Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Ecology of Active Marine Hydrothermal Vent Deposits: The Influence of Geologic Setting on Microbial Communities

i Dedication iii Acknowledgments iv List of Tables ix List of Figures xii Chapter 1: Introduction 1 Preface 1 Geologic Setting 2 Hydrothermal fluid generation 3 Hydrothermal mineral deposits 7 Energy sources 8 Microbial Habitats and Diversity 10 Active vent deposits 11 Inactive vent deposits 14 Biotic and abiotic surfaces 15 Endosymbionts 16 Hydrothermal plumes 17 Scope of Dissertation 18 Chapter 2: Microbial Community Structure of Hydrothermal Deposits from Geochemically Different Vent Fields Along the Mid-Atlantic Ridge 20
Enhancing Resilience to Missing Data in Audio-Text Emotion Recognition with Multi-Scale Chunk Regularization

Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization
Crowdsourcing Experiment and Fully Convolutional Neural Networks for Coastal Remote Sensing of Seagrass and Macroalgae

Recently, convolutional neural networks and fully convolutional neural networks (FCNs) have been successfully used for monitoring coastal marine ecosystems, in particular vegetation. However, even with recent advances in computational modeling and data acquisition, deep learning models require substantial amounts of good quality reference data to effectively self-learn internal representations of input imagery. The classical approach for coastal mapping requires experts to transcribe in situ records and delineate polygons from high-resolution imagery such that FCNs can self-learn. However, labeling by a single individual limits the training data, whereas crowdsourcing labels can increase the volume of training data, but may compromise label quality and consistency. In this article, we assessed the reliability of crowdsourced labels on a complex multiclass problem domain for estuarine vegetation and unvegetated sediment. An interobserver variability experiment was conducted in order to assess the statistical differences in crowdsourced annotations for plant species and sediment. The participants were grouped based on their discipline and level of expertise, and the statistical differences were evaluated using Cochran's Q-test and the annotation accuracy of each group to determine observation biases. Given the crowdsourced labels, FCNs were trained with majority-vote annotations from each group to check whether observation biases were propagated to FCN performance. Two scenarios were examined: first, a direct comparison of FCNs trained with transcribed in situ labels and crowdsourced labels from each group was established. Then, transcribed in situ labels were supplemented with crowdsourced labels to investigate the feasibility of training FCNs with crowdsourced labels in coastal mapping applications. We show that annotations sourced from discipline experts (ecologists and geomorphologists) familiar with the study site were more accurate than experts with no prior knowledge of the site and nonexperts, with our results confirming that biases in participant annotation were propagated in FCN performance. Furthermore, FCNs trained with a combined dataset of in situ and crowdsourced labels performed better than FCNs trained on the same imagery with in situ labels.
Integration of Gradual Release of Responsibility Instructional Model (GRRIM) in the Development of Learning Module in Geometry

This mixed methods study aims to determine the effects of the developed Gradual Release of Responsibility Instructional Model (GRRIM) learning module on the academic achievement of Grade 9 students in Geometry. The topic chosen for the module development is one of the students’ identified least learned competencies, particularly in parallelograms and their properties. The study participants were five (5) module development experts and 110 students grouped into control and experimental groups. The control group was exposed to DepEd SLM, while the experimental group was exposed to GRRIM LM. Data were analyzed using thematic analysis, mean, independent samples t-test, and ANCOVA. The results of the thematic analysis of the focus group discussion revealed a module’s characteristics that embody the GRRIM approach’s different stages. Furthermore, findings indicated that after the intervention, the experimental group’s post-test mean scores were found to be at a moderate level. In contrast, the control group remained at a low level. Finally, the study revealed that when pre-test mean scores were treated as a covariate, there was a significant difference in the post-test mean scores between the control and experimental groups. The findings revealed that the students exposed to GRRIM LM performed better than those exposed to DepEd SLM.
The Mechanical, Thermal, and Biological Properties of Materials Intended for Dental Implants: A Comparison of Three Types of Poly(aryl-ether-ketones) (PEEK and PEKK)

Regarding the dynamic development of 3D printing technology, as well as its application in a growing part of industries, i.e., in the automotive industry, construction industry, medical industry, etc., there is a notable opportunity for its application in producing dental implants, which presents a promising alternative to traditional implant manufacturing methods. The medical industry is very restrictive regarding the applied materials, and it is necessary to use materials that exhibit very good mechanical and thermal parameters, show clinical indifference and biocompatibility, are non-allergenic and non-cancerous, and are likely to sterilize. Such materials are poly(aryl-ether-ketone)s (PAEK)s, mainly poly(ether-ether-ketone) (PEEK) and poly(ether-ketone-ketone) (PEKK), that are found to be high-performance polymers and can be defined as materials that retain their functionality even in extreme conditions. In the present paper, two types of PEEKs and PEKK were compared regarding their structural, mechanical, and thermal properties along with the biological activity toward selected strains. The tested samples were obtained with Fused Deposition Modeling (FDM) technology. The PEKK, after heat treatment, exhibits the most promising mechanical properties as well as less bacterial adhesion on its surface when compared to both PEEKs. Consequently, among the evaluated materials, PEKK after heat treatment stands out as the optimal selection for a dental prosthesis.
Embedding a sustainability mindset in responsible management education

Purpose Organisational responses to the United Nations Sustainable Development Goals depend on the competency and mindset of business leaders to lead responsibly. This study is informed and underpinned by the Principles of Responsible Management Education. This study aims to examine how embedding the “sustainability mindset principles” within a university programme can contribute to responsible management education and, by extension, leadership development. Design/methodology/approach An illustrative case study using 84 students was applied, including undergraduate, postgraduate and executive Master of Business Administration students. An exploratory, qualitative design was followed, primarily adopting focus groups. Findings Evidenced learning gains in connecting sustainability knowledge with personal beliefs and behaviours, provide a compelling basis for educational and business practitioners to focus on the sustainability mindset principles (SMPs). Mapping of mindset against leading global competency frameworks provides important theoretical insight. Learning is illustrated through multiple dimensions (i.e. cognitive, behavioural and affective) to inform leadership development approaches. Research limitations/implications The mapping of sustainability competency frameworks against the SMP, alongside qualitative research insights, provides a compelling basis for further research into the learning gains from embedding the mindset principles. The situated nature of the study and the lack of longitudinal measurement of what students take forward into their lives and workplaces is a limiting factor to be considered. Practical implications This study evidences the value of “whole-person” learning for responsible management, which can helpfully inform the design of both educational and workplace leadership development programmes. Originality/value This study is original in the pedagogic examination of the learning dimensions of the SMPs in a Business and Management programme. It also offers new insights in terms of the implications for leadership development.
Primary Endovascular Thrombectomy in Acute Ischemic Stroke in Patients Ineligible for Intravenous Thrombolysis

Endovascular treatment (EVT) of acute ischemic stroke (AIS) is the main treatment of large vessel occlusion (LVO). Nonetheless, access to EVT is limited specially in developing countries. It is not uncommon in these regions to miss the time window for intravenous thrombolysis (IVT) or to arrive within the IVT window but with a contraindication for it. Hence, a lot of patients with LVO face a situation where either they receive EVT only or conservative management according to available logistics. Aim: To study the outcome of EVT vs best medical treatment (BMT) in two Egyptian stroke centres (Tanta and Alexandria) in AIS patients ineligible for IVT. Results: 390 patients with AIS and LVO were included in the study. 74 underwent EVT while 316 underwent BMT. There were no statistical differences in age, sex, time of onset, initial severity, or imaging scores in both groups. The EVT group had more diabetes mellitus (DM) patients, more smokers and more systolic and diastolic blood pressure (BP) while the BMT had more anticoagulated patients. The EVT group had better functional outcome but more symptomatic intracerebral haemorrhage with no difference in mortality. Conclusion: Endovascular treatment doubles the good functional outcome of AIS in LVO patients but ICH is more with no effect on mortality.
Methods for the determination of antioxidant activity of plant extracts in vitro

Plants are rich sources of secondary metabolites that exhibit diverse biological and pharmacological effects. Some plant ingredients, primarily phenolics, have significant in vitro antioxidant activity, which implies their contribution to the maintenance of redox balance in the body. These potential antioxidant agents are structurally very diverse, having different mechanisms of antioxidant activity. Since there is a growing necessity to detect, develop and understand effective antioxidant compounds, interest in the identification and the measurement of antioxidants in various plant isolates is persistently growing and many methods are being established. Most of the available in vitro tests are affordable and easy to perform, but due to the complex composition of plant extracts, different kinetics, mechanisms and specificity of the chemical reactions underlying these tests, there is no universal parameter for the assessment of antioxidant activity. In this paper, some of the currently most used in vitro methods for investigating and evaluating antioxidant activity of plant extracts are presented, emphasizing their advantages and weaknesses.
Three-Dimensional Bioprinting of Human Organs and Tissues: Bioethical and Medico-Legal Implications Examined through a Scoping Review

Three-dimensional bioprinting is a rapidly evolving technology that holds the promise of addressing the increasing demand for organs, tissues, and personalized medicine. By employing computer-aided design and manufacturing processes, 3D bioprinting allows for the precise deposition of living cells, biomaterials, and biochemicals to create functional human tissues and organs. The potential applications of this technology are vast, including drug testing and development, disease modeling, regenerative medicine, and ultimately, organ transplantation. However, as with any groundbreaking technology, 3D bioprinting presents several ethical, legal, and regulatory concerns that warrant careful consideration. As the technology progresses towards clinical applications, it is essential to address these challenges and establish appropriate frameworks to guide the responsible development of 3D bioprinting. This article, utilizing the Arksey and O’Malley scoping review model, is designed to scrutinize the bioethical implications, legal and regulatory challenges, and medico-legal issues that are intertwined with this rapidly evolving technology.
Emergence of the fourth mobile sulfonamide resistance gene sul4 in clinical Salmonella enterica

The fourth mobile sulfonamide resistance gene sul4 has been discovered in many metagenomic datasets. However, there is no reports of it in cultured bacteria. In this study, a sul4 positive clinical Salmonella enterica SC2020597 was obtained by conventional Salmonella isolation methods and characterized by species identification and antimicrobial susceptibility testing. Meanwhile, the genomic DNA was sequenced using both long-read and short-read methods. Following that, the complete genome was analyzed by bioinformatic methods. The sul4 gene in S. enterica SC2020597 differed from the sul4 identified in metagenomic data by one amino acid and could confer full resistance to sulfamethoxazole. Genetic location analysis showed that the sul4 in SC2020597 was carried by a complex chromosomally integrated hybrid plasmid. ISCR20-like was strongly associated with the mobilization of sul4 by core genetic context analysis. To the best of our knowledge, this is the first report of the emergence of sul4 in clinically cultured S. enterica. More important, the sul4 has the potential to spread to other bacteria with the help of mobile elements.
Establishing research priorities for investigating male suicide risk and recovery: A modified Delphi study with lived-experience experts.

This study uses the Delphi expert consensus method to work with lived-experience experts and establish research priorities to advance our understanding of male suicide risk and recovery. Items for the Delphi were generated via ﬁ ndings from two recent quantitative and qualitative systematic reviews on male suicide, a comprehensive gray literature search, responses to a global survey on male suicide, and feedback from a panel of 10 international academic/clinical male suicide experts. A two-round Delphi study was conducted to gain consensus among 242 lived-experience experts representing 34 countries on 135 potential male suicide research questions. Panelists were asked to rate each item on a 5-point Likert scale from should not be included to essential . Consensus was de ﬁ ned as 80% of respondents scoring an item as “ essential ” or “ important. ” After two Delphi rounds, consensus was reached on 87 items. The ﬁ nal questions were then grouped by the author team and expert academic/clinical panel into thematic clusters to create a 22-point
Controlling the thermoelectric properties of organo-metallic coordination polymers through backbone geometry

Poly(nickel-benzene-1,2,4,5-tetrakis(thiolate)) (Ni-btt), an organometallic coordination polymer (OMCP) characterized by the coordination between benzene-1,2,4,5-tetrakis(thiolate) (btt) and Ni2+ ions, has been recognized as a promising p-type thermoelectric material. In this study, we employed a constitutional isomer based on benzene-1,2,3,4-tetrakis(thiolate) (ibtt) to generate the corresponding isomeric polymer, poly(nickel-benzene-1,2,3,4-tetrakis(thiolate)) (Ni-ibtt). Comparative analysis of Ni-ibtt and Ni-btt reveals several common infrared (IR) and Raman features attributed to their similar square-planar nickel–sulfur (Ni–S) coordination. Nevertheless, these two polymer isomers exhibit substantially different backbone geometries. Ni-btt possesses a linear backbone, whereas Ni-ibtt exhibits a more undulating, zig-zag-like structure. Consequently, Ni-ibtt demonstrates slightly higher solubility and an increased bandgap in comparison to Ni-btt. The most noteworthy dissimilarity, however, manifests in their thermoelectric properties. While Ni-btt exhibits p-type behavior, Ni-ibtt demonstrates n-type carrier characteristics. This intriguing divergence prompted further investigation into the influence of OMCP backbone geometry on the electronic structure and, particularly, the thermoelectric properties of these materials.
Simulations dynamiques des dislocations a une echelle mesoscopique : une etude de la deformation plastique

Ce travail traite de la modelisation numerique de la deformation plastique des materiaux cristallins. Une nouvelle technique de simulation est proposee pour le traitement de la dynamique des dislocations a deux et trois dimensions dans un continuum elastique isotrope. Les echelles spatiale et temporelle choisies (10##6 m et 10##9 s) permettent une prise en compte des proprietes elementaires des dislocations, de leurs interactions a longue et a courte distance, de leurs proprietes collectives, ainsi que de la geometrie du glissement. Grace a cette methode originale il est possible de reproduire l'heterogeneite inherente a la deformation plastique, les effets d'auto-organisation des microstructures ainsi que les proprietes mecaniques correspondantes. En deux dimensions, les simulations conduisent a la formation d'une structure periodique de murs de dislocations dipolaires au cours de la sollicitation cyclique. Les conditions de formation de ces configurations sont etudiees et discutees et un modele phenomenologique est propose pour predire leur dimension caracteristique en fonction de la contrainte appliquee et de la densite de dislocations. Des concordances remarquables apparaissent entre le comportement simule et les donnees de la litterature. En trois dimensions, les simulations sont plus realistes et peuvent etre directement comparees avec l'experience. Elles restent cependant limitees a de faibles deformations plastiques, de l'ordre de quelques 10##3. Les proprietes examinees et discutees a partir de simulations de glissement multiple concernent le modele de la foret, la contrainte interne, dont on montre que la contribution a la contrainte d'ecoulement est d'environ 20%, les mecanismes de durcissement en relation avec les modeles de friedel-saada et de kocks. L'etude des microstructures est plus particulierement focalisee sur deux ingredients indispensables pour les effets d'auto-organisation, la contrainte interne et les intersections entre dislocations non coplanaires. Ces resultats suggerent que le durcissement d'ecrouissage et la formation d'une structure cellulaire en glissement multiple ne peuvent etre compris qu'en faisant intervenir les effets de la contrainte interne locale et du glissement devie sur les mecanismes de franchissement de la foret
Identification of Individual Hanwoo Cattle by Muzzle Pattern Images through Deep Learning

Simple Summary Cattle identification is necessary for precision feeding and management. Cattle muzzles have unique patterns, which can be used as a biometric identification key. This study aimed to identify cattle via a deep learning model based on muzzle images. Muzzle patterns were cropped from images using the YOLO v8-based image cropping model. Various artificial intelligence models based on neural networks were studied through transfer learning cropped images for cattle recognition with four optimizers. Several models showed a high prediction accuracy of over 97 percent, implicating the possibility for real farm usage. Abstract The objective of this study was to identify Hanwoo cattle via a deep-learning model using muzzle images. A total of 9230 images from 336 Hanwoo were used. Images of the same individuals were taken at four different times to avoid overfitted models. Muzzle images were cropped by the YOLO v8-based model trained with 150 images with manual annotation. Data blocks were composed of image and national livestock traceability numbers and were randomly selected and stored as train, validation test data. Transfer learning was performed with the tiny, small and medium versions of Efficientnet v2 models with SGD, RMSProp, Adam and Lion optimizers. The small version using Lion showed the best validation accuracy of 0.981 in 36 epochs within 12 transfer-learned models. The top five models achieved the best validation accuracy and were evaluated with the training data for practical usage. The small version using Adam showed the best test accuracy of 0.970, but the small version using RMSProp showed the lowest repeated error. Results with high accuracy prediction in this study demonstrated the potential of muzzle patterns as an identification key for individual cattle.
Improving the Yearly Profit of Wind Farm with Artificial Intelligence Technique

Owing to the escalating environmental and social problems linked to climate change and the hastily depleting stock of hydrocarbon-based fuels, renewable power generation modes have attained massive prominence. Wind power is an important renewable energy generation technology that contributed to 5% of the planet’s power generation in 2020. However, for sustaining the Paris Agreement targets, the global wind power generation sector necessitates evolving at a fleeter pace. To expand the green switch of the worldwide power generation businesses, wind farms are expected to remain financially more advantageous than fossil fuel-based power plants. The present work focused on elevating the annual profit of wind farms by employing an amended genetic algorithm (GA). A fresh approach to dynamically apportioning the crossover and mutation prospects for a GA-enabled profit growth algorithm was suggested to amplify the capability of the GA. Three dissimilar terrain conditions with diverse obstruction configurations and a randomly generated non-uniform wind flow pattern were used for assessing the competence of the proposed algorithm for profit maximization. The results showed that the annual yields for Terrain Layouts 1, 2 and 3 obtained by the amended GA were higher by 10.34, 5.09 and 0.51%, respectively, than the typical one, which substantiated the superior proficiency of the former.