Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

Numerical Investigation of Copper Foam Adsorption Beds Packed with MOF-801 for Space Cooling and Desalination Applications

In this paper, an emerging Metal Organic Framework adsorbent MOF-801 packed into a recently developed copper foamed adsorbent-bed is numerically investigated under different operating conditions and physical parameters and benchmarked against the widely used silica gel adsorbent. A numerical model using lumped dynamic modelling approach was developed and validated against experimental data. An enhancement in the effective thermal conductivity for MOF-801 and silica gel foam packed bed and hence an improvement for the overall performance. The MOF-801-based system showed a higher performance for desalination application with a maximum production of specific daily water production of 13 m3/ton·day compared to 9 m3/ton·day for the silica gel-based system. MOF-801-based system evidenced its competition in the cooling application, achieving enhancement for the specific cooling power 140% higher than silica gel-based system

Multi-objective optimisation of MOF-801 adsorbent packed into copper foamed bed for cooling and water desalination systems

Recently, there have been several endeavours to enhance the performance of the adsorption systems for cooling cum desalination by developing new materials and adsorbent bed designs. Therefore, this article contributes to the field by computationally studying the utilisation of state-of-the-art MOF-801 adsorbent packed into the emerging copper-foamed adsorbent bed heat exchanger and benchmarking its performance against that utilising silica gel baseline adsorbent. A multi-objective global optimisation aimed simultaneously at the best coefficient of performance, specific cooling power, and clean water productivity was undertaken. The optimisation was built on the insights from a broad parametric study for the geometric and operating conditions. Given the novelty of the adsorbent MOF-801 and bed design combination, a one-dimensional model was developed to imitate the heat transfer in the adsorbent bed and coupled with a previously validated empirical lumped analytical model for the adsorption system using the MATLAB platform. Using copper foam significantly enhanced the effective thermal performance of the adsorbent bed, improving the overall system performance under different operating conditions. Furthermore, the clean water productivity of the MOF-801-based system outperformed that of the SG-based system by 38%, as the former yielded 29.7 m3/(ton.day), while the latter 21.5 m3/(ton.day). Besides, the MOF-801-based system showed specific cooling power of 830.8 W/kg compared to 611.5 W/kg for the silica gel-based system. However, the cooling capacity per unit volume determined the systems’ form factor, and the coefficient of performance was respectively higher by 9.6% and 20.2% for the silica gel-based system than those of the MOF-801-based system, stemming from the low packing density of MOF-801

Myomectomy for fibroids during cesarean section: A randomized controlled trial

Background: There is a considerable debate about the management of myoma during cesarean section (CS). Recently, several studies indicated the safety and feasibility of undertaking myomectomy during CS.Objectives: To evaluate the safety, accessibility, and short-term morbidity of myomectomy for fibroids during cesarean section.Patients and Methods: This was a randomized controlled trial that included 72 patients who were admitted to the Obstetrics & Gynecology Department, Menoufia University Hospital with uterine fibroids during pregnancy; who were randomly allocated equally into a group of cesarean myomectomy (CM; n=36) and another group of CS only (n=36). The operative events and the outcome were recorded and analyzed.Results: CM group showed a longer duration of surgery and longer hospital stay, higher amount of blood loss, and higher mean pain sores, with a highly statistically significant difference (p = 0.000). No cases in both groups required blood transfusion or ICU admission. No statistically significant differences were noted between both groups as regards the fetal outcome measures (p=0.583 & 0.601).Conclusion: CM is safe and applicable in selected cases without deleterious maternal complications. Special precautions ought to be paid during the procedure, particularly in the intramural type and with large fibroids

The Karolinska Institute innovation ecosystem for cancer startups: lessons learned and best practices

The Karolinska Institute's cancer startup innovation ecosystem is a dynamic network of stakeholders who collaborate to develop and commercialise innovative ideas and technologies in cancer research and treatment. This ecosystem has been successful in cultivating an environment of innovation and entrepreneurship, producing several successful startups in the cancer research and treatment space. This paper aims to provide a systematic review of the lessons learned and best practices from the Karolinska Institute innovation ecosystem for cancer startups. The review includes a comprehensive SWOT analysis , as well as insights from interviews with stakeholders from academia, industry, and government. The SWOT analysis identified several key strengths of the Karolinska Institute innovation ecosystem for cancer startups. The interview methodology for this study involved a semi-structured approach, with open questions designed to elicit detailed and nuanced responses from the participants. The Interviews and SWOT analysis identified several key of success of the Karolinska Institute innovation ecosystem for cancer startups is due to a number of key factors, including strong leadership, collaboration, funding mechanisms, supportive policies, and infrastructure. Effective leadership is required to guide the ecosystem and foster an innovation culture. Collaboration among stakeholders is critical for knowledge sharing, resource allocation, and coordination. Funding mechanisms and infrastructure are critical for supporting R&D activities and providing startups with the resources they need to grow and succeed. To protect and incentivize innovation, supportive policies such as intellectual property laws and regulatory frameworks are required. In addition, the paper discovered that incubation programmes are critical to the success of cancer startups in the Karolinska Institute innovation ecosystem. These programmes connect startups with resources, mentorship, and networks that are critical to their growth and development

Foresight for sustainable energy policy in Egypt: results from a Delphi survey

International audienceThis paper presents energy opportunities, particular areas of high potential and reflections on energy challenges in Egypt by the year 2040. Energy foresight significantly contributes in the effective review and formulation of national energy policies and strategies. In this work, 350 experts participated in real-time Delphi survey and responded to a set of structured and cross-linked questionnaires that aim to assess and provide future dimension to the energy sector in Egypt. Priorities are presented across 14 energy cluster-areas with 180 identified topics. The two-round Delphi study with an iterative process was performed to determine and measure the expectations of the different stakeholders with specific emphasis on the prospects of renewable energy and energy efficiency. The designed cross-linkages between survey components allowed the systematic pooling and convergence of knowledge in addition to the technical insights and different perspectives. About 50% of Egypt's energy demand was foresighted to be met by renewable energies around 2030. The results showed that all types of energy would not only provide economic and environmental benefits but also improve living standards. This work demonstrates that involving large diversity of expertise and different stakeholders, comprising heterogeneous groups, in foresight studies would potentiate the forecasting power, reduce the polarization effect, and enhance the reliability of the foresight exercise

Fractional Reverse Coposn's Inequalities via Conformable Calculus on Time Scales

This paper provides novel generalizations by considering the generalized conformable fractional integrals for reverse Copson's type inequalities on time scales. The main results will be proved using a general algebraic inequality, chain rule, Hölder's inequality, and integration by parts on fractional time scales. Our investigations unify and extend some continuous inequalities and their corresponding discrete analogues. In addition, when α = 1, we obtain some well-known time scale inequalities due to Hardy, Copson, Bennett, and Leindler inequalities

Numerical estimation and experimental verification of optimal parameter identification based on modern optimization of a three phase induction motor

The parameters of electric machines play a substantial role in the control system which, in turn, has a great impact on machine performance. In this paper, a proposed optimal estimation method for the electrical parameters of induction motors is presented. The proposed method uses the particle swarm optimization (PSO) technique. Further, it also considers the influence of temperature on the stator resistance. A complete experimental setup was constructed to validate the proposed method. The estimated electrical parameters of a 3.8-hp induction motor are compared with the measured values. A heat run test was performed to compare the effect of temperature on the stator resistance based on the proposed estimation method and the experimental measurements at the same conditions. It is shown that acceptable accuracy between the simulated results and the experimental measurements has been achieved

A Government Decision Analytics Framework Based on Citizen Opinion

This ongoing research aims to develop a Government Decision Support Framework that employs citizen opinions and sentiments to predict the level of acceptance of newly proposed policies. The system relies on a knowledge base of citizen opinions and an Ontological Model comprising aspects and related terms of different policy domains as an input and a Bayesian predictive procedure. The work proceeds in four basic steps. The first step involves developing domain models comprising aspects for different policy domains in government and automatically acquiring semantically related terms for these aspects from associated policy documents. The second step involves computing citizen sentiments and opinions for the different policy aspects. The third involves updating the ontology with the computed sentiments and the last step involves employing a Bayesian Predictive Process to predict likely citizen opinion for a new proposal (policy) based on information available in the ontology. We provide some background to this work, describe our approach in some detail and discuss the progress mad

A predictive government decision based on citizen opinions - tools & results

Research on citizen satisfaction with respect to public policies has significant public and political value. Politicians are generally seeking effective public policies that favourably impacts citizens’ satisfaction. Citizen satisfaction index is a plausible mechanism for public policy makers to monitor and evaluate the public policies. While surveys on citizen satisfaction are common among agile and progressive public administration and governments, automating the computation of citizen's’ satisfaction is challenging. Given that surveys and evaluations related to citizen satisfaction are retrospective, remedial actions when necessary are always somewhat late. We describe in this poster a predictive analytics framework for citizen satisfaction with respect to public policy based on the previous citizen sentiments past related policies