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Objectives: Time-to-treatment initiation is an important consideration for patients undergoing thoracic surgery for early-stage lung cancer because delays have the potential to adversely affect outcomes. This study seeks to quantify time-to-treatment initiation for patients with clinical stage I lung cancer, explore patient factors and predictors that lead to an increased time-to-treatment initiation, and compare surgeon perception of appropriate time-to-treatment initiation to the results. Methods: Time-to-treatment initiation was determined for patients enrolled in the Mount Sinai Initiative for Early Lung Cancer Research on Treatment study who underwent surgical resection for clinical stage I lung cancer between March 2016 and December 2021. The following dates were determined: (1) date of first suspicious radiologic imaging, (2) date of first biopsy, and (3) date of surgery. A total of 15 thoracic surgeons who participated in the Mount Sinai Initiative for Early Lung Cancer Research on Treatment were assessed on their perception on time-to-treatment initiation. Results: For 638 patients, median time from first suspicious imaging findings to biopsy was 40 days, biopsy to surgery was 37 days, and suspicious imaging to surgery was 84 days. Significant factors that resulted in longer time-to-treatment initiation in the multivariate analysis were African American or Black race (P = .005), vascular disease (P = .01), and median household income less than $75,000 (P = .04). Although the surgeon's perception was that the average time from biopsy to surgery was 28 days, it was longer for 63.5% of participants; surgeon perception of maximum time between diagnosis and surgery was 84 days and longer for 28.7% of participants. Conclusions: Patient factors such as race, income, and comorbidities were found to have differences in time-to-treatment initiation. Delays to surgery exceeded the expectations of thoracic surgeons.

Bone illnesses arise at a young age itself, so bone age assessment (BAA) is primarily utilized in paediatrics to identify their growth. Several BAA-related methods are employed to determine bone maturity; however, they do not give accuracy and the rate of error increases. To overcome this issue, in this manuscript, TW3-based Region of Interest (ROI) identification and classification with Chaotic ANN (CANN), as well as Deep Neural Network optimized with Evolved Gradient Direction optimizer (EVGO) architecture, is proposed for an Automated BAA classification. In this, Hand X-Ray images are taken from the Digital Hand Atlas (DHA) dataset and the Radiological Society of North America (RSNA) database. Here, Tanner-Whitehouse (TW3) and Shallow convolution neural network (SCNN) is utilized for extracting the ROI regions such as radius, ulna, and short bones (RUS) from the wrist region of the left hand. After that, the extracted ROI feature data are given to CANN for predicting BAA. Then, the predicted data are classified using the DNN-EVGO network. The bone age evaluation is then classified depending on maturity phases. The proposed approach is activated in MATLAB. Simulation outcomes of TW3- SCNN- CANN- DNN-EVGO- BAA methods for DHA database attain higher accuracy of 45.75%, 37.64%, 24.64% when analysed to the existing models, such as Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for BAA for paediatrics (TW3-F-R-CNN-AF-SFO), Multi-objective segmentation approach for BAA under parameter tuning- U-net architecture, and TW3-based fully automated bone age assessment system using deep neural networks (TW3-F-R-CNN-VGGNet-CNN-BAA). [ABSTRACT FROM AUTHOR]

In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system's unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively. [ABSTRACT FROM AUTHOR]

Thin films of FePtCo ternary alloy with 100 nm thickness have been deposited on an Si substrate using a DC magnetron sputtering system by co-sputtering of Co metal target and Fe40Pt60 alloy target in three different conditions, i.e. as-deposited at room temperature, annealed at 400 °C ${}_{}^ \circ C$ ∘ C , and deposited on the seed- layer. The effects of target sputtering power, leading to different compositions and various deposition conditions on surface roughness of films and magnetic properties, were studied. The average surface roughness of the as-deposited films decreases with the increase in the sputtering power of the Co target. For the annealed and seed-layered films, an increase in Co sputtering power resulted in an increased in roughness. Moreover, the annealed films possess smallerlower roughness due to an increase in the surface mobility. On the other hand, the Cu seed- layer decreases the roughness by preventing the diffusion of the film into the substrate. The room -temperature M-H measurement shows that the films exhibit soft ferromagnetism with coercivity in the order of ${10^3}$ 10 3 A/m and large saturation magnetizationmagnetisation of the order ${10^6}$ 10 6 A/m. The saturation magnetizationmagnetisation decreases with the increase in the sputtering power and is not influenced by the film surface roughness. On the other hand, riseincrease in surface roughness leads to decrease in coercivity of the films. HIGHLIGHTS: Thin films of FePtCo ternary alloy have been deposited by co-sputtering of Co metal target and Fe40Pt60 alloy target. The effects of surface roughness of films on magnetic properties were studied. The room-temperature M-H measurement shows that the films exhibit soft ferromagnetism. The saturation magnetisation decreases with sputtering power and is not influenced by the film surface roughness. On the other hand, increase in surface roughness leads to decrease in coercivity of the films. [ABSTRACT FROM AUTHOR]

Recently, Pt-free counter electrodes (CEs) have emerged as cost-effective alternatives for components in dye-sensitized solar cells (DSSCs), garnering significant interest in the field. In this regard, we have developed a novel hybrid composite consisting of 2D spinel phase CdFe2O4 and 2D reduced graphene oxide (rGO) nanosheets through a one-step ultrasonic-assisted hydrothermal method to serve as the CE for dye-sensitized solar cells. The crystallinity, morphology and textural properties of the composites were analyzed in detail using X-ray diffraction, scanning electron microscopy (FE-SEM), Transmission electron microscope, N2 adsorption–desorption, X-ray photo electron spectroscopy techniques. The findings obtained through electrochemical impedance spectroscopy, cyclic voltammetry and Tafel polarization measurements indicated that the CdFe2O4/rGO demonstrated robust electrocatalytic activity, enabling effective I−/I3− redox reactions and notably reduced charge transfer resistance in comparison with pristine CdFe2O4. Specifically, the DSSC utilizing the CdFe2O4/rGO CE attains a power conversion efficiency of 8.52%, significantly surpassing the efficiencies of 4.02%, 5.21%, and 6.64% observed in solar cells employing CdFe2O4, CdFe2O4-rG1, and CdFe2O4-based counter electrodes, respectively. Therefore, the CdFe2O4/rGO hybrid composite serves as a cost-effective alternative to high-cost Pt as a CE, thereby facilitating cost-efficiency in DSSCs in the future. [ABSTRACT FROM AUTHOR]

Handling of agricultural residues is a major concern today, since they pose a serious threat to the environment. Pyrolysis is one among the different techniques explored across the globe towards the effective conversion of these crop residues into various useful products. In this work, the degradation behaviour of corn husk loaded with tin oxide nanoparticles and raw corn husk are characterized separately by employing thermogravimetric analyzer (TGA). From the thermograms obtained, the energy required for facilitating the degradation process with the aid of the catalyst is found to be 98.48 kJ/mol. Further, the corn husk loaded with prepared tin oxide nanoparticles (0.1 g& 0.2 g) is subjected to pyrolytic degradation at different temperature ranges from 400 °C to 550 °C and compared with thermal pyrolysis. The biochar retained from the pyrolysis process is investigated for its efficacy as an energy storage material after coating them over graphite sheet. Electrochemical analysis reveals the fabricated SnO2/biochar nanocomposite have excellent energy storage characteristics (205 F/g at a scan rate of 10 mV/s) which was attributed to the presence of facile architecture with porous surface area. The outcomes of the present study open fresh gateways for the innovative electrodes in energy domain. [ABSTRACT FROM AUTHOR]

Multiphase fluids are common in many industries, such as oil and petrochemical, and volume fraction measurement of their phases is a vital subject. Hence, there are lots of scientists and researchers who have introduced many methods and equipment in this regard, for example, photon attenuation sensors, capacitance-based sensors, and so on. These approaches are non-invasive and for this reason, are very popular and widely used. In addition, nowadays, artificial neural networks (ANN) are very attractive in a lot of fields and this is because of their accuracy. Therefore, in this paper, to estimate volume proportion of a three-phase homogeneous fluid, a new system is proposed that contains an MLP ANN, standing for multilayer perceptron artificial neural network, a capacitance-based sensor, and a photon attenuation sensor. Through computational methods, capacities and mass attenuation coefficients are obtained, which act as inputs for the proposed network. All of these inputs were divided randomly in two main groups to train and test the presented model. To opt for a suitable network with the lowest rate of mean absolute error (MAE), a number of architectures with different factors were tested in MATLAB software R2023b. After receiving MAEs equal to 0.29, 1.60, and 1.67 for the water, gas, and oil phases, respectively, the network was chosen to be presented in the paper. Hence, based on outcomes, the proposed approach's novelty is being able to predict all phases of a homogeneous flow with very low error. [ABSTRACT FROM AUTHOR]

In this paper, we report a systematic study on effect of film thickness and substrate temperature on structural and magnetic behaviour of Fe-Ga films with Cu buffer layer, deposited on Si and Si/SiO2 substrates at room and high substrate temperatures. Grazing incidence X-ray diffraction studies reveal that all the films are crystalline with disordered alpha-Fe phase, which is BCC A2 phase. At 300 °C of substrate temperature, films deposited on Si/SiO2 substrate found to nucleate L12 phase from matrix of A2 phase, which is not seen in Si based films. Irrespective of substrate material, Fe-Ga films found to exist in dual phases at 500 °C substrate temperature. Lattice strains of the films were calculated from X-ray diffraction patterns, using Williamson-Hall method. From X- ray reflectivity analysis of all the films, film density, thickness and roughness of Fe-Ga layer and buffer layer were obtained after fitting the reflectivity data. Presence of an oxide layer is also evident from reflectivity fitting analysis. From magnetization studies it is clear that, all the films exhibited strong in-plane anisotropy. Saturation magnetization of films was found to vary with change in film density and oxide layer thickness of films. Saturation magnetization of films deposited on Si substrates found to decrease with decrease in film density. Coercivity of films found to vary with change in crystallite size, interface roughness and lattice strain of films. Films deposited on Si substrates are sensitive to strain with change in film thickness and films deposited on Si/SiO2 substrates are sensitive to strain with change in deposition temperature. Coercivity in films found to decrease with decrease in film roughness, increase in crystallite size and lowering the tensile lattice strain. [ABSTRACT FROM AUTHOR]

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for 'held-out data' were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure. [ABSTRACT FROM AUTHOR]

In the present report, we have studied the structural, microstructural, magnetic, exchange blas (EB) properties and magnetoresistance (MR) in Mn rich (Mn at. -50%) Mn50Ni50-xSnx (x = 10) shape memory Heusler alloy ribbons, prepared using the melt spinning method. These ribbons were found to exhibit a first order structural (i.e., martensitic) transition at around 205 K from the high temperature austenite to the low temperature martensite phase. The martensitic transition occurs at comparatively lower temperatures in these ribbons than in the same bulk alloy. Curie temperature of the austenite phase (TCA) is found to be larger i.e., around room temperature than that of bulk alloy. A significant EB field (HEB) of around 960 Oe has been observed at 2 K for these ribbons, which is found to be comparable to that reported for other Heusler systems. The presence of the EB effect in these ribbons is attributed to the coexistence of FM/AFM exchange interactions in the martensite phase. They are also found to show a maximum negative MR of around 12% near the martensitic transition, for 50 kOe field change. Investigation of DC magnetization, AC susceptibility measurements and the observation of training effect (i.e., characteristic feature of EB) strongly corroborates with the coexistence of FM/AFM exchange interactions in the martensite phase of these ribbons, which results in the large EB. The effects of temperature and magnetic field on the EB properties and MR have also been studied here. [ABSTRACT FROM AUTHOR]

There are fewer studies on Trichoderma diversity in agricultural fields. The rhizosphere of 16 crops was analyzed for Trichoderma species in 7 districts of Rajasthan state of India. Based on DNA sequence of translation elongation factor 1α (tef-1α), and morphological characteristics, 60 isolates were identified as 11 species: Trichoderma brevicompactum, species in Harzianum clade identified as T. afroharzianum, T. inhamatum, T. lentiforme, T. camerunense, T. asperellum, T. asperelloides, T. erinaceum, T. atroviride, T. ghanense, and T. longibrachiatum. T. brevicompactum is the most commonly occurring strain followed by T. afroharzianum. No new species were described in this study. T. lentiforme, showed its first occurrence outside the South American continent. The morphological and cultural characteristics of the major species were observed, described, and illustrated in detail. The isolates were tested for their antagonistic effect against three soilborne plant pathogens fungi: Sclerotium rolfsii, Rhizoctonia solani, and Fusarium verticillioides in plate culture assays. One of the most potent strains was T. afroharzianum BThr29 having a maximum in vitro inhibition of S. rolfsii (76.6%), R. solani (84.8%), and F. verticillioides (85.7%). The potential strain T. afroharzianum BThr29 was also found to be efficient antagonists against soil borne pathogens in in vivo experiment. Such information on crop selectivity, antagonistic properties, and geographic distribution of Trichoderma species will be beneficial for developing efficient Trichoderma-based biocontrol agents. [ABSTRACT FROM AUTHOR]

Purpose: This chapter explores the two major schemes applicable to skill development in India: Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP) and Pradhan Mantri Kaushal Vikas Yojana (PMKVY). Need for the Study: The primary objective of this research is to check the role of these schemes in enhancing the skills of socio-economically stressed community members for their livelihoods. The secondary aim is to analyse the outcomes of these schemes through a qualitative inquiry. Methodology: A survey was conducted, and the data was collected from trainees of the skill development programmes. Based on the responses, a qualitative content analysis was performed, which showed that most trainees have the thirst and urge to enhance their life skills for a minimalistic livelihood. Findings: The study concluded that though there are many schemes, only PMKVY is active. They focus on more than just youth communities. Instead, they consider individuals in different age categories. Practical Implications: The Government of India (GOI) is progressing towards a healthy economy to compete with other countries. For this mission to be achieved, skill and labour development is paramount. Appropriate training must be provided and administrated through government schemes. [ABSTRACT FROM AUTHOR]

Data visualization is a powerful tool for communicating complex and multidimensional information in an effective and engaging way. However, creating effective data visualizations requires not only technical skills, but also critical thinking skills to select, analyze, and interpret data. In this paper, we propose an inquiry-based active learning pedagogy approach to heighten learners' critical thinking skills for the course Data Visualization Analytics. We describe the design and implementation of a series of learning activities that involve learners in posing questions, collecting and exploring data, creating and presenting visualizations, and reflecting on their learning outcomes by collecting survey questions from 76 students. We also present the results of a mixed-methods evaluation of the approach, which involved all 76 undergraduate students from Kalasalingam Academy of Research and Education (Deemed to be University) in Tamilnadu, India. The evaluation showed that the approach improved learners' critical thinking skills, as measured by a pre-and post-test, and enhanced their engagement and satisfaction with the course, as indicated by a survey and interviews with four input parameters and one output parameter. We discuss the implications of our findings for the data visualization analytics course and suggest directions for students' efficacy by applying regression and correlation analysis. [ABSTRACT FROM AUTHOR]