Breaking the Blockage for Big Data Transmission: Gigabit Road Communication in Autonomous Vehicles

Recently, the spectrum band beyond 60 GHz has attracted attention with the growth of traffic demand. Previous studies assumed that these bands are not suitable for vehicle communications due to the short range and high rate of blockage. However, it also means that there is no existing service or regulation designed for these bands, which makes this area free to apply. Therefore, in this article, we draw a potential map of THz vehicle transmission for autonomous vehicles to break the blockage of short-range and unstable links. First, we give a brief overview of possible waveforms followed by the specific channel at 0.1-1 THz. Then we propose an autonomous relay algorithm called ATLR for the gigabit-level communication in the high-speed road environment. Finally, we discuss how the THz transmission helps relieve the interference problem and provide extra data to support various instructions in autonomous vehicles

Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN

Recently, the installation of 5G networks offers a variety of real-time, high-performance and human-oriented customized services. However, the current laying 5G structure is unable to meet all of the growing communication needs by these new emerging services. In this paper, we propose a DQL (Deep Q-learning Network) based intelligent resource management method for 5G architecture, to improve the quality of service (QoS) under limited communication resources. In the environment of network function virtualization (NFV), we aim at improving the efficient usage of spectrum resources. In this two-step solution, our first goal is to guarantee the maximum communication quality with the smallest number of infrastructures. Then, a DQL-based wireless resource allocation algorithm is designed to realize the elaborate operation. Unlike previous studies, our system can provide the allocation policy in a more subdivided way and finally maximize the usage of bandwidth resources. The simulation also shows that our proposed MSIO improves 3.12% in the performance of the maximum coverage importance problem and the ARODQ algorithm improves 4.05% than other standard solutions

Enabling Computational Intelligence for Green Internet of Things: Data-Driven Adaptation in LPWA Networking

With the exponential expansion of the number of Internet of Things (IoT) devices, many state-of-the-art communication technologies are being developed to use the lowerpower but extensively deployed devices. Due to the limits of pure channel characteristics, most protocols cannot allow an IoT network to be simultaneously large-scale and energy-efficient, especially in hybrid architectures. However, different from the original intention to pursue faster and broader connectivity, the daily operation of IoT devices only requires stable and low-cost links. Thus, our design goal is to develop a comprehensive solution for intelligent green IoT networking to satisfy the modern requirements through a data-driven mechanism, so that the IoT networks use computational intelligence to realize self-regulation of composition, size minimization, and throughput optimization. To the best of our knowledge, this study is the first to use the green protocols of LoRa and ZigBee to establish an ad hoc network and solve the problem of energy efficiency. First, we propose a unique initialization mechanism that automatically schedules node clustering and throughput optimization. Then, each device executes a procedure to manage its own energy consumption to optimize switching in and out of sleep mode, which relies on AI-controlled service usage habit prediction to learn the future usage trend. Finally, our new theory is corroborated through real-world deployment and numerical comparisons. We believe that our new type of network organization and control system could improve the performance of all green-oriented IoT services and even change human lifestyle habits

Propagation of tidal waves up in Yangtze Estuary during the dry season

Tide is one of the most important hydrodynamic driving forces and has unique features in the Yangtze Estuary (YE) due to the complex geometry of third-order bifurcations and four outlets. This paper characterizes the tidal oscillations, tidal dampening, tidal asymmetry, and tidal wave propagation, which provides insights into the response of the estuary to tides during the dry season. The structural components of tidal oscillations are initially attained by tidal analysis. The increasingly richer spectrum inside the estuary shows an energy transfer corresponding to the generation and development of nonlinear overtides and compound tides. A 2-D numerical model is further set up to reproduce tidal dynamics in the estuary. The results show that the estuary is a strongly dissipative estuary with a strong nonlinear phenomenon. Three amplifications are presented in the evolution process of tidal ranges due to the channel convergence. Tidal asymmetry is spatiotemporally characterized by the M-4/M-2 amplitude ratio, the 2M(2)-M-4 phase difference, and the flood-ebb duration-asymmetry parameter, and the estuary tends to be flood-dominant. There exists mimic standing waves with the phase difference of the horizontal and vertical tide close to 90 degrees when tidal wave propagates into the estuary, especially during the neap tide. In addition, the differences in tidal distortion, tidal ranges, and tidal waves along the two routes in the South Branch (S-B) suggest the branched system behaves differently from a single system

A Social-Network-Optimized Taxi-Sharing Service

Social-network-based taxi sharing is a potential smart city service with social and economic benefits. The authors designed a framework for planning social-network-based taxi travel and successfully applied it in a practical scenario

Battery Maintenance of Pedelec Sharing System: Big Data Based Usage Prediction and Replenishment Scheduling

Pedelecs are an alternative of traditional share bikes by applying the battery-powered motor to assist pedaling and accordingly extend the riding coverage. The large scale deployment of pedelecs, however, requires a careful design of maintenance system to replace the batteries regularly that can be costly. This paper investigates the maintenance of a city-wide pedelec system by developing an offline solution in two steps. First, we develop an optimal and efficient hybrid prediction model which predicts the usage demand of pedelecs in every 48 h on a scale of millions of pedelecs. Our proposal predicts the future usage increment of pedelecs by combining a local predictor, a global predictor, and an inflection predictor, which captures both the short-term and long-term factors affecting the pedelec usage. Second, based on the developed predictor and results of big data analytics, an optimal path planning scheme for the replenishment of pedelec batteries is developed. As compared to other schemes, our scheme can save 40% of the maintenance cost. To verify our proposal, extensive real-data driven simulations are performed which show that the accuracy of the prediction process is high enough than each traditional method and our proposal solves the maintenance problem efficiently

Superior glucose metabolism supports NH4+ assimilation in wheat to improve ammonium tolerance

The use of slow-release fertilizers and seed-fertilizers cause localized high-ammonium (NH4+) environments in agricultural fields, adversely affecting wheat growth and development and delaying its yield. Thus, it is important to investigate the physiological responses of wheat and its tolerance to NH4+ stress to improve the adaptation of wheat to high NH4+ environments. In this study, the physiological mechanisms of ammonium tolerance in wheat (Triticum aestivum) were investigated in depth by comparative analysis of two cultivars: NH4+-tolerant Xumai25 and NH4+-sensitive Yangmai20. Cultivation under hydroponic conditions with high NH4+ (5 mM NH4+, AN) and nitrate (5 mM NO3-, NN), as control, provided insights into the nuanced responses of both cultivars. Compared to Yangmai20, Xumai25 displayed a comparatively lesser sensitivity to NH4+ stress, as evident by a less pronounced reduction in dry plant biomass and a milder adverse impact on root morphology. Despite similarities in NH4+ efflux and the expression levels of TaAMT1.1 and TaAMT1.2 between the two cultivars, Xumai25 exhibited higher NH4+ influx, while maintaining a lower free NH4+ concentration in the roots. Furthermore, Xumai25 showed a more pronounced increase in the levels of free amino acids, including asparagine, glutamine, and aspartate, suggesting a superior NH4+ assimilation capacity under NH4+ stress compared to Yangmai20. Additionally, the enhanced transcriptional regulation of vacuolar glucose transporter and glucose metabolism under NH4+ stress in Xumai25 contributed to an enhanced carbon skeleton supply, particularly of 2-oxoglutarate and pyruvate. Taken together, our results demonstrate that the NH4+ tolerance of Xumai25 is intricately linked to enhanced glucose metabolism and optimized glucose transport, which contributes to the robust NH4+ assimilation capacity

Creating Visible-to-Near-Infrared Mechanoluminescence in Mixed-Anion Compounds SrZn 2 S 2 O and SrZnSO

Abstract(#br)Mechanoluminescence (ML) materials featuring light emission in response to mechanical stimulus have shown promising applications in damage diagnosis, dynamic force detection, and information storage. However, their applications are greatly limited by a very small number of available ML materials as well as unsatisfied ML spectra. In this paper, we developed novel ML materials with intense ML and super-broad visible-to-near-infrared (470-1600 nm) spectra by incorporating lanthanide ions or transition metals into mixed-anion compounds SrZn 2 S 2 O and SrZnSO. These mixed-anion compounds show a linear relationship between ML intensity and applied force, allowing them to be used in non-contact/multi-touch stress sensing. Moreover, the mixed-anion compounds exhibit multiband near-infrared ML enabling a significant bright-field stress sensing approach without the interference of ambient light. This work offers a unique insight for discovering new ML materials and enriching the ML spectral range, thereby promoting their potential applications in stress intelligent sensors, electronic skins, and human-machine interfaces

Characteristics and prevention and control techniques of typical water hazards in coal mines in Shaanxi Province

Shaanxi province is rich in coal resources and is one of the main coal-producing provinces in China, with a coal production of 746 million tons in 2022. Due to the obvious differences in geological and hydrogeological conditions in the coal mining areas of the province, there are various types of water disasters in its coal mines. With the increase of coal mining intensity in recent years, the frequency of water disasters increases significantly, and the situation of water prevention and control is challenging. On the basis of the overall analysis of the hydrogeological conditions in the coal mining areas of Shaanxi province, combined with the types of water disasters revealed in the mining process of various mining areas in the province, the regional distribution of all kinds of water disas-ters is systematically summarized, and the formation mechanism and characteristics of typical water disasters are discussed. Also, the corresponding prevention and control technology and progress are put forward. The results show that ① the main coal producing areas in Shaanxi province are divided into northern Shaanxi, Huanglong and Weibei. The Jurassic coalfield in northern Shaanxi is mainly affected by the water disaster of roof's loose sand layer, the water disaster of thick sandstone, water and sand burst disaster, and the burnt rock flood exists in some coal mines. The Huanglong coalfield is mainly threatened by roof super-thick sandstone water, separated layer water, mud and sand burst disaster or hidden danger. The Carboniferous-Permian coalfields in northern Shaanxi and Weibei are mainly threatened by the water damage of Ordovician limestone on the coal seam floor. ② The hidden danger of roof flood in the Jurassic coalfield in northern Shaanxi is mainly formed by the disturbance of roof aquifer by mining water diversion fracture zone, which can form a continuous large flow water gushing through sandstone aquifer or loose sand layer, and the water filling intensity is large in some areas. Roof flood occurs when the amount of water exceeds the capacity of the drainage system in a short time, and it can be transformed into water and sand burst disaster when the collapse zone in the thin bedrock area directly leads to the loose sand layer. When the water-conducting fracture zone affects the water-rich area of burnt rock, it can form a flood of burnt rock with large instantaneous water volume and continuous flood. The super-thick sandstone aquifer on the roof of the Huanglong coalfield is swept by the mining water-conducting fracture zone to form a high-intensity continuous water gushing and may form roof flood. When the thickness of the lower aquifer is large, the separated layer water burst disaster may be formed. When the roof strata near the coal seam is soft and easy to collapse, it can be transformed into mud and sand burst disaster. In the Carboniferous-Permian coalfield and the Weibei coalfield in northern Shaanxi, when structures such as coal seam mining floor disturbance failure zones or faults lead to the Ordovician limestone aquifers, they will cause extremely serious floor water inrush disasters, which are characterized by strong sudden occurrence and large instantaneous water volume. ③ The roof water disaster treatment technology in each region mainly includes drilling hole drainage, grouting treatment, mining parameter control and so on, and the floor water damage is mainly treated by regional grouting reinforcement and plugging. Burnt rock water disaster is mainly treated by the combination of curtain grouting and drilling exploration