A Review on Progress and Problems of Quantum Computing as a Service (QCaaS) in the Perspective of Cloud Computing

Cloud computing is a global established system Quantum computing is hypothetical model which is still in tentative analysis Cloud system has some weakness in security processing backup and vicinity Somehow quantum computing illustrates some revolutionary solution to overcome cloud weakness Most researchers are optimistic in quantum computing that it will improve cloud system It is not easy to combine these two different systems along We will show two quantum approaches quantum cryptography and blind quantum computing to secure cloud computing Quantum cryptography will secure the user data transmission and communication through cloud form hackers And blind computing will secure the instant eavesdropping or accessing of data processing in cloud from any vicious cloud provider or third party This paper s major target is to show advantages and disadvantages of quantum computing in the viewpoint to integrate it with cloud system Also review some current improvement of quantum computing and compute

GalliformeSpectra: A Hen Breed Dataset

This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry commonly bred worldwide. A total of 1010 original JPG images were meticulously collected, showcasing the physical attributes, feather patterns, and distinctive features of each hen breed. These images were subsequently standardized, resized, and converted to PNG format for consistency within the dataset. The compilation, although unevenly distributed across the breeds, provides a rich resource, serving as a foundation for research and applications in poultry science, genetics, and agricultural studies. This dataset holds significant potential to contribute to various fields by enabling the exploration and analysis of unique characteristics and genetic traits across different hen breeds, thereby supporting advancements in poultry breeding, farming, and genetic research

Screening of barley genotypes for drought tolerance based on culm reserves contribution to grain yield

Grain filling determines the grain weight, a major component of grain yield in cereals. Grain filling in barley depends on current assimilation and culm reserves. A pot experiment was conducted at the Grilled House, Department of Crop Botany, Bangladesh Agricultural University, Mymensingh during October 2015–May 2016 to study the grain filling patterns and the contributions of culm reserves to grain yield under drought stress. The experiment consisted of two factors—barley cultivars (six cultivars) and drought stress treatments (control and drought stress). Drought stress was imposed by limiting the irrigation during grain filling period. The tillers were sampled at anthesis, milk-ripe and maturity to determine the changes in dry weights of different parts, viz., leaf lamina, culm with sheath, spikes, and grains; and to examine the contribution of culm reserves to grain yield. The result in this experiment revealed that the grain yield was reduced by 5–25% due to drought stress. The reduction in grain yield was attributable to reduce number of grains per spike and lighter grain weight due to the stress. Drought stress drastically reduced the grain filling duration by about 30% and the stress induced early leaf senescence. Photosynthesis rate and leaf greenness were also reduced in stress. The stress altered the contribution of culm reserves, water soluble carbohydrates (WSCs) in culms to grains. At milk ripe stage, accumulation reached its peak. It accumulated 29.0 to 70.0 mg and from 15.8 to 40.6 mg culm−1 in control and stressed plants, respectively. The residual culm WSCs ranged from 3.5 to 11.2 mg and 1.0 to 3.5 mg culm−1 under control and stress conditions, respectively. The highest contribution of culm WSCs to grain yield was observed in BARI barley2 and the lowest was in BARI barley5 both in control and stress condition. Among the cultivars studied, BARI barley2 produced higher yield with the higher contribution of culm reserves to grain yield under the drought stress

Citric Acid-Mediated Abiotic Stress Tolerance in Plants

Several recent studies have shown that citric acid/citrate (CA) can confer abiotic stress tolerance to plants. Exogenous CA application leads to improved growth and yield in crop plants under various abiotic stress conditions. Improved physiological outcomes are associated with higher photosynthetic rates, reduced reactive oxygen species, and better osmoregulation. Application of CA also induces antioxidant defense systems, promotes increased chlorophyll content, and affects secondary metabolism to limit plant growth restrictions under stress. In particular, CA has a major impact on relieving heavy metal stress by promoting precipitation, chelation, and sequestration of metal ions. This review summarizes the mechanisms that mediate CA-regulated changes in plants, primarily CA's involvement in the control of physiological and molecular processes in plants under abiotic stress conditions. We also review genetic engineering strategies for CA-mediated abiotic stress tolerance. Finally, we propose a model to explain how CA's position in complex metabolic networks involving the biosynthesis of phytohormones, amino acids, signaling molecules, and other secondary metabolites could explain some of its abiotic stress-ameliorating properties. This review summarizes our current understanding of CA-mediated abiotic stress tolerance and highlights areas where additional research is needed

GalliformeSpectra: A hen breed dataset

This article presents a comprehensive dataset featuring ten distinct hen breeds, sourced from various regions, capturing the unique characteristics and traits of each breed. The dataset encompasses Bielefeld, Blackorpington, Brahma, Buckeye, Fayoumi, Leghorn, Newhampshire, Plymouthrock, Sussex, and Turken breeds, offering a diverse representation of poultry commonly bred worldwide. A total of 1010 original JPG images were meticulously collected, showcasing the physical attributes, feather patterns, and distinctive features of each hen breed. These images were subsequently standardized, resized, and converted to PNG format for consistency within the dataset. The compilation, although unevenly distributed across the breeds, provides a rich resource, serving as a foundation for research and applications in poultry science, genetics, and agricultural studies. This dataset holds significant potential to contribute to various fields by enabling the exploration and analysis of unique characteristics and genetic traits across different hen breeds, thereby supporting advancements in poultry breeding, farming, and genetic research

Vision Intelligence for Smart Sheep Farming: Applying Ensemble Learning to Detect Sheep Breeds

The ability to automatically recognize sheep breeds holds significant value for the sheep industry. Sheep farmers often require breed identification to assess the commercial worth of their flocks. However, many farmers specifically the novice one encounter difficulties in accurately identifying sheep breeds without experts in the field. Therefore, there is a need for autonomous approaches that can effectively and precisely replicate the breed identification skills of a sheep breed expert while functioning within a farm environment, thus providing considerable benefits the industry-specific to the novice farmers in the industry. To achieve this objective, we suggest utilizing a model based on convolutional neural networks (CNNs) which can rapidly and efficiently identify the type of sheep based on their facial features. This approach offers a cost-effective solution. To conduct our experiment, we utilized a dataset consisting of 1680 facial images which represented four distinct sheep breeds. This paper proposes an ensemble method that combines Xception, VGG16, InceptionV3, InceptionResNetV2, and DenseNet121 models. During the transfer learning using this pre-trained model, we applied several optimizers and loss functions and chose the best combinations out of them. This classification model has the potential to aid sheep farmers in precisely and efficiently distinguishing between various breeds, enabling more precise assessments of sector-specific classification for different businesses