Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

Water deficit is a major limiting condition for adaptation of maize in tropical environments. The aims of the current observations were to evaluate the kernel water relations for determining kernel developmental progress, rate, and duration of kernel filling, stem reserve mobilization in maize. In addition, canopy temperature, cell membrane stability, and anatomical adaptation under prolonged periods of pre- and post-anthesis water deficit in different hybrids was quantified to support observations related to kernel filling dynamics. In this context, two field experiments in two consecutive years were conducted with five levels of water regimes: control (D1), and four water deficit treatments [V10 to V13 (D2); V13 to V17 (D3); V17 to blister stage (D4); blisters to physiological maturity (D5)], on three maize hybrids (Pioneer 30B80, NK 40, and Suwan 4452) in Expt. 1. Expt. 2 had four water regimes: control (D1), three water deficit treatments [V10 to anthesis (D2); anthesis to milk stage (D3); milk to physiological maturity (D4)], and two maize hybrids (NK 40 and Suwan 4452). Water deficit imposed at different stages significantly reduced maximum kernel water content (MKWC), kernel filling duration (KFD), final kernel weight (FKW), and kernel weight ear–1 while it increased kernel water loss rate (KWLR), kernel filling rate (KFR), and stem weight depletion (SWD) across maize hybrids in both experiments. The lowest MKWC under water deficit was at D3 in both experiments, indicating that lower KFR results in lowest FKW in maize. Findings indicate that the MKWC (R2 = 0.85 and 0.41) and KFR (R2 = 0.62 and 0.37) were positively related to FKW in Expt. 1 and 2, respectively. The KFD was reduced by 5, 7, 7, and 11 days under water deficit at D3, D4 in Expt. 2 and D4, D5 in Expt. 1 as compared to control, respectively. Water deficit at D5 in Expt. 1 and D4 in Expt. 2 increased KWLR, KFR, and SWD. In Expt. 2, lower canopy temperature and electrical conductivity indicated cell membrane stability across water regimes in NK 40. Hybrid NK 40 under water deficit had significantly higher cellular adaptation by increasing the number of xylem vessel while reducing vessel diameter in leaf mid-rib and attached leaf blade. These physiological adjustments improved efficient transport of water from root to the shoot, which in addition to higher kernel water content, MKWC, KFD, KFR, and stem reserve mobilization capacity, rendered NK 40 to be better adapted to water-deficit conditions under tropical environments.

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Seventeen genotypes of bitter gourd (Momordica charantia L.) were studied in a field experiment conducted at the experimental field of Sher-e-Bangla Agricultural University, Dhaka, during April 2009 to September 2010. The objectives of the study were to measure the variability among the genotypes for yield and yield contributing characters, estimate genetic parameters, association among the characters and their contribution to yield. There was a great deal of significant variation for all the characters among the genotypes. Considering genetic parameters high genotypic co-efficient of variation (GCV) was observed for branches per vine, yield per plant and number of fruit per plant whereas low genotypic co-efficient of variation (GCV) was observed for days to first male and female flowering. In all the cases, it was found that phenotypic co-efficient of variation was greater than genotypic co-efficient of variation. Highest genotypic and phenotypic co-efficient of variation was observed in branch per vine, fruit length, fruit weight and number of fruit plant which indicated a wide variability among the genotypes and offered better scope of selection. The results obtained showed that fruit length showed low direct and positive effect on yield per plant and indirect positive effect on yield per plant via fruit diameter and average fruit weight. Similar result was found for fruit diameter. Average fruit weight and number of fruits per plant showed high direct and positive effect on yield per plant. Path analysis revealed that average fruit weight, number of fruits per plant, days to male flowering and fruit length had positive direct effect on fruit yield. Considering group distance and the agronomic performance, the inter genotypic crosses between G2& G5; G2&G14; G14&G15; G2&G15; G10&G11; G10&G13; G11&G13; G5&G15; G5&G14 might be suitable choice for future hybridization programme.

The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloudbased frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.INDEX TERMS Cloud computing, mobile cloud computing, task offloading, task sequencing, task scheduling, microservices.