Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Language Processing to Understand Human Activities Impacted by Hydroelectric Energy Projects

Sustainability and large infrastructure projects are intricately linked. When considering large and disruptive infrastructure projects, proponents, regulators, and stakeholders consider a variety of information sources to understand how the project and its environmental changes will impact people living in the region. This is often done using small data, through interviews, town halls, and surveys. Big Data approaches have potential to augment these methods; for example, novel approaches natural language processing (NLP) on social media data can reveal human-nature interactions in impacted areas. Existing literature suggested that sentiment and frequency analyses can help us understand public attitudes. As a case study to illustrate this potential, we have examined public engagement with landscapes impacted by large hydroelectric development. We examined S6,122 geotagged image captions within 35 well-defined geographical locations associated with three dam projects in Canada: Mactaquac dam (New Brunswick), Oldman dam (Alberta), and Site C dam (British Columbia) on Instagram. The sentiment results of the study expressed a high positive attitude towards the three landscapes and frequency analysis exposed that the people are more likely to engage with the landscape in the context of fun, recreation, exercise activities and vacations. This can inform planning processes to ensure that impacts to these important interactions are examined and mitigated, achieving long-term environmental, social, and economic well-being.
A Variable Step-Size Regularization-Based Quasi-Newton Adaptive Filter for System Identification

The family of least mean square (LMS) based adaptive filtering algorithms suffers from convergence performance limitation due to the sensitivity of such algorithms to the eigenvalue spread of the input correlation matrix. The quasi-Newton family of adaptive filtering algorithms addresses this limitation, but its performance is restricted by the estimation accuracy of the correlation matrix inverse, especially for highly correlated input signals. Furthermore, the convergence rate and the steady-state performance of both LMS and quasi-Newton families are thoroughly depending on their step-sizes. In this paper, a variable step-size regularized quasi-Newton adaptive algorithm is proposed in the context of system identification. In this algorithm, inspired by the matrix inversion lemma, a modified regularized matrix inverse with a time-varying regularization is computed such that during the convergence, the contribution of matrix inverse in the weight update is reduced, resulting in a more noise-robust algorithm. The paper further provides a convergence analysis of the proposed quasi-Newton algorithm, wherein a variable step-size is proposed to achieve a high initial convergence rate and a low steady-state error in the context of system identification applications.
More than Just a Diagnosis: A Multi-Task Approach to Analyzing Bipolar Disorder on Reddit via DeMHeM

Mental health conditions affect millions of people today. While existing work on predicting mental health conditions from social media text focuses largely on depression and similar conditions, other less prominent disorders like bipolar tend not to receive in-depth analysis. Furthermore, these works tend not to analyze or model the correlated nature of these different disorders and conditions. To account for the coexistence and correlation of multiple mental health conditions, this paper introduces DeMHeM, a novel multitask framework designed for the descriptive classification of bipolar and related mental health topics on online platforms like Reddit. By treating each mental health category as a separate task, DeMHeM leverages both the shared latent and task-specific semantic feature space by integrating sentence-level and topic-level embeddings. It further incorporates Focal Loss for joint learning, inter-task parameter sharing, and regularization decay to optimize the prediction for the naturally skewed imbalanced dataset. Hence, the model distinguishes between different mental health categories and also models the correlation among them by categorizing each post into potentially multiple mental health categories. Next, we focus on a more insightful analysis by leveraging the predicted outcome of the model to study how the discussions differ based on the type and coexistence of different mental disorders. We analyze the entirety of the “r/bipolar” subreddit by applying our trained model to predict a category and then implementing keyword extraction techniques on each predicted combination of mental health conditions to understand the specific nuances in the discussion of bipolar disorder. Our results show that DeMHeM surpassed the baseline models and can be used to understand the multi-faceted discussion of mental health topics for a given community.
Is Active Learning Green? An Empirical Study

Active learning (AL) is a machine learning (ML) approach that entails carefully choosing the most informative samples for annotation during training, aiming to minimize annotation costs. AL has recently emerged as a promising approach in the context of green ML, as an energy-efficient learning method on top of being data-efficient. Nevertheless, given the significant cost of AL, it might lead one to question the effectiveness of this approach in reducing computational costs and promoting green ML. In this paper, we conduct a comparative analysis of both fundamental and advanced active learning methods against a random baseline selection, aiming to demonstrate the efficacy of active learning to reduce the cost of training. This study demonstrates that, with careful tuning of hyperparameters like query size and pool size, AL is able to reduce runtime while maintaining competitive accuracy for classification tasks.
Study of Anti-hyperuricemia, Anti-inflammatory, and Anti-nociceptive Effects of Hylocereus undatus stem bark Extract in Animal Models

Dragon fruit, known as Hylocereus undantus (HU), has traditionally been utilized in Banyuwangi, East Java, Indonesia, for addressing gout and rheumatism. Despite its empirical use, there is a lack of reported pharmacological research on this plant. This study aimed to explore the properties of HU extract in treating hyperuricemia, inflammation, and pain. Adenine-potassium oxonate-induced mice were employed to evaluate anti-hyperuricemia activity, measuring uric acid, blood urea nitrogen (BUN), and creatinine serum levels. Carrageenan-induced paw edema in rats was used to assess anti-inflammatory activity, while acetic acid-induced abdominal writhing and hot plate tests were conducted to investigate anti-nociceptive effects. The results revealed that administering HU extracts at 400 mg/kg notably reduced uric acid levels (p<0.01). Moreover, doses of 100, 200, and 400 mg/kg showed significant reductions in BUN and creatinine serum levels (p<0.0001). At a dose of 400 mg/kg, HU extract exhibited a significant anti-inflammatory effect two hours post-administration, manifesting a 15% and 26% reduction in paw edema for male and female mice, respectively (p<0.005, p<0.001). Additionally, doses of 200 and 400 mg/kg demonstrated anti-nociceptive effects in acetic acid-induced abdominal constriction. Furthermore, at 400 mg/kg, the extract exhibited anti-nociceptive activity (p<0.001) three hours post-administration using the hot plate method. This study underscores the potent anti-hyperuricemia, anti-inflammatory, and anti-nociceptive properties of HU. The implications of these findings contribute significantly to comprehending the therapeutic potential of HU.
Cursos en línea masivos y abiertos (MOOCs, COMA)

La Busqueda Tematica Digital realizada para este numero trata sobre los Cursos en linea masivos y abiertos (MOOC: Massive Open Online Course, COMA: Curso on line masivo y abierto) , tambien conocidos con otras variantes del nombre, tales como: CAEM (Curso Abierto En linea Masivo) o CALGE (Curso Abierto en Linea a Gran Escala). Son cursos que se ofertan en Internet (en linea), que tienen la caracteristica de tener acceso masivo, libre, abierto y gratuito.
Multilocus Phylogeny and Characterization of Five Undescribed Aquatic Carnivorous Fungi (Orbiliomycetes)

The diversity of nematode-trapping fungi (NTF) holds significant theoretical and practical implications in the study of adaptive evolution and the bio-control of harmful nematodes. However, compared to terrestrial ecosystems, research on aquatic NTF is still in its early stages. During a survey of NTF in six watersheds in Yunnan Province, China, we isolated 10 taxa from freshwater sediment. Subsequent identification based on morphological and multigene (ITS, TEF1-α, and RPB2) phylogenetic analyses inferred they belong to five new species within Arthrobotrys. This paper provides a detailed description of these five novel species (Arthrobotrys cibiensis, A. heihuiensis, A. jinshaensis, A. yangbiensis, and A. yangjiangensis), contributing novel insights for further research into the diversity of NTF and providing new material for the biological control of aquatic harmful nematodes. Additionally, future research directions concerning aquatic NTF are also discussed.
Customer Risk Prediction: A Machine Learning Ensemble Approach

In order to understand the links between customer behaviour and growth, researchers and consultants have recently developed several customer metrics. Although metrics like NPS (Net Promoter Score) and Client Sentiment Scores attempt to capture certain customer behaviours, several prominent and widely used indicators of customer health have only shown modest correlations with growth. These metrics are unable to provide a comprehensive view of customer health and lack predictive capacity. Early customer metrics attempted to address consumer motivations; however, the issues that impact consumer loyalty are intricate and go beyond conventional and more general risk metrics. This paper introduces a new methodology for predicting customer risk, focusing on comprehending the important risk factors and providing a holistic overview. The procedure involves gathering and organizing pertinent data that captures various risk factors and features, analysing it, and employing machine learning methods. Once verified, the model is implemented to generate predictions for new client data, assisting businesses in making informed decisions and taking proactive steps to establish stronger client relationships.
Recomendaciones para una caficultura sostenible adaptada al clima en Huehuetenango Guatemala y las Segovias Nicaragua

El cultivo del café, corresponde a un rubro de mucha importancia en Guatemala y Nicaragua ya que en toda la cadena de producción genera efectos relacionados con los medios de vida medioambiente y desarrollo social, sin embargo, por ser un cultivo que es explota de manera extensiva y esta propenso a afectaciones de origen climático, de plagas y enfermedades. Sumado a esto el mal manejo de los cafetales puede incidir en bajas producción y por ende en menos ingresos para las familias productoras. Por lo tanto, este documento está orientado a generar algunas recomendaciones en base a análisis de vulnerabilidad integrales realizados en las partes altas y bajas de las zonas cafetaleras de Guatemala y Nicaragua.
Alternative Erase Verify: The Optimization for Longer Data Retention of NAND FLASH Memory

The NAND FLASH memory is a popular type of memory that is with a DRAM (Dynamic Random Ac-cess Memory). NAND Flash memory is a non-volatile memory that can retain data even without power. NAND FLASH memory is likely to increase the level of integration than the NOR-type cell. Because NAND FLASH memory is advantageous in miniaturization, it has been widely used in the eMMC (mobile devices) and SSD (Solid State Drive). This is because the NAND Flash Memory is advantageous for high bandwidth operations than NOR Flash.
Multi-model genome-wide association studies for appearance quality in rice

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.
Prospective Trial to Compare Direct and Indirect Laryngoscopy Using C-MAC PM® with Macintosh Blade and D-Blade® in a Simulated Difficult Airway

Objective. Evaluation of C-MAC PM® in combination with a standard Macintosh blade size 3 in direct and indirect laryngoscopy and D-Blade® in indirect laryngoscopy in a simulated difficult airway. Primary outcome was defined as the best view of the glottic structures. Secondary endpoints were subjective evaluation and assessment of the intubation process. Methods. Prospective monocentric, observational study on 48 adult patients without predictors for difficult laryngoscopy/tracheal intubation undergoing orthopedic surgery. Every participant preoperatively received a cervical collar to simulate a difficult airway. Direct and indirect laryngoscopy w/o the BURP maneuver with a standard Macintosh blade and indirect laryngoscopy w/o the BURP maneuver using D-Blade® were performed to evaluate if blade geometry and the BURP maneuver improve the glottic view as measured by the Cormack-Lehane score. Results. Using a C-MAC PM® laryngoscope, D-Blade® yielded improved glottic views compared with the Macintosh blade used with either the direct or indirect technique. Changing from direct laryngoscopy using a Macintosh blade to indirect videolaryngoscopy using C-MAC PM® with D-Blade® improved the Cormack-Lehane score from IIb, III, or IV to I or II in 31 cases. Conclusion. The combination of C-MAC PM® and D-Blade® significantly enhances the view of the glottis compared to direct laryngoscopy with a Macintosh blade in patients with a simulated difficult airway. Trial Registration Number. This trial is registered under number NCT03403946.
Big Data Management System Architectures: From Opportunities to Challenges [Vision]

Over the years, data management systems have undergone significant transformations adapting to advancements in hardware, diverse data types, and varying workloads. Numerous architectures have been proposed to more effectively address the customer needs, taking into account rapidly evolving factors like emerging data types, shifting workload and access patterns, and modern hardware innovations. With emergence of cloud infrastructures and their improved capability in seamless scaling, the shared-nothing architecture, which dominated the field for about three decades, is now being replaced with disaggregated architectures. The considerable cost of cloud resources has significantly increased the interest in further disaggregating resources, enabling them to scale independently of one another as required. Consequently, cost-sharing via multi-tenancy and the benefits of pay-for-use billing policies in serverless computing have become pivotal in shaping the design of new database architectures. This paper provides a concise overview of database architecture evolution and their triggers for change, the contemporary state-of-the-art structures, and highlights open research problems for future exploration.
Royal Navy logbooks as secondary sources and their use in climatic investigations: introducing the log‐board

This investigation encompasses the first presentation of the log‐board, which was the primary source used to compile logbooks on board Royal Navy vessels in the 18th century. All wind force and weather descriptions during the day were entered on the log‐board before they were transcribed to the logbook, which is why the logbook should be approached as a secondary source. This study shows that although Royal Navy logbooks represent valuable compilations of what was written on the log‐board, it cannot be ascertained exactly how the logbooks were compiled. Seven logbooks from the Cumberland for August 1782 were evaluated by comparing them to the log‐board from the same vessel, and the purpose was to determine the validity of logbook wind and weather entries. The results show that wind direction was most consistently transcribed according to the log‐board and, furthermore, that a majority of the wind force entries derived from the 1 p.m. entry on the log‐board. The kappa statistic shows that both wind force and weather conditions were most consistently copied from the 1 p.m. entry on the log‐board. The study concludes that the first wind force and weather entries in the logbooks were not made at noon when the vessel was positioned. While the positioning of the vessel was made at the end of the nautical day, the first wind force and weather entries most likely describe the conditions 23 h earlier.
An analysis of the Factors Influencing Psychological Resilience of Chinese Gay Men Based on Qualitative Research Perspectives

The mental health status of homosexuals has attracted more and more attention from the society. Based on the influence of factors such as the pressure of the current social environment, the group faces the pressure from itself, the family and the society, the mental health level is also affected. Psychological resilience plays an important protective factor in mental health, so this study explores the influencing factors of psychological resilience of Chinese gay men, to provide scientific basis for maintaining the mental health level of the group. In this study, 16 gay men were interviewed by qualitative interview method. The results show that the main factors affecting the psychological resilience of gay men are: external environmental resource factors (parental rearing style, social support), internal individual factors (self-esteem, self-efficacy, personality, coping style), and the interaction of all factors, so as to form individual psychological resilience. In the future, the corresponding psychological intervention plan can be designed according to these factors to intervene in the low resilience of the male gay group, so as to promote the harmonious development of their body and mind.
Exploring a Discriminative Metric Network for Few-Shot SAR Target Classification

Few-shot learning aims to learn a classifier for image classification when only a few training samples are available. Recent researches have shown that using local descriptors instead of holistic image representations can provide more informative features. However, local descriptors only capture local information within an image while disregarding global contextual information, leading to decreased accuracy in synthetic aperture radar (SAR) target classification. To address this issue, we propose a novel discriminative metric network (DMnet) for few-shot SAR target classification. The DMnet comprises global-level metric relationship and local-level metric relationship. Among them, the local-level metric relation is computed by an image-to-class metric module that measures the similarity of local descriptors. Simultaneously, we introduce global pooling to obtain the global information for each local descriptor, reducing the influence of local background noise. Subsequently, a prototype-based metric module is utilized to calculate the similarity of descriptors. To better integrate these two types of metric relations, we design an adaptive metric fusion module to achieve more discriminative metric performance. Experimental results on the MSTAR dataset demonstrate the effectiveness of the proposed DMnet.
Wavelet Based Machine Learning Algorithms for Wildfire Prediction

It is estimated that there are 70,000 wildfires every year in the United States alone. These wildfires pose a significant danger to humans, both from property damage and health implications; air quality can drastically decrease from the resulting smoke, leading to over 33000 premature deaths each year [1]. As a result, it is imperative that improvements to wildfire prediction models be made. Previous research has demonstrated the effectiveness of Machine Learning (ML) methods such as XGBoost and Convolutional Neural Network (CNN). In this research, we utilize wavelet analysis to preprocess our data by applying a discrete wavelet transform (DWT) in order to increase the accuracy of our models in predicting the next day's wildfire danger in certain counties in California. We input this DWT transformed data into an XGBoost model and compare the model performance to a traditional model (one trained on non-DWT data). We also apply DWT on aerial images data set from Canada and input the results into a convolutional neural network (CNN) to investigate the effects of DWT transformed image data and to determine if such wavelet based CNN can accurately distinguish between days with and days without a wildfire. We find that the use of DWT significantly improves model performance by denoising the data as well as adding more information in the form of hidden features for the model to train on.
Long-Term Modeling and Forecasting of the Load at a Substation in Lomé Zone A, Togo

Developing an econometric model for estimating long-term electricity demand in Lomé Zone A until 2023 is essential for effective energy planning, enabling optimized demand management and efficient production strategies. Given the complexity of factors affecting electric load, we introduce a hybrid ARIMA/GARCH model, combining ARIMA, GARCH, and LSTM for more precise predictions. This approach facilitates the selection of the optimal model based on performance and reliability. Results show that both ARIMA/GARCH and LSTM models yield good validation results, with the ARIMA/GARCH model excelling in generating realistic predictions, boasting root mean squared errors and mean absolute errors of 17.798 and 16.459, respectively. This highlights the reliability of the ARIMA/GARCH model, providing a solid foundation for enhanced energy management and planning in this growing region.
Multiple inversions of Rayleigh wave dispersion curve for geotechnical site characterization using particle swarm optimization dan genetic algorithm

The inversion of the Rayleigh wave dispersion curve is a crucial step in the multi-channel analysis of surface waves (MASW) method, used to obtain the shear wave velocity (Vs) profile. The nonlinear and multimodal nature of the dispersion curve makes a global optimization approach, such as particle swarm optimization (PSO) and genetic algorithm (GA), the optimal choice for inversion. This study aims to compare the performance of multiple inversions of PSO (MI-PSO) and multiple inversions of GA (MI-GA) in solving the inversion problem of the Rayleigh wave dispersion curve. The test results indicate that the utilized MI-PSO outperforms MI-GA in terms of computational time and accuracy of the obtained model