Zero-sequence voltage and current based single-phase grounding (SPG) fault detection and location methods in non-effectively grounded distribution network have limitations in practical use due to lack of zero-sequence CT and PT in some cases. To deal with this problem, the phase current characteristics under SPG fault are analysed and the SPG fault detection method and section location method is proposed. PSCAD-based simulation results and fault recording data show that the proposed method behaves well when SPG fault with different impedances occurs in different sections. The proposed method can select the faulty phase automatically without using voltage signals, which is suitable for all IEDs.

Inorganic Arsenic (iAs) is one of the largest toxic exposures to impact humanity worldwide. Exposure to iAs during pregnancy may disrupt the proper remodeling of the epigenome of F1 developing offspring and potentially their F2 grand-offspring via disruption of fetal primordial germ cells (PGCs). There is a limited understanding between the correlation of disease phenotype and methylation profile within offspring of both generations and whether it persists to adulthood. Our study aims to understand the intergenerational effects of in utero iAs exposure on the epigenetic profile and onset of disease phenotypes within F1 and F2 adult offspring, despite the life-long absence of direct arsenic exposure within these generations. We exposed F0 female mice (C57BL6/J) to the following doses of iAs in drinking water 2 weeks before pregnancy until the birth of the F1 offspring: 1 ppb, 10 ppb, 245 ppb, and 2300 ppb. We found sex- and dose-specific changes in weight and body composition that persist from early time to adulthood within both generations. Fasting blood glucose challenge suggests iAs exposure causes dysregulation of glucose metabolism, revealing generational, exposure, and sex specific differences. Toward understanding the mechanism, genome-wide DNA methylation data highlights exposure-specific patterns in liver, finding dysregulation within genes associated with cancer, T2D, and obesity. We also identified regions containing persistently differentially methylated CpG sites between F1 and F2 generations. Our results indicate F1 developing embryos and F2 PGCs retain epigenetic damage established during the prenatal period and are associated with adult metabolic dysfunction.

Classical Species Distribution Models are primarily based on climate, land use and other abiotic variables. Despite recent studies showing that biotic information can play an important role in shaping the distribution of species even at large scales, results are not always consistent among studies and the underlying factors that influence the importance of this biotic information to the models, are unclear. To address this knowledge gap, we evaluated how different factors affect the importance of biotic interactions in shaping species distributions, using fine-scale data from plant-pollinator and parasitic interactions in the Netherlands. We found that the models of wild bees improved, when their biotic interaction was included, and the model performance improved the most for parasitic bees. Taxonomic level, resolution and distribution range of the interacting species and degree of specialization of the modelled species all affected the importance of the biotic interactions to the models.

Objectives: To determine the efficacy of cell culture based influenza vaccines in children. Methods: Embase, PubMed, Cochrane and clinical trials were searched.14 randomised controlled trials in children were selected. The current systematic review was done as per the PRISMA guidelines. The pooled estimate of seroconversion and GMT rate was calculated as mean difference. Data was analysed using the Cochrane Collaboration Review Manager Version software. Risk of bias was done as per Cochrane risk of bias tool. The quality of evidence was adjudged using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) using the Grade pro software. Results: Significant results for efficacy were reported for half dose MF59 influenza vaccine control group for GMT at day 1 with a mean difference of 0.78, 95%CI, 0.50 to 1.07, p<0.00001 as compared to full dose MF59 influenza vaccine experimental group .No significant results were reported in half dose MF59 influenza vaccine for GMT at Day 43(mean difference 151.57,95% CI,-29.36 to 332.50 ,p=0.10). Significant results were reported for seroconversion rate for half dose MF59 influenza vaccine control group at day 22 with a mean difference of 17.92,95%CI,10.08 to 25.75,p<0.00001 as compared to half dose MF59 influenza vaccine group at day 43 with a mean difference of 5.00,95%CI,-4.80 to 14.80,p=0.32 . Conclusion: The current systematic review demonstrated that half dose cell derived influenza vaccines was well tolerated and more immunogenic and resulted in high seroconversion rate and Geometric Mean Titres rate in paediatric population.

Responsive deep brain stimulation (DBS) requires recruiting deep brain structures without affecting the superficial neuronal population. Neurosurgeons widely use implanted electrodes, which are highly localized but invasive, to stimulate the deep brain. Temporally interfering stimulation (TIS) excites the deep brain non-invasively. This neuromodulation technique utilizes two high-frequency sinusoidal electric fields that do not recruit superficial neural structures but have a small frequency differential. The small differential causes a low-frequency interference envelope that stimulates deep regions and is steerable by changing the intensity of the electric fields without physically moving the electrodes. Using TIS as a non-invasive DBS method generates high-frequency stimulation artifacts at recording sites, which may saturate a conventional recording front-end. This paper presents a low-power bidirectional 64-channel CMOS neural-ADC that is immune to artifacts such as those in the TIS techniques or conventional biphasic stimulation. The presented DC-coupled chopped analog front-end leverages delta-spectrum shaping to remove electrode DC offset voltage and maintain the input impedance higher than 250 MΩ, which is sufficient for interfacing with non-invasive scalp electrodes. The AFE operates on the input signal difference to detect large and rapid stimulation artifacts. It incorporates both exponential tracking and boosted-rate sampling to recover within 100 μs. Upon recovery, the neural-ADC range and speed are reduced to achieve noise and power efficiency factors of 2.98 and 10.6, respectively. In vivo recordings from anesthetized mice demonstrate the unique capabilities of the presented architecture in resolving local field potentials from the surface and epidural electrodes.

Pulmonary artery sling is a rare childhood vascualr ring that is frequently associated with tracheal stensois. Consequently, neonates may present with a crictal airway obstruction if tehre is long segemt tarcheal stensosi and complete rings. Rapid diagnosis of this cardiac vascular malfomation and extent airway involvement is essentail as longterm outcoem can be excellent follwoing surgicla repair. In this review we focus on airway invetsigation and management for this challenging congential condition.

The median solitary maxillary central incisor syndrome (SMMCI) is a rare developmental disorder consisting of multiple defects found on the body midline. The aim of this report was to describe multiple craniofacial features of a 10-years-old girl presented with SMMCI, and discuss the dental treatment alternatives in such young patients.

Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi-clique, as a generalization of clique, can be used to identify functional and structural properties of various networks. In this paper, we study the maximum weighted quasi-clique problem, and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi-clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into our algorithm. The hybrid strategies utilize a probability-based mechanism for choosing sub-strategies in each round of the local search. We conduct experiments on synthetic networks and real-world networks to show the effectiveness of our algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus our algorithm has a good ability to tackle various networks.

Smoking remains the leading cause of preventable death worldwide. Nicotine Replacement Therapies (NRTs) are the most commonly used smoking cessation medications. However, the scope of these treatments is limited: drop-out rates are high and their effectiveness is modest. We believe that nicotine tapering plays an important role in overall smoking cessation efficacy. However, each of the NRTs currently on the market have been approved on the basis of either a unique tapering strategy or none at all. Therefore, it is unknown whether improved efficacy and safety outcomes could have been achieved by using different approaches. Moreover, dosing regimens of marketed NRTs lack personalization. They are based on a “one-size-fits-all” approach, which is not optimal given that smokers represent a highly heterogeneous group. The emergence of digital health and Electronic Nicotine Delivery Systems (ENDS), which have demonstrated superior outcomes compared to NRTs in terms of smoking cessation rates, give way to the development of new innovative ways to gradually reduce nicotine in a personalized fashion, without the limitations of currently approved NRTs.

Leptomeningeal dissemination of a paraganglioma is an extraordinarily rare phenomenon described in a small number of cases worldwide. Here, we report a case of filum terminale paraganglioma characterized by an indolent, insidious nature even in the setting of leptomeningeal dissemination.

ABSTRACT The anomalous diffusion and reaction process for Riemann-Liouville fractional differential equation is studied for heterogeneously isothermal nth-order reaction. The diffusion coefficient is regarded as a function of the position of the fractal porous catalyst. For a first-order irreversible reaction, new general analytical solutions of transient concentration profiles are derived with Mittag-Leffler function by taking into account of the intraparticle and external mass-transfer resistances. The numerical solution for anomalous diffusion-reaction is present for nth-order reaction; it is found that the results calculating by numerical solution are in satisfactory agreement with those by analytical solution for first-order reaction. The volume-averaged concentration and general expressions for effectiveness factor are present for first-order reaction. The effects of the order of the time fractional derivative, the fractal geometry of porous catalyst, diffusion coefficient, intraparticle and external mass-transfer resistances, and Thiele modulus on transient concentration profiles and catalytic efficiency are examined over a wide range of parameters by analytical solutions and numerical solution.

Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups dataset, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.

The global burden of dengue, a mosquito-borne viral infection, has alarmingly increased in recent decades. The rise in disease occurrence is mainly attributed to changes in the climate, human ecology, globalization, and demography. In such a scenario, an accurate prediction of a dengue outbreak is essential to reduce the morbidity rate significantly. Therefore, this paper employs two classes of autoregressive models for dengue forecasting and a recently proposed approach called Finite Element Machine for Regression (FEMaR). Further, it proposes a variant of the latter, namely FEMaR-KD, which allows the exploration of k -approximate nearest neighbors to interpolate data points based on k -neighborhood instead of the whole dataset. Such models are built considering environmental parameters, which denote one of the main determinants for infection occurrence. Finally, the proposed models’ performance is assessed over two distinct datasets, considering differing spatial scales and regions. Results show that FEMaR obtained Mean Absolute Error up to 51% smaller than the autoregressive models considering univariate scenarios and Root Mean Squared Error up to 63% smaller regarding the univariate models.

This paper aims at increasing the control effect of hybrid magnetic bearings(HMB). In this paper, a Fuzzy-PID control algorithm is used to control the HMB with complex and multivariate characteristics. And the Particle Swarm Optimization (PSO) algorithm is combined to optimize the scale factor parameters of the Fuzzy controller to solve the problem of fixed parameters of the Fuzzy controller, which leads to the limitation of control accuracy. A Simulink model of the HMB system considering the mass of the rotor is built, and the response of the rotor to float and the response under the action of external disturbance forces are simulated respectively. The results show that after optimisation by the PSO algorithm, the system responds quickly, with small overshoot and high stability, which achieves better control results in the process of rotor displacement control of the hybrid magnetic bearing support.

Aim: To test whether the occupancy of shorebirds has changed in the eastern Canadian Arctic, and whether these changes could indicate that shorebird distributions are shifting in response to long-term climate change Location: Foxe Basin and Rasmussen Lowlands, Nunavut, Canada Methods: We used a unique set of observations, made 25 years apart, using general linear models to test if there was a relationship between changes in shorebird species’ occupancy and their Species Temperature Index, a simple version of a species climate envelope. Results: Changes in occupancy and density varied widely across species, with some increasing and some decreasing. This is despite that overall population trends are known to be negative for all of these species, based on surveys during migration. The changes in occupancy that we observed were positively related to the Species Temperature Index, such that the warmer-breeding species appear to be moving into these regions, while colder-breeding species appear to be shifting out of the regions, likely northwards. Main Conclusions: Our results suggest that we should be concerned about declining breeding habitat availability for bird species whose current breeding ranges are centred on higher and colder latitudes.

This article reports a six-year-old case with a complaint of acute respiratory distress syndrome and malaise who had a positive Covid-19 test.After intraoral examination Dehiscence was remarkable on the dentoalveolar area in the posterior Mandible.Surgical resection of Necrotic bone was performed,and the patient was treated completely.

A nonlinear Schrödinger equation with the combined effects of variable nonlinearity and generalized external potentials is investigated. Three soliton solutions are generated by means of Darboux method through constructed Lax pair. We attained two constraints related to gain or loss function for considered equation via compatibility condition. Using three soliton solutions, influences of the inhomogeneous nonlinearity and harmonic potential on soliton structures are analysed by properly tailoring the loss or gain parameter. Specifically, via inelastic collision among three solitons, soliton switching characteristics is observed. Additionally, we explore the Modulation instability (MI) through linear stability analysis (LSA) and impact of nonlinearity profile is examined. The trigonometric, exponential and constant values have been chosen for loss or gain parameter to study the effect on the MI gain spectrum.

Objective: To assess the clinical utility of point of care (POC) capillary blood glucose (CBG) in the assessment of gestational diabetes (GDM) during oral glucose tolerance test (OGTT). Design: Prospective cohort study. Setting: Antenatal clinics at King’s Collage Hospital. Population: Women screening for GDM between March and June 2020. Methods: CBG was measured using POC-StatStrip® (Nova) and venous plasma glucose (VPG) was measured by Roche (Cobas 8000 c702) analyser. GDM was diagnosed based on NICE-2015 criteria. The two methods were compared statistically using Analyse-It (v 5.40.2) Main outcome measures: Diagnostic sensitivity, specificity, positive and negative predictive values (PPV and NPV) for POC-StatStrip® compared to reference laboratory method. Results: 230 women were included. The number and the percentage of women with glucose concentration above the GDM thresholds using POC-StatStrip® vs. Lab-VPG measurement was 15 (6.5%) vs. 8 (3.4%) at fasting and 105 (45%.6) vs. 72 (31.1%) at 2-hour respectively. Sensitivity and specificity for POC-StatStrip® were 88% and 97% at fasting and 97% and 79% at 2-hour respectively. However, the specificity and the NPV for POC-StatStrip® concentrations ≤5.0 mmol/L at fasting or <7.5mmol/L at 2-hour were 100% and the sensitivity and the PPV for concentration >9.5mmol/L at 2-hour was 100 %. Conclusion: In our cohort POC-CBG measurement cannot entirely replace laboratory method in OGTT, however, it can be used to rule out/rule in GDM when the glucose concentrations are ≤5.0mmol/L at fasting or <7.5/>9.5mmol/L at 2-hour. Funding: not applicable. Key Words: Gestational Diabetes Mellitus (GDM), point of care (POC).

Background: Sub-Saharan African (SSA) countries have high stillbirth rates compared to high-income countries, yet research on risk factors for stillbirth in SSA remain scant. Objectives: To identify the modifiable risk factors of stillbirths in SSA and investigate their strength of association using a systematic review. Search Strategy: EMBASE, MEDLINE, Global Health, and CINAHL Plus databases were searched for literature. Selection Criteria: Observational population- and facility-level studies exploring stillbirth risk factors, published between 2013-2019 were included. Data Collection and Analysis: Narrative synthesis of data was undertaken and the potential risk factors were classified into sub-groups. Main Results: Thirty-seven studies were included, encompassing 20,264 stillbirths. The risk factors were categorized as maternal antepartum (0-4 antenatal care visits, multiple gestations, hypertension, birth interval >3 years, history of perinatal death); socioeconomic factors (maternal lower wealth index and basic education, advanced maternal age, grand multiparity (≥5)); intrapartum (direct obstetric complication, non-vaginal delivery); fetal (low birthweight and gestational age <37weeks) and health systems (poor ANC quality, emergency referrals, ill-equipped facility). The proportion of unexplained stillbirths remained very high. No association was found between stillbirths and HIV, BMI, diabetes, and distance from the facility. Conclusion: The overall quality of evidence was low as many studies were facility-based and did not adjust for confounders. This review identified preventable risk factors for stillbirth. Focused programmatic strategies should be developed to improve antenatal care, emergency obstetric care, maternal perinatal education, referral and outreach systems, and birth attendant training. More population-based high-quality research is needed. Funding: Not externally funded

Coronavirus disease 2019 (COVID-19) can cause various complications. Pneumothorax secondary to COVID-19 is relatively uncommon and bilateral pneumothorax is even more so. In patients with poor general health to undergo surgery for pneumothorax, internal treatments are essential to relieving refractory pneumothorax.

Urinary extracellular vesicles (EVs) are potential biomarkers for the early diagnosis of urinary system diseases, but their clinical translation still requires optimization of the EVs isolation process. Size exclusion chromatographic (SEC) is a well-established method for the isolation of EVs, which can obtain high-purity EVs from plasma. In this work, home-made SEC columns were used for the isolation of urinary EVs, and the optimized SEC column can achieve high-efficiency separation of urinary EVs from impurity proteins. Compared with ultracentrifugation (UC) method, SEC can achieve higher EVs recovery rate and purity. RNA-sequencing was performed to assess the value of SEC in clinical biomarker screening, and the results showed that the isolation method has little effect on the sequencing results of EVs. However, the SEC method offered a shorter process time, less operation requirements and better batch-to-batch stability of isolated EVs than UC, thus having a higher potential in clinical translational applications.

Wireless Sensor Networks (WSNs) are vulnerable to various kinds of security attacks that can compromise many nodes and therefore the performance of the network may be degraded. Failures to prevent intrusions could also decrease the credibility of security services, e.g., data confidentiality, integrity, and availability. Traditional Intrusion Detection Systems (IDS) suffer from many issues in performance and increased overhead which are considered the main challenge in WSNs. The common architecture of WSN is that nodes are organized into a set of clusters; each one contains a set of nodes with a specialized node called Cluster Head (CH) node which is responsible for managing activities through the cluster and communicating with other nodes and a Base Station (BS). The CH plays a critical role in the case attacking WSN in the two cases; signature-based and anomaly-based. This paper proposes an efficient hybrid IDS to analyze and secure WSN in multiple phases because it combines the best features of two different approaches to achieve better performance. In the proposed approach, BS evaluates and updates attacks information for the entire network which is the main advantage where BS doesn't suffer from any limitations in sensor nodes. Moreover, BS selects CH among other nodes based on power capabilities and computational resources. The efficiency and adaptability of the proposed method have been tested by simulation experiments deployed on JiST/Swans simulation. The experimental results show that the proposed system is efficient with acceptable performance in comparison with other hybrid IDSs. The experimental evaluation also expresses that the proposed technique reduces the communication costs on the cluster head (CH) which improves the lifetime of the entire WSN.

Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images. This feature enables us to eliminate the necessity of having to explicitly point towards the damaged area (external tissue localization) and classify (classification) as per classical computer vision techniques. In order to provide the useful constraints to guide feature extraction, we incoorporate the edge attention-gated unit. The explicit edge-attention unit is devoted to model the image boundaries as well as enhancing the representation. AGs can easily be integrated within the deep convolutional neural networks (CNNs). Minimal computional overhead is required while the AGs increase the sensitivity scores significantly. We show that the edge detector along with an attention gated mechanism provide a suffcient enough method for brain segmentation reaching an IOU of 0.78. With this methodology, we attempt to bring deep learning closer to the hands of human level performance providing useful information to the process of diagnosis.

Multi-objective optimization is a branch of mathematics used in a large range of applications. It deals with optimization problems involving two or more conflicting objective functions to be optimized. Consequently, there is not a single solution that simultaneously optimizes these objectives, but a set of compromise solutions. These compromise solutions are also called non-dominated, Pareto-optimal, efficient, or non-inferior solutions. The best solution of this set is the one closest point to the utopia point. There are several approaches to perform multi-objective optimization. Undoubtedly the future of multi-objective optimization programming is in artificial intelligence applications. One of the artificial intelligence models is the Corona algorithm. It aims to simulate the epidemic behavior of the Corona virus that affects people's health and its treatment. In this paper, the artificial Corona algorithm is introduced and expanded for solving multi-objective programming problems, in which other models are not effective. The algorithm operates by iteratively selecting the initial values for decision variables of a multi-objective programming problem. The values of objective functions and constraint(s) are calculated. This proposed approach depends on a linear formula to update the solution. An acceptable efficient solution that has a minimum distance value from the utopia point is selected as the best point. To demonstrate the effectiveness of the proposed approach, some illustrative examples are given. These examples include both linear and nonlinear problems. The results indicate that the proposed approach has a high speed and capability to obtain the best solution when compared with other similar works of literature.