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The flow of blood through stenotic arteries is considered one of the most significant areas of investigation in the world of mathematical fluid mechanics. This significance arises from the topic's application to the field of biomedicine. Within an arterial system of blood that has been stenosed, the goal of this research initiative is to investigate the effect that nanoparticles have on the characteristics that define the human circulatory system. The current investigation analyzes blood flow behavior in a controlled, permeable artery employing the Casson hybrid nanofluid model and, additionally, gold, Cu, and silver nanoparticles. The current study examines the investigation of magnetohydrodynamics (MHD) Casson hybrid nanofluid Darcy-Forchheimer flow (DFF) over a stenotic artery with a heat source/sink. By transforming the partial differential equations (PDEs) into ordinary differential equations (ODEs) by utilizing suitable similarity variables. After that, the mathematical solution of these equations used the BVP4c method in the MATLAB solver and analysis for skin and Nusselt number table values for hybrid nanofluid cases. The statistical analysis is used to solve for different physical factors. During a study in which values of both variables are subject to essentially unknown oversights, the task is utilizing statistical techniques to discover the best possible linear equations. In this concept, two different hybrid nanomaterials are used for analyzing heat transfer characteristics. According to these graphical results, the present model concludes that case-2 has better performance than case-1. Rising values of the magnetic parameter improve heat transfer owing to Joule heating impacts and control flow via the Lorentz force, which in turn affects the distribution of shear stresses close to the artery wall. The current work highlights the role of nanoparticles and magnetic fields in enhancing flexibility near arteries' surfaces. This work has substantial repercussions for the treatment of cancer and the treatments that are used to avoid cardiovascular disease, as it suggests that improved heat transfer and shear stress distribution can enhance the effectiveness of therapies targeting tumor cells and improve blood flow in cardiovascular interventions.

Entropy generation (EG) analysis is a fundamental tool for evaluating irreversibility and thermodynamic efficiency in hybrid nanofluid (HNF) flows, as it quantifies energy losses arising from heat transfer and fluid friction. It enables the identification of dominant sources of irreversibility and the determination of optimal operating conditions. Consequently, EG analysis plays a crucial role in enhancing heat transfer performance while minimizing thermodynamic losses in advanced thermal systems. In this study, we have analyzed EG in magnetohydrodynamic Williamson HNF flow with characteristics of transfer of heat over a permeable stretched sheet along with Darcy Forchheimer effects. The impacts of radiation, dissipation, and heat source are accounted in the expression of thermal energy. The relation for concentration is reported by taking the influence of chemical reaction. The HNF is constructed by suspending copper [Formula: see text] and aluminum oxide [Formula: see text] nanoparticle into water-based Williamson liquid. For modeling of EG thermodynamics, second law is utilized. Formulation resulted system of partial differential equations (PDEs). To obtain ordinary differential equations (ODEs) from PDEs, similarity transformations are utilized. ODEs are solved via NDSolve code in Mathematica. The various variables effect on flow velocity, Bejan number, concentration, temperature, and EG are graphically studied. Engineering quantities are scrutinized numerically. The results indicate that as the magnetic variable increases, the temperature also rises. In contrast, an increase in the Prandtl number leads to a decrease in temperature. When both the magnetic parameter and porosity increase, the velocity field of the hybrid nanofluid decreases. The concentration field declines with an enhancement in both the chemical reaction and the Schmidt number. Improvements in EG are observed with higher values of diffusion, the temperature difference ratio, and Brinkman variables. The Bejan number increases with rising diffusion and temperature difference ratio, but it decreases with higher Brinkman variable values.

We study how path-specific delays can be identified from frequency-response measurements in delay-coupled oscillator networks. Although time delays strongly shape collective dynamics, measured transfer curves usually combine many directed routes, making it difficult to determine which delayed path controls a chosen source-detector channel. We show that discrete symmetry can resolve this inverse problem. For delay-coupled Kuramoto populations on a locked Ott-Antonsen branch, a symmetry-preserving operating point enforces an exact detector-source response zero. A controlled symmetry-breaking detuning unfolds this protected zero into a ladder of real-frequency nodal crossings. We prove a general theorem for finite retarded delay networks, solve the minimal four-population motif explicitly, and show that the asymptotic spacing of the nodal ladder reads out a detector-selected delay. In the baseline motif, the recovered spacing matches the predicted delay within 0.08%, while higher-node validations recover effective delays of 2.5027 and 2.7030, in close agreement with the corresponding microscopic path delays. The result provides a swept-frequency protocol for symmetry-assisted delay spectroscopy, requiring only a selected linear detector-source response rather than reconstruction of the full transfer matrix.

Renewable energy will reduce the strain on the energy supply to some degree; however, many challenges exist in its organic integration with the current energy system, thus prompting a new round of transformation of the existing energy system. Inspired by the Internet concepts, methods, and technologies, the Energy Internet, an open and equal facility for convenient access and intelligent use of energy throughout the chain from production and transmission to consumption, has become a significant development trend. Energy, big data has enormous potential value in facilitating the demand-driven allocation of energy resources and the optimization and transition of the energy structure. The green quality evaluation of metropolis energy big data belongs to the MAGDM category. Recently, ExpTODIM and PROMETHEE techniques have been applied to solve MAGDM problems. In the green quality evaluation of metropolis' energy big data, probabilistic linguistic term sets (PLTSs) characterize uncertain information. In this paper, the probabilistic linguistic ExpTODIM-MABAC (PL-ExpTODIM-MABAC) technique is constructed and proposed to solve MAGDM problems with PLTSs. The MEREC technique obtains weights under PLTSs. Finally, an example of the green quality evaluation of metropolis' energy big data is provided to demonstrate the ExpTODIM-MABAC approach.

Ising machines as hardware solvers of combinatorial optimization problems (COPs) can efficiently explore large solution spaces due to their inherent parallelism and physics-based dynamics. Many important COPs such as satisfiability (SAT) assume arbitrary interactions between problem variables, while most Ising machines only support pairwise (second-order) interactions. This necessitates translation of higher-order interactions to pairwise, which typically results in extra variables not corresponding to problem variables, and a larger problem for the Ising machine to solve than the original problem. This in turn can significantly increase time-to-solution and/or degrade solution accuracy. In this paper, considering a representative CMOS-compatible class of Ising machines, we propose a practical design to enable direct hardware support for higher order interactions. By minimizing the overhead of problem translation and mapping, our design can result in up to 4 × lower time-to-solution without compromising solution accuracy.

ObjectivesPatient scheduling is a vital yet complex task that strongly influences patient satisfaction and optimizes healthcare efficiency. Recent studies have emphasized the importance of devising innovative approaches and developing new scheduling frameworks. Therefore, this study establishes a dedicated generative artificial intelligence (GenAI) system for patient scheduling.MethodsThe proposed system first imports scheduling data to formulate the default patient scheduling problem. Subsequently, users enter their scheduling requirements using natural language via the system interface, which are parsed using a deep neural network to establish the corresponding extended three-field notations. A customized genetic algorithm is automatically generated to solve the customized patient scheduling problem.ResultsThe dedicated GenAI system was applied to a real-world case obtained from the literature, involving 12 anesthesiologists, surgeons, and anesthesia resuscitation doctors; 12 operating rooms; and 15 patients undergoing three types of surgeries, each consisting of three operations. The experimental results reveal that the difference in the optimal fitness achieved using this system and branch-and-bound was less than 1% on average, demonstrating that the proposed methodology is effective. In addition, the most complex customized patient scheduling problem could be automatically modeled and solved in 20 s. Furthermore, the scheduling performance achieved using this system was significantly higher (α = 0.05) than those achieved using two current practices. Moreover, customized patient scheduling problems are often substantially more complex than problems addressed using traditional methods reported in previous studies.ConclusionsApplying this dedicated GenAI system improved the effectiveness of patient scheduling. This is expected to considerably enhance patient satisfaction and overall healthcare efficiency.

Rising heat flux in compact electronics demands advanced microchannel cooling with enhanced heat transfer and minimal pressure drop penalties. This study provides a comprehensive assessment of the coupled effects of microchannel geometry, porous-medium characteristics, and operating conditions on the thermo-hydraulic performance of a porous-foam-enhanced wavy microchannel heat sink. A three-dimensional computational fluid dynamics (CFD) model was developed and the governing mass, momentum, and energy equations were solved using the finite volume method. The porous copper foam was modeled as a homogeneous porous medium under the local thermal equilibrium (LTE) assumption. The thermo-hydraulic characteristics of a microchannel with wavy surfaces featuring cubic obstacles and copper foam have been studied through three different parameters: geometry (ratio of the height of the porous layer on the walls and the ratio of the height of the porous layer on the rib, varying from 0.1 to 0.9), microstructure of the material (copper foams with various porosity, permeability, and pore density), and operating conditions (Reynolds number from 100 to 900 and inlet temperature from 293 to 301 K). Results demostrate that the combination of an increase in both the height of the obstacle and the thickness of the porous material of the wall improves heat transfer. The comparison between 0.1/0.1 and 0.5/0.9 configurations indicate that the Nusselt number rises by 96%, while the highest temperature decreases by 1.6%. On the other hand, the friction factor is increased. In terms of operating conditions, increasing the Reynolds number from 100 to 900 boosts Nusselt by 108% and reduces friction by 58%; Re = 800 acts as a knee point, achieving 95% of the maximum PEC with 18-21% lower pressure drop than Re = 900. For Re = 600, an increase in the inlet temperature from 293 to 301 K leads to a relatively moderate improvement in terms of thermal efficiency. Indeed, in such conditions, the Nusselt number is increased by 1.3%, and the friction factor is decreased by 7.2%, leading to the PEC being improved by 3.86%. Therefore, the inlet temperature equal to 301 K provides a more effective thermo-hydraulic performance in the considered case. In general, the obtained data prove the fact that using porous copper foam in wavy microchannel with flow obstacles leads to the improvement of heat transfer in electronic devices. As this process is accompanied by increasing fluid mixing and heat transfer surface, the achieved result is positive considering the acceptable hydraulic losses.

Proteins are studied using a wide variety of methods, each with its inherent limitations. Here we analyze the contributions of different methods to our understanding of one of the most extensively studied proteins, arrestin-1, which plays a key role in the regulation of light-evoked signaling of photopigments in the photoreceptor cells in the retina. The data obtained by biochemical and biophysical methods in vitro are consistent with the results of in vivo studies in genetically modified mice and the symptoms seen in human patients. The structures of free arrestin-1 and its complex with rhodopsin provided very detailed information and stimulated structure-function studies. However, while the results of follow-up experiments confirmed some predictions from the crystal structures, they were inconsistent with others. In particular, the arrestin-1 tetramer in solution and in the photoreceptors of living mice was shown to be dramatically different from that revealed by the crystal structures. The prevalent complex(es) of wild type arrestin-1 with rhodopsin also appear to differ from the one in the solved structure of the two interacting mutant proteins. The lessons learned with arrestin-1 likely also apply to other proteins.

Poor operating room (OR) ergonomics pose risk of injury to OR staff, worsening patient outcomes and threatening workforce wellness and sustainability. How OR staff navigate ergonomic barriers in the interdisciplinary team environment was examined in this qualitative study. Semi-structured, open-ended interviews of surgeons, anaesthesiologists and OR nurses were conducted at single institution using a socio-material approach with interview-to-the-double. Transcripts were coded by two independent researchers. Themes in the data were outlined using the System Engineering Initiative for Patient Safety (SEIPS) framework. Five major themes were identified. First, physical environment issues creating cognitive and physical frictions, such as tripping hazards and improper room layouts. Second, influence of social positionality, with hierarchy by role and experience and social dynamics leading to differential capacity to adjust. Third, team collaboration, with lack of interdisciplinary work and communication and high staff turnover. Fourth, burden of self-initiated work arounds, outlining conflict between system/organizational issues but individual solutions. Fifth, institutional support, with lack of leadership engagement. Participants suggested solutions including accommodations to foster a collaborative approach to managing discomfort in the OR, and new team communication tools. Ergonomic barriers were described within the work system of the SEIPS framework. Social positionality and differential levels of agency, as well as lack of team collaboration and misperceptions of others' roles, were central to ergonomic challenges. Conflicts between problems lying with the system/organization, but solution being left to individuals, were also central to challenges to optimal OR ergonomics. Operating rooms are tough places to work. Staff spend long hours in cramped spaces, holding awkward positions around heavy equipment and tangled cables. Most develop pain or injuries. Up to 100% of surgeons and 98% of anaesthesiologists report muscle or joint problems. When staff are in pain or distracted by hazards, the risk of mistakes during surgery goes up. To understand how surgeons, nurses, and anaesthesiologists deal with physical strain at work and what gets in the way of making the operating room safer. Twenty-four staff members were interviewed for this qualitative study at a large hospital in Toronto, Canada (10 anaesthesiologists, nine surgeons, and five nurses). They were asked to describe a typical day in detail. Two researchers reviewed the interviews and identified common themes. Five main issues were found. First, rooms are cluttered with unneeded equipment, and cables on the floor are constant tripping hazards. Second, there is a clear pecking order. Surgeons’ needs tend to come first. Nurses often wait for surgeons to suggest a break, even when in pain. Junior staff across all roles push through discomfort rather than speak up, because doing so carries real risk. Third, teamwork helps but is inconsistent. When people talk about physical needs, solutions emerge, but these conversations rarely happen. Fourth, staff are left to solve problems alone by creating personal fixes for issues that come from the hospital system, not from individuals. Fifth, staff feel unsupported by leadership, who they believe care more about efficiency and financial targets than worker safety. Worker safety in the operating room depends on how the team works together, who has the power to speak up, and whether the hospital takes responsibility. Right now, most solutions fall on individuals even though the problems come from the system. Hospitals need to invest in better equipment, stable teams, and a culture where everyone feels safe to speak up.

Efficient coordination of berth and quay crane resources is essential for improving the operational performance of container terminals under increasing vessel traffic and limited shoreline capacity. This paper studies an integrated optimization problem that combines continuous berth allocation with time-invariant specific quay crane assignment. Unlike studies that determine only the number of quay cranes assigned to each vessel, this work explicitly determines the identities of assigned quay cranes while considering practical operational constraints, including continuous berth positions, vessel-specific crane quantity limits, contiguous crane assignment, vessel non-overlap, crane capacity, and crane non-crossing requirements. A mixed-integer programming model is formulated to minimize the total time that vessels spend in port. To solve medium- and large-scale instances efficiently, a greedy genetic algorithm is developed by combining greedy initialization, evolutionary search, decoding-based feasibility checking, and repair operations for infeasible offspring. The algorithm is designed to preserve useful inherited information while restoring feasibility with respect to berth-space and crane-resource constraints. Computational experiments are conducted using real operational data from a container port in Liaoning, China, together with synthetic instances of different scales. The results show that the proposed method can obtain high-quality feasible schedules within practical computation times. Additional ablation experiments demonstrate that both greedy initialization and repair contribute to performance improvement, with the repair mechanism providing the most evident gain in average solution quality and stability. These findings indicate that the proposed approach is suitable for integrated seaside scheduling problems requiring explicit crane identity decisions and fast feasible-schedule generation.

The relevance of studying the relationship between sociocultural identity and social immunity in young people stems from the need to find adaptive mechanisms in the context of contemporary sociocultural turbulence. Sociocultural identity, understood as the process and result of an individuals self-identification with a historically rooted system of traditional values, normative codes, and behavioral patterns, is a key mechanism for the reproduction and integration of society. It ensures the continuity of values and semantics and serves as the basis for the development of social immunity a property of a social system that allows it to maintain integrity and stability, protecting itself from destructive external influences. In the context of youth, this identity performs a dual function: it serves as a psychological buffer, reducing the traumatic impact of external instability, and simultaneously becomes a source of meaning and strategies for actively confronting challenges. The nonlinearity of modern identification processes, characterized by the multiplicity and variability of self-determination, does not negate this function; rather, it complicates and enriches the mechanisms of immunity, creating new opportunities for personal development and forming an adaptive resource for society as a whole. The possibilities of developing youth social immunity are examined from the perspective of a socio-technological approach, which views strengthening the positive socio-cultural identity of youth as a constructive task, solved through the implementation of reproducible and measurable social practices. The target outcome of these technologies is the development of a resilient adaptive complex in the new generation, including skills for critically assessing information, resistance to destructive influences, and the ability to maintain social health. Актуальность исследования взаимосвязи социокультурной идентичности и социального иммунитета молодёжи обусловлена необходимостью поиска адаптивных механизмов в условиях современной социокультурной турбулентности. Социокультурная идентичность, понимаемая как процесс самоотождествления личности с исторически укоренённой системой традиционных ценностей, нормативных кодов и поведенческих паттернов, выступает ключевым механизмом воспроизводства и интеграции общества. Она обеспечивает ценностно-смысловую преемственность и служит основой для формирования социального иммунитета свойства социальной системы, позволяющего сохранять целостность и устойчивость, защищаясь от деструктивных внешних воздействий. В контексте молодёжи данная идентичность выполняет двойную функцию: служит психологическим буфером, снижающим травмирующее влияние внешней нестабильности, и одновременно становится источником смыслов и стратегий для активного противостояния вызовам. Нелинейность современных идентификационных процессов, характеризующаяся множественностью и изменчивостью самоопределения, не отменяет эту функцию, а усложняет и обогащает механизмы иммунитета, создавая новые возможности для развития личности и формируя адаптивный ресурс для общества в целом. Цель исследования — определить роль социокультурной идентичности в формировании социального иммунитета молодёжи. Рассмотрены возможности формирования социального иммунитета молодёжи с позиции социально-технологического подхода, который рассматривает укрепление позитивной социокультурной идентичности молодёжи как конструируемую задачу, решаемую через внедрение воспроизводимых и измеримых социальных практик. Целевым результатом этих технологий является формирование у нового поколения устойчивого адаптивного комплекса, включающего навыки критической оценки информации, резистентность к деструктивным влияниям и способность поддерживать социальное здоровье.

To realize the accurate and real-time prediction of shale gas fracturing construction pressure, this study proposes a collaborative innovation method for its pressure signal characteristics. An architecture combining multiscale one-dimensional convolutional neural network (MS-1DCNN) and temporal fusion transformer (TFT) is constructed. The former extracts instantaneous jump to long-term trend features synchronously, and the latter models complex timing dependence. To solve the problem of model failure in long-term prediction, a mild online learning strategy combining FIFO buffer and EMA weight update is designed to realize the smooth adaptation of the model to the change of working conditions. In the actual data experiment, the static model of this method (MAE = 3.21 MPa) has been significantly better than the baseline, and online learning has further reduced its MAE to 1.98 MPa (38% increase), effectively suppressing error drift. It provides high reliability algorithm support for intelligent monitoring of shale gas fracturing and has significant application value for achieving cost reduction and efficiency increase.

In order to solve the problem that butyric acid is difficult to be degraded in butyrate-type fermentation effluent, corn straw was used as a substrate for dark fermentation to produce hydrogen, and different substrates (butyric acid, acetic acid, and fermentation effluent) were used to enrich the bioanode of the microbial electrolysis cell (MEC). The effects of the anode enrichment method, substrate concentration and applied voltage on hydrogen production from butyrate-type straw fermentation effluent were investigated. The microbial community structure was analysed by high-throughput sequencing. The results showed that the maximum hydrogen yield and hydrogen production rate reached 943 mL g-1 and 3.62 m3 m-3 d-1, respectively, when the bioanode enriched with butyric acid was used to treat the straw fermentation effluent at 0.6 V applied voltage. Compared with the enrichment of fermentation effluent, the degradation rate of butyric acid and the removal rate of chemical oxygen demand (COD) increased by 35.1% and 25%, respectively. The anode enriched with butyric acid had higher species richness and diversity, and the abundance of butyric acid oxidizing bacteria Syntrophomonas was as high as 8.7%. It is speculated that butyric acid is first oxidized to acetic acid, and then hydrogen is produced by electrogenic bacteria. Butyric acid oxidation is the rate-limiting step of hydrogen production. The two-stage cascade hydrogen production process significantly improved the straw conversion rate and hydrogen production efficiency, and realized the simultaneous purification of hydrogen fermentation effluent, which provided a reference for the large-scale biological hydrogen production of straw.

Safety valves are critical safety devices in pressure-bearing equipment used in the energy sector. Due to long-term erosion and wear by the medium, the sealing surfaces of their valve seats are prone to damage, which leads to internal leakage. This not only causes ineffective loss of energy medium and exacerbates the pressure on energy conservation and consumption reduction, but also poses a significant safety hazard, threatening the stable operation of the energy system. Acoustic emission signal technology, as a mainstream non-destructive testing method, has been widely used in the detection of internal leakage in safety valves. However, it lacks theoretical support for the acoustic characteristics of internal leakage aerodynamic noise, which limits the accuracy and reliability of the detection. This paper studies the internal leakage aerodynamic noise of a spring-loaded full-lift safety valve. Large eddy simulation (LES) is used to perform numerical calculations of transient internal leakage flow to capture velocity pulsations in the fluid domain. The Lighthill tensor is solved using a energy-conserving mapping method as the excitation source, and the acoustic solution is completed by combining the Ffowcs Williams-Hawkings (FW-H) equations to obtain the sound pressure level curve and contour map of internal leakage aerodynamic noise. The study found that the sound pressure pulsation of internal leakage is dominated by quadrupoles, and the main peak frequency of internal leakage is negatively correlated with the total sound pressure level. The relative error between the numerical simulation and experimental results is ≤ 5%, indicating good accuracy. This research fills a theoretical gap in the acoustic characteristics of internal leakage, provides theoretical guidance for the acoustic detection of internal leakage, can improve the accuracy of leakage detection, achieve early warning, reduce energy waste, help save energy and reduce consumption, and has important engineering application value for ensuring the safe and stable operation of energy systems.

Clinical Practice Guidelines CPGs are the foundation of Evidence-Based Medicine but their long, complex and unstructured format makes them difficult to integrate in Clinical Decision Support Systems CDSS. While Large Language Models LLMs are great at reading text, their tendency to hallucinate and act as black boxes makes them unsafe for autonomous medical decisions. To solve this, we propose an automated pipeline that safely turns CPGs in raw PDF format into a structured, computable database of medical evidence expressed as formal arguments. First, we use computer vision to accurately extract complex tables and preserve the document's layout. Then, we constrain the LLM using strict clinical frameworks PICO and Toulmin to guarantee that every extracted claim is traceable and accurate. Finally, we use clustering and pruning methods to remove duplicate information and organize the data. The result is a clean, trustworthy knowledge base that lays the essential groundwork for formal argumentation graphs and reliable CDSS.

A detailed characterization of the microbial ecosystem involved in the production processes of fermented foods is essential. Although fermented foods are an important part of the human diet and have seen an increasing interest nowadays, some challenges still need to be solved. Specifically, yeast identification through culture-independent methodologies is still limited to the genus level. Unlike bacterial species identifications, long-read sequencing technologies have barely been used for yeast species identification, and, to the best of the authors' knowledge, it has not been validated with mock communities reflecting food fermentation processes yet. Therefore, in the current study, we present an amplicon-based metabarcoding approach targeting the full-length internal transcribed spacer (ITS) region comprising ITS1, the 5.8S rRNA gene, and ITS2, using the PacBio HiFi sequencing platform. This method was validated using DNA-based mock communities composed of yeast species involved in sourdough, lambic beer, and cocoa fermentation processes. Accurate species-level identification was achieved for most of the species. However, special attention should be given to Saccharomyces-rich niches, as accurate species-level identification for this genus is still challenging. Furthermore, underestimation of the relative abundance of species with short ITS regions, such as Pichia and Brettanomyces, occurred. In addition, the method was successfully applied to describe the yeast diversity present in two sourdough and two lambic beer samples. Overall, the current method provides an unprecedented way of determining the species-level yeast composition of complex ecosystems present in fermented food products.IMPORTANCETo date, species-level identification of common yeasts present in food fermentation ecosystems has been difficult, if not impossible, when using short-read sequencing methods. However, species-level identification is essential when evaluating and describing the characteristics of fermented food microbiomes. The current study reports on the development and validation of an amplicon-based metabarcoding approach combined with long-read PacBio HiFi sequencing targeting the full internal transcribed spacer (ITS) region, comprising the ITS1 and ITS2 regions, as well as the 5.8S rRNA gene. The described methodology enables species-level identification of the most common yeasts present in food fermentation ecosystems. This new methodology provides an important tool not only for the investigation of fermented foods but also for other fields engaged in complex microbial community analysis.

The crystal structure of clofarabine (form I) [systematic name: 2-chloro-9-(2-de-oxy-2-fluoro-β-d-arabino-furanos-yl)-9H-purin-6-amine], C10H11ClFN5O3, has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. The oxolane ring adopts an envelope conformation and the angle between the mean ring planes is 88.4 (2)°, resulting in an l-shaped mol-ecule. The mol-ecules stack along the short a-axis direction, and N-H⋯O, O-H⋯N and N-H⋯N hydrogen bonds link them into a three-dimensional network.

Emotions are constantly generated in daily activities. They not only control people's behavioral patterns and thinking decisions, but also affect physical and mental health. Therefore, the emotion recognition technology based on electroencephalogram (EEG) signals has broad application prospects in multiple fields such as human-computer interaction, medical health, and intelligent driving. To address the issues of insufficient labeled data in EEG and significant differences in EEG data among different subjects and at different time periods, this paper introduces the domain adaptation (DA) technique to solve the task of cross-domain EEG emotion recognition under unsupervised conditions. Aiming at the problem that the existing domain adaptation methods ignore the different weights of the global domain and subdomains when reducing domain differences, this paper proposes a dynamic bi-domain discriminator adversarial network (DBDAN). A feature extractor is constructed to extract the low-level domain invariant features of the EEG signals, and a multi-branch domain-specific feature extractor is used to generate domain-specific features. In order to reduce domain differences, adversarial learning for the global domain and subdomains is achieved through a dual-domain discriminator. Meanwhile, dynamic factors are introduced to dynamically adjust adversarial learning, enabling the model to strike a balance between coarse-grained adversarial and fine-grained adversarial and achieve domain adaptation more precisely. The cross-subject accuracy rates on SEED, SEED-IV and DEAP were 89.43%, 75.09% and 63.42%, respectively. These results indicate that our method achieves competitive performance among the evaluated EEG transfer-learning baselines and suggest that dynamic global-subdomain alignment is beneficial for cross-domain EEG emotion recognition.