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Artificial ionic sensory systems, bridging the divide between biological systems and electronics, mimic human skin functions but face critical challenges with biocompatibility, comfort, signal stability, and simplifying packaging. Here, we present a simple and permeable skin-interfaced iontronic mechanosensing (SIIM) architecture that integrates human skin as natural ionic material and hierarchically porous MXene-fiber composite membranes as sensing electrodes. The SIIM system eliminates complex ionic material design and multilayer matrix, exhibiting ultrahigh pressure sensitivities (5.4 kPa–1, <75 Pa), a low detection limit (6 Pa), excellent output stability along with high permeability to minimize the impact of sweating on sensing. The noncytotoxic nature of SIIM electrodes ensures excellent biocompatibility (>97% cell coincubational viability), facilitating long-term wearability and high biosafety. Furthermore, the scalable SIIM configuration integrated with matrix smart gloves, effectively monitors human physical movements. This SIIM-based sensor with marked sensing capabilities, structural simplicity, and scalability, holds promising potential in diverse wearable applications.

Symbiotic radio (SR) is a promising technique to support cellular Internet-of-Things (IoT) by forming a mutualistic relationship between IoT and cellular transmissions. In this paper, we propose a novel multi-user multi-IoT-device SR system to enable massive access in cellular IoT. In the considered system, the base station (BS) transmits information to multiple cellular users, and a number of IoT devices simultaneously backscatter their information to these users via the cellular signal. The cellular users jointly decode the information from the BS and IoT devices. Noting that the reflective links from the IoT devices can be regarded as the channel uncertainty of the direct links, we apply the robust design method to design the beamforming vectors at the BS. Specifically, the transmit power is minimized under the cellular transmission outage probability constraints and IoT transmission sum rate constraints. The algorithm based on semi-definite programming and difference-of-convex programming is proposed to solve the power minimization problem. Moreover, we consider a special case where each cellular user is associated with several adjacent IoT devices and propose a direction of arrival (DoA)-based beamforming design approach. The DoA-based approach requires only the DoA and angular spread (AS) of the direct links instead of the instantaneous channel state information (CSI) of the reflective link channels, leading to a significant reduction in the channel feedback overhead. Simulation results have substantiated the multi-user multi-IoT-device SR system and the effectiveness of the proposed beamforming design approaches. It is shown that the DoA-based beamforming approach achieves comparable performance as the CSI-based approach in the special case when the ASs are small.

Cooperative spectrum sensing (CSS) is a key enabling technology of cognitive radio networks with multiple secondary users (SUs). In conventional CSS systems, when the primary signals are weak, the SUs require long sensing time to achieve a high detection probability for protecting the transmission of the primary user (PU), leading to little remaining time for secondary transmissions. To address this issue, we propose a reconfigurable intelligent surface (RIS) assisted CSS system, where multiple RISs are employed to improve the CSS performance within limited sensing time. Considering that the dependency of the CSS performance on the received primary signal strengths at the SUs differs across various CSS schemes, the RIS configurations could also be optimized differently regarding these CSS schemes. Motivated by this, we investigate the phase shift matrix (PSM) optimization problems to maximize the cooperative detection probability given a maximum tolerable false alarm probability, and we consider two typical kinds of CSS schemes, namely, data fusion and decision fusion. As it is intractable to directly solve these problems due to the complex expressions of the cooperative detection probability with respect to the PSMs, we show that the solutions can be obtained by transforming these problems into channel gain-related optimization problems. Furthermore, we show that the proposed PSM optimization methods can be extended to the more practical scenarios where instantaneous channel state information (CSI) is unavailable. In such cases, we leverage statistical CSI to improve the CSS performance in the sense of expectation. Subsequently, we conduct a numerical analysis on the number of reflecting elements required to achieve a target detection probability in the statistical CSI case. Finally, simulation results demonstrate that the proposed PSM optimization methods can significantly improve the CSS performance within limited sensing time.

To improve real-time operation and management in urban rail transit (URT) systems, accurate and reliable short-term passenger flow forecasting at the network level is a crucial task. Although numerous endeavors have been devoted to this field, the insufficient topological representation for passenger flows in the URT network, the overlooking of intrinsic correlations among multi-source data, and the information loss in deep-learning frameworks are still critical issues that need to be addressed. This study proposes a multi-stage fusion passenger forecasting (MSFPF) model to accomplish short-term multi-step passenger forecasting leveraging multi-source data, and overcome the above-mentioned challenges. Based on the characteristics of passenger flows in the URT network, time-based origin–destination flow data is involved and utilized to enhance the representation of flows and provide spatial-temporal features. Then, the interaction and relationship among multi-source data are estimated to capture their intrinsic correlations. To effectively and comprehensively extract temporal and spatial features, a transformer long short-term memory block and a depth-wise attention block are constructed with attention mechanisms and employed. Furthermore, we construct the multi-stage fusion (MSF) structure to alleviate the information loss during the learning process, which is a significant component in improving the forecasting accuracy. In addition, the model is applied to two large-scale real-world datasets, in which it outperforms nine widely used baselines and four specific variants of itself. The quantitative experiments demonstrate the robustness and superiority of the proposed MSFPF model, and the significant contribution of the MSF structure in the model.

[Display omitted] Surface modification could remarkably improve the oil–water separation ability of membrane, but suffers from limited survival positive effect layer after long-term operation. Herein, we report a systematic comparative study between bulk and surface modification (i.e., synchronous or sequential electrospun/elecrospraying) on hydrophobic polyvinylidene difluoride (PVDF) membrane. A hydrophilic UiO-66-NH 2 @polyacrylic acid micro/nanospheres (UiO@PAA) was selected as functional layer to improve the separation and adsorption capability of PVDF. The surface modification (SMM) and bulk modification membranes (BMM) exhibited the similar water wetting behavior in air as the same surface layer. The spider filament-like nanofibers of BMM could enhance the mechanical stability and dispersion uniformity of active sites among the PVDF fibers. In terms of oil-in-water emulsions separation ability, the BMM was superior to the SMM with the maximumly increase of separation efficiency and filtration flux to 1.3 % and 192.6 L m-2h−1, respectively. As for dye absorption, the BMM showed better adsorption capacity with a removal efficiency over 96 %. For the low concentration of methylene blue, the removal efficiency of SMM was only 94 %, while the removal efficiency of the BMM reached 98 % at room temperature. In addition, the BMM exhibited higher removal efficiency over 94 % compared with that less than 84 % of SMM after eight cycling tests, confirming its advantages in repeatability. This study demonstrates that the bulk modification can endow the initial membrane with better performance and durability, showing more promising prospects in actual wastewater treatment. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing concern regarding health issues arising from exposure to nanoplastics (Nps) in the natural environment. The Nps bioaccumulate within the body viathe circulatory system and accumulate in the liver, resulting in damage. Previous studies have demonstrated that maltol, derived from red ginseng (Panax ginsengC.A. Meyer) as a Maillard product, exhibits hepatoprotective effects by alleviating liver damage caused by carbon tetrachloride or cisplatin. In order to explore the specific mechanism of maltol in improving hepatotoxicity induced by Nps, mice exposed to 100 mg/kg Nps were given maltol at doses of 50 and 100 mg/kg, respectively. The results showed that Nps induced an increase in the levels of liver apoptotic factors BAX and cytochrome c, a decrease in the levels of the autophagy key gene LC3 II/I, and an increase in P62. It also caused oxidative stress by affecting the Nrf2/HO-1 pathway, and a decrease in GPX4 protein expression suggested the occurrence of ferroptosis. However, treatment with maltol significantly improved these changes. In addition, maltol (2, 4, and 8 μM) also protected human normal liver L02 cells from Np (400 μg/mL)-induced damage. Our data suggest that maltol could ameliorate Np-induced L02 cytotoxicity by reducing autophagy-dependent oxidative stress, exhibiting similar protective effects in vitroas in vivo. This study helps shed light on the specific molecular mechanism of Np-induced hepatotoxicity. For the first time, we studied the protective effect of maltol on Np-induced liver injury from multiple perspectives, expanding the possibility of treatment for diseases caused by environmental pollutants.

Amyloid-like aggregation widely occurs during the processing and production of natural proteins, with evidence indicating its presence following the thermal processing of wheat gluten. However, significant gaps remain in understanding the underlying fibrillation mechanisms and structural polymorphisms. In this study, the amyloid-like aggregation behavior of wheat gluten and its components (glutenin and gliadin) during cooking was systematically analyzed through physicochemical assessment and structural characterization. The presence of amyloid-like fibrils (AFs) was confirmed using X-ray diffraction and Congo red staining, while Thioflavin T fluorescence revealed different patterns and rates of AFs growth among wheat gluten, glutenin, and gliadin. AFs in gliadin exhibited linear growth curves, while those in gluten and glutenin showed S-shaped curves, with the shortest lag phase and fastest growth rate (t1/2= 2.11 min) observed in glutenin. Molecular weight analyses revealed AFs primarily in the 10–15 kDa range, shifting to higher weights over time. Glutenin-derived AFs had the smallest ζ-potential value (−19.5 mV) and the most significant size increase post cooking (approximately 400 nm). AFs in gluten involve interchain reorganization, hydrophobic interactions, and conformational transitions, leading to additional cross β-sheets. Atomic force microscopy depicted varying fibril structures during cooking, notably longer, taller, and stiffer AFs from glutenin.

ABSTRACTAgaricusis a species-rich genus with more than 600 species around the world. In this work, three new species, Agaricus cacainus, A. baiyunensis, and A. praeclarefibrillosusare described from the specimens collected at Baiyun Mountain, Guangzhou, China, a subtropical area with a monsoon maritime climate, based on phylogenetic analyses and morphological examinations of internal transcribed spacer (ITS1-5.8S-ITS2 = ITS), D1/D2 domains of the large subunit of ribosomal DNA (28S), and a part of translation elongation factor 1-alpha (TEF1). Agaricus cacainusin A. sect. Amoeniis characterized by a parabolic to applanate, slightly depressed pileus covered with chocolate brown, appressed, triangular squamules against white background, a white, furfuraceous stipe, an unchanging context when cut, a fragile and evanescent annulus, usually 4- or 2-spored basidia, and mostly pyriform cheilocystidia. Agaricus baiyunensisin A. sect. Minoreshas a pileus with a slightly truncate top covered with light brown, downy-wooly fibrillose scales and a light yellowish stipe with membranous annulus. Agaricus praeclarefibrillosusin A. sect. Brunneopictiis characterized by a pileus surface with brownish, triangular, recurved scales and longitudinally splitting lines toward margin, a cottony stipe with white, tiny, recurved fibrils, a single annulus, and variously shaped cheilocystidia, with sparsely ornamented basidiospores. The detailed comparison of their morphological characteristics with closely related species is provided.

The utilization of LNG cold energy is promising to offset the energy penalty of oxy-fuel combustion technology and achieve efficient carbon emissions reduction. In this paper, natural gas oxy-fuel power systems utilizing LNG cold energy are investigated. Supercritical and transcritical CO2cycles are adopted to deeply recover flue gas waste heat. Results show that the thermal efficiency of the supercritical CO2recompression cycle is 6% higher than that of the regenerative cycle, whereas the net power output of the recompression cycle is lower. With transcritical CO2cycle as the secondary subsystem, the efficiency of both integrated systems is enhanced by approximately 9%. Meanwhile, 95% CO2is captured with the power consumption of 0.07 kWh/kg-CO2. The total investment costs of the integrated regenerative and recompression cycle are 53.96 M$ and 54.62 M$. The levelized cost of electricity of the two integrated systems is 3.21 $/MWh and 3.24 $/MWh, indicating great financial availability. Therefore, applying supercritical and transcritical CO2cycles contributes to efficient energy conversion and carbon capture. The research could provide meaningful information on the oxy-fuel combustion power system integrated with supercritical and transcritical CO2cycles from thermodynamic and economic insights.

• A novel Fe-doped RuO 2 / PEDOT/SnO 2 /TNAs electrode was fabricated on Ti substrate. • Such Fe-doped RuO 2 anode showed a remarkable oxygen evolution potential of 3.12 V. • Such Fe-doped RuO 2 anode delivered superior As(III) oxidation ratio of 98.5%. • Amorphous Fe-doped RuO 2 was proven to reduce the generation energy of · OH and · SO 4 −. • Electrocatalytic oxidation reaction mechanism was ascribed to the synergy of · OH and · SO 4 −. Design of electrode materials for stable and efficient electrocatalytic oxidation of As(Ⅲ) in arsenic-contaminated groundwater poses a great challenge due to the rapid deactivation of catalysts resulting from the high oxygen evolution potential (OEP) and considerable barrier to generating reactive oxygen species (ROS). Herein, an innovative TNAs/SnO 2 /PEDOT/Fe(III)-RuO 2 multilayer electrode was synthesized by utilizing a PEDOT-coated SnO 2 interlayer as a supportive framework to combine Fe-doped amorphous RuO 2 catalytic layer with TiO 2 nanotube array substrate. Such electrode exhibited high activity and stability for the oxidation of As(III) to As(V) due to the large surface area provided by the TiO 2 nanotube arrays and the SnO 2 /PEDOT interlayer for facilitating the growth of the catalytic layer. The electrochemically active surface area of the electrode reached as high as 31.7 mF/cm2. Impressively, the doping of Fe into RuO 2 layer led to a remarkable increase in the OEP value to 3.12 V, which boosted the indirect oxidation process mediated by ROS at a lower potential to achieve the As(III) oxidation ratio of 98.5%. DFT calculations revealed that the Fe-doped amorphous RuO 2 weakened the adsorption strength of · OH and · SO 4 − intermediates and lowered the energy barrier for generating ROS. Combined with ESR results, the formation of · OH and · SO 4 − with strong oxidizing properties was fully verified, providing further evidence for the involvement of ROS as the main mediator of the oxidation mechanism of As(III). This work may provide valuable perspectives into the design of catalytic layer structures and heteroatom doping modifications for composite-coated electrodes. [ABSTRACT FROM AUTHOR]

Two-dimensional van der Waals (2D vdW) heterostructure photodetectors have garnered significant attention for their potential applications in next-generation optoelectronic systems. However, current 2D vdW photodetectors inevitably encounter compromises between responsivity, detectivity, and response time due to the absence of multilevel regulation for free and photoexcited carriers, thereby restricting their widespread applications. To address this challenge, we propose an efficient 2D WS2/CuInP2S6vdW heterostructure photodetector by combining band engineering and ferroelectric modulation. In this device, the asymmetric conduction and valence band offsets effectively block the majority carriers (free electrons), while photoexcited holes are efficiently tunneled and rapidly collected by the bottom electrode. Additionally, the ferroelectric CuInP2S6layer generates polarization states that reconfigure the built-in electric field, reducing dark current and facilitating the separation of photocarriers. Moreover, photoelectrons are trapped during long-distance lateral transport, resulting in a high photoconductivity gain. Consequently, the device achieves an impressive responsivity of 88 A W–1, an outstanding specific detectivity of 3.4 × 1013Jones, and a fast response time of 37.6/371.3 μs. Moreover, the capability of high-resolution imaging under various wavelengths and fast optical communication has been successfully demonstrated using this device, highlighting its promising application prospects in future optoelectronic systems.

ABSTRACTMembers of Puccinia(Pucciniaceae, Pucciniales) are known as plant pathogens worldwide, which are characterized by their morphology, host association, and molecular data of various genes. In the present study, 10 specimens of Pucciniawere collected from four herbaceous plants (Anaphalis hancockii, Anthriscus sylvestris, Halenia elliptica, and Pilea pumila) in China and identified based on morphology and phylogeny. As a result, 10 samples represent four undescribed species of Puccinia, viz., P. apdensia, P. decidua, P. dermatis, and P. lianchengensis, spp. nov. P. apdensiais characterized by its smooth teliospores with thickened apex. P. deciduarepresents the first Pucciniaspecies inhabiting the host Anaphalis hancockiiand is distinguished from the other Pucciniaspecies by its telia and uredinia surrounded by the epidermis. P. dermatisfrom Halenia ellipticadiffers from the other Pucciniaspecies on the host genus Haleniaby the telia that have epidermis and teliospores with sparsely irregular granulated protrusions. P. lianchengensisis characterized by its teliospore surface with fishnet ornamentation and urediniospores without prominent caps. All of the new species are described and illustrated in this study.

With the growth of the global population and the development of the economy, the demand for delicious and nutritious meat is increasing. However, the pursuit of high productivity and a high lean meat rate in pig breeding has led to a serious decline in meat quality. How to moderately improve the quality and flavor of pork through nutritional supplementation is of great significance in meeting consumers' demand for high‐quality and delicious pork. Recent studies have shown that fermented feed using probiotics can not only improve the nutritional quality and utilization rate of agricultural by‐products, but also play an important role in improving meat color, increasing intramuscular fat content, regulating the flavor of pork, leading to a sustainable development of agriculture and husbandry. In this paper, we will review the current situation of the probiotics used in fermented feed, the effects of fermentation on the improvement of feed quality, the influence and mechanism of fermented feed on the growth performance, meat quality, and flavor of pigs, and so forth, aiming to provide theoretical basis and technical support for the research and development of fermented feed and its application in the production of high‐quality pork. The use of fermented feed may be a convenient and useful measure for the production of high‐quality pork, ultimately promoting the sustainable development of agriculture and husbandry. Fermented feed used different probiotics, fermented materials, and conditions could modulate feed quality, thereby regulating flavor and nutritional quality of pork. Fermentation degrades the content of antinutritional factors and provides beneficial metabolites in feeds, as well as improves nutrient utilization, and enhances the antioxidant capacity and immune function of animals. The flavor of pork is eventually altered by fermented feed, which also changes the gut microbiota, as well as the deposition of intramuscular fat and myofiber type conversion, and increases the content of biochemical components in muscles, thus regulating the sensory and nutritional qualities of pork.

The identification of Chinese medicinal herbs occupies a crucial part in the development of the food and drug market. Although molecular identification based on real-time PCR offers good versatility and uniform digital standards compared with traditional methods, such as morphology, the dependence on large-scale equipment hinders spot detection and marketable applications. In this study, we developed a DNA nanoclaw for colorimetric detection and visible on-site identification of Chinese medicines. When specific miRNA is present, the DNAzyme is activated and cleaves the substrate strand, triggering the catalytic hairpin assembly (CHA) reaction and forming branched DNA junctions on AuNP-I. This can then capture AuNP-II through hybridization and facilitate their aggregation, resulting in a noticeable color change that is observable to the naked eye. By harnessing the dual amplification of DNAzyme and CHA, this highly sensitive nanoprobe successfully achieved specific identification of Chinese medicines. This offers a new perspective for on-site testing in the herbal market.

Symbiotic radio (SR) is a promising solution to achieve high spectrum- and energy-efficiency due to its spectrum sharing and low-power consumption properties, in which the secondary system achieves data transmissions by backscattering the signal originating from the primary system. In this paper, we are interested in the pilot design and signal detection when the primary transmission adopts orthogonal frequency division multiplexing (OFDM). In particular, to preserve the channel orthogonality among the OFDM sub-carriers, each secondary symbol is designed to span an entire OFDM symbol. The comb-type pilot structure is employed by the primary transmission, while the preamble pilot structure is used by the secondary transmission. With the designed pilot structures, the primary signal can be detected via the conventional methods by treating the secondary signal as a part of the composite channel, i.e., the effective channel of the primary transmission. Furthermore, the secondary signal can be extracted from the estimated composite channel with the help of the detected primary signal. The bit error rate (BER) performance with both perfect and estimated CSI, the diversity orders of the primary and secondary transmissions, and the sensitivity to symbol synchronization error are analyzed. Simulation results show that the performance of the primary transmission is enhanced thanks to the backscatter link established by the secondary transmission. More importantly, even without the direct link, the primary and secondary transmissions can be supported via only the backscatter link.

To evaluate the diagnostic value of musculoskeletal ultrasound measurements of subacromial bursa (SAB) thickness, supraspinatus tendon (SUP) thickness, acromiohumeral distance (AHD), and SUP‐to‐AHD ratio (AHD%) in patients with shoulder impingement syndrome (SIS). This was a prospective cross‐sectional observational study. Thirty patients with SIS (60 shoulders) admitted between January 2019 and January 2020 were enrolled. The SUP thickness, SAB thickness, AHD, and AHD% (calculated as AHD% = [(SUP / AHD) × 100%]) were measured in 60 shoulders using musculoskeletal ultrasound. The affected shoulder displayed thicker SUP and SAB (t= 7.838), narrower AHD (t= 2.324), and larger AHD% (t= 6.875) than the unaffected shoulder (P< .05). The SUP thickness showed a linear positive correlation with AHD (r= .503) and AHD% (r= .792) in the affected shoulder (P< .05). On receiver operating characteristic analysis, AHD*AHD% showed the best diagnostic performance in both measurements (area under the curve: 0.877). This study revealed that SIS symptoms may be related to a larger AHD% with SUP thickening. As diagnostic criteria, the cut‐off values of AHD% (65.6%) and AHD*AHD% (0.504) have good sensitivity and specificity and can help improve the differential diagnosis of patients with SIS.

The WUSCHEL-related homeobox (WOX) gene family is essential for plant growth and development. However, the identification and evolutionary analysis of WOXgenes in the Cucurbitaceae have not been well investigated. In the present work, we performed a genome-wide analysis to identify WOXgenes in eleven cucurbit crops. In total, 127 WOXgenes were identified in eleven cucurbit crops, and classified into three different clades: the ancient clade, the intermediate clade and the WUS clade. The same clade has comparable gene organization and conserved motif patterns. The cis-acting element analysis revealed that WOXgenes were involved in light, hormone and stress in cucurbit crops. Gene duplication analysis indicated that dispersed duplication and whole genome duplication were the two primary causes of WOXgenes expansion. There were 24 and 129 gene pairs found within cucurbit crops, and between Cucumis sativusand other cucurbit crops, respectively. After gene duplication, all WOXgenes were subjected to substantial purifying selection pressure according to the selective pressure analysis. Functional divergence analysis revealed that ancient and intermediate clades may have descended from some essential amino acid sites rather than different evolution rate. Our research provides a comprehensive analysis of WOXgenes which will be helpful for further research into their function study in Cucurbitaceae plants.

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.

Massive multiple-input multiple-output (MIMO) is a key enabling technology for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance the network performance. In this paper, we investigate the sum rate maximization problem in a massive MIMO mobile cellular network, where in each cell a multi-antenna BS serves multiple mobile users simultaneously via downlink beamforming. Although existing optimization-based CBF algorithms can provide near-optimal solutions, they require real-time and global channel state information (CSI), in addition to their high computation complexity. Due to the non-negligible delay of practical backhaul networks and the high-complexity optimization process, it is almost impossible to apply them in mobile cellular networks. Noting that the considered problem under the practical constraints can be modeled as a networked distributed partially observable Markov decision process, we propose a deep reinforcement learning-based distributed dynamic coordinated beamforming (DDCBF) scheme, which enables each BS to determine the beamformers with only local CSI and some historical information from other BSs. Besides, the beamformers can be calculated with a considerably lower computational complexity by exploiting neural networks and expert knowledge, i.e., a solution structure observed from the iterative procedure of the centralized optimization algorithms. Moreover, we provide extensive numerical simulations to validate the effectiveness of the proposed DRL-based approach. With lower computational complexity and less required information, the results show that the proposed approach can achieve comparable performance to the centralized iterative optimization algorithms.

This paper studies the reconfigurable intelligent surface (RIS)-assisted symbiotic radio (SR) system, where an RIS acts as a secondary transmitter to transmit its information by leveraging the primary signal as its RF carrier and simultaneously assists the primary transmission. Conventionally, all reflecting elements of the RIS are used to transmit the secondary signal, which, however, would limit its capability for assisting the primary transmission. To address this issue, we propose a novel RIS partitioning scheme, where the RIS is partitioned into two sub-surfaces, one to assist the primary transmission and the other to transmit the secondary signal. Naturally, there exists a fundamental tradeoff between the assistance and transmission capabilities of RIS regarding the surface partitioning strategy. Considering the coupling effect between the primary and secondary transmissions, we focus on the detection of the composite signal formed by the primary and secondary ones, based on which we propose a novel two-step detector. Then, we formulate the assistance-transmission tradeoff problem to minimize the bit error rate (BER) of the composite signal by jointly optimizing the surface partitioning strategy and the phase shifts of the two sub-surfaces, such that the overall BER of RIS-assisted SR is minimized. By solving this problem, we show that the optimized surface partitioning strategy depends on the channel strength ratio of the direct link to the reflected link. Moreover, performance analysis shows that when the direct link is blocked, exchanging the number of reflecting elements used for assistance and transmission can still achieve almost the same BER of the composite signal thanks to the coupling effect. Finally, extensive simulations show that our proposed RIS partitioning scheme outperforms the conventional schemes which use all reflecting elements for either assistance or transmission.

Deep learning-empowered semantic communication is regarded as a promising candidate for future 6G networks. Although existing semantic communication systems have achieved superior performance compared to traditional methods, the end-to-end architecture adopted by most semantic communication systems is regarded as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. Specifically, a textual knowledge base constructed by inherently readable sentences is introduced into our system. With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. In order to make the proposed system more reliable, the semantic self-information and the source entropy are mathematically defined based on the knowledge base. Furthermore, the knowledge base construction algorithm is developed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. Moreover, the simulation results have demonstrated that the proposed approach outperforms existing baseline methods in terms of transmitted data size and sentence similarity.

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

A mathematical model was developed to get an insight into 3D electromagnetic–thermal phenomena in an induction heated steel slab with a large width/thickness ratio by solving a fully coupled Maxwell equations and energy conservation equation. The spatial heat radiation between the slab surface and the refractory wall in an induction furnace was considered. The electromagnetic induction and temperature distribution features inside the slab with time evolution were described. The effects of electromagnetic induction parameters, thermal conductivity of the material and process parameters on the induction heating process were theoretically examined. Numerical results show that the spatial distribution of induction heat generation inside the slab is non-uniform. The high work frequency and the low heat conductivity of slab increase the non-uniformity of slab induction heating process. The radiative heat loss and heat transfer between the slab and the metal parts used for holding the slab have an important effect on the slab surface temperature. It is necessary to separately control the current input in the multi-layer coil windings along the height direction of furnace and adopt the multi-step heating strategies with time evolution to improve the homogenization of the slab temperature.

Interspecific hybridization in the genus Rosa(Rosaceae) is a common natural phenomenon. Hybrids often exhibit heterosis and new combinations of traits, which can provide raw materials for horticultural breeding. DNA barcodes and microsatellites have been proposed to facilitate species discrimination and hybrid detection. However, most SSR markers developed for roses have been found unapplicable to Rosasect. Synstylaebecause of null alleles or failed amplification. In this study, we designed 15 pairs of microsatellite primers, along with four previously developed primers specifically for Rosasect. Synstylae; we then analyzed 174 individuals of three closely related and sympatrically distributed Rosaspecies as a test case to evaluate the consistency between morphological and genetic hybrid identifications and to compare the discrimination efficiency of the DNA barcodes versus SSRs in detecting admixture. Principle coordinate analysis identified several individuals with intermediate phenotypes among the three rose species. Hybridization, intraspecific morphological polymorphism, and sample collection at different growth stages or phenological phases may have hindered species identification based on morphology and distorted the morphological clustering results. The molecular analyses showed that 12 (6.8%), 13 (7.4%), and 15 (8.6%) individuals were identified as admixed by STRUCTURE, NewHybrids, and nrITSsequences, respectively, of which only seven hybrids showed signs of admixture across all three methods. About 81% of the morphologically identifiable hybrids exhibited admixture based on SSRs. Meanwhile, approximately 69% of morphologically identifiable hybrids were detected, but four morphologically pure species individuals were identified as genetically admixed based on nrITSsequences. Some morphologically pure species individuals were genetically identified as hybrids while some morphological hybrids were identified as pure individuals based on certain molecular markers. Overall, EST-SSRs discriminated morphological hybrids more accurately than nrITS. We inferred that there is ongoing interspecific gene exchange among the three wild Rosaspecies that obscures morphospecies boundaries. Combining multiple data types and analytical approaches offers powerful utility for hybrid detection, regardless of the level of hybridization.

Sequence construction, input form, physical interpretation, machine learning models, and robustness are comprehensively compared for partial charging curves based battery capacity estimation methods.

Wireless communication is vulnerable to malicious jamming attacks due to the inherent broadcasting nature of wireless channels. This paper investigates an anti-jamming communication system that employs a reconfigurable intelligent surface (RIS) to enhance desired signals and suppress jamming signals. To optimize the system performance while guaranteeing fairness, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) at the legitimate user equipments by jointly optimizing the transmit beamforming vectors at the base station (BS) and the reflecting coefficients at the RIS, subject to the BS’s maximum transmit power constraint and the RIS’s reflecting coefficient constraints. To solve the non-convex max-min-fairness optimization problem, we propose an alternating-optimization (AO)-based approach that alternates between optimizing variables using a second-order-cone program and semi-definite relaxation techniques. Considering the piratical limitation of imperfect jammer-related channel state information (CSI), we also adopt the stochastic successive convex approximation technique for tackling imperfect CSI in the AO-based approach. Furthermore, we propose a deep-reinforcement-learning (DRL)-based solving approach that does not require the jammer-related CSI. Numerical results show that both approaches improve the minimum SINR performance significantly. Although the AO-based approach with real-time CSI slightly outperforms the DRL-based approach with historical CSI, the DRL-based approach uses the trained deep neural network to obtain the beamforming decision directly without solving optimization problems.

Symbiotic communication (SC), exploiting the analogy of biological ecosystem to establish communication device ecosystems, can enable cooperative service/resource exchanges across heterogeneous devices thus realizing the complementarity among different communication resources. However, considering unstable wireless links, high network dynamics, and complex electromagnetic interference in such an ecosystem, it is difficult to perform service/resource exchanges without securing a trusted environment. Moreover, multi-dimensional service/resource exchange demanded by massive symbiotic devices (SDs) in the ecosystems exposes additional challenges for exchange decision-making. To deal with the above difficulties, in this article, we propose a blockchain-empowered intelligent coevolution for symbiotic devices (BIO-SD). Specifically, to guarantee the trustworthiness of service/resource exchange and resist malicious attacks, a direct acyclic graph (DAG)-based blockchain architecture is applied to the BIO-SD scheme. Furthermore, a modified multi-agent deep deterministic policy gradient (MADDPG) approach is adopted to make service/resource exchange decisions under this trusted environment. The simulation results show that the proposed BIO-SD scheme outperforms some conventional solutions in terms of transmission rate and transmission latency under both non-attack and malicious attack scenarios.

Tissue adhesion is one of the most common postoperative complications, which is frequently accompanied by inflammation, pain, and even dyskinesia, significantly reducing the quality of life of patients. Thus, to prevent the formation of tissue adhesions, various strategies have been explored. Among these methods, placing anti-adhesion membranes over the injured site to separate the wound from surrounding tissues is a simple and prominently favored method. Recently, electrospun nanofibers have been the most frequently investigated antiadhesive membranes due to their tunable porous structure and high porosities. They not only can act as an essential barrier and functional carrier system but also allow for high permeability and nutrient transport, showing great potential for preventing tissue adhesion. Herein, we provide a short review of the most recent applications of electrospun nanofibrous antiadhesive membranes in tendons, the abdominal cavity, dural sac, pericardium, and meninges. Firstly, each section highlights the most representative examples and they are sorted based on the latest progress of related research. Moreover, the design principles, preparation strategies, overall performances, and existing problems are highlighted and evaluated. Finally, the current challenges and several future ways to develop electrospun nanofibrous antiadhesive membranes are proposed. The systematic discussion and proposed directions can shed light on ideas and guide the reasonable design of electrospun nanofibrous membranes, contributing to the development of exceptional tissue anti-adhesive materials in the foreseeable future.

Spectrum and service sharing among multiple mobile network operators (MNOs) is regarded as a promising technology to construct future mobile networks, because it provides a higher utilization efficiency of the network resources, e.g., spectrum and network infrastructure. However, each individual MNO may not be able to benefit from this technology without joint optimizations. In this paper, we investigate a symbiotic spectrum and service sharing paradigm, which can improve the overall performance of the multi-MNO network while guaranteeing each MNO benefits as well. Particularly, we formulate two user association problems with novel mutual benefit constraints, namely, sum rate maximization and load balancing. Besides, we introduce a sharing coefficient for inter-MNO service sharing, which can be regarded as an inter-MNO service level agreement (SLA) accounting for the regulation of user association behaviors. Then, we propose a successive convex approximation (SCA) based algorithm and a fractional programming (FP) based algorithm to solve the sum rate maximization problems for different inter-MNO sharing strategies. In addition, we also develop a Lagrangian dual decomposition based algorithm for the load balancing problems. Finally, extensive numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. The results show that mutual benefit constraints can help multiple MNOs realize a symbiotic spectrum and service sharing paradigm. By comparing different inter-MNO sharing strategies, it can also be observed that the highest performance gain can be achieved when the multiple MNOs share their spectrum and service simultaneously.

The design of a bending angle sensor consisting of plastic optical fibers (POFs) embedded in transparent tubing is proposed for the detection of the angle of human joints. Highly sensitive sensing is achieved by the bending of the transparent tubing between the two fiber end faces which is functioning as a hollow-core optical fiber with the air inside the tubing as the core and the tubing as the cladding. Simulation results based on the ray tracing method show that when the transparent tubing was bent, both end-face coupling and internal reflection mechanisms coexisted in the beam transmission process, with reflection in the tubing being the main mechanism at a large bending angle. A motor-driven angle measurement system is constructed to achieve accurate control of the bending angle. Experimental results show that the sensor can achieve ultra-wide-angle detection ranging from 0° to 180° with a high sensitivity of 0.136 dB/° and a linear regression coefficient ( $\text{R}^{{2}}{)}$ of 0.997. Finally, the dynamic response characteristics of the sensor are tested on both motor-controlled bending and joint bending, and the results show fast response, good repeatability, and stability of the proposed sensor.

Federated Learning (FL) enables large-scale machine learning without uploading the private data of wireless devices. Due to the heterogeneity and limitation of the devices' resources, the FL accuracy and latency substantially depend on the device participation and training dataset size. In this letter, to strike a trade-off between the FL accuracy and FL latency, a joint device participation, dataset management and resource allocation (DPDMRA) optimization problem is investigated. To solve the non-convex optimization problem, a Markov decision process is formulated for the resource-limited wireless FL. Moreover, due to the high dimensional continuous action space, a multi-agent softmax deep double deterministic policy gradients (MASD3) method is employed to obtain the optimal DPDMRA strategies. The double actor networks and softmax operator are designed to alleviate the underestimation bias. Simulation results demonstrate that the proposed DRL method can obtain the global optimal policy without complete information in the dynamic environment. Compared with the other baseline schemes, the proposed MASD3 approach can achieve the larger system utility with the better convergence performance.

To provide global coverage and ubiquitous wireless services, non-terrestrial networks (NTNs) composed of space-tier, air-tier, and ground-tier stations, have been regarded as a key enabling technology toward the sixth generation (6G) networks. Such a multi-tier architecture, however, induces fundamental challenges in optimizing the overall system performance of the NTN in terms of signaling overheads, complexity, and even analytical formulation of the optimization problem. Interestingly, deep reinforcement learning (DRL) is an effective approach to tackle “complex” optimization problems. When applied to the NTN, however, different DRL methods should be developed for various stations due to different constraints in each tier, which raises fundamental issues involving the interaction, orchestration, and protocol design of multi-tier DRL architecture. In this article, we provide the essential principles for multi-tier DRL for the NTN, and propose a generalized multi-tier architecture to embrace different deployment scenarios of NTNs. A case study of joint optimization of spectrum chunk allocation, trajectory design, and user association for space-tier, air-tier, and ground-tier stations is provided. The simulation results indicate that different trade-offs between the overall throughput, optimization dimension, and computational complexity of stations can be achieved through configuring the proposed architectures.

Mobile crowdsourcing network (MCN) is a promising paradigm to leverage mobile users (MUs) to provide sensing services by data collection. However, owing to limited sensing-computation resource and data security risk, MUs may be unwilling to complete sensing tasks without sufficient incentive. Therefore, a deep reinforcement learning (DRL)-based contract incentive mechanism is proposed by jointly considering participation contribution and sensing-computation cost of MUs. Specifically, considering the heterogeneous sensing and computation abilities of MUs, we formulate a task-reward contract incentive mechanism to achieve the maximum utility of mobile crowdsourcing platform. Moreover, based on the individual rationality and incentive compatibility constraints, we design the optimal contract under the partial information asymmetry scenario. Furthermore, considering the network dynamics and privacy protection, we formulate the contract incentive problem as an Markov decision process. Due to the high-dimensional continuous action and state spaces, we develop the twin-delayed deep deterministic method to obtain the efficient sensing task and incentive reward policy under complete information asymmetry scenario. Simulation results demonstrate the feasibility of the proposed DRL-based contract incentive mechanism.

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Identifying and characterizing genes that control important agronomic traits and finding ways to alter them are vital for crop improvement. Map-based cloning, heterologous gene expression, RNAi mediated gene silencing, T-DNA insertional mutation and TILLING technologies have enabled cloning, characterization, and deployment of a few important genes. Revolution in sequencing technologies and bioinformatics has facilitated us to quickly predict and annotate plethora of genes and regulatory elements with improved precision and spatio-temporal expression of genes in almost all cereal crops. However, their functional validation forms a bottleneck in exploiting useful genetic elements for crop improvement. The CRISPR/Cas9 mediated gene editing has become the tool of choice for precisely introducing targeted modifications in the genome to knock out/in a gene, introducing specific mutation in sequence or modulating its expression in diverse crop species. This can help to rapidly characterize plenty of genes in terms of understanding the function of individual gene/ gene family, involvement in a particular biochemical pathway or interaction of the crop plant with external stimuli at a reasonable cost. In this review, we discuss the power of gene editing for rapid functional characterization of genetic elements as a fundamental requirement to harness the power of precision genetic technologies for increasing crop yield, progress made so far, opportunities and challenges.

Active reconfigurable intelligent surface (ARIS) is a newly emerging RIS technique that leverages radio frequency (RF) reflection amplifiers to empower phase-configurable reflection elements (REs) in amplifying the incident signal. Thereby, ARIS can enhance wireless communications with the strengthened ARIS-aided links. In this letter, we propose exploiting the signal amplification capability of ARIS for channel estimation, aiming to improve the estimation precision. Nevertheless, the signal amplification inevitably introduces the thermal noise at the ARIS, which can hinder the acquisition of accurate channel state information (CSI) with conventional channel estimation methods based on passive RIS (PRIS). To address this issue, we further investigate this ARIS-specific channel estimation problem and propose a least-square (LS) based channel estimator, whose performance can be further improved with the design on ARIS reflection patterns at the channel training phase. Based on the proposed LS channel estimator, we optimize the training reflection patterns to minimize the channel estimation error variance. Extensive simulation results show that our proposed design can achieve accurate channel estimation in the presence of the ARIS noises.

A thin-diameter photonic crystal fiber (TDPCF) magnetic field sensor based on hole collapse and infiltration is proposed and demonstrated. Due to the controlled hole collapse in TDPCF, the interaction between the mode field distribution and the magnetic fluid (MF) is effectively strengthened, causing the direct absorption and scattering of magnetic nanoparticles to transmit light. Consequently, the proposed sensor exhibits a magnetic field sensitivity of up to 0.099 dB/Oe within the range of 0 to 60 Oe. The TDPCF-based magnetic field sensor has several desirable merits such as high sensitivity, convenient integration, and good stability, which provides a promising candidate in environmental and industrial-related magnetic field monitoring applications.