Search

The multi criterion decision making (MCDM) method and experimental investigation on free convective heat transfer performance of oxide-based water nanofluids along a vertical cylinder are the two methods used to compare the performance in this paper. Al2O3, CuO, TiO2, SiO2, Fe3O4, and ZnO were the metal oxide nanoparticles used in the study to make water-based metal oxide nanofluids with volume fractions ranging from 0% to 1%. Two step method was used to create nanofluids. Thermo-physical properties like density, specific heat, viscosity, and thermal conductivity were measured after the various nanofluids were synthesized. Then, the performance of each nanofluid was evaluated based on various attributes using the weighted sum model (WSM) method, and the ranking of nanofluids was given. To begin, water served as the medium for free convection heat transfer experiments to validate the experimental setup. Free convection heat transfer experiments were carried out using metal oxide-based water nanofluids as mediums at volume fractions ranging from 0% to 1% for various heat inputs in the range of 30 W and 50 W. The heat transfer coefficient augments with percentage volume concentration up to 0.1 % for all types of nanofluids and then decreases until it reaches 0.6 % volume fraction. Al2O3-water nanofluid performs better than other metal oxide nanofluids in both WSM and experimental methods.

Copyright of Czech Dental Journal / Ceská Stomatologie a Praktické Zubni Lékarstvi is the property of Czech Dental Chamber and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The literature suggests that the commonly used augmentation method of reject inference achieves no appreciable benefit in the context of logistic and probit regression models. Ranking is not improved and the ability to discern a correct cut-off is undermined. This paper considers the application of augmentation to profit scoring applicants by means of survival analysis and by the Cox proportional hazard model, in particular. This new context involves more elaborate models answering more specific questions such as when will default occur and what will be its precise financial implication. Also considered in this paper is the extent to which the rejection rate is critical in the potential usefulness of reject inference and how augmentation meets that potential. The conclusion is essentially that augmentation achieves negative benefits only and that the scope for reject inference in this context pertains mainly to circumstances where a high proportion of applicants have been rejected. [ABSTRACT FROM AUTHOR]

The treatment of depression may be improved by using an augmentation approach involving selective serotonin reuptake inhibitors (SSRIs) in combination with compounds that focus on antagonism of inhibitory serotonin receptors. Using microdialysis coupled to HPLC, it has recently been shown that the systemic co-administration of 5-HT2C antagonists with SSRIs augmented the acute effect of SSRIs on extracellular 5-HT. In this paper, we have investigated the mechanism through which this augmentation occurs. The increase in extracellular 5-HT was not observed when both compounds were locally infused. However, varying the route of administration for both compounds differentially revealed that an augmentation took place when the 5-HT2C antagonist was locally infused into ventral hippocampus and the SSRI given systemically, but not when systemic 5-HT2C antagonist was co-administered with the local infusion of citalopram. This suggests that the release of extracellular serotonin in ventral hippocampus may be controlled by (an)other brain area(s). As 5-HT2C receptors are not considered to be autoreceptors, this would implicate that other neurotransmitter systems are involved in this process. To investigate which neurotransmitter systems were involved in the interaction, systemic citalopram was challenged with several glutamatergic, GABA-ergic, noradrenergic, and dopaminergic compounds to determine their effects on serotonin release in ventral hippocampus. It was determined that the involvement of glutamate, norepinephrine, and dopamine in the augmentation did not seem likely, whereas evidence implicated a role for the GABA-ergic system in the augmentation.Neuropsychopharmacology (2007) 32, 1550–1557; doi:10.1038/sj.npp.1301287; published online 3 January 2007 [ABSTRACT FROM AUTHOR]

The numerical solution of underwater shock fluid – structure interaction problems using boundary element/finite element techniques became tractable through the development of the family of Doubly Asymptotic Approximations (DAA). Practical implementation of the method has relied on the so-called augmentation of the DAA equations. The fluid and structural systems are respectively coupled by the structural acceleration vector in the surface normal direction on the right hand side of the DAA equations, and the total pressure applied to the structural equations on its right hand side. By formally solving for the acceleration vector from the structural system and substituting it into its place in the DAA equations, the augmentation introduces a term involving the inverse of the structural mass matrix. However there exist at least two important classes of problems in which the structural mass matrix is singular. This paper develops a method to carry out the augmentation for such problems using a generalized inverse technique. [ABSTRACT FROM AUTHOR]

Abstract The purpose of the present paper was to investigate the efficacy of augmentation of clomipramine (CMI) by tandospirone in 36 untreated outpatients with major depressive disorder. Twelve patients were treated with CMI and tandospirone (T group), 12 with CMI and diazepam (D group) and 12 with CMI alone (C group) for 6 weeks. No statistically significant differences in the percentage improvement of the Hamilton Depression Rating Scale (17 items; HDRS-17) and the Hamilton Anxiety Rating Scale (14 items; HARS-14) scores were shown among the three treatment groups. However, at 2 weeks, the percentage improvement of HDRS-17 score in the T group tended to be higher than that in the D and C groups, although there was no statistically significant difference among the three treatment groups. No change in plasma prolactin level or adverse events was induced by the addition of tandospirone. These results suggest that 6 weeks of treatment with tandospirone or diazepam was not effective for augmentation of CMI in major depressive disorder patients. However, augmentation of antidepressants by tandospirone administration for a few weeks might induce early expression of antidepressive effects. [ABSTRACT FROM AUTHOR]

Sleep staging is a crucial aspect of sleep evaluation and disease diagnosis. Numerous automatic schemes have been developed to replace the tedious and expensive task of manual sleep staging. In this paper, we propose SleepViTransformer, a novel scheme that aims to improve the performance of automatic sleep staging. There are three main contributions. (1) A patch-based sleep spectrogram Transformer encoder is proposed for learning more effective feature representations to enhance the automatic sleep staging performance. (2) To reduce the dependence on large amounts of annotated PSG data, cross-modality knowledge from a model pre-trained on image and audio datasets is transferred to SleepViTransformer, significantly improving the model performance. (3) To improve the model robustness under noise and artifacts, a set of PSG augmentations based on the characteristics of the PSG signal is proposed. Experimental results show that SleepViTransformer achieves state-of-the-art performance on four publicly available datasets. On small-scale datasets, SleepViTransformer outperforms the runner-up by 1.8% and 2.5% on SleepEDF-20 and by 1.2% and 1.6% on SleepEDF-78 in terms of accuracy and Cohen's kappa. SleepViTransformer also performs well on large-scale datasets, outperforming the runner-up by 0.8% and 1.2% on Physio-2018 and 0.4% and 0.2% on SHHS, respectively. The ablation experiments show that both the cross-modality pre-training and PSG augmentation module have positive impacts on improving model performance. To the best of our knowledge, this is the first model to adopt the patch encoding technique from Vision Transformer (ViT) on the sleep PSG spectrogram, showing its eminent potential for PSG signal analysis. • Propose a patch-based sleep spectrogram Transformer to learning effective representations. • Transfer cross-modality knowledge from a pre-trained model. • Propose a set of PSG augmentations based on the characteristics of the PSG signal. [ABSTRACT FROM AUTHOR]

The task of grading answers ranging from one phrase to one paragraph using computational techniques is known as Automated Short Answer Grading (ASAG). The performance of existing systems is not good enough due to limited data and the lack of availability of data in many domains. Many ASAG systems were developed as an outcome of the active research in this field. This study builds an effective system for grading short answers in the programming domain by leveraging Pre-trained Language Models and Text Augmentation. We fine-tuned three-sentence transformer models on the SPRAG corpus with five different augmentation techniques: viz., Random Deletion, Synonym Replacement, Random Swap, Backtranslation, and NLPAug. The SPRAG corpus contains student responses involving keywords and special symbols. We experimented with four different data sizes with the augmented data to determine the impact of training data on the fine-tuned sentence transformer model. this paper provides an exhaustive analysis of fine-tuning pretrained sentence transformer models with varying sizes of data by applying text augmentation techniques. we found that applying random swap and synonym replacement techniques together while fine-tuning has given a significant improvement, With a 4.91% increase in accuracy and a 3.36% increase in the F1-score. All the trained models are publicly available 1 1 https://github.com/sridevibonthu/SPRAG/tree/main/augmentation.. • A method to automatically grade short programming related objective answers authored by students. • An Evaluation of the pre-trained sentence transformers on ASAG task. • An extensive analysis of text augmentation techniques. [ABSTRACT FROM AUTHOR]

Historical and ancient murals hold clues to life in the past. This depicts the culture, worship styles, and social life of the community. This fascinates researchers and scholars who study historical bindings to showcase the modern world. Over time, most of these historical monuments have not been preserved in their original state. Most of these are affected due to natural climatic conditions, civil wars, and natural disasters. It remains clueless and imaginary about the size, shape, and color of the distorted historical monuments. This results in limitations for historical studies, archeological research, and geographical surveys. In this paper, we have studied the historical places around Pune city. Identified the locations where the monuments have been distorted and reconstructed recently. The construction age, type, color, shape, and size of the monuments are the major parameters of our study. Based on these criteria, we have captured images of these objects through different angles and camera lens. We have collected and categorized these images into folders with the names of historical places. This image dataset contains both captured and augmented images with distinct angles, scales, and directions. It also includes images captured in the daytime and evening with artificial lighting. This image dataset contains a variety of distinct image patterns that are useful as input to train computer-based supervised learning. The machine learning and deep learning algorithms perform efficiently if the input image dataset is large and distinct. Based on the predictive results generated by the machine learning and deep learning models, it is possible to virtually recreate the original monument. This would add a key value to historical research and studies.

Drone object detection faces numerous challenges such as dense clusters with overlapping, scale diversity, and long-tail distributions. Utilizing tiling inference through uniform sliding window is an effective way of enlarging tiny objects and meanwhile efficient for real-world applications. However, merely partitioning input images may result in heavy truncation and an unexpected performance drop in large objects. Therefore, in this work, we strive to develop an improved tiling detection framework with both competitive performance and high efficiency. First, we formulate the tiling inference and training pipeline with a mixed data strategy. To avoid truncation and handle objects at all scales, we simultaneously perform global detection on the original image and local detection on corresponding sub-patches, employing appropriate patch settings. Correspondingly, the training data includes both original images and the patches generated by random online anchor-cropping, which can ensure the effectiveness of patches and enrich the image scenarios. Furthermore, a scale filtering mechanism is applied to assign objects at diverse scales to global and local detection tasks to keep the scale invariance of a detector and obtain optimal fused predictions. As most of the additional operations are performed in parallel, the tiling inference remains highly efficient. Additionally, we devise two augmentations customized for tiling detection to effectively increase valid annotations, which can generate more challenging drone scenarios and simulate the practical cluster with overlapping, especially for rare categories. Comprehensive experiments on both public drone benchmarks and our customized real-world images demonstrate that, in comparison to other drone detection frameworks, the proposed tiling framework can significantly improve the performance of general detectors in drone scenarios with lower additional computational costs. [ABSTRACT FROM AUTHOR]

Background: Arthroscopic revision rotator cuff repair (ARRCR) is challenging. Biologic strategies seem to be promising. The aim was to evaluate the effectiveness of the combination of microfractures of the greater tuberosity, augmentation with collagen patch graft, and platelet concentrate injections in ARRCR. Methods: A retrospective comparative study was conducted on patients that underwent ARRCR with a minimum follow-up of two years. Patients in the augmentation group underwent ARRCR combined with microfractures, collagen patch graft, and postoperative subacromial injections of platelet concentrate. A standard rotator cuff repair was performed in the control group. Primary outcome: Constant-Murley score (CMS). Secondary outcomes: disease-specific, health-related quality of life using the Disabilities of the Arm, Shoulder, and Hand (DASH) score; assessment of tendon integrity with magnetic resonance at least six months after surgery. Significance was set at p < 0.05. Results: Forty patients were included. Mean follow-up was 36.2 ± 8.7 months. The mean CMS was greater in the augmentation group (p = 0.022). No differences could be found for DASH score. Healing failure rate was higher in the control group (p = 0.002). Conclusion: Biologic augmentation of ARRCR using a combination of microfractures, collagen patch graft, and subacromial injections of platelet concentrate is an effective strategy in improving tendon healing rate. Level of evidence: retrospective cohort study, level III. [ABSTRACT FROM AUTHOR]

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years, and recently, different deep learning approaches have been used. Despite this fact, there has not been any investigation regarding how 3D augmentation can help to create larger datasets, required to train deep networks with millions of parameters. In this study, deep learning was applied to derivatives from resting state functional MRI data, to investigate how different 3D augmentation techniques affect the test accuracy. Specifically, resting state derivatives from 1112 subjects in ABIDE (Autism Brain Imaging Data Exchange) preprocessed were used to train a 3D convolutional neural network (CNN) to classify each subject according to presence or absence of autism spectrum disorder. The results show that augmentation only provide minor improvements to the test accuracy. [ABSTRACT FROM AUTHOR]

This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy. [ABSTRACT FROM AUTHOR]

Three-dimensional bioprinting technology appears to be a promising solution for the treatment and reconstruction of a wide range of maxillofacial bone defects. In this review, the authors discuss different bioprinting solutions and options in the context of the 4 factors of bone healing: structures or scaffolds, osteogenic cells, growth factors, and stabilization (diamond concept of healing), as well as the influence of a 5th factor - vascularization. Bone is a complex tissue; hence, bone bioprinting may require different technical approaches and mixed methods. Ultraviolet (UV) crosslinkable hydrogels, such as gelatin methacryloyl (GelMA), are among the most promising bioinks; they are enhanced by hydroxyapatite or 1-2.5 mm beta-tricalcium phosphate (β-TCP) granules and porous scaffolds with recommended pore sizes greater than 300 μm. The advantages of mesenchymal stem cells (MSCs) are their significant availability, low tumorigenicity, and great potential for differentiation into osteoblasts or endothelial cells (ECs). Although growth factors require advanced delivery systems, they provide excellent improvement in the functionality of printed tissues. A proper vasculature system supplies cells with oxygen and nutrients, removes waste products, promotes osteogenesis, prevents ischemic necrosis, and improves the mechanical properties of bone. With all of these aspects perfectly balanced and working in synergy, the clinical use of bioprinting is only a matter of time. [ABSTRACT FROM AUTHOR]

Ketamine is illegally used as a recreational drug in many Asian countries. Long‑term ketamine abusers often develop irritable bladder symptoms that gradually develop into more severe urinary frequency and urgency and eventually into a painful ulcerated bladder. These patients typically have reduced functional bladder capacity, increased bladder sensation, detrusor overactivity, severe urgency, urinary incontinence, and bladder contracture. Ketamine metabolites can cause severe inflammation of the urothelium, urothelial barrier deficits, vascular endothelial fibrinoid changes, increased oxidative stress, and bladder wall fibrosis. A decrease in bladder compliance, urinary tract infection, severe bladder pain with a full bladder, and painful micturition are also common symptoms. Finally, with continued abuse of ketamine, hydronephrosis, ureteral stricture, vesicoureteral reflux, and renal failure may develop. Cessation of ketamine is the mainstay of treatment. Lower urinary tract symptoms usually relapse if patients reuse ketamine after stopping. In cases of severe ketamine cystitis, only augmentation enterocystoplasty can relieve bladder pain and restore normal lower urinary tract function. This article reviews the underlying pathophysiology, clinical characteristics, and management of ketamine cystitis. [ABSTRACT FROM AUTHOR]

Species translocations are increasingly common in rare plant conservation. Wild populations can provide basic ecological knowledge to improve their chance of success. In the heavily fragmented Florida scrub, USA, many listed species require translocations to persist, including Dicerandra christmanii. In 1994, we began monitoring the only protected population of D. christmanii growing both in gaps (open areas) within the shrub matrix and on roadsides. In 2010, we augmented this population by adding plants and seeds to unoccupied gaps. In 2012, we introduced plants to a separate protected site to create a new population. We evaluated early translocation success using generalized linear mixed‐effect models of vital rate variation among habitat types. Survival probability increased with size, peaking at 0.6–0.8, and was lowest in augmentations and highest in introductions. Growth increased with plant size across all habitat types, except for the largest adults which experienced senescence. Naturally recruited plants in gaps showed the highest reproduction probability and fecundity at smaller sizes, but larger plants in translocations had the highest fecundity. Yearling recruitment was higher in translocated plants relative to naturally recruited plants in gaps during the initial years following outplanting. Experimental components of translocations also affected outplanting performance with positive effects of fire. These analyses suggest a high potential for translocations to become established and contribute to species recovery. [ABSTRACT FROM AUTHOR]

Translocated populations often show vigorous initial dynamics but eventually collapse. Modeling tools that incorporate basic ecological knowledge and allow for propagation of uncertainty can help identify potential risks. Here, we use Bayesian Integral Projection Models to estimate population growth rates (λs), associated elasticities, and extinction risks for the endangered Dicerandra christmanii. Our study compared natural populations in gaps (open areas) within the shrub matrix and roadsides, unoccupied gaps augmented with transplants, and introduced populations. These populations experienced different management, including prescribed fires, and had different initial conditions. Augmented gaps showed lower means but similar variation in λs as natural gaps. Yet, simulations indicate that augmentations can delay quasi‐extinction (40% of simulations) by 4 years at the population level. Introduced populations showed higher means and variation in λs as wild gaps. While vital rate estimates suggested initial translocation success, time to quasi‐extinction was projected to be 7 years shorter for introductions in gaps than for natural gap populations. These contradictory results are partially explained by the lack of established seed banks in introduced populations, which affected the response of early life stage transitions to a prescribed fire. This study highlights the need to account for site‐specific information in models of population dynamics, including initial conditions and management history, and especially cryptic life stages such as dormant seeds. [ABSTRACT FROM AUTHOR]

Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Overemphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG (“what you see is what you get”) urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps.

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

Alveolar ridge bone resorption is a biologic phenomenon that occurs following tooth extraction and cannot be prevented. This paper reviews the vertical and horizontal ridge dimensional changes that are associated with tooth extraction. It also provides an overview of the advantages of ridge preservation as well as grafting materials. A Medline search among English language papers was performed in March 2015 using alveolar ridge preservation, ridge augmentation, and various graft types as search terms. Additional papers were considered following the preliminary review of the initial search that were relevant to alveolar ridge preservation. The literature suggests that ridge preservation methods and augmentation techniques are available to minimize and restore available bone. Numerous grafting materials, such as autografts, allografts, xenografts, and alloplasts, currently are used for ridge preservation. Other materials, such as growth factors, also can be used to enhance biologic outcome.

It is becoming clear that post-traumatic stress disorder (PTSD) is not simply a psychiatric disorder, but one that involves pervasive physiological impairments as well. These physiological disturbances deserve attention in any attempt at integrative treatment of PTSD that requires a focus beyond the PTSD symptoms themselves. The physiological disturbances in PTSD range over many systems, but a common thread thought to underlie them is that the chronic effects of PTSD involve problems with allostatic control mechanisms that result in an excess in what has been termed allostatic load (AL). A pharmacological approach to reducing AL would be valuable, but, because of the large range of physiological issues involved - including metabolic, inflammatory, and cardiovascular systems - it is unclear whether there exists a simple comprehensive way to address the AL landscape. In this paper, we propose that the cannabinoid system may offer just such an approach, and we outline evidence for the potential utility of cannabinoids in reducing many of the chronic physiological abnormalities seen in PTSD which are thought to be related to excess AL.

This paper aims to systematically assess the local radial basis function collocation method, structured with multiquadrics (MQs) and polyharmonic splines (PHSs), for solving steady and transient diffusion problems. The boundary value test involves a rectangle with Dirichlet, Neuman, and Robin boundary conditions, and the initial value test is associated with the Dirichlet jump problem on a square. The spectra of the free parameters of the method, i.e., node density, timestep, shape parameter, etc., are analyzed in terms of the average error. It is found that the use of MQs is less stable compared to PHSs for irregular node arrangements. For MQs, the most suitable shape parameter is determined for multiple cases. The relationship of the shape parameter with the total number of nodes, average error, node scattering factor, and the number of nodes in the local subdomain is also provided. For regular node arrangements, MQs produce slightly more accurate results, while for irregular node arrangements, PHSs provide higher accuracy than MQs. PHSs are recommended for use in diffusion problems that require irregular node spacing.

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model’s capabilities to encompass unstructured data classification and grading, broadening the scope of application.

Embolization of the middle meningeal artery (MMA) is a safe and effective adjunct in the treatment of chronic subdural hematoma. While prior authors describe the use of coils to assist embolization by preventing reflux through eloquent collaterals, we describe the use of coils to further open the MMA, allowing the administration of greater amounts of embolisate for a more robust embolization. The objective of this study was to demonstrate that helical coils can safely open the MMA following the administration of polyvinyl alcohol (PVA) particles. This allows for more embolisate to be administered into the MMA for more effective treatment. A retrospective review was conducted at our institution including intraoperative images and postoperative clinical and radiographic follow up. Failure rates using MMA embolization with PVA and helical coil augmentation were compared to failure rates in the literature of MMA embolization with PVA or ethylene vinyl-alcohol copolymer alone. A total of 8 cases were reviewed in which this technique was implemented. There were no immediate complications after treatment. All patients that underwent helical coil embolization following the administration of PVA had increased amount of embolisate delivered into the MMA. All patients at follow up had resolution of the subdural hematoma on outpatient imaging. Helical coil embolization allows for more embolisate administration into the MMA and provides a technical advantage for patients that fail traditional techniques of embolization. Case series are taking place to further test this hypothesis and identify the ideal patient population that may gain maximal yield from this novel technique. [ABSTRACT FROM AUTHOR]

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions. [ABSTRACT FROM AUTHOR]

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters' initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework. [ABSTRACT FROM AUTHOR]

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach. [ABSTRACT FROM AUTHOR]

The aim of our study was to evaluate the effectiveness of lurasidone augmentation of clozapine in treatment-resistant schizophrenia (SZ) in a retrospective chart review. From the medical records of 916 SZ patients, we identified 16 individuals treated with a combination of clozapine and lurasidone. The detailed clinical data are described separately for each patient. We compared the Clinical Global Impression—Severity (CGI-S) scores between three points of observation: before the treatment and one month and two months after its initiation. CGI Improvement (CGI-I) scores were used to evaluate the treatment response between the first and last points of observation. The vast majority of patients (14/16, 87.5%) responded to lurasidone augmentation of clozapine (CGI-I scores 1 or 2). Therapeutic effects were observable after 3–12 weeks of treatment (median 6 (4–6)). A reduction in CGI-S scores was observed after the first month of observation. There was an observable reduction in positive, depressive and anxiety symptoms, as well as an improvement in psychosocial functioning. Two patients discontinued treatment due to side effects. Our study suggests that lurasidone augmentation of clozapine may lead to improvements in a broad range of SZ symptom dimensions. [ABSTRACT FROM AUTHOR]

A brain tumor can have an impact on the symmetry of a person's face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients' chances of survival will be significantly lowered. Checking the patient's MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model. [ABSTRACT FROM AUTHOR]

Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD's precise identification remains significant for assuring airplane flight security. The material features of FOD remain the very critical criteria for comprehending the destruction rate endured by an airplane. Nevertheless, the most frequent identification systems miss an efficient methodology for automated material identification. This study proffers a new FOD technique centered on transfer learning and also a mainstream deep convolutional neural network. For object detection (OD), this embraces the spatial pyramid pooling network with ResNet101 (SPPN-RN101), which assists in concatenating the local features upon disparate scales within a similar convolution layer with fewer position errors while identifying little objects. Additionally, Softmax with Adam Optimizer in CNN enhances the training speed with greater identification accuracy. This study presents FOD's image dataset called FOD in Airports (FODA). In addition to the bounding boxes' principal annotations for OD, FODA gives labeled environmental scenarios. Consequently, every annotation instance has been additionally classified into three light-level classes (bright, dim, and dark) and two weather classes (dry and wet). The proffered SPPN-ResNet101 paradigm is correlated to the former methodologies, and the simulation outcomes exhibit that the proffered study executes an AP medium of 0.55 for the COCO metric, 0.97 AP for the pascal metric, and 0.83 MAP of pascal metric. [ABSTRACT FROM AUTHOR]

Background: The surgical treatment of proximal humeral fractures (PHFs) with locking plate fixation (LPF) in the elderly is associated with high complication rates, especially in osteoporotic bone. Variants of LPF such as additional cerclages, double plating, bone grafting and cement augmentation can be applied. The objective of the study was to describe the extent of their actual use and how this changed over time. Methods: Retrospective analysis of health claims data of the Federal Association of the Local Health Insurance Funds was performed, covering all patients aged 65 years and older, who had a coded diagnosis of PHF and were treated with LPF between 2010 and 2018. Differences between treatment variants were analyzed (explorative) via chi-squared or Kruskal–Wallis tests. Results: Of the 41,216 treated patients, 32,952 (80%) were treated with LPF only, 5572 (14%) received additional screws or plates, 1983 (5%) received additional augmentations and 709 (2%) received a combination of both. During the study period, relative changes were observed as follows: −35% for LPF only, +58% for LPF with additional fracture fixation and +25% for LPF with additional augmentation. Overall, the intra-hospital complication rate was 15% with differences between the treatment variants (LPF only 15%, LPF with additional fracture fixation 14%, LPF with additional augmentation 19%; p < 0.001), and a 30-day mortality of 2%. Conclusions: Within an overall decrease of LPF by approximately one-third, there is both an absolute and relative increase of treatment variants. Collectively, they account for 20% of all coded LPFs, which might indicate more personalized treatment pathways. The leading variant was additional fracture fixation using cerclages. [ABSTRACT FROM AUTHOR]

This study describes a deep zero-shot transfer learning model (DZTLM) for predicting mild cognitive impairment (MCI) in patients with Alzheimer's disease (AD). The proposed DZTLM combines ResNet and deep subdomain adaptation network (DsAN) blocks with a simple data augmentation and transfer technique, Elastic-Mixup. We test the DZTLM using 3D gray matter images segregated from structural MRI as input. Ablation experiments are conducted to evaluate the proposed model and compare it with existing approaches. Experiments demonstrate that the DsAN network coordinating Elastic-Mixup enhances the accuracy of MCI-AD prediction by more than 18% compared with a standard 3D ResNet50 classifier. The Elastic-Mixup technique contributes more than 16% to this increase in prediction accuracy. Elastic-Mixup also enhances the sensitivity of recognition for stable MCI. When labeled samples are scarce, the unsupervised DZTLM outperforms a semi-supervised transfer learning model. The DZTLM achieves comparable outcomes to existing models despite the absence of tagged MRI data. [ABSTRACT FROM AUTHOR]

(1) Background: Different compositions of biodegradable materials are being investigated to successfully replace non-resorbable ones in bone tissue regeneration in dental surgery. The systematic review tried to address the question, "Can biodegradable polymers act as a replacement for conventional materials in dental surgery procedures?" (2) Methods: An electronic search of the PubMed and Scopus databases was conducted in October 2022. The following keywords were used: (lactide polymers) and (hydroxyapatite or fluorapatite) and (dentistry) and (regeneration). Initially, 59 studies were found. Forty-one studies met the inclusion criteria and were included in the review. (3) Results: These usually improved the properties and induced osteogenesis, tissue mineralisation and bone regeneration by inducing osteoblast proliferation. Five studies showed higher induction of osteogenesis in the case of biomaterials, UV-HAp/PLLA, ALBO-OS, bioresorbable raw particulate hydroxyapatite/poly-L-lactide and PLGA/HAp, compared to conventional materials such as titanium. Four studies confirmed improvement in tissue mineralisation with the usage of biomaterials: hydroxyapatite/polylactic acid (HA/PLA) loaded with dog's dental pulp stem cells (DPSCs), Coll/HAp/PLCL, PDLLA/VACNT-O:nHAp, incorporation of hydroxyapatite and simvastatin. Three studies showed an acceleration in proliferation of osteoblasts for the use of biomaterials with additional factors such as collagen and UV light. (4) Conclusions: Lactide polymers present higher osteointegration and cell proliferation rate than the materials compared. They are superior to non-biodegradable materials in terms of the biocompability, bone remodelling and healing time tests. Moreover, because there is no need of reoperation, as the material automatically degrades, the chance of scars and skin sclerosis is lower. However, more studies involving greater numbers of biomaterial types and mixes need to be performed in order to find a perfect biodegradable material. [ABSTRACT FROM AUTHOR]

This review presents a detailed summary of the current literature regarding RLS and vitamin D deficiency. To our knowledge it is the first review of its kind. We review the prevalence of vitamin D deficiency in RLS as well as the evidence for the use of vitamin D supplementation in RLS management. We further examine the literature for proteomic and genetic evidence of a role for vitamin D in the pathogenesis of RLS. An alteration in vitamin D binding protein in RLS is one of the most consistent findings in the proteomic studies. Furthermore, we examine the interaction of vitamin D with calcium, phosphorus, and parathyroid hormone and the possible role of these connections in RLS. We also explore the possible nexus between RLS and vitamin D in renal disease, cardiovascular and cerebrovascular disease as well as inflammation. In addition, we review the potential interaction between vitamin D and RLS with iron, dopamine and other neurotransmitter systems including the endogenous opiate, serotoninergic, glutamatergic and adenosinergic systems. We also explore the role of vitamin D in RLS Augmentation (i.e., the paradoxical worsening of RLS symptoms when dopaminergic agents are used as a therapy for RLS). Although the literature is not entirely consistent in affirming vitamin D deficiency in RLS or the amelioration of RLS symptoms with vitamin D therapy, the collective studies overall indicate that vitamin D deficiency is common enough in RLS patients to suggest that RLS patients should have their vitamin D levels checked and any deficiency corrected as a standard of care. Highlights Patients with Restless Legs Syndrome (RLS) may be deficient in vitamin D and therapy with vitamin D may ameliorate RLS. We present the first review dedicated solely to evaluating the relationship between RLS and vitamin D and present a case for the role of vitamin D in RLS pathogenesis. [ABSTRACT FROM AUTHOR]

The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option. [ABSTRACT FROM AUTHOR]

“Crossmodal correspondences” are the consistent mappings between perceptual dimensions or stimuli from different sensory domains, which have been widely observed in the general population and investigated by experimental psychologists in recent years. At the same time, the emerging field of human movement augmentation (i.e., the enhancement of an individual’s motor abilities by means of artificial devices) has been struggling with the question of how to relay supplementary information concerning the state of the artificial device and its interaction with the environment to the user, which may help the latter to control the device more effectively. To date, this challenge has not been explicitly addressed by capitalizing on our emerging knowledge concerning crossmodal correspondences, despite these being tightly related to multisensory integration. In this perspective paper, we introduce some of the latest research findings on the crossmodal correspondences and their potential role in human augmentation. We then consider three ways in which the former might impact the latter, and the feasibility of this process. First, crossmodal correspondences, given the documented effect on attentional processing, might facilitate the integration of device status information (e.g., concerning position) coming from different sensory modalities (e.g., haptic and visual), thus increasing their usefulness for motor control and embodiment. Second, by capitalizing on their widespread and seemingly spontaneous nature, crossmodal correspondences might be exploited to reduce the cognitive burden caused by additional sensory inputs and the time required for the human brain to adapt the representation of the body to the presence of the artificial device. Third, to accomplish the first two points, the benefits of crossmodal correspondences should be maintained even after sensory substitution, a strategy commonly used when implementing supplementary feedback.

The necessity of an ‘adequate’ keratinized mucosa at the emergence profile of dental implants is a controversial and debated topic. But, in recent years, research suggests that a minimum of at least 2mm of keratinized mucosa is recommended for the maintenance of peri-implant health. The latest systematic reviews on this topic are largely in favor of keratinized mucosa, as are the recommendations of some expert panels. The present paper presents a novel technique for peri-implant flap suturing that can be used in combination with soft tissue augmentation procedures, such as laterally positioned flaps, roll-flaps, and decellularized dermal matrix xenografts. The technique stabilizes the flap on the buccal surface of the healing screw or the emergence profile of the tissue-level implant with a circumferential continuous mattress design that is anchored on the buccal margins and/or periosteum with a final knot. The technique is carried out with a sterile 6/0 synthetic absorbable glyconate monofilament surgical suture (Monosyn, Bbraun, B. Braun Milano S. p.A., Via Vincenzo da Seregno, 14, 20,161 Milan, Italy). This type of suture is ideal, as it slides smoothly through the tissues and allows for the ideal adjustment of the tension and the position of the flap margins until the final knot is positioned. Furthermore, these augmentative procedures make use of second-intention healing and mattress sutures are always ideal to stabilize flap margins without excess tension in such cases. This case series involves patients with a reduced band of keratinized mucosa and, as such, in need of augmentative flap procedures.