Hasil untuk "Electronic computers. Computer science"

F. P. García de Arquer, D. Talapin, V. Klimov et al.

Advances in colloidal quantum dots The confinement found in colloidal semiconductor quantum dots enables the design of materials with tunable properties. García de Arquer et al. review the recent advances in methods for synthesis and surface functionalization of quantum dots that enable fine tuning of their optical, chemical, and electrical properties. These important developments have driven the commercialization of display and lighting applications and provide promising developments in the related fields of lasing and sensing. Science, aaz8541, this issue p. eaaz8541 A Review highlights advances in the synthesis of colloidal quantum dots that have enabled numerous applications. BACKGROUND Semiconductor materials feature optical and electronic properties that can be engineered through their composition and crystal structure. The use of semiconductors such as silicon gallium arsenide sparked technologies from computers and mobile phones to lasers and satellites. Semiconductor quantum dots (QDs) offer an additional lever: Because their size is reduced to the nanometer scale in all three dimensions, the restricted electron motion leads to a discrete atom-like electronic structure and size-dependent energy levels. This enables the design of nanomaterials with widely tunable light absorption, bright emission of pure colors, control over electronic transport, and a wide tuning of chemical and physical functions because of their large surface-to-volume ratio. ADVANCES The bright and narrowband light emission of semiconductor QDs, tunable across the visible and near-infrared spectrum, is attractive to realize more efficient displays with purer colors. QDs are engineered compositionally and structurally to manipulate energy states and charge interactions, leading to optical gain and lasing, relevant to light emission across visible and infrared wavelengths and fiberoptic communication. Their tunable surface chemistry allows application as optical labels in bio-imaging, made possible by tethering QDs with proteins and antibodies. The manipulation of QD surfaces with capping molecules that have different chemical and physical functions can be tailored to program their assembly into semiconducting solids, increasing conductivity and enabling the transduction of photonic and chemical stimuli into electrical signals. Optoelectronic devices such as transistors and photodetectors lead to cameras sensitive to visible and infrared light. Highly crystalline QDs can be grown epitaxially on judiciously chosen substrates by using high-temperature and vacuum conditions, and their use has led to commercially viable high-performance lasers. The advent of colloidal QDs, which can be fabricated and processed in solution at mild conditions, enabled large-area manufacturing and widened the scope of QD application to markets such as consumer electronics and photovoltaics. OUTLOOK From a chemistry perspective, further advances in QD fabrication are needed to sustain and improve desired chemical and optoelectronic properties and to do so with high reproducibility. This entails the use of inexpensive synthesis methods and precursors that are able to retain laboratory-scale QD properties to market-relevant volumes. A better understanding of the yet-incomplete picture of QD surfaces, atomic arrangement, and metastable character is needed to drive further progress. From a regulatory perspective, added attention is needed to achieve high-quality materials that do not rely on heavy metals such as Cd, Pb, and Hg. The role of nanostructuring in toxicity and life cycle analysis for each application is increasingly important. From a materials and photophysics perspective, exciting opportunities remain in the understanding and harnessing of electrons in highly confined materials, bridging the gap between mature epitaxial QDs and still-up-and-coming colloidal QDs. The yet-imperfect quality of the latter—a price paid today in exchange for their ease of manufacture—remains a central challenge and must be addressed to achieve further-increased performance in devices. From a device perspective, colloidal QD manufacturing must advance to translate from laboratory-scale to large-area applications such as roll-to-roll and inkjet printing. Photocatalysis, in which light is used to drive chemical transformations, is an emerging field in which QDs are of interest. Quantum information technologies, which rely on the transduction of coherent light and electrons, bring new challenges and opportunities to exploit quantum confinement effects. Moving forward, opportunities remain in the design of QD-enabled new device architectures. Semiconductor quantum dot technologies. Quantum dots feature widely tunable and distinctive optical, electrical, chemical, and physical properties. They span energy harvesting, illumination, displays, cameras, sensors, communication and information technology, biology, and medicine, among others. These have been exploited to realize efficient lasers, displays, biotags, and solar harvesting devices available in the market and are emerging in photovoltaics, sensing, and quantum information. In quantum-confined semiconductor nanostructures, electrons exhibit distinctive behavior compared with that in bulk solids. This enables the design of materials with tunable chemical, physical, electrical, and optical properties. Zero-dimensional semiconductor quantum dots (QDs) offer strong light absorption and bright narrowband emission across the visible and infrared wavelengths and have been engineered to exhibit optical gain and lasing. These properties are of interest for imaging, solar energy harvesting, displays, and communications. Here, we offer an overview of advances in the synthesis and understanding of QD nanomaterials, with a focus on colloidal QDs, and discuss their prospects in technologies such as displays and lighting, lasers, sensing, electronics, solar energy conversion, photocatalysis, and quantum information.

Jie Xu, B. Glicksberg, Chang Su et al.

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.

J. Behler

I. Csiszár, J. Körner

Dae-Hyeong Kim, N. Lu, Rui Ma et al.

C. Macal, M. North

M. Wooldridge

Adam Back, Matt Corallo, Luke Dashjr et al.

M. Gordon, Michael W. Schmidt

Анастас К.В.

Современная научная деятельность характеризуется экспоненциальным ростом объема публикаций и данных, что создает значительные трудности в систематизации, анализе и интерпретации информации. В этих условиях технологии искусственного интеллекта (ИИ) становятся ключевым инструментом автоматизации процессов научного исследования. В статье рассматриваются современные подходы к применению методов машинного обучения, глубоких нейронных сетей и обработки естественного языка (NLP) для анализа научной литературы, выявления скрытых закономерностей, генерации гипотез и планирования экспериментальной работы. Особое внимание уделено практическим примерам применения ИИ в биоинформатике, химии, медицине, физике и компьютерных науках, а также анализу ограничений, связанных с интерпретируемостью моделей, надежностью выводов и соблюдением этических норм. Обсуждаются перспективы развития гибридных систем, обеспечивающих совместную работу человека и ИИ, и возможности повышения аналитических компетенций исследователей в условиях цифровизации науки.

Cheryl Akinyi Genga

Digital transformation is reshaping global operations by integrating technology into business, fundamentally changing how value is delivered. In Sub-Saharan Africa, this shift is altering work processes and job content, impacting the demand for skills and leading to the displacement of certain roles across all industries. Understanding the effects of digital technologies on the future of work in the region is essential for developing effective strategies. It is important to recognise how these changes will affect labour markets and workers' ability to transition to new opportunities. While technology can create new paths and improve access, it also exacerbates existing inequalities. This study aimed to explore the challenges shaping the future of work in Sub-Saharan Africa. A qualitative research approach and inductive thematic analysis were utilised for this study. The findings highlight that the major challenges affecting the future of work are digital skills, followed by Diversity, equity and inclusion- digital divide, gender inequality and discrimination and lack of DEI initiatives and finally, workforce- unemployment and inadequately skilled workforce. In conclusion, while the future of work in Africa presents significant challenges, it also offers great promise. Realising this potential depends on bold and proactive decisions by policymakers, educational institutions, and businesses. Strategic investments made today can empower the next generation of African workers, innovators, and entrepreneurs to thrive in an increasingly digital and competitive global economy.

Akbar Asgharzadeh-Bonab, Salar Ghamati, Farid Ahmadi et al.

Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. Wildfire is the leading cause of deforestation, where massive forest areas are annually lost due to the failure to identify and predict forest fires. Accordingly, early detection of wildfires is crucial to inform operational and firefighting teams to prevent fires from advancing. This study analyzes images taken by unmanned aerial vehicles for wildfire detection. For this purpose, the two-dimensional discrete wavelet transform was first performed on the images. Next, due to its superior ability, a convolutional neural network was utilized to extract deep features from wavelet transform sub-bands. Then, the features obtained from each sub-band were merged to create the final feature vector. Afterward, multidimensional scaling was employed to reduce the extracted non-useful features. Ultimately, the presence or absence of wildfire locations in the images was detected using proper classifiers. The proposed method reaches an accuracy and F1 score of 0.9684 and 0.9672, respectively, from the images of the FLAME dataset, indicating its efficiency in detecting the presence of wildfire locations. Thus, this method can significantly contribute to the on-time and prompt firefighting operations and prevent extensive damage to forests.

Mochammad Hannats Hanafi Ichsan, Cecilia Sik-Lanyi, Tibor Guzsvinecz

Abstract New technologies, such as Virtual Reality (VR) / Virtual Environment (VE), which focus on User Experience (UX) to provide more engaging and immersive experiences, can help people grow their skills. Technology advancement is also an essential component of VR development. However, the literature needs to contain more studies on using VR as an assistive tool for skill development. This study aims to explore the impact of VR technological advancements on skill development through UX design taxonomies using a Systematic Literature Review (SLR). Skill development classification was conducted based on social, emotional, and behavioral (SEB) aspects. The selected studies that met the eligibility selection criteria were examined and synthesized. The study’s findings highlight the necessity of technology development for VR technology to accomplish UX for skill development, allowing them to become more self-sufficient. This research can enrich researchers and VR developers, particularly software, hardware, and artificial intelligence (AI) experts. More research should be conducted on the long-term use of VR as an assistive device, particularly for those seeking skill improvement to improve their quality of life.

Norah Saleh Alghamdi, Mohammed Zakariah, Achyut Shankar et al.

Heart disease continues to be a prominent cause of death globally, emphasizing the critical requirement for precise prediction techniques and prompt therapies. This research presents a new method that utilizes the collective capabilities of autoencoder and DenseNet architectures to predict heart illness. Our study is based on the Heart Disease UCI Cleveland dataset, which includes 13 variables that cover clinical and demographic parameters such as age, sex, cholesterol levels, and exercise-induced angina. The dataset presents issues due to its varied attribute types, including category and numerical variables. Furthermore, our approach tackles these difficulties by utilizing a dense autoencoder model, which produced exceptional outcomes. The Model attained a mean accuracy of 99.67% on the Heart Disease UCI Cleveland dataset. Further testing showed it was resilient, with a test accuracy of 99.99%. In addition, the Model demonstrated outstanding macro precision, macro recall, and macro F1 score, with percentages of 99.98%, 99.97%, and 99.96%, respectively. In addition, our results indicate that combining autoencoder and DenseNet designs shows potential for predicting cardiac disease, with substantial enhancements in accuracy and performance metrics compared to current approaches. This methodology can improve clinical decision-making and patient outcomes in cardiovascular care by accurately finding and defining complex patterns within the data. Notwithstanding these encouraging outcomes, our investigation has constraints. The specific attributes of the dataset utilized may limit the applicability of our findings. Subsequent studies could examine the suitability of our method for various datasets and analyze supplementary variables that may improve forecast precision. Furthermore, it is necessary to conduct prospective validation studies to evaluate our strategy’s practical effectiveness in clinical environments.

Kristin Pullyblank, Nicole Krupa, Melissa Scribani et al.

BackgroundTelehealth has undergone widespread implementation since 2020 and is considered an invaluable tool to improve access to healthcare, particularly in rural areas. However, telehealth's applicability may be limited for certain populations including those who live in rural, medically underserved communities. While broadband access is a recognized barrier, other important factors including age and education influence a person's ability or preference to engage with telehealth via video telehealth or a patient portal. It remains unclear the degree to which these digital technologies lead to disparities in access to care.PurposeThe purpose of this analysis is to determine if access to healthcare differs for telehealth users compared with non-users.MethodsUsing electronic health record data, we evaluated differences in “time to appointment” and “no-show rates” between telehealth users and non-users within an integrated healthcare network between August 2021 and January 2022. We limited analysis to patient visits in endocrinology or outpatient behavioral health departments. We analyzed new patients and established patients separately.ResultsTelehealth visits were associated with shorter time to appointment for new and established patients in endocrinology and established patients in behavioral health, as well as with lower no-show rates for established patients in both departments.ConclusionsThe findings suggest that those who are unwilling or unable to engage with telehealth may have more difficulty accessing timely care.

Xiangdong Liu, Yan Bai, Cunhui Yu et al.

The sparrow search algorithm (SSA) is a metaheuristic algorithm developed based on the foraging and anti-predatory behavior of sparrow populations. Compared with other metaheuristic algorithms, SSA also suffers from poor population diversity, has weak global comprehensive search ability, and easily falls into local optimality. To address the problems whereby the sparrow search algorithm tends to fall into local optimum and the population diversity decreases in the later stage of the search, an improved sparrow search algorithm (PGL-SSA) based on piecewise chaotic mapping, Gaussian difference variation, and linear differential decreasing inertia weight fusion is proposed. Firstly, we analyze the improvement of six chaotic mappings on the overall performance of the sparrow search algorithm, and we finally determine the initialization of the population by piecewise chaotic mapping to increase the initial population richness and improve the initial solution quality. Secondly, we introduce Gaussian difference variation in the process of individual iterative update and use Gaussian difference variation to perturb the individuals to generate a diversity of individuals so that the algorithm can converge quickly and avoid falling into localization. Finally, linear differential decreasing inertia weights are introduced globally to adjust the weights so that the algorithm can fully traverse the solution space with larger weights in the first iteration to avoid falling into local optimum, and we enhance the local search ability with smaller weights in the later iteration to improve the search accuracy of the optimal solution. The results show that the proposed algorithm has a faster convergence speed and higher search accuracy than the comparison algorithm, the global search capability is significantly enhanced, and it is easier to jump out of the local optimum. The improved algorithm is also applied to the Heating, Ventilation and Air Conditioning (HVAC) system control optimization direction, and the improved algorithm is used to optimize the parameters of the HVAC system Proportion Integral Differential (PID) controller. The results show that the PID controller optimized by the improved algorithm has higher control accuracy and system stability, which verifies the feasibility of the improved algorithm in practical engineering applications.

Pięta Piotr, Szmuc Tomasz

Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.

XI Runping, XUE Shaohui

The performance evaluation of existing moving target tracking algorithms has many drawbacks,such as massive amount of data,redundant tests and insufficient consideration on algorithm performance under multifactor situation.Therefore,this paper proposes a performance evaluation method for moving target tracking algorithm based on orthogonal test.After a full analysis on the factors and levels that affect algorithm performance,the dataset of orthogonal test is built and then used for algorithm performance test.The data results are analyzed by the range analysis method,so as to obtain the relationship between the factors that affect the algorithm,as well as the combination of factor levels when the algorithm performance is good.Experimental results show that the proposed method can evaluate the performance of the moving target tracking algorithm in a comprehensive and effective way.Besides,this method can reduce the number of tests and data volume,and provide reference for the performance evaluation of other image processing algorithms.

Hao Ce, Wang Hongbin, Cheng Xiaoyan et al.

A continuous finite-time robust control method for the trajectory tracking control of a nonholonomic wheeled mobile robot (NWMR) is presented in this paper. The proposed approach is composed of conventional sliding-mode control (SMC) in the internal loop and modified switched second-order sliding-mode (S-SOSM) control in the external loop. Sliding-mode controller is equivalently represented as stabilization of the nominal system without uncertainties. An S-SOSM control algorithm is employed to counteract the impact of state-dependent unmodeled dynamics and time-varying external disturbances, and the unexpected chattering has been attenuated significantly. Particularly, state-space partitioning is constructed to obtain the bounds of uncertainty terms and accomplish different control objectives under different requirements. Simulation and experiment results are used to demonstrate the effectiveness and applicability of the proposed approach.