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Exclusion of Chromium(VI) Ion in Grueling Activated Carbon Fabricated from Manilkara zapota Tree Wood by Adsorption: Optimization by Response Surface Methodology
The current paper makes obvious the elimination of chromium(VI) ion, from wastewater via adsorption technique with activated carbon generated from Manilkara zapota tree (MZTWAC). Preliminarily MZTWAC has undergone characterization studies which uncovered the suitability of MZTWAC to expel chromium(VI) from aqueous solution. Batch adsorption experimentation was premeditated with the competence of central composite design (CCD) and it was executed. Response surface methodology (RSM) was the key optimization software to appraise the adsorptive chattels of MZTWAC engaged in removing chromium(VI) ion in aqueous solution which explored the interactions flanked between four expounding variables explicitly initial concentration of chromium(VI) ion, pH of the solution, MZTWAC dose and time of exposure, and contact time. The response variable that was concentrated in the study was adsorption capacity. It was deduced a polynomial in quadratic equation was documented amid the adsorption capacity and variables influencing the adsorption with R2=0.9792 which was projected as the best suit for the adsorption process. ANOVA that is expanded as analysis of variance judged the connotation of adsorption process variables. 0.2 g of MZTWAC dosage has removed 87.629% chromium(VI) from aqueous solution. The enhancement of adsorption process reclined on the attainment of maximum adsorption capacity which further depends on the optimization of variables under consideration. This criterion was accomplished by the desirability function optimizing the process variables. 2022 S. Sujatha et al. -
Investigation of detoxification nature of activated carbons developed from Manilkara zapota and de oiled soya
Heavy metals are poisonous and detrimental water contaminant. Their existence affects human beings, animals and vegetation as a outcome of their mobility in aqueous ecosystem, toxicity and nonbiodegradability. This work aimed at the development of new adsorbent in the detoxification of heavy metals using Manilkara zapota tree wood and de oiled soya. The study completely focused on the characterization of the developed activation in the view of using it as a adsorbent. The characterization of activated carbon was effected SEM analysis, FTIR, XRD analysis and surface area determination. Both the activation carbon have showed a tremendous characterization in their employability as adsorbent in adsorption of heavy metals in aqueous solution. 2019 Elsevier Ltd. All rights reserved. -
A critical review of Cr(VI) ion effect on mankind and its amputation through adsorption by activated carbon
A toxic heavy metal is a one which is plausibly dense metal or metalloid that is eminent for its prospective toxicity, particularly in environmental context. Heavy metal poisoning may crop up as an upshot of air or water contamination, exposure to industrial activities, foodstuffs, medicines, coarsely coated food containers, etc. The present review highlights various issues related to the effects of Cr (VI) heavy metal toxicity to human health and its adsorption from wastewater using low cost adsorbents. Many researchers have lay their endeavor to ascertain low-priced adsorbents that are effortlessly available and have power over the sensible adsorption capacity. It is perceptible from the literature survey that the revealed adsorbents have established stupendous removal capabilities for Cr (VI) metal ions. As the convention of heavy metal Cr (VI) is increased, it is implicit that there is a strong need for research to remove Cr (VI) heavy metal ions from wastewater to trim down the problem of soaring anthropogenic pressure and burly tendency to mount up in living organisms. 2020 Elsevier Ltd. All rights reserved. -
Application of response surface methodology to optimize lead(Ii) ion adsorption by activated carbon fabricated from de oiled soya
Lead(II) ion a heavy metal is known for its toxicity. An initiative has been taken in this study, to adsorb toxic lead(II) ion using activated carbon made of de oiled soya, by an aqueous solution through batch adsorption methodology. Adsorption process variables such as adsorbent dose, contact time, solution pH, and lead(II) ion concentration were optimized by central composite design (CCD). To find the interaction between process variables, response surface plots were utilized using response surface methodology. Design-Expert software version 7 was resorted to in this experiment. It was observed that the components from the analysis of variance of the CCD revealed that the selective process independent variables had significant control over adsorption capacity. Desirability function was used to appraise the factors and response in adsorption experiments to find an optimum point where the preferred adsorption could be obtained. Adsorption process with the application of activated carbon developed from de-oiled soya meritoriously removed lead(II) ion with an optimum adsorption capacity of 26.279 mg/g for an initial concentration of lead(II) at 60 mg/L. 2021 Desalination Publications. All rights reserved. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE. -
Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller
Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry. M.S. Sujatha, S. Sreelakshmi, E. Parimalasundar, K. Suresh. -
Smart city initiatives and disaster resilience of cities through spatial planning in Pune city, India
Cities are attracting populations at alarming rate. Cities provide the need of populations in every way from livelihoods to livability. In doing so it is exhausting its resources resulting in increasing threats of risk. An initiative like Smart City Mission is aiming to enhance the capacities of the cities to increase livability and quality of life for its population and decrease threats of risk. This study examines the impact of smart city initiatives on resilience to earthquakes and floods through a spatial planning perspective for the city of Pune in State of Maharashtra through series of structured interviews with key stakeholders. The findings suggest that smart city initiative is still in its primary stage and requires assimilation with the development strategy to contribute to the resilience of the city. The study further proposes the need to integrate the smart city initiative with all the current and future developmental projects. 2023, World Research Association. All rights reserved. -
Implementation of OpenId connect and O Auth 2.0 to create SSO for educational institutes
Increase in the number of users is directly proportional to the need of verifying them. This means that any user using any website or application has to be authenticated first; this leads to the creation of multiple credentials of one user. Now if these different websites or applications are connected or belong to one single organization like a college or school, a lot of redundancy of data is there. Alo ng with this, each user has to remember a wide range of credentials for different applications/websites. So in this paper, we addre ss the issue of redundancy and user related problems by introducing SSO using OpenId Connect in educational institutes. We aim to mark the di fference between the traditional system and proposed login by testing it on a group of users. 2018 Authors. -
Diagnosis of Autism Spectrum Disorder: A Review of Three Focused Interventions
Autism is a neurological developmental disorder that impacts a persons physical, social, and emotional behavior. This disorder develops over time and is characterized by social deficits and repetitive behavior. Although there is no cure for this disorder, an early diagnosis and intervention can do significant wonders and can help the subject to become active functioning members of the family and society. The aim of this study is to minimize the diagnostic period by finding an optimal diagnosis procedure from the existing diagnosis tools. The diagnosis of autism can be done in three ways: 1. clinical evaluation; 2. screening tools; 3. brain images. In this review paper, we have thoroughly gone through all three types of diagnostic procedures and found that there was no single diagnostic tool to confirm the disorder. We also found that the diagnosis period was too long. As the result of this review, we found an ASD diagnosis triad which helps to choose the right diagnosis procedure based on the subjects age which reduces the diagnostic period and helps to aid early diagnosis by eliminating the chaos in choosing the diagnostic tools. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Millets Industry Dynamics: Leveraging Sales Projection and Customer Segmentation
Millets delves into the dynamics of the millets industry, with a particular focus on sales projection and customer segmentation as strategic levers for growth. The research commences with an in-depth analysis of the millets market, encompassing production patterns, consumption trends, and emerging market opportunities. It explores the diverse range of millets varieties, their nutritional profiles, and the factors driving consumer preference. By understanding the market landscape, the study identifies key trends and challenges shaping the industry. A core component of this research is the development of a robust sales projection model. Employing advanced statistical and data-driven techniques, the model forecasts future sales based on historical data, market trends, and relevant economic indicators. The model incorporates factors such as consumer demographics, purchasing behavior, and competitive landscape to provide accurate and actionable insights. Customer segmentation is another critical aspect of the study. By applying clustering and profiling methodologies, the research identifies distinct customer segments based on factors such as age, income, dietary preferences, and purchasing habits. This segmentation enables a deeper understanding of customer needs and preferences, facilitating targeted marketing strategies and product development. The integration of sales projection and customer segmentation empowers businesses to make informed decisions, optimize resource allocation, and enhance overall market performance. By aligning product offerings and marketing efforts with customer segments, companies can achieve higher customer satisfaction, increased market share, and improved profitability. This research contributes to the growing body of knowledge on the millets industry by providing valuable insights into market dynamics, sales forecasting, and customer segmentation. The findings offer practical guidance for industry stakeholders, including farmers, processors, retailers, and policymakers, in navigating the evolving millets landscape. By leveraging the potential of sales projection and customer segmentation, the millets industry can unlock new opportunities and achieve sustainable growth. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE. -
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Enhancing the job scheduling procedure to develop an efficient cloud environment using near optimal clustering algorithm
In this internet era, cloud computing and there are various problems in the cloud computing, where the consumers as well as the service providers facing in their day to day cloud activities. Job scheduling problem plays a vital role in the cloud environment. To provide an efficient job scheduling environment, it is necessary to perform efficient resource clustering. In this regard, the proposed system, concentrated on the resource clustering methodology by proposing an efficient resource clustering algorithm named identicalness split up periodic node size (ISPNS) in the cloud environment. The algorithm proposed helps in forming resource clusters with the help of cloud environment. The proposed system is compared with the existing systems to justify the performance of the proposed resource clustering algorithm and it produces near optimal solution for the resource clustering problem which helps to provide an efficient job scheduling in cloud environment. Copyright 2023 Inderscience Enterprises Ltd. -
Skill sets required to meet a human-centered Industry 5.0: A systematic literature review and bibliometric analysis
The first industrial revolution, known as Industry 1.0, was primarily concerned with mechanical engineering and water and steam. Electric power systems and mass production assembly lines were established during the second industrial revolution (Industry 2.0). The third industrial revolution (Industry 3.0) was defined as automatic manufacturing and the incorporation of electronics, computers, and information technology into manufacturing. The fourth industrial revolution (Industry 4.0) is automating business operations and advancing manufacturing to a level based on connected devices, smart factories, cyberphysical systems (CPS), and the internet of things (IoT), where machines will change how they interact with one another and carry out specific tasks. Industry 5.0, with all modern technologies, is aimed to be a harmonious balance between human and machine interaction, and has an emphasis on sustainable growth. The present study uses an interpretive-qualitative research method to review the skill sets required to meet a human-centered Industry 5.0. 2024, IGI Global. All rights reserved. -
Enablers and Outcomes of Supply Chain Collaboration for Sustainable Growth
This study explores the intricate dynamics, challenges, and potential benefits of supply chain collaboration, emphasizing its pivotal role in achieving sustainability goals. Modern Supply Chain Collaboration (SCC) projects focus on sustainability-related activities, fostering interdependence between partners and driving sustained competitive advantage. The study introduces a comprehensive framework encompassing specific enablers (e.g., Joint Decision Making, Technology Integration) and outcomes (e.g., Social, Economic, and Environmental Sustainability) of supply chain collaboration. It contributes to practical guidelines for businesses seeking to enhance collaboration strategies and delves into theoretical paradigms such as the Cooperative Advantage concept, Triple Bottom Line Theory, Resource-Based View Theory, and Network Theory. The Triple Bottom Line Theory serves as an integrated theory of sustainability, emphasizing economic advantages, environmental impact minimization, and societal benefits. The Resource-Based View Theory underscores the role of internal resources in gaining competitive advantages, aligning with sustainability goals. Network Theory explores collaborative dynamics among competing entities, emphasizing resource sharing. The study's findings offer practical implications, enabling companies to assess and improve the sustainability of their supply chain management. It advocates for the integration of supply chain collaboration into organizational missions, emphasizing the importance of trust-building through standardized guidelines. The insights gained from this study are applicable across sectors, aiding legislators in developing flexible regulations and refining collaboration processes. Additionally, the study highlights the potential cultural variations in supply chain collaboration, paving the way for future research. 2024, Iquz Galaxy Publisher. All rights reserved. -
Experimental Verification of Gain and Bandwidth Enhancement of Fractal Contoured Metamaterial Inspired Antenna
The performance of any antenna cannot be completely assessed purely on the basis of simulation results. All simulations are made by assuming an ideal environment where the fabrication tolerances and practical losses are not accounted for. Therefore, evidencing the performance experimentally becomes a crucial step. In this work, the experimental validation of a fractal contoured square microstrip antenna with four ring metamaterial structure, hereon referred to as optimized metamaterial inspired square fractal antenna has been presented. It is an extension to previously designed antenna and aims to experimentally verify the enhanced gain and bandwidth of this antenna. The design and simulation of the proposed antenna was accomplished by using Ansys HFSS v18.2. The end-to-end antenna spread area is 23 mm x 23 mm on a 46 mm x 28 mm x 1.6 mm FR4 substrate with ?r of 4.4. The simulated design was fabricated using Nvis 72 Prototyping Machine and measured in an anechoic chamber facility using vector network analyzer. The antenna resonates with the deepest S11 of-39.5 dB in a broad bandwidth of 2.53 GHz from 2.265 GHz to 4.79 GHz with experimental verification. The proposed antenna provides an enhanced gain of 8.81 dB at the most popularly used frequency of 2.5 GHz. The simulation and experimental results of resonance, gain and radiation pattern are found to agree maximally. The fractional bandwidth offered by this proposed antenna is 72.28%. The experimental validation confirms enhanced gain-bandwidth performance in a wide resonance band. Hence, this antenna is well recommended for wireless, energy harvesting rectenna and sub-6 GHz (2.5 GHz to 4.20 GHz) 5G applications. 2022, Advanced Electromagnetics. All rights reserved. -
ANN based pattern generation, design and simulation of broadband fractal antenna for wireless applications
The synthesis of microstrip antenna(MSA) remains complex and time consuming from convenient design point of view. The Artificial Neural Network (ANN) on the other hand provides quicker and accurate solutions while multiple parameters controlling MSA designs. This paper proposes a new type of square fractal antenna (SFA) structure iterated and optimized by ANN developed using Advanced C and simulated using HFSS for optimum resonance characteristics covering 1.6-6.6 GHz frequency range. The motivation behind this work is size reduction of MSAs through FA concept with broadband resonance. It is suggested that the proposed antenna can be a right choice for various wireless applications because of its broadband functionality. 2016 IEEE. -
Ground Truncated Broadband Slotted Circular Microstrip Antenna
In this growing era of wireless technology, large sized devices have become obsolete. In response to the increasing demand for miniaturization over the past decades, microstrip antennas have drawn attention due to its various features like light weight, low cost, small in size and its greater resistivity to shock and vibrations. These can also easily get conformed to any surface. These antennas are also capable of operating at high frequencies, providing large bandwidth and gain by using various techniques slots and truncation of shapes. This report describes the design, simulation, fabrication and measurement results of a microstrip fed Slotted Circular Microstrip Antenna for broadband applications. The antenna was designed for an operating frequency of 2.45 GHz on a double side printed FR4 substrate measuring 55 mm x 55 mm x 1.6 mm with ?r of 4.4. It measured a very large resonant band of 1.3 - 9.05 GHz at a return loss level as low as -36.5 dB at 7.98 GHz. A maximum gain of 2.46 dB was achieved at 2.33 GHz. The enhancement in bandwidth was achieved by truncation in ground and inclusion of thin circular slot. The HFSS version 18.2 software and VNA model Anritsu SA20E were used for simulation and measurement respectively. It is found that the simulation and measurement results agree. 2018 IEEE.