Miracle-dz/UniFAR-FTUs huggingface dataset:
TRAVELLING AND DISCOVERING NEW DESTINATIONS AFTER THE COVID-19’S LOCKDOWN: THE ROLE OF ROUTINES AND HABITS

The paper identifies the level of importance determined by the consumer as far as traveling and discovering new destinations are concerned, after the lockdown caused by the health crisis triggered by the coronavirus pandemic. A sample of 445 respondents is used, and an ordered logit econometric model is applied to estimate the importance level of travelling and enjoying a new destination considering the respondents’ sociodemographic characteristics and the level of importance attributed to meeting new people, visiting their families, going back to work, meeting their friends, eating out in a restaurant and attending musical and other cultural events. The main conclusions are related to consumers who believe it is very important to travel for specific reasons, such as meeting new people and friends or visiting their families, eating out in a restaurant or attending musical or other cultural events. Family and friends play an important role, considering that to visit them is seen as a priority. This study provides a relevant assessment regarding the priorities defined by the consumers in the scope of the discovery of new destinations, thus providing inputs to the different stakeholders involved in the definition of strategies for the promotion of tourism destinations.
Additive manufacturing in the wood-furniture sector: Sustainability of the technology, benefits and limitations of adoption

Purpose In the world economy there is the emergence of advanced manufacturing technologies that are enabling more cost and resource-efficient small-scale production. Among them, additive manufacturing, commonly known as 3D printing, is leading companies to rethink where and how they conduct their manufacturing activities. The purpose of this paper is to focus in the Italian wood-furniture industry to understand if the companies in this sector are investing in additive manufacturing techniques, to remain competitive in their reference markets. The research also attempts to investigate the potential sustainable benefits and limitations to the implementation of 3D printing in this specific sector, considering the companies that have already implemented this technology. Design/methodology/approach Data were collected using a structured questionnaire survey performed on a sample of 234 Italian companies in this sector; 76 companies claimed to use 3D printing in their production system. The questionnaire was distributed via computer-assisted web interviewing and it consisted of four sections. Findings The research has highlighted how Italian 3D companies have a specific profile; they are companies aimed at innovating through the search for new products and product features, putting design and Made in Italy in the first place. They pay high attention to the image they communicate to the market and are highly oriented to the final customer, and to the satisfaction of its needs. Originality/value The study is attempting to expand a recent and unexplored research line on the possible advantages and disadvantages of the implementation of emerging production technologies such as 3D printing.
Gas Turbine Diagnostic Algorithm Testing Using the Software ProDiMES

Since gas turbines are very complex and potentially unreliable machines, the improvement of their monitoring systems becomes an essential part. Considering this necessity, the present paper performs a gas turbine diagnostic algorithm testing. The methodology proposed is formed by three stages. In the first stage, the commercial software ProDiMES (Propulsion Diagnostic Method Evaluation Strategy) is used to simulate an engine fleet and generate data with fault and no-fault conditions. In the second stage, a baseline model testing is implemented to improve the healthy engine performance approximation. Finally, a fault recognition stage based on a pattern recognition technique (Multi-Layer Perceptron) performs the diagnosis and calculates the probability of correct diagnostic decisions. The results obtained show that: a) the software ProDiMES is an easy and convenient tool to evaluate gas turbine diagnostic methods, b) the baseline model testing is a key step because it allows reducing the errors that can negatively influence the diagnostic process and c) the algorithm correctly performs the fault recognition task.
Analysis of the dynamic characteristics of a suspension bridge based on RTK-GNSS measurement combining EEMD and a wavelet packet technique

To investigate the dynamic characteristics of a self-anchored suspension bridge (i.e. Tianjin Fumin Bridge), a real time kinematic-global navigation satellite system (RTK-GNSS) is used to achieve the displacement responses of the structure. Seeing the defect in the positioning accuracy of RTK-GNSS, a combined method (EEMDWP) of ensemble empirical mode decomposition (EEMD) and wavelet packet (WP) analysis is firstly put forward to improve the precision of the vibration signals. Subsequently, fast Fourier transform and the random decrement technique are applied to estimate the natural frequency and the corresponding damping ratio of the structure. Meanwhile, to contrast the field measurement results, the finite element model (FEM) of the structure is established. Finally, the analysis results indicate that: (1) the RTK-GNSS technique is a powerful tool for monitoring the deformation of long-span bridges; (2) the proposed EEMDWP method is demonstrated to be better than the single EEMD or WP method; (3) the structural dynamic parameters are successfully obtained (i.e. the first natural frequency: 0.5873 Hz, the corresponding damping ratio: 2.12%); (4) the results from the field measurement are in agreement with the FEM with a difference of about 5% from each other.
Grape Leaf Disease Diagnosis using Convolutional Neural Network and Support Vector Machines

The agricultural industry plays an important role in Thailand as it has an impact on the domestic economy and exportation. Well timed control and management of plant diseases are essential to help the quality control of products and reduce the damage of plant diseases or controlling appropriate chemicals amount possibly affects soil and water quality in the surrounding environment. This research presents a hybrid deep learning model for grape leaf disease diagnosis using a convolutional neural network (CNN) and support vector machine (SVM). This algorithm can be called Convolutional Support Vector Machine (cSVM). The deep learning algorithm is used in feature extraction part to increase the ability to extract distinct features using the CNN’s filters model, in which the user does not have to choose the parameters for testing by himself. The Grape leaf is tested for the diagnosis of 4 grape leaf cases including Healthy, Downey, Rust, and Scap. The performance of proposed algorithm showed the accuracy value equals 94.1 percent in average. Thus, the proposed algorithm can efficiently identify the infection types of the disease.
Coupling mechanism of basin and orogen in the Tianshan Orogenic Belt since Late Mesozoic: Influence on the source–sink process of sandstone‐type uranium mineralization

The coupling mechanism of the Tianshan Orogenic Belt (TOB) and the basins on its sides (Junggar, Turpan–Hami, Tarim, Yili, etc.) since the Late Mesozoic was complex and multi‐levelled. Based on gravity, magnetism, Moho discontinuity, and lithospheric velocity structure of the TOB, together with the structural and sedimentary data of the basins, a model is proposed to reflect the influence of the basin–orogen coupling on the regional source–sink processes of sandstone‐type uranium mineralization since the Late Mesozoic. Generally, there are four main stages of tectonic movements that have changed the coupling mechanism of basin and orogeny: including the Early Yanshanian Movement, the Late Yanshanian Movement, the Middle Himalayan Movement, and the Late Himalayan Movement. These four movements together controlled the mineralization of the sandstone‐type uranium deposits in the TOB together. The mineralization of sandstone‐type uranium deposits in the TOB can be divided into three metallogenic stages: the ore‐bearing formation stage (T‐J2), the tectonic reversal‐related metallogenic stage (J3‐N1), and the epigenetic regenerating ore‐preserving stage (N2‐Q). The tectonic reversal‐related metallogenic stage can be subsequently divided into three sub‐stages: the pre‐metallogenic stage (J3), the sub‐metallogenic stage (K), and the main metallogenic stage (E‐N1). The associated uplift led to the deformation of the mineralized strata in the basins, while the reducing agent migration along faults enhanced the reduction environment within the strata, which affected the enrichment and distribution of the uranium mineralization in the strata. Meanwhile, the recharge and the change of flow state of groundwater also contributed to the later transformation and mineralization. The intra‐continental subduction caused deep lithospheric buckling and depression in the TOB, accompanied by local‐scale mantle convection, which accounts for both tectonic and geodynamic settings for the regional basin–orogen coupling mechanism. In addition, the far‐field effect of the India–Eurasian collision also provided the driving force for the sandstone‐type uranium mineralization in the TOB.
Motor skills in children with specific learning disorder: A controlled study

E-mail: onurtuuce@hotmail.com Received: February 22, 2022; Revised: April 15, 2022; Accepted: May 29, 2022 ABSTRACT Objective: This study aimed to compare the motor skills of children with specific learning disorders (SLD) with those of typically developing children, controlling for attention deficit hyperactivity disorder (ADHD) symptoms. Second, we aimed to examine the relationship between motor skills and children’s academic achievement. Method: The sample consisted of 57 children with SLD (63.2% males, mean age=9.52±0.94), and 30 children as a control group (66.7% males, mean age=9.68±1.08). Wechsler Intelligence Scale for Children-Revised, Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version, Purdue Pegboard Test (PPT), Developmental Coordination Disorder Questionnaire (DCDQ’07), and Swanson, Nolan and Pelham Scale-IV (SNAP-IV) were used. Results: Of children with SLD, 87.7% had any comorbid psychiatric disorder, mainly ADHD (78.9%). The SLD group had lower scores on both the DCDQ’07 total score and all subtests, but the statistical difference remained only in the DCDQ’07 total score and Fine Motor and Handwriting (FMHW) subtest after controlling for the SNAP-IV scores. Children with SLD scored lower than the control group on the nondominant hemisphere and assembly subtests of PPT, and significant differences remained after controlling for SNAP-IV scores. Academic achievement and motor skills were not correlated in the SLD and control groups, but the FMHW subtest showed the strongest correlation (r=0.618, p<0.001) with the grade point average in the entire sample. SNAP-IV total score and having an SLD diagnosis were predictive of the DCDQ’07 total score according to regression analysis. Conclusion: Many children with SLD suffer from motor skill problems, and comorbid ADHD symptoms contribute significantly to them.
Biodegradable Mg-based alloys: biological implications and restorative opportunities

Mg-based alloys as revolutionary implantable biomaterials have increasingly attracted considerable attention, owing to their biodegradability in vivo and beneficial effects on biological systems. The degradation process and products of Mg-based alloys have been reported to exhibit significant biological effects on host-tissue responses. However, these effects have not yet been fully understood. This review systemically summarizes and analyses the current understandings and recent research progress in this area. The primary focal points are the biological effects and related mechanisms associated with the degradation behaviour of Mg-based alloys. The biological impacts of the degradation products are elucidated and the arguable or controversial issues are also discussed, providing a pathway toward a greater understanding of the biological implications of Mg-based alloys. Furthermore, based on these biological implications, the restorative potential of Mg-based alloys for applications in tissue repair and regeneration is summarized. Finally, outlooks on biosafety evaluation and design strategies for Mg-based alloy implants are briefly discussed.
High Challenge Exercise and Learning Safe Landing Strategies among Community-Dwelling Older Adults: A Randomized Controlled Trial

There is limited research on optimal exercise programs that effectively decrease falls and fall-related injuries in older populations. This randomized controlled trial (RCT) aimed to explore the effects of a 12-week Judo4Balance program on falling techniques, physical and psychological functions, health status, and physical activity levels among 200 community-dwelling older adults (79% women and 21% men) with a mean age of 72 years. The 200 participants were randomly allocated for the Judo4Balce program (n = 100) or control group (n = 100). The RCT intervention started in mid-January 2020 and was abruptly interrupted because of the COVID-19 pandemic. A restart of the RCT was initiated in September 2021, and the 12-week intervention was offered to two groups. This study reports the results from three points of assessment: baseline, 20-month follow-up, and 12-week postintervention. At 20 months follow-up, the control group had significantly decreased physical activity levels (summer p = 0.002 and winter p = 0.003); similar changes were not seen in the exercise group. In the exercise group, learning falling techniques in 6–9 weeks led to sustained fall competence at 20 months follow-up. Further, significant improvements in physical function (exercise group p = 0.009 and control group p < 0.001) and learning falling techniques (p < 0.001 for both groups) were noted in both groups after the 12-week intervention. This effective, supervised, group-based, high-challenge multicomponent exercise program needs to be further evaluated for possible impact on falls and fall-related injuries.
PREDICTION OF ABSORPTION RATE IN HOUSING PROJECT BY ARTIFICIAL NEURAL NETWORK

When performing feasibility study and undertaking project design of housing projects, the absorption rate, e.g., the number of units sold per month, is one of the most important parameters to be predicted. However, the conventional prediction approach relies a lot on the knowledge and experience of the experts, which is various and rare. In this research, an Artificial Neural Network (ANN) was adopted for developing an absorption rate prediction model. The research was done by collecting the factors affecting the absorption rates of housing projects from literature review and from expert interviews. Then, the factors of 400 housing projects were collected and analyzed. The acquired model has two hidden layers with 10 and eight nodes, respectively. When testing with the new set of data, the model had R2 = 0.914. Real estate developers can use the acquired factors and model when making decisions to invest in new projects, as well as for finding the forms of projects that will lead to the required investment results. Keywords: Absorption Rate, Prediction Model, Housing Project, Artificial Neural Network.
It Is Not THEM and US – Working together for sustainable outcomes for children with autism

With a conservative estimate of 1 in 100 children thought to be on the autism spectrum (Ministry of Health, n.d.), this article reports on findings from a preliminary exploratory national study of parents of children aged 0-21 with ASD. Using an e-survey questionnaire and focus groups, the study sought to understand the supports that were available to children with autism and their families. More importantly, it explored the supports parents perceived were most useful and effective for their children at home, after school, and in educational settings. The key findings highlighted the need for autism-specific knowledge and training for educators and caregivers, and flexibility of funding for parents to access supports and services that they perceived to be the most useful. These findings were juxtaposed by issues of availability, accessibility and affordability of services and supports. There was unequivocal support from participants for a one-stop-shop for better coordination of services, funding flexibility and closer partnerships with families. Implications are discussed and limitations identified.
No priming, just fighting – endophytic yeast attenuates the defense response and the stress induced by Dutch elm disease in Ulmus minor

One century after the first report of Dutch Elm Disease (DED), there is still no practical solution for this problem threatening European and American elms (Ulmus spp.). The long breeding cycles needed to select resistant genotypes and the lack of efficient treatments keep disease incidence at high levels. In the present work, the expression of defense-related genes to the causal agent of DED, Ophiostoma novo-ulmi, were analyzed in in vitro clonal plantlets from two DED-resistant and two DED-susceptible U. minor trees. In addition, the effect of the inoculation of an endophytic pink-pigmented yeast (Cystobasidium sp.) on the plant’s defense system was tested both individually and in combination with O. novo-ulmi. The multifactorial nature of the resistance to DED was confirmed, as no common molecular response was found in the two resistant genotypes. However, the in vitro experimental system allowed to discriminate the susceptible from the resistant genotypes, showing higher levels of oxidative stress and phenolic compounds in the susceptible genotypes after pathogen inoculation. Inoculation of the endophyte before O. novo-ulmi attenuated the plant molecular response induced by the pathogen and moderated oxidative stress levels. Niche competition, endophyte-pathogen antagonism, and molecular crosstalk between the host and the endophyte are discussed as possible mechanisms of stress reduction. In sum, our results confirm the multifactorial nature of DED resistance mechanisms and highlight the possibility of using certain endophytic yeasts as biological tools to improve tree resilience against biotic stress.
Multi-fault Signal Feature Extraction of Mud Pump Based on Parallel Factorization

The multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode is a technical bottleneck and problem encountered in the application of fault diagnosis in the process industrial production line. It needs not only to extract the time-frequency characteristics of the single-source fault signal but also to ensure the corresponding relationship between the nonlinear variable, multi-fault mode and multi-source fault features in time, frequency and space after feature extraction. Mechanical multi-source signals also have coupling, aliasing, and other phenomena that cause the characteristic signals between the channels to interfere with each other and overlap. In order to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines, this paper develops multi-source dynamic feature recognition and adaptive diagnosis. This paper studies the multi-scale parallel factorization theory and proposes a three-dimensional time-frequency space model reconstruction algorithm for multi-source feature factors, which improves the accuracy of mechanical fault detection and intelligent levels Introduction When the mechanical equipment is subjected to unsteady state excitation in the process industrial production line, the vibration signal contains an obvious frequency shift characteristic. The fault feature signal will flow at each vibration collection point as the nonlinear failure mode changes. The two-dimensional time-frequency characteristics of the extracted signal are fuzzy, uncertain and incomplete. The method of single-channel signal extracting two-dimensional time-frequency feature like wavelet theory cannot reflect the intrinsic relationship of non-linear changes between multi-source channel feature signals and cannot eliminate information interference[1-3]. Dual-channel signal feature extraction methods such as holographic spectrum and global vector spectrum are used for the axis motion of the rotating machinery to establish signal frequency, amplitude, cross-section frequency or cross-sectional phase, where each channel signal is analyzed independently[4-5]. It can be seen that we cannot separate multi-source signals into single signals to extract two-dimensional time-frequency features independently. Multi-fault mode multi-source dynamic feature extraction is a multi-scale parallel factorization of multi-source signal matrix including process state variables, vibration signals, etc[6]. Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 73 8th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2018) Advances in Computer Science Research (ACSR), volume 83
Supporting the Implementation of Evidence-Based Practices for Children with ASD

The primary objective of this small-scale study was to identify ways in which Resource Teachers: Learning and Behaviour (RTLB) can work with teachers to implement evidence-based practices for children with Autism Spectrum Disorder (ASD) within aninclusive educational system. With the increasing prevalence of children with ASD in mainstream classrooms, there is a critical need to support teachers with evidence-based practices (EBPs) to improve outcomes for all students. In this study, twenty four RTLB were surveyed about professional learning and development (PLD) related to their work with teachers to implement EBPs for children with ASD. Qualitative and quantitative methods of data gathering were employed in the form of an anonymous online self-administered questionnaire. The findings revealed that, overall, this group of RTLB has a preference for PLD in the form of ongoing coaching and support with the implementation of evidence-based practices tailored to the needs of their caseload students.
Path-ATT-CNN: A Novel Deep Neural Network Method for Key Pathway Identification of Lung Cancer

Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very conspicuous that is shown in the prediction model of disease aftermath. Biological data are commonly revealed in a nominal grid data structured pattern. ATT-CNN cannot be applied directly. In order to figure out these issues, a novel method which is called the Path-ATT-CNN is proposed by us, making an explicable ATT-CNN model based on united omics data by making use of a recently characterized pathway image. Path-ATT-CNN shows brilliant predictive demonstration difference in primary lung tumor symptom (PLTS) and non-primary lung tumor symptom (non-PLTS) when applied to lung adenocarcinomas (LADCs). The imaginational tool adoption which is linked with statistical analysis enables the status of essential pathways which finally exist in LADCs. In conclusion, Path-ATT-CNN shows that it can be effectively put into use elucidating omics data in an interpretable mode. When people start to figure out key biological correlates of disease, this mode makes promising power in predicting illness.