Miracle-dz/UniFAR-FTUs huggingface dataset:
ABA-Induced Vegetative Diaspore Formation in Physcomitrella patens

The phytohormone abscisic acid (ABA) is a pivotal regulator of gene expression in response to various environmental stresses such as desiccation, salt and cold causing major changes in plant development and physiology. Here we show that in the moss Physcomitrella patens exogenous application of ABA triggers the formation of vegetative diaspores (brachycytes or brood cells) that enable plant survival in unfavorable environmental conditions. Such diaspores are round-shaped cells characterized by the loss of the central vacuole, due to an increased starch and lipid storage preparing these cells for growth upon suitable environmental conditions. To gain insights into the gene regulation underlying these developmental and physiological changes, we analyzed early transcriptome changes after 30, 60, and 180 min of ABA application and identified 1,030 differentially expressed genes. Among these, several groups can be linked to specific morphological and physiological changes during diaspore formation, such as genes involved in cell wall modifications. Furthermore, almost all members of ABA-dependent signaling and regulation were transcriptionally induced. Network analysis of transcription-associated genes revealed a large overlap of our study with ABA-dependent regulation in response to dehydration, cold stress, and UV-B light, indicating a fundamental function of ABA in diverse stress responses in moss. We also studied the evolutionary conservation of ABA-dependent regulation between moss and the seed plant Arabidopsis thaliana pointing to an early evolution of ABA-mediated stress adaptation during the conquest of the terrestrial habitat by plants.
Structural, Spectroscopic, and Chemical Bonding Analysis of Zn(II) Complex [Zn(sal)](H2O): Combined Experimental and Theoretical (NBO, QTAIM, and ELF) Investigation

The Zn(II) complex of salen-like scaffold [Zn(sal)](H2O) was synthesized and characterized by elemental analysis, IR, UV–Vis, and 1H-NMR spectroscopic techniques. The structure of complex was confirmed by single crystal X-ray diffraction studies. In the complex, Zn (II) was placed in the inner N2O2 compartment of the salen scaffold in square planar geometry and crystallized in the monoclinic space group P21/n. DFT and TDDFT calculations were performed to reproduce the experimentally observed structural and spectroscopic (IR and UV–vis) findings. The bonding of the Zn(II) framework in the [Zn(sal)](H2O) complex was explored in depth. The theoretical approaches employed were perturbation theory within the context of the natural bond orbital (NBO) framework, and quantum theory of atoms in molecule (QTAIM) and electron localization function (ELF) analysis. The study begins by delineating the difference between the NBO and QTAIM approaches. This paper thus exhibits the supportive nature of NBO theory and QTAIM in discussion of the bonding in the [Zn(sal)](H2O) complex, when both the methodologies are used in combination.
Safe Coordination of Human-Robot Firefighting Teams

Wildfires are destructive and inflict massive, irreversible harm to victims' lives and natural resources. Researchers have proposed commissioning unmanned aerial vehicles (UAVs) to provide firefighters with real-time tracking information; yet, these UAVs are not able to reason about a fire's track, including current location, measurement, and uncertainty, as well as propagation. We propose a model-predictive, probabilistically safe distributed control algorithm for human-robot collaboration in wildfire fighting. The proposed algorithm overcomes the limitations of prior work by explicitly estimating the latent fire propagation dynamics to enable intelligent, time-extended coordination of the UAVs in support of on-the-ground human firefighters. We derive a novel, analytical bound that enables UAVs to distribute their resources and provides a probabilistic guarantee of the humans' safety while preserving the UAVs' ability to cover an entire fire.
Musical Creativity-Based Ornament Learning Model of Cianjuran Sundanese Songs

This research concern to the development of Creativity Based Ornament Learning Model of Cianjuran Sundanese Songs. The development of learning model is based on the concept of, High Order Thinking Skill from Anderson Krathwohl, Exercises Creative Music Education from Murray Schaffer. Implementing the theory of Cianjuran Sundanese Songs by Zanten and Sukanda, and the theory of Cianjuran Sundanese song ornament. The research method used is design based research, with the stage of practical analysis in the field, organizing design, evaluation and reflection of learning model. Result of this research indicating that the ornament of Cianjuran Sundanese songs has aesthetic values, affective music values, and has strength in the practice of music creativity. The prototype model of Musical Creativity Based Cianjuran Sundanese Song learning modal is succeeded in delivering all participants to master the ornament of Sundanese songs creatively.
Numerical Analysis of Temperature Uniformity of Liquid Cooling Based Battery Module With Incremental Heat Transfer Area

Battery thermal management (BTM) has an important significance for electronic vehicles to keep them operating in a reasonable temperature range and reduce local temperature differences. In this study, a novel structure of liquid cooling-based lithium-ion battery module with a variable contact area of heat-conductive blocks is proposed. Three-dimensional transient simulations are carried out to investigate the thermal performance of the proposed structure. The effects of block height, height gradient, and inlet velocity are discussed. The results indicate that simply increasing the height of heat-conductive blocks could have a negative effect on cooling performance and that a variable heat transfer area could efficiently improve the temperature uniformity of the battery module. In addition, the thermal performance of the proposed battery module is sensitive to inlet velocity, but the positive effect can be decreased when the velocity is adequately increased. The temperature difference (ΔT) of the battery module with a variable contact area can achieve below 4 °C, and its reduced percentage can be 47.7% compared with that of the module with a consistent contact area when the inlet velocity is 0.2 m/s.
Model-Based Task Transfer Learning

A model-based task transfer learning (MBTTL) method is presented. We consider a constrained nonlinear dynamical system and assume that a dataset of state and input pairs that solve a task T1 is available. Our objective is to find a feasible state-feedback policy for a second task, T1, by using stored data from T2. Our approach applies to tasks T2 which are composed of the same subtasks as T1, but in different order. In this paper we formally introduce the definition of subtask, the MBTTL problem and provide examples of MBTTL in the fields of autonomous cars and manipulators. Then, a computationally efficient approach to solve the MBTTL problem is presented along with proofs of feasibility for constrained linear dynamical systems. Simulation results show the effectiveness of the proposed method.
Earthquake Education Through the Use of Documentary Movies

Unscientific, false, inaccurate and/or exaggerated reporting about anything in media or other platforms is a serious concern that needs a solution. This is particularly important when reporting about disasters (e.g. earthquakes). The lack of authentic scientific input into science news reporting may lead to news disasters, which may prove to be much more critical and dangerous than say -earthquake disasters. Therefore, this paper explores such a problem in a portion of NW Borneo and offers solution to improve the existing norms on the earthquake science, education and awareness programs in SE Asia. The explored field location is Sabah, Malaysia, which is targeted to map the level of earthquake science education and awareness of local people, and to examine the co-seismic deformation associated with the 5th June, 2015 earthquake. This event has surprised the local communities because the region is geographically located away from the active tectonic plate boundaries, and has traditionally been considered a low earthquake risk region. This is in contrast to the existence of high earthquake hazard and risk regions in the neighboring Indonesia and the Philippines. Therefore, not surprisingly, the residents of Borneo where puzzled, surprised and worried when a medium magnitude earthquake occurred and caused significant loss of life and property. The lack of scientific education on the causes, and remedies of earthquake hazards in most of the South and Southeast Asian regions is a reality, which needs a real solution. Therefore, through this work a small initiative has been started in Sabah, Malaysia where stories from the earthquake victims were recorded after the devastation caused by the June 2015 earthquake. Their real time experiences were blended with the updated scientific data on the occurrence of earthquakes in Borneo, which are mostly gathered from previously published works and the work presented here. The entire work is converted into a small documentary movie that highlights the causes of earthquake and how it impacts human life.
Describing human activities in video streams

This thesis outlines and advances a video description framework that describes human activities and their spatial and temporal relationships that can be used for video indexing, retrieval and summarisation applications. Generating natural language description of video streams demands the extraction of high-level features (HLFs) that sufficiently represent the events. This research centres around the issues combined with this task. One of these issues relates to identifying and segmenting participant human objects and identifying their visual attributions due to a broad range of scene setting variations, occlusion and background clutter. To that end a five-stage approach is developed to investigate the video description task. Firstly, a proper corpus that can be used for development and evaluation is created which contains relatively long video clips of human activities crafted from the Hollywood2 dataset, depicting a variety of action classes along with human textual annotations for each. Extensive analysis of the hand annotations associated with this corpus results in the conclusion that annotators are most interested in human presences and their visual attributions in the video stream, especially their actions, and interaction with other objects. Secondly, based on analysis outcome a novel framework that can detect, segment and track human body regions over video frames is proposed in order to efficiently describe video semantic. The proposed framework leverages the advances of low-level image cues and highlevel part detectors information. Thirdly, the visual attributions of extracted human objects are extracted as an efficient human action recognition framework is introduced. The video representation is improved by using extracted spatio-temporal human regions combined with the extended spatio-temporal locality-constrained linear coding (LLC) technique in order to identify the action class. Human action classification benchmarks are used to assess the performance of this model. The results reveal that the outcome of this approach outperforms the state-of-the-art, owing to its efficient representation of complex actions in video stream. Fourthly, as spatial and temporal relations of prominent objects play a vital role in describing video semantic content, a comprehensive representation is developed to efficiently extract spatial and temporal relations between interacted objects present in a video clip using their approximate oriented bounding box. The final stage aims to convert extracted HLFs into sentential descriptions using a template-based approach. By calculating the overlap between
Rehabilitation Strategies and Key Related Mechanisms Involved in Stroke Recovery

Poststroke rehabilitation requires a thorough understanding of the neural mechanisms underlying motor function recovery. This chapter outlines these mechanisms and also discusses the corresponding rehabilitation strategies based on the functional characteristics of the brain. The main topics we discuss are as follows: Although ipsilateral brain region activity is inhibited when using the limbs under normal conditions, it is thought that a decrease in this inhibition and the subsequent increased ipsilateral brain area activity post-injury promote recovery in the damaged contralateral neural network. For optimal poststroke motor function recovery, it is important to normalize the resulting imbalance in brain activity. Therefore, increased corticomotor excitation in the injured hemisphere or decreased excitation in the non-injured hemisphere must be promoted. Rehabilitation strategies include reducing non-paretic limb somatosensory input to decrease excitation in the non-injured hemisphere, increasing paretic limb somatosensory input to increase excitation in the injured hemisphere, increasing excitation in the injured hemisphere through movement training of the paretic hand and anesthesia of the paretic upper arm, increasing excitation in the injured hemisphere, or reducing excitation in the non-injured hemisphere. Considering the functional characteristics of the primary motor area, during the early stages after stroke, it is important to increase the somatosensory input to the paralyzed side and combine mental practices using motor imagery.
The evaluation of the repeatability of the 13C-ketoisocaproate breath test for assessing hepatic mitochondrial function

ABSTRACT The 13C-ketoisocaproate (13C-KICA) breath test (BT) has been recently proposed as a non-invasive test for assessing hepatic mitochondrial function. Results of the 13C-KICA BT can be expressed as different parameters. However, the best parameter for expressing the 13C-KICA BT result is uncertain which hinders use of the BT in routine clinical practice. We have investigated the repeatability of different parameters of 13C-KICA BT. Thirteen healthy adult subjects (5 men and 8 women) underwent a 13C-KICA BT on two occasions separated by a gap of approximately 30 days. There were no significant differences between the repeated measurements for all the test parameters over 30 days. Furthermore, the Bland Altman statistics showed no fixed or proportional bias for any of the test parameters. The cumulative 13C-dose enrichment over 60 min had the lowest within-subject variability of 12% compared to all other test parameters. The cumulative 13C-dose enrichment over 60 min could be a very useful parameter for the 13C-KICA BT to detect impaired hepatic mitochondrial function in patients with chronic liver diseases.
Exploring sustainable experiences in tourism

ABSTRACT This study explores the vaguely defined concept of sustainable experiences. Specifically, it questions how perceived experience value at tourism destinations can be enhanced through sustainable experience dimensions. Although experiences and sustainable tourism are intrinsically interlinked, knowledge of sustainable experiences and how they can be included in experience design to enhance perceived value is limited. Within a lake context, local stakeholders, researchers and students were invited to actively identify and co-design sustainable experience dimensions using, among others, interviews with residents and tourists. Our findings suggest four sustainable experience dimensions: interaction with the natural environment; interaction with the cultural environment; insights and views; and lake-based activities. The study advocates for future research and management to better incorporate sustainable experience dimensions to holistically enhance tourists’ perceived experience value and destination sustainability.
Perceptions of partner support among pregnant plurisexual women: A qualitative study

ABSTRACT Although partner support is an established determinant of mental health, we know little about bisexual and other plurisexual people's experiences of support from their partners. Furthermore, very limited research has examined how bisexual or plurisexual people experience partner support during pregnancy, a significant life stage for many couples. This paper draws from semi-structured interviews with 29 plurisexual women partnered with different-gender (i.e. cisgender male or transgender) partners to examine women's perceptions of partner support during pregnancy. While participants reported many of the same partner support issues and dynamics that have been described in research with monosexual childbearing women, their experiences as plurisexual women were unique in two regards: (a) unconditional acceptance from partners was connected to the partner's support for their plurisexual identities/histories; and (b) social integration support often included shared integration into social networks related to their plurisexual experiences, including sexual networks. These findings offer important implications for sexual and relationship therapists, who can play an important role in helping to foster these plurisexual-specific forms of partner support, and in so doing, improve outcomes for women during this significant life stage.
Topology Poisoning Attack in SDN-Enabled Vehicular Edge Network

The development of the Internet of Vehicles (IoV) has made people’s lives and travels safer, more efficient, and more comfortable. The combination of edge computing and IoV can provide processing and storage capabilities close to vehicles, thus becoming a potential paradigm. At this time, the software-defined networking (SDN) architecture is extremely necessary to realize centralized control and convenient management for complex and dynamic vehicular edge networks. However, as the brain of the SDN architecture, little attention has been paid to the security of the SDN controller. Once the controller is threatened, severe global chaos may happen. Therefore, in this article, we study the attack against the SDN controller, which is the topology poisoning attack. We successfully implement this attack in four mainstream controllers and analyze its impact from multiple levels. To the best of our knowledge, we are the first to study this attack in the vehicular edge network. In addition, in view of the counter-attacks of the existing defence mechanisms, we propose an attack-tolerance scheme based on deep reinforcement learning (DRL) to enhance the vehicular edge network with a certain degree of self-recovery.
Crowdsourced Truth Discovery in the Presence of Hierarchies for Knowledge Fusion

Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values are not mutually exclusive even for functional predicates due to their hierarchical structures. Thus, we need to consider the hierarchical structure to effectively estimate the trustworthiness of the sources and infer the truths. We propose a probabilistic model to utilize the hierarchical structures and an inference algorithm to find the truths. In addition, in the knowledge fusion, the step of automatically extracting information from unstructured data (e.g., text) generates a lot of false claims. To take advantages of the human cognitive abilities in understanding unstructured data, we utilize crowdsourcing to refine the result of the truth discovery. We propose a task assignment algorithm to maximize the accuracy of the inferred truths. The performance study with real-life datasets confirms the effectiveness of our truth inference and task assignment algorithms.
SmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms

Due to the increasing success of machine learning techniques, nowadays, thay have been widely utilized in almost every domain such as financial applications, marketing, recommender systems and user behavior analytics, just to name a few. In practice, the machine learning model creation process is a highly iterative exploratory process. In particular, an effective machine learning modeling process requires solid knowledge and understanding of the different types of machine learning algorithms. In addition, all machine learning algorithms require user-defined inputs to achieve a balance between accuracy and generalizability. This task is referred to as Hyperparameter Tuning . Thus, in practice, data scientists work hard to find the best model or algorithm that meets the specifications of their problem. Such iterative and explorative nature of the modeling process is commonly tedious and time-consuming. We demonstrate SmartML , a meta learning-based framework for automated selection and hyperparameter tuning for machine learning algorithms. Being meta learning-based, the framework is able to simulate the role of the machine learning expert. In particular, the framework is equipped with a continuously updated knowledge base that stores information about the meta-features of all processed datasets along with the associated performance of the different classifiers and their tuned parameters. Thus, for any new dataset, SmartML automatically extracts its meta features and searches its knowledge base for the best performing algorithm to start its optimization process. In addition, SmartML makes use of the new runs to continuously enrich its knowledge base to improve its performance and robustness for future runs. We will show how our approach outperforms the-state-of-the-art techniques in the domain of automated machine learning frameworks.
Economic weights of current and new breeding objective traits in Aberdeen Angus

Breeding values estimated for growth, calving performance, and exterior traits are currently combined into simple selection indices for bulls, cows, and heifers of the Aberdeen Angus breed. To establish a comprehensive economic index for this breed, the absolute and relative economic weights (EW) for a complex of 16 production, functional, carcass, and feed efficiency traits were calculated. The absolute EW of a trait expressed the difference in the present values of profit that will be obtained from the descendants of a bull with the average breeding value for this trait, and descendants of a bull with the breeding value one unit higher than the average one. The relative EW of a trait was defined as the standardised EW of a trait (i.e. EW per genetic standard deviation) expressed as percentage of the sum of standardised EWs of all evaluated traits. Sensitivity analysis was conducted to explore the EW of traits under variable production and economic conditions. Variability in the marketing strategy, in product prices and costs, and in trait means was considered in this analysis. Relative EW of the feed efficiency of breeding heifers and of cows reached 4%. The highest relative EW was obtained in three growth traits: weight gains of calves from birth to 120, from 120 to 210, and from 210 to 365 days of age (66% combined). The survival rate of calves until weaning and cow productive lifetime reached 11% and 8% of the total economic importance of traits, respectively. These growth and functional traits accounted for 84% (in marketing strategy involving selling breeding animals) to 90% (in populations with high growth intensity) of the total economic weight of all 16 evaluated traits. Therefore, these traits should be considered as new selection criteria when constructing a comprehensive selection index for the Czech Aberdeen Angus population in future.