Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment Methods to ICT Platform Used in Educational Guidance: Application to an Immersive World

School counselling is a decision-making process in which high school students have to decide what higher education course they will register for. Many digital devices support high school students in this counselling process. Our objective is to evaluate the impact of those digital devices. The research literature offers indicators to evaluate digital devices, however, evaluation protocols are very context-dependent and there are many types of digital devices. We want to determine how to measure the impact of these digital devices in the orientation process. This document presents an evaluation protocol and its application with an immersive world (VirBELA)
Leveraging Machine Learning for Capacity and Cost on a Complex Toolset: A Case Study

In this case study, we introduce two ML techniques, Long Short-Term Memory (LSTM) and an optimized Random Forest (RF), to address challenges related to capacity and cost, by addressing problems of unscheduled downtime and Process Time (PT) variation in the case of a complex chamber processing tool. We show that by using these ML techniques, traditional methods of Predictive Maintenance (PdM) and PT analysis can be enhanced with new insights and lead to significant productivity improvements. We demonstrate that, with these methods, by detecting states and attributes of the tool, trends in the tool’s behavior can be more effectively identified to reduce its unscheduled downtime and improve its run-rate, thereby resulting in significant capacity and cost improvements. This is achieved by reducing the variability of availability; extending the Mean Time Between Failures (MTBF); and removing variability in PT between lots and chambers.
Multiplicity of positive solutions for nonlinear singular Neumann problems

We consider a nonlinear Neumann problem driven by the p-Laplacian and a reaction which consists of a singular term plus a (p-1) - linear perturbation which is resonant at +∞ with respect to the principal eigenvalue. Using variational methods together with suitable truncation, comparison and approximation techniques, we show that the problem admits two positive smooth solutions.
Frequency-Domain Diffusion Bias-Compensated Adaptation With Periodic Communication

When the input signal of each node is interfered by noise, the distributed frequency-domain adaptive algorithm yields biased estimation. To eliminate the noise-induced bias with reduced communication load, this article proposes the frequency-domain diffusion bias-compensated adaptive filtering with periodic communication. By minimizing the bias-eliminating cost function, the frequency-domain diffusion bias-compensated LMS (FD-BCLMS) is first derived. Subsequently, to achieve lower computational complexity and communication cost, we design the double periodic FD-BCLMS (DPFD-BCLMS) algorithm by resorting to periodic update and communication strategies. Moreover, the DPFD-BCLMS with power normalized scheme (DPFD-BCNLMS) is developed to improve the convergence rate in the case of colored input. The transient and steady-state behaviors are investigated. For the steady-state performance degradation in the DPFD-BCNLMS, we modify the combination step near steady-state, resulting in the switched DPFD-BCNLMS (SDPFD-BCNLMS). A new estimation method for the input noise variance is also provided. Finally, the superiority of the proposed algorithms is validated by numerical simulations.
Interactive Learning for Network Anomaly Monitoring and Detection with Human Guidance in the Loop

With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators’ expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel’s guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human–computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence.
Ecological Niche Modeling of Aedes and Culex Mosquitoes: A Risk Map for Chikungunya and West Nile Viruses in Zambia

The circulation of both West Nile Virus (WNV) and Chikungunya Virus (CHIKV) in humans and animals, coupled with a favorable tropical climate for mosquito proliferation in Zambia, call for the need for a better understanding of the ecological and epidemiological factors that govern their transmission dynamics in this region. This study aimed to examine the contribution of climatic variables to the distribution of Culex and Aedes mosquito species, which are potential vectors of CHIKV, WNV, and other arboviruses of public-health concern. Mosquitoes collected from Lusaka as well as from the Central and Southern provinces of Zambia were sorted by species within the Culex and Aedes genera, both of which have the potential to transmit viruses. The MaxEnt software was utilized to predict areas at risk of WNV and CHIKV based on the occurrence data on mosquitoes and environmental covariates. The model predictions show three distinct spatial hotspots, ranging from the high-probability regions to the medium- and low-probability regions. Regions along Lake Kariba, the Kafue River, and the Luangwa Rivers, as well as along the Mumbwa, Chibombo, Kapiri Mposhi, and Mpika districts were predicted to be suitable habitats for both species. The rainfall and temperature extremes were the most contributing variables in the predictive models.
Long-term Assessment of Precipitation Behaviour in Bihar (1901-2021): Patterns, Trends and Observed Variability

Rainfall is an important variable that governs the climate and hydrological conditions of any region. During the past century, an increase in global emissions of greenhouse gases has caused changes in the patterns of hydro-meteorological conditions across the world. It has resulted in changed patterns and trends of rainfall, causing episodic occurrences of droughts and floods. This increasing variability of rainfall has triggered and intensified extreme events that pose potential future risks under different climate change scenarios. This projected variability may induce drastic changes in the flood affected states like Bihar where even a slightest change in rainfall variability may affect hydrological cycle and agriculture conditions.
Integrating genetic markers and adiabatic quantum machine learning to improve disease resistance-based marker assisted plant selection

The goal of this research was to create a more accurate and efficient method for selecting plants with disease resistance using a combination of genetic markers and advanced machine learning algorithms. A multi-disciplinary approach incorporating genomic data, machine learning algorithms and high-performance computing was employed. First, genetic markers highly associated with disease resistance were identified using next-generation sequencing data and statistical analysis. Then, an adiabatic quantum machine learning algorithm was developed to integrate these markers into a single predictor of disease susceptibility. The results demonstrate that the integrative use of genetic markers and adiabatic quantum machine learning significantly improved the accuracy and efficiency of disease resistance-based marker-assisted plant selection. By leveraging the power of adiabatic quantum computing and genetic markers, more effective and efficient strategies for disease resistance-based marker-assisted plant selection can be developed.
Deteksi Pengendara Mengantuk dengan Kombinasi Haar Cascade Classifier dan Support Vector Machine

Microsleep sering terjadi pada aktivitas kita tanpa disadari, terutama pada saat berkendara. Hal tersebut menjadi salah satu faktor penyebab kecelakaan yang diakibatkan kesalahan manusia seperti mengantuk, tidak fokus, dan kelelahan menyumbangkan porsi 61%, selain itu 9% dikarenakan faktor kendaraan, serta 30% disebabkan oleh faktor prasarana dan lingkungan. Beberapa teknik deteksi microsleep melalui kedipan mata telah dikembangkan antara lain menggunakan eye aspect ratio, yaitu thresholding manual dengan menetapkan detik minimal mata menutup/berkedip. Pada penelitian ini dilakukan deteksi pengendara mengantuk dengan kombinasi Haar Cascade yang mengklasifikasi wajah pengendara, serta metode Support Vector Machine yang mampu menentukan mata menutup dan membuka. Implementasi pada sistem deteksi pengendara mata mengantuk secara real time akurasi mencapai 99%. Penelitian ini diharapkan dapat membantu dalam mengurangi bahkan mencegah terjadinya kecelakaan yang disebabkan oleh microsleep yang dapat dideteksi secara dini melalui kedipan mata pengendara.
Medical and healthcare waste generation, storage, treatment and disposal: a systematic scoping review of risks to occupational and public health

Abstract Systems to safely store, handle, treat and dispose of medical (healthcare) waste are well developed in the 21st century. Yet, across many parts of the Global South (low-income and middle-income countries) such systems, resources and knowhow are lacking; to the extent that medical waste could pose a serious threat to the health, safety and lives of millions of healthcare workers and waste handlers who frequently interact with this category of materials. We present here a novel scope and dimension to investigating specifically the risks and hazards to people who come into contact with medical waste, focusing on activity types and established medical practice. A systematic scoping review of evidence (PRISMA-Scr) was used to critically analyze, compare and summarize data. Prevalent combinations of hazards, exposure and risk are semi-quantitatively scored and ranked. Our results signpost three core topics posing a major risk to human health: (1) Open, uncontrolled burning and rudimentary incineration of medical waste by waste handlers who have to make difficult choices between burning or discarding on land (e.g. in dumpsites) from where it risks pathogen infection; (2) A small but non-negligible trade in reused medical equipment (e.g. hypodermic needles), proliferated by a cohort of waste reclamation specialists (sub-group of waste pickers); and (3) The mismanagement of medical sharps at the point of generation, handling and storage in the Global North and South. A combination of immediate action and further research are recommended to address and inform on these topics which threaten the health and mortality of millions. GRAPHICAL ABSTRACT
Incidence of Aflatoxins and Ochratoxin A in Wheat and Corn from Albania

In this study, aflatoxins (AFs) and ochratoxin A (OTA) were analyzed in grains, specifically wheat and corn, from Albania. To summarize, 71 wheat and 45 corn samples from different growing areas were collected. The multi-toxin analytical procedure involved sample extraction and liquid chromatography–tandem mass spectrometry (LC–MS/MS). The incidence of AF was 18% in the analyzed wheat and 71% in the corn samples. The concentration of AFs was much higher in the corn samples than in the wheat samples. The maximum permitted levels for aflatoxin B1 (AFB1) and total AFs were not exceeded in the wheat samples, while they were exceeded in 36% of the corn samples. In the wheat samples, the AFB1 concentration varied between 0.2 and 0.4 µg kg−1. However, the highest concentrations in the corn samples were 2057, 2944, and 3550 µg kg−1. OTA was present in only three corn samples and one wheat sample. However, all contaminated samples exceeded the maximum permitted levels. This report reveals the presence of AFs and OTA in grain commodities, specifically wheat and corn, grown in Albania.
Lesbian , Gay , and Bisexual Voters in the 2000 US Presidential Election

Lesbians, gay men, and bisexuals (LGBs) in the United States are strikingly more likely to vote for Democratic presidential candidates than are heterosexuals. LGBs are one of the Democratic Party’s most loyal voting blocs, despite the absence of one of the most important mechanisms for creating party identification: inter-generational transmission. We use the 2000 Presidential election to examine whether LGB voters overwhelmingly chose Al Gore because they viewed him as superior to George W. Bush on LGB-related policy issues or because of their greater overall liberalism and Democratic Party identification. We also examine the impact of socialization within the LGB community for generating political liberalism, Democratic Party identification, and interest in LGB policies. Using logit analysis on a 2000 Harris Interactive poll of 13,000 Americans, including 1,000 LGBs, we find that concern for LGB rights, policy liberalism, and party identification all played a role in the LGB vote. Analysis of the LGB subsample supports a model of political socialization within the LGB community leading to stronger interest in LGB rights, liberalism, Democratic party identification, and support
An Analysis of Gender Pay Disparity in the Nonprofit Sector : An Outcome of Labor Motivation or Gendered Jobs ?

Although pay differences between men and women with comparable characteristics are generally smaller in the nonprofit than in the for-profit sector, gender pay gaps in the nonprofit sector vary widely across industries. In some industries, gender pay gaps are as large as in the for-profit sector, but in others, women make more than comparably qualified men. Using Hierarchical Linear Modeling on the combined 2001-2006 American Community Surveys, we test nonprofit labor motivation theories against a gendered-job hypothesis to explain this variation. We find that gender pay gaps in the nonprofit sector are smaller in industries where nonprofits outnumber for-profits and where higher proportions of female-dominated occupations exist.
Characterization and Functional Analysis of Chalcone Synthase Genes in Highbush Blueberry (Vaccinium corymbosum)

Chalcone synthase (CHS) is the first key enzyme-catalyzing plant flavonoid biosynthesis. Until now, however, the blueberry CHS gene family has not been systematically characterized and studied. In this study, we identified 22 CHS genes that could be further classified into four subfamilies from the highbush blueberry (Vaccinium corymbosum) genome. This classification was well supported by the high nucleotide and protein sequence similarities and similar gene structure and conserved motifs among VcCHS members from the same subfamily. Gene duplication analysis revealed that the expansion of the blueberry CHS gene family was mainly caused by segmental duplications. Promoter analysis revealed that the promoter regions of VcCHSs contained numerous cis-acting elements responsive to light, phytohormone and stress, along with binding sites for 36 different types of transcription factors. Gene expression analysis revealed that Subfamily I VcCHSs highly expressed in fruits at late ripening stages. Through transient overexpression, we found that three VcCHSs (VcCHS13 from subfamily II; VcCHS8 and VcCHS21 from subfamily I) could significantly enhance the anthocyanin accumulation and up-regulate the expression of flavonoid biosynthetic structural genes in blueberry leaves and apple fruits. Notably, the promoting effect of the Subfamily I member VcCHS21 was the best. The promoter of VcCHS21 contains a G-box (CACGTG) and an E-box sequence, as well as a bHLH binding site. A yeast one hybridization (Y1H) assay revealed that three anthocyanin biosynthesis regulatory bHLHs (VcAN1, VcbHLH1-1 and VcbHLH1-2) could specifically bind to the G-box sequence (CACGTG) in the VcCHS21 promoter, indicating that the expression of VcCHS21 was regulated by bHLHs. Our study will be helpful for understanding the characteristics and functions of blueberry CHSs.
Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation

Digital reconstruction through Building Information Models (BIM) is a valuable methodology for documenting and analyzing existing buildings. Its pipeline starts with geometric acquisition. (e.g., via photogrammetry or laser scanning) for accurate point cloud collection. However, the acquired data are noisy and unstructured, and the creation of a semantically-meaningful BIM representation requires a huge computational effort, as well as expensive and time-consuming human annotations. In this paper, we propose a fully automated scan-to-BIM pipeline. The approach relies on: (i) our dataset (HePIC), acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models; (ii) a novel ad hoc deep network (BIM-Net++) for semantic segmentation, whose output is then processed to extract instance information necessary to recreate BIM objects; (iii) novel model pre-training and class re-weighting to eliminate the need for a large amount of labeled data and human intervention.
Abnormal Transactions Detection in the Ethereum Network Using Semi-Supervised Generative Adversarial Networks

Numerous abnormal transactions have been exposed as a result of targeted attacks on Ethereum, such as the Ethereum Decentralized Autonomous Organization attack. Exploiting vulnerabilities in smart contracts, malicious users can pursue their own illicit objectives through abnormal transactions. Consequently, identifying these malevolent users, implicated in fraudulent activities and their attribution, becomes exceedingly complex. Cryptocurrency transactions used for malicious purposes, employing pseudo-anonymous accounts to send and receive ransom payments and accumulating funds under various identities, further highlight the need to control and detect these abnormal transactions for maintaining a high level of security within the Ethereum network. Although existing Intrusion Detection Systems (IDSs) help mitigate abnormal transaction occurrences, their performance necessitates improvement. To address this issue, this study presents a novel approach, named Abnormal Transactions Detection Using a Semi-Supervised Generative Adversarial Network (ATD-SGAN), which efficiently detects abnormal attacks within the Ethereum network. ATD-SGAN leverages a semi-supervised generative adversarial network for this purpose. The results demonstrate that ATD-SGAN significantly enhances the performance of state-of-the-art IDSs. It achieves an increase in detection accuracy from 3.78% to 11.05% and reduces the false alarm rate from 42.29% to 0.15%. Moreover, ATD-SGAN notably improves the F1-measure, ranging from 10.39% to 3.79%, compared to the current IDSs.
Pilot study investigating estrus length and estrus behavior in Norwegian Red cattle on a commercial dairy farm

Introduction Norwegian Red has been shown to have high levels of estrus behavior under experimental conditions. However, the estrus behaviors of Norwegian Red cows have not been studied under commercial conditions. Methods A herd of 89 Norwegian Red cows housed in free stalls on concrete, slatted floors, were continuously video monitored for 21 days. Ovarian cyclicity was confirmed in a final study sample group (n = 18) using milk progesterone concentrations. All mounting and standing activities were recorded, and the duration of mount estrus, standing estrus and the differences between these; prestand and poststand, were determined. The cycle stages metestrus, diestrus and proestrus were estimated based on the starting time and ending time of mount estrus. Results All cows in the final study sample group exhibited the primary estrus sign, ‘standing to be mounted’ during estrus. Two (11%), eleven (61%) and six (33%) cows exhibited the behavior ‘standing to be mounted’ during metestrus, diestrus and proestrus, respectively. The number of mounts initiated by individual cows was higher during mount and stand estrus than during the rest of the estrous cycle. This study reports a median duration of mount estrus and stand estrus of 21.0 h (interquartile range (IQR) 15.0 to 27.3) and 14.3 h (IQR 12.0 to 18.8), respectively. The median counts per hour of all mount behaviors were 8.6 (IQR 5.6 to 11.3), 1.51 (IQR 0.3 to 3.8) and 1.7 (IQR 0.8 to 6.0) for standing estrus, prestand and poststand, respectively. Discussion This study shows that under commercial conditions the Norwegian Red cow displays a high level of mount and stand activity associated with estrus.
Indonesia's New Capital: An Analysis Of Transparency, Public Participation, And Deliberative Democracy

This study aims to assess the government's policy regarding establishing a new capital city, focusing on the dimensions of transparency, public participation, and deliberative democracy. This study will provide valuable insights for policymakers in Indonesia and other developing nations, enabling them to make informed decisions of a comparable nature. The present study employs a qualitative case study approach, incorporating interviews, observations, and secondary data collection from government reports and research conducted by non-governmental organizations (NGOs). According to this study, the formulation of the IKN Law has successfully adhered to the principles of deliberative democracy, encompassing the dimensions of influence, inclusiveness, and deliberation. However, it is essential to note that certain limitations are associated with this process, specifically insufficient transparency and limited engagement with local indigenous communities. In order to ensure effective community engagement and inclusivity, it is imperative for the government to enhance transparency in disseminating information regarding the progress of the Ibu Kota Negara (IKN) and actively involve local indigenous populations in the project. In the context of a developing nation such as Indonesia, it is imperative for policymakers to accord utmost importance to public engagement and transparency in formulating critical policies that bear substantial consequences on the social, economic, and environmental fronts.