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Green synthesis is an easy, safe, and environmentally beneficial nanoparticle creation method. It is a great challenge to simultaneously improve the capping and stabilizing agent carrier separation efficiency of photocatalysts. Herein, Zn-doped Titanium dioxide (TiO 2 ) nanoparticles with high exposure of 360 nm using a UV/visible spectrophotometer were prepared via a one-step hydrothermal decomposition method. A detailed analysis reveals that the electronic structures were modulated by Zn doping; thus, the responsive wavelength was extended to 600 nm, which effectively improved the visible light absorption of TiO 2 . We have optimized the different parameters like concentration, time, and temperature. The peak for TiO 2 is located at 600 cm- 1 in FTIR. A scanning electron microscope revealed that TiO 2 has a definite shape and morphology. The synthesized Zn-doped TiO 2 NPs were applied against various pathogens to study their anti-bacterial potentials. The anti-bacterial activity of Zn-doped TiO 2 has shown robust against two gram-ve bacteria ( Salmonella and Escherichia coli ) and two gram + ve bacteria ( Staphylococcus epidermidis and Staphylococcus aureus ). Synthesized Zn-doped TiO 2 has demonstrated strong antifungal efficacy against a variety of fungi. Moreover, doping TiO 2 nanoparticles with metal oxide greatly improves their characteristics; as a result, doped metal oxide nanoparticles perform better than doped and un-doped metal oxide nanoparticles. Compared to pure TiO 2 , Zn-doped TiO 2 nanoparticles exhibit considerable applications including antimicrobial treatment and water purification.

Introduction Narcolepsy is a sleep disorder of hypersomnia with typical features. Some narcolepsy patients may also have with autonomic dysfunction including bradycardia and hypotension1. We report a unique case where armodafinil was used to treat high-grade atrioventricular (AV) block in a narcolepsy patient. Report of Case A 30 year-old woman presented with multiple events of transient dizziness, syncope, and excessive daytime sleepiness since age 16. Holter monitor demonstrated high-grade AV block with multiple sinus pauses up to 4–5 seconds in length. A tilt table test and EP study were negative. The patient was unable to tolerate a trial of theophylline due to tremors. Due to excessive daytime sleepiness, a sleep study was done which showed narcolepsy with cataplexy, and the patient was started on armodafinil. During follow-up, her syncope resolved, and repeat holter monitoring showed no more evidence of AV block or sinus pauses. Two years later, the patient stopped armodafinil, because she wanted to become pregnant. However, her syncope and arrhythmias recurred, and a pacemaker was implanted at the recommendation of her cardiologist. Conclusion Armodafinil is the (R)-enantiomer of the wakepromoting compound modafinil with a longer half-life2. The α-1-adregnergic agonist properties of modafinil may promote wakefulness but appears to lack peripheral sympathomimetic effects seen with amphetamines2. Modafinil is known to increase norepinephrine in hypothalamus, and may also have a slight role on cardiac α1-B receptors. Similarly noradrenaline4 may have played a role in the stimulation of cardiac alpha receptor signal transduction pathways, leading to resolution of a high grade AV block with armodafinil. While armodafinil is not typically used to manage arrythmias, it may be used in highly select cases.

Plants coevolved with their antioxidant defense systems, which detoxify and adjust levels of reactive oxygen species (ROS) under multiple plant stresses. We performed whole-genome identification of ascorbate peroxidase (APX) and catalase (CAT) families in cultivated and wild soybeans. In cultivated and wild soybean genomes, we identified 11 and 10 APX genes, respectively, whereas the numbers of identified CAT genes were four in each species. Comparative phylogenetic analysis revealed more homology among cultivated and wild soybeans relative to other legumes. Exon/intron structure, motif and synteny blocks are conserved in cultivated and wild species. According to the Ka/Ks value, purifying selection is a major force for evolution of these gene families in wild soybean; however, the APX gene family was evolved by both positive and purifying selection in cultivated soybean. Segmental duplication was a major factor involved in the expansion of APX and CAT genes. Expression patterns revealed that APX and CAT genes are differentially expressed across fourteen different soybean tissues under water deficit (WD), heat stress (HS) and combined drought plus heat stress (WD + HS). Altogether, the current study provides broad insights into these gene families in soybeans. Our results indicate that APX and CAT gene families modulate multiple stress response in soybeans.

Increased quantities of the same sort of item are not nearly as critical to client happiness as a high-quality product. The requirements and expectations of the consumer have an impact on the overall quality of a product or service. The term “quality” may also be defined as the sum total of all the features that contribute to the production of goods and services that are satisfactory to the consumer. Certain imported commodities have lately seen an improvement in quality thanks to efforts by importing nations. Additionally, it safeguards food imported from other nations by confirming that it is safe for human consumption before it is released. This article describes a technique for monitoring perishable goods that is based on the Internet of Things and machine learning. Pictures are recorded using high-resolution cameras in this suggested architecture, and then these images are sent to a cloud server using Internet of Things devices. When uploaded to a cloud server, these photos are segmented using the K-means clustering method. Then, using the principal component analysis technique, features are extracted from the photos, and the images are categorized using machine learning models that have been trained. This proposed model makes use of the Internet of Things, image processing, and machine learning to monitor perishable food.

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In the digital world, automatic component classification is becoming increasingly essential for industrial and logistics applications. The ability to automatically classify various machine parts, such as bolts, nuts, locating pins, bearings, plugs, springs, and washers; using computer vision is challenging for image-based object recognition and classification. Despite varying shapes and classes, components are difficult to distinguish when they appear identical in several ways–particularly in images. This paper proposes identifying machine parts by a deep feature fusion classification model (DFFCM)-variance based designed through the convolutional neural network (CNN), by extracting features and forwarding them to an AdaBoost classifier. DFFCM-v extracts multilayered features from input images, including precise information from image edges, and processes them based on variance. The resulting deep vectors with higher variance are fused using weighted feature fusion to differentiate similar images and used as input to the ensemble AdaBoost classifier for classification. The proposed DFFCM-variance approach achieves the highest accuracy of 99.52% with 341,799 trainable parameters compared with the existing CNN and one-shot learning models, demonstrating its effectiveness in distinguishing similar images of machine components and accurately classifying them.

Industries are increasingly shifting towards unmanned and intelligent systems that require efficient processing and monitoring of structures across various applications, ranging from machine manufacturing to waste disposal. In order to achieve the goal of intelligent processing, it is crucial to accurately classify and differentiate various components and parts. However, existing studies have not focused on simultaneously classifying electro-mechanical machinery components. This poses a challenge as these components, including capacitors, transistors, ICs, inductors, springs, locating pins, washers, nuts, and bolts, exhibit high intra- and inter-class similarity, making their accurate classification a tedious task. Furthermore, many of these components have symmetrical shapes but are asymmetrical among different classes. To address these challenges, this article introduces a new double-single (D-S) pooling method that focuses on the higher resemblance of seventeen electro-mechanical component classifications with minimum trainable parameters and achieves maximum accuracy. The industrial machine component classification model (IMCCM) consists of two convolutional neural network (CNN) blocks designed with a D-S pooling method that facilitates the model to effectively highlight the differences for the higher similar classes, and one block of grey-level co-occurrence matrix (GLCM) to strengthen the classification outcome. The extracted fused features from these three blocks are then forwarded to the random forest classifier to distinguish components. The accuracy achieved by this proposed model is 98.15%—outperforming the existing state of the arts (SOTAs) models, and has 141,346 trainable parameters– hence, highly effective for industrial implementation.

Green synthesis is an easy, safe, and environmentally beneficial nanoparticle creation method. It is a great challenge to simultaneously improve the capping and stabilizing agent carrier separation efficiency of photocatalysts. Herein, Zn-doped Titanium dioxide (TiO ) nanoparticles with high exposure of 360 nm using a UV/visible spectrophotometer were prepared via a one-step hydrothermal decomposition method. A detailed analysis reveals that the electronic structures were modulated by Zn doping; thus, the responsive wavelength was extended to 600 nm, which effectively improved the visible light absorption of TiO . We have optimized the different parameters like concentration, time, and temperature. The peak for TiO is located at 600 cm- in FTIR. A scanning electron microscope revealed that TiO has a definite shape and morphology. The synthesized Zn-doped TiO NPs were applied against various pathogens to study their anti-bacterial potentials. The anti-bacterial activity of Zn-doped TiO has shown robust against two gram-ve bacteria (Salmonella and Escherichia coli) and two gram + ve bacteria (Staphylococcus epidermidis and Staphylococcus aureus). Synthesized Zn-doped TiO has demonstrated strong antifungal efficacy against a variety of fungi. Moreover, doping TiO nanoparticles with metal oxide greatly improves their characteristics; as a result, doped metal oxide nanoparticles perform better than doped and un-doped metal oxide nanoparticles. Compared to pure TiO , Zn-doped TiO nanoparticles exhibit considerable applications including antimicrobial treatment and water purification.

ABSTRACT Seed rate is one of the most pivotal factors that significantly impact grain quality and yield in wheat. In wheat-based crop production system, it can be easily managed. An indiscrimination in seeding rate can lead to higher production cost but will also result in decline of crop yield and quality. Hence, the present experiment was designed to establish the optimal seeding density of three different wheat genotypes (Fatehjang-2016, Dharabi-11 and 16FJ17) of the Barani Areas to attain maximum economic yield. The research experiment was managed in split amidst the cropping season of Rabi 2019-20; one at the laboratory by growing three diverse genotypes of wheat at four different levels of seeding densities in pots and the other at field area of Barani Agricultural Research Station, Fateh Jang at four discrete levels of seeding density of 80, 100, 120 and 140 kg ha-1, respectively. The experiment was laid down using a RCBD with three replication and four treatments. Different seed rates and genotypes significantly affected all plant traits except days to 50% heading, plant height and germination percentage. Contrarily, the interactive effect of genotype and seeding rate on all growth and yield attributes was found non-significant. Whereas, seeding density of 120 kg ha-1 exhibited more germination percentage (85 %), shoot length (11.57 cm), coleoptile length (3.97 cm), days to 50% heading (130), plant height (110 cm), nodes per stem (5.0), 1000 grain weight (49.53 g), grain per spike (41.0), days to maturity (172.67) and grain yield (3755 kg ha-1) in Fatehjang-2016. Whereas Dharabi-11 showed maximum root length (17.64 cm) and tiller count (405.67 m-2) at same level of seeding density. In contrast, 16FJ17 stood second in all parameters except root length and number of tillers m-2. The value for grain per spike (40.0) and shoot length (10.55 cm) is at par with Fatehjang-2016 and Dharabi-11, respectively at 120 kg ha-1 of seeding level. It is evident from the results that wheat variety Fatehjang-2016 can effectively be planted at an optimum seed rate of 120 kg ha-1 for general cultivation and better economic returns in Barani Areas of Pakistan, provided all the agronomic and crop management practices must be kept optimum.

Due to the global increase in temperature, heat stress has become a great threat for the crops production worldwide. This research was conducted to assess the genotypic variability and relationship of accumulation of glycine betaine, proline, chlorophyll contents, and cell membrane thermostability with yield in different cauliflower varieties under heat stress. Heat stress was imposed by early sowing of genotypes (in July as compared to September sowing). Under the early sown condition, the high temperature was experienced by the plants during the curd development stage. Heat tolerance ability of the genotypes was assessed by their ability to curd induction and development at high temperature and also based on different physiological traits. Heat susceptible genotypes showed lengthened curd induction stage. Further, decrease in chlorophyll and osmoprotectants contents was also seen in heat susceptible genotypes. TSX-C40 was identified as most susceptible genotype to heat stress due to low accumulation of glycine betaine and proline, and greater relative cell injury percentage, along with lengthened curd induction stage. Curd induction in TSX-C40 was seen when the maximum temperature was between 21.5-26.0 °C. While CF-Early was identified as heat tolerant genotype as curd induction was started at an average maximum temperature of 34 °C. Further CF-Early showed high chlorophyll contents, and more glycine betaine, and proline accumulation. Less relative cell injury percentage resulted in less damage to cell membranes and the protection of the photosynthetic apparatus during the heat stress of CF-Early. In addition, the accumulation of more osmoprotectants in CF-Early may lead to reactive oxygen species scavenging to protect cell membranes.