User Side Research Articles

Experimental investigation of thermal performance of ground source heat pump system for summer and monsoon seasons of Himalayan region of India: A case study

In the present experimental research work examines the thermal interactions of borehole heat exchangers on the ground side and psychrometric process involved inside the room during cooling mode operation using a ground source heat pump system (GSHP). An experimental facility of 17.5 kW cooling capacity GSHP system, consisting of five parallelly connected double U-tube borehole heat exchangers fitter with a commercially available heat pump system has been employed. The experiments were performed during peak summer and monsoon (rainy) seasons for space cooling applications for 45 hours of continuous operation in a city located in the Himalayan range of India. Analysis of results obtained indicate that during the summer season the hourly heat rejected to the ground is maximum for peak weather conditions due to high cooling load demand on the user side. On increasing the building load (increase in ambient temperature), there is a slight decrease in COPsys in both the seasons. During heavy rain season, the latent cooling load increases up to 1.5 times of the sensible cooling load. The average value of sensible heat factor is found to be 0.6 for the case of the peak summer season and 0.51 for the rainy season.

Research on the radioactive safety of industrial steam supplied by PWR nuclear power plant

The heating source of the nuclear power steam supply installation is PWR secondary steam, but the secondary steam is potentially radioactive, and the radioactive material may migrate to the industrial steam, which goes through the leakage of the heat exchanger. Compared with the conventional industrial steam, the steam supplied by the nuclear power has potential radioactivity. Nuclear power is first introduced to produce the industrial steam, in order to analyze the specific activity and radiation effects of the industrial steam on the staffs at the user side, an analytical model of industrial steam radioactivity is developed based on the technological process of industrial steam under operating conditions, and then the exposure pathways of the staff at the user side from the industrial steam is analyzed. The specific activity of the industrial steam and the exposure dose of the staff is evaluated. According to the evaluation results, it can be seen that the dose to staff caused by the industrial steam is far smaller than the radiation impact on the public caused by the nuclear power plant, and can meet the requirements of relevant regulations and standards. Therefore from the radiation safety point of view, the industrial steam supplied by the nuclear power steam supply installation is safe and acceptable for industrial use.

Optimal operation of multi-energy microgrids with shared energy storage clusters by master-slave games

Abstract As shared energy storage power plants have become a research hotspot, the cooperation between them and microgrids has attracted attention. In this paper, we propose a master-slave game optimization operation strategy for multi-energy microgrids, taking into account shared energy storage (ESS) clusters. Firstly, the inclusion of ESS clusters is considered in a combined heat and power (CHP) type multi-energy microgrid. Then, the optimal operation model is established and solved in two layers. The master-slave game model in the context of ESS clusters is proposed by analyzing the game relationship between the microgrid side and the user side, the arithmetic simulation with MATLAB, and the modeling and solving are carried out by the Yalmip tool and the CPLEX solver, and the combination of heuristic algorithms and the solver is used to optimize the strategies of the microgrid side and the user side. The optimization of the system after the optimization of this paper’s methodology can enhance the system economy.

Nurses’ Experiences with the Use of Electronic Nursing Record: A Qualitative Study

Background: The electronic nursing record has contributed to improving nursing practice and ensuring nursing quality, but it is still incomplete in supporting nursing workflow. The purpose of this study is to explore the experience of using the electronic nursing record by nurses working in frontline care for trauma, emergency, and infectious disease-exposed patients.Methods: The study employed a qualitative descriptive design and qualitative content analysis. In-depth interviews were conducted with 10 nurses working in emergency, trauma, and critical care units in the tertiary hospital.Results: The nurses’ experiences with the use of the electronic nursing record emerged into three subjects (improvements to electronic nursing record implementation, benefits of electronic nursing record implementation, electronic nursing record user factors), five categories (information finding and sharing function, information input function, facilitating efficient communication, ensuring record quality, need for education), and eight subcategories.Conclusions: Electronic nursing record supports the visualization of special information such as emergency and infection, and has the advantage of facilitating communication between medical staff. Both support better clinical decision-making. However, system instability and low nursing friendliness delay nursing work in the process of inputting, confirming, and sharing information. Copy-andpaste practices, poor quality of records, and insufficient electronic nursing record education were identified as factors to be changed on the user side. Improvement of nursing-friendly interfaces, optimization of nursing workflows, and participation of nurses in the improvement process are necessary. We should also focus on developing electronic nursing record education programs.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Meta concept recommendation based on knowledge graph

Massive Open Online Courses (MOOCs) are playing a key role in improving educational ways. Abundant learning resources make it difficult for online users to find suitable learning content. The current personalized service in the field of online education relies more on course recommendations. However, coarse-grained recommendations cannot help users discover the defects of their knowledge network effectively. In this paper, we propose a Knowledge-Aware Meta-Concept (KAMC) framework to provide fine-grained recommendation services. We innovatively incorporate Knowledge Graph (KG) into the field of educational recommendation to provide abundant auxiliary information. However, simply combining knowledge graphs with educational recommender systems cannot improve the performance of existing recommendation models, and may even weaken the performance of the models. Because the modeled KG ignores the enhancements on the user side and only considers the enhancements on the item side. We further propose to enrich the semantic representation of users with collaborative information in user-item interactions, and at the same time enrich the semantic representation of items with information in KG. Furthermore, to provide users with more accurate and fine-grained personalized recommendation services, we propose a user-based attention mechanism to capture users’ fine-grained semantic information. Our method is experimentally validated on three real-world datasets. In the three datasets, KAMC’s AUC evaluation index is 6.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.2\\%$$\\end{document}, 6.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.9\\%$$\\end{document}, and 2.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.2\\%$$\\end{document} higher than the latest baseline method (KGAN), respectively. Experimental results show that the KAMC method outperforms the current state-of-the-art baseline methods.

Performance Analysis of Internet-based DGPS and Commercial Satellite-based Augmentation System: A Case Study in Peninsular Malaysia

Global Navigation Satellite Systems (GNSS) has become an essential component in modern times for positioning, navigation, and timing. A fast-growing economic region in Malaysia required a GNSS-based augmentation positioning and navigation service. To improve navigation solution, several augmentation techniques exist, such as a differential Global Positioning System (DGPS) assisted by reference station, or augmentation service provided by commercial communication satellite. The DGPS correction is applied through a real-time communication medium and can be received at the user side by several communication methods including internet-based, and this method is favourable for land and near-coast areas. This case study aims to investigate the reliability of internet-based DGPS and SBAS along the Peninsular Malaysia. A test was conducted and data were collected over 15 hours at a rate of 1 Hz from the available GNSS satellite using a geodetic-grade receiver mounted on a moving vehicle. The obtained results showed that DGPS and SBAS perform better than the navigation solution with an accuracy of 1.536 m and 0.955 m respectively, compared to the navigation solution with an accuracy of 3.159 m. The limitations of both augmentation techniques were also analysed and discussed in this study.

Drivers of the use of digital platforms by Portuguese parishes – how small government entities struggle with the demand for transparency

PurposeThe provision of online information through the use of digital platforms (websites and social networks) can be helpful for public sector organisations to promote transparency and accountability, thereby bringing it closer to citizens, more accessible and with higher added value for society. Whereas the call for government transparency is manifest and seems to be equal for all parts of government, this paper investigates whether small government entities are able to cope with this demand. In addition, the drivers of digital platforms are investigated in the context of Portuguese parishes.Design/methodology/approachThe paper follows a quantitative research approach based on an original data set with more than 3,000 observations collected in two different time periods (2015 and 2020), which looks at the parishes’ use of website and Facebook.FindingsFindings show that about half of Portuguese parishes, which are mostly small entities with on average fewer than 3,500 inhabitants, have an institutional website, whereas almost three out of four have Facebook. The empirical analyses reveal that a lack of organisational capacity of small entities can be an obstacle for the use of an institutional website and of Facebook. Furthermore, factors from the provider side (e.g. being part of a merger, participation in previous elections) and the user side (e.g. having access to broadband internet) are also found to have an influence on the adoption, use, and dropping of a digital platform.Originality/valueThe use of an original data set with more than 3,000 observations, and the research of the influence of size as a proxy of organisational capacity on the presence of parishes on the internet and Facebook, are innovative elements of this paper. In addition, this study adds to the knowledge about the influence of organisational, political, and socio-demographic characteristics on the use of digital platforms by small government entities.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0171

Optoelectronics Interfaces for a VLC System for UHD Audio-Visual Content Transmission in a Passenger Van: HW Design.

This work aims to provide the hardware (HW) design of the optoelectronics interfaces for a visible-light communication (VLC) system that can be employed for several use cases. Potential applications include the transmission of ultra-high-definition (UHD) streaming video through existing reading lamps installed in passenger vans. In this use case, visible light is employed for the downlink, while infrared light is used for the uplink channel, acting as a remote controller. Two primary components -a Light Fidelity (LiFi) router and a USB dongle-were designed and implemented. The 'LiFi Router', handling the downlink channel, comprises components such as a visible Light-Emitting Diode (LED) and an infrared receiver. Operating at a supply voltage of 12 V and consuming current at 920 mA, it is compatible with standard voltage buses found in transport vehicles. The 'USB dongle', responsible for the uplink, incorporates an infrared LED and a receiver optimized for visible light. The USB dongle works at a supply voltage of 5 V and shows a current consumption of 1.12 A, making it well suited for direct connection to a universal serial bus (USB) port. The bandwidth achieved for the downlink is 11.66 MHz, while the uplink's bandwidth is 12.27 MHz. A system competent at streaming UHD video with the feature of being single-input multiple-output (SIMO) was successfully implemented via the custom hardware design of the optical transceivers and optoelectronics interfaces. To ensure the system's correct performance at a distance of 110 cm, the minimum signal-to-noise ratio (SNRmin) for both optical links was maintained at 10.74 dB. We conducted a proof-of-concept test of the VLC system in a passenger van and verified its optimal operation, effectively illustrating its performance in a real operating environment. Exemplifying potential implementations possible with the hardware system designed in this work, a bit rate of 15.2 Mbps was reached with On-Off Keying (OOK), and 11.25 Mbps was obtained with Quadrature Phase Shift Keying (QPSK) using Orthogonal Frequency-Division Multiplexing (OFDM) obtaining a bit-error rate (BER) of 3.3259 × 10-5 in a passenger van at a distance of 72.5 cm between the LiFi router and the USB dongle. As a final addition, a solar panel was installed on the passenger van's roof to power the user's laptop and the USB dongle via a power bank battery. It took 13.4 h to charge the battery, yielding a battery life of 22.3 h. This characteristic renders the user's side of the system entirely self-powered.

Modeling and operational characteristics analysis of a dual-source synergetic heat pump based on air energy and biomass waste heat

Air source heat pumps have gradually become a widely used heating solution worldwide due to their advantages of high efficiency, energy conservation, and environmental protection. However, air source heat pumps also face some challenges in practical applications, such as unstable heating and performance degradation in low-temperature environments. In order to improve these problems, based on the cascade heat technology, a new type of dual-source synergetic heat pump system is designed using air energy and biomass waste heat dual heat sources. In order to study its performance, a simulation model of the heat pump system was first constructed using distributed-parameter method. In order to verify the effectiveness of the model, an 18-kW heat pump experimental unit was constructed. By comparing the three key parameters of user side outlet water temperature, high-temperature compressor power, and system coefficient of performance (COP), the results show that the maximum error does not exceed 11 %; Secondly, in order to improve the stability and operational efficiency of the system, a three-level control strategy was designed based on considering the coordination of different grade heat sources; Finally, based on the above, the operating characteristics of the heat pump system were investigated through testing under different external input conditions. The research results indicate that by utilizing cascade heating and parallel heat exchange technology, the heat pump system can achieve stable heating even under large fluctuations in external working conditions. At the same time, compared with air source heat pumps, the COP can be improved by 69.7 % in low-temperature environments. The system results not only provide reference for the design and operation of new heat pump systems, but also provide practical and feasible solutions for the problem of decreased heat pump performance in low-temperature environments.

Series arc‐fault diagnosis using convolutional neural network via generalized S‐transform and power spectral density

AbstractIt is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

A training-free non-intrusive air conditioning load monitoring method based on fuzzy comprehensive evaluation

Air conditioner (AC) is a grid friendly load, monitoring its operating status plays a key role in evaluating user side potential for participation in demand response. With continuous innovations in AC technology, the AC operation characteristics have shown increasing complexity, which poses significant challenges for non-intrusive AC load monitoring. Furthermore, obtaining labeled training data for target scenarios is often challenging, which hinders deployment of many Non-Intrusive Load Monitoring (NILM) methods in practice. In this regard, this article proposes a training-free non-intrusive AC load monitoring method based on fuzzy comprehensive evaluation (FCE), which is very easy to deploy and has good robustness. Firstly, considering various influence factors, a FCE model is constructed based on common knowledge about AC. This model simulates the manual labeling process to locate AC operation periods. Secondly, an AC power estimation method based on weighted filtering is established, in which weighted filter is used to eliminate the non-AC load components that may overlap during AC operation periods. Specifically, an optimization model is constructed to make the cumulative power of the AC events in its working cycle approach zero, with the filter weight vector as the objective variable, in which a heuristic greedy algorithm is used to find the optimal filter weight vector. When multiple feasible solutions with similar objective function values are derived in the solving process, the probability of load events appearing in AC and non-AC operation periods are compared. The final solution is selected, taking into account the likelihood of each feasible solution corresponding to a load event sequence belonging to AC load. The testing results on the PecanStreet dataset and multiple real-world measured datasets prove the effectiveness of the proposed method in monitoring complex ACs with poor waveform consistency.

Lightweight ring-neighbor-based user authentication and group-key agreement for internet of drones

As mobile internet and Internet of Things technologies continue to advance, the application scenarios of peer-to-peer Internet of Drones (IoD) are becoming increasingly diverse. However, the development of IoD also faces significant challenges, such as security, privacy protection, and limited computing power, which require technological innovation to overcome. For group secure communication, it is necessary to provide two basic services, user authentication and group key agreement. Due to the limited storage of IoD devices, group key negotiation requires lightweight calculations, and conventional schemes cannot satisfy the requirements of group communication in the IoD. To this end, a new lightweight communication scheme based on ring neighbors is presented in this paper for IoD, which not only realizes the identity verification of user and group key negotiation, but also improves computational efficiency on each group member side. A detailed security analysis substantiates that the designed scheme is capable of withstanding attacks from both internal and external adversaries while satisfying all defined security requirements. More importantly, in our proposal, the computational cost on the user side remains unaffected by the variability of the number of members participating in group communication, as members communicate in a non-interactive manner through broadcasting. As a result, the protocol proposed in this article demonstrates lower computational and communication costs in comparison to other cryptographic schemes. Hence, this proposal presents a more appealing approach to lightweight group key agreement protocol with user authentication for application in the IoD.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

DataLoom: Simplifying Data Loading with LLMs

Schema discovery and data loading is a crucial step in any data analysis pipeline. While this used to be a rare task, in the highly dynamic field of machine learning and modern business intelligence on top of data lakes, today it has become a frequent, but often underestimated, activity. Existing tools often focus on single files, presume prior knowledge of the data on the user's side or a significant amount of manual labor. In this paper, we improve the process of mapping a "chaotic" set of files to an initial database schema that can then be iteratively refined and loaded. The idea is to take the previously tedious parts of this process and automate them through the use of Large Language Models (LLMs) while leaving already well-understood problems such as constraint discovery to existing algorithms. We thus carefully orchestrate the use of LLMs for the "soft" problems and traditional algorithms for the "hard" problems. This creates a more seamless schema discovery and data loading experience that minimizes the time to first insight for users. We show this vision on modern schema discovery and data loading in our web-based prototype called DataLoom that serves as our demonstration.

Optimized scheduling of integrated energy systems for low carbon economy considering carbon transaction costs

With the introduction of the “dual carbon” goal and the continuous promotion of low-carbon development, the integrated energy system (IES) has gradually become an effective way to save energy and reduce emissions. This study proposes a low-carbon economic optimization scheduling model for an IES that considers carbon trading costs. With the goal of minimizing the total operating cost of the IES and considering the transferable and curtailable characteristics of the electric and thermal flexible loads, an optimal scheduling model of the IES that considers the cost of carbon trading and flexible loads on the user side was established. The role of flexible loads in improving the economy of an energy system was investigated using examples, and the rationality and effectiveness of the study were verified through a comparative analysis of different scenarios. The results showed that the total cost of the system in different scenarios was reduced by 18.04%, 9.1%, 3.35%, and 7.03%, respectively, whereas the total carbon emissions of the system were reduced by 65.28%, 20.63%, 3.85%, and 18.03%, respectively, when the carbon trading cost and demand-side flexible electric and thermal load responses were considered simultaneously. Flexible electrical and thermal loads did not have the same impact on the system performance. In the analyzed case, the total cost and carbon emissions of the system when only the flexible electrical load response was considered were lower than those when only the flexible thermal load response was taken into account. Photovoltaics have an excess of carbon trading credits and can profit from selling them, whereas other devices have an excess of carbon trading and need to buy carbon credits.

Credible calculation method for regional corrected carbon emission factors taking into account the nearby consumption of renewable energy

Abstract The carbon emission factor of the power grid is a key data indicator for measuring the carbon emission level on the user side. Currently, the carbon emission factor of the power grid is not sufficient to accurately express the regional carbon emission level, and especially cannot accurately reflect the regional new energy consumption level, which affects user confidence in emission reduction and the accuracy of carbon emission quota issuance. Therefore, this article utilizes blockchain technology and carbon emission flow theory to realize the impact of user-side self-generated and self-used photovoltaics (PV) on carbon emission factors. We have constructed a residual power grid correction model, designed a blockchain intelligent terminal module and functional requirements, ensured the correctness of the model and results from the source, solved the problem of data tampering and insufficient data credibility generated by terminal devices at the source, and ultimately helped achieve the dual carbon goal.

Study on phase selection control characteristics of fast vacuum circuit breakers suitable for new power systems

Abstract With a high proportion of new energy access to the power system, the user side of the randomness and volatility of distributed photovoltaic large-capacity access to the 10 kV medium-voltage distribution grid operation results in more frequent fluctuations in the system voltage and more frequent switching of reactive compensation of the shunt capacitor bank on the 10 kV side of the conventional substation. Conventional reactive compensation switching circuit breakers adopt a spring mechanism whose mechanical stability is insufficient and the selection of the relevant fit capacitor bank accuracy is not enough. The 12 kV fast vacuum circuit breaker of small mechanical dispersion for selection related to the combination was proposed. First of all, based on the neutral grounded and ungrounded system of the capacitor bank, the strategy of capacitor bank phase selection closing was developed. For the action characteristics of the phase selection of the 12 kV fast vacuum circuit breaker, the variation laws of mechanical dispersion and the influence factors such as ambient temperature, capacitance voltage, capacitance degradation, mechanical wear, and conductive rod deformation were obtained by simulation and experiment. The results showed that the ambient temperature and capacitance voltage had a great influence on the switching time of the fast circuit breaker, while other factors had a small influence. Again, the pre-breakdown characteristics of the 12 kV fast vacuum circuit breaker were obtained by experiments. The results showed that the influence of contact opening distance and pre-breakdown voltage was a linear relationship, and the pre-breakdown slope was 30 kV/mm. According to the pre-breakdown closing characteristic curve, the pre-breakdown time of the fast circuit breaker was 0.45 ms at most. The feedforward compensation mechanism based on a neural network was proposed to compensate for the action characteristics of the fast circuit breaker and aimed to improve the phase selection accuracy. Finally, two rounds of capacitive current switching tests of 12 kV fast vacuum circuit breakers with C2 level were carried out in a third-party test station. The accuracy of phase closing is within the ±0.5 ms closing window, effectively preventing the occurrence of closing inrush current.

Entities recommendations using contextual information

Generating entities recommendations has attracted considerable interest in recent years. Most recently published works mainly focus on providing a user with the most relevant and/or personalized entity recommendations that score highly against the query and/or the user’s preference. Some works consider user side information, such as the user network, user relations, and user’s demographic information, and propose to integrate them into the framework of recommender systems. These approaches have been shown to increase the users’ satisfaction and engagement with the system. In this paper, we investigate entities recommender systems and summarize the recent efforts in this domain by categorizing approaches. The first category presents different approaches that utilize knowledge graph as side information. The second category gathers work that consider both the current query, and the users’ previous interactions with the system. These latter works have considered the full user history to personalize the ranking of recommended entities related to the query. In this review paper, we emphasize contextual information-based approaches that utilize user’s context and feedback to improve the recommendations. We accomplished a summary of the literature and synthesized the papers according to different perceptions. Finally, a comparison between approaches is provided and some drawbacks are identified.

Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.