Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship Between Self-Efficacy And Acadmic Stress In Psychology Study Program Student At Muhammadiyah University Of Sidoarjo

Penelitian ini mempunyai tujuan guna menguji hubungan antara self efficacy dengan stress akademik pada mahasiswa Program Studi Psikologi Universitas Muhammadiyah Sidoarjo. Metode penelitian ini menggunakan metode pendekatan kuantitatif. Populasi dalam penelitian ini adalah 762 dengan jumlah sampel 238 mahasiswa Program Studi Psikologi Universitas Muhammadiyah Sidoarjo. Penelitian ini menggunakan teknik simple random sampling dalam pengmbilan sampel. Teknik pengumpulan data dalam penelitian ini menggunakan dua skala, yaitu skala self efficacy dan stres akademik dengan menggunakan penyusunan skala psikologi yang menggunakan skala likert. Analisis data menggunakan kolerasi product moment dengan bantuan SPSS for windows. Hasil dari nilai R-Square pada penelitian ini adalah 0,102 atau 10,2% sehingga membuktikan bahwa self efficacy memiliki pengaruh terhadap stres akademik. Hasil dari uji hipotesis menunjukkan hasil rxy = -319 dengan taraf signifikansi 0,000. Hal ini membuktikan bahwa variabel self-efficacy dan stress akademik pada mahasiswa Program Studi Psikologi Universitas Muhammadiyah Sidoarjo memiliki hubungan yang negatif. Hubungan negatif ini dapat diartikan jika self efficacy semaki ntinggi maka tingkat stres akademik akan semakin rendah, begitupun sebaliknya apabila self efficacy semakin rendah yang dimiliki oleh mahasiswa maka tingkat stres akademik yang dirasakan oleh mahasiswa akan semakin tinggi.
Analyzing and Optimizing an Array of Low-Cost Gas Sensors for use in an Air Quality Measurement Device with Machine Learning

Low-cost gas sensors have been proposed in place of conventional expensive instruments however they have issues due to cross-sensitivity with other pollutants. Several different types of metal oxide and electrochemical sensors and machine learning methods were evaluated. The objectives were to determine which type of sensor, metal oxide or electrochemical, is better at measuring traffic-related air pollution and hether deep neural networks (DNN) and recurrent neural networks (RNN) improve sensor performance. Three devices were deployed across three sites, two in Toronto and one in Beijing to evaluate the performance of calibration. Calibration was performed with two weeks of data from only one site and evaluated with the remaining data. The combination of metal oxide and electrochemical sensors were more accurate when measuring NOx. When targets were normalized, the RNN performed better than DNN and linear calibration, however, not when applied to measuring data well outside the range for calibration.
Kill-Spill: Integrated biotechnological solutions for combating marine oil spills

Executive Summary: Oil spill disasters remain a worldwide problem despite the fact that the number of large oil spills incidents have steadily decreased over the last five decades. It is important to consider that current emergency response technologies with booms and skimmers seldom go beyond an oil recovery of 10 %. Dispersants represent the classical remediation approach for open seas where the spilled oil is converted to billions of micron-sized droplets that are subsequently removed by intrinsic bioremediation. When an oil spill reaches the shoreline, the ecological and economic damaged is significantly increased. On the other hand, heavier agglomerates of oil and particulate matter often reach the sediments where bioremediation rates are practically negligible due to insufficient oxygenation. The problem of hydrocarbon-contaminated sediments
Video Based Fish Species Detection Using Faster Region Convolution Neural Network

Fish recognition and classification represent significant challenges in marine biology and agriculture, promising fields for advancing research. Despite advancements in real-time data collection, underwater fish recognition and classification still require improvement due to challenges such as variations in fish size and shape, image quality issues, and environmental changes. Feature learning approaches, particularly utilizing convolutional neural networks (CNNs), have shown promise in addressing these challenges. This study focuses on video-based fish species classification, employing a feature learning-based extraction method through CNNs. The process involves two main stages: detection and classification. To address the detection and classification in video a Faster Region Convolutional Neural Network (RCNN) with transfer learning techniques are applied, achieving a mean average precision of 84% for detection and classification tasks. These techniques offer promising avenues for enhancing fish recognition and classification in diverse environments
Evolución de la evapotranspiración y el estrés hídrico de la vegetación de dehesa en la Península Ibérica (2001-2015)

Este trabajo pretende profundizar en el efecto del deficit de agua en el suelo sobre la vegetacion de los ecosistemas de dehesa. Para ello, se ha realizado un seguimiento de la evapotranspiracion (ET) y el estres hidrico de este sistema en la Peninsula Iberica a lo largo quince anos, mediante la aplicacion de un modelo de balance de energia basado en datos termicos procedentes de imagenes de satelite. El modelo SEBS (Surface Energy Balance System) se ha aplicado, a escala mensual desde enero de 2001 a diciembre de 2015, haciendo uso de datos remotos e informacion meteorologica procedente de reanalisis. Los resultados han permitido evaluar el funcionamiento del modelo en estas condiciones usando observaciones experimentales, obteniendo estimaciones suficientemente precisas (RMSD=14-20 W·m –2 para los flujos radiativos y RMSD=26-29 W·m –2 en los convectivos) para realizar un seguimiento de los flujos de energia, incluyendo ET, y el estres hidrico de la vegetacion en las condiciones estudiadas. Se han caracterizado los principales episodios de sequia ocurridos en dicho periodo, cuantificando su efecto sobre la cobertura de la vegetacion y su produccion. Por ultimo, se ha profundizado en el analisis de los dos anos mas secos de la serie (2004/2005 y 2011/2012) y su impacto sobre los diferentes estratos de vegetacion, herbaceos y arboreo, del ecosistema de dehesa.
A review of deep learning based intrusion detection systems

Network security is a key issue in the era of rapid Internet development and new technologies are needed to enhance the security protection of network systems. As the first line of defence for the security of network systems, IDS is considered to be one of the important network tools for managing network security. Traditional network intrusion detection methods usually use machine learning techniques. However, deep learning, a more powerful machine learning method, has the advantages of mature technology, wide applicability and high accuracy, and has shown its advantages in various fields such as image recognition, natural language processing and computer vision. It can also be applied to network security detection, as it can effectively handle large-scale, complex datas and detect unknown, sophisticated network attacks. This paper will provide a summary of machine learning techniques and deep learning techniques in intrusion detection systems, as well as examples of IDSs using deep learning, and conclude with a summary and outlook.
Image Steganography by Using Multiwavelet Transform

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego images. The proposed algorithm is compared with wavelet based algorithm which shows a favorable results in terms of PSNR reaches to 18 dB.
Social Media Metrics as Predictors of Publishers’ Website Traffic

The relationship between legacy media and social media has become a crucial topic in the discussions about new media. The debate intensified after Facebook announced a reduction in news posts in user timelines in 2018. In the era of the “Like economy”, social media holds significant economic value, prompting media outlets to adopt a “let’s try and see” approach to reach new audiences and increase their online advertising share. The present study, based on a large-scale survey of 50 publishers’ websites, Facebook pages, and Twitter accounts, deepens our understanding of the relationship between legacy and social media as indicators of audience feedback. Through the lens of network gatekeeping and reciprocal journalism theories, it contributes to the development of new evaluation tools that predict publishers’ website traffic based on social media metrics. Results show that Facebook and Twitter metrics can predict publishers’ website traffic indicators at a rate exceeding 60%. This study underscores the importance of social media metrics in evaluating media practices and the need to shift research toward specific indicators to understand the relationship between legacy and social media.
An Analysis into the Factors Affecting Online Customers’ Satisfaction: A Developing Country Perspective

This article investigates the factors that affect online customers’ satisfaction from the perspective of Bangladesh. The study is a descriptive type of research. Data have been collected from 201 respondents who make online purchasing, using a structured survey questionnaire. For analyzing data, frequency tables, factor analysis and multiple regressions have been used. The findings from factor analysis reveal that information availability, convenience, ease of use, attractive website design and customization of product or service affect online customers’ satisfaction mostly. The study from multiple regression analysis reveals that there is a high degree of positive correlation between dependent variable and independent variables. The findings from multiple regression analysis reveal that product quality, delivery quality, after sales service and fair price are highly significant factors that affect online customers’ satisfaction in Bangladesh. The results of this research is suitable for the online product sellers or online service providers as they can take ideas about which factors they need to take into consideration with greater importance and caution for online customers’ satisfaction in a developing country perspective like Bangladesh.
Kinematics of Foreland‐Vergent Crustal Accretion: Inferences From the Dinarides Evolution

One of the most common observation in Mediterranean areas is the migration of contractional deformation and associated slabs through time toward external orogenic areas, associated with lower plate crustal accretion. The Dinarides orogen of Central Europe is an optimal place to study such a sequence of contractional deformation. Compared with other areas, contraction in the Dinarides was less overprinted by subsequent extension, while a remnant of the subducted slab is observed in a far external orogenic position. Understanding the deformational evolution of the Dinarides is hampered by the reduced availability of kinematic studies. Therefore, we have performed a surface kinematic study in the external parts of the Dinarides. By correlating with available geophysical and evolutionary constraints, we constructed two large‐scale, kinematically controlled regional transects. The results demonstrate a long‐lived evolution of shortening that affected the Dinarides lower orogenic plate. While the Late Jurassic‐earliest Cretaceous deformation was associated with an earlier obduction moment, the latest Cretaceous onset of continental collision has gradually focused deformation at inherited rheological weakness zones. We show that shortening was interrupted by a period of Miocene extension that affected all orogenic areas and created the Dinarides Lake System. The extension was followed by renewed shortening, which started during the latest Miocene and remains presently active, whose kinematics in the central and SE part of the Dinarides is revealed for the first time by our study. These results indicate a lower plate crustal accretion mechanism that was spatially and temporally connected with gradual slab retreat in the Dinarides.
Suppression of Inflammation in Adipocyte-Macrophage Coculture by Passion Fruit Seed Extract: Insights into the p38 and NF-ҡB Pathway

Obesity, which is characterized by chronic low-grade inflammation, involves the infiltration of immune cells into adipose tissue, leading to the secretion of inflammatory cytokines and subsequent inflammation. Therefore, the aim of this study was to examine the potential of passion fruit seed extract (PSEE) in mitigating lipopolysaccharide (LPS)-induced inflammation in a coculture system comprising macrophages and adipocytes. PSEE demonstrated significant reductions in reactive oxygen species (ROS) and nitric oxide (NO) levels, primarily achieved through the downregulation of inducible nitric oxide synthase (iNOS) protein expression in LPS-induced adipocyte-macrophage cocultures. Furthermore, PSEE effectively suppressed the secretion of TNF-α and IL-1β by attenuating the gene expression of these cytokines, as well as other inflammation-related genes such as MMP-2, IL-6, and MCP-1. Notably, PSEE exhibited potent inhibitory effects on the p38 and NF-κB signaling pathways, thus alleviating inflammation in the LPS-induced adipocyte-macrophage cocultures. Additionally, PSEE led to a decrease in the expression of ACC, HSL, and FaSN, while aP2 and ATGL showed increased expression in LPS-induced cocultured macrophages and adipocytes. These findings suggest that passion fruit seed extract effectively combats inflammation by suppressing the p38 and NF-κB signaling pathways, resulting in reduced levels of proinflammatory cytokines, NO, and ROS production.
Comparative assessment of Nanotrap and polyethylene glycol-based virus concentration in wastewater samples

Abstract Wastewater-based epidemiology is now widely used in many countries for the routine monitoring of SARS-CoV-2 and other viruses at a community level. However, efficient sample processing technologies are still under investigation. In this study, we compared the performance of the novel Nanotrap® Microbiome Particles (NMP) concentration method to the commonly used polyethylene glycol (PEG) precipitation method for concentrating viruses from wastewater and their subsequent quantification and sequencing. For this, we first spiked wastewater with SARS-CoV-2, influenza and measles viruses and norovirus and found that the NMP method recovered 0.4%–21% of them depending on virus type, providing consistent and reproducible results. Using the NMP and PEG methods, we monitored SARS-CoV-2, influenza A and B viruses, RSV, enteroviruses and norovirus GI and GII and crAssphage in wastewater using quantitative PCR (qPCR)-based methods and next-generation sequencing. Good viral recoveries were observed for highly abundant viruses using both methods; however, PEG precipitation was more successful in the recovery of low-abundance viruses present in wastewater. Furthermore, samples processed with PEG precipitation were more successfully sequenced for SARS-CoV-2 than those processed with the NMP method. Virus recoveries were enhanced by high sample volumes when PEG precipitation was applied. Overall, our results suggest that the NMP concentration method is a rapid and easy virus concentration method for viral targets that are abundant in wastewater, whereas PEG precipitation may be more suited to the recovery and analysis of low-abundance viruses and for next generation sequencing.
CNV DETECTION AND ASSOCIATION STUDIES IN THE BROWN SWISS CATTLE BREED

English . 10 Chapter 1 . 10 Chapter 2 . 11 Chapter 3 . 12 Abbreviations . 13 General Introduction . 14 Copy Number Variants . 14 CNVs identification and CNV studies. 15 CNVs and Phenotypes . 17 CNVs and Genome-Wide Association Studies . 18 Aims . 19 References . 20 1. Identification and validation of copy number variants in Italian Brown Swiss dairy cattle using the Illumina Bovine SNP50 Beadchip 28 Abstract . 28 Introduction . 29 Material and Methods . 31 Sampling and genotyping . 31 Editing data . 32 Copy number variations detection . 32 PennCNV detection . 33 SVS7 detection . 33 Copy number variation regions validation by quantitative polymerase chain reaction . 34 Copy number variation regions annotation . 34 Results and discussion . 35 Copy number variations and copy number variation regions detection . 35 Copy number variation regions validation by quantitative polymerase chain reaction . 38 Copy number variation regions annotation . 38 Conclusions . 41 5 References . 42 Supporting information . 55 2. A genome-wide scan of copy number variants using high-density SNPs in Brown Swiss dairy cattle . 103 Abstract . 103 Introduction . 105 Material and methods . 107 Sampling and genotyping . 107 Quality assurance of CNV raw data . 107 Copy Number Variation detection . 108 CNVRs and consensus CNVRs definition . 108 Consensus CNVR annotation . 109 CNVR validation by qPCR . 109 Results and discussion . 110 Quality assurance of CNV Raw Data . 110 CNVs detection . 110 CNVRs and consensus CNVRs definition . 112 Consensus CNVR annotation . 115 CNVR validation by qPCR . 117 Conclusion . 118 References . 118 Supporting information . 128 3. A genome wide association study between CNVs and quantitative traits in Brown Swiss cattle . 163 Abstract . 164 Introduction . 164 Material and methods . 166 Sampling and genotyping . 166 Phenotypes . 166 CNV-association with productive, udder and health traits . 167 CNVs annotation. 167 Results and discussion . 168 CNV-association and annotation results . 168 Conclusion . 180 Conflict of interest statement . 181 Acknowledgements . 182 References . 182
Applying self-attention model to learn both Empirical Risk Minimization and Invariant Risk Minimization for multimedia recommendation

Multimedia recommendation systems have many applications in our daily life. However, how accurately capture a customer's preference is an issue that is difficult to deal with. The proposed Invariant Risk Minimization (IRM) and Empirical Risk Minimization (ERM) are ways to learn a customer's preference. Still, both frameworks show some limitations: although ERM performs excellently in a single environment, it fails to generalize well when faced with multiple and new domains. On the other hand, IRM learns invariant features across heterogeneous environments, but it lacks theoretical guarantees and performs less effectively where the invariants are unclear. This paper proposes an ERM and IRM Optimized Rating Framework (EIOR) as our final recommender model with direct rating scores. The EIOR enhances the accuracy and functionality of the multimedia recommendation systems by utilizing self-attention mechanisms to combine IRM and ERM with adjusted attention weights. Specifically, IRM learns invariant parts across different environments, while ERM learns variant parts. With self-attention, we can adaptively allocate attention weights for the two pieces and seek the optimal pair of attention weights based on the loss function. We demonstrate EIOR on a cutting-edge recommender model UltraGCN and use the open multimedia dataset of TikTok to finish all the experiments. The results validate the effectiveness of EIOR by comparing purely operating invariant representations alone with the framework of IRM.
Fluid Boundaries: the hyporheic zone of a tropical tidal river

While the hyporheic zone is increasingly acknowledged as an important component of the hydrological cycle, being critical to ground water and surface water interactions, knowledge of its hydrological functioning is still limited, particularly at local scales. Tropical and tidal hyporheic zones principally are underrepresented in academic studies, in spite of their geographical significance. The distinct seasonality of the tropics, combined with known tidal influences, such as tidal pumping, suggests different hydrological processes dominate in tropical and tidal systems to those of non-tidal or temperate rivers. This research is an initial attempt to articulate the complexity of local hyporheic zone characteristics of a site on the Bang Pakong River, Thailand. This characterization began with a literature-based conceptual model of the hyporheic flux paths and velocity and zone morphology of a generic site on the river that was then refined into a field model, using field observations and limited field data gathered during a dry-season field trip to a specific site. The resultant field model clearly demonstrated increased hydrological complexity. It was argued that this complexity was a response to local-scale features; features invisible to regional-scale research. For example, the conceptual model predicted hyporheic zone morphology included level layers, however the presence of a secondary channel at the research site suggested a concave morphology. With limited knowledge of the Bang Pakong River’s hyporheic zone, and tropical and tidal hyporheic zones generally, this research represents a modest, yet important, starting point to better understanding the hydrological function of these zones. Such knowledge contributes to an increased appreciation of hyporheic zone behaviour more broadly, as well as providing specific information to incorporate in policies and practices guiding the management of tropical tidal rivers.
Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Permanent magnet synchronous motor (PMSM) is a vital component of modern industry which is widely used in transport, aerospace and intelligent machinery. In the light of its relatively high frequency of inter-turn short circuit (ITSC) fault, it is valuable to detect the fault accurately and efficiently. Due to the convenience of motor vibration signal measurement, more attention has been paid to fault detection methods based on vibration signal. As a nonstationary, nonlinear signal, vibration signal is highly susceptible to external noise interference. Variational mode decomposition (VMD) has a wide range of applications in the field of nonlinear signal analysis, but the processing ability to the signal is affected by the parameter setting. Hence, a Bayesian optimized adaptive parameter selection variational mode decomposition (BOAPS-VMD) signal processing method is proposed and applied to detect the ITSC in PMSM. Firstly, the motor vibration signal is decomposed through BOAPS-VMD, and the intrinsic mode functions (IMFs) are obtained. Secondly, the cumulative variance contribution rate (C-VCR) is applied to identify the IMFs that contain fault signature information. Finally, using Hilbert transform (HT) to further analyze the IMFs that are identified. The results are output in 3D time-frequency diagrams to enhance the representation of fault signatures. The effectiveness and accuracy of the BOAPS-VMD were validated through the finite element simulation and experiment. The study shows that the BOAPS-VMD effectively improves the modal mixing phenomenon, has better noise robustness, improves the accuracy of fault detection, and has better engineering applicability.