Search Results (28)

Intracranial aneurysms are a serious cerebrovascular condition with a risk of subarachnoid hemorrhage due to rupture, leading to high mortality and morbidity. Flow Diverter Stents (FDSs) have become an important endovascular treatment option for unruptured large or wide-neck aneurysms. Hemodynamic factors significantly influence treatment outcomes in aneurysms treated with FDSs, and Computational Fluid Dynamics (CFD) has been widely used to evaluate post-deployment flow characteristics. However, conventional wire-resolved CFD methods require extremely fine meshes to reconstruct individual FDS wires, resulting in prohibitively high computational costs. This severely limits their feasibility for use in clinical treatment planning, where fast and robust simulations are essential. To address this limitation, we developed a computationally efficient CFD method that incorporates a porous media model accounting for local variations in wire density after FDS deployment. Based on Virtual Stent Simulation, the FDS region was defined as a hollow cylindrical domain with spatially varying resistance derived from cell-specific wire density. We validated the proposed method using 15 clinical cases, demonstrating close agreement with conventional wire-resolved CFD results. Relative errors in key hemodynamic parameters, including velocity, shear rate, inflow rate, and turnover time, were within 5%, with correlation coefficients exceeding 0.98. The number of grid elements, the data size, and total analysis time were reduced by over 90%. The method also allowed comparison between Total-Filling (OKM Grade A) and Occlusion (Grade D) cases, and evaluation of different FDS sizing, positioning, and coil-assisted strategies. The proposed method enables practical and efficient CFD analysis following FDS treatment and supports hemodynamics-based treatment planning of aneurysms. Full article

Background and Purpose: Mechanical thrombectomy in the context of internal carotid artery dissection (ICA-D) lesions is an undesirable procedure that may necessitate carotid stenting. Flow-diverting stents (FDSs) are promising devices with numerous advantages, particularly in cases involving tortuous anatomy. Here, we investigate the use of FDSs in the acute management of carotid dissection during mechanical thrombectomy procedures in patients with dissection-related strokes. Materials and Methods: This was a multicentric retrospective observational study of consecutive patients admitted for mechanical thrombectomy due to acute ischaemic stroke with ICA-D and treated with an FDS in the acute setting between July 2018 and February 2023. Patient records, procedural details, and post-procedural outcomes, including follow-up data, were reviewed. Results: A total of 11 patients (10 patients with unilateral ICA-D and one patient with bilateral ICA-D) were included, 10 of whom were male, with a median age of 54 years (range: 35–85 years) and NIHSS scores at admission ranging from 3 to 32 (median 13). Eight cases (73%) involved intracranial occlusion (tandem stroke), with the intracranial occlusion managed first each time. An FDS was selected when the dissection was long and/or the ICA was tortuous, and successful deployment was achieved in all patients with a favourable angiographic outcome (TICI 2B-3). A favourable outcome (modified Rankin scale 0–2 at 90 days) was observed in five patients (45%), with four patients (36%) experiencing symptomatic ICH and three patients having stent occlusion out of the 12 treated ICA-D cases. Conclusions: The use of FDSs for acute stenting in ICA-D-related stroke can be performed efficiently, resulting in excellent angiographic outcomes and an acceptable rate of favourable outcomes specific to the pathology. Larger prospective studies are still needed to confirm the potential benefits of FDSs in acute situations. Full article

Background and clinical significance: Functional/dissociative seizures (FDSs) in adolescents are paroxysmal events which superficially resemble epileptic seizures or syncope. This study evaluated the effectiveness of brief cognitive analytic therapy (CAT). Case presentation: The patient was a 17-year-old white cisgender male with a diagnosis of non-epileptic attack disorder. The functional/dissociative seizures were treated with 8-session CAT, with follow-up at 5 weeks. Two target problems (TPs) and associated target problem procedures (TPPs) were rated for recognition and revision at each session and at follow-up. An A-B-C-FU single-case experimental evaluation of the TP/TPPs was conducted. Nomothetic outcome measures (DES-2 and RCADS) were administered at session 1, session 8, and at follow-up, and the YP-CORE and the Session Rating Scale were completed at each session. The patient was independently interviewed using the Change Interview 13 weeks after completing therapy. The results show that CAT effectively increased the recognition and revision of TPs/TPPs, four specific changes occurred (including cessation of functional seizures). There were pre–post reliable and clinically significant improvements to psychological wellbeing, but these were not maintained at follow-up. Conclusions: This study indicates that CAT was a partially effective intervention. The use of CAT as a treatment for FND in adolescents holds promise, but more research is needed. Full article

Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. Full article

This study addresses the critical challenge of data imbalance in credit card fraud detection (CCFD), a significant impediment to accurate and reliable fraud prediction models. Fraud detection (FD) is a complex problem due to the constantly evolving tactics of fraudsters and the rarity of fraudulent transactions compared to legitimate ones. Efficiently detecting fraud is crucial to minimize financial losses and ensure secure transactions. By developing a framework that transitions from imbalanced to balanced data, the research enhances the performance and reliability of FD mechanisms. The strategic application of Meta-heuristic optimization (MHO) techniques was accomplished by analyzing a dataset from Kaggle’s CCF benchmark datasets, which included data from European credit-cardholders. They evaluated their capability to pinpoint the smallest, most relevant set of features, analyzing their impact on prediction accuracy, fitness values, number of selected features, and computational time. The study evaluates the effectiveness of 15 MHO techniques, utilizing 9 transfer functions (TFs) that identify the most relevant subset of features for fraud prediction. Two machine learning (ML) classifiers, random forest (RF) and support vector machine (SVM), are used to evaluate the impact of the chosen features on predictive accuracy. The result indicated a substantial improvement in model efficiency, achieving a classification accuracy of up to 97% and reducing the feature size by up to 90%. In addition, it underscored the critical role of feature selection in optimizing fraud detection systems (FDSs) and adapting to the challenges posed by data imbalance. Additionally, this research highlights how machine learning continues to evolve, revolutionizing FDSs with innovative solutions that deliver significantly enhanced capabilities. Full article

Lake Avernus is a volcanic lake located in southern Italy. Since ancient times, it has inspired numerous myths and legends due to the occurrence of singular phenomena, such as coloring events. Only recently has an explanation been found for them, i.e., the recurring color change over time is due to the alternation of cyanobacterial blooms that are a consequence of natural nutrient inputs as well as pollution resulting from human activities. This current report specifically describes the red coloring event that occurred on Lake Avernus in March 2022, the springtime season in this region of Italy. Our innovative multidisciplinary approach, the ‘Fast Detection Strategy’ (FDS), was devised to monitor cyanobacterial blooms and their toxins. It integrates remote sensing data from satellites and drones, on-site sampling, and analytical/bioinformatics analyses into a cohesive information flow. Thanks to FDS, we determined that the red color was attributable to a bloom of Planktothrix rubescens, a toxin-producing cyanobacterium. Here, we report the detection and identification of 14 anabenopeptins from this P. rubescens strain, seven of which are known and seven are newly reported herein. Moreover, we explored the mechanisms and causes behind this cyclic phenomenon, confirming cyanobacteria’s role as reliable indicators of environmental changes. This investigation further validates FDS’s effectiveness in detecting and characterizing cyanobacterial blooms and their associated toxins, expanding its potential applications. Full article

We present our experience with the implantation of flow diverter stents (FDSs) for the management of internal carotid artery (ICA) dissections in tortuous tonsillar loop segments. A total of 16 patients (10 women, 62.5%; mean age 39 ± 8 years; median baseline NIHSS 13; median ASPECTS 8.5) with acute ischemic stroke due to ICA dissection in a tortuous tonsillar loop segment, with/without large intracranial vessel thrombotic occlusion diagnosed between June 2015–February 2022 were included in this retrospective study under a waiver of informed consent. An FDS device was deployed from the petrous ICA toward the upper cervical ICA, completely covering the tonsillar loop. Stentriever-assisted thrombectomy was performed when indicated. A dual antiplatelet regimen was used during and after the procedure. Thrombocyte inhibition levels were evaluated before, during, and after the intervention. The ICA occlusion/near occlusion was successfully recanalized in all 16 patients with mean postangioplasty residual stenosis of 34 ± 14% (range 0–50%). Stent-assisted thrombectomy was performed in 15/16 patients (93.7%), achieving revascularization (TICI 2b–3) in all. There were no procedural complications and no intraprocedural embolic events; one asymptomatic petechial hemorrhage was detected. At 3-month follow-up, mRS 0–2 was seen in all patients. This report provides pilot data for a subsequent study on the use of flow diverter stents for ischemic cerebrovascular conditions. Our encouraging preliminary results await confirmation from further experience and prospective randomized studies. Full article

Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology. Full article

With distributed generation (DG) being continuously connected into distribution networks, the stochastic and fluctuating nature of its power generation brings ever more problems than before, such as increasing operating costs and frequent voltage violations. However, existing robust scheduling methods of flexible resources tend to make rather conservative decisions, resulting in high operation costs. In view of this, a two-stage robust optimal scheduling method for flexible distribution networks is proposed in this paper, based on the pairwise convex hull (PWCH) uncertainty set. A two-stage robust scheduling model is first formulated considering coordination among on-load tap changers, energy storage systems and flexible distribution switches. In the first stage, the temporal correlated OLTCs and energy storage systems are globally scheduled using day-ahead forecasted DG outputs. In the second stage, FDSs are scheduled in real time in each time period based on the first-stage decisions and accurate short-term forecasted DG outputs. The spatial correlation and uncertainties of the outputs of multiple DGs are modeled based on the PWCH, such that the decision conservativeness can be reduced by cutting regions in the box with low probability of occurrence. The improved column-and-constraint generation algorithm is then used to solve the robust optimization model. Through alternating iterations of auxiliary variables and dual variables, the nonconvex bilinear terms induced by the PWCH are eliminated, and the subproblem is significantly accelerated. Test results on the 33-bus distribution system and a realistic 104-bus distribution system validate that the proposed PWCH-based method can obtain much less conservative scheduling schemes than using the box uncertainty set. Full article

Drug-induced seizure liability is a significant safety issue and the basis for attrition in drug development. Occurrence in late development results in increased costs, human risk, and delayed market availability of novel therapeutics. Therefore, there is an urgent need for biologically relevant, in vitro high-throughput screening assays (HTS) to predict potential risks for drug-induced seizure early in drug discovery. We investigated drug-induced changes in neural Ca²⁺ oscillations, using fluorescent dyes as a potential indicator of seizure risk, in hiPSC-derived neurons co-cultured with human primary astrocytes in both 2D and 3D forms. The dynamics of synchronized neuronal calcium oscillations were measured with an FDSS kinetics reader. Drug responses in synchronized Ca²⁺ oscillations were recorded in both 2D and 3D hiPSC-derived neuron/primary astrocyte co-cultures using positive controls (4-aminopyridine and kainic acid) and negative control (acetaminophen). Subsequently, blinded tests were carried out for 25 drugs with known clinical seizure incidence. Positive predictive value (accuracy) based on significant changes in the peak number of Ca²⁺ oscillations among 25 reference drugs was 91% in 2D vs. 45% in 3D hiPSC-neuron/primary astrocyte co-cultures. These data suggest that drugs that alter neuronal activity and may have potential risk for seizures can be identified with high accuracy using an HTS approach using the measurements of Ca²⁺ oscillations in hiPSC-derived neurons co-cultured with primary astrocytes in 2D. Full article

Integrated sensing and communications (ISAC) is emerging as a key technology of 6G. Owing to the low peak-to-average power ratio (PAPR) property, discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is helpful to improve the sensing range and suitable for high-frequency transmission. However, compared to orthogonal frequency-division multiplexing (OFDM), the sensing accuracy of DFT-s-OFDM is relatively poor. In this paper, frequency-domain spectral shaping (FDSS) is adopted to enhance the performances of DFT-s-OFDM including sensing accuracy and PAPR by adjusting the correlation of signals. Specifically, we first establish a signal model for the ISAC system, followed by the description of performance indicators. Then, we analyze the influence of amplitude fluctuation of frequency domain signals on sensing performance, which shows the design idea of FDSS-enhanced DFT-s-OFDM. Further, a FDSS-enhanced DFT-s-OFDM framework is introduced for ISAC, where two types of FDSS filters including a pre-equalization filter and an isotropic orthogonal transform algorithm (IOTA) filter are designed. The simulation results show that the proposed scheme can obtain about 4 dB performance gain in terms of sensing accuracy over DFT-s-OFDM. In addition, FDSS-enhanced DFT-s-OFDM can significantly reduce PAPR and improve the power amplifier efficiency. Full article

The main theme of the present study is to analyze numerically the effects of the magnetic field on the hybrid nanofluid flow over a flat elastic surface. The effects of the thermal and velocity slips are also analyzed in view of the hybrid nanofluid flow. It is considered a combination of titanium oxide (TiO₂) and copper oxide (CuO) nanoparticles that are suspended in the incompressible and electrically conducting fluid (water). The behavior of the Brownian motion of the nanoparticles and the thermophoretic forces are contemplated in the physical and mathematical formulations. Moreover, the impact of the Joule heating and viscous dissipation are also discussed using the energy equation. The mathematical modeling is simulated with the help of similarity variables. The resulting equations are solved using the Keller–Box method with a combination of finite difference schemes (FDSs). Hybrid nanofluids provide significant advantages over the usual heat transfer fluids. Therefore, the use of nanofluids is beneficial to improve the thermophysical properties of the working fluid. All of the results are discussed for the various physical parameters involved in governing the flow. From the graphical results, it is found that the hybrid nanoparticles improve the concentration, temperature, and velocity profiles, as well as the thickness of the relevant boundary layer. The conjunction of a magnetic field and the velocity slip, strongly opposes the fluid motion. The boundary layer thickness and concentration profile are significantly reduced with the higher levels of the Schmidt number. Full article

The demand for food delivery services (FDSs) during the COVID-19 crisis has been fuelled by consumers who prefer to order meals online and have it delivered to their door than to wait at a restaurant. Since many restaurants moved online and joined FDSs such as Uber Eats, Menulog, and Deliveroo, customer reviews on internet platforms have become a valuable source of information about a company’s performance. FDS organisations strive to collect customer complaints and effectively utilise the information to identify improvements needed to enhance customer satisfaction. However, only a few customer opinions are addressed because of the large amount of customer feedback data and lack of customer service consultants. Organisations can use artificial intelligence (AI) instead of relying on customer service experts and find solutions on their own to save money as opposed to reading each review. Based on the literature, deep learning (DL) methods have shown remarkable results in obtaining better accuracy when working with large datasets in other domains, but lack explainability in their model. Rapid research on explainable AI (XAI) to explain predictions made by opaque models looks promising but remains to be explored in the FDS domain. This study conducted a sentiment analysis by comparing simple and hybrid DL techniques (LSTM, Bi-LSTM, Bi-GRU-LSTM-CNN) in the FDS domain and explained the predictions using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The DL models were trained and tested on the customer review dataset extracted from the ProductReview website. Results showed that the LSTM, Bi-LSTM and Bi-GRU-LSTM-CNN models achieved an accuracy of 96.07%, 95.85% and 96.33%, respectively. The model should exhibit fewer false negatives because FDS organisations aim to identify and address each and every customer complaint. The LSTM model was chosen over the other two DL models, Bi-LSTM and Bi-GRU-LSTM-CNN, due to its lower rate of false negatives. XAI techniques, such as SHAP and LIME, revealed the feature contribution of the words used towards positive and negative sentiments, which were used to validate the model. Full article

During the COVID-19 crisis, customers’ preference in having food delivered to their doorstep instead of waiting in a restaurant has propelled the growth of food delivery services (FDSs). With all restaurants going online and bringing FDSs onboard, such as UberEATS, Menulog or Deliveroo, customer reviews on online platforms have become an important source of information about the company’s performance. FDS organisations aim to gather complaints from customer feedback and effectively use the data to determine the areas for improvement to enhance customer satisfaction. This work aimed to review machine learning (ML) and deep learning (DL) models and explainable artificial intelligence (XAI) methods to predict customer sentiments in the FDS domain. A literature review revealed the wide usage of lexicon-based and ML techniques for predicting sentiments through customer reviews in FDS. However, limited studies applying DL techniques were found due to the lack of the model interpretability and explainability of the decisions made. The key findings of this systematic review are as follows: 77% of the models are non-interpretable in nature, and organisations can argue for the explainability and trust in the system. DL models in other domains perform well in terms of accuracy but lack explainability, which can be achieved with XAI implementation. Future research should focus on implementing DL models for sentiment analysis in the FDS domain and incorporating XAI techniques to bring out the explainability of the models. Full article

With the increasing population of cities, expanding roads as one of the essential urban infrastructures is a necessary task; therefore, adverse effects such as increased fuel consumption, pollution, noise, and road accidents are inevitable. One of the most efficient ways to mitigate congestion-related adverse effects is to introduce effective intelligent transportation systems (ITS), using advanced technologies and mobile communication protocols to make roads smarter and reduce negative impacts such as improvement in fuel consumption and pollution, and reduction of road accidents, which leads to improving quality of life. Smart roads might play a growing role in the improved safety of road transportation networks. This study aims to evaluate and rank the potential smartification measures for the road network in Calabria, in southern Italy, with sustainable development goals. For this purpose, some potential smartification measures were selected. Experts in the field were consulted using an advanced procedure: four criteria were considered for evaluating these smartification measures. The Integrated fuzzy decision support system (FDSS), namely the fuzzy Delphi analytic hierarchy process (FDAHP) with the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) were used for evaluating and ranking the potential smartification measures. The results demonstrated that the repetition of signals in the vehicle has the highest rank, and photovoltaic systems spread along the road axis has the lowest rank to use as smartification measures in the roads of the case study. Full article