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Diagnosing a disease consumes a part of time, needs high technical methods but nowadays the smart technologies have been grown rapidly in the field of healthcare industries and also it improves the routine life of the patients, reduces the amount of work, treatment cost in the health care organization. Diseases prediction is one of the major challenges faced by society nowadays. The recent survey also stated that the death rate is remarkably high in CAD because most of the people are affected by cardiovascular diseases. Prediction and diagnosis of cardiovascular diseases is very essential nowadays to reduce the death rate and diagnosing it preliminary stage itself. In earlier studies, they worked with machine learning techniques to predict the diseases, but they are not given proper attention to identifying the feature with the help of proper feature selection methods. This paper proposed a new-found feature selection technique HRFLC (RANDOM FOREST + ADABOOST + PEARSON COEFFICIENT). This method helps to predict the diseases in a very efficient manner, and it improves the accuracy level in prediction.

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed in detail in the AI community, but have received increased attention lately. In this study, we aim to analyse the commonly selected features using a proposed framework of several feature selection methods and their effect on the classification results, which will provide an interpretation of the depression detection model. The developed framework aggregates and selects the most promising features for modelling depression detection from 38 feature selection algorithms of different categories. Using three real-world depression datasets, 902 behavioural cues were extracted from speech behaviour, speech prosody, eye movement and head pose. To verify the generalisability of the proposed framework, we applied the entire process to depression datasets individually and when combined. The results from the proposed framework showed that speech behaviour features (e.g. pauses) are the most distinctive features of the depression detection model. From the speech prosody modality, the strongest feature groups were F0, HNR, formants, and MFCC, while for the eye activity modality they were left-right eye movement and gaze direction, and for the head modality it was yaw head movement. Modelling depression detection using the selected features (even though there are only 9 features) outperformed using all features in all the individual and combined datasets. Our feature selection framework did not only provide an interpretation of the model, but was also able to produce a higher accuracy of depression detection with a small number of features in varied datasets. This could help to reduce the processing time needed to extract features and creating the model.

IoT has started capturing the market all over the world. Nearly all household appliances are available and consumer can buy them. Lack of security leaves the customer potentially exposed to various risk factors. Risk might be ranging daily routine data theft to great financial risk, even in some cases there is life threating risk for specific or group of persons. On analyzing various devices, through that come to know that many are not devices enforced strong passwords are not devices applied, or brute-force attacks is not protected, physical replacement of faulty sensors or wiretapping the traffic. As per approximation, every two among ten applications of mobile which are being used for managing and handling IOT devices are vulnerable and are not using SSL for communication data encryption. IoT involve billions of sensors spreading all around the world. Many of them are not physically protected. Attacker could access those sensors/devices and manipulate them or replace them. Captured data through sensors is also not safe if transmitted without encrypting them. Further many devices don’t have such power to perform heavy encryption. In this research Proposed a PUF (Physically unclonable function) and light weight encryption technique-based solution. Physically unclonable function create a class for new embedded and its security purpose. PUF mainly concentrate on shift the security challenges and issues. It has a very simple architecture and can solve problem of energy-constrained IoT devices. Through this research work introduced PUF functionality with hardware sensors. Since PUF function cannot be cloned therefore sensors are safe and any modification or replacement can be easily detected. Further data received from sensors will be encrypted using light weigh encryption technique. In future this proposed methodology explore a how to store safely vast amount of data generated through billions of sensors.

Supply chain collaborations may generate substantial cost savings. Many such initiatives cease to exist, however, due to not reaching an agreement on how to share the gains. We describe an exercise to understand the challenges in collaborative gain sharing. Funding: This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration (FP7 Transport) [Grant Agreement 284926].

The Internet of things is gaining a lot of interestfrom day to day forvarious applications like Smart cities, Industrial health monitoring, smart homes, etc. IoT systems consist of sensor devices with communication capabilities, actuators, edgegateways, Back end Servers, and IoT applications. The Ultimate goal of an IoT network is to collect sensor data, reliable Transfer sensor data to the backend serverthrough the edgegateways. Back end server delivers the sensor data to the IoT Applications. IoT application process the Sensor data and apply the appropriate decision. Reliable Transfer of sensor data tothe Backend server isessential considering wireless sensor network characteristics, low computation power, and sensor devices' bandwidth. The IoT network deployment signaling protocol plays a crucial role in communicating sensor data to the IoT program. Various application messaging protocols are currently available, like MQ TELEMETRY TRANSPORT, COAP, HTTPetc. These protocols aredesigned, developed considering richcomputation powerand bandwidth. These protocolsare notenergy efficientfor sensor devices. MQ TELEMETRY TRANSPORT-SN protocol specified for sensor devices considering wireless sensor networks. In this article, Message Telemetry Transfer Protocol-sensor networks were planned, built, and validated according to the MQ TELEMETRY TRANSPORT-SN specification.

Organic fertilizers have been used since the dawn of mankind to improve soil fertility and promote crop growth. However, the process of preparing organic fertilizers for use in agricultural activities is a labour-intensive and time-consuming procedure. Thus, in order to reduce labour costs and improve the efficiency of organic fertilizer preparation, automated organic fertilizer mixers were developed. For this research work, an improved organic fertilizer mixer based on the Internet of Things (IoT) technology capable of monitoring the status of fertilizer production remotely and providing updates and alerts to workers was developed. The aim of this work was to further reduce labour workload and costs, thus increasing efficiency of the organic fertilizer mixing process. In order to accommodate the IoT monitoring system, several modifications and design changes were carried out on the prototype of an automated organic fertilizer mixer. Tests conducted on the prototype unit showed that it performs as required. The IoT monitoring system successfully captures and uploads information on the weight of agricultural waste mixture in the storage drum to the IoT platform. This information can then be used to alert workers on when to add more agricultural waste material to the mixture or remove the processed organic fertilizer. Furthermore, an overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems. Future work includes further automation to the agricultural waste filling and organic fertilizer removal process.

The suffix array, perhaps the most important data structure in modern string processing, is often augmented with the longest common prefix (LCP) array which stores the lengths of the LCPs for lexicographically adjacent suffixes of a string. Together the two arrays are roughly equivalent to the suffix tree with the LCP array representing the tree shape. In order to better understand the combinatorics of LCP arrays, we consider the problem of inferring a string from an LCP array, i.e., determining whether a given array of integers is a valid LCP array, and if it is, reconstructing some string or all strings with that LCP array. There are recent studies of inferring a string from a suffix tree shape but using significantly more information (in the form of suffix links) than is available in the LCP array. We provide two main results. (1) We describe two algorithms for inferring strings from an LCP array when we allow a generalized form of LCP array defined for a multiset of cyclic strings: a linear time algorithm for binary alphabet and a general algorithm with polynomial time complexity for a constant alphabet size. (2) We prove that determining whether a given integer array is a valid LCP array is NP-complete when we require more restricted forms of LCP array defined for a single cyclic or non-cyclic string or a multiset of non-cyclic strings. The result holds whether or not the alphabet is restricted to be binary. In combination, the two results show that the generalized form of LCP array for a multiset of cyclic strings is fundamentally different from the other more restricted forms.

The Crime against the women in our country today is becoming a daily routine. There continues to be increased demand in the development of safety devices to ensure a secure and safe environment for the women. The key objectives are to develop a kind of “Virtual Friendly” machine to protect you from serious troubles. These suggested systems are made up of the GPS, the GSM transmitter, the Microcontrollers (LPC812), Voice Recognizer, a RF transceiver and Temperature Sensors. GPS Receiver receives satellite locations information’s I n the indirect form of the longitude and the latitude. It information is handled by the Microcontroller and this gathered information the sent to the main control point of POLICE. These special temperature sensors continuously sense the body temperature. In a case of a very low temperature indicating some emergency conditions, a kind of SMS alert would be automatically sent. The device can be triggered by the consumer on its own when self-defense is necessary. To ensure maximum performance, functionality such as warning tone, voice recognition is applied to the device. Using the SAVE ME and Support ME commands, for the voice recognizers this circuit is utilized to transmit voice commands. The device is designed as a small package and is easy to operate, so that a simple women can conveniently take these along everywhere.

The uniaxial ratchetting-fatigue interaction of extruded AZ31 magnesium (Mg) alloy is investigated by uniaxial stress-controlled cyclic tests at room temperature and with addressing the roles of different plastic deformation mechanisms. Different stress levels are prescribed to reflect the cyclic plasticity of the alloy controlled by diverse deformation mechanisms (i.e., dislocation slipping, deformation twinning and detwinning ones), and then the influences of stress level and stress rate on the ratchetting and fatigue life are discussed. The experimental results demonstrate that different evolution characteristics of whole-life ratchetting and fatigue life presented during cyclic tests with various mean stresses, stress amplitudes and stress rates are determined by the dominated plastic deformation mechanisms. It's worth noting that the ratchetting can occur in the compressive direction even in the cyclic tests with a positive (tensile) mean stress, and the fatigue life increases first and then decreases with the increase of mean stress on account of the interaction between dislocation slipping and twinning/detwinning mechanisms. Comparing the fatigue lives obtained in the asymmetric stress-controlled and symmetrical strain-controlled cycle tests, it is seen that the ratchetting deformation causes an additional damage, and then leads to a shortening of fatigue life.

To clarify the anodic dissolution mechanism of Mg, the hydrogen evolution from pure Mg in acidic solutions under galvanostatic conditions were systematically measured. With increasing anodic current density, the cathodic hydrogen evolution rate decreased, and the anodic hydrogen evolution became faster while some surface area on the Mg was becoming dark under anodic polarization. Based on the surface analysis results and the generally accepted basic electrochemical equations, the evolution kinetics of hydrogen from Mg was deduced, and the most possible surface intermediate active species that could facilitate the anodic Mg dissolution and anodic hydrogen evolution were proposed. This paper further develops the model of incomplete film Mg+ dissolution, explains many reported experimental phenomena, and clarifies misunderstandings of current mechanism.

The study aimed to identify important variables associated with policies, processes and procedures for culturally diversified organizations. Further to identify which factor among the policies, processes and procedures is the most prerequisite for culturally diversified organizations. For the study, a survey method was employed. 153 Data were collected from companies in the service industry. For the study HR managers were considered as the samples. The data samples were chosen using a simple random sampling technique. From the result obtained, it was found that; the Retention Strategies, Talent Management and Salary Reviews policies are most important for the culturally diversified organization. Also, evaluation by observing how people spend their time in the job, interviewing with existing company people periodically and Understand the career path progression of employees and their desire are important process to be considered for a culturally diversified organization. Similarly, the Compensation system, hiring guidelines and Chain of Command procedures are considered to be important for evaluating procedure in a culturally diversified organization. It can be further perceived that the male HRs indicates Procedure and Policy are an important factor to be considered for a culturally diversified organization. While the female HRs indicates Procedure and Process are an important factor to be considered for a culturally diversified organization.

Carbon Nanotube Aluminum metal grid composites are viewed as most encouraging composite materials nearby Aerospace, Automotive, Naval and other assembling applications. The momentum research work is made to comprehend the impact of carbon nanotube (CNT) inclusion on the Hardness and Wear trademark conduct of Al6061-CNT combination. The CNT particles of 500 nm size is executed as the fortifications in the Al6061 compound medium. Nano composites amalgamated by powder metallurgy strategy arranged by 1, 1.5 and 2 wt% in the Al6061 amalgam medium. Examples were considered for microstructural portrayal utilizing filtering electron magnifying lens (SEM) and energy dispersive spectroscope (EDS). Wear and Hardness strength were evaluated according to ASTM norms. Checking electron miniature pictures found the unvarying spreading of CNT in Al6061 amalgam. Further, Wear and Hardness strength of the establishment medium Al6061 compound is improved with the gathering of CNT.

Indian air travel and advanced technological development in the tourism society in India lead to the growth of Indian Hotel Industry. The Indian Industry staged a good progress due to the emergence of business hotel groups such as Taj Group of hotels, ITC Group of hotels, Ashok group. The growth and development of Indian hotel Industry is growth and development in India taken place with the growth and development of Human Resources Management and their practices in the Indian hotel which pave the way for their better performance. In this context, an attempt is made in this research paper to overview and analyse the organisational commitment of employees working in Indian Hotel Industry.

Nano-electro-mechanical (NEMS) systems are compacted to be of dimension less than a micron by MEMS. Extremely high fundamental frequencies can be reached in this size regime while at the same time maintaining extremely elevated mechanical sensitivity (minute strength constant). This powerful mixture of attribute directly translate in to elevated strength sensitivities, ultra-low power operability's, and the capability to cause functional nonlinearities by very diffident controlling powers. Current paper addressed NEMS in this overview and will detail numerous of their stimulating original application. At the threshold for this new domain, however, there is a steep entry fee: modern innovation is key to realizing NEMS ' full potential. Some of the foundations of MEMS ' methods would obviously not usefully fit into the NEMS system. The main problem is the dimensions of the systems relative to the circuits in which they will be embedded, its excessive volume-to-surface ratio and its alternative “characteristic operating range.” These create some of the main challenges currently facing the development of NEMS. The majority of well-known of these is the requirement for: Very sensitive, elevated values of bandwidth dislocation transducer; unprecedented controls of quality of surface and adsorbents; new mode of capable Nano-scale actuations; and new surface as well as bulk Nano-machining approaches that are precise, robust and routinely reproducible.

The cloud server stores its unique DE-duplicate data replication cloud storage system as outsourced only data copies. Deduplication cloud storage audits can verify the integrity of data stored in the cloud, while the cloud calculates the need to maintain only one copy of the copied file. The existing de-duplication auditing solutions for cloud storage are vulnerable to brute-force dictionary attacks, which is a disclosure leakage of user privacy and more time consumption to find a duplicate files in cloud. Users must check the integrity of lightweight cloud computing data to create data authentication and retrieve files in the cloud. The proposed system uses a secure Array Preserving Triple Data Encryption (AP3DE) algorithm to verify with confidence the expertise of aggregate technology. In this method to analysis the user upload document before store on cloud. The aggregator to provide an authentication access the data access role to cloud user. Cloud protects the privacy, confidentiality, and integrity of data stored in the cloud by generating the code of the authentication file and the encryption key of the file through the proposed system test. The Index-level Boundary Pattern Convergent Encryption (IBPCE) algorithm integrates dynamic data handling and data exclusion capabilities for secure cloud storage privacy security public audits. The cloud data security using a Convergent international data encryption algorithm used to encrypted data. It is a 64 bit plaintext and cipher text blocks is controlled by 128 bit key. Analysis of safety and efficiency shows that the proposed mechanism is highly efficient and safe. In this proposed IBPCE method to manage and reduce the cloud storage compare to other existing method.

Graph Mining has gained importance due to its wide range of applications. As graph mining is a form of data mining where data is depicted in the form of graphs. The data when represented in the form of graphs helps to model the real scenario and relationship well. Discovery of interesting pattern from real relationship is of utmost beneficial. Frequent Subgraph Mining (FSM) is a significant area of graph mining. We accompany our approach in the field of FSM by adding a graph sampling function. This sampling function takes the single graph and a sampling percentage as input and produces a sampled graph which is similar in properties with original graph. This sampled graph is then taken as input while implementing the GraMi framework. Our sampling approach successfully mines the subgraphs with lower frequency threshold thus significantly improving the efficiency of the original GraMi framework.

Silver nanoparticles are known to have antimicrobial properties. In the present paper, antibacterial activity of biosynthesized silver nanoparticles coated on low density poly ethylene films was evaluated against Escherichia coli as the representative organism. Silver nanoparticles were biosynthesized using banana peel extract as the reducing agent and characterized by means of UV–Visible spectroscopy, Scanning Electron Microscopy and Energy Dispersive X-ray analyses. Formation of silver nanoparticles was confirmed by the Surface Plasmon Resonance spectrum with absorption maxima at 431 nm. Particles were seen to be highly polydispersed with the average size in the range of 50–390 nm. Presence of elemental silver was confirmed by the Energy dispersive X-ray spectroscopy analysis. Biosynthesized silver nanoparticles showed excellent antibacterial activity against Escherichia coli in the agar well diffusion test, with the zone of inhibition diameter measuring 19 mm. The silver nanoparticle coated low density poly ethylene film was prepared successfully and our antimicrobial studies revealed that the coated films declined the growth profile of bacteria starting from the log phase. Hence biosynthesized silver nanoparticles incorporated low density poly ethylene films can be suggested as an active packaging material for food packaging after further characterization and migration studies.

In this paper, we propose a new class of optimal one-coincidence FHS (OC-FHS) sets with respect to the Peng-Fan bounds, including prime sequence sets and HMC sequence sets as special cases. Thereafter, through investigating their properties, we determine all of the FHS distances in the OC-FHS set. Finally, for a given positive integer, we also propose a new class of wide-gap one-coincidence FHS (WG-OC-FHS) sets where the FHS gap is larger than the given positive integer. Moreover, such a WG-OC-FHS set is optimal with respect to the WG-Lempel-Greenberger bound and the WG-Peng-Fan bounds simultaneously.

Hydrogen is an outstanding source of energy and plays very important part in energy system. Hydrogen is one of the best energy carrier alternatives to fossil fuels because of its high energy density. It is very important to produce clean and renewable energy sources due to increasing population and pollution day by day. In 2018, the maximum hydrogen (94%) is manufactured from fossil fuels by steam reforming of natural gas, partial oxidation of the methane gas as well as coal gasification. The hydrogen production plays important role in any industrialized society, since hydrogen is required for many essential chemical processes. Several designed metal-based ideas can be employed as an efficient catalyst for hydrogen production for the alternate source of energy and society benefits. Proposed review is highlighting the different efficient strategies for hydrogen production.

We introduce a cross-entropy (CE) indicator to quantify the extent to which two input-output tables or two tables with results based on input-output analysis differ from each other. Our work deploys a unique feature of the CE indicator: it can be decomposed, allowing for matrix comparisons at various levels within one coherent framework. To illustrate the power of this approach, we apply the technique to five multi-region input-output (MRIO) tables for 2011, derived from the Eora, EXIOBASE, GTAP, OECD and WIOD databases. We make pairwise comparisons between MRIOs and between global value chain (GVC) computations based on these MRIOs. We find that answers to questions related to broader aggregates are generally quite similar, but that answers to questions at the level of single industries can be rather different across MRIOs.

In this work comparative analysis of the effect of various active materials and use of photonic crystal in active layer of organic photodetector in the wavelength range between 400 nm and 700 nm is described. The active organic material used in the photodetector is PH3T: PCBM. The various materials used for the analysis of absorbance and transmittance between the active layer are nc_ZnO organic material and Al2O3, GaAs and silicon nitride (Si3N4) semiconductor materials. The maximum absorbance reported in this work are 80.0969%, 80.0451%, 75.4784%, 80.8878%, 76.8223% for without PC on nc_ZnO organic layer, with nc_ZnO PC on nc_ZnO layer, Al2O3, GaAs and silicon nitride (Si3N4) semiconductor materials with PC. The absorption is almost flat between 70% and 80% for without PC and for with PC having materials nc_ZnO, Al2O3 and GaAs between the wavelength range 450 nm to 630 nm. We have achieved an improvement in the absorption at wavelength ranging from 630 nm to 700 nm by using PC with nc_ZnO organic material.

Stock price forecasting is a type of time series problem that forecasts the future price or status of a company on the basis of analysis of time respective values. As the price of stock or company varies with respect to time, its behavior can be analyzed by different machine learning approaches. In this work, methodology is proposed to evaluate the stock position with variation in time using deep learning approach such as recurrent neural network (RNN). This methodology used the technical parameters to evaluate the long term and short-term analysis of any stock or share. This approach also evaluates and gives suggestions to investors either to buy or sell any stock for long term and gives return at very low risk. In this paper work, hybridization of co-relation analysis and deep learning approach for stock price and long-term behavior analysis. The proposed work is termed as time lagged weight optimized RNN (TL-WO-RNN) that is adopted in this work and effectively predict the technical parameters and on the basis of that stock behavior is also predicted. The result analysis was performed on data from different sectors and such as Telecom, Powers, Manufacturing, Finance, Software sectors, etc. The result analysis shows the effectiveness of the TL-WO-RNN algorithm as compared to existing work.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

Hotel business is one of the successful businesses in India. Most of the Indian hotels use banana leaves to serve the food to the customers. Banana leaves are large, bendable, and waterproof. They impart a fragrance to food that is cooked and served on them, steaming with banana leaves imparts an intelligent sweet flavor and a good smell to the dish. Many hotels serve food in a full banana leave which increases the cost. When the leaves are cut into round plates from a half leaves it reduces the cost as well as the consumption of leaves. Hence most of the hotels started to reduce the consumption of leaves for cost saving as well as to utilize the leaves efficiently. Two or more labors are employed to cut and arrange the leaves manually. To overcome the difficulties faced by the labor, it is essential to automate the process. The Banana leaf is cut into round shape by the machine with minimum wastage. The components of the project are hydraulic cylinder, pneumatic cylinders, circular blade, motor, ultrasonic sensor and Arduino module. Initially the leaf is fed into machine and is kept below the hydraulic cylinder. When the sensor detects the presence of leaf, the pneumatic cylinder is actuated which forces the blades on the leaf to cut into the round shape and collected in the basket. In the proposed method, the issues faced by hotels in the banana leaf plate cutting process are considered and remedies are proposed. About 60 plates can be manually cut per hour but in this machine the outcome is increased to 100 plates per hour. Power consumption is about 30 units per day. Factors like economy, more yield, rapid process, saving manpower and many others are concentrated.

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

Silver nanoparticles are generally used in various products due to their distinctive and antibacterica activities. Nanosilver are used in electronics bio-sensing, clothing, cosmetics, sunscreen and medical devices but a large numbers of in vitro shows that AgNPs has toxic effects on various cultured mammalian cells. In this paper I reviewed about the toxicity effect of silver nanoparticles(AgNPs) on humans Health. In addition we studied to which content the silver ion fraction contribute the toxicity to cells. However by green formation of silver nanoparticles(AgNPs) the toxicity of silver nanoparticles can be reduced. More studies and research is required that confer AgNPs at lower toxicity only then their use would be proved useful in several industry biomedical contexts and for human health also. The ambition or goal of this review is to estimate the literature on the toxicity of silver nanoparticles. The aim of this review to inspire more studies in the field of toxicity of nanoparticles of silver or silver nanoparticles.

The water quality index has been universally accepted as an indicator to represent the water quality status of the surface water body comprehensively. The prevalent conventional Water Quality Indices (WQIs) suffer from limitations such as 'eclipsing' and 'ambiguity'. Artificial intelligence techniques such as artificial neural networks (ANNs) have gained importance to overcome the limitations of conventional WQIs. In the present study, the Levenberg Marquardt (LM) algorithm and the Scaled Conjugate Gradient (SCG) algorithm have been compared to develop WQI based on the ANN approach (i.e ANNWQI). It is observed that the LM algorithm outperforms the SCG algorithm for prediction of ANNWQI of Indian streams, while the Bayesian Regularization algorithm has not been found suitable for the same purpose in the present study. It is also observed that both LM and SCG algorithm gives robust predictions when the hidden layer contains ten neurons. The combination of data set partitioning of training (75%), Validation (15%), and testing (10%) have been found to give the robust performance of prediction of ANNWQI for Indian streams. The predicted ANNWQI model using the LM algorithm has a very high correlation with the measured WQI values and therefore recommended to be adopted as an effective alternative, to avoid lengthy calculations involved in prevalent conventional WQI.

Coconut Shell is a waste product derived from agriculture which is utilized as bio adsorbent for treating the wastewater discharged from electroplating industries. The physio chemical characterization of raw waste water and batch adsorption study using coconut shell charcoal (CSC) with a reference to effluent characterization is conducted to identify the potential contaminant and efficiency of adsorbent. The parameters such as effective particle size of adsorbent, contact time and dosage influencing the treatment efficiency of zinc and potassium which are identified as the potential contaminant in the waste water. Increase in dosage and specific surface area with reduced particle size increases the treatment efficiency up to the approved level, beyond which the portion adsorbed which remains constant. It is apparent that a dosage of 15 g/l and duration of 90 min is satisfactory to remove 90% of phosphate and 97% of zinc for 150 µm particle size. The adsorption equilibrium data were tailored well with the Freundlich isotherm for the removal of zinc and potassium. The baseline study is manifested the potential of CSC in the reduction of zinc and potassium from the industrial waste water.

Secure and reliable identity is essential for identifying a user accurately and providing services. The traditional centralized identity systems have several security weaknesses and do not support user control. Self-sovereign identity (SSI) has been proposed to provide user control and secure identity model. User record privacy and security is a critical factor for any healthcare information system. Healthcare is also a domain where SSI solutions can counter patient information privacy and security threats. This paper reviews the applicability of blockchain-based SSI solution in healthcare, their advantages and requirements. It further provides a model use case for demonstrating the SSI application in healthcare.

Additive manufacturing (AM) of Mg alloys has become a promising strategy for producing complex structures, but the corrosion performance of AM Mg components remains unexploited. In this study, wire and arc additive manufacturing (WAAM) was employed to produce single AZ31 layer. The results revealed that the WAAM AZ31 was characterized by significant grain refinement with non-textured crystallographic orientation, similar phase composition and stabilized corrosion performance comparing to the cast AZ31. These varied corrosion behaviors were principally ascribed to the size of grain, where cast AZ31 and WAAM AZ31 were featured by micro galvanic corrosion and intergranular corrosion, respectively.

The agriculture industry is one that is highly resource- and labour-intensive. As such, farmers are increasingly turning to technology and automation to address this issue. However, agricultural robots are far too complicated, slow, and costly to be made publicly available. As a result, the agriculture sector still lags behind in integrating modern technologies. This research paper details the development of a low-cost agricultural robot for spraying fertilizers and pesticides in agriculture fields as well as for general crop monitoring. The prototype system is a two-wheeled robot that consists of a mobile base, a spraying mechanism, a wireless controller for controlling the robot movement, and a camera for crop health and growth monitoring as well as detecting the presence of pests in the agriculture field. Tests conducted on the prototype system show that while the productivity of the robot in terms of crop coverage is slightly lower than a human worker, the labour cost savings afforded by the agricultural robot prototype is much greater as it functions completely in an autonomous mode and only requires the operator to control the robot when placing it at the start of the crop path. Furthermore, the prototype system also provides greater resource savings and reduction in the contamination of underground water sources due to leeching process, thus achieving precision agriculture goals. Lastly, the excellent battery life of the prototype system ensures that there will be no increase in the operation times and reduction in the efficiency of the fertilizer and pesticide spraying process due to the recharging times when replacing human workers. Future recommendations include making the agricultural robot fully autonomous, using either a rail- or line-following system, to further reduce the labour requirements and costs.

India is an agricultural country. Nearly 70% of the populations thrive in rural areas, engaged in agriculture making the backbone of our economy. The Trichoderma genus is widely distributed and provides an alternative to chemical pesticides in crops and plants growth. Trichoderma species are free living and commonly found in soil and root ecosystems. Root colonization by Trichoderma also regularly promotes growth and development of plants. They also perform beneficial and significant role in enhancing crop productivity, fight against pathogens, resistant to abiotic stress and increase of nutrients uptake from soil. The present study was undertaken to investigate quantitative and qualitative estimation of phosphate solubilizer by using nineteen isolates of Trichoderma grown on Pikovskaya’s Agar and NBRIP media. It was found that all isolates were capable of differentially utilizing 5 g/L TCP in NBRIP broth. This was indicated by the soluble phosphate concentrations and increase in acidity of growth medium. Out of 19 isolates, all isolates were capable in phosphate solubilization. However, the P- solubilizing efficiency was found higher in case of the isolate number-59 and 89. While isolate number-102 was the strongest and isolate number-23 was the weakest P-solubilizer. Minimum Phosphate solubilization concentration was observed in 3rd day (0%) that simultaneously increased on 7th day (52.63%) followed by 9th day (100%) and remaining all days 13th, 17th and 21st days also observed 100% efficiency.

This study shows the experimental investigation on Inconel 718 to find the parametric response of abrasive water jet cutting on top Kerf width (Wt), bottom Kerf width (Wb) and Kerf wall inclination. The machining are planned with Box Behnken Design (BBD) based on four factors at three levels each. Four important process parameters e.g P, AFR, SOD and TS, were selected in cutting. The quadratic regression models were established to forcast the Kerf quality by using response surface methodology. ANOVA has been employed to find the influence of operating factors. Result reports that traverse speed is the most influenced parameter in affecting the Kerf taper.

Subfield codes of linear codes over finite fields have recently received a lot of attention, as some of these codes are optimal and have applications in secrete sharing, authentication codes and association schemes. In this paper, a class of binary subfield codes is constructed from a special family of MDS codes, and their parameters are explicitly determined. The parameters of their dual codes are also studied. Some of the codes presented in this paper are optimal or almost optimal.

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

A tweakable enciphering scheme (TES) is a length preserving (tweakable) encryption scheme that provides (tweakable) strong pseudorandom permutation security on arbitrarily long messages. TES is traditionally built using block ciphers and the security of the mode depends on the strong pseudorandom permutation security of the underlying block cipher. In this paper, we construct TESs using public random permutations. Public random permutations are being considered as a replacement of block cipher in several cryptographic schemes including AEs, MACs, etc. However, to our knowledge, a systematic study of constructing TES using public random permutations is missing. In this paper, we give a generic construction of a TES which uses a public random permutation, a length expanding public permutation based PRF and a hash function which is both almost xor universal and almost regular. Further, we propose a concrete length expanding public permutation based PRF construction. We also propose a single keyed TES using a public random permutation and an AXU and almost regular hash function.

The tool and die applications are preferred the unconventional machining processes as they are popular in machining the difficult to cut type complex contour/ profile. The complex profile machined on hard material. Some special materials like Stainless Steel 12X18H10T withstand its inherent properties even it exposes at higher temperature. This investigation aims to expand its application by investigating its machinability in Electro Chemical machining process for surface finish. The process parameters like gap between work material and tool, discharge current, concentration of electrolyte and applied voltage at three levels. The Taguchi approach is used for optimizing the process parameters and confirmation runs also conducted for developing the mathematical modelling. For optimizing number of experiment the Taguchi L9 is utilized for this investigation.

In this study we report a facile, cost effective one step preparation method for the synthesis of Mn doped cerium oxide (CeO2) nanoparticles (NPs) through co-precipitation technique. The synthesis was performed at relatively low temperature using a green reagent oxalic acid as reducing agent. The structural and optical attributes of the samples were characterized by X-Ray Diffraction spectroscopy (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV–Visible spectroscopy and SEM (Scanning electron microscopy). The XRD and FTIR results show the formation of Mn doped ceria NPs having a cubic fluorite structure. The morphology and chemical composition of the prepared sample was realized using SEM and EDX spectrum. The synthesized Mn doped ceria NPs shows greater absorption in the visible region and reduced particle size compared to pure ceria NPs.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

Nanocomposites hold the possible to reform the field of conventional composites together in conditions of recital and prospective applications. Polymer nanocomposites enclose enormous market future together as replacements for present composites and in the making of new markets during their great behaviour. It offer advanced technologies and enhances business opportunities to all industrial sectors. This review gives a broad resource for polymer nanocomposite, nanoparticles synthesis, fabrication and testing methods of polymer nanocomposites.

In the present paper, we have studied the dependence of thermal conductivity on shape, size and dimension of nanomaterial. By using the quantitative expression of melting temperature, the expression for thermal conductivity is deduced in this work. Kapitza resistance term is also considered in the study to better explain the thermal conductivity variation in nanomaterials with scattering effect on thermal properties. The thermal conductivity is found to decreased as size of nanomaterials decease. The variation in thermal conductivity with size is calculated for nanowire, nanofilm, spherical, regular tetrahedral particle, regular octahedral particle, and regular icosahedral particle shapes. The results calculated in the present work are compared with the available experimental results to judge the validity of the present model.

The Constitution and the geometry aspects of any sensor is influenced by several factors involved in the assembling of sensor. It is basically influenced by parameter to be measured, method of measuring the measurand, nature of the parameter i.e. weather the parameter is chemical, biological, physical, optical, electrical, magnetic, mechanical or acoustic etc., environment around it, etc. In most of the cases, several sensors are developed, meant to measure a single parameter with maximum achievable sensitivity. In the present paper a fiber optic multipurpose sensor is described, which can be used to measure various physical quantities of liquids with well defined combination at room temperature (30 °C), employing a specific set of materials and methods in the design operation of the sensor. Exploiting all the advantages offered by the fiber optic technology, the sensor was calibrated to measure ultrasonic velocity, refractive index, density, dielectric constant, viscosity, molar volume, acoustic impedance, molar refraction, viscous relaxation time, adiabatic compressibility, free intermolecular length, Gibb’s free energy, absorption coefficient, internal pressure, free volume, mole fraction, concentration, effective mass at room temperature and examined to exhibit superiority in all respects compared to the conventional sensors.

The tyre could be among the most critical and complicated elements of automobile dynamics and roadway pressure analysis as it serves just about only connection among road surface as well as the automobile. Substantial study and growth efforts on automobile dynamics and tyre models are being reported in last centuries, but the implementations and parameter recognition involved with both of these tyre models remain elusive. The goal of this research paper is to research and analysis the impact on tyre performance of the different operational conditions of the tyre the parameters of tire operation. Experimentally checking tire performance parameters allows the designer to compare parameter relationships and plan the tire, so it's necessary to test the tire. Information of tires ' Vibrant properties are essential for any type of automobile dynamics study and growth activities. The purpose is to distinguish the characteristics of the tyre from vehicle, reach a good degree of validity and minimise prices. This analysis study offers details on the work of numerous researcher in conducting inter-laboratory tyre development work with tyre performance characteristics, but this review article also assist to explain the variables on which tyre quality factors rely.

A composite system consisting of aminosilicate (APS) functionalized-fullerene quantum dots (C60 QDs) and bismuth vanadate (BiVO4-APS-C60) has been developed via a facile one-step hydrothermal process. The structural analysis by XRD revealed that the integrated QDs have not affected the crystal structure of the host BiVO4, while the XPS studies showed that these QDs have been integrated via the Bi-V-O-Si-C network. The optical studies indicated that both the C60 and APS-C60 QDs have hardly influenced the optical absorption properties of the composite system. On the other hand, the dispersion of aminosilicate functionalized-QDs considerably reduced the aggregation in the layers and yielded a few nanometer thick BiVO4 layers due to their inter-layer occupancy as seen from their high-resolution TEM images. The photoluminescence, photocurrent and electrochemical impedance studies showed that the integration of APS-C60 QDs greatly improved the photoactive properties of the composite by effectively enhancing the charge recombination resistance, charge separation and transfer process between the integrated materials and surroundings. As a result, BiVO4-APS-C60 composites showed the enhanced photocatalytic efficiency towards degradation of ciprofloxacin (CIP) molecules under solar light irradiation as compared to that of bare-BiVO4 and BiVO4-C60.

Soft gamma ray irradiation is done on Silver nano colloids prepared using Pepper Nigrum as reducing and stabilizing agent. Change in surface morphology and the structure of the colloid is obtained from Transmission Electron Microscope images and Selected area Electron Diffractogram. Morphology of the gamma irradiated sample is devoid of nano particle clusters and the Selected Area Electron Diffraction pattern confirms the crystal nature as face centered cubic structure of nano silver. UV–visible spectroscopic analysis gives the absorption peaks in the green region. Gamma irradiation results in the reduction in peak intensity without any change in wavelength. A statistical approach is done on treating the gamma irradiated colloidal sample with the as prepared silver nano colloid for measuring the area of affecting fungal infection on a potato-agar medium. It is found that the fungal growth is declined rapidly with the introduction of gamma irradiated silver nano colloid than the as prepared colloid.

The vicinity of the Rozprza archaeological site (central Poland) has been the area of a series of palaeoecological studies tracking the environmental history of the Luciąża River valley up to ca. 13,200 cal. BP. Numerous subfossil palaeomeanders of different sizes have been discovered in the valley floor. Here, we present the first results of multiproxy research on the paleo-oxbow lake fill, one of the oldest in the region. The wide range of palaeoecological analyses resulted in reconstructions of vegetation history, climatic, hydrological and habitat changes. The studied oxbow was an aquatic ecosystem with diverse invertebrate fauna until the end of Younger Dryas when it transformed into a limno-telmatic habitat. The sediment composition indicates active denudation processes and several episodes of turbulent hydrological conditions. Such an increased river activity could have caused flooding, resulting in an allochthonous matter supply to the oxbow lake in Late Vistulian. Environmental changes were strictly related to the regional features of the catchment, the transformation of soils, and the hydrogeological conditions. The chironomid- and pollen-inferred climatic reconstructions indicate periods of high and low continentality. The chironomid record indicates relatively cool summer conditions in the Allerød, especially ca. 13,000 cal. BP, possibly related to the Gerzensee Oscillation. On the other hand, a distinct increase of summer temperatures in Younger Dryas (up to 16 °C) was recorded. Such a situation was also confirmed in some other studies from the region, suggesting that it might be the effect of some specific, local palaeoclimatic conditions. Fundator badań: Fundator badań: Optional.empty

This study compiles information on the different construction systems used in mining camps in Peru in order to determine guidelines and criteria for architectural design and their different construction systems, considering improving the quality of life, comfort and optimal development of social activities that are made there. The particular case study is located in Quellaveco, Moquegua Region in southern Peru, and the “Las Bambas” mining camp located in the Apurimac Region will also be used as an example for comparison. The research has made it possible to identify the main variables that make up the design of its architecture, modular concepts and the use of different construction systems and to propose design recommendations that can be implemented to improve comfort.

In the current exploration an impact of 44 µm estimated B4C support expansion on the mechanical conduct of Al2618 combination has been contemplated. The Al2618 compound metal composites with 2, 4, 6 and 8 changing weight rates of B4C were created by stircast strategy. The orchestrated 44 µm estimated B4C particles built up Al2618 combination composites were exposed to microstructural considers, mechanical properties testing. Microstructural portrayals of acquired examples were completed by SEM microscopy and EDS spectrums. The B4C particles were uniformly disseminated and existences of these particles were affirmed by the EDS patterns. The hardness and tensile qualities of metal composites have been upgraded with the expansion of B4C support. There was decline in the ductility of the Al2618 compound after the joining of the support. Different crack and failure systems were seen in the Al2618-B4C composites utilizing SEM.

Agricultural drones are Unmanned Aerial Vehicles (UAVs) applied to farming in order to help increase crop production and improve farm efficiency. Crop-dusting drones are able to spray powdered fertilizer, pesticide, or insecticide on crops from the air, resulting in reduced costs and increased efficiency compared to traditional methods. The objective of this research work was to improve and enhance the performance of the crop-dusting process using agricultural drones. This research paper details the development of an improved storage container design and stabilized release mechanism for crop-dusting. To stabilise the release of payload when the drone is moving or swaying in the air, a gimbal mechanism was added to the release mechanism in which weights were used to produce constant downward force which balances the mechanism. Tests conducted on the prototype system showed that it performs as required. The stabilization tests show an improvement in accuracy of granule spread when using the stabilized release mechanism developed in this research work. As such, the wastage of fertilizers and pesticides can be reduced. The prototype system was also found to much more capable at carrying heavier loads for a longer period of time compared to current designs. Thus, using the prototype system developed for this research work, the size of storage container as well as the weight of fertilizer or pesticide granule payloads can be increased.