Optimization of Quantum Cost for Low Energy Reversible Signed/Unsigned Multiplier Using Urdhva-Tiryakbhyam Sutra

One of the elementary operations in computing systems is multiplication. Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems. In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity. This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost. The Vedic multiplier is considered fast as it generates all partial product and their sum in one step. This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output. First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra. This multiplier consists of bitwise multiplication and adder compressors. Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design. It has a garbage output of 30 with optimization of the best-compared design. Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers. Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters. DesignI has separate binary two’s complement (B2C) and MUX circuits, while DesignII combines binary two’s complement and MUX circuits in one circuit. DesignI shows the lowest quantum cost, 231, regarding state-of-the-art. DesignII has a quantum cost of 199, reducing to 86.14% of DesignI. The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2.     

The composition of fatty acids in the tissues of Tunisian swordfish (Xiphias gladius)

The objective of this study was to compare the composition of fatty acids stored in the various parts of swordfish. Muscle and organ sections of a series of swordfish samples were collected. Their chemical analysis allowed the classification of swordfish as a semi-fatty fish, with its byproducts totalling 35.6% of the total fatty acids (TFA) and its white and red muscles (MR) 7.2%. The highest contents were found in the liver (26%), the gonads (4.7%) and the red muscle (RM) (4.5%). The UFA/SFA ratio as recommended by nutritionists is 3; in the liver, skin and RM samples this ratio was, respectively, 3.5, 2.8 and 2.7. There is a high level of unsaturated fatty acids (UFA) in swordfish. More than 40% of the muscular tissues are made up of polyunsaturated fatty acids (PUFA) of the n-3 series (EPA + DHA). The byproducts contain more than 30% of monounsaturated acids characterised by the fatty acids of the n-9 series and particularly by oleic acid.     

Effects of Extraction Solvents and Provenances on Phenolic Contents and Antioxidant Activities of Cumin (Cuminum cyminum L.) Seeds

Polyphenol contents and 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) scavenging activity of cumin (Cuminum cyminum L.) seed extracts were compared depending on their geographical origin (Tunisia and India: TCS and ICS, respectively) and the extraction solvent polarity. The ??-carotene bleaching assay, the chelating ability and the reducing power of the most promising solvent extracts were also assessed. In addition, TCS and ICS extracts were acid-hydrolyzed and the phenolics identified by reversed-phase high-performance liquid chromatography (HPLC). Seed phenolic contents and antioxidant activity appeared to be accession and solvent dependent. Extraction with 80% acetone led to the highest polyphenol (18.60 and 16.50?mg gallic acid equivalents (GAE)/g dry weight (DW)), flavonoid (5.91 and 4.99?mg catechin equivalents (CE)/g DW) and tannin (83.23 and 80.23?mg CE/g DW) contents, respectively for TCS and ICS. DPPH scavenging activity, ??-carotene bleaching assay, chelating ability and reducing power were maximal in 80% acetone for both TCS and ICS. HPLC analysis revealed several phenolic compounds in C. cyminum seeds, with p-coumaric (4.83 and 2.33?mg/g DW), trans-2-dihydrocinnamic (1.09 and 1.20?mg/g DW) and rosmarinic (0.70 and 1.04?mg/g DW) acids as major phenolics in TCS and ICS, respectively. Thus, phenolic composition of cumin seeds is also origin dependent. Taken together, our findings indicate that cumin might constitute a rich and novel source of natural antioxidants as food additives in food industry and that acetone 80% would be the most appropriate solvent for seed extraction.     

钢筋混凝土厚板裂缝间距的分析模型

建立一个简单、实用的新模型预测正交钢筋混凝土板的开裂行为。新的模型能解释箍筋对开裂行为的影响,同时也考虑了影响钢筋混凝土构件开裂行为的主要参数,包括混凝土抗拉强度、配筋率、钢筋直径、间距。采用平衡和协调方程,对钢筋混凝土单元进行研究,以求出钢筋和混凝土的平均应力。对本文试验研究结果以及涉及不同结构单元、荷载条件的已有研究成果进行一系列验证比较可知。所提出的裂缝间距模型能够预测不同荷载阶段的裂缝宽度值。在所研究案例中,试验值与模型预测值吻合较好。     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

Composition and insecticidal activity of essential oil from Pistacia lentiscus L. against Ectomyelois ceratoniae Zeller and Ephestia kuehniella Zeller (Lepidoptera: Pyralidae)

This study reports investigations on the chemical constituents and fumigant toxicity of Pistacia lentiscus L. (Anacardiaceae) essential oil against two major stored-date insects, carob moth Ectomyelois ceratoniae Zeller and Mediterranean flour moth Ephestia kuehniella Zeller. Results showed that P. lentiscus essential oil contained terpinene-4-ol (23.32%), α-terpineol (7.12%) and β-caryophyllene (22.62%) as major compounds. Fumigant toxicity tests showed that P. lentiscus oil was more toxic to E. kuehniella (LC₅₀ = 1.84 μl/l, LC₉₅ = 5.14 μl/l) than E. ceratoniae (LC₅₀ = 3.29 μl/l, LC₉₅ = 14.24 μl/l). The fecundity and hatching rate of both insects decreased with increases in concentration or exposure time to the oil. At 136 μl/l air, fecundities and hatching rates were respectively 35 eggs/female and 42.86% for E. ceratoniae and 78 eggs/female and 29.49% for E. kuehniella.     

Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

Journal of Materials Science - We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving...     

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection. This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes. These sensors produce a huge volume of physical activity data that necessitates real-time recognition, especially during emergencies. Falling is one of the most important problems confronted by older people and people with movement disabilities. Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people. But, the costs incurred upon installation and operation are high, whereas the technology is relevant only for indoor environments. Currently, commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements. Against this background, the current study develops an Improved Whale Optimization with Deep Learning-Enabled Fall Detection for Disabled People (IWODL-FDDP) model. The presented IWODL-FDDP model aims to identify the fall events to assist disabled people. The presented IWODL-FDDP model applies an image filtering approach to pre-process the image. Besides, the EfficientNet-B0 model is utilized to generate valuable feature vector sets. Next, the Bidirectional Long Short Term Memory (BiLSTM) model is used for the recognition and classification of fall events. Finally, the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method, which shows the novelty of the work. The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.     

Structural Characteristics and Electrical Conductivity of Vanadium-doped lithium Ultraphosphate Glasses

Glasses of the 33.5Li₂O-66.5P₂O₅-xV₂O₅ (mol. %) system with (0 ≤ x ≤ 5) were prepared by the normal melting and annealing technique. Their physical and spectroscopic properties such as X-ray powder diffraction (XRD), density, infrared spectroscopy and electrical conductivity were investigated. The composition dependent density shows an initial decrease at 0.1 mol.% of V₂O₅ followed by an increase in density with further addition of the V₂O₅ content. The structure of these glasses has been studied by recording the Fourier-transform infrared spectroscopy (FTIR) over the range 400–1800 cm^?1. Vibrational modes are assigned to different types of phosphate groups in the glass network. The temperature (333–452 K) and composition dependence of direct current (DC) electrical conductivity were measured. The electric conductivity is observed to increase with the first addition of vanadium oxide reflecting the presence of V⁴⁺ions that take part within modifying positions in the glass network .Then, the electric conductivity is found to decrease with the further increase in the content of V₂O₅ up to 5 mol.% reflecting the formation of V⁵⁺ions that take part as network forming positions in the glass network. The variations in density and electrical conductivity with V₂O₅ content are discussed in terms of changes in the glass structure.