Octuplet Loss: Make Face Recognition Robust to Image Resolution

Image resolution, or in general, image quality, plays an essential role in the performance of today's face recognition systems. To address this problem, we propose a novel combination of the popular triplet loss to improve robustness against image resolution via fine-tuning of existing face recognition models. With octuplet loss, we leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification on various datasets without meaningfully exacerbating the performance on high-to-high resolution images. Our method applied on the FaceTransformer network achieves 95.12% face verification accuracy on the challenging XQLFW dataset while reaching 99.73% on the LFW database. Moreover, the low-to-low face verification accuracy benefits from our method. We release our code to allow seamless integration of the octuplet loss into existing frameworks

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm's applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained. The results demonstrate the algorithm's vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal

Complementarity of wind and solar power in North Africa: Potential for alleviating energy droughts and impacts of the North Atlantic Oscillation

With growing gas and oil prices, electricity generation based on these fossil fuels is becoming increasingly expensive. Furthermore, the vision of natural gas as a transition fuel is subject to many constraints and uncertainties of economic, environmental, and geopolitical nature. Consequently, renewable energies such as solar and wind power are expected to reach new records of installed capacity over the upcoming years. Considering the above, North Africa is one of the regions with the largest renewable resource potential globally. While extensively studied in the literature, these resources remain underutilized. Thus, to contribute to their future successful deployment and integration with the power system, this study presents a spatial and temporal analysis of the nature of solar and wind resources over North Africa from the perspective of energy droughts. Both the frequency and maximal duration of energy droughts are addressed. Both aspects of renewables’ variable nature have been evaluated in the North Atlantic Oscillation (NAO) context. The analysis considers the period between 1960 and 2020 based on hourly reanalysis data (i.e., near-surface shortwave irradiation, wind speed, and air temperature) and the Hurrel NAO index. The findings show an in-phase relationship between solar power and winter NAO index, particularly over the coastal regions in western North Africa and opposite patterns in its eastern part. For wind energy, the connection with NAO has a more zonal pattern, with negative correlations in the north and positive correlations in the south. Solar energy droughts dominate northern Tunisia, Algeria, and Morocco, while wind energy droughts mainly occur in the Atlas Mountains range. On average, solar energy droughts tend not to exceed 2–3 consecutive days, with the longest extending for five days. Wind energy droughts can be as prolonged as 80 days (Atlas Mountains). Hybridizing solar and wind energy reduces the potential for energy droughts significantly. At the same time, the correlation between their occurrence and the NAO index remains low. These findings show the potential for substantial resilience to inter-annual climate variability, which could benefit the future stability of renewables-dominated power systems.Graphical abstrac

Nonalcoholic Fatty Liver Disease and the Risk of Atrial Fibrillation

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is now the factor behind the development of liver cirrhosis, liver cell failure, and liver transplantation in many cases. However, its relation to atrial fibrillation (AF) could not be cleared up. AIM: The purpose of the study was to evaluate prevalence of AF in the setting of NAFLD; the association between them, and to evaluate risk factors of AF in this category of patients. METHODS: This cross-sectional study was performed on 400 patients between January 2018 and June 2019. These patients were analyzed for the presence of NAFLD and presence of persistent or chronic AF. RESULTS: There were 138 patients with NAFLD, and 20 patients with persistent or permanent AF. Factors associated with AF were old age, male gender, and high values of aspartate aminotransferase, alanine-aminotransferase, γ-glutamyltranspeptidase, and serum uric acid. The participants with AF had a significantly greater prevalence of NAFLD than those without AF. CONCLUSION: Incidence and prevalence of atrial fibrillation in NAFLD patients were high. Severity of liver disease was an important predictor of new-onset atrial fibrillation

Drug induced liver injury: causative agents and predictors for the outcome – a retrospective study at Tanta University Hospital, Egypt

OBJECTIVE: Drug induced liver injury is a rare but an important cause of acute liver failure. It is associated with significant morbidity and mortality. This study aimed to analyze this disorder, causes, different patterns, and outcomes in Egyptian patients. PATIENTS AND METHODS: This retrospective study collected data of 87 patients diagnosed with drug induced liver injury from 2019 through 2020 at Tanta University. Pattern of liver injury was classified as hepatocellular, cholestatic, and mixed. Data including Model for End-Stage Liver Disease (MELD), Glasgow coma scale, and Poison Severity Score were statistically analyzed. Predictors of mortality and fulminant hepatic failure were determined. RESULTS: Participants were 46 females and 41 males with age ranging from 12-70 years. 39 patients had hepatocellular liver injury, 15 cholestatic, and 33 mixed. Fulminant hepatic failure was diagnosed in 40 patients. Acetaminophen was the most common causative agent. Overall mortality was 17%. Dead patients had significantly deteriorated liver functions (Model for End-Stage Liver Disease). On multivariate logistic regression analysis, Model for End Stage Liver Disease and SO2 independently predicted mortality, and Model for End Stage Liver Disease and random blood glucose were predictors of fulminant hepatic failure development. CONCLUSIONS: Drug induced liver injury is an important health problem in Egypt. Further studies are needed to know the natural history of this disorder. Acetaminophen is one of the most common leading causes. The MELD score is a useful predictor of the outcome of drug induced liver injury such as fulminant hepatic failure and mortality

Reliability Improvement of Power Distribution Systems using Advanced Distribution Automation

<p class="Abstract"><a name="OLE_LINK15"></a><a name="OLE_LINK14"></a>Towards the complete vision of smarter distribution grid, advanced distribution automation system (ADAS) is one of the major players in this area. In this scope, this paper introduces a generic strategy for cost-effective <a name="OLE_LINK555"></a><a name="OLE_LINK554"></a>implementation and evaluation of ADAS. Along with the same line, fault location, isolation and service restoration (FLISR) is one of the most beneficial and desirable applications of ADAS for self-healing and reliability improvement. Therefore, <a name="OLE_LINK24"></a><a name="OLE_LINK23"></a>a <a name="OLE_LINK567"></a><a name="OLE_LINK566"></a>local-centralized-based FLISR (LC-FLISR) <a name="OLE_LINK677"></a><a name="OLE_LINK676"></a>architecture is implemented on a real, urban, underground medium voltage distribution network. For the investigated network, the complete procedure and structure of the LC-FLISR are presented. Finally, the level of reliability improvement and customers’ satisfaction enhancement are evaluated. The results are presented in the form of a comparative study between the proposed automated and non-automated distribution networks. The results show that the automated network with proposed ADAS has a considerable benefit through a significant reduction in reliability indices. In addition, it has remarkable benefits observed from increasing customers’ satisfaction and reducing penalties from industry regulators.</p

The value of frontal sinusotomy in cases of sinonasal polyposis

Abstract Background Chronic frontal sinusitis is being treated with less invasive transnasal endoscopic treatments. Surgery relieves illness and prevents recurrence. Our study was conducted on 80 patients who suffer from frontal opacity with sinonasal polyposis and divided randomly into 2 groups: group A: 40 patients candidate for anterior ethmoidectomy without identification of frontal sinus ostium and group B: 40 patients candidate for anterior ethmoidectomy with identification of frontal sinus ostium. The study was conducted in the otolaryngology department  faculty of medicine Cairo university. Results CT score and sinonasal endoscopy score were statistically significantly improved postoperatively compared to preoperative scores in both groups. There are no significant differences between the 2 groups regarding recurrence rate and complications. Conclusion Ethmoidectomy without frontal sinusotomy is a potential substitute for frontal sinusotomy for the treatment of chronic frontal sinusitis with sinonasal polyposis, and it can achieve similar improvements in symptoms and radiological evidence of frontal sinusitis

Optimal design and energy management of an isolated fully renewable energy system integrating batteries and supercapacitors

The continuous rise of global electricity demand and significant dependency on fossil fuel-based centralised power plants are the main indicators of increasing greenhouse gas emissions, thus negatively affecting climate change and human health and increasing the earth temperatures. This alarming situation has demanded the transition to 100 % renewable energy to decarbonise energy use. However, the fluctuating weather resources, and high investment cost are the major challenges with renewable energy implementation. In this context, the combined integration of multiple sources with energy storage in a so-called hybrid renewable energy system was developed as a durable remedy for the previous issues. This paper proposes a joint and conceptual approach for techno-economic design and dynamic rule-based power control of an off-grid solar/wind hybrid renewable energy system integrated with a hybrid energy storage system that comprises a lithium-ion battery, lead-acid battery, and a supercapacitor. Such concurrent integration of 100 % renewable energy systems and hybrid energy storage systems is lacking in the existing literature. First, with the aid of HOMER software, the feasibility and optimisation analysis of nine different configurations was performed in 1 min resolution to find the optimal component sizes. Second, MATLAB/Simulink models were assembled for the winning design based on a dynamic rule-based strategy to investigate and analyse the system’s dynamic response, power equilibrium, DC-bus voltage supervision and load voltage/frequency control against instantaneous and dynamic changes in the load or renewable energy resources. The proposed approach was promoted and validated on an actual case study for isolated residential community electrification in Saudi Arabia, in which a multi-tier framework was adopted to accurately simulate the stochastic energy consumption of the community’s households. From the design results, the hybrid renewable energy system, which integrates solar, wind, lead-acid batteries, and converter with optimal capacities of 55 kW, 18 kW, 325 kWh and 42 kW converter, respectively, is the most cost-effective alternative with the minimum net present and energy costs of $232,423.3 and$0.3458/kWh, respectively. The system has the least unmet load with 13.5 kWh/years (0.02 %), thus ensuring maximum reliability and customer satisfaction. Meanwhile, the developed control strategy efficiently improved the dynamic response, the DC-bus voltage stability, and the load voltage/frequency during different climatological and load interruptions. Further, the use of lead-acid batteries and supercapacitors has effectively diminished the maximum overshoot of the DC-bus and load voltages by 50 % during all disturbances. In addition, the integration of supercapacitors proficiently saved 2.3–6 kW during wind speed change and 2–9 kW during simultaneous changes in the radiation and load. Overall, the presented method, together with the reported results, provides an improved understanding and highlights the extent to which the successful integration of hybrid renewable energy system and hybrid energy storage system can be used to develop reliable and sustainable energy access for off-grid areas