Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.
Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning

This study reports a method for the detection of mechanical signaling anomalies in cardiac tissue through the use of deep learning and the design of two anomaly detectors. In contrast to anomaly classifiers, anomaly detectors allow accurate identification of the time position of the anomaly. The first detector used a recurrent neural network (RNN) of long short-term memory (LSTM) type, while the second used an autoencoder. Mechanical contraction data present several challanges, including high presence of noise due to the biological variability in the contraction response, noise introduced by the data acquisition chain and a wide variety of anomalies. Therefore, we present a robust deep-learning-based anomaly detection framework that addresses these main issues, which are difficult to address with standard unsupervised learning techniques. For the time series recording, an experimental model was designed in which signals of cardiac mechanical contraction (right and left atria) of a CD-1 mouse could be acquired in an automatic organ bath, reproducing the physiological conditions. In order to train the anomaly detection models and validate their performance, a database of synthetic signals was designed (n = 800 signals), including a wide range of anomalous events observed in the experimental recordings. The detector based on the LSTM neural network was the most accurate. The performance of this detector was assessed by means of experimental mechanical recordings of cardiac tissue of the right and left atria.
Pastoral community's vulnerability under extreme floodings accelerated by rangeland degradation among Turkmen transhumant, Northern Iran

Flood disaster is considered a significant natural hazard due to their devastating effects. The 2019 flood in Northern Iran drastically affected the lives of Indigenous Turkmen pastoralists. This work assesses the vulnerability of the pastoral families living in flood regions. We used the Geographical Information System, Multi‐Criteria Decision Analysis, and semi‐structured interviews to analyze the flood vulnerable areas. Initially, we interviewed 20 individuals and then formulated a questionnaire completed by 69 pastoralists in rangelands affected by floods in Golestan province. Result showed that 91% of the participants thought road networks and 76% thought livestock were the most affected components of the rangeland ecosystems in the 2019 flood. Moreover, we found supplementary feeding, watering costs, and forage quantity to be the most affected items in the economy of pastoral families. The main rangeland degrading factors were overgrazing, climate change, drought, and water shortages. The main reasons for overgrazing were the high prices of supplementary feeding, low forage productivity of the rangelands, and financial difficulties of pastoralists. We indicated that pastoral communities in the semi‐arid rangeland of Northern Golestan were highly vulnerable to flood. Hence, proper management of rangelands and building pastoral family resilience requires the attention of nature conservationists and management organizations at the national level.
A method of attitude measurement and level assessment for skiers based on wearable inertial measurement

Quantitative analysis of sports is an important development direction of scientific skiing training, and the digital expression of human movement patterns during skiing is the basis for scientific quantitative analysis. A human motion capture and attitude reconstruction system based on a wearable BSBD inertial measurement unit was designed and built, combined with the human multi-rigid body motion model to realize the human body reconstruction during the skiing, and applied to the auxiliary training of slewing movements in alpine skiing. At the same time, for the indoor training scene based on the multi-degree-of-freedom simulated ski training platform, a digital evaluation method suitable for ski slalom is proposed. The method uses motion capture system and posture reconstruction system to extract five kinds of sliding characteristic data of skiers, and realizes the evaluation of skiers’ technical parameters through similarity measurement and linear fitting with high-level athletes’ motion parameters, so as to assist scientific training. Finally, experiments are carried out on the indoor Olymp simulated ski training bench to verify the effectiveness of the method.
When past meets future in Persian: A construction grammar approach to futurity

ABSTRACT In Persian, two of the verbal constructions used for expressing futurity – the Past Simple and the Future Simple – are built around the past form of the main verb. This paper seeks to demystify this connection between the past and the future by investigating how these past forms contribute to the expression of futurity and setting this within an overall analysis of the future constructions in this language. Based on the form of the main verb in 940 sentences with verbal future constructions, six constructions were identified and categorized into future-oriented past constructions (FPST), which include the Past Simple and the Future Simple, and future-oriented present constructions (FPRT), which include the Present Simple, the Present Progressive, the Present Subjunctive and the WANT construction. A construction grammar approach together with Reichenbach and neo-Reichenbach terminology was then utilized to distinguish the differing relations between tense, aspect, mood and speaker perspective, and assign syntactic-semantic maps in terms of features such as level of formality, perfectness, intended future, certainty and inner aspect (aktionsart) to each of the FPST and FPRT constructions. It is argued that the use of the past form of the main verb in the Past Simple and the Future Simple (FPST constructions) owes much to aspectual and ontological factors, with the past element indicating certainty, perfectness and a speaker perspective of looking back.
Assessing Emotional Expressions During a Cycling-Based Initiative for Older Care Home Residents Using Video-Based Recordings

Objective: Through Cycling Without Age, trained volunteers use specially designed trishaws to provide rides for older adults living in care homes and other supported living environments. Qualitative and quantitative research suggests benefits in terms of improvements in mood and wellbeing. Those studies have predominantly been interviews with participants reflecting on previous rides, or as pre-/post-assessments. The current study assessed emotional experiences using video recordings acquired during participants’ rides. Methods: Twelve older adults (50% female; 67-92 years old (M = 81.8, SD = 7.4)) living in care homes or supported living environments were recruited. During a Cycling Without Age ride, participants were filmed using an action camera mounted on the trishaw. Recordings were rated using the Facial Expression Coding System by two researchers to assess the frequency, duration and intensity of positive and negative emotions. Results: On average, 23.7 positive emotional expressions were observed per ride, significantly higher than negative emotions (0.4). As well as more frequent, positive emotions were observed over a longer duration in total (139.5 seconds vs. 1.3) and rated as more intense (1.9 out of 5 vs. 0.3). Conclusion: The study supported the value of directly assessing emotional responses during this cycling-based initiative, including minimising the input required from participants. The predominantly positive emotional expressions observed were consistent with both qualitative and quantitative assessments of Cycling Without Age, and suggests a potential pathway by which those benefits manifest. Future studies might adopt a triangulated approach, using in-activity monitoring, quantitative assessments and participant reflections.
Antibacterial, Antiparasitic, and Cytotoxic Activities of Chemical Characterized Essential Oil of Chrysopogon zizanioides Roots

This study aimed to investigate the chemical composition as well as the antibacterial, antiparasitic, and cytotoxic potentialities of the Brazilian Chrysopogon zizanioides root essential oil (CZ-EO) In addition, CZ-EO cytotoxicity to LLCMK2 adherent epithelial cells was assessed. The major compounds identified in CZ-EO were khusimol (30.0 ± 0.3%), β-eudesmol (10.8 ± 0.3%), α-muurolene (6.0 ± 0.1%), and patchouli alcohol (5.6 ± 0.2%). CZ-EO displayed optimal antibacterial activity against Prevotella nigrescens, Fusobacterium nucleatum, Prevotella melaninogenica, and Aggregatibacter actinomycetemcomitans, with Minimum Inhibitory Concentration (MIC) values between 22 and 62.5 µg/mL and Minimum Bactericidal Concentration (MBC) values between 22 and 400 µg/mL. CZ-EO was highly active against the L. amazonensis promastigote and amastigote forms (IC50 = 7.20 and 16.21 µg/mL, respectively) and the T. cruzi trypomastigote form (IC50 = 11.2 µg/mL). Moreover, CZ-EO showed moderate cytotoxicity to LLCMK2 cells, with CC50 = 565.4 µg/mL. These results revealed an interesting in vitro selectivity of CZ-EO toward the L. amazonensis promastigote and amastigote forms (Selectivity Index, SI = 78.5 and 34.8, respectively) and the T. cruzi trypomastigote form (SI = 50.5) compared to LLCMK2 cells. These results showed the promising potential of CZ-EO for developing new antimicrobial, antileishmanial, and antitrypanosomal drugs.
Cyanobacteria-Mediated Immune Responses in Pepper Plants against Fusarium Wilt

Research in plant pathology has increasingly focused on developing environmentally friendly, effective strategies for controlling plant diseases. Cyanobacteria, including Desmonostoc muscorum, Anabaena oryzae, and Arthrospira platensis, were applied to Capsicum annuum L. to induce immunity against Fusarium wilt. Soil irrigation and foliar shoots (FS) application were used in this investigation. The disease symptoms, disease index, osmotic contents, total phenol, Malondialdehyde (MDA), hydrogen peroxide (H2O2), antioxidant enzymes (activity and isozymes), endogenous hormone content, and response to stimulation of defense resistance in infected plants were assessed. Results demonstrated that using all cyanobacterial aqueous extracts significantly reduced the risk of infection with Fusarium oxysporum. One of the most effective ways to combat the disease was through foliar spraying with Arthrospira platensis, Desmonostoc muscorum, and Anabaena oryzae (which provided 95, 90, and 69% protection percent, respectively). All metabolic resistance indices increased significantly following the application of the cyanobacterial aqueous extracts. Growth, metabolic characteristics, and phenols increased due to the application of cyanobacteria. Polyphenol oxidase (PPO) and peroxidase (POD) expressions improved in response to cyanobacteria application. Furthermore, treatment by cyanobacteria enhanced salicylic acid (SA) and Indole-3-Acetic Acid (IAA) in the infected plants while decreasing Abscisic acid (ABA). The infected pepper plant recovered from Fusarium wilt because cyanobacterial extract contained many biologically active compounds. The application of cyanobacteria through foliar spraying seems to be an effective approach to relieve the toxic influences of F. oxysporum on infected pepper plants as green and alternative therapeutic nutrients of chemical fungicides.
Interfacial charge transfer and Schottky barriers at c-Si/a-In heterojunctions

Metal-Semiconductor (M/S) heterojunctions, better known as Schottky junctions play a crucial role in modern electronics. At present, the mechanisms behind the M/S junctions are still a subject of discussion. In this work, we investigate the interfaces between semiconducting crystalline Si and amorphous metallic indium, Si{0 0 1}/a-In and Si{1 1 1}/a-In using both ab initio molecular dynamics simulations and a Schottky-Mott approach. The simulations reveal the formation of a distinct border between the Si substrates and amorphous In at the interfaces. The In atoms adjacent to the interfaces exhibit atomic ordering. Charge transfer occurs from In to Si, forming c-Si−q/a-In+q charge barriers at the interfaces. This indicates that a crystalline p-Si/a-In heterojunction will have rectifying properties, which agrees with an analysis using the Schottky-Mott model which predicts a Schottky barrier height of 1.3 eV for crystalline p-Si/a-In using the calculated work function for a-In (3.82 eV). We further discuss the interfacial charge transfer, related hole-depletion regions in Si adjacent to the interfaces and the Schottky-Mott approximations.
Towards roadmap to implement blockchain in healthcare systems based on a maturity model

Healthcare systems face various issues related to complex networks of intermediaries and a lack of transaction traceability. The most critical issues are the fragmentation of healthcare data, obstacles in providing efficient research and services, lack of clinical trial reporting, high cost and mismanagement of the drug supply chain, patient data security, and fake drugs. Blockchain technology has the potential to address these criticalities as it has in build traceability mechanisms and promises new business models by enabling incentive structures. This potential of blockchain gathers a high interest in the health industry. However, the implementation of blockchain in healthcare faces various issues as well. Currently, there are no practice‐oriented maturity models to improve such an implementation. In this paper, we present a roadmap to develop a maturity model for blockchain in healthcare (MMBH) based on critical barriers (CBs), critical success factors (CSFs), and the best practices for blockchain implementation in healthcare systems. As a first step to develop the MMBH, in this paper, we present the initial results of a systematic literature review (SLR) to identify critical success factors for implementing blockchain in healthcare systems. We also applied fuzzy technique order preference by similarity to ideal solution (TOPSIS) to prioritize the identified CSFs.
Development of a High-Linearity Voltage and Current Probe with a Floating Toroidal Coil: Principle, Demonstration, Design Optimization, and Evaluation

As the conventional voltage and current (VI) probes widely used in plasma diagnostics have separate voltage and current sensors, crosstalk between the sensors leads to degradation of measurement linearity, which is related to practical accuracy. Here, we propose a VI probe with a floating toroidal coil that plays both roles of a voltage and current sensor and is thus free from crosstalk. The operation principle and optimization conditions of the VI probe are demonstrated and established via three-dimensional electromagnetic wave simulation. Based on the optimization results, the proposed VI probe is fabricated and calibrated for the root-mean-square (RMS) voltage and current with a high-voltage probe and a vector network analyzer. Then, it is evaluated through a comparison with a commercial VI probe, with the results demonstrating that the fabricated VI probe achieved a slightly higher linearity than the commercial probe: R2 of 0.9967 and 0.9938 for RMS voltage and current, respectively. The proposed VI probe is believed to be applicable to plasma diagnostics as well as process monitoring with higher accuracy.
The horizontal distribution of water in the stratospheres of Jupiter and Saturn with Herschel/PACS

Abstract Thedetectionofwater in thestratospheresofthe giantplanets[1]andofTitan[2]withtheInfraredSpaceOb-servatory raised the question of the origin of oxygencompounds in these atmospheres. This external oxy-gen supply may have several sources: (i) a permanentu x from interplanetary dust particles produced fromasteroid collisions and from comet activity [3], (ii) lo-cal sources from planetary environments (rings, satel-lites) [4, 5], (iii) cometary Shoemaker-Levy 9 (SL9)type impacts [6]. Disentangling the various sourcesat Jupiter andSaturn is a keyobjectiveof the HerschelSpace Observatory key program HssO (Herschel So-lar System Observations) [7]. In particular, we lookfornewevidenceoftheSL9originofwaterinJupiter'sstratospherefollowing[8,9,10]andforapossiblesig-nature in Saturn's stratosphere of water coming fromthe Enceladus torus [11]. In this paper, we will present disk-resolved obser-vations in the strong water line at 66.4  m in thestratospheres of Jupiter and Saturn, recorded with thePACS (Photodetector Array Camera and Spectrome-ter) instrument [12] of the Herschel Space Observa-tory [13] on December 15
A Coordinated Droop Control Method Using a Virtual Voltage Axis for Power Management and Voltage Restoration of DC Microgrids

In this paper, a coordinated droop control method through virtual voltage axis is proposed for voltage restoration and energy management of dc microgrids (MGs). Droop control is a control method that operates a dc MG using only local information. However, there are several drawbacks in that output voltage deviations occur, and the utilization priorities of various sources are not considered. To solve these problems, the voltage compensation term is defined as the virtual axis voltage value. It can minimize the voltage deviation of the dc MG and enable energy management by determining the operation band. However, the value of the virtual voltage axis is affected by communication delays. Therefore, its influence on the values of the virtual voltage axis was analyzed. The reference voltage and the droop gain of the droop control were designed, and the validity of the proposed method was verified using a lab-scale dc MG.
Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features

Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.
Advancing sustainable water futures for Melbourne: analysis of expert opinion on structural and non-structural approaches

This social research project investigated industry leader perspectives and decision values in relation to promoting management techniques to address sustainable water supply issues across urban regions. The conditions under which structural (engineering) and non-structural (behavioural change) solutions are prioritised and implemented was investigated through semi-structured interviews of senior representatives from water authorities, State Government agencies, local government, developers, consultants and academics across Metropolitan Melbourne. In theory, a mixed structural and non-structural approach is typically advocated for advancing sustainable urban water futures. However, there is a lack of knowledge of the decision criteria of how to select and systematically integrate these approaches. The results reveal that industry leaders would not promote non-structural solutions without the primary support of a structural solution. Largely due to lack of certainty about social receptivity, this was perceived as presenting a substantially higher risk to achieving a sustained reduction in potable water demand. Therefore, structural approaches were viewed as much easier to implement, monitor and enable reliable outcomes. This evidence strongly suggests that without social research directed at quantifying both the uncertainties and outcomes of non-structural solutions, it is unlikely that industry leaders will be in a position to effectively promote and resource their implementation as an independent initiative.
Catching a mouse on a tree

In this paper we consider a pursuit-evasion game on a graph. A team of cats, which may choose any vertex of the graph at any turn, tries to catch an invisible mouse, which is constrained to moving along the vertices of the graph. Our main focus shall be on trees. We prove that $\lceil (1/2)\log_2(n)\rceil$ cats can always catch a mouse on a tree of order $n$ and give a collection of trees where the mouse can avoid being caught by $ (1/4 - o(1))\log_2(n)$ cats.