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Interdiffusion analysis in multicomponent alloy systems plays a pivotal role in controlling various processes and in designing materials. Interdiffusion of elements also leads to changes in microstructure and properties during service, especially for the materials operating at elevated temperatures. The urge of increasing efficiency of gas turbine engines has led to the demand of higher service temperatures and longer life, which is achieved by the application of thermal barrier coatings (TBC) on Ni based superalloys. To prevent oxidation damage to the superalloy substrate, bond coats are used in which diffusion acts as a key factor influencing the stability and durability of the engine components. Over the last few decades, β-(Ni,Pt)Al coatings have been widely employed as bond coat materials because the presence of Pt enhances oxidation resistance by accelerating diffusion of Al to generate a continuously growing TGO (Thermally grown oxide) layer. However, this also encourages the interdiffusion of Ni and Al between the bond coat and the substrate, which weakens the TBC system. Therefore, a thorough understanding of diffusion behaviour is required in the bond coat materials. In the present study, ternary interdiffusion coefficients are determined in β-(Ni,Pt)Al at 1100 °C. Knowledge of such diffusional interactions would further open new avenues to design bond compositions and process sequences in order to minimize the microstructural changes caused by interdiffusion processes in service.

Crises such as the COVID-19 pandemic are known to exacerbate depression and anxiety, though their temporal trajectories remain under-investigated. The present study aims to investigate fluctuations in depression and anxiety using the COVID-19 pandemic as a model crisis. A total of 1512 adults living in the United States enrolled in this online study beginning April 2, 2020 and were assessed weekly for 10 weeks (until June 4, 2020). We measured depression and anxiety using the Zung Self-Rating Depression scale and State-Trait Anxiety Inventory (state subscale), respectively, along with demographic and COVID-related surveys. Linear mixed-effects models were used to examine factors contributing to longitudinal changes in depression and anxiety. We found that depression and anxiety levels were high in early April, but declined over time. Being female, younger age, lower-income, and previous psychiatric diagnosis correlated with higher overall levels of anxiety and depression; being married additionally correlated with lower overall levels of depression, but not anxiety. Importantly, worsening of COVID-related economic impact and increase in projected pandemic duration exacerbated both depression and anxiety over time. Finally, increasing levels of informedness correlated with decreasing levels of depression, while increased COVID-19 severity (i.e., 7-day change in cases) and social media use were positively associated with anxiety over time. These findings not only provide evidence for overall emotional adaptation during the initial weeks of the pandemic, but also provide insight into overlapping, yet distinct, factors contributing to depression and anxiety throughout the first wave of the pandemic.

Renewable and sustainable sources such as SPV systems and WT energy conversion systems are now seen as a promising and growing alternative energy source in view of global emissions and deterioration of the FP. the main objective of the study designing of a solar PV system with varying irradiation in MATLAB/SIMULINK and enhance its output capacity before its integration with the grid. Also, the system has to be made to drive an unbalanced load and dynamic load so that it is capable of handling the change in the power demand of the system. And to design a suitable controller for the multilevel power converter such that it produces better output results than the traditional converter. AI technique has to be incorporated in the converter designing. This work provides a comprehensive design and implementation of power regulatory per phase inverter with proposed MF-CSA (Multi-Function Crow Search Algorithm) optimization controller. The Inverter has been provided with a proposed optimization technique while integrating it with the grid. The voltage output of the system from the modeled solar system with varying irradiation and temperature control is being fed to the inverter for DC to AC conversion

A slow wind tunnel should be designed and built. This project involved the design and construction of the small wind tunnel. During this project, a computer aided design (CAD) called ANSYS will be used to design the wind tunnel.

The circular central solar receiver helps in the generation of power output in a solar power plant. This paper of research encompasses mathematical development and modelling of a circular central solar receiver with variable diameter header. This modelling is utilized for analyzing the power output in a year. A desired electrical output is generated by the modeling. Methodologies were utilized for evaluating the desired parameters involved in the modelling of the entire system such as; size of the receiver, height of the tower etc. In this modelling system, solar simulator is utilized in the place of heliostat. The working fluid in the system is water. The type of receiver involved in the modelling is a circular central solar receiver. Simulation of the thermal solar receiver system is done. The solar irradiation heat flux of 3500 W/m2 is applied at the input.

PURPOSE: To assess the anatomy of the spinal accessory nerve (SAN), its variations and the landmark of level II B lymph nodes. METHODS: Prospective study included 50 patients from 2016 to 2018.The predictor variables were drawn from demographic details of the patients; SAN was analyzed intraoperatively with the parameters like the nerve relationship with the IJV, SCM muscle, contributions of cervical plexus and a new parameter of length from midpoint of clavicle to entry of nerve in the trapezius muscle in the lower part of neck which was studied for the first time and forms the prime identification landmark to preserve the nerve. Outcome variables were details of anatomic variations and branches and utility of these landmarks in prevention of nerve injury. RESULTS: Sample consisted of 38 (76%) male and 12 (24%) female patients. The SAN with respect to the IJV was dorsal in 42% patients and ventral in 58%. In 54% cases, SAN gave a branch to the SCM without penetrating the muscle and in 46% gave a branch to the SCM penetrating the muscle. SAN received contributions from the C2 root of the cervical plexus in 68%, both C2 and C3 in 54% and C3 in 50% cases. Mean length from measurements recorded between mid-line of clavicle to insertion of SAN to trapezius muscle and entry of SAN into trapezius muscle was 59 mm with variations recorded in gender and short/long neck. CONCLUSION: The result of this study suggests that parameters used are simple clinical tools for identification of the SAN and its variations resulting in no nerve injury. It is prudent for the surgeon to have knowledge of sound anatomical landmarks with the variations in the SAN course which avoids morbidity and improves the quality of life.

A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach—connectome-wide similarity—we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.

Objective Condylar fractures remain the most controversial topic in maxillofacial trauma. Open reduction and internal fixation (ORIF) with use of an extraoral approach has certain benefits over the nonsurgical treatment. Concerns, however, remain about the risk of facial nerve injury, postoperative facial nerve weakness, and facial scarring when operating in this region despite the various extraoral approaches that have been developed. The recently popularized endoscope-assisted open reduction and internal fixation (EAORIF) is claimed to provide better results because it is minimally invasive, provides excellent visibility, and eliminates surgical scarring and the risk of facial nerve injury. This study, therefore, aims to compare the retromandibular (extraoral) approach and EAORIF for the treatment of condylar fractures. Study Design A prospective analysis of 32 cases of condylar fractures that reported to the SDM Craniofacial Centre (Dharwad, India) was carried out. Sixteen patients had been treated with the retromandibular (RM) approach, and 16 had undergone EAORIF in the period from 2012–2017. Patients were evaluated for clinical parameters, such as fracture site, displacement, and surgical duration, as well as for functional parameters, such as occlusion, maximum interincisal opening, deviation of mouth on lateral movements, temporomandibular joint (TMJ) pain and clicking, and facial nerve weakness. Statistical significance was elicited with P Results The patients subjected to either approach had suffered fractures of the low condylar neck and the subcondyle. Maximum interincisal opening, mandibular deviation, occlusion, and TMJ function at postoperative month 6 were comparable between the groups. Although no permanent facial nerve injury was seen in this study, the incidence of transient facial nerve weakness was higher in the RM group (56.25%) compared with the EAORIF group (6.25%) (P = .036). The surgical time was longer in the EAORIF group (RM: 107+/– 19.7 minutes and EAORIF: 155 +/– 18.2 minutes) (P = .04). The rest of the parameters were comparable between both groups. Conclusions Although there is consensus on closed reduction in pediatric and adult condylar head fractures, the role of a surgical approach to treatment of displaced condylar neck and subcondylar fractures remains controversial. In our study, both surgical approaches were found to be suitable for the treatment of these fractures.

Human prosocial behaviors are constantly shaped by the push-and-pull between societal need for cooperation and one’s natural tendency to self-prioritize. Nevertheless, it remains elusive how our valuation and perceptual systems might contribute to altruistic acts under the influence of a real-world crisis. Here, using computational modeling and a game-theoretic approach, we investigated how the coronavirus pandemic perturbed altruistic choices in the United States, April-May, 2020. Overall, people made more altruistic choices as the pandemic worsened, an effect primarily driven by increased preference for social welfare. Paradoxically, participants also processed self-relevant information (i.e., “self-prioritization”) more efficiently at the perceptual level, as the pandemic became worse. These effects were not observed one year later (May-June, 2021) when the variability of the pandemic diminished. Furthermore, individuals’ prosocial choices and preferences did not correlate with their self-prioritization efficiency. Collectively, these results revealed a more nuanced view of human altruism — that as a dynamic and context-dependent construct, altruism can co-exist with increased attention to the self.

We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The temporal convolutional neural network model developed using 2D pose estimation performs multiclass classification of these 11 table tennis strokes with a validation accuracy of 99.37%. Moreover, the neural network generalizes well over the data of a player excluded from the training and validation dataset, classifying the fresh strokes with an overall best accuracy of 98.72%. Various model architectures using machine learning and deep learning based approaches have been trained for stroke recognition and their performances have been compared and benchmarked. Inferences such as performance monitoring and stroke comparison of the players using the model have been discussed. Therefore, we are contributing to the development of a computer vision based sports analytics system for the sport of table tennis that focuses on the previously unexploited aspect of the sport i.e., a player’s strokes, which is extremely insightful for performance improvement.

BackgroundPsychiatric neuroimaging typically proceeds with one of two approaches: encoding models, which aim to model neural mechanisms, or decoding models, which aim to predict behavioral or clinical characteristics from brain imaging data. In this study, we seek to combine these aims by developing interpretable decoding models that offer both accurate prediction and novel neural insights. We demonstrate the effectiveness of this combined approach in a case study of chronic marijuana use.MethodsChronic marijuana (MJ) users (n=195) and non-using healthy controls (n=128) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic MJ users and non-users based on task-evoked, whole-brain functional connectivity. We then used graph theoretic analyses to identify ‘predictive functional connectivities’ among brain regions that contributed most substantially to the classification of chronic marijuana use.ResultsWe obtained high (~80% out-of-sample) accuracy across four different classification models, demonstrating that task-evoked, whole-brain functional connectivity can successfully differentiate chronic marijuana users from non-users. Subsequent network analyses revealed key predictive regions (e.g., anterior cingulate cortex, dorsolateral prefrontal cortex, and precuneus) that are often implicated in neuroimaging studies of substance use disorders, as well as some key exceptions. We also identified a core set of networks of brain regions that contributed to successful classification, comprised of many of the same predictive regions.ConclusionsOur dual aims of accurate prediction and interpretability were successful, producing a predictive model that also provides interpretability at the neural level. This novel approach may complement other predictive-exploratory approaches for a more complete understanding of neural mechanisms in drug use and other neuropsychiatric disorders.

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

ObjectiveCrises such as the COVID-19 pandemic are known to exacerbate depression and anxiety, though their temporal trajectories remain unclear. The present study aims to investigate fluctuations in depression and anxiety using COVID-19 as a model crisis.Methods1,512 adults living in the U.S. enrolled in this online study on April 2nd, 2020 and were assessed weekly for 10 weeks (until June 4th, 2020; final n=537). Depression and anxiety were measured using the Zung Self-Rating Depression scale and State-Trait Anxiety Inventory (state subscale), respectively, along with demographic and COVID-related questions. Mixed-effects linear regression models were used to examine factors contributing to longitudinal changes in depression and anxiety.ResultsDepression and anxiety levels were high in early April, but declined over time (F(9,4824)=17.53, pF(9,4824)=23.35, pConclusionsAlongside evidence for overall emotional adaptation, these findings highlight overlapping (economic), yet distinct (change in COVID-19 cases, social media use, and projected COVID-19 duration) factors contributing to fluctuations in depression and anxiety throughout the first wave of COVID-19. These results provide insight into socioeconomic policies and behavioral changes that can increase emotional adaptation in times of crisis.

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional magnetic resonance imaging data, we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation-pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified “task-positive” and “task-negative” activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals “rested,” suggesting task-focused activity may occur during rest. Together our results suggest that brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states potentially reflecting a wide variety of cognitive processes.

This review focused on central tubular receiver modelling to obtain concentrated solar energy. Required specifications involved with efficient low cost receiver with suitable material which able to absorb concentrated solar radiations. This type of advance receivers shows excellent performance in the commercial and domestic applications. Review shows compiles literature engaged in mechanical and thermal modelling of receiver. Also highlighted on various geometries, tube sizes and various heat transfer fluids and their effect on overall performance of receiver. Recently authors concentrated on CFD simulations of different kind of receiver and put forth advanced design which offers superior flexibility and accuracy also explains stresses generated in the tube of receiver. The selection of stress theory is impacting on the mechanical life of the receiver with different approaches presented. Extensive technical analysis is investigated on different receivers for internal and external tubular geometry. Effect of heat transfer for different geometry is also observed. In this paper different aspects discussed thoroughly like receiver design, Numerical simulation, outdoor and indoor testing facilities, thermal efficiency and desired outlet temperature, benefits, future challenges, and research needs.