Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Insight into Winter Native Fish Assemblages in Guadiana Estuary Salt Marshes Coping with Environmental Variability and Non-Indigenous Fish Introduction

This work aims to undertake a preliminary characterization of winter fish assemblages in the salt marsh areas of Guadiana lower estuary (South-East Portugal) and discusses the potential risks of habitat dominance by a non-indigenous species (NIS). To this effect, six field campaigns were carried out in four sampling sites during winter season targeting the collection of fish species. A total of 48 samples were collected. Individuals from seven different taxa (marine and estuarine) were collected, although the assemblage was dominated by two estuarine species—the native Pomatoschistus sp. (goby) and the NIS Fundulus heteroclitus (mummichog). Goby was the most abundant taxa in the majority of salt marsh habitats, except for one specific, marsh pool, where extreme environmental conditions were registered, namely high temperature and salinity. Such conditions may have boosted the intrusion of mummichog in this area. This species is well adapted to a wide range of abiotic factors enabling them to colonize habitats where no predators inhabit. Impacts of mummichog introduction in the Guadiana salt marsh area are still unpredictable since this is the first time they have been recorded in such high density. Nevertheless, in scenarios of increased anthropogenic pressure and, consequently, habitat degradation, there is a potential risk of mummichog spreading to other habitats and therefore competing for space and food resources with native species.
The Lived Experiences of Non-Thai Teachers in Code-Switching at Nakhon Pathom Rajabhat University

This study investigates the lived experiences of non-Thai teachers' code-switching in delivering their English lessons in EFL classrooms. The study employed a qualitative approach, specifically phenomenology. The teachers were interviewed using semi-structured interview questions. The responses were transcribed and analyzed using Colaizzi’s seven-step descriptive phenomenological data analysis approach. The findings of this study showed that teachers’ experiences of code-switching were a helpful, practical, and essential tool to foster active participation from the students. The teachers’ reasons for utilizing code-switching are to relieve students’ anxiety and amplify socialization, word power, understanding of grammar, and instruction. Furthermore, the study also emphasizes that excessive use of code-switching can hinder the student’s learning of the target language. Additionally, the teachers face a challenge when they code-switch due to the tone-sensitive nature of the Thai language. Thus, it is suggested that one must be at least conversational in the students' language to use code-switching effectively. It is concluded in this study that teachers should not cease using code-switching since it helps students grasp complex ideas and allows them to be engaged in the classroom.
Graph of Concepts for Semantic Annotation of Remotely Sensed Images based on Direct Neighbors in RAG

Abstract Image annotation is a challenging task and is widely studied in several fields such as image interpretation and retrieval. With the overwhelming volume of image data, manual annotation is no longer practical. Therefore, automatic image annotation (AIA) techniques have become a necessity where various approaches have been proposed mainly to resolve the semantic gap problem. Nevertheless, further efforts are still required in order to obtain more faithful semantic image annotations. In this paper, we propose a method to semantically annotate remote sensing (RS) images based on region adjacency graphs (RAG). Specifically, we simultaneously consider contextual, spatial and spectral information of image regions for AIA. Spectral libraries are used as a source of knowledge where several radiometric indices are computed to identify objects in the images. As a result, a graph of concepts representing objects is produced in scenes that exploit spatial and spectral attributes. Our approach is validated based on popular RS image datasets such as the Indian Pines and Jasper Ridge datasets. Promising results are reported based on the measurements of precision, recall, F1 and N + scores.
Mowing wet meadows reduces the health of their snail communities

Wet meadows harbor rich biodiversity, making them pivotal ecosystems worldwide. These habitats are commonly used for grazing or hay production for livestock. However, regular mowing can influence these habitats, potentially leading to significant repercussions for the animals residing within them. In order to investigate the effects of land management practices, we conducted an experimental study to compare snail communities in mowed and unmowed wet meadows in northern Hungary. We found that overall, mowing decreases snail populations, as well as species richness and diversity. Thus, our results suggest that routine mowing of wet meadows is deleterious to their snail communities. Based on these results, we suggest that designated patches of meadows that are regularly managed should be left uncultivated in their natural state. These patches can serve as potential colonization sites, facilitating the restoration of the entire meadow’s ecological balance.
The impact of mindfulness on nurses’ perceived professional benefits: the mediating roles of workplace spirituality and work-life balance

This study delves into the effects of mindfulness on workplace spirituality, work-life balance, and perceived professional benefits among nurses operating in the high-pressure environments of hospitals in Jiangxi Province, China. Utilizing a robust sample of 303 valid questionnaires and employing partial least squares (PLS) analysis, the research uncovers a significant positive relationship between mindfulness and workplace spirituality. Furthermore, it demonstrates how both workplace spirituality and work-life balance serve as crucial mediators in enhancing nurses’ perception of their professional benefits. The findings illuminate the potential of mindfulness training in substantially elevating job satisfaction and reducing burnout among nurses. The study not only reinforces the value of mindfulness in the healthcare sector but also advocates for its integration into professional development programs and healthcare policies. By doing so, it aims to bolster the overall wellbeing and professional effectiveness of nurses facing the myriad challenges inherent in demanding healthcare environments. This study contributes to the growing discourse on mindfulness in occupational settings, highlighting its pivotal role in enhancing both the personal wellbeing and professional capabilities of healthcare professionals.
A novel decomposition-based ensemble broad learning system for short-term load forecasting

Load forecasting of the power system plays a key role in the production planning and actual operation scheduling of power systems. However, as the power system becomes larger and more complex, it is very important to propose a forecasting model with high accuracy and low computational cost. In this paper, a novel short-term load forecasting model is proposed, which combines the empirical wavelet transform (EWT) and the broad learning system (BLS). The advantage of EWT is that it can decompose the signal into multiple local frequency bands, select the local wavelet function adaptively, and overcome the modal mixing problem caused by the discontinuity of the signal time-frequency scale. While, the advantage of BLS is that it allows the model to learn more by stretching the width, and uses ridge regression to make it less time consuming and faster. To verify the effectiveness, the model is compared with some other state-of-the-art methods, and several performance estimations indicate that the model has high accuracy and low computational cost.
LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.
STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis and treatment. In this paper, a multitask learning architecture has been proposed for accurate tooth segmentation and numbering in panoramic X-ray images. A graph convolution network was applied for the automatic annotation of the target region, a modified convolutional neural network-based detection subnetwork (DSN) was used for tooth recognition and boundary regression, and an effective region segmentation subnetwork (RSSN) was used for region segmentation. The features extracted using RSSN and DSN were fused to optimize the quality of boundary regression, which provided impressive results for multiple evaluation metrics. Specifically, the proposed framework achieved a top F1 score of 0.9849, a top Dice metric score of 0.9629, and an mAP (IOU = 0.5) score of 0.9810. This framework holds great promise for enhancing the clinical efficiency of dentists in tooth segmentation and numbering tasks.
Methods for Improving the Germination of Rhodotypos scandens (Thunb.) Makino Seeds through Endocarp Removal

Rhodotypos scandens (Thunb.) Makino is known to have a seed dispersal that is thick and stony (endocarp + seeds) and has potential as a landscaping tree seed. In several Rosaceae species, seeds are covered with a hard endocarp, making the internal seeds water-impermeable and germination difficult. Here, we analyzed the morphoanatomical traits and germination properties of R. scandens seeds. To identify ideal seed propagation conditions, we immersed R. scandens seeds in sulfuric acid for varying durations and subjected them to phytohormone (gibberellic acid A3 and fluridone) and a cold stratification (CS) (5 °C) treatment after endocarp removal (ER). The R. scandens stony seeds did not increase in mass by ≥25.0%. Following ER, the seed mass increased by ≥50.0% with water absorption when compared to the initial dry mass. Seed surfaces showed damage and cracks through scarification after 1 h of immersion in sulfuric acid, failing to germinate. A combination of ER, phytohormone treatment, and CS improved seed germination compared to ER alone (26.0 ± 5.3%). Overall, R. scandens seeds showed a dispersal with a hard endocarp from the parent plant, and a pre-treatment with ER, phytohormones, and CS was required for effective seed propagation.
Urtica pilulifera leaves extract mitigates cadmium induced hepatotoxicity via modulation of antioxidants, inflammatory markers and Nrf-2 signaling in mice

Introduction: Cadmium (Cd) is a harmful heavy metal that results in many toxic issues. Urtica pilulifera showed potential pharmaceutical applications. This study investigated the possible ameliorative mechanism of Urtica pilulifera leaves extract (UPLE) against hepatotoxicity induced by cadmium chloride (CdCl2) in mice. Methods: In vitro phytochemical screening and the metal-chelating activity of UPLE were ascertained. Four groups of forty male mice were used (n = 10) as follows; Group 1 (G1) was a negative control. G2 was injected i.p., with UPLE (100 mg/kg b. wt) daily. G3 was injected i.p., with Cd (5 mg/kg b. wt) daily. G4 was injected with Cd as in G3 and with UPLE as in G2. On day 11, the body weight changes were evaluated, blood, and serum samples were collected for hematological and biochemical assessments. Liver tissues were used for biochemical, molecular, and histopathological investigations. Results: The results showed that UPLE contains promising secondary metabolites that considerably lessen the negative effects of Cd on liver. Furthermore, UPLE inhibited oxidative stress and inflammation; restored antioxidant molecules; and promoted nuclear-related factor-2 (Nrf-2) expression. Also, UPLE improved the histopathological alterations induced by Cd. Discussion: This study explored the beneficial role of UPLE treatment in Cd-induced liver injury through enhancing Nrf-2 signaling and antioxidant enzyme gene expression in the liver of mice. Therefore, UPLE could have valuable implications against hepatotoxicity induced by environmental cadmium exposure. Which can be used as a chelating agent against Cd.
Interlinked changes in fish populations and their environments in Lower Lough Erne, Northern Ireland

Lower Lough Erne (LLE) is a mesotrophic to eutrophic lake in north-western Ireland, l 09 km' in area and with a maximum depth of 65 m (Fig. 1). The generallimnology of the lake was first described by GmsoN et al. (1980). Current water quality status is given in Table l. The largest lake on the Erne system, it is a noted mixed recreational fishery for pike Exox Lucius L. , roach Rutilus rutilus L. , perch Perca jluviatilis L. , bream Abramis brama L. , and brown trout Salmo trutta L. Pike and eels Anguilla anguilla L. are also exploited commercially, yielding annually
On Local Temporal Embedding for Semi-Supervised Sound Event Detection

Semi-supervised sound event detection (SSED) task requires recognizing the categories of events and marking each event's onset and offset times in a mixed audio recording using a small amount of weakly labeled and a large scale of unlabeled data. So, exploring local temporal information, i.e., local discrimination and local correlations in the time domain, is essential for SSED, and in particular, for precise event boundary detection. Besides, as manual-labeled datasets are scarce, SSED tasks require effectively exploiting unlabelled data to reduce overfitting, typically through regularization techniques. Recently, self-supervised learning provided a viable solution to leverage unlabeled data for effective feature learning in various downstream tasks. In this paper, we propose LTE-Net, a novel multitask framework, to learn the Local Temporal Embedding for SSED. Specifically, LTE-Net first locally down-samples the input spectrogram and learns the token embeddings with a high temporal resolution (i.e., local discrimination). Then, LTE-Net effectively models the local correlations among the token embeddings through self-supervised masked spectrogram modeling. Finally, a novel joint (self- and semi-supervision) regularization framework is employed for the training of LTE-Net to effectively leverage unlabeled data in SSED. Extensive experiments on DCASE 2019, 2020 and 2021 SSED datasets show that LTE-Net significantly outperformed existing methods and achieved 2.1% to 8.7%, 2.1% to 3.9% and 1.2% to 6.1% performance gains on the evaluation set in 2019, 2020 and 2021 datasets, respectively.
Explaining the lived experiences of school principals regarding the promotion of psychological capital of high school girls through promoting their health

BACKGROUND: Psychological capital, a vital factor in adolescents’ success, interacts with health and the dimensions of health and is especially important in adolescent girls. The purpose of this research is to explain the lived experiences of school principals regarding the promotion of psychological capital of high school girls through promoting their health. MATERIALS AND METHODS: The present study, which is based on a qualitative approach and a descriptive phenomenological method, has been carried out through studying 23 participants who were the principals of girls’ high schools in Tabriz in 2022. Purposive sampling method was used and 23 semistructured interviews were conducted until theoretical saturation was reached. Data analysis was conducted based on the Colaizzi’s seven-step method. RESULT: Findings indicate that the lived experiences of school principals regarding the promotion of psychological capital of high school girls through promoting their health can be classified as follows: physical health including physical activity and sports, healthcare (teeth and mouth, eyes, skin, and hair), disease prevention, nutrition, and physical fitness; mental health including spirituality, life skills, academic skills, counselling, and peer relationships; and puberty health including awareness of puberty, appropriate modeling, and sex education. CONCLUSION: The programs and behaviors related to health dimensions at schools are among the key grounds promoting psychological capital of high school girls.
Slope and soil nutrients can explain the distribution of Phragmites australis and Phragmites japonica in riparian wetlands

Selecting appropriate plant species is required for effective vegetational restoration. Phragmites australis and Phragmites japonica are large perennial grasses frequently used for restoration of wetland ecosystems. They have different distribution ranges along stream. We analysed plant density, stream slope, and physicochemical characteristics of soil at their habitats to distinguish environmental factors responsible for the distribution ranges. P. japonica was distributed at upper stream side with average slope of 8.4 m/km whereas P. australis inhabited at lower stream side with average slope of 1.7 m/km. Moisture content, organic matter, PO4‐P, NH4‐N, K+, and Ca2+ contents in soil were significantly higher at habitats of P. australis than those at habitats of P. japonica. Canonical correspondence analysis showed that NH4‐N, K+, and organic matter contents had the strongest influences on the density of these two species. The density of P. australis and P. japonica can be explained by the following multiple linear regression models with soil NH4‐N and K+ contents: DPJ = 97.471–0.052CNH4‐N − 0.185CK and DPA = 7.533 + 0.135CNH4‐N + 0.032CK (DPA = Density of P. australis, DPJ = Density of P. japonica, CNH4‐N = Content of NH4‐N, CK = Content of K+). These models can be used as references to select P. japonica or P. australis for restoring stream ecosystem.
"Hey, I'm walking here!'' - An explorative study of spatial encounters between older adults and autonomous robots

This thesis is an explorative study of spatial encounters between older adults and autonomous robots. We have collected data from a wide range of contexts to explore the domain; using field studies, observations, focus groups, and interviews. Our primary empirical context has been an institution where older adults live independent in separate apartments, but with common areas for social activities. A robot platform based on the Robot Operating System framework has been used to observe how older adults react when encountering a robot. We have found that the deployment of an autonomous robot is a pervasive alteration in the life of an older adult, and that it will require a substantial amount of facilitating tasks to be able to move autonomously. Thorough investigations and collaboration with participants for each particular context are required prior to deployment of the robot. A robot should communicate congruent information, utilizing multiple communication modalities. We have seen that spatial conflicts can emerge from encounters between robots and older adults, and propose five design implications to mitigate these conflicts. The most important proposals are to identify possible problem areas, and develop navigation strategies that not only focuses on efficiency, but has an explicit focus on avoidance of such conflicts and challenges.
Structural development of the northern Dutch offshore: Paleozoic to present

Abstract We used new high-quality 2D and 3D seismic data covering the northern Dutch offshore to develop a structural framework for Paleozoic–recent times. Early Carboniferous extension was accommodated predominantly along WNW–ESE-trending faults, and was characterized by an alternation of highs and lows; in the northern Dutch offshore, the principal high coincides with the present-day Elbow Spit Platform. An Early Carboniferous low, the North Elbow Basin, is present north of this high in the A and B quadrants. Lower Carboniferous deposits have been preserved here and on the Elbow Spit Platform. Late Carboniferous–Early Rotliegend deformation is accommodated primarily along normal faults with a NE–SW trend. These faults commonly show no significant offset of Upper Rotliegend and younger units. Development of the Dutch Central Graben and Step Graben occurred during the Triassic–Early Cretaceous, primarily along north–south-trending faults, but reactivation of pre-existing faults as oblique-slip faults occurred as well. Associated with these north–south-trending faults was another, not previously described, family of WSW–ENE-trending dextral strike-slip faults which are proposed to represent transfer faults that accommodated strain partitioning.