Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme rainfall in West Africa: A regional modeling

In a world of increasing exposure of populations to natural hazards, the mapping of extreme rainfall remains a key subject of study. Such maps are required for both flood risk management and civil engineering structure design, the challenge being to take into account the local information provided by point rainfall series as well as the necessity of some regional coherency. Two approaches based on the extreme value theory are compared here, with an application to extreme rainfall mapping in West Africa. The first approach is a local fit and interpolation (LFI) consisting of a spatial interpolation of the generalized extreme value (GEV) distribution parameters estimated independently at each station. The second approach is a spatial maximum likelihood estimation (SMLE); it directly estimates the GEV distribution over the entire region by a single maximum likelihood fit using jointly all measurements combined with spatial covariates. Five LFI and three SMLE methods are considered, using the information provided by 126 daily rainfall series covering the period 1950–1990. The methods are first evaluated in calibration. Then the predictive skills and the robustness are assessed through a cross validation and an independent network validation process. The SMLE approach, especially when using the mean annual rainfall as covariate, appears to perform better for most of the scores computed. Using the Niamey 104 year time series, it is also shown that the SMLE approach has the capacity to deal more efficiently with the effect of local outliers by using the spatial information provided by nearby stations.
Research on the Problem of Constructing Scent Lineups of Olfactory Analysis

The paper considers the problem of construction scent lineups for olfactory research. This problem has a combinatorial nature with multiple constraints dictated by the practical field. We propose to consider this problem as a selection of words from some languages. The construction algorithm of words for the first two lineups of the olfactory research is developed and the words number estimations of languages for these lineups are given.
Sea surface dynamic height topography and the North Equatorial Countercurrent as inferred from a linear model

Hellerman and Rosenstein's (1983) climatological winds are used to force a linear, continuously stratified model of the tropical Atlantic. The dynamic height topography relative to 500 meters is compared with available data. Special attention is paid to the computed North Equatorial Countercurrent. We conclude that most of the observed seasonal variations can be explained using simple linear theory.
The Impact of Symbolic Culture on the Understanding of Visual Figuration in a Cross-Cultural Environment

This paper focuses on the understanding of visual rhetorical figures as they appear in today’s globalized advertising. A sound theoretical model (i.e., Phillips and McQuarrie 2004) is first reviewed to select a good representative sampling (i.e., 9 advertisements) of the behaviour domain measured. Then, a population of 60 English language students are questioned regarding these 9 advertisements. This study uses a combined methodology, resorting to basic quantitative data to reveal qualitative findings. Our discussion supports prior research (Callister and Stern 2008: 148) indicating that culture is essential for image understanding in a cross-cultural environment and stands in opposition to those who act on the assumption that visuals are universal and therefore understandable in all cultures of the world (Levitt 1983).
Variance characteristic preserving common spatial pattern for motor imagery BCI

Introduction The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space. Methods This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly. Results The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm. Discussion The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.
Development of a semi-automated MHC-associated peptide proteomics (MAPPs) method using streptavidin bead-based immunoaffinity capture and nano LC-MS/MS to support immunogenicity risk assessment in drug development

Major histocompatibility complex (MHC)-Associated Peptide Proteomics (MAPPs) is an ex vivo method used to assess the immunogenicity risk of biotherapeutics. MAPPs can identify potential T-cell epitopes within the biotherapeutic molecule. Using adalimumab treated human monocyte derived dendritic cells (DCs) and a pan anti-HLA-DR antibody (Ab), we systematically automated and optimized biotin/streptavidin (SA)-capture antibody coupling, lysate incubation with capture antibody, as well as the washing and elution steps of a MAPPs method using functionalized magnetic beads and a KingFisher Magnetic Particle processor. Automation of these steps, combined with capturing using biotinylated-Ab/SA magnetic beads rather than covalently bound antibody, improved reproducibility as measured by minimal inter-and intra-day variability, as well as minimal analyst-to-analyst variability. The semi-automated MAPPs workflow improved sensitivity, allowing for a lower number of cells per analysis. The method was assessed using five different biotherapeutics with varying immunogenicity rates ranging from 0.1 to 48% ADA incidence in the clinic. Biotherapeutics with ≥10%immunogenicity incidence consistently presented more peptides (1.8-28 fold) and clusters (10-21 fold) compared to those with <10% immunogenicity incidence. Our semi-automated MAPPs method provided two main advantages over a manual workflow- the robustness and reproducibility affords confidence in the epitopes identified from as few as 5 to 10 donors and the method workflow can be readily adapted to incorporate different capture Abs in addition to anti-HLA-DR. The incorporation of semi-automated MAPPs with biotinylated-Ab/SA bead-based capture in immunogenicity screening strategies allows the generation of more consistent and reliable data, helping to improve immunogenicity prediction capabilities in drug development. MHC associated peptide proteomics (MAPPs), Immunogenicity risk assessment, in vitro/ex vivo, biotherapeutics, Major Histocompatibility Complex Class II (MHC II), LC-MS, Immunoaffinity Capture, streptavidin magnetic beads
Valuing water resources in Switzerland using a hedonic price model

In this paper, linear and spatial hedonic price models are applied to the housing market in Switzerland, covering all 26 cantons in the country over the period 2005–2010. Besides structural house, neighborhood and socioeconomic characteristics, we include a wide variety of new environmental characteristics related to water to examine their role in explaining variation in sales prices. These include water abundance, different types of water bodies, the recreational function of water, and water disamenity. Significant spatial autocorrelation is found in the estimated models, as well as nonlinear effects for distances to the nearest lake and large river. Significant effects are furthermore found for water abundance and the distance to large rivers, but not to small rivers. Although in both linear and spatial models water related variables explain less than 1% of the price variation, the distance to the nearest bathing site has a larger marginal contribution than many neighborhood‐related distance variables. The housing market shows to differentiate between different water related resources in terms of relative contribution to house prices, which could help the housing development industry make more geographically targeted planning activities.
A robotIc Radial palpatIon mechaniSm for breast examination (IRIS)

In this paper we present IRIS, a manipulator that is capable of applying contact forces when interacting with an object/stimulus, in increments in the order of mN up to 6N at 5 radial locations simultaneously. IRIS is based upon the contractual mechanism of its namesake: the iris diaphragm often found in cameras. Complete coverage of the surface of a realistic breast phantom is demonstrated using this contractual mechanism combined with control over the angle of incidence between the sensors and the stimulus using sim-to-real concepts. A significant amount of the complexity in control is outsourced to the morphology and compliance of the mechanism. The manipulator demonstrates the technological feasibility of a robotic clinical breast examination.
Semisupervised Deep Features of Time-Frequency Maps for Multimodal Emotion Recognition

Traditional approaches for emotion recognition utilize unimodal physiological signals. The effectiveness of such systems is affected by some limitations. To overcome them, this paper proposes a new method based on time-frequency maps that extract the features from multimodal biological signals. At first, the fusion of electroencephalogram (EEG) and peripheral physiological signal (PPS) is performed, and then, the two-dimensional discrete orthonormal Stockwell transform (2D-DOST) of the multimodal signal matrix is calculated to obtain time-frequency maps. A convolutional neural network (CNN) is then utilized to extract the local deep features from the absolute output of the 2D-DOST. Since there are uninformative deep features, the semisupervised dimension reduction scheme reduces them by balancing the generalization and discrimination. Finally, the classifier recognizes the emotion. The Bayesian optimizer finds the proper SSDR and classifier parameter values to maximize the recognition accuracy. The performance of the proposed method is evaluated on the DEAP dataset considering the two- and four-class scenarios through extensive simulations. This dataset consists of electroencephalograph (EEG) signals in 32 channels and peripheral physiological signals (PPSs) in eight channels from 32 subjects. The proposed method reaches the accuracy of 0.953 and 0.928 for two- and four-class scenarios, respectively. The results indicate the efficiency of the multimodal signals for detecting emotions compared to that of unimodal signals. Also, the results indicate that the proposed method outperforms the recently introduced ones.
Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach

This study examines the communications of English- and Spanish-speaking Twitter users through traditional and deep learning algorithms to automatically recognize whether they live with one of nine mental health conditions. We created two datasets in English and Spanish. The “diagnosed” set comprises the timeline of 1,500 users who explicitly reported in one or more of their posts having been diagnosed with one of the following: ADHD, Anxiety, Autism, Bipolar, Depression, Eating disorders, OCD, PTSD, and Schizophrenia. The “control” set comprises the timeline of 1,700 randomly selected users who had not disclosed a diagnosis. We extracted a variety of text features from the collected data, such as n-grams, q-grams, Part-of-speech (POS) tags, topic modeling, Linguistic Inquiry and Word Count (LIWC), and word embeddings, and trained traditional machine-learning and deep learning classifiers for two tasks: binary classification, to distinguish between diagnosed and non-diagnosed users, and multiclass classification, to identify the specific diagnosis. Overall, XGBoost and convolutional neural network (CNN) performed the best in the two classification tasks. Moreover, lexical attributes based on n-grams and q-grams are the ones that performed well in both datasets. Using our collected datasets, for binary classification, we achieved an AUC of 0.835 on the Spanish Twitter dataset using n-grams of words from one to three (UBT) and 0.846 on the English Twitter dataset with a 5-gram characters (C5) model. In multiclass classification, we obtained an AUC of 0.712 and 0.697 in the Spanish and English Twitter datasets, respectively.
Reducing the Vulnerability of Rural Households through Agroecological Practice : Considering the System of Rice Intensification (SRI)

The System of Rice Intensification is a cropping system based on agroecological principles that mobilizes existing biological potentials in crops and soil systems. SRI has shown in more than two dozen countries that yields can be raised substantially by managing plants, soil, water and nutrients differently, with less reliance on fertilizer and agrochemicals, lowering costs of production, and even reducing labor. This article considers how the vulnerability particularly of resource-limited households can be reduced by reorienting current agricultural practices.
Predicting Soldering Failure in Die-Cast Products Using a Surrogate Model Based on Geometrical Features

Die casting is a widely used technology in industrial production, particularly in the automotive industry, for its ability to mass-produce complex shapes quickly. A significant challenge in the process is predicting and minimizing defects such as "soldering failure. " This paper presents the development of a surrogate model that predicts soldering failure in die-cast products based on their geometrical features. By comparing the performance of the surrogate model to a conventional rule-based method, we find a significant improvement in accuracy. The surrogate model is constructed using 3D shape data of die-cast products and corresponding information on the occurrence of soldering defects, employing a machine-learning algorithm to create the model. Our results indicate that the surrogate model based on geometrical features effectively predicts the soldering phenomenon between the die and molten metal in the die-casting process. This research has potential applications in defect prediction for various die-casting processes and automatic shape optimization using VAE latent space.
VAFOR: Proactive Voice Assistant for Object Retrieval in the Physical World

In this paper, we present a proactive robotic voice assistant with a perceive-reason-act loop that carries out pick-and-place operations based on verbal commands. Unlike existing systems, our robot can retrieve a target object not only when the target is explicitly spelled out, but also given an indirect command that implicitly reflects the human intention or emotion. For instance, when the verbal command is “I had a busy day, so I didn’t have much to eat. ”, the target object would be something that can help with hunger. To successfully estimate the target object from indirect commands, our framework consists of separate modules for the complete perceive-reason-act loop as follows. First, for perception, it runs an object detector on the robot’s onboard computer to detect all objects in the surroundings and records a verbal command from a microphone. Second, for reasoning, a list of available objects as well as a transcription of the verbal command are integrated into a prompt for a Large Language Model (LLM) in order to identify the target object in the command. Finally, for action, a TurtleBot3 with a 5 DOF robotic arm finds the target object and brings it to the human. Our experiments show that with a properly designed prompt, the robot can identify the correct target object from implicit commands with at most 97% accuracy. In addition, it is shown that the technique of fine-tuning a language model based on the proposed prompt designing process amplifies the performance of the smallest language model by a factor of five. Our data and code are available at https://github.com/bekatan/vafor
Analysis of C-shape Slotted MSPA for 5G Sub Band Applications on Three Different Substrates

— A comparative analysis of a compact planar Square patch Microstrip Multiband antenna on three different substrates is proposed. The proposed design has a C-shaped slot etched on the square radiating part and the antenna is energized using microstrip feed line. RT Duroid ( 𝜺 𝒓 = 𝟐.𝟐) , Taconic ( 𝜺 𝒓 = 𝟑. 𝟐 ) and FR4 ( 𝜺 𝒓 = 𝟒. 𝟒 ) substrates are used for simulation analysis. The flow of current is modified by the C-shaped slot making the antenna to resonate at 3/4 and 6 bands for RT Duroid/Taconic and FR4 substrates respectively suitable for 5G sub GHz applications. The antenna has a compact dimension of 𝟑𝟐 × 𝟑𝟐 × 𝟏. 𝟔𝒎𝒎 𝟑 and exhibits a return loss, S 11 of less than -10dB for all the resonating frequencies for all three substrates. The analysis has been done by considering the S 11 (Return loss <-10 dB), Directivity, Antenna Gain, VSWR and surface current distribution. Table II provides the comparison of parameters for different substrate material.
Will common agricultural policy support save farm employment in the wine sector?

ABSTRACT The aim of this paper is to analyze whether different types of Common Agricultural Policy (CAP) subsidies maintained jobs or even created them on wine farms during the years 2013–2019 using a Hungarian Farm Accountancy Data Network panel dataset. Farm employment is separated into paid and unpaid labor. The initial decline in farm employment during the first three years of analysis was due to the decline in paid labor, but later there was rather stable development in both paid and unpaid labor. The dynamic panel regression results confirm that the employment in the previous year is the crucial driver for the employment of labor on wine farms. CAP subsidies for direct payments to farmers and, to a lesser extent, economic farm size, are positively associated with the employment of paid labor (and thus the employment of total wine farms-related labor), but not with employment of unpaid labor, and vice versa rented land share. These results and findings are robust independent of year-fixed effects. While CAP subsidies for direct payments to farmers supported the maintenance or increase in paid labor employment on wine farms, trade-offs arose regarding the efficiency of wine farms and their entrepreneurial-, innovation-, and market-added value-chain efforts.
Science Mapping of Meta-Analysis in Agricultural Science

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences.
Bioconversion of sludge from integrated paper and pulp industries.

Bioconversion is the environmentally sound alterna te available for the stabilization and ultimate dis posal of sludge. The main objective of the present study is to contextualize the knowledge, application and inv estigate systems for the onsite treatment of organic waste t hrough bioconversion by Vermicomposting. A blend of primary and secondary sludge of 80:20 and 60:40 rat ios respectively from integrated pulp and paper mil l was used for composting separately with and without foo d and nutrients. It was observed that blend in rati o of 80:20 with food and nutrients proved to be very useful as value added compost. Further bioactivity of these blends was performed through microbial assay.
Vertical Power Delivery and Heterogeneous Integration for High-Performance Computing

This paper presents a heterogeneously integrated vertical power delivery (power tree) system that combines a 180nm HV BCD 20V:4V hybrid converter, a 65nm 4V:1V switched-capacitor converter, and an interposer filled with $3\mathrm{D}$ nano-porus deep-trench flying and decoupling capacitors. A prototype of the overall system achieves a total efficiency of 87% from 20V to 0.9V, all while featuring high-voltage vertical delivery that minimizes lateral conduction losses and the total number of pins required for power/ground.
Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms

Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.