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The present study evaluates the effect of water and acetic acid washing on the chemical composition of spruce bark in-depth. Also, washing with steam explosion condensate (SEC) was investigated which is a novel attempt. The leaching kinetics of troubling elements (TE) was studied to understand the leaching behavior of TEs and for upscaling the process. Furthermore, to study the ash transformation behavior of TEs in pre-treated and raw bark at high temperatures (500–1500 °C), thermodynamic equilibrium modeling (TEM) was also performed. The result of washing pre-treatment shows high removal of TEs: 22–97% Na, 46–82% Cl, 14–79% K, 14–65% Mg, 25–50% S, 3–22% Ca, 12–36% P, 3–43% Si, and 6–35% N. Continuous removal of TEs was seen with increasing washing duration where most of TEs followed a second-order leaching kinetics. Acid washing results in a much higher and quicker removal for all TEs than water washing. Due to the acidic nature of the SEC, it shows similar removal of TEs as the 0.1 M acetic acid solution. TEM reveals that the transformation behavior of TEs in bark changes considerably after pre-treatment. Pre-treated bark shows the formation of fewer problematic compounds responsible for fouling, slagging, and corrosion at typical gasification and combustion temperatures, such as KCl, K2SO4, K2CO3, KOH, Na2SO4, NaCl, and K-, Na-, P-, and Ca-slag. Though best washing efficiency was seen for longer washing durations, 10 min washing with 0.1 M acetic acid or SEC may be adequate for practical applications. publishedVersion

Surveillance system is a method of securing resources and loss of lives against fire, gas leakage, intruder, earthquake, and weather. In today’s time, people own home, farm, factory, office etc. It has become more crucial to monitor everything for securing resources and loss of lives against fire, gas leakage, intruder, earthquake. As a part of surveillance, monitoring weather is also essential. Climate change and agriculture are interrelated processes, Today's sophisticated commercial farming like weather monitoring, suffers from a lack of precision, which results huge loss in farm. Monitoring residential and commercial arenas throughout is an efficient technique to decrease personal and property losses due to fire, gas leakage, earthquake catastrophes. Internet of Things make it possible and can be implemented separately for each thing or site. But it is very difficult to monitor each site and have centralized access of it across the world. This arises the need of heterogenous system which will monitor all IoTs and perform decision making accordingly. IoT itself a large-scale thing. For single IoT application, sensors used are more in number. These sensors generate thousands of records for an instance of time, some of those are valuable and some requires just analysis. This huge amount of data on servers requires better data processing and analytics. Maintenance is also a critical task. Cloud extends these functionalities but storing all the data on cloud entail users to pay tremendous cost to the cloud service providers. This problem is catered by “CoTsurF” framework. This paper presents novel and cost effective “CoTsurF” framework, CoT-enabled robust Surveillance system using fog machine learning, a Proof-Of-Concept implementation of heterogenous and robust surveillance system based on internet of things and cloud computing by leveraging a groundbreaking concept of Fog machine learning that is Fog Computing and machine learning in Cloud of Things.

This study investigated biomass blending and water washing to improve product quality from two-step pyrolysis of rice straw. Rice straw (RS) was mixed with groundnut shells (GNS) and wheat straw (WS) in different weight ratios. Blending RS with GNS/WS in a 1:1 ratio increased the total bio-oil yields by 7–9% and reduced the pyrolysis gas and char yields by 5–7% and < 2%, respectively. RS was washed with water separately to examine the effect of removing water-soluble ash elements. The optimum washing duration was 60 min; the ash removal efficiency was then 26%. The bio-oil yields from washed straw increased by 4% over unwashed straw, and pyrolysis gas yields decreased. Combining the washing and blending processes increased the levoglucosan yield by 1.6–2.1 times compared to unwashed RS, and the water content in bio-oil was reduced by ∼ 10%. Moreover, the biochar samples obtained after pyrolysis of washed biomass blends had potential fuel applications owing to low fouling or slagging propensity. They also had possible use in the soil for adsorption of soil contaminants and increasing acidic soil pH, with likely stability of ∼ 1000 years in the ground. These results provide a promising alternative for efficiently converting rice straw to multiple value-added products. publishedVersion

In signal processing applications, it is often required to have multi-notch filters (MNFs) which simultaneously possess an excellent quality factor, ‘Q’, and a brief transient response. Though, the duration of transient response can be improved by decreasing ‘Q’ which, however, also degrades the quality of the filter. Due to their contradictory nature, dealing with ‘Q’ and transient response becomes a difficult task. This paper is a sincere attempt to address this problem for MNFs by exploring time-varying pole-radius infinite impulse response MNF designs employing a hyperbolic tangent sigmoid (HTS) function-based pole-radius variation. Two-notch and three-notch filters, based on HTS function, are designed, analyzed for stability and tested for transient suppression, selectivity and noise removal. Experimental validations supported by simulations on sinusoids and ECG signals are performed in the LabVIEW $$^{\mathrm{TM}}$$ environment and have been presented as complete performance assessment. In contrast to the recently reported and traditional MNF designs, significant performance enhancements were recorded for the proposed designs. Therefore, based on the presented investigations, it is concluded that the proposed MNFs are efficient designs for removing harmonics from sinusoids and ECG signals.

To improve the efficiency and applicability of the washing pre-treatment for combustion, pyrolysis and gasification, a modified approach was developed in the present study. Two novel washing approaches were tested using wheat straw and empty fruit bunches of oil palm: multiple-step washing with fresh water (SWFW) and wastewater recirculation (SWWR). SWFW showed the high removal of K (

Osteosarcoma is one of the most common malignant bone tumor mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify images obtained from different sources. Here, we propose an automatic detection system Integrated Features-Feature Selection Model for Classification (IF-FSM-C) that detect osteosarcoma from the high-resolution whole slide images (WSIs). The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted feature extraction techniques and deep learning models. It is quite possible that the integrated features may contain some redundant and irrelevant features which may unnecessarily increases the computation time and leads to wastage of resources. To avoid this, we perform feature selection (FS) before giving the integrated features to the classifier. To perform feature selection, we propose two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed and the results prove the superiority of the proposed IF-FSM-C that uses integrated features and feature selection in classifying WSIs as compared to the classifiers which use handcrafted or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection.

In the dynamic and competitive world, technology has changed the pace of all the industry. Artificial intelligence is a technology which enables the industry to grow at faster pace and efficiently finishing their work. This technology has entered into various departments such as finance department, human resource department, marketing, production etc. AI system has enabled the organization to enhance their existing performance and efficiently performing functions on a day-to-day basis. Currently, due to dynamic and competitive environment people working at different managerial level are working under pressure and understanding the need of artificial intelligence at workplace. Authors have used quantitative research to conduct the research and regression methods has been used to analyse the data. AI as a technology has a role in the different HR practices starting from talent acquisition and extending it to the assessing the performance of the people at work place. This research will study the relation of artificial intelligence and HR functions and different functions performed by HR department. The objective is to understand the factor like innovativeness and how use of HR operations. To conduct the study HR professionals from different IT companies were considered. Through the analysis the result indicated the positive linkage between different factors such ease of use and innovativeness which clearly indicates that AI has a influence on both the factors. This research paper will provide indepth knowledge about artificial intelligence which is at present a new revolution in industry and referred as industry 4.0.

Sentiment analysis and opinion mining have taken a leading position ever since the boom of the internet which led to an enormous amount of opinionated data being posted online. Hence, it has become essential to structure an efficient algorithm for analysing sentiments from text. Even though traditional machine learning algorithms were a breakthrough in the domain, there remained a demand for a better solution. This paper presents a systematic & qualitative literature review for various approaches to sentiment analysis using hybridized soft-computing techniques to cater to noise and improve the flexibility of a fuzzy inference-based sentiment analysis system. This qualitative literature review adds to the body of evidence that soft-computing and fuzzy logic techniques can be effectively applied to a range of applications where noise is inherent, especially in the case of sentiment analysis and opinion mining.

The present work is the second part of a study conducted to evaluate the effect of basic parameters on the efficiency of water washing pre-treatment. As per the results of Part-1, large sizes and shorter washing durations are favourable for industrial applications, yet low to moderate fouling and corrosion can be expected from them on combustion. The aim of the present study was to further improve the efficiency of the washing pre-treatment for rather short washing durations (5 and 10 min) and a large feedstock size (3 cm) by optimizing the temperature and solid-to-liquid (S:L) ratio. The washing temperatures considered were 20 °C, 40 °C, 60 °C, and 80 °C, while the S:L ratios considered were 1:15, 1:20, 1:30, 1:40, and 1:50. Fuel composition, heating values, energy loss, and fouling and slagging propensity were evaluated. Increasing the washing temperature resulted in the greater removal of ash (25–37%), K (42–55%), Cl (60–81%), S (32–61%), Mg (52–68%), N (17–25%), P (11–37%), and Ca (14–29%). On increasing the S:L ratio, improvement in the removal of ash (27–35%), Cl (73–84%), S (41–59%), and N (21–40%) was also observed. For practical applications, an S:L ratio of 1:15 was found adequate compare to higher S:L ratios. The washing of 3 cm sized wheat straw for 10 min at 40 °C with a 1:15 S:L is recommended for industrial applications, as it shows the least fouling propensity and energy loss. publishedVersion

As quantities and composition of municipal solid waste (MSW) vary significantly with seasons, a seasonal characterization study is critical for developing an efficient MSW management system. MSW was characterized in three different seasons for selecting an appropriate waste treatment and management strategy for Guwahati city. Results of the study shows that the major components of the MSW were organics (42.2%) and plastic wastes (25.2%), which show high variations on a seasonal basis (22–49%). The chemical characterization of MSW revealed that on seasonal basis moisture content varies between 43.4% and 58.3%, pH between 5.5 and 6.5, volatile solid content from 32.9 to 58.9%, and the calorific value between 1203 and 3015 kcal/kg. Waste collected in the present study was a mixture of organics, recyclables, and inert material which is difficult and uneconomical for treatment in its present form. However, with proper waste segregation, bio-methanation, and composting could be sustainable waste treatment solutions due to the high moisture and volatile content of the MSW. Due to inadequate quantity, low calorific values, requirement of skilled supervision, and high capital investment, the thermochemical conversion of MSW may not be economically feasible for the present case. Implications: Present study is a novel attempt to analyze in-depth variation in the municipal solid waste (MSW) composition and properties in different seasons and how does it influence the selection and feasibility of the available waste treatment technologies. Search on Google scholar shows that only seven articles have been published till now which evaluated seasonal impact of MSW. Out to these published studies only one study have calculated energy potential of MSW on seasonal basis which is mainly restricted to incineration only. In-depth analysis of seasonal variation on anaerobic digestion, composting, refuse derived fuel (RDF), pyrolysis, and gasification is yet to determine. Furthermore, to best of our knowledge so far in India there was no such in-depth study has been published related to seasonal variation in MSW on large scale (city level). Present study provides in-depth valuable information regarding degree of variation in MSW composition and how does it affect resource recovery out of waste, which was not studied before in-depth before. Outcomes of the present study will definitely assist engineers and policymaker involved MSW management and planning for large urban areas to fulfil their sustainability goals. publishedVersion

Google Earth provides high-resolution satellite images over a long period of historical time period which can be used to study land use/land cover (LULC) changes in any area over an extended period of time. In this study, Google Earth Pro was used to identify problems with current locations of solid waste dumping sites and conduct a time-series analysis of the areas occupied by waste dumping sites in Kharagpur city. Five dumping locations were studied of which three are official dumping sites, one is an illegal dumping site and the last one is an official dumping site which was cleaned in 2015. Satellite images of the same dumping locations were taken to evaluate changes in the areas of the dumping sites from 2010 to 2017. The results of the study show that most of the sites are situated very close to an airbase runway, railway line, residential area or highway/road which is in contravention of regulations. Time-series analysis shows that the sizes of all dumps have varied significantly with respect to time except for the unauthorized dumping site. The reasons behind the fluctuations in area are frequent burning of garbage and partial clean-up of the site due to local complaints. The methodology used in this study can be extended to an entire city or even several cities to find problems related to the existing illegal or official waste dumping site within a specific time period. acceptedVersion

Impacts by over-height (OH) vehicles on bridges, commonly known as `bridge hits', cause significant risk to safety and preservation of transportation infrastructure in the USA. Currently, available over-height vehicle detection systems (OHVDs) have specific site requirements, extremely high installation costs, and propensity for false alarms, which limit their field deployment to few locations. This study describes a new, enhanced LADAR-based OHVDs (L-OHVDs), which can be installed on the face of a structure to be protected and can measure the height of an approaching vehicle before the safe stopping distance from the structure. Built using off-the-shelf components and a patent pending optical design, it exhibits enhanced features like vehicle detection, actual height measurement, and collision prediction with no reported false alarms. The developed prototype has a detection range of 220 ft with height measurement accuracy (±0.66 inches) that is better than currently available OHVDs. This system has exceptional precision and is well suited to detect OH trucks and tractor trailers approaching a low vertical clearance bridge. With superior performance and cost-effective installation, the proposed L-OHVDs has the potential to reduce occurrences of `bridge hits', thereby limiting consequences such as congestion and damages to bridge while sustaining safety of motorists.