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We have investigated the effect of gamma (γ)-irradiation on the structural and superconducting properties of FeTe0.55Se0.45 single crystals grown by the self-flux method. The impact of γ-irradiation on the superconducting transition temperature (TC), critical current density (JC), and vortex pinning mechanism has been systematically studied. The x-ray diffraction study reveals the growth of single crystals along the c-axis. The superconductivity has been confirmed in pristine and γ-irradiated samples through temperature-dependent resistivity (ρ (T)) and magnetization [M(T)] measurements. After irradiation, a slight improvement is observed in the upper critical field H c 2 (0) values. The values of thermally activated energy have been calculated and a crossover from a single to collective vortex pinning regime is observed. Additionally, we have analyzed the vortex phase diagrams, revealing a transition from vortex liquid to vortex glass state. Furthermore, the presence of second magnetization peak (SMP) or fishtail effect has been noticed in the M(H) loops, and with increasing temperature, the position of SMP (H s p ) shifts toward lower magnetic field regions. The critical current density has been estimated by Bean's critical state model at different magnetic fields [JC(H)] and temperatures [JC(T)]. The defects through gamma-irradiation lead to a significant threefold increase in JC compared to pristine samples in self-field and at 2 K. The pinning mechanisms have been explained using collective pinning theory and the Dew-Hughes model by analyzing the normalized pinning force density. Our analysis indicates that δl-pinning is dominant and point defects are present in all the samples. [ABSTRACT FROM AUTHOR]

In this study, we have synthesized CuO, ZnO, and CuO@ZnO composites with different mole ratios of CuO and ZnO, like CZ11 (1:1), CZ21 (2:1), and CZ12 (1:2) for NO2 gas sensing. Furthermore, the synthesized CuO, ZnO, and CuO@ZnO composites have been mixed with 10 wt% rGO. For electrical measurements, thin films of the synthesized samples have been fabricated by drop casting method which is abbreviated as CuO/rGO, ZnO/rGO, CZG11, CZG21, and CZG12. The measured percentage response values of CuO/rGO, ZnO/rGO, CZG11, CZG21, and CZG12 for 10 ppm NO2 gas at room temperature are 30, 45, 56, 55 and 77, respectively. Thus, among these samples, CZG12 has the best percentage response, approximately 2.6 and 1.7 times greater than CuO/rGO and ZnO/rGO sensors, with a response time of 13 s. XPS results confirm that this sample has more oxygen vacancies as compared to bare CZ12. Moreover, we investigated the sensing behavior of the CZG12 sensor at different temperatures (27–100 °C) and relative humidity (RH) levels (20–95%). The sensor shows a better percentage response at room temperature and for intermediate humidity (∼50%) levels. Additionally, at room temperature, the sensor shows negligible response to other gases such as NH3, CO, H2S, and Cl2, proving its excellent selectivity towards NO2 gas. Moreover, it has been observed that after 100 days, the response is almost constant, suggesting the long-term stability of this sensor. The remarkable gas sensing capabilities of CZG12 sensor strongly imply its immense potential for environmental surveillance and regulation. [ABSTRACT FROM AUTHOR]

Summary Software‐defined networking (SDN) is an emerging networking architecture paradigm that decouples the control and data planes. The problem of figuring out the number and positions of controllers and mapping of switches to them is known as the controller placement problem. To provide the resilience against the failure of a controller, each switch is mapped to a primary controller (first reference controller or FRC) and a backup controller (second reference controller or SRC). An existing work aims to minimize the worst‐case latency (WCL) from switch to controller when a controller fails. But this work misses the constraint specifying the definition of a switch's SRC, which might cause an increase in the latency between some switches and their controllers in the event of a controller failure. In order to address this issue, a model is proposed in this paper by incorporating the missing constraint. But the addition of this constraint can potentially cause an increase in the minimum number of required controllers. In order to address this issue, a second model is proposed in this paper by modifying the capacity constraint based on the observation that the capacity of a controller need not be reserved for all the switches for which it acts as SRC. The two proposed models aim at minimizing the WCL from switch to controller when a controller fails. Three network topologies are used to test the proposed models and compare their performance with the existing model in terms of principal and subsidiary metrics. The results demonstrate that the proposed models perform on equal level with the existing model in terms of WCL from switch to SRC while outperforming it in terms of average latency (AL). For example, the first proposed model achieves an average AL reduction of 21.63%, 8.55%, and 25.13% compared with the existing model on three networks. Similarly, the second proposed model achieves an average AL reduction of 21.3%, 8.55%, and 24.19% in each network on three networks. Moreover, the second proposed model achieves a fair trade‐off between the minimum number of controllers required and AL while outperforming both the existing and the first proposed models in terms of the average percentage of reserved controller capacity. [ABSTRACT FROM AUTHOR]

Though the Hyper-spectral images (HSI) are associated with rich spectral information for discriminating the class-specific objects, the high dimensional data generates Hughes effect for additional processing. So, during pre-processing, band Selection (BS) is done to lower the dimension. The proposed unsupervised BS technique follows ‘3ʹ subsequent steps, such as 1) spatial residual (Gaussian) filtering, 2) spatial feature-based band clustering (using K-means) and 3) Entropy-based ranking. Initially, the bands are filtered and the self residual images are obtained. The filtered residual images are mapped to k clusters, where each cluster represents unique spatial information. Further, each cluster undergoes selection through the information-theoretic (Entropy) approach. Also, from the selected optimal bands, SpatioSpectral information is extracted to appraise the performance using Support Vector Machine (SVM) classifiers with different state-of-art approaches. Performance measures like Average Accuracy (AA), Kappa (κ) and Overall Accuracy (OA) are contrasted and are presented in tables. The experiment shows propitious results as contrasted to other approaches, and suggests that pre clustering of the bands is informative as the adjacent bands are strongly correlated. Also, on comparing other approaches, the proposed one is computationally cheap and faster, which may best suit for online (in-camera) BS purposes.

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

Grade 630 martensitic precipitation hardening stainless steel has a combination of high hardness and strength after suitable heat treatment. The great advantage of this grade is that they are usually supplied in the condition treated with the solution and then aged to achieve a sufficient high strength. Chosen for this research work, AISI 630 stainless steel samples were subjected to salt bath nitriding process. Three samples were chosen in the shape of tapered cylindrical shapes with diameter of 10 mm and length of 40 mm. The specimens were subjected to 60 Minutes, 120 Minutes, 180 Minutes and named as SBN1, SBN2, SBN3 respectively. One untreated specimen is kept aside for the comparison purpose with the treated specimens. After the heat treatment process wear test were conducted by pin on disc wear test machine. Then the hardness test is carried out using the Rockwell hardness test. The microscopic images were taken using Scanning electron microscope. The results were compared and the best wear resistant material is chosen for the required application.

Gülfizar Sözeri-Varma,1 Nalan Kalkan-Oğuzhanoglu,1 Muharrem Efe,1 Yilmaz Kiroglu,2 Taçlan Duman11Department of Psychiatry, 2Department of Radiology, Faculty of Medicine, Pamukkale University, Denizli, TurkeyBackground: Previous studies have determined the neurochemical metabolite abnormalities in major depressive disorder (MDD). The results of studies are inconsistent. Severity of depression may relate to neurochemical metabolic changes. The aim of this study is to investigate neurochemical metabolite levels in the prefrontal cortex (PFC) of patients with mild/moderate MDD.Methods: Twenty-one patients with mild MDD, 18 patients with moderate MDD, and 16 matched control subjects participated in the study. Patients had had their first episode. They had not taken treatment. The severity of depression was assessed by the Hamilton Rating Scale for Depression (HAM-D). Levels of N-acetyl aspartate (NAA), choline-containing compounds (Cho), and creatine-containing compounds (Cr) were measured using proton magnetic resonance spectroscopy (1H-MRS) at 1.5 T, with an 8-cm3 single voxel placed in the right PFC.Results: The moderate MDD patients had lower NAA/Cr levels than the control group. No differences were found in neurochemical metabolite levels between the mild MDD and control groups. No correlation was found between the patients’ neurochemical metabolite levels and HAM-D scores.Conclusion: Our findings suggest that NAA/Cr levels are low in moderate-level MDD in the PFC. Neurochemical metabolite levels did not change in mild depressive disorder. Our results suggest that the severity of depression may affect neuronal function and viability. Studies are needed to confirm this finding, including studies on severely depressive patients.Keywords: major depressive disorder, magnetic resonance spectroscopy, N-acetyl aspartate, creatine, choline

Partial shading is the prevailing problem in Photovoltaic arrays. It results in severe consequences like multiple peaks in electrical characteristics, hot spots on Photovoltaic panels, disruptions in tracking maximum power and reduced output power. During most of the shading conditions, the shade concentrates in a particular area of the Photovoltaic (PV) array. This shading reduces PV array efficiency. Several physical reconfiguration schemes based on puzzle patterns are proposed to minimize this problem. The majority of these methods deduced to fixed PV array size for which the algorithm has proposed. Further, few of them are applicable only to lower size PV array. This paper presents a novel magic square puzzle for scattering the shade in the PV array to overcome the above constraints. In this method, the module physical position remains the same. However, the electrical wiring alters according to the puzzle pattern to change the row current in the Total-Cross-Tied topology PV array. The efficiency of the proposed system is examined under different shading conditions using characteristic curves. The relative analysis was carried between the conventional Total-Cross-Tied topology and the proposed Novel Magic Square-based Total-Cross-Tied topology. The MATLAB/SIMULATION results demonstrate that placing the modules according to the proposed reconfiguration method minimizes the peaks and enhances the output power (11.53%), with the global peak always on the right side of the PV curve under partial shading conditions. [ABSTRACT FROM AUTHOR]

In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing. [ABSTRACT FROM AUTHOR]

Superconducting thin films of two different thicknesses have been fabricated on (100) LaAlO3 substrates using a polycrystalline target of composition Fe1.05Te0.50Se0.50 by the pulsed laser deposition technique. The onset of superconducting transition temperatures (T C o n s e t) at 0 T magnetic field of the grown thin films of thickness of ∼74 nm and ∼185 nm are ∼12.65 and 13.15 K, respectively. The upper critical field BC2(0) values have been calculated by the Ginzburg–Landau theory as well as by the Werthamer–Helfand–Hohenberg model, and the corresponding coherence lengths have been estimated. In the present work, the thermally activated energy (TAE) has been obtained using the conventional Arrhenius law as well as by a modified thermally activated flux flow (TAFF) theory. For both models, the TAEs of vortices show a crossover at a magnetic field of ∼2 T corresponding to the transition from the single vortex pinning regime to the collective vortex pinning regime. Based on the analysis of the field dependence of TAE, the planer/point defect dominating magnetic field regions have been identified. The analysis of the magnetotransport data with the modified TAFF model reveals the dominating three dimensional behavior of vortices for both thin films. Moreover, in the vortex phase diagram, a narrow vortex melting region and a vortex glass-liquid phase transition below BC2 have been observed for both of the grown thin films. Furthermore, the X-ray photoelectron spectroscopy results show that Fe is present in Fe2+ and Fe3+ chemical states in both thin films. [ABSTRACT FROM AUTHOR]

The present manuscript reports an energy efficient g-Fe2O3-rGO-based humidity tolerant nanohybrids, fabricated on flexible tempered glass (FTG), for NO2 gas sensing at room temperature. An interaction between n-type g-Fe2O3 and p-type rGO semiconductors assisted in forming a p-n heterojunction at their interface, creating oxygen vacancies (Ov) in the g-Fe2O3 phase, as revealed by XPS analysis. The O2 adsorbed on these sites produced O2 - species, which facilitated the charge-transfer from O2 - to NO2 to eventually reduce the resistivity. The voltage at 0.3 volt restricted the effect of humidity on NO2 gas sensing in a wide RH range (15-97%), making it not only humidity tolerant but also energy efficient. The bending of the FTG up to about 1501 resulted in a negligible change in %response (from 56% to 50%) with a minor increase in recovery time, suggesting its potential for usage in flexible electronic devices. Its high selectivity for NO2 gas sensing is manifested by a minor decrease in %response from 56% to 49% in the presence of common air pollutant gases like Cl2, NO2, CO, NH3, C3H6O, H2S, and C2H5OH at 10-ppm of each. The role of rGO in the nanohybrids towards enhancing the conductivity is corroborated by the significantly lower %response observed (9%) for bare g-Fe2O3 film on FTG. The efficient sensing of NO2 gas has been correlated based on the comparison of the electron affinity (eV) values with other probe gases, following the order: NO2 (2.30) 4 CO (1.32) 4 H2O (1.3) 4 NH3 (0.16) 4 C3H6O (0.00152) 4 H2S (-1.16). Thus, the as-designed g-Fe2O3-rGO-based NO2 gas sensor, operating at 0.3 V at RT, demonstrating selective sensing with rapid response/recovery times (0.08 min/0.25 min) for 0.5 ppm NO2, having high %response, reproducibility, humidity tolerance in a wide RH range and long-term stability (452 weeks), is suggested to be a novel NO2 gas sensing device. [ABSTRACT FROM AUTHOR]

Consequences of the variation of the particle size and external magnetic field on the magnetic and transport characteristics have been investigated in the perovskite manganite of composition Nd0.50Ca0.50MnO3. The size of the crystallites (particles) enhances from ~ 17 nm (144 nm) to ~ 25 nm (600 nm) as TS enhances from 900 to 1300 ℃, with a simultaneous increase in the orthorhombic lattice parameters. The ZFC–FC magnetization-temperature (M-T) data recorded at H = 500 Oe show the coexistence of ferromagnetic (FM) and antiferromagnetic charge–ordered (AFM-CO) phases, and the accompanying ZFC–FC divergence gives signature of a cluster glass-like phase in the lower temperature regime. The FM region close to the Curie temperature (TC) exhibits scaling of the type M = M O (T C - T) β . The value of exponent β is found to be ~ 0.491 and 0.548 at 144 nm and 600 nm particle size, respectively. The value of β is found to increase as the M-T measurement field increases; e.g., for 600 nm particle size, its value increases from 0.5484 to 0.7551. In the absence of a magnetic field, all the samples remain insulators. A zero-field insulator–metal transition (IMT) is conspicuously absent. The application of a magnetic field induces IMT in all the samples, and it occurs at H = 60 kOe for 900 ℃, 40 kOe for 1100 ℃, and 20 kOe for 1300 ℃ sintered samples. Such behavior brings out the softening of the AFM-CO phase with an increase in particle size. [ABSTRACT FROM AUTHOR]

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate. [ABSTRACT FROM AUTHOR]