Holy Cross College

This paper explores how nasogastric force-feeding in the treatment of anorexia nervosa can function as both a life-saving intervention and a profoundly dehumanizing act. Drawing on Sophia Gore’s memoir The Rustle of a Wing: Finding Hope Beyond Anorexia and historical accounts of the force-feeding of suffragettes, the paper analyses the violation of embodied autonomy through a phenomenological lens, particularly Husserl’s distinction between the lived body (Leib) and the objectified body (Körper). By comparing medical treatment with political coercion and examining the language, procedures, and trauma of enforced feeding, this research questions the ethical implications of coercive care. It also reflects on the failure of narrative and empathetic engagement in clinical settings, proposing the integration of narrative medicine as a more humanizing framework. Ultimately, the paper argues that force-feeding, while medically justified in some cases, may perpetuate a form of violence when it neglects the lived experiences and subjectivity of patients.

Abstract Catechin is a strong bioactive substance that exhibits anticancer properties. This study investigates the anticancer efficacy of catechin from the leaves of Psidium guajava on breast cancer targets through in-silico analyses. The objectives of the current study were to predict the Lipinski rule of 5, examine its ADMET profile, and analyze protein-ligand interactions relevant to breast cancer progression and apoptosis regulation. The results from the in-silico analyses supported that the Catechin follows the Lipinski rule of 5 and has favourable ADMET properties. Catechin showed higher binding affinity towards Catalase, CDK4, and CDK6, as (-9.4, -8.3, and − 8.0) respectively. Catechin showed more hydrogen bond interactions with Bak, Caspase 9 and Superoxide dismutase. The findings provide compelling in-silico evidence for the anticancer potential of catechin, supporting its role as a therapeutic lead compound. Further in vitro and in vivo studies are warranted to validate these interactions and establish its clinical applicability. This study highlights catechin as a powerful herbal bioactive candidate with potential for the development of novel breast cancer therapeutics.

Aim: Lung cancer is the top leading cause death globally, and early and precise detection is required to achieve better survival rates. This research tends to create an optimized Vision Transformer (ViT) model that can be used for real-time lung cancer detection with better generalization and efficiency. Leveraging deep learning and transformer-based models, the proposed model endeavors to realize high diagnostic accuracy along with computational efficiency for real-world usage. Materials and Methods: This research consists of two groups. Group 1: Recently, several start-ups explore the adoption of Vision Transformers (ViTs) to overcome some of the limitations CNNs exhibited. Group 2: Hybrid approaches that leverage the advantages of both ViTs and convolutional architectures, as well as advanced attention mechanisms, represent a strong opportunity to enhance lung cancer detection performance. Results: The Hybrid Attention Vision Transformer (HViT) model classification shows a 96.7% accuracy of 96.7%, , Recall of 96.63%, Precision of 94.8%, F1-score of 96.1% and AUC of 0.97. The model exhibits a substantial inference time reduction, processing images at 0.32 seconds per scan, and is most applicable for hence well-suite for real-time scenario. The model also generalizes well across datasets, with a 5.6% performance gain over conventional CNN-based models. Conclusion: The optimized Vision Transformer model proposed here effectively improves lung cancer detection with enhanced generalization and efficiency. The real-time processing capabilities with improved accuracy of this work make it a potential tool for early diagnosis, which can assist healthcare professionals in making timely decisions and enhancing patient outcomes.

Through AI, companies in the digital economy can make better decisions, handle their operations more efficiently and build up their competitiveness. This paper explores the various ways AI impacts business strategies by linking information from data to common strategic guidance. It analyzes many industries by explaining how technology such as machine learning, natural language processing and robotics process automation shape strategy, market studies, customer interaction and new ideas. Important case studies demonstrate how AI is effectively utilised in business strategies, guiding companies seeking to succeed in the digital age. Matters involving ethics, data privacy and complicated system integration are included in these debates. The combination of scholarly sources and case studies in the study outlines the main strategy aspects of AI and recommends an approach for adopting it well in business strategy. According to the findings, AI improves a company’s ability to respond fast, but it has to fit into what the organization aims for and its environment if it is to succeed.

Continuous surveillance in a reservoir is necessary for managing resources properly; yet traditional cloud services encounter delays, limited bandwidth and high energy costs. To overcome these limitations, the study presents a new system that merges edge computing with Wireless Sensor Networks (WSNs). The system achieves faster response times and decreased power use by processing information close to where sensors are placed. ESNs and other reservoir computing structures are key components of the platform for processing unbalanced sensor information smoothly. Real-time detection and prediction of unusual patterns is possible with ESNs because their reservoirs can automatically handle input signals. This process is made better by using a fault-tolerant edge provisioning framework that helps choose the best placement for nodes that balance being reliable, using less energy and delivering services fast. Specifically, the devices can preprocess their data and filter out noisy parts and then encode these important features before they send the state vectors to servers for deeper study. Experiments in water quality monitoring reveal how the system processes all the intended measurements (such as pH and dissolved oxygen) despite not having much bandwidth. The use of inkjet-printed sensors along with analog pulse-based telemetry helps to both lower costs and use less energy. There is a 40% decrease in delay time for transferring data and a 30% rise in successful fault detection, as shown by the results, compared to models that depend on the cloud.

In the current energy landscape, power quality (PQ) emerges as a critical concern. Even when there is no fault on a line, PQ issues are common in all power networks since 90% of power systems’ loads are variable or inductive in nature. Variable loads cannot be avoided; hence, PQ concerns such as voltage swelling and sag will always arise. Voltage sag is one of the main issues within a distribution network, resulting in financial losses for the utility company and the customer. The Dynamic Voltage Restorer (DVR) effectively addresses voltage sags and minimizes total harmonic distortion (THD) in the distribution network. This paper proposed a novel control strategy to increase the PQ in a system. A Frilled Lizard Optimization-optimized fuzzy PI controller is proposed in this work to control the inverter. This proposed method improves the DVR’s ability to correct voltage sag and reduce total harmonic distortion as soon as possible. The PI control scheme is utilized initially to reduce the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the PI method is applied to a fuzzy logic-based compensatory stage. The proposed approach is validated using pro-type models, as well as mathematical and Simulink modelling. In the Results Section, the performance of the proposed controllers with the DVR is tabulated and compared with other DVR controller schemes described in other research papers.

Green investment emphasis allocating capital for the industries and research works that promote environmental sustainability and reduce GHG (Greenhouse gas) emissions. It holds up the energy consumption, toxic pollution and the sustainable environment. This research examines the impact of green investment in the inventory model for deteriorating items with planned backorders to balance the economic efficiency and the environmental sustainability. The items are deteriorating at a natural decay process and their rate is proportional to the inventory level per unit time. It leads to the level of inventory. Back ordering happens when inventory is null and it will deliver after production takes place in the industries. The purpose of this framework is to create a green inventory mathematical model for reducing the total cost and carbon (CO2) emissions.