Recommender systems have generated tremendous value for both users and businesses, drawing significant attention from academia and industry alike. However, due to practical constraints, academic research remains largely confined to offline dataset optimizations, lacking access to real user data and large-scale recommendation platforms. This limitation reduces practical relevance, slows technological progress, and hampers a full understanding of the key challenges in recommender systems. In this survey, we provide a systematic review of industrial recommender systems and contrast them with their academic counterparts. We highlight key differences in data scale, real-time requirements, and evaluation methodologies, and we summarize major real-world recommendation scenarios along with their associated challenges. We then examine how industry practitioners address these challenges in Transaction-Oriented Recommender Systems and Content-Oriented Recommender Systems, a new classification grounded in item characteristics and recommendation objectives. Finally, we outline promising research directions, including the often-overlooked role of user decision-making, the integration of economic and psychological theories, and concrete suggestions for advancing academic research. Our goal is to enhance academia's understanding of practical recommender systems, bridge the growing development gap, and foster stronger collaboration between industry and academia.
Wideband digital phased array radar offers the advantages of high range resolution, which improves the recognition ability for multiple targets, group targets, and high-speed targets. Traditional wideband phased arrays use true time delay to compensate for aperture fill time; however, the cost increases significantly. In this paper, a wideband elemental digital array architecture based on the stretch processing method is proposed. By utilizing the time-domain and frequency-domain translation equivalence of the LFM (Linear Frequency Modulation) signal waveform, the equivalent aperture fill time is compensated for through frequency shift and phase shift after stretch processing. Compared to traditional wideband digital arrays, this method can dramatically reduce the required sampling rate and lower the requirements on antenna hardware, thereby reducing the manufacturing cost of system. A comprehensive analysis of the signal processing process and stretch processing method is provided. And an antenna array prototype is developed to verify the T/R channel compensation and wideband beamforming. Measured results show that the antenna is capable of ±60° scanning in azimuth plane and ±40° scanning in elevation plane, with a bandwidth of 500 MHz in S-band. The results demonstrate excellent wideband beam performance and accurate lobe scanning, which confirms the validity of the proposed wideband architecture for stretch processing, frequency shift, and phase shift. This method can be widely applied to the low-cost design and wideband performance improvement of wideband digital array radar.
We report on the theoretical design and experimental verification of a high efficiency microwave wireless power transmission (MWPT) system operating in the Fresnel region. To achieve high conversion efficiency over a transmit distance of 11 meters, the transmit reflector antenna was optimized to locate the focal point at 11 m. The size of the receive antenna was decided by calculating the field distribution and the received power at different positions in the receive antenna aperture. Furthermore, an accurate model of the diode is presented, which was imported into the ADS software for high-precision rectifying circuit design. As a result, an overall DC-DC conversion efficiency of 20% was achieved, as measured in an anechoic chamber at a given distance of 11 m. The experimental results validated the proposed method.
Javier Penuela, Cecile Ben, Stepan Boldyrev
et al.
Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO$_2$ concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.
In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of masters programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 masters programs, with substantial growth since 2021. Over time masters have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore masters programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, quantum program enhancements, which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of masters programs as a route to the quantum industry.
Wesley Hanwen Deng, Solon Barocas, Jennifer Wortman Vaughan
Recent years have witnessed increasing calls for computing researchers to grapple with the societal impacts of their work. Tools such as impact assessments have gained prominence as a method to uncover potential impacts, and a number of publication venues now encourage authors to include an impact statement in their submissions. Despite this push, little is known about the way researchers assess, articulate, and address the potential negative societal impact of their work -- especially in industry settings, where research outcomes are often quickly integrated into products. In addition, while there are nascent efforts to support researchers in this task, there remains a dearth of empirically-informed tools and processes. Through interviews with 25 industry computing researchers across different companies and research areas, we first identify four key factors that influence how they grapple with (or choose not to grapple with) the societal impact of their research. To develop an effective impact assessment template tailored to industry computing researchers' needs, we conduct an iterative co-design process with these 25 industry researchers and an additional 16 researchers and practitioners with prior experience and expertise in reviewing and developing impact assessments or broad responsible computing practices. Through the co-design process, we develop 10 design considerations to facilitate the effective design, development, and adaptation of an impact assessment template for use in industry research settings and beyond, as well as our own ``Societal Impact Assessment'' template with concrete scaffolds. We explore the effectiveness of this template through a user study with 15 industry research interns, revealing both its strengths and limitations. Finally, we discuss the implications for future researchers and organizations seeking to foster more responsible research practices.
The accurate location of an unknown radio emitter (URE) is a critical task in wireless communication security. The URE localization method based on the received signal strength difference (RSSD) has become popular due to the identification of unknown transmitting power and frequency. However, high computational complexity and low positioning accuracy have been caused by the RSSD fingerprint data’s redundancy and cross-correlation. In this article, an indoor RSSD-based positioning algorithm combining principal component analysis (PCA) and Pearson correlation coefficient (PCC), called RSSD-PCA-PCC, is proposed to realize efficient feature extraction and reduce false fingerprint matching. Firstly, to achieve reduction and decorrelation, the principal components of the RSSD fingerprint database are extracted by the singular value decomposition (SVD) method. Secondly, the PCC is applied to measure the relative distance between the principal component features. In particular, the PCC is used for selecting the reference points (RPs) in order to match the position accurately. The results show that the proposed algorithm can obtain a more superior performance compared with the conventional RSSD-based weighted k-nearest neighbor algorithm (RSSD-WKNN) and COS matching algorithm (RSSD-PCA-COS) in the case of different selected RP numbers, AP numbers, and grid distances.
Ali Kaan Kurbanzade, Ansaar M. Baig, Sanjay Mehrotra
Unmanned aerial vehicles, commonly known as drones, have emerged as a disruptive technology with the potential to revolutionize operations across various industries. Drones are the fast-growing internet-of-things technology and are estimated to have a $100 billion market value in the next decade. Exploring drone operations through research has the potential to yield innovative academic insights and create significant practical effects in diverse industries, offering a competitive edge. Drawing insights from both academic and industry literature, this article describes how technological advancements in UAVs may disrupt traditional operational practices in different industries (e.g., commercial last-mile delivery, commercial pickup and delivery, telecommunication, insurance, healthcare, humanitarian, environmental, urban planning, homeland security), identifies the value of this evolving disruptive technology from sustainability and operational innovation perspectives, argues the significance of this area for operations management by conceptualizing a research agenda. The current state of the art focuses on the computing aspect of analytical models to tackle a variety of synthetic drone-related problems, with mixed integer optimization being the primary tool. There is a very significant research gap that should focus on drone operations management with industry know-how by partnering with actual stakeholders and using a variety of tools (i.e., econometrics, field experiments, game theory, optimal control, utility functions). This article aims to promote research on UAVs from operations management and industry-specific point of view.
Laurent Castanet, Valentin Le Mire, Julien Queyrel
et al.
Current spatial resolutions achieved by mesoscale weather forecast models allow them to be used to generate the state of the lowest layers of the atmosphere over areas as small as a few square kilometers which corresponds to the typical size of the tropospheric area crossed by Earth-space links. Furthermore, they allow the evolution of the troposphere to be predicted with a time stamp of five minutes instead of every hour with large-scale weather forecast models which makes them attractive for radio propagation predictions for satellite communication applications. This paper aims at studying the capability of the Weather Research and Forecast (WRF) model coupled with an electromagnetic physical model to reproduce rain attenuation statistics for Earth-space paths at Ka-band. To this purpose, one year of propagation measurements collected at 20 GHz in different places at midlatitudes in Toulouse and Salon de Provence (France), Spino d’Adda (Italy), Aveiro (Portugal), and Madrid (Spain), at high latitudes in Svalbard (Norway) and at low latitudes in Kourou are used to make comparisons between simulations and measurements. Comparisons between the simulated and the experimental annual statistics considered in this paper provide encouraging results, with a similar accuracy as Recommendation ITU-R P.618–13 for midlatitude European locations and with better accuracy for a high latitude area in Svalbard and for an equatorial location in French Guiana.
Seung Hwan Kim, Jeong hwan Jeon, Anwar Aridi
et al.
This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms' patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm's footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support for R&D activities, we further analyzed the role of government policy in supporting firms' knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms' technological capabilities into I4Ts.
Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.
The SEC teaching management platform provides a good support for school-enterprise cooperation. This paper conducts research on the SEC management information platform based on the B/S architecture. On the basis of the analysis of the relevant functional needs of the system, the overall functional framework of the system includes the college management platform, enterprise management platform, resource-sharing platform, and user login platform, then the B/S architecture is used to develop the SEC teaching management platform, and the test experiments of the SEC teaching management platform are carried out. The maximum number of concurrent tests shows that the maximum number of concurrencies of the platform is 989. Through the system response time test, the system response time is between 0.2 and 0.45, which meets the system response time requirements. In the CPU use test, the CPU share of the system was between 25% and 31%, meeting the needs of the system. From the above experimental results, the system is relatively high.
This paper studies a federated learning (FL) system, where \textit{multiple} FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising \emph{intra-service} resource allocation and \emph{inter-service} resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.
Fatima Khalid, Warda Saeed, Nosherwan Shoaib
et al.
This paper presents a quad-band, 3D mountable rectenna module for ambient energy harvesting. With the aim of powering up Internet of Things (IoT) nodes in practical ambient environments, a hybrid approach of combining power, both at RF and DC, is adopted using 98 MHz FM band, GSM900 (Global System for Mobile Communications), GSM1800, and Wi-Fi 2.4 GHz band. A dual polarized cross-dipole antenna featuring asymmetric slots as well as central ring structure enables multiband response and improved matching at the higher three frequency bands, whereas a loaded monopole wire antenna is used at the lower FM band. Four identical multiband antennas are placed in a 3D cubic arrangement that houses a 4-to-1 power combiner and matching circuits on the inside and the FM antenna on the top. In order to maintain stable rectenna output at varying input power levels and load resistances, a novel transmission line based matching network using closed form equations is proposed. Integrated in form of a 10 × 10 × 10 cm3 cube using standard FR4 substrate, the rectenna generates a peak output voltage of 2.38 V at −10 dBm input power. The RF to DC conversion efficiency is 70.28%, 41.7%, 33.37%, and 27.69% at 98 MHz, 0.9 GHz, 1.8 GHz, and 2.4 GHz, respectively, at −6 dBm. The rectenna also exhibits a measured conversion efficiency of 31.3% at −15 dBm for multitone inputs in ambient environment. The promising results in both indoor and outdoor settings are suitable to power low power IoT devices.
Improving learning efficiency is paramount for learning resource allocation with deep neural networks (DNNs) in wireless communications over highly dynamic environments. Incorporating domain knowledge into learning is a promising way of dealing with this issue, which is an emerging topic in the wireless community. In this article, we first briefly summarize two classes of approaches to using domain knowledge: introducing mathematical models or prior knowledge to deep learning. Then, we consider a kind of symmetric prior, permutation equivariance, which widely exists in wireless tasks. To explain how such a generic prior is harnessed to improve learning efficiency, we resort to ranking, which jointly sorts the input and output of a DNN. We use power allocation among subcarriers, probabilistic content caching, and interference coordination to illustrate the improvement of learning efficiency by exploiting the property. From the case study, we find that the required training samples to achieve given system performance decreases with the number of subcarriers or contents, owing to an interesting phenomenon: "sample hardening". Simulation results show that the training samples, the free parameters in DNNs and the training time can be reduced dramatically by harnessing the prior knowledge. The samples required to train a DNN after ranking can be reduced by $15 \sim 2,400$ folds to achieve the same system performance as the counterpart without using prior.
With the commercial launch of 5G technologies and fast pace of expansion of cellular network infrastructure, it is expected that cellular and mobile networks traffic will exponentially increase. In addition, new services are expected to spread widely, such as the Internet of Things connected to mobile networks. This will add additional burden in terms of traffic load. As a result, some studies suggest that mobile traffic may increase more than 1000 times compared to the amount of traffic that is generated nowadays. This means that network resources for mobile services must be managed and controlled in a smart way, because resources are always limited, but the demand for services and the need for keeping user equipment always connected to mobile networks can be considered unlimited, leaving gap between huge service demands and available resources. In order to narrow this gap, major consideration should be given to the management of network resources to avoid network congestion and performance degradation during peak hour/s and traffic spikes, and allow access to network services to more customers when demand is high. On the other hand, guaranteeing quality of service requirements for the wide range of new services is another challenge that must be met in 5G networks. In this paper we will review 5G networks characteristics and specifications, then carry out a survey on resource management and QoS provisioning to improve and manage resource utilization in 5G networks.
The plasma sheath is known as a popular topic of computational electromagnetics, and the plasma case is more resource-intensive than the non-plasma case. In this paper, a parallel shift-operator discontinuous Galerkin time-domain method using the MPI (Message Passing Interface) library is proposed to solve the large-scale plasma problems. To demonstrate our algorithm, a plasma sheath model of the high-speed blunt cone was established based on the results of the multiphysics software, and our algorithm was used to extract the radar cross-section (RCS) versus different incident angles of the model.
Besides the advantages derived from the ever present communication properties, it increases the attack surface of a network as well. As industrial protocols and systems were not designed with security in mind, spectacular attacks on industrial systems occurred over the last years. Most industrial communication protocols do not provide means to ensure authentication or encryption. This means attackers with access to a network can read and write information. Originally not meant to be connected to public networks, the use cases of Industry 4.0 require interconnectivity, often through insecure public networks. This lead to an increasing interest in information security products for industrial applications. In this work, the concept for holistic intrusion detection methods in an industrial context is presented. It is based on different works considering several aspects of industrial environments and their capabilities to identify intrusions as an anomaly in network or process data. These capabilities are based on preceding experiments on real and synthetic data. In order to justify the concept, an overview of potential and actual attack vectors and attacks on industrial systems is provided. It is shown that different aspects of industrial facilities, e.g. office IT, shop floor OT, firewalled connections to customers and partners are analysed as well as the different layers of the automation pyramid require different methods to detect attacks. Additionally, the singular steps of an attack on industrial applications are characterised. Finally, a resulting concept for integration of these methods is proposed, providing the means to detect the different stages of an attack by different means.