Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Control of a Climbing Robot for Autonomous Vertical Gardening

This paper focuses on the development of a novel climbing robot that is designed for autonomous maintenance of vertical gardens in urban environments. The robot, designed with a unique five-legged structure, is equipped with a range of electrical and mechanical components, enabling it to autonomously navigate and maintain a specially designed vertical garden wall facilitating interactive maintenance and growth monitoring. The motion planning and control of the robot were developed to ensure precise and adaptive movement across the vertical garden wall. Advanced algorithms were employed to manage the complex dynamics of the robot’s movements, optimizing its efficiency and effectiveness in navigating and maintaining the garden structure. The operation of the robot in maintaining the vertical garden was evaluated during a two-week trial where the robot successfully performed nearly 8000 leg movements, with only 0.6% requiring human intervention. This demonstrates a high level of autonomy and reliability. This study concludes that the pentapod robot demonstrates significant potential for automating the maintenance of vertical gardens, offering a promising tool for enhancing urban green spaces.
Augmentation of Fingerprints for Indoor BLE Localization Using Conditional GANs

Location estimation in indoor environments using radiofrequency (RF) has garnered considerable attention in recent years owing to the widespread adoption of mobile devices. RF-based fingerprinting—a direct approach that allows location estimation based on observed signals—relies on manual surveys during the offline phase to create a radio map with coordinates and RF measurements at multiple locations. The accuracy of RF fingerprint-based localization is proportional to the number of reference points. However, conventional site survey procedures incur substantial expenses. To alleviate the workload of site surveys and address the challenge of incomplete fingerprint databases, we propose a data-augmentation method to complement existing fingerprint data. Our approach leverages a conditional generative adversarial network with long short-term memory (CGAN-LSTM) prediction model to effectively learn the intricate patterns inherent in the initial training data and generate high-quality synthetic data that align with the underlying data distribution. In an experimental evaluation conducted on a real testbed, our data augmentation framework increased the average localization accuracy by 15.74% compared with fingerprinting without data augmentation. Compared with linear interpolation, inverse distance weighting, and Gaussian process regression, the proposed approach demonstrates an average accuracy improvement ranging from 1.84% to 14.04%, achieving average accuracies of 1.065 and 1.956 m in both scenarios. In experiments conducted in two typical indoor environments using sparse data, the proposed approach substantially reduced localization error and proved comparable to state-of-the-art data-augmentation methods.
The Prevalence of Clinically Undiagnosed Depression in Patients With Autoimmune Bullous Diseases Seen at Inkosi Albert Luthuli Central Hospital, Durban, South Africa

Background Chronic autoimmune bullous diseases have been associated with major depression in previous studies. This has been attributed to inflammatory cytokines, chronic pain, and the chronicity and debilitating nature of the disease. As no similar studies have been conducted in our setting, we aimed to determine the prevalence and severity of clinically undiagnosed depression in patients with autoimmune bullous diseases. Methodology We performed a cross-sectional study among outpatients managed in a bullous disease clinic at Inkosi Albert Luthuli Central Hospital, a quaternary provincial hospital in Durban, South Africa. Results A total of 44 participants were recruited and included in this study. The majority of the participants were females (29, 65.9%). The most common autoimmune bullous diseases were pemphigus vulgaris (19, 43.2%), bullous pemphigoid (18, 40.9%), and pemphigus foliaceus (5, 11.4%). The overall prevalence of at least mild and at least moderate depression in patients with autoimmune bullous diseases in our clinic was 52.3% and 20.5%, respectively. Pemphigus vulgaris showed the highest median Patient Health Questionnaire-9 score compared to other bullous dermatoses. Statistically significant differences were observed between females and males for the duration with the bullous disease (p = 0.014) and between intraepidermal and subepidermal disease for both the mean age (p = 0.038) and age at onset (p = 0.015). Conclusions Clinically undiagnosed depression is common in patients with autoimmune bullous disease. Its frequency and severity may differ depending on the underlying autoimmune bullous disease and possibly other factors. Dermatologists should always be alert to this fact and prompt psychiatric consultation as required to comprehensively manage these patients.
Sistemática y distribución espacio-temporal del ictiopláncton en la costa catalana

SE HA ESTUDIADO EL CONJUNTO DEL ICTIOPLANCTON PROCEDENTE DE SEIS CAMPANAS OCEANOGRAFICAS REALIZADAS DE ABRIL A OCTUBRE DE 1983 EN LA COSTA CATALANA. ESTA ZONA PRESENTA UNA ALTA HETEROGENEIDAD, TANTO TOPOGRAFICA COMO HIDROGRAFICA, Y SE HA OBSERVADO QUE ESTA HETEROGENEIDAD INFLUYE EN LAS PAUTAS DE DISTRIBUCION DE LAS ESPECIES. EL ESTUDIO DEL ICTIOPLANCTON EN LA COSTA CATALANA SE HA ABORDADO TANTO A NIVEL DE ESPECIES, CONSIDERADAS INDIVIDUALMENTE, COMO A NIVEL DEL CONJUNTO DE LA POBLACION DE LARVAS. PARA CADA ESPECIE SE HA REALIZADO UN ESTUDIO SISTEMATICO, SE HA DESCRITO SU DISTRIBUCION ESPACIO-TEMPORAL, Y EN LAS MAS ABUNDANTES SE HA ESTUDIADO SU DISTRIBUCION DE FRECUENCIAS DE TALLA. LA DETERMINACION DEL AREA DE DISTRIBUCION Y DE LA PRESENCIA ESTACIONAL DE CADA ESPECIE, HA PERMITIDO AMPLIAR O DELIMITAR SU AREA Y PERIODO DE PUESTA Y CONOCER LOS FACTORES GEOGRAFICOS, HIDROGRAFICOS Y TOPOGRAFICOS QUE INTERVIENEN EN LA DELIMITACION DE LA FREZA. SE HAN IDENTIFICADO UN TOTAL DE 106 ESPECIES, DE LAS CUALES SE HA DESCRITO POR PRIMERA VEZ LA SERIE DE DESARROLLO LARVARIO DE CITHARUS MACROLEPIDOTUS Y ALGUNOS ESTADIOS LARVARIOS DE CATAETYX SP. SE HAN AMPLIADO. Y EN ALGUNOS CASOS COMPLETADO LAS DESCRIPCIONES DEL DESARROLLO LARVARIO DE OTRAS 13 ESPECIES. SE HA CITADO POR PRIMERA VEZ EN EL MEDITERRANEO LA PRESENCIA DE GYMNAMMODYTES SEMISQUAMATUS.
Combining inception-V3 and support vector machine for garbage classification

The global volume of trash has increased due to population growth and consumption, with a growing variety of materials and materials being generated. Inadequate garbage disposal practices, particularly in plastics, have led to environmental contamination and pollution in various regions. Artificial Intelligence (AI) technologies, particularly in machine learning, have demonstrated significant potential in trash sorting, particularly in the realm of machine learning. The Inception-V3 model and Support Vector Machines (SVM) are used in this study to extract relevant features and classify garbage categories. The Inception-V3 and SVM combination exhibits superior performance, with greater accuracy and F1 score compared to other methods. The radial basis function (RBF) kernel is the most optimal model of SVM, but it faces challenges in accurately categorizing the "trash" category due to limited data and resemblance to the "paper" class. The system developed in this study has a high level of effectiveness, with superior accuracy and F1 scores of 0.876 and 0.874, respectively.
Estimating transit times of noninteracting pollutants through compacted soil materials

Pollutant travel times through compacted soil materials cannot be accurately predicted from the permeability (saturated hydraulic conductivity) alone, Travel time is also dependent on the effective porosity of the material; i.e., the portion of the total porosity that contributes significantly to fluid flow. Once permeability and effective porosity are determined for a selected material, travel times for noninteracting pollutants through specified thicknesses of compacted material at specified hydraulic gradients can easily be predicted. This paper presents a straightforward method of determining the effective porosity of compacted soil materials and compares measured and predicted solute breakthrough times for three compacted soil materials, The determination of effective porosity is based upon the total porosity and the spread on a log scale in the pore sizes of a compacted sample, Once the total porosity and pore size distribution information are obtained for a particular sample, the effective porosity can be determined by using a graphical solution. Pollutant travel time, T, through a compacted soil (i.e., when pollutant first appears at the bottom of the liner) can be predicted by T=EL/KI; where E is effective porosity, L is thickness of compacted sample, K is sample permeability, and I is hydraulic gradient. Chloride travel times through compacted samples of glacial till, loess, and paleosol materials were measured as less than 208 minutes (min.), between 15 and 30 min., and between 250 and 380 min., respectively. Predictions of noninteracting solute breakthrough for the glacial till, loess, and paleosol materials were 227 min., 32 min., and 270 min., respectively. Thus, the predictions are reasonably close to measurements. Further experiments using thicker soil samples are under way. Disciplines Agricultural Science | Agronomy and Crop Sciences | Soil Science Comments This proceeding was published as Horton, R. , M. L. Thompson, and J. F. McBride. 1985. Estimating transit times of noninteracting pollutants through compacted soil materials. Land Disposal of Hazardous Waste: Proceedings of the Eleventh Annual Solid Waste Research Symposium: 275-282. Rights Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. This conference proceeding is available at Iowa State University Digital Repository: http://lib.dr.iastate.edu/agron_conf/68 ESTIMATING TRANSIT TIMES OF NONINTERACTING POLLUTANTS THROUGH COMPACTED SOIL MATERIALS Robert Horton, Michael L. Thompson, and John F, McBride Department of Agronomy Iowa State University Ames, Iowa 50011
Anthropometric and Biochemical Correlations of Insulin Resistance in a Middle-Aged Maltese Caucasian Population

Background Insulin resistance (IR) is associated with increased cardiovascular disease risk, and with increased all-cause, cardiovascular, and cancer mortality. A number of surrogate markers are used in clinical practice to diagnose IR. The aim of this study was to investigate the discriminatory power of a number of routinely available anthropometric and biochemical variables in predicting IR and to determine their optimal cutoffs. Methods We performed a cross-sectional study in a cohort of middle-aged individuals. We used receiver operator characteristics (ROC) analyses in order to determine the discriminatory power of parameters of interest in detecting IR, which was defined as homeostatic model assessment-insulin resistance ≥2.5. Results Both the lipid accumulation product (LAP) and visceral adiposity index (VAI) exhibited good discriminatory power to detect IR in both males and females. The optimal cutoffs were 42.5 and 1.44, respectively, in males and 36.2 and 1.41, respectively, in females. Serum triglycerides (TG) and waist circumference (WC) similarly demonstrated good discriminatory power in detecting IR in both sexes. The optimal cutoffs for serum TG and WC were 1.35 mmol/L and 96.5 cm, respectively, in men and 1.33 mmol/L and 82 cm, respectively, in women. On the other hand, systolic and diastolic blood pressure, liver transaminases, high-density lipoprotein cholesterol, serum uric acid, ferritin, waist-hip ratio, “A” body shape, thigh circumference, and weight-adjusted thigh circumference all had poor discriminatory power. Conclusions Our data show that LAP, VAI, TG, and WC all have good discriminatory power in detecting IR in both men and women. The optimal cutoffs for TG and WC were lower than those currently recommended in both sexes. Replication studies are required in different subpopulations and different ethnicities in order to be able to update the current cut points to ones which reflect the contemporary population as well as to evaluate their longitudinal relationship with longer-term cardiometabolic outcomes.
The Phytochemical Agathisflavone Modulates miR146a and miR155 in Activated Microglia Involving STAT3 Signaling

MicroRNAs (miRs) act as important post-transcriptional regulators of gene expression in glial cells and have been shown to be involved in the pathogenesis of neurodegenerative diseases, including Alzheimer’s disease (AD). Here, we investigated the effects of agathisflavone, a biflavonoid purified from the leaves of Cenostigma pyramidale (Tul.), on modulating the expression of miRs and inflammatory mediators in activated microglia. C20 human microglia were exposed to oligomers of the β-amyloid peptide (Aβ, 500 nM) for 4 h or to lipopolysaccharide (LPS, 1 µg/mL) for 24 h and then treated or not with agathisflavone (1 µM) for 24 h. We observed that β-amyloid and LPS activated microglia to an inflammatory state, with increased expression of miR-146a, miR-155, IL1-β, IL-6, and NOS2. Treatment with agathisflavone resulted in a significant reduction in miR146a and miR-155 induced by LPS or Aβ, as well as inflammatory cytokines IL1-β, IL-6, and NOS2. In cells stimulated with Aβ, there was an increase in p-STAT3 expression that was reduced by agathisflavone treatment. These data identify a role for miRs in the anti-inflammatory effect of agathisflavone on microglia in models of neuroinflammation and AD.
End-to-End Bayesian Networks Exact Learning in Shared Memory

Bayesian networks are important Machine Learning models with many practical applications in, e.g., biomedicine and bioinformatics. The problem of Bayesian networks learning is <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math><alternatives><mml:math><mml:mi mathvariant="script">NP</mml:mi></mml:math><inline-graphic xlink:href="zola-ieq1-3366471.gif"/></alternatives></inline-formula>-hard and computationally challenging. In this article, we propose practical parallel exact algorithms to learn Bayesian networks from data. Our approach uses shared-memory task parallelism to realize exploration of dynamic programming lattices emerging in Bayesian networks structure learning, and introduces several optimization techniques to constraint and partition the underlying search space. Through extensive experimental testing we show that the resulting method is highly scalable, and it can be used to efficiently learn large globally optimal networks.
Projecting biological impacts from climate change like a climate scientist

Our ability to project changes to the climate via anthropogenic forcing has steadily increased over the last five decades. Yet, biologists still lack accurate projections about climate change impacts. Despite recent advances, biologists still often rely on correlative approaches to make projections, ignore important mechanisms, develop models with limited coordination, and lack much of the data to inform projections and test them. In contrast, atmospheric scientists have incorporated mechanistic data, established a global network of weather stations, and apply multi‐model inference by comparing divergent model projections. I address the following questions: How have the two fields developed through time? To what degree does biological projection differ from climate projection? What is needed to make similar progress in biological projection? Although the challenges in biodiversity projections are great, I highlight how biology can make substantial progress in the coming years. Most obstacles are surmountable and relate to history, lag times, scientific culture, international organization, and finances. Just as climate change projections have improved, biological modeling can improve in accuracy by incorporating mechanistic understanding, employing multi‐model ensemble approaches, coordinating efforts worldwide, and validating projections against records from a well‐designed network of biotic stations. Now that climate scientists can make better projections of climate change, biologists need to project and prevent its impacts on biodiversity.
Stability Analysis of Buck Converter Based on Passivity-Based Stability Criterion

Recently, the stability of DC microgrids has attracted increasing attention. The traditional stability analysis method cannot not meet the requirements for the complexity and bidirectional energy flow of the system. In this paper, a passivity-based stability criterion (PBSC) is proposed to analyze the stability of the cascade system. In order to realize the passivity of the system, an improved feedback control method based on the traditional double-loop control strategy is proposed, which will improve the stability region and guarantee the passivity of the system. Moreover, a Buck-CPL simulation model is established based on MATLAB/Simulink R2008, and the correctness of the theoretical analysis is verified by experiments.
Aqua-Sense: Relay-Based Underwater Optical Wireless Communication for IoUT Monitoring

Underwater wireless optical communication (UWOC) performance is negatively impacted by challenges such as water turbulence, restricted range, and misalignment. These challenges can impact the feasibility of large-scale deployment. In comparison to conventional acoustic and radio frequency (RF) communication, UWOC can play a pivotal role in connecting various Internet of Underwater Things (IoUT) devices. This research presents the design and evaluation of “aqua-sense,” a relay-based UWOC system aimed at enhancing communication link performance and expanding the reception area of the optical receiver. To harness diversity gain, the optical relay incorporates combining techniques such as Equal Gain Combining (EGC), Majority Logic Combining (MLC), and Selection Combining (SC) to enhance its performance. Additionally, the optical relay, referred to as the “opto-relay,” is empowered by a channel-aware algorithm to further improve communication link performance. In moderately turbid water conditions with a turbidity level of 25 NTU, the aqua-sense system achieved a packet success rate of 68% at a communication link distance of up to 7.5 meters, transmitting at a rate of 0.2 Mbps. Additionally, the sensor node, “opto-sense,” achieved a consistent transmission rate of 0.5 Mbps in clear water with a turbidity of 0.01 NTU. These results remained stable even when moderate water waves with a displacement rate of 5 liters/min and air bubbles with an airflow rate of 5 liters/min were present within a 2-meter communication link range.
Revisiting Lateral-BTBT Gate-Induced Drain Leakage in Nanowire FETs for 1T-DRAM

In this work, we revisit the lateral band-to-band tunneling (L-BTBT) gate-induced drain leakage (GIDL) in gate-all-around (GAA) nanowire field-effect transistors (NWFETs) which is otherwise detrimental to logic operation and propose design guidelines for amplifying hole accumulation and floating body effects for realizing scalable one-transistor (1T)-dynamic random access memory (DRAM) with appreciable sense margin, high retention, and low write energy. The performance of the proposed NWFET-based 1T-DRAM was evaluated using calibrated 3-D TCAD simulations. The proposed NWFET-based 1T-DRAM exploiting L-BTBT GIDL for write operation exhibits an ultralow write energy of 3.78 fJ, a high sense margin of 89.19 nA, and an appreciable retention time of 5 ms even for an ultrashort gate length of 20 nm.
Uma Busca Ordenada Branch-and-Bound para solução do Problema de Inferência Transdutiva usando Máquinas de Vetores Suporte

Nesse artigo é apresentado um novo método para resolver o problema de inferência transdutiva cujo objetivo é predizer os rótulos binários de um subconjunto de pontos de interesse de uma função de decisão desconhecida. É utilizada a Máquina de Vetores Suporte para tentar encontrar um limite de decisão. Para obter a hipótese de margem máxima sobre as amostras rotuladas e não rotuladas, é empregada uma busca ordenada (best-first) admissível com base nos valores de margem. Evidências empíricas sugerem que esta solução globalmente ótima pode obter excelentes resultados no problema de transdução. Devido à estratégia de seleção usada, o algoritmo de busca explora apenas uma pequena fração de amostras não rotuladas, tornando-a eficiente para bases de dados de tamanho médio. Os resultados obtidos foram comparados com os resultados da Transductive Support Vector Machine, demonstrando melhores resultados em valores de margem.
A Comparison Analysis of a New Switched-Inductor and Conventional Split Source Inverter Structures

Several impedance source converters are receiving more attention currently due to their bucking-boosting capability in a single conversion step, reduced system cost, size, footprint, and control system complexity compared to dual-stage structures. This paper introduces a comparative analysis of a recent Split Source Inverter (SSI) topology and one of its modified versions which is called Switched-inductor Split Source Inverter (SL-SSI). Obviously, the SSI structure advances many features compared to the conventional Z-Source (ZSI), and quasi-Z-Source Inverters (qZSI) such as minimized component voltage and current stress for single-stage voltage boosting properties. In addition, the mathematical modulation of the SSI and SL-SSI are analyzed for inverter power elements design and selection strategy, and control system simplification. The theoretical operation of the two topologies is investigated and analyzed considering the Sinusoidal Pulse-width Modulation (SPWM) strategy. The SSI and SL-SSI are designed for 10 kW system ratings and implemented using Opal RT OP5410.
Impacts of Assimilating Vertical Velocity, Latent Heating, or Hydrometeor Water Contents Retrieved From a Single Reflectivity Data Set

Assimilation of observation data in cloudy regions has been challenging due to the unknown properties of clouds such as cloud depth or cloud drop size distributions (DSD). Attempts to assimilate data in cloudy regions generally assume a DSD, but most assimilation systems fail to maintain consistency between models and the observation data, as each has its own set of assumptions. This study tries to retain the consistency between the forecast model and the retrieved data by developing a Bayesian retrieval scheme that uses the forecast model itself for the a priori database. Through the retrieval algorithm, vertical profiles of three variables related to the development of tropical cyclones, including vertical velocity (VV), latent heating (LH), and hydrometeor water contents (HYDRO), are derived from the same reflectivity observation. Each retrieved variable is assimilated in the data assimilation system using a flow‐dependent forecast error covariance matrix. The simulations are compared to evaluate the respective impact of each variable in the assimilation system. In this study, three assimilation experiments were conducted for two hurricane cases captured by the Global Precipitation Measurement satellite: Hurricane Pali (2016) and Hurricane Jimena (2015). Analyses from these two hurricane cases suggest that assimilating LH and HYDRO have similar impacts on the assimilation system while VV has less of an impact than the other two variables. Using these analyses as an initial condition for the forecast model reveals that the assimilations of retrieved LH and HYDRO were able to improve the track forecast as well.