bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote Detection of Human Brain Reaction to Speech by AI Speckle Pattern Analysis
Authors: Segal Natalya , Zalevsky Zeev , Beiderman Yafim , Beiderman Yevgeny , Kalyuzhner Zeev , Agdarov Sergey
Published: September 2024

A fully remote, portable, contactless, affordable alternative to fMRI, EEG, and fNIRS for brain cortex analysis can accelerate innovation in understanding brain function across various fields. We leverage laser speckle pattern tracking technology, which has proven valuable in engineering and bioengineering, and empower it with AI to implement remote brain monitoring. This study investigated brain cortex responses to clear versus incomprehensible speech by projecting a laser beam over Wernicke’s area and analyzing the reflected speckle patterns with a convLSTM-based DNN classifier. The classifier could distinguish brain reactions in unseen subjects with a mean area under the ROC curve (AUC) of 0.94 when classifying at least 1 second of input speech. The ability to remotely distinguish brain reactions has practical applications in dynamic settings such as sports and real-life activities and for individuals with sensory sensitivities to scalp contact, helmets, or claustrophobic environments.
Thermoelectric Response of Graphite/Na 1.4 Co 2 O 4 Thermocouple on Paper
Authors: Badampudi Chandrababu , Anadkat Devang , Dungani Shreya , Pandya Anil , Sanchela Anup V.
Published: September 2024

There is a demand for high-performance, environmentally friendly, mechanically robust, and economically viable thermoelectric generators (TEGs), with potential applications in electronic and energy conversion units as well as practical preparation techniques. We demonstrate the solid-state based synthesis and thermoelectric behavior of a Graphite/Na1.4Co2O4 flexible thermocouple device that was printed on ordinary paper which acts as substrate. Four pair of TE legs fabricated with alternate graphite and Na1.4Co2O4 traces, yielding of electrical conductivity, Seebeck coefficient and power factor for graphite traces 3333 Ω-1m-1, 26.78 µVK-1 & 2.39 µWm-¹K-2 and Na1.4Co2O4 traces 331 Ω-1m-1, 67.97 µVK-1 & 1.53 µWm-¹K-2, respectively are noteworthy. Our thermoelectric generator is cost effective and ecofriendly which provides good output performance. The thermocouple device's exhibits output voltage of 31.0 mV, this work provides insight into the potential for flexible thermoelectric heading beyond.
Evaluation of Friction in Deep Drawing of DP600 Steel under Oil-Based and Water-Based Lubrication Conditions
Authors: Kalkan Hakan , Somay Mehmet
Published: September 2024

This study conducts a comprehensive investigation into the deep drawing process, with a focus on the crucial role of lubricants in influencing its performance. It evaluates the effects of five different lubricants, each with a unique formulation, on key aspects of deep drawing operations. Deep drawing, as an essential metal forming technique, involves converting flat sheet metal into complex three-dimensional shapes, where the choice of lubricant significantly impacts friction reduction, surface quality, and formability. Through a systematic series of experimental trials, the research aims to quantify and compare the effectiveness of these lubricants, supported by Finite Element (FE) analysis, providing valuable insights for industries involved in metal forming processes. The expected results will be useful to assist engineers and manufacturers in optimizing lubricant selection, thereby bridging the gap between theoretical knowledge and practical applications. This research contributes to a broader understanding of lubricant behavior in metal forming, potentially leading to advancements and further exploration in the field.
Multiscale fracture characterization method for transitional shale reservoir based on post-stack multi-attribute ant-tracking fusion and pre-stack wide-azimuth gathers AVAZ analysis
Authors: Peng Hongjie , Zhao Jingtao , Qiu Zhen , Zhang Qin , Liu Wen , Kong Weiliang , Gao Wanli , Sheng Tongjie , Li Wenyu , Yang Yugang
Published: September 2024

Transitional shale gas is an important area for oil and gas exploration. It has a wide distribution area and large resource potential, accounting for about 25% of the total shale gas resources in China. The method of reservoir fracture prediction by seismic attributes has been relatively established. However, seismic attributes are often limited by factors such as seismic data frequency and resolution. Characteristics of transitional shale reservoirs, such as complex combinations of lithologies, frequent lithological changes, low resolution and frequent frequency changes of seismic data and the existence of strong reflection shielding in coal seams, greatly affect the prediction of fracture. A single seismic attribute technique is difficult to provide a comprehensive and accurate prediction of fractures in transitional reservoirs. In this study, the discontinuity information of large fractures, fine fractures, and fractures shielded by strong reflections from coal seams are extracted by variance attributes, amplitude contrast attributes and amplitude of diffracted waves, respectively. The discontinuity features extracted by the three methods are tracked using ant-tracking technique and fused. Fractures in the work area are systematically characterized. The pre-stack wide-azimuth gathers is analyzed by the near-offset Rüger formula method. Anisotropic gradients and anisotropic directions are obtained. The prediction results of the fracture are interpreted and verified with assistance. A combined pre-stack and post-stack seismic multi-scale fracture prediction methodology is developed. The strong reflection shielding effect of the coal seam is effectively removed. The characterization of fractures in transitional shale reservoirs is improved.
A Data-driven Optimal Control Approach for A Class of Nonlinear SEIRS Epidemic Model
Authors: Chen Yadong , Cai Wenli
Published: September 2024

In this paper, we introduce the data-driven optimal control problem combined with a class of nonlinear SEIRS epidemic model. By extending the classical SEIR model framework, we formulate a nonlinear SEIRS epidemic model by considering the immune loss rate of recovered population and a nonlinear incidence rate with saturation effect. In the SEIRS model, the number of population in each compartment is unknown and the parameters are time-varying. Combined with the ODE system derived from theSEIRS model, we leverage real-time data to define the loss function and obtain a data-drivenoptimal control problem. Employing the generalized Pontryagin's Maximum Principle,we state the necessary conditions for optimal solution to the data-driven optimal controlproblem. Futhermore, we meticulously devise an algorithmic framework, inclusive of detailed steps, to address this complex optimization task.We conduct numerical experiments using reported COVID-19 data, which enable us to estimateunknown population numbers and obtain time-varying parameters within our SEIRS model.The results of numerical experiments validate the effectiveness and rationality of our algorithm. Ultimately, by controlling the growth of the number of infected population and dead population over the subsequent 30 days, we obtain the temporal evolution trend of key parameters. Notably, our findings emphasize the strategic importance of reducing both the effective contact rate and the immune loss rate as effective means to control propagation of epidemics. This conclusion, rooted in our data-driven approach, offers a fresh perspective on epidemic control strategies.
DeepMiRBP: A Hybrid Model for Predicting MicroRNA-Protein Interactions Based on Transfer Learning and Cosine Similarity
Authors: Azizian Sasan , Cui Juan
Published: September 2024

Background Interactions between microRNAs and RNA-binding proteins are crucial for microRNA-mediated gene regulation and sorting. The molecular mechanisms underlying these protein interactions with small RNAs remain largely under-studied, apart from sequence motifs identified on microRNAs. To date, only a limited number of microRNA-binding proteins have been confirmed, typically through labor-intensive experimental procedures. Advanced bioinformatics tools are urgently needed to facilitate this research.
Methods DeepMiRBP is a novel hybrid deep learning model designed to predict microRNA-binding proteins by modeling molecular interactions. This innovation approach is the first to target the interactions between small RNAs and proteins specifically. DeepMiRBP consists of two main components. The first component employs bidirectional long short-term memory (Bi-LSTM) neural networks to capture sequential dependencies and context within RNA sequences, attention mechanisms to enhance the model’s focus on the most relevant features and transfer learning to apply knowledge gained from a large dataset of RNA-protein binding sites to the specific task of predicting microRNA-protein interactions. Cosine similarity is applied to assess RNA similarities. The second component utilizes Convolutional Neural Networks (CNNs) to process the spatial data inherent in protein structures based on Position-Specific Scoring Matrices (PSSM) and contact maps to generate detailed and accurate representations of potential microRNA-binding sites and assess protein similarities.
Results Our DeepMiRBP method reported a prediction accuracy of 87.4 via training and 85.4 using testing, with an F score of 0.860. Additionally, we have applied three case studies to further validate the method using recently reported evidence on miRNA interactions, including miR-451, -19b, -23a, -21, -223, and -let-7d. Using DeepMiRBP, we accurately predict known microRNA interactions with recently discovered RNA binding proteins identified in different exosomes, including AGO, YBX1, and FXR2.
Conclusions Our proposed DeepMiRBP strategy represents the first of its kind designed for microRNA-protein interaction prediction. Its promising performance underscores the model’s potential to identify novel interactions involved in small RNA sorting and packaging and infer new RNA transporter proteins. The methodologies and insights from DeepMiRBP offer a scalable template for future small RNA research, from mechanistic discovery to modeling disease-related cell-to-cell communication, emphasizing its adaptability and potential for developing novel small RNA-centric therapeutic interventions and personalized medicine.
Research on the mechanism of water-jet guided laser micro-hole machining of GH4169 superalloy
Authors: Yu Shuo , Zhao Guoyong , Zhao Yugang , Gao Yingqi , Xu Shuming , Liu Huayi
Published: September 2024

The air film holes in aero-engine turbine blades are closely arranged, with small hole diameters, and high requirements for surface quality and positional accuracy, which makes it difficult for traditional machining to meet its manufacturing accuracy requirements. To solve this problem, micro-hole machining simulation is carried out in the paper, using water-jet guided laser technology with GH4169 superalloy as the research object. First of all, the three-dimensional transient temperature field and material removal model of GH4169 superalloy was established based on the simulation software of Comsol, and the material removal mechanism of water-jet guided laser micro-hole machining was revealed. Then, comparative experiments between water-jet guided laser and traditional laser micro-hole machining were carried out with different laser powers. Finally, through the measurement of the morphology and roughness of the hole wall, it is possible to verify the feasibility of water-jet guided laser micro-hole machining and to obtain the optimal machining parameters. The experimental results show that the micro-hole surface roughness obtained by the processing is 1.362µm with a smaller heat-affected zone in the laser power of 200W, pulse frequency of 6000Hz, and water beam pressure of 2.0MPa, while the micro-hole surface roughness obtained by the traditional dry laser under the same processing parameters is 13.517µm. Therefore, water-jet guided laser processing can make a better balance of the uneven heat transfer phenomenon in the processing, and improve the processing efficiency with better surface morphology.
The Multifaceted Effects of Migraine on Syrian Society: Insights from a Multicenter Cross- Sectional Study
Authors: Ataya Jamal , Bannoud Nour , Daaboul Rawan , Hasan Yaman Haj , Hanifa Hamdah , Watfa Masa , Alnabi Nour Alhuda Abd , Salama Raghad , Mackieh Rustum
Published: September 2024

Background
Migraine is the most common primary headache. It's alone responsible for 1.3% of disability in the world. Migraine both worsen quality of life of individuals and place a significant burden on the society. Despite this, there exists a knowledge gap regarding its repercussions on the Syrian population.
Methods
This cross-sectional study enrolled 679 migraine-diagnosed patients from all over Syria through electronic questionnaires disseminated on official social media platforms. Depending on demographic characteristics, physical and mental debility assessed using the Chalder scale, the Migraine Specific Quality of Life Questionnaire (MSQ) and the Generalized Anxiety Disorder 2-item (GAD_2) scale.
Results
There are many demographic and biological factors impact migraine's incidence. Being female is a significant risk factor for migraines especially during puberty and menarche. Age also plays a role in migraine incidence, peaking between 30 and 39 years. Environmental factors include urbanization, healthcare accessibility, and air pollution contribute to the increasing prevalence of migraines among younger individuals. In addition to many systemic diseases such as depression, anxiety, irritable bowel syndrome, fibromyalgia, sleep disturbances, chronic fatigue, and cognitive impairments.
Conclusions
This study aimed to elucidate the multifaceted impact of migraines on daily life, considering diverse demographic variables to enhance diagnostic accuracy, treatment efficacy, and disease management strategies.
Tracing Localised Nutrient Pollution Events to Posidonia oceanica Epiphyte Community Assemblages, in the Eastern Aegean Sea
Authors: Naccarato Gian Luca , Cierjacks Arne , Malcolm-McKay Alice , García María Moran , Macrina Laura , Miliou Anastasia
Published: September 2024

The occurrence of localised eutrophication events in small coastal communities of the Mediterranean threatens seagrass ecosystem functionality, necessitating the reliable identification of the onset and magnitude of these processes. This study examined the suitability of epiphyte assemblages on Posidonia oceanica leaves as an indicator of anthropogenic nutrient enrichment, for the first time in the eastern Aegean Sea. Seagrass biometric and meadow parameters were studied at control and potentially disturbed sites (n=3), in addition to epiphyte community composition and structure at a depth of 5 m during winter 2022. Sites were classified using a modified version of the Land Use Simplified Index. Potentially disturbed meadows exhibited decreased leaf length and shoot density, and increased epiphyte cover and biomass. Epiphyte community composition shifted significantly, with clear differences observed in red coralline algae, filamentous brown algae, bryozoans, and annelids. Results highlighted that reduced water quality, which is discontinuous and therefore difficult to tackle with a single direct measurement in the water column, from point and diffuse sources including a sewage outlet, agricultural activity and tourism, strongly affected seagrass bed condition. Here, substantial epiphyte cover was observed, causing direct (shading) and indirect (intensified grazing; physical leaf instability) effects. The study revealed that epiphyte communities, in combination with the index, serve as accurate and cost-effective indicators of coastal water pollution. Regular monitoring could aid in detecting higher nutrient loading at an early stage and potentially prevent long-term meadow decline.
Fruit and Pulse Synergy: Evaluating Vigna mungo performance in Himalayan wild pomegranate based agroforestry systems
Authors: Bhatia Avinash Kumar , Pant K S , Prakash Prem , Kumar Praveen , Sharma Harish , Chauhan Saakshi , Bishnoi Prakash , Kumari Babita
Published: September 2024

The integration of fruit-based agroforestry models with pulse cultivation, supported by integrated nutrient management practices, presents a sustainable approach for developing nations aiming to enhance productivity while maintaining soil and human health. This study evaluated the impact of planting conditions and integrated nutrient management on the growth, yield, and soil attributes of Vigna mungo (mash) under a wild pomegranate-based agroforestry system. The experiment was designed using a randomized block design factorial approach with twelve treatments involving organic and inorganic manures (T1: RDF (Recommended dose of Fertilizer), T2: FYM (100% N equivalent basis), T3: Vermicompost (100% N equivalent basis), T4: Goat manure (100% N equivalent basis), T5: Jeevamrut @ 500 l/ha, T6: 50% RDF + 50% FYM, T7: 50% RDF + 50% VC, T8: 50% RDF + 50% GM, T9: 50% RDF + 50% Jeevamrut, T10: 50% RDF + 25% FYM + 25% VC, T11: 50% RDF + 25% GM + 25% Jeevamrut and T12: Control) and two planting conditions (under wild pomegranate canopy and open conditions), each replicated thrice. The results indicated that Vigna mungo exhibited better growth and yield parameters in open conditions compared to the wild pomegranate canopy. Among the nutrient treatments, T6 (50% RDF + 50% FYM) demonstrated superior growth and yield. Post-harvest soil analysis revealed improved physical and chemical properties under the agroforestry system with 100% FYM application (T2). Economically, the wild pomegranate + Vigna mungo system yielded the highest net returns (US$ 1094.09 ha− 1) and benefit-cost ratio (2.26) compared to sole cropping of Vigna mungo. This study accentuates the potential of integrating fruit trees with pulse crops under appropriate nutrient management to achieve sustainable agricultural practices, improved farmer incomes, and better soil and human health.
Weed Tolerance and Suppressive Ability of Potato Cultivars to Natural Weed Infestations
Authors: Siddiqui Ahmad Omid , Jabran Khawar
Published: September 2024

The concerns such as herbicide-resistant weeds and environmental pollution have increased interest in using weed competitive crop cultivars for weed management. Potato plants are highly vulnerable to weed competition, especially during early and late growing season. In this context, a two-year field experiment was performed to assess weed tolerance and suppressive ability of 15 genetically diverse potato cultivars to natural weed. The results showed that weed interference severely reduced potato plant growth and yield. For instance, weed competition decreased potato plant height by 10–37%, plant fresh weight by 21–44%, plant dry weight by 15–40% and yield by 15–48%. Potato cultivars in the study differed in their ability to suppress and tolerate weeds, with weed dry weight ranging from 146.7 g m− 2 in Lady Olympia to 428.3 g m− 2 in Arizona and tuber yield from 13.84 t ha− 1 in Electra to 25.15 t ha− 1 in Agria under weedy conditions. Key traits of weed-suppressive cultivars included early emergence, rapid canopy development, and greater plant biomass. Cultivars with greater plant height and higher yields were generally more weed tolerant. Among the evaluated cultivars, Lady Olympia demonstrated the highest weed suppression, reducing weed dry weight by 60% or more, making it suitable for organic farming systems. Agria exhibited superior weed tolerance with minimal yield loss, making it suitable for conventional high-yield farming systems.
Robust Dynamic Event-Triggered Sliding ModeControl of Lateral Dynamics of Intelligent ElectricVehicle with Designable Triggering Characteristics
Authors: Liu Zhi , Chu Xing , Mao Suqin , Ding Pengfei , Yao Shaowen , Lei Jilin , Cheng Shouhu
Published: September 2024

This paper proposes a dynamic event triggering mechanism (ETM) whose triggering characteristics can be designed for a novel sliding mode controller to solve the lateral motion control problem of intelligent electric vehicle (IEV) in the presence of bounded matched disturbances. The motivation of current work mainly aims to alleviate unnecessary computing and communication of energy-constrained IEV control systems by a large margin while ensuring its stability and robustness. The main contributions of the paper are summarized as follows. We design a continuous-time robust sliding mode controller (RSMC) which is proved to have the capability to effectively suppress the bounded matched disturbances. We derive a dynamic event triggering rule, under which the positive minimum value and the evolution characteristics of the inter-event times are able to be designed. We analyze the stability via the Lyapunov theory, exclude Zeno behavior and prove that the semi-global robust event-separation property (RESP) holds for the above constructed event-triggered control system theoretically. We numerically evaluate the effectiveness of our theoretical results and the advantages of the proposed methods which is highlighted in an overtaking case for the IEV. To the best of our knowledge, this is the first paper which studies lateral motion control of IEV with the consideration of the robustness not only for the stability but also for the positive minimum inter-event times.
Advanced Influential Node Identification in Social Networks: A Hybrid Copula and Quantum-Based Deep Learning Model
Authors: P Vimal Kumar , C Balasubramanian
Published: September 2024

Social networks are one the foremost origins of information transmission at present. Nevertheless, not all nodes in social networks are indistinguishable. As a matter of fact, certain nodes are said to be more influential than others, or to be more specific, their information gravitates to proliferate more. Identifying the most influential nodes in a social network called as the Influence Maximization problem remains one of the hot issue with the evolution of Internet and social media. Several methods have been proposed to identify influential nodes in composite networks, ranging from parallel algorithm to distribution difference and non-overlapping communities. However, most of the previous methods do not consider error involved in overlapping communities in social network. To solve the problem, we propose a method called, Copula Tubular Quantum Based Spearman Deep Neural for influential node tracing in social network (CTQ-SDNINT) is proposed. Besides, three algorithms are used in our work to find the influential node in social network with maximum influence spread, minimal time, and minimal error rate namely Copula Probability Node Pre-processing algorithm and Tubular Neighborhood Intensity-based Feature extraction algorithm Network Curb Quantum-based Spearman Deep Neural Influential Node Tracing algorithm respectively. Extensive experiments on The Telecom dataset demonstrate the efficiency and influence performance of the proposed algorithms on Performance metrics such as sensitivity, specificity, response time, accuracy, and influence spread are used to compare the proposed methods against existing ones. Results indicate that our approach significantly improves the accuracy and efficiency of influential node tracing in social networks.
Machine Learning Enhanced Autonomous ADCS for an IoT CubeSat
Authors: S Amaresh , S Anaswara B , Albert Aleeta , S Abhirami S , Daniel Sanil K
Published: September 2024

This paper discusses the design and development ofa hardware prototype of an Autonomous Attitude Determination and Control Subsystem (ADCS) for an Internet of Space Things (IoST) CubeSat with Machine Learning (ML) integrated into the system. It is a novel design to the existing ADCS by integrating ML into it and making the whole system autonomous thereby improving reliability and efficiency. The prototype developed is based on the design and structure of a one-unit (1U) CubeSat which are widely used in the IoST domain. A CubeSat encompasses several subsystems, out of them ADCS holds significant importance. The ADCS must be designed within stringent mass, size and energy constraints. Additionally, CubeSat typically has limited sensing, computing, and communication capabilities, highlighting the necessity for autonomous and computationally efficient algorithms. In such constrained environments, developing an effective attitude control system presents a significant challenge. The proposed design discussed in the paper upgrades the existing ADCS to a better efficient and reliable one with optimum energy consumption and less failure rate with the integration of ML technologies and enabling it to be autonomous. The selection of hardware and algorithms takes into account system requirements, limitations, past experience, and both theoretical and practical considerations. The paper thoroughly explores the design methodologies, hardware components, and algorithms utilized in the experimental hardware prototype.
Fine-tuning multilingual language models in Twitter/X sentiment analysis: a study on Eastern-European V4 languages
Authors: Filip Tomas , Pavlicek Martin , Sosik Petr
Published: September 2024

The aspect-based sentiment analysis (ABSA) is a standard NLP task with numerous approaches and benchmarks, where large language models (LLM) represent the current state-of-the-art. We focus on ABSA subtasks based on Twitter/X data in underrepresented languages. On such narrow tasks, small tuned language models can often outperform universal large ones, providing available and cheap solutions.

We fine-tune several LLMs (BERT, BERTweet, Llama2, Llama3, Mistral) for classification of sentiment towards Russia and Ukraine in the context of the ongoing military conflict. The training/testing dataset was obtained from the academic API from Twitter/X during 2023, narrowed to the languages of the V4 countries (Czech Republic, Slovakia, Poland, Hungary). Then we measure their performance under a variety of settings including translations, sentiment targets, in-context learning and more, using GPT4 as a reference model. We document several interesting phenomena demonstrating, among others, that some models are much better fine-tunable on multilingual Twitter tasks than others, and that they can reach the SOTA level with a very small training set. Finally we identify combinations of settings providing the best results.
Assessing landslide velocity scales with acoustic emission active waveguides for early warning system
Authors: Kumar Deepak , Mahapatro Ajit K. , Singh Sushil Kumar
Published: September 2024

This study proposes a strategy to predict the different landslide velocity scales of susceptible slopes by analysing the acoustic emission (AE) behaviour of the active waveguide system (AWS). Laboratory compression tests were conducted on models of AWS utilizing a universal testing machine to induce strain-induced interactions within the backfill material, resulting in the generation of AE signals. AE characteristics of AWS has been analysed at deformation rates ranging from slow (0.003 mm/min) to rapid (30.0 mm/min) rates of Varnes’ landslide velocity scales. Two intermediate scales (0.03 and 3.0 mm/min) have been introduced between slow, moderate, and rapid rates of landslide velocity scales. AE characteristics, including signal duration, counts, acoustic signal level, amplitude, signal strength, and their derivatives were meticulously analysed for each velocity scale. A strong proportionality relationship was observed between cumulative AE counts and deformation rate of AWS. Quadratic correlation was established between AE signal strength and amplitude. AE activity of the AWS for different velocity scales were also analysed. Significant results observed and correlations were validated using another different set of AWS. Additionally, one test consisting all the velocity scales in sequential order was conducted on the AWS and results were reverified. This study can significantly contribute to developing real-time landslide early warning systems that issue alerts based on varying landslide velocities and slope instability stages, as reflected in the AE data of AWS.
GPU-Based Acceleration for Conflict Resolution in Prioritized DL-Lite Knowledge Bases
Authors: Tarek ABABSA , Adelmoutia TELLI
Published: September 2024

Computing of conflicting elements in prioritized \dllite{} knowledge bases when the assertions are provided by multiple and conflicting sources is an important task to repair these kinds of knowledge bases. For this purpose, several algorithms have been proposed in the literature for computing one minimal conflicts set in \dllite{} knowledge base. Even though, the proposed algorithms for repairing \dllite{} knowledge bases have proven their effectiveness, they are still lacking in efficiency. In this paper, we demonstrate how GPUs can accelerate the selection of a set assertional base conflicting (contradictory elements), leading to further reduction in runtime. This way of programming uses data parallelism to minimize execution time compared to sequential programming. Our experimental studies demonstrate the potential to achieve a speed-up of up to 12.68\(\times\) by employing modern GPUs for computing conflicting sets under inconsistency in lightweight knowledge bases.
RETRACTED: Assessment of Workplace Resilience in Reducing Burnout Among Postgraduates Across Multiple Medical Specialties
Authors: Karoli Ritu , Karoli Ritu , Karoli Ritu , Karoli Ritu , Karoli Ritu , Gupta Ashok Kumar , Dixit Sumeet
Published: September 2024

The authors have requested that this preprint be removed from Research Square.
RETRACTED: Assessment of Workplace Resilience in Reducing Burnout Among Postgraduates Across Multiple Medical Specialties
Authors: Srivast Prof )Shetanshu , Sehgal Garima , Kaur Apjit , Rai Priyanka , Karoli Ritu , Gupta Ashok Kumar , Dixit Sumeet
Published: September 2024

The authors have requested that this preprint be removed from Research Square.
T_SELEX program: Theoretical SELEX tool for Rational Design and Selection of RNA Aptamers Targeting Macromolecules
Authors: Mokgopa Kabelo Phuti , Lobb Kevin Alan , Tshiwawa Tendamdzimu
Published: September 2024

Selection of aptamers is crucial iterative process which is significant for many applications from biosensors to biomedical applications. The process of selecting aptamers is called systematic evolution of ligands by exponential enrichment (SELEX). This process is costly, time consuming and labour-intensive. In this communication we present T_SELEX API together with its algorithms and application. This program is an autonomous robust python package specifically engineered for designing aptamers and screening of RNA aptamers against macromolecular targets. The tool is evaluated from generation of sequence library of RNA aptamers, multiscale folding or secondary structure predictions, multiscale tertiary structure predictions, virtual screening and detailed analysis. The application was tested by screening a generated aptamer library against HIV-1 protease. The source code of the T_SELEX tool is available on GitHub [https://github.com/CMCDD/T_SELEX] and can be used directly following instructions on the readme file.
TOKOH AISYIYAH DI INDONESIA: KAJIAN ATAS ‎PEMIKIRAN DAN KONTRIBUSINYA PADA ‎PENDIDIKAN ISLAM DAN SOSIAL (TAHUN 1917 - 1960 M)‎
Authors: Dianthiny Suci , Arsyad Junaidi , Sapri Sapri
Published: September 2024

Penelitian ini bertujuan untuk menganalisis tokoh intelektual Aisyiyah di Indonesia tahun 1917-1960 M, dan menganalisis kontribusi tokoh-tokoh intelektual Aisyiyah tersebut, serta pengaruh pemikirannya di Indonesia. Penelitian ini merupakan penelitian kualitatif dengan mengutamakan studi kepustakaan terhadap sumber-sumber yang relevan, seperti karya-karya tulis mengenai jejak tokoh Aisyiyah di Indonesia dan kajian terhadap pemikirannya serta kontribusinya. Pendekatan dalam penelitian ini adalah pendekatan studi tokoh atau disebut juga studi biografi. Analisis data yang ada pada penelitian ini adalah teknik analisis isi (content analysis). Hasil penelitian ini menunjukkan bahwa Peran Muhammadiyah dan Aisyiyah dalam meningkatkan pendidikan kaum perempuan, khususnya warga Muhammadiyah amat kuat. Ada empat tokoh Aisyiyah di Indonesia yang mengiringi perkembangan pendidikan dan sekaligus sebagai tokoh emansipasi pendidikan perempuan. Keempat tokoh tersebut ialah; Siti Walidah, Siti Bariyah, Siti Munjiyah, dan Siti Aisyah Hilal. Keempat tokoh Aisyiyah tersebut memiliki cara dan model masing-masing dalam mendorong perkembangan pendidikan dan kesetaraan gender di Indonesia, seperti; Memperkenalkan pemikirannya bahwa perempuan mempunyai hak yang sama untuk menuntut ilmu setinggi-tingginya dan peran Wanita dalam membentuk karakter sebuah bangsa.
An Adequate Model for the Development of English Reading Skills
Authors:   Department of Educational Studies, The Ohio State University, V. Paul Peter
Published: September 2024

The focus of this article is on the development of adequate English reading skills. To delineate necessary components for a phonemic-based language, it is necessary to compare two broad frameworks--the Qualitative Similarity Hypothesis (QSH) and the American Sign Language (ASL)— English sign-print bilingual model. Both frameworks have been invoked to address the language and literacy challenges of d/Deaf and hard of hearing (d/Dhh) children and adolescents. Descriptions of the QSH and ASL are provided, and a synthesis of salient research findings is presented. The selection of relevant sources for both frameworks is the result of both a systematic and a professional review, focusing on early and recent investigations. There are benefits associated with the use of American Sign Language in a sign-print ASL-English bilingual education program, especially when combined with fingerspelling and English orthography. However, these benefits seem to diminish during the later early literacy period, particularly in the alphabetic stage. Thus, it is argued that, at present, the QSH and other extant developmental models proffer not only stronger evidence, but also a better explanation for the difficulties of d/Dhh students in developing early and advanced English reading skills. The article concludes with recommendations for further research.
Text Analysis to Enhance Flow of Ideas Fluency and Conjugation Accuracy of EFL Students’ Writing
Authors: Rustipa Katharina , Soepriatmadji Liliek , Rahayu Endang Yuliyani , Kasprabowo Teguh
Published: September 2024

Empirical studies revealed the EFL students’ writing problems, such as incomplete paragraph development and conjugation accuracy. These problems affected the overall text coherence.  These problems have inspired teacher researchers to investigate alternative instructional techniques to improve the flow of ideas and conjugation accuracy of EFL students' paragraphs. This current research aims to investigate mood and paragraph structure analysis effectiveness to develop the conjugation accuracy and flow of ideas fluency in EFL students' paragraphs. This study followed an experimental design utilizing control and experimental groups. The research subjects comprised 30 2nd-year undergraduate EFL learners who were at an intermediate level of English proficiency. Data for this research were collected using multiple methods, including tests, documentation, and interviews. The research results revealed that mood analysis and paragraph-structure analysis were effective techniques for improving the conjugation accuracy and idea flow fluency of EFL students' writing. Thus, it is recommended that mood and paragraph structure analyses be alternative techniques to teach paragraph writing.
Resocialización de la persona privada de la libertad en el contexto carcelario y penitenciario colombiano a través de la modalidad de enseñanza
Authors: Mogollón Villamizar Adriana Alexandra
Published: September 2024

El presente artículo busca abordar el uso dado y la aplicación de los aspectos de la resocialización como fin de la pena, desde el enfoque criminológico y de las implicaciones en los procesos de vida de las personas privadas de la libertad.
Para ello, se explican sucintamente algunas teorías sobre la función que cumple la pena en el sistema penal colombiano, dentro de la condición descrita de un estado de cosas inconstitucional. Se precisan los pormenores de la resocialización como fin de la pena dentro del sistema penitenciario y carcelario, para finalmente aportar algunos elementos que enriquezcan la discusión académica sobre los hechos y datos relacionados con el modelo de resocialización colombiano.
Satisfacción con la vida y consumo en jóvenes chilenos durante la pandemia por COVID-19
Authors: Quintano-Méndez Felipe , Denegri-Coria Marianela , Riquelme-Segura Leonor , Palavecinos Mireya , Sepúlveda-Aravena Jocelyn
Published: September 2024

La pandemia por COVID-19 generó una crisis de salud pública a nivel mundial, reconfigurando prácticas cotidianas y de consumo, particularmente en la adquisición de bienes y servicios. El objetivo de la presente investigación es analizar la relación de variables psicológicas vinculadas al consumo respecto a la satisfacción con la vida durante la pandemia por COVID-19 en jóvenes de la región de La Araucanía, Chile. Con este propósito se realizó un estudio con diseño no experimental transversal de alcance correlacional con una muestra de 402 jóvenes chilenos, a quienes se les aplicaron las escalas Satisfacción con la vida, Actitudes hacia la compra, Actitudes hacia el endeudamiento, Valores materiales y el Test de alfabetización económica. Se llevó a cabo un análisis de regresión lineal múltiple con la variable satisfacción con la vida como dependiente. Los resultados evidenciaron que a medida que aumentan los niveles de actitud hacia la compra compulsiva y los valores materiales (creencias respecto a la importancia de las posesiones materiales para su satisfacción personal) de los participantes, disminuyen sus niveles de satisfacción con la vida; caso contrario sucede con las actitudes hacia la compra racional, alfabetización económica y compra impulsiva. Se recomienda avanzar en estudios que contribuyan a comprender como estas prácticas pueden ser producto de un escenario pandémico y/o se materializan en hábitos que se mantienen en el tiempo.
Gaudibilidad y Sensibilidad en el Procesamiento Sensorial como predictores de la desregulación emocional
Authors: Montoya-Pérez Karina Salud , Padrós-Blázquez Ferran
Published: September 2024

La Sensibilidad en el Procesamiento Sensorial (SPS) alude a las diferencias en el grado de susceptibilidad a las influencias ambientales que presentan los individuos, y según se manifieste puede tener efectos positivos y negativos en su vida; entre los negativos se encuentra la desregulación emocional (DE). Por otro lado, la regulación emocional se relaciona con la gaudibilidad (conjunto de moduladores del disfrute), no obstante, hay poca investigación al respecto. El objetivo de esta investigación fue analizar el papel de la gaudibilidad y la SPS como predictores de la DE. Para tal efecto se llevó a cabo un estudio transversal, descriptivo y observacional. Participaron 1,040 estudiantes universitarios a quienes se administró la Escala para Personas Altamente Sensibles (EPAS), la Escala de Gaudibilidad para Adultos y la Escala de Dificultades en la Regulación Emocional en español (DERS-E). Los resultados del análisis de regresión mostraron que el mejor predictor positivo de la DE fue la dimensión sensibilidad procesada de la SPS (t= 14.48), mientras que el mejor predictor negativo fue la dimensión concentración de la gaudibilidad (t= -10.31). Este hallazgo, sumado a las correlaciones moderadas obtenidas entre la DE y ambas variables, positiva para la SPS y negativa para la gaudibilidad, sugieren, que un nivel alto de SPS conlleva dificultades para regular las emociones, independiente de si se cuenta con alta gaudibilidad, aunque la DE será mayor si el nivel de gaudibilidad es bajo. Se evidencia que la gaudibilidad efectivamente funciona como un importante regulador emocional específico del disfrute, que comparte elementos con la regulación emocional de forma genérica.
Síndrome de Burnout em estudantes de medicina: efeito das emoções, variáveis sociodemográficas e acadêmicas
Authors: Bittencourt Romeiro Fernanda , Carlotto Mary Sandra , Goergen Brust-Renck Priscila
Published: September 2024

A Síndrome de Burnout (SB) em estudantes é caracterizada como uma resposta ao estressores crônicos vinculados ao papel e às atividades presentes no contexto acadêmico, que causam sérios prejuízos para a saúde física e mental e para o desempenho acadêmico. O objetivo do estudo foi identificar o poder preditivo das variáveis demográficas, acadêmicas, demanda emocional, dissonância emocional e empatia pelos pacientes sobre a Síndrome de Burnout em uma amostra não probabilística de 85 estudantes de medicina. Para tanto, foi desenhado um estudo observacional, analítico e transversal. Os participantes responderam a um questionário online contendo um questionário de dados demográficos e acadêmicos, a Escala de Burnout em Estudantes Universitários, Subescalas de demanda emocional e de dissonância emocional, e a Escala Jefferson de Empatia Médica. Os resultados, obtidos por meio da análise de regressão linear múltipla, revelaram como preditores as variáveis sexo feminino, estar em semestres mais distantes do final do curso, maiores demandas emocionais e maior dissonância emocional. Resultados indicam a importância de intervenções, por parte das instituições universitárias de ensino, voltadas para o desenvolvimento de habilidades socioemocionais e estratégias de regulação das emoções e criação de um ambiente de ensino que proporcione liberdade e individualidade para manifestações de emoções a fim de formar profissionais com maior capacidade de interpretar adequadamente os afetos, seus e de seus pacientes.
Teste de Abordagens de Aprendizagem de Jogadores de Videogame – Dois Fatores: Validades Estrutural e Preditiva
Authors: De Araujo Jhonys , Mauro Assis Gomes Cristiano
Published: September 2024

As abordagens de aprendizagem têm sido medidas por testes voltados ao contexto escolar/acadêmico, a despeito da teoria não assumir que as abordagens ocorrem apenas nesse contexto. O Teste de Abordagens de Aprendizagem de Jogadores de Videogame – Dois Fatores (TAb-Videogame-2F) pretende superar essa limitação buscando medir as abordagens profunda e superficial no contexto do videogame. Neste artigo, apresentamos esse teste e avaliamos sua validade estrutural e preditiva. A amostra do estudo foi de 709 participantes, majoritariamente universitários. O modelo das abordagens superficial e profunda correlacionadas mostrou-se o mais adequado para representar a validade estrutural do teste. Esse resultado indica que o TAb-Videogame-2F mensura as abordagens no contexto dos videogames. Os resultados da validade preditiva complementam a validade estrutural ao mostrar que a abordagem profunda em videogame prediz em 81% a autodeclaração das pessoas como gamer.
An Investigation of Subjective Metacognitive Awareness and Objective Absolute Monitoring Accuracy among Undergraduate Students from China, Colombia, Spain, and the U.S.: The Role of Culture
Authors: Gutierrez de Blume Antonio P. , Wang Ying , Du Hongcui , Montoya Londoño Diana Marcela
Published: September 2024

Metacognition is understood as the knowledge and regulation of one’s own cognitive activity that deals with one’s agency over mental processes in a conscious and deliberate way. However, the role culture plays on individuals’ experiences of metacognition and metacognitive awareness is not well understood. Thus, the present study explored the influence of culture on self-report metacognitive awareness (via the Metacognitive Awareness Inventory) and objective absolute metacognitive monitoring accuracy (i.e., a comparison of individuals’ confidence in performance judgments against actual performance) across three academic domains (vocabulary, probabilities, and paper folding [assesses visual-spatial reasoning]) in a sample of 366 undergraduate students from four countries (China, Colombia, Spain, and the U.S.). Results revealed that metacognitive awareness is not a universally conceptualized construct. Not only were correlational patterns distinct across cultures, but there were significant differences among the four cultures in both a finer- and coarser-grained analysis. Further, results of objective absolute monitoring accuracy indicated that, incongruent with the self-report findings, participants from the Chinese sample exhibited significantly poorer absolute monitoring accuracy in vocabulary and paper folding, but that participants from all four countries manifested similar absolute monitoring accuracy in a probabilities task. These findings have profound repercussions for how culture influences theory, applied research, measurement, and practice regarding metacognitive awareness and absolute metacognitive monitoring accuracy.
Behavior of LSTM and Transformer deep learning models in flood simulation considering South Asian tropical climate
Authors: Madhushanka G.W.T.I. , Jayasinghe M. T. R. , Rajapakse R. A.
Published: September 2024

The imperative for a reliable and accurate flood forecasting procedure stems from the hazardous nature of the disaster. In response, researchers are increasingly turning to innovative approaches, particularly machine learning models, which offer enhanced accuracy compared to traditional methods. However, a notable gap exists in the literature concerning studies focused on the South Asian tropical region, which possesses distinct climate characteristics. This study investigates the applicability and behavior of long short-term memory (LSTM) and transformer models in flood simulation considering the Mahaweli catchment in Sri Lanka, which is mostly affected by the Northeast Monsoon. The importance of different input variables in the prediction was also a key focus of this study. Input features for the models included observed rainfall data collected from three nearby rain gauges, as well as historical discharge data from the target river gauge. Results showed that the use of past water level data denotes a higher impact on the output compared to the other input features such as rainfall, for both architectures. All models denoted satisfactory performances in simulating daily water levels, with Nash–Sutcliffe Efficiency (NSE) values greater than 0.77 while the transformer encoder model showed a superior performance compared to encoder–decoder models.
Influence of weather research and forecasting model microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka
Authors: Perera P. L. L. N. , Neluwala N. G. P. B. , Wijetunge J. J.
Published: September 2024

Creating precise quantitative precipitation forecasts is essential for reducing losses and damages. This study aimed to identify the best microphysics and cumulus schemes for forecasting monsoon rainfall over the Kelani River basin, Sri Lanka, using the WRF-ARW model. Four extreme rainfall events from the 2020 and 2021 monsoon seasons were simulated with various microphysics and cumulus parameterizations to find the optimal combinations. These combinations were then tested for their ability to forecast two monsoon events with a 24-h lead time. Simulated and forecasted rainfalls were compared with observations from 15 gauging stations. Results indicate that WSM3 and WSM6 microphysics schemes with the Betts–Miller–Janjic (BMJ) cumulus scheme are optimal for simulating rainfall, with WSM3_BMJ being the most suitable for forecasting. The findings of this study provide valuable initial data for research in regions with similar environmental conditions, offering insights into the suitability of various physics schemes for simulating and forecasting monsoon rainfall, particularly under extreme conditions. Furthermore, given the prevalence of monsoons in many tropical and subtropical climates, these results will be instrumental in enhancing the use of numerical weather prediction models for forecasting monsoon rainfall on a global scale.