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Abstract Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Abstract Objectives To determine how fetuin-A contributes to diagnosing and assessing MASLD severity. Methods Fifty MASLD patients and fifty healthy control participants were involved in this retrospective case-control research. Abdominal ultrasonography, fibroscan with controlled attenuated parameter scan (CAP scan), laboratory investigation (including fetuin-A assessment), clinical examination, and history-taking were performed on every case. Results Fetuin-A level was considerably higher in the Cases group (1154.85 ± 629.89) than in the Control group (505.29 ± 150.4) (p 702.5 with 82% sensitivity, 90% specificity, and 86% overall accuracy. Conclusion One possible marker for MASLD diagnosis could be fetuin-A. Furthermore, a substantial association between such marker and the severity of the disease as it revealed a significant correlation with ultrasound grading and fibroscan with controlled attenuated parameters. Trial registration 1- Pan African Clinical Trial Registry. Unique Identifying number/registration ID: PACTR202309644280965. URL: https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=26860 . Registration Approval date: 21/09/2023. 2- ClinicalTrials.gov. Unique Identifying number /registration ID: NCT06097039. URL: https://clinicaltrials.gov/study/NCT06097039?cond=NCT06097039&rank=1 . Registration Approval date: 25/10/2023.

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Abstract The most important function of the deep learning (DL) channel equalization and symbol detection systems is the ability to predict the user’s original transmitted data. Generally, the behavior and performance of the deep artificial neural networks (DANNs) rely on three main aspects: the network structure, the learning algorithms, and the activation functions (AFs) used in each node in the network. Long short-term memory (LSTM) recurrent neural networks have shown some success in channel equalization and symbol detection. The AFs used in the DANN play a significant role in how the learning algorithms converge. Our article shows how modifying the AFs used in the tanh units (block input and output) of the LSTM units can significantly boost the DL equalizer's performance. Additionally, the learning process of the DL model was optimized with the help of two distinct error-measuring functions: default (cross-entropy) and sum of squared error (SSE). The DL model's performance with different AFs is compared. This comparison is conducted using three distinct learning algorithms: Adam, RMSProp, and SGdm. The findings clearly demonstrate that the most frequently used AFs (sigmoid and hyperbolic tangent functions) do not really make a significant contribution to perfect network behaviors in channel equalization. On the other hand, there are a lot of non-common AFs that can outperform the frequently employed ones. Furthermore, the outcomes demonstrate that the recommended loss functions (SSE) exhibit superior performance in addressing the channel equalization challenge compared to the default loss functions (cross-entropy).

Multiterminal High-Voltage Direct Current (MT-HVDC) systems offer numerous benefits compared to conventional alternating current (AC) power systems, including higher power density and improved efficiency. However, the need for adequate protection schemes for HVDC systems remains a significant obstacle to their widespread adoption. Much attention has been given to developing HVDC protection methods to address this. Moreover, the protection of MT-HVDC systems presents a challenge due to bidirectional power flow, dynamic system characteristics, and fault current characteristics that cannot be addressed using conventional methods. This paper represents a centralized protection unit based on a distance protection scheme that involves a two-stage relay process. The first stage involves fault detection by measuring voltage and current to obtain the system impedance, which is then compared to the reach point. The second stage involves identifying the fault location by selecting the correct faulty zone. This technique provides both main and backup protection. A central control unit supports the approach presented in this paper to communicate relays and update their settings. The LoRaWAN communication protocol is employed, as it provides more excellent coverage than other standardized communication technologies and can cover long distances. The proposed method is studied under different scenarios, including system contingency, simultaneous faults, fault resistances, locations, and types. The results of this technique provide the effectiveness of the proposed method. The fault can be cleared within 1.32–1.8 ms.

Background: Antenatal administration of magnesium sulfate (MgSO4) to women in preterm labor has gained widespread use. This study examined the relationship between MgSO4 exposure with neonatal respiratory outcomes. Methods: Very low birth weight (VLBW) infants exposed to antenatal MgSO4 were included. Infants who were intubated anytime during the first three days of life were compared to those who were not intubated regarding their demographic and clinical characteristics, MgSO4 therapy, immediate respiratory outcomes, and occurrence of intraventricular hemorrhage (IVH) using student t-test, chi square testing and logistic regression analysis to control for confounding variables. Correlation coefficient of MgSO4 cumulative dose given and duration of infusion with delivery room resuscitation and need for mechanical ventilation in the first 3 days of life were also calculated. Multilinear regression analysis was used to control for confounding factors. Results: Intubated group included 96 infants while non-intubated group included 171 infants. Although, intubated group has younger gestational age (26 vs. 29 weeks, p

Orthogonal frequency division multiplexing (OFDM) wireless systems rely heavily on channel state estimation (CSE) to mitigate the effects of multipath channel fading. Achieving a high data rate with OFDM technology requires efficient CSE and accurate signal detection. In contrast to more traditional CSE methods that depend on a model-based strategy, machine learning (ML)-based CSE techniques have attracted increased interest in recent years due to their data-driven, learning-based flexibility. In light of this, a deep learning (DL) convolutional neural network (CNN) is utilized to acquire reliable CSE over OFDM wireless system Rayleigh-fading channels. The suggested CSE utilizes offline training to gather channel information from transmit/receive pairs. In addition, it employs pilots to provide additional guidance on channels of communication. Compared to conventional estimation approaches, the proposed CNN-based CSE shows considerable improvement in experimental results. Furthermore, the trained CNN model performs better than the state-of-the-art DL channel estimators. The simulation findings also confirm that the suggested CNN-based CSE is effective when there are fewer pilots, with/without cycle prefixes (CP), and this reduces the bandwidth required to convey the same quantity of data. In addition, there is no background knowledge of the channel's statistics in the proposed estimator. Consequently, the proposed method shows potential for addressing CSE issues in OFDM systems with a significant spectrum resource reduction.

ObjectiveTo examine disparity in hospital mortality among Caucasian (C) and African American (AA) neonates born at different gestational ages (GA).MethodsDe-identified national inpatient data were obtained from the Healthcare Cost and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality (AHRQ) for the years 2011–2018. We compared the odds ratio for mortality among C and AA infants by sex and GA category. Analyses were repeated after controlling for multiple maternal and neonatal confounding variables in a logistic regression model.ResultsThe study included 18,758,233 infants; 78.3% of them were C and 21.7% were AA. Compared to C population, AA population has a significantly higher mortality in term infants born at GA ≥ 36 weeks. The racial/ethnic disparity in preterm infants was inconsistent without any difference at 35–36 weeks in male and female infants. The overall aOR for mortality in AA in all male preterm infants ≤36 weeks was 1.44 (1.39–1.49), 36 weeks and less AA mortality in infants born with GA 24–26 weeks.

As one of the principal protections of UHVDC transmission lines, pilot protection requires strict synchronization and small data flow by exchanging the current on both terminals under the limitation of DC protection action rapidity. To solve this problem, this paper finds that the DC voltage and current on both terminals of the line have different plane distribution characteristics when faults occur internally and externally in the system area, and then advances a novel pilot protection method for UHVDC transmission lines based on U-I characteristics. By setting up the U-I plane with DC voltage and current as variables, analyzing the dynamic trajectory of the real-time captured U-I plane, constructing protection criteria based on the U-I plane on the rectifier side and inverter side separately, and finally converting the judgment results into state information. Both terminals of the line identify the fault location by exchanging the longitudinal comparison of state information. This protection project only utilizes the instant tracks of voltage and current in the rectifier side and inverter side at the time of fault to judge. The principle is simple and can quickly locate faults. The state messages of the two terminals are not modified by the data asynchrony on both terminals. This protection method is convenient for setting, has high sensitivity, and can quickly identify interior and exterior faults. The results are testified by theoretical analysis and simulation verification.

This scoping review investigates instructional strategies implemented in recent studies to enhance the digital application usage experience for the elderly, addressing emerging issues in the context of a rapidly aging global population. With the World Health Organization predicting a significant increase in the proportion of individuals aged 60 years and above by 2030, the imperative for digital literacy among the elderly becomes crucial. The review, drawing from 14 eligible articles sourced from Web of Science and Scopus, categorizes findings into two main themes: i) intergenerational strategies of instruction and ii) contemporary issues associated with intergenerational approaches. By exploring these dimensions, the paper provides valuable insights for researchers seeking to understand and tackle current challenges in instructing the elderly on digital applications, contributing to the ongoing discourse on improving the quality of life for the aging population through digital technology.

The authors wish to make the following corrections to this paper [...]

Background: Reports on the survival of infants born at periviable gestation (GA of ≤24 weeks and birth weight of Methods: We utilized the de-identified National Inpatient Sample (NIS) dataset of the Healthcare Cost and Utilization Project (HCUP) from the Agency for Healthcare Research and Quality (AHRQ). National data were analyzed for the years 2010–2018. Hospitals were categorized according to delivery volume, USA regions, and teaching status. Results: We identified 33,998,014 infants born during the study period; 76,231 infants were ≤24 weeks. Survival at birth and first 2 days of life was greatest in urban teaching hospitals in infants Conclusions: Hospital factors are associated with increased survival rates. Improved survival in large teaching hospitals supports the need for the regionalization of care in infants born at the limits of viability. There was a significant delay in the postnatal mortality day.

Low-speed wind tunnel experiments are conducted to study the aerodynamic performance of a half-span delta wing with 45° leading-edge sweep at subsonic flow regime. The experiments are carried out at a Reynolds number of 8.37 × 105, a free-stream Mach number of 0.1 and angles of attack up to 25°, in steps of 5°. The test model was designed with thirty-two pressure taps fixed on its surfaces (sixteen on each side). Multi-tube manometers were connected to these taps using long tubes to enable recording the pressure readings. Surface pressure distributions and aerodynamic characteristics were calculated at different span-wise locations along the non-dimensional chord-wise distance. Results exhibited that most lift on the studied wing is generated in the region close to the leading edge for all the studied incidence angles. Additional lift is created in the region close to the root chord rather than the tip chord, whereas drag forces increases from tip to root. This can be attributed to the formation of trailing edge vortexes due to the flow separation at the wing leading edge that produces more drag, hence suppressing lift. The study showed also that angle of attack increases the drag coefficient from tip to root, especially at high angle of attack, indicating unfavourable behaviour for manoeuvring. Moreover, the angle of attack increased the pitching moment coefficient up to 10° before it drops sharply until it reaches the tip of the wing model.

In this paper, an innovative fault section location method is introduced for enhancing the resilience of distribution systems. After analyzing the flaws in existing section location methods, it has been argued that certain types of distortion in some nodes can lead to misjudgment and overestimation of section location. In order to solve such a problem, this paper proposes a three-line defense scheme. The first line of defense is to build a fault section location model based on data from multiple sources. Multi-source data includes data collected by remote fault indicators (RFIs), as well as data collected by smart meters (SMs), distributed phasor measurement units (DPMUs), and reinforced section data. The second line of defense is to use the result of the last section location as a known quantity to create a dynamic location model that can reduce the rate of node states distortion at that position. The third line of defense is to use fault isolation and power recovery to resolve the site model and then validate the results of the solution. With these three lines of defense, over 95% position accuracy can be achieved under incomplete and incorrect fault data. Simulations on IEEE 33-node and IEEE 123-node systems show the adaptability of the proposed method to improve the resilience of the distribution network.

The traditional current protection uses the RMS value of the current to compare with a setting-value to identify faults, which is the typical protection scheme for the transmission lines of the power distribution systems. The current is calculated by the fast Fourier transform (FFT) algorithm. As the calculation of the RMS current requires a long time, the speed of the current quick break protection is reduced. In this regard, a fast current protection algorithm based on the discrete setting value is presented in this paper to solve this problem. The main emphasis is placed on the construction of the discrete sequence of setting-value and the protection criterion. The protection startup criterion and action criterion are constructed based on comparisons between discrete sequence of setting-value and sampling values of the current fault component. The operation characteristics and reliability of the fast current protection are analyzed. The performance of the novel fast current protection algorithm is compared with that of the conventional current protection through simulations based on PSCAD/EMTDC. The results verified that the novel fast current protection algorithm has good operation characteristics under different fault conditions.

Abstract Although celebrating its golden jubilee, rapamycin’s importance keeps increasing by the day. Starting as a promising antifungal agent, then as a potent immunosuppressant, strong anticancer drug, and now rapamycin is attracting serious attention as a rejuvenative agent and a possible contributor in treating this era pandemic, COVID-19. Due to its diverse biological activities and promising medical applications, we aimed in this review to put rapamycin under the spot and highlight its discovery, famous microbial producers, reported biological activities, chemical structure, famous analogues, and biosynthesis. Moreover, discuss some rapamycin production approaches including solid-state fermentation, and stressing out producing strain. On the other hand, describe its action mechanism and trials to use it in treatment of COVID-19. Additionally, we highlighted some of the side effects accompanying its use, and describe some approaches reported to minimize these undesired effects. Finally, we report the current status of rapamycin and its analogues in global market, and discuss future prospects of this potent drug.

As an important index of risk protection, the safety distance is crucial to ensure the safe and stable operation of the power system and the safety of personnel’s life. Traditional monitoring methods are difficult to balance recognition accuracy and convenience. Therefore, this paper presents a power safety distance sensing method based on monocular visual images to achieve the recognition of the safety distance of external damage in complex scenes of transmission corridors, and proposed a power density depth distance model. In this model, a codec network with skip-connection to extract features and aggregate shallow and deep features for input power system images. Then, the regularization method, migration learning strategy, cosine annealing learning strategy, and data enhancement strategy are used to further optimize the model, so as to obtain a model with good accuracy and generalization in complex conditions. The effectiveness and superiority of the proposed method are verified in comparison to other external damage monitoring methods. The experimental results showed that the proposed method has high accuracy for the distance of external damage in the actual scenario. Moreover, the method has good generalizability, which can be easily deployed in video monitoring systems on different transmission corridors.

Background: Necrotizing enterocolitis (NEC) is a multifactorial gastrointestinal disease which mostly occurs in very low birth weight (VLBW) infants. In addition to decreasing gestational age (GA) or birth weight (BW), artificial formula, delayed initiation or rapidly advanced feeding, severe anemia and systemic infections were associated with NEC. Several studies demonstrated that breast milk, standardized feeding advancement regimens and treatment of anemia are associated with less incidence of NEC. It is not known if including all these interventions in one multifaceted program will lead to significant reduction in NEC. Methods: The NICU team at The George Washington University Hospital created a multifaceted interdisciplinary quality improvement project to tackle several aspects of NEC prevention that addressed researched risk factors for NEC. The program was made of four quality improvement protocols: 1) Standardized Structured Feeding Program, 2) Feeding Intolerance Management Algorithm, 3) Enteral Osmolality Control Tool, and 4) Packed Red Blood Cell (RBC) Standardized Transfusion Protocol. This time-series, quasi experimental study design examined the differences in the incidence of NEC between infants with BW < 1500 g who were admitted to the GW Hospital NICU before and after the program implementation. Results: Data from 408 VLBW infants were included in the study. Although not statistically significant, there was a decreasing trend of NEC incidence in the post-implementation group (n = 199) compared to the pre-implementation group (n = 209), (3.5% vs. 5.3%, p = 0.88). The trend in the incidence of NEC declined further after the introduction of RBC transfusion protocol which was introduced ten month after starting the other elements of the program. Conclusion: Integration of the multifaceted quality improvement program may be associated with a decline in the occurrence of NEC. Further analysis with a larger sample size is required to determine if the changes seen are statistically significant.

Transmission and storage of medical data using cloud-based Internet-of-health-systems (IoHS) necessitate important prerequisites, such as secrecy, legitimacy, and integrity. This paper recommends a novel hybrid encryption/decryption scheme that can be applied in e-healthcare, or IoHS, for the protection of medical images. The proposed system explores innovative perturbation algorithms that utilize novel chaotic maps. The proposed perturbation-based data encryption is employed in both rounds of confusion and diffusion to cope the drawbacks of traditional chaos-based confusion and diffusion architectures. Particularly, (i) the new maps parameters and chaotic sequences are used to control the permutation and diffusion properties of the scheme, and (2) the derived properties control pixel shuffling and operations of substitution. Different techniques and tests are used to analyzed the chaotic behaviors of the proposed system, including the bifurcation diagram, Lyapunov exponents, as well as the NIST and DIEHARD tests. Moreover, evaluation using various test images indicated that the proposed cryptosystem is fast, have high efficiency, showed high robustness and protection of medical images, and documented the good ability to withstand a variety of cyber-attacks. Furthermore, quantitative results using benchmark color and greyscale images prove the high security levels, sensitivity, and low residual intelligibility with high quality recovered data of our technique than several typical and state-of-the-art encryption schemes. This has been documented using statistical and security analysis metrics, such as number of pixels change rate (NPCR, 99.814%), unified average changing intensity (UACI, 33.694%), peak signal-to-noise-ratio (PSNR, 7.723), and Shannon entropy (7.998).

Multifunctional membrane technology has gained tremendous attention in wastewater treatment, including oil/water separation and photocatalytic activity. In the present study, a multifunctional composite nanofiber membrane is capable of removing dyes and separating oil from wastewater, as well as having antibacterial activity. The composite nanofiber membrane is composed of cellulose acetate (CA) filled with zinc oxide nanoparticles (ZnO NPs) in a polymer matrix and dipped into a solution of titanium dioxide nanoparticles (TiO2 NPs). Membrane characterization was performed using transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), and Fourier transform infrared (FTIR), and water contact angle (WCA) studies were utilized to evaluate the introduced membranes. Results showed that membranes have adequate wettability for the separation process and antibacterial activity, which is beneficial for water disinfection from living organisms. A remarkable result of the membranes’ analysis was that methylene blue (MB) dye removal occurred through the photocatalysis process with an efficiency of ~20%. Additionally, it exhibits a high separation efficiency of 45% for removing oil from a mixture of oil–water and water flux of 20.7 L.m−2 h−1 after 1 h. The developed membranes have multifunctional properties and are expected to provide numerous merits for treating complex wastewater.

This paper presents case studies for replacing diesel generators (DGs) that are used as the main and emergency power sources for alternating current (AC) auxiliary services in substations (SS) within the transmission network (voltage greater than or equal to 230 kV). The objective of this research is to present a solution that is more reliable, environmentally friendly, and financially viable than DGs. To achieve this, the proposed solutions incorporate Battery Energy Storage Systems (BESSs) with or without the integration of Photovoltaic (PV) Systems. These solutions were simulated using the HOMER PRO Version 3.14.5 software for the Messias SS /AL, and the results were analyzed and compared to the DG in terms of reliability, financial viability, and environmental impact. Based on the conducted analyses, the BESS solution with the PV system was found to be the most suitable for the main source. However, in the case of the emergency source, if one of the main sources is a DG/BESS, maintaining the emergency DG is the preferable option. If both main sources are independent, the BESS solution with the PV system is a suitable solution.

A novel intelligent hybrid model is proposed in this paper for accurate prediction and modeling of solar power plants using the wavelet transform package (WTP) and generative adversarial networks (GANs). In the proposed model, the wavelet transform is deployed for decomposing the solar energy signal into the sub-harmonics followed by the statistical feature selection analyses. The GAN model as a deep learning approach is proposed for learning each sub-frequency and predicting the future of the solar energy in the short-time window. Due to the high complexity of the solar irradiance data when training, an evolutionary algorithm based on dragonfly algorithm (DA) is suggested to train the generative and discriminator networks in the GAN. Moreover, a three-phase adaptive modification is suggested to enhance the search capabilities of the DA optimization. The efficiency and appropriate performance of the proposed model is examined and compared with the most successful models such as artificial neural networks (ANNs), support vector machine (SVM), time series, auto-regressive moving average (ARMA), and original GAN on several benchmarks for varied forecast time horizons. The simulation results on the datasets of two regions show the mean absolute percentage error (MAPE) of 0.0282 and 0.0262, when 1-pace forecast horizon, for the two regions which increase up to 0.0531 and 0.0631 for 6-pace forecast horizon. Moreover, the root mean absolute error (RMSE) is 0.0473 and 0.0479 for the two regions at 1-pace forecast horizon which increased up to 0.0895 and 0.0946 for 6-pace forecast horizon. These results show the more precise performance of the proposed forecast deep learning as well as the more optimal performance of the modified DA over the other algorithms shown in the results.

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (PSO) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method.

The power operation site typically faces high voltages, strong currents, and other environmental hazards. Safety fence as a boundary for power operation is of great importance to ensure the safety of operators. The preparation of operation fence is temporary and random, and the risk identification is a complex problem that involves a dynamic interaction between individuals and fence. The existing methods of determining the safety area based on prior knowledge are insufficient. Therefore, this paper proposes a risk identification method based on neighborhood correlation networks and vector calculation. In this method, firstly, the key targets of fence operation are detected by the target detection method. Then, considering that the fence operation target has the characteristics of flat shape and small size; the region proposal network by guided anchoring (GA-RPN) and neighborhood-related network module is introduced to improve the detection accuracy. Finally, the vector calculation criterion is used to judge whether the operator is in the safety fence area, to realize the risk identification of the operation fence overrun. Experiments are conducted on available power fence operation datasets, and the obtained results showed that the proposed method presents better performance than the classical target detection methods.

In the context of refining countries’ transition plans toward 100% shares of renewable systems in the electric power grids by 2050, this study aims to investigate, for the first time, the potential of covering the increasing energy demand for electric vehicles (EVs) using the previously planned hybrid renewable energy system (RES) for Jordan, which comprised wind, solar photovoltaic (PV), and battery energy storage systems (ESS). The optimal RES sizing is revisited to examine the influence of EV penetration percentages and charging rates (normal, medium, and fast) and optimize the charging periods to reduce system capacity. The results showed that for typical nighttime charging and relatively smaller EV penetrations, the RES system capacity remains the same, but the size of ESS must be increased by up to 16.67% for 25% EV penetration. As EV penetration increases, the required ESS capacity drops slightly, but the RES capacity increases by up to 24.53% for 100% EV penetration. Optimal charging periods were found to be during morning hours (6:00 to 9:00 a.m.). Adopting these early charging hours and compared to 0% EV, the required ESS capacity remained nearly the same, except for smaller EV penetration percentages. However, for 100% EV penetration, the increase in required capacity ranged from 24.5% for fast charging to 50.01% for normal charging. For lower penetration percentages, the three modes of charging resulted in comparable capacities of ESS and RES. As the penetration percentage increased to 100%, the fast charging mode required considerably lower RES capacity (by 17.0%) and the same ESS capacity of the 0% EV penetration.

Projects usually need continuous coordination among the participants in terms of scope of work, quickness, and timing during the execution phase to be completed without delay. Sub-contracting is a common practice in civil engineering projects. Subcontractors carry out a large part of the work done in construction projects. Thus, selecting the right subcontractor basically contributes to the project’s success. In this study, it was targeted to explore the causes of the mainconflicts between the contractor and subcontractors and the responsibility distribution of these causes. The basic columns of any project are main contractor and subcontractor, so the relationship between them must be good and effective in achieving the project's goals on time. A questionnaire was surveyed among the projects' parties (including the owner, the main contractor, the sub-contractor, and the consultant). The survey covered how to determine the party responsible for factors leading to dispute, and factors contributing to effective interface between main contractors and sub-contractors. Statistical analysis was carried out on the observations of the respondents of the survey using SPSS software. The mean score of each factor, degree of importance, and ranking was determined and the p-value was deduced using the Kruskal-wallis test to compare between the projects' parties. Also, the significance of each factor used in the questionnaire was determined. The conclusions of the study revealed factors that enhance the relationships, such as contract progress payment done on time, the problems related to the main contractor such as delay in contract progress payment, the problems related to subcontractor such as work delay and the problems related to other sources such as price escalation of material and labor. The results showed good acceptable limits of homogeneity and reliability including communications, financial, workper formance, contract, and other sources. The study recommended a framework that can be implemented to improve such relationships such as compliance with the terms of the contract and choosing contractors specialized in the required works. The main contribution of the study is improving the relationship between main contractor and sub contractor, state the problems facing the relationship to set the methods that can be used to processed with and that helps to success in the projects and development the construction sector in Egypt. This study opens the way for continued development of model parameters that will aid in effectively treating this subject area.

Summary: Background: Substantial differences exist in the approach to resuscitating infants born at periviable gestation. Evaluation of current survival may help guide prenatal counselling and provide accurate expectations of clinical outcomes. We aimed to assess the US national survival trends in periviable infants born at gestational age (GA) ≤24 weeks. Methods: We used de-identified patient data obtained from the US Healthcare Cost and Utilization Project (HCUP) from 2007 to 2018. All infants with documented GA ≤24 weeks were included. The Cochran-Armitage test was used for trend analyses. Regression analyses were conducted for variables associated with survival. Findings: A total of 44,628,827 infant records were identified with 124,345 (0.28%) infants born ≤24 weeks; of those, 77,050 infants 500 g compared to ≤500 g (aOR = 4.62, CI:3.23–5.02 & aOR = 5.44, CI: 4.59–5.84, respectively). Black (aOR = 1.33, CI: 1.31–1.36 & aOR = 1.24, CI: 1.20–1.32, respectively) and Hispanic (aOR = 1.29, CI: 1.27–1.32 & aOR = 1.27, CI: 1.22–1.30, respectively) had higher survival than White. Interpretation: There is a national increase in survival over the years in infants born at periviable GA. BW >500 is associated with >4 folds higher survival compared to ≤500 g. The results of this study should be cautiously interpreted as long-term outcomes are unknown Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

This paper describes the design, micro-fabrication and testing of a novel Micro-Aerial Vehicle (MAV) that mimicking a real locust. Actual parameters of locust insect are used to create a micro-scale MAV that can replace the traditional types that mimicking dragonfly and birds. Based on the obtained results, the novel MAV crucial parameters are its weight and strength to take-off under normal locust performance parameters fashion. Computational Fluid Dynamics (CFD) simulations are carried out at angles of attack of 10°, 20° and 30° and flapping frequencies of 19 Hz, 24 Hz, 30 Hz, 35 Hz and 40 Hz to investigate the aerodynamic performance of this designed MAV and optimize its flapping frequency. The simulation results defined the frequency at which the MAV is capable of hovering and take-off. In addition, the simulation results showed that the MAV is able to utilize some lift enhancement mechanisms that are being actually used by insects. These results enhances the manufacturing process of future MAV’s, especially in the material selection and manufacturing method, and the transmission mechanism for flight.

In the field of thermodynamics, entropy is a measure of the degree of disorder of the system considered focus. The direct measurement of entropy for microscopic systems involving molecule-level variations exists. However, for a macroscopic system, it still remains a challenge. The temperature reading is used as the primary index for understanding the quality of heat. In certain applications, the induced errors in the temperature measurement create complexity in decision making. One such case is the measurement of human body temperature for fever-like symptoms. This research aims to develop a direct entropy measuring device to indicate heat transfer for macroscopic systems. The developed device measured the mean entropy of the human body as 0.042 and 0.146 kJ/K during the daytime and nighttime, respectively. In comparison to the simulated entropy values, the measured values differed by 4% during the trials on the human body. Key decisions on macroscopic systems based on mild temperature variations can be made confidently with the measured entropy values.

Electrical power communication networks undertake complicated power services. They interact with physical power grids all the time, which will result in a high risk to power grids when their electric power communication network is attacked. Therefore, to improve the reliability of power systems, it is of great significance to identify the key links of communication networks. Existing research studies focus more on the risk of power communication services undertaken by communication links, which ignores the frequent interaction between power grids and communications networks. In this paper, from a multi-layer network perspective, an assessment method of communication equipment is proposed in view of the operation characteristics of nodes in different systems as well as the dynamic fault propagation between homogeneous communication nodes. Firstly, we build a model of cyber-physical power interdependent network. Then, for communication nodes, a dynamic model is adopted for cascading failure among the nodes based on the load-capacity model. Afterward, we define an index to evaluate the node importance considering characteristics of the operation of both communication networks and power grids. Finally, a local power grid is taken as an example for verification by comparing the effects of different indexes. The simulation results demonstrate the validity of the evaluation method proposed.

Abstract Background Bioprospecting lipase producers in non-conventional habitats are the way to find special enzymes of diverse applications. Halo-alkaline marshes in Wadi El Natrun in Egypt are some of the most stable ecological systems in the world, and because of the double extremities of alkalinity and salinity, they harbor individual microbes capable of adapting stress conditions. Results Eight strains were recovered from the coastline soil of Al-Beida Lake in Wadi El Natrun and have been tested for lipase production. Among the eight isolates, the strain SBLWN_MH2 was the most active producer of lipase (7.5 U/ml). The crude SBLWN_MH2 lipase showed activity over a wide pH range (3.5 to 13) with an optimum pH at 10.5, and it was able to show more than 75% of its highest activity at pH elevated up to 13. The identification using phenotypic and genotypic methods strongly indicated that the strain SBLWN_MH2 belonged to the genus Streptomyces with a similarity of 99%. Thus, it has been given the suggested name Streptomyces sp. SBLWN_MH2 (MG593538). SBLWN_MH2 produced extracellular lipase in modified starch casein medium supplemented with different oils or Tween-80, and the potential production rate has been attained in the case of linseed oil after 3 days. Further experiments have been carried out to optimize medium composition through Box-Behnken design and response surface methodology, and it was possible to achieve more than 3.5-fold increase in lipase production. Conclusions The present study indicates that Streptomyces sp. SBLWN_MH2 is a potential lipase producer and could be fruitfully employed in the large-scale production of highly alkaline lipase.

Distributed optimization methods have been vastly investigated and approved by the researchers due to their major advantages including high accuracy, secured performance and low time-consuming structure compared to the centralized frameworks. This paper aims to provide a novel method based on fuzzy primal-dual method of multipliers (PDMM) to manage the optimal energy scheduling problem in the smart grids. The proposed method illustrates some unrivaled points of interest which are more preferable than the conventional alternating direction method of multipliers (ADMM) in terms of preciseness and convergence speed. The proposed smart grid is constructed of different components such as generators, wind park and storage devices as two of the most profitable and applicable energy sources in the power grids. In order to model the uncertainty effects, a stochastic method based on fuzzy cloud theory is developed to capture the high-dimension uncertainty in a more realistic way. The units are scheduled to exchange energy in the smart grid in a fully distributed manner when meeting the active/reactive generation and demand balance. Such an energy exchanging process continues until a proper solution would be found through which all the agents in the system are satiated. The simulation results on the IEEE 24-bus test system indicate that the proposed stochastic distributed energy management framework yields an error of less than 0.018% compared to the centralized approach.

Recently, many renewable energy (RE) initiatives around the world are based on general frameworks that accommodate the regional assessment taking into account the mismatch of supply and demand with pre-set goals to reduce energy costs and harmful emissions. Hence, relying entirely on individual assessment and RE deployment scenarios may not be effective. Instead, developing a multi-faceted RE assessment framework is vital to achieving these goals. In this study, a regional RE assessment approach is presented taking into account the mismatch of supply and demand with an emphasis on Photovoltaic (PV) and wind turbine systems. The study incorporates mapping of renewable resources optimized capacities for different configurations of PV and wind systems for multiple sites via test case. This approach not only optimizes system size but also provides the appropriate size at which the maximum renewable energy fraction in the regional power generation mix is maximized while reducing energy costs using MATLAB’s ParetoSearch algorithm. The performance of the proposed approach is tested in a realistic test site, and the results demonstrate the potential for maximizing the RE share compared to the achievable previously reported fractions. The results indicate the importance of resource mapping based on energy-demand matching rather than a quantitative assessment of anchorage sites. In the examined case study, the new assessment approach led to the identification of the best location for installing a hybrid PV / wind system with a storage system capable of achieving a nearly 100% autonomous RE system with Levelized cost of electricity of 0.05 USD/kWh.

This paper proposes a novel high voltage gain structure of DC-DC converter with soft-switching ability for photovoltaic (PV) applications. A small size coupled inductor with one magnet core is utilized to improve the voltage conversion ratio in the proposed converter. The converter has one active MOSFET with low conducting resistance ( $R_{DS}-_{ON}$ ), which in turn reduces the conduction losses and complexity of the control section. Due to the low input current ripple, the lifetime of the input PV panel is increased, and the maximum power point (MPP) of the PV panel can be easily tracked. The MOSFET’s zero-voltage and zero-current switching and diodes are the other countenance of the proposed converter, which improve its efficiency. Additionally, an improved Perturb and Observe MPP tracking (IP&O MPPT) algorithm is introduced to boost the extracted power of the input PV sources. To validate the performance of this converter, the operation modes principle, steady-state and efficiency survey, and comparison results with other same family converters are carried out. Finally, an experiential prototype is built with 20 V input, 200 V output, power rate of 200 W, and 50 kHz operating frequency to validate the mathematical analysis and effectiveness of the proposed structure. The efficiency of the proposed converter was estimated by over 95% at various power levels.

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids’ components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.

Assets deteriorate over time, as well as being covered, corroded, or becoming old in less obvious ways. Maintenance can extend the remaining useful life (RUL) of an asset system, but sooner or later it must surely be replaced. In this study, we propose a new RUL estimation methodology to assist in decision making for the maintenance and replacement of assets from prioritizing equipment in a renovation plan. Our methodology uses advanced data analysis techniques that consider multiple competing criteria with the goal of maximizing values of the asset throughout its life cycle, while considering the rules of remuneration and service quality of the current regulation, as well as the values at risk according to the decisions and actions taken. Experimental results with real datasets show the efficiency of the proposed approach. Finally, this work also presents the development of an analytical tool to optimize asset renewal decisions applying the RUL estimation methodology proposed and its application to the Brazilian electric sector.

The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers’ expertise, which sometimes might be imprecise. To fill that gap, in this paper, a comparative analysis using the multi-criteria method ELECTRE TRI and clustering algorithms is performed to obtain an auxiliary procedure to define initial thresholds for the ELECTRE TRI method. In this proposed methodology, K-Means, K-Medoids, Fuzzy C-Means algorithms, and Bio-Inspired metaheuristics such as PSO, Differential Evolution, and Genetic algorithm for clustering are tested considering a dataset from a fundamental problem of sorting in Water Distribution Networks. The computational performances indicate that Fuzzy C-Means was more suitable for achieving the desired response. The practical contributions show a relevant procedure to provide an initial view of boundaries in multi-criteria sorting methods based on the datasets from specific applications. Theoretically, it is a new development to pre-define the initial limits of classes for the sorting problem in multi-criteria approach.

Over the past few years, the Bitcoin-based financial trading system (BFTS) has created new challenges for the power system due to the high-risk consumption of mining devices. Briefly, such a problem would be a compelling incentive for cyber-attackers who intend to inflict significant infections on a power system. Simply put, an effort to phony up the consumption data of mining devices results in the furtherance of messing up the optimal energy management within the power system. Hence, this paper introduces a new cyber-attack named miner-misuse for power systems equipped by transaction tech. To overwhelm this dispute, this article also addresses an online coefficient anomaly detection approach with reliance on the reinforcement learning (RL) concept for the power system. On account of not being sufficiently aware of the system, we fulfilled the Observable Markov Decision Process (OMDP) idea in the RL mechanism in order to barricade the miner attack. The proposed method would be enhanced in an optimal and punctual way if the setting parameters were properly established in the learning procedure. So to speak, a hybrid mechanism of the optimization approach and learning structure will not only guarantee catching in the best and most far-sighted solution but also become the high converging time. To this end, this paper proposes an Intelligent Priority Selection (IPS) algorithm merging with the suggested RL method to become more punctual and optimum in the way of detecting miner attacks. Additionally, to conjure up the proposed detection approach’s effectiveness, mathematical modeling of the energy consumption of the mining devices based on the hashing rate within BFTS is provided. The uncertain fluctuation related to the needed energy of miners makes energy management unpredictable and needs to be dealt with. Hence, the unscented transformation (UT) method can obtain a high chance of precisely modeling the uncertain parameters within the system. All in all, the F-score value and successful probability of attack inferred from results revealed that the proposed anomaly detection method has the ability to identify the miner attacks as real-time-short as possible compared to other approaches.

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE’s processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

The insertion of renewable sources to diversify the energy matrix is one of the alternatives for the energy transition. In this sense, Brazil is one of the largest producers of renewable energy in the world, mainly in wind generation. However, the impact of integrating intermittent sources into the system depends on their penetration level, causing problems in the electrical network. To evaluate this scenario, the present article aims to investigate the power quality problems generated by wind turbines in connection with the electrical system and how battery energy storage systems (BESS) solve or mitigate these disturbances in the network. Knowing the impacts of high generation power variability, the focus of the work is the application of power smoothing. However, results are presented for five applications (factor correction, voltage control, power factor smoothing, frequency control and time shift) that can be carried out at the studied wind farm. This article presents a real BESS, which has a capacity of 1 MW/1.29 MWh, connected in parallel to a group of wind turbines that provides a power of approximately 50.4 MW located in Brazil. In addition to presenting the system simulation in HOMER Pro software, this study validates the effectiveness of this BESS by presenting real operation data for each application.

Steel beams’ shear strength is one of the most important factors that influence how quickly webs buckle. Despite extensive studies having been performed over the previous three decades, the existing procedures did not achieve the necessary reliability to predict the ultimate shear resistance of plate girders. New techniques called Learner Techniques have started to be used over the last few years; these techniques were applied to calculate the steel beam shear strength. In this study, a Regression Learner Techniques model was built using data from 100 test results from previously published research. Based on the geometric and material properties of the web and flanges available in the published tests, a model was built using Artificial Neural Networks. Based on sensitivity analysis, a Cascade Forward Backpropagation Neural Networks (CFBNN) approach was utilized to anticipate the shear strength of steel beams. The proposed models outperformed current hybrid artificial intelligence models developed using the same collected datasets and demonstrated to accurately predict the ultimate shear strength. The performance of the models was evaluated using a range of statistical assessment methods, which led to a valuable conclusion. The CFBNN model achieved the highest root mean square (R2 = 0.95). The results corresponding to each test were verified by specimen shear strength values calculated by a theoretical approach. The resultant maximum shear force obtained by the proposed modified equation was compared with the experimental results and the shear force was estimated using two different approaches proposed by the European code. Finally, two approaches were used to verify the proposed model. The first approach was the data reported from an experimental shear test program conducted by the authors, and the second was the results of the shear values acquired experimentally by other researchers. Based on the test results of the previous studies and the current work, the suggested model gives an adequate degree of accuracy for estimating the shear strength of steel beams.

Nonorthogonal multiple access (NOMA) is a relevant technology for realizing the primary goals of next-generation wireless networks, such as high connectivity and stability. Because a rising number of users are becoming connected, user data security has become a critical issue. Many chaotic communication systems have been established to address this important issue via exhibition of affordable physical-layer-security solutions. In this study, we propose a chaotic downlink NOMA (C-DL-NOMA) system over the additive white Gaussian noise and Rayleigh-fading channels to enhance the security of the DL-NOMA system. The proposed algorithm is based on a coherent analog modulation technique that combines various chaotic maps for chaotic masking of encrypted data. On the transmitter, chaotic encryption was used for transmitted data with fixed power-allocation-level control, whereas on the receiver, successive interference-cancellation demodulation was utilized to detect multiple users, after which chaotic decryption was performed. Simulation results were evaluated based on security analyses, such as statistical analysis (histogram and correlation analyses and information entropy), bit-error-rate performance, and achievable-data-rate performance. According to these security analyses and numerical results, the proposed C-DL-NOMA system outperformed traditional unencrypted NOMA systems.

Acute heat stress (AHS) is known to affect rabbit production and well-being negatively. Yet, early acute heat stress effect on later physiological response is poorly studied. Our study aimed to provide more knowledge of rabbit physiological response to early acute heat stress in two distinguished rabbit breeds. A total of 120 males of New Zealand White (NZW) and Baladi Black (BB) rabbits five weeks old were recruited and randomly divided into two sub-groups, control groups (Control) reared in a controlled environmental chamber 28 °C, and 40% RH and early acute heat stress groups (HS) exposed to 36 °C and 62% RH for 6 hours. At 13 weeks, rabbits of all groups were subjected to the same AHS conditions. Our results, irrespective of breeding differences, demonstrate a significant (p

The high growth of the automotive industry reveals the very bright future of this technology and its high penetration effects on the human society. No doubt that the random and volatile charging demand of these devices would affect the power grid optimal operation and scheduling which may be regarded as a new challenge. Therefore, this paper investigates the stochastic scheduling of hybrid AC/DC microgrids considering the plugin hybrid electric vehicles charging demands, distributed all over the grid. Three different charging patterns, called coordinated, uncoordinated and smart charging models with different characteristics for the charger type, capacity and market share are proposed. Moreover, different types of renewable energy sources including wind turbine, solar panel and fuel cell are modeled and considered in the scheduling process of the hybrid microgrid. In order to mitigate the charging effects of electric vehicles on the hybrid AC–DC microgrid operation, some remotely switches are considered in the system which make it possible for changing the topology and power flow way. In order to model the uncertainty effects, a data-driven framework based on point estimate method and support vector machine is developed. This would make it possible to extract out the standard deviation value of the uncertain parameters and reflect their impacts on the microgrid operation problem through the limited concentration points. A novel evolving solution based on flower pollination algorithm is also proposed to solve the problem optimally. An IEEE standard test system is used as the hybrid AC/DC microgrid case study to assess the performance of proposed model.

The unified power quality conditioner (UPQC) has become one of the most proficient solutions for compensating the effects of voltage fluctuations, harmonics, sudden load changes and enhancing the power quality of distribution networks. Aiming at the shortcomings of conventional compensation control strategies and enhancing the compensation effect of UPQC, this paper proposes an effective control strategy for UPQC based on dq0 detection method. Furthermore, the space vector pulse width modulation (SVPWM) algorithm is improved in this paper. The fundamental and harmonic detection on the power supply side current and load side voltage are performed through the proposed strategy. In order to verify the effectiveness of the proposed control strategy, the simulation model of the system is established by MATLAB/Simulink under three-phase voltage unbalance conditions, voltage sag and load sudden changes. In addition, an experimental verification is carried out on an installed experimental platform. Under normal and unbalance operating conditions of the three-phase power supply voltage, the proposed control strategy has a better fundamental and harmonic detection effect compared with the conventional detection-based control strategy. Furthermore, under different load sudden changes conditions, it is verified that the proposed control strategy has better voltage tracking performance. Finally, the outcomes showed the effectiveness of the proposed control strategy in maintaining load side voltage stability, maintaining power side current sinusoidal, and enhancing the power quality of the distribution network.

The Unmanned Aerial Vehicle (UAV) inspection mode has been gradually implemented in the power system. The UAV inspection image is checked by the target detection technology, but there is no high-precision target detection algorithm as the technical support. In this regard, this paper proposes a target detection algorithm based on the improved RetinaNet which is suitable for transmission lines defect detection. In this algorithm, the shortcomings of the RetinaNet anchor frame extraction mechanism based on Apriori are corrected. At the same time, the number and size of anchor frames are redesigned by using the improved K-means + + algorithm, so that the anchor frame of the improved algorithm gets the highest average IoU (Intersection over Union) value, which matches the actual size of the transmission line defects. Then, in RetinaNet, the feature pyramid network based on DenseNet is built as the backbone network to improve the model accuracy and make the model lighter. The improved model is trained and tested by using the data set of transmission line defects for validation. The results show that the proposed method has advantages and effectiveness in the detection of transmission line defects, and meets the requirements of intelligent inspection in terms of accuracy.

In complex power systems, when power equipment fails, multiple concurrent failures usually occur instead of a single failure. Concurrent failures are so common and hidden in complex systems that diagnosis requires not only analysis of failure characteristics, but also correlation between failures. Therefore, in this paper, a concurrent fault diagnosis method is proposed for power equipment based on graph neural networks and knowledge graphs. First, an electrical equipment failure knowledge map is created based on operational and maintenance records to emphasize the relevance of the failed equipment or component. Next, a lightweight graph neural network model is built to detect concurrent faults in the graph data. Finally, a city’s transformer concurrent fault is taken as an example for simulation and validation. Simulation results show that the accuracy and acquisition rate of graph neural network mining in Knowledge Graph is superior to traditional algorithms such as convolutional neural networks, which can achieve the effectiveness and robustness of concurrent fault mining.

Background Star Rating Assessment (SRA) was initiated in 2015 in Tanzania aiming at improving the quality of services provided in Primary Healthcare (PHC) facilities. Social accountability (SA) is among the 12 assessment areas of SRA tools. We aimed to assess the SA performance and its predictors among PHC facilities in Tanzania based on findings of a nationwide reassessment conducted in 2017/18. Methods We used the SRA database with results of 2017/2018 to perform a cross-sectional secondary data analysis on SA dataset. We used proportions to determine the performance of the following five SA indicators: functional committees/boards, display of information on available resources, addressing local concerns, health workers’ engagement with local community, and involvement of community in facility planning process. A facility needed four indicators to be qualified as socially accountable. Adjusted odds ratios (AOR) with 95% confidence intervals (CI) were used to determine facilities characteristics associated with SA, namely location (urban or rural), ownership (private or public) and level of service (hospital, health centre or dispensary). Results We included a total of 3,032 PHC facilities of which majority were dispensaries (86.4%), public-owned (76.3%), and located in rural areas (76.0%). On average, 30.4% of the facilities were socially accountable; 72.0% engaged with local communities; and 65.5% involved communities in facility planning process. Nevertheless, as few as 22.5% had functional Health Committees/Boards. A facility was likely to be socially-accountable if public-owned [AOR 5.92; CI: 4.48–7.82, p = 0.001], based in urban areas [AOR 1.25; 95% CI: 1.01–1.53, p = 0.038] or operates at a level higher than Dispensaries (Health centre or Hospital levels) Conclusion Most of the Tanzanian PHC facilities are not socially accountable and therefore much effort in improving the situation should be done. The efforts should target the lower-level facilities, private-owned and rural-based PHC facilities. Regional authorities must capacitate facility committees/boards and ensure guidelines on SA are followed.