Phase and frequency-resolved microscopy of operating spin Hall nano-oscillator arrays.

Coherent optical detection is a powerful technique for characterizing a wide range of physical excitations. Here, we use two optical approaches (fundamental and parametric pumping) to microscopically characterize the high-frequency auto-oscillations of single and multiple nano-constriction spin Hall nano-oscillators (SHNOs). To validate the technique and demonstrate its robustness, we study SHNOs made from two different material stacks, NiFe/Pt and W/CoFeB/MgO, and investigate the influence of both the RF injection power and the laser power on the measurements, comparing the optical results to conventional electrical measurements. To demonstrate the key features of direct, non-invasive, submicron, spatial, and phase-resolved characterization of the SHNO magnetodynamics, we map out the auto-oscillation magnitude and phase of two phase-binarized SHNOs used in Ising machines. This proof-of-concept platform establishes a strong foundation for further extensions, contributing to the ongoing development of crucial characterization techniques for emerging computing technologies based on spintronics devices.

Novel Role of CD34+ Stromal Cells/Telocytes in Oral Submucous Fibrosis Development and its Malignant Transformation.

BACKGROUND: Telocytes are interstitial cells widely distributed in the extracellular matrix of numerous tissues distinguished by their long, thin, and moniliform projections. Telocytes have a role in the stimulation of angiogenesis and contribute to the development and progression of fibrosis. AIM: The current study aimed to assess and compare the telocyte distribution in normal mucosa, oral submucous fibrosis (OSF), and OSCC associated with OSF (OSCC-OSF). MATERIALS AND METHODS: Formalin-fixed and paraffin-embedded tissue blocks of 30 OSF cases, 15 OSCC-OSF cases, and 15 normal oral mucosae were obtained. Immunohistochemical staining was done with antibodies to CD34 to assess the vasculature and telocytes. The mean vascular density (MVD) and mean telocyte density were compared between the groups using the Kruskal-Walli test. RESULTS: A statistically significant high MVD (3.4 ± 1.22) and mean telocyte density (3.8 ± 1.35) was observed in OSCC-OSF cases while it was lowest in advanced OSF cases. MVD was higher in early OSF cases than in normal mucosa. CONCLUSION: This study showed a decrease in CD34-positive telocytes in OSF, indicating that telocyte loss promotes the development of fibrosis.Increased angiogenesis coexisted with an increase in telocytes in OSCC-OSF.

Association of Pharmacist Interventions With Adverse Drug Events and Potential Adverse Drug Events.

BACKGROUND: Adverse drug events (ADEs) are a frequent cause of injury in patients. Our aim was to assess whether pharmacist interventions compared with no pharmacist intervention results in reduced ADEs and potential adverse drug events (PADEs). METHODS: We searched MEDLINE, Embase, and two other databases through September 19, 2022 for any RCT assessing the effect of a pharmacist intervention compared with no pharmacist intervention and reporting on ADEs or PADEs. The risk of bias was assessed using the Cochrane tool for RCTs. A random-effects model was used to pool summary results from individual RCTs. RESULTS: Fifteen RCTs met the inclusion criteria. The pooled results showed a statistically significant reduction in ADE associated with pharmacist intervention compared with no pharmacist intervention (RR = 0.86; [95% CI 0.80-0.94]; p = 0.0005) but not for PADEs (RR = 0.79; [95% CI 0.47-1.32]; p = 0.37). The heterogeneity was insignificant (I2 = 0%) for ADEs and substantial (I2 = 77%) for PADEs. Patients receiving a pharmacist intervention were 14% less likely for ADE than those who did not receive a pharmacist intervention. The estimated number of patients needed to prevent one ADE across all patient locations was 33. CONCLUSIONS: To our knowledge, this is the first systematic review and meta-analysis of RCTs seeking to understand the association of pharmacist interventions with ADEs and PADEs. The risk of having an ADE is reduced by a seventh for patients receiving a pharmacist care intervention versus no such intervention. The estimated number of patients needed to be followed across all patient locations to prevent one preventable ADE across all patient locations is 33.

Highly efficient uranium uptake by the eco-designed cocamidopropyl betaine-decorated Na-P1 coal fly-ash zeolite.

In some locations around the globe, the U concentrations may exceed WHO standards by 2-folds therefore, effective yet environmentally wise solutions to purify radioactive waters are of significant importance. Here, the optimized and fully controlled coal-fly-ash based Na-P1 zeolite functionalization by employing novel, biodegradable biosurfactant molecule - cocamidopropyl betaine (CAPB) is showcased. The zeolite's surface decoration renders three composites with varying amounts of introduced CAPB molecule (Na-P1 @ CAPB), with 0.44, 0.88, and 1.59-times External Cation Exchange Capacity (ECEC). Wet-chemistry experiments revealed extremely high U adsorption capacity (qmax = 137.1 mg U/g) unveiling preferential interactions of uranyl dimers with CAPB molecules coupled with ion-exchange between Na+ ions. Multimodal spectroscopic analyses, including Fourier-Transformed Infra-Red (FT-IR), X-ray Photoelectron (XPS), and X-ray Absorption Fine Structure (XAFS), showed the hexavalent oxidation state of U, and no secondary release of the CAPB molecule from the composite. The EXAFS signals fingerprint changes in the interatomic distances of adsorbed U, showing the impact of the O and N, heteroatoms present in the CAPB molecule on U binding mechanism. The presented research outcomes showcase the easy, scalable, optimized, and environmentally friendly synthesis of biofunctional zeolite effectively purifying the real-life U-bearing wastewaters from the vicinity of the Pribram deposit (Czech Republic).

Enhancing handwritten text recognition accuracy with gated mechanisms.

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

Comparison of TL characteristics of natural quartz and zircon.

The dosimetry of different minerals is carried out to investigate the dose received by the population in case of a nuclear accident. Retrospective dosimetry is a field where there is a continuous search to find new materials. Beach sand minerals, namely quartz and zircon, were exposed to beta and gamma radiation and studied separately. A comparison of the thermoluminescence (TL) output of different peaks of quartz for beta and gamma was studied. Comparison of quartz peaks with the TL output of zircon peaks was carried out. TL output for a constant dose of gamma is always higher compared to the TL output received due to beta.

Cardiac arrhythmia detection using artificial neural network.

This paper outlines the development of the 'Cardiac Abnormality Monitoring' wearable medical device, aimed at creating a compact safety monitor integrating advanced Artificial Neural Network (ANN) algorithms. Given power consumption constraints and cost-effectiveness, a strategy combining sophisticated instruments with neural network algorithms is proposed to enhance performance. This approach aims to compete with high-end wearable devices, utilizing innovative manufacturing techniques. The paper evaluates the feasibility of employing the Levenberg-Marquardt (LM) ANN algorithm in power-conscious wearable devices, considering its potential for offline embedded systems or IoT gadgets capable of cloud-based data uploading. The Levenberg-Marquardt ANN is chosen primarily for its practicality in prototype development, with other neural network algorithms also explored to identify potential alternatives. We have compared the six neural network models and determined the model that has the potential to replace the primary neural network model. We found that the 'Kernelized SVC with PCA' can test accuracy. To be specific, in this paper, we will evaluate the performance of the ANN model and also check its feasibility and practicality by integrating it with a constructed prototypical working model.

Salvaging the second molar with platelet-rich fibrin and photobiomodulation (970 nm diode laser) following third molar extraction.

Second molars are more susceptible to periodontitis when present adjacent to periodontally hopeless third molars. It is crucial to restore the health of the second molar and to prevent a dry socket after third molar extraction. To ensure complete eradication of infection along with regeneration, mere nonsurgical periodontal therapy might be inadequate. Thus, in this case report, high-level laser therapy (HLLT), advanced-platelet-rich fibrin (A-PRF), and low-level laser therapy (photobiomodulation [PBM]) were adapted to obtain a synergistic effect. HLLT was accomplished for decontamination and clot stabilization following the third molar extraction. Further, A-PRF was placed and irradiated with PBM on 3rd, 7th, 15th, and 21st days, postoperatively. There was reduced clinical probing depth and gain in clinical attachment level with a significant radiographic bone fill distal to second molar at 3 months follow-up. This combined use of HLLT, and PBM along with PRF could be a reliable treatment approach for regeneration, particularly in acute infections.

Field tuning Kitaev systems for spin fractionalization and topological order.

The honeycomb Kitaev model describes aZ2spin liquid with topological order and fractionalized excitations consisting of gappedπ-fluxes and free Majorana fermions. Competing interactions, even when not very strong, are known to destabilize the Kitaev spin liquid. Magnetic fields are a convenient parameter for tuning between different phases of the Kitaev systems, and have even been investigated for potentially counteracting the effects of other destabilizing interactions leading to a revival of the topological phase. Here we review the progress in understanding the effects of magnetic fields on some of the perturbed Kitaev systems, particularly on fractionalization and topological order.

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform.

Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.

Baryon Electric Charge Correlation as a Magnetometer of QCD.

The correlation between net baryon number and electric charge, χ_{11}^{BQ}, can serve as a magnetometer of QCD. This is demonstrated by lattice QCD computations using the highly improved staggered quarks with physical pion mass of M_{π}=135 MeV on N_{τ}=8 and 12 lattices. We find that χ_{11}^{BQ} along the transition line starts to increase rapidly with magnetic field strength eB≳2M_{π}^{2} and by a factor 2 at eB≃8M_{π}^{2}. Furthermore, the ratio of electric charge chemical potential to baryon chemical potential, µ_{Q}/µ_{B}, shows significant dependence on the magnetic field strength and varies from the ratio of electric charge to baryon number in the colliding nuclei in heavy ion collisions. These results can provide baselines for effective theory and model studies, and both χ_{11}^{BQ} and µ_{Q}/µ_{B} could be useful probes for the detection of magnetic fields in relativistic heavy ion collision experiments as compared with corresponding results from the hadron resonance gas model.

The effect of material properties and process parameters on die filling at varying throughputs: A PLS-model-based analysis.

When tablets are manufactured on a rotary tablet press and the throughput is increased, it leads to changes in powder dynamics during die filling due to formulation characteristics and changing powder flow in the feed frame. This may result, a.o. in increased tablet weight variability, poorer content uniformity, capping and lamination. This research focuses on explaining the die filling performance depending on material properties and process settings, including throughput for small and large tablets. It was concluded that throughput had a negative impact on die filling variability, which is related to reduced residence time and lower fill fraction of the feed frame and dies. Furthermore, the die filling mechanism was inherently different for large tablets in comparison to small tablets. Higher die filling consistency was observed for dense, less porous, less compressible and better flowing powders. As a result of this work, a model was developed to predict the impact of formulation properties and process settings on die filling variability and its dependency on changes in throughput. This model will benefit formulation development at an early stage when active ingredient availability may be challenging as it will avoid the need to conduct experiments at high throughputs.

Effect of Detector Orientation and Influence of Jaw Position in the Determination of Small-field Output Factor with Various Detectors for High-energy Photon Beams.

Background: Accurate dose measurements are difficult in small fields due to charge particle disequilibrium, partial source occlusion, steep dose gradient, and the finite size of the detector. Aim: The study aims to determine the output factor using various detectors oriented in parallel and perpendicular orientations for three different tertiary collimating systems using 15 MV photon beams. In addition, this study analyzes how the output factor could be affected by different configurations of X and Y jaws above the tertiary collimators. Materials and Methods: Small field output factor measurements were carried out with three detectors for different tertiary collimating systems such as BrainLab stereotactic cones, BrainLab mMLC and Millennium MLC namely. To analyze the effect of jaw position on output factor, measurements have been carried out by positioning the jaws at the edge, 0.25, 0.5, and 1.0 cm away from the tertiary collimated field. Results: The data acquired with 15 MV photon beams show significant differences in output factor obtained with different detectors for all collimating systems. For smaller fields when compared to microDiamond, the SRS diode underestimates the output by up to -1.7% ± 0.8% and -2.1% ± 0.3%, and the pinpoint ion chamber underestimates the output by up to -8.1% ± 1.4% and -11.9% ± 1.9% in their parallel and perpendicular orientation respectively. A large increase in output factor was observed in the small field when the jaw was moved 0.25 cm symmetrically away from the tertiary collimated field. Conclusion: The investigated data on the effect of jaw position inferred that the position of the X and Y jaw highly influences the output factors of the small field. It also confirms that the output factor highly depends on the configuration of X and Y jaw settings, the tertiary collimating system as well as the orientation of the detectors in small fields.

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization.

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

Life cycle GHG emissions assessment of vanadium recovery from bitumen-derived petcoke fly ash.

Petcoke generated during bitumen upgrading is a potential source of vanadium for the global market. Recovering vanadium from the fly ash originating from the combustion of petcoke appears to be a suitable route for commercial implementation, given its high extraction rate. Although the technical feasibility of the recovery process has been proven, the environmental impact should be addressed. Information on the greenhouse (GHG) emissions from the process is scarce in the public domain. Therefore, a framework was developed for assessment of life cycle GHG emissions for extraction of vanadium from petcoke-based fly ash. This framework was used to perform a life cycle GHG emissions assessment of a water leaching and salt roasting process to extract vanadium from fly ash. For the upstream GHG emissions, we collected direct emissions data and energy consumption from the literature, and, for the process emissions, we developed a model to estimate energy and material balances based on process conditions. The emission factors for electricity production, fuel combustion, production of consumables, and gas treatment were used to obtain the life cycle GHG emissions. The results show that the life cycle GHG emission of vanadium recovery are 26.6-3.9+0.9 kg CO2eq/kg V2O5; 66% of these are direct GHG emissions. The process GHG emissions from fly ash decarbonization contribute the most to the life cycle GHG emissions. The air-to-fuel ratio for roasting and the GHG emission factors for petcoke combustion and the gas treatment operation are the inputs that most effect the model output. Compared with the production of V2O5 from vanadium titano-magnetite ore and bitumen upgrading spent catalyst, the petcoke fly ash pathway generates about twice the life cycle GHG emissions. This study's results can help determine areas of improvement in the upstream operations and the recovery process to reduce the life cycle GHG emissions to levels that can compete with primary and alternative routes to produce vanadium pentoxide. The results of this study can help in decision-making associated with vanadium extract from fly ash produced from combustion of petcoke.

AI in ECG: Validating an ambulatory semiology labeller and predictor.

OBJECTIVES: Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques. METHODS: We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance. RESULTS: Our final system using the Temple University Hospital's Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8-15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively. CONCLUSIONS: The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction. SIGNIFICANCE: EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient's quality of life.

Timeliness metrics for screening and preventing TB in household contacts of pulmonary TB patients in Kenya.

BACKGROUND: The study assessed whether a "7-1-7" timeliness metric for screening and TB preventive therapy (TPT) could be implemented for household contacts (HHCs) of index patients with bacteriologically confirmed pulmonary TB under routine programmatic settings in Kenya. METHODS: A longitudinal cohort study conducted among index patients and their HHCs in 12 health facilities, Kiambu County, Kenya. RESULTS: Between January and June 2023, 95% of 508 index patients had their HHCs line-listed within 7 days of initiating anti-TB treatment ("First 7"). In 68% of 1,115 HHCs, screening outcomes were ascertained within 1 day of line-listing ("Next 1"). In 65% of 1,105 HHCs eligible for further evaluation, anti-TB treatment, TPT or a decision for no drugs was made within 7 days of screening ("Second 7"). Altogether, 62% of screened HHCs started TPT during the "7-1-7" period compared with 58% in a historical cohort. Main barriers to TPT uptake were HHCs not consulting clinicians, HHCs being unwilling to initiate TPT and drug shortages. Healthcare workers felt that a timeliness metric was valuable for streamlining HHC management and proposed "3-5-7" as a workable alternative. CONCLUSIONS: The national TB programme must generate awareness about TPT, ensure uninterrupted drug supplies and assess whether the "3-5-7" metric can be operationalised.

CONTEXTE: L'étude a évalué si une mesure de rapidité "7-1-7" pour le dépistage et le traitement préventif de la TB (TPT) pouvait être mise en œuvre pour les contacts familiaux des patients index atteints de TB pulmonaire confirmée bactériologiquement dans le cadre d'un programme de routine au Kenya. MÉTHODES: Étude de cohorte longitudinale menée auprès de patients index et de leurs contacts familiaux dans 12 établissements de santé du comté de Kiambu, au Kenya. RÉSULTATS: Entre janvier et juin 2023, 95% des 508 patients index ont eu leur centre de santé inscrit sur la liste dans les 7 jours suivant le début du traitement antituberculeux (« First 7 ¼ ). Dans 68% des 1 115 centres de santé, les résultats du dépistage ont été vérifiés dans le jour suivant l'inscription sur la liste (« Next 1 ¼). Dans 65% des 1 105 centres de santé éligibles pour une évaluation plus approfondie, le traitement antituberculeux, le TPT ou la décision de ne pas prendre de médicaments a été prise dans les 7 jours suivant le dépistage (« Second 7 ¼). Au total, 62% des patients dépistés ont commencé un traitement antituberculeux au cours de la période « 7-1-7 ¼, contre 58% dans une cohorte historique. Les principaux obstacles à l'adoption du TPT étaient les suivants : les centres de santé ne consultaient pas les cliniciens, les centres de santé n'étaient pas disposés à commencer le TPT et les pénuries de médicaments. Les professionnels de la santé ont estimé qu'une mesure de la rapidité d'exécution était utile pour rationaliser la gestion des centres de santé et ont proposé le « 3-5-7 ¼ comme solution de rechange viable. CONCLUSION: Le programme national de lutte contre la TB doit sensibiliser au TPT, garantir un approvisionnement ininterrompu en médicaments et évaluer si la mesure « 3-5-7 ¼ peut être mise en œuvre.