MgZn_(2)-type(Hf,Ta)Fe_(2),known for its negative thermal expansion during magnetic transition,is a key component in the production of zero thermal expansion *** paper presents a basic approach for designing such comp...
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MgZn_(2)-type(Hf,Ta)Fe_(2),known for its negative thermal expansion during magnetic transition,is a key component in the production of zero thermal expansion *** paper presents a basic approach for designing such composites by introducing an additional Laves phase through atomic ***,Co,Ni,A1 and V were chosen to substitute Fe in(Hf,Ta)Fe_(2).The addition of Co or Ni results in the creation of an extra MgCu_(2)(C15)phase in the MgZn_(2)(C14)*** C15phase exhibits positive thermal expansion,which effectively compensates for the negative thermal expansion of the C14 *** adjusting the amount of Co or Ni,zero thermal expansion can be achieved in a given temperature ***,the replacement of Fe by Al or V yields another C14 phase with a higher doping element content relative to the C14 *** two C14 phases possess different magnetic transition temperatures and negative thermal expansion temperature *** combination of the two C14 phases results in zero thermal expansion due to the effective thermal expansion compensation between *** results serve to identify potential approaches for designing(Hf,Ta)Fe_(2)-based zero thermal expansion composites.
Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of arti...
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In recent years, IoT has transformed personal environments by integrating diverse smart devices. This paper presents an advanced IoT architecture that optimizes network infrastructure, focusing on the adoption of MQTT...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elasti...
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Load forecasting plays a crucial role in mitigating risks for utilities by predicting future usage of commodity markets transmission or supplied by the utility. To achieve this, various techniques such as price elastic demand, climate and consumer response, load analysis, and sustainable energy generation predictive modelling are used. As both supply and demand fluctuate, and weather and power prices can rise significantly during peak periods, accurate load forecasting becomes critical for utilities. By providing brief demand forecasts, load forecasting can assist in estimating load flows and making decisions that prevent overloading. Therefore, load forecasting is crucial in helping electric utilities make informed decisions related to power, load switching, voltage regulation, switching, and infrastructure development. Forecasting is a methodology used by electricity companies to forecast the amount of electricity or power production needed to maintain constant supply as well as load demand balance. It is required for the electrical industry to function properly. The smart grid is a new system that enables electricity providers and customers to communicate in real-time. The precise energy consumption sequence of the consumers is required to enhance the demand schedule. This is where predicting the future comes into play. Forecasting future power system load (electricity consumption) is a critical task in providing intelligence to the power grid. Accurate forecasting allows utility companies to allocate resources and assume system control in order to balance the same demand and availability for electricity. In this article, a study on load forecasting algorithms based on deep learning, machine learning, hybrid methods, bio-inspired techniques, and other techniques is carried out. Many other algorithms based on load forecasting are discussed in this study. Different methods of load forecasting were compared using three performance indices: RMSE (Root Mean Square Err
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1...
Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.
Semitransparent organic photovoltaics(ST-OPVs)for building integration represent a pivotal direction in the development of photovoltaic ***-processed silver nanowires(AgNWs)are considered promising candidates for tran...
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Semitransparent organic photovoltaics(ST-OPVs)for building integration represent a pivotal direction in the development of photovoltaic ***-processed silver nanowires(AgNWs)are considered promising candidates for transparent electrodes in semitransparent devices due to their high transparency-conductivity-efficiency merit,large-scale processability,and low *** this work,we develop two solution-processed organic–inorganic hybrid electrodes,named AgNWs-PD and AgNWsPC,utilizing AgNWs as the conductive framework and aliphatic amine-functionalized perylene-diimide(PDINN)as the sandwiched material,while AgNWs-PC exhibits significantly improved electrical conductivity and enhanced contact area with the underlying electron transport *** optimized device achieves a power conversion efficiency of 9.45%with an open circuit voltage of 0.846 V,a high filling factor of 75.4%,and an average visible transmittance(AVT)of 44.0%,delivering an outstanding light utilization efficiency(LUE)of 4.16%,which is the highest reported value for all solution-processed *** addition,by coupling a 30-nm tellurium dioxide atop AgNWs-PC,the bifaciality factor of derivative devices improves from 73.7%to 99.4%,while maintaining a high bifacial LUE over 3.7%.Our results emphasize the superiority and effectiveness of PDINN-sandwiched AgNWs electrodes for highperformance and all solution-processed ST-OPVs.
Deep learning technology has extensive application in the classification and recognition of medical images. However, several challenges persist in such application, such as the need for acquiring large-scale labeled d...
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Low-light image enhancement is highly desirable for outdoor image processing and computer vision applications. Research conducted in recent years has shown that images taken in low-light conditions often pose two main...
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We introduce a novel BiMoeFormer based on the Transformer architecture for 3D human motion prediction. Previous approaches primarily focus on the relationships between body joints in human poses, while neglecting thei...
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