Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding tourist behaviour in terms of activeness and intra-destination movement patterns for managing tourism experience

The dissertation is focusing on the demand side of tourism, in particular on tourists’ consumption of a destination and on the conceptualization of their decision process during visitation. Travel behaviour can be related to and motivated by several factors and the analysis of these factors allows a better understanding of tourists’ movements and activity participation. This information can be used for creating customized tourism products that can bring additional benefits to the tourists and the destination. In this context, destination cards are analysed in the thesis which are integrated products to facilitate tourists in their consumption by offering public transport usage and entrance to activities and attractions at the destination. The dissertation is divided into four chapters and refers to four research articles; three of them are published in refereed journals and one of them is presented in an edited book. The first article investigates the relation among motivation with variables introduced for the first time in this study, namely activeness indicators and destination card requirements. In addition, it attempts to identify the customer profile of destination cards. The second paper models the correlated choices of the spatial extent of the visited destination and the selection of transport modes, and determines the influential variables in this context applying bivariate probit models. The third article investigates the existence of spatial or product clustering in consumption with destination cards and looks for the discriminating factors among the clusters. Finally, the fourth paper reviews and elaborates theories on movement patterns and their influencing factors. The implications of the thesis are beneficial for destination marketing organizations, regional transport authorities and tourism operators.
Association Between the Presence of Coronary Artery Disease or Peripheral Artery Disease and Left Ventricular Mass in Patients Who Have Undergone Coronary Computed Tomography Angiography

Background Left ventricular mass (LVM) is a critical marker of future cardiovascular risk. We determined the association between LVM measured by coronary computed tomography angiography (CCTA) and the presence of coronary artery disease (CAD) or peripheral artery disease (PAD) in patients who had undergone CCTA for screening of CAD. Methods We enrolled 1,307 consecutive patients (66 ± 12 years old, 49% males) who underwent CCTA for screening of CAD at the Fukuoka University Hospital (FU-CCTA registry), and either were clinically suspected of having CAD or had at least one cardiovascular risk factor. Patients with coronary stenosis of ≥ 50% by CCTA were diagnosed as CAD. Patients with an ankle brachial pressure index < 0.9 or who had already been diagnosed with PAD were considered to have PAD. Left ventricular mass index (LVMI), left ventricular ejection fraction (LVEF), end-diastolic volume (EDV) and end-systolic volume (ESV) were measured. The patients were divided into CAD (-) and CAD (+) or PAD (-) and PAD (+) groups. Results The prevalences of CAD and PAD in all patients were 50% and 4.8%, respectively. Age, %males, %hypertension (HTN), %dyslipidemia (DL), %diabetes mellitus (DM), %smoking and %chronic kidney disease in the CAD (+) group were significantly higher than those in the CAD (-) group. Age, %males, %HTN, %DM and %smoking in the PAD (+) group were significantly higher than those in the PAD (-) group. CAD was independently associated with LVMI (odds ratio (OR): 1.01, 95% confidence interval (CI): 1.01 - 1.02, P < 0.01) in addition to age, male, HTN, DL, DM, and smoking. PAD was also independently associated with LVMI (OR: 1.01, 95% CI: 1.0 - 1.02, P = 0.018) in addition to age, DM, and smoking. Conclusions LVMI determined by CCTA may be useful for predicting atherosclerotic cardiovascular diseases including both CAD and PAD, although there were considerable differences between %CAD and %PAD in all patients.
Design and Implementation of MQTT-Based Over-the-Air Updating Against Curious Brokers

The over-the-air (OTA) updating mechanism is one critical mechanism for Internet of Things (IoT); the OTA updating mechanism facilitates the systems remotely update the firmware/software without recalling the devices or visiting the remote sites. However, the existent MQTT-based OTA models (like Amazon, Infineon, etc.) ignore the privacy against a curious broker: they assume the users trust the broker and the broker would not peek at the transmission contents. This strong assumption is dangerous as we can see that many high-profile-security IT servers (like Coinbase) have been compromised either from the internal staff or from the external hackers. This article, based the end-to-end (E2E) channel establishment, designs, implements, and evaluates a new MQTT-based OTA scheme. The E2E channel ensures the security and the privacy. The AVISPA-based formal proof validates its security goals. The evaluation, based on the implementations, shows that the proposed scheme enhances the security/privacy while keeping the efficiency.
The Happiness of the Sandwich Generation in Bali: the Roles of Family, Social, and Balinese Culture

The sandwich generation in Bali, especially Gianyar, which is still thick with its culture, has a dual role in the family, as caregivers for children and parents, breadwinners, and is actively involved in community activities. This study aims to analyze the influence of cultural norms in moderating the effect of family roles and social support on the happiness of the sandwich generation. The samples were 203 generations of sandwiches obtained through accidental sampling techniques and analyzed using Moderated Regression Analysis (MRA). The results showed that cultural norms, social support, and family roles significantly influence the happiness of the sandwich generation. Meanwhile, cultural norms were not variables that moderate the effect of family roles and social support on the happiness of the sandwich generation. This study proposed two central policies to break the sandwich generation chain in Gianyar Regency: socializing the importance of financial literacy so that in the future, a person does not depend on his child for life and forming a parent empowerment group to reduce the burden of the sandwich generation.JEL Classification: M14, J12, I31, L31How to Cite:Yuniari, N. K. A. , & Saskara, I. A. N. , (2023). Happiness of the Sandwich Generation in Bali: the Role of Family, Social and Balinese Culture. Signifikan: Jurnal Ilmu Ekonomi, 12(2), 355-370. https://doi.org/10.15408/sjie. v12i2.32315.
Comparing continental and local distribution patterns of protists: A case study of silica-scaled chrysophytes

: Free–living protists play important roles in biogeochemical cycling and food–web chains in both aquatic and terrestrial environments. However, their biogeography and spatial distribution remain poorly resolved. Here, we used silica–scaled chrysophytes to determine the most important climatic, historical, and environmental variables that explain biodiversity patterns across different geographical scales; the continental scale was represented by 473 European sites (gradient of 3,800 km), the local scale comprised 69 Aquitanien (France) sites (gradient of 186 km). Generalized Additive Models were used to evaluate the relationship between species richness and explanatory variables. Partition of community variance into fractions, explained separately by local environment, history, and climate helped us to track the main drives of community composition. While species richness was evenly influenced by all factors at the European scale and predominantly by environment (pH) at the local scale, the main factor shaping community composition was climate at both scales and history (geographical distance) at the European scale. Our results showed that silica–scaled chrysophytes respond strongly to climate–related variables at a broad geographical scale. Our work highlights the effect of climate on protist biogeographic structuring and suggests that climate–related variables may represent one of the main drivers of protist distribution, similarly as in macroorganisms.
Exploring objective measures for assessing team performance in healthcare: an interview study

Introduction Effective teamwork plays a critical role in achieving high-performance outcomes in healthcare. Consequently, conducting a comprehensive assessment of team performance is essential for providing meaningful feedback during team trainings and enabling comparisons in scientific studies. However, traditional methods like self-reports or behavior observations have limitations such as susceptibility to bias or being resource consuming. To overcome these limitations and gain a more comprehensive understanding of team processes and performance, the assessment of objective measures, such as physiological parameters, can be valuable. These objective measures can complement traditional methods and provide a more holistic view of team performance. The aim of this study was to explore the potential of the use of objective measures for evaluating team performance for research and training purposes. For this, experts in the field of research and medical simulation training were interviewed to gather their opinions, ideas, and concerns regarding this novel approach. Methods A total of 34 medical and research experts participated in this exploratory qualitative study, engaging in semi-structured interviews. During the interview, experts were asked for (a) their opinion on measuring team performance with objective measures, (b) their ideas concerning potential objective measures suitable for measuring team performance of healthcare teams, and (c) their concerns regarding the use of objective measures for evaluating team performance. During data analysis responses were categorized per question. Results The findings from the 34 interviews revealed a predominantly positive reception of the idea of utilizing objective measures for evaluating team performance. However, the experts reported limited experience in actively incorporating objective measures into their training and research. Nevertheless, they identified various potential objective measures, including acoustical, visual, physiological, and endocrinological measures and a time layer. Concerns were raised regarding feasibility, complexity, cost, and privacy issues associated with the use of objective measures. Discussion The study highlights the opportunities and challenges associated with employing objective measures to assess healthcare team performance. It particularly emphasizes the concerns expressed by medical simulation experts and team researchers, providing valuable insights for developers, trainers, researchers, and healthcare professionals involved in the design, planning or utilization of objective measures in team training or research.
A Deep Learning Approach for Detecting and Classifying Cat Activity to Monitor and Improve Cat’s Well-Being Using Accelerometer, Gyroscope, and Magnetometer

Activity detection of pets using sensor technology and artificial intelligence (AI) approaches has gained momentum in recent years. These technologies have revamped up the monitoring systems for the well-being of pets and are emerging as revolutionary technologies. Most of the research works have been conducted on the activity detection of humans, dogs, sheep, and horses; however, cat activity detection has not been explored meticulously using the aforementioned latest technologies. In this article, we proposed an long short-term memory (LSTM)-based deep learning model and investigated the activity detection of cats using three wearable sensors. Our research content can be summarized as follows. First, we collected 1765825 data samples from ten cats of different ages, breeds, and gender using an accelerometer, gyroscope, and magnetometer. Second, the raw data were preprocessed by applying data transformation approaches and feature engineering techniques. Third, we developed three different kinds of deep learning models, namely; artificial neural network (ANN), 1-D convolutional neural network (CNN), and LSTM, to classify ten different activities of cats. Hyperparameter tuning was performed to optimize the performance of the models. LSTM model outperformed other models and showed good accuracy. The model achieved an accuracy of 95.94%. To the best of our knowledge, this work is the first of its kind, which applied a deep learning-based model for the detection of cat activity using three kinds of sensor data, such as accelerometer, gyroscope, and magnetometer. This proposed model will help improve the well-being of cats by detecting the activities of cats with high accuracy using wearable sensor devices and deep learning technology.
HER2+ Early Breast Cancer: From Escalation via Targeted and Post-Neoadjuvant Treatment to De-Escalation

Background: Human epidermal growth factor receptor 2 positive (HER2+, also referred to as ERBB2+) breast cancer is a subtype, historically associated with a particularly poor prognosis. Research into biological and molecular pathomechanisms of breast cancer has resulted in the development and adoption of several therapies targeting HER2. In parallel, various escalation/de-escalation strategies have been examined to further optimize patient outcomes and care. Summary: In this review, we highlighted the landmark trials in the evolution of treatment and management of HER2+ early breast cancer (eBC). Key Messages: Continuous research over the last two decades has gradually prolonged survival in patients with early HER2+ eBC. Incorporation of post-neoadjuvant setting into clinical practice improved long-term outcomes in high-risk patients with residual disease after neoadjuvant therapy. In parallel, use of modern anti-HER2 agents may potentially allow omission of chemotherapy without compromising the survival in a significant number of selected patients. Current research focused on exploring the molecular heterogeneity of HER2+ breast cancer resulted in identification of new prognostic and predictive biomarkers which could pave the way toward the development of truly personalized therapy.
Remotely triggered micro‐earthquakes in the Larderello‐Travale Geothermal Field (Italy) following the 2012 May 20, Mw 5.9 Po‐plain earthquake

We report observations of remotely triggered earthquakes at the Larderello‐Travale Geothermal Field (Italy), following the Mw = 5.9 Po‐Plain earthquake on 20 May 2012. Four distinct triggered events are recognized within a short (~25 s) time interval accompanying the sweeping of ~10s Rayleigh waves. Triggered sources are clustered at depths in between 4 km and 6 km. The magnitude and distance of the mainshock agree well with the triggering threshold previously proposed for The Geysers, California. For three out of four earthquakes, the Rayleigh wave dynamic stresses are mostly associated with extensional vertical (szz) and shear (sxz) components, which range up to 5 KPa. Once considering the structural setting of the area, the most likely triggering mechanism involves the rupture of normal faults which are kept close to failure by high‐pressure crustal fluids.
Predictive analytics identifies key factors driving hyperalgesic priming of muscle sensory neurons

Hyperalgesic priming, a form of neuroplasticity induced by inflammatory mediators, in peripheral nociceptors enhances the magnitude and duration of action potential (AP) firing to future inflammatory events and can potentially lead to pain chronification. The mechanisms underlying the development of hyperalgesic priming are not well understood, limiting the identification of novel therapeutic strategies to combat chronic pain. In this study, we used a computational model to identify key proteins whose modifications caused priming of muscle nociceptors and made them hyperexcitable to a subsequent inflammatory event. First, we extended a previously validated model of mouse muscle nociceptor sensitization to incorporate Epac-mediated interaction between two G protein-coupled receptor signaling pathways commonly activated by inflammatory mediators. Next, we calibrated and validated the model simulations of the nociceptor’s AP response to both innocuous and noxious levels of mechanical force after two subsequent inflammatory events using literature data. Then, by performing global sensitivity analyses that simulated thousands of nociceptor-priming scenarios, we identified five ion channels and two molecular processes (from the 18 modeled transmembrane proteins and 29 intracellular signaling components) as potential regulators of the increase in AP firing in response to mechanical forces. Finally, when we simulated specific neuroplastic modifications in Kv1.1 and Nav1.7 alone as well as with simultaneous modifications in Nav1.7, Nav1.8, TRPA1, and Kv7.2, we observed a considerable increase in the fold change in the number of triggered APs in primed nociceptors. These results suggest that altering the expression of Kv1.1 and Nav1.7 might regulate the neuronal hyperexcitability in primed mechanosensitive muscle nociceptors.
Evaluation of Hydraulic Efficiency of Sedimentation Basins

Detention time distribution curves of a pilot sedimentation basin were analyzed by the conventional method using central tendency values of the flow curve and by a new method in which an F(t) function is used. A straight line resulted when experimental values of log [1-F (t)] versus t/T were plotted. Good correlation was obtained between results by both methods. The new method enables a quantitative analysis of the flow regime. It was shown that the flow in a sedimentation basin, with inlet baffle, consists of approximately 60% mixing, the remainder being plug flow.
A High-Performance and Energy-Efficient Photonic Architecture for Multi-DNN Acceleration

Large-scale deep neural network (DNN) accelerators are poised to facilitate the concurrent processing of diverse DNNs, imposing demanding challenges on the interconnection fabric. These challenges encompass overcoming performance degradation and energy increase associated with system scaling while also necessitating flexibility to support dynamic partitioning and adaptable organization of compute resources. Nevertheless, conventional metallic-based interconnects frequently confront inherent limitations in scalability and flexibility. In this paper, we leverage silicon photonic interconnects and adopt an algorithm-architecture co-design approach to develop MDA, a DNN accelerator meticulously crafted to empower high-performance and energy-efficient concurrent processing of diverse DNNs. Specifically, MDA consists of three novel components: 1) a resource allocation algorithm that assigns compute resources to concurrent DNNs based on their computational demands and priorities; 2) a dataflow selection algorithm that determines off-chip and on-chip dataflows for each DNN, with the objectives of minimizing off-chip and on-chip memory accesses, respectively; 3) a flexible silicon photonic network that can be dynamically segmented into sub-networks, each interconnecting the assigned compute resources of a certain DNN while adapting to the communication patterns dictated by the selected on-chip dataflow. Simulation results show that the proposed MDA accelerator outperforms other state-of-the-art multi-DNN accelerators, including PREMA, AI-MT, Planaria, and HDA. MDA accelerator achieves a speedup of 3.6, accompanied by substantial improvements of 7.3×, 12.7×, and 9.2× in energy efficiency, service-level agreement (SLA) satisfaction rate, and fairness, respectively.
CHA2DS2-VASc Score as a Predictor of New Onset Atrial Fibrillation in Patients With Non-ST Segment Elevation Myocardial Infarction Who Underwent Percutaneous Coronary Intervention

Background: New-onset atrial fibrillation (NOAF) often complicates acute coronary syndromes (ACSs) with adverse short- and long-term consequences. However, the current risk classification model for estimating NOAF during non-ST-segment elevation myocardial infarction (NSTEMI) remains unclear. Some parameters in the CHA2DS2-VASc score are closely related to the development of atrial fibrilation (AF). Methods: This retrospective study was conducted among 670 consecutive NSTEMI patients who applied to our cardiovascular center between June 2020 and June 2022 and underwent percutaneous coronary intervention (PCI). Results: NOAF developed during hospitalization in 55 patients (12.5%). NOAF patients were older and had higher high sensitivity C-reactive protein (hs-CRP), left atrial volume index, Post PCI thrombolysis in myocardial infarction (TIMI) grade <3, CHA2DS2-VASc score, peak troponin I (ng/ml), and SYNTAX score (SS). After univariate logistic regression analysis for the predictors of NOAF development, CHA2DS2-VASc score, post PCI TIMI grade <3, hemoglobin, hsCRP, SS alone were predictors of NOAF, but after multivariate analysis, CHA2DS2-VASc score, post PCI TIMI grade <3, hemoglobin had a determining effect. Conclusions: The CHA2DS2-VASc score can be evaluated as a predictor of NOAF after PCI in NSTEMI. Except for the components of the CHA2DS2-VASc score, post PCI TIMI grade <3 and low hemoglobin levels are independent risk factors for NSTEMI-NOAF.
Static of tomographic inversion by first breaks in complex areas

The solution of static problem in complex mountainous areas is one of the most important aspects of better seismic imaging.The refraction static method is based on the hypothesis of horizontal refraction interface.Therefore,both the precondition and the practical applications have proved its incapability for the complicated areas characterized by severely relief surface and rapidly changed velocity both laterally and vertically.This paper recommends the tomographic inversion static by first breaks,that is to say,the first breaks of seismic records are used to inverse the surface velocities and calculate the long-period static corrections and the short-period static corrections.By using it to the forward modeling and the practical seismic data,the tomographic inversion method well solves the static problem caused by complicated surface.
THE EFFECT OF CLIMATE CHANGES ON PHENOLOGICAL PHASES IN PLUM TREE (Prunus domestica L.) IN SOUTH-WESTERN ROMANIA

This paper's aim is to study phenological changes in plum tree species (Prunus domestica L.) that recently appeared in Oltenia region (Romania). It was found that in the years with early spring season and high temperatures, vegetation phenological phases have developed much earlier than in normal years. Analysis of data obtained showed that "flowering time duration" is a feature which is influenced by climatic factor and genetic factor. The influence of meteorological factor is manifested in different years, thus causing, in the same cultivar, different time durations between the beginning and the end of flowering. In general, the earlier the flowering is developing, the shorter its time duration. The studies on the environmental factors' impact on phenology in fruit-tree species are allowing making decisions on forming out suitable assortments for different culture zones, depending on local ecological conditions.
THE ANNALS OF “DUNAREA DE JOS” UNIVERSITY OF GALATI

The method proposed in this paper uses the Independent Component Analysis (ICA) for an application of unsupervised recognition of textures. The analysed texture is modelled by a weighted sum of almost statistically independent random signals that are extracted with FastICA algorithm. Each resulting signal is described by its negentropy, more precisely, by one of the approximations used by FastICA algorithm. The approximated negentropies are sorted into descending order and represented by a curve. The final step of the algorithm is the averaging of a certain number of such curves obtained from different zones of the texture. The resulting mean ”negentropy curve” displays a good discriminating power on the tested textures.