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It is well established that several Homo sapiens populations experienced admixture with extinct human species during their evolutionary history. Sometimes, such a gene flow could have played a role in modulating their capability to cope with a variety of selective pressures, thus resulting in archaic adaptive introgression events. A paradigmatic example of this evolutionary mechanism is offered by the EPAS1 gene, whose most frequent haplotype in Himalayan highlanders was proved to reduce their susceptibility to chronic mountain sickness and to be introduced in the gene pool of their ancestors by admixture with Denisovans. In this study, we aimed at further expanding the investigation of the impact of archaic introgression on more complex adaptive responses to hypobaric hypoxia evolved by populations of Tibetan/Sherpa ancestry, which have been plausibly mediated by soft selective sweeps and/or polygenic adaptations rather than by hard selective sweeps. For this purpose, we used a combination of composite-likelihood and gene network-based methods to detect adaptive loci in introgressed chromosomal segments from Tibetan WGS data and to shortlist those presenting Denisovan-like derived alleles that participate to the same functional pathways and are absent in populations of African ancestry, which are supposed to do not have experienced Denisovan admixture. According to this approach, we identified multiple genes putatively involved in archaic introgression events and that, especially as regards TBC1D1, RASGRF2, PRKAG2, and KRAS, have plausibly contributed to shape the adaptive modulation of angiogenesis and of certain cardiovascular traits in high-altitude Himalayan peoples. These findings provided unprecedented evidence about the complexity of the adaptive phenotype evolved by these human groups to cope with challenges imposed by hypobaric hypoxia, offering new insights into the tangled interplay of genetic determinants that mediates the physiological adjustments crucial for human adaptation to the high-altitude environment. [ABSTRACT FROM AUTHOR]

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the diverse demands of mobile users and vertical industries, it is crucial for 5G-advanced (5G-A) networks to exhibit flexibility in system design across both the physical and higher layers. A single-frame structure alone cannot provide the necessary flexibility to accommodate multiple traffic modes. Therefore, we investigate a heterogeneous frame structure that meets the requirements. However, this structure will cause severe cross-link interference (CLI) and degrade the performance. To solve this problem, four CLI mitigation methods are proposed: slot level adaptive modulation and coding (AMC) scheduling (method 1), interference mitigation algorithm (method 2), adaptive slot turn-off (method 3), and edge user adaptive scheduling (method 4). The experimental results show that the CLI problem can be completely mitigated by deployed method 3 in a macro-base station (BS) with a low load scenario, the micro-BS uplink (UL) throughput was improved by 75.8%. Otherwise, with macro-BS in the high load scenario, when method 4 was implemented in macro-BS and methods 1 and 2 were deployed in micro-BS, a nearly complete CLI mitigation effect was obtained, reflecting that the micro-BS UL throughput increased to 57.4%. These methods are elaborated upon and evaluated within practical 5G-A networks, and the experimental results not only demonstrate the viability and efficacy of the above methods but also highlight their potential applications in future usage scenarios. [ABSTRACT FROM AUTHOR]

Featured Application: This study will focus on enhancing vehicle design by integrating advanced multisensory and culturally adaptive emotion modulation systems to meet the diverse needs of drivers globally and improve overall driving safety. Road accidents are caused by multiple factors. Aggressive driving and traffic violations account for 74% of road traffic accidents. In total, 92% of fatalities occur in low- and middle-income countries. Drivers' emotions significantly influence driving performance, making emotional modulation critical during vehicle interaction. With the rise of smart vehicles, in-vehicle affective computing and human-centered design have gained importance. This review analyzes 802 studies related to driver emotional regulation, focusing on 74 studies regarding sensory stimuli and cultural contexts. The results show that single-sensory methods dominate, yet multisensory approaches using auditory and visual elements are more effective. Most studies overlook cultural factors, particularly the differences in East–West cultural values, indicating a need to tailor modulation methods based on cultural preferences. Designs must emphasize adaptability and cultural consistency. This review aims to analyze driver emotional modulation thoroughly, providing key insights for developing vehicle systems that meet the diverse emotional and cultural needs of global drivers. Future research should focus on creating multisensory emotional modulation systems that offer positive reinforcement without causing excessive relaxation or aggression, accommodating subtle cultural and individual differences, thus enhancing the safety of autonomous driving. [ABSTRACT FROM AUTHOR]

Free-space optics (FSO)-based non-terrestrial networks (NTN) have garnered significant attention as a potential technology for forthcoming 6G wireless communications due to their exceptional data rate and extensive global coverage capability. Nevertheless, atmospheric attenuation, cloud attenuation, geometric loss, and atmospheric turbulence present numerous difficulties in developing these networks. To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. We develop mathematical models and derive closed-form expressions to implement the proposed algorithm and solve the optimization problem. The numerical results confirm that the JAMPC scheme helps NTN provide better energy efficiency and the ability to adapt to various channel conditions.

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]

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links. [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]

Adaptive free-space optical (FSO) communication links have been extensively studied in order to adapt to variable atmospheric channel environments due to factors such as atmospheric turbulence. As a supporting technology, an all-in-one BPSK/QPSK switchable transmission and reception method is proposed and experimentally demonstrated for adaptive modulation format switching in FSO links. The transmission and reception of both modulation formats are realized based on the same IQ modulator and single-photodetector coherent receiver. Simulation and experimental results show that the QPSK signal has a power penalty of about 3–4 dB compared to the BPSK signal with a BER of about 1 × 10−3. The basis for format switching is given according to the various atmospheric channel conditions. The proposed method provides a flexible and efficient solution for variable FSO communication environments to improve their performance. [ABSTRACT FROM AUTHOR]

Although the dead-time optimization design of resonant converters has been widely researched, classical design methods focus more on achieving zero-voltage switching (ZVS) operation. The body diode loss is always ignored, which results in low-efficiency of the converter, especially, in energy router (ER). To deal with this problem, this paper proposes an adaptive deadtime modulation scheme for bidirectional LLC resonant converters in ER. First, the power loss of the MOSFET is analyzed based on the dead-time. Then, a novel dead-time optimization modulation principle is proposed. It can eliminate the body diode loss of MOSFET compared with existing literature. Based on the optimization modulation principle, this paper proposes an adaptive dead-time modulation scheme. To this end, the converter adopting the scheme no longer needs to calculate dead-time, which simplifies the parameter design process. Meanwhile, this scheme enables dead-time to dynamically change with working conditions according to the dead-time optimization modulation principle. With these effects, the ZVS operation is achieved, and the body diode loss of MOSFET is also eliminated. Furthermore, a digital implementation method is designed to make the proposed modulation scheme have fast-transient response. Finally, experimental results show that the proposed dead-time modulation scheme enables converters to achieve ZVS operation in all working conditions, and has higher efficiency than classical dead-time design methods.

There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive M-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-light channels’ secrecy capacity and bit error rate (BER) performances. The proposed design is motivated by higher-order modulation, which results in better secrecy capacity at the expense of a higher BER. On the other hand, a proper precoding design, which can manipulate the received signal quality at the legitimate user and the eavesdropper, can also enhance secrecy performance and influence the BER. A reward function that considers the secrecy capacity and the BERs of the legitimate user’s (Bob) and the eavesdropper’s (Eve) channels is introduced and maximized. Due to the non-linearity and complexity of the reward function, it is challenging to solve the optical design using classical optimization techniques. Therefore, reinforcement learning-based designs using Q-learning and Deep Q-learning are proposed to maximize the reward function. Simulation results verify that compared with the baseline designs, the proposed joint designs achieve better reward values while maintaining the BER of Bob’s channel (Eve’s channel) well below (above) the pre-FEC (forward error correction) BER threshold.

The cell-free (CF) systems are a key technology for future wireless networks, which exploit the scenario spatial macro-diversity to offer high channel capacity and link reliability, uniform user quality of service (QoS), and better coverage. A technique for access point selection (APS) must be applied to improve the use of the macro-diversity. This paper proposes a low-complexity APS technique based on the derived signal-to-interference-plus-noise ratio (SINR) theoretical expression. Additionally, to increase the system’s spectral efficiency (SE), an expression for the bit error probability (BEP) of the generic square $M_{m}$ -ary quadrature amplitude modulation ( $M_{m}$ -QAM) scheme for each user is proposed and applied to an adaptive modulation (AM) algorithm. The results revealed that the proposed APS technique outperforms techniques with similar simplicity and that the AM algorithm provides higher system SE maintaining the error rate below a given threshold.

Abstract 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.

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]

Speech coding is a method to reduce the amount of data needs to represent speech signals by exploiting the statistical properties of the speech signal. Recently, in the speech coding process, a neural network prediction model has gained attention as the reconstruction process of a nonlinear and nonstationary speech signal. This study proposes a novel approach to improve speech coding performance by using a gated recurrent unit (GRU)-based adaptive differential pulse code modulation (ADPCM) system. This GRU predictor model is trained using a data set of speech samples from the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus actual sample and the ADPCM fixed-predictor output speech sample. Our contribution lies in the development of an algorithm for training the GRU predictive model that can improve its performance in speech coding prediction and a new offline trained predictive model for speech decoder. The results indicate that the proposed system significantly improves the accuracy of speech prediction, demonstrating its potential for speech prediction applications. Overall, this work presents a unique application of the GRU predictive model with ADPCM decoding in speech signal compression, providing a promising approach for future research in this field. [ABSTRACT FROM AUTHOR]

This study introduces a brand-new modified QAM modem that transmits data at a rate of 14.4 kbps over a 32 kbps Adaptive Differential Pulse Code Modulation (ADPCM) channel. This modified QAM modem's goal is to lessen the nonlinear distortion that ADPCM introduces. The effectiveness of ADPCM is investigated using modified and regular QAM modems with various constellations. The simulation findings demonstrate that ADPCM performs better when using a customized QAM modem than when using a normal QAM modem. Additionally, a circular constellation performs better than a rectangular one. [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]

To solve the problems of low spectral efficiency and poor performance of physical layer security (PLS) in single-input single-output (SISO) wireless systems with adaptive modulation in the presence of an active eavesdropper, multiple-input multiple-output (MIMO) wireless systems with secure adaptive modulation (SAM) were proposed.The design idea was to improve the spectral efficiency of the main channel by antenna diversity technology, which used the transmit antenna selection (TAS) at the transmitter and the maximal ratio combining (MRC) at the legitimate receiver to improve the signal-to-noise ratio.Simultaneously, the PLS of the legitimate receiver was guaranteed by limiting the bit error rate of the active eavesdropper so that it could not demodulate the confidential information from the main channel.With the joint constraints of the average transmission power and PLS, the optimal switching thresholds and the analytical expressions of spectral efficiency and the eavesdropper’s average bit error rate of the SAM-MIMO systems were derived.Numerical and simulation results show that SAM systems using TAS/MRC technology can not only improve the spectral efficiency of the main channel, but also ensure the PLS against the active eavesdropper, compared with the SAM-SISO systems.

This paper explores the use of load modulation feedback (LMF) in adaptive matching networks (MN) for low-coupling inductive wireless power transfer systems, with an emphasis on its use in implantable medical devices. After deriving the handy expressions of link efficiency and modulation depth in the case of LMF in the case of loose coupling, a brief overview of the most common capacitive resonance networks is presented. In particular, the MN employing two capacitors in Series–Parallel and in Parallel–Series configurations allow adaptivity with a wide range of load conditions. Then, the authors describe an effective design procedure of an adaptive matching network with LMF for an inductive wireless power transfer system, exploring the trade-off between power efficiency and modulation depth. Analytical and electrical simulations show that the proposed simple modulation strategy can successfully achieve high power transfer efficiency while maintaining steady back telemetry under varying loading conditions. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) has been employed to assign transmission parameters to all sub-carriers in a set frequency band for anti-jamming Orthogonal Frequency Division Multiplexing (OFDM) systems. However, prior works often overlooked the influence of wireless environment fading and convergence issues stemming from overly large parameter sets. To address these challenges, an anti-jamming scheme was proposed based on the Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) communication system integrated with reinforcement learning. First, all sub-carriers were divided into sub-bands, and a Finite State Markov Sub-bands (FSMS) model was established to describe the time-varying fading characteristics of each sub-band by combining Adaptive Modulation and Coding (AMC) technology. To mitigate instability due to the fading channel, a joint sub-band and modulation anti-jamming decision scheme was adopted, enabling the transmitter to select the optimal sub-band and transmission rate. Ultimately, this decision-making process was modeled as a Markov Decision Process (MDP), and an Upper Confidence Bound based Q-learning (UCB-Q) anti-jamming algorithm was proposed for obtaining the joint sub-band and transmission rate selection strategies. Simulation results indicate that the proposed algorithm demonstrates enhanced speed and superior average throughput. Additionally, the algorithm showcases the same commendable anti-jamming performance in scenarios with time-varying dynamic jamming. [ABSTRACT FROM AUTHOR]

Arbitrary-style-per-model (ASPM) style transfer algorithms transfer arbitrary styles based on a single model. Statistics-based learning algorithms of ASPM, represented by adaptive instance normalization (AdaIN), conduct instance normalization and then perform an affine transformation on target features. These algorithms are computationally efficient and easy to embed in convolutional neural networks. Consequently, they are widely used in image synthesis tasks to control the style of the resulting images. However, the style of stylized images may be a combination of content and stylized images, which suggests that these methods do not transform styles accurately. In this work, we rethink the function of AdaIN in controlling style. We show that the role of AdaIN is to (1) give each input content image a specific optimization target, (2) dynamically set cross-channel correlations, and (3) act as a feature selector after combining it with an activation function. Accordingly, we propose adaptive style modulation (AdaSM), which fully leverages the three roles mentioned above and thereby enables more precise control of global style. Experimental results show that AdaSM provides superior style controllability, alleviates the style blending problem, and outperforms state-of-the-art methods in artistic style transfer tasks. [ABSTRACT FROM AUTHOR]

The signal quality in high-bandwidth free space optical (FSO) systems deteriorates due to atmospheric turbulence and pointing errors. Employing techniques such as adaptive transmission and relay selection (RS) can mitigate their effects. This paper analyzes the performance of a dual-hop decode-and-forward multi-relay FSO system with an adaptive M-ary phase shift keying scheme. This analysis is based on the recently proposed Fisher–Snedecor  F channel model and considers the impact of pointing errors. We propose two partial relay selection schemes based on the source-to-relay or relay-to-destination channel state information to reduce the complexity of the optimal relay selection scheme. In this investigation, we derive closed-form expressions for the outage probability, modulation level selection probability, and spectral efficiency (SE) and compare the performance of the proposed RS schemes under balanced and unbalanced link cases. We observe an improvement in the SE with an increase in the number of modulation levels and the number of relays. Moreover, it is noted that the performance of the system can be restricted by the quality of either the source-to-relay or the relay-to-destination link, even if the quality of the other link is perfect. Finally, the outcomes obtained through the derived expressions are validated using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper proposed a fully integrated adaptive on-time (AOT) controlled buck converter with fast load transient. An adaptive on-time generator is presented to stabilize the output frequency. To enhance the light load efficiency, the converter could transfer from the pulse width modulation (PWM) to pulse skip modulation (PSM) as the load current decreases. The buck converter can switch between these two modulation modes adaptively with the assistance of a zero current detection circuit. Implemented in the TSMC 0.18 µm BCD (BiCMOS/DMOS) process, the proposed buck converter works with an input voltage ranging from 5.5 to 15 V, an output voltage ranging from 0.5 to 5 V, and an output load ranging up to 5 A. The experimental results show that based on the dual modulation adaptive on-time controlled mode, the transient recovery time from light to heavy load and from heavy load to light load is 13 µs and 15 µs, respectively. An overshot voltage of 57 mV and an undershot voltage of 53 mV are also achieved. [ABSTRACT FROM AUTHOR]

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links.

Deep learning-based methods have recently advanced image generation by exploiting the valuable information within immense training data, but they struggle to synthesize new images for rare categories that are scarce enough to cover some visual concepts. Image generation based on few-shot learning is developed to capture the generalizable generative feature from limited data to produce new images for unseen categories. Existing few-shot methods focus on the high-level semantic difference between conditional images and fuse a generative feature based on the semantic metric. However, it ignores the impact of semantic information underlying different level feature subspaces throughout the generation process, leading to the degradation of visual quality when the diversity of synthetic images improves. In this work, we propose a novel Adaptive Multi-scale Modulation Generative Adversarial Network (AMMGAN) for few-shot image generation, leveraging the U-Net with skip connections to exploit multi-scale semantic metric information. Specifically, an adaptive self-metric fusion module is introduced at the junction between the encoder and decoder of generator, which measures the pixel-wise semantic information of conditional images as a self-metric attention map in each level of deep features to fuse a general feature of interest and adaptively adjust the mean and variance of the fused feature based on the high-level semantic feature of decoder. Meanwhile, the part network of discriminator is developed to co-learn with the generator to formulate a reference-metric modulation code embedding channel-wise metric information, which is integrated into the decoder of generator by learnable residual transformation to refine the unsampled fused feature from the channel dimension. Extensive experiments on several benchmark datasets demonstrate the effectiveness of AMMGAN on few-shot image generation and downstream visual classification tasks. [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]

Recent advancements in immunology and chemistry have facilitated advancements in targeted vaccine technology. Targeting specific cell types, tissue locations, or receptors can allow for modulation of the adaptive immune response to vaccines. This review provides an overview of cellular targets of vaccines, suggests methods of targeting and downstream effects on immune responses, and summarizes general trends in the literature. Understanding the relationships between vaccine targets and subsequent adaptive immune responses is critical for effective vaccine design. This knowledge could facilitate design of more effective, disease-specialized vaccines. [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]

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]

Under the current trend of power electronics in energy systems, a high percentage of renewable energy transports clean energy to the grid through grid-connected inverters. The pulse-width modulation (PWM) technique brings high-order harmonics near to the switching frequency, and LCL filters with low-pass characteristics become the common choice for grid-connected inverters. However, the low-order harmonics caused by nonideal switching characteristics are difficult to filter out, and the new resonance point introduced by the LCL filter causes a security problem for the energy systems. Firstly, the generation mechanism of the 6 k ± 1 order harmonic and high-frequency resonance from a PV grid-connected inverter is analyzed. Then, a virtual resistor is constructed by the active damping method to absorb the resonant component. Meanwhile, this paper also presents an adaptive modulation voltage compensation method to decrease the low-order harmonics. Finally, the actual measured data of user photovoltaic (PV) and multiple comparative simulations verify these theories. Simulation results show that the proposed coordinated control algorithm reduces the peak of the resonance point, and the rate of low-order harmonics mitigation is more than 50%. The proposed method is suitable for various operating conditions. [ABSTRACT FROM AUTHOR]

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Optical multicasting has been considered resource efficient for multicast services. Light-tree and light-trail are two technologies that support optical multicasting while the former requires many splitters and thus experiences significant power loss. In this paper, we consider using the light-trail technology for the accommodation of multicast requests in elastic optical networks with adaptive modulation. For better spectrum efficiency, we consider accommodating each multicast by multiple light-trails. We formulate the problem by Mixed Integer Linear Programming (MILP) and propose efficient heuristic algorithms. For the impact of accommodation sequence on the algorithm performance, apart from the traditional sequence among different requests, we consider an additional sequence among the destinations of a multicast. For efficient multicast accommodation, we propose several strategies and compare their performances through a range of cases. To avoid a destination occupying excessive resources in certain cases of joining multiple light-trails, we propose an efficient algorithm to delete some duplicated destinations. Numerical results show that the proposed heuristic algorithms significantly outperform a benchmark algorithm and one performs close to the optimal MILP. Also, the algorithm for deleting certain destination replicas largely reduces the spectrum and transmitter usages, up to 41% and 20% for the cases considered, respectively.

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]

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]

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.

In order to improve the data sharing and comprehensive information processing capabilities between multi-microgrids in the power system, the multi-microgrids scheduling system based on laser communication has been proposed. In order to reduce the error rate of laser communication and reduce the impact of atmospheric turbulence on signal acquisition, an adaptive modulation algorithm has been designed. A mathematical model for laser communication modulation and demodulation based on adaptive modulation algorithm has been constructed. In simulation analysis, the target signal was extracted from the original signal superimposed with atmospheric turbulence noise through filtering and demodulation. The energy fluctuation of the extracted signal decreased from 47.3 to 5 mV. The energy attenuation trend of communication lasers within the range of 0–6 km was experimentally tested. Within 2.0 km, the energy demodulation results of both algorithms are similar, both below 10%. After exceeding 2.0 km, the calculation error of the adaptive modulation algorithm remains basically unchanged, while the error of traditional algorithms increases by about twice. For the APD response value, the adaptive modulation algorithm demodulation has a higher response range concentration ratio and the higher envelope recognition. Under different nominal atmospheric turbulence values, the maximum error rates of the adaptive modulation algorithm are 5.8 × 10−8, 8.9 × 10−8, and 1.2 × 10−7, respectively, while the maximum error rates of the amplitude coherent algorithm are 2.9 × 10−5, 6.3 × 10−5, and 1.05 × 10−4, respectively. It can effectively suppress the impact of atmospheric turbulence on the error rate of laser communication by adaptive modulation algorithm.

Zetomipzomib (KZR-616) is a selective inhibitor of the immunoproteasome currently undergoing clinical investigation in autoimmune disorders. Here, we characterized KZR-616 in vitro and in vivo using multiplexed cytokine analysis, lymphocyte activation and differentiation, and differential gene expression analysis. KZR-616 blocked production of >30 pro-inflammatory cytokines in human peripheral blood mononuclear cells (PBMCs), polarization of T helper (Th) cells, and formation of plasmablasts. In the NZB/W F1 mouse model of lupus nephritis (LN), KZR-616 treatment resulted in complete resolution of proteinuria that was maintained at least 8 weeks after the cessation of dosing and was mediated in part by alterations in T and B cell activation, including reduced numbers of short and long-lived plasma cells. Gene expression analysis of human PBMCs and tissues from diseased mice revealed a consistent and broad response focused on inhibition of T, B, and plasma cell function and the Type I interferon pathway and promotion of hematopoietic cell lineages and tissue remodeling. In healthy volunteers, KZR-616 administration resulted in selective inhibition of the immunoproteasome and blockade of cytokine production following ex vivo stimulation. These data support the ongoing development of KZR-616 in autoimmune disorders such as systemic lupus erythematosus (SLE)/LN. [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]

Pericytes are perivascular multipotent cells wrapping microvascular capillaries, where they support vasculature functioning, participate in tissue regeneration, and regulate blood flow. However, recent evidence suggests that in addition to traditionally credited structural function, pericytes also manifest immune properties. In this review, we summarise recent data regarding pericytes' response to different pro-inflammatory stimuli and their involvement in innate immune responses through expression of pattern-recognition receptors. Moreover, pericytes express various adhesion molecules, thus regulating trafficking of immune cells across vessel walls. Additionally, the role of pericytes in modulation of adaptive immunity is discussed. Finally, recent reports have suggested that the interaction with cancer cells evokes immunosuppression function in pericytes, thus facilitating immune evasion and facilitating cancer proliferation and metastasis. However, such complex and multi-faceted cross-talks of pericytes with immune cells also suggest a number of potential pericyte-based therapeutic methods and techniques for cancer immunotherapy and treatment of autoimmune and auto-inflammatory disorders. [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]

Chaos-based communications are a promising application of chaos theory and nonlinear dynamics. Their key features include concealed transmission, high security, and native broadband signals. Many studies have recently been published devoted to this technology. However, the practical implementations of chaos-based communications are rare due to multiple shortcomings: high hardware requirements, complex signal processing algorithms, and a lack of efficient modulation techniques for chaotic signals. In this study, we consider a simple hardware prototype of a coherent chaos-based communication system based on a novel type of modulation: adaptive symmetry of the finite-difference scheme used in a chaos generator. We explicitly demonstrate the possibility of covertly transmitting data using a chaotic transmitter and receiver implemented in a general-purpose microcontroller unit. A comparison between traditional parameter and symmetry modulation is given through a return map analysis and bit error rate estimation. The communication secrecy is analyzed using quantified return map analysis. The obtained results confirm the possibility of creating chaos-based communication systems based on symmetry modulation. [ABSTRACT FROM AUTHOR]

Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively. [ABSTRACT FROM AUTHOR]

Advancement of 5G new radios has enabled more robust communication for the Machine-to-Machine (M2M) communication node, using filter bank multicarrier (FBMC). This paper focuses on robust transmission over random fluctuations of the channel and also enhances the battery life for the massive machine type communication (mMTC) node. Filter bank multicarrier and Adaptive Modulation Coding (AMC) have been utilized together to enhance the performance of the 5G (NR) PHY layer. A frame-to-frame implementation is used to diminish the impact of fading using AMC, while efficient utilization of spectrum is achieved using FBMC. The selection of the AMC profile is obtained through the analysis of uplink packets using the Distance Statistics (DS). The FBMC is incorporated with 5G PHY in place of OFDM to achieve the optimum utilization of spectrum and also obtain a significant reduction in peak to average power ratio (PAPR) for robust transmission, which saves 10% of the battery requirement. On the basis of channel state information, distance statistics were employed to optimize the AMC. The optimum selection of AMC with FBMC will reduce the bit error rate (BER) against multipath fading and ensure the better utilization of available spectrum to attain the optimum utilization of the power amplifier. [ABSTRACT FROM AUTHOR]

Innovative solutions are required for the intervention of implant associated infections (IAIs), especially for bone defect patients with chronic inflammatory diseases like diabetes mellitus (DM). The complex immune microenvironment of infections renders implants with direct antibacterial ability inadequate for the prolonged against of bacterial infections. Herein, a synergistic treatment strategy was presented that combined sonodynamic therapy (SDT) with adaptive immune modulation to treat IAIs in diabetes patients. A multifunctional coating was created on the surface of titanium (Ti) implants, consisting of manganese dioxide nanoflakes (MnO 2 NFs) with cascade catalytic enzyme activity and a responsive degradable hydrogel containing a sonosensitizer. The reactive oxygen species (ROS) generated by glucose-hydrogen peroxide (H 2 O 2) cascade catalysis and ultrasound (US) activation sonosensitizer helped kill bacteria and release bacterial antigens. Meanwhile, Mn2+ facilitated dendritic cells (DCs) maturation, enhancing antigen presentation to activate both cellular and humoral adaptive immunity against bacterial infections. This approach effectively eliminated bacteria in established diabetic IAIs model and activated systemic antibacterial immunity, providing long-term antibacterial protection. This study presents a non-antibiotic immunotherapeutic strategy for fighting IAIs in chronic diseases. [ABSTRACT FROM AUTHOR]