Selective Channels Research Articles

Highly Selective and Excitable Artificial Ion Channel for Spike Generation.

Cation channels of the neuron can be excited by neurotransmitters and have high ionic selectivity, which are crucial for the generation of spikes. Intuitively, if a channel has both a high ionic selectivity and excitability, it may be used to generate spikes. However, integrating the high selectivity and excitability in a single channel remains highly challenging. In this work, we demonstrate an artificial ion channel based on a wetting film between an oil droplet and a glass substrate. The channel height is dominated by the repulsive electrostatic interaction between the glass/water and water/oil interfaces and is therefore highly adaptable to ambient ions and charged surfactant, setting the basis for flexible control of the open/close state and ion selectivity. The channel stays in a closed state unless excited by an anionic surfactant. Once excited, the channel opens wide for monovalent ions but narrows for divalent ions, exhibiting a selectivity up to >6800. By exploiting such excitability and selectivity, we constructed a prototype artificial neuron for spike generation. Using the anionic surfactant to simulate the neurotransmitter, our artificial neuron can automatically and step-wisely open and close the channel, generating a current spike with adjustable magnitude. We expect our work to inspire the development of biomimetic devices for potential neuromorphic computing applications.

Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks

This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) have varying channel availability. Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. Extensive simulations demonstrate that the RL-based algorithm significantly reduces the expected TTR (ETTR) compared to the JS algorithm, particularly in asymmetric scenarios where M-sequence-based approaches are less effective. This suggests that RL-based approaches not only offer robustness in asymmetric environments but also provide a promising alternative in more predictable settings.

RADD-CycleGAN: unsupervised reconstruction of high-quality ultrasound image based on CycleGAN with residual attention and dual-domain discrimination.

Plane wave imaging (PWI) is fast, but limited by poor imaging quality. Coherent plane wave compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio (PSNR) and a 22.2% increase in the structural similarity index measure (SSIM). The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Primary Channel Selection in Dynamic Channel Bonding Using Laser Chaos Decision Maker

Primary Channel Selection in Dynamic Channel Bonding Using Laser Chaos Decision Maker

The role of online platform selling mode in recycling channel selection: A game-theoretic analysis of profit and environmental impact

The role of online platform selling mode in recycling channel selection: A game-theoretic analysis of profit and environmental impact

Research on Intelligent Screening of International Communication Information and National and Regional Communication Strategies Based on Deep Learning Algorithm

In the context of globalization, the importance of international communication has become increasingly prominent. However, in the face of massive international communication information, how to effectively select and formulate communication strategies has become a big challenge. The traditional manual screening method is not only time-consuming and labor-intensive but also easy to be affected by subjective factors. This study aims to address the efficiency and accuracy issues in current international communication information screening and dissemination strategy formulation through the application of deep learning algorithms. It is of great theoretical and practical value to use deep learning algorithms to conduct intelligent screening of international communication information and research on national and regional communication strategies. To explore the application of deep learning algorithms in the intelligent screening of international communication information and the research of national and regional communication strategies, first, we will introduce the basic principles and methods of deep learning, as well as its applications in natural language processing (NLP), image recognition, and other fields. Then, we will explore how to use deep learning algorithms for intelligent screening of international dissemination information, including information classification, clustering, topic modeling, etc. Combined with practical cases, the application of deep learning algorithms in national and regional communication strategies will be studied, including communication content generation, communication channel selection, and communication effect evaluation.

Pharmacological Targeting of Sodium and Calcium Channels as a Therapeutic Approach for Chronic Pain Relief

Background: It is determined that chronic pain is a common, worsening health issue that reduces patient’s quality of life and is a huge strain on health systems. Sodium and calcium channels are key for the process of transferring pain signals. Pharmacological targeting of these ion channels has been identified as a potential and efficient long-term therapy of chronic pain in the absence of non-specific side effects of traditional analgesics.Objectives: In a clinical trial, to determine the effectiveness of the drugs, selective sodium and calcium channel blockers in the management of chronic pain symptoms by comparing patients’ pain scores and the number of effectively treated patients over time.Study design: A Double-blind, randomized, controlled trialPlace and duration of the Study: Department of Biochemistry, Saidu Medical College, Saidu Sharif Swat KP – Pakistan from March 2022 to March 2023.Methodology: The effect of sodium and calcium channel blockers was examined in a double-blind, randomized comparison study of 150 patients in a chronic pain clinic. Subjects rated their pain using an anchored pain intensity scale at baseline, two weeks, and four weeks. In analyzing the results, the use of mean, standard deviation and P-value were used whereby the P value used to test hypotheses in an experiment is the probability of obtaining the observed results. Hypothesis derived parameters included; the primary end point being pain score reduction, and the secondary end points including functional changes and side effects respectively.Results: In 150 patients these three groups recognized 23 percent achieving reasonable pain heading for sodium and calcium channel blockers who achieved extra pain cut off the placebo mean pain heading of 7.3 ±1.5 SD reducing to mean pain heading of 3.8 ±1.2 SD in the same patients. Actual placebo group demonstrate a slight improvement (mean change from 7.4 to 6.8). Analysis of the data by the G * Power software showed that the effect size was highly statistically significant, with a p-value = < 0.001. Hundred percent increase in the functional scores for the treatment group, but the adverse effects were reported to be less and were comparable with that of the placebo group. These results suggest that selective sodium and calcium channel blockade can attenuate neuropathic pain.Conclusion: This class of drugs has a substantial potential and does not trigger such severe side effects as constant use of traditional analgesics does for relieving chronic pain. The present work provides evidence that ion channel modulation can be used for chronic pain management, which should be further investigated and considered for implementation into therapeutic pain paradigms.Conclusion: Results indicate that G6PD deficiency is a leading cause of neonatal jaundice, particularly in populations at higher risk due to genetic factors. Early diagnosis and treatment significantly improve outcomes, though complications such as kernicterus remain a risk in severe cases.

Selective inhibition of T-type calcium channel preserves ischemic pre-conditioning mediated neuroprotection during cerebral ischemia reperfusion injury in diabetic mice.

Ischemic preconditioning (IPC) provides ischemic tolerance and neuroprotection during cerebral ischemia reperfusion (CI/R) injury. Diabetes abolishes the beneficial effects of conditioning phenomenon during CI/R. The study investigates the role of T-type calcium ion channel in IPC mediated protection during diabetes mellitus. The study employed Swiss Albino mice. Animals were divided into 3 normoglycaemic groups (Sham, CI/R, and IPC) and 4 hyperglycaemic groups (Sham, CI/R, IPC, and ML218 + IPC). CI/R injury was induced in Swiss Albino mice by occlusion of common carotid arteries followed by reperfusion. IPC was given prior to CI/R injury and diabetes was induced using streptozotocin (STZ). Animals were assessed for learning, memory, motor coordination, neurological function, cerebral infarction, edema, and histopathological alterations. Biochemical assessments were performed for calcium binding proteins (Calmodulin (CaM), calcium/calmodulin-dependent protein kinase II (CaMKII), and S100B), oxidative stress (4-hydroxy-2-nonenal (4-HNE)), glutathione (GSH), inflammation (nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), tumor necrosis factor (TNF-α), interleukin (IL-10)), inducible nitric oxide synthase (iNOS) levels, and acetylcholinesterase activity (AChE) in brain supernatants. NF-kB, iNOS, and S100B serum levels were also assessed. CI/R animals (normoglycemic and hyperglycaemic) showed impairment in learning, memory, motor coordination, and neurological function along with increase in cerebral infarction, edema, and histopathological alterations. Furthermore, increase in brain calcium-binding proteins, oxidative stress, inflammation, and AChE along with serum NF-kB, iNOS, and S100B levels were recorded in CI/R animals. IPC ameliorated CI/R induced behavioral, biochemical, and histopathological impairment, however no beneficial effects were observed in IPC (diabetic) mice. Administration of ML218 (10mg/kg; i.p.), a selective T-type calcium channel re-established the IPC mediated neuroprotection in CI/R diabetic animals. In conclusion, IPC-mediated neuroprotection was abolished in diabetic mice. T-type calcium ion channel antagonism plays an important role in the IPC-mediated neuroprotection during hyperglycaemia.

Selection of spillover channels in spatial dynamic panel models using heterogeneous shrinkage on spatial parameters

ABSTRACT This paper proposes a Bayesian approach to estimating heterogeneous spatial dynamic panel models, subject to possible shrinkage on spatial dependence parameters. This amounts to heterogeneous selection of candidate spatial weight matrices that represent different spillover channels. The shrinkage methods include both the traditional and more flexible ones that allow the shrinkage strength to vary across spatial parameters. Monte Carlo results indicate that when the true model has a relatively low proportion of nonzero spatial parameters, flexible shrinkage in general leads to lower average root mean squared errors in estimating these parameters. An empirical study using this approach shows that there exists substantial heterogeneity in spillover channels across counties that determine the correlation patterns of county COVID-19 vaccination rates in four states in the United States.

RSNC-YOLO: A Deep-Learning-Based Method for Automatic Fine-Grained Tuna Recognition in Complex Environments

Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to significant overfishing, resulting in a sharp decline in tuna populations. For sustainable tuna fishing, it is essential to accurately identify the species of tuna caught and to count their numbers, as these data are the foundation for setting scientific catch quotas. The traditional manual identification method suffers from several limitations and is prone to errors during prolonged operations, especially due to factors like fatigue, high-intensity workloads, or adverse weather conditions, which ultimately compromise its accuracy. Furthermore, the lack of transparency in the manual process may lead to intentional underreporting, which undermines the integrity of fisheries’ data. In contrast, an intelligent, real-time identification system can reduce the need for human labor, assist in more accurate identification, and enhance transparency in fisheries’ management. This system not only provides reliable data for refined management but also enables fisheries’ authorities to dynamically adjust fishing strategies in real time, issue timely warnings when catch limits are approached or exceeded, and prevent overfishing, thus ultimately contributing to sustainable tuna management. In light of this need, this article proposes the RSNC-YOLO algorithm, an intelligent model designed for recognizing tuna in complex scenarios on fishing vessels. Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. Additionally, the number of parameters is reduced by approximately 30%, and the model’s weight size is reduced by 9.6 MB, while maintaining an inference speed comparable to that of YOLOv8s-seg.

KRAFS-ANet: A novel framework for EEG-based stress classification using channel selection and optimized ensemble stacking

KRAFS-ANet: A novel framework for EEG-based stress classification using channel selection and optimized ensemble stacking

Nitric Oxide and Small and Intermediate Calcium-Activated Potassium Channels Mediate the Vasodilation Induced by Apigenin in the Resistance Vessels of Hypertensive Rats.

Apigenin (4',5,7-trihydroxyflavone), a flavonoid with potential cardiovascular benefits, has unclear mechanisms of action. This study investigates its effects on vascular function in Spontaneously Hypertensive Rats (SHRs). Mesenteric vascular beds (MVBs) were isolated from SHRs and perfused with increasing doses of apigenin after pre-contraction with phenylephrine. To explore the mechanisms, different MVBs were pre-perfused with antagonists and inhibitors, including indomethacin, L-NAME, and potassium channel blockers (tetraethylammonium, a non-specific potassium channel blocker; glibenclamide, an ATP-sensitive potassium channel blocker; 4-aminopyridine, a voltage-gated potassium channel blocker; charybdotoxin a selective intermediate-conductance calcium-activated potassium channel blocker; and apamin, a selective small-conductance calcium-activated potassium channel blocker). Apigenin induced a dose-dependent reduction in perfusion pressure in MVBs with intact endothelium, an effect abolished by endothelium removal. L-NAME reduced apigenin-induced vasodilation by approximately 40%. The vasodilatory effect was blocked by potassium chloride and tetraethylammonium. The inhibition of small and intermediate calcium-activated potassium channels with charybdotoxin and apamin reduced apigenin-induced vasodilation by 50%, and a combination of these blockers with L-NAME completely inhibited the effect. Apigenin promotes vasodilation in resistance arteries through endothelial nitric oxide and calcium-activated potassium channels. These findings suggest that apigenin could have therapeutic potential in cardiovascular disease, warranting further clinical research.

EXPRESS: Beyond the Pair: Media Archetypes and Complex Channel Synergies in Advertising

Prior research on advertising media mixes has mostly focused on single channels (e.g., television), pairwise cross-elasticities, or budget optimization within single campaigns. This is starkly detached from advertising practice where (i) there is an increasingly large number of media channels available to marketers, (ii) media plans employ complex combinations of channels, and (iii) marketers manage complementarities among many (i.e., more than pairs) channels. This research empirically learns complex channel complementaries using Latent Class analysis. Latent classes have three useful properties: (i) they account for non-random selection of channels into campaigns, (ii) they capture pairwise and higher-order interactions between channels, and (iii) they allow for meaningful interpretation. We empirically describe the most common media channel archetypes and estimate their relationship to the effectiveness of a set advertising campaigns on a set of common brand-related performance metrics. We use a dataset of 1,083 advertising campaigns from around the world run between 2008 and 2019. We find that there is not a systematically “best” media mix that correlates to dominant performance across all metrics, but clear patterns emerge given specific metrics. We find that traditional channels (TV, outdoor) are commonly paired with digital channels (Facebook, YouTube) in high-performing campaigns. We also find that current marketing practice appears far from optimal, and simple strategies have the potential to increase brand mindset metric lifts by 50% or more.

Dynamic combustion optimization of a pulverized coal boiler considering the wall temperature constraints: A deep reinforcement learning-based framework

Coal-fired power generation boilers are susceptible to low combustion efficiency, pollutant exceedance, and over-temperature tube burst during deep peak shaving due to load fluctuation and mass flow rate variation, which seriously affects unit economics, environmental protection, and safety. In this paper, the issue of boiler combustion optimization under variable load conditions is investigated using a data-driven approach. (1) Based on the published literature, a multi-objective prediction model based on Channel Selection Convolutional Neural Network (CS-CNN) for boiler thermal efficiency, NOx emission and wall temperature was developed, taking into account the safety of the unit under fast variable loads (the range of wall temperature varies widely and is prone to over-temperature). (2) Conventional multi-objective optimization algorithms are time-consuming and hard to satisfy the real-time decision-making of combustion in the furnace. In this research, we developed a combustion decision-making agent based on the Twin Delayed Depth Deterministic Policy Gradient (TD3). By interacting with the predictive model and learning the combustion strategy, the agent has the ability to optimize the unfamiliar operating conditions of the boiler. Simulation experimental tests were carried out on real historical data from a 600 MW down-fired boiler and the results showed that thermal efficiency increased by 0.411 %, and NOx emissions decreased by 17.701 mg/m3, with the safety of the wall temperature maintained. A single decision by the TD3 is almost instantaneous, with an average time of 0.004 s. The decision-making efficiency was much better than the traditional search-based optimization algorithms.

Abstract 4137646: Transcriptomic Profiling of Human Myocardium at Sudden Death to Define Vulnerable Substrate for Lethal Arrhythmias

Background: While some chronic pathological substrates for sudden cardiac death (SCD) are well-known (e.g., coronary disease and left ventricular [LV] dysfunction), the vulnerable myocardial state predisposing to fatal arrhythmia remains a critical barrier to near-term SCD prevention. Hypothesis: Myocardium of autopsy-defined SCDs exhibit distinct expression profiles vs. non-cardiac sudden deaths and trauma deaths that reflect its acute vulnerable state in the hours to days before SCD, as expression arrests upon death. Goals/Aims: Define the vulnerable myocardial substrate for lethal arrhythmia by targeted RNA profiling. Methods: We used autopsy to adjudicate arrhythmic from non-arrhythmic causes in 1,265 sudden deaths in San Francisco from 2011-2018. We performed a transcriptomic evaluation of LV sampled at the time of SCD from 245 consented cases using a curated panel of 448 genes with known or hypothesized association with SCD. Results: Comparison between Arrhythmic (n=129) and Non-Arrhythmic (n=116) cohorts revealed 31 differentially upregulated and 36 downregulated genes (p-adj<0.05), related to collagen-containing extracellular matrix (upregulation of FAP , FMOD , LTBP2 ), ion transport (upregulation of KCNA5 and KCNN3 , and downregulation of KCNJ8, KCNK1, KCNJ5 ), and contraction (downregulation of MYH6 ). Fibrosis-related genes showed the highest magnitude increased expression in Arrhythmic vs. Non-arrhythmic deaths and vs. published transcriptomes from end-stage heart failure. After molecular stratification by known markers for mature ( COL1A1, COL1A2, COL3A1) and active ( POSTN , MEOX1 ) fibrosis, cases with highest expression of both had the highest proportion of arrhythmic cause of death (27/36 [75%]) vs. cases with low expression of both (87/181 [38%], p=0.006) or vs. mature only (10/14 [71%]) or active only (5/14 [36%]). Activated fibroblast gene expression was enriched in Arrhythmic female vs. Arrhythmic male cases, among other sex-specific differences in ion channel and myosin (upregulation of SCN4B , SCN8A , KCNAB1 in females, KCNJ4 and MYH7B in males) expression. Conclusions: RNA profiling of myocardium at SCD identifies active fibrosis, undetectable by conventional clinical methods, in the presence of fixed scar and selected ion channel dysregulation (more pronounced among female cases) as an acute vulnerable substrate for fatal arrhythmias, and may identify patients at elevated near-term risk for SCD and novel pathways for intervention.

GO-TO-MARKET STRATEGIES FOR CONSUMER TECHNOLOGICAL PRODUCTS WITH SUBSCRIPTION MONETIZATION MODEL

This study examines go-to-market (GTM) strategies for consumer technological products utilizing subscription monetization models. As the digital landscape evolves, businesses are increasingly adopting subscription-based approaches to generate recurring revenue and establish long-term customer relationships. Our research investigates the key components of successful GTM strategies in this context, focusing on market segmentation, value proposition development, pricing strategies, distribution channel selection, and customer acquisition and retention tactics. Through a comprehensive analysis of scientific papers, industry reports, and case studies, we identify best practices and trends in subscription-based GTM strategies for consumer tech products. Our findings highlight the importance of market segmentation, value proposition development, pricing strategies, marketing and communication, customer acquisition and retention practices in optimizing customer lifetime value. This study contributes to the growing body of literature on subscription business models and provides practical insights for managers and entrepreneurs in the consumer tech industry.

Twinned Metal-Organic Framework Nanoplates for Hydrocarbon Separation Membranes.

Filler morphology control is critical for enhancing the gas separation performance of mixed matrix membranes (MMMs). A vertical transport channel using the crystal twinning phenomenon is designed on the zeolitic imidazolate framework (ZIF) nanoplate. Twinned ZIF-8 (TZIF-8) nanoplate is prepared by controlling the shape of ZIF-L from nanosheet to twinned flake, followed by conversion into the ZIF-8 phase. With the addition of TZIF-8 in 6FDA-DAM polymer, propylene/propane selectivity is dramatically enhanced, showing propylene permeability of 40 Barrer and propylene/propane selectivity of 82. The separation performance surpasses the performance of MMMs reported so far, and the selectivity is comparable to that of polycrystalline ZIF-8 membranes. A transport mechanism study using mathematical models implies that the percolated twin fillers create rapid and selective gas channels for desired molecules and substantial tortuous pathways for undesired molecules.

Cyclodextrin Metal-Organic Framework Functionalized Carbon Materials with Optimized Interface Electronics and Selective Supramolecular Channels for High-Performance Lithium-Sulfur Batteries.

During the reaction process in lithium-sulfur batteries, Lewis acidic lithium polysulfides (LiPSs) affect ion distribution and overall electrolyte stability, degrading battery performance and product distribution (e.g., Li2S). Here, a microenvironment regulation strategy with optimized interface electronics and selective supramolecular channels, is proposed to enhance LiPS reaction kinetics through Lewis basic γ-cyclodextrin metal-organic framework (γ-CDMOF). To validate this concept, γ-CDMOF is rapidly synthesized on 3D graphene foam (GF) via a microwave-assisted method, resulting in a γ-CDMOF/GF cathode for high-performance Li-S batteries. A range of analytical techniques combined with density functional theory (DFT) calculations confirm that introducing a Lewis basic supramolecular microenvironment mitigates the LiPSs shuttle effect, enhances polysulfide capture, and improves sulfur redox conversion. Additionally, COMSOL simulations reveal that the γ-CDMOF framework and oxygen sites significantly reduce volumetric expansion stress during the LiPS solid-liquid phase transition. Impressively, the γ-CDMOF/GF cathode exhibits exceptional performance, including a high specific capacity (1253.01 mAh g⁻¹ at 0.1C), excellent rate performance (589.68 mAh g⁻¹ at 5C), and long cycle life (over 1200 cycles). This study introduces a new concept of supramolecular microenvironment regulation and interfacial interaction strategy, offering a unique approach for the development of multifunctional electrode materials.

Radar-based contactless heart beat detection with a modified Pan–Tompkins algorithm

Background.Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge.Materials and methods.The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins.Results.The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute.Conclusion.The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare.

Nanoarchitecture via Synchronic Stacking of Metallic and Nonmetallic Two-Dimensional Nanosheets for Optimal Light-Driven Ion Transport.

The exceptional selectivity and responsive ion transport in biological channels inspire technology breakthrough in energy, environmental, and resource sectors. However, existing nanofluidic systems with a high photothermal conversion efficiency often exhibit excessive thermal conductivity, which impedes the creation of effective temperature gradients and results in a low ion transport efficiency. In this study, a strategy based on the synchronic stacking of metallic and nonmetallic two-dimensional (2D) nanosheets was presented to construct heterogeneous nanofluidic channels. This specific nanoconfined architecture sustained high temperatures in the illuminated area while maintaining low temperatures in the nonilluminated area, thus obtaining a robust driving force from sunlight for directional ion transport. As a result, our light-responsive ion transport system demonstrated significant potential in solar energy conversion and osmotic energy harvesting, surpassing those of all previously reported nanofluidic systems. Additionally, although it is still at the proof-of-concept stage, it shows great promise in light signal monitoring. This work provides an effective strategy for developing advanced light-responsive ion transport systems and their important applications.