Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting Adoption of Online Community Water User Participation

Social networks are the most extensively utilized online virtual communities. It is a technology platform that is currently used to interact with other members of any virtual online community, including preserving social ties. This study is aimed at determining the elements that influence the participation of water users in the online community. It suggests combining the commitment-trust theory with the technology acceptance model (TAM) to include stakeholders, social networks, and transparency about content and user perceptions to better understand the elements that influence the online community and user participation. A questionnaire with a five-point Likert scale was issued to 1,000 community water consumers, and the researcher received 627 valid responses. Data were collected using a basic random sampling technique with a sampling ratio of 14 villages. Subsequently, descriptive statistics characterize the data (frequency distributions, percentages, averages, medians, and standard deviations). Furthermore, PLS-SEM was utilized to study the interactions between variables and launch the conceptual model via partial least square (PLS) path modeling. This study reveals that community water user engagement is founded on a commitment-trust paradigm that acts as an effective role model for community participation behavior. The results and consequences are discussed at length in the discussion section, which offers valuable recommendations. This article discusses overcoming ethical divisions in community water management through increased communication between customers and community water management teams using social media networks. Additionally, several elements contribute to the monitoring and supervision advancement, boosting the openness of community water management.
The Born Global Phenomenon - Case Study from Slovakia

: The traditional theory of international trade suggests that companies will first establish a strong domestic market and only expand abroad in the later stages of their life cycle. This is in line with the so-called Uppsala model, whose basic idea is that sales and success abroad requires an understanding of the previously unknown environment and adaptation to it. However, this view is called into question by research, which shows that some companies internationalize very quickly (in some cases immediately) - they are so- The existence of companies that sell a substantial part of their production abroad immediately or within a few years of its establishment is therefore a theoretical challenge. The aim of our article is to examine the concept of born-global companies from a theoretical point of view and to demonstrate the functioning of such companies in the Slovak environment on the case study in communication industry. Using the method of a case study, the origin and development of the Slovak company Slido is described, focusing on the typical features of BGC. After subsequent analysis and comparison, it can be stated that the company Slido meets all the characteristics of the so-called born-global company.
Major depressive disorder: Current progress and new treatment perspective

: Major depressive disorder (MDD) is a mental disorder that affects about 3.8% of the world population. It brings negative effect on individuals, including low mood, decreased sex desire, decreased appetite, and thought of death or suicide. Since 20 th century, theories have been trying to explain the cause of major depressive disorders. Most of the theories focus on the interrelationship between social, psychological and biological causes. However, no one theory has found out the real pathopsychological causes of the diseases and treatments to this disorder are limited with medication and methods with huge side effects. The review summarizes four theories about the pathophysiology of MDD with their corresponding treatments and proposes a potential treatment of genetic approaches.
Parental Knowledge and Educational Intervention on Behavioural Disorders in Pre-school Children in Kolkata

Parents need to know the common behavioural disorders and their prevention in order to detect deviations early and promote healthy childhood. A prospective interventional study was conducted to assess the impact of an educational intervention on the awareness of behavioural disorders among the mothers of pre-school children attending child guidance clinic in a tertiary care hospital in Kolkata, India. Knowledge of 50 mothers of children between 3 to 6 years of age was assessed before and one week after the educational intervention by administration of a prevalidated questionnaire and analysed with inferential statistics like paired t test. The pre-test scores of parents on knowledge of behavioural problem of children did not vary significantly with age of the child, birth order of child, type of the family, educational background of parents and behavioural disorder of child. There was a significant improvement in post-test score (17.26 ± 4.11) over pre-test score (8.28 ± 4.54) which was statistically significant (p-value 4.494e-12 < 0.0001). This study establishes the significance of educating parents on behavioural disorders in children and reflects the need for sustained structured educational services for parents to improve mental health of the children in a developing nation like India
Design and Experimental Validation of a Monopod Robot With 3-DoF Morphable Inertial Tail for Somersault

Animals and human often use their external appendages (such as tails or arms) to achieve spectacular maneuverability and various agile motions, which cannot be easily realized in the existing legged robots. Many animals can bend or straighten up their tails in about a tenth of a second to facilitate rapid adjustment in the moment of inertia and the center of mass. However, existing robotic tails have a fixed length, limiting their inertial adjustment capability for agile locomotion. Therefore, in this article, a monopod robot with a compact, 3-DoF morphable inertial tail, is proposed to explore the effect of tail’s inertial adjustment capability on robot agility, particularly on somersault motion. The proposed 3-DoF tail composes of tail orientation structure and scissor-lift parallel mechanism based morphable inertial structure, enabling effective inertial adjustment within 0.15 s. A novel springy underactuated leg with a large stroke is also proposed to facilitate the robot’s jumping capability, necessary for somersault. We also develop a dynamic model and propose motion controllers to study the tail-assisted somersault. Simulation and experimental results demonstrate the effectiveness of the proposed mechanical design and controllers in doing tail-assisted somersault. Using our proposed adjustable-length tail, the stabilized body rotation speed during flight phase can become twice of that with a fixed-length tail. Lastly, for the first time, our robot successfully completed an advanced somersault (somersault with a twist) by utilizing the tail’s 3-D inertial adjustment capability.
The Classification of Music and Art Genres under the Visual Threshold of Deep Learning

Wireless networks are commonly employed for ambient assisted living applications, and artificial intelligence-enabled event detection and classification processes have become familiar. However, music is a kind of time-series data, and it is challenging to design an effective music genre classification (MGC) system due to a large quantity of music data. Robust MGC techniques necessitate a massive amount of data, which is time-consuming, laborious, and requires expert knowledge. Few studies have focused on the design of music representations extracted directly from input waveforms. In recent times, deep learning (DL) models have been widely used due to their characteristics of automatic extracting advanced features and contextual representation from actual music or processed data. This paper aims to develop a novel deep learning-enabled music genre classification (DLE-MGC) technique. The proposed DLE-MGC technique effectively classifies the music genres into multiple classes by using three subprocesses, namely preprocessing, classification, and hyperparameter optimization. At the initial stage, the Pitch to Vector (Pitch2vec) approach is applied as a preprocessing step where the pitches in the input musical instrument digital interface (MIDI) files are transformed into the vector sequences. Besides, the DLE-MGC technique involves the design of a cat swarm optimization (CSO) with bidirectional long-term memory (BiLSTM) model for the classification process. The DBTMPE technique has gained a moderately increased accuracy of 94.27%, and the DLE-MGC technique has accomplished a better accuracy of 95.87%. The performance validation of the DLE-MGC technique was carried out using the Lakh MIDI music dataset, and the comparative results verified the promising performance of the DLE-MGC technique over current methods.
Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population.
Investigation into the effects of various processing treatments on the flexural performance of carbon fiber reinforced polymer-bamboo scrimber composites

ABSTRACT Carbon fiber-reinforced polymer (CFRP) laminates can significantly improve the flexural performance of the bamboo scrimber. This study evaluated the effects of three fabrication methods (one-time hot pressing, secondary hot pressing, and secondary cold pressing) on the flexural performance of CFRP-bamboo scrimber composites. Four-point bending experiments and theoretical analysis were conducted to study the failure modes, flexural performance, load-displacement relationships and strain curves over time of CFRP-bamboo scrimber composites. Besides a theoretical model was proposed to describe the flexural stiffness of CFRP-bamboo scrimber composites. The results indicated that the CFRP-bamboo scrimber composite specimens demonstrated four failure modes depending on the treatment methods. Overall, the static flexural modulus of the one-time hot pressed specimens was superior (up to 1.93 times that of the untreated bamboo scrimber specimen), and the static flexural strength of the secondary cold pressing specimens was superior (up to 3.58 times greater than that of the untreated bamboo scrimber specimen), although neither the static flexural modulus nor the static flexural strength of the secondary hot pressing specimens was satisfactory. Finally, it was illustrated that the theoretical models, established to describe the load-displacement, could accurately predict the experimental results.
Signaling the Intent to Change Online Communities: A Case From a Reddit Gaming Community

This study builds on existing research about churn and community movement, examining if language use on Reddit can be used to determine if people signal their intent to relocate to a new community before they actually do so. Using a computational and semantic approach, we studied the subreddits for the game series Fallout at the time Fallout 76 (FO76) was released to see if the users of the Fallout 4 (FO4) subreddit signaled how they would react to the new subreddit. The main difference we found was that those who stay in the FO4 subreddit or use both subreddits on average post more often and create longer posts than those who move to the FO76 subreddit or leave. This adds further evidence to support theories about community as communication, and we suggest this finding can help online community managers identify which users may be about to leave the community, aiding retention and the overall health of the community.
On-line estimation of fuel consumption for producing electrical power in a vehicle

This paper describes a method to estimate on-line the additional costs for producing electrical power in a vehicle. The alternator power is excited with a small excitation signal of zero mean dP . In a feedback-loop the fuel-injection is excited with a similar signal with amplitude df . This amplitude is controlled with help of the correlation between the engine speed and the excitation on the alternator power dP . The aim of df is to compensate dP . The correlation determines whether dP is exactly compensated by df or not. The amplitude-ratio between dP and df provides the Incremental Fuel Rate (IFR= λ = df/dP ). This quantity represents the derivative of the joint engine and the alternator map in an operating point. The method is designed and analysed on its stability and noise influence, which are verified with simulations.
The analysis and optimization of CNN Hyperparameters with fuzzy tree modelfor image classification

: The meaningful performance of convolutional neural network (CNN) has enabled the solution of various state-of-the-art problems. Although CNNs achieve satisfactory results in computer-vision problems, they still have some diﬀiculties. As the designed CNN models are deepened to achieve much better accuracy, computational cost and complexity increase. It is significant to train CNNs with suitable topology and training hyperparameters that include initial learning rate, minibatch size, epoch number, filter size, number of filters, etc. because the initialization of hyperparameters affects classification results. On the other hand, it is not possible to make a definite inference for the hyperparameter initialization and there is uncertainty. This study is carried out to model uncertainty using fuzzy inference system (FIS). The designed fuzzy model provides estimation of classification result depending on CNN topology and training hyperparameters. GoogleNet and Inceptionv3 that contain inception-modules, ShuffleNet that contains shuffle-blocks, DenseNet201 that contains dense-blocks, EﬀicientNet, ResNet18, ResNet50, ResNet101, and MobileNetv2 that contain residual-blocks, and InceptionResNetv2 that includes both inception-modules and residual-blocks were evaluated as CNN models. Test sample dataset was obtained by training CNN models with various training hyperparameter combinations. CNN models were trained on Animal Diagnostics Lab (ADL) which is a histopathological dataset includes healthy and inflamed kidney, lung, and spleen images. A new FIS tree model that is more computationally eﬀicient and easier to understand than a single FIS was designed and classification accuracy prediction of CNN models depending on hyperparameter combinations was performed. The best, the worst, and the average classification accuracies obtained with CNN models that use best training hyperparameter set are 97.70%, 93.60%, and 96.30%, respectively. Moreover, Cifar10 and Cifar100 benchmark datasets were experimented to reveal true capability and limitations of the proposed approach. Experimental results indicate that the designed FIS tree model provides a successful hyperparameter evaluation mechanism with an average RMSE value of 1.2652.
Safety-II and the study of healthcare safety routines: Two paths forward for research

Safety routines such as the WHO surgical safety checklist and SBAR have gained widespread attention and implementation in healthcare. However, there has also been criticism. With the ongoing Covid-19 pandemic, the need for knowledge about how safety routines work in practice is larger than ever. In light of these obstacles, I suggest two approaches to the study of healthcare safety routines, based on a human factors perspective and a safety II mind-set that so far has gained little attention. The WHO surgical safety checklist, is used as an example. However, the suggestions presented here applies to other safety routines as well. The first approach is that instead of being preoccupied with what people do not do, investigate what they value with the routine. The perceived importance of different parts of the routine can expose the rationality behind the personnel's choice of actions when using the routine. Knowledge that could be used both to investigate the dynamics of everyday performance and for redesign and adjustment of the routine. The second approach is that instead of looking for failure, investigate and highlight when the routine works. Examples of when the routine works, i.e. avert adverse events, can be used both as positive reinforcement, and as an opportunity for learning with regards to everyday performance variability. Since a safety-II perspective is largely missing in the literature on healthcare safety routines, the two approaches suggested here comes with a huge potential for learning about how to improve safety.
Smart city planning and development shortcomings

This paper explores smart city planning and development shortcomings. In particular, it investigates eleven cases of smart city strategies and the shortcomings that were observed during their planning and implementation stages. The cases include: Barcelona Smart City, PlanlT Valley, Stockholm Smart City, Cyberjaya, King Abdullah Economic City, Masdar City, Skolkovo, Songdo International Business District, Chicago Smart City, Rio de Janeiro Smart City, and Konza Technology City. The paper proceeds with the synthesis of the findings and their critical appraisal. Shortcomings are classified into economic and budget shortages, bureaucratic and organizational challenges, challenges in the development and layout of digital services, poor physical planning, struggle to attract investment and support the development of new businesses, low performance in attracting and engaging users, and stakeholder resistance. In turn, the shortcomings are clustered in two distinct groups and analyzed in terms of causes and effects. The paper closes with mitigation propositions, accounting for past experience and novel approaches to this end.
Isolation of antifungal-resistant Candida from the blowholes of captive dolphins

In this study, we isolated eight strains of Candida albicans from the blowhole air cultures of eight dolphins (one Pacific white-sided dolphin and seven bottlenose dolphins) housed at the Enoshima Aquarium. The minimum inhibitory concentrations of antifungals for these isolates were determined by conducting E-test and broth microdilution assays using the CLSI M27-A3 protocol antifungal susceptibility testing method. Only one of the eight dolphins from which Candida had been isolated had been treated with amphotericin B (AMB), and four had been treated with itraconazole (ITZ). All isolates were identified as Candida albicans, and all were resistant to both ITZ and voriconazole, though the isolates exhibited susceptibility to AMB and micafungin. Based on our findings, we suspect that the frequency of occurrence of azole-resistant Candida species is increasing in captive dolphins as well as in their aquarium environments.
The future of zoological taxonomy is integrative, not minimalist

Roughly 18,000 species are described annually as new to science, while estimated extinction rates are comparable to or even exceeding these new discoveries. Considering the estimates of up to 15 million extant eukaryotic species on Earth, of which only about 2 million have been described so far, there has been a recent ‘boom’ of new potential approaches to more quickly discover and describe the millions of unknown species. This deficit is particularly noted in hyperdiverse taxa, as the current rate of species discovery is considered too slow. Recently, a ‘minimalist’ alpha taxonomic approach was proposed, relying solely on DNA barcoding and a habitus photograph, in a claimed effort to expedite the naming of new species to combat the so-called taxonomic impediment. In this paper, we point to limitations of minimalist taxonomy, present arguments in favour of the integrative approach, and finally explore a number of potential solutions to combat the taxonomic impediment in hyperdiverse taxa without sacrificing utility and quality for apparent speed and quantity.
Admission Patterns and Outcome in a Pediatric Intensive Care Unit at Tobruk Hospital

This study aims to describe the admission pattern and outcome of patients in the pediatric intensive care unit (PICU) at Tobruk medical center. We have retrospectively reviewed medical files of PICU over the period from January to December of 2016 for children aged from 1month-15 years. A total of 1026 children were admitted to PICU at Tobruk medical center. Of the total, 55% were males and 44.9% were females with male to female ratio of 1.2: 1. Age distribution showed that 64% were infants (1month -1 year). Majority of admission during cold months (December, January, and February) had the highest admission rate. Central nervous system and respiratory system diseases (23%, 12%) respectively were the most diseases requiring PICU admission. Congenital heart diseases 10/26 (38.4%) was the most common cause of total death. 668(65%) patients improved and were discharged from ICU, and 201 (19.5%) were transferred to pediatric ward for further management and discharged in satisfactory condition. 99 (9.6%) patients left against medical advice, and 32 (3%) patients transferred to more specified hospital and 26 patient died (2.5%). The observed difference in the mortality was with the respect to age, length of stay, and the involved systems. Neurological system remains a major cause for admission in ICU mainly for febrile convulsion and was associated with considerable morbidity and mortality. Respiratory system was the second cause mainly for pneumonia, and CHD was the major cause of death. The results concluded that epidemiologic analysis of the pattern of patients admitted to our PICU showed different etiologies for admission
Postglacial range expansion of high‐elevation plants is restricted by dispersal ability and habitat specialization

Species' ecological traits influence their spatial genetic patterns. Bedrock preference strongly shapes the phylogeography of alpine plants, but its interactions with other ecological traits have rarely been disentangled. Here, we explore whether dispersal ability and degree of habitat specialization account for divergent postglacial expansion patterns of high‐elevation plants in spite of similar bedrock preference.