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Fog computing (FC) is a contemporary computing paradigm that gives additional support to cloud environment by carrying out some local data analysis in edge of the devices, facilitating networking, computing, infrastructure and storage support as backbone for end user computing. Still enterprises are not convinced to use this as security and privacy are most of the open and challenging issues. Availability among the security requirements is the one which is about rendering on demand service to different client applications without any disruptions. It can often be demolished by Denial of service (DoS) and distributed denial of service (DDoS) attacks in fog and cloud computing environment. In this paper we propose a novel Source based DDoS defence mechanism which can be used in fog environment as well as the cloud environment to mitigate DDoS attacks. It makes use of Software Defined Network (SDN) to deploy the DDoS defender module at SDN controller to detect the anomalous behavior of DDoS attacks in Network/Transport level. The proposed work provides deep learning (DL) based detection method which makes use of the network traffic analysis mechanisms to filter and forward the legitimate packets to the server and can block the infected packets to cause further attacks.

The desiccant wheel based air conditioning systems are superior over conventional vapour compression systems in terms of the efficiency and power consumption. Researchers are consistently focusing on the various aspects of the desiccant wheel such as the architecture of the wheel, effect of operating parameters on the performance of the wheel and the overall configuration of air-conditioning systems. For the simulations of the air-conditioning systems it is essential to have an accurately performing model of the desiccant wheel. Thus, for solving this purpose, the radial basis neural network is applied to forecast the performance of a desiccant wheel data. The input parameters are the inlet process air humidity, temperature, regeneration temperature, number of rotations per hour of the desiccant wheel and the ratio of the volume flow rates of the process air and the regeneration air. The output parameters are the temperature and humidity of the process air outlet stream. The model is able to predict the original data within ± 5% accuracy and the r-square value is obtained to be 0.9963. A parametric study presenting the variations of the process air outlet temperature and humidity with respect to the various input parameters is also included in the article.

Quinoline Schiff bases are notified for their extensive pharmacological applications which paved way for the continuous exploration of their properties by the chemists of various fields. The theoretical exploration of organic compounds finds new dimensions in the recent advances of science intend us to study the nonlinear optical behaviour of Schiff Bases. In this context, four series of substituted hydrazinoquinoline Schiff bases 1–4 [ 4-methyl-2-salicylidenehydrazinoquinoline (1), 4,6-dimethyl-2-salicylidene hydrazine quinoline (2), 6-chloro-4-methyl-2salicylidenehydrazinoquinoline (3), 4-methyl-6-methoxy-2-salicylidene hydrazine quinoline (4)] were synthesised and characterised by IR and 1H NMR spectral studies. The DFT studies of the synthesised compounds viz., Frontier Molecular Orbital analysis and first hyperpolarisabilty were carried out by using B3LYP functional 6-31G (d,p) basis level. From the DFT results, the first hyperpolarisability suggest that the studied compounds have good non-linear properties. The global reactivity descriptors value indicates that the synthesised compounds are hard with greater kinetic stability. Having lower band gap energy of 4.1 eV–3.73 eV and a low lying LUMO, the synthesised compounds might be a excellent NLO candidate and also act as superconductors, and may have prospective used in both technological as well as pharmaceutical related applications.

In a Wireless Sensor Network (WSN), one of the most important issue is to minimize the energy consumption without losing accuracy during faster data transmission. During information broadcast, message communication is to be sent in an optimized way to increase energy efficiency in the networks. By applying various techniques and methodology in cluster WSN the network lifetime is increased and delay is minimized with the load balanced network. To accomplish load balance, Adelson-Velskii and Landis (AVL) tree rotation clustering algorithm is simulated considering the cluster sensor node. A single large area network is divided into multiple clusters using modified K-means clustering algorithm. Computational complexity is reduced through the construction of Minimum Connected Dominating Set with Multi-hop Information (MCDS-MI) and Bi-Partite Graph (BG) technique. Cluster Head (CH) assortment mechanism is implemented to find maximum cover set count of the sensor nodes. In addition, the enactment of the anticipated design is established through simulations during scalable data transmission in a WSN. Hypothetical investigation and experimental simulations are studied by measuring various performance evaluation metrics namely Virtual Dominators, Size Reduction, Network Lifetime and Residual Energy. The results shows that the proposed MSDS-MI system has maximum reduction in network size of 50%, maximum increase in network lifetime of 60% and saved maximum residual energy consumption of 47.76%. The results are encouraging and our proposed method is found to be more efficient than Connected Dominating Sets (CDS), Pseudo Dominating Sets (PDS), Dynamic Cluster Head Genetic Algorithm (DCH-GA) and Distributed Self-Healing Approach (DSHA).

The outbreak of the novel Coronavirus in late 2019 brought severe devastation to the world. The pandemic spread across the globe, infecting more than ten million people and disrupting several businesses. Although social distancing and the use of protective masks were suggested all over the world, the cases seem to rise, which led to worldwide lockdown in different phases. The rampant escalation in the number of cases, the global effects, and the lockdown may have a severe effect on the psychology of people. The emergency protocols implemented by the authorities also lead to increased use in the number of multimedia devices. Excessive use of such devices may also contribute to psychological disorders. Hence, hence it is necessary to analyze the state of mind of people during the lockdown. In this paper, we perform a sentiment analysis of Twitter data during the pandemic lockdown, i.e., two weeks and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral tweets would assist us in determining how people are dealing with the pandemic and its effects on a psychological level. Our study shows that the lockdown witnessed more number positive tweets globally on multiple datasets. This is indicative of the positivity and optimism based on the sentiments and psychology of Twitter users worldwide. The study will be effective in determining people's mental well-being and will also be useful in devising appropriate lockdown strategies and crisis management in the future.

As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind a series of words may essentially be used to understand the attitude, opinions, and emotions of users. We propose a novel long short-term memory (LSTM)–convolutional neural networks (CNN)–grid search-based deep neural network model for sentiment analysis. The study considers baseline algorithms like convolutional neural networks, K-nearest neighbor, LSTM, neural networks, LSTM–CNN, and CNN–LSTM which have been evaluated using accuracy, precision, sensitivity, specificity, and F-1 score, on multiple datasets. Our results show that the proposed model based on hyperparameter optimization outperforms other baseline models with an overall accuracy greater than 96%.

Titanium alloys, especially Ti–6Al–4V, were reported as a promising biomaterial featuring excellent corrosion resistance and good biocompatibility, but it lacks anti-infection capacity and osseointegration. In this work, we aimed to improve the bio-functions and corrosion resistance of AT-Ti-6Al–4V by applying cerium incorporated hydroxyapatite using a spin coater. The triple layer of Ce-HAP coatings was systematically accumulated using different speeds such as 2000, 3000 and 4000 RPM, which were sintered for 2 h at 500 °C. XRD and SEM-EDS characterize the developed Ce-HAP coatings to confirm the phase formation and surface morphology. The Colony Forming Unit confirms that Ce-HAP coatings exhibited significant inhibition efficiency towards the pathogens. In vitro bioactivity study evaluated in SBF indicates excellent apatite layer formation on Ce-HAP coatings. The cell viability studies using MG-63 cells showed good cell proliferation and attachments on a coated implant. The electrochemical properties of coatings were analysed by EIS and the result displayed pronounced anti-corrosion property. This study suggests that the cerium incorporated HAP promotes bioactivity with rapid osteointegration and presented higher corrosion resistance that makes it essential for the desired biomedical applications.

The unexpected emergence of a novel and dangerous virus widely known as coronavirus (COVID-19) has brought in global health crisis and has socioeconomically disrupted the lives of people to a significant extent. In order to curb the spread of the contagious infection, nationwide lockdown and sometimes shutdown is announced by the state which has adversely affected the lives of many members of transgender community who are solely dependent on social interactions for their livelihood such as begging in streets and trains, entertaining in marriage functions and baby showers, engaging in prostitution. Consequently, the lost livelihood, clogged income stream, exhausted savings, burden of debt have imbued psychological distress among them. This study has analyzed the association of social distancing and lockdown constraints to psychological outcomes (i.e., depression, anxiety issues, financial stress, lack of social support, and loneliness) among transgender people amid COVID-19. Transgender participants (N = 150, mean age = 42.5) were selected through snowball sampling from India who responded to the questionnaire through both online platform and offline modes. Findings show that there is positive association between any two of the variables and the degree of association is quite reasonable with the range from 0.534 to 0.945. Further, in order to evaluate the hypothesis a series of regression analysis is conducted. Results highlight the negative impact of social distancing and lockdown constraints on psychology of transgender individuals.

This paper presents a hybrid AC/DC microgrid consisting of multiple distributed energy sources, modeled in MATLAB/Simulink environment that undergoes islanding and non-islanding (symmetrical and unsymmetrical Faults) disturbances. These disturbances result in various changes in circuit parameters (voltage, current, and frequency) and this paper focuses only on disturbed current signals that are extracted from the solar and wind-connected buses. Further to extract rich and useful fault features from the extracted current signals, an adaptive detection technique, local mean decomposition (LMD) is proposed that results in series of product functions (PFs). Various feature extraction indices are used to extract the underlying features of the decomposed PFs that are processed through a deep-stacked random vector functional link network (RVFLN) based auto-encoder (AE) technique for classifying the faults. The effectiveness of the proposed RVFLN based AE technique is evidenced in terms of classification accuracy (CA) and confusion matrix (CM). The performance of the proposed technique has been evaluated through reliability analysis (MAPE, MAE, RMSE, and CM) by comparison with various artificial neural networks under both islanding and non-islanding mode of operation.

Cubic phase of MgO nanoparticles were prepared by microwave-assisted combustion synthesis and investigated for the removal of toxic dyes like Congo Red (CR) and Trypan blue (TB). The crystallite size of the MgO nanoparticle was calculated to be 18 nm from XRD pattern. The sample was further characterized by FTIR, TGA and FESEM techniques. The dyes were subjected to prototypical batch adsorption process, including investigation of different parameters like MgO dosage, dye concentration, solution pH, agitation speed and temperature. It was found that, 0.2 g of MgO NPs showed maximum removal efficiency for both the dyes (more than 98%), having 25 ppm of dye concentration at an acidic pH (3–4). The maximum loading capacity of MgO NPs was obtained to be 136 mg/g and 132 mg/g for CR and TB, respectively. Different thermodynamic parameters like ΔG0, ΔH0 and ΔS0 were measured. The negative ΔH0 and the positive ΔS0 values for both the dyes correspond to an exothermic process and an increase in randomness of the adsorbent and dye. The isotherm analysis exhibited that the Freundlich model fits better to the experimental equilibrium data, suggesting heterogeneous surface of the nanoparticles. Whereas, the kinetic data revealed a pseudo 2nd order rate for adsorption process.

This paper describes a novel implicit fourth-order approximation for solving the 2D quasilinear elliptic partial differential equations on an irrational domain. In this technique, we use 9-grid points compact cell with half-step unequal mesh discretization in both x- and y-coordinates. We also study fourth-order explicit approximations for the solution of normal derivatives on an irrational domain. The proposed method has been extended to solve quasilinear elliptic equations in vector form. Stability analysis is discussed in order to verify the convergence of the proposed computational method. On applications, we solve several benchmark nonlinear elliptic problems including steady-state Burgers’ and Navier–Stokes equations. The outcome of numerical results is shown the reliability, accuracy and the stability character of the method even for high values of Re.

Children learn and develop their abilities at their own pace. One of the most basic skills that they acquire is reading. However, some children struggle with reading longer than their friends, and in such a case, it is possible that they have a learning disorder known as dyslexia. The paper aims to use neural networks, namely generative neural networks, for generating raw audio data of two- or three-letter Hindi words. Using the generated data, a system will be built that will pronounce generated words for children recuperating from dyslexia. The system aims to be an effective helping tool for teachers to speed up the recuperation process by making the child repeat the correct pronunciation of the word. The system uses advance Mel-generative adversarial network neural network for working with Mel-spectrograms of the raw audio, by which the system will model its own audio iteratively, until a satisfactory result is achieved. Generated audio sample contains the Hindi words which will be taught to children. Mel-generative adversarial network will be used to generate audio samples since it provides better results compared to other existing models. 300 basic two- or three-letter Hindi words are taken as an input for assisting 5- to 8-year children. Minimum opinion score is calculated for comparison.

BACKGROUND Hypothyroidism is a common endocrine disorder which affects 11 % of the adult population in western countries. Hypothyroidism can cause a wide variety of anaemic disorders. Patients with both hypothyroidism and Vitamin B12 deficiency also have similar symptoms. Thus, this study was conducted to evaluate the relationship between the two and also hypothyroidism due to autoimmune cause. METHODS This was a descriptive study of 50 newly detected hypothyroid patients from Rajarajeswari Medical College evaluated for vitamin B12 deficiency. The study was conducted between January 10th 2019 to July 21st 2019. Lab parameters analysed included haemoglobin, thyroid function test (TFT), vitamin B12 levels and antithyroid peroxidase (anti-TPO) antibody levels. RESULTS Of the 50 hypothyroid patients evaluated, 23 were males and 27 were females between the age of 18 to 70 years. Anti TPO antibodies were present in 24 patients (48 %) out of 50, out of which 17 (70 %) patients had vitamin B12 deficiency. Out of 50 hypothyroid patients, 26 patients (52 %) had vitamin B12 deficiency. Thus hypothyroidism and autoimmune thyroid disease was associated with Vitamin B12 deficiency which was statistically significant. CONCLUSIONS Patients with hypothyroidism were studied for the incidence of deficiency of vitamin B12. It was found that 26 of 50 (52 %) patients had low B 12 levels. Incidence in females (54 %) was more than in males (46 %). Our study showed association between hypothyroidism and vitamin B12 deficiency and also autoimmune thyroid disease. KEYWORDS Hypothyroidism, Autoimmune Thyroid Disease, Vitamin B12 Deficiency

Semiconducting nanowires with modulated structures are potential candidates for application in nanoelectronics and photonics devices and are crucial for the fundamental understanding of electron tr...

Internet of Things (IoT) is one of the most powerful platforms that incorporates several other technological components within itself. The IoT ecosystem comprises devices, communications, protocols, analytics, cloud, automation, etc. Its magnitude keeps on increasing with the addition of tools and services. While IoT has many advantages like connectivity, efficiency, and convenience, it is a known fact that privacy and security issues are prevalent in the IoT network. To minimize the security and privacy issues, we propose a blockchain-based solution. In this paper, we design policies based on smart contacts, which is a self-enforcing agreement embedded in computer code managed by a blockchain. We propose three different policies: Hardware and Device Security Policies, Access and Authentication policies, and Application security for the IoT network. Since blockchain-based solutions ensure trust and stability, this may be one of the most robust techniques to alleviate the IoT network’s security and privacy issues. Also, we calculate the throughput and latency of the IoT enabled blockchain network and compare the power consumption of the IoT device at the time of data request with other proposed systems.

In recent years, photocatalysis and photoelectrocatalysis (PEC) have emerged as efficient strategies for facing the energy crisis and environmental pollution. In this regard, the very suitable and exciting material, graphitic carbon nitride (g-C3N4), has drawn spectacular attention because of its fascinating physicochemical properties. However, its practical application in the field of photocatalysis and PEC is still a bottleneck due to its narrow solar energy absorption range, low surface area, which provides less active sites, low electronic conductivity due to deficient CN donor density, poor electron–hole separation efficiency, insufficient redox potentials, and difficulty in the formation of thin films due to its poor dissolution in common solvents. To optimize the redox potential and provide effective spatial charge separation, Z scheme systems are being fabricated by imitating the natural photosynthesis process. In this review, the historical development of g-C3N4-based Z scheme systems is summarized along with their construction, synthesis, and various photocatalytic and PEC applications. g-C3N4-based Z scheme systems, such as the redox mediator solution-phase Z scheme, solid-state Z scheme, and direct Z scheme, and their characterization methods have been discussed briefly. Also, the methodology for the detection of Z scheme charge transfer processes has been explored. Lastly, conclusions and various future perspectives regarding the challenges in the construction and progress of the Z scheme photocatalytic systems are presented.

The photosensitivity of amorphous chalcogenide thin films brings out light-induced changes in the nonlinear and linear optical parameters upon sub-bandgap and bandgap laser irradiation. The present work reports the in situ laser irradiated changes in the optical properties of As50Se40Sb10 thin films. The thermally evaporated film showed an exponential decrease in optical bandgap and increase in Urbach energy. The decay rate of the bandgap was 6.24 min and growth rate of Urbach energy was 6.67 min respectively. The dynamics of photo-induced changes were observed from the changes in linear refractive index and its dependent parameters such as 3rd order nonlinear susceptibility, nonlinear refractive index, dispersion and dielectric parameters. The conversion of heteropolar to homopolar bonds induced the photodarkening mechanism that changed the dispersion parameters. The decrease in Ed and Eo reduced the oscillator strength along with the zero-frequency dielectric constant. The optical and electrical conductivity changed significantly with time. The changes were saturated with time which brings stability in the film properties that is useful for various optical applications. However, no structural and compositional changes upon laser irradiation were noticed from the X-ray diffraction and EDX studies respectively. The surface homogeneity was checked from the FESEM picture.

Recently, the development of cathode materials for Na-ion batteries has gained much attention due to the abundance, low cost, and easy availability of resources. Apart from the usual metal oxides, multi-electron redox materials grabbed attention due to their high energy density and practical capacity with long cycle life. Polyoxometalates (POMs) are inorganic clusters of higher valent metals, and act as electron sponges with multi-electron redox properties. Herein we report a Keggin-type polyoxometalate [PMo10V2O40]5− with Na+ and H+ counter cations as a cathode material for Na-ion batteries. Further the formation of POM is evidenced by PXRD, FT-IR, flame photometry and XPS studies. In Na-POM, Na+ ions in the intercluster cavities provide a better pathway and easy diffusion during the charge/discharge process, and contribute to better electrochemical properties than H-POM. The DFT studies further explore the detailed mechanistic pathway of Na+ ions around the clusters in the normal and super-reduced states. Na-POM enables better cycling stability and capacity retention with a specific discharge capacity of 123 mA h g−1 at 0.1C rate at room temperature.

In this research work, rice husk ash has been used as an admixture and the strength properties have been studied. The influence of 15% rice husk ash on strength properties has been compared with normal M30 concrete. The water cement ratio has been kept constant as 0.45. Strength tests like compressive strength, split tensile strength, flexure strength has been carried out at 28 days for normal concrete and 15% rice husk ash replaced concrete mix. For practical application of rice husk ash, reinforced beams were cast with normal concrete and rice husk ash. Both the beams were checked for load deflection. From strength tests, rice husk ash mix gave good compressive strength, split tensile strength and flexure strength values. Test on reinforced beam indicated that rice husk ash beam gave higher ultimate load value compared to normal M30 concrete beam.

As the civilization is evolving and rapid growing modernization many technical advancements has been made, which resulted in depletion of natural resources. One of the main natural construction materials is river sand, the demand for the stand increasing day-by-day. To meet the desired needs at economical ways partial usage of coir pith may be used. The outer fibre, which is taken out from the dried coconut husk is named as coir pith. These fibres are commonly discarded as an agricultural waste product. Dumping of huge quantity of coir pith in fertile or any good quality land creates numerous health problems and these lands get disturbed both physically and chemically due to its less deterioration in soil. The major properties of coir pith are an excellent moisture retention material even after drying. Enhancing internal curing maintains uniform temperature and retained all over the process of concrete to reduce or completely rid of certain crack problems like thermal shrinkage. The process of maintaining the moisture content inside the concrete for a particular period gives or enhance mechanical strength and thus it was names as curing period of concrete. This concrete possesses greater physical resiliency that withstands compression better, resulting in greater compressive strength, also 100% degradable, organic and a renewable resource and also making it economical. This paper describes various methods adopted to partially replace coir pith to fine aggregate and suggest some of the methods for utilizing coir pith in construction industry. Experimental analysis to find the physical and strength properties of coir pith are done. Comparatively treated coir pith gives better result than untreated coir pith on the basis of strength and bonding property with concrete.

The nanoscale memristor device SPICE modeling is significant for memristive circuits and neuromorphic system design applications. In this paper, a novel modified piece-wise linear (PWL) window function proposed for the nonlinear property of the memristor SPICE model. The PWL window function used here can address the shortcoming of the parabola and Heaviside- step function based typical window functions proposed in the literature, those are established by symmetrical functions. However, in most of the typical window functions, the monotonically increasing and the asymmetrical functions have not been addressed. Also, using this PWL-window function based memristor model, a binary-weighted memristor-based artificial synaptic circuit has been designed. The performance of the device model demonstrated by the efficiency of designed artificial synapse sensitivity to the external input signal.

Phosphorus, sulfur, and boron doped graphitic carbon nitride were successfully synthesized by solid-state calcination of melamine, as precursor of g-C3N4 and ammonium dihydrogen phosphate [NH4(H2PO4)], thiourea (CH4N2S), and boric acid (H3BO3) as dopant. The as synthesized materials were further subjected to chemical exfoliation using hydrochloric acid as exfoliating agent. The different physicochemical properties of the synthesized materials were well characterized by means of Powder X-ray diffraction (PXRD), UV–Visible diffuse reflectance absorption spectra (UV-DRS), photoluminescence spectra (PL), and electrochemical measurement. Boron doping into the bulk CN lattice enhance the absorption of light, electron-hole separation as revealed from absorption and PL emission spectra. From the photocurrent and impedance spectra it is clear that boron doping not only enhances the photocurrent value but also decreases the charge transfer resistance of the parent material. All synthesised materials were tested for photocatalytic hydrogen evolution reaction, out of which CNB shows highest rate of hydrogen generation (18.2 μmolh−1) which is around three times more than that of g-C3N4.

This work presents a low noise active pixel sensor. It uses one additional transistor compared to standard 4T pixel to obtain 1/f noise reduction and high conversion gain. 1/f noise is reduced by periodically switching the in-pixel source follower between depletion and inversion. The depletion state is achieved by re-configuring the source follower into a MOS capacitor and depleting the channel by controlling the source-drain terminal. The state change of the in-pixel source follower also enhances the conversion gain. The test chip has a $64 \times 64$ pixel array with a $10~ {\mu }\text{m}$ pixel pitch fabricated in 350 nm AMS process. The measured integrated noise of a pixel in the frequency band 128 Hz to 50 KHz is $728~ {\mu V_{rms}}$ without switching. At a switching frequency of 4 MHz, the integrated noise reduces to $306~ {\mu V_{rms}}$ after switching resulting in a 7.5 dB reduction. The input referred noise is reduced from $26 {e_{rms}^{-}}$ to $7.3 {e_{rms}^{-}}$ after applying switching. When both, switching and conversion gain enhancement are applied, the input referred noise is further reduced to $2.3 {e_{rms}^{-}}$ with an enhanced conversion gain of $398\,\, {\mu V/e^{-}}$ .

Four mononuclear cobalt (II) complexes of substituted hydrazino quinoline Schiff bases 1(a–d) were synthesised and characterized by UV, IR, NMR and TGA studies. The geometry of cobalt complexes 1(a–d) unambiguously attested as distorted octahedral’ and the ligand was coordinated through NNO donor fashion of tridentate nature. Structure of the proposed complexes were optimized using Density Functional theory (DFT) with Gaussian 09/ Gauss view software. Mulliken charges, global softness and electrophilicity index were derived for the optimized structure and the energy of highest occupied orbital (HOMO) and lowest unoccupied orbital (LUMO) and energy gap were calculated. The photophysical properties of the synthesised complexes were analyzed by UV–Visible and photoluminescence spectral studies, the results revealed that the emission bands centered in the range of 445–455 nm with higher luminescence intensity and relatively large Stoke’s shift observed (198 nm–215 nm) in the absorption and emission shoed a promising novel material towards OLED’S.

Amorphous Li3PS4 (LPS) solid-state electrolytes are promising for energy-dense lithium metal batteries. LPS glass, synthesized from a 3 : 1 mol ratio of Li2S and P2S5, has high ionic conductivity and can be synthesized by ball milling or solution processing. Ball milling has been attractive because it provides the easiest route to access amorphous LPS with a conductivity of 3.5 × 10−4 S cm−1 (20 °C). However, achieving the complete reaction of precursors via ball milling can be difficult, and most literature reports use X-ray diffraction (XRD) or Raman spectroscopy to confirm sample purity, both of which have limitations. Furthermore, the effect of residual precursors on ionic conductivity and lithium metal cycling is unknown. In this work, we illustrate the importance of multimodal characterization to determine LPS phase and chemical purity. To determine the residual Li2S content in LPS, we show that (1) XRD and 31P solid state nuclear magnetic resonance (ssNMR) are insufficient and (2) Raman loses sensitivity at concentrations below 12 mol% Li2S. Most importantly, we show that 7Li ssNMR is highly sensitive. Using 7Li ssNMR, we investigate the effect of ball milling parameters and develop a robust and highly reproducible procedure for pure LPS synthesis. We find that as the residual Li2S precursor content increases, LPS conductivity decreases and lithium metal batteries exhibit higher overpotentials and poor cycle life. Our work reveals the importance of multimodal characterization techniques for amorphous solid-state electrolyte characterization and will enable better synthetic strategies for highly conductive electrolytes for efficient energy-dense solid-state lithium metal batteries.

In this present article, zinc oxide nanoparticles (NPs) have been synthesized by a greener approach using Acacia concinna fruit extract via sol–gel technique. These biosynthesized ZnO NPs were characterized by XRD, SEM, FTIR, BET and HRTEM techniques. The XRD pattern revealed the samples are crystalline and having hexagonal ZnO phase with wurzite type structure. The morphology of zinc oxide nanoparticles were analysed by SEM, which suggested that the particles are in agglomerated form and porous in nature. The adsorption of Congo red dye by using these ZnO NPs was investigated extensively. The kinetics, thermodynamics and adsorption isotherm data were obtained from the batch experiment analysis of the Congo red dye, where the efficiency of adsorption of the sample was found to be ∼ 92%. The kinetics data analysis, indicated the adsorption process to be of pseudo-second order type. The Freundlich isotherm was found to be best fitted with R2 = 0.998, signifying a multilayer adsorption. Thermodynamic parameters, ΔH0 and ΔS0 were calculated. The negative value of ΔH0 (−5.644 kJ/mol) corresponded to an exothermic adsorption process and the positive value of ΔS0 (0.0108 J mol−1 K−1) signified the increased randomness at the interface of the adsorbate and the adsorbent.

PurposeIndustry 4.0 is driving an incremental shift in paradigms for the construction industry. Current research in the built environment is limited to exploring the exponential technological prowess of Industry 4.0 with very little work on its implications to the construction business model, strategy and competitive advantage. There arises a challenge for researchers to understand how appropriate technologies can be assembled to assist in achieving the goals of construction businesses. The overarching aim of this research is to develop a construction Business Model Transformation Canvas (BMTC) to map the transformation of construction enterprises in Industry 4.0.Design/methodology/approachThe research was carried out by conducting an expert forum with academics from nine universities across Australia and New Zealand. The study employed purposive sampling, and the academics were selected in a strategic manner in order to provide data that are relevant to the research.FindingsThe research identifies that technology-based partnerships supporting strategy and capability building, platforms enabling enterprises to conceive, design, manufacture and assemble buildings and competition with stakeholders having superior capabilities not in building but in other areas of business are fundamental to Industry 4.0 transformation.Originality/valueThe results present state-of-the-art development of business model research in construction that intends to support the strategic planning of construction enterprises in Industry 4.0. This research is the first and only research that uses a business model canvas (BMC) for strategy-reformulation in incumbent construction enterprises to maintain a competitive advantage in Industry 4.0. Merits of the construction BMTC lie in its holistic approach, visual representation and simplicity.

Dopants and impurities can adsorb to surfaces and grain boundaries in ceramics, influencing the sintering kinetics and grain boundary mobility during processing. Ca is a common impurity in alumina that promotes grain growth at concentrations below the solubility limit, in contrast to the commonly observed solute-drag effect. Knowledge of the Ca solubility limit at temperatures relevant to processing is important to control microstructural evolution. Here we report on the experimentally measured solubility limit of Ca in α-alumina at 1400 °C and 1500 °C from equilibrated and quenched samples using standardized wavelength dispersive spectroscopy to be 32 ± 3 ppm and 45 ± 3 ppm, respectively. Together with the previously measured Ca solubility limit at 1600 °C, the enthalpy (0.95 eV per Ca) and vibrational entropy (-7.35 × 10−4 eV K-1 per Ca) of solution were determined. A negative vibrational entropy of solution indicates coulombic interactions between the Ca cations and charge compensating defects in α-alumina.

Introduction Continuing pharmacy education (CPE) and continuing professional development (CPD) programs have been useful for enhancing the skills and performance of pharmacists. Despite its adoption worldwide, the practice of such programs has been limited in Nepal. The current pilot study aimed to assess the perception of pharmacists regarding CPE/CPD in one of the oncology centers in Nepal. The goal of this study was to provide suggestions for developing and implementing the CPE/CPD programs. Methods This is a cross-sectional, questionnaire-based pilot study, whereby the pharmacists and assistant pharmacists at a single-center were provided with a structured questionnaire inquiring about their perception of CPE/CPD. A mixed-method approach was followed for data collection. Descriptive statistics were used for all the variables. Results A total of 15 pharmacy professionals were enrolled in the study. Recent innovations in pharmacy practices were the most commonly desired topics (n= 11, 73.3%) for CPE/CPD. Live in-person presentations (n= 10, 66.7%) and handouts method (n= 10, 66.7%) were the most recommended practices for CPE/CPD. The most commonly cited deterrent was poor quality and method of CPE delivery (n=11, 73.3%), while the most common motivation was professional competence (n= 12, 80%). Conclusion This pilot study shows the benefits of developing CPE/CPD packages in future for pharmacy professionals focusing on advances in pharmacy practices and skill development, using in-person presentations and handouts, and focusing on the curiosity of the participant to improve his/her professional practice.

Biofilm and thrombus formation on surfaces results in significant morbidity and mortality worldwide, which highlights the importance of the development of efficacious fouling-prevention approaches. In this work, novel highly robust and superhydrophobic coatings with outstanding multiliquid repellency, bactericidal performance, and extremely low bacterial and blood adhesion are fabricated by a simple two-step dip-coating method. The coatings are prepared combining 1H,1H,2H,2H-perfluorooctyltriethoxysilane (FAS-17)-coated hydrophobic zinc oxide and copper nanoparticles to construct hierarchical micro/nanostructures on commercial polyurethane (PU) sponges followed by polydimethylsiloxane (PDMS) treatment that is used to improve the binding degree between the nanoparticles and the sponge surface. The micro/nanotextured samples can repel various liquids including water, milk, coffee, juice, and blood. Relative to the original PU, the superhydrophobic characteristics of the fabricated sponge cause a significant reduction in the adhesion of bacteria (Staphylococcus aureus) by up to 99.9% over a 4-day period in a continuous drip-flow bioreactor. The sponge is also highly resistant to the adhesion of fibrinogen and activated platelets with ∼76 and 64% reduction, respectively, hence reducing the risk of blood coagulation and thrombus formation. More importantly, the sponge can sustain its superhydrophobicity even after being subjected to different types of harsh mechanical damage such as finger-wiping, knife-scratching, tape-peeling, hand-kneading, hand-rubbing, bending, compress-release (1000 cycles) tests, and 1000 cm sandpaper abrasion under 250 g of loading. Hence, this novel hybrid surface with robustness and the ability to resist blood adhesion and bacterial contamination makes it an attractive candidate for use in diverse application areas.

SPICE models are constructed for memristive devices to form associated biophysical neuron circuit models such as the Hodgkin–Huxley (HH) type II excitability neuron circuit model, the HH type III excitability neuron circuit model, the simplified HH neuron circuit model, the Morris–Lecar neuron circuit model, and the memristive based direct-current (DC) circuit model. Rigorous nonlinear circuit-theoretic principles are also applied to analyze the different behaviors of the generic memristor Na $$^{+}$$ -ion, K $$^{+}$$ -ion, and Ca $$^{++}$$ -ion channels forming these biophysical neuron circuit models. Detailed explanations and clarifications are presented on the memristive HH type II and HH type III axonal excitabilities based on mathematical analysis as well as the circuit models. This is done from the perspective of the spike patterns generated by both of these biophysical neuron circuit models. Moreover, various experimental studies have revealed a synchronous brain state known as gamma rhythms that are responsible for sensory, memory, and motor processes. This suggests that understanding how the gamma oscillation (30–100 Hz) is generated in the brain will be extremely important to unravel the link between the activity of an individual neuron and the cognitive processing achieved by a population of networked neurons. We thus also study the dynamics of an interconnected excitatory–inhibitory (E–I) network population, which is ubiquitous in the brain. Utilizing biophysical models of the E–I network, we investigate the generation of pyramidal-interneuronal network gamma (PING) rhythms caused by the external input to the network and the connectivity heterogeneities. The results reveal that synchronous strong PING and sparsely firing weak PING rhythms are generated based on the network connectivities and external input heterogeneities in simulations of 100 memristive HH type II excitability neurons forming an E–I network.

The occurrence of antibiotic resistant microorganisms in poultry litter is due to the increasing use of antibiotics. Poultry litter is used as a fertilizer for its high nutrient content. The presence of antibiotic resistant microorganisms in poultry litter makes its handling, amendment and application as fertilizer a challenging task. This study is designed to evaluate the antibiotic susceptibility and plasmid profiling of beta-lactamase producing multi-drug resistant Staphylococcus aureus in poultry litter. Sample collected from a poultry farm was screened for the presence of Staphylococcus aureus strains. The in vitro antibiotic susceptibility test by disk diffusion method were carried out in all the isolates. All the isolates were highly susceptible to oxacillin and gentamycin and intermediate for erythromycin and were completely resistant to vancomycin and penicillin. The isolated Staphylococcus aureus strains showed beta-lactamase activity which is the basis for the resistance to beta-lactum antibiotics. These strains when exposed to curing agent namely ethidium bromide, the beta-lactamase activity was lost. Results indicate that Staphylococcus aureus isolated from poultry litter are beta-lactamase producers and relatively resistant to commonly used antibiotics. The resistance among the isolated strains is found to be plasmid-mediated. The outcome of our present study exercises both usefulness in shaping the existing antibiotics prescription and thus helps to reduce or prevent the development of multiple drug resistant microorganism.

Tungsten heavy alloys are widely used in the manufacturing of weights for aircraft, missiles, boats and race cars; penetrators; radiation shielding; and radioisotope containers. Manufacturing these components needs machining as a secondary operation. Since tungsten heavy alloys are difficult to machine, the in-depth analysis of tool wear growth and mechanism during machining of these alloys becomes essential. Hence, this work focuses on the experimental study of flank wear growth and its effect on other machining outputs for two different tool geometries (−5° and 2° rake angles) during turning of 90 tungsten heavy alloys. The predominant wear mechanism was identified as adhesion based on scanning electron microscopic analysis. Finally, three commonly used analytical tool wear rate models and one newly proposed model (modified Zhao model) were utilized for the prediction of flank wear growth and tool life. It was observed that the modified Zhao model could predict tool flank wear fairly well within error percentage of 4%–7% and thus could be used as a benchmark while machining difficult-to-cut alloys.

Intelligence has been defined in many ways like logic, awareness, reasoning, critical thinking, etc. Many researchers insist on the possibility of a Technological Singularity shortly, which may see...

India's COVID-19 lockdown has left transgender people at heightened risk of hunger and poverty since most of them make their living on streets by begging, street entertainment, and paid sex. They are not socially privileged to operate within the online world as they are predominantly dependent on social interactions and functions such as weddings or baby showers. Ideology is endangering transgender people from coronavirus. Transgender people are a socially marginalized community who is forced into slums where social distancing is challenging, making it a hotbed for Coronavirus. Further, the absence of healthcare backup and lack of awareness increases the looming fear among the transgender community regarding the budding impact of COVID-19. A substantial rate of economic instability, as well as social discrimination, was visible before the pandemic, thereby worsening their situation with a lack of food, fund, insecurity, safety, and mental health concern during COVID-19. This study focuses on the impact of the Coronavirus crisis on the socioeconomic life of Transgender people taking into account the explosion in unemployment, adding to an already disproportionate healthcare support system pushing them further to the margins. The study comes forward with useful suggestions based on content analysis of published and aired information to reduce the existing woes of transgender people during COVID-19.

The development of gasoline compression ignition engines operating in a low temperature combustion mode depends heavily on robust control of the heat release profile. Partial fuel stratification is an effective method for controlling the heat release by creating a stratified mixture prior to autoignition, which can be beneficial for operation across a wide load range. In this study, three-dimensional large eddy simulations were used to model a double direct injection strategy for which 80% of the fuel was injected during the intake stroke, and 20% of the fuel was injected at varying timing during the compression stroke. The simulations replicated a set of experiments performed at Sandia National Laboratories on a 1-L single-cylinder research engine using E10 gasoline (gasoline fuel containing 10% vol. ethanol). The objective of this study is to analyze the effects of the double direct injection strategy on the compositional and thermal stratification of the mixture, and understand the best use of this operating strategy. The modeling results indicated that by retarding the start of the second injection, the mixture stratification increases, which can be used to control the autoignition timing and the combustion phasing. Ignition and CA50 (crank angle of 50% mass fraction burned) are dictated by the mass concentration of the richest zones in the combustion chamber, as well as their location. The richer zones have the lowest temperatures before ignition primarily due to evaporative cooling from direct fuel injection. Overall, this study enhances the understanding of partial fuel stratification that can be used for controlling the heat release in gasoline compression ignition engines.

Multiple vehicle detection is a promising and challenging role in intelligent transportation systems and computer vision applications. Most existing methods detect vehicles with bounding box representation and fail to offer the location of vehicles. However, the location information is vigorous for several real-time applications such as the motion estimation and trajectory of vehicles moving on the road. In this paper, we propose an advanced deep learning method called enhanced you only look once v3 and improved visual background extractor algorithms are used to detect the multi-type and multiple vehicles in an input video. More precisely, tracking is to find the trace of the upcoming vehicles using a combined Kalman filtering algorithm and particle filter techniques. To improve the tracking results, further, we propose the technique, namely multiple vehicle tracking algorithms, and tested with different weather conditions such as sunny, rainy, night and fog in input videos of 30 frames per second. The major research issues were found in the recent kinds of literature in ITS sector which is closely related to the real-time traffic environmental problems such as occlusions, camera oscillations, background changes, sensors, cluttering, camouflage, varying illumination changes in a day- and sunny and at nighttime vision. The experimental results are tested with the ten different input videos and two benchmark datasets KITTI and DETRAC. The most eight high- level features have been considered for automatic feature extraction and annotation. The attributes are length, width, height, number of mirrors and wheels and windscreen shielding glass to detect the target region of interest (vehicles) on road. In addition, further experiments are carried out in multiple-input videos of high definition quality using a monocular camera, and the average accuracy is 98.6%, and the time complexity of the algorithm is O(n) and also tracking results attained 96.6%. The dataset and input videos are discussed in comparative results with the F-test measure done for multiple vehicles.

Hypothesis: Previous works have shown that many-body interactions induced by dispersants with increasing correlation length will generate a diminishing two-phase region [Soft Matter 14, 6921 (2018)]. We conjecture that the attenuation of the depletion attraction due to many-body interactions is a ubiquitous phenomenon in medium-induced interactions. We propose mixtures of colloidal particles and rod-like polymers as a feasible experimental system for verifying these predictions, since the intra-molecular correlations are not screened in a good solvent for rod-like polymers as they are in flexible polymers. The length of the rods can grow and become the dominant length scale that determines the range of the depletion interactions for the imbedded non-adsorbing particles. Simulations: We study many-body depletion forces induced by polymerizing rod-like polymers on spherical non-adsorbing colloids, using Metropolis Monte Carlo simulations. We also employ a simple mean-field theory to further justify our numerical predictions. Findings: We demonstrate that the phase diagram displays the same qualitative features that have previously been predicted by many-body theory, for mixtures containing flexible polymers under theta solvent conditions. The contraction of the particle two-phase region that we observe, as the correlation length increases beyond some specific value, could be a signature of the weakening of the depletion caused by many-body effects.

Most of the urban heat island (UHI) studies are carried out in densely populated cities but core industrial areas are also potential sites of heat island effect despite having a comparatively lower population. In the present study, heat island assessment has been carried out for Angul-Talcher industrial area (ATIA) which is one of the oldest industrial areas of India and is still undergoing a transformation to accommodate more industries and mining operations. As the major contributors towards influencing local meteorology were expected to be industrial (and mining) activities, the heat island was studied as “industrial heat island” (IHI) rather than urban heat island. Industrial and mining sites were the most frequent nighttime canopy-layer heat island intensity (HIN) hotspots due to anthropogenic heat of associated industrial processes as well as built structures. During the daytime, croplands experienced the most frequent canopy-layer HIN hotspots which could be attributed to low moisture of the soils during the non-farming period of the field campaign. Hourly maximum atmospheric heat island intensities were observed in the range of 7–9 °C. Monthly maximum HINs ranged from 2.97 to 4.04 °C while 3-month mean HINs varied from 1.45 to 2.74 °C. Amongst different land use/land cover classes, the highest mean canopy-layer heat island intensity for the entire 3-month-long duration of field campaign during nighttime was assessed at the mining sites (3-month mean 2.74 °C) followed in decreasing order by the industrial sites (2.52 °C), rural and urban settlements (2.13 °C), and croplands (2.06 °C). Corresponding daytime canopy-layer heat island intensity was highest for the croplands (2.07 °C) followed in decreasing order by the mining sites (1.70 °C), rural and urban settlements (1.68 °C), and industry (1.45 °C).

Cross-linked polymer electrolytes containing structurally dynamic disulfide bonds have been synthesized to investigate their combined ion transport and adhesive properties. Dynamic network polymers of varying cross-link densities are synthesized via thiol oxidation of a bisthiol monomer, 2,2'-(ethylenedioxy)diethanethiol, and tetrathiol cross-linker, pentaerythritol tetrakis(3-mercaptopropionate). At optimal loading of lithium bis(trifluoromethane-sulfonyl-imide) (LiTFSI) salt, the ionic conductivities (σ) at 90 °C are about 1 × 10

Hydrogen sulfide (H2S) is an endogenous gasotransmitter in the human body involved in various physiological functions including cytoprotection, maintaining homeostasis, and regulation of organ development. Therefore, H2S-releasing polymers that can imitate endogenous H2S release can offer great therapeutic potential. Despite decades of research, the use of H2S donors in medical device applications is mostly unexplored largely due to the challenge of the steady H2S release from a suitable polymeric platform that does not compromise the normal cellular functions of the host. In this work, an exogenous H2S release system was developed by integrating sodium sulfide (Na2S), a common H2S donor, into a medical-grade thermoplastic silicone-polycarbonate-urethane polymer, Carbosil 20 80A (hereon as Carbosil), via a facile solvent evaporation technique. The spatial distribution and nature of Na2S in Carbosil were characterized through X-ray diffraction (XRD) spectroscopy and field emission scanning electron microscopy (FESEM) with energy-dispersive spectroscopy (EDS), indicating an amorphous phase shift upon incorporating Na2S in Carbosil. The composite, Na2S-Carbosil, is responsive in physiological conditions, resulting in sustained H2S release measured for 3 h. In vitro cellular responses of 3T3 mouse fibroblasts, human lung epithelial (HLE), and primary human umbilical vein endothelial cells (HUVEC) were investigated. Fibroblast cells showed cell proliferation in 24 h and complete cell migration in 42 h in vitro. The Na2S-Carbosil composites were cytocompatible toward HUVEC and HLE cells. This study provided important in vitro proof of concept that warrants potential use of these H2S-releasing platforms in engineering biomedical devices, tissue engineering, and drug delivery applications.

An alkaliphile bacterial strain designated as CH11 was isolated from the sediments of Chilika Lake, Odisha. The isolate showed stupendous growth and production of α-amylase at pH 10.0. Through 16S rRNA gene based molecular technique this isolate was identified as Bacillus cereus strain SP-CH11 having GenBank Accession No. KT992791. Homogenous ~55 kDa extracellular α-amylase was extracted with 241.304, 26.26 and 3.2-fold acceleration in specific activity, purification fold and yield respectively. The alkaline α-amylase AA11 was further characterized. At pH 9.0 the purified enzyme AA11 was highly stable while retaining 88–100% functional viability at temperature range from 35 to 65 °C, confirming its thermostability nature. It showed stability with powdered and liquid detergents at 7 mg/mL and 100-fold dilutions respectively. AA11 efficiently removed the starch stain from cotton fabrics. The findings of this study indicate that the isolate CH11 is a source of novel alkaline α-amylase that has promising application in food and detergent industries.

The most adverse outcome of increasing industrialization is contamination of the ecosystem with heavy metals. Toxic heavy metals possess a deleterious effect on all forms of biota; however, they affect the microbial system directly. These heavy metals form complexes with the microbial system by forming covalent and ionic bonds and affecting them at the cellular level and biochemical and molecular levels, ultimately leading to mutation affecting the microbial population. Microbes, in turn, have developed efficient resistance mechanisms to cope with metal toxicity. This review focuses on the vital tolerance mechanisms employed by the fungus to resist the toxicity caused by heavy metals. The tolerance mechanisms have been basically categorized into biosorption, bioaccumulation, biotransformation, and efflux of metal ions. The mechanisms of tolerance to some toxic metals as copper, arsenic, zinc, cadmium, and nickel have been discussed. The article summarizes and provides a detailed illustration of the tolerance means with specific examples in each case. Exposure of metals to fungal cells leads to a response that may lead to the formation of metal nanoparticles to overcome the toxicity by immobilization in less toxic forms. Therefore, fungal-mediated green synthesis of metal nanoparticles, their mechanism of synthesis, and applications have also been discussed. An understanding of how fungus resists metal toxicity can provide insights into the development of adaption techniques and methodologies for detoxification and removal of metals from the environment.

Nowadays, routing in three-dimensional environments is necessary since sensor nodes are organized in those kinds of areas. In this routing mechanism, data packets are routed using geographic routing by constructing a forwarding area. It is assumed that nodes in the network are homogeneous which contain the same energy level and sensing parameter. In this paper, we propose a new method to reduce the void node problem called Adaptive Void Recovery Mechanism, which is implemented by two folds namely position management and forwarding management concepts. Position management is implemented by sensing the surroundings using the base station and location management. Forwarding management is implemented using the assured factor value and cumulative value from the gathered data. The sensor nodes are elected with a minimized congestion packet latency value. Cluster-based routing technique is implemented to improve the network lifetime and network throughput. The proposed method is evaluated by simulation against the related methods like CREEP, EECS, FABC-MACRD in terms of End to End Delay, Residual Energy, Energy Consumption, Routing Overhead, Network Lifetime, and Network Throughput.

Geospatial data analysis using cloud computing platform is one of the promising areas for analysing, retrieving, and processing volumetric data. Fog computing paradigm assists cloud platform where fog devices try to increase the throughput and reduce latency at the edge of the client. In this research paper, the authors discuss two case studies on geospatial data analysis using Fog-assisted cloud computing namely, (1)Ganga River Basin Management System; and (2)Tourism Information Management of India. Both case studies evaluate proposed GeoFog architecture for efficient analysis and management of geospatial big data employing fog computing. The authors developed a prototype of GeoFog architecture using Intel Edison and Raspberry Pi devices. The authors implemented some of the open source compression methods for reducing the data transmission overload in the cloud. Proposed architecture performs data compression and overlay analysis of data. The authors further discussed the improvement in scalability and time analysis using proposed GeoFog architecture and Geospark tool. Discussed results show the merit of fog computing that holds an enormous promise for enhanced analysis of geospatial big data in river Ganga basin and tourism information management scenario.

The present work shows the effect of annealing conditions on the linear and nonlinear optical properties of two-year-old thermally evaporated 800 nm As40Se50Ge10 thin films. The aging effect in this film is clearly noticeable as compared with the old observation. The two-year-old films were annealed at different temperatures like 373, 413, 453 and 493 K for 1 h. The optical parameters were calculated from the optical transmittance and reflectance spectra measured at normal incidence of light by spectrometer in the 500–1000 nm range. The linear refractive index (n) and extinction coefficient (k) were decreased with annealing temperature. The observation reveals the increase in optical band gap with increase in annealing temperature while the width of the tail in the gap has an opposite behaviour. The oscillator energy, dispersion energy, dielectric constant, the loss factor, and optical conductivity were discussed in detail. The nonlinear refractive index and optical susceptibility were calculated by using Miller's formula which decreased with annealing temperature. The changes in both linear and nonlinear optical parameters with annealing temperature showed that annealing temperature can be considered as a useful factor for controlling the optical properties of As40Se50Ge10 chalcogenide films which could be the candidate for numerous photonic applications. The structural study was done by X-ray diffraction and Raman spectroscopy.

Electro-discharge machining (EDM) is a widely used process for manufacturing industries. It is a non-conventional machining process, where material is removed by the thermal energy of the generated electrical sparks between the electrode and workpiece. It is generally used to machine high strength temperature-resistance alloys which are difficult-to-machine. Titanium alloys being a difficult-to-machine alloy, is generally used for parts in aero-space industries, thus its machining should have a higher material removal rate, lower tool wear rate with better surface finish. To achieve these suitable machining responses the process is successfully optimized using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

CdS as an n–type layer plays an important role in the fabrication of solar cells. Cd1-xCoxS thin-films with variable Co doping levels (x = 0 to 0.06) were deposited on glass and indium tin oxide-coated (ITO) glass substrates by simple and inexpensive chemical bath deposition. The effects of Co doping on the structural, optical, morphological, elemental, and vibrational properties of CdS films were systematically examined. XRD results revealed that Co concentrations increase and the crystallite size of the film decreases from 20.4 to 13.6 nm due to the incorporation Co in Cd sites. The optical bandgap was reduced from 2.44 to 2.32 eV due to the incorporation of Co, which was found with a UV–Vis spectrophotometer. Urbach energy, refractive index and extinction coefficient of all deposited films was estimated, and the results were discussed. The intensity of PL peaks was reduced by increasing the Co concentration in the red region. The resistivity and carrier concentration were measured from Hall measurement system and the film current density was increased from 7 to 26 mA/cm2 by cobalt doping.

Now-a-days metallic biodegradable biomaterial (MBB) has taken more attention by research people. From last few decades an enormous research has been done in magnesium, zinc and iron based MBB. Magnesium and zinc based alloys are now in final implementation stage whereas iron based alloys still in research stage. To take iron based alloys to the implement stage, a lot of research has been done by many research people for different aspects from last few decades. Among all iron based alloys, Fe-35Mn is one of them which show higher degradation rate in vitro as well as in vivo studies. In this study, effect of average particle size varies (from 30 to 80 µm) on degradation behavior and was completely investigated of the above alloy which is being prepared via powder metallurgy route. Fe-35Mn alloy pellets were sintered in a tube furnace at 1200 °C under Ar-5%H2 atmospheric pressure. SEM and DLS particle size analysis showed that 3 h milling time was the optimum time for the milling of initial powder sample. Smaller average particle size (35 µm) was responsible for uniform corrosion of Fe-35Mn alloy. Optical microscope analysis showed uniform corrosion on the surface of the sample. XRD results were also showed anti-ferromagnetic behavior as γ-Fe phase was present after sintering. Sintered density and porosity were calculated for each sample and showed that 3 h milling time particle size has higher porosity which affects the degradation rate during vitro study. Hardness results depicted 3 h alloy sample had better micro hardness value (143 ± 4Hv). Almost 34% porosity and 5.068 g/cm3 sintered density sample showed better corrosion rate (1.065 mm/yr) than pure iron (0.16 mm/yr) during static immersion test.

Mineral admixture concrete is proved to be beneficial to enhance strength and resistance of concrete against aggressive chemical environment. However, water binder (w/b) ratio is the major responsible factor to govern all the concrete material properties. So, for a strong and durable concrete, it is imperative to design a proper mix with suitable w/b ratio. The present study investigates the effects of w/b ratio on the compressive strength and acid resistance property of mineral admixture incorporated concrete. Fly ash (FA) and crusher dust (CD) were used in concrete as partial cement replacement at the level of 20%, 40%, and 60% to cast concrete cube of size (150 × 150 × 150) mm3 using three w/b ratios of 0.35, 0.38 and 0.4. The fresh concrete properties, the compressive strength of the different types of concrete samples at 365 days of water curing and the acid (5% H2SO4) resistance of those water cured concrete samples after 365 days of acid exposure have been investigated to study the impact of w/b on the strength and acid resistance properties of concrete. Full factorial DOE with ANOVA was conducted to determine the significance of the concrete type and w/b ratio on strength and strength loss of concrete under acid exposure. In addition, the Grey relational Analysis and the Response Surface Methodology (RSM) have been also used to find the impact of w/b ratio towards simultaneous optimization of strength and % strength loss on acid exposure to concrete.