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This paper provides a detailed analysis of the important performance metrics like effective capacity and symbol error rate over fluctuating Nakagami-m fading channel. This distribution is obtained from the ratio of two random variables, following the Nakagami-m distribution and the uniform distribution. Our study derives exact analytical expressions for the EC and SER under different modulation schemes, considering the effect of channel parameters. Recognising the importance of additive Laplacian noise in today scenario, it has been considered for the error performance analysis of the system. This work may be utilised for the design and optimization of the systems operating in environments characterized by fluctuating Nakagami-m fading., Comment: 18 pages

Given the imperative for advanced wireless networks in the next generation and the rise of real-time applications within wireless communication, there is a notable focus on investigating data rate performance across various fading scenarios. This research delved into analyzing the effective throughput of the shadowed Beaulieu-Xie (SBX) composite fading channel using the PDF-based approach. To get the simplified relationship between the performance parameter and channel parameters, the low-SNR and the high-SNR approximation of the effective rate are also provided. The proposed formulations are evaluated for different values of system parameters to study their impact on the effective throughput. Also, the impact of the delay parameter on the EC is investigated. Monte-Carlo simulations are used to verify the facticity of the deduced equations., Comment: 18 pages

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow., Comment: 12 pages, 2 figures, 1 table

Entanglement is a valuable resource in quantum information technologies. The practical implementation of entangled photon sources faces obstacles from imperfections and defects inherent in physical systems, resulting in a loss or degradation of entanglement. The topological photonic insulators, however, have emerged as promising candidates, demonstrating an exceptional capability to resist defect-induced scattering, thus enabling the development of robust entangled sources. Despite their inherent advantages, building programmable topologically protected entangled sources remains challenging due to complex device designs and weak material nonlinearity. Here we present a development in entangled photon pair generation achieved through a non-magnetic and tunable anomalous Floquet insulator, utilizing an optical spontaneous four-wave mixing process. We verify the non-classicality and time-energy entanglement of the photons generated by our topological system. Our experiment demonstrates a substantial enhancement in nonclassical photon pair generation compared to devices reliant only on topological edge states. Our result could lead to the development of resilient quantum sources with potential applications in quantum technology., Comment: 24 pages, 14 figures

Peripheral nerve blocks are crucial to treatment of post-surgical pain and are associated with reduction in perioperative opioid use and hospital stay. Accurate interpretation of sono-anatomy is critical for the success of ultrasound (US) guided peripheral nerve blocks and can be challenging to the new operators. This prospective study enrolled 227 subjects who were systematically scanned for supraclavicular and interscalene brachial plexus in various settings using three different US machines to create a dataset of 227 unique videos. In total, 41,000 video frames were annotated by experienced anaesthesiologists using partial automation with object tracking and active contour algorithms. Four baseline neural network models were trained on the dataset and their performance was evaluated for object detection and segmentation tasks. Generalizability of the best suited model was then tested on the datasets constructed from separate US scanners with and without fine-tuning. The results demonstrate that deep learning models can be leveraged for real time segmentation of supraclavicular brachial plexus in neck ultrasonography videos with high accuracy and reliability. Model was also tested for its ability to differentiate between supraclavicular and adjoining interscalene brachial plexus. The entire dataset has been released publicly for further study by the research community.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space.The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.

Efficient artificial photosynthesis systems are currently realized as catalyst- and surfacefunctionalized photovoltaic tandem- and triple-junction devices, enabling photoelectrochemical (PEC) water oxidation while simultaneously recycling CO2 and generating hydrogen as a solar fuel for storable renewable energy. Although PEC systems also bear advantages for the activation of dinitrogen - such as a high system tunability with respect to the electrocatalyst integration and a directly controllable electron flux to the anchoring catalyst through the adjustability of incoming irradiation - only a few PEC devices have been developed and investigated for this purpose. We have developed a series of photoelectrodeposition procedures to deposit mixed-metal electrocatalyst nanostructures directly on the semiconductor surface for light-assisted dinitrogen activation. These electrocatalyst compositions containing Co, Mo and Ru in different atomic ratios follow previously made recommendations of metal compositions for dinitrogen reduction and exhibit different physical properties. XPS studies of the photoelectrode surfaces reveal that our electrocatalyst films are to a large degree nitrogen-free after their fabrication, which is generally difficult to achieve with traditional magnetron sputtering or e-beam evaporation techniques. Initial chronoamperometric measurements of the p-InP photoelectrode coated with the Co-Mo alloy electrocatalyst show higher photocurrent densities in the presence of N2(g) than in the presence of Ar at -0.09 V vs RHE. Indications of successful dinitrogen activation have also been found in consecutive XPS studies, where both, N 1s and Mo 3d spectra, reveal evidence of nitrogen-metal interactions.

A transparent polarisation sensitive phase pattern exhibits a position and polarisation dependent phase shift of transmitted light and it represents a unitary transformation. A quantum ghost image of this pattern is produced with hyper-entangled photons consisting of Einstein-Podolsky-Rosen (EPR) and polarisation entanglement. In quantum ghost imaging, a single photon interacts with the pattern and is detected by a stationary detector and a non-interacting photon is imaged on a coincidence camera. EPR entanglement manifests spatial correlations between an object plane and a ghost image plane, whereas a polarisation dependent phase shift exhibited by the pattern is detected with polarisation entanglement. In this quantum ghost imaging, the which-position-polarisation information of a photon interacting with the pattern is not present in the experiment. A quantum ghost image is constructed by measuring correlations of the polarisation-momentum of an interacting photon with polarisation-position of a non-interacting photon. The experiment is performed with a coincidence single photon detection camera, where a non-interacting photon travels a long optical path length of 17.83~$m$ from source to camera and a pattern is positioned at an optical distance of 19.16~$m$ from the camera., Comment: 10 pages, 4 figures

A transparent polarisation sensitive phase pattern makes a polarisation dependent transformation of quantum state of photons without absorbing them. Such an invisible pattern can be imaged with quantum entangled photons by making joint quantum measurements on photons. This paper shows a long path experiment to quantum image a transparent polarisation sensitive phase pattern with hyper-entangled photon pairs involving momentum and polarisation degrees of freedom. In the imaging configuration, a single photon interacts with the pattern while the other photon, which has never interacted with the pattern, is measured jointly in a chosen polarisation basis and in a quantum superposition basis of its position which is equivalent to measuring its momentum. Individual photons of each hyper-entangled pair cannot provide a complete image information. The image is constructed by measuring the polarisation state and position of the interacting photon corresponding to a measurement outcome of the non-interacting photon. This paper presents a detailed concept, theory and free space long path experiments on quantum imaging of polarisation sensitive phase patterns., Comment: 13 pages, 7 figures

The seamless forecast approach of subseasonal to seasonal scale variability has been succeeding in the forecast of multiple meteorological scales in a uniform framework. In this paradigm, it is hypothesized that reduction in initial error in dynamical forecast would help to reduce forecast error in extended lead-time up to 2-3 weeks. This is tested in a version of operational extended range forecasts based on Climate Forecast System version 2 (CFSv2) developed at Indian Institute of Tropical Meteorology (IITM), Pune. Forecast skills are assessed to understand the role of initial errors on the prediction skill for MJO. A set of lowest and highest initial day error (LIDE & HIDE) cases are defined and the error-growth for these categories are analysed for the strong MJO events during May to September (MJJAS). The MJO forecast initial errors are categorized and defined using the well-known multivariate MJO index introduced by Wheeler &Hendon (2004). The probability distribution of bivariate RMSE and error growth evolution (first order difference of index error for each successive lead days) with respect to extended range lead-time are used as metrics in this analysis. The result showed that initial error is not showing any influence in the skill of model after a lead time of 7-10 days and the error growth remains the same for both set of errors. A rapid error growth evolution of same order is seen for both the classified cases. Further the physical attribution of these errors is studied and found that the errors originate from the events with initial phase in Western Pacific and Indian Ocean. The spatial distribution of OLR and the zonal winds also confirms the same. The study emphasises the importance of better representation of MJO phases especially over Indian ocean in the model to improve the MJO prediction rather than focusing primarily on the initial condition, Comment: 27 pages, 9 figures

This paper investigates the Cyber-Physical behavior of users in a large indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators. Our analysis shows that many users exhibit a high correlation between their cyber activities and their physical context. To find this correlation, we propose a mechanism to semantically label a physical space with rich categorical information from DBPedia concepts and compute a contextual similarity that represents a user's activities with the mall context. We demonstrate the application of cyber-physical contextual similarity in two situations: user visit intent classification and future location prediction. The experimental results demonstrate that exploitation of contextual similarity significantly improves the accuracy of such applications., Comment: Accepted in ACM Transactions on Sensor Networks, 2020

It is a widely accepted view that COVID 19 is either transmitted via surface contamination or via close contact of an un-infected person with an infected person. Surface contamination usually happens when infected water droplets from exhalation/sneeze/cough of COVID sick person settle on nearby surfaces. To curb this, social distancing and good hand hygiene advise is advocated by World health Organization (WHO). We argue that COVID 19 coronovirus can also be airborne in a puff cloud loaded with infected droplets generated by COVID sick person. An elementary calculation shows that a $5~\mu m$ respiratory infected droplet can remain suspended for about 9.0 minutes and a $2~\mu m$ droplet can remain suspended for about an hour! And social distancing advise of 3 feet by WHO and 6 feet by CDC (Centers for Disease Control and Prevention) may not be sufficient in some circumstances as discussed in the text., Comment: 6 pages and 1 figure

We obtained a density-dependent analytical expression of binding energy per nucleon for different neutron-proton asymmetry of the nuclear matter (NM) with a polynomial fitting, which manifests the results of effective field theory motivated relativistic mean-field (E-RMF) model. This expression has the edge over the Br$\ddot{u}$ckner energy density functional [Phys. Rev. {\bf 171}, 1188 (1968)] since it resolves the Coster-Band problem. The NM parameters like incompressibility, neutron pressure, symmetry energy, and its derivatives are calculated using the acquired expression of energy per nucleon. Further, the weight function calculated by E-RMF densities are folded with calculated NM parameters within coherent density fluctuation model to find the properties of closed/semi-closed-shell even-even $^{16}$O, $^{40}$Ca, $^{48}$Ca, $^{56}$Ni, $^{90}$Zr, $^{116}$Sn, and $^{208}$Pb nuclei. The values obtained for the neutron pressure $P^{A}$, symmetry energy $S^{A}$ and its derivative $L_{sym}^A$ known as slope parameter, lie within a narrow domain whereas there is a large variation in isoscalar incompressibility $K^{A}$ and surface incompressibility $K_{sym}^{A}$ while moving from light to heavy nuclei. The sizable variation in $K^{A}$ and $K_{sym}^{A}$ for light and heavy nuclei depicts their structural dependence due to the peculiar density distribution of each nucleus. A comparison of surface quantities calculated in the present work has also been made with ones obtained via Br$\ddot{u}$ckner energy density functional., Comment: 8 pages, 5 figures, 3 tables. Published in Phys. Rev. C 103 , 024305 (2021)