Hasil untuk "q-bio"

Ran Wang, Weiquan Huang, Junyu Wu et al.

To address the rapid population diversity loss and premature convergence of the Artificial Lemming Algorithm (ALA) in complex optimization problems, this paper proposes an Improved Artificial Lemming Algorithm (IALA) with multi-strategy enhancements inspired by lemming behavior. First, a non-uniform mutation operator and a nonlinear step-size strategy are introduced to strengthen local optima escape capability and optimization precision. Second, inspired by the foraging and positioning behavior of lemmings, a relative advantage learning strategy is designed to reduce dependence on the global best individual, further enhancing the algorithm’s exploration ability. Finally, a Q-learning-based adaptive mechanism is integrated to intelligently orchestrate five lemming-inspired behavioral modes through a nonlinear reward function, enabling adaptive switching among search patterns. Comparative experiments on the CEC2022 benchmark suite demonstrate that IALA achieves a Friedman mean rank of 1.25, ranking first with a significant margin. Compared with the original ALA and other six classical and state-of-the-art metaheuristic algorithms, and four recently proposed improved ALA variants (EALA, IALA_Tan, DMSALAs, and MsIALA), the Wilcoxon rank-sum test confirms that IALA is significantly outperformed in only 2 out of 120 pairwise comparisons, exhibiting remarkable advantages in optimization accuracy, convergence speed, and robustness. Ablation experiments validate the synergistic necessity of all three strategies, with the Q-learning adaptive mechanism identified as the most critical contributor. Exploration–exploitation balance analysis and search history visualization further confirm that IALA achieves a smooth adaptive transition from global exploration to local exploitation. Space complexity analysis reveals that the Q-table introduces only approximately 19.5 KB of fixed additional overhead, which becomes negligible for high-dimensional problems. Furthermore, IALA is successfully applied to the parameter tuning of underwater manipulator controllers, verifying its efficiency and reliability in real-world engineering applications.

Jorge Torres Gómez, Pit Hofmann, Lisa Y. Debus et al.

Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.

Shaher Momani, Shaher Momani, Rabha W. Ibrahim

We propose a novel framework for modeling thermal transport in biological tissues based on a fractional bio-heat diffusion equation regularized by a generalized (q, τ)-entropy functional. The model incorporates a Caputo-Numerical simulations demonstrate the evolution of temperature profiles and entropy dynamics, revealing the interplay between fractional memory, metabolic heat generation, and entropy-induced resistance. A stability theorem this framework offers a physically consistent and flexible approach grounded in non-equilibrium statistical mechanics and bio-thermal regulation, making it suitable for applications in complex biological media with long-range.

Wensen Huang, Jihua Chen, Hongge Yan et al.

This study examines the grain characteristics, dynamic precipitation phase characteristics, and texture evolution of Mg-Ga-xZn alloys produced through medium-high strain rate rolling. It investigates the impact of Zn on the mechanical and damping properties of Mg-Ga sheet. The addition of Zn reduces the solid solubility of Ga in α-Mg, facilitating dynamic precipitation, grain refinement, and weakening of the basal texture of the sheet, ultimately enhancing strength and damping performance. The yield strength of the sheet initially increases and then decreases with increasing Zn content. The Mg-5Ga-0.6 Zn alloy demonstrates the best overall mechanical properties, with a yield strength, tensile strength, and elongation of 221 MPa, 304 MPa, and 28.6%, respectively, primarily attributed to fine-grained strengthening. Damping performance at low strain amplitudes also follows a similar trend with increasing Zn content, with Mg-5Ga-0.6 Zn showing the highest damping values. The study suggests that the decrease in damping performance due to Zn can be linked to the reduced solid solubility of Ga in α-Mg. Specifically, at a strain amplitude of 1 × 10–3, the damping values Q-1 of Mg-5Ga, Mg-5Ga-0.6 Zn, and Mg-5Ga-1.2 Zn alloy sheets are 0.0167, 0.0152, and 0.0174, respectively. These findings have implications for the development of bio-implantable magnesium alloys with high damping properties.

Guozhong Zheng, Jiqiang Zhang, Shengfeng Deng et al.

Recent paradigm shifts from imitation learning to reinforcement learning (RL) is shown to be productive in understanding human behaviors. In the RL paradigm, individuals search for optimal strategies through interaction with the environment to make decisions. This implies that gathering, processing, and utilizing information from their surroundings are crucial. However, existing studies typically study pairwise games such as the prisoners' dilemma and employ a self-regarding setup, where individuals play against one opponent based solely on their own strategies, neglecting the environmental information. In this work, we investigate the evolution of cooperation with the multiplayer game -- the public goods game using the Q-learning algorithm by leveraging the environmental information. Specifically, the decision-making of players is based upon the cooperation information in their neighborhood. Our results show that cooperation is more likely to emerge compared to the case of imitation learning by using Fermi rule. Of particular interest is the observation of an anomalous non-monotonic dependence which is revealed when voluntary participation is further introduced. The analysis of the Q-table explains the mechanisms behind the cooperation evolution. Our findings indicate the fundamental role of environment information in the RL paradigm to understand the evolution of cooperation, and human behaviors in general.

Christopher R. Kitching, Tobias Galla

We consider evolutionary games in which the agent selected for update compares their payoff to q neighbours, rather than a single neighbour as in standard evolutionary game theory. Through studying fixed point stability and fixation times for 2x2 games with all-to-all interactions, we find that the flow changes significantly as a function of q. Further, we investigate the effects of changing the underlying topology from an all-to-all interacting system to an uncorrelated graph via the pair approximation. We also develop the framework for studying games with more than two strategies, such as the rock-paper-scissors game where we show that changing q leads to the emergence of new types of flow.

Guozhong Zheng, Zhenwei Ding, Jiqiang Zhang et al.

The inherent complexity of human beings manifests in a remarkable diversity of responses to intricate environments, enabling us to approach problems from varied perspectives. However, in the study of cooperation, existing research within the reinforcement learning framework often assumes that individuals have access to identical information when making decisions, which contrasts with the reality that individuals frequently perceive information differently. In this study, we employ the Q-learning algorithm to explore the impact of information perception on the evolution of cooperation in a two-person Prisoner's Dilemma game. We demonstrate that the evolutionary processes differ significantly across three distinct information perception scenarios, highlighting the critical role of information structure in the emergence of cooperation. Notably, the asymmetric information scenario reveals a complex dynamical process, including the emergence, breakdown, and reconstruction of cooperation, mirroring psychological shifts observed in human behavior. Our findings underscore the importance of information structure in fostering cooperation, offering new insights into the establishment of stable cooperative relationships among humans.

Wout Laeremans, Midas Segers, Aderik Voorspoels et al.

Coarse-grained models have emerged as valuable tools to simulate long DNA molecules while maintaining computational efficiency. These models aim at preserving interactions among coarse-grained variables in a manner that mirrors the underlying atomistic description. We explore here a method for testing coarse-grained vs. all-atom models using stiffness matrices in Fourier space ($q$-stiffnesses), which are particularly suited to probe DNA elasticity at different length scales. We focus on a class of coarse-grained rigid base DNA models known as cgDNA and its most recent version cgDNA+. Our analysis shows that while cgDNA+ follows closely the $q$-stiffnesses of the all-atom model, the original cgDNA shows some deviations for twist and bending variables which are rather strong in the $q \to 0$ (long length scale) limit. The consequence is that while both cgDNA and cgDNA+ give a suitable description of local elastic behavior, the former misses some effects which manifest themselves at longer length scales. In particular, cgDNA performs poorly on the twist stiffness with a value much lower than expected for long DNA molecules. Conversely, the all-atom and cgDNA+ twist is strongly length scale dependent: DNA is torsionally soft at a few base pair distances, but becomes more rigid at distances of a few dozens base pairs. Our analysis shows that the bending persistence length in all-atom and cgDNA+ is somewhat overestimated.

Qizhi Pei, Lijun Wu, Kaiyuan Gao et al.

Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e.g., IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data. These enhancements allow BioT5+ to bridge the gap between molecular representations and their textual descriptions, providing a more holistic understanding of biological entities, and largely improving the grounded reasoning of bio-text and bio-sequences. The model is pre-trained and fine-tuned with a large number of experiments, including \emph{3 types of problems (classification, regression, generation), 15 kinds of tasks, and 21 total benchmark datasets}, demonstrating the remarkable performance and state-of-the-art results in most cases. BioT5+ stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology. Our code is available at \url{https://github.com/QizhiPei/BioT5}.

Qingzhong Hua, Lu Ren

Abstract Background At present, the treatment options available for idiopathic pulmonary fibrosis are both limited and often come with severe side effects, emphasizing the pressing requirement for innovative therapeutic alternatives. Myofibroblasts, which hold a central role in pulmonary fibrosis, have a close association with the Smad signaling pathway induced by transforming growth factor-β1 (TGF-β1) and the transformation of myofibroblasts driven by oxidative stress. Liquiritigenin, an active compound extracted from the traditional Chinese herb licorice, boasts a wide array of biomedical properties, such as anti-fibrosis and anti-oxidation. The primary objective of this study was to examine the impact of liquiritigenin on bleomycin-induced pulmonary fibrosis in mice and the underlying mechanisms. Methods The anti-pulmonary fibrosis and anti-oxidant effects of liquiritigenin in vivo were tested by HE staining, Masson staining, DHE staining and bio-chemical methods. In vitro, primary mouse lung fibroblasts were treated with TGF-β1 with or without liquiritigenin, the effects of liquiritigenin in inhibiting differentiation of myofibroblasts and facilitating the translocation of Nrf2 were valued using Quantitative real-time polymerase chain reaction (Q-PCR), western blotting and immunofluorescence. Nrf2 siRNA and SIRT1 siRNA were used to investigate the mechanism underlies liquiritigenin’s effect in inhibiting myofibroblast differentiation. Results Liquiritigenin displayed a dose-dependent reduction effect in bleomycin-induced fibrosis. In laboratory experiments, it was evident that liquiritigenin possessed the ability to enhance and activate sirtuin1 (SIRT1), thereby facilitating the nuclear translocation of Nrf2 and mitigating the oxidative stress-induced differentiation of primary mouse myofibroblasts. Moreover, our investigation unveiled that SIRT1 not only regulated myofibroblast differentiation via Nrf2-mediated antioxidant responses against oxidative stress but also revealed liquiritigenin's activation of SIRT1, enabling direct binding to Smad. This led to decreased phosphorylation of the Smad complex, constrained nuclear translocation, and suppressed acetylation of the Smad complex, ultimately curtailing the transcription of fibrotic factors. Validation in live subjects provided substantial evidence for the anti-fibrotic efficacy of liquiritigenin through the SIRT1/Nrf2 signaling pathway. Conclusions Our findings imply that targeting myofibroblast differentiation via the SIRT1/Nrf2 signaling pathway may constitute a pivotal strategy for liquiritigenin-based therapy against pulmonary fibrosis.

Rajeev Sharma, Neha Mishra, Simon Cousens et al.

Arpan Garg, Y.D. Sharma, Subit K. Jain

This paper presents a novel study on the stability of thermo-bioconvection due to gravitactic microorganisms into an anisotropic porous fluid layer saturated with Jeffrey liquid. A Jeffrey-Darcy model along with Boussinesq approximations is utilized. The field equations are treated with non-dimensionalization, linear stability analysis, and the normal mode technique to formulate a set of ordinary differential equations. These equations along with Robin boundary conditions are then analytically solved by employing the weighted residual Galerkin method utilizing the trigonometric trial functions. The traditional thermal Rayleigh-Darcy number Ra,c is obtained as a well-compiled function of the mechanical anisotropy parameter ξ, Jeffrey parameter γ, the thermal anisotropy parameter η, bioconvection Rayleigh-Darcy number Rb, and Péclet number Q, while it is independent from bioconvection Lewis number Lb. It is observed that mounting ξ and γ in between 0 to 1 hasten the formulation of bioconvection patterns and also enlarges the size of convective cells. The results demonstrate that increasing bioconvection Péclet number and microorganism concentration constitute an unstable system. η ranged between 0 to 1 has shown dual effect which is dominated by the concentration of gravitactic microorganism. For small microorganism concentration, augmenting thermal anisotropy strength stabilizes the system and increases the size of the convective cells. Mathematically, the stabilizing nature of η is bounded by the feasibility of the inequality (π2+δc2ξ)(1+γ)ξ>2Rbδc2(eQ−1)π2Q2(π2+δc2)(π2+Q2)(4π2+Q2). This study may find relevance in applications related to pharmaceutical, bio-mechanics, and in microbial enhanced oil recovery (MEOR). The experimental Ra,c value of measure 4π2 at critical wave number value δc=π is also regained as a limiting case from this study.

Yan Shi, Shilin Yu, Hao Li et al.

Combining with the bound states in the continuum (BICs) theory in all-dielectric metasurfaces has become an extensively used method to excite multiple high quality(Q) factor Fano resonances, which remarkably enhance the performance of structures to be applied to refractive index sensors. In this article, a novel silicon pinwheel-shaped all-dielectric metasurface in the near-infrared region is designed and numerically investigated. Two Fano resonances are excited in the original structures. After breaking the symmetry of the original structures in combination with the BIC theory, four sharp Fano resonances are excited and the maximum Q-factor exceeds 3.9 &#x00D7; 10⁵ when <italic>&#x03B4;</italic> &#x003D; 10 nm. With the asymmetric parameter <italic>&#x03B4;</italic> &#x003D; 80 nm, multiple Fano resonances could be turned on and off by turning the polarization of the incident light, which performs excellent characteristics in optical switches. Both in the original structures and in the asymmetric state it offers outstanding sensing characteristics. Furthermore, with <italic>&#x03B4;</italic> &#x003D; 80 nm and the polarization angle 90 degrees, the sensitivity and the figure of merit (FOM) could respectively reach up to 355 nm&#x002F;RIU and 1375.97 RIU^&#x2212;1. The designed structures may provide a way to enhance the performance of bio-chemical sensors, optical switches, and nonlinear optics.

Henri Riihimäki

Directed graphs are ubiquitous models for networks, and topological spaces they generate, such as the directed flag complex, have become useful objects in applied topology. The simplices are formed from directed cliques. We extend Atkin's theory of $q$-connectivity to the case of directed simplices. This results in a preorder where simplices are related by sequences of simplices that share a $q$-face with respect to directions specified by chosen face maps. We leverage the Alexandroff equivalence between preorders and topological spaces to introduce a new class of topological spaces for directed graphs, enabling to assign new homotopy types different from those of directed flag complexes as seen by simplicial homology. We further introduce simplicial path analysis enabled by the connectivity preorders. As an application we characterise structural differences between various brain networks by computing their longest simplicial paths.

Shireen Adeeb Mujtaba Ali, R. Z. Sayyed, Mohammad I. Mir et al.

Surfactin lipopeptide is an eco-friendly microbially synthesized bioproduct that holds considerable potential in therapeutics (antibiofilm) as well as in agriculture (antifungal). In the present study, production of surfactin by a marine strain Bacillus velezensis MS20 was carried out, followed by physico-chemical characterization, anti-biofilm activity, plant growth promotion, and quantitative Reverse Transcriptase—Polymerase Chain Reaction (q RT-PCR) studies. From the results, it was inferred that MS20 was found to produce biosurfactant (3,300 mg L–1) under optimized conditions. From the physicochemical characterization [Thin layer chromatography (TLC), Fourier Transform Infrared (FTIR) Spectroscopy, Liquid Chromatography/Mass Spectroscopy (LC/MS), and Polymerase Chain Reaction (PCR) amplification] it was revealed to be surfactin. From bio-assay and scanning electron microscope (SEM) images, it was observed that surfactin (MIC 50 μg Ml–1) has appreciable bacterial aggregation against clinical pathogens Pseudomonas aeruginosa MTCC424, Escherichia coli MTCC43, Klebsiella pneumoniae MTCC9751, and Methicillin resistant Staphylococcus aureus (MRSA) and mycelial condensation property against a fungal phytopathogen Rhizoctonia solani. In addition, the q-RTPCR studies revealed 8-fold upregulation (9.34 ± 0.11-fold) of srfA-A gene compared to controls. Further, treatment of maize crop (infected with R. solani) with surfactin and MS20 led to the production of defense enzymes. In conclusion, concentration and synergy of a carbon source with inorganic/mineral salts can ameliorate surfactin yield and, application wise, it has antibiofilm and antifungal activities. In addition, it induced systemic resistance in maize crop, which makes it a good candidate to be employed in sustainable agricultural practices.

Yue Zheng, Junjun Xu, Ke Tang

Activity distance, which is commonly used to describe the transformation from a bound state to a transition state in the potential energy landscape model, is the key factor in the bio-molecular system to study dynamic properties. Adopted both in phenomenological theory and the statistical model, activity distance is the fundamental parameter to describe the kinetic characteristics of bio-molecules and is usually connected with the change in the pulling force F. The effects of activity distance Δx‡ are easy to be detected in a single dimensional landscape model because the force only expresses the mechanical work −Fx, which mainly overlaps with the fluctuation of the configuration of bio-molecules. However, as the force cannot affect the transformation directly in the multidimensional landscape model, the deflection angle φ is introduced in our work to discuss the pulling force, which has partial effects on the Q dimension. By comparing the mean waiting time ⟨t⟩ under the conditions of normal kinetics and dynamic disorder, in this study, we show the typical results from the effects of activity distance on the multidimensional potential energy model.

Mona Najaf Najafi, Neshat Najaf Najafi, Farzaneh Rashidi Fakari et al.

Abstract Objective The aim of the present systematic review meta-analysis is to assess the effect of olfactory stimulation on reducing dysmenorrhea. Methods Systematic search was conducted in several databases, such as PubMed, Web of Science, Cochrane, and Scopus, to identify relevant research up to October 26, 2019. The identified studies were evaluated based on a modified Jadad scale. The intervention involves aromatherapy alone or in combination with essential oils. There was no restriction for the control group such as a placebo group or other common treatments. The Comprehensive Meta-Analysis Version 2 (Bio stat, Englewood, NJ, USA) was used for meta-analysis. Cochran’s Q and I2 tests were utilized. Results The findings of our meta-analysis, which contained 13 trials (15 data), showed that dysmenorrhea decreased significantly in the group receiving aromatherapy with herbal compared with the control group (standardized mean difference [SMD] =-0.795; 95% confidence interval [CI]: -0.922 to- 0.667; 17 trials O < 0.001); heterogeneity; I2 = 19.47%; p = 0.236). In addition, four studies with insufficient data were not included in our meta-analysis. The results of all studies suggested that aromatherapy with herbal medicine group compared with control group is effective. Conclusion Aromatherapy with herbal medicine decreased dysmenorrhea. This treatment was particularly effective when aroma oil was combined with massage or when a mixture of aroma oil was used for the treatment of dysmenorrhea.

Meiping Li, Yanpeng Shi, Xiaoyu Liu et al.

Waveguide structures effectively controlling and guiding terahertz (THz) waves can achieve interesting resonance effects when combined with resonators. At present, achieving high quality factor (Q-factor) resonance in THz resonator-coupled waveguide structure is still a critical consideration to expand its practical application. Here, a high Q-factor Fano resonance based on a metal-dielectric-metal (MDM) waveguide consisting of a stub resonator and a metal wall with an aperture in the center is investigated theoretically and numerically in the THz region. The results show that the sharp and asymmetric Fano resonance peak is induced by the destructive interference between the stub resonator and metal wall which act as a Fabry-P&#x00E9;rot cavity. Q-factor is obviously improved about 60 times (3.72 to &#x007E;225) by introducing the metal wall into the stub-coupled MDM waveguide. Moreover, Fano resonance can be effectively tuned by varying different structure parameters. Owing to the high sensitivity of Fano resonance peak to dielectric surroundings, a large-range refractive index (RI) sensor based on the proposed structure with a high sensitivity of 96480 nm/RIU is obtained. The figure of merit (FOM) of 195 is greatly improved compared to other THz Fano-based RI sensors. These results provide possibilities for subwavelength MDM waveguide structure to apply for THz bio/chemical sensing, bandpass filters, and on-chip highly integrated plasmonic device.

Ruimin Wang, Ruiping He, Zhaohui Li et al.

Clausena indica (Datz.) Oliv fruit pericarps (CIOPs) is an important agro-industrial by-product rich in active components. In this article, the effects of traditional and green deep eutectic solvents (DESs) on the high-performance liquid chromatography (HPLC) characterization, antioxidant activities, and α-glucosidase-inhibitory activity of phenolic extracts from CIOPs were investigated for the first time. The results showed that ChCl-Gly and Bet-CA had higher extraction efficiency for the total phenolic content (TPC, 64.14–64.83 mg GAE/g DW) and total flavonoid content (TFC, 47.83–48.11 mg RE/g DW) compared with the traditional solvents (water, methanol, and ethyl acetate). LC-Q-Orbitrap-MS/MS was adopted to identify the phenolic compositions of the CIOPs extracts. HPLC-diode array detection (HPLC-DAD) results indicated that arbutin, (–)-epigallocatechin, chlorogenic acid, procyanidin B1, (+)-catechin, and (–)-epicatechin were the major components for all extracts, especially for deep eutectic solvents (DESs). In addition, ChCl-Xyl and ChCl-Gly extracts showed higher antioxidant activities against 2,2-diphenyl-1-picrylhydrazyl (DPPH•), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid (ABTS+•), ferric reducing antioxidant power (FRAP), reducing power (RP), and cupric ion reducing antioxidant capacity (CUPRAC) than extracts extracted by other solvents. A strong α-glucosidase-inhibiting activity (IC50, 156.25-291.11 μg/ml) was found in three DESs extracts. Furthermore, in silico analysis of the major phenolics in the CIOPs extracts was carried out to explore their interactions with α-glucosidase. Multivariate analysis was carried out to determine the key factors affecting the antioxidant activity and α-glucosidase-inhibiting activity. In short, DES can be taken as a promising solvent for valorization and recovery of bioactive compounds from agro-industrial by-products. The results verified that CIOPs can be used as a prospective source rich in bio-active compounds applied in the food and pharmacy industries.

Young-Jin Park, Ji-Eun Kim, Kyung-Min Na et al.

In this study, small multilayer planar spiral coils were analyzed and optimized to wirelessly charge an in-ear wearable bio-signal monitoring device in a wine-glass-shaped transmitter (Tx) based on magnetic resonance wireless power transfer (MR-WPT). For analysis of these coils, a volume filament model (VFM) was used, and an equivalent circuit formulation for the VFM was proposed. The proposed method was applied to design effective multilayer coils with a diameter and height of 6 and 3.8 mm, respectively, in the wearable device. For the coils, a printed circuit board having a 0.6 mm thick dielectric substrate and a 2 oz thick copper metal was used. Moreover, the coils on each layer were connected in series. The dimensions of the double-, four-, and eight-layer coils were optimized for the maximum quality factor (Q-factor) and coupling efficiency. The operating frequency was 6.78 MHz. The optimal dimensions for the maximum Q-factor varied depending on the number of coil layers, pattern width, and turn number. For verification, the designed coils were fabricated and measured. For the four-layer coil, the coupling efficiency and Q-factor using the measured resistance and mutual inductance were 58.1% and 32.19, respectively. Calculations showed that the maximum Q-factor for the four-layer coil was 40.8 and the maximum coupling efficiency was 60.1%. The calculations and measurement were in good agreement. Finally, the entire system of the in-ear wearable bio-signal monitoring device, comprising a wine-glass-shaped transmitter, the designed receiving coil, and a monitoring circuit, was fabricated. The measured dc-dc efficiency of the MR-WPT system was 16.08%.