Multi-Class Skin Cancer Classification Using Vision Transformer Networks and Convolutional Neural Network-Based Pre-Trained Models

Peer reviewedPublisher PD

Model predictive control of consensus-based energy management system for DC microgrid

The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1µsec and 5% overshoot

The Effect of Transformational Leadership on Employee Job Performance through Mediating Role of Organizational Commitment in Logistic Sector of Pakistan

The purpose of this paper is to examine the effect of transformational leadership on the employee job performance through organizational commitment in the logistics sector of Pakistan. Data were collected from the employees of logistic sectors headquarter, Islamabad througha self-administered questionnaire usingpurposive sampling technique. 280 self-administered questionnaires were distributed among the employees of PTCL but 210 questionnaires were returned observing response rate as 75 %. Data were analyzed using structural equational modeling (SEM). Results of the study indicate that transformational leadership has both the direct significant effect on employee job performance and also the indirect significant effect on employee job performance through organizational commitment. In the light of the findings of this research, it is recommended for PTCL management to adopt a transformational leadership style to cope with todays fast changing environment

A Comprehensive Overview of Large Language Models

Large Language Models (LLMs) have shown excellent generalization capabilities that have led to the development of numerous models. These models propose various new architectures, tweaking existing architectures with refined training strategies, increasing context length, using high-quality training data, and increasing training time to outperform baselines. Analyzing new developments is crucial for identifying changes that enhance training stability and improve generalization in LLMs. This survey paper comprehensively analyses the LLMs architectures and their categorization, training strategies, training datasets, and performance evaluations and discusses future research directions. Moreover, the paper also discusses the basic building blocks and concepts behind LLMs, followed by a complete overview of LLMs, including their important features and functions. Finally, the paper summarizes significant findings from LLM research and consolidates essential architectural and training strategies for developing advanced LLMs. Given the continuous advancements in LLMs, we intend to regularly update this paper by incorporating new sections and featuring the latest LLM models

Theoretical Framework Development for Supply Chain Risk Management for Malaysian Manufacturing

Globalization, rapid technological changes and growing competition not only facilitate but also make the supply chain more complex and fragile. Any disruption can disturb many organizations and even whole system. There are many theories and frameworks that present solution but no study is available that theoretically development framework for supply chain risk management. Due to the lack of structured supply chain risk management system the focus of this study is to develop a novel framework for identifying the potential risks and assessment of their effects on supply chain performance. Additionally, evaluate the role of supply chain collaboration in risk mitigation and performance improvement in Malaysian manufacturing sector. This study has highlighted numerous problems of Malaysian organizations and developed a theoretical framework. This framework will guide Malaysian organizations and will present better understanding for managers in resolving these issues. This is a conceptual paper, systematic as well as content analysis have been done for literature review. For future study, there is need to empirically verification of this theoretical framework. The proposed methodology to achieve this framework is; questionnaire will be developed from a pool and will be validate by exploratory view for risk identification. This questionnaire will be distributed among Malaysian manufacturing and data will be analyzed through Structural Equation Modeling (SEM) for risk assessment and mitigation. The theoretical contribution of this study is support of theory of swift, even flow as underpinning theory and information processing theory as supportive theory

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Modifiable risk factors in patients with cerebrovascular accident

To determine the frequency of modifiable risk factors of stroke. Study Design: Descriptive Case series. Place and duration of study: Ayub Teaching Hospital, Abbottabad from March to December 2012. Methodology: 106 cases of cerebrovascular accident (CVA) above 20 years of age and of either sex admitted in medical units of Ayub Teaching Hospital, Abbottabad were included in this study. The patients below 20 years of age, those with recurrent history of CVA, patients not fulfilling the required criteria of risk factors set in operational definitions and patients having intracranial space occupying lesion such as tumors or brain abscess or neurological deficit secondary to head injury were not included in the study. The subjects were taken by non-probability sampling. Results: There were 106 patients in this study. Of these 62(58.5%) were male and 44(41.5%) were females. Mean age of the patients was 62.3 years. 66 (62.3%) patients had hypertension, 29 (27.3%) had Diabetes mellitus, 10 (9.4%) were smokers, 8 (7.5%) had dyslipidemia, 3 (2.83%) had atrial fibrillation, 4 (3.77%) were obese and 2 (1.88%) had carotid bruit. Conclusions: This study showed that hypertension was the commonest modifiable risk factor of CVA followed by diabetes mellitus, smoking, dyslipidemia, obesity, atrial fibrillation, and carotid bruit. CVA was more common in males as compared to female and mostly occurred in middle and old age patient

Diversity in conception of COVID-19 in pastoral and metropolis areas of Pakistan

Background: SARS (severe acute respiratory syndrome) COV-2 has become a global problem since its emergence in 2019 from Wuhan, China as it is most deadly pandemic since Spanish flu in 1918. Primary Study Objective: This study analyzed the people ‘s perception regarding COVID-19 and precautionary measures taken by them during COVID- 19 peak time. Methods/Design: An observational concurrent study was performed in which data was collected from population in rural and urban areas of Punjab and Azad Kashmir using questionnaires. Setting: This survey was carried out in rural and urban areas of Punjab and Azad Jammu and Kashmir Participants: 305 participants were included in the study Intervention: Data was collected from the educated participants of different age groups belongs to different profession. Primary Outcome Measures: Purpose of the study is to know about that conception of COVID -19 in both rural and urban areas of Pakistan so that effective therapy can be given to the nation. Results: It was found that people from both populations had adequate knowledge about COVID-19 and followed restriction imposed by the government during lockdown period. Moreover, population in rural areas was more likely to believe in the myths and rumors about COVID-19 existence. Conclusion: It was concluded that there is need of generating awareness among general population through adequate means which provide authentic information to people and lockdown is not a solution. Everyone should take of his own life by wearing masks and following protocols

Tri-Allelic Autosomal STR Patterns Observed in Pakistani Population during Forensic Case Work

The tri-allelic pattern is a genotyping abnormality that can be observed during routine short tandem repeat (STR) profiling in the field of forensic science. Fourteen tri-allelic patterns have been observed in nine different loci during routine forensic casework consisting of 20,000 STR profiles. All these 20,000 STR unrelated profiles were profiled using AmpFlSTR Identifiler Plus® and Global Filer® kits. Tri-allelic patterns can be divided into two types based on RFUs (relative fluorescent units) in peaks of three component alleles. Unequal RFUs of all three peaks are observed in the Type-I pattern, whereas in the Type-II pattern, RFUs of all three peaks are nearly equal. A total of nine novel tri-allelic genotypes were observed out of 20,000 unrelated individuals in the Pakistani population. All of these forensic cases belonged to the Type-I pattern which means that the sum of the height of two smaller peaks is nearly equal to the height of the third larger peak. The frequency of occurrence for all these patterns was compared with already reported data. In this study, eight novel tri-allelic patterns have been reported which are not listed in the National Institute of Standards and Technology (NIST) database as well as in any published article