Miracle-dz/UniFAR-FTUs huggingface dataset:
Caterpillar Envenomation (Lepidopterism) in a Panamanian Jungle: About a Case

Caterpillar venom has the potential to cause acute pain and systemic symptoms in individuals seeking medical attention in the jungles of Panama. Although this is not an obligatory notifiable disease, the hazards associated with exposure to this animal are widely recognized within the local community. Here, we present a case of a patient who sought medical attention after being rescued from a river in a Panamanian jungle after feeling acute pain in an upper extremity associated with shortness of breath and how tropical medicine teleconsult allowed for quick identification of the cause and assisted in the management. About his case, we examine the phenomenon of caterpillar envenomation and suggest that further research is needed to assess the potential impact of climate change on this disease. Of particular concern is the likelihood of an increase in contact accidents. We recommend that scientists and public health officials work together to understand the mechanisms of this disease better and to develop effective strategies for prevention and treatment. Our analysis underscores the importance of ongoing monitoring and surveillance to ensure we are prepared for future outbreaks.
Lyapunov-Based Long Short-Term Memory (Lb-LSTM) Neural Network-Based Adaptive Observer

Long short-term memory (LSTM) neural networks excel at capturing short- and long-term dependencies, making them powerful tools for system identification and state estimation. Their unique design improves memory capabilities by retaining important information and discarding irrelevant data over time. However, due to mathematical challenges involved in developing adaptive control methods for LSTMs, their training is predominantly limited to offline methods. This letter develops a Lyapunov-based (Lb-) LSTM observer for state estimation in nonlinear systems. The Lb-LSTM weights adapt in real-time using Lyapunov-based stability-driven adaptation laws. A nonsmooth Lyapunov-based stability analysis ensures state estimation error convergence and stability of the overall Lb-LSTM architecture. To validate the developed observer design, simulations were performed to estimate the unknown angular velocity states of a two-link robot manipulator. The developed method yielded a 41.13% improvement in the root mean square estimation error when compared to an adaptive RNN observer.
THE ROLE OF PERSONALITY AS A MEDIATOR IN THE RELATIONSHIP BETWEEN LEADER-MEMBER EXCHANGE AND JOB PERFORMANCE

Academic staffs are the main asset to Higher Education Institutions (HEIs) since education is the institution's core business. The ability of academic staff to meet the goals set by the HEIs is of utmost importance. They must possess strong competencies and excellent personality to improve their performance and achieve the desired target. Although the effect of organizational and individual factors has been verified in previous studies, it remains unclear what underlying mechanisms explain the relationship. This study examined the role of personality as a mediator in the relationship between leader-member exchange (LMX) and job performance. This research used a quantitative method with a total sample of 260 academic staff in private HEIs, which were analyzed using the SEM Amos software. The findings showed that LMX and job performance (β= -0.26) and LMX and personality (β= 0.42) were significantly related, and academic staff personality was found to have the most substantial effect on job performance (β= 1.07). The result also indicated that personality partially mediates the relationship between LMX and job performance. The study's findings propose that the management of HEIs can improve the organizational factors through the proper implementation of LMX, which will nurture individual factors' effectiveness and improve academic staff job performance.
African Border-Crossings in a ‘City of Others’: Constellations of Irregular Im/Mobility and In/Equality in the Everyday Urban Environment of Athens

Abstract: This paper ethnographically explores the border-crossing experience of sub-Saharan African migrants in Athens. The everyday social visibility and spatial mobility of migrants in the city is viewed through a critical phenomenological perspective centering on irregularity and border-crossing as a lived experience which, in times of financial crisis, is shared by other vulnerable urban dwellers beyond the migrant/non-migrant dichotomy. The salience of such practices for current discourses and policies concerning migration, urban public space, citizenship and identity in a series of socio-political scales is explored. As migrants’ im/mobility and in/visibility intersect with emerging, crisis-induced patterns of social in/equality in Greece, they become strategic actors who construct new understandings of self and other, indicate emergent political and social configurations, and daily reassert their ‘right to the city’ in the ‘city of Others’.
Study on Influencing Factors of Consumer Engagement and Effect of Time Pressure in Chinese Online Shopping Festival

The purpose of this study is to examine the impact of promotional product characteristics, environmental factors, and personal shopping preferences on shoppers' participation in online shopping events. As the background of the study is the e-commerce shopping festival "618", an S-O-R model is constructed by adding the moderating effect of consumers' internal organismal psychological activities. Using SPSS and AMOS software to analyze the data of 255 questionnaires, it is found that the diversity of products, the infectivity of the environment and the impulse of online shopping have obvious positive effects on "618" and other shopping festivals. In addition, we found that time pressure, a moderating variable, moderates the effect of product diversity on participation. The influence of product richness on participation can be regulated positively by time pressure, while online shopping impulsivity can be weakened by it, while environmental contagiousness can’t be controlled by it. In the context of economic downturn, it is meaningful to figure out how to stimulate online shopping engagement which is the main way of boosting economic growth these years.
DETEKSI USIA BERBASIS CITRA RADIOGRAF PANORAMIK DENGAN METODE GRAY LEVEL CO-OCCURENCE MATRIX (GLCM) SERTA KLASIFIKASI LEARNING VECTOR QUANTIZATION (LVQ)

Identifikasi usia manusia yang akurat diperlukan untuk menyelesaikan masalah forensik dalam mengidentifikasi korban bencana alam atau korban kecelakaan. Identifikasi bisa dilakukan menggunakan gigi, pada penelitian ini dipilih gigi bagian molar pertama. Untuk menemukan akurasi terbaik sistem ini menggunakan metode ekstraksi ciri Gray Level Co-occurence (GLCM) dan klasifikasi Learning Vector Quantization (LVQ). Langkah pertama, dilakukan ekstraksi ciri pada setiap citra dengan menggunakan parameter contrast, correlation, energy, dan homogeneity. Selanjutnya, LVQ digunakan untuk klasifikasi data kelas usia. Proses evaluasi pada jaringan LVQ menghasilkan akurasi 65,08% saat pengujian pada parameter correlation dan energy sudut 0 O , parameter homogeneity sudut 45 o, dan parameter energy sudut 90 o .
Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation

Background From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations. Objective In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations. Methods We built a tobacco research domain–specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group. Results In total, 33 studies were identified as relevant to sexual and gender diverse individuals’ nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies. Conclusions Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool’s performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis.
An innovative decentralised data fusion using electromechanical impedance techniques

This paper describes the electromechanical impedance (EMI) method based structural health monitoring using sensor data fusion approach. The data fusion of different attributes is more effective over a single data in achieving reasonable accuracy and precision. The paper investigates an electromechanical impedance (EMI) method applied to the structural health monitoring of sensor network of thin composite plates using distributed sensor data fusion techniques and a single sensor for different levels of delamination. The information from multiple sensors and single sensor is studied in the frequency domain and a new optimized fused criteria of variable admittance (Y) and conductance (G) is explained by damage metric root mean square deviation (RMSD). The experiments are performed on a thin composite plate with attached piezoelectric transducers at different locations and a plate with single transducer with different delamination levels.
To climb or not to climb? : The sacred deed done at Australia's mighty heart

"This thesis investigates visitor motivations for climbing or not climbing, and visitors’ responses to the Anangu request. It uses a cultural geography approach encompassing historical research and a survey of park visitors to examine the way that the landscapes of Uluru and of the climb are imagined, produced, read and experienced. This research has specific application in informing climb management at Uluru. Its findings may also be applicable to understanding how visitors respond to management for Indigenous cultural heritage values more generally. " - Abstract
Perceptions and Perspectives of Black Male Athletic Directors at National Collegiate Athletic Association Division I Institutions

The purpose of this study was to investigate the relationship among self- identified Black male National Collegiate Athletic Association (NCAA) Division I Football Bowl Subdivision (FBS) athletic directors (ADs) and their lived experiences of career attainment through the lens of Critical Race Theory (CRT). This research examined both the factors impacting the ADs’ careers and the strategies they used to overcome barriers. Semi-structured, in-depth interviews were conducted with - at the time of the study - all of the FBS Black male ADs (N=12) regarding their perspectives of their lived AD experiences. The findings revealed three overarching dimensions (i.e., allyship, nepotism, and networking with other minority leaders) and seven emergent themes associated with success factors (i.e., diverse network, lack of privilege, mentorship, and cultural differences) and barrier removal strategies (i.e., intrinsic motivation, gathering extensive experience, and being intentional about networking). This study advances the sport management field and CRT research by revealing insights regarding Black ADs’ perceptions, challenges, opportunities, and strategies.
Modelling and validation of agricultural and forest biomass potentials for Germany and Austria

The Biosphere Energy Transfer Hydrology Model (BETHY/DLR), a process model that estimates the net primary productivity of agricultural and forested areas was used to assess biomass potentials for Germany and Austria. The model is driven by remote sensing data and meteorological data. Remotely sensed datasets including a time series of the leaf area index, which describes vegetation condition, and a land cover classification, which provides information about land use, are needed. Leaf area indices and land cover data derived from the sensor VEGETATION are used. Both datasets have spatial resolutions of about 1km x 1km and are freely available for the area of investigation. The meteorological input parameters are air temperature (at 2m height), precipitation, cloud cover, wind speed (at 10m height) and soil water content (inthe four uppermost soil layers); these are obtained from the European Centre for Medium-Range Weather Forecasts, with a spatial resolution of about 0.25° x 0.25° and a temporal resolution up to four times daily. The output of the model, the gross primary productivity, is calculated at daily resolution. By subtracting the cumulative plant maintenance and growth respiration, the net primary productivity is then determined. In order to validate the modelled net primary productivity, crop yield estimates and mean above ground biomass increases derived from the national statistics of Germany and Austria are used. After estimating above-ground biomass using plant-specific above- to below-ground ratios, conversion factors (corn-to-straw and leaf-to-beet relations) are applied to estimate total biomass. Finally the carbon content of dry matter is estimated. To correlate model results with these statistical data, the modelled data are aggregated to net primary productivity per administrative district. The results show that a process model using remote sensing data as input can deliver reliable estimates of agricultural and forest biomass potential which are highly correlated with statistically derived estimates of actual biomass produced. In addition, theoretical energy potentials calculated from the modelled and validated NPP are examined, assuming sustainable agricultural and forest management and using species-specific heating values. Such estimated sustainable biomass energy potentials play an important role in the sustainable energy debate. In order to improve the model BETHY/DLR a multi-layered soil water budget model was developed and applied. It uses van Genuchten parameters which were estimated for 128 globally available FAO soil types.
A meta-analysis of the impact of AR and VR technologies on mathematics learning

Using Augment reality (AR) technologies and Virtual reality (VR) technologies in education is a new tendency. The aim of this study is to test the effect of AR and VR technologies on the development of students’ mathematics learning. Articles that met the inclusion criteria from a systematic database like Web of Science were included in the meta-analysis. A total of 20 research articles published from 2010 to 2023 were included in this study. The overall result showed that AR and VR technologies had a large positive impact (SMD=0.892, p 0.001) on students’ mathematics learning. Sub-group results showed that the effect were significantly moderated by outcome, school level, content area, but were not significantly moderated by technologies and experimental period. According to the results, some suggestions were given for the future studies.
The effect of and correction for through‐slice dephasing on 2D gradient‐echo double angle B1+ mapping

Abstract Purpose To show that B0 variations through slice and slice profile effects are two major confounders affecting 2D dual angle B1+ maps using gradient‐echo signals and thus need to be corrected to obtain accurate B1+ maps. Methods The 2D gradient‐echo transverse complex signal was Bloch‐simulated and integrated across the slice dimension including nonlinear variations in B0 inhomogeneities through slice. A nonlinear least squares fit was used to find the B1+ factor corresponding to the best match between the two gradient‐echo signals experimental ratio and the Bloch‐simulated ratio. The correction was validated in phantom and in vivo at 3T. Results For our RF excitation pulse, the error in the B1+ factor scales by approximately 3.8% for every 10 Hz/cm variation in B0 along the slice direction. Higher accuracy phantom B1+ maps were obtained after applying the proposed correction; the root mean square B1+ error relative to the gold standard B1+ decreased from 6.4% to 2.6%. In vivo whole‐liver T1 maps using the corrected B1+ map registered a significant decrease in T1 gradient through slice. Conclusion B0 inhomogeneities varying through slice were seen to have an impact on the accuracy of 2D double angle B1+ maps using gradient‐echo sequences. Consideration of this confounder is crucial for research relying on accurate knowledge of the true excitation flip angles, as is the case of T1 mapping using a spoiled gradient recalled echo sequence.
Actuality about Satellite Thermal Infrared Data Applied in Earthquake Forecast

This paper summarized the history and actuality of the study on satellite thermal infrared anomaly before tectonic earthquake.This study went through about 4 stages: discovery of the anomaly phenomena,summary of the anomaly phenomena,study of the mechanism and improvement of the method of the anomaly phenomena detecting before earthquake;and it further summed up the applicability and specialty of the methods for detecting the anomaly phenomena.
Caratterizzazione geotecnica del sottosuolo di San Giuliano di Puglia

Sommario Al fine di correlare il danneggiamento sulle costruzioni prodotto dal terremoto del Molise del 2002 con i caratteri litostratigrafici e con le proprietà meccaniche dei terreni è stata intrapresa una approfondita campagna di indagini, in sito e in laboratorio sul sottosuolo di San Giuliano di Puglia (CB). Le indagini sono state orientate sia allo studio del deposito di argille marnose di Toppo Capuana, in corrispondenza delle quali si sono rilevati i maggiori danni all’edificato, sia, in misura minore, della formazione del flysch di Faeto, che di essa costituisce il substrato rigido. Il deposito argillo-marnoso è distinguibile in diversi spessori, differenziati in relazione all’intensità di fessurazione, classificabile secondo un criterio razionale. Le indagini in-situ hanno permesso di riconoscere i caratteri litostratigrafici dell’area di studio e di ricostruire, con diversi metodi, la sezione stratigrafica principale che tiene conto della variabilità di alterazione e fessurazione della porzione argillosa. Tali caratteri, benché non influenzino le proprietà fisiche e mineralogiche dei terreni, sembrano invece condizionarne la risposta meccanica e, in particolare, il comportamento tensio-deformativo influente sulla risposta sismica locale del deposito.
Enhancing healthcare data security: a two-step authentication scheme with cloud technology and blockchain

In the modern world, medical data leakage has many external and internal threats. Information systems of medical organizations are constantly subject to various types of cyber-attacks and unauthorized penetration attempts, which leads to the publication of patient medical data online. Existing authentication schemes using blockchain technologies in medical organization systems ensure the integrity of medical data and secure access to patient data. However, one of the serious reasons for unauthorized access to the healthcare system is the human factor, which manifests itself in a negligent attitude towards account security, non-compliance with the rules and policies of information security, and transferring to third parties personal login details to the information system of a medical organization. This paper proposes a solution to this problem through an improved two-step authentication scheme using cloud technology and blockchain. The combined use of cloud technologies and blockchain is a distinctive feature of the proposed authentication scheme since it provides two levels of protection: 1) two-step authentication, the second stage of which includes biometrics through a mobile application. It prevents unauthorized access to the system by third parties; 2) cloud encryption keys for decrypting medical data, which are also accessed through the user's biometrics. The practical part of the paper includes the implementation of biometric login in Python using the OpenCV library. As a result of the practical part, unique fingerprint samples were obtained. The biometric user verification algorithm is designed for a mobile application, which we plan to implement in the future