This work develops a novel observer-based finite-time methodology to make disturbed wheeled mobile robots (WMRs)  follow a desired reference signal. With the kinematic and reference models, two decoupled systems are computed. Then, each subsystem’s control signal is split into three terms. The first part estimates the disturbances affecting the system, transforming the closed-loop system into two new second-order systems with uniformly bounded and convergent disturbance terms. Then, based on a new sliding surface and a design function, the second and third controller’s parts are employed to accomplish the trajectory tracking task despite disturbances. The proposed controller is compared against a finite-time and a feedback controller. Then, detailed numerical simulations show the outstanding performance of the novel proposal.

This study focused on developing a persuasive technology-based mobile intervention system to support users to adopt malaria prevention and control measures.  As a first step towards contributing to research, user-centered research to uncover the factors preventing the adoption of the existing malaria preventive measures and the appropriate persuasive strategies that could be employed to motivate users to adopt the expected malaria prevention and control behaviors was conducted. Findings from this study were mapped into their corresponding persuasive strategies during the research design before being operationalized. The intervention system developed is a mobile phone-based application, code-named Go-Malaria. The intervention system embodies text notifications, and audio and video tutorials made in English, Igbo, and Abakaliki languages to engage users based on their preferences. This research was carried out in Ebonyi State. Three Local Government Areas (LGAs) were selected for the study based on three inclusion criteria: high rate of malaria fatality, proximity to the river, and availability of mobile phone network. The research was conducted in 3 phases viz: Pre-evaluation study to determine the factors militating against malaria prevention, research design and intervention system development and deployment, and evaluation phase to measure the impact of the intervention system using quantitative analysis tool (SPSS). The result of the evaluation study indicated reductions in the rate of malaria cases when compared to the cases recorded during the baseline study, from 96.9% to 68.5%, and ownership of mosquito nets and usage increased from 54% to 85.5% within the intervention group

The multichannel SAR system is an advanced imaging technique that enables achieving high resolution and wide swath simultaneously. However, the fundamental hindrance in the multichannel SAR system is the failure of reception by the multiantenna system due to being in hostile working conditions. As a result of this, the received multichannel SAR data often suffer from the presence of missing samples in the azimuth domain. Besides this problem, the multichannel SAR system also has to deal with the azimuth ambiguities to obtain high azimuth resolution and wide swath. This kind of received multichannel SAR data consisting of missing azimuth samples and azimuth ambiguities cannot be processed with the conventional multichannel SAR algorithms. Therefore, we propose a novel multichannel reconstruction method to address both challenges, enabling imaging of wide swath width with high resolution from the received incomplete multichannel SAR data. Our method uses the minimum variance distortionless response (MVDR) algorithm to reconstruct an unambiguous wide azimuth spectrum. In the end, we have provided simulation results that validate the efficiency of the proposed reconstruction method. Index Terms-Synthetic aperture radar (SAR), minimum variance distortionless response (MVDR) algorithm, azimuth ambiguities, high-resolution wide-swath (HRWS), and azimuth sampling frequency.

This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by-11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors.

Microfinance emerges as a pivotal catalyst within India's micro, medium, and small-scale industries. A notable exemplar is the SIDBI-PRAYAAS initiative, under the auspices of SIDBI, strategically engineered to empower the lower echelons of the financial pyramid. The latest study unveils a compelling triumph, spotlighting an impressive 6.02% Compound Annual Growth Rate (CAGR) surge in the commercial ventures of beneficiaries who availed themselves of this pioneering scheme. In this context, the infusion of digital mechanisms, including B-POS and B-Bank, has admirably streamlined the application, disbursement, and meticulous execution of loan reimbursements. The efficacy of these interventions reverberates triumphantly. Notably, the synergy of the Patanjali Ayurved Limited collaboration has further amplified the scheme's resonance, as eager borrowers harness its potential to nurture the expansion of Gramin Arogya Kendra. Considering these advancements, the current case study stands as a compelling testament to the efficacy of the PRAYAAS schemes, seamlessly interwoven with the digital dexterity of contemporary banking paradigms. This holistic approach underscores not only the scheme's operational prowess but also engenders profound customer gratification, thus forging a harmonious synthesis of financial inclusion and technological ingenuity.

Many businesses seeking new capabilities that blockchains may offer are deterred from fully embracing the technology due to fears of the classic "vendor lock-in" and platform-capture into one specific blockchain. From an asset-centric perspective, most business applications seek certain desirable functional guarantees with regard to the state of the tokenized asset on the blockchain. These new capabilities must be accessible through standardized service interfaces. The emerging tokenized asset networks based on decentralized ledger technology must integrate seamlessly into existing financial IT systems through similar standard interfaces. As such, if blockchains are to be a foundational technology in the future Web3 Internet of Value, then several classes and types of standardized APIs must be specified, published, and widely deployed by the nascent tokenized asset industry. These standard APIs must provide business applications with a single uniform interface to the many and varied blockchains today, thereby reducing business IT costs and preventing platform-capture.

In the context of India, a country with a rich tapestry of regional sign languages, effectively recognizing and interpreting Indian Sign Language (ISL) presents a formidable challenge for individuals with hearing and speaking impairments. This system introduces an innovative method for ISL recognition by leveraging the YOLOv5s (You Only Look Once version 5) object detection framework. Complementing the YOLOv5s, the project integrates Microsoft Azure’s cognitive app service, specifically the computer vision capabilities, and utilizes Mesa, a Python agent development framework. This comprehensive approach aims to enhance the expression and communication of individuals with hearing and speaking impairments in a predominantly spoken language-oriented world.

Fault-tolerant methods have been recognized for improving the reliability of multi-phase bidirectional DC-DC converters. Few fault-tolerant methods for current-fed dual active bridge (CF-DAB) converters have been developed because it becomes more challenging due to the asymmetrical structure between primary and secondary sides. In this paper, the behaviors of the CF-DAB converter are comprehensively analyzed when an open-circuit fault occurs in different fault scenarios. Analysis and proposed fault-tolerant method are aimed to determine the open-circuit cases which can recover full conversion power after the fault occurs. Moreover, this paper also proposed an auto-balancing control method to prevent the converter from being out of control. The proposed balancing control method can be utilized for general multi-phase current-fed converters. A 6-kW prototype is built to verify the proposed method.

In this paper, we propose a novel self-optimization framework that incorporates an automatic weight function for determining Handover Control Parameters (HCP) and utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm for target cell selection. The proposed algorithm estimates suitable HCP values automatically based on key factors such as Signal-to-Interference-plus-Noise Ratio (SINR), traffic load, and user equipment (UE)'s speed. Additionally, TOPSIS algorithm is employed to select the most suitable target cell based on three evaluation criteria: SINR, Reference Signal Received Power (RSRP), and load level. The performance of the proposed algorithm is evaluated using load level, throughput, and Radio Link Failure (RLF) as key performance indicators (KPIs). Extensive simulations are carried out to assess the performance of the proposed approach. The findings indicate that the proposed algorithm outperforms the existing solutions in terms of load level, throughput, and RLF.

Kähler forms with associated metrics and cohomology classes are shown taking Kähler–Einstein metric.

Proposed neural network accurately classifies stars as RGB or HeB based on distinct oscillation patterns.

Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.

This study introduces two innovative methodologies aimed at augmenting energy efficiency in satellite-to-ground communication systems through the integration of multiple Reflective Intelligent Surfaces (RISs). The primary objective of these methodologies is to optimize overall energy efficiency under two distinct scenarios. In the first scenario, denoted as Ideal Environment (IE), we enhance energy efficiency by decomposing the problem into two sub-optimal tasks. The initial task concentrates on maximizing power reception by precisely adjusting the phase shift of each RIS element, followed by the implementation of Selective Diversity to identify the RIS element delivering maximal power. The second task entails minimizing power consumption, formulated as a binary linear programming problem, and addressed using the Binary Particle Swarm Optimization (BPSO) technique. The IE scenario presupposes an environment where signals propagate without any path loss, serving as a foundational benchmark for theoretical evaluations that elucidate the systems optimal capabilities. Conversely, the second scenario, termed Non-Ideal Environment (NIE), is designed for situations where signal transmission is subject to path loss. Within this framework, the Adam algorithm is utilized to optimize energy efficiency. This non ideal setting provides a pragmatic assessment of the systems capabilities under conventional operational conditions. Both scenarios emphasize the potential energy savings achievable by the satellite RIS system. Empirical simulations further corroborate the robustness and effectiveness of our approach, highlighting its potential to enhance energy efficiency in satellite-to-ground communication systems.

As machine learning is being used in numerous applications, there is even more concern regarding algorithms within artificial intelligence. This paper focuses on analyzing the biases incorporated in a setup like FOLD-R++. The findings hold significance to both academia and industry in establishing what a fair and neutral machine learning algorithm is. The article contains an exhaustive literature review about biases in machine learning and a detailed explication. The chapter re-examines previous studies on the efficacy of this algorithm, unearthing the limitations in earlier literature and urging more research.This involves purposefully choosing a dataset from Kaggle, metrics applied in evaluating the algorithm, and a detailed experimental design. The results of these tests of different test scenarios have displayed that the algorithm is correct but vulnerable to bias and efficient across different domains. The results are discussed, and comparisons with what is available in the table regarding system-biased algorithms are made. In conclusion, this study contributes to the existing literature on machine learning and highlights certain shortcomings concerning the use of the FOLD-R++ algorithm. Thus, it demonstrates the need to address issues of unexplained inequality and develop reliable algorithms for decision-making. The study acknowledges these shortcomings to the extent that it becomes a vital stage toward developing more justified machine learning.

The current study presents a novel, non-invasive method for estimating both systolic and diastolic blood pressure by combining photoplethysmogram (PPG) signals with physiological data, such as sex, age, weight, height, heart rate, and BMI, using two Gated Recurrent Units (GRUs) models. The first model processes dynamic patterns in PPG signals, while the second model incorporates physiological parameters. Both models are connected through a series of dense layers. To prepare the datasets for the GRU framework, rigorous preprocessing was conducted. This resulted in a robust architecture capable of accurately predicting systolic and diastolic blood pressure. The proposed method achieved a Mean Absolute Error (MAE) of 1.458 for systolic and 1.164 for diastolic blood pressure. These findings demonstrate the potential of this approach for continual and non-intrusive blood pressure monitoring in wearable health technology. The studyâ\euro™s results also make a significant contribution to the field of medical monitoring technology. The proposed solution addresses a major limitation in traditional blood pressure measurement practices and paves the way for advancements in personalized health monitoring, particularly for managing hypertension and cardiovascular conditions. Â

This work addresses the challenge of producing chip level predictions on satellite imagery when only label proportions at a coarser spatial geometry are available, typically from statistical or aggregated data from administrative divisions (such as municipalities or communes). This kind of tabular data is usually widely available in many regions of the world and application areas and, thus, its exploitation may contribute to leverage the endemic scarcity of fine grained labelled data in Earth Observation (EO). This can be framed as a Learning from Label Proportions (LLP) problem setup. LLP applied to EO data is still an emerging field and performing comparative studies in applied scenarios remains a challenge due to the lack of standardized datasets. In this work, first, we show how simple deep learning and probabilistic methods generally perform better than standard more complex ones, providing a surprising level of finer grained spatial detail when trained with much coarser label proportions. Second, we provide a set of benchmarking datasets enabling comparative LLP applied to EO, providing both fine grained labels and aggregated data according to existing administrative divisions. Finally, we argue how this approach might be valuable when considering on-orbit inference and training. Source code is available at https://github.com/rramosp/llpeo. [+] This work has been submitted to the IEEE for review and possible publication. Copyright might be transferred without notice, after which this version may no longer be accessible.

The rapid expansion and deployment of AI technologies in various sectors have raised critical concerns about fairness, bias, and ethical implications. Addressing these concerns, the novel integration of structural fairness mechanisms and active learning frameworks into the Mistral Large model presents a significant advancement in creating AI systems that are both highly accurate and equitable. The proposed approach involves enhancing the model architecture with fairness-aware layers and incorporating a modified loss function that penalizes biased predictions, thereby promoting equitable outcomes across diverse demographic groups. Additionally, the implementation of an active learning framework prioritizes the selection of the most informative data points, which not only improves model accuracy but also maintains fairness throughout the learning process. Experimental evaluations demonstrate substantial improvements in performance metrics such as accuracy, precision, recall, and F1-score, alongside significant reductions in bias as measured through disparate impact and equal opportunity difference. The results underscore the feasibility and effectiveness of embedding fairness considerations into the core design and training processes of AI models, paving the way for the development of robust LLMs that adhere to ethical standards and promote social justice. The methodologies and findings provide a comprehensive framework for future research and practical implementations aimed at achieving both excellence and fairness in AI systems.

This work outlines the procedure to establish a system for assessing materials' complex permittivity and permeability within the 110-170 GHz frequency range. We present the employed methodology and the calibration procedure for the system, incorporating a dual approach using TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line) methods. Following that, a smoothing technique is used to enhance the accuracy of the results. Tests were conducted on a HIPS sample to validate the system's performance, demonstrating the results' reliability across the entire measured bandwidth.