bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and modelling of surface roughness in milling of JFRP composite using central composite design
Authors: Akmam Noor Rashid Mir , Mohd Zain Zakaria , Nor Khairusshima M.K. , Ibne Noor Wazed , Mullah Momin , Ahmed Khan Shaheer
Published: March 2021

Nowadays, the JFRP composite is known as an eco-friendly, cost-effective, lightweight, higher stiffness product, and demand for this composite is increasing tremendously in various applications like automotive, aerospace, marine, and domestic upholstery. In order to achieve the required shape and design of this composite, machining is essential during the assembly stage. Thus, machining arises some difficulties in where surface roughness is one of the major drawbacks during machining of the milling process. The cutting parameter of machining influences the output performance of the product. The main purpose of this study is to find out the effect of milling parameters such as feed rate, spindle speed, depth of cut on the output responses like surface roughness, which generates during milling on JFRP composite. The machining was done by using a solid carbide cutting tool of 8 mm width and the experiments were conducted according to the Central Composite Design (CCD). It was found that whenever the spindle speed increases from 671.57 to 6328.43 rev/min then the Surface roughness decreases 18.58%. On the contrary, Surface roughness increased by 31.22% due to the increase in feed rate from 108.58 to 391.42 mm/min. A mathematical model was also developed in this study to correlate the milling parameters with the output parameter of the Surface roughness. It was found that the feed rate is the most significant factor to affects the Surface roughness. Based on the RSM, the predicted optimized input parameter were spindle speed 4293.8 rev/min, feed rate 1.50 mm/min, and depth of cut 1 mm in where Surface roughness would be lower (1.188 µm).
Comparative study between product- and service-based operations using time-driven activity-based costing
Authors: Zaini S N A M , Abu M Y
Published: March 2021

Product-based companies are consumer suppliers that can meet the customer order and consumption. Service-based operation is a company offering an intangible and non-including sales and satisfactions type of product. This work aims to summarize the strength of time-driven activity-based costing (TDABC) implementation in product- and service based operation using selected criteria. There are 8 criteria to be discussed are cost allocation, determination of drivers, transparency, cost consideration for implementation, transparency, overestimation of cost, oversimplification of activities and capacity forecast and planning. For product-based operation, this work is conducted at the plantation located in Prosper Palm Oil Mill Sdn. Bhd., Muadzam Shah, Pahang. The estate is divided into three main activity centers which are nursery, replanting and ramp. For service-based operation, data are taken from academic library in Universiti Malaysia Pahang, Pekan, Pahang. However in this work, only two activity centers are been used which are acquisition and cataloging. In this work, the application of TDABC in product- and service-based operations does not present any substantial differences in unused capacity. However, the activities in a product-based company depend on one another in terms of process dependence. While activities are independent in service-based operations.
Characterisation of bitumen, refined waste oil, and emulsifiers for coating and insulation purposes
Authors: Ishag Obi Nasreldeen , Najib Razali Mohd , Hamid Nour Abdurahman
Published: March 2021

The current study aims to characterise bitumen, refined waste oil, and emulsifiers to formulate emulsified modified bitumen for coating and insulation purposes. These raw materials were characterised in terms of their physical properties and elemental composition. This analysis provides significant information for setting the formulation parameters and determining the coating application conditions. Bitumen is a non-renewable petroleum substance; thus, the utilisation of refined waste oil in bitumen modification will reduce the dependency on neat bitumen and solve the increasing environmental problems in Malaysia. The physical characterisation was carried out using the tests of viscosity, flash point, density, penetration, softening point, moisture content, and X-ray diffraction. Fourier transform infrared analysis was conducted to determine the molecular composition and structure of the materials. In conclusion, the characterised materials showed the potential properties to formulate a highly adhesive, stable, and homogeneous emulsion.
Calibration and validation of hydrological model using HEC-HMS for Kuantan River Basin
Authors: Othman N , Romali N S , Samat S R , Ahmad A M
Published: March 2021

Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. This study aims to calibrate and validate the rainfall-runoff transformation model for Bukit Kenau Station in Kuantan River Basin (KRB) using HEC-HMS (Hydrologic Engineering Centre Hydrology Modeling System). For the loss rate, SCS Curve Number method was selected while Clark Unit Hydrograph was used for the transform method. The model calibration and validation efficiency were verified using the Nash-Sutcliffe model efficiency (ME). As a result, the model calibration and validation were found to be satisfactory with ME between 0.5 to 0.8. The model can be used to forecast the river flow and helps in the flood mitigation works to reduce the impacts along with the cost use. Besides, the result obtained from this study can be used as a guideline for future flood risk assessment works in the study area.
Methodological Review of Malaysia’s Energy Security Measurement: A Systems Approach using Stakeholder Engagement
Authors: Shadman S , Chin C M M , Sakundarini N , Yap E H , Velautham S
Published: March 2021

This study considers the analysis of Malaysia’s Energy Security (ES) using a mixed-method of inquiry. A combination of qualitative and quantitative methods known as the ‘triangulation of methods’ was used to build a solid understanding of ES from the standpoint of Malaysia within the Association of Southeast Asian Nations (ASEAN). Qualitative data were collected from 16 stakeholders and experts in the field of energy security and sustainability from Malaysia and the ASEAN region using semi-structured interviews (SSI). These data were coded using grounded theory (GT) that follows emerging coding techniques to allow for emerging ES themes to develop, hence creating our ES framework, used as the basis for a systems approach – quantitative research on ES. Quantitative data were collected from energy reports and statistics published by ministries, agencies, and relevant departments in Malaysia and ASEAN. The dimensional indicators identified from the ES framework are causally-linked using system dynamics (SD) modelling, underpinned by systems thinking, where SD models are designed and simulated to yield meaningful insights on Malaysia’s energy security for future policy recommendations. This mixed-method approach has enabled our futuring research through the modelling of scenarios to inform policy-making within the contexts of the problem space.
Post-cutting surface evaluation of the Cu foil substrate grown with single-layer graphene
Authors: Arissa N F , Aid S R
Published: March 2021

A large-sized graphene purchased for research purposes can be cost effective as its usage is not limited to one experimental study. It can be cut into many smaller pieces for other experimental studies. In this study, Cu foil substrate grown with single-layer graphene via chemical vapor deposition (CVD) with a size of 2” x 2” was cut into small pieces with a size of 10 × 10 mm2 using a pair of high-quality stainless-steel scissors. The surface quality of the cut substrate was evaluated using three microscopes with different types of illumination sources. In the post-cut substrate, a wrinkle and roughness at the edge of the substrate surface were observed, which can be attributed to the uneven surface contact and force distribution during the handling and cutting process using a tweezer and a pair of scissors. However, the wrinkle in the substrate is not the main reason for the degraded quality of graphene grown on the substrate. Meanwhile, the roughness at the edge area of the cut substrate may be reduced by controlling the speed and angle of the cutting forces. The information obtained from this preliminary study of sample preparation is important for future work in the synthesis of graphene nanocomposites for its application in nanofluids.
Enhanced Oil Palm Waste Properties through Torrefaction and Pelletization
Authors: Sambeth S K , Chandran T S T , Saleh S B , A Samad N A F B
Published: March 2021

Torrefaction is a thermal pre-treatment process to treat biomass at temperature range of 200-300°C under an inert atmosphere. It was known that torrefaction process strongly depended on the decomposition of the lignocellulosic constituents in oil palm waste namely hemicellulose, cellulose and lignin. The objective of this research is to study the effect of torrefaction on the characteristics of pellet formed from palm mesocarp fiber (PMF), oil palm frond (OPF) and palm kernel shell (PKS) as a solid fuel. The samples were torrefied at 270°C for 30 minutes. The torrefied samples were then densified into pellets through hot press machine at different temperatures (110 °C, 130 °C, 150 °C) and different pressure (10 MPa, 12 MPa, 14 MPa) for 20 minutes to find the optimum pelletization condition. The pellets were analyzed through mass yield, high heating value, fourier-transform infrared spectroscopy (FTIR) analysis, moisture absorption and compression test. Compression test is to study the strength of pellet by identifying the force required to break the pellet. The result shows that the mass yield of PKS was higher than PMF and OPF. The high heating value of torrefied oil palm waste improved as compared to the raw oil palm waste. Through FTIR, the changes of chemical bonds in oil palm waste after torrefaction have been identified. From the analysis of the pellet, the optimum conditions for pelletization were obtained, which are 130 °C and 12 MPa. The moisture absorption rate of torrefied pellet was lesser than raw pellets. Lastly, the torrefaction and pelletization process produced better quality pellets in term of high heating value, durability and more hydrophobic compared to raw pellets which can used as biofuel to replace the coal.
A Review Paper on The Effect of Waste Paper on Mechanical Properties of Concrete
Authors: Solahuddin B. A. , Yahaya F. M.
Published: March 2021

Cement, sand, coarse aggregate and water are the materials to make concrete. The waste paper has been dumped as waste and causes environmental pollution behind mill or landfill. The industry paper wastage for every year is increasing gradually. More spaces are being needed for landfills, uses energy loss of natural resources and increase of expenditure and various types of pollutions. Utilizing waste paper as cement replacement or addition in concrete production will reduce environmental pollutions. This review paper is to investigate the effect of waste paper on mechanical properties of concrete such as compressive strength and flexural strength. From many previous studies before this, 5% and 10% waste paper as cement replacement and additions were the ideal percentages to increase the compressive and flexural strengths of concrete. This study indicates waste paper can give benefit by using it as additional material in concrete production.
GIS Mapping of CO2 Emission from Population of Metropolis: Case Study of Ho Chi Minh City in Vietnam
Authors: Thi Thanh Ho Van , Vu Thi Hang , Quoc Pham Hau , Nguyen Phuc Hua , Ha Thanh Phuong Nam
Published: March 2021

The issue of CO2 emissions, the major caused leads to climate change has always been of particular interest in the context of the industrialization process and urbanization taking place more and more complex. Ho Chi Minh City is a large city of Vietnam with a high rate of urbanization and thus the CO2 emissions management process needs to be studied. In this work, we have developed the Geographic Information System (GIS) to build the mapping of CO2 emission from population of Metropolis, case study of Ho Chi Minh City in Vietnam for the first time. The mapping CO2 emissions for Ho Chi Minh city from the population have been calculated from human respiration and combustion gas in daily activities. We found that the highest level of CO2 emissions is located the western suburbs of the city including Binh Tan district, Tan Phu district, Tan Binh district, Go Vap district, District 12, Binh Chanh and Hoc Mon district. In seven districts with emission levels high, Binh Tan has the highest CO2 emissions and opposite Can Gio district has the lowest CO2 emission due to Can Gio district is a district adjacent to the sea and mainly mangroves forests, traffic condition is not conducive to the process of industrial development, therefore no attract workers from the others district, the province as Binh Tan district and local people trend to industrial zones making the district’s population the lowest. The results of this work can make reasonable policy in the management and reduction of CO2 emissions for future cities with low carbon city.
Extraction and analysis of chemical composition of Ocimum gratissimum L essential oil in the North of Vietnam
Authors: Dung P N T , Dao T P , Le T T , Tran H T , Dinh T T T , Pham Q L , Tran Q T , Pham M Q
Published: March 2021

Ocimum gratissimum L. is a fragrant plant of the Lamiaceae family. O. gratissimum has been used in folk medicine, cosmetics, and food industry. In this study, herbs of O. gratissimum were collected in the summer in Northern Vietnam and extracted for essential oils by hydrodistillation, followed by gas chromatography - mass spectrometry (GC-MS) analysis. The efficiency of essential oil extraction was 0.2258% ± 0.0735. According to the analysis results, twenty-seven components were identified. The main ingredients include Eugenol (65.135%), Ocimene <(Z) -b-> (7.20%), Caryophyllene  (6.64%), and Germacrene D (12.03%). This result suggests that the O. gratissimum essential oil is a potential source of antimicrobial, preservative and flavoring agent.
Character Recognition on Time Series Data collected from Smartphone Sensors
Authors: Raval Deep , Suhagiya Jaymin , Macker Sukriti
Published: March 2021

In the modern era, smartphones have become part and parcel of life. The exponential increase in the number of smartphone users has given rise to a copious amount of data. Although the data produced by smartphones are of various types, this paper mainly focuses on the usage of sensory data produced by smartphones. This paper explores the field of recognizing characters with the aid of time-series sensor data. The primary focus of the research is to utilize recurrent neural networks to predict the digits 0 - 9 and characters A - Z. The objective achieved was by the help of sensor data that included the readings of Accelerometer, Magnetometer, Gyroscope, and Linear Accelerometer sensors providing information with respect to three-axis x, y, and z, having an interval of 0.01 seconds between two corresponding values. We succeeded in achieving the accuracy of 93.60% on the training data and 89.51% on the testing data.
Pixort: A Novel Approach for Effective Photo Album Clustering
Authors: Mathias Cheryl L , D’Souza Crystal F , Alexander Job , Hudson Mariah S , Tantry Renuka
Published: March 2021

Retrieving digital records on the internet and capturing, uploading images on social media or other platforms has become such a notable part of one’s daily existence. Digital content and videos constitute majority of this ever-growing deluge of information. Major problems are faced by people on a daily basis when they have to manually scan large databases to search for particular images thereby exhausting valuable time and resources. To overcome this, Content Based Image Retrieval in addition to Emotion Detection must be performed to efficiently store and retrieve very specific images. An effective image clustering application should be capable of accurately extracting images based on the given query image. It should also provide functionalities like Facial Detection and Recognition for customized usage and retrieval. In this study, texture-based feature extraction followed by the usage of a feed forward backpropagation neural network has been proposed for CBIR. For emotion detection, correlation has been proposed in addition to the Viola Jones algorithm which has been used for facial detection.
A Heuristic Review on Analog Performance and Accomplishment of Activation Functions at RTL Level
Authors: Jyothula Sudhakar
Published: March 2021

In this article, the behavior of many functional activities used for Artificial Neural Networks (ANNs) study is demonstrated together with their similar performance under transistor conditions. ANN plays an important role in computer science, technology, machine learning, automation, speech and voice processing. The accuracy of any automated device largely depends on ANN training. Therefore, choosing an appropriate operating system affects the performance of the entire system. Performing operational movements at the transistor levels with low power, latency, and power requirements without adjusting their properties is a major challenge. Process actions are performed on an analog or digital one. Analog performance simulations are clearly demonstrated in this article, as the field and power limit for simulation applications is greater than for digital applications. The efficiency of the operating system relies on the % of errors between its manufacture and quality.
ICT for Cyber Security in Business
Authors: Hristev R , Veselinova M
Published: March 2021

In recent years, the financial value of the stored information repeatedly exceeds the one of the equipment which is needed for its storage. Therefore, the process of business development needs the implementation of up-to-date ICT for data security from cyber-attacks. The scalability of IT infrastructures in this direction leads to development of malware creators. Thus, file servers become inefficient and give away to private clouds. Attacks against data stored on file servers and workstations are becoming more widespread. Standard security methods (for example, backup) are not always secure enough and can also be the target of a cyber-attack if not properly managed. In addition, information from the time of the last backup to the time of attack will be lost. The paper aims to research and propose approaches which can be used for recovery of the information arrays. We consider some software for implementation of private cloud which correspond to the ISO 27001 standard. The methods for accessing data will be analyzed and the techniques by which clouds deal with some of cyber-attacks will be addressed.
A Cost-Efficient Proof-of-Stake-Voting Based Auditable Blockchain e-Voting System
Authors: Sharma Trishie , Rama Krishna C , Bahga Arshdeep
Published: March 2021

In recent years, blockchain-based systems have emerged powerfully and led to the development of several new and improved applications in terms of increased security, trust, accountability and reliability. One important trust-based application is electronic voting system which still lacks in providing good throughput while also reducing the costs in voting given the deployment of state-of-the-art technologies. So, there is a need to imply an efficient consensus protocol to impart cost-efficiency as well as better performance of a blockchain-based electronic voting system. This paper focuses on boosting up the cost-efficiency in the voting process, while also being auditable, thus, strengthening the trust of voters on the government and lessening the overall costs incurred in voting. In this paper, Proof-of-Stake-Voting is used as consensus protocol which is observed to give a throughput of 4-5K transactions per second. Then its performance is compared with the other existing consensus protocols such as Proof-of-Work and Proof-of-Authority in terms of gas consumption, gas cost, and throughput. It has been shown through the results that there is a significant difference in the total gas cost, with Proof-of-Stake-Voting protocol consuming 3.1 x 10−7 Ethers (ETH), as compared to the Proof-of-Work, taking 5.42536 x 10−4 ETH and Proof-of-Authority protocols taking 2.49759 x 10−4 ETH.
A Computational Model to Automate the Design of Reinforced Concrete Tee Beam Girder Bridge Using Python Ecosystem
Authors: Jain Utkarsh , Sharma Mahima , Tumrate Charanjeet Singh
Published: March 2021

The design of tee beams girder bridge members such as deck slab, main girder, and cross girder is automated by using a computation model based on the python ecosystem. The slab, main girder, cross girder section, and reinforcement detailing are generated by using the purposed computational model. The strength and serviceability checks are as per the Indian Road Congress: 112-2011, Code of Practice for Concrete Road Bridges. The model takes the design parameters of dead and live load from Indian Road Congress 5 and 6 as an input and provides structural detailing of elements involved in the tee beam girder bridge system. The validation of the model architect is performed by comparing it with the solved illustration as per the Indian Road Congress norms. The section sizes and the steel requirement is calculated efficiently and with accuracy thus the time required to generate the structural drawings has reduced drastically. Moreover, with help of the purposed model, a more intensive database of tee beam girder bridges including bridge bearings, piers and foundation can be modelled towards the automation of complete reinforced concrete bridge design as per the Indian Road Congress.
Statistical Analysis of Influencing Design Parameters for Two Way Slabs
Authors: Sancheti Gaurav , Raitka Hinshal , Bhatnagar Vaibhav , Prasad Jagdish
Published: March 2021

The slab is a fundamental component of any building that are used as floors and roofs. It is, most commonly, made up of reinforced concrete. Slabs are the structural elements whose thickness is significantly less than their other dimensions. Slabs can have different shapes and conditions of support. It may be a solid, waffle-type or a ribbed slab. They are categorized as one way and two-way slab according to their load-carrying nature. In this paper, a comprehensive statistical analysis of factors that influence the design parameters of slab is presented. It is found that there is low correlation between that there is high but negative correlation found between Effective Depth Required and spacing_x and Inputs variable is able to explain well only two variables Effective_depth require and Spacing_x. The effect of magnitude of different input variables on output variables and interaction of combination of different input variables on output variables for design of Two-Way Slab is also performed.
Real Estate Cost Estimation Through Data Mining Techniques
Authors: Khare Sandali , Gourisaria Mahendra Kumar , Harshvardhan GM , Joardar Subhankar , Singh Vijander
Published: March 2021

Real estate is one of the most fast-paced and emerging industries today. Nowadays everyone wants to be the owner of their house rather than live on rent. Therefore, people are very cautious in searching for the most suitable house. Different people have a different budgets and so varies their desire. This paper draws attention to the house rate predictions based on different objectives like financial status and expectations of non-house holders. It consists of two prediction sets, one with all the available features required for buying the house and the other with a few selected features. It involves varying machine learning regression techniques like linear regression, polynomial regression, decision tree, and random forest. Here, all the above techniques are compared, and it is found that polynomial regression with all the features gives the best results.
Problems of Analyzing Socio-Political Content of Internet Resources Based on Neural Network Technologies
Authors: Rogachev A F
Published: March 2021

Ensuring information security requires identifying undesirable information content of Internet resources. Semantic and lexicological diversity of Internet content requires improvement of methods of neural network analysis of natural language texts (NLP). The problem is complicated by the presence of” information garbage”, which is a specific information noise that complicates the task of classifying texts. Well-known NLP technologies using artificial neural networks (ANN) include substantiation of the structure and construction of a subject-oriented database of text data bodies, frequency analysis and construction of dictionaries. To identify semantic content and latent threats, a dense vector representation of the analyzed texts in a multidimensional space (embedding) is justified. The authors substantiate a modified NLP approach to identifying sociocultural and cyber threats, contained in the information content of Internet resources. To justify and research the ANN architecture and hyperparameters focused on the socio-political content under study, the ANN family was built in Python 3. The ANN architecture included combinations of fully connected, convolutional, and/or recurrent layers. The number of neurons of the recognizing fully connected layer with the “softmax [[CHECK_DOUBLEQUOT_ENT]] activation function (or sigmoid in multiple classification) was taken by the number of classes marked in the text corpus.
Opinion Classification of Product Reviews Using Naïve Bayes, Logistic Regression and Sentiwordnet: Challenges and Survey
Authors: Dadhich A , Thankachan B
Published: March 2021

In recent years, automated opinion classification has evolved as one of the most demanding area in natural language processing. Many such systems have been implemented and developed for the summarization and classification of text and reviews of online products. There are many data sources and domains which sells the online products, such as Amazon, Flipkart, Snapdeal etc. In the same direction, this paper is intended to present a detailed review and comparative analysis of various existing sentiment analysis algorithms especially for the Amazon products, which have worked upon the supervised learning techniques called Naïve Bayes, logistic regression and SentiWordNet. Various key parameters and aspects of such a comparative tour are the use of feature reduction method, sentiment polarity, dataset domain and sources, product name, data set size and classifier. Further this paper includes the discussion on their accuracy results; additional results including important findings; and needs, challenges and limitations. Lastly, the performance of these algorithms is evaluated by comparing the % usage of the key parameters.
Structuring Rich Agriculture using Pervasive Computing
Authors: Bhatnagar Anurag , Bhatnagar Nikhar , Kumar Pradeep
Published: March 2021

Now a day almost all humans carry gadgets which are smaller in size but has maximum utilization in term of functionality i.e., mobile or smart watches. Cloud Computing is proven one of the best hope for farmers for better cultivation of crops by using digital compilation. In almost every year, more than 40% downfall in the cultivation has observed due to unfavourable conditions for instance changes in the climate, the storage of water and shortage of labour due to increased urbanized population. The solution of this problem turn onto the form of a new technology named ‘eplant’. With the minimized input the best possible crops production for the best health is the focus of this technique. Thus the term “Pervasive Computing” came into existence. “Pervasive Computing” is also known as “Ubiquitous Computing”. This paper is about introducing this new technique in farming. Data sensors are used to provide all necessary resources for better crop development through and different compatible actuators are also used as per the environmental conditions. The role of sensors is the collection of the data about soil, the climate and for specific plants conditions. In order to minimize the hard work and to maximize the profit of the farmer this technique provided desired parameters by different calculations.
A Cluster Based Classification for Imbalanced Data Using SMOTE
Authors: Tripathi Rajesh Kumar , Raja Linesh , Kumar Ankit , Dadheech Pankaj , Kumar Abhishek , Nachappa M N
Published: March 2021

There is tremendous upturn in data repositories because of data generation by various organizations like government, cooperates, health caring in large amounts. Large amount of data is being produced, processed, collected, and analysed online. So there comes a requirement to transform this data into valuable information. This process of extracting the knowledge from large amount of data is referred as data mining. The proposed hybrid approach can be checked on different classifiers like Naïve Bayes, Random forest classifier etc. In proposed methodology we find that SMOTE algorithm which used K-nearest neighbour algorithm is limited to some minority class instances for creating synthetic samples, which sometimes leads to over fitting, so an effective oversampling approach can be developed.
Using Improved MPPT Charge Controller Improvement Functioning Efficiency of Power Grid Connected Solar Photovoltaic System
Authors: Dua Surendra Singh , Sharma Ruchi , Patidar Raghavendra
Published: March 2021

In recent decades, the world’s energy demand is closely linked to the rise of manufacturing, transport and the media. Today, large amount of the power is provided by non-renewable energy including coal, natural gas, oil and uranium. Pace of regeneration of energy is very sluggish, which adds to the possibility of short-term depletion. This paper focuses on power grid-connected photovoltaic systems. The primary objective is to regulating power pumped into the grid from solar panels and improving the configuration and regulation of inverters as used to supply electricity to the grid with optimum efficiency and according to networking requests as an interface between the grid and photovoltaic systems. The infused power management not only contains the aggressive force control but also the reactive power control. An algorithm is proposed for the design of a simple and robust grid-connected inverter power. It is based on the strategy for digital power. The proposed work analyses and improves the constraints of the VSCs’ Voltage Source Converters as converters for supplying active and reactive power into the grid, ensuring the best possible connection from the solar panels, ensuring effective operation of the inverter, increasing the current injection into the grid power and harmonic levels.
Acridone and acridinium constructs with red-shifted emission
Authors: Tikhomirova Anastasiia A , Swift Kerry M , Haack Richard A , Macdonald Patrick J , Hershberger Stefan J , Tetin Sergey Y
Published: March 2021

Acridinium 9-carboxylic acid derivatives have been extensively used as chemiluminescent labels in diagnostic assays. Triggering acridinium with basic hydrogen peroxide produces a highly strained dioxetanone intermediate, which converts into an acridone in an electronically excited state and emits light at 420–440 nm. Here, we introduce a novel acridinium-fluorescein construct emitting at 530 nm, in which fluorescein is covalently attached to the acridinium N-10 nitrogen via a propyl sulfonamide linker. To characterize the spectral properties of the acridinium-fluorescein chemiluminophores, we synthesized the analogous acridone-fluorescein constructs. Both acridinium and acridone were linked to either 5- or 6-carboxyfluorescein and independently synthesized as individual structural isomers. Using fluorescent acridone-fluorophore tandems, we investigated and optimized the diluent composition to prevent dye aggregation. As monomolecular species, the acridone isomers demonstrated similar absorption, excitation, and emission spectra, as well as the expected fluorescence lifetimes and molecular brightness. Chemical triggering of acridinium-fluorescein tandems, as well as direct excitation of their acridone-fluorescein analogs, resulted in a nearly complete energy transfer from acridone to fluorescein. Acridone-based dyes can be studied with steady-state spectroscopy. Thus, they will serve as useful tools for structure and solvent optimizations, as well as for studying chemiluminescent energy transfer mechanisms in related acridinium-fluorophore tandems. Direct investigations of the light-emitting molecules generated in the acridinium chemiluminescent reaction empower further development of chemiluminescent labels with red-shifted emission. As illustrated by the two-color HIV model immunoassay, such labels can find immediate applications for multicolor detection in clinical diagnostic assays.
Effects of roasting and NH4Cl catalysis on the direct electrotransformation products from an AlCl3 solution
Authors: Han Xiuxiu , Zhang Ting-an , Lv Guozhi , Pan Xijuan , Fu Daxue
Published: March 2021

Contemporary processes for extracting alumina from fly ash involve various disadvantages, including high-energy consumption, heavy slag discharge, and severe equipment corrosion, so a new method for extracting alumina via electrotransformation was developed in this work. This novel method reduced slag discharge, enabled recycling, and produced no acid or alkali waste. The theoretical solution pH associated with Al(OH)3 precipitation and the actual solution pH during electrotransformation were also compared herein. Differential thermogravimetric analysis was performed for the direct electrotransformation products generated with and without NH4Cl as a catalyst; the activation energy and reaction order of these reactions revealed large fluctuations. The effects of the roasting temperature on the phase, morphology, D(50), and D(90) of the roasting products were also studied. The results indicated that the solution reached the theoretical pH (4.7) of complete Al(OH)3 precipitation in a short time when NH4Cl was implemented as a catalyst. Additionally, the D(50) and D(90) of the roasting products were nearly ten times higher when NH4Cl was used, relative to when it was not, reaching 227.1 and 519.6 μm, respectively. The activation energies of each reaction in the electrotransformation and roasting process were also reduced in the presence of the NH4Cl catalyst. Between 100 °C–250 °C, the physical adsorption of water occurred, and some of the structural water of the electrotransformation products generated with NH4Cl were removed in an endothermic reaction (reaction order, n1 = 0.43; activation energy, E1 = 2.13J mol−1). At temperatures between 250 °C–500 °C, the nitrogen-containing substances were removed in another endothermic reaction (reaction order, n2 = 0.4697; activation energy, E2 = 5.316J mol−1). Increasing the roasting temperature was beneficial for improving the crystal form of the roasting products, and the reduced electrotransformation products appeared more compact after roasting.
Boston Naming Test: Lose the Noose
Authors: Eloi Janelle M , Lee Jennifer , Pollock Erica N , Tayim Fadi M , Holcomb Matthew J , Hirst Rayna B , Tocco Carly , Towns Stephanie J , Lichtenstein Jonathan D , Roth Robert M
Published: March 2021

Objective

Administering the noose item of the Boston Naming Test (BNT) has been questioned given the cultural, historical, and emotional salience of the noose in American culture. In response, some have modified the BNT by skipping/removing this item and giving the point as if the examinee responded correctly. It is unknown, however, whether modifying standardized administration and scoring in this manner affects clinical interpretation. In the present study, we examined the prevalence of noose item failure, whether demographic and clinical characteristics differed between those who responded correctly versus failed the item, and whether giving a point to those who failed affected clinical interpretation.

Method

Participants included a mixed clinical sample of 762 adults, ages 18–88 years, seen for neuropsychological evaluation at one of five sites within the USA.

Results

Those who failed the item (13.78%) were more likely to be female, non-White, and have primary diagnoses of major neurocognitive disorder and epilepsy. Noose item failure was associated with lower BNT total score, fewer years of education and lower intellectual functioning, expressive vocabulary, and single word reading. Giving a point to those who failed the item resulted in descriptor category change for 17.1%, primarily for patients with poor overall BNT performance.

Conclusions

Only a small percentage of patients fail the noose item, but adding a point for these has an impact on score interpretation. Factors associated with poorer overall performance on the BNT, rather than specific difficulty with the noose item, likely account for the findings.
Event-specific quantitative polymerase chain reaction methods for detection of double-herbicide-resistant genetically modified corn MON 87419 based on the 3′-junction of the insertion site
Authors: Long Likun , Yan Wei , Li Congcong , Dong Liming , Liu Na , Xing Zhenjuan , Li Feiwu
Published: March 2021

MON 87419 was one of the new transgenic corn events developed in US with the trait of herbicide resistance to both dicamba and glyphosate. To monitor unintended release of genetically modified organism in the future, as well as to meet GM-labeling requirements, it is requisite to develop a reliable method for the detection and quantification of MON 87419, an event-specific primer pair was designed to amplify the 3′-junction site between the endogenous genome sequence and the transferred DNA of GM event MON 87419, amplicons of desired size were produced by qualitative polymerase chain reaction (PCR) assay. For the validation of this quantitative method, the mixed samples containing 10%, 1%, and 0.1% MON 87419 ingredient were quantified. The precisions were expressed as relative standard deviations, deviated by 7.87%, 12.94%, and 19.98%, respectively. These results clearly demonstrate that the PCR methods we developed herein can be used for event-specific quantitative testing of the double-herbicide-resistant corn MON 87419.
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients
Authors: Mahmud S M Hasan , Al-Mustanjid Md , Akter Farzana , Rahman Md Shazzadur , Ahmed Kawsar , Rahman Md Habibur , Chen Wenyu , Moni Mohammad Ali
Published: March 2021

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
Individual Efficacy and Community Impact of Ivermectin, Diethylcarbamazine, and Albendazole Mass Drug Administration for Lymphatic Filariasis Control in Fiji: A Cluster Randomized Trial
Authors: Hardy Myra , Samuela Josaia , Kama Mike , Tuicakau Meciusela , Romani Lucia , Whitfeld Margot J , King Christopher L , Weil Gary J , Grobler Anneke C , Robinson Leanne J , Kaldor John M , Steer Andrew C
Published: March 2021

Background

Bancroftian filariasis remains endemic in Fiji despite >10 years of mass drug administration (MDA) using diethylcarbamazine and albendazole (DA). The addition of ivermectin to this combination (IDA) has improved efficacy of microfilarial clearance at 12 months in individually randomized trials in nocturnal transmission settings, but impact in a setting of diurnally subperiodic filarial transmission has not been evaluated.

Methods

This cluster randomized study compared the individual efficacy and community impact of IDA vs DA as MDA for lymphatic filariasis in 35 villages on 2 islands of Fiji. Participants were tested at enrollment for circulating filarial antigen and, if positive, for microfilariae. Weight-dosed treatment was offered according to village randomization. Communities were visited at 12 months and retested for lymphatic filariasis. Infected individuals from Rotuma were retested at 24 months.

Results

A total of 3816 participants were enrolled and 3616 were treated. At 12 months, microfilariae clearance was achieved in 72 of 111 participants detected with infection at baseline, with no difference in efficacy between treatment groups: DA, 69.2% (95% confidence interval [CI], 57.2%–79.1%) vs IDA, 62.5% (95% CI, 43.6%–78.2%); risk difference, 11.3 % (95% CI, –10% to 32.7%); P = .30. There was no difference between treatment groups in community prevalence of microfilariae at 12 months or individual clearance at 24 months.

Conclusions

We found no difference between IDA and DA in individual clearance or community prevalence of lymphatic filariasis at 12 months, and no improved efficacy following a second annual round of IDA. Possible explanations for the apparent lack of benefit of IDA compared to DA include drug and parasite factors affecting clearance, and higher than expected reinfection rates.

Clinical Trials Registration: NCT03177993 and Australian New Zealand Clinical Trial Registry: N12617000738325.
Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements
Authors: Woodsend Brénainn , Koufoudaki Eirini , Lin Ping , McIntyre Grant , El-Angbawi Ahmed , Aziz Azad , Shaw William , Semb Gunvor , Reesu Gowri Vijay , Mossey Peter A
Published: March 2021

Background

Previous studies embracing digital technology and automated methods of scoring dental arch relationships have shown that such technology is valid and accurate. To date, however there is no published literature on artificial intelligence and machine learning to completely automate the process of dental landmark recognition.

Objectives

This study aimed to develop and evaluate a fully automated system and software tool for the identification of landmarks on human teeth using geometric computing, image segmenting, and machine learning technology.

Methods

Two hundred and thirty-nine digital models were used in the automated landmark recognition (ALR) validation phase, 161 of which were digital models from cleft palate subjects aged 5 years. These were manually annotated to facilitate qualitative validation. Additionally, landmarks were placed on 20 adult digital models manually by 3 independent observers. The same models were subjected to scoring using the ALR software and the differences (in mm) were calculated. All the teeth from the 239 models were evaluated for correct recognition by the ALR with a breakdown to find which stages of the process caused the errors.

Results

The results revealed that 1526 out of 1915 teeth (79.7%) were correctly identified, and the accuracy validation gave 95% confidence intervals for the geometric mean error of [0.285, 0.317] for the humans and [0.269, 0.325] for ALR—a negligible difference.

Conclusions/implications

It is anticipated that ALR software tool will have applications throughout clinical dentistry and anthropology, and in research will constitute an accurate and objective tool for handling large datasets without the need for time intensive employment of experts to place landmarks manually.
Importance of environmental factors over habitat connectivity in shaping bacterial communities in microbial mats and bacterioplankton in an Antarctic freshwater system
Authors: Ramoneda Josep , Hawes Ian , Pascual-García Alberto , J. Mackey Tyler , Y. Sumner Dawn , D. Jungblut Anne
Published: March 2021

Freshwater ecosystems are considered hotspots of biodiversity in Antarctic polar deserts. Anticipated warming is expected to change the hydrology of these systems due to increased meltwater and reduction of ice cover, with implications for environmental conditions and physical connectivity between habitats. Using 16S rRNA gene sequencing, we evaluated microbial mat and planktonic communities within a connected freshwater system in the McMurdo Wright Valley, Antarctica, to determine the roles of connectivity and habitat conditions in controlling microbial assemblage composition. We examined communities from glacial Lake Brownworth, the perennially ice-covered Lake Vanda and the Onyx River, which connects the two. In Lake Vanda, we found distinct microbial assemblages occupying sub-habitats at different lake depths, while the communities from Lake Brownworth and Onyx River were structurally similar. Despite the higher physical connectivity and dispersal opportunities between bacterial communities in the shallow parts of the system, environmental abiotic conditions dominated over dispersal in driving community structure. Functional metabolic pathway predictions suggested differences in the functional gene potential between the microbial mat communities located in shallower and deeper water depths. The findings suggest that increasing temperatures and meltwater due to future climate change will affect bacterial diversity and functioning in Antarctic freshwater ecosystems.