In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patient’s health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys. Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data. However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E-Healthcare Records Management System (PRMS) that focuses on latency and throughput. A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively. The experimental results confirm the superiority of the PRMS under the high workload scenario over SRHB and its applicability in cloud data centers.

This paper evaluates the performance of six different machine learning (ML) algorithms for classifying power quality disturbances (PQDs), with statistical features extracted using discrete wavelet transform (DWT) as feature input. The statistical features have been extracted from coefficients of multi-resolution analysis (MRA) using four different mother wavelets: Daubechies 4 (‘db4’), ‘haar’, Discrete Meyer (‘dmey’), Coiflets 4 (‘coif4’). The performance analysis has been carried out with 5,500 synthetic signals pertaining to eleven different PQDs generated in accordance with IEEE 1159-2019. Moreover, the performance of the classifiers trained with synthetic signals has been investigated under the influence of unseen noisy signals, hardware PQD signals obtained from the experimental setup, and real PQD events. The analysis indicates that the performance of the extra tree (ET) classifier with the features extracted using ‘haar’ as a mother wavelet is superior and robust in comparison to other classifiers, viz k-nearest neighbor (kNN), random forest (RF), decision tree (DT), logistic regression model (LRM), and gaussian naïve bayes (GNB) with features extracted using different mother wavelets. Furthermore, the ‘haar-ET’ based technique demonstrated remarkable performance in classifying PQDs, showing strong generalization to both unseen hardware and noisy signals, and achieving consistent results when tested with real PQD events.

A graphene disk metasurface-inspired refractive index sensor (RIS) with a subwavelength structure is numerically investigated to enhance the functionality of flexible metasurface in the biosensor sector. The main aim behind the sensor development is to detect amino acids with high sensitivity. The results in form of transmittance and the electric field intensity are carried out to verify the sensor’s performance. The optimal design of the proposed sensor is also obtained by varying several structural parameters such as glass-based substrate thickness, the inner radius of the graphene disk metasurface, and the angle of incidence. The proposed sensor is also wide-angle insensitive for the angle of incidence ranging from 0° to 60°. Furthermore, the sensor’s attributes are analyzed based on numerous parameters with an achieved maximum sensitivity of 333.33 GHz/RIU, Figure of Merit (FOM) of 3.11 RIU ^-1 , and Q-factor of 7.3 are achieved. As a result, these insights offered an enhanced direction for designing metasurface biosensors with a high Q-factor and FOM with high sensitivity for the detection of amino acids.

Lead (Pb) is a highly toxic heavy metal that causes significant health hazards and environmental damage. Thus, the detection and removal of Pb²⁺ ions in freshwater sources are imperative for safeguarding public health and the environment. Moreover, the transformation of single resources into multiple high-value products is vital for achieving sustainable development goals (SDGs). In this regard, the present work focused on the preparation of two efficient materials, i.e., biochar (R-BC) and carbon dots (R-CDs) from a single resource (rice straw), via a novel approach by using extraction and hydrothermal process. The various microscopic and spectroscopy techniques confirmed the formation of porous structure and spherical morphology of R-BC and R-CDs, respectively. FTIR analysis confirmed the presence of hydroxyl (–OH), carboxyl (–COO) and amine (N–H) groups on the R-CDs’ surface. The obtained blue luminescent R-CDs were employed as chemosensors for the detection of Pb²⁺ ions. The sensor exhibited a strong linear correlation over a concentration range of 1 µM to 100 µM, with a limit of detection (LOD) of 0.11 µM. Furthermore, the BET analysis of R-BC indicated a surface area of 1.71 m²/g and a monolayer volume of 0.0081 cm³/g, supporting its adsorption potential for Pb²⁺. The R-BC showed excellent removal efficiency of 77.61%. The adsorption process followed the Langmuir isotherm model and second-order kinetics. Therefore, the dual use of rice straw-derived provides a cost-effective, environmentally friendly solution for Pb²⁺ detection and remediation to accomplish the SDGs.

The goal of this research is to figure out how to calculate academic achievements and students’ cognitive quotients for placement. This study will attempt to forecast students’ intelligence quotients or academic grades to measure the IQ of a student in a holistic manner using all kinds of parameters, from students’ academic records to input from their professors and even their family background, thus creating a dataset of 9000 instances with all these data. We implemented and trained multiple machine learning algorithms on the data and collected the outcomes to select the best algorithm. Students’ quantitative reasoning ability was selected as a parameter that could be assessed by their performance on aptitude tests. Certifications of the student during their bachelor’s degree have been considered, which would also give us an idea about the student’s critical and logical thinking ability. All the parameters were rated on a scale of 1-10. The driving motivation behind this investigation was to discover what parameters force a student to be placed in a company then the final overall “student score” is calculated to determine a student’s intelligence quotient. The final IQ score of the student-generated was graded on a scale of 0-3 and a suitable salary package range for the student was estimated giving the company a good idea of the student’s capability.