The Role of Metacognitive Strategies in Second Language Writing

Abstract - Metacognition is defined as one’s own awareness ofthe thought process; specifically it concerns the ability toregulate the cognitive processes of the learners in their learning.In the context of writing, it refers to ‘thinking about one’s ownwriting’ or ‘awareness of one’s own writing processes. Thisexperimental study investigates the role of metacognitive strategiesin promoting effective English writing. The data was collectedfrom 27 Indian ESL learners using strategy questionnaire,writing tasks and classroom observation. It is found that successfulemployment of Metacognitive strategies facilitates to meet thechallenges in writing in producing comprehensive content. Theresults of the differential and correlation analysis reveal thatthe employment of effective metacognitive strategies hassignificantly correlated with the development of writing skills

Computational intelligent sensor-rank consolidation approach for Industrial Internet of Things (IIoT).

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches

Training Teachers for Teaching English in Higher Institutes of Engineering and Technology: Challenges and Perspectives

Abstract - Higher Education in India and abroad hasundergone radical and seamless changes. The advent ofinformation technology has redefined the attitude to Englishlanguage learning and teaching. In order to meet thechallenges and demands of the profession, teachers of English inEngineering colleges need to upgrade their professional andsubject competence. This paper aims to explore the need forteachers of English in Higher Institutes of Engineering andTechnology, to equip themselves to face their professionalchallenges. This necessitates teachers of English to undergospecific training programmes designed based on their targetneeds that would in turn enable them to become betterteachers. A preliminary data was collected from135 teachers ofEnglish from various Engineering colleges in Tamilnadu,India. The findings reveal that more than 75 percent of theteachers expressed their views that there is an existing needfor in-service training and development for teachers to teachEnglish in Engineering colleges in Tamilnadu

Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems.

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands >10Gbit/s transfer rate and >1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP (>71%) with processing delay (< 3.5 x 106bits/slot), and transfer delay (< 3Sms). Our simulation results indicate that our approach is highly effective

Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Comparative studies on poly-β-hydroxybutyrate (PHB) with gelatin and PHB with starch as a finished fabric

ABSTRACT The aim of the study was to isolate the PHB producing strains from soil samples, quantitative screening, extraction and estimation of PHB. The preliminary step involves the preparation of PHB, gelatin, starch, PHB with gelatin and PHB with starch as a polymer solution. The production of PHB with gelatin and starch coated textiles laminates by pad dry cure method. The determination of the physical, chemical and functional properties of the developed textile laminates. Comparative studies of invitro degradation of developed textile laminate (PHB with gelatin / PHB with starch) for biomedical application

Apathy and functional disability in behavioral variant frontotemporal dementia

Background Behavioral variant frontotemporal dementia (bvFTD) has profound consequences on patients and their families. In this multicenter study, we investigated the contribution of cognitive and neuropsychiatric factors to everyday function at different levels of overall functional impairment. Methods In a retrospective cross-sectional study, 109 patients with bvFTD from 4 specialist frontotemporal dementia centers (Australia, England, India, and Brazil) were included. The measures administered evaluated everyday function (Disability Assessment for Dementia [DAD]), dementia staging (Clinical Dementia Rating [CDR]), general cognition (Addenbrooke’s Cognitive Examination–revised [ACE-R]), and neuropsychiatric symptoms (Neuropsychiatric Inventory [NPI]). Patients were then subdivided according to functional impairment on the DAD into mild, moderate, severe, and very severe subgroups. Three separate multiple linear regression analyses were run, where (1) total DAD, (2) basic activities of daily living (BADL), and (3) instrumental activities of daily living (IADL) scores were dependent variables; ACE-R total score and selected NPI domains (agitation/aggression, euphoria, apathy, disinhibition, irritability, aberrant motor behavior) were used as independent variables. Age, sex, education, and country of origin were controlled for in the analyses. Results Cognitive deficits were similar across the mild, moderate, and severe subgroups but significantly worse in the very severe subgroup. NPI domain scores (agitation/aggression, euphoria, apathy, disinhibition, irritability, aberrant motor behavior) did not differ across the DAD subgroups. In the multiple regression analyses, a model including ACE-R and NPI apathy explained 32.5% of the variance for total DAD scores. For IADL, 35.6% of the variance was explained by the ACE-R only. No model emerged for BADL scores. Conclusions Cognitive deficits and apathy are key contributors to functional disability in bvFTD but factors underlying impairment in BADLs remain unclear. Treatments targeting reduction of disability need to address apathy and cognitive impairment to ensure greater efficacy, especially in regards to IADLs