Business & Knowledge Alliance

Decisions on location selection are critical for the survival of small-to-medium entrepreneurial organizations from the time they are established until later stages of operation and expansion. The selection of location for small and medium entrepreneurial businesses requires a selection strategy that incorporates relevant factors, quantifies these factors and develops a methodology that analyzes data for better decision-making. In the era of globalization where borders have become easier to transcend, many small ventures tend to choose more attractive international markets as a potential location for their operations where they can obtain higher returns on their investment. Thus, significant changes in the location decision process of the small and medium entrepreneurial companies have received great attention in the literature about small firms with global orientation as a response to the international entrepreneurship phenomenon. Therefore, consideration should be given to factors and attributes that reinforce the appeal of the international market to new businesses. These factors and attributes will provide the decision maker with an effective methodology for data analysis that will provide a framework for decision-making in the selection of locations for the entrepreneurial organization. In this research, the most frequent and critical attributes to select the best location for the entrepreneurial firms (globally) are extracted from relevant literature. Then, a similarity-based iii cluster analysis approach is introduced to quantify these attributes based on the existing data of economic metrics, such as technological advancement, expenditures on education, expenditures on research and development, the quality of the labor force, unemployment rates, domestic competitiveness, etc. Subsequently, the resulting outcomes are used to identify groups of prospective sites that fit the needs of the entrepreneurial firm. Last, the validity of the adopted methodology will be tested via numerical examples.

The selection of suitable composite material for high-strength industrial applications, from the list of available alternatives, is a tedious task as it requires an optimized structural performance-based solution. This study aimed to optimize the concentration of fillers, i.e., vinyl tri-ethoxy silane and absorbed gamma-dose, to enhance the properties of an industrial scale polymer, i.e., ultra-high molecular weight polyethylene (UHMWPE). The UHMWPE hybrids, in addition to silane, were treated with (30, 65, and 100 kGy) gamma dose and then tested for ten application-specific structural and performance attributes. The relative importance of attributes based on an 11-point fuzzy conversation was used for establishing the material assessment graph and corresponding adjacency matrix. Afterwards, the normalized values of attributes were used to establish the decision matrix for each alternative. The normalization was performed after the identification of high obligatory valued (HOV) and ...

The potential of quantifying the variations in IR active bands was explored while using the chemometric analysis of FTIR spectra for selecting orthopedic biomaterial of industrial scale i.e., ultra-high molecular weight PE (UHMWPE). The nano composites UHMWPE with multi-walled carbon nano-tubes (MWCNTs) and Mg-silicate were prepared and irradiated with 25 kGy and 50 kGy of gamma dose. Principal component analysis (PCA) revealed that first three principal components (PCs) are responsible for explaining the >99% of variance in FTIR data of UHMWPE on addition of fillers and/or irradiation. The factor loadings plots revealed that PC-1 was responsible for explaining the variance in polyethylene characteristics bands and the IR active region induced by fillers i.e., 440 cm−1, 456 cm−1, from 900−1200 cm−1, 1210 cm−1, 1596 cm−1, PC-2 was responsible for explaining the variance in spectra due to radiation-induced oxidation and cross linking, while the PC-3 is responsible for explaining th...

This research aims to assess the impact of campus culture on the satisfaction level of engineering students. Five latent variables, namely, respect, creativity, support, extracurricular activity, and individual responsibility, were used to examine the impact of campus culture. The data were collected with the help of an online questionnaire, which was distributed among male and female students. Subsequently, 304 responses were collected, which were analyzed using descriptive statistics, multicollinearity, and structural equation modeling (SEM) via AMOS 23 software. Internal consistency and reliability of each construct were done through Cronbach’s alpha, while confirmatory factor analysis was used for the validation of results. The findings of the metaheuristics-based research indicate that the campus culture has a positive impact on the satisfaction level of students (r = 0.774, p < 0.001 ). Improving these factors reciprocally increases the satisfaction level, besides raising t...

Introduction of modern technologies and methods and quality analysis for the gemstone industry are the main strategic initiatives of the Small and Medium Development Authority (SMEDA) of Pakistan. In this regard, four natural gemstones Quartz, Pyrope-Almandine Garnet, Black tourmaline, and Amethyst brought from Hunza valley Pakistan were analyzed by state-of-the-art spectroscopic techniques including EDX, UV-VIS, and FTIR spectroscopy. EDX revealed the traces of Fe, Mg, and Ca in Pyrope-Almandine garnet, Mg and Fe in Black tourmaline, Au and Ca in Amethyst. UV-VIS data revealed the values of Urbach energies 520, 210, 460, and 430 meV, and the values of direct bandgap energies 5.14, 6.12, 5.54, 5.74 eV, respectively. The higher structural disorder due to the presence of Fe and other impurities in stones except Quartz was attributed to the higher values of Urbach energies and decrease in band gaps: FTIR data Fe-O and Si-O stretching vibration in Pyrope-Almandine garnet, Si-O bending v...

Wireless sensor networks (WSNs) based on the Internet of Things (IoT) are now one of the most prominent wireless sensor communication technologies. WSNs are often developed for particular applications such as monitoring or tracking in either indoor or outdoor environments, where battery power is a critical consideration. To overcome this issue, several routing approaches have been presented in recent years. Nonetheless, the extension of the network lifetime in light of the sensor capabilities remains an open subject. In this research, a CUCKOO-ANN based optimization technique is applied to obtain a more efficient and dependable energy efficient solution in IoT-WSN. The proposed method uses time constraints to minimize the distance between sources and sink with the objective of a low-cost path. Using the property of CUCKOO method for solving nonlinear problem and utilizing the ANN parallel handling capability, this method is formulated. The resented model holds significant promise si...

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new fede...

Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging o...

The global community is now coping with such a significant issue as the Covid-19 virus, public gatherings are experiencing certain restrictions in order to stop the virus from spreading further. The issue takes on a bigger significance during religious pilgrimages such as the Hajj and the Umrah, when tens of thousands, if not hundreds of thousands, of people gather in holy cities to participate in religious rituals. During such a time period, it is quite difficult to single out an infected person from among the big crowd that is there. The current screening approach only includes a single element of identity, which means that there is a possibility that the screening process may fail because there will not be enough identification. The use of thermal imaging provides a higher level of accuracy when compared to more conventional ways of testing for viral infections in the detection of these symptoms in crowded locations. The primary method that is utilised to determine whether or not...

Present study investigates the impact of manager’s emotional intelligence (EI) on the stress and motivation level of employees. Fifty managers from small and medium enterprises were randomly selected from public and private sector in city of Jeddah. Each of these managers were responsible for the performance of minimum seven workers. Two questionnaires were used to calculate the stress and motivation level of workers, while one questionnaire was used to calculate the EI scores of managers. Results indicate a negative correlation between managers EI and workers’ stress levels, however, a strong positive correlation is observed between managers EI with workers motivation. Managers belonging to multinational organizations exhibit higher level of EI followed by managers of private and public sector organizations. EI domains of social skills and empathy are major contributors influencing the stress and motivation of workers. However, Self-awareness and self-management strongly affect the...

This research seeks to propose an innovative mathematical approach for measuring student performance in engineering education. This paper also includes a detailed analysis and the numerical solutions to this mathematical model. The designed mathematical model is constructed upon four categories, average candidates, weak candidates, below average candidates and good candidates. For the numerical results of the designed nonlinear mathematical model, the analysis through the Adams numerical scheme is applied for solving the differential system based on the migration rate and average student rate moves to weak and above average. Moreover, artificial neural network is also applied to get the stochastic results ANNs-LMB, also known as Levenberg-Marquardt training algorithm. The ANNs-LMB procedures have been implemented with three samples of data scales using the authentication, testing and training, which are chosen as 75%, 15% and 10%, respectively. According to the findings, when the ra...

The objective of our study was to assess awareness, attitudes, and practices regarding artificial intelligence (AI) among healthcare workers in private polyclinics in Jeddah, Saudi Arabia. Methods: We conducted cross-sectional study among healthcare workers in private clinics in Jeddah. Data was collected using a structured, validated questionnaire in Arabic and English on awareness, attitudes, and behaviors regarding AI. Cronbach's alpha for the questionnaire ranged from 0.6 to 0.8. Descriptive and bivariate analysis was done to assess the scores and their association of various sociodemographic variables with awareness, attitudes, and behaviors regarding AI. Multiple linear regression was performed to predict the scores of awareness, attitudes, and behaviors based on the sociodemographic variables. Results: We recruited 361 participants for this study. Approximately, 62% of the healthcare workers were female. The majority (36%) of healthcare workers were nurses, while 25% were physicians. The median awareness, attitude, and behavioral scores were 5/6 (IQR 3-6), 5/8 (IQR 4-7), and 0/3 (IQR 0), respectively. Approximately three-fourths (74%) of the healthcare workers believed that they understood the basic computational principles of AI. Only half of the participants were willing to use AI when making future medical decisions. We found that male healthcare workers had better knowledge scores regarding AI as compared to female healthcare workers (Beta = 0.555, 95%, p value = 0.010), while for attitude scores, being administrative employee as compared to other employees was found to have negative attitude towards AI (Beta = 0.049, 95%, p value = 0.03). Conclusion: We found that healthcare workers had an overall good awareness and optimistic attitude toward AI. Despite this, the majority is worried about the potential consequences of replacing their jobs with AI in the future. There is a dire need to educate and sensitize healthcare workers regarding the potential impact of AI on healthcare.