Lingaya's Vidyapeeth is a private deemed-to-be university located in Faridabad, Haryana, India. It was established in 1998, as Lingaya's Institute of Management and Technology..
The world is experiencing an increased power demand, hence the need for sustainable sources of power at the expense of conventional sources. This paper explores the minimization of energy waste using the organic flash cycle in combination with a regenerator to recover the waste heat with 1,1,1,3,3-pentafluoropropane as a working fluid. A thermodynamic model was designed to optimize the operating conditions primarily in the heat recovery vapor generator (HRVG) pressure. When HRVG pressure is raised, both cycle efficiency and power will go up, but turbine inlet temperature has a greater influence on the overall performance of the system. The results indicate that boiler pressure reaches a minimum level during waste heat recovery operations compared with traditional fuel-fired facilities, and increasing source temperatures leads to increased pressure in both cases. The optimal operation pressure for the boiler reaches 12.79 bar when the source temperature stands at 150 degrees C. Operating under those conditions allows the system to achieve 13.20% energy efficiency and 19.53% exergy efficiency.
This paper presents the design and analysis of an multiband uniform metasurface antenna. The proposed metasurface comprises a three-by-three array of identical circular radiating elements with cross-slot configurations. Characteristic Mode Analysis (CMA) is employed to investigate and optimize the antenna’s inherent resonant behaviour. Key CMA parameters, including modal significance, characteristic angle, and eigen value responses, are analyzed to identify and excite the dominant modes. Surface current distributions are further examined using CMA, and a micro strip line feeding technique is implemented to efficiently excite the desired operating frequency bands. The proposed antenna exhibits multiple resonant frequencies at 5.5 GHz, 6.3 GHz, 10.0 GHz, 17.4 GHz, and a wideband response spanning 19.5 GHz to 30 GHz, making it suitable for 5G and advanced wireless communication applications. The antenna achieves an average gain of approximately 10 dBi with a radiation efficiency of 85
One of the most prevalent signs of depression is insomnia, which refers to the inability to fall or stay asleep, and can worsen the severity and duration of depressive episodes. Early diagnosis and effective treatment of insomnia are crucial to prevent related health issues. In this work, we present an empirical mode decomposition (EMD) based methodology to identify insomnia from prefrontal EEG signals available in the CAPSleep database. The EEG signals are decomposed with EMD to produce Intrinsic Mode Functions (IMFs), from which features are extracted for each IMF band. These features are then reduced via principal component analysis (PCA). Four machine learning techniques, such as Ensemble KNN (EKNN), Support Vector Machine (SVM), k-nearest neighbor (KNN), and Decision Tree (DT), are used to classify the resultant feature vectors and evaluation through 5-fold cross-validation. The study reports a 79.7% accuracy with the Decision Tree classifier, highlighting the potential of prefrontal EEG signals in diagnosing insomnia.
This paper investigates human-centred XAI resolutions for financial inclusiveness, digital enablers and responsibility and confidence in AI-enabled finance in emerging markets. It examines how AI aids financial audit across credit, fraud, regulation, audit, compliance, digital payment services and risk classification; and it enables exclusionary hazards of unequal data, unaccountable tools, inscrutable docs and opaque management; ultimately aiming to develop an Human-Centred XAI Trust Structure for contextually-appropriate audibility and contestability in identifying if financial audit is technically reliable, intelligible, challengeable, socially inclusive and institutionally accountable. It employs qualitative secondary data-based conceptual research using an integrative literature review, thematic investigation, qualitative content analysis, stakeholder examination, case-based comparative analysis, risk-control activity mapping and conceptual synthesis; drawing evidence from open access academic journals, policy papers, financial innovation policies, AI ethical codes, fintech management policies, emerging markets exemplars. Its essentials signify that AI-enabled financial audit provides dual accountability due to the double nature of financial activity and its automated audit; and technical transparency even cannot generate peoples' understanding, capability, correction and inquiry of the automated decisions. Financial digital exclusion can arise from incomplete and unrepresentative data, proxy-based bias, uneven error rate, opaque docs, natural language translation, human fragility, disintegrated accountability lines and weak grievance response. The context-sensitive human-centred trust structure weights up contextual perception, data and modeller integrity, human-centred explication, auditability, participation, inclusive management and empowerment outcomes. It documents Stakeholder-centred explication, responsible decisions, Trust calibration and Guide to contextual implementation for responsible AI in digital finance.
The need of the hour is the optimization of digital marketing campaigns, especially in businesses that want to refine customer targeting and conversion rates. This paper will study deep learning-based predictive analytics for the purpose of predicting user purchase behavior based on online platform data, email analytics, and ad platforms, mainly based on the demographic, behavioral, and transactional data about user browsing history, clickstream patterns, social media activities, and email campaign response. Four deep learning models—Bidirectional Encoder Representations from Transformers (BERT), XGBoost, long short-term memory (LSTM), and ResNet50—were employed to predict the buying intentions of users. The data were preprocessed and split into 70
