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Cardiovascular diseases are the leading cause of mortality and morbidity globally. Clinicians must know cutaneous signs of cardiovascular disease, including petechiae, macules, purpura, lentigines, and rashes. Although cutaneous manifestations of diseases like infectious endocarditis and acute rheumatic fever are well established, there is an indispensable need to evaluate other important cardiovascular diseases accompanied by cutaneous signs. Moreover, discussing the latest management strategies in this regard is equally imperative. This review discusses distinctive skin findings that help narrow the diagnosis of cardiovascular diseases and recommendations on appropriate treatment.

Major focus of this study was on visual, auditory and kinesthetic learning preferences of college students as given in Dunn and Dunn model (Dunn & Dunn, 1993) and their relationship with academic achievement of students. The nature of study was quantitative, descriptive and correlational. All (male and female, science and arts) students enrolled in B.A. /B.Sc. and BS programs of all public sector colleges of Punjab province constituted the population of study. The study was delimited to six districts of Punjab province with two districts from each region (North, Central, & South Punjab). Three-stage cluster sampling technique was used to select the sample of 1200 students. Barsch Learning Preferences Inventory (BLPI) (visual, auditory, & kinesthetic) developed by Barsch (1996) was used as a research instrument. Product Moment correlation coefficient was used to find out relationship between each learning preference and academic achievement. Furthermore, differences among low achieving and high achieving students with respect to their learning preferences were analyzed by applying z-test. It was concluded that college students were predominantly visual in their learning preference. High achieving students were also more visual and kinesthetic. Further, there was also a significant relationship between auditory learning preference and academic achievement.

The deployment of solar panels at airports offers numerous benefits, such as clean energy production, cost savings, emission reduction, improved energy security, and a positive public image. In this study, the performance of various solar panel technologies is investigated based on the 7E framework (i.e. energy, exergy, economic, energoenvironmental, exergoenvironmental, energoenviroeconomic, and energoenviroeconomic) at airports in Pakistan. Initially, available spaces at five international airports are identified followed by energy assessments conducted with PVSyst simulation software. Next, a mathematical model is developed to evaluate exergy, economic, energoenvironmental, exergoenvironmental, energoenviroeconomic, and exergoenviroeconomic parameters. Results show that all airports demonstrate favorable performance ratios. Specifically, Quetta airport emerges as the optimal location as per the 7E assessment, showcasing a reference yield of 2752 kWh/kW, final yield of 2420.8 kWh/kW, 27.63% capacity utilization factor, 0.031 $/kWh levelized cost of electricity, 5730 tons of CO2 avoided annually, and $488,826 per year in greenhouse gas revenue, achieved through thin film-based technology with single axis tracking. Peshawar airport stands out for its high energy efficiency, while Karachi airport excels in exergy analysis. The outcome of the study will provide insights into the potential of these systems to mitigate energy challenges, considering economic feasibility and environmental implications.

This paper reports the undermined potential of broad range of (Information and communication technologies) ICTs that remained effective yet unnoticed in different flood-phases to exchange traffic, travel, and evacuation related information. The objective was to identify convenient ICTs that people found operational in life cycle of a flood. For the purpose, ICTs were tested in relation to 18 different variables based on personal capabilities, demographic, and vehicle-based information etc.

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers. [ABSTRACT FROM AUTHOR]

This work focuses on the thermodynamic property calculations of seven copper‐based species, namely copper, copper oxide, copper hydroxide, copper nitrate, and copper hydroxide nitrate. The structures of these species were optimized to achieve stable geometries. The density functional theory (DFT) calculations were employed to obtain various thermodynamic properties such as entropy, enthalpy, Gibbs free energy, and heat capacity at constant pressure. A comparative investigation was performed on the temperature‐dependent behavior of key thermodynamic parameters. Species characterized by a higher quantity of atoms tend to demonstrate elevated thermodynamic properties. Copper and copper hydroxide nitrate had higher thermodynamic values than their oxides and other counterparts. It should be noted that the thermodynamic properties of copper hydroxide nitrate were newly computed, and the results showed that the thermodynamic values of the compound structure were higher than their crystalline counterparts. Moreover, due to the large structure size and solid phase, these thermodynamic values exhibited discrepancies with previously calculated computational and experimental values. The thermodynamic property values that depended on temperature were transformed into NASA 7‐Coefficient polynomials parameterization. The newly determined thermodynamic data and polynomials provide valuable insights into the thermodynamic behavior of copper‐based species. It will help better understand their surface sites and different crystalline structures. Such data can be used to better understand a variety of industrial processes, including combustion, gasification, chemical synthesis, and further to enhance efficiency, reduce costs, and minimize hazardous environmental emissions. [ABSTRACT FROM AUTHOR]

The prevalence of cyberbullying has reached an alarming rate, affecting approximately 54% of teenagers who experience various forms of cyberbullying, including offensive hate speech, threats, and racism. This research introduces a comprehensive dataset and system for cyberbullying detection in Urdu tweets, leveraging a spectrum of machine learning approaches including traditional models and advanced deep learning techniques. The objectives of this study are threefold. Firstly, a dataset consisting of 12,500 annotated tweets in Urdu is created, and it is made publicly available to the research community. Secondly, annotation guidelines for Urdu text with appropriate labels for cyberbullying detection are developed. Finally, a series of experiments is conducted to assess the performance of machine learning and deep learning techniques in detecting cyberbullying. The results indicate that fastText deep learning models outperform other models in cyberbullying detection. This study demonstrates its efficacy in effectively detecting and classifying cyberbullying incidents in Urdu tweets, contributing to the broader effort of creating a safer digital environment. [ABSTRACT FROM AUTHOR]