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In any service-based computing environment, performance pertains to the effectiveness of a system or application in managing user tasks. The key performance assessment metrics include makespan, responsiveness, speed, throughput, resource utilization, etc. In any distributed landscape, like cloud computing, optimal performance relies on resource management techniques such as scheduling, load balancing, etc. Cloud environments often exhibit varying levels of heterogeneity arising from the diverse characteristics of cloudlets and virtual machines. This research paper focuses on the impact of this heterogeneity and proposes two scheduling algorithms to address it effectively: the Variance Managed Heuristic Scheduler (VMHS) and the Adaptive Heterogeneity Index Cloudlet Scheduler (AHICS). AHICS aims to minimize makespan, virtual machine underutilization, the degree of load imbalance, and the deviation of completion time among virtual machines. AHICS functions as the main scheduler, whereas VMHS and MaxMin act as sub-schedulers in this proposed work. AHICS is designed to be flexible and adjust its scheduling strategy based on the level of heterogeneity within the cloud environment. AHICS utilizes the VMHS scheduler in scenarios with a low heterogeneity index or the MaxMin scheduler when the cloudlets and the virtual machines characteristics are highly diverse or heterogeneous. This multi-objective AHICS scheduling algorithm harnesses the strengths of both schedulers as a hybrid algorithm. Implemented using the CloudSim 3.0.3 simulator, experimental results demonstrate that AHICS outperforms other heuristic scheduling algorithms, including MinMin, TASA, HAMM, PTFR, and RSSM, in terms of makespan, virtual machine utilization ratio, the degree of load imbalance, and the deviation of completion time among the virtual machines in both low and high heterogeneity levels

Wireless Sensor Networks (WSNs) are utilised in a variety of applications due to their capacity to capture and transmit environmental data. Clustering has emerged as an efficient method for improving energy efficiency in WSNs. To resolve these issues, we propose an Adaptive Wind Driven Optimisation based Energy Aware Clustering Scheme (AWDO-EACS) for WSNs. The AWDO-EACS model presents an extended form of the Wind Driven Optimisation (WDO) algorithm, designated AWDO, with optimised inherent term values. The proposed model takes into account multiple objectives, such as energy consumption, distance, and end-to-end latency, in order to achieve superior energy efficiency and an extended network lifetime. To validate the efficacy of the AWDO-EACS model, extensive experiments with varying node counts were carried out. In terms of network stability, energy efficiency, end-to-end latency, packet delivery ratio, throughput, packet loss rate, and network lifetime, the results demonstrate that the AWDO-EACS outperforms contemporary clustering strategies. Specifically, the AWDO-EACS obtained a significant increase in energy efficiency, with a 27.35 percent improvement over existing clustering techniques for 20 nodes and an 83.41 percent improvement for 100 nodes. In addition, the end-to-end latency was considerably reduced, with a 96-round lifetime for 20 nodes and a 74-round lifetime for 100 nodes, compared to 37 and 20 rounds, respectively, for GA-LEACH and MW-LEACH. In addition, the AWDO-EACS demonstrated superior packet delivery performance, with a 99.32% delivery ratio for 100 nodes, eclipsing the 76.90% and 82.65% of GA-LEACH and MW-LEACH, respectively. Moreover, the AWDO-EACS model demonstrated a remarkably low packet loss rate of 0.68 percent for 100 nodes, compared to 23.10 percent for GA-LEACH and 17.35 percent for MW-LEACH. The effectiveness of the proposed AWDO-EACS model in enhancing the overall performance of WSNs is demonstrated.

The cooperative QoS Aware Based Data Congestion Control (QoS-ADCC) algorithm is proposed to achieve this goal. It leverages QoS indicators such as connection probability, packet arrival rate, and delay to determine the best route from the source to the destination. These indicators are evaluated for each road segment and combined with estimated relay quality. The QoS-ADCC algorithm combines passive and preventive elements to establish and maintain the routing of data packets. If the source route to the target path is unavailable, the algorithm employs forward and backward reactions to search for candidate routes. The selected route is implemented in the wireless network, and pheromone levels are updated as packets pass through intersections. This approach reduces overhead and transmission delay, utilizing dedicated lanes on urban buses to establish a mobile backbone network and enhance vehicle connectivity. The proposed QoS-ADCC algorithm selects relay data packets between intersections based on QoS dynamic road segments, aiming for a straightforward and efficient routing mechanism. The research involves evaluating different routing protocols and congestion control mechanisms, considering factors such as route stability, end-to-end delay, throughput, and packet loss rate. Simulation experiments using network simulators like NS-3 and OMNET++ as well as real-world testbed experiments will be conducted to assess the performance of the proposed system. The findings of this research will contribute to the development of efficient and reliable data routing mechanisms in MANETs for intelligent transportation systems, ensuring high QoS and improved network performance. Overall, the proposed QoS-ADCC algorithm demonstrates promising results with a throughput value of 31000 Mbps, a Link Stability Speed of 40 meters, a Packet Delivery Ratio of 1.2%, and a routing overhead of 1. These outcomes highlight the potential of the algorithm to enhance data delivery in MANETs within ITS.

Abstract Background The lactate receptor GPR81 contributes to cancer development through unclear mechanisms. Here, we investigate the roles of GPR81 in three-dimensional (3D) and in vivo growth of breast cancer cells and study the molecular mechanisms involved. Methods GPR81 was stably knocked down (KD) in MCF-7 human breast cancer cells which were subjected to RNA-seq analysis, 3D growth, in situ- and immunofluorescence analyses, and cell viability- and motility assays, combined with KD of key GPR81-regulated genes. Key findings were additionally studied in other breast cancer cell lines and in mammary epithelial cells. Results GPR81 was upregulated in multiple human cancer types and further upregulated by extracellular lactate and 3D growth in breast cancer spheroids. GPR81 KD increased spheroid necrosis, reduced invasion and in vivo tumor growth, and altered expression of genes related to GO/KEGG terms extracellular matrix, cell adhesion, and Notch signaling. Single cell in situ analysis of MCF-7 cells revealed that several GPR81-regulated genes were upregulated in the same cell clusters. Notch signaling, particularly the Notch ligand Delta-like-4 (DLL4), was strikingly downregulated upon GPR81 KD, and DLL4 KD elicited spheroid necrosis and inhibited invasion in a manner similar to GPR81 KD. Conclusions GPR81 supports breast cancer aggressiveness, and in MCF-7 cells, this occurs at least in part via DLL4. Our findings reveal a new GPR81-driven mechanism in breast cancer and substantiate GPR81 as a promising treatment target.

One of the biggest issues faced by farmers is the lack of labor for harvesting vegetables. Harvesting brinjals requires a deep understanding and intuitive expertise. Transferring the knowledge of human involved methods to visual system of the harvesting robots is difficult due to the complex background and the nature of the plant bearing multiple brinjals on a single branch. And it is observed that all the brinjals belong to one cluster may not have the same maturity level, this add complexity to identify the moderately matured brinjals through a computer vision. This study proposes an algorithm to detect and identify the moderately matured KKM-1 brinjals. The images are taken from the actual fields through the mobile camera and are considered as the basic data for this study. The algorithm involves two stages, (i) detecting the brinjal and (ii) predicting the moderately matured brinjal. The size of the brinjal determines its maturity and the size prediction is found to be influenced by the shades that is caused due to many reasons. In this study, the k-means clustering algorithm is employed to (i) identify the shaded region of the brinjal and (ii) segment the area of interest. The brinjals are detected from the segmented image using contour method and the non-brinjals are removed based on the shape. A threshold value is calculated using median function that takes the sample area from brinjal dataset. It is observed that the suitability to harvest is more when the area of the brinjal exceeds the threshold. An extensive experimental study is conducted on the collected dataset. During the detection phase, the proposed technique achieved a precision of 79 % and an F1-Score of 85 %. During the detection phase and in the moderately maturity prediction process, the precision is 96 % and the F1-Score is 91 %.

In this study, we have investigated the oscillatory properties of the following fractional difference equation: ∇α+1χ(κ)⋅∇αχ(κ)−p(κ)г(∇αχ(κ))+q(κ)G∑μ=κ−α+1∞(μ−κ−1)(−α)χ(μ)=0,{\nabla }^{\alpha +1}\chi \left(\kappa )\cdot {\nabla }^{\alpha }\chi \left(\kappa )-p\left(\kappa )г\left({\nabla }^{\alpha }\chi \left(\kappa ))+q\left(\kappa ){\mathcal{G}}\left(\mathop{\sum }\limits_{\mu =\kappa -\alpha +1}^{\infty }{\left(\mu -\kappa -1)}^{\left(-\alpha )}\chi \left(\mu )\right)=0, where κ∈N0\kappa \in {{\mathbb{N}}}_{0}, ∇α{\nabla }^{\alpha } denotes the Liouville fractional difference operator of order α∈(0,1)\alpha \in \left(0,1), pp, and qq are nonnegative sequences, and гг and G{\mathcal{G}} are real valued continuous functions, all of which satisfy certain assumptions. Using the generalized Riccati transformation technique, mathematical inequalities, and comparison results, we have found a number of new oscillation results. A few examples have been built up in this context to illustrate the main findings. The conclusion of this study is regarded as an expansion of continuous time to discrete time in fractional contexts.

The study highlights the importance of integrating organic resources, such as vermicompost and biofertilizers with inorganic fertilizers to sustain coconut yields and manage costs. The experimental trial was conducted from 2016 to 2020 in a 47-year-old East Coast Tall coconut garden at Coconut Research Station in South India. The research evaluated four nutrient management treatments viz., T1 (75% Recommended Dose of Fertilizer (RDF) + 25% N organic), T2 (50% RDF + 50% N organic), T3 (100% N organic) and a control (100% RDF). Intercrops included black pepper (Piper nigrum), banana (Musa acuminata) and cocoa (Theobroma cacao). Organic manure significantly improved soil physical properties, water retention and overall soil health. The T2 treatment achieved the highest yields for coconut, cocoa, banana and black pepper. The added soil health parameters supported to these findings, with T2 showing the highest fungal (15.27 × 103 cfu/g of soil) and bacterial populations (17.25 × 105 cfu/g of soil), along with a significant earthworm population (26/m2), indicating enhanced soil ecosystem health. Additionally, soil moisture content was highest under T3 (100% organic) across various depths, followed by T2, highlighting the critical role of organic matter in improving soil moisture conservation. The economic feasibility analysis, including a net present value (NPV), benefit/cost ratio (B/C ratio) and an internal rate of return (IRR), revealed T2 to be the most economically viable nutrient management strategy. This study highlights the economic benefits of intercropping coconuts using an integrated nutrient management (INM) approach, demonstrating its superiority over traditional monocropping practices.

Abstract The evolution of digital health care system in medical imaging has become a contemporary area. Human eye contain numerous nerves and sensitive tissues in it which are highly prone to eye diseases like hemorrhage, exudates, micro aneurysms etc. These eye diseases may affect blood vessels of retina to a greater extent. There are different research contributions made in this research work. They are (i) Identification of OD/OS (Right eye / Left eye) using DWT. (ii) A clip limit estimation technique which identifies the suitable clip limit value for enhancement. (iii) A framework for blood vessel extraction using estimated clip limit value and thresholding technique. Using DWT the localization of optic disc is done. The clip limit estimation technique involves various analysis to estimate the suitable clip limit for enhancement namely qualitative analysis, quantitative analysis, intensity distribution analysis and statistical analysis. A framework is proposed for retina blood vessel segmentation using clip limit estimation technique. The performance of the proposed methods are measured in terms of classification accuracy, sensitivity, specificity. The framework is tested on different datasets which produced an accuracy of 96.87% on DRIVE dataset, 96.89% on HRF dataset, 97.34% on STARE dataset, and 97.52% on DIARETDB1 dataset respectively.

Background: Cluster of differentiation 44 (CD44) is a cell surface adhesion protein involved in the progression and metastasis of oral squamous cell carcinoma. The current study aims to evaluate the expression of CD44 in oral lichen planus and related lesions and thereby assess the relative risk of malignant transformation of these lesions. Materials and Methods: Formalin-fixed paraffin-embedded tissue blocks of 10 oral lichen planus (Group 1), 10 oral lichenoid lesions (Group 2), 8 with oral lichen planus with dysplasia (Group 3), and 5 with lichenoid dysplasia (Group 4) were included in the study. Immunostaining was done for the tissue sections using CD44 mouse monoclonal antibody. Staining density, staining intensity, and immunoreactive scores of CD44 were evaluated in all four groups. Statistical analysis was done by Statistical Package for the Social Sciences® software and the Kruskal–Wallis test was used. Results: CD44 staining pattern of lichenoid dysplasia and lichen planus with dysplasia changed from membranous to cytoplasmic. The membranous CD44 immunoreactivity was mild with a score of 2.25 for Group 3 and 1.6 for Group 4 whereas moderate for other groups with a P-value of 0.009. The cytoplasmic immunoreactivity was significantly high in Group 3 (5.3 ± 2.6) followed by Group 4 (3.2 ± 1.2), Group 2 (1 ± 1.8), and Group 1 (0.7 ± 1.3) with a P-value of 0.001. Conclusion: The CD44 membranous immunoreactivity scores were low while the cytoplasmic immunoreactivity was high in oral lichen planus with dysplasia and oral lichenoid dysplasia when compared to oral lichen planus and oral lichenoid lesions. CD44 immunostaining pattern can help in assessing the malignant transformation of oral lichen planus or lichenoid lesions.

In this paper, we study the forced oscillation of solutions of a fractional partial differential system with damping terms by using the Riemann-Liouville derivative and integral. We obtained some new oscillation results by using the integral averaging technique. The obtained results are illustrated by using some examples.

Background: Long-term exposure to silica dust makes people prone for silicosis which raises the risk of the individual in developing pulmonary tuberculosis (PTB). Silicosis is a progressive lung disease characterize with shortness of breath, cough, fever, and bluish skin, caused due to inhalation of crystalline silica dust found in abundance in sand, rock, and quartz. Aims and Objectives: The disease is known to effect the functioning of macrophages making it unable to defend mycobacterium species, thus allowing the bacterium to invade and develop tuberculosis. Materials and Methods: A retrospective cross-sectional study is carried out to estimate the prevalence of PTB in newly diagnosed silicosis patients at Madurai Medical College, Madurai, Tamil Nadu. All the patients who got registered in Occupational Lung Diseases Registry from January 2016 to December 2021 are included in the study. Silicosis patients tested positive on sputum CBNAAT are considered as silicotubercular patients, whose prevalence is calculated. Results: Our study included a total of 54 subjects (90.8% males and 8.2% female) with mean SD age of 38±6.7 years ranging from 26 to 53 years. Of which 22 patients, 40.7% tested positive for Mycobacterium tuberculosis (MTB) on sputum CBNAAT. The prevalence of MTB in among silicosis patients was found to be 40.7%. Conclusions: Association of TB and silicosis is very strong with the prevalence of TB among silicosis patients as 40.7%. Our study also observed the male sex and elder age as elevated risk of developing silicotuberculosis. All the people getting exposed to silica dust need to be educated in the usage and usefulness of protective equipment.

Climate change is one of the current threats facing the world. Pollution is the primary factor causing climate change, in it, air pollution plays a major part. Almost all developed and developing countries emit a lot of greenhouse gases (GHG). The transportation sector is responsible for the majority of GHG emissions. Nowadays, almost all nations make an effort to lower CO2 emissions from transportation. India also has a strategy to achieve zero emissions through several programmes. When considering ways to lower GHG emissions from the transportation sector, electric vehicles (EVs) are the first choice that comes to mind. The main goal of this case study is to identify why and how India is having trouble launching EVs. India faces significant obstacles in the areas of infrastructure, electricity, battery technology, and consumer behaviour. India already has the infrastructure necessary for the general usage of fuel-powered automobiles. Suddenly changing to another technology and expecting to complete the requirement is a little problematic in emerging nations like India. The majority of electric vehicles (EVs) use lithium-ion batteries, and India is in a position to buy these batteries from other nations. As a result, the battery is a little expensive in India. Nothing is difficult to overcome the barriers compared to the benefits of EVs. Finally, this study makes several recommendations for eliminating the barriers to India's EV adoption.