University of Bridgeport

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s best results were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model, with Precision, Recall, F1-score, and Accuracy all reaching 0.99. This study highlights how advanced pre-trained models, such as Bidirectional Encoder Representations from Transformers, can significantly enhance the accuracy and reliability of fraud detection systems.

The purpose of this research is to contribute to the development of approaches for the classification and segmentation of various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, dyed resection margins, esophagitis, normal cecum, normal pylorus, normal Z line, polyps, and ulcerative colitis. This research is relevant and essential because of the current challenges related to the absence of efficient diagnostic tools for early diagnostics of GI cancers, which are fundamental for improving the diagnosis of these common diseases. To address the above challenges, we propose a new hybrid segmentation model, U-MaskNet, which is a combination of U-Net and Mask R-CNN models. Here, U-Net is utilized for pixel-wise classification and Mask R-CNN for instance segmentation, together forming a solution for classifying and segmenting GI cancer. The Kvasir dataset, which includes 8000 endoscopic images of various GI cancers, is utilized to validate the proposed methodology. The experimental results clearly demonstrated that the novel proposed model provided superior segmentation compared to other well-known models, such as DeepLabv3+, FCN, and DeepMask, as well as improved classification performance compared to state-of-the-art (SOTA) models, including LeNet-5, AlexNet, VGG-16, ResNet-50, and the Inception Network. The quantitative analysis revealed that our proposed model outperformed the other models, achieving a precision of 98.85%, recall of 98.49%, and F1 score of 98.68%. Additionally, the novel model achieved a Dice coefficient of 94.35% and IoU of 89.31%. Consequently, the developed model increased the accuracy and reliability in detecting and segmenting GI cancer, and it was proven that the proposed model can potentially be used for improving the diagnostic process and, consequently, patient care in the clinical environment. This work highlights the benefits of integrating the U-Net and Mask R-CNN models, opening the way for further research in medical image segmentation.

To enhance the response speed and accuracy of the motors in a four-wheel motor-driven electric vehicle, precise control of the wheel side motor speed is essential to improve vehicle handling stability. This paper proposes a chaotic adaptive sparrow-optimized PID control algorithm. Firstly, the structural principles of the four-wheel side motor-driven electric vehicle are analyzed. A mathematical model and a simulation model of the electric vehicle are established, and the accuracy of the tram model is verified under ECE urban working conditions. Subsequently, the traditional sparrow algorithm is improved using Logistic-Tent chaotic mapping, adaptive search parameters, and the Levy flight strategy. These modifications enable the algorithm to escape local optima and achieve better convergence speed and accuracy. Based on these improvements, the ALSSA-PID control algorithm is developed, and the motor model is simulated and tested. Finally, a physical control platform for the wheel-edge motor is constructed to test the control algorithm. The results indicate an average reduction of 48.83% in rise time and 11.27% in rise time, as well as a 27.31% reduction in stabilization time and 7.17% reduction in stabilization time for the ALSSA-PID control algorithm when compared to the open-loop control and SSA-PID control, respectively, at the required speed under the ECE condition. These findings demonstrate that the proposed control algorithm significantly improves the control performance of the wheel-edge motor control system.

Drug-induced neuropsychiatric symptoms are not uncommon, particularly in patients with multiple comorbidities and polypharmacy. Sacubitril/valsartan is a combination medication used in the treatment of heart failure, especially for reducing hospitalizations in patients with heart failure with preserved ejection fraction (HFpEF). However, its neurological side effects, including delirium, hallucinations, and confusion, have been increasingly reported. This case report explores the occurrence of drug-induced delirium, specifically visual hallucinations, in a 79-year-old male with HFpEF and end-stage kidney disease (ESKD) after starting sacubitril/valsartan.

Optimal egg size theory implies that female organisms balance between fecundity and individual offspring investment according to their environment. Past interspecific studies suggest that fishes in large marine systems generally produce smaller eggs than those in small freshwater systems. We tested whether intraspecific egg size variation reflected a similar pattern by comparing egg size among yellow perch (Perca flavescens) populations inhabiting a range of system sizes. In 2018, 2019, and 2023, we collected yellow perch egg samples from 12 locations in systems ranging in surface area from 37 to 5,390,492 ha. First, we found that egg diameter significantly increased with maternal total length in five of eight individually tested populations. After accounting for these maternal effects, we found a significant interaction, where females inhabiting larger lakes, such as the main basins of Lakes Erie and Michigan, produced smaller eggs than those in smaller inland lakes, and the greatest differences were demonstrated among females of greater total length. This egg size variation in the largest females is consistent with interspecific egg size comparisons between marine and freshwater fishes. However, by examining a single species across vastly different environments, we were able to support theoretical expectations that maternal investment in offspring should vary with environmental conditions controlling early‐life resource acquisition and competition.

Starting with the origins of professional law enforcement agencies in the mid-1800s, police officials and their organizations were under the control of local government leaders. Political influence and interference were rampant. The same phenomena have been apparent within the police of Turkey since organizational foundation in the nineteenth century. This was further compounded and negatively impacted by a long and persistent history of military coups that left police officials to follow the demands of the reigning government. It was not until 2001 that a major reorganization process within the Turkish National Police resulted in dramatic enhancements to professionalism, ethics, and training. These actions aligned with Turkey’s ambition for European Union integration. However, a transition to a more authoritarian government and a major purge of police and rule of law officials across the country following the commencement of a major corruption scandal involving numerous government officials, followed by a failed coup attempt in 2016, have resulted in a decline in public trust in the Turkish National Police, both nationally and globally. It is therefore time for reform and reorganization within all criminal justice agencies in Turkey in order to return legitimacy and elevate professional standards for the Turkish National Police and partner public agencies.

The ability to manipulate the flux of ions across membranes is a key aspect of diverse sectors including water desalination, blood ion monitoring, purification, electrochemical energy conversion and storage. Here we illustrate the potential of using daily changes in environmental humidity as a continuous driving force for generating selective ion flux. Specifically, self-assembled membranes featuring channels composed of polycation clusters are sandwiched between two layers of ionic liquids. One ionic liquid layer is kept isolated from the ambient air, whereas the other is exposed directly to the environment. When in contact with ambient air, the device showcases its capacity to spontaneously produce ion current, with promising power density. This result stems from the moisture content difference of ionic liquid layers across the membrane caused by the ongoing process of moisture absorption/desorption, which instigates selective transmembrane ion flux. Cation flux across the polycation clusters is greatly inhibited because of intensified charge repulsion. However, anions transport across polycation clusters is amplified. Our research underscores the potential of daily cycling humidity as a reliable energy source to trigger ion current and convert it into electrical current.

Acoustic surveys are important for fish stock assessments, but fish responses to survey vessels can bias acoustic estimates. We leveraged quiet uncrewed surface vessels (USVs) to characterize potential bias in acoustic surveys. Five conventional motorized ships overtook USVs from astern over 2 km transects at night in Lake Superior in 2022. We examined the difference in acoustic backscatter, average target depth, and average target strength (TS) between USV and motorized vessels. Although sound level measurements from the motorized vessels sometimes exceeded recommendations for scientific vessels, we did not detect differences in acoustic measures among survey vessels. However, the USVs recorded 2 dB higher acoustic backscatter and TS than motorized vessels, leading to ∼15% higher fish densities with drones when using in situ TS and echo integration. Differences in fish density would increase to 30%–60% if a standard TS value was applied. Target depth did not differ between USVs and motorized ships. These results are consistent with a change in orientation but not depth of insonified fish and limited horizontal avoidance of motorized survey vessels.

Researchers are becoming more interested in novel barium-nitride-chloride (Ba3NCl3) hybrid perovskite solar cells (HPSCs) due to their remarkable semiconductor properties. An electron transport layer (ETL) built from TiO2 and a hole transport layer (HTL) made of CuI have been studied in Ba3NCl3-based single junction photovoltaic cells in a variety of variations. Through extensive numerical analysis using SCAPS-1D simulation software, we investigated elements such as layer thickness, defect density, doping concentration, interface defect density, carrier concentration, generation, recombination, temperature, series and shunt resistance, open circuit voltage (VOC), short circuit current (JSC), fill factor (FF), and power conversion efficiency (PCE). The study found that the HTL CuI design reached the highest PCE at 30.47% with a VOC of 1.0649 V, a JSC of 38.2609 mA cm⁻², and an FF of 74.78%. These findings offer useful data and a practical plan for producing inexpensive, Ba3NCl3-based thin-film solar cells.

Quantum machine learning holds the potential to revolutionize cancer treatment and diagnostic imaging by uncovering complex patterns beyond the reach of classical methods. This study explores the effectiveness of quantum convolutional layers in classifying ultrasound breast images for cancer detection. By encoding classical data into quantum states through angle embedding and employing a robustly entangled 9-qubit circuit design with an SU(4) gate, we developed a Quantum Convolutional Neural Network (QCNN) and compared it to a classical CNN of similar architecture. Our QCNN model, leveraging two quantum circuits as convolutional layers, achieved an impressive peak training accuracy of 76.66% and a validation accuracy of 87.17% at a learning rate of 1 × 10−2. In contrast, the classical CNN model attained a training accuracy of 77.52% and a validation accuracy of 83.33%. These compelling results highlight the potential of quantum circuits to serve as effective convolutional layers for feature extraction in image classification, especially with small datasets.

In Chloé Zhao's 2020 film Nomadland, Fern's commitment to eschewing a geolocalizable “home” uproots her from conventional patterns of domesticity and transforms her into an inhabitant of planet earth as a whole, enabling a new way of thinking about identity, environment, and the nature of human freedom. The kind of freedom that Fern's narrative evokes stands in deliberate contrast to the masculinized, heroic style of freedom that American films have done so much to promulgate. In contrast to this conventional representation of freedom, new materialist criticism has examined the many ways that human agency emerges from within a dense web of interconnections that bind together human beings with one another, with other life forms, with inanimate matter, and with the cosmic expanses of time and space. Nomadland can be understood as a film that picks up the iconography of conventional American cinematic freedom in order to redirect this iconography into a new materialist worldview.

In this chapter, we use the scholarship of teaching and learning (SoTL) to explore the application of universal design for learning (UDL) (CAST 2018) to health care education. We begin by setting a historical context of health care education. Next, we provide an overview of UDL, a review of the literature of UDL in health care education, and then discuss one institution's exploration of applying UDL in health science and other program teaching practices, arguing that UDL's main concepts can be effectively applied to health care education to create inclusive and effective learning experiences.