bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Feasibility for Novel Modular Block Construction
Authors: Bhaliya Pradhyuman , Rajgor Mamta , Shah Devang A.
Published: April 2022

The construction industry needs fast construction technology and modular construction in the future. Living in poverty is a problem for 21.9 percent of the population in India, with 98 percent of the shortage occurring in low-income and Economically Weaker Sections (EWS). In addition, 40 percent of agricultural products go to waste due to inadequate handling and storage infrastructure. We all know that plastic harms the environment. Further CO2 production during cement manufacturing is a major environmental issue. A ton of cement emits 1.25 tonnes of CO2. After considering all these issues, we propose to use gypsum, steel curls, and coconut shell fiber to produce ideal-sized modular units and test their strength and economy. The sample casting had been carried out, and the testing of the basic parameters was accomplished in the study.
Online Teaching and Online Assessment: A Critical Study
Authors: Chinnathambi Kodhandaraman , Anandan Latha , Yousuf Ayari Chokri Mohammed
Published: April 2022

The COVID-19 outbreak forced educational institutions to close, and it had a wide range of consequences. As a stopgap measure, online education is being used around the world. Although it has its own drawbacks and advantages, this change has provided a way for students to continue their education despite the pandemic. Policies were made to accommodate the alternative pedagogical and assessment methods. At this stage, it is essential to study to what extent these alternative teaching and assessment methods are effective. Hence this study investigates teachers’ opinions of online teaching and online assessment during the COVID-19 pandemic adopting quantitative research methodology. Teachers' responses were gathered via a structured questionnaire (N=55) and the respondents were the teachers of English, University of Technology and Applied Sciences-Ibra, Oman. The study results have implications for policy redirections. It's clear from the study's results that online instruction and assessment worked well during the COVID-19 outbreak.
Design Guidelines for Daylight Distribution in Design Studios in Dhaka: An Experiment on Wall Color and Light Shelf
Authors: Rahman Sabrina
Published: April 2022

Light is a significant factor to accelerate creative productivity specially in architectural design studios, where students have to stay for a long period every day. Being a densely populated urban city, buildings in Dhaka experience insufficient daylight and architectural design studios also face the same. Artificial lighting can be an alternate solution to satisfy visual needs of the users, however it may be tiresome after long intervals and can create pressure on the overall energy demand. Since daylighting has psychological, physiological, and aesthetic benefits, the architectural features may also be explored by ensuring adequate daylight in design studios during various learning sessions. The objective of this study is to identify design guidelines for architectural design studios, located in Dhaka, by experimenting on two parameters, wall color and light shelf, which has not been investigated before.
Prospects and Challenges of Community Based Tourism in Heritage and Sculpture Village Shivarapattana
Authors: BG Mukunda , G Vijay
Published: April 2022

Sculpture tourism is a form of tourism which includes exploring different places associated with sculpture and learning about it first hand. Shivarapattana is one of the heritage and sculpture villages famous for stone sculptures. Located in the Malur Taluk of Kolar District in Karnataka, it is surely a place worth visiting especially for a sculpture enthusiast. The main occupation of eighty percentage of the local community here works on crafting idols mostly in stone. The village is a non-descript rural tourism village in Karnataka. Community-based tourism can help the village to bring up entrepreneurial business opportunities among the local community and enhance rural development, subsequently helping the villagers realize reasonable economic returns on the investment and reducing the economic leakages. The focus and aim of this study are to know about the significance of community-based tourism in promotion of sculpture tourism and also to identify prospects and problems faced by the people involved in marking this sculpture village as a prominent rural destination in the tourism map of Karnataka. To complete the study and to derive the necessary findings data has been collected through questionnaire. For analysis, relevant statistical tools were used.
A Study to Assess the Impact of Mindfulness Meditation on Perceived Stress Levels and Subjective Well-Being Among Housewives
Authors: Kaur Manpreet , Sood Meenakshi , Singh Gurvinder Pal
Published: April 2022

The prevalence of stress-related problems and deprived overall subjective well-being are very common among women. Aim and objectives: To assess the impact of mindfulness meditation by comparing the perceived scores of stress levels and subjective well-being. Methodology: The researcher collected the data from Mohali, Chandigarh, Panchkula. The total sample for the pilot study was 40. The sociodemographic data were filled with two scales perceived stress scale and subjective well-being inventory by the housewives. Results 3(15%) were under high perceived stress, followed by 14(70%) moderate, and 3(15%) under low perceived stress level. In SUBI, 6(30%) were belongs to high subjective well-being followed by 13(65%) were belongs to moderate, and 1(15%) belongs to low subjective well-being score. Conclusion: The stress score were found to be significantly associated with the age of housewives, socio-economic status. In SUBI, there is an association of subjective well-being with the family source of income.
Evaluating Durability Properties of Composite Fibre Reinforced Concrete by Using Mineral Admixtures
Authors: Patel Priya A , Pandya Prachi V , Vyas Gaurav J , Trivedi Dhruv
Published: April 2022

This study is aimed at investigating durability properties of fibre reinforced concrete. The sorptivity test to assess rate of absorption, and the rapid chloride penetration test was performed to assess the permeability of concrete. The weight loss and strength loss measured after 28 and 56 days immersion in 5% concentrated HCL, H2SO4 solution to check aggressive ions of concrete, such as chlorides and sulphates. Normal concrete is used for the above assessment. The addition of nano silica by fractions such 0%, 1.5%, 3.0%, and 4.5% by weight of cement. For each of above combination 1% steel and 0.25% polypropylenes fibres respectively by volume of binders were added. A constant aggregate binder ratio and water binder ratio were maintained throughout this investigation process. At the end of above testing, the aim is to find out optimum percentage combination of ultra fine fly ash and nanosilica to achieve better durability of concrete.
Understanding Material Chemistry of Mobile Handset for Recycling Feasibility Analysis
Authors: MN Shruthi , Narahari NS , MG Bhaskar , Teli Savita C
Published: April 2022

Mobile phones and the smartness associated with them can be attributed to as the single most important factor for such transformation. With volume of mobile handsets sold seeing an increasing trend, emphasis on recycling and waste policy has increased by multifold. Schematic of metal components constituting production of a typical handset and each of the segmented parts has been showcased along with data of waste generation from various states of India. This paper discusses the economic viability of metal extraction considering factors such as metal process time, relative effort and general requirement which identifies high extraction efficiency of metal. Comparison chart has been tabulated for each of the metals with regard to expenditure, revenue generated, processing methods and processing difficulty. The results obtained provide satisfactory evidence to further improve the process to enhance the metal, calculation comparison chart represent each metal expenditure and revenue of Gold and Platinum takes more time for process i.e Au = 24hr, Pt =24hr, Zinc Copper and Aluminum takes minimum time for process Zn=1hr Cu=1hr Al=2hr. Extraction of gold and platinum process are complex compare to other metal highest expenditure cost of metal Au = 1653Rs, Minimum expenditure cost of metal Al = 620Rs Zn =620Rs, Highest revenue from Aluminum = 9000Rs. Minimum Revenue from Silver =1720Rs, Identified high extraction efficiency metals – Cu =99.95%, Al= 99%, Ag =99%. Identified maximum level of purity accepted metals Pt = 99% Au = 99%.
Nostalgia: An Antecedent of Tourist Motivations in Senior Citizens
Authors: Hajra Vandita , Aggarwal Arun
Published: April 2022

Nostalgia can be described as a poignant yearning for the bygone times and is a universally familiar as well as a collective phenomenon experienced by all and sundry. Nostalgia contributes significantly to the overall tourism experience and recently some researchers have theorized nostalgia as a push travel motive. Nostalgia has not been much explored in the arena of senior tourism except for few researchers who have studied nostalgia as a push factor for travel in senior tourists. Following the work of Prayag and Chiappa in 2021, nostalgia has not been treated as a travel motivation in this study. Since nostalgia itself has several motivational implications, this research work aims to explore its role as an antecedent of tourist motivations in senior citizens. The study provides a rather unexplored point of view for senior tourism marketers to design new marketing strategies that would take into account the nostalgic element in order to propel senior citizens to undertake travel activities.
Role of Internet of Things (IoT) During COVID-19: Applications and Challenges
Authors: Thakare Vaibhav , Kumbhar Manisha
Published: April 2022

The World Health Organization (WHO) had declared novel coronavirus (COVID-19) as a global pandemic. Almost all sectors of the industry have been affected. Every country in the world is trying to stabilize their economy. The healthcare industry is under tremendous pressure to keep up with demands and expectations. This pandemic situation has imposed a war like situation on whole world. A majority of the world is facing challenges like scarcity of resources, overburdened healthcare systems, lockdown and unemployment. This paper aims at exploring various technologies related to IoT to understand applications and challenges which needs to be addressed. IoT is being used along with various other technology like Blockchain and Artificial Intelligence. This can be very effective for remote diagnosis and treatment, which is very important in today’s situation where social distancing is most effective to battle COVID-19. This situation gave us the opportunity to understand challenges and develop solutions for the pandemic situation.
A Review on Risk Factors and Diagnosis of Carpal Tunnel Syndrome
Authors: Gautam Nikita , Shrivastava Amit
Published: April 2022

CTS is the most common occupational disease. It is a form of median nerve compression and is caused by prolonged or repetitive motions of the wrist. CTS led to numbness and tingling sensation in the hand. CTS hinder activities of daily life and therefore diagnosis in good time is very important. The conventional diagnosis of CTS involves manual examination through electrophysiological testing. Manual diagnosis is not only time consuming but it is also not a proactive approach. With the recent advancement in computer vision, imaging, and soft computing techniques, predictive diagnosis of CTS has become an important research field. The aim of this research paper is to review the state of art of early detection and prediction of CTS. Major methods and algorithms used in this context are reviewed with their advantages and limitations. The research paper also points out the research gap and recommends direction for future research.
Compressive Behavior of Tamarind Shell Powder and Fine Granite Particles Reinforced Epoxy Matrix Based Hybrid Bio-Composites
Authors: Murugan Giri , Loganathan Ganesh Babu , Sivaraman G , Shilaja C , Mayakannan S
Published: April 2022

Nowadays, hybrid bio-composites are being developed by combining different natural resources as reinforcement and filler components, and this has raised their necessary qualities dramatically. An epoxy resin matrix for compressive qualities was tested experimentally with the inclusion of fine granite powder and tamarind shell powder particles. As reinforcement materials, fine granite powder and tamarind shell powder are employed. Specimens of hybrid bio-composite were created by altering the reinforcement material weight % while maintaining the epoxy resin weight percentage the same. Utilizing a compression moulding process, composite boards made of hybrid biomaterials were created. Water jet machining is used to remove hybrid bio-composite specimens for compression tests in accordance with ASTM standards from the hybrid bio-composite boards. When fine granite and tamarind shell powder particles are added to the epoxy resin matrix, experimental results show that compressive characteristics of the hybrid bio-composites are greatly improved.
A Comprehensive Study of XSS Attack and the Digital Forensic Models to Gather the Evidence
Authors: Kumar Saurabh , Pathak S.K. , Singh Jagendra
Published: April 2022

Cyber Crime and fraud are growing at a rapid rate and the law practitioners require proper methodology to collect the digital evidences. When the criminals use digital platform in order to commit the crime, forensic examiners apply practical frameworks and methods to collect digital evidences. Digital forensics method involves the analysis of network traffic for detecting intrusions and investigating them. Different models have been proposed for the cyber crime investigation with the merits and demerit. This research paper explores cross-site scripting (XSS) attacks, features of the existing digital forensic investigation models and discusses some of the issues related to digital forensic research.
Smart Chronic Disease Consultation Using Machine Learning
Authors: Aravindan M , Shilaja C , Nalinashini G
Published: April 2022

In the quick world with such countless savvy frameworks infer our energy, the most alluring thing of inserted development is the applications usable by everyday person for day-by-day expectation purposes which accommodating for them a ton. A particularly intelligent clinical assistant framework is executed here with installed frameworks and MATLAB IDE for re- enactment. Plan and execution of a powerful integrative MATLAB model is made for identifying the regular constant infections and their manifestations present with the patients. The proposed framework goes about as a pre-screening model application hence the patients would self is able to dissect and get the idea of the drugs for normally happening persistent sicknesses through their live manifestations. The proposed model zeroed in on carrying out a Weighted Bias Network which look at and run various iterative circles to anticipate the practical infections and their side effects.
Phytoconstituents Based Novel Nano-Formulations: An Approach
Authors: Kumari Sapna , Goyal Anju , Garg Madhukar
Published: April 2022

Over the last few eras, plant-based drugs have been receiving extensive attention as these were used from the ancient times for the cure of diseases. However, the dose accuracy, dose efficacy, and treatment has been based on the observed symptoms only. Instead of crude drug, current research mainly focused on medicinal active phytoconstituents. Several novel drug formulations like nano-capsules, polymer micelles, liposomes, nanogel, phytosomes, nano-emulsions, transferosomes, microsphere, ethosomes, injectable hydrogels, polymeric nanoparticles, dendrimers etc. has been developed using bioactive molecules and plant extracts. The tremendous applications of the novel formulations are enhancement in solubility, therapeutic efficacy, bioavailability, stability, tissue distribution, protection from physical and chemical damage, and sustained and targeted delivery. Most of the material used are eco-friendly, biodegradable, plant-based material and bioadhesive, that provide extensive benefits as well as distinctive size to the nano-formulations. The present review is a concise representation of various herbal bioactive nano-formulations and pharmacological activity.
Recent Advances in Efficient Nanostructured Photocatalysts for Hydrogen Fuel Production: A Short Review
Authors: M Kaushik , B Vijeth , SM Darshan , M Gopal Krishna Hegade , Shetty Samarth Kumar , C Manjunatha
Published: April 2022

Various techniques developed to produce the H2 gas, photocatalytic H2 generation from water splitting is found to be clean and green technology which is in line with the global environment and energy suitability. It involves separation of charge, migration of charge, and chemical reaction on the surface. Various kinds of photocatalysts are used which are made of metal oxides, metal chalcogenides, and graphene-based nanocomposites. This review describes the development of various photocatalysts, such as TiO2, ZnO, MoS2, and their composites. The three-way nitrogen-doped carbon tube covered MoS2 TNCT@MoS2 was reported to be a more efficient photocatalyst. SEM images, XRD results, and diffraction patterns of the above composites were described to correlate with the photocatalytic property. The production of hydrogen using TNCT@MoS2 was found to be much more productive than usual MoS2. In addition to metal oxide and sulfide, the review also covers the photocatalytic behavior of graphene, and S/Se based metal chalcogenide-based compounds and composites (MCCs) were also discussed. The structural features of all these important photocatalysts using XRD, SEM, EDX, and UV-V is techniques were revealed. Finally, the photocatalytic H2 generation efficiency, possible mechanism of all these catalysts, was compared. In summary, we have reviewed the photocatalytic hydrogen production efficiency of metal oxides, metal chalcogenides, and graphene-based nanocomposites.
COVID-19 Social Distancing IoT Application Using Image Processing and Deep Learning
Authors: T Naga Swathi , V Megala , S Yokesh babu , Nath MalayaKumar , S Jayadev , U Rezin
Published: April 2022

The Internet of Things is now a groundbreaking area with the potential to influence our lives and bring major changes to the globe. Many IoT approaches have been proposed to enable data-driven and smart applications for users. Therefore, architecture, applications, and technological systems must be created for fast and prompt enforcement of guidelines, regulations, and government directives to control such future epidemics. This paper describes a new architecture, possible use-cases, and some future possibilities for building such applications utilizing Smart City. The COVID-19 epidemic is an existing pandemic induced by that of the Coronavirus. It is a severe acute respiratory syndrome (SARS-CoV-2). It was discovered for the first time in December 2019 in Wuhan, China. Social distancing is a technique for controlling the transmission of infectious illnesses. It suggests that individuals must physically separate away from each other, decreasing intimate contact, and therefore lowering the spreading of a deadly virus also including Coronavirus.
A Study of Measuring of Brand Awareness and Brand Perception with Reference to Non-stick Cookware in Maharashtra
Authors: Hatole Sachin Shankarrao , Gaikwad Vishal B , Vilasini D S
Published: April 2022

Brand awareness and brand perception focus on branding towards products and services. Today’s branding plays the important role of engaging customer. Brand equity becomes a part of brand management. Branding is endorsing products and services. The study is to examine the dimensions of brand awareness, brand perception, and customer purchase intensions on buying behaviour. The purpose of this research is to explore the brand awareness and brand perception of non-stick cookware with special reference to Maharashtra region in India. Researchers tried to find how consumer responds to the brand awareness and accesses the brand perception, which dimension impact brand awareness among consumers and researcher also tried to assess do the consumer brand perception towards the non-stick cookware have significant impact on consumers.
Role of Eye Tracking in Human Computer Interaction
Authors: Modi Nandini , Singh Jaiteg
Published: April 2022

With the invention of computers arises the need of an interface for users and interacting with a computer has become a natural practice. For all the opportunities a machine can bring, it is now a limiting factor for humans and their interaction with machines. This has given rise to a significant amount of research in the area of human computer interaction to make it more intuitive, simpler, and efficient. Human interaction with computers is no longer confined to printers and keyboards. Traditional input devices give way to natural inputs like voice, gestures, and visual computing using eye tracking. This paper provides useful insights in understanding the use of eye gaze tracking technology for human-machine interaction. A case study was conducted with 15 participants to analyze eye movements on an educational website. Visual attention was measured using eye gaze fixations data and heat maps were utilized to illustrate the results.
A Randomized Pilot Study of a Cognitive Intervention for the Elderly with Mild Cognitive Impairment
Authors: Karim M , Venkatachalam Jonnadula
Published: April 2022

The extension of life increased the prevalence of many non-communicable and chronic diseases, including neurodegenerative diseases. Identifying Mild Cognitive Impairment (MCI) in the early stage would allow to providing intervention at early that may reduce the progression of Alzheimer’s and other related cognitive disorders. The present study aimed to examine the effects of a new Indian adapted Motivationally Enhanced Compensatory Cognitive Training program for the elderly with MCI (IAME-CCT-MCI). This is a 4-week intervention program, utilized randomized pilot study with single-group pretest-posttest design conducted with 32 elderly participants screened with MCI using Tamil version of Montreal Cognitive Assessment (T-MoCA). The primary outcome measure was T-MoCA used to measure cognitive impairment, followed by PGI Battery for Assessment of Mental Efficiency in the Elderly (PGI-BAMEE). PGI Memory Scale (PGI-MS) was used to measure as the secondary outcome. The PGI-BAMEE consists of four subtests to assess mental efficiency, general information, orientation towards time and place, perceptuo-motor functions, and depressive symptomology. Whereas, the PGI-MS contains a detailed assessment of ten types of memory, viz., remote memory, recent memory, attention-concentration, delayed recall, immediate recall, verbal retention for similar pairs, verbal retention for dissimilar pairs, visual retention, and recognition. Through the paired t-test analysis, the IAME-CCT-MCI intervention program significantly improved the T-MoCA score and PGI-Memory Scale scores. The subtests of PGI-BAMEE were also significantly improved during the immediate pretest-posttest period. The findings provides preliminary support for the potential efficacy of the IAME-CCT-MCI cognitive intervention when delivered to the elderly with MCI.
Predictors of Severe COVID-19 Outcome: A Meta-Analysis of Common Laboratory Biomarkers
Authors: N Anush , Jasmin A , Prabhakar Pranav K , Mathew Riju , Bineesh C P
Published: April 2022

COVID-19 is a multisystem sickness caused by the complex interaction of inflammatory, immunological, and coagulative cascade. Risk assessment, choice of appropriate treatments, monitoring, and timely discharge are important aspects of COVID patient management. Along with proper clinical evaluation, laboratory markers can offer extra information that can have a substantial influence on these aspects of patient management. To identify potential markers of COVID-19-related mortality, a meta-analysis of papers concerning C-Reactive Protein (CRP), D-Dimer, Ferritin, Interleukin-6 (IL-6), Neutrophil-to-Lymphocyte ratio (N/L ratio), Procalcitonin, and mortality was conducted. D-Dimer and N/L ratio were found to be significantly different between the non-survivor and survivor groups. Overall meta-risk of death associated with the laboratory values was as follows: CRP HR 2.39 (1.05-3.74), D-Dimer HR 1.25 (1.03-2.48), Ferritin HR 3.37 (0.86-5.88), IL-6 HR 3.07 (-1.35-7.49), N/L ratio HR 1.20 (1.14-1.63), and Procalcitonin OR 3.60 (1.60-8.10). More study is needed to determine whether these laboratory biomarkers can be employed in the development of a clinical scoring system to help in patient triage.
3D Printed Dual-Band Microwave Imaging Antenna
Authors: Borra Vamsi , Itapu Srikanth , Garretto Joao , Yarwood Ronald , Morrison Gina , Cortes Pedro , MacDonald Eric , Li Frank
Published: April 2022

Microwave imaging utilizes low-power near-field electromagnetic fields at microwave frequencies to detect the internal structure of an object. Sufficient resolution through the thickness is crucial in biomedical applications to detect small objects of concern. Parameters such as the frequency of microwave signals, the design, and the material of the antenna are the most important factors to consider for microwave-based biomedical sensing. The proposed antenna yields merits of: compactness in size, ease of fabrication, wider impedance bandwidth, simple design, and good RF performance. An Asymmetric-fed Coupled Stripline (ACS) antenna is 3D-printed on an FR4 substrate with return loss measurements ranging from 2 GHz to 20 GHz. The impedance bandwidth is obtained between 6 GHz to 8 GHz and 15 GHz to 17 GHz. The proposed microwave antenna was simulated using Ansys HFSS. The parameters are designed to ensure optimum radiation efficiency. The radiation patterns obtained were omnidirectional in H-plane and bidirectional in E-plane.
Effect of Kernel Size in Deep Learning-Based Convolutional Neural Networks for Image Classification
Authors: Sood Shivani , Singh Harjeet
Published: April 2022

In recent years deep learning models are very popular for image classification. Convolution Neural Networks (CNNs) are commonly used for image classification and based on the architecture of these models there are various transfer learning models which are pre-trained on the ImageNet dataset. The accuracy of these models depends upon the various parameters, such as the number of layers, kernel size, image size, etc. In the current study, we analyze the effect of kernel size on the CNNs model for image classification. We consider the CGIAR wheat rust disease image dataset for experimentation. After performing various experiments, it is found that using a small kernel size leads to the reduction of several parameters of the model and also has a significant effect on the accuracy of the model.
Non-Antibacterial Carbon Nanoparticles and Its Fluorescence Properties
Authors: Johnson Annpriya , Santhosh Devika , Thomas Riya , Daniel Elcey C , Balachandran Manoj
Published: April 2022

Highly fluorescent carbon nanoparticles are synthesized from corn starch via one-pot hydrothermal method. Upon treatment with the lime juice as the catalyst, carbon nanoparticles are functionalized with potassium, and an improvement in the luminescence behavior is also observed. The synthesized nanoparticles did not exhibit any antibacterial activity against gram-positive (Staphylococcus aureus, Bacillus subtilis) and gram-negative (Pseudomonas fluorescence, E.coli) bacteria. The excellent photoluminescence coupled with non-toxic behavior of the carbon nanoparticles would be best suited for biomedical applications.
Content-Based Video Retrieval Based on Multifaceted Feature Extraction Using Statistical Methods
Authors: Kalakoti Gowrisankar , Prabakaran G
Published: April 2022

For content-dependent video recovery, this paper proposes a multifaceted feature extraction approach based on color string extraction, local texture characteristics, and amount of Absolute Difference, which extracts the features such as color components , texture components, and motion behavior. The color string function is derived using the string duration count, and the color histogram approach is used for detecting scene shifts. Based on scene shift recognition, the key-frames are retrieved. Based on the color string and local texture functions, the color and texture characteristics are derived from the key-frame. The Amount of Absolute Difference is measured, and the motion function of a video is derived using it. To compare the function vectors of the question and main frames of the video to be recovered, the Jeffrey's Divergence measure is implemented. The suggested approach has the greatest capture of both spatial and temporal aspects, and the recovery efficiency is increased by utilizing multifaceted features. The system suggested outperforms the current approaches.
Formulation and Characterization of Phytophospholipid Complex of Andrographis Paniculata Extract
Authors: Saini Vandana , Bala Rajni , Arora Sandeep
Published: April 2022

Active constituents extracted from various parts of the plants have wide therapeutic potential for the treatment of diseases. Number of these compounds obtained elicits positive in vitro pharmacological effect; however poor in vivo absorption of polyphenolic compounds limited the widespread applicability. One of the novel strategies to improve the bioavailability and thereby oral absorption of these compounds is the use of phytophospholipid complexes. The present work is aimed to formulate and characterize phytophospholipid complex of methanolic extract of leaves of Andrographis paniculata by anti-solvent precipitation technique. Four different formulation batches (F1-F4) were prepared by combining the phospholipid and Andrographis paniculata leaves extract in different molar ratios. Formulated batches were characterized for particle size, polydispersity index, entrapment efficiency, FTIR, SEM, XRD, and in vitro drug release characteristic. On the basis of various experimental resulted the formulation batch (F4) with molar ratio of extract and phospholipids 1:2 was found to be stable giving highest drug loading and sustained drug release for prolonged period of time.
Higher-Order Iterative Method to Solve Non-Linear Equations
Authors: Kakarlapudi Navya , Mylapalli Mani Sandeep Kumar , M Sowjanya , Sri Ramadevi
Published: April 2022

This paper introduces a new tenth-order iterative method for solving non-linear equations. Modification of Newton’s method with higher-order convergence is presented. Analysis of convergence finalized that the order of convergence is ten. Some numerical examples illustrate that the algorithm is more efficient and performs better than classical Newton’s method and other methods with the same order.
Understanding Information Seeking Behavior of Users in Academic Context (A Critical Review of Literature)
Authors: Parveaz Salik , Khan Asif
Published: April 2022

Information has become quintessential for everyone. It is hailed as the most valuable resource in modern times1. Owing to the rapid proliferation of technology and increased Internet adoption rate, interacting with this valuable resource has become easy2. Users seek information from myriad sources to satisfy their information needs3. The information needs are secondary needs that arise from some basic needs, which arise in some contexts4. Information seeking behavior is "purposive seeking of information in response to a need to satisfy a goal"
5
. It is a concept of great importance in library and information sciences6, with studies dating back to the early 1900s. With time and advancement in technology, the focus of researchers and scholars shifted from information systems to users' information needs and information use5. The present study has reviewed literature pertaining to various aspects of users' information seeking behavior, such as information sources, information needs, factors, and challenges affecting it in academic settings. The studies reviewed here are mainly from the field of library sciences. This paper concisely attempts to elucidate the basic concepts of information seeking behavior and gauge the influence of technology on users' information seeking practices in academic settings. The review findings suggest that faculty and students' information seeking behavior is concerned chiefly with their academic lives. The Internet is the most used and popular source/medium for academic information seeking processes. The paper shall assist future researchers, scholars, students interested in understanding the concept of information seeking behavior qua affective factors and technology usage.
Responsible Causes of Payment Delays in Indian Construction Industry
Authors: Kathpalia Amit , Jhamb Deepika
Published: April 2022

Payment delays are endemic in construction projects across the world. Research has shown that payment delays are leading causes of time and cost overruns, disputes, and bankruptcy. Payment delays also have significant economic consequences for companies, jobs, and growth. While adequate research has been conducted in India on causes of delays in construction projects, there has been almost nil research on why payments delays occur and what are the most feasible solutions for reducing payment delays. This research analyses responses of comprehensive questionnaire survey based on literature review and in consultation with industry experts and academia in India and abroad. The survey was administered to select group of approximately equal numbers of major categories of stakeholders directly involved in construction projects. Statistical analysis of the results eventually reflected major implementable points in terms of capacity building, dispute resolution mechanisms, and contract formulation for achieving major impacts in reducing payment delays.
Short-Term Electric Load Forecasting Using Support Vector Machines
Authors: Chauhan Madhur , Gupta Sheifali , Sandhu Mamatha
Published: April 2022

This research paper proposes a short-term electrical load forecasting model based on support vector machines and the ensemble machine learning method. Historical data is reviewed, and acceptable features for developing the model are discovered. Load demands from the previous twenty-four hours are utilized as the goal value, with the day of the week and the hour of the day as the input values. The effect of temperature has been also analyzed as an input. It has been found that temperature is not always a very good feature for designing the short-term load forecast model. In order to train the model efficient data and then prediction of short-term electrical load achieved. The comparative result between SVM and ensemble method have been carried out, which shows that SVM performed better in STLF model.
An Online Estimation Method of State of Health for Lithium-Ion Batteries Based on Constant Current Charging Curve
Authors: Liu Wei , Zhao Jinbao
Published: April 2022

Accurate estimation of state of health (SOH) is of great significance for the safety and reliability of lithium-ion batteries. In this paper, a novel method to estimate SOH online based on constant current charging curve is presented. In order to incorporate the factor of rates, a simple two-step data transformation process is carried out to make the method suitable for SOH estimation at different charging rates. Then polynomial is used to fit the transformed curve, and the coefficient sets of analytic expression obtained by fitting are taken as the battery aging feature variables. Finally, linear regression algorithm, the simplest machine learning algorithm, is employed to construct the mapping between feature variables and SOH, thus accomplishing the SOH estimation. When estimating SOH, only the charging curve of the whole constant current charging process is needed, regardless of the charging process at whatever rates. This method takes low computational cost, making it suitable for online estimation. The verification results on battery test data show that the method is of high accuracy and effectiveness.
Evaluation of early bilateral ovariectomy in mice as a model of left heart disease
Authors: Joll J. Ethan , Bersi Matthew R. , Nyman Jeffry S. , Merryman W. David
Published: April 2022

This article uses in vivo and ex vivo analysis to track the development of osteoporosis and left heart cardiovascular disease in an aged, high-cholesterol diet, mouse ovariectomy model. Mice develop early left ventricle hypertrophy without concurrent fibrosis or aortic valve stenosis. These findings allow for a new model of the study of left ventricle hypertrophy in postmenopausal osteoporosis that more closely mimics the natural progression of disease in female patients.
Research on Video Captioning Based on Multifeature Fusion
Authors: Zhao Hong , Guo Lan , Chen ZhiWen , Zheng HouZe
Published: April 2022

Aiming at the problems that the existing video captioning models pay attention to incomplete information and the generation of expression text is not accurate enough, a video captioning model that integrates image, audio, and motion optical flow is proposed. A variety of large-scale dataset pretraining models are used to extract video frame features, motion information, audio features, and video sequence features. An embedded layer structure based on self-attention mechanism is designed to embed single-mode features and learn single-mode feature parameters. Then, two schemes of joint representation and cooperative representation are used to fuse the multimodal features of the feature vectors output by the embedded layer, so that the model can pay attention to different targets in the video and their interactive relationships, which effectively improves the performance of the video captioning model. The experiment is carried out on large datasets MSR-VTT and LSMDC. Under the metrics BLEU4, METEOR, ROUGEL, and CIDEr, the MSR-VTT benchmark dataset obtained scores of 0.443, 0.327, 0.619, and 0.521, respectively. The result shows that the proposed method can effectively improve the performance of the video captioning model, and the evaluation indexes are improved compared with comparison models.
Spectral Normalized CycleGAN with Application in Semisupervised Semantic Segmentation of Sonar Images
Authors: Zhang Zhisheng , Tang Jinsong , Zhong Heping , Wu Haoran , Zhang Peng , Ning Mingqiang
Published: April 2022

The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a pretrained model, the experimental results demonstrate that our simple yet effective method helps to achieve reasonably accurate sonar targets segmentation results.
The Risk Assessment of Debris Flow in the Duba River Watershed Using Intuitionistic Fuzzy Sets: TOPSIS Model
Authors: Gu Xin-Bao , Li Wang  , Wu Qi-Hong
Published: April 2022

The occurrence of debris flow hazards has great destructive effects on the life and property safety of the people. Many factors control its occurrence. The intuitionistic fuzzy sets-TOPSIS model is introduced to assess the risk level of debris flow in the Duba river watershed at first; secondly, the decisive matrix of the intuitionistic fuzzy sets is established, and the index weight coefficients are determined by using the entropy weight method, and then the weighed decisive matrix is obtained. Finally, the degree of membership at different levels is calculated. The risk level corresponding to the maximum degree of membership is regarded as the final assessment level. The conclusions are drawn, estimating that the risk level of debris flow hazards in the Duba river watershed based on the intuitionistic fuzzy sets-TOPSIS model is feasible when its results are compared with the actual investigation and Liu X.L model, and the model not only can reflect the risk level of debris flow hazards accurately, but also further determines the risk ranking of debris flow in four different gullies, so that it can provide a new method and thoughts to estimate the risk level of debris flow hazards in the future.
Classical and Bayesian Inference Using Type-II Unified Progressive Hybrid Censored Samples for Pareto Model
Authors: Nagy M. , Alrasheedi Adel Fahad
Published: April 2022

In the lifetime and reliability experiments, the censored samples play a fundamental and important role in order to control time and cost. The researchers developed the censored sample schemes to solve the problems that arise by applying the previous methods. Recently, Górny and Cramer (2018) proposed a new general type of censored sample called Type-II unified progressive hybrid censored sample. In this paper, we present an overview of the Type-II unified progressive hybrid censored sample. We used this censored sample to compute the maximum likelihood estimates of unknown parameters from the Pareto distribution, as well as Bayesian estimates for unknown parameters under three different error loss functions. The point and interval Bayesian predictions one- and two-sample Bayesian predictions from the Pareto distribution are shown. Simulation studies are carried out to compare the efficacy of the various inference approaches. Finally, real data sets are examined to determine the applicability of the proposed model and various estimating approaches.
A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis Technology
Authors: Chen Xiang , Chen Zhiwei , Xiao Lei , Zhou Ming
Published: April 2022

With the development of virtual reality and digital reconstruction technology, digital museums have been widely promoted in various cities. Digital museums offer new ways to display and disseminate cultural heritage. It allows remote users to autonomously browse displays in a physical museum environment in a digital space. It is also possible to reproduce the lost heritage through digital reconstruction and restoration, so as to digitally present tangible cultural heritage and intangible cultural heritage to the public. However, the user’s experience of using digital museums has not been fully and deeply studied at present. In this study, the user’s experience evaluation data of digital museum are classified and processed, so as to analyze the user’s emotional trend towards the museum. Considering that the user’s evaluation data are unbalanced data, this study uses an unbalanced support vector machine (USVM) in the classification of user evaluation data. The main idea of this method is that the boundary of the support vector is continuously shifted to the majority class by repeatedly oversampling some support vectors until the real support vector samples are found. The experimental results show that the classification obtained by the used USVM has a good practical reference value. Based on the classification results of the evaluation data, the construction of the digital museum can be further guided and maintained, thereby improving the user experience satisfaction of the museum. This research will make an important contribution to the construction of the museum and the inheritance of culture.
Construction and Application of SPD Automated Management Module for Interventional High-Value Consumables Based on RFID Sensors
Authors: Cheng Jifang , Wen Chunjie , Jiang Shengbo , Zhang Lihuan , Zhu Xia , Qian Weiming
Published: April 2022

In order to discuss the construction and application of the SPD automated management module for interventional high-value consumables based on RFID sensors, a high-value medical consumables management information system based on radio frequency identification (RFID) technology is designed and implemented in this paper. It not only realizes the scientific, effective, and full-process monitoring of high-value medical consumables management but also provides managers with scientific and efficient management methods, combined with HIS and SPD’s “Unified Receiving Platform” system and barcode technology, to achieve drug barcode acceptance. The experimental results show that the goal of zero inventory management of high value medical consumables is realized and the whole tracking management of use, warehousing, billing, and payment, the high value consumables can be traced back to suppliers and manufacturers and patients, and the inventory, accounting, and payment status can be checked at any time. It proves the effectiveness of RFID technology in the construction and application of SPD automatic management module for high-value consumables.
Selective Partial Update Adaptive Filtering Algorithms for Block-Sparse System Identification
Authors: Wei Dandan , Xu Qing
Published: April 2022

The block-sparse normalized least mean square (BS-NLMS) algorithm which takes advantage of sparsity, successfully shows fast convergence in adaptive block-sparse system identification, adaptive control, and other industrial informatics applications. It is also attractive in acoustic processing where long impulse response, highly correlated and sparse echo path are encountered. However, the major drawback of BS-NLMS is largely computational complexity. This paper proposes a novel selective partial-update block-sparse normalized least mean square (SPU-BS-NLMS) algorithm. Compared with conventional BS-NLMS for block-sparse system identification, the proposed elective partial-update block-sparse NLMS algorithm takes partial-update blocks scheme which is determined by the smallest squared Euclidean-norm at each iteration instead of entire block coefficients to save computations. Computational complexity analysis is conducted to help researchers select appropriate parameters for practical realizations and applications. Computer simulations on acoustic echo cancellation are conducted to verify the results and the effectiveness of the proposed algorithm.
Integrated Analysis Identifies Four Genes as Novel Diagnostic Biomarkers Which Correlate with Immune Infiltration in Preeclampsia
Authors: Yang Mu-yi , Ji Ming-hui , Shen Tian , Lei Lei
Published: April 2022

Preeclampsia remains a high cause of incidence and death for mothers and fetuses in developing nations. Preeclampsia has numerous clinical and biochemical markers that have been tested, but they have failed to provide a conclusive diagnosis in the different phases of the disease’s progression. Herein, our team intended to determine potential diagnostic biomarkers for preeclampsia and analyzed associations with immune cells. Two microarray data from mankind’s preeclampsia and control specimens were acquired from GSE75010 and GSE44711 datasets. Differentially expressed genes (DEGs) were identified between77 normal samples and 80 preeclampsia samples. Candidate biomarkers were discovered using the least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) analysis. The expressions and diagnostic values of genes in preeclampsia were further demonstrated in the GSE44711 dataset (8 control samples and 8 preeclampsia samples). The correlation of critical genes with the proportion of immune cells was analyzed. We identified 20 DEGs in preeclampsia. Diseases enriched by DEGs were mainly related to preeclampsia, gestational diabetes, ovarian disease, female reproductive system disease, and endocrine system disease. COL17A1, FLT1, FSTL3, and SERPINA3 were identified as diagnostic genes of preeclampsia and validated in the GSE44711 datasets. Immune cell infiltration assays suggested that COL17A1, FLT1, FSTL3, and SERPINA3 were related to several immune cells. Overall, we identified four critical diagnostic genes in preeclampsia. Furthermore, more well-designed research studies with larger cohorts were warranted to confirm the value of the four genes for the diagnosis and outcome of preeclampsia patients.