General Electric Research Articles

Towards Efficient Electricity Management in Benghazi

In Libya, the general electricity company is tasked with managing peak electricity demand, often resorting to load shedding. This practice, while necessary, results in power outages, particularly impacting areas like the Benghazi Electrical Grid. This study aims to bring predictability to these events by exploring time series forecasting models namely: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Dynamic Regression ARIMA (DRARIMA). The models were trained using data from May 2020 and 2021, and subsequently tested on May 2022. Performance was evaluated using metrics such as mean squared error, mean absolute error, mean absolute percentage error, and mean absolute percentage accuracy. The ARIMA model achieved the highest accuracy at 78.88% mean absolute percentage accuracy with a mean absolute error of 0.9. The SARIMA model, which considers seasonal patterns, achieved an accuracy of 73.86% and mean absolute error of 0.11, but its complexity may lead to overfitting. The DRARIMA, which incorporates exogenous variables, demonstrated an accuracy of 65.36% and mean absolute error of 0.15. Future projections for May 2024 and 2025 using ARIMA models indicate potential improvements in load shedding management and highlight the importance of model selection for accurate forecasting. By improving load forecasting accuracy, this research aims to enhance the effectiveness of load shedding management, thereby reducing power outages and their socio-economic impacts in regions like Benghazi. These findings are particularly valuable for energy planners and managers in similar contexts, providing practical insights and data-driven strategies.

Count-rate management in 131I SPECT/CT calibration

BackgroundSystem calibration is essential for accurate SPECT/CT dosimetry. However, count losses due to dead time and pulse pileup may cause calibration errors, in particular for 131I, where high count rates may be encountered. Calibration at low count rates should also be avoided to minimise detrimental effects from e.g. background counts and statistical fluctuations. This paper aims to present experimental data illustrating count-rate dependencies and to propose practical routines to mitigate errors in the 131I calibration procedure without needing advanced analysis tools.ResultsThe sensitivities of two General Electric (GE) Discovery 670 Pro systems were assessed using two Jaszczak phantom geometries. SPECT/CT data were collected over two months, starting with an initial 131I content of > 2 GBq, decaying to approximately 20 MBq. This allowed for a detailed analysis of count losses due to dead time and pulse pileup. From the sensitivity analysis, it was shown that robust calibration was obtained for 131I phantom activities ranging between 250 and 1500 MBq.ConclusionsThe results show that adequate corrections for dead-time and pulse-pileup counting losses are essential for accurate calibration. It is argued that loss corrections should be based on total spectrum count rates in projections and not only on the 364.5 keV energy window data. The measurement campaigns presented in this paper, using basic tools and equipment, may serve as a model for establishing routines for count-loss corrections as well as for system calibration and regular control of system sensitivity. The data suggest that analysis of source and count concentration in a homogeneous Jaszczak phantom offers robust calibration, whereas analysis of source strength and counts in a delineated phantom insert offers a practical and robust method for regular quality control.

Equity investment portfolio in renewable energy companies to influence the UN's 2030 agenda

Companies need more and more financing, in particular energy companies can be a good instrument to generate a higher return by investing in their shares and to collaborate so that these companies have income from the purchase of their shares. In this work 5 companies related to the topic of renewable energies were considered, these are: Iberdrola, Gamesa, Vestas, General Electric (GE), and Brookfield Renewable Partners L.P. (BEP), this portfolio will be analyzed following the Markowitz model. The results yield a weighting in the distribution of money when invested in this way: 55% to Iberdrola shares, 20% to Gamesa, 2% to Vestas, 0% to GE, and 23% to BEP. It was found that the stocks with the highest risk are Vestas, and the lowest risk are Iberdrola. These results could motivate investment in this type of companies and the result would be to have more economically sound companies and thus meet the Sustainable Development Goals of the United Nations (UN).

Manifestation of Adult children of patients having Alzheimer’s dementia

AbstractBackgroundIndividuals with his parents having Alzheimer’s dementia (AD) have a 4 to 10 times higher risk of developing AD compared to those without family history. The increasing risks of developing AD may result from several underlying mechanisms to be clarified for further strategies of management. We are going to report the demographic and biological characteristics of adult children (AC) of AD parents cohort with its clinical significances in Taiwan.MethodAC of AD patients age from 50‐79 years old were recruited from the neurological outpatient department of Kaohsiung Municipal Ta‐Tung Hospital, Taiwan. The paired individuals, adult children with his/her parents were received annual following up in clinical, neuropsychological, and neuroimaging including PET/CT with 18F‐Florbetaben (FBB) imaging session, using DiscoveryTM MI DR PET/CT system (General Electric Medical Systems, Waukesha, Wisconsin, USA) examinations and assays of blood for genetic and protein analyses. This study was approved by Kaohsiung Medical University Hospital Institutional Review Board. Written informed consent was obtained from all participants before including in this study.Result63 AC with age 59.3 ± 5.8 years, female predominant (76.2%), baseline MMSE 28.3 ± 1.8, CASI 94.7 ± 3.4, and having at least one of APOE e4 allele 34.9%, have recruited. 19 voluntary participants (25.4%) completed 18F‐FBB PET. 9 of 19 (47.4%) were classified as amyloid‐positive based on visual assessment, rated by a senior board‐certificated nuclear medicine physician with experience in interpretation of 18F‐FBB PET who evaluated all images according to the guideline of 18F‐FBB PET interpretation, blind to all clinical information.ConclusionAC of AD patients have higher proportion of having APOE4 allele and higher proportion of having amyloid PET (+). It may imply AC does increase the risk of developing AD, and AC with amyloid PET (+) could be preclinical AD.

Waste to Energy Sustainability Model as a Waste Power Plant: A Bibliometric and Visualization Analysis

More than half of the world's population lives in cities, so the increase in urban waste production globally is mainly due to population growth, urbanization, and economic development, in the past decade, the utilization of waste to energy has provided positive benefits in terms of energy generation. By 2040 power generation will increase globally by 49%. By 2040, renewable energy sources are expected to meet 8% of the total global energy demand, With the development of the economy, waste management goes through several stages to reach the level of high technology as seen today. A sophisticated collection system, combined with an efficient separation process, enables high recovery and recycling rates. The method used in this study is qualitative with a literature study approach. The findings in this study show that China is the country that researches the most about wasto to energy, then the process carried out in the PLTSa Waste to Energy Policy Model recommendations in this study involves several related actors such as Business Entities, authorized Local Governments, and communities. In this case, waste to energy needs attention by local governments that have the authority to manage electricity by establishing provincial regulations in the electricity sector, establishing general electricity plans, and determining business licenses for providing electricity to business entities.

A hybrid forecasting model for general hospital electricity consumption based on mixed signal decomposition

Current research into electricity consumption forecasting for General Hospital still has considerable scope for further development, particularly in its failure to incorporate hospital-specific energy usage characteristics as input variables. This study explores the impact of the usage frequency of sizeable medical equipment on the electricity demand of general hospitals. It proposes a hybrid forecasting algorithm that integrates the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Variational Mode Decomposition (VMD) for signal decomposition with the Hyperband-LSTM deep learning algorithm to enhance prediction accuracy. ICEEMDAN is employed for preprocessing the power consumption series, while VMD is used for the secondary decomposition of high-frequency signals within the series. The Hyperband Pruner is utilized to efficiently adjust the hyperparameters of the LSTM, which is then used for electricity consumption forecasting. The predictive performance of the developed method is assessed by comparing it with 15 different forecasting models. The results indicate that the proposed method demonstrates superior forecasting performance. Applying the model to a real-case scenario, it has reduced the hospital’s electricity consumption by about 15%, providing a referable energy management solution for other medical institutions.

Evaluating Energy Consumption in Residential Buildings in Qatar: A Case Study on Compounds

The global urgency to cut carbon emissions and pollution is clear. Qatar, rich in fossil fuels, is shifting towards sustainability to reduce carbon emissions. This paper analyzes the energy consumption patterns in residential buildings in Qatar, categorizing them by size and ownership, and establishing energy benchmarks for each building type, offering insights to guide energy efficiency policies. By examining the building size and ownership, the study helps establish benchmarks, supporting Qatar’s sustainability goals in reducing carbon emissions. The study was conducted from 1 January 2019 to 31 December 2021, utilizing data from the Qatar General Electricity and Water Corporation (KAHRAMAA). A total of 172,796 residential buildings were analyzed, with data on building characteristics and demographic information incorporated into the analysis. A quantitative analysis revealed that the building size, ownership, and demographics significantly impact energy consumption, guiding efficiency strategies. The ownership and floor area significantly impact energy consumption. A strong positive correlation (R = 0.97) was found between energy consumption (kWh) and the total built area (m2). The patterns of energy use varied across different residential building types. The findings highlight the importance of considering the ownership and building size in energy efficiency policies. Identifying specific energy use patterns supports the development of targeted strategies. This research offers valuable data on residential energy consumption in Qatar, providing a foundation for energy benchmarks. These benchmarks can guide policy decisions and strategies to enhance energy efficiency and promote sustainability in the residential sector. This study uniquely connects the ownership and building size with energy consumption patterns in Qatar, supporting the development of effective energy policies and contributing to global sustainability goals.

A Rare Case of Pulmonary Vascular Anomaly: MDCT Pulmonary Angiography in Diagnosis of Arteriovenous Malformation and Post-operative Imaging

Objective. To demonstrate the diagnostic algorithm and tactics of a surgical management of patients with pulmonary arteriovenous malformations.Materials and Methods. Based on a patient`s clinical data analysis, further examination tactic was chosen: in the pre and postoperative periods multispiral computed tomography (MSCT) angiopulmonography (on a Canon Aquillion Prime, Japan) was performed before and after intravenous administration of a iodine-containing contrast agent (Ultravist, 370 mg/ ml) at the rate of 1 ml/kg. Brain magnetic resonance tomography was performed on a Signa Voyager, General Electric, China, 1,5 Tl in the pulse sequences T1, T2, T2 FLAIR, DWI, SWAN in three mutually perpendicular planes with a slice thickness of 1-5 mm to examine the neurological complications. Interventional angiopulmonography was implemented by using General Electric Innova IGS530, France, angiographic system before and after endovascular occluder ("AMPLATZER Vascular PLUG 4", USA) and spirals ("2D-Helical-35" 9 mm in diameter and 2,7 mm long (4 spirals) and "Complex–Helical-18" 11 mm in diameter and 17 mm long, USA) placement. A literature review in Russian and English languages for the last 5 years was searched using CyberLeninka, PubMed Central, Elibrary data bases, by keywords: pulmonary arteriovenous malformation, MSCT angiopulmonography, endovascular treatment. The prospective longitudinal study was conducted in compliance with the principles of ethics in accordance with the Helsinki Declaration, and informed voluntary consent was obtained from the patient.Results. According to clinical and instrumental data, the tactics of combined endovascular treatment of a vascular malformation with subsequent postoperative control was collectively chosen. During the curation, an increase in saturation parameters, general wellbeing improvement, decrease in pulmonary pressure parameters, and diminution in the size of the right heart's parts were achieved.Conclusion. Arteriovenous malformations (AVMs) are rare vascular abnormalities that represent a pathological communication between the arterial and venous bed. The fistulous link leads to an abnormal discharge of blood, bypassing the physiological capillary channel. Radiation and interventional visualization methods play the most important diagnostic, and the latest — therapeutic role in management of patients and are able to establish indications for surgical intervention, prevent the occurrence of life-threating complications: abscesses, paradoxical embolism and heart attacks, progressive respiratory and heart failure.

طرق التعلم الآلي للتنبؤ بالأحمال متوسطة المدى: دراسة على محطات شمال بنغازي التابعة للشركة العامة للكهرباء في شرق ليبيا

The General Electricity Company of Libya (GECOL) has experienced a surge in electricity demand in recent years, leading to power shortages, particularly during peak summer months. These shortages, often exacerbated by system outages caused by large generating unit failures or transmission line disruptions, have significantly impacted the country's stability. This is further compounded by the ongoing political instability in Libya, which, coupled with electricity supply issues, has negatively affected oil and gas production in some of the country's largest fields. This research addresses the challenge of electricity load demand forecasting by employing Machine Learning (ML) techniques, specifically focusing on Medium Term Load Forecasting (MTLF) based artificial intelligence algorithms. The study compares the accuracy and convergence of different ML methods against actual consumption data, aiming to identify the most effective approach. Accurate load forecasting is crucial for electrical utilities like GECOL to effectively meet customer demands and optimize power generation and transmission. Focusing on Benghazi, this research pioneers the application of Machine learning techniques to predict total energy consumption and demand. The study's findings are validated against real-world data obtained from GECOL's Benghazi Regional Control Center (BRCC), demonstrating the potential of ML for improving electricity load forecasting in Libya. The study concluded with the following results: The Extra Trees Regressor algorithm produced the best results for pregnancy as a target, with an accuracy value of 85%. The Huber Regressor algorithm produced the best results for deficit quantity as a target, with an accuracy value of 77%. Keywords: GECOL, Machine Learning, Load Forecasting, Medium Term Load Forecasting, MTLF.

تطبيق شبكات العصب الاصطناعي وأساليب المنطق الضبابي لتوقع الحمل على المدى القصير لدى شبكة كهرباء غرب ليبيا

This research paper investigates the application of artificial neural networks (ANNs) and fuzzy logic methods for short-term load forecasting in the Western Libyan Electric Network. Historical hourly load data collected during the year 2023 by the General Electricity Company of Libya (GECOL). The study aims to develop accurate and reliable load forecasting models to support efficient grid operation and resource planning. Historical load data and meteorological variables are utilized to train and evaluate the forecasting models. The performance of the ANNs and fuzzy logic methods is compared, and the suitability of each approach for load forecasting in the Western Libyan Electric Network is assessed. The results demonstrate the effectiveness of both ANNs and fuzzy logic methods in short-term load forecasting, providing valuable insights for electricity providers and policymakers in the region. Keywords: neural' networks, fuzzy logic, load forecasting

Three-Dimensional Assessment of the Effects of Kinesio Taping on Mandibular Condylar Volume with Skeletal Class II Malocclusions

This study aimed to perform a three-dimensional assessment of the effects of Kinesio taping (KT) on mandibular condylar volume during Twin Block (TB) appliance therapy in individuals with skeletal Class II malocclusions. Thirty children (16 female, 14 male) aged between 10 and 15 years (12.80 ± 1.08) who were scheduled for TB therapy were randomly assigned to two groups of 15 patients each. One group (Group 1) received KT on the temporomandibular joint (TMJ) area with TB therapy while the other group (Group 2) only received the TB. KT was applied in cycles of 3 days, followed by 1 day off for 3 months. All patients underwent computed tomography (CT) scanning before KT and TB therapy (T0) and 3 months later (T1). Changes in right and left condylar volumes were measured (cm3) using a GE Advantage Workstation (General Electric Medical Systems, USA) and compared between the two groups using the Mann–Whitney U test and Wilcoxon signed-rank test. The KT group showed a significant differences in condylar volumes for both right (mean 0.13; p = 0.015; [0.04: 0.22]) and left condyle (0.30; p = 0.001; [0.18: 0.42]) from baseline (T0) to 3 months (T1). The comparisons between Group 1 and Group 2 revealed no statistically significant difference in initial and final condylar volumes for both the right (0.13; p > 0.05; [−0.16: 0.09]) and the left condyle (0.30; p > 0.05; [−0.04: 0.42]). Kinesio taping, which demonstrated a relative and slight increase on condylar volume, may provide clinical benefits such as a reduction in the duration of functional appliance use, decreased risk of relapse, and effective correction of overjet.

Associations of physical activity, sedentary time, and fitness with cardiovascular risk and atherosclerosis over 3 years in women with systemic lupus erythematosus

IntroductionCardiovascular (CV) diseases are among the main causes of death in systemic lupus erythematosus (SLE). Physical activity (PA) and fitness are potential protective factors against the progression of CV risk factors and atherosclerosis. AimTo analyze trends over time in PA, sedentary time (ST) and physical fitness and their associations of with traditional and novel markers of CV risk and subclinical atherosclerosis in women with SLE over a 3-year follow-up period. MethodsIn this exploratory study, 77 White Hispanic women with SLE (43.3±13.8 years) with mild disease activity were followed after 3 years (n=44). HDL and LDL cholesterol (blood samples), BMI and muscle mass (stadiometer and bioimpedance device), blood pressure (BP), pulse wave velocity (PWV, Mobil-O-Graph® monitor), carotid plaques and intima–media thickness (General Electric Medical Systems, LOGQ-6 model) were assessed. PA and ST were measured using triaxial accelerometers. Physical fitness was assessed with the back-scratch, handgrip strength, 30-s chair stand, and 6-min walk, tests. ResultsAfter 3 years, LDL-c (estimated mean change [est]=13.77mg/dL) and PWV (0.13m/s) increased while diastolic BP (−2.80mmHG) decreased (all, p<0.05). In mixed models, 6-min walk test was positively associated with HDL-c (est=0.07); back scratch (est=0.33) and chair-stand (est=1.19) tests were positively associated with systolic BP (all, p<0.05). No other trends or associations over time were identified (all, p>0.05). ConclusionsPA, ST, fitness, and most studied CV risk factors remained stable over time, with only marginal changes in LDL-c, PWV, and diastolic BP. Overall, PA and ST were not longitudinally associated with CV risk factors and subclinical atherosclerosis and contradictory weak associations were found for physical fitness.

In vitro Study to Compare Stability and Homogeneity of Various Routinely Used Meals for Gastric Emptying Scintigraphy (GES) for Solids and Validating Radiolabeled Idli (Savory Cake) as an Improved Alternate Vegetarian Meal

Purpose: The purpose of the study was to compare the stability and homogeneity of various routinely used meals for gastric emptying scintigraphy (GES) for solids and to validate an improved vegetarian well uniformly radiolabeled solid meal for GES, which is easily fully consumed by patients in view of their common symptoms in such indications and in keeping with Indian food culture. Materials and Methods: Following various types of solid meal preparations were labeled with 3 mCi of 99 mTc sulfur colloid (SC), and image of each meal was acquired on General Electric (GE) Infinia Hawkeye 4 camera: (1) Meal of 4 slice bread + 20 g butter with calorific value ≈ 356 kcal. (2) 4 slice bread + 30 g jam with calorific value ≈ 332 kcal. (3) 4 slice bread + 70 ml of milk with calorific value ≈ 243 kcal. (4) 150 g of sagu (sabudana) +5 ml refined vegetable oil + 10 g peanuts (sagu khichdi) with calorific value ≈ 324 kcal. (5) 4 idli + 2 tablespoon green chutney with calorific value ≈ 280 kcal. (6) 70 g wheat + 50 ml water + 5 ml refined vegetable oil with calorific value ≈ 237 kcal. (7) Meal of 75 g of idli mix + 80 ml water + 10 g butter with calorific value ≈ 310 kcal. All radiolabeled meals were subjected to the radiolabeled stability test procedure to calculate labeling efficiency which was compared. Results: Idli meal prepared with radiotracer added to the batter mixture itself, with a calorific value ≈ 310 kcal showed the highest uniform Tc99 m SC distribution in comparison to all other meal preparations. Conclusions: Radiolabeled idli (savory cake) prepared with radiotracer added to the batter mixture itself, appears to be an ideal vegetarian meal in comparison to all other meal preparations for gastric emptying scintigraphy studies, especially among patients with specific dietary restrictions.

Evaluating the accuracy of planar gated blood pool processing software using simulated patient studies

Planar gated blood pool (GBP-P) radionuclide imaging is a valuable non-invasive technique for assessing left ventricular ejection fraction (LVEF). Serial cardiac imaging can be performed to monitor the potential decline in LVEF among patients undergoing cardiotoxic chemotherapy. Consequently, accurate LVEF determination becomes paramount. While commercial software programs have enhanced the LVEF values' reproducibility, concerns remain regarding their accuracy. This study aimed to generate a database of GBP-P studies with known LVEF values using Monte Carlo simulations and to assess LVEF values' accuracy using four commercial software programs.We utilised anthropomorphic 4D-XCAT models to generate 64 clinically realistic GBP-P studies with Monte Carlo simulations. Four commercial software programs (Alfanuclear, Siemens, General Electric Xeleris, and Mediso Tera-Tomo) were used to process these simulated studies. The accuracy and reproducibility of the LVEF values determined with these software programs and the intra- and inter-observer reproducibility of the LVEF values were assessed.Our study revealed a strong correlation between LVEF values calculated by the software programs and the true LVEF values derived from the 4D-XCAT models. However, all the software programs slightly underestimated LVEF at lower LVEF values. Intra- and inter-observer reliability for LVEF measurements was excellent.Accurate LVEF assessment is crucial for determining the patient's cardiac function before initiating and during chemotherapy treatment. The observed underestimation, particularly at lower LVEF values, emphasises the need for the accurate and reproducible determination of these values to avoid excluding suitable candidates for chemotherapy. The software programs' excellent intra- and inter-observer reliability highlights their potential to reduce subjectivity when using the semi-automatic processing option.This study confirms the accuracy and reliability of these commercial software programs in determining LVEF values from simulated GBP-P studies. Future research should investigate strategies to mitigate the underestimation biases and extend findings to diverse patient populations.Gated blood pool studies, left ventricular ejection fraction, Monte Carlo simulations, 4D-XCAT models.

Development and validation of a perivascular space segmentation method in multi-center datasets

BackgroundPerivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies. MethodsWe annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images. ResultsIn the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region. ConclusionThe mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.

Achieving zero-bill for the grid-connected PV systems in Saudi Arabia governmental schools: a techno-economic analysis

Governmental schools are characterized as ideal places for installing grid-connected PV systems due to the availability of large spaces on their roofs. Schools are also characterized by their good annual load profile, in which most of the loads occur during the day, and there are no loads on nights or weekends or during summer and vacations. Moreover, in the winter, the loads drop dramatically due to the lack of air conditioning. This special annual load profile provides a relative property to government schools with regard to exporting the energy generated during off days to the general electricity grid. The main objective of this research is to attempt to design a grid-connected PV system that can balance imported and exported energy to the grid to achieve an annual zero bill based on the energy exchange tariff in Saudi Arabia. Three different schools in Buraidah City were selected for investigation. The annual energy consumption was estimated from energy bills for 3 years and compared with actual installed loads. The performance analysis was done by applying three widely used indicators: yield factor, capacity factor, and performance ratio. Also, the economic analysis was done using the life cycle analysis methodology based on the local market prices to find the levelized cost of energy (LCOE) and the payback time. The results of economic and performance analysis revealed the professionality of installing grid-connected PV systems in government schools.

Preliminary design for the sizing of an emergency thermal power plant (GE) for the continuity of service of a production chain. case of the Kakula concentrator

This work concerns the preliminary project for the sizing of a thermal power station with emergency generators capable of supplying electrical energy to the Kamoa concentrator located in kakula. The Kakula concentrator, beneficiary of this preliminery project is a plant that concentrates the ore coming from the Kakula mine and having a grade of 5%. After treatement, a copper concentrate of about 55% is obtained and transported for sale. For the choice of generators, the most important element was the determination of the power to be supplied. To do this, we based ourselves on some principales of general electricity. The results enabled us to choose thirteen (13) generators of 4500 kVA of the brand SDMO that we coupled in parallel to obtain a power of 58500 kVA. Then, we chose a 50 MVA transformer to raise the 11 kV voltage produced by generators to a voltage of 33 kV which must be available on the main busbar of the plant. In the end, we proposed a general scheme of the plant with an automatic start system for the generators using an inverter.

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 ( P < 0.001). The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.

Positron Emission Tomography (PET) imaging after Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing parameter lowered RC, COV and BV, while CNR was maximized at = 4000; further increase resulted in oversmoothing. For quantification purposes, = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

A novel economic and technical dispatch model for household photovoltaic system considering energy storage system in “Duhok” City/Iraq as a case study

Photovoltaic (PV) systems harnessing solar power to generate electricity have gained widespread adoption worldwide due to clean innovations. The geographic position of the Kurdistan region, north of Iraq, is very favourable for the photovoltaic power generation system. In Duhok City, the average daily hours of sunshine per year are 8.5 h; this is a good point, which makes installing photovoltaic systems more interesting because the performance of PV systems depends mainly on solar radiation. This study analyses the design of a PV system that supplies electricity to households in Duhok City during the period when there is no general electricity considering the economic and technical dispatch aspects. The system is to be designed to power the household for 10 h (Grid-connected PV system) since it is assumed that the general electricity supply only lasts for 10 h per day. The system will be an alternative source of electricity to the local generators. The full design of the proposed system is implemented, so that the data on the electrical load and site radiations of a specific household at the site under consideration are collected during the design stages. Further, the technician's and economic evaluations are considered as well during each step of analysis and selection for system components. The cost estimate was based on the following data: the electrical load of the house is 6239.1 Wh/day, the electricity cost is $0.005 per Wh and the total investment amount is $3475. The ultimate outcomes, when juxtaposed with the local expenses of diesel generators and PV solar systems, indicate that within 4.5 years, PV solar systems will recoup their initial costs, providing a clean and noise-free energy source thereafter.