This comprehensive review article delves into the evolving landscape of outlier detection techniques, shedding light on their significance in modern data analysis. In an era characterized by advanced technology and dynamic digital environments, the demand for robust anomaly detection methods spans across diverse domains. Anomaly detection plays a pivotal role in streamlining data analysis, saving valuable time, and offering versatile applicability across various data types. It encompasses an array of methods and approaches, each contributing to our understanding of data irregularities. This paper explores these techniques, emphasizing their role in the broader context of fuzzy methods’ applications. Furthermore, it provides an invaluable resource for those seeking a holistic review of the existing literature in this field. By examining current trends in the use of fuzzy methods and their potential impact on different facets of human life and the environment.

The microgrid is the modern solution for power surges and blackouts as it can act as a standalone system when the grid is under attack as it works along with the grid which helps in achieving higher reliability of the system and also helps to overcome existing problems associated with a grid to reach out remote areas but unlike advantages, there are disadvantages for this kind of system to overcome such as reliability, quality and continuity. When it is considered in a contest of power systems, The major uncertainties are nonlinear, nonconvex, and mixed-integer problems, hence to overcome such types of uncertainties there is a need to develop an optimization technique that can help to reduce the effect on generation and dispatch of power. Microgrid systems which comprised of renewable energy resources such as wind, solar and battery storage units that can be connected to the grid & also can act as a standalone system. This paper reviews the different soft computing techniques involved in optimizing the power system & furthermore, the appropriate & most efficient soft computing technique is determined as well as future scope of these techniques is discussed.

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. In particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose two sets of merit criteria for their efficient development and performance assessment, namely the criteria for evaluating and benchmarking ML-enabled CPSs and the criteria for evaluation and benchmarking of the tools intended to support users through the modeling and development pipeline. This study deploys multiple empirical and qualitative research methods based on literature review and survey instruments, including expert interviews and an online questionnaire. We collect, analyze, and integrate the opinions of 77 experts from over 25 organizations in 10 countries to devise and validate the proposed framework.

A document by Amit Kumar Singh. Click on the document to view its contents.

With the global adoption of Internet services, service providers are having a difficult time securing their systems, especially against new attacks and intrusions. Various anomalous detection approaches have been developed for protecting WSN from cyber-attacks. However, those systems suffer from the major issues of a high number of false alarms, increased over-fitting, and complexity. Therefore, this paper motivates to develop a novel and intelligent IDS framework for protecting WSN from cyber-attacks. For this purpose, an Intensive Binary Pigeon Optimization (IBiPO) and Bi-directional Long Short-Term Memory (Bi-LSTM) mechanisms are developed for accurate intrusion detection and classification.

This study analyzed the temporal variation of scour hole dimensions caused by a plain wall jet, which is one of the most hazardous issues faced by hydraulic structures. The study employed two recently developed artificial intelligence-based models, Extreme Learning Machine (ELM) and Multi-Gen Genetic Programming (MGGP), to predict scour hole dimensions and identify effective parameters. Both models accurately predicted the scour hole dimensions, with MGGP outperforming ELM for both training and testing data. MGGP presented four equations that can be used by designers to predict the temporal variations of scour hole dimensions with high accuracy. The non-dimensional form of the scouring time was found to be the most effective parameter, while the channel width ratio and standard deviation of sediments had negligible effects on the accuracy of the models. The study found that the effectiveness of the densimetric Froude number should be considered for predicting the temporal variation of scour hole dimensions due to plain wall jets. The proposed equations from both models had higher accuracy than previous empirical models. Overall, this study provides valuable insights into predicting and mitigating jet scour problems in hydraulic structures.

Chronic throat symptoms such as cough, dysphonia, globus sensation, throat clearing and sensation of mucus in the throat are prevalent and bothersome symptoms, which are now understood to cause significant morbidity when untreated. A dilemma for clinicians is the frequent lack of organic or structural disease explanation for these non-specific symptoms, even with often multidisciplinary assessment. Whilst associations to reflux or functional syndromes are often implied, these diagnostic paradigms lack interdisciplinary consensus and rely on empirical practices in the absence of clear disease links. The role of respiratory allergy or respiratory allergy sensitisation in contributing to presentations involving unexplained chronic throat symptoms (UCTS) remains poorly defined, but is emerging as a potentially undervalued relationship. For the clinician, clinical considerations and potential therapeutic strategies for the patient with UCTS and respiratory allergy disease or sensitisation remain ambiguous concepts, devoid of consensus or guidelines. This scoping review addresses the intersection of respiratory allergy and chronic throat complaints, offering an up-to-date assessment of this potential relationship, postulated symptom mechanisms and evidence to guide therapeutic decisions.

In automotive industry, the paint surface inspection process is still mainly performed by manual methods based on subjective human vision system, which may not only be inaccurate but time consuming and costly as well. Recent promises by machine vision, image processing and machine learning, techniques have led to emergence of tools may now allow development of robust models that may be successfully used to perform automatic paint surface inspection. Hence, modeling of such a system for the purpose of automation provides opportunity in reducing the cost and time attributed to inspection and repairs. This paper reports a novel study towards an effective detection and classification of various surface defects attributed to different color mixes or paint textures. Proposed method performs the detection using the combination of non-linear spatial order statistics filter with Gaussian filter for preprocessing followed by Canny Edge Detection and Morphological Transformation. Next feature extraction and classification using a voting classifier selected among eight different classifiers and their performance analysis. Results show that our model performs defect detection with high accuracy, precision and recall (91.17%, 91.14 %, 91.17%).

The study examined human performance and related neural mechanisms in a pop-out search with different probabilities of target location and the relation between the target location and feature. In a search array, we introduced the binding relation between two target features and two kinds of location probability. Moreover, in the second half of the experiment, such a probability pattern for location/feature binding was reversed. Behavioral results revealed successful statistical learning of probability for both absolute target location and target’s location-feature binding indicated by faster RTs in the high-probability conditions for both location and location-feature binding. Moreover, the learning benefit for the probability of location-feature binding acquired during 1st (training) phase was still expressed in the 2nd (reversal) phase despite the actual binding probability was reversed. ERP results suggested that both the attentional selection and response selection process were affected by such learning revealed in the difference in N2pc and LRP amplitudes between the two conditions with different binding probability in the reversal phase. An expectation to the high probability for location-feature binding was also suggested from time-frequency analysis and Multi-Variate Pattern Classification (MVPC) indicated by larger alpha ERD magnitude and lower decoding accuracy, respectively, when the target appeared at the high-binding location in the training phase instead of the reversal phase. Overall, we have demonstrated behavioral evidence and 4 EEG markers for the associative learning of the probability of relation between location and feature of target in a pop-out search.

Psychoneuroimmunology is the branch of science that shows the relation between the nervous system, emotions, and immune system. In this study, we differentiate the interaction procedures between mind and body when exposed to stress. The symptoms of psychoneuroimmunological stress are depression, aggression, fear and social withdrawal which can affect the body. According to Charles Darwin humans and animals show the same characteristics and symptoms so they help identify the pharmacological mechanism and potential clinical effects of the drug. Animal models of stress are based on the difference of opinion giving rise to a motivation state to induce and approach the avoidance situations. This review contains the different model of stress induction that are also used worldwide. Here in this review, we used the word “Ethological” which is used for the assessment of unlearned and unpunished behaviors like EPS, EZM, light-dark box, open field test, etc. The learned and punished models are used under the category “conditioned operant conflict test like the Vogel conflict test. We also discussed the models of classic conditioning like fear conditioning and psychosocial models like social defeat, and physical and chronic unpredictable stress. This review also shows the effect of the immune system response on the mind and body of animals.

Objective To investigate whether there are changes in cognitive control among Tibetan indigenous residents exposed to long-term high altitude. Methods The Backward Masking Majority Function Task (MFT-M) was used to collect the capacity of cognitive control (CCC) of 93 Tibetans living in different high-altitude areas (2900m, 3700m, 4200m) and 92 Han people controls living in the plain. Results There were significant differences in CCC of the four groups. Specifically, the CCC of the plain control group and the 2900-m group was significantly higher than that of the 3700-m and 4200-m groups. However, there were no significant differences in CCC between the plain control group the 2900-m group as well as between the 3700-m and 4200-m group . Conclusion Long-term high-altitude exposure began to have negative impact on the capacity of cognitive control of Tibetan residents at altitudes between 2900-m and 3700m, indicating that 2900-m to 3700-m was the altitude threshold for CCC decline in Tibetan residents.

In the environmental security monitoring of smart cities, the infrared and visible image fusion method deployed on intelligent systems based on cloud and fog computing plays an vital role in providing enhanced images for target detection systems. However, the fusion quality can be significantly influenced by the illumination of the monitoring scenario in visible images. Therefore, conventional methods typically suffer a severe performance drop under the condition of insufficient illumination. To tackle this issue, we propose an illumination enhancement and target-aware fusion method (IETAFusion) based on artificial intelligence, which breaks the boundaries between the task of illumination enhancement and image fusion and provide a fusion result with better visual perception in nighttime scene. Specifically, we use a light-weight contrast enhancement module (CEM) restore the brightness of the visble image. Moreover, a Swin Transformer-based backbone network (STBNet) is utilized to facilitate information exchangement between the source images and enhance the capabilities of target awareness. Finally, the fused images are reconstructed by the contrast-texture retention module (CTRM) and reconstructor. The extensive experiments indicates that the proposed approach achieves improved performance both in human perception and quantitative analysis compared with the state-of-the-art (SOTA) methods.

In plants, salicylic acid (SA) hydroxylation regulates SA homoeostasis, playing an essential role during plant development and response to pathogens. This reaction is catalyzed by SA hydroxylase enzymes, which hydroxylate SA producing 2,3- dihydroxybenzoic acid (2,3-DHBA) and/or 2,5-dihydroxybenzoic acid (2,5-DHBA). Several SA hydroxylases have been recently identified and characterized from different plant species; however, no such activity has been previously reported in maize. In this work, we describe the identification and characterization of a new SA hydroxylase in maize plants. This enzyme, with high sequence similarity to previously described SA hydroxylases from Arabidopsis and rice, converts SA into 2,5-DHBA; however, it shows different kinetics properties to those from previously characterized enzymes, and it also catalyzes the conversion of the flavonoid dihydroquercetin into quercetin in in vitro activity assays, suggesting that the maize enzyme may have different roles in vivo as those previously reported from other species. Despite this, ZmS5H can complement the resistance to pathogen and early senescence phenotypes of Arabidopsis s3h mutant plants. Finally, we characterized a maize mutant in the S5H gene ( s5hMu) that has altered growth, senescence and increased resistance against Colletotrichum graminicola infection, showing not only changes in SA and 2,5-DHBA but also variations in flavonol levels. Together, the results presented here provide evidence that SA hydroxylases in different plant species have evolved to show differences in catalytic properties that may be important to fine tune SA levels and other phenolic compounds such as flavonols to regulate different aspects of plant development and defense against pathogens.

At present, most of the deep learning-based building extraction is based on semantic segmentation,it is strongly influenced by the data scene, as the geometric features of the building are not taken into account. The traditional edge detection methods ignore the different effects of different edge detection operators on images when processing remotely sensed buildings.In order to extract buildings more effectively, we propose a method for extracting buildings named fusion of adaptive edge features and geometric features for building extraction from remote sensing images. Firstly, calculate the final contribution of 5 common operators to edge detection and perform edge enhancement by adaptively determining the weight coefficients; Secondly,the processed image is then texture smoothed using the RTV model; then this is followed by the marking of the connected areas.Specific filters are constructed to filter out non-building noise based on the geometric characteristics of the building; Finally, hollow filled reserved area,a more accurate map of the building results is generated. In this paper, six remote sensing images of buildings situated in different landscapes were selected. The experimental results show that the algorithm has advantages over classical algorithms and deep learning algorithms.

Large rivers are difficult to sample due to their size yet critical to monitor because humans heavily rely upon and alter them. Aquatic invertebrates are commonly used to assess the ecosystem quality of streams, but methods to sample these animals in large rivers are still being developed. We sampled aquatic invertebrates using two methods in the Snake River near Jackson, Wyoming. We used a Hess sampler to collect aquatic invertebrates in areas of the river that were <42 cm in depth and rock baskets in deeper areas that were near the bank. Hess samples collected more aquatic invertebrate taxa, and a higher proportion of Ephemeroptera, Plecoptera and burrowing taxa. Rock baskets collected a higher proportion of Trichoptera, filterers and clinging taxa. Bioassessment metrics differed between sampling methods; richness, diversity, evenness, Ephemeroptera, Plecoptera and Trichoptera (EPT) and Hilsenhoff’s biotic index produced higher values in Hess samples, and percent EPT was higher in rock baskets. Non-metric multidimensional scaling and analysis of similarity indicated that the samplers collected different assemblages (p < 0.001). The standard error of total invertebrate density was smaller when at least seven samples were collected and most species were collected when 6-7 replicate samples were processed within a reach. Understanding how sampling method alters the aquatic invertebrates collected will help managers develop monitoring protocols that are best suited to the river and collect the most unbiased invertebrate assemblages.

Background Cystic fibrosis (CF) related diabetes affects up to half of all adults with CF and is associated with higher morbidity and mortality. Our aim is to co-design an ideal model of care that integrates diabetes technology and better meets the needs of adults living with the condition to improve attendance, engagement, service satisfaction and clinical outcomes. Methods Using qualitative research methods, we evaluated disease perceptions, barriers and enablers to optimal CF-related diabetes management and service delivery. Integration of continuous glucose monitoring (CGM) was also explored. An initial broad purposive consumer survey was followed by focus groups with end-users. Grounded theory approach was utilized with major problem-areas identified then explored, coded and grouped into requisites for an ‘ideal model of care’ for adults living with CF-related diabetes. Results Two key themes emerged i) CGM was acceptable for use in adults with CF-related diabetes with many perceived benefits and should be integrated into the model of care, ii) an ideal model of care consisted of a dual-specialty service co-led by endocrinology and CF physicians and supported by diabetes nurse educator and CF dietitian with a goal to provide consistent and personalized diabetes management. Barriers to optimizing glycaemic control included diet, finger-prick testing, reduced access to CGM and pulmonary exacerbations. End-user feedback on CGM was overwhelmingly positive with regards to user operability. CGM was also identified as a tool that could be used to engage, educate and empower adults living with CF-related diabetes and facilitate constructive and personalized clinical decision-making by healthcare providers. Conclusion For adults living with CF, a diagnosis of diabetes is associated with increased treatment burden. End-users agreed CGM had many benefits and should be integrated into an ‘ideal model of care’ for CF-related diabetes that was co-led by endocrinology services integrated within a pre-existing CF service.

Infants bear a significant malaria burden but are usually excluded from participating in early dose optimisation studies that inform dosing regimens of antimalarial therapy. Unlike older children, infants’ exclusion from early-phase trials has resulted in limited evidence to guide accurate dosing of antimalarial treatment for uncomplicated malaria or malaria preventive treatment in this vulnerable population. Subsequently, doses used in infants are often extrapolated from older children or adults, with the potential for under or overdosing. Population pharmacokinetic-pharmacodynamic (PK-PD) modelling, a quantitative methodology that applies mathematical and statistical techniques, can aid the design of clinical studies in infants that collect sparse pharmacokinetic data as well as support the analysis of such data to derive optimised antimalarial dosing in this complex and at-risk yet understudied subpopulation. In this review, we reflect on what PK-PD modelling can do in programmatic settings of most malaria-endemic areas and how it can be used to inform antimalarial dose optimisation for preventive and curative treatment of uncomplicated malaria in infants. We outline key developmental physiological changes that affect drug exposure in early life, the challenges of conducting dose optimisation studies in infants, and examples of how PK-PD modelling has previously informed antimalarial dose optimisation in this subgroup. Additionally, we have discussed the limitations and gaps of PK-PD modelling when used for dose optimisation in infants and best practices for using population PK-PD methods in this subgroup.

A document by Katrine Gorham. Click on the document to view its contents.

Many studies suggest that aside from environmental variables, such as topography and climate, species-specific ecological traits are relevant to explain the geographic distribution of intraspecific genetic lineages. Here, we investigated whether and to what extent incorporating such traits systematically improves the accuracy of random forest models in predicting genetic differentiation among pairs of localities. We leveraged available ecological datasets for birds and tested the inclusion of two categories of ecological traits: dispersal-related traits (i.e., morphology and foraging ecology) and demographic traits (such as species survival rate and generation length). We estimated genetic differentiation from published mitochondrial DNA sequences for 31 species of birds (1,801 total genetic samples, 526 localities) in the Atlantic Forest of South America. Aside from the aforementioned ecological traits, we included geographic, topographic and climatic distances between localities as environmental predictors. We then created models using all available data to evaluate model uncertainty both across space and across the different categories of predictors. Finally, we investigated model uncertainty in predicting genetic differentiation individually for each species (a common challenge in conservation biology). Our results show that while environmental conditions are the most important predictors of genetic differentiation, model accuracy largely increases with the addition of ecological traits. Additionally, the inclusion of dispersal traits improves model accuracy to a larger extent than the inclusion of demographic traits. Similar results are observed in models for individual species, although model accuracy is highly variable. We conclude that ecological traits improve predictive models of genetic differentiation, refining our ability to predict phylogeographic patterns from existing data. Additionally, demographic traits may not be as informative as previously hypothesized. Finally, prediction of genetic differentiation for species with conservation concerns may require further careful assessment of the environmental and ecological variations within the species range.

Anthropogenic climate change is expected to lead to forest conversion to grass and shrubland due to more extreme fire behavior and hotter and drier post-fire conditions. However, field surveys of wilderness areas in the Northern Rocky Mountains of the United States indicate robust conifer regeneration on burned sites, indicating this region might be more resistant to conversion than dryer southerly forests. This study utilizes a machine learning approach to monitor canopy cover on burned sites in two large wilderness areas from 1985 to 2021 and quantify fire impact and recovery. We used a GBM model to integrate LiDAR and Landsat observations of the region to predict canopy cover. A time series was then calculated for 352 distinct sites. Fire impact was highly correlated with severity and was consistent with the mixed severity fire regime characteristic of the region. Of the burned area studied, 85% showed evidence of recovery. Sites that are failing to recover are occurring more recently than their recovering counterparts, with 60% of non-recovering sites burning for the first time after 2003. Extended secondary mortality was common even on recovering sites, so more monitoring time might be required to determine which sites are truly not going to recover. While median recovery time has remained stable through the study period, in recent years there have been more outlier fires with long recovery times. Of the 18 sites with projected recovery times greater than 150 years, 14 occurred since 2002. While fires that are failing to recover or recovering slowly make up proportionally small portions of the landscape, they are of particular management interest because they may be harbingers of future forest conversion.

Increased elliptical surface area gives Elliptical Annular Fins an edge in heat transmission. This concept enveloped EAF with Multi-Walled Carbon Nano Tubes to improve convective heat transmission, the primary goal. The coated and uncoated surfaces were compared for convective heat transfer. This research examines fin efficiency, thermal conductivity, and temperature dispersion. Empirical evidence shows that carbon nanotube coated EAF transmits 9% more heat than uncoated fins. Empirical and computational approaches differ in shaped tube and fin efficiency within an acceptable range. Additionally, the Shape Factor is expected to significantly affect the fin’s surface temperature.

ABSTRACTA 24-year-old man was being followed for dilated cardiomyopathy. He presented with bipolar aphthosis and a painless pulsatile abdominal mass. A CT scan showed abdominal aortic and superior mesenteric artery aneurysms and pulmonary embolism. The diagnosis of Behçet’s disease was accepted. The initial course was favorable under corticosteroid therapy.

Optic neuritis after mRNA COVID-19 vaccination; A case reportRaghad Tarcha, Afraa Ghazal, Lama Al-Darwish, Hoda Abdoh, Maysoun Kudsi.

Hashimoto Thyroiditis in a lingual thyroid: an interesting case