A large number of studies over different experimental sites globally has successfully shown the utility of ICESat-2 LiDAR sensor-based datasets for the evaluation of topography (elevation) and water level. The current study evaluates the different filtering strategies for the selection of ATL08 footprints from the Advanced Topographic Laser Altimeter System (ATLAS) instrument based on the terrain uncertainty available in the ATL08 data. The openly accessible digital elevation models (DEMs), namely ASTER GDEM V003, CartoDEM V3 R1, TanDEM-X EDEM Global 30m, and SRTM 1 Arc Second Global, were evaluated for the Madurai Region. The footprints with unknown (null) uncertainties were removed first, and thereafter different datasets were generated for DEM assessment based on uncertainties of <10m, < 5m, < 2.5, < 1, <0.75, <0.5m, <0.25m, and <0.1m. It is observed that the CartoDEM performance is better than ASTER among the optical photogrammetrically derived DEMs, whereas TanDEM-X is better than the SRTM among the SAR Interferometry (InSAR)-based DEMs. Overall, the accuracy of TanDEM-X EDEM was found to be best upon comparison of RMSE among the four openly accessible DEMs used in the analysis. For example, with the filtering strategy of footprints selected with <0.75m uncertainty, the RMSE values for ASTER GDEM V003, CartoDEM V3 R1, TanDEM-X EDEM Global 30m, and SRTM 1 Arc Second Global were 7.14m, 3.68m, 1.52m, and 3.16m, respectively. It is also observed that reducing the uncertainties beyond an uncertainty level does not provide clear estimates due to inherent qualities and errors in different DEMs as well as techniques employed in DEM generation. The results may vary from one DEM to another, thus indicating that a careful selection of DEMs shall be performed before using in any application.

Part-of-speech (POS) tagging involves tagging each word in a text with the appropriate part of speech. POS tagging is regarded as one of the fundamental technologies required in Natural Language Processing (NLP) applications. For many natural language processing jobs, this procedure is regarded as one of the pre-processing processes. Recently, with the development of machine learning-based algorithms, the process of part-of-speech tagging improved, and there are now a respectable number of taggers accessible for high-resource languages like English. However, low-resource languages like Hausa continue to lack accurate and effective computational approaches for part-of-speech tagging. Despite the recent exponential expansion of Hausa online content on websites like BBC.com/Hausa, Freedomradio.com.ng, Hausa Leadership.ng, Aminiya and dailytrust.com.ng, part-of-speech tagging on such Hausa web content has not been investigated by the research community. Therefore, part-of-speech tagging on Hausa-based web contents is a new topic that can be researched. This research work proposed a machine learning-based method for Hausa part-of-speech tagging. We implement three architectures, namely, long short-term memory (LSTM), bi-directional long short-term memory (BLSTM) and gated recurrent unit (GRU), to perform part-of-speech tagging on a Hausa data set. The labeled data are transformed into a one-hot-vector encoding and then sent through a deep neural network using LSTM, BLSTM and GRU hidden layers. We obtain precision, recall, accuracy and f1-score as the evaluation matrix of the three architectures. In conclusion, the system achieves an overall result of 99%, and this shows that the proposed approach outperforms the previous approach (with a result of 79.14%) in terms of precision, recall, accuracy and f1-score.

The emergence of Unmanned Aerial Vehicles (UAVs), commonly known as drones, has presented numerous transformative opportunities across sectors such as agriculture, commerce, and security surveillance systems. However, the proliferation of these technologies raises significant concerns regarding security and privacy, as they could potentially be exploited for unauthorized surveillance or even targeted attacks. Various research endeavors have proposed drone detection models for security purposes. Yet, deploying these models on edge devices proves challenging due to resource constraints, which limit the feasibility of complex deep learning models. The need for lightweight models capable of efficient deployment on edge devices becomes evident, particularly for the anonymous detection of drones in various disguises to prevent potential intrusions. This study introduces a lightweight deep learning-based drone detection model by fusing knowledge distillation with Bayesian optimization. Knowledge distillation is utilized to transfer knowledge from a complex model (teacher) to a simpler one (student), preserving performance while reducing computational complexity, thereby achieving a lightweight model. However, selecting optimal hyperparameters for knowledge distillation is challenging due to a large number of search space and complexity requirements. Therefore, through the integration of Bayesian optimization with knowledge distillation, we present an enhanced CNN-KD model. This novel approach employs an optimization algorithm to determine the most suitable hyperparameters, enhancing the efficiency and effectiveness of the drone detection model. Validation on a dedicated drone detection dataset illustrates the model's efficacy, achieving a remarkable accuracy of 96% while significantly reducing computational and memory requirements. With just 102,000 parameters, the proposed model is five times smaller than the teacher model, underscoring its potential for practical deployment in real-world scenarios.

In this study, we present a groundbreaking approach to aspect-based sentiment analysis (ABSA) using transformer-based models. ABSA is essential for understanding the intricate nuances of sentiment expressed in text, particularly across diverse linguistic and cultural contexts. Focusing on movie reviews in Hausa, a language under-represented in sentiment analysis research, we propose HauBert, a biredirectional transformer-based approach tailored for aspect and polarity classification, by fine-tuning a pre-trained mBert model. Our work addresses the scarcity of resources for sentiment analysis in under-represented languages by creating a comprehensive Hausa ABSA dataset. Leveraging this dataset, we preprocess the text using state-of-the-art transformer techniques for feature extraction, enhancing the model's ability to capture nuanced aspects of sentiment. Furthermore, we manually annotate aspect-level feature ontology words and sentiment polarity assignments within the reviewed text, enriching the dataset with valuable semantic information. Our proposed transformer-based model utilizes self-attention mechanisms to capture long-range dependencies and contextual information, enabling it to effectively analyze sentiment in Hausa movie reviews. The proposed model achieves significant accuracy in aspect term extraction and sentiment polarity classification, with scores of 96% and 94%, respectively, outperforming traditional machine models. This demonstrates the transformer's efficacy in capturing complex linguistic patterns and sentiment nuances. Our study not only advances ABSA research but also contributes to a more inclusive sentiment analysis landscape by providing resources and models tailored for under-represented languages.

The poultry sector in Ecuador relies heavily on non-renewable energy sources, particularly conventional electricity from the public grid. A typical poultry plant consumes an average of 57.313 MWh per year, resulting in an annual cost of USD 7100. The sheds constitute the largest portion of its energy consumption, accounting for 36% of the total. The objective of this study was to model an optimal photovoltaic system that could contribute to the energy supply of the area with the highest consumption. The aim was to reduce the operating costs and facilitate a transition in the energy matrix. To achieve this, historical and exploratory data were collected, including solar radiation levels, estimation of geographical resources, and energy consumption patterns in the business. Based on the analysis, an isolated photovoltaic system was designed. The system comprises four solar panels, eight batteries, one charge regulator, one current inverter, five types of conductors, and three types of electrical protections. The photovoltaic system was sized to meet the energy requirements of the Type A shed, which consumes 5.89 kWh, and the Type B shed, which consumes 6.59 kWh. The design considered the lower annual solar radiation values of 4.58 kWh/m², ensuring that the system could function effectively even with reduced solar input. This approach not only addresses the immediate energy needs of the poultry sector but also contributes to the broader goal of reducing dependency on non-renewable energy sources. By transitioning to photovoltaic systems, poultry plants can significantly lower their operating costs and reduce their environmental impact.

Piezoelectric sensors are widely used due to their high sensitivity, fast response, and ability to convert mechanical energy to electrical signals for diverse applications in pressure sensing, wearable devices, and energy harvesting. This study investigates the design and performance analysis of two piezoelectric pressure sensors with rectangular and circular patches using COMSOL Multiphysics. Each sensor consists of a Polydimethylsiloxane (PDMS) substrate, a Polyvinylidene Fluoride (PVDF) piezoelectric layer, and aluminium electrodes on the top and bottom. Fixed boundary conditions are applied to secure the sensor's four edges, while a pressure range of 0–60 kPa is applied to its top surface using the boundary load feature in the solid mechanic interface. The electric potential interface within the electrostatic interface connects the sensor patches in series, grounding one patch's bottom electrode while terminating the other to the electric circuit interface to measure the output voltage across a 1 kΩ load. The rectangular patch sensor yields a maximum output voltage of 26 mV, while the circular patch sensor produces a higher output of 30 mV. Additionally, to enhance sensor output, the piezoelectric element is replaced with Zinc Oxide (ZnO). The sensor employing ZnO material generates a higher output voltage of 40 mV and 47 mV for the rectangular and circular patches, respectively, compared to the PVDF material. A comparative study reveals that the circular patch sensor outperforms the conventional rectangular one, offering enhanced output voltage and optimized geometry for superior performance, which makes it a better choice for pressure sensing applications.

Due to the fast progression of technology, the dependence on electronic and electrical devices like axial flux permanent magnet machines (AFPMMs) has increased greatly, making printed circuit boards (PCBs) crucial components in modern designs. PCBs, made up of numerous ICs connected by copper traces, are essential to current lightweight AFPMMs. This study systematically reviews the role of PCBs in the core design of AFPMMs for both low- and high-power applications, synthesizing research published between 2019 and 2024. Utilizing the PRISMA methodology, 38 articles indexed in IEEE Xplore and Web of Science were analyzed. This review explores advancements in PCB manufacturing, defect mitigation strategies, winding topologies, software tools, optimization algorithms, and the associated losses from varying winding configurations. A structured Boolean search strategy (“Printed Circuit Board” OR “PCB” AND “axial flux permanent magnet machine” OR “AFPM”) guided the literature retrieval process. Articles were meticulously screened using Rayyan software for titles, abstracts, and content, with duplicate removal performed via Mendeley software V2.120.0. The findings demonstrate substantial progress in the design of lightweight AFPMMs that incorporate PCB components, resulting in enhanced power quality and improved electromagnetic performance. Research activity over the past 6 years has shown inconsistent growth, with concentrated trapezoidal windings emerging as the dominant configuration, followed by distributed winding designs. These configurations were particularly applied in single-stator double-rotor (SSDR) coreless AFPM machines, characterized by minimal defects and associated losses and optimized single-layer winding designs utilizing tools such as ANSYS and COMSOL. Additionally, there has been growing interest in double-stator single-rotor (DSSR) and multi-disk configurations, reflecting the potential for alternative designs. These insights underscore the evolving role of PCBs in AFPMM development, highlighting opportunities for integrating advanced optimization methods and innovative configurations to address future technological demands.

This research paper investigates the feasibility of integrating photovoltaic batteries storage systems into a direct current (DC) microgrid for reliable electricity supply. The proposed system consists of a photovoltaic array, DC/DC boost converter, battery bank energy storage, bidirectional DC/DC converter and DC loads. First the elements of our system are modeled than the control of this elements is given.
A fuzzy logic-based control algorithm is used as a maximum power point tracking to optimize power extraction from the photovoltaic panels.The energy management system (EMS) operator regulates the power output of the photovoltaic array/battery bank by transmitting reference power signals to the input side regulation unit. These units, in turn, control the DC link voltage and the state of charge of the battery energy storage system. Depending on the energy management system proposed, our system works as an islanded microgrid and as a connected to an alternating current (AC) Grid.
The contribution of this paper is the use of fuzzy logic based MPPT this technique give better performance than the classical perturb and observe in terms of robustness, efficiency and response time.
The system is simulated using Matlab software; variable sunshine and temperature are taken to test the effectiveness of our system. The results simulation demonstrates the high performance of the fuzzy logic maximum power point tracking controller used and the effectiveness of the energy management system (EMS) proposed.

Vibrations are a physical effect to which buildings in cities are continuously subjected, and depending on the vibration frequency, they can be associated with a level of risk. In high-seismic-risk areas, buildings endure continuous vibrations that can produce small fractures in the building's structure and increase material damage from periodic and random seismic events. The early detection of structural damage allows for preventive decisions to minimize the risk of a potential collapse.

To provide a tool that helps detect the vibrations a building is subjected to and assess the associated risk from large vibrations, this work presents the development of a simple and practical system that remotely detects vibrations in a building. The system's development involves using vibration sensors and a signal capture system. Data processing and a simple prognosis are performed to evaluate the risk of possible fractures in the building's structure.

The preliminary results allow us to identify that most of the vibrations a building's structure endures are due to vehicular traffic around it, coupled with vibrations from surrounding constructions. The critical vibrations that increase the risk of fracture are due to natural earthquakes exceeding 3 degrees on the Gutenberg–Richter scale, detected by sensors with frequency and amplitude magnitude components. The implemented system offers a quick and effective alternative for detecting and quantifying vibrations in buildings and serves as an alert system for potential fracture risks in a building's structure.

Globally, women entail physical assault and harassment, which emphasizes the critical requirement for proactive and efficient safety measures. However, a lot of present technologies are inadequate and often lack comprehensive information protection, integration, and real-time responsiveness, which reduces user confidence and reliability. A complete integrated platform in the form of GUARDHER and GUARDIANSTEP has been designed to provide a safety system that serves women with precautionary measures. GUARDHER is a companion app, where the user has to register and create a secure wallet that contains their personal photo and other key information within a passkey protection. This application will be linked to GUARDIANSTEP, which consist of pair of shoes with the innovative feature of force sensor connected to a high-performance Microcontroller unit. Once the threshold pressure is applied by the user to the force sensor, it eventually results in transmission of the current location with their personal profile in the wallet to the control panel using Internet of Things (IoT) for immediate remedies. Within a delay time, the user has to deactivate/turn-off the emergency button via the application after entering the passkey of their respective wallet to prevent false alerts. The difference it holds from the current-day safety apps or devices, that it is a combination of personal profile protection, transfer of personal details along with their photo, real-time location tracking, and an advanced force sensor in footwear. It is a unique combo that offers user authentication, alerts in case of emergencies, delay time to turn it off and proactive safety measures that will build user confidence and responsive ability in extreme conditions. The “GUARDHER & GUARDIANSTEP” product offers a holistic solution by providing a secure platform and also ensures the integrating safety features in mobility, fostering a society where women can move freely with confidence and empowerment.