This paper aims to understand and analyse the concept, impacts and factors causing outbound tourism leakage, as well as to guide decision-makers and tourism entrepreneurs on how to reduce outbound ...tourism leakage and mitigate its impacts. This study used stakeholder theory as a theoretical base for analysing the perspectives of the stakeholders of the tourism and hospitality (T&H) sector. Qualitative research method was used in this study. Semi-structured interviews were conducted. Thematic analysis was used. Interview quotes were categorized into themes. The findings revealed that outbound tourism leakage resulted in lost tourism revenues, investor reluctance, a balance-of-payments deficit, a reduction in the economic multiplier effect, and inflationary pressures on the economy. The main causes of outbound tourism leakage were the high prices, high constructional and operational costs, lower quality of infrastructure and superstructure services, limited marketing, and lack of participation and collaboration. There is a lack of conceptual and qualitative research that analysed the phenomenon of outbound tourism leakage. Therefore, this study was developed to fill a knowledge gap and qualitatively analyse the concept, impacts and factors causing outbound tourism leakage.
This research presents an enhanced approach for Aspect-Based Sentiment Analysis (ABSA) of Hotels’ Arabic reviews using supervised machine learning. The proposed approach employs a state-of-the-art ...research of training a set of classifiers with morphological, syntactic, and semantic features to address the research tasks namely: (a) T1:Aspect Category Identification, (b) T2:Opinion Target Expression (OTE) Extraction, and (c) T3: Sentiment Polarity Identification. Employed classifiers include Naïve Bayes, Bayes Networks, Decision Tree, K-Nearest Neighbor (K-NN), and Support-Vector Machine (SVM).The approach was evaluated using a reference dataset based on Semantic Evaluation 2016 workshop (SemEval-2016: Task-5). Results show that the supervised learning approach outperforms related work evaluated using the same dataset. More precisely, evaluation results show that all classifiers in the proposed approach outperform the baseline approach, and the overall enhancement for the best performing classifier (SVM) is around 53% for T1, around 59% for T2, and around 19% in T3.
This paper proposes a state-of-the-art research for aspect-based sentiment analysis of Arabic Hotels’ reviews using two implementations of long short-term memory (LSTM) neural networks. The first one ...is (a) a character-level bidirectional LSTM along with conditional random field classifier (Bi-LSTM-CRF) for aspect opinion target expressions (OTEs) extraction, and the second one is (b) an aspect-based LSTM for aspect sentiment polarity classification in which the aspect-OTEs are considered as attention expressions to support the sentiment polarity identification. Proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Results show that our approaches outperform baseline research on both tasks with an enhancement of 39% for the task of aspect-OTEs extraction and 6% for the aspect sentiment polarity classification task.
Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to ...its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of cone beam computed tomography with magnetic resonance imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated the procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed for precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.
Employee turnover is expensive and disruptive for an organization. Studies have already mentioned that the economic cost of turnover is huge, ranging from 90% to 200% of the existing employee’s ...salary. With an increase in turnover rate, the social fabric of an enterprise may be disrupted. Additionally, organizations with an increasing turnover are expected to lose intangible knowledge and skills, operational effectiveness, customer satisfaction, and product or service quality. In a healthcare context, an increasing turnover rate has more consequences than other sectors because the healthcare sector worldwide is already identified as a sector facing resource scarcity. Exacerbating the situation, current evidence suggests that employee turnover has been increasing globally in the healthcare sector. The literature suggests that an ethical leadership style may reduce employees’ likelihood of quitting an organization. However, such literature is sparse in healthcare, especially from the perspective of a developing economy in the Global South, which is more resource-deficient than the Global North. To fill this knowledge gap, this study investigates the relationship between ethical leadership style and turnover intentions in the healthcare context of the Global South. This study also tests the mediating effect of intrinsic motivation and psychological contract fulfillment in the above-proposed relationship. Furthermore, the conditional indirect effect of resilience is also tested. The data are collected from the hospital employees through a self-administered questionnaire. The hypothesized relationships are tested through structural equation modeling. The empirical evidence indicates that ethical leadership reduces employees’ turnover intentions significantly. The results further confirm the mediating and moderating effects of intrinsic motivation, psychological contract fulfillment, and resilience. These results have different theoretical and practical implications for the healthcare sector. The results especially highlight the role of ethical leaders in a hospital to deal with the challenge of turnover, which has been rising worldwide.
In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image ...segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others.
1,2,3-Thiadiazole, 1,2,3-selenadiazole, and semicarbazones that are prepared from ketones are promising moieties for lead compound development. New 1,2,3-thiadiazole (2c-4c) and 1,2,3-selenadiazole ...derivatives (2d–4d) were prepared from the corresponding semicarbazones (2b-4b). The semicarbazones (2b-4b) were prepared from the corresponding ketones (2a-4a). Characterization of the synthesized compounds was performed using infrared spectra (IR), proton nuclear magnetic resonance (1H-NMR) spectra, carbon nuclear magnetic resonance (13C-NMR), ultraviolet spectra, mass spectrometry, and elemental analysis. The antimicrobial activity of the prepared compounds was explored in vitro against various pathogenic microbes. All heterocyclic compounds had positive antimicrobial activity, but these activities varied in the extent of antimicrobial coverage. Compounds (2c) and (2d) had positive activity against Staphylococcus aureus and Escherichia coli but without any antipseudomonal activity. Compound (3c) had the most activity against Candida albicans with potential as a novel antifungal agent along with activity against some Gram-positive and Gram-negative bacteria. Compounds (4c) and (4d) exhibited broad-spectrum coverage in which both compounds demonstrated antimicrobial activity against all microorganisms explored. Interestingly, they both had substantial antipseudomonal activity against local resistant Pseudomonas aeruginosa and reference P. aeruginosa (ATCC 27853). This may suggest the potential for compounds (4c) and (4d) as novel broad-spectrum antibacterial agents with promising antipseudomonal activity. In conclusion, new 1,2,3-thiadiazole (2c-4c) and 1,2,3-selenadiazole (2d-4d) derivatives were identified as potential lead compounds for novel antibacterial agents.
The interchangeably connected Web technologies and the advancements that accompany the semantic web content's leaps, have raised many challenges in the results' retrieval process especially for the ...Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users' queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions' Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users' questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.
Assessing diabetes self care management is essential for nursing care for diabetes. There is a need to have valid and reliable scales that assess the actual performance of diabetes self management. ...The purpose of this study was to revise and conduct psychometric testing and analysis of the Diabetes Self Management Scale (DSMS).
A cross-sectional methodological design was used. A convenience sample was used and 78 adults with diabetes and taking insulin from five sites in the Midwest area of the U.S participated in the study. Reliability analysis was done using Ferketich techniques to make decisions about whether any given item should be retained or deleted.
A descriptive analysis for the 60 items of the scale was conducted; several items had low variability compared to the other items on the scale. The correlation matrices showed that a total of 20 items had poor item characteristics. These 20 items were deleted resulting in developing 40- item version of the scale. The 40 - item scale had high level of internal consistency (Cronbach's α = 0.947). The validity testing of the 40 - item scale was guided by the Research Model for Diabetes Self Care Management; results were congruent with the model and showed strong correlation with self efficacy, moderate correlation with self care agency, and weak correlation with diabetes knowledge.
The items and the scale (DSMS) have undergone careful psychometric testing. The 40-item DSMS is a reliable and valid instrument to measure diabetes self care management among people with diabetes.
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