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Smart windows with excellent near-infrared (NIR) light shielding capacity and controlled visible light transmittance are expected to reduce building energy consumption and improve indoor comfort. However, the ability of current thermochromic materials to regulate solar gain with the tunable extinction of phase-change materials is still far from optimum. In this work, an fluorine (F)-doped ATO (FATO) nanocrystals (NCs) are designed as a photothermal material to optimize the solar modulation of thermochromic hydrogels. F ion doping can modify the electronic structure of ATO, resulting in a high concentration of oxygen vacancies and free electrons, thereby enhancing the NIR absorption performance and photothermal conversion performance of the ATO NCs. On this premise, the designed smart window based on FATO NCs and thermochromic hydrogels exhibits the remarkable features of excellent NIR shielding, high initial visible transmittance and fast response speed. The window is capable of shielding 99.5% of the solar radiation from 300 to 2500 nm and permitting 75.0% of initial visible light. Moreover, the simulated indoor temperature of the smart window was 17.2 °C lower compared with the black one, demonstrating potentials in practical application of building energy saving. [Display omitted] • Novel chain-like FATO NCs were prepared by precipitation-hydrothermal method. • F-doping optimized electronic structure of ATO and improved the LSPR absorption. • The fabrication of FATO-PNC hydrogel provides a novel idea for smart window. • The smart window could shield 99.5% of solar radiation after optical switching. [ABSTRACT FROM AUTHOR]

Introduction Allergic Rhinitis is defined clinically as having two or more symptoms of anterior or posterior rhinorrhoea, sneezing, nasal blockage and or itching of nose during two or more consecutive days for more than one hour on most days. Exercise induces modulation in innate and adaptive immune system, dependent on host defence, activity level and disease susceptibility. This study was conducted to evaluate the effect of exercise on allergic rhinitis. Methods We report a comparative study conducted among 43 MBBS students of Mysore medical college and research institute. The study was a conducted from March 2022 to December 2022. The study subjects were selected based on SFAR (score for allergic rhinitis) score and they were divided into two groups. Those students with allergic rhinitis and performed exercise of 30 minutes for 5 days a week were considered cases and those who didn't exercise but had allergic rhinitis were placed in the control group. They were followed up for a period of 9 months. The effect of exercise on allergic rhinitis was determined by SFAR score obtained by the students at the end of the study period. Result 43 MBBS students were included in this study. 22 were cases (those with allergic rhinitis and performed exercise) and 21 were controls (those with allergic rhinitis but didn't exercise). 13 students (59.1%) from the case group reported improvement in their allergic symptoms with exercise. 8 (61.5%) out of the 13 students who reported improvement were involved in indoor sports activity. 5 (22.7%) of the cases reported no change in severity of symptoms with exercise. While 4 (18.2%) of the cases reported worsening of their symptoms with exercise. Conclusion During exercise, autonomic reflexes improve nasal efficiency. In dynamic exercise training due to an increase of nasal sympathetic activity, venous sinusoids constrict. This study showed that exercise has a beneficial effect on allergic rhinitis. [ABSTRACT FROM AUTHOR]

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid control method with adaptive phase-shifting modulation (PSM) and pulse-frequency modulation (PFM) is proposed to optimize the steady-state performance of an LLC resonant converter in wide voltage-gain range application. For the primary-side switches under the hybrid control method, zero-voltage switching (ZVS) performance is maintained over wide voltage-gain range and the turn-off loss is reduced; therefore, the converter reconciles wide voltage-gain range and high efficiency. Mode characteristics under different phase-shifting-angle-θ and switching-frequency-fs combinations is analyzed, and then the soft-switching characteristic is revealed. By introducing time domain analysis, the turn-on current of the primary-side switches is calculated, and thus the ZVS boundary of different θ and fs combinations is inferred. In addition, to acquire the optimum steady-state operation of the converter, the turn-off current is calculated; by making the converter work near to the minimum turn-off current operating point, the turn-off loss can be reduced greatly. With the principles of achieving ZVS performance and minimizing turn-off current, the phase-shifting angle θ of PSM is designed to be adaptive to the reference output voltage, no additional circuits are needed, and the two control degrees (θ and fs) are simplified to one (fs). The simulation and experiment are developed to verify the feasibility and effectiveness of the hybrid control method; the results show that ZVS performance is maintained at wide voltage-gain range and the turn-off current of the hybrid control method is reduced to that of the single PFM, and thus the turn-off loss is reduced. The efficiency comparison validates the fact that the hybrid control method has less power loss than single PSM and single PFM. [ABSTRACT FROM AUTHOR]

Helicobacter pylori, a widely renowned bacterium, has recently gained attention owing to its potential impact on extragastric health. The emergence of research linking H. pylori infection with metabolic syndrome (MetS)-related cardiovascular diseases (CVDs) has raised intriguing questions about the pathogenic linkage and its translational implications for clinicians. MetS encompasses a collection of metabolic abnormalities that considerably elevate the risk of CVDs and cerebrovascular diseases. Emerging evidence supports a potential pathogenetic role of H. pylori for MetS-related disorders through mechanisms implicating chronic smoldering inflammation, insulin resistance (IR), and modulation of immune responses. One intriguing aspect of this possible connection is the role of mast cells (MCs), a subset of immune cells representing innate immune system effector cells. They play a fundamental role in innate immune responses and the modulation of adaptive immunity. Activated MCs are commonly found in patients with MetS-related CVD. Recent studies have also suggested that H. pylori infection may activate MCs, triggering the release of pro-inflammatory mediators that contribute to IR and atherosclerosis. Understanding these intricate interactions at the cellular level provides new insights into the development of therapeutic strategies targeting both H. pylori infection and MetS-related MCs activation. This review investigates the current state of research regarding the potential impact of H. pylori infection and MetS-related MCs activation on the pathophysiology of CVD, thereby opening up new avenues for related research and paving the way for innovative approaches to prevention and treatment in clinical practice. [ABSTRACT FROM AUTHOR]

The voltage source converter-based multi-terminal DC transmission (VSC-MTDC) system can use additional frequency control to respond to the frequency change of faulty AC system. However, the control coefficient of traditional additional frequency control is mostly fixed, and the control flexibility is insufficient, so it cannot be adjusted adaptively according to the frequency change of the system. Therefore, a frequency control strategy of the VSC-MTDC system based on fuzzy logic control is proposed. Based on the DC voltage slope controller, this strategy introduces an additional frequency controller based on fuzzy logic control, takes the frequency deviation and frequency change rate as the additional controller input, and dynamically adjusts the control quantity through the fuzzy logic control link to realize the adaptive adjustment of the VSC-MTDC system to the AC system's frequency. Finally, a three-terminal flexible HVDC system is built on the PSCAD/EMTDC simulation platform for simulation verification. The results show that the proposed control strategy can effectively use the flexible DC system to support the frequency of the AC system and significantly improve the frequency stability of the faulty AC system. [ABSTRACT FROM AUTHOR]

Summary: Communication systems that simultaneously combine orthogonal (OMA) and nonorthogonal multiple access (NOMA) are currently under research. Such systems are called hybrid and have more flexibility in user multiplexing, which allows for increased system capacity. One of the main focus areas of research in such systems is radio resource scheduling. The existing research works on scheduling approaches are based on the Shannon's formula, which can be used to obtain the limit capacity by ideal modulation and coding schemes (MCS). However, it is more correct to consider scheduling based on the real capacity. To get it, one should consider the performance of real MCSs with finite code length; however, they are very rarely taken into account in NOMA research. The reason is that one of the basic unresolved problems in NOMA is MCS selection for users. To solve it, we have presented a table of joint pairwise MCS combinations with their adaptive selection for NOMA. In this work, we propose a radio resource scheduling approach for the OMA/NOMA system based on real MCSs that uses our own method of joint MCS selection for two NOMA users. By performing the simulation of the proposed scheduling approach, we have obtained both total and user capacity in the OMA/NOMA system, as well as the distribution of traffic among users, demonstrating the execution of the scheduling strategies. [ABSTRACT FROM AUTHOR]

This study illustrates the salinity tolerance mechanisms in Volkameria inermis (a mangrove-associate), making it an ideal candidate for establishment in saline lands. The plant was exposed to 100, 200, 300, and 400 mM NaCl and the TI value indicates that the stress-imparting concentration was 400 mM. There was a decrease in biomass and tissue water, and a gradual increase in osmolytes like soluble sugars, proline, and free amino acids content was observed in plantlets with the increase in NaCl concentrations. Higher number of lignified cells in the vascular region of the plantlet's leaves treated with NaCl (400 mM) may influence the transport through the conducting tissues. SEM data reveals the presence of thick-walled xylem elements, an increased number of trichomes, and partially/fully closed stomata in the 400 mM NaCl-treated samples of V. inermis. In general, macro and micronutrient distribution tend to be affected in the NaCl-treated plantlets. However, Na content increased remarkably in plantlets treated with NaCl, and the highest accumulation was observed in roots (5.58-fold). Volkameria inermis can be a good option for phytodesalination in salt-affected areas since it is equipped with strong NaCl tolerance strategies and can be exploited for desalinization purpose of salt affected lands. The phytodesalination potential of V. inermis was proved with the aid of physiochemical and anatomical studies, which was not yet revealed. The present study elucidated the level of NaCl tolerance in V. inermis and the development of associated adaptive responses. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

Summary: The work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI. [ABSTRACT FROM AUTHOR]

Digestion is driven by digestive enzymes and digestive enzyme gene copy number can provide insights on the genomic underpinnings of dietary specialization. The "Adaptive Modulation Hypothesis" (AMH) proposes that digestive enzyme activity, which increases with increased gene copy number, should correlate with substrate quantity in the diet. To test the AMH and reveal some of the genetics of herbivory vs carnivory, we sequenced, assembled, and annotated the genome of Anoplarchus purpurescens, a carnivorous prickleback fish in the family Stichaeidae, and compared the gene copy number for key digestive enzymes to that of Cebidichthys violaceus, a herbivorous fish from the same family. A highly contiguous genome assembly of high quality (N50 = 10.6 Mb) was produced for A. purpurescens, using combined long-read and short-read technology, with an estimated 33,842 protein-coding genes. The digestive enzymes that we examined include pancreatic α-amylase, carboxyl ester lipase, alanyl aminopeptidase, trypsin, and chymotrypsin. Anoplarchus purpurescens had fewer copies of pancreatic α-amylase (carbohydrate digestion) than C. violaceus (1 vs. 3 copies). Moreover, A. purpurescens had one fewer copy of carboxyl ester lipase (plant lipid digestion) than C. violaceus (4 vs. 5). We observed an expansion in copy number for several protein digestion genes in A. purpurescens compared to C. violaceus, including trypsin (5 vs. 3) and total aminopeptidases (6 vs. 5). Collectively, these genomic differences coincide with measured digestive enzyme activities (phenotypes) in the two species and they support the AMH. Moreover, this genomic resource is now available to better understand fish biology and dietary specialization. [ABSTRACT FROM AUTHOR]

The objective of fifth generation networking technologies seems to provide faster data throughput, exceptional user exposition, power efficiency, and incredibly low delay. To provide clients with the appropriate quality of services, these wireless networks will employ a variety of multi-layer designs made up of device-to-device connections, macro-cells, as well as various types of smaller cells. Machine-to-machine (M2M) technologies are continuing to increase rapidly in quantity. Without careful and effective utilization of resources, as more gadgets accumulate, M2M connectivity will be irregularly impeded by the speed of service shortage. To solve such problems, we provide a dynamical rate adapting (DRA) method in this study that combines time-dependent with event-based M2M technologies to produce optimized rates of service dispersion. By utilizing the median-valued theory of integrals with generalized processing share, DRA enables real-time tracking of M2M data arriving rate, a foundation upon which the speed of service allocation amongst M2M apps will be briefly modified. [ABSTRACT FROM AUTHOR]

A method for modulating transverse magnetic intrinsic graphene plasmons by temperature is proposed. In contrast to existing 2D or 3D infinite graphene structures, an intrinsic graphene 3D finite structure with periodicity in all three directions is designed. At the matching point, where the dispersion curve of the intrinsic graphene plasmons intersects with that of electromagnetic radiation, the intrinsic graphene plasmons and electromagnetic radiation can be excited by each other; in other words, the plasmon can be excited by light without additional mechanisms. Furthermore, the influence of environment temperature on the matching point is shown. The study reveals that the plasmon modulation is adaptive to the temperature. These results are beneficial for the design of compact graphene‐based optoelectronic devices. [ABSTRACT FROM AUTHOR]

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70. [ABSTRACT FROM AUTHOR]

The neuropeptide orexin is involved in motor circuit function. However, its modulation on neuronal activities of motor structures, integrating orexin's diverse downstream molecular cascades, remains elusive. By combining whole‐cell patch‐clamp recordings and neuropharmacological methods, we revealed that both non‐selective cationic conductance (NSCC) and endocannabinoids (eCBs) are recruited by orexin signalling on reticulospinal neurones in the caudal pontine reticular nucleus (PnC). The orexin–NSCC cascade provides a depolarizing force that proportionally enhances the firing‐responsive gain of these neurones. Meanwhile, the orexin–eCB cascade selectively attenuates excitatory synaptic strength in these neurones by activating presynaptic cannabinoid receptor type 1. This cascade restrains the firing response of the PnC reticulospinal neurones to excitatory inputs. Intriguingly, non‐linear or linear interactions between orexin postsynaptic excitation and presynaptic inhibition can influence the firing responses of PnC reticulospinal neurones in different directions. When presynaptic inhibition is in the lead, non‐linear interactions can prominently downregulate or even gate the firing response. Conversely, linear interactions occur to promote the firing response, and these linear interactions can be considered a proportional reduction in the contribution of depolarization to firing by presynaptic inhibition. Through the dynamic employment of these interactions, adaptive modulation may be achieved by orexin to restrain or even gate the firing output of the PnC to weak/irrelevant input signals and facilitate those to salient signals. Key points: This study investigated the effects of orexin on the firing activity of PnC reticulospinal neurones, a key element of central motor control.We found that orexin recruited both the non‐selective cationic conductances (NSCCs) and endocannabinoid (eCB)–cannabinoid receptor type 1 (CB1R) system to pontine reticular nucleus (PnC) reticulospinal neurones. The orexin–NSCC cascade exerts a postsynaptic excitation that enhances the firing response, whereas the orexin–eCB–CB1R cascade selectively attenuates excitatory synaptic strength that restrains the firing response.The postsynaptic and presynaptic actions of orexins occur in an overlapping time window and interact to dynamically modulate firings in PnC reticulospinal neurones.Non‐linear interactions occur when presynaptic inhibition of orexin is in the lead, and these interactions can prominently downregulate or even gate firing responses in PnC reticulospinal neurones.Linear interactions occur when postsynaptic excitation of orexin is in the lead, and these interactions can promote the firing response. These linear interactions can be considered a proportional reduction of the contribution of depolarization to firing by presynaptic inhibition. [ABSTRACT FROM AUTHOR]

The concept of cognitive radio (CR) as a tool to optimize the obstacle of spectral coexistence has promoted the development of shared satellite–terrestrial wireless networks. Nevertheless, in some applications like Earth Exploration Satellite Services, which demand high spectral efficiency (bps/Hz) for downlink transmissions, spectral coexistence amidst interferences from cellular Base Stations is still challenging. Our research aims to mitigate these interferences on low-orbit satellite downlinks carrying imaging data received from a ground station. In order to fulfill this, we present cognitive radio approaches to enhance spectrum exploitation and introduce the adaptive modulation and coding (MODCOD) technique to increase RF power and spectral efficiencies. Therefore, we propose a combined methodology using CR and adaptive MODCOD (ACM) techniques. Afterwards, we applied the solution by monitoring the signal to interference plus noise ratio and the MODCOD strategy. Finally, we provide a real in situ case study at the Cuiabá ground station located in Brazil's central area, which receives images from an Earth observation satellite (EOS). In addition to demonstrating the strategy effectiveness in this scenario, we conducted a bench test emulating the interfering wireless communication system. In this sense, we demonstrated the proposed approach, successfully mitigating the harmful effects on the received EOS images. [ABSTRACT FROM AUTHOR]

Transformers have been widely applied in various vision tasks processing different data, such as images, videos and point clouds. However, the use of transformers in 3D mesh analysis remains largely unexplored. To address this gap, we propose a mesh transformer (MeT) that utilizes local self-attention on edges. MeT is based on a transformer layer that uses vector attention for edges, which is a kind of attention operator that supports adaptive modulation to both feature vectors and individual feature channels. Based on the transformer block, we build a lightweight mesh transformer network that consists of encoder and decoder. MeT provides general backbones for subsequent 3D mesh analysis tasks. To evaluate the effectiveness of our network MeT, we conduct experiments on two classic mesh analysis tasks: shape classification and shape segmentation. MeT achieves the state-of-the-art performance on multiple datasets for two tasks. We also conduct ablation studies to show the effectiveness of key designs in our network. [ABSTRACT FROM AUTHOR]

Animals' sensory systems adjust their responsiveness to environmental stimuli that vary greatly in their intensity. Here we report the neural mechanism of experience-dependent sensory adjustment, especially gain control, in the ASH nociceptive neurons in Caenorhabditis elegans. Using calcium imaging under gradual changes in stimulus intensity, we find that the ASH neurons of naive animals respond to concentration increases in a repulsive odor 2-nonanone regardless of the magnitude of the concentration increase. However, after preexposure to the odor, the ASH neurons exhibit significantly weak responses to a small gradual increase in odor concentration while their responses to a large gradual increase remain strong. Thus, preexposure changes the slope of stimulus–response relationships (i.e. , gain control). Behavioral analysis suggests that this gain control contributes to the preexposure-dependent enhancement of odor avoidance behavior. Mathematical analysis reveals that the ASH response consists of fast and slow components, and that the fast component is specifically suppressed by preexposure for the gain control. In addition, genetic analysis suggests that G protein signaling may be required for the regulation of fast component. We propose how prior experience dynamically and specifically modulates stimulus–response relationships in sensory neurons, eventually leading to adaptive modulation of behavior. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an adaptive pulse position modulation—Gaussian minimum shift keying—subcarrier intensity modulation (PPM-GMSK-SIM) system over Ғ distribution channel model with pointing error. Here, in our proposed system, an adaptive method is used to mitigate the performance degradation due to atmosphere turbulence (AT). In this method, the modulation order adapts according to instantaneous AT under a target an average bit error rate (ABER) and automatically adjust the modulation order at the receiver accordingly and inform it to the transmitter to adapt the modulation order via a feedback path. We derived ABER for PPM-GMSK-SIM system over Ғ distribution channel model. Moreover, we derived an achievable spectral efficiency and ABER expressions for the adaptive PPM-GMSK-SIM system. Here, we compared the performance of proposed PPM-GMSK-SIM modulation system with the PPM-MSK-SIM modulation system over Ғ distribution channel model and observed that the proposed PPM-GMSK-SIM gives better performance than the PPM-MSK-SIM system and the improvement in the ABER performance is approximately 6 dB at ABER value is 10 - 3 in weak, moderate and strong atmospheric turbulence conditions. Also, the ABER versus received average irradiance performance is approximately 2 dB better in the weak AT and it is approximately 3 dB for moderate AT and strong AT conditions at ABER value is 10 - 3 . We compared the spectral efficiency of the adaptive PPM-GMSK-SIM for target ABER values 10 - 2 and 10 - 3 and observed that the spectral efficiency improves as target ABER decreases and compared the ABER performance of the PPM-GMSK-SIM with the adaptive PPM-MSK-SIM system over Ғ distribution channel model and observed that the system performance improves about 11 dB at SNR value is 10 - 3 . [ABSTRACT FROM AUTHOR]

Removing raindrops adhering to lenses or glass is challenging since raindrops have more complex internal structures and optical effects than rain streaks. Existing raindrop removal methods typically employ single-channel masks for raindrop representation. However, these methods ignore the complementarity of priors between different channels, thereby losing the color and transparency information of raindrops. Moreover, simply applying the same processing across different pixels is insufficient to deal with spatially-varying degradation. To address these limitation, we propose a novel Mutual channel prior guided Dual-domain Interaction Network (MDINet) for single image raindrop removal. Specifically, to effectively guide the restoration of the raindrop region, we propose a mutual channel prior by modeling the multi-channel raindrop soft mask, in which the enriched color and transparency information of raindrops are represented and learned by our model. To tackle the spatially-varying degradation, we design the dual-domain interaction block to capture global low-frequency content and local high-frequency details. Simultaneously, a spatially adaptive modulation block is devised to handle complicated and diverse raindrops. Moreover, we propose a contrastive discrimination strategy to motivate the restored image to approach the clean image and stay away from the raindrop image in the representation space. Extensive experiments on benchmark datasets and real-world images indicate that the proposed method achieves qualitative and quantitative superiority against state-of-the-art methods. [Display omitted] • We propose a novel mutual channel prior for raindrop representation. • We design a dual-domain interaction network to tackle diverse raindrops. • Contrastive discrimination is proposed to mine the mutually exclusive property. [ABSTRACT FROM AUTHOR]

Automatic modulation classification (AMC) has recently acquired a lot of interest in the optical wireless communication (OWC) community. The OWC channel has variable characteristics. Hence, there is a need for an adaptive modulation scheme to cope with the varying channel characteristics. Adaptive modulation requires the implementation of AMC at the receiver side. Instead of using complex classification with deep learning (DL) techniques, a simple proposed scheme for AMC is introduced in this paper. This scheme is based on chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (2/4/8/16 PSK), and (8/16/32/64 QAM). First, the constellation diagrams are acquired and scrambled through the CBM, and they are merged using the wavelet image fusion and stored as reference templates in the system database. After that, the classification of each modulation format depends on estimated correlation scores and a thresholding strategy similar to the strategy adopted in cancellable biometric systems that depend on encrypted templates. Simulation results prove good classification for all studied modulation formats. [ABSTRACT FROM AUTHOR]

Platelets are classically recognized for their important role in hemostasis and thrombosis but they are also involved in many other physiological and pathophysiological processes, including infection. Platelets are among the first cells recruited to sites of inflammation and infection and they exert their antimicrobial response actively cooperating with the immune system. This review aims to summarize the current knowledge on platelet receptor interaction with different types of pathogens and the consequent modulations of innate and adaptive immune responses. [ABSTRACT FROM AUTHOR]

Smart windows with light management and indoor solar heating modulation capacities are of paramount importance for building energy conservation. Thermochromic poly(N‐isopropylacrylamide) (PNIPAm) hydrogel smart windows exhibit advantages of the relatively suitable transition temperature of 32 °C, high cost‐effective and automatic passive sunlight regulation, but sustain slow response rate and unsatisfactory solar modulation efficiency. Herein, a strategy of one‐step copolymerization of NIPAm and different olefine acids (OA) using reverse atom transfer radical polymerization method is developed to fabricate various chain/microparticle hybrids (CMH) for liquid energy‐saving windows. Synergetic mechanisms of thermal‐induced dissolution and aggregation of linear polymer chains integrated with water capture and release of microgel particles contribute to tunable light‐scattering behaviors and adaptive solar modulation. Without any post‐treatment, the as‐prepared poly(N‐isopropylacrylamide‐co‐acrylic acid) (P(NIPAm‐co‐AA))‐based CMH suspension is injected into sandwich glass to construct energy‐saving windows, which exhibits appreciated near‐room‐temperature transition (26.7 °C), rapid response (5 s), extraordinary luminous transmittance (91.5%), and solar modulation efficiency (85.8%), resulting in a substantial decline of indoor temperature of 24.5 °C in simulation experiment. Combining the versatile strategy with flexible adjustment on transition temperature, multifarious P(NIPAm‐co‐OA)‐based CMH windows with eminent light management capacity are obtained. This work will powerfully promote the development and renovation of energy‐efficient windows. [ABSTRACT FROM AUTHOR]

Contour error compensation is an active research topic in five-axis CNC machining, especially in the manufacturing of sculptured surface parts. Nevertheless, current methods are mainly based on the mirror compensation principle, and fail to obtain a desired level of accuracy when processing parts with tight curvature feature. To address this issue, a global toolpath modulation-based contour error pre-compensation method is developed in this paper, which incorporates the error compensation issue into the stage of toolpath planning with a linear analytical solution. In this method, the nominal toolpaths used to machine the products is first expressed by dual B-spline curves, and then the instantaneous tracking error model of each individual drive is built with respect to control points of splined path. Afterward, the satisfaction condition of the spline control points for eliminating the contour error is yielded, which provides a possibility for compensating contour error in a global manner, and the neighbor-dependent coupling issue in error compensation between adjacent cutter location points is capable of being handled as well. On this basis, by applying the least-squares technique, the complicated contour error pre-compensation problem is further converted into a solution of simpler linear equation system. For enhancing its robustness when processing long toolpaths, an adaptive piecewise modulation strategy is also developed. Finally, both experiment and simulation are conducted to validate the proposed method, and the results demonstrate that the proposed method can significantly improve contour precision at low computational costs when compared with the existing pre-compensation method. [ABSTRACT FROM AUTHOR]

Background: During myocardial damage, the sex hormone estrogen and CD73, the main enzyme that converts AMP into adenosine, are cardioprotective molecules. However, it is unclear how these two molecules work together to provide cardioprotection. The current study aimed to elucidate the interaction between estrogen and CD73 under chronic stress. Methods: Ovariectomy and SHAM operations were done on FVB wild-type (WT) female mice. Two weeks after the operation, the mice were treated with daily isoproterenol (10 mg/kg/day) injections for 14 days. The effect of E2 on relevant cardiac injury biomarkers (BNP, ANP), myocardial morphology (cardiomyocyte surface area), electrocardiography, CD73 protein expression and activity, and macrophage (CD86 + and CD206 +) infiltrations were assessed. In vitro, H9C2 cells were treated with 1 nM of estrogen and 10 mM APCP (CD73 inhibitor α, β-methylene adenosine-5'-diphosphate), 10 µM isoproterenol and 20 µm LY294002 (PI3K inhibitor) for 24 h and western blot was done to elucidate the mechanism behind the effect of estrogen on the CD73/adenosine axis. Results: Estrogen deficiency during chronic catecholamine stress caused myocardial injury, thereby triggering the hyperactivity of the CD73/adenosine axis, which aggravated myocarditis, adverse remodeling, and arrhythmias. However, estrogen normalizes CD73/Adenosine axis via the upregulation of PI3K/Akt pathways to prevent adverse outcomes during stress. In vivo results showed that the inhibition of PI3K significantly decreased PI3K/Akt pathways while upregulating the CD73/adenosine axis and apoptosis. Conclusion: Estrogen's pleiotropy cardioprotection mechanism during stress includes its normalization of the CD73/Adenosine axis via the PI3K/Akt pathway. 9z1eijmQFGaVTY6LcoepL6 Video Abstract [ABSTRACT FROM AUTHOR]

Background: Emerging evidence of immunological dysfunction have been described in endometriosis. Dendritic cells (DCs), one of the main antigen-presenting cells, are specialized in the initiation and modulation of the adaptive immune response. Emerging studies demonstrated both endometrial and circulating differences in DCs populations in women with endometriosis. However, the role and mechanism of peritoneal DCs in endometriosis is still unclear. The present study was undertaken to explore the features of peritoneal DCs in the pathogenesis of endometriosis. This study is beneficial to further clarify the cause of endometriosis and provide a new insight into the medical treatment for endometriosis. Methods: The study included 12 women with endometriosis and 11 women without endometriosis. The C57BL6 mouse model of endometriosis was established by intraperitoneal injection of endometrial segments. The peritoneal DCs of endometriosis patients and mouse models were analyzed by fluorescence associated cell sorting (FACS) examination. Results: Increased cell density of peritoneal DCs were observed in endometriosis patients. Moreover, the proportion of mature DCs (mDCs, CD80highCD1alow cells) in the peritoneal DCs was lower whereas the proportion of immature DCs (iDCs, CD80lowCD1ahigh cells) was increased in endometriosis patients. Similarly, the cell density of peritoneal DCs in murine models increased immediately after the injection of endometrial tissues and reached the highest level at 14 days. In addition, the proportion of mDCs (CD11chighCD80high cells) in the peritoneal DCs decreased immediately after the injection of endometrial tissues and then increased with the time until 42 days, but still lower than the control group. In contrast, the proportion of iDCs (CD11chighCD80low cells) in the peritoneal DCs showed the opposite dynamic changes. However, after treated with LPS, the mDCs proportion was significantly increased, leading to lower volume and weight of the endometriosis lesions. Conclusions: Increased level of peritoneal DCs facilitated the pathogenesis of endometriosis lesions, especially in the early stage of the disease. Furthermore, peritoneal DCs maturation played an important role in the development of endometriosis. [ABSTRACT FROM AUTHOR]

Semantic face editing has achieved substantial progress in recent years. However, existing face editing methods, which often encode the entire image into a single code, still have difficulty in enabling flexible editing while keeping high-fidelity reconstruction. The one-code scheme also brings entangled face manipulations and limited flexibility in editing face components. In this paper, we present IA-FaceS, a bidirectional method for disentangled face attribute manipulation as well as flexible, controllable component editing. We propose to embed images onto two branches: one branch computes high-dimensional component-invariant content embedding for capturing face details, and the other provides low-dimensional component-specific embeddings for component manipulations. The two-branch scheme naturally enables high-quality facial component-level editing while keeping faithful reconstruction with details. Moreover, we devise a component adaptive modulation (CAM) module, which integrates component-specific guidance into the decoder and successfully disentangles highly-correlated face components. The single-eye editing is developed for the first time without editing face masks or sketches. According to the experimental results, IA-FaceS establishes a good balance between maintaining image details and performing flexible face manipulation. Both quantitative and qualitative results indicate that the proposed method outperforms the existing methods in reconstruction, face attribute manipulation, and component transfer. We release the code and weights at: https://github.com/CMACH508/IA-FaceS. [ABSTRACT FROM AUTHOR]

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency. [ABSTRACT FROM AUTHOR]

Resonant mode dual active bridge (DAB) bidirectional dc–dc converters using either an inductive–capacitive–inductive (LCL) or a capacitive–inductive–capacitive (CLC) ac-port network offer several performance benefits compared to more conventional nonresonant filter DAB topologies. This includes a reduction in the circulating power between the two bridges, which consequently enables the conduction loss to be minimized and the physical converter volume to be reduced. However, these gains also require that soft-switching is maintained, which is very challenging to achieve across all operating conditions, particularly at light loads. This article investigates the zero-voltage-switching (ZVS) capability of resonant mode DAB converters, applying Fourier analysis to the converter modulation patterns to quantify the ZVS boundaries analytically. The resulting analytic formulations also quantify the DAB real and reactive power flows, which subsequently enables the identification of modulation conditions that improve the converter's soft-switching and reactive circulating power flow behavior. The analysis is verified using detailed time-domain PSIM simulations matched to experimental DAB converters equipped with both LCL and CLC filters. [ABSTRACT FROM AUTHOR]

CpG Oligodeoxynucleotides (ODNs) are established TLR9 ligands; however, their functional responses in CD4+ T cells are believed to be independent of TLR9 and MyD88. We studied ligand‐receptor interactions of ODN 2216 and TLR9 in human CD4+ T cells and assessed their consequences in terms of TLR9 signalling and cell phenotype. We demonstrated that the uptake of ODN 2216, a synthetic TLR9 agonist, is controlled by TLR9 signalling molecules and results in an increase in the expression of TLR9 signalling molecules, regulated via a feedback mechanism. Next, the uptake of ODN 2216 resulted in TLR9 signalling dependent but MyD88 independent increase in expression of TGF‐β. Finally, ODN 2216 treated CD4+ T cells showed an anti‐inflammatory phenotype that was similar to Th3 type of regulatory T cells. These Th3‐like cells were able to suppress the proliferation of untreated CD4+ T cells. Collectively, our results demonstrate a direct and interdependent relationship between ODN 2216 uptake and TLR9 signalling in CD4+ T cells. Our findings thus pave the way for future research to explore direct modulation of adaptive immune cells, using innate immune ligands, to subvert exaggerated inflammatory responses. [ABSTRACT FROM AUTHOR]

The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the $k$ nearest neighbor ($k$ -NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost. [ABSTRACT FROM AUTHOR]

• We propose a novel network for better stereo image super-resolution. • We propose an adaptive modulation alignment mechanism to modulate the aligned features. • We add rectangular convolutional layers to expand the receptive field along the disparity epipolar line. • Experimental results show that our method outperforms other state-of-the-art methods on the three stereo benchmarks. Deep learning based stereo image super-resolution algorithm can make use of the additional information in the stereo image pairs to improve the quality of the reconstructed images. However, extracting the similarity information of one image to another precisely is challenging when partially occluded regions exist in stereo images. In this paper, we propose an adaptive modulation alignment mechanism to modulate the aligned features calculated from the parallax attention mechanism and effectively deal with the inaccuracy caused by occlusion. Furthermore, because disparities of stereo image exist only along the epipolar line, we use rectangular convolution kernel in some convolution layers, to extend the receptive field horizontally. Finally, experimental results demonstrate that our method achieves state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 stereo benchmarks. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) adaptive modulation (AM) requires feedback about channel state information (CSI) but the long propagation delays and time-varying features of UWA channels can cause the CSI feedback to be outdated. When the AM mode is selected by outdated CSI, the mismatch between the outdated CSI and the actual CSI during transmission degrades the performance and can even lead to communication failure. Reinforcement learning has the ability to learn the relationships between adaptive systems and the environment. This paper proposes a deep Q-network (DQN)-based AM method for UWA communication that uses a series of outdated CSI as the system input. Our study showed that it could extract channel information and select appropriate modulation modes in the expected channels more effectively than single Q-learning (QL) without needing a deep neural network structure. Furthermore, to mitigate any decision bias that was caused by partial observations of UWA channels, we improved the DQN-based AM by integrating a long short-term memory (LSTM) neural network, named LSTM-DQN-AM. The proposed scheme could enhance the DQN's ability to remember and process historical input channel information, thus strengthening its relationship mapping ability for state-action pairs and rewards. The pool and sea experimental results demonstrated that the proposed LSTM-DQN-AM outperformed DQN-, QL- and threshold-based AM methods. [ABSTRACT FROM AUTHOR]

Existing spectral super-resolution (SSR) methods have achieved satisfactory performance by designing complicated deep convolution neural networks (DCNNs) to extract spectral and spatial features. However, these methods ignore the fact that the significance of spatial and spectral information in each hyperspectral image (HSI) is different, and most of them directly fuse two kinds of information with concatenation and convolution operation, which resulting in generating redundant information and have negative effects on reconstruction. To address such inadequacies, this paper proposes a novel adaptive spatial–spectral modulation network (ASSM-Net). Specifically, we propose a new adaptive feature fusion module (AFFM) to replace traditional convolutional fusion schemes. Through explicitly measuring the weights of spatial information and spectral features of HSI, AFFM can select dominant features for each pixel to modulate spatial and spectral information. Additionally, we develop a pixel-weighted aware attention (PAA) mechanism to enhance the feature interdependences for recovering finer structure information. Finally, a large number of quantitative and qualitative experiments reveal that the proposed network achieves competitive reconstruction results on three benchmark datasets (NTIRE 2022, CAVE and Harvard). [ABSTRACT FROM AUTHOR]

• In order to realize that the electrical stimulation parameters can be adjusted in real time according to the amount of feedback, a predictive control algorithm based on the Hammerstein model for closed-loop feedback electrical stimulation is investigated. • A Hammerstein model of the stimulated muscle group is established, and a recursive least-square identification algorithm is used to identify online parameters of the Hammerstein model with muscle time-varying. • The experiments show that the model parameters are well identified, with a relative RMS error of 3.83 % between actual and predicted values. • The predictive control algorithm based on the motor tasks is able to adaptively adjust the stimulus parameters so that the stimulated muscle groups achieve the desired sEMG characteristic trajectory. Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model. [ABSTRACT FROM AUTHOR]

Nowadays low Earth orbit (LEO) Earth observation (EO) satellites commonly use constant coding modulation (CCM) or variable coding modulation (VCM) schemes for data transmission to ground stations (G/S). Compared with CCM and VCM, the adaptive coding modulation (ACM) could further improve the data throughput of the link by making full use of link resource and the time-varying characteristics of atmospheric attenuation. In order to comprehensively study the data transmission performance, one new index which could be utilized as a quantitative index for the satellite-to-ground data transmission scheme selection, the transmission efficiency factor (TEF) of LEO satellites is proposed and defined as "the product of the link availability and the average useful data rate". Then, the transmission efficiency of CCM, VCM and ACM at typical G/S with different weather characteristics at Ka-band is compared and analyzed. The results show that ACM is more suitable for the G/S with moderate and abundant rainfall. Compared with the CCM of MCS 28, for Beijing G/S and Sanya G/S, ACM not only improves the transmission efficiency with the TEF increased by 3.62% and 24.51%, respectively, but also improves the link availability with the outage period reduced by 82.47% and 75.18%, respectively. [ABSTRACT FROM AUTHOR]

In harsh interference environments, there are all kinds of electromagnetic interference to disturb the communication links. In this article, by integrating transform domain communication system (TDCS) into cognitive radio (CR), transform-domain-based cognitive radio (TD-CR) is proposed to flexibly adopt TD-based spread spectrum technology to generate an adaptive anti-interference modulation waveform over the idle spectrum, which will improve both the anti-interference performance and spectrum efficiency of CR in the harsh interference environments. First, the transmitter and receiver of the TD-CR system are designed, where the key technologies, including spectrum sensing, spectrum magnitude shape, random phase mapping, fundamental modulation waveform, and signal detection, are described. Second, novel interleave-division multiple access and clustering multiple access for the TD-CR are proposed to improve the multiuser spectrum access ability. Thirdy, artificial-intelligence-enabled TD-CR is proposed to achieve intelligent spectrum sensing and optimal waveform transform by a cognitive engine. Then simulation results are provided to verify the anti-interference performance of the TD-CR. Finally, some future research challenges are discussed. [ABSTRACT FROM AUTHOR]

Membrane transport proteins are potential targets for medical and biotechnological applications. However, more than 30% of reported membrane transporter families are either poorly characterized or lack adequate functional annotation. Here, adaptive laboratory evolution was leveraged to identify membrane transporters for a set of four amino acids as well as specific mutations that modulate the activities of these transporters. Specifically, Escherichia coli was adaptively evolved under increasing concentrations of L–histidine, L–phenylalanine, L–threonine, and L–methionine separately with multiple replicate evolutions. Evolved populations and isolated clones displayed growth rates comparable to the unstressed ancestral strain at elevated concentrations (four-to six-fold increases) of the targeted amino acids. Whole genome sequencing of the evolved strains revealed a diverse number of key mutations, including SNPs, small deletions, and copy number variants targeting the transporters leuE for histidine, yddG for phenylalanine, yedA for methionine, and brnQ and rhtC for threonine. Reverse engineering of the mutations in the ancestral strain established mutation causality of the specific mutations for the tolerant phenotypes. The functional roles of yedA and brnQ in the transport of methionine and threonine, respectively, are novel assignments and their functional roles were validated using a flow cytometry cellular accumulation assay. To demonstrate how the identified transporters can be leveraged for production, an L–phenylalanine overproduction strain was shown to be a superior producer when the identified yddG exporter was overexpressed. Overall, the results revealed the striking efficiency of laboratory evolution to identify transporters and specific mutational mechanisms to modulate their activities, thereby demonstrating promising applicability in transporter discovery efforts and strain engineering. [Display omitted] • ALE was utilized to assign a functional role to individual transport proteins without a priori knowledge. • A naïve E. coli strain evolved tolerance towards four amino acids separately based on modulated transport activities. • WGS revealed a diverse number of mutational mechanisms that can modulate both efflux and influx transporters. • Mutational causality, novel transport functions, and the utility of enhanced transport activities were confirmed in vivo. • ALE can be scaled to additional molecules and microbes to discover membrane transporters for various applications. [ABSTRACT FROM AUTHOR]

Nowadays, the satellite segment in telecommunications occupies an important place and provides positioning of the global coverage system. However, the development of satellite technologies, compared to terrestrial wireless technologies, is slow. For example, the new DVB-S2 (Digital Video Broadcasting via Satellite) satellite standard contains a small number of improvements and refinements over the previous DVB-S standard. The main improvements are the introduction of codes with low density of LDPC (Low Density Parity Check) and the introduction of adaptive modulation and coding. Given the above, the object of research is modern satellite communication system. The subject of the research is the way to increase the efficiency of modern satellite communication systems. The research aims to analyze the feasibility of using a number of effective technologies in modern wireless systems, such as OFDM, UWB and MIMO, in satellite communication systems. The implementation of the considered options for the use of MIMO technology in satellite communication systems will increase the bandwidth and efficiency of these systems. However, there is a need for additional research to adapt this technology in satellite communication systems. Thus, the analysis allows forming the main directions of improving the efficiency of modern satellite communication systems. This analysis allows: – to formulate new approaches to increase the efficiency of modern satellite communication systems; – to substantiate new technological solutions for the construction of transceivers of satellite communication systems; – to identify possible areas of research to improve the efficiency of modern satellite communication systems. [ABSTRACT FROM AUTHOR]

Hyperpolarization activated cyclic nucleotide-gated channel 1 (HCN1) is expressed throughout the nervous system and is critical for regulating neuronal excitability, with mutations being associated with multiple forms of epilepsy. Adaptive modulation of HCN1 has been observed, as has pathogenic dysregulation. While the mechanisms underlying this modulation remain incompletely understood, regulation of HCN1 has been shown to include phosphorylation. A candidate phosphorylation-dependent regulator of HCN1 channels is 14-3-3. We used bioinformatics to identify three potential 14-3-3 binding sites in HCN1. We confirmed that 14-3-3 could pull down HCN1 from multiple tissue sources and used HEK293 cells to detail the interaction. Two sites in the intrinsically disordered C-terminus of HCN1 were necessary and sufficient for a phosphorylation-dependent interaction with 14-3-3. The same region of HCN1 containing the 14-3-3 binding peptides is required for phosphorylation-independent protein degradation. We propose a model in which phosphorylation of mouse S810 and S867 (human S789 and S846) recruits 14-3-3 to inhibit a yet unidentified factor signaling for protein degradation, thus increasing the half-life of HCN1. [ABSTRACT FROM AUTHOR]

Here, the authors consider the uplink of a cellular massive multiple‐input multiple‐output (MIMO) system with maximum ratio combiner (MRC) at base stations (BSs). To achieve a better QoS provisioning, users in the cells employ link adaptation using adaptive modulation and coding (AMC) in conjunction with packet retransmission. The authors present a cross‐layer approach for the AMC design exploiting the time variation feature of MIMO wireless channels. At first, a fast AMC (FAMC) scheme is proposed to adapt the fast small‐scale fading of the wireless channel based on the instantaneous channel state information. Since the fast fading is asymptotically averaged out due to the channel hardening property of massive MIMO system, we then suggest a slow AMC (SAMC) scheme which adjusts the adaptation parameters based on only the large‐scale channel fading statistics including shadow fading and pass loss of the channel. The SAMC substantially reduces the feedback rate and implementation cost because of using a larger coherence time compared to that of the FAMC. For the proposed schemes, the probability density function of signal‐to‐interference‐plus‐noise ratio (SINR) for MRC receivers at BSs is analytically derived and verified by simulation. Also, closed‐form expressions are presented for the throughput and packet error outage of the system. Index Terms—Massive MIMO, MRC, AMC, ARQ, Performance analysis. [ABSTRACT FROM AUTHOR]

Sparse Code Multiple Access (SCMA) is one of the competitive nonorthogonal multiple access (NOMA) techniques for the fifth-generation (5G) communication system. The SCMA codebook design is particularly important for SCMA systems. However, according to the results of our investigation of as many literature as possible, there is no research on adaptive modulation SCMA at present. Also, there is no systematic study concerning adaptive modulation SCMA codebook optimization. In this paper, an adaptive modulation SCMA transmission mechanism in different fading channels is proposed. Then, we optimize the adaptive modulation SCMA codebook design based on constellation rotation (AMSCR) for each adaptive mode including different modulation methods. The codebook optimization criterion is to maximize the minimum Euclidean distance between joint codewords. Since the space of the set of joint codewords is too large, the calculation of the minimum Euclidean distance has become an NP-hard problem. We use a genetic algorithm to solve this problem. As compared with the traditional SCMA codebook, the proposed AMSCR has better bit error rate performance in Gaussian channel and Rayleigh channel. The adaptive modulation strategy can maximize the throughput and has the best throughput performance compared with the traditional SCMA system and adaptive modulation SCMA system using traditional codebook. [ABSTRACT FROM AUTHOR]

Nowadays all-metal induction heating (IH) cooktops have become popular for heating various pots quickly and safely. A load adaptive modulation (LAM) method was proposed to improve the IH power delivery capability and the cost-effectiveness of an all-metal IH system. However, this method shows poor power conversion efficiency under light-load conditions since a high switching loss is induced using the pulse frequency modulation (PFM) method in the series resonant inverters for IH applications. In this paper, a multi-mode control strategy for an IH system employing the LAM method is proposed to improve light-load efficiency when compared with the conventional LAM method. The proposed multi-mode control algorithm automatically changes its modulation according to the pots' material-dependent resistance. The modulation method is selected to improve the light-load efficiency using the proposed control strategy. Experimental results demonstrate the validity and performance of the proposed multi-mode control algorithm with a 2-kW prototype IH series resonant inverter and various-material pots. [ABSTRACT FROM AUTHOR]

Sparse Code Multiple Access (SCMA) is one of the competitive nonorthogonal multiple access (NOMA) techniques for the fifth-generation (5G) communication system. The SCMA codebook design is particularly important for SCMA systems. However, according to the results of our investigation of as many literature as possible, there is no research on adaptive modulation SCMA at present. Also, there is no systematic study concerning adaptive modulation SCMA codebook optimization. In this paper, an adaptive modulation SCMA transmission mechanism in different fading channels is proposed. Then, we optimize the adaptive modulation SCMA codebook design based on constellation rotation (AMSCR) for each adaptive mode including different modulation methods. The codebook optimization criterion is to maximize the minimum Euclidean distance between joint codewords. Since the space of the set of joint codewords is too large, the calculation of the minimum Euclidean distance has become an NP-hard problem. We use a genetic algorithm to solve this problem. As compared with the traditional SCMA codebook, the proposed AMSCR has better bit error rate performance in Gaussian channel and Rayleigh channel. The adaptive modulation strategy can maximize the throughput and has the best throughput performance compared with the traditional SCMA system and adaptive modulation SCMA system using traditional codebook. [ABSTRACT FROM AUTHOR]

[Display omitted] • Designing a smart window with an adaptive cooling performance up to ∼13.9 °C. • Extending high-efficiency spray assembly method for scale-up of smart windows. • Proving the pressure-dependent adsorption kinetic of Au NPs in spray assembly. Smart windows for adaptive solar modulation have attracted considerable attention because of their broad prospects in energy-saving buildings. Herein, energy-saving smart windows are fabricated by the high-efficiency spray assembly of thermoplasmonic gold nanoparticles (Au NPs). First, the adsorption kinetics of the Au NPs in the spray assembly are investigated based on a pseudo-first-order kinetics equation, which exhibit a spray-pressure dependency. The amount of adsorbed Au NPs after spraying at 30 psi for 20 s is up to ∼80% the saturation adsorption amount, demonstrating the high-speed characteristic of the spray assembly. The spray-assembled Au NP coating, as a solar heating surface, is used to construct smart windows for adaptive solar modulation with the thermochromic poly(N-isopropylacrylamide) hydrogel. The obtained smart window shields 76.6% of the total solar irradiation in the range 300–2500 nm and allows 80.3% of the visible light to enter indoors before optical switching by the hydrogel ensures visual comfort. The cooling performance of the smart window is determined to be ∼13.9 °C by a model house test under a stimulated solar irradiation of 2.8 kW·m−2 (2.8 suns). Moreover, the fabrication method is suitable for flexible substrates to prepare bendable smart windows. This work provides a speed-up fabrication strategy for the large-scale manufacturing of energy-saving smart windows. [ABSTRACT FROM AUTHOR]

Selective Harmonics Elimination is a high-efficiency modulation method for multilevel inverters that allows handling very high voltage applications. It eliminates the most significant harmonics and fixes the desired fundamental component. The main issue of these techniques is the complex process to obtain the appropriate switching-angles, being necessary to calculate them offline, meaning that if some disturbances occur, the system will not be compensated. This article proposes a real-time selective harmonic elimination for a single-phase cascaded multilevel inverter. The control strategy maintains constant the fundamental component of the output voltage while removing its third, fifth, and seventh order harmonics. The switching-angles are dynamically adapted to compensate for variations in the input voltage and the load. This is done by obtaining a virtual dynamic system using Groebner basis, an adaptation of the Newton-Raphson method, and implementing a digital PI controller into the virtual dynamical model. This adaptive modulation technique is validated experimentally in a 200 W, 9-levels Cascaded Full Bridge Inverter, canceling the harmonics and regulating the fundamental components in all the tests. The developed theory can be adapted or extended for any multilevel inverter modulated by selective harmonic elimination. [ABSTRACT FROM AUTHOR]

Adaptive modulation received significant attention for underwater acoustic (UA) communication systems with the aim of increasing the system efficiency. It is challenging to attain a high data rate in UA communication, as UA channels vary fast, along with the environmental factors. For a time-varying UA channel, a self-adaptive system is an attractive option, which can choose the best method according to the channel condition to guarantee the continuous connectivity and high performance constantly. A real-time orthogonal frequency-division multiplexing (OFDM)-based adaptive UA communication system is presented in this paper, employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. In this paper, the received SNR is considered as a performance metric to select the transmission parameters, which are sent back to the transmitter for data transmission. In this research, a UA OFDM communication system is developed, employing adaptive modulation schemes for a nonstationary UA environment which allows to select subcarriers, modulation size, and allocate power adaptively to enhance the reliability of communication, guarantee continuous connectivity, and boost data rate. The recent UA communication experiments carried out in the Canning River, Western Australia, verify the performance of the proposed adaptive UA OFDM system, and the experimental results confirm the superiority of the proposed adaptive scheme. [ABSTRACT FROM AUTHOR]

Summary: As the core technology of mobile communication system, multiple access technology directly determines the performance and capacity of the system. However, with the rapid growth of the number of mobile communication system users and the high rate service demand, these orthogonal multiple access technologies are limited by the spectrum efficiency and access capacity, so it is difficult to meet the future mobile communication demand. Therefore, for the fifth generation mobile communication system (5G), the research of non‐orthogonal multiple access (NOMA) technology to effectively improve the system capacity has been widely concerned. The purpose of this paper is to analyze the influence of interference and power distribution on system performance simulation based on the simulation results of two most representative NOMA technologies, sparse code division multiple access (SCMA) and NOMA. This paper firstly briefly analyzes the SCMA system model and key technologies and, on this basis, introduces the SCMA system simulation platform, including basic platform parameters and platform simulation flow chart. Furthermore, the influence of adaptive modulation coding on the performance improvement of SCMA system is analyzed by simulation. Then, based on the NOMA system model, this paper carried out the research on fairness among NOMA users. Based on Lagrange multiplier method, this paper simulates fairness under the restriction of power allocation optimization algorithm, the number of users in different area, different radius and different transmission power, the village of village fair index, the change of the frequency spectrum efficiency and energy efficiency curve, the throughput rate between 0.8 and 1.4 changes, and the conclusion that has a guiding significance for NOMA system design. [ABSTRACT FROM AUTHOR]