bluuebunny/crossref_metadata_2025 huggingface dataset:
CLASSIFICATION OF PRICE RANGES FOR MOBILE PHONES USING MACHINE LEARNING
Authors: Dr. Rakesh Kumar B  , Jovish Dsouza  , Raksha
Published: July 2023

Mobile phones have become a crucial requirement in the modern day world. They have emerged as the most common and straightforward means of global communication and information access. Setting the appropriate pricing range for mobile phones is crucial. To determine pricing, all of their features must be considered. This study paper's major goal is to achieve mobile pricing. In this paper we have used different machine learning algorithms along with ensemble algrithms and made a study between Gradient Boosting and Artificial Neural Networks(ANN’s) to find which suits best for our data. We got an accuracy of 91.25% for MLP classifier. An interactive web sites employing flask is also designed to make customers easier to choose a price range.
AIRLINE PASSENGER SATISFACTION PREDICTION USING MACHINE LEARNING ALGORITHMS
Authors: Ashwika  , Dishali G K  , Hemalatha N
Published: July 2023

Airlines are always focused on providing excellent customer service. It is vital to determine which of the two levels of satisfaction with the airline the passenger belongs to: satisfied or dissatisfied. Customer satisfaction scores from 120,000+ airline passengers, including additional information about each passenger, have been collected to determine the factors that are highly correlated to a satisfied or dissatisfied passenger to predict the level of satisfaction the passenger belongs. This study presents a machine learning approach to analyze the information and improve the customer’s experience. The dataset has 24 columns of data, some are categorical, some are integer, one is float and many contain discrete values between 0-5. This paper contains various methods and techniques that we have used to determine the best accurate model and various visualization tools to deeply understand the data. This paper also helps to understand which features are important in predicting passenger satisfaction and to find the correlation between features. We have implemented various machine learning classification algorithms of which we observed that the Random Forest Classifier is the best model giving us the best accuracy result of 94% further helping in accurate predictions among all other models we have used. Hence, this could help airline companies adjust their service value and demand to satisfy customers’ demands.
HR ANALYTICS FOR EMPLOYEE ATTRITION PREDICTION USING MACHINE LEARNING
Authors: Reshma  , Sweedal Tellis  , Vanitha T
Published: July 2023

The practice of gathering and analyzing Human Resource data to enhance an organization's workforce performance is known as HR analytics. Employee data is used in HR analytics to help the firm make better decisions for future expansion. The term "attrition" refers to when employees leave the organization either voluntarily or involuntarily. This article uses machine learning and data analysis methods to examine employee attrition. The procedures include addressing missing numbers, visualizing the data, and using classification algorithms. The likelihood that an employee will leave the organization is predicted using different models, including Random Forest, Decision Tree, SVC, and Logistic Regression. Performance metrics such as the confusion matrix, accuracy score, precision score, and ROC Curve were utilized to evaluate how well the prediction was made. Our research led us to the conclusion that the Random Forest Classifier had the highest accuracy, with a 98% accuracy rate. This study's main objective is to retain employees by understanding the factors responsible for the employee to leave the company.
CASE STUDY ON SPEECH TO SCRIPT TRANSLATION FROM ENGLISH TO BEARY
Authors: Sahana M  , UT Mohammad Mashfooq Hussain
Published: July 2023

Humans communicate with one another using language. Nowadays, most people face difficulties when conversing with others who do not know their language or other language. To mitigate this problem there has been a rise in interest in speech to script translation have been increasing rapidly. Speech to Script translation play a significant role in facilitating communication between speakers of various languages. Attempt was made automatically identify the speech in English language and translate script into Beary language. The proposed paper has two phases: During first phase the system converts the speech which is given as a source into Kannada text which is then transliterated to English language and in the second phase the converted text is transliterated to Beary language. In this study, the system uses independently created audio for both English and Kannada, resulting in a fair and balanced text pronunciation in both languages and also it uses the corpus of Kannada language that acts as a mediator between English and Beary language. The approaches used are rule-based and example-based, and the future work will focus on direct translation of speech to Beary language using sequence methods.
A STUDY ON VIRTUAL MACHINE PLACEMENT ALGORITHMS IN CLOUD DATA CENTER
Authors: Cleona Jyothsna Monteiro  , Rubania Gonsalves  , Mr. Srinivas B.L
Published: July 2023

A large number of data centres have appeared all over the world as a result of the expansion of cloud computing. Instead of owning resources, cloud computing allows users to consume and access resources from the server room as needed. Because of the large number, power-hungry data centers grows at an alarming rate, power administration has emerged as a critical architectural constraint in cloud data centres. A VM consolidation strategy that distributes as many simulated machines (VMs) as possible across as few servers as possible can reduce the amount of electricity used in cloud data centres. However, unnecessary migrations can lead to service outages, performance degradation, and SLA violations. The VM placement's goal challenge is to reduce the overall number of migrations in the system and place idle servers to sleep. This study aims to examine how well VM placement algorithms function in cloud data centres.
A CASE STUDY ON FAKE NEWS DETECTION USING MACHINE LEARNING ALGORITHMS
Authors: Santhosh B  , Sanjana Shetty  , Walsten Glen Monteiro
Published: July 2023

The vast majority of the world's population reads or analyses news produced on the internet, whether via social media or elsewhere. The news is disseminated over the internet without the need to check its veracity. In this work, we experimented with a system to categorise the news in the dataset in order to determine if the news is real or false. We combined the Count Vectorizer with the MultinomialNB (multinomial Naive Bayes classifier) and the term frequency-inverse document frequency (TF-IDF) vectorizer with the PassiveAggressiveClassifier. A dataset called "fake or real news.csv" was used to train and test the algorithm. The output of the system reflects its efficiency.
COMPARATIVE ANALYSIS OF CONVOLUTIONAL NEURAL NETWORK MODELS APPLIED IN HUMAN BEHAVIOUR RECOGNITION
Authors: Sadiya Ayub Humbarkar  , Sumedha E  , Anushree Raj
Published: July 2023

Human behaviour recognition is a crucial area of scientific research in the science of computer vision that has significant applications in a variety of industries, including intelligent surveillance, smart homes, and virtual reality. Traditional manual approaches have a hard time meeting the demands of high recognition accuracy and applicability in the contemporary complicated environment. Deep learning's arrival has opened up new avenues for behaviour recognition research. The major focus of this paper is behaviour recognition using convolutional neural networks (CNN). Before discussing and analysing the classical learning methods and deep learning methods of behaviour recognition, the research context and importance of behaviour recognition are first introduced. Based on the convolution neural network designed for the specific human behaviour in public areas, we develop a series of human behaviour recognition systems. In order to extract behaviour or characters of the body, the video and images of human behaviour data set will be processed. Then the planned convolution neural network is trained with the training data sets, and the deep learning network is built. Finally, using the developed network model, the numerous sample behaviours are categorized and recognized, and the recognition outcomes are then evaluated. The findings demonstrate that CNN is capable of studying human behaviour models automatically and recognizing human behaviours without the need for manually annotated training.
DETECTION OF FACE MASKS USING DEEP LEARNING
Authors: K. Annapoorneshwari Shetty  , Christin Jose  , Christo M Kuriakose
Published: July 2023

The COVID-19 pandemic has quickly disrupted global trade and travel, affecting our day-to-day lives. In its 209th report, released on August 16, 2020, The World Health Organization (WHO) reported that the acute respiratory syndrome (SARS-CoV2) coronavirus illness (COVID-19) has killed more than 379,941 people and infected more than 6000000 people worldwide. The custom of donning a safety mask has evolved. Many public service providers may soon need their clients to wear the necessary masks in order to use their services. In order to serve the worldwide society, face mask detection is becoming a key responsibility. The face mask alone can reduce the risk of contracting the infection by more than 70%. Infections have swiftly spread in some regions as a result of incorrect use of face masks. To solve this issue, we needed a dependable mask monitoring system. The several government agencies who are attempting to make wearing a face mask mandatory can benefit from face mask identification software. This study presents a concise deep learning approach for achieving this objective.
AUTOMATIC FACIAL MAKEUP DETECTION METHODS IN FACE RECOGNITION: A REVIEW
Authors: Rachana Alva  , Sonal J Fernandes  , Nausheeda B S
Published: July 2023

In the realm of cosmetics, facial recognition technology could potentially be used for the detection and selection of cosmetics based on a person's unique facial features. The use of makeup can sometimes make it more difficult for facial recognition technology to accurately detect and analyze a person's facial features. This is because makeup can alter the appearance of a person's face, making it harder for the technology to identify certain characteristics. For example, heavy makeup or makeup that changes the shape or color of a person's features, such as contouring or dramatic eye makeup, can make it more difficult for facial recognition technology to accurately detect and analyze the face. In these cases, the technology may not be able to identify the person's unique facial features, which could lead to incorrect or inaccurate results. Overall, the use of makeup can sometimes make it more difficult for facial recognition technology to accurately detect and analyze a person's facial features, but with the proper training and development, it is possible for the technology to account for the effects of makeup.
COMPARATIVE ANALYSIS TOWARDS PREDICTION OF LUNG CANCER USING MACHINE LEARNING MODELS
Authors: Dr. Rakesh Kumar B  , Devika. S  , Prathvi Saldhana
Published: July 2023

One of the most prevalent cancers that negatively affects a person's health is lung cancer. It is a condition in which certain cells in the lungs develop abnormally and multiply rapidly, resulting in the formation of a tumor. If lung cancer is discovered in its initial stages, several lives can be saved. The number of human deaths will keep increasing if this disease is not detected and treated before the second stage. This research paper is mainly intended to do a comparative study and detect lung cancer at earlier stages through various data pre-processing techniques and machine learning algorithms like Logistic Regression, K-Nearest Neighbour(KNN), Support Vector Machine(SVM), Decision Tree, and Random Forest(RF). It was observed that Logistic Regression is the most accurate mode with accuracy 96%l. The best model was deployed for predicting future trends in real-time providing a user interface using Flask, a Python web framework, which accepts patient information as input and predicts whether or not a person has lung cancer.
APPLICATIONS OF DEEP LEARNING IN AGRICULTURE (PEST-DETECTION)
Authors: Neeke Golatkar  , Krithika  , Hemalatha N
Published: July 2023

Due to increased global trade and demand for food products, agriculture has grown dramatically in recent years. The nation's economy is significantly impacted by this. As the second-largest producer of food in the world, agriculture plays a significant role in the Indian economy. The agricultural sector is one that could benefit from digital technologies. Additionally, it supports a number of small-scale industries by creating jobs. Deep learning approaches are used to improve performance across a wide range of industries. Deep learning is a component of machine learning. It has been used in many smart agriculture applications, including water and soil management, crop cultivation, crop disease detection, weed removal, crop distribution, robust fruit counting, and yield prediction. Pests are a type of arthropod that causes significant problems in agriculture. They have ruined a lot of food, costing farmers money all around and having a variety of negative effects on food production. The purpose of this paper is to discuss how to utilize deep learning in pest detection.
VERIFICATION OF OFFLINE SIGNATURE USING MACHINE LEARNING
Authors: Nishmitha U  , Ahalya Durgadas Kini
Published: July 2023

Every person has a distinctive signature which is primarily used for personal identification and to confirm the authenticity of significant papers or legal transactions. The process of implementing offline or online signature verification will depend on the application. Online systems make advantage of dynamic data from a signature which is recorded while signing. Offline systems utilize a signature's scanned image for operation. Initially system is trained using a database of signatures gathered from individuals whose signatures it must authenticate. The lack of sufficient data for testing is the main issue with signature verification. High accuracy must be used in the development of signature security. In this paper, Offline signature verification that makes use of grid-based extraction of features using ML and CNN have been introduced.
PREDICTION OF CARDIOVASCULAR ILLNESS WITH MACHINE LEARNING
Authors: Ruban S  , Mohammed Safwan  , Manisha Preshal Dsouza  , Mohammed Moosa Jabeer
Published: July 2023

Cardiovascular disease has recently emerged as a significant health risk for people. The term "cardiovascular disease" refers to a broad variety of illnesses that affect the heart and blood arteries. It is been rising daily as a result of inherited and lifestyle factors. Cardiovascular disease is been caused by physical inactivity, unhealthy diet, tobacco use and harmful use of alcohol. One of the largest cardiovascular disease burdens is seen in India. The age-standardized cardiovascular disease death rate of 272 per 100000 population in India is higher than the global average of 235per 100000 population.As the reason due to the patient’s unhealthy symptom’s doctors will ask to go through the basic tests as ECG, X-ray and some other tests and then on the basis of the reports, conclusions about the diseases will be made. At some sort of time patients will be at very weak condition of their health and wait for the treatments to be done. To avoid all such process through the help of machine learning application a lot of time and treatments can also be done as early as possible. Due to a number of risk factors, such as high blood pressure, excessive cholesterol, and an irregular heart rate, it is difficult to diagnose. By using machine learning it collects the required data from the patient and measures whether that patient is having the disease related to cardiovascular or some other diseases. So that the patient can save up the time, test and expenses.
Filling the blank space: Branched 4-nonylphenols isomers are responsible for robust constitutive androstane receptor (CAR) activation by nonylphenol
Authors: Rashidian Azam , Dusek Jan , Drastik Martin , Smutná Lucie , Fritsche Kristin , Braeuning Albert , Poso Antti , Kronenberger Thales , Pavek Petr
Published: July 2023

4-Nonylphenol (4-NP), a para-substituted phenolic compound mixture comprising a straight or branched carbon chain, is a widespread ubiquitous environmental pollutant and food contaminant. 4-NP, particularly the branched form, has been identified as an endocrine disruptor with potent activities on estrogen nuclear receptors. Constitutive Androstane Receptor (CAR, NR1I3) is another crucial nuclear receptor that regulates hepatic lipid, glucose, and steroid metabolism and is involved in endocrine disruption mechanism. The nonylphenol mixture has been described as an extremely potent activator of both human and rodent CAR. However, detailed mechanistic aspects of CAR activation by NP are enigmatic and it is not known if 4-NP can directly interact with the CAR ligand binding domain (LBD). Here we examined interactions of individual branched (22NP, 33NP, and 353NP) and linear 4-NPs with CAR and its variants using molecular dynamics (MD) simulations, cellular experiments with various CAR constructs or CAR ligand binding domain (LBD) mutants, or in differentiated HepaRG hepatocyte cellular model. Our results demonstrate that branched 4-NPs display more stable poses to activate both wild-type (wt)CAR1 as well as CAR3 variant LBDs in MD simulations. Consistently, branched 4-NP activated CAR3 and wtCAR1 LBD more efficiently than linear 4-NP or tert-nonylphenol (22NP). Furthermore, in HepaRG cells, we observed that all NP significantly up-regulated CYP2B6, a relevant hallmark for CAR activation. This is the first study to provide detailed insights into the direct interaction between individual 4-NPs and the human CAR-LBD, as well as its dominant variant CAR3 The work could contribute to the safer use of individual NPs in many areas of industry.
Room-Temperature Multiple Phosphorescence from Functionalized Corannulenes: Temperature Sensing and Afterglow Organic Light-Emitting Diode
Authors: Si Changfeng , Wang Tao , Kumar Gupta Abhishek , Cordes David , Slawin Alexandra , Siegel Jay , Zysman-Colman Eli
Published: July 2023

Corannulene-derived materials have been extensively explored in energy storage and solar cells, but, however, are rarely documented as emitters in light-emitting sensors and organic light-emitting diodes (OLEDs), due to low exciton utilization. Here, we report a family of multi-donor and acceptor (multi-D-A) motifs, TCzPhCor, TDMACPhCor, and TPXZPhCor, using corannulene as the acceptor and carbazole (Cz), 9,10-dihydro-9,10-dimethylacridine (DMAC), and phenoxazine (PXZ) as the donor, respectively. By decorating corannulene with different donors, multiple phosphorescence is realized. Theoretical and photophysical investigations reveal that TCzPhCor shows room-temperature phosphorescence (RTP) from the lowest-lying T1; however, for TDMACPhCor, dual RTP originating from a higher-lying T1 (T1H) and a lower-lying T1 (T1L) can be observed, while for TPXZPhCor, T1H-dominated RTP occurs resulting from a stabilized high-energy T1 geometry. Benefiting from the high-temperature sensitivity of TPXZPhCor, high color-resolution temperature sensing is achieved. Besides, due to degenerate S1 and T1H states of TPXZPhCor, the first corannulene-based solution-processed afterglow organic light-emitting diodes (OLEDs) is investigated. The afterglow OLED with TPXZPhCor shows a maximum external quantum efficiency (EQEmax) and a luminance (Lmax) of 3.3% and 5167 cd m-2, respectively, which is one of the most efficient afterglow RTP OLEDs reported to date.
Hindu versus christian perspectives on victory, god’s will and love: a comparative study
Authors: Mahmoud Amel , Abdulkarim Chiad
Published: July 2023

This research paper investigates two literary texts, one is an ancient Indian epic Bhagavad Gita (The Song of the Lord) anonymously written between (500 -200 BC) while the second is a historical novel Manuscript Found in Accra by Paulo Coelho. Though the two texts may vary in their genre and their religious tradition, they still share significant features. The first is related to the setting. The first shared feature is army preparation for a war which is going to happen soon based on the setting of both texts. The second feature, in common with the two texts, is in connection with their narrative structures. Both texts are based on a question-answer dialogue, a discourse between two parties of characters. The third significant characteristic shared by the two texts is in connection with their core religious, spiritual, and philosophical themes. Arjuna, in Bhagavad Gita, and the people of Jerusalem, in Manuscript Found in Accra, agitatedly ask their spiritual leaders, Krishna, in the first text, and the Copt, in the second, religious and ethical questions. These questions evidently refer to key subjects which are victory in a military combat, God’s will and volition and love between man and God and among human beings. While analyzing these themes, the discussion thoroughly probes through the two texts’ religious contexts: the Hindu theologian tradition and Biblical teachings. The aim is to meticulously examine the points of disparity between the Hindu versus the Christian understanding of victory in the battlefield, God’s will and love, which are the core themes in this analysis.
Penerapan Teori Keadilan Terhadap Pembagian Harta Bersama Pasca Perceraian
Authors: Utami Safira Maharani Putri , Dalimunthe Siti Nurul Intan Sari
Published: July 2023

This research aims to examine the application of the division of marital assets after divorce by analyzing the case in decision number 2802/Pdt.G/2018/Dpk. The focus of this research is on the division of marital assets and how the theory of justice is applied in the division process. This research is important as it highlights the division of marital assets as a sensitive issue in society, and the importance of justice as a key factor in the division that can influence court decisions. Therefore, it is necessary to analyze the factors considered by the panel of judges in cases involving the division of marital assets. This research adopts a normative legal research method by analyzing the applicable legal regulations and supported by other legal materials, including primary, secondary, and tertiary legal sources. The findings of this research indicate that the existing regulations governing the division of marital assets do not fully meet the criteria of justice for wives who bear a double burden in marriage. Judges are allowed to deviate from existing laws and engage in contra legem practices to achieve justice for all parties involved in the marriage.

 

Penelitian ini memiliki tujuan untuk mengkaji penerapan terhadap pembagian harta bersama pasca perceraian dengan menganalisis perkara pada putusan Nomor 2802/Pdt.G/2018/Dpk. Penelitian ini berfokus pada pembagian atas harta bersama dan bagaimana teori keadilan diterapkan dalam proses pembagian tersebut. Penelitian ini menjadi penting dengan menyoroti pembagian harta bersama sebagai isu yang cukup sensitif dalam masyarakat dan pentingnya keadilan sebagai faktor utama dalam pembagian tersebut yang dapat mempengaruhi keputusan pengadilan. Maka dari itu, dirasa perlu adanya urgensi untuk menganalisis faktor-faktor pertimbangan oleh majelis hakim dalam kasus pembagian atas harta bersama. Penelitian ini menggunakan metode penelitian hukum normatif (yuridis normatif) dengan menganalisis peraturan-peraturan hukum yang berlaku dengan didukung oleh bahan hukum lainnya, antara lain bahan hukum primer, sekunder, serta  tersier. Hasil dari penelitian ini menjelaskan bahwa peraturan yang telah diundangkan yang mengatur mengenai pembagian atas harta bersama belum sepenuhnya memenuhi kriteria keadilan bagi istri yang memiliki beban ganda dalam perkawinan. Hakim dalam memutuskan terkait diizinkan untuk melanggar hukum yang ada dengan melakukan contra legem guna mencapai keadilan bagi semua pihak dalam perkawinan.

 

 

 

 
Red kidney bean (Phaseolus vulgaris L.) instant porridge: Effect of isomaltooligosaccharides and Fibercreme as sucrose replacement on lipid profile improvement in hypercholesterol-induced rats
Authors: Marsono Y. , Putri R.G. , Gunawan H. , Indrawanto R.
Published: July 2023

The aim of the present study was to evaluate the effect of consumption pregelatinized kidney bean porridge with variation of sweetener including sucrose (BKM S), isomaltooligosaccharides (BKM IMO) and Fibercreme (BKM FC) in hypercholesterolemia rats on the lipid profile, bile acid binding capacity, digesta cholesterol excretion and digesta characters including weight, water content and pH. The diets included standard diet for Negative Control rats (NC diet) and kidney bean porridge diets with different sweeteners: sucrose (BKM S diet), IMO (BKM IMO diet) and fibercreme (BKM FC diet), respectively. Red kidney bean instant porridge substituted 30% of total calories of the diets. All of the diets were formulated based on AIN 93M standard and were fed for 28 days. The result showed that BKM IMO and BKM FC diets improve lipid profile of rats by reducing total cholesterol, LDL cholesterol and triglycerides and increasing HDL cholesterol. In addition, both diets increased cholesterol excretion. In vitro measurement of bile acid binding capacity showed that BKM IMO and BKM FC have a higher binding capacity than BKM S. It can be argued that increasing of cholesterol excretion and bile acids binding capacity of these diets are responsible for the improving of the lipid profile. BKM IMO and BKM FC have higher digesta weight and water content but lower pH. It is concluded that substitution of sucrose as sweetener with isomalto-oligosaccharides and Fibercreme in the formulation of pregelatinized kidney bean porridge improve the beneficial health effect of kidney bean porridge.
Effect of using different clarifying agents and temperature on physicochemical and sensory properties of sweet sorghum syrup extract
Authors: Harlen W.C. , Ristiarini S.
Published: July 2023

The utilization of sorghum crops to produce value-added products such as alternative natural sweeteners has been increasing. Knowledge of the sweet sorghum juice clarification process to yield sweet sorghum syrup as an alternative sweetener is essential to get the best result and improve the quality of the syrup. This research aimed to study the sorghum syrup extraction process by using clarifying agents: Ca(OH)2, bentonite, and activated carbon, followed by the heating process at 70oC or 80oC. Sorghum stalks were milled to produce sorghum juice then clarified and heated up to make sweet sorghum syrup to reach 68.78-76.78% Bx. The clarification process was done to reduce particles in sorghum juice before the heating process. In this work, the psycho-chemical (pH, colour, turbidity, reducing sugar) and sensory properties (flavour and aroma) of sorghum syrup after clarification and heating process were analyzed. A randomized block design was used as the experimental design. Adding Ca(OH)2 and heating at 80oC resulted in sorghum syrup with higher pH and lower turbidity than that heated at 70oC. The evaluation revealed that the combination of Ca(OH)2 and 5% bentonite has the sweetest flavour, caramel aroma, and preferred psycho-chemical properties showed in pH (5.28), turbidity (35.56 NTU), and reducing sugar (57.52%).
Effect of rice bran and spent soymilk on the dough rheological properties and quality of bread
Authors: Wang C.C.R. , Nugrahedi P.Y. , Johan Y.
Published: July 2023

The utilization of agricultural residues (ARs) has been involved in the bakery product, particularly due to the functional ingredients of AR. The concept of glycemic index (GI) is indicated to classify carbohydrate-based foods according to their postprandial glucose response. Long term consumption of food with high GI will lead to the risk of chronic disease. The objectives of this study are to investigate the effect of the substitution of AR on the physical properties of dough and physicochemical and sensory properties of breads. Spent soymilk (SS) and rice bran (RB) were used in this study. The results observed that SS and RB contain high fiber ranging from 9.16 to 20.48% (d.b.). The stability and extensibility of rheological properties of dough decreased with the increases of the substitution of AR. The substitution of SS significantly decreased the volume and height of bread but increased hardness and chewiness. The substitution of AR significantly (p<0.05) reduced the value of estimated glycemic index (eGI) from 94.6 to 62.9. Moreover, the breads containing 10%, 20% RB, and 10% SS were similar with the control sample according to the result of sensory evaluation, which can be used to improve the nutritional quality of bread.
Synergistic effect of kappa-carrageenan and konjac flour in enhancing physicochemical and organoleptic properties of wheat-based edible straw
Authors: Jati I.R.A.P. , Natasha L. , Nugraha D.T. , Virly  , Setijawaty E.
Published: July 2023

The development of edible straws is an affordable solution to reduce the use of plastic tableware as well as a promising innovation to promote an eco-friendly lifestyle. This study was aimed at producing wheat-based edible straw made by combining high-protein wheat flour with kappa-carrageenan, konjac flour, salt and water. All ingredients were introduced to mixing, kneading, resting, dough rolling, dough flattening, molding, and baking. The effect of six different proportions of kappa-carrageenan and konjac flour (100:0, 80:20, 60:40, 40:60, 20:80, 0:100) on the physicochemical and organoleptic properties of wheat-based edible straw was evaluated. The water content of edible straws from all treatments ranged from 7.07-8.12%. Among the six treatments, the maximum synergy of kappa-carrageenan and konjac flour in producing high gel strength and desired edible straw characteristics was obtained from the proportion of 60:40 with the produced edible straw possessed the lowest water activity (aw), percentage of water absorption at tested temperatures (0-5, 25-30, and 65-70oC), turbidity, and fracturability. The results of the organoleptic evaluation showed that the panelists slightly liked and could accept the aroma and color of wheat-based edible straw made with the proportion of kappacarrageenan and konjac flour 60:40.
The Synthesis and Biological Evaluation of Ligands for C-Type Lectin Receptors
Authors:   , Khan Ayesha
Published: July 2023

Macrophage inducible C-type lectin (Mincle), expressed on antigen presenting cells (APCs), is an important player in innate immunity due to its capacity to recognise pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), which are involved in pathogen recognition and in tissue homeostasis, respectively. Several ligands can bind Mincle and activate APCs to generate an inflammatory immune response, with prominent examples including the mycobacterial glycolipid, trehalose dimycolate (TDM), and the synthetic analogue thereof, trehalose dibehenate (TDB). Trehalose glycolipids exhibit anti-tumour properties and an ability to switch the phenotype of macrophages from one that is tumour promoting to one that is tumour suppressive. Insomuch, Mincle ligands have potential as adjuvants for both prophylactic and therapeutic (e.g. cancer) vaccines. Maradolipids, which were originally extracted from the nematode Caenorhabditis elegans, are glycolipids containing trehalose with symmetrical or asymmetrical iso-branched and straight chain fatty acids. The incorporation of the iso-branch makes them distinct from linear trehalose diesters (TDEs) and might provide the compounds with enhanced immunomodulatory properties. 
Analysis of Clinical and Imaging Characteristics of Suspected and Confirmed COVID-19 Patients
Authors: Chen Lijun , Xu Wang , Liu Jing , Zhang Huifang , Chen Juanxia , Ma Xiaoyong
Published: July 2023

Objective: To analyze the clinical characteristics and chest CT imaging characteristics of patients with confirmed COVID-19 (COVID-19) and patients with suspected COVID-19. Methods: The study time span was from February 2020 to May 2020. The case samples were selected from 72 patients with confirmed covid-19 and suspected covid-19 diagnosed and treated by The First People’s Hospital of Yinchuan and Yinchuan Temporary Emergency Hospital, including 38 patients with confirmed covid-19 and 34 patients with suspected covid-19. All patients underwent laboratory examination and chest CT examination, and the specific examination results were compared and analyzed. Results: There were significant differences in number of white blood cell, percentage of lymphocytes, creatine kinase and erythrocyte sedimentation rate between confirmed and suspected COVID-19 patients (P<0.05). The CT imaging characteristics of COVID-19 patients were compared with those of suspected COVID-19 patients. The lesions of COVID-19 patients were mostly characterized by mixed ground glass density and pure ground glass density. There were vascular thickening and interstitial thickness increase, and accompanied by bronchiectasis or air bronchogram. The distribution of lesions was mostly subpleural without pleural effusion. The lesion area of suspected COVID-19 patients mostly showed solid density and mixed ground glass density. The lesion was distributed along bronchovascular and pleural effusion was observed. Conclusion: There are some differences in biochemical indexes and chest CT images between confirmed and suspected covid-19 patients, which can be used for differential diagnosis.
A Review of the Mechanism of Cardiac Benefit of Sodium-Glucose Cotransporter 2 Inhibitors
Authors: Chen Yuezheng , Wang Xian
Published: July 2023

Sodium-glucose cotransporter 2 inhibitor (SGLT2i) is a new type of hypoglycemic drug, which can reduce the excitability of sympathetic nerve by regulating the metabolism of energy substrate, electrolyte, tissue fluid and circulating blood volume in the ganglion, interfering with inflammation and oxidative stress, offering protection to vascular endothelial cells, maintaining vasodilation function, regulating volume and pressure load and myocardial energy metabolism, as well as protecting cardiomyocytes and cardiac function.
The legitimacy of artificial intelligence and the role of ChatBots in scientific publications
Authors: Melnyk Yu. B. , Pypenko I. S.
Published: July 2023

Background and Aim of Study: Developing and using ChatBots based on artificial intelligence (AI) has raised issues about their legitimacy in scientific research. Authors have increasingly begun to use AI tools, but their role in scientific publications remains unrecognized. In addition, there are still no accepted norms for the use of ChatBots, and there are no rules for how to cite them when writing a scientific paper. The aim of the study: to consider the main issues related to the use of AI that arise for authors and publishers when preparing scientific publications for publication; to develop a basic logo that reflects the role and level of involvement of the AI and the specific ChatBots in a particular study. Results: We offer the essence of the definition “Human-AI System”. This plays an important role in the structure of scientific research in the study of this new phenomenon. In exploring the legitimacy of using AI-based ChatBots in scientific research, we offer a method for indicating AI involvement and the role of ChatBots in a scientific publication. A specially developed base logo is visually easy to perceive and can be used to indicate ChatBots’ involvement and contributions to the paper for publication. Conclusions: The existing positive aspects of using ChatBots, which greatly simplify the process of preparing and writing scientific publications, may far outweigh the small inaccuracies they may allow. In this Editorial, we invite authors and publishers to discuss the issue of the legitimacy we give to AI, and the need to define the role and contribution that ChatBots can make to scientific publication.
The effect of self-compassion on academic burnout in undergraduate students
Authors: Farisandy E. D. , Yulianto A. , Kinanti A. D. , Ayu B. A.
Published: July 2023

Background and Aim of Study: Due to high academic demands, undergraduate students are prone to academic burnout. Academic burnout is emotional exhaustion caused by the pressures of academic tasks. Having self-compassion can be a protective factor against academic burnout. When dealing with numerous academic pressures and demands, undergraduate students with high self-compassion will be able to see themselves and their situations positively, resulting in lower academic burnout. The aim of the study: to investigate the effect of self-compassion on academic burnout in undergraduate students in Indonesia. Material and Methods: This study was conducted using the quantitative approach with a non-experimental design. The Maslach Burnout Inventory-Student Survey (MBI-SS) was used to measure academic burnout (α=0.824), and the measurement of self-compassion used the Self-Compassion Scale (SCS) (α=0.878), which has been adapted into the Indonesian language. A sample of undergraduate students in Indonesia between the ages of 17 and 25 was taken using the convenience sampling technique. This study used simple linear regression analysis. Results: The regression analysis from data of 474 undergraduate students shows that there is a negative and significant effect of self-compassion on academic burnout in undergraduate students (R2=0.076, F=38.960, p<0.001). Furthermore, the variable of gender and a current semester each has a significant effect on academic burnout, but the finding reveals the opposite with the age variable. Conclusions: Self-compassion has a negative effect on academic burnout in undergraduate students. This explains that an increase in self-compassion will be followed by reduction in academic burnout in undergraduate students.
Role of lipid profile, apolipoproteins, and their ratio for prediction of cardiovascular disease in essential hypertension
Authors: Dubey R. , Baghel D. S. , Gaikwad K. , Rathore V. , Saxena R. , Ansari Y. M.
Published: July 2023

Background and Aim of Study: Dyslipidemia is a risk factor for cardiovascular disease, and lipid metabolism changes are linked to essential hypertension. The aim of the study: to investigate the significance of lipid parameters, apolipoproteins, and their ratio in predicting cardiovascular disease among individuals with essential hypertension. Material and Methods: 250 patients with essential hypertension and 250 healthy control subjects were enrolled in this case-control study and their serum lipids and apolipoproteins were analyzed. Differences between cases and controls were examined using independent sample t-test and, a p-value <0.05 was considered significant. Results: In the essential hypertensive group, fasting blood glucose (FBG), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), apolipoprotein B100 (Apo B100) and Apo B100/Apo A1 ratio were increased significantly compared to control subjects. Essential hypertensive patients had significantly decreased levels of apolipoprotein A1 (Apo A1) and high-density lipoprotein cholesterol (HDL-C) compared to controls. Moreover, age, body mass index (BMI), FBG, TC, TG, LDL-C, and VLDL-C, as well as the Apo B100/Apo A1 ratio, were significantly positively correlated with both systolic blood pressure (SBP) and diastolic blood pressure (DBP), but HDL-C and Apo A1 were significantly negatively correlated in essential hypertensive subjects. There was a significant positive correlation between apo B100 and SBP in people with essential hypertension. Apo B100 and DBP showed a positive association, however, it was not statistically significant. Conclusions: Essential hypertensive people with dyslipidemia and an elevated Apo B100/Apo A1 ratio are at an increased risk for the development of cardiovascular disease.
Influence of behavioral and social factors on gastric cancer incidence and mortality
Authors: Nikolov A. , Georgieva L.
Published: July 2023

Background and Aim of Study: The significance of the risk factors, including behavioral and social characteristics of the patients, for the occurrence of stomach cancer is constantly increasing and largely determines the development and outcome of the disease. The aim of the study: to provide empirical data for the significance of behavioral and social factors on gastric cancer incidence and mortality. Material and Methods: Data from a study of gastric cancer patients (234 men and 144 women) examined at the Heidelberg University Clinic (Germany) were used. Risk factors, gastric cancer survival and mortality, factors of fatal outcome of gastric cancer patients were compared. Multiple binary logistic regression analysis was used to quantitatively assess their influence. To analyze the possible statistical significance between different groups, we used Fisher’s exact test and chi-squared test for the relationship between categorical variables and Student’s t-test. The significance level (rejecting the null hypothesis) is p<0.05. Results: The most common risk factors are lack of physical activity, smoking, accompanying cardiovascular diseases, as well as emotional stress. Unhealthy lifestyle increases the risk by 56.8%. With a lower but significant percentage are overweight and alcohol abuse. We found the presence of emotional stress in 25.3% of patients with stomach cancer. Jobs with primary mental work and high stress level increase the probability of negative outcome. Mainly mental work is associated with about 8 times the risk of death compared to physical work. The presence of stress increases the lethal risk about 3.2 times. Conclusions: Risk factors related to healthy lifestyle of the patients, including mental health, play an important role in the development of stomach cancer. They largely determine the status, disease control, treatment, survival and mortality of gastric cancer patients.
An analysis of inhalation technique errors in metered-dose inhaler and dry powder inhaler users
Authors: Chaudhary K. , Kumar A.
Published: July 2023

Background and Aim of Study: Decreased efficacy of metered dose inhaler and dry powder inhalers are associated with errors in inhalational techniques. The aim of the study: to study the association of errors in the technique for using two types of inhalers with demographic and other variables. Material and Methods: Five hundred adult patients with respiratory diseases who were currently using an inhaler device were enrolled in this study. Patient’s demographics and duration of inhaler therapy and assessment of inhaler technique were recorded by interview and direct observation. Results: Out of 500 enrolled patients, 465 patients were using the device with wrong technique. Among 465 patients, 188 patients were using metered dose inhaler and 277 patients were using dry powder inhalers. Technical errors were common in both the devices but more common with metered dose inhaler device. Failure to exhale before the inhale through device was most common error with metered dose inhaler (68.6%) and dry powder inhalers (71.4%). Association of errors with female gender is seen in both metered dose inhaler and dry powder inhalers users. Reduction in the numbers of errors is seen with increase in the duration of therapy and regular training on follow-up visits. Conclusions: Dry powder inhalers and metered dose inhalers are commonly used in management of respiratory patients. Therefore, the errors in using these devices, technique and handling errors are common in both dry powder inhalers and metered dose inhaler users. More error was found in old age, female and short-term users. However regular training on follows up visits can solve this current problem.
Integrating meditation practice into thanatophobia therapy
Authors: Ho T. T. H.
Published: July 2023

We explore the potential effectiveness of incorporating maranassati meditation into therapeutic practices, highlighting its ability to mitigate thanatophobia and improve psychological well-being. Drawing upon Buddhist psychological perspectives, maranassati emphasizes the contemplation of mortality, impermanence, and interconnectedness. The aim of the study: to integrate Buddhist meditative practice into thanatophobia therapy. This article presents research evidence from real-world scenarios involving individuals with coronary artery disease, cancer patients, and the elderly. The initial findings provide a solid foundation, reinforcing the efficacy of mindfulness-based therapies in reducing death anxiety and improving psychological well-being. The proposed approach offers valuable insights for counselors and therapists in assisting future clients in cultivating a positive outlook on death, thereby alleviating fear and anxiety associated with mortality.
Peculiarities of the psychological well-being and social adaptation of young students and cadets in wartime conditions
Authors: Stadnik A. V. , Melnyk Yu. B. , Mykhaylyshyn U. B. , de Matos M. G.
Published: July 2023

Background and Aim of Study: The mental health and social adaptation of young students and cadets in wartime conditions is a new and understudied problem. The aim of the study: to identify the particularities of psychological well-being and social adaptation of students and cadets in wartime conditions. Materials and Methods: Our study was conducted among the students and cadets of two Ukrainian universities (KNUIA, UzhNU) in 2023, February. 327 participants were divided into 3 groups: 1) 112 cadets whose permanent disposition was changed in Ukraine; 2) 108 students who were displaced and who are in Ukraine and EU countries; 3) 107 students who did not change their place of permanent residence and who are in Ukraine in the combat zone or near it. The 28-item General Health Questionnaire (GHQ-28) to assess psychological well-being and emotional stability, and the Social Support Questionnaire (F-SozU K-22) to determine the particularities of emotional support, practical support, and social integration were used in the study. Appropriate internal consistency values (Cronbach α between 0.817 and 0.903) were found for both scales. Results: Group 1 has the best general health indicator: 1.65 times better than Group 3. The levels of somatic symptoms, anxiety and insomnia are lower in Group 1 (practically the same in women and men), and higher in Group 3. The highest rates of social dysfunction are found in Group 3. Men in all groups have more pronounced social dysfunction; women in all groups have more pronounced severe depression. Group 1 total perceived social support is 1.89 times better than Group 3. Emotional support, social integration are highest in Group 1, practical support is highest in Group 2. Conclusions: The lowest levels of psychological well-being and social adaptation were found in Group 3. This is probably due to uncertainty about the future, constant instability and insecurity. Teachers and psychologists are urged to consider the identified psychological and social peculiarities in the educational and clinical process when dealing with young students.