Miracle-dz/UniFAR-FTUs huggingface dataset:
An Alternative Approach to Bode’s Noise Theorem

Bode’s noise theorem is an efficient way of determining the total noise in passive, linear RLC networks. This theorem has been traditionally derived in the frequency-domain using contour integration. We present an alternative (and more intuitive) derivation of Bode’s theorem. Our technique works in the time-domain and exploits energy conservation. Finally, we apply our technique to determine the autocorrelation function of the noise at the output of the network.
Consumers’ Preference in Online Purchasing of Ornamental Plants in the Klang Valley, Malaysia

The internet’s growth has opened doors for global sales, particularly in Malaysia, where a rising trend in online shopping is evident. Floriculture entrepreneurs are seizing this opportunity to boost ornamental plant sales online, driven by the increasing internet-savvy Malaysian customer base. Despite the overall surge in online commerce, ornamental plants face challenges in popularity compared to items like clothing or food. Various factors, including plant pricing, quality attributes, and reliable delivery services, influence consumer preferences for online ornamental plant purchases. This study aims to explore the factors influencing consumers toward purchasing ornamental plants online, assess consumer preferences, and understand how these patterns may impact future buying behavior. The conceptual framework includes five independent variables: price, quality, time-saving, convenience, and delivery service, with consumer preferences as the dependent variable. Data was collected from 154 respondents in Klang Valley, Malaysia, experienced in purchasing ornamental plants online, and analyzed using descriptive and mean ranking analysis. Findings show that the key platforms for purchases were Facebook, Shopee, and WhatsApp, with seedlings and adult plants priced under RM20 being the preferred choices. Respondents identified price, quality, time-saving, convenience, and delivery service as crucial factors in their online purchasing decisions. Notably, delivery service emerged as the most influential factor, earning 100% preference. Consumers expressed a strong willingness to buy ornamental plants online if informed about the seller’s quality and packaging methods, as highlighted by a mean score of 4.533. The study emphasizes that consumers prioritize “quality delivery service” when making online purchases. “Convenience” is another key factor, allowing consumers to compare prices before buying. Overall, consumers focus on price, quality, convenience, and time savings in their decision to buy ornamental plants online. Sellers are encouraged to go beyond these factors, employing diverse strategies to attract consumers to the online ornamental plant market.
The differentiation and speciation of Scapania javanica and S. undulata complexes in the Eastern Sino-Himalayas and perimeters for Scapania Sect. Stephania (Scapaniaceae, Hepaticae)

Three taxa of Scapania with Sino-Himalayan and meta-Himalayan distribution are described as new to science: S. pseudojavanica from the southern portion of the eastern spur of the Tibetan Plateau in southern China and North Vietnam, S. metahimalayana with a disjunct distribution that ranges from Nepal (where its distribution is questionable) to North Vietnam, and S. sichuanica from high elevation in Sichuan in SW China. Each species is morphologically similar to taxa distributed in the more humid and warmer climates of East and South-East amphi-oceanic Asia. The differentiation is presumably the result of recent speciation in the Sino-Himalayas and adjacent lands and the observed differentiation and distribution patterns are in support of the geographic approach in splitting hepatic species. The initial differentiation of these Scapania in sect. Stephania was the specialization into two groups: with brown and colorless gemmae, both being predominantly unicellular.
A Joint Architecture of Mixed-Attention Transformer and Octave Module for Hyperspectral Image Denoising

Convolutional neural networks (CNNs) recently have achieved impressive performance for hyperspectral image denoising. However, current CNNs have limitations in exploring spectral correlations across various bands and the interactions among features within each band. Although transformers are introduced to capture spatial-spectral correlation in hyperspectral image (HSI), generally, they either explore intercorrelation in bands or intracorrelation between bands, neglecting the combination of intra- and intercorrelation in HSI cubes. Besides, transformer methods rarely address hierarchical (i.e., the low- and high-level) features in an adaptive manner. That is, features at different levels are of different importance, whereas these features are tackled equally in these methods. To alleviate these limitations, we introduce a joint architecture (the so-called MAOTformer) of mixed-attention transformers and octave modules for HSI denoising. On the one hand, the mixed attention transformer (MATB) is designed to simultaneously capture pixel relationships inter- and intrabands by incorporating naive spatial self-attention, bidirectional recurrent channel attention, and progressive channel attention. Besides, a U-net based on mixed attention is equipped with attentive skip connections in MATB, which enables the proposed MAOTformer to explore hierarchical features by U-net and adaptively connect these hierarchical features by the attentive skip connections. On the other hand, we introduce an octave module behind each MATB to utilize multiscale features for separating noise in high-frequency components. Extensive experiments are conducted on synthetic and real-world HSIs, showing that the proposed method outperforms state-of-the-art methods.
Mitochondrial Glutathione in Cellular Redox Homeostasis and Disease Manifestation

Mitochondria are critical for providing energy to maintain cell viability. Oxidative phosphorylation involves the transfer of electrons from energy substrates to oxygen to produce adenosine triphosphate. Mitochondria also regulate cell proliferation, metastasis, and deterioration. The flow of electrons in the mitochondrial respiratory chain generates reactive oxygen species (ROS), which are harmful to cells at high levels. Oxidative stress caused by ROS accumulation has been associated with an increased risk of cancer, and cardiovascular and liver diseases. Glutathione (GSH) is an abundant cellular antioxidant that is primarily synthesized in the cytoplasm and delivered to the mitochondria. Mitochondrial glutathione (mGSH) metabolizes hydrogen peroxide within the mitochondria. A long-term imbalance in the ratio of mitochondrial ROS to mGSH can cause cell dysfunction, apoptosis, necroptosis, and ferroptosis, which may lead to disease. This study aimed to review the physiological functions, anabolism, variations in organ tissue accumulation, and delivery of GSH to the mitochondria and the relationships between mGSH levels, the GSH/GSH disulfide (GSSG) ratio, programmed cell death, and ferroptosis. We also discuss diseases caused by mGSH deficiency and related therapeutics.
Challenges in big data adoption for Malaysian organizations: a review

Big data has played an ever-increasing role in various sectors of the economy. Despite the availability of big data technologies, many companies and organizations in Malaysia remain reluctant to adopt them. Numerous studies have been published on big data adoption; however, there is a lack of research focusing on identifying the challenges faced by Malaysian organizations. Therefore, this study will implement the technology-organization-environment (TOE) framework to examine the challenges faced by Malaysian organizations with regards to big data adoption. A systematic literature review (SLR) was conducted to examine the challenges. From the result of this study, it was found that the factors from technology context are deemed to be the major challenge faced in big data adoption followed by organization and environment factors. Furthermore, the insights derived from the TOE framework-based information can help address concerns that hinder big data adoption among organizations in Malaysia. Finally, this study concludes with several recommendations.
Multiloss-Based Optimization for Time Series Data Augmentation

Data augmentation plays an important part in the current success of machine learning and deep learning models. In particular, state-of-the-art architectures in the image recognition field include data augmentation modules as an integral part. However, there is still room for progress in the time series domain. In this work, we introduce OptimAug, a novel method for time series data augmentation. We deviate from the current state-of-the-art comprised of random transformations, pattern mixing, generative models, and decomposition methods, to develop the first multiloss-based optimization method. We evaluate our method with its two variants on datasets from the University of California Riverside (UCR) archive and compare it to multiple baseline algorithms from the literature.
An Interactive Framework of Balancing Evaluation Cost and Prediction Accuracy for Knowledge Tracing

The development of online intelligent educational systems has revolutionized personalized learning, presenting an opportunity for the integration of knowledge tracing (KT). KT is an essential task that leverages students’ historical interactions to model their knowledge states, enabling accurate predictions of their future performance. The application of deep learning models on KT tasks, also known as deep learning based knowledge tracing (DLKT) models, has accelerated the process of KT tasks in recent years. Although DLKT models have achieved promising results, there is a huge challenge to avoid models generating wrong estimations that yield bad guidance in educational contexts. Hence, in this work, we propose a simulation framework to explore the feasibility of minimizing the evaluation cost while guaranteeing prediction performance. In the framework, we initially design a simple yet efficient DLKT model that learns the actual process of students’ knowledge acquisition. We then select the reliable predictions generated by the proposed model and assign the unreliable ones to teaching professionals based on confidence estimations. We present the results of a proof-of-concept experiment on three real-world publicly available datasets to demonstrate that our framework can obtain the balance of human cost and automatic evaluation accuracy, which can be flexibly deployed to real-world educational contexts in the future. To encourage reproducible research, we make our code publicly available at https://github.com/gwbnwnwh/human-in-the-loop.
National Data Management: A Modern Data Stewardship Approach for the Swiss Administration Enabled by the New Platform “I14Y Interoperability”

To create the basis for the implementation of the Once-Only-Principle in Switzerland, the Swiss Federal Council launched a program called “National Data Management” in 2019. The Once-Only-Principle aims to reduce the administrative burden on citizens and companies. Instead of having to contact them repetitively for the same information, the responsible authorities should strive to exchange the required data. To enable this, we need to actively work on the data landscape of the Swiss administration. The concept of data stewardship is known to be an efficient tool to promote and ensure a consistent high level of data quality. Nowadays public administrations of many countries strive to embrace the idea of data stewardship. This paper describes the approach we have developed for the Swiss administration.
The value of luteinizing hormone basal values and sex hormone-binding globulin for early diagnosis of rapidly progressive central precocious puberty

Objective This study aimed to investigate the diagnostic value of luteinizing hormone (LH) basal values and sex hormone-binding globulin (SHBG) for rapidly progressive central precocious puberty (RP-CPP). Methods A total of 121 girls presenting with secondary sexual characteristics were selected from the Department of Pediatric Endocrinology, Lianyungang Clinical Medical College of Nanjing Medical University, from May 2021 to June 2023. The children were followed up for 6 months and were divided into three groups: RP-CPP group (n=40), slowly progressive central precocious puberty (SP-CPP) group (n=40), and premature thelarche (PT) group (n=41). The differences in LH basal values and SHBG among girls in the three groups were compared. ROC curves were drawn to analyze the value of LH basal values and SHBG in identifying RP-CPP. Results Significant differences were observed in age, height, predicted adult height (PAH), weight, body mass index (BMI), bone age (BA), BA-chronological age (CA), LH basal, LH peak, FSH basal, LH peak/FSH peak, estradiol (E2), testosterone, and SHBG levels between the RP-CPP group and the SP-CPP and PT groups (P < 0.05). The LH basal value in the RP-CPP group was higher than that in the SP-CPP group and the PT group, while SHBG levels were lower than in the latter two groups, and these differences were statistically significant (P < 0.05). When the LH basal value was ≥0.58 IU/L and SHBG was ≤58.79 nmol/L, the sensitivity for diagnosing RP-CPP was 77.5% and 67.5%, and the specificity was 66.7% and 74.1%. Conclusion Detection of basal LH and SHBG levels allows for early diagnosis of the progression of central precocious puberty.
Determinan Rekrutmen Online Generasi Y dan Z di Perusahaan ABC

Penelitian ini bertujuan menguji dan menganalisis pengaruh daya tarik perusahaan, reputasi perusahaan, dan rekrutmen online dengan reputasi perusahaan sebagai mediasi daya Tarik perusahaan terhadap minat melamar pekerjaan pada generasi Y dan Z pengguna platform TikTok di Perusahaan ABC. Metode yang digunakan pada penelitian ini adalah kuantitatif asosiatif. Teknik pengambilan sampel yang digunakan purposive sampling. Data dikumpulkan dengan menyebarkan kuesioner secara online melalui google form pada 96 responden. Data yang terkumpul dianalisis dengan software SmartPLS versi 3.0. Hasil penelitian menunjukkan bahwa reputasi perusahaan dan rekrutmen online berpengaruh positif dan signifikan terhadap minat melamar pekerjaan pada generasi Y dan Z pengguna platform TikTok di Perusahaan ABC. Reputasi perusahaan memediasi pengaruh daya tarik perusahaan terhadap minat melamar pekerjaan pada generasi Y dan Z pengguna platform TikTok di Perusahaan ABC. Implikasi praktis, Perusahaan ABC hendaknya mengembangkan strategi dalam proses rekrutmen online dan meningkatkan reputasi perusahaan untuk menarik minat pelamar dari generasi Y dan Z.
Firm Value in Indonesia: Will Foreigners be the Determinant?

Despite considerable scholarly attention, the literature on foreign investors is characterized by inconsistency and mixed findings. Using a sample of 427 non-financial firms during the period 2012 to 2019, this study provides robust evidence of the relationship between foreign investors and changes in firm value in the Indonesian setting. The investigation results using robust panel regression show that the trading behavior of foreign investors is the cause of the decline in firm value. This finding is robust with a number of repeated tests and endogeneity bias. The results of this study can be generalized to the capital markets of developing countries in Asia with thin market characteristics, high information inequality, and weak investor protection. This study is especially useful for regulators in formulating policies and academics.
Corporate Voluntary Codes of Conduct for Preventing Human Trafficking through Corporate Social Responsibility Approach : An Empirical Study

This study explores the effectiveness of Corporate Voluntary Codes of Conduct(CVCC) for preventing human trafficking through a Corporate Social Responsibility (CSR) approach. The research involved a diverse sample size of 543 participants, including corporate governance professionals, international justice authorities, human trafficking combating establishments, and non- governmental organizations (NGOs). Both descriptive and inferential statistics were employed to test hypotheses, utilizing a range of statistical methods such as, Mean, Standard Deviation, Coefficient of Variation, Skewness and Kutosis and t-test, ANOVA, Correlation, Multiple Regression, and Confirmatory Factor Analysis (CFA ) respectively. The study utilized a 5-point Likert scale to assess 125 factors reduced to 15 combined factors out 77 factors, and 48 factors become redundant by administering data reduction technique related to preventing human trafficking, encompassing Corporate Voluntary Codes of Conduct and CSR. The findings were employed to develop a comprehensive frameworks to hold corporations accountable for human rights violations. Human Trafficking Controlling and Prevention Model. The results highlight the significance of CVCC inconjuction with CSR initiatives in addressing human trafficking issues beyond the confines of corporate codes. This research contributes to the evolving discourse on corporate responsibility and its potential impact on preventing human trafficking, emphasizing the need for a holistic approach that extends beyond regulatory frameworks
Comparing ML OCR Engines on Texts from 19th Century Written in the Romanian Transitional Script

Many 19th century Romanian texts are written in the Transitional Script (RTS) combining Cyrillic and Latin characters in variable proportions depending on specific factors (period, region, publishing house, literary trends, and personal beliefs). Thousands of heritage documents in numerous libraries across Romania have yet to be digitized. Reading texts written in RTS is challenging for modern scholars, today’s students and OCR engines which due to specific particularities have difficulties in processing them. Transfer learning can be used to train state-of-the-art tools such as Tesseract or Transkribus, but their efficiency varies according to the underlying deep neural network, the training parameters, and the particularities of the scanned images and used scripts. In this paper, we compare the performance of two architectures on 180 OCR models for 5 scenarios (best CER <4.6% for Tesseract) and discuss OCR challenges on RTS texts.
Dynamics of soil microbial communities involved in carbon cycling along three successional forests in southern China

Dynamics of plant communities during forest succession have been received great attention in the past decades, yet information about soil microbial communities that are involved in carbon cycling remains limited. Here we investigated soil microbial community composition and carbohydrate degradation potential using metagenomic analysis and examined their influencing factors in three successional subtropical forests in southern China. Results showed that the abundances of soil bacteria and fungi increased (p ≤ 0.05 for both) with forest succession in relation to both soil and litter characteristics, whereas the bacterial diversity did not change (p > 0.05) and the fungal diversity of Shannon-Wiener index even decreased (p ≤ 0.05). The abundances of microbial carbohydrate degradation functional genes of cellulase, hemicellulase, and pectinase also increased with forest succession (p ≤ 0.05 for all). However, the chitinase gene abundance did not change with forest succession (p > 0.05) and the amylase gene abundance decreased firstly in middle-succession forest and then increased in late-succession forest. Further analysis indicated that changes of functional gene abundance in cellulase, hemicellulase, and pectinase were primarily affected by soil organic carbon, soil total nitrogen, and soil moisture, whereas the variation of amylase gene abundance was well explained by soil phosphorus and litterfall. Overall, we created a metagenome profile of soil microbes in subtropical forest succession and fostered our understanding of microbially-mediated soil carbon cycling.
Mathematical Validation of 100nm n-MOSFET using Silvaco TCAD

This paper attempts to prove the mathematical validation of 100nm n-channel Metal Oxide Semiconductor-FET (MOSFET), with their characteristics and extraction of parameters, i.e. Characterization, the theoretical and practical analysis using Silvaco-TCAD. While performing mathematical validation, a comprehensive study was made, which covers the functioning and mechanism of MOS Devices. Various aspects of MOS devices have been analyzed and validated in this paper, including the “drain current $(\mathrm{I}_{\mathrm{D}})$ versus drain voltage $(\mathrm{V}_{\text{DS}})$ “relation, the “drain current $(\mathrm{I}_{\mathrm{D}})$ versus gate voltage $(\mathrm{V}_{\text{GS}})$ “relation, transconductance $(\mathrm{g}_{\mathrm{m}})$, drain resistance $(\mathrm{r}_{\mathrm{D}})$, or ON resistance and threshold voltage $(\mathrm{V}_{\mathrm{T}})$.
Transforming Agriculture with Intelligent Data Management and Insights

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the de facto data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.
Bullying and Unhealthy Psychological Atmosphere in Classroom

In the realm of education, the classroom symbolizes intellectual exploration and youth development. Unfortunately, bullying mars this ideal, pervading the social and emotional fabric. This pervasive issue, spanning verbal abuse to cyberbullying, demands a nuanced understanding. Beyond individual impacts, bullying disrupts group dynamics, hindering collaborative learning. A holistic approach is imperative to reclaim the classroom as a fear-free space for academic growth.