Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying the Deep Learning Techniques to Solve Classification Tasks Using Gene Expression Data

This manuscript explores the application of deep learning (DL) techniques for classifying gene expression data. A key aspect of our research is the comparative analysis of various DL neural network architectures, including Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Recurrent Neural Networks (RNN), as well as hybrid models that combine these networks. We applied the Bayesian optimization algorithm using 5-fold cross-validation for optimal hyperparameter tuning, which is crucial for DL algorithm performance. Significantly, we have advanced the methods for applying RNNs in processing gene expression data, particularly focusing on LSTM and GRU types. Our study introduces also a novel hybrid quality criterion for data classification, calculated as a weighted sum of partial quality criteria, incorporating an integrated F1-score derived through the Harrington desirability method. Furthermore, we investigate hybrid models that leverage various DL methods, enhancing decision-making objectivity in sample identification. This model uses a step-by-step information processing procedure, initially applying different DL models to gene expression data and subsequently processing these through a CART-based classifier for final decision-making. Our experiments, performed on gene expression data from patients with eight cancer types and one subset with normal samples (without cancer), demonstrated that GRU-RNN-based models, particularly a two-layer GRU-RNN, achieved the highest classification efficacy, with an accuracy of 97.8% on the test dataset. The performance of this model exceeded that of other models, whose accuracy varied between 96.6% and 97.3%. Comparative analysis with other studies in this field suggests that the proposed techniques demonstrate higher efficacy compared to similar research regarding the application of DL models for cancer-type diagnosis.
EUROPEAN PRACTICES OF RURAL GREEN TOURISM: CHALLENGES AND PROSPECTS

The relevance of researching rural green tourism in Europe is an important field that offers tourists the opportunity to enjoy nature and the countryside, interact with locals and participate in authentic rural life. Implementing environmentally sustainable green tourism practices will help conserve natural resources and reduce the environmental impact of tourism. Encouraging the development of local entrepreneurship and crafts can contribute to preserving the authenticity and economic development of rural areas. Rural green tourism can become a forum for cultural exchange between tourists and local people, promoting mutual understanding. A balanced approach that takes into account infrastructure needs, environmental sustainability, cultural aspects and the participation of local communities is key to the successful development of rural green tourism in Europe. The purpose of the study is to consider the theoretical and practical aspects of European practices of rural green tourism in the context of challenges, to analyse the prospects and opportunities for the development of rural green tourism. The research methodology of the topic includes the systemic method, which recognises the importance of studying the rural green tourism system in Europe, considering its elements and their interaction. The structural method aims at revealing the structure of the object, identifying the key elements and the relationships between them. Results. The article presents directions for the development of rural green tourism, proving that it has significant potential for promoting the development of rural areas and stimulating the economy. This issue is of practical importance, as the growing popularity of rural green tourism creates a demand for hotel, restaurant, excursion and other entertainment services, which can lead to the creation of new jobs, regional economic development and the preservation of cultural and traditional aspects of rural communities. Increased tourism potential can encourage local governments and businesses to invest in infrastructure such as hotels, restaurants, transport and entertainment facilities, and attracting tourists to natural areas encourages more environmentally conscious behaviour and the conservation of natural resources. It was concluded that the development of rural tourism is an effective tool for attracting tourists and creating a positive image of regions. The research on rural green tourism in Europe not only contributes to the development of the tourism industry, but also has an important impact on the socio-economic development and the preservation of the natural and cultural heritage of the regions.
Measures for Persons with Spinal Cord Injury to Monitor Their Transitions in Care, Health, Function, and Quality of Life Experiences and Needs: A Protocol for Co-Developing a Self-Evaluation Tool

Evaluating the experiences of persons with spinal cord injury (PwSCI) regarding their transitions in care and changes in health, function, and quality of life is complex, fragmented, and involves multiple tools and measures. A staged protocol was implemented with PwSCI and relevant expert stakeholders initially exploring and selecting existing measures or tools through a modified Delphi process, followed by choosing one of two options. The options were to either support the use of the 10 selected tools from the Delphi method or to co-develop one unique condensed tool with relevant measures to evaluate all four domains. The stakeholders chose to co-develop one tool to be used by persons with SCI to monitor their transition experiences across settings and care providers. This includes any issues with care or support they needed to address at the time of discharge from acute care or rehabilitation and in the community at 3, 6, and 12 months or longer post-discharge. Once developed, the tool was made available online for the final stage of the protocol, which proposes that the tool be reliability tested prior to its launch, followed by validation testing by PwSCI.
Automated Network Intrusion Detection for Internet of Things: Security Enhancements

Security attacks are becoming more sophisticated and common as connected devices rapidly exchange personal, sensitive, and important data. Security solutions are therefore required for Internet of Things (IoT) environments. System administrators receive alerts through an automatic Network Intrusion Detection (NID) system when security breaches occur. An automatic NID can be an effective tool to protect IoT networks against various attacks. It is possible to detect intrusions using a variety of intrusion detection techniques, but the performance and class imbalance in the dataset make this a difficult process. To improve detection rates and decrease false alarms, intrusion detection accuracy must be improved. In this paper, an automatic NID system is proposed leveraging a renowned machine learning model named Random Forest (RF) on the (UNSW-NB15) dataset collected from Kaggle. The experimental results indicate that the proposed model not only has higher accuracy at 90.17% surpassing the baseline approach by 7.34%, but also has precision, recall, and F1 scores up to 90.14%, 90.17, and 90.14%, respectively. Moreover, 98.83% accuracy is achieved with a balanced class dataset by using random resampling techniques to generate synthetic data of minority attacks.
Extrema-Triggered Conversion for Non-Stationary Signal Acquisition in Wireless Sensor Nodes

While wireless sensor node (WSNs) have proliferated with the rise of the Internet of Things (IoT), uniformly sampled analog–digital converters (ADCs) have traditionally reigned paramount in the signal processing pipeline. The large volume of data generated by uniformly sampled ADCs while capturing most real-world signals, which are highly non-stationary and sparse in information content, considerably strains the power budget of WSNs during data transmission. Given the pressing need for intelligent sampling, this work proposes an extrema pulse generator devised to trigger ADCs at significant signal extrema, thereby curbing the volume of data points collected and transmitted, and mitigating transmission power draw. After providing a comprehensive signal-theoretic rationale, we construct and experimentally validate these circuits on a system-on-chip field-programmable analog array in a 350 nm complementary metal-oxide-semiconductor (MOS) process. Operating within a power range of 4.3–12.3 µW (contingent on the input bandwidth requirements), the extrema pulse generator has proven to be capable of effectively sampling both synthetic and natural signals, achieving significant reductions in data volume and signal reconstruction error. Using a nonideality-resilient reconstruction algorithm, that we develop in this work, experimental comparisons between extrema and uniform sampling show that extrema sampling achieves an 18-fold lower normalized root mean square reconstruction error for a quadratic chirp signal, despite requiring 5-fold fewer sample points. Similar improvements in both the reconstruction error and effective sampling rate objectives are found experimentally for an electrocardiogram signal. Using both theoretical and experimental methods, this work demonstrates the potential of extrema-triggered systems for extending Pareto frontiers in modern, resource-constrained sensing scenarios.
Harmonic Solution of Higher-Dimensional Second Order Kuramoto Oscillator Network

Obtaining the solution of Kuramoto oscillator model is an effective way to understand the mechanism behind the dynamical behaviors it represents. However, it is not an easy work to get, even the approximation solution for the second order Kuramoto oscillator network because of its complex coupling interaction and strong nonlinearity. Hence, this paper first establishes a new polynomial mathematical model for the second order Kuramoto oscillator network, which is partly decoupled in a polynomial fashion. Then, a quadratic harmonic balance method (HBM) is developed to get the approximate solution for quadratic differential algebraic equation (QDAE) system. An equivalent quadratic technique is introduced to convert the polynomial Kuramoto oscillator model into a QDAE system aiming to obtain its harmonic solution. Finally, the suggested harmonic solution is utilized to explain the morphological characteristics of the stable motion of oscillators, and the relative motions for the oscillators in Kuramoto network is concluded by a set of nonlinear state output equations. It is also found that the distance between the maximum phase angle and the controlling unstable equilibrium point (UEP) can be used as a stability margin for the oscillator network. A single-machine infinite bus (SMIB) power system, an IEEE 3-machine 9-bus power system and an IEEE 10-machine 39-bus power system are employed as different dimension second order Kuramoto oscillator network cases to demonstrate the validation and accuracy of the proposed method and analysis.
Cloning and Characterization of a Novel N-Acetyl-D-galactosamine-4-O-sulfate Sulfatase, SulA1, from a Marine Arthrobacter Strain

Sulfation is gaining increased interest due to the role of sulfate in the bioactivity of many polysaccharides of marine origin. Hence, sulfatases, enzymes that control the degree of sulfation, are being more extensively researched. In this work, a novel sulfatase (SulA1) encoded by the gene sulA1 was characterized. The sulA1-gene is located upstream of a chondroitin lyase encoding gene in the genome of the marine Arthrobacter strain (MAT3885). The sulfatase was produced in Escherichia coli. Based on the primary sequence, the enzyme is classified under sulfatase family 1 and the two catalytic residues typical of the sulfatase 1 family—Cys57 (post-translationally modified to formyl glycine for function) and His190—were conserved. The enzyme showed increased activity, but not improved stability, in the presence of Ca2+, and conserved residues for Ca2+ binding were identified (Asp17, Asp18, Asp277, and Asn278) in a structural model of the enzyme. The temperature and pH activity profiles (screened using p-nitrocatechol sulfate) were narrow, with an activity optimum at 40–50 °C and a pH optimum at pH 5.5. The Tm was significantly higher (67 °C) than the activity optimum. Desulfation activity was not detected on polymeric substrates, but was found on GalNAc4S, which is a sulfated monomer in the repeated disaccharide unit (GlcA–GalNAc4S) of, e.g., chondroitin sulfate A. The position of the sulA1 gene upstream of a chondroitin lyase gene and combined with the activity on GalNAc4S suggests that there is an involvement of the enzyme in the chondroitin-degrading cascade reaction, which specifically removes sulfate from monomeric GalNAc4S from chondroitin sulfate degradation products.
Patient-derived parathyroid organoids as tracer and drug-screening application model

Parathyroid diseases are characterized by dysregulation of calcium homeostasis and alterations in parathyroid hormone (PTH) excretion. The understanding of parathyroid hyperplastic growth and the development of parathyroid-targeted treatment and imaging tracers could benefit from in vitro models. Therefore, we aim to establish stem cell-derived, three-dimensional organoids representing human parathyroid tissue in vitro. Patient-derived hyperplastic parathyroid tissue was dispersed and parathyroid organoids (PTO) were cultured and characterized. PTO-derived cells were shown to exhibit in vitro self-renewal over several passages, indicative of the presence of putative stem cells. Immunofluorescence and RNA-sequencing confirm that PTO phenocopy hyperplastic parathyroid tissue. Exposure of PTO to increasing calcium concentrations and to PTH-lowering drugs resulted in a significantly reduced PTH excretion. Next to this, the PTO showed specific binding of 11C-methionine to the targeted receptor. Additionally, when organoids were incubated with 99mTc-sestamibi, we observed a higher uptake in PTOs from patients with a 99mTc-sestamibi positive scan compared to patients with a negative scan. These data show functionality of PTOs resembling the parathyroid. In conclusion, we present a patient-derived PTO culture, that recapitulates the originating tissue on gene and protein expression and functionality. This PTO model paves the way for future physiology studies and therapeutic target and tracer discovery.
EXAMINING ACHIEVEMENT GOAL ORIENTATION AND SELFCONCEPT AS PREDICTORS OF SECONDARY SCHOOL STUDENTS’ ACADEMIC ACHIEVEMENT IN MATHEMATICS IN ANAMBRA STATE, NIGERIA

Achievement goal orientation and academic self-concept to be considered as adaptive learning behaviours have been a debated in educational psychology research. The study aimed to examine achievement goal orientation, and academic self-concept as predictors of secondary school students’ academic achievement in Mathematics in Anambra State. Four research questions and three null hypotheses guided the study. The study adopted predictive correlational research to provide answers to the research questions and testing of the hypotheses. The population of the study comprised 21204 from which a sample of 750 was drawn. Multi-stage procedure was used to select the sample. Standardized research instruments namely; Achievement Goal Orientation Questionnaire (AGOQ) and Self-Description Questionnaire (SDQ) were used for data collection. Students’ Mathematics Achievement Scores (SMAS) from the state-wide promotion examination were used to represent mathematics achievement. Cronbach’s alpha was used to determine the reliability of the items in the instruments. A reliability index of 0.84, 0.72, 0.86, 0.74 and 0.87, for mastery-approach, mastery-avoidance, performance-approach, performance-avoidance and academic self-concept respectively. The standard multiple regression was used to analyze the collected data. The research question 1 was answered using multiple regression. Research question 2 was answered using unstandardized β. Research question 3 was answered using adjusted R2. Research question 4 was answered using standardized β. The null hypothesis 1 was tested using the Ftest for the regression model. The null hypothesis 2 was tested using a t-test for adjusted R2. The null hypothesis 3 was tested using a t-test for β at a . 05 level of significance. Findings showed that students’ achievement goal orientation and academic self-concept scores jointly predicted their academic achievement scores in mathematics. It was indicated that mastery avoidance, and work avoidance, relatively and significantly predicted academic achievement in mathematics, while mastery-approach, performance approach, performance avoidance and academic self-concept do not relatively and significantly predict academic achievement in mathematics. Finally, it was recommended that students should adopt achievement goal orientation and academic self-concept in their learning process since the variable had jointly predicted achievement scores in mathematics.
Evolving and Emerging Trends in Digital Libraries User Interfaces

This paper reports on the ways in which two national and two public digital libraries from four different countries (Canada, United States, Britain, Australia) have made use of metadata elements and social media features such as social tags and recommendations to support searching, browsing and navigation of digital information. Cette communication porte sur les facons dont deux bibliotheques numeriques nationales et deux bibliotheques numeriques publiques de quatre pays (Canada, Etats-Unis, Royaume-Uni, Australie) ont fait usage des metadonnees et des fonctionnalites sociales telles que l’etiquetage social et les recommandations pour aider la recherche et la navigation de l’information numerique.
Cloud water in windward and leeward mountain forests: The stable isotope signature of orographic cloud water

Cloud water can be a significant hydrologic input to mountain forests. Because it is a precipitation source that is vulnerable to climate change, it is important to quantify amounts of cloud water input at watershed and regional scales. During this study, cloud water and rain samples were collected monthly for 2 years at sites on windward and leeward East Maui. The difference in isotopic composition between volume‐weighted average cloud water and rain samples was 1.4‰ δ18O and 12‰ δ2H for the windward site and 2.8‰ δ18O and 25‰ δ2H for the leeward site, with the cloud water samples enriched in 18O and 2H relative to the rain samples. A summary of previous literature shows that fog and/or cloud water is enriched in 18O and 2H compared to rain at many locations around the world; this study documents cloud water and rain isotopic composition resulting from weather patterns common to montane environments in the trade wind latitudes. An end‐member isotopic composition for cloud water was identified for each site and was used in an isotopic mixing model to estimate the proportion of precipitation input from orographic clouds. Orographic cloud water input was 37% of the total precipitation at the windward site and 46% at the leeward site. This represents an estimate of water input to the forest that could be altered by changes in cloud base altitude resulting from global climate change or deforestation.
Parang Romang (Boehmeria virgata (Frost.) Guill.): Correlation of Phytochemistry with Antioxidant and Xanthine Oxidase Inhibitory Activities

Secondary metabolites such as phenolic and flavonoid from the plant are essential in their activity, especially activities related to the prevention of oxidation and the inhibition of xanthine oxidase. One plant that is believed to have prevention of oxidation and XOI and correlates with secondary metabolites is parang romang. The purpose of this study is to assess the antioxidant capacity and xanthine oxidase inhibitory activities of parang romang, and to examine their relationship with the TPC and TFC. Total phenolic and flavonoid were measured for all parts, and the DPPH, FRAP, and CUPRAC methods determined antioxidant capacity. The activity of xanthine oxidase inhibition was also assessed. Flavonoids, alkaloids, and coumarin were found in parang romang. The steroid/triterpenoid was found in the stem, leaves, and flower; saponin was in the roots and flower; quinone and tannin were only in the roots. The root gave the highest of TPC, while the flower had the highest TFC. The highest antioxidant capacity was found in roots, leaves, flowers, and stems. The highest xanthine oxidase inhibitory was given by leaves (9.74±0.14 µg/ml), followed by flower, steam, and rood. The correlation was shown between the phenolic and flavonoid quantities and the actions of antioxidants and xanthine oxidase inhibitors.
Diversification of Agricultural Crops to Adapt to Climate Change in the Guadiana River Basin

Adaptation is gaining attention as an inevitable answer to the challenges posed by climate change. The increasingly uncertain climatic conditions to which actors are exposed are becoming a constraint for their well-being. This paper looks at diversification as a key factor in reducing risk and means of coping with an uncertain climate. The aim of this work is to identify combinations of agricultural crops that reduce current and future climate related risks in the Guadiana River Basin. The Guadiana is a transboundary river between Spain and Portugal. The paper uses the variance of the revenue of agricultural crops as a proxy for climate risk, associating minimum variance with a region that is well adapted to the climatic conditions. The paper assesses risks as a function of the key agricultural crops and the climatic conditions that the Guadiana is subjected to. Crop yields under different climatic conditions are simulated by linking climate scenarios from General Circulation Models to the regional crop simulation model CropSyst. Borrowing from economic theory, the paper shows what cropping patterns reduce climate related risk. Crops with yields that have a low correlation provide the highest potential to reduce climate risks. Comparing model simulations of the scenario period 1961-1990 to the period 2071-2100, results suggest that the climate will have modest effects on the average crop yield, but will significantly reduce the variance of crop yields and change crop yield correlation. This changes the cropping pattern that provides the highest risk security.
ANALYSIS OF METHODS FOR ORGANIZING DISTRIBUTED TELECOMMUNICATION SYSTEMS USING THE PARADIGM OF EDGE COMPUTING

The article analyzes modern architectures of edge and fog computing systems, including OpenFog, F2c2C (Cloudlet), MELINDA, and architectures based on SDN and NFV. Particular attention is given to the study of Fog Computing from the conceptual and programmatic points of view. The advantages and limitations of the studied architectures in the context of IoT application are determined. Opportunities for enhancing telecommunication systems and improving the quality of service through the use of appropriate architectures are identified. The necessity of taking into account the specific needs and features of each system when choosing the appropriate fog computing architecture is proved. The need and relevance of further development and improvement of these architectures for optimal use are substantiated.
Experimental Registration of Simultaneous Radiation at the First and Third Cyclotron Harmonics in a High-Current Relativistic Gyrotron

We report the experimental studies of radiation on fundamental and third cyclotron harmonic from a Ka-band high-current relativistic gyrotron with a maximum output power of 40 MW. Helical electron beam is formed by the electron optics system based on using the kicker field for initiation of electrons’ gyration and its subsequent pitching in increasing magnetic field. Based on the spectral measurements, we estimate indirectly the radiation power level at the third harmonic of the gyro-frequency as about −30 dB with respect to the first-harmonic radiation level. This estimation is in good agreement with the results of 3-D particle-in-cell (PIC) simulations.