Novel oil spill indices for sentinel-2 imagery: A case study of natural seepage in Qaruh Island, Kuwait

Oil spills are a paramount and immediate challenge affecting marine ecosystems globally. Effective and timely monitoring tools, such as oil detection indices, offer a swift means to track oil spill spread across vast oceanic expanses. Moreover, these indices enhance data clarity, making it more conducive for machine learning and deep learning algorithms. This study leverages the natural seepage occurring around Qaruh Island, Kuwait as a unique context for the spectral analysis of oil spills using Sentinel-2 multispectral imagery due to repeated occurrences in the same region. This research evaluated 859 single band and 455 multichannel combinations to identify the most effective combinations in oil-water separability, employing the Jeffries-Matusita (JM) distance measure as a key metric. Bands 1, 2, 3, 8A, 11, and 12 consistently featured among the top-performing indices combinations B 1 - B 11 B 1 + B 11 ; B 1 + B 2 B 3 + B 11 ; B 1 + B 2 B 3 + B 12 ; B 1 + B 2 B 3 + B 8 A affirming the significant effect of oil spills on visible, Near-Infrared (NIR), and Shortwave Infrared (SWIR) bands. Notably, the indices developed in this study outperformed those from prior research in terms of suitability to unsupervised classification algorithms. A significant conclusion of this study is that incorporating a higher number of bands in the analysis did not correlate with an increase in JM values, suggesting that the selection of specific, informative bands is more critical than the volume of input data. These findings underscore the indispensable role of Sentinel-2 imagery in environmental investigations and highlight the potential for focused, efficient analysis using strategic band combinations for effective oil spill detection.•This study identified optimized Sentinel-2 band combinations for oil-water separability, benefiting from naturally occurring spills around Qaruh Island.•The proposed indices outperformed the previous indices for oil spill visualization and clustering.•The new indices highlighted the critical role of specific band selection over the volume of input data for effective oil spill detection.

Emergence of Long-Range Angular Correlations in Low-Multiplicity Proton-Proton Collisions

This Letter presents the measurement of near-side associated per-trigger yields, denoted ridge yields, from the analysis of angular correlations of charged hadrons in proton-proton collisions at sqrt[s]=13  TeV. Long-range ridge yields are extracted for pairs of charged particles with a pseudorapidity difference of 1.4<|Δη|<1.8 and a transverse momentum of 1 Collapse

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First Measurement of the |t| Dependence of Incoherent J/ψ Photonuclear Production

The first measurement of the cross section for incoherent photonuclear production of J/ψ vector mesons as a function of the Mandelstam |t| variable is presented. The measurement was carried out with the ALICE detector at midrapidity, |y|<0.8, using ultraperipheral collisions of Pb nuclei at a center-of-mass energy per nucleon pair of sqrt[s_{NN}]=5.02  TeV. This rapidity interval corresponds to a Bjorken-x range (0.3-1.4)×10^{-3}. Cross sections are given in five |t| intervals in the range 0.04<|t|<1  GeV^{2} and compared to the predictions by different models. Models that ignore quantum fluctuations of the gluon density in the colliding hadron predict a |t| dependence of the cross section much steeper than in data. The inclusion of such fluctuations in the same models provides a better description of the data.

Observation of Seven Astrophysical Tau Neutrino Candidates with IceCube

We report on a measurement of astrophysical tau neutrinos with 9.7 yr of IceCube data. Using convolutional neural networks trained on images derived from simulated events, seven candidate ν_{τ} events were found with visible energies ranging from roughly 20 TeV to 1 PeV and a median expected parent ν_{τ} energy of about 200 TeV. Considering backgrounds from astrophysical and atmospheric neutrinos, and muons from π^{±}/K^{±} decays in atmospheric air showers, we obtain a total estimated background of about 0.5 events, dominated by non-ν_{τ} astrophysical neutrinos. Thus, we rule out the absence of astrophysical ν_{τ} at the 5σ level. The measured astrophysical ν_{τ} flux is consistent with expectations based on previously published IceCube astrophysical neutrino flux measurements and neutrino oscillations.

ANN: adversarial news net for robust fake news classification

With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

ψ(2S) Suppression in Pb-Pb Collisions at the LHC

The production of the ψ(2S) charmonium state was measured with ALICE in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV, in the dimuon decay channel. A significant signal was observed for the first time at LHC energies down to zero transverse momentum, at forward rapidity (2.5 Collapse

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Micro-segmentation of retinal image lesions in diabetic retinopathy using energy-based fuzzy C-Means clustering (EFM-FCM)

Diabetic retinopathy (DR) is a prevalent cause of global visual impairment, contributing to approximately 4.8% of blindness cases worldwide as reported by the World Health Organization (WHO). The condition is characterized by pathological abnormalities in the retinal layer, including microaneurysms, vitreous hemorrhages, and exudates. Microscopic analysis of retinal images is crucial in diagnosing and treating DR. This article proposes a novel method for early DR screening using segmentation and unsupervised learning techniques. The approach integrates a neural network energy-based model into the Fuzzy C-Means (FCM) algorithm to enhance convergence criteria, aiming to improve the accuracy and efficiency of automated DR screening tools. The evaluation of results includes the primary dataset from the Shiva Netralaya Centre, IDRiD, and DIARETDB1. The performance of the proposed method is compared against FCM, EFCM, FLICM, and M-FLICM techniques, utilizing metrics such as accuracy in noiseless and noisy conditions and average execution time. The results showcase auspicious performance on both primary and secondary datasets, achieving accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s. The proposed method holds significant potential in medical image analysis and could pave the way for future advancements in automated DR diagnosis and management. RESEARCH HIGHLIGHTS: A novel approach is proposed in the article, integrating a neural network energy-based model into the FCM algorithm to enhance the convergence criteria and the accuracy of automated DR screening tools. By leveraging the microscopic characteristics of retinal images, the proposed method significantly improves the accuracy of lesion segmentation, facilitating early detection and monitoring of DR. The evaluation of the method's performance includes primary datasets from reputable sources such as the Shiva Netralaya Centre, IDRiD, and DIARETDB1, demonstrating its effectiveness in comparison to other techniques (FCM, EFCM, FLICM, and M-FLICM) in terms of accuracy in both noiseless and noisy conditions. It achieves impressive accuracy rates of 99.03% in noiseless conditions and 93.13% in noisy images, with an average execution time of 16.1 s.

Attention-based deep learning framework to recognize diabetes disease from cellular retinal images

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0-4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.

Measurements of Groomed-Jet Substructure of Charm Jets Tagged by D^{0} Mesons in Proton-Proton Collisions at sqrt[s]=13 TeV

Understanding the role of parton mass and Casimir color factors in the quantum chromodynamics parton shower represents an important step in characterizing the emission properties of heavy quarks. Recent experimental advances in jet substructure techniques have provided the opportunity to isolate and characterize gluon emissions from heavy quarks. In this Letter, the first direct experimental constraint on the charm-quark splitting function is presented, obtained via the measurement of the groomed shared momentum fraction of the first splitting in charm jets, tagged by a reconstructed D^{0} meson. The measurement is made in proton-proton collisions at sqrt[s]=13  TeV, in the low jet transverse-momentum interval of 15≤p_{T}^{jet ch}<30  GeV/c where the emission properties are sensitive to parton mass effects. In addition, the opening angle of the first perturbative emission of the charm quark, as well as the number of perturbative emissions it undergoes, is reported. Comparisons to measurements of an inclusive-jet sample show a steeper splitting function for charm quarks compared with gluons and light quarks. Charm quarks also undergo fewer perturbative emissions in the parton shower, with a reduced probability of large-angle emissions.

Detection of Lung Tumors in CT Scan Images using Convolutional Neural Networks

Changing the human being's lifestyle, has caused, or exacerbated many diseases. One of these diseases is cancer, and among all kind of cancers like, brain and pulmonary; lungs cancer is fatal. The cancers could be detected early to save lives using Computer Aided Diagnosis (CAD) systems. CT scans medical images are one the best images in detecting these tumors in lung that are especially accepted among doctors. However, location and random shape of tumors, and the poor quality of CT scans images are one the biggest challenges for physicians in identifying these tumors. Therefore, deep learning algorithms have been highly regarded by researchers. This paper presents a new method for identifying tumors and pulmonary nodules in CT scans images based on convolution neural network algorithm with which tumor is accurately identified. The active counter algorithm will show the detected tumor. The proposed method is qualitatively measured by the sensitivity assessment criteria and dice similarity criteria. The obtained results with 98.33% accuracy 99.25% validity and 98.18% dice similarity criterion show the superiority of the proposed method.

Measurement of the Lifetime and Λ Separation Energy of _{Λ}^{3}H

The most precise measurements to date of the _{Λ}^{3}H lifetime τ and Λ separation energy B_{Λ} are obtained using the data sample of Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV collected by ALICE at the LHC. The _{Λ}^{3}H is reconstructed via its charged two-body mesonic decay channel (_{Λ}^{3}H→^{3}He+π^{-} and the charge-conjugate process). The measured values τ=[253±11(stat)±6(syst)]  ps and B_{Λ}=[102±63(stat)±67(syst)]  keV are compatible with predictions from effective field theories and confirm that the _{Λ}^{3}H structure is consistent with a weakly bound system.

Measurement of the J/ψ Polarization with Respect to the Event Plane in Pb-Pb Collisions at the LHC

We study the polarization of inclusive J/ψ produced in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV at the LHC in the dimuon channel, via the measurement of the angular distribution of its decay products. We perform the study in the rapidity region 2.5 Collapse

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First Measurement of Antideuteron Number Fluctuations at Energies Available at the Large Hadron Collider

The first measurement of event-by-event antideuteron number fluctuations in high energy heavy-ion collisions is presented. The measurements are carried out at midrapidity (|η|<0.8) as a function of collision centrality in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV using the ALICE detector. A significant negative correlation between the produced antiprotons and antideuterons is observed in all collision centralities. The results are compared with a state-of-the-art coalescence calculation. While it describes the ratio of higher order cumulants of the antideuteron multiplicity distribution, it fails to describe quantitatively the magnitude of the correlation between antiproton and antideuteron production. On the other hand, thermal-statistical model calculations describe all the measured observables within uncertainties only for correlation volumes that are different with respect to those describing proton yields and a similar measurement of net-proton number fluctuations.

Enhanced Deuteron Coalescence Probability in Jets

The transverse-momentum (p_{T}) spectra and coalescence parameters B_{2} of (anti)deuterons are measured in p-p collisions at sqrt[s]=13  TeV for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest p_{T} in the event (p_{T}^{lead}>5  GeV/c) is used as an approximation for the jet axis. The event is consequently divided into three azimuthal regions, and the jet signal is obtained as the difference between the toward region, that contains jet fragmentation products in addition to the underlying event (UE), and the transverse region, which is dominated by the UE. The coalescence parameter in the jet is found to be approximately a factor of 10 larger than that in the underlying event. This experimental observation is consistent with the coalescence picture and can be attributed to the smaller average phase-space distance between nucleons in the jet cone as compared with the underlying event. The results presented in this Letter are compared to predictions from a simple nucleon coalescence model, where the phase-space distributions of nucleons are generated using pythia8 with the Monash 2013 tuning, and to predictions from a deuteron production model based on ordinary nuclear reactions with parametrized energy-dependent cross sections tuned on data. The latter model is implemented in pythia8.3. Both models reproduce the observed large difference between in-jet and out-of-jet coalescence parameters, although the almost flat trend of the B_{2}^{Jet} is not reproduced by the models, which instead give a decreasing trend.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

Observation of high-energy neutrinos from the Galactic plane

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Effects of ileocolonic delivered vitamin B2, B3 and C (ColoVit) or the Groningen anti-inflammatory diet on disease course and microbiome of patients with Crohn's disease (VITA-GrAID study): a protocol for a randomised and partially blinded trial

BACKGROUND

Diet plays a pivotal role in the onset and progression of Crohn's disease (CD). Nutritional interventions revealed effects on intestinal inflammation and gut microbial composition. However, data from well-designed and controlled dietary trials are lacking. Therefore, evidence-based dietary recommendations are still unavailable to patients and physicians. Here, we aim to investigate the effects of an evidence-based anti-inflammatory diet, and an ileocolonic-targeted capsule containing vitamin B₂, B₃ and C (ColoVit) on patients with CD and their healthy household members.

METHODS AND ANALYSIS

In this multicentre, randomised, placebo-controlled, partially blinded nutritional intervention trial, we aim to recruit 255 CD patients with Harvey-Bradshaw Index <8 and a faecal calprotectin (FCal) cut-off of ≥100 µg/g at baseline. Participants will be randomised into two experimental intervention groups and one placebo group. In the experimental groups, participants will either adhere to the Groningen anti-inflammatory diet (GrAID) or ingest an ileocolonic-delivered oral vitamin B₂/B₃/C capsule (ColoVit). The study consists of a 12-week controlled interventional phase, which proceeds to a 9-month observational follow-up phase in which patients allocated to the GrAID group will be requested to continue the intervention on their own accord. Household members of participating patients will be asked to participate in the trial as healthy subjects and are allocated to the same group as their peer. The primary study outcome for patients is the change in FCal level from baseline. The primary outcome for household members is the change in gut microbial composition, which is set as secondary outcome for patients.

ETHICS AND DISSEMINATION

The protocol has been approved by the Institutional Review Board of the Stichting Beoordeling Ethiek Biomedisch Onderzoek in Assen, the Netherlands. Written informed consent will be obtained from all participants. Results will be disseminated through peer-reviewed journals and conference presentations.

TRIAL REGISTRATION NUMBER

NCT04913467.

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence

Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics.

An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs

Explainable Artificial Intelligence is a key component of artificially intelligent systems that aim to explain the classification results. The classification results explanation is essential for automatic disease diagnosis in healthcare. The human respiration system is badly affected by different chest pulmonary diseases. Automatic classification and explanation can be used to detect these lung diseases. In this paper, we introduced a CNN-based transfer learning-based approach for automatically explaining pulmonary diseases, i.e., edema, tuberculosis, nodules, and pneumonia from chest radiographs. Among these pulmonary diseases, pneumonia, which COVID-19 causes, is deadly; therefore, radiographs of COVID-19 are used for the explanation task. We used the ResNet50 neural network and trained the network on extensive training with the COVID-CT dataset and the COVIDNet dataset. The interpretable model LIME is used for the explanation of classification results. Lime highlights the input image's important features for generating the classification result. We evaluated the explanation using radiologists' highlighted images and identified that our model highlights and explains the same regions. We achieved improved classification results with our fine-tuned model with an accuracy of 93% and 97%, respectively. The analysis of our results indicates that this research not only improves the classification results but also provides an explanation of pulmonary diseases with advanced deep-learning methods. This research would assist radiologists with automatic disease detection and explanations, which are used to make clinical decisions and assist in diagnosing and treating pulmonary diseases in the early stage.

Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence.

Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters.

Evidence for neutrino emission from the nearby active galaxy NGC 1068

A supermassive black hole, obscured by cosmic dust, powers the nearby active galaxy NGC 1068. Neutrinos, which rarely interact with matter, could provide information on the galaxy’s active core. We searched for neutrino emission from astrophysical objects using data recorded with the IceCube neutrino detector between 2011 and 2020. The positions of 110 known gamma-ray sources were individually searched for neutrino detections above atmospheric and cosmic backgrounds. We found that NGC 1068 has an excess of 79 − 20 + 22 neutrinos at tera–electron volt energies, with a global significance of 4.2σ, which we interpret as associated with the active galaxy. The flux of high-energy neutrinos that we measured from NGC 1068 is more than an order of magnitude higher than the upper limit on emissions of tera–electron volt gamma rays from this source.

Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions.

Search for Unstable Sterile Neutrinos with the IceCube Neutrino Observatory

We present a search for an unstable sterile neutrino by looking for a resonant signal in eight years of atmospheric ν_{μ} data collected from 2011 to 2019 at the IceCube Neutrino Observatory. Both the (stable) three-neutrino and the 3+1 sterile neutrino models are disfavored relative to the unstable sterile neutrino model, though with p values of 2.8% and 0.81%, respectively, we do not observe evidence for 3+1 neutrinos with neutrino decay. The best-fit parameters for the sterile neutrino with decay model from this study are Δm_{41}^{2}=6.7_{-2.5}^{+3.9}  eV^{2}, sin^{2}2θ_{24}=0.33_{-0.17}^{+0.20}, and g^{2}=2.5π±1.5π, where g is the decay-mediating coupling. The preferred regions of the 3+1+decay model from short-baseline oscillation searches are excluded at 90% C.L.

Community- vs. hospital-based management of multidrug-resistant TB in Pakistan

BACKGROUND Multidrug-resistant TB (MDR-TB) treatment takes 18-24 months and is complex, costly and isolating. We provide trial evidence on the WHO Pakistan recommendation for community-based care rather than hospital-based care.METHODS Two-arm, parallel-group, superiority trial was conducted in three programmatic management of drug-resistant TB hospitals in Punjab and Sindh Provinces, Pakistan. We enrolled 425 patients with MDR-TB aged >15 years through block randomisation in community-based care (1-week hospitalisation) or hospital-based care (2 months hospitalisation). Primary outcome was treatment success.RESULTS Among 425 patients with MDR-TB, 217 were allocated to community-based care and 208 to hospital-based care. Baseline characteristics were similar between the community and hospitalised arms, as well as in selected sites. Treatment success was 74.2% (161/217) under community-based care and 67.8% (141/208) under hospital-based care, giving a covariate-adjusted risk difference (community vs. hospital model) of 0.06 (95% CI -0.02 to 0.15; P = 0.144).CONCLUSIONS We found no clear evidence that community-based care was more or less effective than hospital-based care model. Given the other substantial advantages of community-based care over hospital based (e.g., more patient-friendly and accessible, with lower treatment costs), this supports the adoption of the community-based care model, as recommended by the WHO.

Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.

Molecular detection and therapeutic study of Trypanosoma brucei evansi from naturally infected horses in Punjab, Pakistan

Trypanosomiasis is one of the severe pathogenic infections, caused by several Trypanosoma species, affecting both animals and humans, causing substantial economic losses and severe illness. The objective of this study was to determine the molecular diagnosis and the risk factors associated with trypanosomiasis in District Jhang, Punjab, Pakistan. For this purpose, blood samples were randomly collected from 200 horses. A predesigned questionnaire was used to collect data on risk factors before the sample collection. The microscopy examination through Giemsa staining, formol gel test and PCR techniques were used to find the prevalence. The prevalence was recorded as 22.5% with microscopy examination, 21% through formol gel test and 15.5% with PCR based results. Analysis of risk factors associated with Trypanosoma brucei evansi occurrence was carried out using Chi-square test. It showed the prevalence of Trypanosoma brucei evansi was significantly (p⟨0.05) associated with sex, age, rearing purpose and body condition whereas non-significantly (p⟩0.05) with insects control practices. This study supports the idea that PCR is a sensitive, robust and more reliable technique to diagnose trypanosomiasis. It was concluded that Trypanosoma brucei evansi is widely prevalent in Jhang (Pakistan), highlighting a dire need to develop control strategies and education programmes to control this disease in developing countries.

Convolutional neural network transformer (CNNT) for free-breathing real-time cine imaging

Funding Acknowledgements

Type of funding sources: None.

Background

Real-time cine imaging does not require breath-holding and is a robust cine imaging technique in the presence of irregular heartbeats. It is a good alternative to the conventional breath-hold retro-gated cine for simplified acquisition and improved patient comfort. Real-time acquisition is achieved with the single-shot BSSFP readout without retro-gating. To maintain good temporal and spatial resolution, higher acceleration (e.g. &gt;4x parallel imaging) is required. As a result, the real-time cine images experience reduced signal-to-noise ratio (SNR), which limits its clinical acceptance.

Purpose

We developed a novel deep learning model architecture, the Convolutional Neural Network Transformer (CNNT), to improve the quality of real-time cine, under 4x, 5x and 6x acceleration.

Method

Convolutional Neural Networks (CNN) are widely used in CMR research to process cardiac images. Cardiac images are often acquired as a time series with strong inter-phase correlation. We combined the CNN with the more recent transformer model to develop a novel CNNT architecture. It takes in the entire 2D+T time series as input and has advantages of CNN for efficient computation and spatial invariance. It further inherits the advantages of attention layer in the transformer and is able to efficiently utilize the temporal correlation within a time series.

A CNNT model is developed to improve the SNR of real-time cine imaging. N=10 patients were scanned at a heart center, with 4x, 5x and 6x acceleration. Typical imaging parameters are: FOV 360×270mm2, flip angle 50°, acquired matrix size 160×90 for R=4 acceleration, 192×108 for R=5 and 6, temporal resolution 40ms for R=4, 42ms for R=5 and 35ms for R=6. The real-time images went through a TGRAPPA reconstruction [1] and the CNNT model. The SNR of TGRAPPA was measured with SNR units [2]. The Monte-Carlo pseudo-replica test was used to measure SNR for the CNNT model. For every cine series, two phases were picked for the end-systole and end-diastole. For every image picked, two region-of-interests were drawn in the myocardium and in the LV blood pool. The CNNT model was deployed inline on the MR scanner using the Gadgetron InlineAI [3].

Results

Figure 1 gives real-time cine images for three accelerations, reconstructed with TGRAPPA and CNNT. The parallel imaging TGRAPPA reconstruction suffers significant SNR loss from elevated g-factor and less acquired data. The deep learning CNNT model recovered SNR even at the very high 6x acceleration, without observed loss of boundary sharpness.

Table 1 lists the SNR measurement results. The TGRAPPA SNR decreased ∼4x from R=4 to R=6 for both the blood and myocardium. For the blood, the CNNT increased the SNR by 170%, 335%, 371% at R=4, 5 and 6. For the myocardium, the SNR increases were 335%, 634% and 828%.

Conclusion

We developed a convolutional neural network transformer model to recover the SNR for real-time cine imaging at higher acceleration.

CNNT DB-LGE: free-breathing dark blood late enhancement imaging using the convolutional neural network transformer speeds acquisition by 50%

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Supported in part by the Division of Intramural Research of the National Heart, Lung, and Blood Institute, National Institutes of Health (grants Z1A-HL006214-05 and Z1A-HL006242-02).

Background

Dark blood late gadolinium enhancement (DB-LGE) imaging shows superior delineation of myocardial infarction (MI), especially at the sub-endocardial boundary. Our previous study [1] developed a free-breathing DB-LGE with the single shot SSFP readout, phase sensitive inversion recovery (PSIR) reconstruction, and respiratory motion corrected averaging. To compensate the potential signal-to-noise ratio loss, our previous DB-LGE doubled the measurements, thereby increasing the acquisition time.

Purpose

In this study, we developed a deep learning image enhancement model using a novel neural network architecture called the convolutional neural network transformer (CNNT) to improve the image quality of DB-LGE and to reduce the acquisition time by decreasing the number of measurements.

Methods

A novel image enhancement model was developed using a novel network architecture called the Convolutional Neural Network Transformer (CNNT) proposed by us. This architecture is suitable for the 2D+Time CMR acquisition, by exploiting the temporal correlation between images over multiple averages.

The evaluation was first retrospectively conducted on a cohort of 12 patients acquired with the original protocol [1] using the full 16 measurements. For every subject, a complete short-axis stack (typically 12 slices) was acquired to cover the entire left ventricular. The imaging data was reconstructed in three ways. Original: using all acquired 16 measurements. This is our base-line protocol. Original 50%: using only the first 8 measurements. CNNT 50%: using only the first 8 averages, but performing the CNNT deep learning image enhancement before MOCO PSIR reconstruction. Two experienced imaging researchers (PK and MF, &gt;10 years of experience for both) scored all DB-LGE images for the overall quality, diagnostic confidence and delineation of MI/boundaries (5 = excellent, 4 = good, 3 = fair, 2 = poor, and 1 = non-diagnostic). The CNNT DB-LGE was deployed to the MR scanner using the Gadgetron InlineAI [2].

Results

Figure 1 gives examples of DB-LGE with three reconstruction methods. The CNNT image has higher SNR and well delineated MI. The Original images with the longest acquisition have good quality and the Original-50% acquired with 8 measurements are good quality but have reduced SNR. The mean scores for overall image quality, diagnostic confidence and MI delineation of two reviewers were 4.88±0.23, 4.88±0.23, 4.83±0.25 for CNNT and 4.96±0.14, 4.96±0.14, 4.67±0.39 for the original approach. No significant differences were found between the original and the CNNT (P&gt;0.15 for all).

Figure 2 shows an acute MI patient prospectively acquired with the 50% scan time reduction, with and without the CNNT enhancement. The resulting PSIR images well delineate the MVO due to the acute MI, with improved SNR.

Conclusion

A novel CNNT model was proposed and evaluated to speed up the free-breathing MOCO DB LGE by 50% without sacrificing image quality.

19 A Rare Case of Parapharyngeal Schwannoma Mimicking Peritonsillar Abscess in a Young Female Patient

Introduction

Parapharyngeal Space (PPS) tumours are responsible for 0.5% of head and neck cancers. Presenting features can mimic peritonsillar abscess. A differential diagnosis of PPS tumour should therefore be considered in young, systemically well patients with resistant peritonsillar masses.

Aim

To report a rare case of parapharyngeal schwannoma in a fit and well adult female with a focus on investigations and optimal management.

Method

A 29-year-old, Caucasian female presented with a three-week history of a right sided oropharyngeal mass, odynophagia, and dysphonia. A stagnant clinical picture prompted further investigation with Magnetic Resonance Imaging (MRI). This demonstrated a 7cm right sided PPS mass arising from the deep lobe of the right parotid gland. Mass excision and partial, ipsilateral parotidectomy confirmed Schwannoma (neurilemoma).

Results & Discussion

Schwannomas account for a third of PPS tumours. Arising from neurological Schwann cells, they are often encapsulated, benign tumours which are characterised by slow and asymptomatic growth. Presentation often follows mass effects leading to dysphagia, odynophagia, and dysphonia. MRI and FNAC remain the gold standard in confirmation of diagnosis. Insensitivity to radiotherapy ensures that surgical resection is the mainstay of treatment and may utilise the transcervical, transparotid or transmandibular approach.

Conclusion

There is a significant degree of overlap in the epidemiology and presenting features of peritonsillar abscess and PPS tumours. In cases of treatment resistant, chronic peritonsillar masses a differential diagnosis of PPS tumour must therefore be excluded.

Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers

Skin cancer occurrences increase exponentially worldwide due to the lack of awareness of significant populations and skin specialists. Medical imaging can help with early detection and more accurate diagnosis of skin cancer. The physicians usually follow the manual diagnosis method in their clinics but nonprofessional dermatologists sometimes affect the accuracy of the results. Thus, the automated system is required to assist physicians in diagnosing skin cancer at early stage precisely to decrease the mortality rate. This article presents an automatic skin lesions detection through a microscopic hybrid feature set and machine learning-based classification. The employment of deep features through AlexNet architecture with local optimal-oriented pattern can accurately predict skin lesions. The proposed model is tested on two open-access datasets PAD-UFES-20 and MED-NODE comprising melanoma and nevus images. Experimental results on both datasets exhibit the efficacy of hybrid features with the help of machine learning. Finally, the proposed model achieved 94.7% accuracy using an ensemble classifier.

Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale ν_{μ} Disappearance at IceCube

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter ε_{μτ}. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of -0.0041≤ε_{μτ}≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date.

Hypertriton Production in p-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

The study of nuclei and antinuclei production has proven to be a powerful tool to investigate the formation mechanism of loosely bound states in high-energy hadronic collisions. The first measurement of the production of _{Λ}^{3}H in p-Pb collisions at sqrt[s_{NN}]=5.02  TeV is presented in this Letter. Its production yield measured in the rapidity interval -1<y<0 for the 40% highest-multiplicity p-Pb collisions is dN/dy=[6.3±1.8(stat)±1.2(syst)]×10^{-7}. The measurement is compared with the expectations of statistical hadronization and coalescence models, which describe the nucleosynthesis in hadronic collisions. These two models predict very different yields of the hypertriton in charged particle multiplicity environments relevant to small collision systems such as p-Pb, and therefore the measurement of dN/dy is crucial to distinguish between them. The precision of this measurement leads to the exclusion with a significance larger than 6.9σ of some configurations of the statistical hadronization model, thus constraining the theory behind the production of loosely bound states at hadron colliders.

Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study

Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.

POS0661 MAJOR COST SAVINGS ASSOCIATED WITH BIOLOGIC DOSE REDUCTION IN PATIENTS WITH INFLAMMATORY ARTHRITIS

Background

Anti-TNF drugs have dramatically improved the management of inflammatory arthritis (IA).

Although the introduction of biosimilars have reduced the cost, chronic use of biologic agents

has a high impact on healthcare expenditure. This study evaluated the cost effectiveness of a

dose reduction strategy for the most commonly used anti- TNF drugs over a period of 10 years

in patients with IA in remission.

Objectives

The purpose of this study was to explore whether patients with Inflammatory Arthritis (IA) (Rheumatoid Arthritis (RA), Psoriatic Arthritis (PsA) or Ankylosing Spondylitis (AS) would remain in remission after 10 year period, following a reduction in biologic dosing frequency and to calculate the cost savings associated with dose reduction.

Methods

This prospective, non-blinded, non-randomised study was commenced in 2010. Patients

with IA, Rheumatoid arthritis (RA),ankylosing spondylitis (AS) and Psoriatic arthritis (PsA)

who were in remission as defined by disease activity indices (DAS28<2.6, BASDAI<4), and

were offered Anti TNF dose reduction. Patients on etanercept were reduced from 50mg

weekly to fortnightly, adalimumab 40mg once monthly instead of fortnightly. Patients were

assessed for disease activity at 1, 4 and 10 years following reduction in dosing

frequency.Cost saving was calculated by deducting the total annual cost of the biologic

agent used over 10 years compared with the cost if the dosing interval had not changed.

Results

Seventy nine patients with inflammatory arthritis in remission were recruited. 57% had rheumatoid arthritis (n=45), 13% psoriatic arthritis (n=10) and 30% ankylosing spondylitis (n=24). 57% (n=45) were taking etanercept and 43% (n=34) adalimumab. The percentage of patients who maintained dose reduction at 10 years was 9% (n=7). Of the total 48 patients who were successfully dose reduced at year 1 (n=42), (69%, n=29) were able to maintain the dose reduction up to 4 years and 9% (n=7) maintained this dose reduction up to year 10. The estimated cost saving was €4,928 per patient per year. Estimated cost savings for 7 patients on reduced dose was €344,952.88 over 10 years.

Conclusion

Anti TNF dose reduction strategy in patients with IA results in substantial cost savings. Implementation of a dose reduction strategy while monitoring of disease activity reduces the financial impact of the use of biologic therapies. Further studies should be done to identify which patients are more likely to remain in remission while on dose reduction.

References

[1]Bonafede MM, Gandra SR, Watson C, Princic N, Fox KM. Cost per treated patient for etanercept, adalimumab, and infliximab across adult indications: a claims analysis. Adv Ther. 2012 Mar;29(3):234-48. doi: 10.1007/s12325-012-0007-y. Epub 2012 Mar 9. PMID: 22411424.

[2]Joaquín Borrás-Blasco, Antonio Gracia-Pérez, J Dolores Rosique-Robles, MD Elvira Casterá & F Javier Abad (2014) Clinical and economic impact of the use of etanercept 25 mg once weekly in rheumatoid arthritis, psoriatic arthropathy and ankylosing spondylitis patients, Expert Opinion on Biological Therapy, 14:2, 145-150, DOI: 10.1517/14712598.2014.868433

[3]Carter CT, Changolkar AK, Scott McKenzie R. Adalimumab, etanercept, and infliximab utilization patterns and drug costs among rheumatoid arthritis patients. J Med Econ. 2012;15(2):332-9. doi: 10.3111/13696998.2011.649325. Epub 2012 Jan 6. PMID: 22168788.

Disclosure of Interests

None declared

Polarization of Λ and Λ[over ¯] Hyperons along the Beam Direction in Pb-Pb Collisions at sqrt[s]_{NN}=5.02 TeV

The polarization of the Λ and Λ[over ¯] hyperons along the beam (z) direction, P_{z}, has been measured in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV recorded with ALICE at the Large Hadron Collider (LHC). The main contribution to P_{z} comes from elliptic flow-induced vorticity and can be characterized by the second Fourier sine coefficient P_{z,s2}=⟨P_{z}sin(2φ-2Ψ_{2})⟩, where φ is the hyperon azimuthal emission angle and Ψ_{2} is the elliptic flow plane angle. We report the measurement of P_{z,s2} for different collision centralities and in the 30%-50% centrality interval as a function of the hyperon transverse momentum and rapidity. The P_{z,s2} is positive similarly as measured by the STAR Collaboration in Au-Au collisions at sqrt[s_{NN}]=200  GeV, with somewhat smaller amplitude in the semicentral collisions. This is the first experimental evidence of a nonzero hyperon P_{z} in Pb-Pb collisions at the LHC. The comparison of the measured P_{z,s2} with the hydrodynamic model calculations shows sensitivity to the competing contributions from thermal and the recently found shear-induced vorticity, as well as to whether the polarization is acquired at the quark-gluon plasma or the hadronic phase.

Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.

Measurement of the Groomed Jet Radius and Momentum Splitting Fraction in pp and Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV

This article presents groomed jet substructure measurements in pp and Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV with the ALICE detector. The soft drop grooming algorithm provides access to the hard parton splittings inside a jet by removing soft wide-angle radiation. We report the groomed jet momentum splitting fraction, z_{g}, and the (scaled) groomed jet radius, θ_{g}. Charged-particle jets are reconstructed at midrapidity using the anti-k_{T} algorithm with resolution parameters R=0.2 and R=0.4. In heavy-ion collisions, the large underlying event poses a challenge for the reconstruction of groomed jet observables, since fluctuations in the background can cause groomed parton splittings to be misidentified. By using strong grooming conditions to reduce this background, we report these observables fully corrected for detector effects and background fluctuations for the first time. A narrowing of the θ_{g} distribution in Pb-Pb collisions compared to pp collisions is seen, which provides direct evidence of the modification of the angular structure of jets in the quark-gluon plasma. No significant modification of the z_{g} distribution in Pb-Pb collisions compared to pp collisions is observed. These results are compared with a variety of theoretical models of jet quenching, and provide constraints on jet energy-loss mechanisms and coherence effects in the quark-gluon plasma.

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations.

Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears

The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.

Search for Relativistic Magnetic Monopoles with Eight Years of IceCube Data

We present an all-sky 90% confidence level upper limit on the cosmic flux of relativistic magnetic monopoles using 2886 days of IceCube data. The analysis was optimized for monopole speeds between 0.750c and 0.995c, without any explicit restriction on the monopole mass. We constrain the flux of relativistic cosmic magnetic monopoles to a level below 2.0×10^{-19}  cm^{-2} s^{-1} sr^{-1} over the majority of the targeted speed range. This result constitutes the most strict upper limit to date for magnetic monopoles with β≳0.8 and up to β∼0.995 and fills the gap between existing limits on the cosmic flux of nonrelativistic and ultrarelativistic magnetic monopoles.

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.

Microscopic retinal blood vessels detection and segmentation using support vector machine and K-nearest neighbors

The retina is the deepest layer of texture covering the rear of the eye, recorded by fundus images. Vessel detection and segmentation are useful in disease diagnosis. The retina's blood vessels could help diagnose maladies such as glaucoma, diabetic retinopathy, and blood pressure. A mix of supervised and unsupervised strategies exists for the detection and segmentation of blood vessels images. The tree structure of retinal blood vessels, their random area, and different thickness have caused vessel detection difficulties at machine learning calculations. Since the green band of retinal images conveys more information about the vessels, they are utilized for microscopic vessels detection. The current research proposes an administered calculation for segmentation of retinal vessels, where two upgrading stages depending on filtering and comparative histogram were applied after pre-processing and image quality improvement. At that point, statistical features of vessel tracking, maximum curvature and curvelet coefficient are extracted for each pixel. The extracted features are classified by support vector machine and the k-nearest neighbors. The morphological operators then enhance the classified image at the final stage to segment with higher accuracy. The dice coefficient is utilized for the evaluation of the proposed method. The proposed approach is concluded to be better than different strategies with a normal of 92%.

Measurement of Prompt D^{0}, Λ_{c}^{+}, and Σ_{c}^{0,++}(2455) Production in Proton-Proton Collisions at sqrt[s]=13 TeV

The p_{T}-differential production cross sections of prompt D^{0}, Λ_{c}^{+}, and Σ_{c}^{0,++}(2455) charmed hadrons are measured at midrapidity (|y|<0.5) in pp collisions at sqrt[s]=13  TeV. This is the first measurement of Σ_{c}^{0,++} production in hadronic collisions. Assuming the same production yield for the three Σ_{c}^{0,+,++} isospin states, the baryon-to-meson cross section ratios Σ_{c}^{0,+,++}/D^{0} and Λ_{c}^{+}/D^{0} are calculated in the transverse momentum (p_{T}) intervals 2<p_{T}<12 and 1<p_{T}<24  GeV/c. Values significantly larger than in e^{+}e^{-} collisions are observed, indicating for the first time that baryon enhancement in hadronic collisions also extends to the Σ_{c}. The feed-down contribution to Λ_{c}^{+} production from Σ_{c}^{0,+,++} is also reported and is found to be larger than in e^{+}e^{-} collisions. The data are compared with predictions from event generators and other phenomenological models, providing a sensitive test of the different charm-hadronization mechanisms implemented in the models.