bluuebunny/crossref_metadata_2025 huggingface dataset:
Information technology of quality improvement planning of process subsets of the spice model
Authors: Hodlevskyi Mykhailo , Burlakov Georgii
Published: December 2023

The object of research is the software development process. The subject of the study is models, methods and information technology for planning quality improvement of a subset of processes of the SPICE maturity model. The aim of the work is to implement the developed models, method and technology within the framework of information technology and check its performance on test examples. Research methods: quality engineering of software systems; the theory of utility, on the basis of which the target function of the model is built; the method of options sequential analysis; the decision-making theory and modeling of business processes in the development of decision-making support information technology. Applied models: the SPICE maturity model is formalized, which is presented in the form of a hierarchical system; a model for evaluating the level of possibility of a separate process/subprocess of the SPICE model was synthesized; the quality improvement-planning model of a subset of the SPICE model processes under conditions of limited resources is synthesized. The method of options sequential analysis was applied, which was implemented on the basis of the "Kyiv Broom" algorithm. The obtained results: a technology for solving the given task was developed, which is presented in the form of a sequence of execution of individual business processes using the IDEFO standard; the requirements for software were defined; a data model of the subject area was developed; the toolkit for software development is substantiated; the software architecture was chosen; a diagram of software components placement is presented; the performance of the developed information technology was verified on test examples. The practical significance of the work lies in the fact that the obtained information technology allows the managers of IT companies within the limits of the allocated resources in a certain planning period to build the trajectory of the development of the most significant processes of software, to determine how their level of opportunity will increase in each planning sub-period in order to obtain quality software.
ASSESSING AND FORECASTING THE STATE OF DETERIORATING SYSTEMS WITH THE USE OF MODIFIED REGRESSION POLYNOMIALS ON THE BASIS OF FUNCTIONAL APPROXIMATION OF THEIR COEFFICIENTS
Authors: Raskin Lev , Sukhomlyn Larysa , Sokolov Dmytro , Vlasenko Vitalii
Published: December 2023

Object of research is technical state of deteriorating systems whose operating conditions depend on a large number of interacting factors. The caused inhomogeneity of the sample of initial data on the technical state leads to impossibility of correct use of traditional methods of assessing the state of a system (meaning methods using mathematical tools of regression analysis). Subject of research is developing a method for constructing a regression polynomial based on the results of processing a set of controlled system parameters. Non-linearity of the polynomial describing the evolution of the technical state of real systems leads to an increase in the number of regression polynomial coefficients subject to estimation. The problem is further complicated by the growing number of factors affecting the technical state of the system. In these circumstances, the so-called  occurs.  Goal the research consists in developing a method for constructing an approximation polynomial that describes evolution of the system state in a situation where the volume of the initial data sample is insufficient for correct estimating coefficients of this polynomial. The results obtained. The paper proposes a method for solving the given problem, based on implementation of a two-stage procedure. At the first stage a functional description of the approximation polynomial coefficients is performed; and this radically reduces the number of regression polynomial parameters to be estimated. This polynomial is used for preliminary estimation of its coefficients with the aim of filtering out insignificant factors and their interactions. At the second stage, parameters of the truncated polynomial are estimated by means of using standard technologies of mathematical statistics. Two approaches to constructing a modified polynomial have been studied: the additive one and the multiplicative one. It has been shown that the additive approach is, on average, an order of magnitude more effective than the multiplicative one.
HYBRID APPROACH FOR DATA FILTERING AND MACHINE LEARNING INSIDE CONTENT MANAGEMENT SYSTEM
Authors: Poliarush Oleh , Krepych Svitlana , Spivak Iryna
Published: December 2023

The object of research is the processes of data filtering and machine learning in content management systems. The subject of research is developing a hybrid approach to data filtering based on a combination of supervised and unsupervised machine learning. The article explores machine learning approaches to content management and how they can change the way we organize, categorize, and derive value from vast amounts of data. The main goal is to develop and use a hybrid approach for data filtering and training that will help optimize resource consumption and perform supervised training for better categorization in the future. This approach includes elements of supervised and unsupervised learning using the BERT architecture that uses this kind of flow that help reduce resource usage and adjust the algorithm to perform better in a specific area. As a result, thanks to this approach, the intelligent system was able to independently optimize for a specific field of use and help to reduce the costs of using resources. Conclusion. After applying a hybrid approach of data filtering and machine learning to existing data streams, we obtain a performance increase of up to 5%, and this percentage increases depending on the running time of the application.
AlexNet Convolutional Neural Network Architecture with Cosine and Hamming Similarity/Distance Measures for Fingerprint Biometric Matching
Authors: AL-Jumaili Ahmed Sabah Ahmed , Tayyeh Huda Kadhim , Alsadoon Abeer
Published: December 2023

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compared to traditional image filtering techniques. This paper aimed to utilize a specific CNN architecture known as AlexNet for the fingerprint-matching task. Using such an architecture, this study has extracted the significant features of the fingerprint image, generated a key based on such a biometric feature of the image, and stored it in a reference database. Then, using Cosine similarity and Hamming Distance measures, the testing fingerprints have been matched with a reference. Using the FVC2002 database, the proposed method showed a False Acceptance Rate (FAR) of 2.09% and a False Rejection Rate (FRR) of 2.81%. Comparing these results against other studies that utilized traditional approaches such as the Fuzzy Vault has demonstrated the efficacy of CNN in terms of fingerprint matching. It is also emphasizing the usefulness of using Cosine similarity and Hamming Distance in terms of matching.
Synthesis, Characterization and Properties of Trimethylolpropane Triesters from Coconut (Cocos nucifera) Methyl Esters
Authors: Mustapha Aliru Olajide , Rasaq SofiatOmowumi , Raimi Mukaila Tunde , Amisu Suliat Kemi , Bello Faimot Peju , Afolabi Yemisi Tokunbo , Oshilaru Dayo
Published: December 2023

In this study, the acid-alkaline transesterification of refined coconut seed oil (RCOSO) to fatty acid methyl ester was followed by the production of a trimethylolpropane-based thermosensitive biolubricant using potassium hydroxide, and its physicochemical characteristics were evaluated. The American Standard Test for Materials (ASTM) was employed to ascertain the biolubricant's pour point and index of viscosity, which were found to be -4 oC and 283.75, respectively. The opposite connection between lubricant viscosity and temperature was shown by the measured viscosities at varied transesterification to be transformed into biodiesel. Following this, a biolubricant was created by further transesterifiedtemperature. The ester group is verified by comparing the Fourier Transfer Infra-red (FTIR) spectra of the biodiesel and the biolubricant between absorption bands (1755.07-1737.21 cm-1). Gas chromatographic data shows that sweet almond oil contains 71.725 % unsaturated fatty acids for use as a biolubricant, 53.478 % more unsaturated fatty acids than saturated fatty acids, and 9.52 g iodine per 100 g of oil sample more than unsaturated glycerides. At 31.44 and 45.93 %, respectively, linoleic acid made up the majority among the fatty acids in the oil and synthetic biolubricant. In addition, palmitic, linoleic, and oleic acids were present in the biolubricant and coconut oil. The biolubricant's characterization results correlated highly with the ISO VG-32 standards, suggesting that it has the capacity to serve as light gear oil.
Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
Authors: Darwich Ayham , Ismaiel Ebrahim , Al-kayal Ayman , Ali Mujtaba , Masri Mohamed , Nazha Hasan Mhd
Published: December 2023

Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforward neural network (FNN) model. Data acquisition involved 60 subjects diagnosed with the studied cases. The implementation of FNN achieved an accuracy of 96.6% using 50% of the dataset as training data and 92.8% using only 30% training data. The comparison with related work shows good impact of using the differential values of pressure points as input for neural networks compared with raw data.
An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction
Authors: Mitra Suranjana , Majumder Annwesha Banerjee , Saha Tanusree
Published: December 2023

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately.  The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.
Polymerization of Acrylamide N-methylene Lactic and Glycolic Acid
Authors: Khazratkulova Sevara , Zokirova Nodira , Mukhamedova Busora , Lal Basant , Khamidov Orifjon , Berdimurodov Elyor , Alieva Guloy , Hosseini-Bandegharaei Ahmad , Aliev Nizomiddin
Published: December 2023

In this research work, the novel polymer base on acrylamide N-methylene lactic and glycolic acid was synthesized and its structural performances were identified by the IR, 1H NMR and 13C NMR spectroscopic investigations. The influencing factors and kinetics of polymerization, viscosity performance were studied and quantum chemical calculations were used to identify the correlation between the structure and properties. It was determined that the polymerization rate of the examined monomers in an aqueous solution, in the presence of DAA, adheres to the standard rules for radical polymerization of acrylamide monomers in solution. An investigation into the pH solution's impact on the kinetics of radical polymerization of acrylamido-N-methylene glycolic and acrylamido-N-methylene lactic acids revealed an extreme dependence with a minimum in a neutral medium. It was found the linear correlation between pH and viscosity. The physical and chemical performance of this polymer depends on the structural parameters related the results of quantum chemical calculation. Biological tests conducted on polyacrylamido-N-methylene lactic acid indicated its potential as a plant growth stimulator. The polymeric form of lactic acid was found to enhance the growth of Dustlik variety wheat seedlings by 40% more efficiently than lactic acid alone.
Synthesis of Multifunctional Polymers Based on Tert-Butyl Acrylamide/Octyl Acrylate as Lubricant Additives and Adsorbents for Copper Ion from Aqueous Solution
Authors: Mohammed Ameen Hadi , Mousa Enaam Fadel , Hamed Ameera Hassan , Ahmad Mansor
Published: December 2023

Two homopolymeric and three copolymeric additives for base oil were synthesized using octyl acrylate (OA) and tert-butyl acrylamide (TBA) monomers. The two additives named P1 and P2 are the homopolymers of TBA and OA, respectively, whereas copolymeric additives named Co1, Co2, and Co3 were synthesized by varying the ratios of TBA:OA as 1:3, 3:1 and 1:1, respectively. The prepared polymers were characterized by Fourier Transform Infrared (FTIR). Based on the solubility of synthesized polymers in base oil and reactivity ratios of TBA/OA copolymer (0.222, 0.434) calculated by Fineman-Ross method, P2, Co1, Co2 and Co3 were selected to evaluate their performance as pour point depressant (PPD), viscosity improver (VII), and anticorrosion additives in base oil. Additives P2 and Co1 showed the best performance as (PPD) and (VII) whereas Co2 and Co3 revealed the best performance as anticorrosion additives. The flash point of base oil increased as a concentration of polymeric additives increased in the base oil. In addition to study the performance of synthesized polymers as lubricant additives, the capability of polymers for removing CuII from synthetic wastewater was also investigated. Different parameters such as pH of solution, contact time, and CuII concentration were changed in order to study their effect on adsorption capacity of polymers. Generally, polymers with high TBA content such as P1, Co2, and Co3 showed good performance in removing CuII.
AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach
Authors: Sholihin Miftahus , Fudzee Mohd Farhan Md , Ismail Mohd Norasri
Published: December 2023

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.
A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology
Authors: Badr Aymen Mudheher , Fourati Lamia Chaari , Ayed Samiha
Published: December 2023

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings.
Bem-estar psicológico dos jovens universitários recém ingressados no mercado de trabalho do estado de São Paulo após a pandemia do Covid-19
Authors: Giusa Chiferi Mariana , Santana Feitosa Flávia
Published: December 2023

O presente trabalho busca entender como está o bem-estar psicológico dos jovens universitários que estão ingressando no mercado de trabalho do Estado de São Paulo depois da pandemia do COVID-19. O objetivo é identificar como as variáveis que afetam o bem-estar psicológico desses jovens universitários se desdobram no cotidiano, no contexto pós-pandemia. Para isto foi aplicada uma pesquisa quantitativa utilizando uma survey baseada na escala de bem-estar psicológico (BEP) desenvolvida por Ryff (2007). Foram coletadas respostas de 78 indivíduos e os resultados são que jovens universitários recém-ingressados no mercado de trabalho em São Paulo apresentaram maior bem-estar psicológico na capital em comparação ao interior. Foi observado uma maior BEP entre os jovens do gênero feminino e com trabalhos híbridos.
As ações do PET-Letras da UFSC no retorno presencial
Authors: Butturi Junior Atilio , Cassariego Perusso Angelo Gabriel , Dos Santos Raymundo Manoela Beatriz , Karolaine de Lavega Daniely , Giovanna Lima Pereira Ingryd , Da Silva Quarezemin Sofia , Klug Débora , Machado Serafim Laiara , Bayerl Bonatto Izabel , A. Muller Conselvan Taynara , Leonardo Salas Garcés Andrés , Caroline Fanton Boassi Hanna
Published: December 2023

Este texto parte de uma política do cuidado e tem por objetivo fazer um relato das atividades do PET-Letras da Universidade Federal de Santa Catarina (PET-Letras UFSC) desde o retorno presencial, ou seja, desde março de 2022 e 2023. Inicialmente, descrevemos sucintamente os objetivos do Programa de Educação Tutorial do Brasil, baseados numa política linguística do cuidado, para, então, passarmos a uma breve narrativa da constituição do PET-Letras UFSC. A partir daí, passamos a elencar as atividades dos seis projetos que compõe o PET: Idiomas, Eventos, Escrita, Grupos, Mídias, Gestão e Acessibilidade. Fazemos notar que os projetos estão apoiados no tripé pesquisa, ensino e extensão e têm forte diálogo com a comunidade – acadêmica ou não. Destacamos algumas ações fundamentais, como os cursos de idiomas (entre eles, um curso de Português para Refugiados), o Slam Estrela D’Alva, a revista Preguiça e a série de grupos de estudos propostos. Ademais, damos a ver o papel da acessibilidade, notadamente em Libras, de nosso PET, e o papel central que, no grupo, as redes sociais ganharam, tanto como ferramenta de divulgação quanto de visibilização de pautas socialmente prementes e se apresentam como espaço fundamental, hoje, de contato da Universidade com a sociedade. Por fim, concluímos o texto apontando para o compromisso ético-político que nos move e para a importância de se construir uma Universidade que garanta a permanência e a autonomia a seus e a suas discentes.
Experimental study of homogeneous charge compression ignition in a light-duty diesel engine operated with commercial gasoline-ethanol
Authors: Guzmán Juan José Milón , Braga Sergio Leal
Published: December 2023

An experimental bench was designed and developed to carry out experimental tests of a Diesel cycle engine using gasoline as fuel. The engine (originally a diesel cycle) was fitted with different devices to run in HCCI mode. These modifications did not affect the engine's original condition, which can be returned to diesel mode at any time. Instrumentation was set up to measurement the temperature (exhaust gases, inlet charge inlet, lubricating oil), pressure (intake air, gasoline inlet, combustion chamber), torque, rpm, air flow and fuel flow. The engine was subjected to different operating conditions and control parameters to study and analyze the effects of engine speed, air-fuel ratio and mixture temperature (air-gasoline). The results indicate that higher temperatures or amounts of fuel cause an earlier ignition phase, which would have a direct influence on the beginning of combustion. The results also indicate that, by increasing the amount of fuel and varying the inlet temperature, more useful energy is generated until reaching the limit of the detonation zone. Small gains in combustion efficiency translate into significant energy savings, also reducing exhaust gas pollution levels.
RETRACTED: Significant improvement in machining process performance and elimination of harmful environmental effects using the advanced and innovative cooling method
Authors: Tazehkandi Ahmadreza Hosseini , Shabgard Mohammadreza , Tutunchi Abolfazl
Published: December 2023

The authors have requested that this preprint be removed from Research Square.
The effect of intrahepatic cholestasis in pregnancy combined with different stages of hepatitis B virus infection on pregnancy outcomes: a retrospective study
Authors: Gao Qian , Li Xuejiao , Wang Li , Tan Xiaozhi , Li Zhe , Xu Chengfang
Published: December 2023

Background and Aims: To investigate the impact of intrahepatic cholestasis of pregnancy (ICP) with hepatitis B virus (HBV) infection on pregnancy outcomes.
Methods: We selected 512 pregnant women, collected the data including maternal demographics, main adverse pregnancy outcomes and maternal HBV infected markers HBeAg and HBV-DNA loads status, then have a comparative analysis.
Results: There were 319 cases of isolated ICP, 113 cases of chronic hepatitis B (CHB) combined with ICP, 80 cases of asymptomatic HBV carriers combined with ICP. All HBV-infected pregnant women with ICP were divided into hepatitis B e antigen (HBeAg)-positive group (102 cases) and HBeAg-negative group (91 cases), according to the level of the serum HBeAg status; and into high viral load group (92 cases), moderate viral load group (46 cases) and low viral load group (55 cases) according to the maternal HBV-DNA level. CHB+ICP group had a higher level of serum total bile acids, transaminase, bilirubin as well as a higher percentage of premature delivery, neonatal transfer and meconium-stained amniotic fluid (MSAF) compared with the other two groups(P<0.05), but there were no significant differences in the the above indicators between the group of isolated ICP and asymptomatic HBV carriers with ICP.Among the HBV-infected patients with ICP, HBeAg-positive group had a higher level of serum transaminase, bilirubin and bile acid as well as shorter gestational weeks of delivery, lower birth weight of newborns and a higher rate of neonatal intensive care unit (NICU) admission than HBeAg-negative group (P<0.05); Those with a high viral load (HBV-DNA>106IU/ml) had a higher level of transaminase, bilirubin, and bile acid as well as shorter gestational weeks of delivery, lower birth weight of newborns and a higher rate of NICU admission compared with those with a low or moderate viral load (P<0.05).
Conclusion: ICP combined with CHB patients have more severe liver damage, a higher percentage of preterm birth and NICU admission. HBeAg-positive status and a high HBV-DNA load will increase the severity of conditions in HBV-infected pregnant women with ICP. Patients who have CHB combined with ICP, HBeAg-positive or a high viral load should be treated more actively.
Dynamic modeling of three-dimensional muscle wrapping based on absolute nodal coordinate formulation
Authors: Tang Yimin , Guo Jianqiao , Tian Qiang , Hu Haiyan
Published: December 2023

Muscle wrapping influences its moment arm and alters the dynamic response of the musculoskeletal system. Conventional musculoskeletal multibody models often simplify muscle wrapping as the shortest path problem, while these models cannot consider the three-dimensional muscular geometry. In comparison, the finite element description of skeletal muscles using three-dimensional solid element discretization leads to expensive computational costs. This paper established a novel flexible multibody modeling method of muscle wrapping based on absolute nodal coordinate formulation (ANCF), which can simultaneously deal with its fusiform geometry, distributed inertia, and active contractions. The skeletal muscle is discretized by full-parameterized ANCF beam elements with variable cross section. The three-dimensional shape of the muscle belly can be expressed by an explicit function, and its time-dependent change can be captured via the surface-to-surface contact detection algorithm. The muscle stress contains the effect of Hill-type active-passive contractions, muscular fascia hyperelasticity, and volume constancy based on continuum mechanics description. As a validation, a forward dynamics simulation of the sagittal elbow rotation driven by surface electromyography (sEMG) data was performed and compared with literature data and experimental results. The established numerical method provides an efficient analysis tool to describe muscle-bone force transmissions, presenting an approach for understanding the hidden biomechanics during human locomotion and human-machine interactions.
Unlock Tigrigna NLP - Design and Development of Morphological Analyzer for Tigrigna Verbs Using Hybrid Approach
Authors: Gebremeskel Hagos Gebremedhin , Chong Feng , Heyan Huang
Published: December 2023

Morphological analyzer is the basis for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for the analysis of Tigrigna verbs morphological analyzer using the hybrid of memory learning and rule based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross-validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.
Neural networks for the approximation of Euler's elastica
Authors: Celledoni Elena , Çokaj Ergys , Leone Andrea , Leyendecker Sigrid , Murari Davide , Owren Brynjulf , de Almagro Rodrigo T. Sato Martín , Stavole Martina
Published: December 2023

Euler's elastica is a classical model of flexible slender structures, relevant in many industrial applications. Static equilibrium equations can be derived via a variational principle. The accurate approximation of solutions of this problem can be challenging due to nonlinearity and constraints. We here present two neural network based approaches for the simulation of this Euler's elastica. Starting from a data set of solutions of the discretised static equilibria, we train the neural networks to produce solutions for unseen boundary conditions. We present a \textit{discrete} approach learning discrete solutions from the discrete data. We then consider a \textit{continuous} approach using the same training data set, but learning continuous solutions to the problem. We present numerical evidence that the proposed neural networks can effectively approximate configurations of the planar Euler's elastica for a range of different boundary conditions.
Fragment Molecular Orbital-based Variational Quantum Eigensolver for Quantum Chemistry in the Age of Quantum Computing
Authors: Lim Hocheol , Kang Doo Hyung , Kim Jeonghoon , Pellow-Jarman Aidan , McFarthing Shane , Pellow-Jarman Rowan , Jeon Hyeon-Nae , Oh Byungdu , Rhee June-Koo Kevin , No Kyoung Tai
Published: December 2023

Despite the potential of quantum computers in analyzing complex systems, their applications for larger systems are hindered by the limited qubit availability. Addressing this, our study presents the novel FMO/VQE algorithm that combines fragment molecular orbitals and variational quantum eigensolver methods, strategically designed for optimal qubit utilization in fragment-based quantum chemistry simulations. The FMO/VQE/UCCSD achieved an absolute error of just 0.053 mHa with 8 qubits in a H24 system using the STO-3G basis set, whereas traditional VQE methods require 48 qubits. In a H20 system with the 6-31G basis set, the FMO/VQE/UCCSD showed an error of 1.376 mHa with 16 qubits, as opposed to the 80 qubits required for the VQE/UCCSD. Consequently, the FMO/VQE not only outperforms in accuracy and scalability compared to conventional VQE, but also would facilitate quantum chemistry simulations of increasingly complex systems, as the field of quantum computing progresses and the availability of qubits expands.
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese
Authors: Tran Vinh Khiem , Phan Phu Hao , Nguyen Van Kiet , Nguyen Luu Thuy Ngan
Published: December 2023

In recent years, Visual Question Answering (VQA) has gained significant attention for its diverse applications, including intelligent car assistance, aiding visually impaired individuals, and document image information retrieval using natural language queries. VQA requires effective integration of information from questions and images to generate accurate answers. Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English.To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. The dataset comprises over 26,000 images and 30,000 question-answer pairs (QAs), each question annotated to specify the type of reasoning involved. Leveraging this dataset, we conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations.Furthermore, we present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions. The architecture effectively employs transformers to enable simultaneous reasoning over textual and visual data, merging both modalities at an early model stage. The experimental findings demonstrate that our proposed model achieves state-of-the-art performance across seven evaluation metrics.
Assessment of seasonal variation of heavy metal pollution and health risk in surface sediments at the Karnaphuli River confluences of Chattogram, Bangladesh
Authors: Islam Md. Ariful , Karim Md. Rezaul , Abedin Md. Jainal , Uddin Md. Ripaj , Hos Syed Md. Minhaz , Karmakar Shuvo , Bhuiyan Md Nurul Huda , Moniruzzaman Mohammad
Published: December 2023

The sessional variation of heavy metals pollution and potential health risks in sediments to local people were studied using a flame atomic absorption spectroscopy (FAAS). Ten (10) different khals that serve as the natural drainage of Chittagong City were chosen for collecting the surface sediments samples from the confluence of the Karnaphuli River. The mean concentration of Cu, Cr, Cd, Pb, Ni, Mn, As and Zn in sediments samples were 38.83, 46.92, 2.18, 42.98, 43.39, 63.04, 1.34 and 91.93 mg kg− 1 respectively for the wet session, while the respective values in dry season were found 51.12, 59.23, 2.52, 61.04, 49.66, 58.54, 2.21 and 104.44 mg kg− 1. The enrichment factor (EF), contamination factor (CF), modified contamination degree (mCd), degree of contamination (Cd), geoaccumulation index (Igeo), potential ecological index (PERI) and nemerow pollution index (PN) were calculated to estimate the pollution level of the study areas. Potential carcinogenic and non-carcinogenic health risks through dermal contact were also calculated using respective mean values of heavy metals. Statistical analyses were applied in order to findout the establish the association and source of heavy metals between pollutants in river sediments. The results showed that concentration of heavy metals was higher in the dry season than in the wet season at most of the locations. The obtained comprehensive data for toxic heavy metals in the surface sediment of the Karnafuly River along the Chittagong City Corporation may help the policy maker to take effective legislation for monitoring and protecting the ecological risks of the Karnafuli River.
Exploring chalcone-sulfonyl piperazine hybrids as anti-diabetes candidates: Design, synthesis, biological evaluation, and molecular docking study
Authors: Nasab Narges Hosseini , Raza Hussain , Eom Young Seok , Shah Fahad Hassan , Kim Song Ja
Published: December 2023

To combat the rising rates of diabetes mellitus over the world, novel compounds are required. The demand for more affordable and efficient methods of managing diabetes is increasing due to the unavoidable side effects associated with the existing antidiabetic medications. In order to develop inhibitors against alpha-glucosidase and alpha-amylase, various chalcone-sulfonyl piperazine hybrid compounds (5a–k) were designed and synthesized in this present research. In addition, several spectroscopic methods, including FT-IR, 1H-NMR, 13C-NMR, and HRMS, were used to confirm the exact structures of the synthesized derivatives. All synthetic compounds were evaluated for their ability to inhibit alpha-glucosidase and alpha-amylase in vitro using acarbose as the reference standard and they showed excellent to good inhibitory potentials. Compound 5k exhibited excellent inhibitory activity against alpha-glucosidase (IC50 = 0.31 ± 0.01 µM) and alpha-amylase (IC50 = 4.51 ± 1.15 µM), which is 27-fold more active against alpha-glucosidase and 7-fold more active against alpha-amylase compared to acarbose, which had IC50 values of 8.62 ± 1.66 µM for alpha-glucosidase and 30.97 ± 2.91 µM for alpha-amylase. It was discovered from the Lineweaver-Burk plot that 5k exhibited competitive inhibition against alpha-glucosidase. Furthermore, cytotoxicity screening assay results against human fibroblast HT1080 cells showed that all compounds had a good level of safety profile. To explore the binding interactions of the most active compound (5k) with the active site of enzymes, molecular docking research was also conducted, and the results obtained supported the experimental data.
Was the 2022 drought in the Yangtze River Basin, China more severe than other typical drought events by considering the natural characteristics and the actual impacts?
Authors: Yang Siquan , Sun Hongquan , Zhao Ruxin , Xing Lisong , Tan Zhuoyan , Ning Yuan , Li Ming
Published: December 2023

The Yangtze River Basin (YRB), China, experienced record-breaking multiple season droughts in 2022, but also other severe drought events in recent history. This study examined the spatiotemporal characteristics of the 2022 drought in the YRB and compared this event with other extreme drought events in 1951 to 2022 from multiple perspectives, including spatial distribution, temporal evolution, return period, and drought losses. Six other extreme drought events were selected by the severity of water deficiency. The results showed that a “whole-basin” drought, which covered nearly the entire region, was evident in the summer and autumn of 2022 compared with other drought years. The return period was more than 1000 years (considering both temperature and precipitation), also severer than the six other drought years. Although the 2022 drought was much more extreme than other drought years from a natural perspective, the actual crop impacted area ratio was less than those in other drought years. This indicates the importance of drought relief measures. As for the drought attribution in the YRB, the El Niño/Southern Oscillation (ENSO) played a key role in explaining its occurrence, significant at different lag times. These results may help policymakers to comprehensively understand the typical extreme droughts in the YRB and rationally allocate funds for drought relief.
Explaining the information needs related to couples' childbearing in the context of interaction with modern media: a qualitative content analysis study
Authors: Ghorbani Faeze , Khadivzadeh Talat , Karimi Fatema Zahra , deldar kolsoum
Published: December 2023

Introduction:Family is the most basic social institution and childbearing is considered as the basic pillar of its unit. The important role of the media in filling the information gap regarding childbearing is very important in improving the health of the family. Therefore, this study was conducted with aim to explain the informational needs of couples' childbearing in the context of interaction with new media.
Methods:This research was conducted with a qualitative method and a conventional content analysis approach from July to December 2022. Interviews were conducted with 13 couples in different cities of the country (Tehran, Ahvaz, Mazandaran, Tabriz, Kerman) due to the diversity of childbearing culture and with experts in the field of childbearing and policy making. Data were analyzed by MAXQDA 20 software.
Results:Three main categories extracted from qualitative data included the educational content of fertility and infertility (marriage, pregnancy and childbirth, abortion and children), need to meet the multifaceted needs of parents (counseling, welfare information, multifaceted support, life skills, parents' worries and solutions), and forming a social network of empowered mothers (sharing experiences, sharing daily routines, empowering mothers by presenting the model of empowered mothers).
Conclusion:Couples and experts recognized the informational needs of childbearing, the existence of scientific information about childbearing, the need to pay attention to parents and support them in various fields, and sharing experiences with other mothers in the context of new media (Internet). Accordingly, it is necessary to design and produce content in new media to promote childbearing.
Perception of students about Continuous Assessment Methods and its relation to Students’ Achievement and Motivation
Authors: Abdi Hussein
Published: December 2023

Background Assessment is one of the most important aspects of university education since it has such a big influence on students' learning processes and results. Continuous assessment (CA) is a technique for improving the quality of students' learning throughout time. It offers a lot of benefits and a few drawbacks. The purpose of this research is to examine students' perceptions of CA and correlate it with their academic results and intrinsic motivation. Methods The study employed a mixed study design, using both qualitative and quantitative data. Quantitative data has been collected using an online-based questionnaire using the Google form platform and the students' grades, while qualitative data was collected through focus group discussions. Factor analysis using varimax rotation was used to analyse the perception of students about CA and presented using descriptive statistics alongside correlation tests to correlate students’ perception with achievement and intrinsic motivation while the qualitative data was thematically analyzed. Results The factor analysis of the students' perception of CA revealed three factors: the usefulness of CA, beneficial effects of CA, and adverse effects of CA. CA's perceived usefulness was high (Mean: 4; SD:.53) on a 5-point Likert scale, and almost all students saw CA as a motivator to study on a regular basis. The perceived beneficial effects were very high (Mean: 4.33; SD:.59), whereas the perceived adverse unwanted effects were very low (Mean: 1.67; SD:.55). The students’ achievement (grade) was positively correlated with the usefulness and beneficial effects of CA, while it was negatively correlated with the adverse effects of CA, which was statistically significant. Qualitative data also revealed three major themes, namely "advantages of CA", "disadvantages of CA", and "intrinsic vs. extrinsic motivation". The study also found that CA has improved extrinsic motivation rather than intrinsic motivation. Conclusion Even though the present study found that CA is useful for students' regular study and improved students' achievement both in CA and the final grades, on the other hand, the study also revealed that CA had increased students' extrinsic motivation rather than the intrinsic one. Therefore, for better application and use of CA, the structure of the course and student characteristics should be taken into account. Discussing with students about the goal and advantages of continuous assessment may be the key to mitigating the potential adverse effects of continuous graded assessments while also raising students' engagement in non-graded regular assessment
Resonant ultrasonic testing can quantitatively assess the microscopic porosity of complex-shaped additively manufactured AlSi10Mg components.
Authors: Skiadopoulos Michail , Prato Dominic J. , Bozek Evan P. , Dickman Corey J. , Reutzel Edward W. , Corbin David J. , Shokouhi Parisa
Published: December 2023

The utility of resonant ultrasonic testing for quality control of complex-shaped additively manufactured (AM) components in terms of porosity variations is investigated. A fully non-contact test setup is used to investigate differences in the volumetric porosity between AM AlSi10Mg samples. A set of 96 samples with programmatically induced pores varying in nominal total porosity between 0% and 2% is tested: one half of the samples are prismatic, and the other half have a complex internal Triply Periodic Minimal Surface (TPMS) structure. In addition, a subset of the samples is scanned using X-ray micro-computed tomography (µ-CT). It is found that the resonance frequency corresponding to the 1st compressional mode can predict the total nominal porosity even in TPMS samples. From statistical analysis, the smallest detectable porosity difference is found to be 0.25% for the prismatic samples and 0.5% for the TPMS samples. The experimental results agree well with the predictions of finite element (FE) simulations and analytical models. However, X-ray µ-CT appears to underestimate the porosity, possibly due to its inability to resolve the small pores. Our findings suggest that resonant ultrasonic testing can quantitatively assess the total porosity of AM parts having complex geometries.
Bayesian versus Frequentist approaches in Psychometrics: A Bibliometric Analysis
Authors: Zagaria Andrea , Lombardi Luigi
Published: December 2023

The increasing popularity of the Bayesian approach in Psychology has prompted metascientific efforts to quantify its prevalence. However, despite enduring debates between proponents of Frequentist and Bayesian approaches, no systematic comparison of their prominence has been conducted in existing literature. This brief report fills this gap, examining Bayesian and Frequentist Trends in the period from 1964 to 2021 through a meticulous search in PsycINFO. The findings reveal that within Psychometrics and Statistical Psychology, the Frequentist approach has consistently been more popular than the Bayesian approach. However, a plateau or decline in frequentist contributions has emerged in recent years (around 2010), with Bayesian contributions surpassing them after 2014 in some portions and after 2019 in all investigated sources. Although this observation applies primarily to specialized literature rather than the entire psychology domain, it underscores the growing prevalence of the Bayesian approach, signaling a shift among specialists in the field.
GÜMÜŞ EKONOMİ ÜZERİNE SWOT ANALİZİ
Authors: ASLAN Özgür , GÜLTEKİN Tule , IRMAK Hatice Selin
Published: December 2023

Gümüş ekonomi ile yaşlı bireylerin yaşam kalitesinin arttırılması için, ihtiyaçlarını karşılayacak nitelikte yenilikçi politikaların, ürünlerin ve hizmetlerin geliştirilmesi ve aktif yaşlanmanın sağlanması amaçlanmaktadır. Bu çalışma kapsamında doküman inceleme ve SWOT analizi tekniği kullanılarak gümüş ekonominin, içsel (güçlü ve zayıf yönler) ve dışsal (fırsatlar ve tehditler) faktörleri belirlenmeye çalışılmıştır. SWOT Analizi ile gümüş ekonominin, ekonomideki payının belirlenmesi için olası fırsat ve tehditleri ile üstün ve zayıf yönleri belirlenmiştir. Bu bağlamda gümüş ekonominin potansiyelinin farkına varılmasının sağlanması için yaşlı dostu iş ortamlarının oluşturulması, çalışma yaşamında yaş ayrımcılığının önlenmesi ve yaşlı istihdam kotası gibi noktalarda düzenlemeler yapılmalıdır. Ayrıca bunların yanında yaşlılara yönelik istihdam düzeyinin artırılmasına ve özel politikalara ihtiyaç duyan grupların işgücü piyasasına kazandırılmasına yönelik yapılan faaliyetler desteklenmelidir.