Point University

Social networking sites, now with thousands in existence, have ushered in a revolution in digital communication, while also giving rise to serious security threats like fake profile creation and online impersonations. The perpetrators engaged in these deceitful acts use them for cyberbullying, spreading misinformation, and identity theft, among other evils. Traditional detection methods relying on rule-based systems and shallow, machine learning algorithms have had modest success at best against the increasing complexity of fake profiles. Deep learning approaches have emerged in recent years as powerful, complementary alternatives capable of modeling highly complex patterns from large-scale, heterogeneous sources of data. This review paper presents an in-depth evaluation of state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, and Transformer-based architecture in the domain of fake profile detection. The paper also looks at multimodal methods combining textual, image, behavioral, and network-based features to enhance detection accuracy. Challenges tackled herein include class imbalance, data privacy, adversarial evasion, and real-time implementation.

In Haiti, surviving pregnancy and childbirth is especially tenuous for young women, who are already marginalized. One of the four leading causes of death for women 20-24years old is pregnancy- or childbirth-related. An important gap remains regarding youth perspectives of maternal health and well-being within their communities. We conducted a community-based participatory research study using Photovoice to understand the lived experiences of 17 young women from La Pointe, Haiti, and its surrounding rural communities, who survived a maternal near-miss, a grave obstetric event that one narrowly escapes. Youth participants took photographs and participated in individual interviews and focus groups to represent and share their pregnancy and birthing experiences. Visual and narrative data were analyzed by participants and our community research team using reflexive thematic analysis. The five main themes represent a perilous maternal journey. The themes included (a) the social context youth are embedded within, (b) structural, systemic and political forces youth face, (c) pain and suffering throughout youth maternal experiences, (d) search for support during the maternal trajectory, and (e) youth-generated solutions for improved maternal health and well-being. Our findings amplify youth voices on the pressing need to address multiple, intersecting issues that shape maternal health and well-being for all women with particular attention to the needs of young women. Understanding the experiences of Haitian youth survivors of maternal near-misses can inform research, practice, and policy to curb maternal deaths and maternal near-misses among young women within low-resource settings.

Traffic Management is one of the major issues which is a rising rapidly because of significant increase in number of vehicles. To address this there is the need of a smart traffic management system that will help control and improve the flow of traffic. Today there are traffic signals which work on the timeframe setting and switch signals after the certain fixed time frame. This system works but a problem arises that is if within the timeframe if the lane becomes empty before the time ends then the signal will not immediately switch as it will complete the timeframe and then switch according to its default setting

The paper additionally depicts an abnormal state perspective of the framework engineering. With the increasing urbanization and the rise in the number of vehicles, managing parking spaces has become a significant challenge in modern cities. This paper presents a Smart Parking System integrated with a real-time booking application, designed to optimize parking space utilization and provide a seamless user experience. The proposed system uses sensors to monitor parking space occupancy in real-time, offering users the ability to search, reserve, and pay for parking spots through a mobile application. By providing live updates the system reduces the time spent searching for vacant spaces, minimizes congestion, and enhances the overall efficiency of urban parking management., the integration of a real-time booking system ensures that users can plan their parking in advance. The system’s adaptability to different parking structures, combined with its user-friendly interface, presents a scalable solution for smart cities aiming to streamline urban mobility and reduce environmental impact through reduced traffic congestion and improved traffic flow.

This project focuses on the development of a Zigbee-based alert system for two autonomous robots. Each robot is equipped with sensors capable of detecting hazards such as smoke, fire, and obstacles. Controlled by an Arduino Uno, the robots communicate wirelessly using Zigbee modules to coordinate their actions in real-time. When a hazard is detected by one robot, it sends a signal to the other robot, prompting it to halt or take necessary action. The status is displayed on an I2C LCD, and an alarm buzzer provides an audible warning. Robot movement is managed using the L293D motor driver. This system demonstrates efficient wireless communication, real-time hazard detection, and coordinated response, improving safety and collaboration among autonomous robots

A portable universal solar charging station is an innovative solution designed to provide off-grid power to a wide range of electronic devices in various environments. This compact and lightweight system harnesses solar energy to generate electricity, making it an eco-friendly alternative to traditional charging methods. With multiple output ports, it is compatible with a variety of devices such as smartphones, laptops, cameras, and portable appliances. The station is equipped with high- efficiency solar panels that charge the built-in battery, ensuring reliable power availability even in remote or outdoor locations. Its portability and ease of use make it ideal for camping, emergency situations, or outdoor activities. The solar charging station offers a sustainable energy solution that reduces dependence on fossil fuels and promotes renewable energy use, contributing to environmental conservation. Its versatility, convenience, and energy efficiency make it an essential tool for people seeking reliable power on the go

Abstract MG1-1995959 is a short-period (∼0.586 day) eclipsing binary, exhibiting at times an extraordinarily large O’Connell effect (OE), for which this paper reports its discovery, time series photometry, and spectroscopic observation. The photometry is scattered across years 2001–2023, with spectroscopic observations from 2023. Photometric curves exhibit classical OE asymmetries, whereby maxima following primary and secondary eclipses are of different magnitudes. Short time span photometric time series provide static characterizations of OE stars yielding incomplete representations of critical macrobehavior. In contrast, MG1-1995959 photometry is of sufficient duration to indicate pronounced changes in such behavior, suggesting that intense observing programs can provide key benchmark data to better inform complex modeling efforts. Spectra indicate the primary to be K1 Vk(e), and the secondary type is ∼K5 V. PHOEBE modeling of the light curve from 2014 enables estimation of key stellar parameters (primary/secondary): T eff [K] (5110/4260), and, in solar units, mass (0.86/0.68) and radius (0.93/0.65). The inclination of the system was found to be ∼78°. PHOEBE modeling of light curves from 2013, 2022, and 2023 is presented, demonstrating (1) the light curves can be well fit by invoking distributions of star spots, (2) optimal spot sizes and distributions vary dramatically as a function of time, and (3) this is a powerful tool with which to investigate the short term evolution of this star. MG1-1995959 is an accessible star with dynamic and unusual behavior deserving of intensive study to better understand nuances of the OE and the nature of late-type main-sequence stars.

This paper describes a method of identifying the transfer function for small DC gear motors such as those used in academic control systems laboratories and for student robotics projects. Many of these motors incorporate a quadrature encoder on the armature shaft and provide ready access to the armature's angular position. The approach that is described in this paper uses only this angular position and the magnitude of the armature voltage to obtain a first-order model for the armature angular velocity in terms of the applied armature voltage. This is accomplished by first identifying and compensating for Coulombic friction using break-away tests. Then, the step response is obtained in response to a Coulombic friction compensated input signal. From both the slope and intercept of the straight line approximation of the steady-state portion of the response curve, which is obtained via least-squares approach, the parameters of the model are calculated. This approach, unlike conventional step response approaches, does not require angular velocity, and therefore no filtered finite-differences or dynamic state-estimation is required, making this method quick, easy to understand, and ideal for obtaining transfer functions for controller design.

ObjectivesTo analyze the clinical and biological characteristics and to evaluate the risk factors associated with the mortality of patients with COVID-19 in Commune IV of the District of Bamako.MethodsThe cohort consisted of COVID-19 patients managed from March 2020 to June 2022 at the Bamako Dermatology Hospital and the Pasteur Polyclinic in Commune IV in Bamako. The studied variables were sociodemographic, clinical, and biological. For the analysis of deaths, explanatory variables were grouped into sociodemographic factors, comorbidities and symptoms. Binomial logistic regression models were used to identify mortality associated risk factors.ResultsAmong the 1319 included patients, 38.4% were asymptomatic, 46% and 15.5% developed moderate or severe COVID-19 respectively. The predominant signs were cough (48.5%), respiratory difficulty (24.6%) and headache (19.7%). Male were more common (58.2%). High blood pressure (19.9%) and diabetes (10%) were the main comorbidities. D-dimers < 0.5 μg/l was found in 53.3% of cases and the mean hemoglobin level was 12.9 ± 1.7 g/l. The case fatality rate was 3.71% in our series. In bivariate analysis, age > 60 years, high blood pressure, diabetes, clinical severity, D-dimers < 0.5 μg/l were associated with death. Using binomial logistic regression method, age > 60 years, increased heart rate, disease severity level and mainly acute respiratory distress syndrome (polypnea, difficulty breathing) were the factors found associated with death. After adjusting for all the assessed factors, age < 60 years [aHR = 0.15 (0.06–0.35)] and administration of azithromycin [aHR = 0.31 (0.1–0.97)] were protective factors while higher respiratory rate [aHR = 1.14 (1.07–1.22)] and difficulty breathing [aHR = 3.06 (1.03–9.13)] were risk factors associated with death.ConclusionThese main findings elucidate the factors associated with severity and lethality external of health care system constraints. Advanced age, higher heart rate and the development of respiratory distress were the factors significantly associated with increased fatalities.