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Revenue maximization (RM) is one of the most important problems on online social networks (OSNs), which attempts to find a small subset of users in OSNs that makes the expected revenue maximized. It has been researched intensively before. However, most of exsiting literatures were based on non-adaptive seeding strategy and on simple information diffusion model, such as IC/LT-model. It considered the single influenced user as a measurement unit to quantify the revenue. Until Collaborate Game model appeared, it considered activity as a basic object to compute the revenue. An activity initiated by a user can only influence those users whose distance are within k-hop from the initiator. Based on that, we adopt adaptive seed strategy and formulate the Revenue Maximization under the Size Budget (RMSB) problem. If taking into account the product's promotion, we extend RMSB to the Revenue Maximization under the Community Budget (RMCB) problem, where the influence can be distributed over the whole network. The objective function of RMSB and RMCB is adatpive monotone and not adaptive submodular, but in some special cases, it is adaptive submodular. We study the RMSB and RMCB problem under both the speical submodular cases and general non-submodular cases, and propose RMSBSolver and RMCBSolver to solve them with strong theoretical guarantees, respectively. Especially, we give a data-dependent approximation ratio for RMSB problem under the general non-submodular cases. Finally, we evaluate our proposed algorithms by conducting experiments on real datasets, and show the effectiveness and accuracy of our solutions.

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large.

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for sometime and then transfer to another one, MHRS helps in attaining 30\% lower cost and 20\% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services., Comment: This is an extended version of the work presented in NeurIPS Workshop 2019. arXiv admin note: text overlap with arXiv:1903.03882

The advancements in Internet of Things have augmented the importance of innovation and smart technologies, such as self-driving, self-monitoring, and self-controlling as key enablers for future intelligent transport system (ITS). In ITS, critical services such as rescue and disaster, medical emergency, etc., require an efficient route planning by considering performance parameters such as delay, energy, and service-level agreements during peak and off-peak hours. The ignorance of these performance parameters can lead to loss of life, or financial property, or service users. Therefore, Third Eye, a secure information management scheme is proposed by satisfying all the performance parameters as well as satisfying the trust metrics among the vehicles and devices. The proposed scheme is evaluated using various parameters such as qualifying service set mean route between the source and destination (as $s$ to $t$ ), average energy consumption for data transmission, average hop counts, probability of attack strength and error, end-to-end delay, and users query response. The performance results demonstrated the superior performance of the proposed scheme in comparison to the other state-of-the-art schemes.

We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

In this era of 5G technology, the ever‐increasing demands for high data rates lead researchers to develop hybrid satellite‐terrestrial (HST) networks as a substitution to the conventional cellular terrestrial systems. Since an HST network suffers from a masking effect which can be mitigated by adopting the terrestrial relaying strategy, in this work, wireless multicasting is focused on through an HST relaying network (HSTRN) in which a satellite sends messages to multiple terrestrial nodes via multiple relays under the wiretapping efforts of multiple eavesdroppers. The concern is to protect the multicast messages from being eavesdropped taking advantage of the well‐known opportunistic relaying technique. It is considered that the satellite links follow Shadowed Rician fading whereas the terrestrial links undergo α−μ fading. The secrecy performance of the proposed HSTRN model is accomplished by deriving expressions for the probability of nonzero secrecy multicast capacity, ergodic secrecy multicast capacity, and secure outage probability for multicasting in closed‐form. Capitalizing on the derived expressions, the authors analyze how a perfect secrecy level can be preserved in spite of harsh channel conditions and also present a secrecy trade‐off in terms of the number of relays, multicast users, and multiple eavesdroppers. Finally, the numerical analyses are corroborated via Monte‐Carlo simulations.

In order to enhance the service provisioning to users in the indoor environment, a hybrid visible light communication (VLC)/radio-frequency (RF) Internet of Things (IoT) system is proposed based on simultaneous lightwave information and power transfer (SLIPT). A mobile user equipment, which serves as an off-the-grid relay, is able to extract information from the light-emitting diode source and then forward the processed information to the destination far away, thus dividing the signal transmission into two hops. Specifically, the optical signal received at the relay is separated into alternating current and direct current components for information decoding and energy harvesting, respectively, in the first hop. Then, the energy harvested is used to forward the processed source information to the destination by using RF in the second hop. Subject to the constraints imposed on both the average and the peak powers of the source, the end-to-end outage probability of the system is analytically derived in a closed form. On this basis, the minimization of the end-to-end outage probability is formulated as an optimization problem. This problem is then solved with a joint design of the peak amplitude and the direct current bias of the source transmitter, by trading-off between the performance of two successive hops. Simulation results demonstrate that by using the proposed optimal solution, the information flow and energy flow can be dynamically balanced, resulting in significant performance gains in terms of outage probability and throughput.

In this work, we investigate the physical layer security of a jamming-based underlay cognitive satellite-terrestrial network consisting of a radio frequency link at the first hop and an optical feeder at the second hop. Particularly, one secondary user (SU) is transmitting data to an end-user optical ground station through the aid of a relay satellite, in the presence of an active eavesdropper at each hop. Moreover, another SU located in the first hop is acting as a friendly jammer and continuously broadcasting an artificial noise that cannot be decoded by the wiretapper so as to impinge positively on the system’s secrecy. Owing to the underlying strategy, the SUs are permanently adjusting their transmit powers in order to avoid causing harmful interference to primary users. The RF channels undergo shadowed-Rician and Rayleigh fading models, while the optical link is subject to Gamma-Gamma turbulence with pointing error. Closed-form and asymptotic expressions for the intercept probability (IP) are derived considering two different scenarios regardless of the channel’s conditions, namely (i) absence and (ii) presence of a friendly jammer. The effect of various key parameters on IP, e.g., sources’ transmit power, artificial noise, maximum tolerated interference power, and fading severity parameters are examined. Precisely, we aim to answer the following question: could a friendly jammer further enhance the security of such a system even in a low SNR regime? All the derived results are corroborated by Monte Carlo simulations and new insights into the considered system’s secrecy are gained.

Vehicular ad hoc networks are characterized as the ad hoc networks with dynamic and dense network topology which faces issues like routing, data congestion, and overhead. One technique which has proved to be useful in managing VANETs is clustering. Clustering is a technique to divide the network into smaller, distributed and more stable hierarchical structure. The parameters like speed, position, distance, direction and mobility are used for clustering the networks. Clustering helps in load balancing, improving scalability, efficient resource allocation and reducing overhead. In this paper a multi-hop cluster-based algorithm (MhCA) for VANET is proposed which uses Fuzzy TOPSIS for CH selection based on Rank Index of nodes. The flowchart of the algorithm along with the description of the algorithm is given below in the paper. Extensive simulation experiments are run using the ns3 and SUMO to evaluate & compare the performance of proposed algorithm with the existing multi-hop algorithms like VMaSC and n-hop. Keywords: CH, CM, CH Change Duration, CH Duration, OSM, NS3.

Objective The purpose of the current study was to determine the dynamic knee valgus angulation (DKVA) among sedentary young adults and the relationship of DKVA with triple hop distance and athletic single leg stability. Methods A cross sectional study was conducted on a sample of 72 healthy sedentary young adults aged 18-26 years out of which 29 (38.33%) were males and 43 (61.76%) were females. DKVA, single leg triple hop distance and athletic single leg stability were the outcome measurements for the study. Shapiro-Wilk and Kolmogorov Smirnov tests were used to determine normality of data. Independent t-test and Mann Whitney-U test were used for gender based comparison, paired t-test and Wilcoxon signed-rank test for comparing values of right and left leg, Freidman test for comparing the values of DKVA in different tasks and Pearson and Spearman correlation was used to determine relationship between two variables. Confidence interval was kept at 95% and p

Recent technological advancements in wireless communication technologies and networking have enabled the Internet of Things (IoT) systems’ designing. Since the wireless sensor networks refer to networks of embedded IoT devices to provide sensing services with limited energy and storage resources, the energy consumption is an important requirement for communication in IoT applications. Clustering algorithms have been investigated as a technique for energy conservation in networks with scarce resources. The appropriate cluster head balances the load in the network, lowering energy consumption and extending the network lifetime. An energy‐saving solution is proposed based on an effective cluster head selection method that assigns cluster heads to nodes based on which have the highest energy level. The proposed algorithm is called Adaptive Dynamic Multi‐Hop‐Low Energy Adaptive Clustering Hierarchy (ADMH‐LEACH). The proposed algorithm is intended for IoT applications in the oil and gas (O&G) industry. The deployment of the adopted algorithm leads to improved field communication, lower maintenance costs, real‐time monitoring, digital oil field infrastructure, lower power consumption, increased asset safety and protection, and thus increased productivity. The ADMH‐LEACH is based on a variety of variables, including initial energy, residual energy, and energy consumed per round, in order to meet quality‐of‐service constraints by following service level agreements for O&G IoT applications. The simulation results show that the proposed algorithm improves network reliability, network lifetime, and load balance significantly in comparison to current algorithms.

Internet access has become a primary human need. During the COVID 19 pandemics, almost all activities such as studying and working online. However, several rural areas still did not have internet access due to weak cellular signals. Internet access in rural areas can have various alternatives, such as VSAT (satellite). It needs an inexpensive and precise way to complete the internet access coverage because many customers were concentrated in areas far from the VSAT terminal. WLAN 802.11n was an alternative way to expand the range of internet coverage with low cost and easy implementation. Our case study uses the 5GHz frequency with a sectoral antenna as the multi-hop point-to-point backhaul network's frequency to avoid the risk of channel interference on the backhaul. On the other hand, 2.4GHz frequency with an omnidirectional antenna serves smartphone customers. On the test results, the network latency between multihop access point, has a maximum value of 19,894 ms. This result means that the latency obtained can be categorized as preferred VoIP services based on ITUT G.1010 in the local network. Then for the customer on the fourth hop, 3968 meters from the VSAT terminal, the UDP data rate of 1.04Mbps was stable, and the TCP data rate decreased to 1.26Mbps. This paper emphasizes the use of multihop 5Ghz WLAN 802.11n as a backhaul to expand internet access coverage from VSAT in rural areas, where the concentration of customers was far apart and there were many buffalo horn barriers in traditional homes and buildings.

After years of research and discussion on edge technologies, the importance of Fog Computing for the Internet of Things (IoT) is not hidden from anyone. This technology serves at the closest distance to the operating environment and has a multi-layered architecture that penetrates deep into the network. Many resource-constraint and heterogeneous IoT devices send their requests to the Fog. The variety of equipment in this layer is remarkable, but the most important of them is Nano Datacenters. These nodes are located a hop away from the operating environment and provide processing, data storage, and communication services. Fog Nano Datacenters have beneficial features that enhance the ability of developers and researchers to properly design service delivery for IoT real-time applications. In this paper, we tried to introduce them comprehensively, remove ambiguities and describe its services and values. Finally, we presented the challenges and remarks for future research directions in this context.

Let G be a connected graph. A subset S of V (G) is a connected co-independent hop dominating set in G if the subgraph induced by S is connected and V (G)\S is an independent set where for each v ∈ V (G)\S, there exists a vertex u ∈ S such that dG(u, v) = 2. The smallest cardinality of such an S is called the connected co-independent hop domination number of G. This paper presents the characterizations of the connected co-independent hop dominating sets in the join, corona and lexicographic product of two graphs. It also discusses the corresponding connected co-independent hop domination numbers of the aforementioned graphs.

When studying the Influence Maximization (IM) problem in social networks, it is found that the initial seed nodes selected by some heuristic algorithms have the characteristics of aggregation. This situation is called the rich club phenomenon of seed sets. Once the seed node is over-aggregated, it will limit the spread of influence. Therefore, analyzing the rich-club phenomenon is necessary for solving the IM problem. The paper proposes the rich club coefficient and reactivation rate to quantitatively analyze this phenomenon. The analysis mainly focuses on the relationship between the hop distance and the propagation probability. In addition, the relationship between the rich club phenomenon and the IM problem is also an aspect of concern. When dealing with the main aspects, a key problem needs to be solved, which is that the optimal range of hop is different under different propagation probabilities. To solve this problem, the Multi Hop Remove (MHR) algorithm is proposed, which is based on the independent cascade model. By the MHR algorithm, the hop range is determined under different propagation probabilities. According to our experimental results, the more serious the rich club phenomenon accumulates, the smaller the influence spread. To reduce the obstruction of this phenomenon, the multi-hop selection of seed nodes is a superior solution.

Dependence among cyber risks has been an essential and challenging component of risk management. The current study characterizes cyber dependence from both qualitative and quantitative perspectives based on L-hop propagation model. From the qualitative side, it is shown that cyber risks always possess positive association based on the proposed risk propagation model. From the quantitative side, an explicit formula for computing the fundamental dependence measure of covariance is provided for an arbitrary network. In particular, we study the impacts of factors—especially external and internal compromise probabilities, propagation depth, and network topologies—on dependence among cyber risks. We conclude by presenting some examples and applications.

Communication in Wireless Sensor Networks (WSN) by its environment further disposed to danger attacks and it harms data. Secured information or maintain data confidentiality is a big challenge in today life, but main issue is which needs a security for the purpose of localization. Including to this one more complication now focused on sensation in acoustic motes which practices Particle Swarm Optimization (PSO) method for locality or Position Assessment. Here propose Contemporary Secured Target Locality (CSTL) algorithm mainly addresses security and target location issues, Sybil and on-off attack and wicked motes occurrence at altered stages of target locality at the end results are compared with existing method General Localization (GLS), Approximate Point in Triangle, Distance Vector hop localization and multi hop method with proposed method. Here outcome of simulation prove better accuracy and progressive reaction in attack recognition presently contributes in build a secured wireless sensor network.

Hops (Humulus lupulus L.) are known worldwide for their application in the food industry by providing both aroma and flavor to the beer. Considering its popularity as an indispensable brewing crop, a lot of information can be found in the literature about the bioactive compounds present in hop cones. However, current research pointed that hop application can go further than brewing. The present review updates the emerging technologies used to extract hop bioactive compounds in comparison with the conventional ones. The main characteristics, extraction conditions, and extracted compounds of these environmentally friendly extraction methods are highlighted, helping to understand their importance and industrial applications, especially considering hop phytochemicals and their potential for the food industry. In addition, considering this still little addressed potential, this work summarizes the use of hops in different food matrices and emphasizes the relevant aspects that circumvent it. The development of “hopped products” in addition to their potential use as food preservatives expanded the hop industrial perspectives. Among the non-beer foods, meat products, minimally processed vegetables, bread, and premium products, including non-alcoholic beverages, showed up as great options to hop compound incorporation. Consumer perception based on current trends for hopped products also indicates interesting prospects for industrial spread.

Intelligent reflecting surface (IRS) has emerged as a promising and low-cost technology for improving wireless communications by collecting dispersed radio waves and redirecting them to the intended receivers. In this letter, we characterize the achievable rate when multiple IRSs are utilized in the manner of decode-and-forward (DF) relaying. Our performance analysis is based on the Nakagami- $m$ fading model with perfect channel state information (CSI). Tight upper bound expressions for the ergodic rate are derived. Moreover, we compare the performance of the multi-IRS DF relaying system with that of the one with a single IRS and confirm the gain. We then optimize the IRS configuration considering the numbers of IRSs and IRS reflecting elements, which provides useful insights for practical design.

The localization problem of unknown nodes in wireless sensor networks (WSN) has drawn increasing scholarly attention along together the popularity of meta-heuristic algorithms. To overcome the shortcomings of low accuracy that traditional least square method (LSM) inevitably produces, this paper introduces an improved adaptive genetic algorithm (IAGA) to handle the aforementioned problem and uses a modified evaluation function to reduce the error of distance measurement in a topological structure. The experimental results prove that the IAGA algorithm based on DV-Hop has superior performance in comparison of original DV-Hop and other meta-heuristic algorithms. The conclusion can be drawn that meta-heuristic algorithms have an better superiority over DV-Hop in locating nodes in WSN and the IAGA is more promising than other meta-heuristic algorithms.

Multi-hop localization has attracted considerable research interests, recently. Most of the existing methods assume that the distribution of anchors is non-collinear, which often does not represent real-world scenes, particularly for irregular networks. In this paper, we present a novel multihop localization scheme to address the ill-posed issue caused by the inappropriate distribution of anchors. First, we recognize whether the preliminary estimated location of nonanchor obtained from existing methods deviates from its true location. So it becomes a new anchor (also called the virtual anchor) after it is identified that the preliminary location of a nonanchor is within an unfit area. With the support of both the virtual anchor and other anchors, a smaller and delimited area can be found in which the target node is situated. Next, we employ the weighted Bounding-Box algorithm to compute the non-anchors’ locations that are close to their true locations. We evaluate our scheme with different network topologies based on other state-of-the-art research and analyze its performance. The results demonstrate that our proposed scheme can improve the accuracy of localization with lower computation complexity.

This paper concerns maximizing the minimum achievable secrecy rate in a two-way multi-antenna relay network in which two single-antenna nodes aim at exchanging their confidential messages with the help of some multi-antenna relays incorporating the Amplify and Forward (AF) strategy, while a single-antenna passive eavesdropper attempts to overhear the exchanged information. The communication is carried out in two hops. During the first hop, two source nodes transmit their information, hence, the relays get a combination of transmitted signals. Then, throughout the second hop, each relay applies a beamforming matrix to its received signal vector and broadcasts the resulting signal vector to the transmitting ends. Also, the eavesdropper overhears the exchanged information of two hops. The main goal persuaded in the current work is to devise proper beamforming strategies at the relays to improve the minimum secrecy rate. The problem is cast as an optimization problem, where it is shown that the underlying problem is not convex in general. Hence, two suboptimal approaches, called null-space (NS) and interference-leakage alignment (ILA) are devised, where using the so-called semi-definite relaxation (SDR) and Charnes–Cooper transformation techniques, the underlying problems are formulated as semi-definite programming (SDP) problems which can be numerically solved.

Over the last 10 years, there has been an exponential growth of Internet services over cellular and wireless network. This has resulted in multiple wireless networks: macro-cell, micro-cell, femto-cell, legacy networks, etc. co-existing simultaneously. Multihop cellular networks have been well-investigated to support the heterogeneous wireless network scenario. However, this results in a very distributed and a non-optimum distribution of radio resources, leading to sub-optimal throughput of the network. Keeping this in mind, this paper proposes a three-hop heterogeneous wireless network architecture, wherein each hop would be from same network or other networks. Importantly, this paper proposes and provides a detailed analysis of a specific cluster-based architecture: three-hop network. This is the first instance where a three-hop architecture is analyzed. Accordingly, in every cell, three communicating pairs communicate using a given radio resource, but for only $${1/3}^\mathrm{{rd}}$$ the time slot period. This three-hop design enables a frequency reuse ratio of one. A comprehensive performance analysis indicates that the capacity obtained with a three-hop architecture is up to 20% higher than that can be obtained by two-hop architecture and 40% higher than traditional single-hop network. This is an extremely important result. It indicates the improvement that can be obtained in using specific design for multihop-based next generation LTE-advanced network.

Introduction. The research featured the effect of various hopping conditions on the content of polyphenolic compounds associated with the extraction and biotransformation of hop compounds. This mechanism is responsible for uncharacteristic beer flavor in the traditional production method. The research objective was to study the migration routes, influence factors, and changes in the content of hop polyphenols in model experiments in order to reduce various factors in the production process chain. The experiment was important from the point of view of identifying the polyphenols contribution to the beer colloidal system. Study objects and methods. The study involved granulated aromatic hop of Tetnanger variety harvested in 2019, aqueous and 4% aqueous-alcoholic solutions simulating the wort and young beer liquid phase, and brewing yeast Sacharomyces cerevisiae of races Rh (lager) and Nottingham (ale). The work used the generally accepted methods for assessing the content of polyphenolic compounds. Results and discussion. The research established various factors that affected the migration of hop polyphenolic groups. The acidity effect on the polyphenol was established as follows: pH 4.4 contributed to a 12% greater isoxanthohumol accumulation, while pH 5.2 promoted a six times greater accumulation of anthocyanogens than pH 4.4. The total content of polyphenols during boiling was constant and correlated with the phenolic compound in different groups. The conditions of “dry” hopping, simulating the wort clarification in Wirpool, increased the dissolution of anthocyanogens by six times in comparison with kettle hopping, which was associated with the turbulent flow. The isoxanthohumol sorption and formation rate during “dry” hopping was established when modeling the maturation conditions for different temperatures, oxygen levels, and yeast races. A lower temperature (5°C) had a negative effect on the isoxanthohumol sorption. The quercetin content was found to be in the range of 0.9–2.0 mg/dm³ at 5°C and 0.8–4.7 mg/dm³ at 20°C, which determined the temperature effect on extraction during “dry” hopping. The presence of yeast cells in the medium promoted the quercetin accumulation: the quercetin content doubled at 5°C and quadrupled at 20°C compared with the control. The rutin content in the control increased for two days, and minor fluctuations in the content of yeast cells were 5.0 ÷ 7.4 mg/dm3. A comparative analysis of the simple phenolic acids and aldehydes amounts under “dry” hopping conditions showed a greater decrease in their concentration because they were involved in the yeast consumption and biotransformation processes. Conclusion. The research made it possible to establish the phenolic compounds in various groups of migration routes under the conditions of classical (kettle) and “dry” methods of hopping, as well as their dependence on such factors as medium acidity, stirring intensity, temperature, oxygen content, and yeast race. The sorption rates of the polyphenolic compounds were established as follows: absorption of isoxanthohumol was at its highest during the first day of “dry” hopping, and that of rutin – within two days, while quercetin was not absorbed at all. Therefore, an additional fermentation stage can be considered as the most expedient method of “dry” hopping.

The influence of growing region on hop quality was examined in three American aroma hop varieties (Mosaic®, Simcoe® and Strata®) that were harvested from two to five distinct sites within each of eleven commercial fields located throughout the Willamette Valley, Oregon. Weather, climate, and management data were collected for each of the eleven fields, while site-specific soil characterization was performed at each of the 50 sites. While hop variety had the largest influence on chemical differences, Multiple Factor Analysis revealed regional groupings attributable to differences in soil, weather, and management. Supplemental data for this article is available online at at https://doi.org/10.1080/03610470.2021.1968272 .

The influence of growing region on hop quality is examined in three American aroma hop varieties (Mosaic®, Simcoe® and Strata®) from eleven locations throughout the Willamette Valley, OR. Hops from each site were analyzed for total oil content, α- and β-acids, and the oil was analyzed via GC-FID to quantify selected aroma compounds. Single-field IPAs were brewed using composite samples from each field and assessed via sensory evaluation. While hop variety had the largest influence on chemical differences, as expected, Analysis of Variance revealed differences between fields within the same variety. Sensory discrimination and descriptive testing of the single-field IPAs also showed significant region-based differences among samples within the same variety. Supplemental data for this article is available online at

The traditional relay deployment problem typically assumes that the locations of users are known and stationary, which is not realistic in practice. The prevalence of mobile devices has made it possible to collect user trajectory and account for user movement while deploying relays. Under this background, a novel problem trajectory-based relay deployment (TBRD) is put forward. This problem considers communication-related metrics and is aimed at maximizing user connection time as users roam through the target area under relay resource constraints, which is more reasonable than the goal of expanding the relay coverage. To figure out the TBRD, we first propose the concept demand nodes (DNs), which are virtual weighted nodes representing the locations where users frequently pass or stay for a long period. Next, we design a Demand Node Generation algorithm that can transform the continuous historical user trajectory into a number of discrete DNs. By generating DNs, we convert the TBRD problem into a demand node coverage (DNC) problem, which is proved to be NP-complete. Followed by that, we introduce an approximation algorithm, named Submodular Iterative Deployment Algorithm, which solves the DNC problem with the approximation factor $1-\frac{1}{\sqrt{e\cdot (1-1/k)}}$, where $e$ is the mathematical constant, and $k$ is the relay number constraint. Finally, five real trajectory datasets are used to evaluate our proposed algorithm, and the simulation results demonstrate that our algorithm can obtain high coverage for users in motion, which can lead to better user experience. In addition, we also analyze the impact of different parameters on the coverage performance, and under this circumstance, we may safely come to the conclusion that our work is at the leading edge to utilize user trajectories for relay deployment in wireless networks.

Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks (WSNs). Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of source location privacy to some extent. It is urgent to solve the problems such as complicate the ac path between nodes, improve the centralized distribution of Phantom nodes near the source nodes and reduce the network communication overhead. In this paper, PSSPR routing is proposed as a visable approach to address SLP issues. We use the coordinates of the center node V to divide sector-domain, which act as important role in generating a new phantom nodes. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead.

В статье представлены результаты комплексного исследования на рынке производства и переработки хмеля. Проанализированы отчеты фирмы «Барт» и годовые отчеты International Hop Growers’ Convention в период 2007-2020 гг. Приведены и систематизированы данные Федеральной службы государственной статистики Российской Федерации, по Чувашской Республике и официального интернет-портала органов власти Чувашской Республики по формированию внутреннего рынка хмеля. Предложены меры регионального регулирования отрасли, которые согласованы с утверждённой Концепцией развития хмелеводства в Чувашской Республике на 2020-2025 гг. в рамках государственной программы. The article presents the results of a comprehensive study on the market of hop production and processing. The reports of the company «Bart» and the annual reports of International Hop Growers'are analyzed Convention in the period 2007-2020. The data of the Federal State Statistics Service of the Russian Federation on the Chuvash Republic and the official Internet portal of the authorities of the Chuvash Republic on the formation of the domestic hop market are presented and systematized. The measures of regional regulation of the industry are proposed, which are coordinated with the approved Concept of hop growing development in the Chuvash Republic for 2020-2025 within the framework of the state program.

Influence maximization is a classic network optimization problem, which has been widely used in the field of viral marketing. The influence maximization problem aims to find a fixed number of active nodes. After a specific propagation model, the number of active nodes reaches the maximum. However, the existing influence maximization algorithms are overly pursuing certain indicators of efficiency or accuracy, which cannot be well accepted by some researchers. This paper proposes an effective algorithm to balance the accuracy and efficiency of the influence maximization problem called local two-hop search algorithm (LTHS). The core of the proposed algorithm is a node not only be affected by one-hop neighbor nodes, but also by two-hop neighbor nodes. Firstly, this paper selects initial seed nodes according to the characteristics of the node degree. Generally, the high degree of nodes regards as influential nodes. Secondly, this paper proposes a node two-hop influence evaluate function called two-hop diffusion value (THDV), which can evaluate node influence more accurately. Furthermore, in order to seek higher efficiency, this paper proposes a method to reduce the network scale. This paper conducted full experiments on five real-world social network datasets, and compared with other four well-known algorithms. The experimental results show that the LTHS algorithm is better than the comparison algorithms in terms of efficiency and accuracy.

Cooperative Communication (CC) is implemented extensively in mobile Ad hoc networks to leverage the benefits of CC technique. Energy consumption and routing are major challenges for large scale Cooperative Mobile Ad hoc Networks since each node in the network have mobility. To address these challenges, a hybrid multi-hop cooperative routing algorithm is formulated by combining clustering and location-based routing strategies. The main idea of our algorithm is to establish communication between similar mobility nodes to reduce the mobility effect since the link between (approximately) equal mobility nodes was reliable. All the equal mobility nodes are grouped to form a cluster; one of the nodes in this is selected as a cluster head based on its location. Further, we optimize the number of transmitters and receivers in every hop; and an optimal number of cooperative relays are obtained in every hop thereby reducing the end-to-end energy utilization. The evaluation result shows that the proposed algorithm saves energy consumption by up to 53.42% compared to traditional algorithms.

In this paper, we investigate the optimum energy allocation of dual-hop communication systems with AF protocol where we consider both coherent and differential transmission schemes. First, we demonstrate the performance of dual-hop communication systems in terms of symbol error rate (SER). Then, we carry out the optimum energy allocation which minimizes the error rate. Based on the optimized energy allocation, its effects on system performance are analyzed, and these results are compared with DF-based systems. We also compare the performance of AF-based systems depending on the transmission scheme.

Learning to infer missing links is one of the fundamental tasks in the knowledge graph. Instead of reasoning based on separate paths in the existing methods, in this paper, we propose a new model, Sequential Relational Graph Convolutional Network (SRGCN), which treats the multiple paths between an entity pair as a sequence of subgraphs. Specifically, to reason the relationship between two entities, we first construct a graph for the entities based on the knowledge graph and serialize the graph to a sequence. For each hop in the sequence, Relational Graph Convolutional Network (R-GCN) is then applied to update the embeddings of the entities. The updated embedding of the tail entity contains information of the entire graph, hence the relationship between two entities can be inferred from it. Compared to the existing approaches that deal with paths separately, SRGCN treats the graph as a whole, which can encode structural information and interactions between paths better. Experiments show that SRGCN outperforms path-based baselines on both link and fact prediction tasks. We also show that SRGCN is highly efficient in the sense that only one epoch of training is enough to achieve high accuracy, and even partial datasets can lead to competitive performance.