Enhancing Query Processing on Stock Market Cloud-based Database

Cloud computing is rapidly expanding because it allows users to save the development and implementation time on their work. It also reduces the maintenance and operational costs of the used systems. Furthermore, it enables the elastic use of any resource rather than estimating workload, which may be inaccurate, as database systems can benefit from such a trend. In this paper, we propose an algorithm that allocates the materialized view over cloud-based replica sets to enhance the database system\u27s performance in stock market using a Peer-to-Peer architecture. The results show that the proposed model improves the query processing time and network transfer cost by distributing the materialized views over cloud-based replica sets. Also, it has a significant effect on decision-making and achieving economic returns

Identification of parameters in photovoltaic models through a runge kutta optimizer

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively

Thyroid Hormone Indices in Computer Workers with Emphasis on the Role of Zinc Supplementation

AIM: This study aimed to investigate the effects of computer monitor-emitted radiation on thyroid hormones and the possible protective role of zinc supplementation.MATERIAL AND METHODS: The study included three groups. The first group (group B) consisted of 42 computer workers. This group was given Zinc supplementation in the form of one tablet daily for eight weeks. The second group (group A) comprised the same 42 computer workers after zinc supplementation. A group of 63 subjects whose job does not entail computer use was recruited as a control Group (Group C). All participants filled a questionnaire including detailed medical and occupational histories. They were subjected to full clinical examination. Thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4) and zinc levels were measured in all participants. RESULTS: TSH, FT3, FT4 and zinc concentrations were decreased significantly in group B relative to group C. In group A, all tested parameters were improved when compared with group B. The obtained results revealed that radiation emitted from computers led to changes in TSH and thyroid hormones (FT3 and FT4) in the workers. CONCLUSION: Improvement after supplementation suggests that zinc can ameliorate hazards of such radiation on thyroid hormone indices

Evaluation of Biological Activities of Chemically Synthesized Silver Nanoparticles

Silver nanoparticles were synthesized by the earlier reported methods. The synthesized nanoparticles were characterized using ultraviolet-visible spectrophotometry (UV/Vis), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), and X-ray powder diffraction (XRD). The synthesized materials were also evaluated for their antibacterial activity against Gram positive and Gram negative bacterial strains. TEM micrograph showed the spherical morphology of AgNPs with size range of 40–60 nm. The synthesized nanoparticles showed a strong antimicrobial activity and their effect depends upon bacterial strain as AgNPs exhibited greater inhibition zone for Pseudomonas aeruginosa (19.1 mm) followed by Staphylococcus aureus (14.8 mm) and S. pyogenes (13.6 mm) while the least activity was observed for Salmonella typhi (12.5 mm) at concentration of 5 µg/disc. The minimum inhibitory concentration (MIC) of AgNPs against S. aureus was 2.5 µg/disc and less than 2.5 µg/disc for P. aeruginosa. These results suggested that AgNPs can be used as an effective antiseptic agent for infectious control in medical field

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

State dependent parameter control applied to construction robots and other nonlinear systems

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Exact linearization by feedback of state dependent parameter models applied to a mechatronics demonstrator

The paper develops an exact linearization by feedback approach for State Dependent Parameter (SDP), Proportional-Integral-Plus (PIP) control. The method is demonstrated using a simple automated belt driven by a DC motor equipped with a single board Reconfigurable Input-Output (sbRIO-9631) card, within a Field Programmable Gate Array (FPGA), and with a real time processor for control. The demonstrator is first modelled using a discrete-time SDP model structure, in which the parameters are functionally dependent on measured system states. An exact linearization step returns a linear model with unity coefficients, which is subsequently used to design a PIP control algorithm based on linear system design strategies, including pole assignment and optimal linear quadratic design. Preliminary experimental results demonstrate that the new approach yields an acceptable control performance for the nonlinear system

Parametric Analysis on the Progression of Mechanical Properties on FSW of Aluminum-Copper Plates

The contemporary work manifests that friction stir welding (FSW) is a viable avenue for joining AA1100 aluminium (Al) to C12200 copper (Cu) plates. In this present study, the response of distinctive welding parameters (viz. tool geometries, tool rotational speed, tool travel speed, and tool plunging depth) on weld quality has been investigated. The present work focused on both microstructural investigation and mechanical properties examination. It has been observed that the process parameters have significant effects on weld quality. The design of the experiments has been executed considering four welding input parameters in two variables and selected L-16 orthogonal array to limit the experimental replications. It has been observed that good quality of welds produced by keeping the tool pin offset around 4mm towards the aluminium side and 2mm towards the copper side. And it has also been noticed that right-hand threaded tool pins are giving good weld quality compared to left-handed thread. The joint efficiencies for the welds E2, E14 which were welded by RHT tools were 75.3% and 74.61% and the Strength (UTS) of the welds for the same tools exhibit’s greater than the LHT tools i.e., 98 and 95Mpa. Moderate hardness values are observed for the same welds E1 and E14 with the parameters 1100rpm, 98welding speed, and 1.6mm tool plunge depth. . It also noticed that the weld quality can be significantly enhanced by using proper tool plunge and tool pin geometries compared to the other process parameters

Multiple Strategies Boosted Orca Predation Algorithm for Engineering Optimization Problems

Abstract This paper proposes an enhanced orca predation algorithm (OPA) called the Lévy flight orca predation algorithm (LFOPA). LFOPA improves OPA by integrating the Lévy flight (LF) strategy into the chasing phase of OPA and employing the greedy selection (GS) strategy at the end of each optimization iteration. This enhancement is made to avoid the entrapment of local optima and to improve the quality of acquired solutions. OPA is a novel, efficient population-based optimizer that surpasses other reliable optimizers. However, owing to the low diversity of orcas, OPA is prone to stalling at local optima in some scenarios. In this paper, LFOPA is proposed for addressing global and real-world optimization challenges. To investigate the validity of the proposed LFOPA, it is compared with seven robust optimizers, including the improved multi-operator differential evolution algorithm (IMODE), covariance matrix adaptation evolution strategy (CMA-ES), gravitational search algorithm (GSA), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), Harris hawks optimization (HHO), and the original OPA on 10 unconstrained test functions linked to 2020 IEEE Congress on Evolutionary Computation (CEC’20). Furthermore, four different design engineering issues, including the welded beam, the tension/compression spring, the pressure vessel, and the speed reducer, are solved using the proposed LFOPA, to test its applicability. It was also employed to address node localization challenges in wireless sensor networks (WSNs) as an example of real-world applications. Results and tests of significance show that the proposed LFOPA performs much better than OPA and other competitors. LFOPA simulation results on node localization challenges are much superior to other competitors in terms of minimizing squared errors and localization errors