(1) Background: This retrospective study aimed to compare the efficacy and safety of one-stage computed tomography (OSCT)- to that of two-stage computed tomography (TSCT)-guided localization for the surgical removal of small lung nodules. (2) Methods: We collected data from patients with ipsilateral pulmonary nodules who underwent localization before surgical removal at Veteran General Hospital Kaohsiung between October 2017 and January 2022. The patients were divided into the OSCT and TSCT groups. (3) Results: We found that OSCT significantly reduced the localization time and risky time compared to TSCT, and the success rate of localization and incidence of pneumothorax were similar in both groups. However, the time spent under general anesthesia was longer in the OSCT group than in the TSCT group. (4) Conclusions: The OSCT-guided approach to localize pulmonary nodules in hybrid operation room is a safe and effective technique for the surgical removal of small lung nodules.

The objective of this study is to preliminarily evaluate a new CT-biopsy guidance device, an origami needle guide. The device is created by laser cutting the structure from a sheet of cardboard, 3D printing two radiocontrast agent grids on to the surface and folding the structure into a rectangular prism with a viewing window. An abdominal imaging phantom was used to evaluate the device through CT imaging and the targeting of lesions for needle insertion. The lesion targeting trials resulted in a mean targeting error of 1.88 mm with a standard deviation of 0.73 mm. The device attaches to the patient and is rigid enough to adequately support standard biopsy needles, reducing the effect of gravity and the risk of laceration by the needles, making it potentially advantageous for biopsy of superficial lesions and lesions approached from a horizontal orientation. The device supports insertion of multiple needles at once, making it particularly suitable for composite ablation using multiple needles. Another advantage of the device is that it can guide off-axial needle insertion. The low-cost and disposability of the device make it well-suited for the minimally invasive image guided therapy environment.

Transcutaneous injection laryngoplasty is a well-known procedure for treating paralyzed vocal fold by injecting augmentation material into it. Hence, vocal fold localization plays a vital role in the preoperative planning as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcome in general anatomic landmark localization problem in the recent years. However, such methods suggest training individual agent for localizing each fold, though the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both the folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session which utilizes the inter-fold correlation and avoids redundant feature learning. Experiment with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.

Extracellular vesicles represent a large heterogeneous class of near and long-distance intercellular communication mediators, released by both prokaryotic and eukaryotic cells. Specifically, the scientific community has shown a growing interest in exosomes, which are nano-sized vesicles with an endosomal origin. Not so long ago, the physiological goal of exosome generation was largely unknown and required more investigation; at first, it was hypothesized that exosomes are able to remove excess, rejects and unnecessary constituents from cells to preserve cellular homeostasis. However, thanks to recent studies, the central role of exosomes in regulating cellular dialogue has emerged. Exosomes act as vectors on cell-cell signaling by their cargo, proteins, lipids, and nucleic acids, and influence physiological and pathological processes. The findings on exosomes are widespread in a large spectrum of biomedical applications from diagnosis and prognosis to thera-pies. In this review, we describe exosome biogenesis and the current methods for their isolation and characterization, emphasizing the role of their cargo in female reproductive processes, from gametogenesis to implantation, and the potential involvement in human female disorders.

Two-dimensional ultrasound (2D US) is commonly used in clinical settings for its cost-effectiveness and non-invasiveness, but it is limited by spatial orientation and operator dependency. Three-dimensional ultrasound (3D US) overcomes these limitations by adding a third dimension and enhancing integration with other imaging modalities. Advances in deep learning (DL) have further propelled the viability of freehand image-based 3D reconstruction, broadening clinical applications in intraoperative and point-of-care (POC) settings. This review evaluates state-of-the-art freehand 3D US reconstruction methods that eliminate the need for external tracking devices, focusing on experimental setups, data acquisition strategies, and reconstruction methodologies. PubMed, Scopus, and IEEE Xplore were searched for studies since 2014 following PRISMA guidelines, excluding those using additional imaging or tracking systems other than inertial measurement units (IMUs). Fourteen eligible studies were analyzed, showing a shift from traditional speckle decorrelation towards DL-based methods, particularly Convolutional Neural Networks (CNNs). Variability in datasets and evaluation methods hindered a comprehensive quantitative comparison, but notable accuracy improvements were observed with IMUs and integration of contextual and temporal information within CNNs. These advancements enhance freehand 3D US reconstruction feasibility, though variability limits definitive conclusions about the most effective methods. Future research should focus on improving precision in complex trajectories and adaptability across clinical scenarios.

This review article is focused on the analysis of the state of the art of sensors for guided 9 ultrasonic waves for the detection and localization of impacts, therefore of interest for the structural 10 health monitoring (SHM). The recent developments in sensor technologies are then reported and 11 discussed through the many references in recent scientific literature. The physical phenomena re-12 lated to impact event and the main physical quantities are then introduced to discuss their im-13 portance in the development of the hardware and software components for SHM systems. An im-14 portant aspect of the article is the description of the different ultrasonic sensor technologies cur-15 rently present in the literature and what advantages and disadvantages they could bring, in relation 16 to the various phenomena investigated. In this context, the analysis of the front-end electronics is 17 deepened, the type of data transmission both in terms of wired and wireless technology and in terms 18 of online and offline signal processing. The integration aspects of sensors for the creation of net-19 works with autonomous nodes with the possibility of powering through energy harvesting devices 20 and the embedded processing capacity is also studied. Finally, the emerging sector of processing 21 techniques using deep learning and artificial intelligence concludes the review by indicating the 22 potential for the detection and autonomous characterization of the impacts.

Mitochondrial DNA copy number (mtDNAcn) is associated with mitochondrial function contributing various diseases. Understanding of the association between mtDNAcn and infant neurodevelopment, as well as the role of racial disparities, remains unknown. A cohort study was conducted with 55 preterm infants and a single blood sample was collected from each infant during their NICU stay to measure mtDNAcn. NICU Network Neurobehavioral Scale (NNNS) and Bayley Scale of Infant Developmental testing Edition III was assessed during NICU until 2 years of corrected age (CA). Linear regression models were performed to investigate the relationship between the clinical characteristics, neurobehavioral outcomes and mtDNAcn. Majority of our subjects were male, White, non-Hispanic, had C-section, and without preterm premature rupture of membrane (PPROM). Increased mtDNAcn was found associated with younger birth gestational age (GA), nonoptimal neurodevelopment. However, the opposite associations between mtDNAcn and neurodevelopmental outcomes were observed between Black and White infants up to 1 year of CA. Adverse early life experience, together with increased mtDNAcn in White infants, and decreased mtDNAcn in Black infants may be considered as significant positive predictors of poor early life neurodevelopmental outcomes in infants.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

This review provides an overview of eXplainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between March 25 and April 14 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post-hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.

Background: To assess effectiveness, safety of computed tomography (CT)-guided cryoablation for treating adrenal metastases (AMs). Materials and Methods: This study involved 12 patients treated with 13 CT-guided cryoablation procedures for AMs between 2016 and 2020. Patients’ ineligible for surgery, with tumors ≤5 cm, and no significant health risks were included. The procedures, conducted by expert radiologists, included initial non-contrast CT scans to determine lesion morphology, followed by cryoablation with monitoring for complications. Post-treatment, patients underwent regular physical and laboratory checks, including hormonal analysis, and CT scans at specified intervals to assess the treatment's effectiveness and detect disease recurrence or progression. Results: In this study, 12 patients with AMs successfully underwent cryoablation, achieving a primary technical success rate of 91.7% and a secondary rate of 100% after a repeat procedure for one patient. All cryoablation sessions were completed without major complications, with ice ball formations extending 1 cm beyond tumor margins to ensure safety. Over an 8–24-month follow-up (average 16.3 months), six patients required further systemic therapy, with three developing extra-adrenal tumors. Local progression was noted in two patients (16.7%), one of whom underwent a repeat cryoablation. Systemic progression was observed in five patients, involving renal and NSCLC cancer recurrences and bone metastasis. The study reported five deaths due to tumor progression and heart failure, with an average overall survival duration of 32.4 ± 7.6 months.

The popularity of handheld devices for point-of-care ultrasound (POCUS) has increased in recent years due to their portability and cost-effectiveness. However, POCUS has the drawback of lower imaging quality compared to conventional ultrasound, because of hardware limitations. Improving the quality of POCUS through post-image processing would therefore be beneficial, with deep learning approaches showing promise in this regard. This review investigates the state-of-the-art progress of image enhancement using deep learning suitable for POCUS applications. A systematic search was conducted from January 2024 to February 2024 on PubMed and Scopus. From the 457 articles that were found, the full text was retrieved for 69 articles. From this selection, 15 articles were identified addressing multiple quality enhancement aspects. A disparity in the baseline performance of the low-quality input images was seen across these studies, ranging between 8.65–29.24 dB for the Peak Signal-to-Noise Ratio (PSNR) and 0.03-0.71 for the Structural Similarity Index Measure (SSIM). In six studies, where both PSNR and the SSIM metrics were reported for the baseline and the generated images a mean difference of 6.60 (SD ± 2.99) and 0.28 (SD ± 0.15) was observed for the PSNR and SSIM, respectively. The reported performances demonstrate the potential of deep-learning-based image enhancement for POCUS. However, variability in the extent of performance gain across datasets and articles was notable, and the heterogeneity across articles makes quantifying the exact improvements challenging.

53% of adults in the US acknowledge racial bias as a significant issue, 23% of Asian adults experience cultural and ethnic bias, and more than 60% conceal their cultural heritage after racial abuse. AI models like ChatGPT and Google Bard, trained on historically biased data, inadvertently amplify racial and ethnic bias and stereotypes. This paper addresses the issue of racial bias in AI models using scientific, evidence-based analysis and auditing processes to identify biased responses from AI models and develop a mitigation tool. The methodology involves creating a comprehensive database of racially biased questions, terms, and phrases from thousands of legal cases, Wikipedia, and surveys, and then testing them on AI Models and analyzing the responses through sentiment analysis and human evaluation, and eventually creation of an 'AI-BiasAudit,' tool having a racial-ethnic database for social science researchers and AI developers to identify and prevent racial bias in AI models.

3D-printed models with high anatomic fidelity are an increasingly viable tool in simulation-based medical education. One advantage of 3D models is they provide enhanced tactile and spatial understanding of complex anatomy to develop technical skills used in minimally invasive procedures. We propose that 3D anatomical models can improve the development of Interventional Radiology vascular access skills – first described in the 1950s as the Seldinger Technique – for pre-clerkship medical students. Early adoption of 3D-printed technology in pre-clinical medical education can lead to improved student engagement and satisfaction when learning procedural techniques. This study involved creating a 3D model of the upper extremity vasculature from an anonymized CT angiogram, using it as a medical education tool for 31 pre-clinical medical students practicing the Seldinger Technique on a prefabricated venipuncture arm, and assessing student satisfaction with this form of learning. Overall, attendees responded positively to the incorporation of the 3D model in medical education to improve their anatomic understanding and application to the Seldinger Technique. These results indicate that the use of 3D models in simulation-based medical education can provide benefits to acquiring technical skills and the potential to decrease training costs without harming a patient.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

Motivation is essential in education, with highly motivated learners engaging more deeply with content and more ably transferring knowledge to new contexts. However, the validity of scales to measure motivation has been underexplored in pre-clerkship medical education. This study evaluates the validity of the Situational Motivation Scale (SIMS) for measuring motivation among pre-clerkship medical students in post-situational and short-term contexts. Using a sample of n = 156 students from the Frank H. Netter MD School of Medicine at Quinnipiac University, SIMS was tested for construct validity with attention paid to content, response process, internal structure, relationships to other variables, and consequences of use evidence. Small modifications from present to past tense in English were made for clarity following focus group feedback, and content validity was ensured via expert consultation. SIMS demonstrated strong internal consistency with a satisfactory Cronbach’s alpha for all subscales and anticipated patterns of correlations. Factor analysis confirmed appropriate factor loadings, with a stronger model fit for the short-term context, and no observed adverse effects on student engagement. These findings support the robustness of SIMS in capturing intrinsic, extrinsic, and amotivation in pre-clerkship medical students, highlighting its applicability for short-term and situational motivational assessment.

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Background: SARS-CoV-2 Omicron JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation. The immune evasion capability of JN.1 is a subject of scientific investigation. The US CDC used RT-qPCR SGTF as the proxy indicator of JN.1 infections for evaluation of the effectiveness of updated monovalent XBB.1.5 COVID-19 vaccines against JN.1 and recommended that all persons aged ≥6 months should receive an updated COVID-19 vaccine dose. Objective: Recommend Sanger sequencing for diagnosis of JN.1 infections to generate the data based on which guidelines are made to direct vaccination policies. Methods: The RNA in nasopharyngeal swab specimens from patients with clinical respiratory infection was subjected to nested RT-PCR, targeting a 398-base segment of the N-gene and a 445-base segment of the RBD of SARS-CoV-2 for amplification. The DNA sequences were analyzed for amino acid mutations. Results: The N-gene sequence showed Q229K, usually associated with Omicron BA.2.86 (+JN.1). The RBD sequence showed 24 amino acid mutations, including the hallmark L455S mutation for JN.1 and the V483del for BA.2.86 lineage. Conclusions: The CDC should consider Sanger sequencing of the RBD to diagnose JN.1 infections for statistical analysis in making vaccination policies.

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as Smart Dust. Several efforts focused primarily on computing and networking capabilities but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data was processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc, an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 meters in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

Weight-bearing computed tomography (WBCT) enables acquisition of three dimensional bony structure images in a physiological weight-bearing position, which is fundamental in under-standing the pathologic lesions and deformities of the ankle joint. Over the past decade, re-searchers focused on validating and developing WBCT measurements, which has significantly enhanced our knowledge of common foot and ankle disease. Consequently, understanding the application of WBCT in clinical practice is becoming more important to produce improved out-comes in the treatment of disease around the ankle joint. This review will describe an overview of what is currently being evaluated in the foot and ankle surgery using WBCT and where the course of research will be heading in the future.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcel-lular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

The objective of this study was to develop and evaluate artificial intelligence (AI) models for the detection and instance segmentation of vertebrae and spinal metastases in computer tomography (CT) scans. The models were trained on datasets consisting of patients with polytrauma and relatively undamaged spines, as well as patients diagnosed with spinal metastases. Our results indicate that the models achieved high performance in vertebra segmentation, with F-beta scores ranging from 0.88 to 0.96 across all vertebra classes. For spinal metastases, the model attained F-beta scores of 0.68 for lytic type and 0.57 for sclerotic type metastases. Additionally, the models demonstrated the capability to detect isolated metastatic nodes in other bones, highlighting their robustness and potential for broader clinical application. Despite these promising results, the study faced limitations including a relatively small and homogeneous dataset, variability in segmentation mask quality, and the need for real-world clinical validation. Future clinical trials are necessary to evaluate the practical utility and effectiveness of these AI models in improving patient outcomes. Our study emphasizes the potential of AI-assisted detection and segmentation models in enhancing diagnostic accuracy and efficiency in clinical practice.

Owing to the nondiffracting, self-accelerating, and self-healing properties, Airy beams of different nature have become a subject of immense interest in the past decade. Their interesting properties have opened doors to many diverse applications. Consequently, the questions of how to properly design spatial manipulation of Airy beams or how to implement them in different setups have become important and timely in the development of various optical devices. Here, based on our previous work, we present a short review on the spatial control of Airy beams, including the interactions of Airy beams in nonlinear media, beam propagation in harmonic potential, and the dynamics of abruptly autofocusing Airy beams in the presence of a dynamic linear potential. We demonstrate that under the guidance of nonlinearity and external potential, the trajectory, acceleration, structure, and even the basic properties of Airy beams can be adjusted to suit specific needs. We describe other fascinating phenomena observed with Airy beams, such as self-Fourier transformation, periodic inversion of Airy beams, and the appearance of spatial solitons in the presence of nonlinearity. These results have promoted the development of Airy beams, and have been utilized in various applications, including particle manipulation, self-trapping, and electronic matter waves.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

Introduction Blunt carotid injuries (BCI) in pediatric trauma are quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/ BCVI on pediatric patients after blunt trauma is, what routine diagnostics look like, if a CT/ CTA scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects? Methods This literature review includes reviews, systematic reviews, case reports and original studies in english language between 1999 and 2020 that have dealt with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure on children were included in the study. For literature research, Medline (PubMed) and the Cochrane library were used. Results Pediatric BCI/ BCVI, shows an overall incidence between 0.03 – 0.5% of confirmed BCI/ BCVI cases due to pediatric blunt trauma. 1.1 – 3.5% of pediatric blunt trauma patients underwent CTA to detect BCI. Only 0.17 – 1.2% of all CTA scans shows a positive diagnosis for BCI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligray, a computed tomography angiography needs at least 138 mGy. A cumulative doses of about 50 mGy almost triples the risk of leukemia, and doses of about 60 mGy triples the risk of brain cancer. Discussion Knowing that a BCI could have extensive neurologic consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. Computed tomography scans (CT) and computed tomography angiography (CTA) are mostly used in routine BCI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are reflect established BCI screening options, as well as using duplex ultrasound.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Lung involvement in rheumatic diseases is a major determinant of patient morbidity and mortality; however, its diagnosis depends on the detection methods used. We compared the incidence of pulmonary involvement in newly diagnosed treatment-naive patients with rheumatic diseases, as assessed by pulmonary function tests (PFTs) and chest high-resolution computed tomography (HRCT). This retrospective study reviewed the symptoms, spirometry results, and HRCT scans of 22 newly diagnosed, treatment-naïve patients and age-matched controls from the University General Hospital “Attikon”, Athens, Greece, between January 2021 and December 2023. Correlations between clinical findings and spirometry parameters were tested. HRCT scans revealed lung abnormalities in all patients, including peribronchial wall thickening (61.5%), ground glass opacities (59.1%), parenchymal bands (54.5%), air-trapping (50%), reticular patterns (45.5%), bronchiectatic changes (40.9%), and parenchymal opacities (31.8%). PFTs, performed in 17/22 (77.3%) patients, revealed definite pathology in 5 patients (restrictive: N=4; obstructive: N=1). No association was found between spirometric indices and clinical parameters. The results indicate a higher prevalence of abnormal chest CT than of PFT abnormalities, suggesting that HRCT is more sensitive in detecting early lung involvement in pediatric patients with rheumatic disease and should be considered in the routine evaluation of these patients.

Penile cancer (PeCa) is a rare urological malignancy characterized by significant geographic variations in both incidence and mortality rates. Due to its rarity and the consequent lack of randomized trials, current management is based on retrospective studies and small prospective trials. In addition, the diagnostic pathways and treatment strategies exhibit substantial heterogeneity, differing significantly between less developed and well-developed countries. The prognosis of PeCa is determined by the presence and extent of regional lymph node (LN) involvement. Therefore, early detection and treatment of LN metastasis is paramount to ensure better outcomes. In recent decades, the overall survival for PeCa has increased, mainly due to advancements in imaging techniques and risk stratification. We aim to provide an overview of the current role of PET CT imaging in the management of patients with PeCa.

Several bio-actors are involved in the occurrence of leishmaniasis, which is made too complex for prevention and control options. Currently, all forms of leishmaniasis are being treated with chemical drugs, which have limitations and adverse effects on patients. Searching the bioactive molecules from plant sources allows the development of a novel drug. The essential oil and solvent extract of the roots of E. kebericho showed antileishmanial activity. But looking for biological activities of the isolated active ingredient from tubers of E. kebericho was scant. Thus, the isolation of the leishmanicidal compound from the roots of E. kebericho through a bioassay-guided technique was an aim of this work. Dehydrocostus lactone showed doubled potent than the standard drug miltefosine on leishmanicidal activities against L. major and L. tropica. Besides, the broad-spectrum biological activities of dehydrocostus lactone could be a potential drug candidate for co-infected patients caused by an association of leishmania and another pathogen.

Accurate prostate volume estimation is crucial for effective prostate disease management. Ultrasound (US) imaging, particularly transrectal ultrasound, offers a cost-effective and rapid assessment. However, US images often suffer from artifacts and poor contrast, making prostate volume estimation challenging. This review explores recent advancements in deep learning (DL) techniques for automatic prostate segmentation in US images as a primary step for prostate volume estimation. We examine various DL architectures, including traditional U-Net modifications and innovative designs incorporating residual connections, multi-directional image data, and attention mechanisms. Additionally, we discuss pre-processing methods to enhance image quality, the integration of shape information, and strategies to improve the consistency and robustness of DL models. The effectiveness of these techniques is evaluated through metrics such as Dice Similarity Coefficient, Jaccard Index, and Hausdorff Distance. The review highlights the potential of DL in improving prostate volume estimation accuracy and reducing clinical workload, while also identifying areas for future research to enhance model performance and generalizability.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Oritavancin is a novel long-acting lipoglycopeptide with in vitro activity against methicillin-resistant (MR) Gram-positive pathogens and a good bactericidal activity even in presence of biofilm forming bacteria. It has been approved for acute bacterial skin and skin structure infections (ABSSSI), but recent reports have demonstrated possible off-label uses, as for prosthetic joint infections (PJI), which, in more than half of cases, are caused by MR Gram positive organisms. We reported a case of a man in his eighties with a late shoulder PJI caused by methicillin resistant Staphyloccus epidermidis (MRSE) with contraindications for surgical replacement and few oral therapeutic options for a long term suppressive antibiotic therapy. The prosthesis was retained, and the patient received ten outpatient sequential doses of 1200 mg of oritavancin during 28 weeks, based on therapeutic drug monitoring (TDM) as a guide for correct timing of administration of each dose. During oritavancin administration, the patient achieved clinical cure, with disappearance of the pain and regaining pre-infection joint mobility, with no side effects reported and no further surgery or hospitalization needed. The treatment is ongoing as a long-lasting suppressive antimicrobial therapy. Oritavancin could represent an excellent solution for treating PJI caused by MR organism, especially in patients who need a long-term suppressive therapy.

Computed tomography (CT) is a widely employed imaging modality with diverse clinical applications in pediatric and adult populations, offering numerous advantages. Despite several alternatives to imaging modalities with ionising radiation, CT scans have demonstrated persistent global popularity, contributing to a substantial amount of effective radiation doses for patients. This exposure results in hazards, including stochastic effects that amplify the radiation-induced cancer risk proportionally to the radiation dose received. CT-related radiation comprises a substantial portion of cumulative effective doses within diagnostic radiology, specifically impacting younger patient cohorts undergoing repeated investigations for chronic diseases over time. Scientific literature over recent decades has explored various methods to mitigate radiation doses during medical imaging. One notable strategy involves implementing low-dose and ultralow-dose CT protocols, maintaining diagnostic efficacy in acute and non-acute medical and surgical conditions. Utilising innovative image reconstruction algorithms, such as iterative reconstruction and emerging deep-learning algorithms based on artificial intelligence, plays a pivotal role in achieving an approximately 80% reduction in patient radiation compared to standard doses. This review article discusses the significance of low-dose protocols across a spectrum of acute and chronic medical conditions affecting different body systems.

Scaffold guided breast tissue regeneration (SGBTR) can both transform reconstructive and cosmetic breast surgery. Implant-based surgery is the most common method. However, there are inherent limitations as there is replacement of tissue rather than regeneration. Regenerating autologous soft tissue has the potential to provide a more like-for-like reconstruction with minimal morbidity. Our SGBTR approach regenerates soft tissue by implanting additively manufactured bioresorbable scaffolds filled with autologous fat graft. A pre-clinical large animal study was conducted implanting 100 ml breast scaffolds (n=55) made from medical-grade polycaprolactone in 11 minipigs for 12 months. Various treatment groups were investigated where immediate or delayed autologous fat graft, as well as platelet rich plasma were added to scaffolds. Computed Tomography and Magnetic Resonance Imaging was performed on explanted scaffolds to determine volume and distribution of regenerated tissue. Histological analysis was performed to confirm tissue type. At 12 months we were able to regenerate and sustain a mean soft tissue volume of 60.9 4.5 ml (95% CI) across all treatment groups. There was no evidence of capsule formation. There were no immediate or long-term post-operative complications. In conclusion, we were able to regenerate clinically relevant soft tissue volumes utilizing SGBTR in a pre-clinical large animal model.

: In this paper we present an optimization of the planar manufacturing scheme for stretch-free, shape induced metal interconnects to simplify the fabrication with the aim of maximizing the flexibility in the structure regarding to stress and strain. The Formation of trenches between silicon islands are actively used in the lithographic process to create arc shape structures by spin coating resist into the trenches. The resulting resist form is used as a template for the metal lines, which are structured on top. Because this arc shape is beneficial for the flexibility of these bridges. The trench depth as a key parameter for the stress distribution is investigated by applying numerical simulations. The simulated results show that the increase in penetration depth of the metal bridge into the trench increases the tensile load which is converted into a shear Force Q(x), that usually leads to increased strains the structure can generate. For the fabrication the filling of the trenches with resist is optimized by varying the spin speed. Compared to the theoretical resistance, the current-voltage measurements of the metal bridges show a similar behavior and almost every structural variation is capable of functioning as a flexible electrical interconnect in a complete island-bridge array.

Background: Glioma surgery was remarkably renewed in the past 2 decades, with improved safety and limited but yet improved life expectations. Fluorescence-guided resection of high-grade gliomas (HGG) plays a central role in this sense, by allowing higher degrees of extent of resection (EOR). The introduction of the exoscopic guided surgery may be considered an implementation for fluorescence techniques over the traditional microscopes. We present the application and the advantages of the exoscopic guided surgery compared to the microscopic surgery in tumor resection guided by 5-ALA fluorescence in patients with HGG. Methods: ten consecutive patients underwent a surgery for HGG resection. The surgery was performed via an exoscopic-guided procedure (Olympus Orbeye) and after oral administration of Gliolan 5 hours before the procedure. During surgery, the procedure was shifted to microscope (Kinevo 900, Zeiss) view. The intensity of the fluorescence under the two different was subjectively measured in different picture samples during the surgery in a 1 to 5 (from minimum to maximum) scale. The brightness of the surgical field and the detailing of the anatomy were also analyzed comparatively. Results: among the ten patients the histopathological diagnosis was an high grade glioma in all cases. In 9 cases was possible to achieve the gross total resection. There was no perioperative mortality. The median fluorescence intensity, on a scale of 1–5, was 4.5 in the exoscope group and 3.5 in the microscope group (p<0.01). Conclusions: The exoscopic guided surgery adds advantages to the traditional fluorescence- guided surgery with 5-aminolevulinic acid. Beyond the important advantage of low cost and the possibility to perform collaborative surgeries, it adds a plain and continuous visualization of the tumor and the surgical field in the fluorescence guided glioma surgery compared to the microscopic guided one.

Abstract: Giant Cell Arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial-GCA and Large Vessel Vasculitis (LV)-GCA, involving the thoracic aor-ta and its branches. 18F-fluorodeoxyglucose positron emission tomography-computed tomogra-phy (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosis of patients with systemic disease. However, its association with soluble inflammatory markers is still elu-sive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT pos-itive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The Most-Disease-Segment Target-to-Background Ratio (TBRMDS) was calculated for 9 LV-GCA patients, while 12 cranial-GCA and 7 Polymyalgia Rheumatica patients with negative initial PET/CT served as disease controls. Serum macrophage-related cytokines were evaluated by Cytometric Bead Array (CBA). Finally, previously published NMR-metabolomics data acquired at the same blood sampling were associated with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.45 vs. 2.55, p=0.008). The analysis identified 6 serum metabolites, as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group, but not the disease controls. Cytokine levels were not associated with PET/CT activity. Combining with CRP, ESR, and TBRMDS a composite index was generated to depict well the differences between active and inactive systemic LV-GCA (25.45 vs 11.45, p=0.0039). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for the everyday clinical practice.

Ultrasound imaging is a vital imaging tool in musculoskeletal medicine, with the number of publications on ultrasound-guided surgery increasing in recent years. However, ultrasound imaging has drawbacks, such as operator dependency and image obscurity. Artificial Intelligence (AI) and Deep Learning (DL), a subset of AI, can address these issues. DL methods, including segmentation, detection, and localization of target tissues and medical instruments, potentially allow physicians and surgeons to perform ultrasound-guided procedures more accurately and efficiently. This review summarizes recent advances in ultrasound-guided procedures for musculoskeletal diseases and provides a comprehensive overview of the utilization of AI/DL in ultrasound for musculoskeletal medicine, particularly focusing on ultrasound-guided surgery.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter row environments for agricultural machinery. The paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines is less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on four zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results show that within a range of 6m, the average relative error of distance measurement does not exceed 1%, and within a range of 9m, the maximum relative error is 2.03%. The average distance measurement time is 30 milliseconds, which can accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduces the RMSE index of absolute trajectory error compared to ORB-SLAM2 algorithm, which is less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 has a higher degree of agreement between the estimated trajectory and the true trajectory when the car is traveling in straight and circular directions. The RMSE index of the absolute trajectory error is less than 0.03m/s, and the average tracking time is 47ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper can meet the accuracy and real-time requirements of agricultural machinery positioning in orchard environments.

Organised hiking and trekking tours are promoted by many providers in adventure travel. Such guided tours shall be engaging for the tourists and evoke positive attitudes towards enjoying nature. We present the Trekking Engagement Profile as a tool to evaluate engagement factors of such guided tours, based on engagement research performed in a different domain, namely evaluating engagement in museums and science centres. This tool shall strengthen guides’ soft skills and enable them to increase engagement during an ongoing arrangement. For instance, the Trekking Engagement Profile can be applied when tourists indicate that the quality of a guided tour is not entirely to their liking. We show the viability of using the Trekking Engagement Profile in a small study with guides and participants of trekking tours offered by the Norwegian Trekking Association. Further, we offer guidance and charts to promote its practical use.

The trigeminal nerve is responsible for innervating the periorbita. Ultrasound-guided trigeminal block is employed in humans for trigeminal neuralgia or periorbital surgery. There are no studies evaluating this block in dogs. This study aims to evaluate and compare two approaches (coronoid and temporal) of the trigeminal nerve block. We hypothesized superior staining with the coronoid approach. Thirteen dog heads were used. After a preliminary anatomical study, two ultrasound-guided injections per head (right and left, coronoid and temporal approach, randomly assigned), with an injectate volume of 0.15 mL cm-1 of cranial length were performed (iodate contrast and tissue dye mixture). The ultrasound probe was placed over the temporal region, visualizing the pterygopalatine fossa. For the temporal approach, the needle was advanced from the medial aspect of the temporal region in a dorsoventral direction. For the coronoid approach, it was advanced ventral to the zygomatic arch in a lateromedial direction. CT scans and dissections were conducted to assess and compare the position of the needle, spread of injectate, and nerve staining. No significant differences were found. Both approaches demonstrated effective interfascial distribution of injectate, with some minimal intracranial spread. Although the coronoid approach did not yield superior staining as hypothesized, it presents a viable alternative to the temporal approach. Studies in live animals are warranted to evaluate clinical efficacy and safety.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

G-protein coupled receptors (GPCRs), one of the largest superfamilies of cell-surface receptors, are heptahelical integral membrane proteins that play critical roles in virtually every organ system. G-protein-coupled receptors operate in membranes rich in cholesterol, with an imbalance in cholesterol level within the vicinity of GPCR transmembrane domains affecting the structure and/or function of many GPCRs, a phenomenon that has been linked to several diseases. These effects of cholesterol could result in indirect changes by altering the mechanical properties of the lipid environment or direct changes by binding to specific sites on the protein. There are a number of studies and reviews on how cholesterol modulates class A GPCRs, however, this area of study is yet to be explored for class C GPCRs, which are characterized by a large extracellular region and often form constitutive dimers. This review highlights specific sites of interaction, functions, and structural dynamics involved in the cholesterol recognition of the class C GPCRs. We summarize recent data from some typical family members to explain the effects of membrane cholesterol on the structural features and functions of Class C GPCRs and speculate on their corresponding therapeutic potential.

Hantaviruses are enveloped viruses that possess a tri-segmented, negative-sense RNA genome. The viral S-segment encodes the multifunctional nucleocapsid protein (N), which is involved in genome packaging, intracellular protein transport, immunoregulation and several other crucial processes during hantavirus infection. In this study we have generated fluorescently tagged N protein constructs derived from Puumalavirus, the dominant hantavirus species in Central, Northern and Eastern Europe. We have comprehensively characterized this protein in the rodent cell line CHO-K1, monitoring the dynamics of N protein complex formation and investigating co-localization with host proteins as well as the viral glycoproteins Gc and Gn. We found a significant spatial correlation of N with vimentin, actin and P-bodies, but not with microtubules. N constructs also co-localized with Gn and Gc, albeit not as strong as the glycoproteins associated with each other. Moreover, we as-sessed oligomerization of N constructs, observing efficient and concentration-dependent multi-merization, with complexes comprising more than 10 individual proteins.

We introduce quantum spatio-temporal dynamics (QSD) as modeled by the Nexus Paradigm (NP) of quantum gravity to resolve the problem of energy- momentum localization in a gravitational field. Currently, the gravitational field as described using the language of geometry modeled under General Relativity (GR) fails to provide a generally accepted definition of energy-momentum. Attempts at resolving this problem using geometric methods have resulted in various energy-momentum complexes whose physical meaning remain dubious since the resulting complexes are non-tensorial under a general coordinate transformation. In QSD, the tangential manifold is the affine connection field in which energy-momentum localization is readily defined. We also discover that the positive mass condition is a natural consequence of quantization and that dark energy is a Higgs like field with negative energy density everywhere. Finally, energy-momentum localization in quantum gravity shows that a free falling object will experience large vacuum fluctuations (uncertainties in location) in strong gravity than in weak gravity and that the amplitudes of these oscillations defines the energy of the free falling object.

Guided image filter implicitly balances noise and details based on local variance, achieving noise suppression while preserving prominent edges. However, we have observed limitations in using variance to distinguish between noise and image details, resulting in the suppression of fine structures alongside noise. Furthermore, the goal of enhancing image details, which is crucial in image filtering, has not been adequately addressed in edge-preserving filter design. In this paper, we approach image filtering as a problem of spectral energy reconfiguration, where the gain of each spectral band is adaptively regulated to achieve simultaneous noise suppression and detail enhancement. Instead of relying on variance, we employ a linear noise estimator to obtain the Signal-to-Noise Ratio (SNR) measurement necessary for dynamic gain regulation. Similar to the guided image filter, we determine the filter coefficients by solving the closed-form solution of the optimizer. We demonstrate that the proposed extended guided filter effectively integrates information from multiple spectral bands locally, allowing for the joint enhancement of meaningful details while simultaneously smoothing noise.

Image synthesis poses a challenging problem that researchers in computer vision and machine learning have been grappling with for several decades. Numerous machine learning techniques have emerged and proven effective in generating high-fidelity artificial images. This study breaks new ground by exploring image synthesis through generative learning using the D-Wave 2000Q quantum annealer, marking the first attempt to address the issue of generative image synthesis on a Quantum Processing Unit (QPU). Alongside executing image synthesis on the quantum annealer, this research also compares its performance with existing classical models and delves into resolving the Generative Learning Trilemma.

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.

Synchrotron radiation computed tomography (SRCT) allows more accurate calcified plaque and coronary stenosis assessment as a result of its superior spatial resolution, however, typical micro-computed tomography (micro-CT) systems have even higher resolution. The purpose of this study was to compare performance of high-resolution micro-CT with SRCT in the assessment of calcified plaques and a previously published dataset of coronary stenosis assessment. This experimental study involved micro-CT scanning of three-dimensional printed coronary artery models with calcification in situ used in our previously published SRCT study on coronary stenosis assessment. Measurements of coronary stenosis utilizing both modalities were compared using a paired sample t-test. The degrees of stenosis measured on all but one micro-CT dataset were statistically significantly lower than the corresponding SRCT measurements reported in our previous paper (p&lt;0.0005-0.05). This indicates that the superior spatial resolution of micro-CT was able to further reduce over-estimation of stenosis caused by extensive calcification of coronary arteries and hence false positive results. This study shows that high-resolution micro-CT outperforms SRCT in both calcified plaque and coronary stenosis assessment. This finding will become clinically important for cardiovascular event prediction and enable reclassification of individuals with low and intermediate risk into appropriate risk categories when the technical challenges of micro-CT in clinical practice such as the small field of view and demanding on image processing power are addressed.

Clustered regularly interspaced short palindromic repeats (CRISPR) based gene-editing has begun to transform the treatment landscape of genetic diseases. The history of the discovery of CRISPR/CRISPR-associated (Cas) proteins/single guide RNA (sgRNA)-based gene-editing since the first report of repetitive sequences of unknown significance in 1987 is fascinating, instructive, and inspiring for future advances. The recent approval of CRISPR-Cas9-based gene therapy to treat patients with severe sickle cell anemia and transfusion-dependent beta thalassemia has renewed hope for treating other hematologic diseases, including patients with germline predisposition to hematologic malignancies, who would benefit greatly from the development of CRISPR-based gene therapies. The purpose of this manuscript is three-fold: first, a chronological description of the history of CRISPR-Cas9-sgRNA-based gene editing; second, a brief description of the current state of clinical research in hematologic diseases, including selected applications in treating hematologic diseases with CRISPR-based gene therapy; and third, the current progress in gene therapies in inherited hematologic diseases and bone marrow failure syndromes, to hopefully stimulate efforts towards developing these therapies for patients with inherited bone marrow failure syndromes and other inherited conditions with germline predisposition to hematologic malignancies.

Quantum computers are invaluable tools to explore the properties of complex quantum systems. We show that dynamical localization of the quantum sawtooth map, a highly sensitive quantum coherent phenomenon, can be simulated on actual, small-scale quantum processors. Our results demonstrate that quantum computing of dynamical localization may become a convenient tool for evaluating advances in quantum hardware performances.

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

Abstract Background: Occipital neuralgia is a debilitating condition and traditional treatments often provide limited or temporary relief. Recently, ultrasound-guided hydrodissection of the greater occipital nerve (GON) has emerged as a promising minimally invasive approach. Objectives: To describe two novel ultrasound-guided hydrodissection with 5% dextrose for GON and discuss their advantages, disadvantages, and considerations. Methods: Two case reports are presented. Case 1 describes a lateral decubitus approach for hydrodissecting the GON between the semispinalis capitis (SSC) and obliquus capitis inferior (OCI) muscles. Case 2 details a cranial-to-caudal approach for hydrodissecting the GON within the SSC and upper trapezius (UT) muscles when the GON pass through these two muscles. Results: Both patients experienced significant and sustained pain relief with improvements in function and quality of life. Conclusions: Ultrasound-guided GON hydrodissection using 5% dextrose is a promising treatment for occipital neuralgia. The lateral decubitus and cranial to caudal approaches provide additional options to address patient-specific anatomical considerations and preferences.

This study aims to evaluate the patient’s dose exposure from Computed Tomography Pulmonary Angiography (CTPA) examination and to estimates the cancer risk induced from the examinations based on the patient’s size. One hundred patients were recruited, and data information was collected retrospectively. A multi-detector (MDCT) (Philips Brilliance 128, USA) scanner were utilized for the CTPA examination, and dose data were obtained from the system. The effective diameter of each subjects’ image was measured for the Size-specific dose estimates (SSDEs). All subjects divided into Group 1 (19 – 25 cm), Group 2 (25-28 cm) and Group 3 (28-38 cm), where the association between gender were analysed. Effective dose (E), SSDE, organ dose and cancer risk of each group were evaluated and compared statistically using independent t-test and one-way ANOVA. The range of mean CTDI_vol, DLP and E values were (6.44 – 17.42 mGy), (239 – 631 mGy), (5.19 – 13.90 mSv), respectively. In respective with the patient’s effective diameter, the mean SSDE value for Group 1, Group 2 and Group 3 were 9.93 ± 3.89 mGy, 13.70 ± 9.04 mGy and 22.29 ± 7.35 mGy. The organ dose and cancer risk attained for breast, lung and liver were 17.05 ± 10.40 mGy (194 per one million procedure), 17.55 ± 10.86 mGy (192 per one million procedure) and 15.04 ± 9.75 mGy (53 per one million procedure), respectively. Lung and breast with more massive patient’s effective diameter received the highest dose exposure which increases the probability of the cancer risk. CTDI_vol was found to be underestimated, and SSDE provides more accurate in describing the radiation dose and cancer risk. Body effective diameter found to be significant on the estimation except for gender. Therefore, it is essential to apply optimised protocols in order to reduce patient’s exposure during CTPA examination.

Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educative counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasability study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone unsupervised during their daily routine over a period of 4 months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in the tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of .408, while the improvements on the THI were much smaller with an effect size of .168. Analysis on the user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively, reported a stronger improvement of the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.

Photon-counting Computed Tomography (PCCT) is a promising cardiovascular-imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although PCCT is well established for cardiac imaging in assessing coronary artery disease, its application in vascular imaging remains limited. This review summarizes the available literature and provides an overview of the current use of PCCT for the diagnosis of vascular conditions. Of the 13 records selected in the initial search, 7 articles were included in the present review: 6 cohort studies and 1 case report. This review synthesized the available literature on PCCT in vascular imaging, focusing mainly on endoleaks detection and characterization after endovascular aneurysm repair (EVAR), contrast dose volume, and radiation exposure reduction, particularly in patients with chronic kidney disease and in those requiring follow-up CT.

Gene therapy is a medical approach capable of correcting genetic and epigenetic alterations in pathological conditions, including cancer. Different strategies have been pursued for cancer gene therapy, aiming to restore the function of tumor suppressor genes or inhibit oncogenes. These comprise the use of CRISPR/Cas9 gene editing technology and therapeutic oligonucleotides, such as microRNAs, anti-microRNAs, small interfering RNAs, DNAzymes, and epigenetic modifiers. However, despite great potential, their broad clinical use is hindered due to the lack of safe and efficient systems for targeted delivery to cancer cells. To this purpose, aptamers could represent an ideal tool. They are short single-stranded oligonucleotides that bind receptors selectively present or overexpressed on cancer cell membranes, often undergoing cell-specific internalization. Thanks to this last feature, numerous internalizing aptamers have been investigated as targeting moiety to deliver therapeutic oligonucleotides, viral vectors, and nanoparticle-based systems in preclinical studies. Here, we summarize the state-of-the-art of aptamer applications in cancer gene therapy, providing key examples and discussing their advantages, limitations, and clinical perspectives.

Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) is an alternative to surgery for acute cholecystitis (AC) in poor operative candidates. However, the role of EUS-GBD in non-cholecystitis (NC) indications has not been well studied. We compared clinical outcomes of EUS-GBD for AC and NC indications. Consecutive patients undergoing EUS-GBD for all indications at a single center were retrospectively analyzed. Fifty-one patients underwent EUS-GBD during the study period. Thirty-nine (76%) patients had AC while 12 (24%) had NC indications. NC indications included malignant biliary obstruction (n = 8), symptomatic cholelithiasis (n = 1), gallstone pancreatitis (n = 1), choledocholithiasis (n = 1), and Mirizzi’s syndrome (n = 1). Technical success was noted in 92% (36/39) for AC and 92% (11/12) for NC (p &gt; 0.99). Clinical success rate was 94% and 100%, respectively (p &gt; 0.99). There were 4 adverse events in the AC group and 3 in the NC group (p = 0.62). Procedure duration (median 43 vs 45 minutes, p = 0.37), post-procedure length of stay (median 3 vs 3 days, p = 0.97), and total gallbladder related procedures (median 2 vs 2, p = 0.59) were similar. EUS-GBD for NC indications is similarly safe and effective as EUS-GBD in AC.

In the last decade, the role of Computed Tomography Pulmonary Angiography (CTPA) in diagnosing and managing pulmonary embolism (PE) has significantly evolved. Once a purely diagnostic tool, CTPA now plays a crucial role in initial emergency department assessments and treatment planning tailored to the patient’s condition. Advances in CTPA technology, including image quality enhancements and artificial intelligence (AI) applications, necessitate a reassessment of its current utility. This narrative review updates on new CTPA tools and techniques and focuses on data extraction challenges in emergency settings. CTPA studies, often conducted with multidetector scanners, provide essential information for risk stratification and treatment planning. The quantification of thrombotic burden using CTPA is vital for predicting mortality and determining appropriate treatments. Classical scoring systems, such as those developed by Qanadli, Mastora, and Ghanima, convert CTPA findings into quantifiable data to assist clinical decision-making. Despite CTPA not being a standard mortality predictor, significant research suggests including CTPA data, especially RV dysfunction indicators, for prognostic purposes. Recent studies highlight automated techniques for quantifying pulmonary perfusion and thrombus composition, enhancing the accuracy of PE severity assessment. The integration of AI in CTPA, particularly through deep learning algorithms, shows promise in automating thrombus load assessment and improving risk stratification. The continuous development of imaging techniques positions CTPA as a potential tool for comprehensive PE management, aiding in diagnosis, prognosis, and treatment decisions.

Significant global challenges, particularly in the areas of economics, society, and the environment, are posed by urbanization. In this researcher-led study, we look at how Jordan is trying to bring the Sustainable Development Goals (SDGs) to life in its capital, Amman, by bringing urban laws in line with these global goals. The paper finds legislative flaws that prevent SDG incorporation through a thorough analysis of the Amman Building and Regulation Act of 2019. Some of the main points covered are public participation, the use of renewable energy sources, sustainable construction practices, resilience in urban areas, and the management of natural resources. Although a lot of work has gone into it, the analysis shows that if Jordan wants to accomplish the 2030 Agenda and have sustainable urban growth in the future, it must overcome these institutional and legal barriers. As a model for other cities facing a similar challenge, Amman is being asked to lead the way in SDG localization through more public involvement, new laws, and innovative resource management.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

In recent years, video anomaly detection technology , which can intelligently analyze massive video and quickly find abnormal phenomena, has attracted extensive attention with the wide application of video surveillance technology . To address the complex and diverse problem of abnormal human behavior detection in surveillance videos, a surveillance video abnormal behavior detection and localization supervised method based on the deep network model and the traditional method is proposed. Specifically, we combined AGMM and YOLACT methods to obtain more accurate foreground information by fusing the foreground maps extracted by each technique. To further improve the accuracy , we use the PWC-Net technique to extract features of the foreground images and input them into an anomaly classification model for classification. The proposed method effectively detects and locates the abnormal behavior in the monitoring scene. In addition to the aforementioned methods, this paper also employs YOLOV5 and DeepSORT networks for object detection and tracking in the video, which allows us to track the detected objects for better understanding of the scene in the video. Experiments on the UCSD benchmark dataset and the comparison with state-of-the-art schemes prove the advantages of our method.

With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, it is required an active monitoring of all passengers. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in hardware the sound localization algorithms.

The increasing detection of infections of Trypanosoma cruzi, the etiological agent of Chagas disease, in non-endemic regions beyond Latin America has risen to be a major public health issue. With an impact in the millions of people, current treatments rely on antiquated drugs that produce severe side effects and are considered nearly ineffective for the chronic phase. The minimal progress in the development of new drugs highlights the need for advances in basic research on crucial biochemical pathways in T. cruzi to identify new targets. Here, we report on the T. cruzi presenilin-like transmembrane aspartyl enzyme, a protease of the aspartic class in a unique phylogenetic subgroup with T. vivax separate from protozoans. Computational analyses suggests it contains 9 transmembrane domains and an active site with the characteristic PALP motif of the A22 family. Multiple linear B-cell epitopes were identified by SPOT synthesis analysis with Chagasic patient sera. Two were chosen to generate rabbit antisera, whose signal was primarily localized to the flagellar pocket, intracellular vesicles and endoplasmic reticulum in parasites by whole cell immunofluorescence. The results suggest that the parasitic presenilin-like enzyme could have a role in the secretory pathway and serve as a biomarker for infections.

The important characteristic that could assist in autonomous navigation is the ability of a mobile robot to concurrently construct a map for an unknown environment and localize itself within the same environment. This computational problem is known as Simultaneous Localization and Mapping (SLAM). In literature, researchers have studied this approach extensively and have proposed a lot of improvement towards it. More so, we are experiencing a steady transition of this technology to industries. However, there are still setbacks limiting the full acceptance of this technology even though the research had been conducted over the last 30 years. Thus, to determine the problems facing SLAM, this paper conducted a review on various foundation and recent SLAM algorithms. Challenges and open issues alongside the research direction for this area were discussed. However, towards addressing the problem discussed, a novel SLAM technique will be proposed.

Application of toxic antibacterial agents is considered necessary to control prevalent fresh water microorganisms in evaporative cooling water systems, but these agents can adversely affect the environment and human health. Alternatively, natural antibacterial water chemistry has been applied in industrial cooling water systems for over 10 years with excellent results. The tower water chemistry method concentrates natural salts in highly-softened water to produce elevated pH and dissolved solids, with low calcium and magnesium. This practice conserves water while generating only a small volume of non-toxic natural salt concentrate for cost efficient separation and disposal if required. This review presents a novel perspective of natural antimicrobial chemistry for inhibiting parasitic microbiome functional relationships within the bio-triad of Legionella outbreaks, "Trojan Protozoans" and biofilms. The review further examines practical application and function of polyvalent metal ions in the inhibition of biofilms. Reducing global dependence on toxic antibacterial agents discharged to the environment is an emerging concern due to their impact on the natural microbiome, plants, animals and humans. Discharge of antibacterial agents also contributes to development of pathogen resistance. Use of natural antibacterial chemistry can play a key role in managing the cooling water environment in a more ecologically sustainable manner.

Background The acknowledgment of computed tomography (CT) defined diagnosis in high prevalence northern Italy may identify more patients with Coronavirus Disease-2019 (COVID 19) infection, than RT-PCR alone. Methods We retrospectively reviewed 148 chest CT scans of oncological patients who were referred to the Radiological Unit of Policlinico S. Marco from 1st of February 2020 to 30th of April 2020, during the Covid-19 outbreak in Bergamo area. Therefore, we analyzed RT-PCR tests of these 148 patients. Results Among 32 patients with diagnosis of COVID-19 infection: 17 patients were asymptomatic or had mild symptoms (53.1%), while 15 developed severe disease (46.8%). The incidence of COVID-19 infection is 22.9%, the mortality rate is 18.8%. Severe COVID-19 disease is associated with higher median age. We did not find any correlation between disease severity and sex, smoke or cardiovascular comorbidities. Remarkably, patients who were on treatment developed milder disease than cancer patients who were not on treatment. Conclusions The acceptance of CT-defined diagnosis in high prevalence area like Bergamo highlighted a larger number of COVID-19 oncological population than RT-PCR alone, in particular asymptomatic and mild symptomatic patients. We observed that actively treated patients had milder disease, according to previous studies that suggested a protective role of immunosuppression.

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared to the traditional timestamp approach. We demonstrated that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

Sr2FeMoO6-δ (SFMO) double perovskite is a promising candidate for room-temperature spintronic applications since it possesses a half-metallic character (with theoretically 100% spin polarization), a high Curie temperature of about 415 K, and a low-field magnetoresistance (LFMR). However, due to different synthesis conditions of ceramics as well as thin films, different mechanisms of electrical conductivity and magnetoresistance prevail. In this work, we consider the weak localization effect in SFMO occurring in disordered metallic or semiconducting systems at very low temperatures due to quantum interference of back-scattered electrons. We calculate the quantum corrections to conductivity and the contribution of electron scattering to the resistivity of SFMO. We attribute the temperature dependence of SFMO ceramics resistivity in the absence of a magnetic field to the fluctuation induced tunneling model. Also, we attribute the decreasing resistivity in the temperature range from 409 K up to 590 K to adiabatic small polaron hopping and not to localization effects. Both fluctuation induced tunneling and adiabatic small polaron hopping do not favor quantum interference. Additionally, we demonstrate that the resistivity upturn behavior of SFMO cannot be explained by weak localization. Consequently, to the best of our knowledge, there is still no convincing evidence for the presence of weak localization in SFMO.

Meiotic crossovers/chiasmata are not randomly distributed and strictly controlled. The mechanisms behind COs patterning remain largely unknown. In many species COs predominantly occur in the distal 2/3 of the chromosome arm and suppression of COs is observed in the proximal regions. The exceptions are some species, among which Allium fistulosum has strictly localized COs in the proximal region. The ability to manipulate the COs localization can be useful for onion breeding. We investigated the factors that may contribute to the pattern of COs in two closely related onion species A. cepa and A. fistulosum, which differ significantly in the localization of chiasmata, and their F1 diploid and triploid hybrids in pollen mother cells. We demonstrate a significant shift in the COs localization to the distal and interstitial region in F1 triploid hybrid, which has a complete diploid set of A. fistulosum chromosomes and haploid set of A. cepa chromosomes. This observation points to a possible genetic control of COs distribution. We did not find the differences in the assembly and disassembly of ASY1 and ZYP1 between A. cepa and A. fistulosum while the difference between parental species and their hybrids was observed. In diploid F1 hybrids at pachytene the chromosome pairing delay marked by ASY1 was revealed. Immunolocalization of MLH1, a marker for class I of the interference-dependent COs, and MUS81, a marker for class II of the interference-free COs, and mlh1/mus81 gene expression profiling in different stages of meiosis showed a spatiotemporal asymmetry among A. cepa, A. fistulosum and their diploid and triploid F1 hybrids. Our results support the hypothesis of genetic control of CO distribution as one of the players that affect meiotic recombination and exchange of genomic material.

Recently deep cross-modal hashing networks have received increasing interests due to its superior query efficiency and low storage cost. However, most of existing methods concentrate less on hash representations learning part, which means the semantic information of data cannot be fully used. Furthermore, they may neglect the high-ranking relevance and consistency of hash codes. To solve these problems, we propose a Self-Attention and Adversary Guided Hashing Network (SAAGHN). Specifically, it employs self-attention mechanism in hash representations learning part to extract rich semantic relevance information. Meanwhile, in order to keep invariability of hash codes, adversarial learning is adopted in the hash codes learning part. In addition, to generate higher-ranking hash codes and avoid local minima early, a new batch semi-hard cosine triplet loss and a cosine quantization loss are proposed. Extensive experiments on two benchmark datasets have shown that SAAGHN outperforms other baselines and achieves the state-of-the-art performance.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

This study introduces a robust methodology utilizing Multi-Criteria Decision Analysis (MCDA) combined with the Analytic Hierarchy Process (AHP) to repurpose abandoned hydrocarbon fields for energy storage, supporting the transition to renewable energy sources. We use a geostatistical approach integrated with Python scripting to analyze reservoir parameters—including porosity, permeability, thickness, lithology, temperature, heat capacity, and thermal conductivity—from a decommissioned hydrocarbon field in Southeast Hungary. Our workflow leverages stochastic simulation data to identify potential zones for energy storage, categorizing them into high, moderate, and low suitability scenarios. This innovative approach provides rapid and precise analysis, enabling effective decision-making for energy storage implementation in depleted fields.

Background: The transversus abdominis plane (TAP) block has gained increasing importance as a very effective pain relief in for caesarean section. This study aimed to compare bupivacaine (Group B), bupivacaine with dexmedetomidine (Group BD) and bupivacaine with dexamethasone (Group BDx) applied in ultrasound-guided TAP block in the treatment of CS postoperative pain. Material and Method: American Society of Anesthesiologists I and II pregnant women who underwent cesarean section were included in the study. Forty patients were admitted in each group. All patients were applied TAP block with ultrasound guidance by administering the medications of the group in which they were included. Results: Body mass index (BMI) and operation duration were higher in Group B (p*=0.01 and 0.004, respectively). Instant tramadol requirement and amount were higher in Group B at some of follow up times. Patient satisfaction scale were better in Group BD and Group BDx. Conclusion: TAP block significantly reduces pain after cesarean section, prolongs the time to first analgesic request, and reduces the need for additional opioid analgesics. The addition of adjuvant to the TAP block usually improves the postoperative clinical condition. However, some special conditions such as body mass index (BMI) may negatively affect the clinical situation. Comprehensive, standardized, randomized controlled studies are needed to compare results under different conditions.

Human detection from Unmanned Aerial Vehicles (UAV) is gaining popularity in the field of disaster management, crowd counting, people monitoring. Real time human detection from UAV is a challenging task, because of many constraints involved. This study proposes a system for real time detection of humans on videos captured from UAVs addressing three of these constraints namely, flying height, computation time and scale of viewing. The proposed method integrated an android application with a binary classifier based on Haar-features to automatically detect human / non-human class from UAV images. The video frames were parsed and detected humans from image frames were geo-localized and visualized on Google Earth. The performance was evaluated for geo-localization accuracy, computation time and detection accuracy, considering human coverage – pixel size relationship for various heights and scale factor. Based on flying height - human size relationship and tradeoff between detection accuracy vs computation time, the study came up with optimal parameters for OpenCV’s cv2.cascadeClassifier. detectMultiScale function. This paper establishes a strong ground for further research relating to real time human detection from UAV.

Because of the increasing relevance of the Internet of Things and location-based services, researchers are evaluating wireless positioning techniques, such as fingerprinting, on Low Power Wide Area Network (LPWAN) communication. In order to evaluate fingerprinting in large outdoor environments, extensive, time-consuming measurement campaigns need to be conducted to create useful datasets. This paper presents three LPWAN datasets which are collected in large-scale urban and rural areas. The goal is to provide the research community with a tool to evaluate fingerprinting algorithms in large outdoor environments. During a period of three months, numerous mobile devices periodically obtained location data via a GPS receiver which was transmitted via a Sigfox or LoRaWAN message. Together with network information, this location data is stored in the appropriate LPWAN dataset. The first results of our basic fingerprinting implementation, which is also clarified in this paper, indicate a mean location estimation error of 214.58 m for the rural Sigfox dataset, 688.97 m for the urban Sigfox dataset and 398.40 m for the urban LoRaWAN dataset. In the future, we will enlarge our current datasets and use them to evaluate and optimize our fingerprinting methods. Also, we intend to collect additional datasets for Sigfox, LoRaWAN and NB-IoT.

Water distribution networks are often susceptible to pipeline leaks caused by mechanical damages, natural hazards, corrosion, and other factors. This paper focuses on the detection of leaks in water distribution networks (WDN) using a data-driven approach based on machine learning. A hybrid Autoencoder neural network (AE) is developed, which utilizes unsupervised learning to address the issue of unbalanced data (as anomalies are rare events). The AE consists of a 3DCNN encoder, a ConvLSTM decoder, and a ConvLSTM future predictor, making the anomaly detection robust. Additionally, spatial, and temporal attention mechanisms are employed to enhance leak localization. The AE first learns the expected behavior and subsequently detects leaks by identifying deviations from this expected behavior. To evaluate the performance of the proposed method, the Water Network Tool for Resilience (WNTR) Simulator is utilized to generate water pressure and flow rate data in a water supply network. Various conditions such as fluctuating water demands, data noise, and the presence of leaks are considered using the pressure-driven demand (PDD) method. Datasets with and without pipe leaks are obtained, where the AE is trained using the dataset without leaks and tested using the dataset with simulated pipe leaks. The results, based on a benchmark WDN and a confusion matrix analysis, demonstrate that the proposed method successfully identifies leaks in 96% of cases and a false positive rate of 4% compared to a random forest model baseline based on supervised learning with a false positive rate of 15% due to unbalanced data. Furthermore, a real case study demonstrates the applicability of the developed model for leak detection in operational conditions of water supply networks using inline sensor data.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

The current paper presents a case of a 33-year-old female with an uncommon localization of a leiomyoma in the oral cavity - the anterior palatal fibromucosa and the incisive papilla. The patient referred to the Oro-Maxillo-Facial Surgery Clinic of the Emergency City Hospital Timisoara, Romania accusing a slight discomfort in the act of mastication and the occurrence and persistence of a diastema between the upper central incisors, due to the presence of a tumoral mass located in the anterior palatal mucosa, between the upper central incisors, without any changes of the subjacent bone structure in the anterior hard palate visible on the cone beam computed tomography image (CBCT). The tumor was removed by a surgical excisional biopsy and a histopathological examination was performed using morphological Hematoxylin–Eosin (HE) staining and additional immunohistochemical (IHC) reactions, in order to confirm the diagnosis. On microscopic examination, there were found bundles of spindle cells with eosinophilic cytoplasm and vesicular nuclei, with finely granular chromatin. The immunohistochemically staining found positive results for smooth muscle actin (SMA) and desmin and negative result for vimentin. The treatment of choice for leiomyoma of the oral cavity is surgical excision with clear margins, followed by periodical clinical monitoring.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

Many applications in the context of Industry 4.0 require precise localization. However, indoor localization remains an open problem, especially in complex environments such as industrial environments. In recent years, we have seen the emergence of Ultra WideBand (UWB) localization systems. The aim of this article is to evaluate the performance of a UWB system to estimate the position of a person moving in an indoor environment. To do so, we implemented an experimental protocol to evaluate the accuracy of the UWB system both statically and dynamically. The UWB system is compared to a ground truth obtained by a motion capture system with a millimetric accuracy.

This study addresses the challenge of treating skin cancer effectively, especially in visible areas like the face, where traditional methods may be undesirable. Current options, including surgery and radiation therapy, have limitations such as complexity, expense, and imprecise dose delivery. The proposed solution involves additively manufacturing personalized patches using diagnostic imaging data, offering a targeted and controlled dose of beta-emitting isotopes like Y-90. The patches, tailored to the tumor's shape and depth, aim to minimize damage to healthy tissues and structures. Through 3D printing technology we demonstrated the feasibility of creating individualized applicators filled with radioisotopes, that have the potential to deliver therapeutic doses while ensuring patient comfort and affordability. This innovative approach, based on radiation delivery simulations, offers a promising future alternative to conventional radiation therapy methods. Clinical trials are required to validate its efficacy and safety, but the potential for reduced radiation exposure and enhanced treatment precision positions this personalized and targeted therapy as a valuable advancement in skin cancer treatment.

Polymyalgia rheumatica (PMR) is an inflammatory disease common in people aged 50 years and older. This condition is characterized by the presence of pain and stiffness involving mainly the shoulder and pelvic girdle. Besides the frequent association with giant cell arteritis (GCA), several conditions may mimic PMR or present with PMR features. Since the diagnosis is basically clinical, an adequate diagnosis of this condition is usually required. Positron emission tomography-computed tomography (PET-CT) has proved to be a useful tool for the diagnosis of PMR. The use of 18F-FDG-PET imaging appears promising as it provides detailed information on inflammatory activity that may not be evident with traditional methods. However, since PET-CT is not strictly necessary for the diagnosis of PMR, clinicians should consider several situations in which this imaging technique can be used in patients with suspected PMR.

The 3D reconstruction of vermiculite-based sealing material was carried out based on CT scanning experiments and Avizo software. The precision and representation of the reconstructed digital core were tested by calculating the pore structure parameters and comparing them with the experimental results of mercury compression. Avizo is a perfect interface with Fluent software to perform micro-scale seepage simulation to explore the distribution of pressure and velocity fields during fluid transport. In this paper, a new approach is proposed to study the penetration leakage of non-metallic gaskets by using equivalent pore networks.

Underwater robots often encounter the influence of confined underwater environments during underwater exploration. These environments include underwater caves, sunken ships, submerged houses, and pipeline structures. Robot positioning in these environments is strongly disturbed, leading not only to the failure of some commonly used positioning methods but also to an increase in errors in positioning systems that normally function well in open water. In order to overcome the limitations of positioning methods in underwater confined environments, researchers have studied different underwater positioning methods and selected suitable methods for positioning in such environments. These methods can achieve high-precision positioning without relying on assistance from other platforms, and are referred to as autonomous positioning methods. Autonomous positioning methods for underwater robots mainly include SINS/DR positioning and SLAM positioning. In addition, in recent years, researchers have also developed some bio-inspired autonomous positioning methods. This article introduces the applicable robot positioning methods and sensors in underwater confined environments and discusses the research directions of robot positioning methods in such environments.

As an established and widely available infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization of both mobile and stationary users. In planning a mobile communications network and radio system design, the critical role of coverage prediction becomes evident, empowering network operators to optimize cellular networks and elevate the overall customer experience. Moreover, WLANs present several challenges that must be fulfilled when it comes to localization based on Wi-Fi signals to get a proper coverage prediction map. This paper presents a study based on application of the extra trees regression (ETR) for indoor localization by using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected received signal strength indicator (RSSI) values. The proposed scheme investigates several machine learning (ML) regression algorithms for localization, where the training dataset is obtained from the REM by using the nearest neighbors method. Parameter tuning is conducted to optimize the performance of the ETR scheme by using 10-fold cross-validation. In the numerical results, we first demonstrate the effectiveness of utilizing ML regression techniques for generating coverage maps, which enables accurate estimation of the Wi-Fi signal strength in indoor environments. Then, we showcase the superior performance of the proposed ETR-based method compared to several other ML schemes for indoor localization using the REM. ML algorithms, including decision tree regression and the ETR, are compared to evaluate the system model. Based on error metrics, the proposed ETR-based approach exhibits the best performance among the evaluated techniques. The combination of coverage map generation and localization using regression techniques offers a powerful approach for analyzing the radio frequency (RF) environment in indoor spaces.

The widespread availability of digital image processing softwares has given rise to various forms of image manipulation and forgery which can pose a significant challenge in different fields such as law enforcement, journalism etc,. it can also lead a privacy concern. We are proposing a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for purpose of Investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images if the forged portion (dummy image) has a compression quality different from the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10x10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.

Indoor localization is an active area of research dominated by traditional machine-learning techniques. Deep learning-based systems have shown unprecedented improvements and have accomplished exceptional results over the past decade, especially the Transformer network within natural language processing (NLP) and computer vision domains. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer with multiple classification heads (one per class) and learnable embeddings, to investigate the effectiveness of Transformer-based models for received signal strength (RSS) based WiFi fingerprinting. HyTra leverages learnable embeddings and the self-attention mechanism to determine the relative position of the wireless access points (WAPs) within the high-dimensional embedding space, improving the prediction of user location. From an NLP perspective, we consider a fixed order sequence of all observed WAPs as a sentence and the captured RSS value(s) for every given WAP at a given reference point from a given user as words. We test our proposed network on public and private datasets of different sizes, proving that the quality of the learned embeddings and overall accuracy improves with increments in samples.

Cooperative localization under complex urban environments has become a solution to replace the Global Navigation Satellite System (GNSS) positioning. Due to utilizing distance measured and information exchanged between vehicles, cooperative localization results in high computational complexity and heavy communication overhead. This paper proposes a cooperative localization method based on evolutionary coalitional game theory, which implements vehicles' location estimation with less communication cost. We select the neighboring vehicles to form a coalition based on the node's square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method holds low communication overhead while maintaining localization accuracy.

Background. Restoring large defects with proximal caries extending below the cemento-enamel junction and cavity margins located beneath the gingival tissues represents a very common clinical situation. The aim of this article is to propose a clinical classification of the localization subgingival margins of cavity.Material and methods. Diagnosis is carried out by periodontal probe perpendicular to the long axis of the tooth in the deepest point of the cavity’s margin. To describe the localization subgingival margins of cavity we use exponent which value is equal to the distance (integer number expressed in millimeters) of the level of epithelial attachment to the margin of the cavity. Three periodontal examiners, with >10 years of periodontal practice, were required to attend a calibration session aimed at the validation of the proposed classification. Results. The intrarater and interrater agreement among the localization subgingival margins of cavity: for intrarater agreement ranged from 0.74 to 0.95 (almost perfect agreement), whereas interrater agreement ranged from 0.26 to 0.59 (moderate agreement).Conclusions. The classification the localization subgingival margins of cavity is useful for reaching a more precise diagnosis.

COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.