Search Results (34)

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud. Meanwhile, a Long Short-Term Memory (LSTM) model in the cloud analyzes time-series data for predictive failure analysis, enhancing maintenance scheduling and operational efficiency. The framework’s dynamic workload management algorithm optimizes task distribution between edge and cloud resources, balancing latency, bandwidth usage, and energy consumption. Experimental results show that the hybrid approach achieves a 35% reduction in latency, a 28% decrease in energy consumption, and a 60% reduction in bandwidth usage compared to cloud-only solutions. This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments. Full article

This study investigates cellular negative stiffness honeycomb (NSH) structures in the prospect of energy absorption and vibration isolation. The energy absorption properties for different geometrical parameters have been investigated for a specific displacement in the range of linear compression. The force–displacement characteristics of NSH structures with various AM materials have been investigated using the finite element analysis method. The results have revealed the parametric relation of the NSH with the energy absorbed, specific energy absorption, stiffness, and compressive stress. The analysis highlights the NSH structures’ potential to enhance material efficiency and performance optimization, contributing to material and manufacturing engineering. Full article

This research compares the performance of structural steel and general aluminum alloys in identical crank arm designs when bearing loads are applied at different stages of paddling, such as starting, climbing, and racing. Finite element analysis (FEA) was utilized to evaluate fatigue life and safety factors. A design modification strategy was proposed to reduce critical stress in failure zones, resulting in an increased fatigue life. Although steel and aluminum alloys both have significant life and nominal high fatigue life during racing and climbing, respectively, aluminum alloys are unable to withstand a 1815 N starting load, even after modification. Full article

Hybrid PVT solar systems offer an innovative approach that allows solar energy to be used to simultaneously generate thermal and electrical energy. It is still a challenge to develop an energy-efficient hybrid PVT system. The aim of this work is to develop a mathematical model, investigate the system’s performance based on parameters, include sensitivity analysis in the upper layer mainly photovoltaic part, and provide an efficient and innovative system. Performance analysis of the hybrid system is obtained by establishing a mathematical model and efficiency analysis. The electrical model and thermal model of the hybrid system is also obtained by appropriate and complete mathematical modeling. It establishes a good connection of the system in the context of electrical analysis and power generation. The parameters variation impact and sensitivity analysis of the most important parameters, namely, irradiance, ambient temperature, panel temperature, wind speed, and humidity in the PV panel section, are also obtained using a MATLAB model. The results show the effective increase or decrease in the electrical power and sensitiveness in the output of the system due to this modification. Related MPP values as a result of these parameters variation and their impact on the overall output of the hybrid PVT system are also analyzed. Full article

Building on previous studies on a mid- and late-twentieth-century recasting of Islam’s doctrine of monotheism, or tawhid, as a distinctly Islamic framework for liberative praxis, this article considers the interplay between the particular and the universal in the tawhidic paradigms of Iranian lay theologian Ali Shariati (1933–1977) and African-American pro-faith and pro-feminist theologian amina wadud (b. 1952). The article proposes that although it was developed in a distinctly Islamic register by means of Quranic exegesis and intrareligious conversations, the tawhidic paradigm has always been conversant with a range of non-Islamic liberative paradigms, and these conversations have been integral to the negotiation of a more inclusive concept of tawhid. To continue to recast tawhid in a more inclusive register, the article further argues, requires taking account of the non-Muslim ‘other’ as an equal moral agent in liberative struggles and embracing Islam’s theological and ideological ‘others’ as equally significant repositories of liberative potential. Full article

Transcranial doppler (TCD) ultrasound is a non-invasive imaging technique that can be used for continuous monitoring of blood flow in the brain through the major cerebral arteries by calculating the cerebral blood flow velocity (CBFV). Since the brain requires a consistent supply of blood to function properly and meet its metabolic demand, a change in CBVF can be an indication of neurological diseases. Depending on the severity of the disease, the symptoms may appear immediately or may appear weeks later. For the early detection of neurological diseases, a classification model is proposed in this study, with the ability to distinguish healthy subjects from critically ill subjects. The TCD ultrasound database used in this study contains signals from the middle cerebral artery (MCA) of 6 healthy subjects and 12 subjects with known neurocritical diseases. The classification model works based on the maximal blood flow velocity waveforms extracted from the TCD ultrasound. Since the signal quality of the recorded TCD ultrasound is highly dependent on the operator’s skillset, a noisy and corrupted signal can exist and can add biases to the classifier. Therefore, a deep learning classifier, trained on a curated and clean biomedical signal can reliably detect neurological diseases. For signal classification, this study proposes a Self-organized Operational Neural Network (Self-ONN)-based deep learning model Self-ResAttentioNet18, which achieves classification accuracy of 96.05% with precision, recall, f1 score, and specificity of 96.06%, 96.05%, 96.06%, and 96.09%, respectively. With an area under the ROC curve of 0.99, the model proves its feasibility to confidently classify middle cerebral artery (MCA) waveforms in near real-time. Full article

A crucial element of computer-assisted pronunciation training systems (CAPT) is the mispronunciation detection and diagnostic (MDD) technique. The provided transcriptions can act as a teacher when evaluating the pronunciation quality of finite speech. The preceding texts have been entirely employed by conventional approaches, such as forced alignment and extended recognition networks, for model development or for enhancing system performance. The incorporation of earlier texts into model training has recently been attempted using end-to-end (E2E)-based approaches, and preliminary results indicate efficacy. Attention-based end-to-end models have shown lower speech recognition performance because multi-pass left-to-right forward computation constrains their practical applicability in beam search. In addition, end-to-end neural approaches are typically data-hungry, and a lack of non-native training data will frequently impair their effectiveness in MDD. To solve this problem, we provide a unique MDD technique that uses non-autoregressive (NAR) end-to-end neural models to greatly reduce estimation time while maintaining accuracy levels similar to traditional E2E neural models. In contrast, NAR models can generate parallel token sequences by accepting parallel inputs instead of left-to-right forward computation. To further enhance the effectiveness of MDD, we develop and construct a pronunciation model superimposed on our approach’s NAR end-to-end models. To test the effectiveness of our strategy against some of the best end-to-end models, we use publicly accessible L2-ARCTIC and SpeechOcean English datasets for training and testing purposes where the proposed model shows the best results than other existing models. Full article

One of the most significant linchpins of the socioeconomic and livelihood milieu for rural farmers around the world is agroforestry. Several agroforestry practices are being employed by farmers in Bangladesh’s northern region, with Choi Jhal (Piper chaba)-based agroforestry being one of the most prevalent. Numerous researches have been conducted in different regions of Bangladesh to determine the potential livelihood for farmers who engage in diversified agroforestry, but hardly any comprehensive research has been carried out considering the aforementioned system as one of the most sustainable practices. To address this knowledge void, the present research was conducted in the Chinai union of Rajarhat Upazila in the Kurigram district of Bangladesh, surveying 105 Piper chaba farmers to assess the impact of this existing agroforestry system on their livelihood predicament. A mixed-method approach, including secondary data review, questionnaire survey, key informant interviews, focus group discussions and direct observations, were used for data collection and triangulation. To evaluate livelihoods and the problem severity, the Livelihood Improvement Index (LII) and the Problem Facing Index (FPI) were utilized, respectively. The findings demonstrate that the most suitable tree for Piper chaba cultivation is the betel nut (74.3%), and the majority (64.8%) of farmers have 41 to 90 Piper chaba plants. By strengthening farmers’ constant availability of food, fruit, timber, fodder, and fuelwood, this agroforestry system has markedly increased the sustainability of their livelihoods. This practice is thought to boost farmers’ livelihood capitals, with natural capital improving the most, while social capitals improve the least. However, eight major problems have been identified that farmers face while growing the crop and these must be remedied if different livelihood capitals are to be vastly improved. This research gives a full insight into the current Piper chaba production scenario and livelihood dynamics of local farmers, allowing some bold propositions to be formulated for further upgrading of their subsistence. Full article

This paper will explore ways in which dynamics of visibility/invisibility of American Muslim women activists are transformed in secular places like USA, while these women struggle surviving on the borderlands. Borderland and boundary are perceived as lived spaces that are culturally hybrid and are seen as a theatre for radical action. In this paper I contend that Muslim women activists in the USA operate from geographies of borderland and while inhabiting this hybrid third space they generate discourses of dissent that challenge stereotypes about them. Hailing from diverse backgrounds and countries, with different cultural roots yet same belief system and faith, American Muslim women activists adapt varied resistance strategies to challenge the Muslim patriarchy and the western hegemony that has persisted to portray Muslim women as an oppressed group of people in need of saving. Tracing Muslim women activists’ emotional and experiential geographies I will look at ways in which dynamics of solidarity between them have moved beyond dichotomous divisions of global-local, global North-global South, and empire-colony. With the discussion of lives and activism of Amina Wadud, Linda Sarsour and Asra Nomani, this paper will contextualize these activists within the spaces of resistance which they inhabit, while navigating their challenges in the context of geopolitical tensions and conflicts which are their lived realities in the USA. Full article

Fake news detection techniques are a topic of interest due to the vast abundance of fake news data accessible via social media. The present fake news detection system performs satisfactorily on well-balanced data. However, when the dataset is biased, these models perform poorly. Additionally, manual labeling of fake news data is time-consuming, though we have enough fake news traversing the internet. Thus, we introduce a text augmentation technique with a Bidirectional Encoder Representation of Transformers (BERT) language model to generate an augmented dataset composed of synthetic fake data. The proposed approach overcomes the issue of minority class and performs the classification with the AugFake-BERT model (trained with an augmented dataset). The proposed strategy is evaluated with twelve different state-of-the-art models. The proposed model outperforms the existing models with an accuracy of 92.45%. Moreover, accuracy, precision, recall, and f1-score performance metrics are utilized to evaluate the proposed strategy and demonstrate that a balanced dataset significantly affects classification performance. Full article

The chapter highlights the importance of lived realities to the hermeneutics of the Qur’an and questions the classical interpretation of the Qur’an, evidencing that the dominant and prolific model of centering analysis of the sacred text and religious practices around men and men’s experience. Discussing attitudes and specific Qur’an passages, neutral terminology in relation to creation and cosmology, the story of Lot/Lut, and themes such as the question of Shari‘ah, the paper offers a personal reflection on gender and Qur’anic or Islamic interpretative possibilities. The author also explains how she came to a theological perspective on the equality of gender and gender identity over the last two decades, and gives specific examples of a unique Qur’anic analysis. Full article

A significant increase has been observed in the use of Underwater Wireless Sensor Networks (UWSNs) over the last few decades. However, there exist several associated challenges with UWSNs, mainly due to the nodes’ mobility, increased propagation delay, limited bandwidth, packet duplication, void holes, and Doppler/multi-path effects. To address these challenges, we propose a protocol named “An Efficient Routing Protocol based on Master–Slave Architecture for Underwater Wireless Sensor Network (ERPMSA-UWSN)” that significantly contributes to optimizing energy consumption and data packet’s long-term survival. We adopt an innovative approach based on the master–slave architecture, which results in limiting the forwarders of the data packet by restricting the transmission through master nodes only. In this protocol, we suppress nodes from data packet reception except the master nodes. We perform extensive simulation and demonstrate that our proposed protocol is delay-tolerant and energy-efficient. We achieve an improvement of 13% on energy tax and 4.8% on Packet Delivery Ratio (PDR), over the state-of-the-art protocol. Full article

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency. Full article

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results. Full article

There will be a dearth of electrical energy in the prospective world due to exponential increase in electrical energy demand of rapidly growing world population. With the development of internet-of-things (IoT), more smart devices will be integrated into residential buildings in smart cities that actively participate in electricity market via demand response (DR) programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, an energy management strategy using price-based DR program is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Subsequently, we devised a strategy based on our proposed WBFA to systematically manage the power usage of IoT-enabled residential building smart appliances by scheduling to alleviate peak-to-average ratio (PAR), minimize cost of electricity, and maximize user comfort (UC). This increases effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR programs to combat the major problem of the DR programs, which is the limitation of consumer’s knowledge to respond upon receiving DR signals. To endorse productiveness and effectiveness of the proposed WBFA-based strategy, substantial simulations are carried out. Furthermore, the proposed WBFA-based strategy is compared with benchmark strategies including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm in terms of energy consumption, cost of electricity, PAR, and UC. Simulation results show that the proposed WBFA-based strategy outperforms the benchmark strategies in terms of performance metrics. Full article