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A low cost, non-explosive process for the synthesis of graphene oxide (GO) is demonstrated. Using suitable choice of reaction parameters including temperature and time, this recipe does not require expensive membranes for filtration of carbonaceous and metallic residues. A pre-cooling protocol is introduced to control the explosive nature of the highly exothermic reactions during the oxidation process. This alleviates the requirement for expensive membranes and completely eliminates the explosive nature of intermediate reaction steps when compared to existing methods. High quality of the synthesized GO is corroborated using a host of characterization techniques including X-ray diffraction, optical spectroscopy, X-ray photoemission spectroscopy and current-voltage characteristics. Simple reduction protocol using ultra-violet light is demonstrated for potential application in the area of photovoltaics. Using different reduction protocols together with the proposed inexpensive method, reduced GO samples with tunable conductance over a wide range of values is demonstrated. Density functional theory is employed to understand the structure of GO. We anticipate that this scalable approach will catalyze large scale applications of GO.

Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.

In this study, the load frequency control of a two-area thermal generation system based on renewable energy sources is considered. When solar generation is used in one of the control areas, the system becomes nonlinear and complicated. Zero deviations in the frequencies and the flow of power through the tie lines are achieved by considering load disturbances. A novel grey wolf optimizer, which is a metaheuristic algorithm motivated by grey wolves is utilized for tuning the controller gains. The proportional, integral, and derivative gains values are optimized for the two-area Solar integrated Thermal Plant (STP). As the load connected to the system varies continuously with time, random load variation is also applied to observe the effectiveness of the proposed optimization method. Sensitivity analyses have also been adopted with the deviation in the time constants of different systems. Inertia constant variations of both areas are considered from −25% to +25%, with or without STP. The proposed algorithm shows good dynamic performance as shown from the simulation results in terms of settling time, overshoot values, and undershoot values. The power in the tie line achieves zero deviation quite rapidly in solar-based cases compared to those without STP.

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.

[...]history revealed the presence of similar symptoms not only in other members of her family but also in many of the co-villagers, even the village animals were showing some vague symptoms such as decreased intake of food and drinks along with reduced milk output. [...]the chances of it related to water source were strong, but the hint was still vague enough to arrive at any diagnosis. {Figure 1}{Figure 2} Meanwhile, a thorough literature review of chemical contamination from industries or heavy metal contamination, selenium toxicity seemed a relevant differential by the exclusion of others.

BackgroundThe symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described.MethodsRetrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness.Results75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge.ConclusionsPatients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19.

The present study examines how climatic conditions and pond design affect the growth performance of microalgae. From January to April of 2011, outdoor batch cultures of Nannochloropsis salina were grown in three replicate 780 L conventional raceways, as well as in an experimental 7500 L algae raceway integrated design (ARID) pond. The ARID culture system utilizes a series of 8–20 cm deep basins and a 1.5 m deep canal to enhance light exposure and mitigate temperature variations and extremes. The ARID culture reached the stationary phase 27 days earlier than the conventional raceways, which can be attributed to its superior temperature management and shallower basins. On a night when the air temperature dropped to −9°C, the water temperature was 18°C higher in the ARID pond than in the conventional raceways. Lipid and fatty acid content ranged from 16 to 25% and from 5 to15%, respectively, as a percentage of AFDW. Palmitic, palmitoleic, and eicosapentaenoic acids comprised the majority of fatty acids. While the ARID culture system achieved nearly double the volumetric productivity relative to the conventional raceways (0.023 versus 0.013 g L−1day−1), areal biomass productivities were of similar magnitude in both pond systems (3.47 versus 3.34 g m−2day−1), suggesting that the ARID pond design has to be further optimized, most likely by increasing the culture depth or operating at higher cell densities while maintaining adequate mixing.

The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.

This study highlights the development of a lab-scale, indigenously designed; Packed-Bed Biofilm Reactor (PBBR) packed with brick pieces. The developed biofilm in the reactor was used for the decolourisation and biodegradation of the textile industry effluent. The PBBR was continuously operated for 264 days, during which 301 cycles of batch and continuous treatment were operated. In batch mode under optimised conditions, more than 99% dye decolourisation and ≥ 92% COD reduction were achieved in 6 h of contact time upon supplementation of effluent with 0.25 g L−1 glucose, 0.25 g L−1 urea, and 0.1 g L−1 phosphates. A decolourisation rate of 133.94 ADMI units h−1 was achieved in the process. PBBR, when operated in continuous mode, showed ≥ 95% and ≥ 92% reduction in ADMI and COD values. Subsequent aeration and passage through the charcoal reactor assisted in achieving a ≥ 96% reduction in COD and ADMI values. An overall increase of 81% in dye-laden effluent decolourisation rate, from 62 to 262 mg L−1 h−1, was observed upon increasing the flow rate from 18 to 210 mL h−1. Dye biodegradation was determined by UV–Vis and FTIR spectroscopy and toxicity study. SEM analysis showed the morphology of the attached-growth biofilm.