Knowledge, Volume 2, Issue 3 (September 2022) – 12 articles

SMEs are one of the leading solutions for reducing unemployment and poverty and boosting economic growth. Due to this, the determinants of the survival and success of SMEs have received increasing scrutiny in recent years. Empirical evidence has been uncovered in different countries. Enriching this growing body of evidence, this paper explores the internal and external factors that affect the survival and success of SMEs in the middle eastern country of Oman. We used a quantitative approach to collect the data by distributing a survey questionnaire among SME owners and prospective entrepreneurs. The survey was distributed in different industries throughout Oman. The findings, based on 344 responses, show that for SME survival and success, the education system needs intervention. Other areas of intervention include transforming Omani business culture, focusing on managerial skills, and improving the procedures required for establishing a business. These findings offer vital implications for Oman’s economy and for SME owners. The findings of this study can help policymakers make the appropriate interventions at various levels to enhance SME survival and success in Oman. The study also provides insights for existing and prospective entrepreneurs to bridge the skillset gaps to keep pace with ever-changing market demands. Full article

Piezoelectrics have been used in several recent works to extract energy from the environment. This study examines the average wind speed across Iran and evaluates the amount of extracted voltage from vortex-induced vibrations with the piezoelectric cantilever beam (Euler–Bernoulli beam). This study aims to compute the maximum extracted voltage from polyvinylidene fluoride piezoelectric cantilever beam at the resonance from vortex-induced vibration to supply wireless network sensors, self-powered systems, and actuators. This simulation is proposed for the first-ranked meteorological station at its mean velocity over six years (2015–2020), and the finite element method is used for this numerical computation. The wind data of 76 meteorological stations in Iran over the mentioned period at the elevation of 10 m are collected every three hours and analyzed. Based on the statistical data, it is indicated that Zabol, Siri Island, and Aligudarz stations had recorded the maximum mean wind speed over the period at 6.42, 4.73, and 4.42 m/s, respectively, and then energy harvesting at the mean wind speed of top-ranked station (Zabol) is simulated. The prevailing wind directions are also studied with WRPLOT view software, and the wind vector field of 15 top-ranked stations is plotted. For energy harvesting simulation, periodic vortex shedding behind the bluff body, known as vortex-induced vibration, is considered numerically (finite element method). The piezoelectric cantilever beam is at a millimeter-scale and has a natural frequency of 630 Hz in its mode shapes to experience resonance phenomenon, which leads to maximum extracted voltage. The maximum extracted voltages for three piezoelectric cantilever beams with the natural frequency of 630 Hz with the wind speed of 6 m/s are 1.17, 1.52, and 0.043 mV, which are suitable for remote sensing, supplying self-power electronic devices, wireless networks, actuators, charging batteries, and setting up smart homes or cities. To achieve this, several energy harvesters with various dimensions should be placed in different orientations to utilize most of the blown wind. Full article

Among all sources of technical information, patent information is one of the richest and most comprehensive. Knowing how to search in this mass of documents is becoming increasingly crucial. However, many users have limited knowledge of patents and search strategies, so they must use intuitive, often approximate approaches that can lead to highly inaccurate searches and be time-consuming. To address this problem, there are tools that help expand queries to increase recall so as not to miss good documents, however, it remains an open problem dealing with misspellings-based strategies. Typically, the problem of the presence of misspellings in patent text is underestimated even by experts in the field, and there is no specific functionality to handle it in the tools available, both free and paid. The goal of the article is to raise awareness about the difficulties in making a proper patent strategy that also takes into account the possible presence of misspellings. It is important to know where we expect to find them and how much these may affect the final result. In particular, it is chosen to divide misspellings into categories, distinguishing between misspellings associated with a generic keyword or multiword from misspellings in acronyms, chemical formulas, names of applicants, inventors, or names of specific formulas or theorems. At least one example case is given for each category, showing when and how it may affect the result. Finally, an integrated approach combining word and contextual embedding models based on deep learning with a rule-based algorithm based on wild cards and truncation operators is suggested for correcting the query, automatically suggesting the most consistent misspellings, thus achieving a more accurate and reliable result. Full article

An approach to controlling the three-link Robogymnast robotic gymnast and assessing stability is proposed and examined. In the study, a conventionally configured linear quadratic regulator is applied and compared with a fuzzy logic linear quadratic regulator hybrid approach for stabilising the Robogymnast. The Robogymnast is designed to replicate the movement of a human as they hang with both hands holding the high bar and then work to wing up into a handstand, still gripping the bar. The system, therefore has a securely attached link between the hand element and the ‘high bar’, which is mounted on ball bearings and can rotate freely. Moreover, in the study, a mathematical model for the system is linearised, investigating the means of determining the state space in the system by applying Lagrange’s equation. The fuzzy logic linear quadratic regulator controller is used to identify how far the system responses stabilise when it is implemented. This paper investigates factors affecting the control of swing-up in the underactuated three-link Robogymnast. Moreover, a system simulation using MATLAB Simulink is conducted to show the impact of factors including overshoot, rising, and settling time. The principal objective of the study lies in investigating how a linear quadratic regulator or fuzzy logic controller with a linear quadratic regulator (FLQR) can be applied to the Robogymnast, and to assess system behaviour under five scenarios, namely the original value, this value plus or minus ±25%, and plus or minus ±50%. In order to further assess the performance of the controllers used, a comparison is made between the outcomes found here and findings in the recent literature with fuzzy linear quadratic regulator controllers. Full article

Extensively digitized workplaces require advanced competence profiles from employees, not least due to new options for teleworking and new complex digital tools. The acquisition of advanced competence profiles is to be addressed by formal education. For example, the method of Building Information Modeling (BIM) aims at digitizing the design, construction, and operation of structures and as such requires advanced competence profiles. In this study, two educational scenarios based on teleworking and complex digital tools are compared, each with one cohort and consisting of two learning activities. The first cohort initially completes, as the first learning activity, a semester-long course that aims at BIM-domain competences. The semester-long course of the second cohort fosters meta competences, such as communication, collaboration, and digital literacy. At the end of the semester, both cohorts solve a BIM practice task in a second learning activity. The research questions are: (1) Do the two educational scenarios promote the competences to be addressed? and related: (2) What is the impact of the initial course that fosters domain competences or meta competences? Methodologically, the learning outcomes are assessed by measuring the domain competences three times during the educational scenario using online tests in the two cohorts (n = 11). Further, students’ perceptions are surveyed in parallel, using online questionnaires. In addition, semi-structured interviews are conducted at the end of the educational scenarios. The quantitative and qualitative results of the study—designating the training of meta competencies partly as a substitute for imparting domain competences—are presented. Further, the influence of both educational scenarios on competence development for extensively digitized workplaces is discussed. Full article

With the rapid speed of globalization and technological breakthroughs, current social issues have become more complex than in past decades. As many issues such as pandemics, terrorism, and interracial conflict are realistically unpredictable, the idea of resilience offers an intuitively plausible and attainable strategy to deal with these potential adversities. The current narrative review explores the cultivation of positive emotions and traits as a plausible way to achieve a resilient society. Based on research in the social and industrial organizational psychology literature, we reviewed the role of positive emotions and traits on resilience. Lastly, we highlight important experiences and interventions that have been shown to be effective in cultivating positivity and discuss several potential considerations and boundary conditions. Full article

Investing in quality information contributes to the relationship between demand and suply. In this sense, this paper’s objective is to analyze the quality of the information generated by the social media (Instagram) of the Acolhida na Colônia association. To identify the relevance of each attribute in the consumers’ perception, categories and dimensions for quality information were analyzed based on the user’s vision and semantic criteria. The main results revealed the difference in the quality of Instagram from the families who participated in the training. The quality of images and content of property’s posts has also fed the Association’s institutional Instagram. However, there is a need to expand the amount of information and constant updating. Full article

An advanced method of training artificial neural networks is presented here which aims to identify the optimal interval for the initialization and training of artificial neural networks. The location of the optimal interval is performed using rules evolving from a genetic algorithm. The method has two phases: in the first phase, an attempt is made to locate the optimal interval, and in the second phase, the artificial neural network is initialized and trained in this interval using a method of global optimization, such as a genetic algorithm. The method has been tested on a range of categorization and function learning data and the experimental results are extremely encouraging. Full article

Background: Preventive and positive online coping strategies are essential for harm reduction associated with alcohol abuse among older adults in pandemic and social isolation scenarios. The objectives were to examine the relationship between alcohol use/abuse and physical capacity/self-care to perform the physical activities of daily living or impairment of the functional capacity of the elderly in the COVID-19 pandemic. Methods: An online cross-sectional survey was carried out. One hundred and one elderly people in the city of São Paulo, Brazil, participated in a community program. Results: Most participants (52.5%) showed excellent self-care skills. Approximately 12% of participants reported problems related to alcohol use/abuse. There was no association between self-care ability and abuse and probable alcohol dependence. Conclusions: Although most participants have excellent self-care and functional capacity and have not evidenced alcohol use/abuse, health professionals need to systematically provide information to prevent alcohol abuse, especially in scenarios of great emotional distress, such as in a pandemic. In addition, the online meetings held by the UAPI program were shown to be opportunities for social interaction and were essential to minimize the negative effects of the possible presence of alcohol use/abuse and the deteriorating performance of physical activities of daily living during a pandemic outbreak for the elderly. Full article

Sentiment analysis is one of the most important fields of natural language processing due to its wide range of applications and the benefits associated with using it. It is defined as identifying the sentiment polarity of natural language text. Researchers have recently focused their attention on Arabic SA due to the massive amounts of user-generated content on social media and e-commerce websites in the Arabic world. Most of the research in this fieldwork is on the sentence and document levels. This study tackles the aspect-level sentiment analysis for the Arabic language, which is a less studied version of SA. Because Arabic NLP is challenging and there are few available Arabic resources and many Arabic dialects, limited studies have attempted to detect aspect-based sentiment analyses on Arabic texts. Specifically, this study considers two ABSA tasks: aspect term polarity and aspect category polarity, using the text normalization of the Arabic dialect after making the classification task. We present a Seq2Seq model for dialect normalization that can serve as a pre-processing step for the ABSA classification task by reducing the number of OOV words. Thus, the model’s accuracy increased. The results of the conducted experiments show that our models outperformed the existing models in the literature on both tasks and datasets. Full article

The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents. Full article

The present paper focuses on adaptive audio detection, segmentation and classification techniques in audio broadcasting content, dedicated mainly to voice data. The suggested framework addresses a real case scenario encountered in media services and especially radio streams, aiming to fulfill diverse (semi-) automated indexing/annotation and management necessities. In this context, aggregated radio content is collected, featuring small input datasets, which are utilized for adaptive classification experiments, without searching, at this point, for a generic pattern recognition solution. Hierarchical and hybrid taxonomies are proposed, firstly to discriminate voice data in radio streams and thereafter to detect single speaker voices, and when this is the case, the experiments proceed into a final layer of gender classification. It is worth mentioning that stand-alone and combined supervised and clustering techniques are tested along with multivariate window tuning, towards the extraction of meaningful results based on overall and partial performance rates. Furthermore, the current work via data augmentation mechanisms contributes to the formulation of a dynamic Generic Audio Classification Repository to be subjected, in the future, to adaptive multilabel experimentation with more sophisticated techniques, such as deep architectures. Full article