Explore the vast range of titles available.

\"Between our launch of up to 100-gigabit speeds to Little Rock's business community last year, and today's announcement, our strong investment here continues,\" Comcast Regional Senior Vice President Doug Guthrie said in a news release. Comcast Business announced in November that it had completed a multimillion-dollar fiber optic network that spans greater Little Rock and is capable of delivering up to 100-gigabit speeds to business customers.

Cable service providers face significant challenges in managing Hybrid Fiber–Coaxial (HFC) networks due to the growing demand for high-speed services. Ensuring high service availability is critical to preventing customer attrition. This study employs machine learning techniques, specifically Decision Tree and Random Forest models, for proactive fault detection in HFC networks using data from the Simple Network Management Protocol (SNMP). Two operational scenarios were considered: a network-wide model and node-specific models. The dataset for fault detection exhibited a severe class imbalance, with outage events being extremely rare. To address this, the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic samples of the minority class to balance the dataset, was applied. This significantly improved recall and F1-scores—the harmonic mean of precision and recall—while maintaining high precision. The results demonstrate that these machine learning algorithms achieve up to 98% accuracy, and the SMOTE-enhanced models provide more reliable detection of connectivity faults. This approach is highly effective for cable operators in maintaining quality of service, enabling proactive management of problems and enhancement of network performance.

Objective To measure the effect of free access to the scientific literature on article downloads and citations.Design Randomised controlled trial.Setting 11 journals published by the American Physiological Society.Participants 1619 research articles and reviews.Main outcome measures Article readership (measured as downloads of full text, PDFs, and abstracts) and number of unique visitors (internet protocol addresses). Citations to articles were gathered from the Institute for Scientific Information after one year.Interventions Random assignment on online publication of articles published in 11 scientific journals to open access (treatment) or subscription access (control).Results Articles assigned to open access were associated with 89% more full text downloads (95% confidence interval 76% to 103%), 42% more PDF downloads (32% to 52%), and 23% more unique visitors (16% to 30%), but 24% fewer abstract downloads (−29% to −19%) than subscription access articles in the first six months after publication. Open access articles were no more likely to be cited than subscription access articles in the first year after publication. Fifty nine per cent of open access articles (146 of 247) were cited nine to 12 months after publication compared with 63% (859 of 1372) of subscription access articles. Logistic and negative binomial regression analysis of article citation counts confirmed no citation advantage for open access articles.Conclusions Open access publishing may reach more readers than subscription access publishing. No evidence was found of a citation advantage for open access articles in the first year after publication. The citation advantage from open access reported widely in the literature may be an artefact of other causes.

The need to determine which wireless IoT technology, currently available on the market, will be able to support a sizeable simultaneous load of information, types of electrical emergency alarms, required bandwidths, delays-latency, area coverage, and economic cost to serve areas of 600 square kilometers of surface and several users of around two million homes. This article compares Sigfox, LoRa, NB-IoT, and WiMAX technologies as possible solutions for last-mile communications. For this purpose, a network of hundreds of smart meters has been simulated, each transmitting different types of information. In conclusion, it has been obtained that, for a context with the priority of alarms and a large amount of information transport in massive last-mile communications in smart grids and for electrical metering traffic, it is highly recommended that it be done through WiMAX, which in the evaluation shows the best result compared to the other technologies.

Large telecom-service-provider networks are typically based on complex communications infrastructures comprising millions of network devices. The performance monitoring of such networks is a very demanding and challenging task. A large amount of data is collected and processed during performance monitoring to obtain information that gives insights into the current network performance. Using the obtained information, providers can efficiently detect, locate, and troubleshoot weak spots in the network and improve the overall network performance. Furthermore, the extracted information can be used for planning future network expansions and to support the determination of business-strategy decisions. However, traditional methods for processing and storing data are not applicable because of the enormous amount of collected data. Thus, big-data technologies must be used. In this paper, a big-data platform for the performance monitoring of telecom-service-provider networks is proposed. The proposed platform is capable of collecting, storing, and processing data from millions of devices. Typical challenges and problems in the development and deployment process of the platform, as well as the solutions to overcome them, are presented. The proposed platform is adjusted to HFC (Hybrid Fiber-Coaxial) network and currently operates in the real HFC network, comprising millions of users and devices.

DECLARACIÓN CONJUNTA LAUNDEX-REDALYC-CLACSO-IBICT SOBRE EL USO DE LA LICENCIA CC BY-NC-SA PARA GARANTIZAR LA PROTECCIÓN DE LA PRODUCCIÓN ACADÉMICA Y CIENTÍFICA EN ACCESO ABIERTO El Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (LATINDEX), la Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (REDALYC), El Consejo Latinoamericano de Ciencias Sociales (CLACSO) y el Instituto Brasileiro de Informagao em Ciencia e Tecnología (IBICT). Reconociendo que el obstáculo para el acceso al conocimiento internacional no es la tecnología, sino el poder pagar el acceso a las bases de datos comerciales, por lo cual se crearon consorcios en los países para enfrentar el continuo aumento de los precios, pero el costo y las restricciones al uso de los documentos han aumentado y ahora se suma el costo de pagar por publicar en acceso abierto (APC-article processing charges y BPC-book processing charges), y el hecho de que dichas bases comerciales se han convertido en la materia prima de la evaluación. Advirtiendo que los sistemas comerciales tienen todos los recursos cerrados y además están sumando recursos abiertos de forma totalmente legal, porque la licencia Creative Commons CC-BY les permite tomar, insertar, modificar, integrar, añadir Identificadores Digitales de Objecto (DOI), vender, revender, entre otras cosas; e inconformes porque las revistas de los grandes monopolios editoriales niegan el depósito en los repositorios nacionales e institucionales de la versión final del artículo (PDF diagramado) -el cual es financiado por las instituciones- y cuando permiten su depósito exigen una licencia no comercial.

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system.

Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.