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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]

Radiative cooling has been recently intensively explored for thermal management and enhancing energy efficiency. Yet, traditional materials with singular emissivity fall short in dynamic thermal management, highlighting the need for materials that can adjust their thermal radiation in real time. Active modulation methods, requiring external stimuli such as mechanical stretch, electric potential, or humidity change, offer adaptability but can increase energy use and complexity. Passive approaches, using materials' inherent thermal‐responsive properties, face manufacturing and scalability challenges. Here, a scalable yet effective passive approach is introduced for adaptive thermal modulation based on gold (Au) and liquid crystal elastomer (LCE) with a reversible response to environmental temperature changes. This modulator enables a "low thermal resistance" state through actuation‐induced microcracks that expose a high‐emissivity polymer substrate, and a "high thermal resistance" state by closing these microcracks and forming a high thermal resistance air gap between the modulator and the target object. The flexible design and fixed external dimensions of the Au‐LCE thermal modulator make it adaptable to various surface geometries. Furthermore, by adjusting the LCE's chemical composition, the modulator's transition temperature can be tailored, broadening its applications from enhancing building energy efficiency to improving clothing thermal comfort. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output systems with spatial modulation has evolved into an energy efficient and less complex wireless transmission system due to the use of limited radio frequency chains. In this work, a novel physical layer security scheme with enhanced spatial modulation (ESM) and secret key generation using multi-level adaptive threshold binary-coded decimal quantization algorithm is introduced. The proposed method, as SPACESM, enhances spectral efficiency using ESM and achieves confidentiality by adaptively selecting and shuffling the codebook in ESM. For selection of ESM codebook, SPACESM uses physical layer signature called channel signal to noise ratio (SNR) and for shuffling of codebook and rotation of transmit vector, secret key is generated at the transmitter and receiver separately by threshold-based quantization algorithm, which uses channel state information. Adaptive modulation technique selects the modulation order for different SNR. The performance of the proposed method is evaluated in terms of confidentiality, spectral efficiency, and robustness of secret key through MATLAB simulation.The proposed SPACESM technique provides 2 bpcu improvement for both N t = 2 and N t = 4 with M = 64 than the existing SM. Also it provides 3 dB and 4 dB SNR gain than SM for N t = 2 and N t = 4 respectively. Similarly, it provides 2 dB and 4 dB SNR gain than the SMX technique for N t = 2 and N t = 4 respectively. In summary, the proposed method outperforms than the SMX and SM technique in all aspects. [ABSTRACT FROM AUTHOR]

The digital voice is multimedia content of great importance, given the range of applications where it can be found. This paper addresses the shortcomings of existing voice authentication algorithms, presenting a completely blind speech authentication and recovery method based on fragile watermarking using the Least Significant Bit (LSB) method. This scheme obtains a compressed version of the original speech signal by Adaptive Differential Pulse Code Modulation (ADPCM) coding and the Discrete-Time Wavelet Transform (DTWT). Authentication bits are then generated by the SHA256 hash function, and the watermark is afterward embedded in the last three LSBs of the original audio samples. Experimental results evaluated on five different audio databases, each comprising speech signals recorded in different situations, contexts, and languages, have demonstrated a high embedding payload and imperceptibility of the watermark, obtaining an average Signal-to-Noise Ratio (SNR) value above 40 d B . Furthermore, the proposed method demonstrates a strong ability to accurately locate and restore up to 50% of a speech signal that has been tampered with, using no additional information. Moreover, the recovered speech signal is intelligible and has an SNR value higher than other recovery schemes, justifying the efficiency of the proposed method. [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]

Ground-based thermal infrared observations face substantial challenges in correcting the predominant background emitted as thermal radiation from the atmosphere and the telescope itself. With the upcoming 40 m class ELTs, unprecedented sensitivities from ground will be reached, underlining the need of even more sophisticated background correction strategies. This study aims to investigate the impact of thermal backgrounds on ground-based observations and identify possible limiting factors in dedicated correction strategies. We evaluate temporal and spatial characteristics of the thermal background in direct imaging data obtained with different telescopes and observation modes. In particular, three distinct data sets, acquired using VLT/NACO and KECK/NIRC2, are analyzed. Our analysis reveals that the observations are not fully photon shot noise limited, but exhibit additional sensitivity losses caused by imperfect background compensation in the different data sets. We identify correlations between background fluctuations and the activity of the adaptive optics system. We hypothesize that the pupil modulation of the adaptive optics mirrors introduces high frequency spatial and temporal fluctuations to the background, which could ultimately constrain the detection limit if they are not compensated adequately. [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]

The quadratic buck converter is renowned for its steep step‐down capability. It encounters increased losses due to its high component count. In scenarios with light loads, switching losses become the dominant factor. Additionally, the presence of two right‐half plane zeros impairs transient response, even at high‐frequency switching operations. Incorporating this converter into the digital domain introduces an undesired phenomenon known as subharmonic oscillation, rendering the system unstable, albeit potentially mitigated over time—a drawback particularly undesirable for converters tasked with rapid load dynamics. This paper introduces an adaptive pulse skipping modulation scheme to control metal–oxide–semiconductor field‐effect transistor (MOSFET) switching actions, enhancing overall efficiency in discontinuous conduction mode. Furthermore, the effects of right half‐plane (RHP) zeros on stability are analyzed within these switching schemes. The proposed scheme is integrated with voltage‐mode control. Simulation and theoretical analyses are conducted to validate this converter. A flat efficiency of 89% to 86% for the load range of 25 to 700 mA is obtained, outperforming other existing schemes. The results demonstrate that the adaptive pulse modulation scheme effectively improves efficiency and stability in discontinuous conduction mode converters. This research provides valuable insights for optimizing power electronics systems with varying load dynamics. [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]

Automatic Modulation Format Identification(AMFI) is an important issue in Visible Light Communication (VLC). There are several factors that affect the performance of vehicular VLC (V-VLC) systems. To accomplish the required communication quality for different traffic scenarios, It needs adaptive modulation techniques. Adaptive modulation requires modulation format identification at the receiver to avoid the overhead required to determine the proper modulation format. In this paper, we propose an AMFI scheme based on Deep Learning (DL) for Infrastructure-to-Vehicle (I2V) VLC. Our scheme utilizes the Hough transform to project the constellation diagram of the received modulated signals onto another space to extract features efficiently. Hough transform is estimated for eight different VLC modulation formats, including Q/8/16-Phase Shift Keying (PSK) and 4/16/32/64-Quadrature Amplitude Modulation (QAM) at signal to noise ratio (SNR) ranging from 5 to 25 dB. Conventional classifiers such as Alexnet, Sequeeznet and Googlenet are used to perform the task of AMFI. A study of the impact of the weather conditions and the communication distance on the accuracy of the used classifiers is introduced. The obtained results prove that the proposed scheme can identify the modulation format with high classification accuracy up to 100% at lower SNR levels and different weather conditions. [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]

Clustering is one of the most promising approaches to reducing energy usage in Wireless Sensor Networks, where power remains a bottleneck (WSNs). The vast majority of advances in energy efficiency result in exorbitant computing costs. Because of its superiority in simulating human decision making and its capacity to turn several inputs into a single output, the fuzzy-logic based clustering algorithm is preferred over others. This paper proposes a technique to energy-efficient clustering based on fuzzy logic (FLEE). Energy consumption is a significant barrier for geographically dispersed wireless sensor networks (WSNs). Because of the rapid improvement of energy harvesting technology, a novel approach known as simultaneous wireless information and power transmission (SWIPT) has emerged as a hot research topic due to its use in tackling the energy scarcity problem in WSNs. The bulk of existing SWIPT systems rely on time switching (TS) or power splitting due to their high complexity, lack of flexibility, and implementation issues (PS). In this paper, we provide a novel Fuzzy Logic-based Energy Efficient—SWIPT approach for adaptively dividing data packets into energy data packets and information data packets using fountain codes and real-time node decoding status information. The modulation switching thresholds and transmit power allocation at the SWIPT transmitter, as well as the power splitting ratios at the SWIPT receiver, are jointly changed to maximise the spectrum efficiency of wireless information transfer in wireless sensor networks. The numerical demonstration of the FLEE-SWIPT performance of many fixed modulation schemes under different fading situations. The FLEE-SWIPT transceiver with adaptive modulation has been proved to be useful. The results show an increase in Network Lifetime of 12.342 s, an increase in Network Throughput of 1579.41 kbps, a decrease in Energy consumption of 23.789 J, a decrease in Packet Loss Ratio of 38.61%, a decrease in Packet Delivery Ratio of 96.33%, a decrease in Encryption Time of 2.987 s, a decrease in Decryption Time of 3.567 s, and a decrease in Execution. [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]

Atmospheric turbulence channels experience quasi-static fading, which makes it relatively straightforward to utilize feedback information for link adaptive transmission. Applying adaptive modulation to MIMO systems can effectively mitigate the degradation of Free-Space Optical (FSO) signals under adverse turbulence conditions. In this paper, the joint influence of pointing error and Malaga distribution turbulence fading on the probability density function (PDF) of the received light intensity of a MIMO channel employing maximal ratio combining (MRC) is considered. The PDF of MRC combined channel is derived based on the moment generation function. Then, using the cumulative density function (CDF)-based method, we analyze the asymptotic spectral efficiency (SE) and average bit error rate (BER) performance of the adaptive MPSK-MIMO system under different turbulence intensities, beam waist radius, jitter standard deviation, and the number of transmit/receive apertures. In addition, we also compare the average BER performance of adaptive and non-adaptive MIMO systems with MRC receivers. Monte Carlo simulation further verifies our analysis. The derived results help quantify the diversity order of the systems we are considering. [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.

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]

Non-orthogonal multiple access (NOMA) is the most emerging radio access scheme for fifth-generation (5G) cellular networks. However, the major drawbacks of present NOMA systems are limited channel feedback and the difficulty of combining them with other advanced adaptive modulation and coding systems. Thus, this paper proposes an optimized NOMA system using a stacked Bi-GRU-based deep learning method for estimating the channel. Initially, the messages are coded by utilizing hybrid polar low-density parity-check (LDPC) code for error-free and efficient coding. The coded messages are then adjusted for frame error rate reduction and data sub-block security using a rate optimization approach, and they are modulated with quadrature amplitude modulation (QAM). Finally, channel estimation is performed using a novel stacked bidirectional Gated recurrent unit (Bi-GRU) technique to manage long-term relationships in information. After transmitting the messages via the estimated channel, decoding is performed by a selective extended segment successive cancellation list (SES-SCL) method on the receiver side. For experimentation, the MATLAB platform is preferred, and the results are evaluated and compared with other existing methods. The comparison analysis demonstrates that the proposed method performs better in terms of outage probability, loss, root mean square error (RMSE), bit rate error (BER), and frame error rate (FER). The obtained findings demonstrate that the proposed mechanism is extremely useful for the NOMA system in providing effective services. [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]

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 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]

The article "Adaptive rate and modulation scheme for M2M communication using quantum optical and 5G communication" published in Optical & Quantum Electronics has been retracted by the Publisher due to concerns about compromised peer-review processes, inappropriate references, and being out of scope for the journal. Authors S. Markkandan and G.D. Vignesh disagree with the retraction, while authors Prameela Devi Chillakuru, S. Maheswari, R.S. Sabeenian, and B. Mouleswararao have not responded to correspondence regarding the retraction. Springer Nature, the Publisher, remains neutral in this matter. [Extracted from the article]

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.

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.

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]

[Display omitted] • NIPAM-AM hydrogel with tunable LCST is developed by free radical polymerization. • ATO nanoparticles with high NIR and UV shielding efficiency are synthesized. • Thermoresponsive hydrogel film is used for smart window and display case. • High solar modulation ability and thermal insulation are demonstrated. Improving the solar regulation and reducing the response temperature of thermochromic smart windows are pivotal importance for energy-saving buildings. However, these two strategies are challenging to be integrated into one window. Herein, a novel composite hydrogel based on N-isopropylacrylamide (NIPAM) doped with antimony-doped tin oxide (ATO) nanoparticles and acrylamide (AM) was developed for producing a high-performance smart window, which successfully achieved enhanced solar energy regulation and reduced response temperature. This smart window was fabricated by combining a reversible thermoresponsive hydrogel (TAH) that acted as a thermochromic material with a ATO multilayer film that performed as a transparent heater. The as-prepared smart window could modulate solar light over a range from ultraviolet (UV) to infrared (IR) radiation and achieved active responses to high-temperature weather. The smart window showed high luminous transmittance (T lum , 82.92 %) together with an excellent solar modulation performance (ΔT lum = 73.31 %, ΔT IR = 38.26 %, and ΔT sol = 60.74 %), and a lower critical solution temperature of 32 °C. Even after 120 high- and low-temperature cyclic durability tests, the smart windows still exhibited a high solar modulation capability. In outdoor demonstrations, the as-prepared smart window exhibited a promising temperature regulation ability under strong solar irradiation. Therefore, the present universal modification framework provided some insights for the future design of energy-saving windows. [ABSTRACT FROM AUTHOR]

The production and manufacturing processes of steel inevitably generate various types of surface defects. The real-time and accurate detection of these surface defects is of great practical significance. To realise real-time detection of steel surface defects with significant differences in shape and size on resource constrained edge computing equipment, this paper proposes a lightweight real-time steel surface defect detection model SD-YOLO based on a dynamic parameterisation strategy. Firstly, a Dynamic Parameterised Enhancement Module is proposed, which dynamically assigns routing weights to parallel convolutional kernels based on input features, thereby enhancing the representation of defect features in the feature map and improving the network's ability to capture rich and detailed features. Secondly, the Efficient Intersection over Union loss function is employed to optimise the regression process of the prediction boxes. This enhances the model's fitting performance on bounding boxes with significant aspect ratio differences and improves the accuracy of detecting defects of various scales. Experimental results indicate that for the NEU-DET and GC10-DET datasets, SD-YOLO achieves a mean average precision of 83.1% and 74.1% respectively, with a stronger focus on defective regions, and detection speeds of 169.5 Frames Per Second (FPS) and 178.6 FPS, respectively. When SD-YOLO is deployed on the NVIDIA Jetson Orin NANO, the detection speed reaches 33.9 FPS and 66.7 FPS respectively, and maintains the same detection accuracy as the server-side, which realises real-time, accurate, and automatic detection of steel surface defects on edge computing devices with limited computational resources. Furthermore, SD-YOLO also demonstrates excellent generalisation ability and accuracy on images of steel surface defects collected in real industrial environments. In conclusion, SD-YOLO provides a practical and effective solution for real-time steel surface defect detection in resource-constrained environments, making it highly suitable for deployment in industrial applications. Source code is available at https://github.com/Xcy0512/SD-YOLO. [ABSTRACT FROM AUTHOR]

• Balancing security and intra-block pixel correlation by adaptive pixel modulation. • A predictor based on block correlation is proposed to improve prediction accuracy. • More efficient high bit-plane code method is proposed to enhance the embedding rate. With the rapid development of telemedicine technology, electronic medical records (EMR) are facing security issues such as illegal access, leakage and malicious tampering during the transmission of electronic patient information (EPI). In view of this, a high-capacity RDHEI based on adaptive pixel modulation and high bit-plane based redundancy matrix coding (HBP-RMC) is proposed to effectively achieve patient privacy protection by embedding private data such as electronic medical records and authentication information into encrypted medical images. Specifically, the carrier image is encrypted using designed block-level disruption and adaptive pixel-modulation to preserve pixel-correlation. This method provides the basis for high embedding rates while ensuring the overall security of the image. Next, an accurate block-level pixel prediction is proposed to make full use of the high correlation to generate considerable redundancy and in balance with high prediction accuracy. Then the proposed HBP-RMC is used to maximally convert the redundant information into the embeddable space and effectively limit the size of the auxiliary information. At the decoding end, users can realize separable image restoration and information extraction according to their own keys. The maximum ERs of our scheme on the BOWS-2 and BOSSbase datasets can reach 2.9332 bpp and 3.0249 bpp, respectively. Extensive experiments prove that our algorithm outperforms some existing state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Semantic segmentation has been one of the most critical tasks in computer vision. Recent works mainly focus on improving segmentation performance by designing high-capacity transformer architectures. They try to solve the high data consumption and computing costs required for model training and deployment in the cloud, but the high computation overhead still makes it difficult to be directly applied to limited resource devices. In this paper, we propose a novel fast Fourier Transform (FFT) based Context-aware Feature Mixer under the transformer-like architecture for precise and efficient semantic segmentation, called SegCFT. Different from the self-attention-based transformer, SegCFT uses a Hierarchical Fourier Transform (HFT) to reduce computational cost via non-parametric calculation and promote segmentation performance by fusing the channel-wise and pixel-wise contexts. To integrate the features from the frequency domain of DFT into the spatial domain of the transformer-like architecture, an Adaptive Modulation Unit (AMU) is designed to modulate the frequency-domain features and ensure consistency between the frequency domain and the spatial domain. Experimental evaluation on two semantic segmentation benchmarks, ADE20k and Cityscapes, shows that SegCFT achieves competitive segmentation performance, while the training and inference costs are superior to the previous methods. • Context-aware Fourier Transform is proposed to replace self-attention for segmentation. • Hierarchical Fourier Transform fuses the token-channel contexts to reduce computation cost. • Adaptive Modulation Unit fuses the spatial-frequency features for performance improvement. • SegCFT achieves the best trade-off between segmentation and inference speed. [ABSTRACT FROM AUTHOR]

Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. This task is mostly regarded as an image retrieval problem. The key underpinning this task is to design a series of deep neural networks to learn discriminative image descriptors. However, existing methods meet large performance drops under realistic weather, such as rain and fog, since they do not take the domain shift between the training data and multiple test environments into consideration. To minor this domain gap, we propose a Multiple-environment Self-adaptive Network (MuSe-Net) to dynamically adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network. As the name implies, the multiple-environment style extraction network is to extract the environment-related style information, while the self-adaptive feature extraction network utilizes an adaptive modulation module to dynamically minimize the environment-related style gap. Extensive experiments on three widely-used benchmarks, i.e. , University-1652, SUES-200, and CVUSA, demonstrate that the proposed MuSe-Net achieves a competitive result for geo-localization in multiple environments. Furthermore, we observe that the proposed method also shows great potential to the unseen extreme weather, such as mixing the fog, rain and snow. • Identifying one key challenge in visual geo-localization: weather and illumination changes. • Presenting MuSe-Net to alleviate the interference caused by environmental changes. • Designing Residual SPADE for efficient training and feature discrimination boosting. • Results on three geo-localization benchmarks confirm the superiority of our MuSe-Net. [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]

Adaptive modulation and coding schemes play a crucial role in ensuring robust data transfer in wireless communications, especially when faced with changes or interference in the transmission channel. These schemes involve the use of variable coding rates, which can be achieved normally through code puncturing or shortening, and have been adopted in 4G and 5G communication standards. In recent works, auto-encoders for wireless communications have demonstrated the ability to learn short code representations that achieve gains over conventional codes. Such a methodology is attractive as it can learn optimal representations under a variety of channel conditions. However, due to its structure the auto-encoder does not currently support multiple code rates with a single model. This article draws upon the discipline of multi-task learning, as it applies to deep learning and therefore devises a branching architecture for the auto-encoder and custom training algorithm in training transmitter and receiver for adaptive modulation and coding. In this article we aim to demonstrate improvements in Block Error Rate over conventional methods in the Additive White Gaussian Noise channel, and to analyse the performance of the model under Rayleigh fading channels without retraining the auto-encoder on the new channel. This article demonstrates a novel approach towards training auto-encoder models to jointly learn adaptive modulation and coding schemes framed as a multi-task learning problem. The research outcomes extend end-to-end learning approaches to the design of adaptive wireless communications systems. [Display omitted] • Proposes an end-to-end machine learning architecture enabling multiple code rates. • Custom training/multi-task learning produces competitive error-rate performance. • Proposed approach outperforms several traditional coding techniques for short codes. • Proposed approach is versatile in adapting to varying channels without retraining. • Demonstrates machine learning design methods for resilient wireless communications. [ABSTRACT FROM AUTHOR]

The manipulation of interactions between light and matter plays a crucial role in the evolution of organisms and a better life for humans. As a result of natural selection, precise light-regulatory systems of biology have been engineered that provide many powerful and promising bioinspired strategies. As the "king of disguise", cephalopods, which can perfectly control the propagation of light and thus achieve excellent surrounding-matching via their delicate skin structure, have made themselves an exciting source of inspiration for developing optical and thermal regulation nanomaterials. This review presents cutting-edge advancements in cephalopod-inspired optical and thermal regulation nanomaterials, highlighting the key milestones and breakthroughs achieved thus far. We begin with the underlying mechanisms of the adaptive color-changing ability of cephalopods, as well as their special hierarchical skin structure. Then, different types of bioinspired nanomaterials and devices are comprehensively summarized. Furthermore, some advanced and emerging applications of these nanomaterials and devices, including camouflage, thermal management, pixelation, medical health, sensing and wireless communication, are addressed. Finally, some remaining but significant challenges and potential directions for future work are discussed. We anticipate that this comprehensive review will promote the further development of cephalopod-inspired nanomaterials for optical and thermal regulation and trigger ideas for bioinspired design of nanomaterials in multidisciplinary applications.

Lambda interferons (IFNλs), also termed type III interferons (IFNs) or interleukins-28/29, have been in the shadow of type I IFNs for a long time. Their common induction mechanisms and signalling cascades with type I IFNs have made difficult the unwinding of their unique nonredundant functions. However, this is now changing with mounting evidence supporting a major role of IFNλs as a specialized antiviral defense system in the body, mediating protection at mucosal barrier surfaces while limiting immunopathology. Here, we review the latest progress on the complex activities of IFNλs in the respiratory tract, focusing on their multiple effects in IFNλ receptor-expressing cells, the modulation of innate and adaptive immune responses in the context of infections and respiratory diseases, and their similarities and differences with type I IFNs. We also discuss their potential in therapeutic applications and the most recent developments in that direction. [ABSTRACT FROM AUTHOR]