition of Facial expression in technology plays a major role in many sectors. It has many advantages because of which it is very important. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural networks (CNN). The main objective here is to classify each face based on the emotions shown into seven categories which include Anger, Disgust, Fear, Happiness, Sadness, Surprise and Neutrality. The main objective here in this project is, to read the facial expressions of the people and displaying them. OpenCV is used for automatic detection of faces and drawing bounding boxes around them. Face detection using the Hear cascades is a machine learning based algorithm where a cascade function will be trained with a set of input data. OpenCV contains many pre-trained classifiers for face, eyes, smile etc. The deep learning is a subset of machine learning. Deep learning is used by Google to translate the information form one language to another using deep learning approach. The network should be trained with relatively more data in deep learning.

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the genetic algorithm technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology.