Rare microbial taxa as potential drivers of yield variation in sauce-flavor baijiu fermentation: Insights from microecology and machine learning.

Transferability and integration of physics-informed neural network models for scalable multi-zone occupant-centric ventilation control

The eukaryotic cell cycle is one of the most fundamental biological processes, ensuring the accurate duplication and segregation of the genome during mitosis. Decades of research across model systems have shown that this process is orchestrated by a family of protein kinases known as cyclin-dependent kinases (Cdks). Together with their cyclin partners, Cdks act as master regulators of cell division, coordinating DNA replication, chromosome segregation, and cytokinesis with remarkable precision. The discovery of Cdks and cyclins in yeast and sea urchins, celebrated with the Nobel Prize of Hartwell, Hunt, and Nurse (awarded in 2001), established the conceptual framework for understanding how oscillations in kinase activities drive cell cycle progression in a unidirectional and irreversible manner. Over the past thirty years, a central question has been whether cell cycle control relies primarily on the quantitative level of Cdk1 activity or whether distinct qualitative functions of cyclin-Cdk1 complexes ensure the correct ordering of events. Addressing this question required new genetic and biochemical tools capable of controlling Cdk1 activity with high temporal resolution and specificity. A turning point came in 2000 with the development of the analogue-sensitive Cdk1 allele by the Shokat laboratory. This approach replaced classical temperature-sensitive alleles with a version of Cdk1 that can be selectively inhibited by bulky ATP analogues. Beyond specific inhibition, the system was soon adapted to directly label and identify Cdk1 substrates, coupling chemical genetics with the emerging power of mass spectrometry. This review outlines the conceptual frameworks of quantitative and qualitative models of Cdk1 control. It also highlights how these ideas have been experimentally dissected, tracing the development of the Cdk1 Shokat system and advances from synthetic biology and phosphoproteomics in decoding phosphorylation logic, and how these concepts apply to meiosis. These studies draw primarily on budding yeast and fission yeast which have a single Cdk, making them convenient models for studying core principles of cell cycle regulation. Key insights from vertebrates are also integrated to illustrate principles that extend to other eukaryotes.

The development of an intelligent connected monorail transit system offers an effective solution to the mismatch between passenger flow and system capacity at various time intervals within urban rail networks. As the core of such a system lies the virtual coupling (VC) technology, which dynamically adjusts train configurations in response to real-time passenger demand, thereby improving resource utilization. However, during VC operations, severe communication delays between vehicles or the sudden emergence of obstacles ahead may still result in rear-end collisions among coupled vehicles, posing significant safety risks. To address these challenges, this paper focuses on the active collision avoidance control of intelligent connected monorail vehicles operating within the VC environment. At the modeling level, a control model is developed to facilitate VC between leading and following vehicles, and the dynamic characteristics of typical operational scenarios—including station approach coupling, tracking coupling, and departure decoupling—are thoroughly analyzed. Building upon this foundation, the train’s behaviors under collision avoidance during accelerated departures, decelerated arrivals, and unexpected obstacle encounters are further investigated. In terms of control strategy, a Model Predictive Control (MPC) algorithm is introduced to enable efficient coordination and proactive collision avoidance among trains. Ultimately, a simulation platform based on Chongqing Rail Transit Line 3 is established for validating the proposed model and algorithm under representative operating scenarios. The evaluation demonstrates gains in system flexibility and safety and technical foundation for the practical implementation of intelligent rail transit systems.

High−Altitude wind is a critical factor affecting the recovery safety of reusable rockets, significantly altering aerodynamic loads, flight attitudes, and trajectories—especially during the aerodynamic deceleration phase (engine shutdown) of reentry, posing severe challenges to high-precision guidance and stable control. Currently, accurate advance prediction of landing site wind fields is difficult with poor real-time performance, necessitating a real-time estimation and prediction method independent of additional measurement equipment. This study addresses this gap by proposing a deep learning-based approach for wind field estimation and prediction, using directly measurable attitude angles and apparent acceleration deviations of the rocket as inputs to train a dedicated deep neural network. Furthermore, to solve the attitude control problem of Reusable Launch Vehicles (RLVs) during recovery, a non-recursive simplified high-order sliding mode control method with online wind disturbance compensation is designed to achieve finite-time convergence. First, a dynamic model for the attitude control of RLVs during recovery is established; second, based on homogeneity theory, a non-recursive simplified homogeneous high-order sliding mode controller is developed to realize finite-time tracking control during RLV recovery with uncertainties, effectively suppressing the chattering inherent in sliding mode control; finally, simulation results verify the effectiveness and engineering feasibility of the proposed method. The combined approach significantly reduces wind-induced disturbance torque and required control torque, enhancing the adaptability and control robustness of vertically recoverable rockets to wind fields.

Sickle cell disease (SCD) is a major Public Health concern in India with highest prevalence amongst the tribal ethnic groups. It is an autosomal recessive disorder which results in haemoglobin polymerisation leading to sickle shaped red blood cells. It can manifest as SCD (Hemoglobin SS [HbSS]), Sickle Cell Trait (HbSS) or Sickle β-Thalassemia (HbS). HbSS is the most severe form, presenting as vaso-occlusion crisis, chronic hemolysis, eventually resulting in progressive multiorgan damage. Given the diversity of clinical presentation, SCD poses a considerable burden on health care system. National Sickle Cell Anemia Elimination Mission (NSCEM) launched by Government of India in 2023 proposes comprehensive framework for SCD screening, diagnosis and management. This article provides an overview of recent updates on SCD, existing programmatic challenges and future directions for advancing sickle cell care in India. Although geospatial epidemiology has been well understood, the reach at the grassroots level has been impacted by lapses in implementation leading to difficulty in health care access in remote tribal regions. The review highlights the need of identifying context-specific gaps and developing an evidence-based optimal model for SCD screening, diagnosis, treatment, and care. Further, newborn screening clubbed with timely follow up ensuring continuum of care can be programmatically measured as a composite care continuum index for every diseased child tracking from diagnosis till treatment initiation. While innovations in point of care testing for SCD diagnosis and liquid oral formulation of hydroxyurea for treatment are promising, there is imperative need of efforts towards improving overall quality of life of SCD individuals, spanning beyond medical management. Integrating Social and Behavioural Change Communication and Advocacy, Communication and Social Mobilization may culminate in the socio-cultural transformation for long-term success. With a robust political commitment, microplanning strategies, intersectoral collaboration, and community participation, NSCEM, India can serve as a global model for sickle cell control in low resource settings.

Sensorimotor control models traditionally consist of two types of internal models: inverse models, which compute the motor commands needed to reach a desired movement goal, and forward models, which predict the resulting sensory feedback. These models are usually considered separate entities, but it is unclear whether such separation exists in the nervous system. Additionally, maintaining separate networks may be more computationally expensive. Therefore, we investigated whether these functions could be executed within a single neural circuit: an inverse-forward-recognition model (InFoRM). We implemented InFoRM using neural networks and compared their ability to reproduce cyclic reaching movements with that of control architectures based on classical, separated inverse and forward models. Desired movement trajectories were represented by recorded three-dimensional kinematics, while efferent (muscle activation) and afferent (muscle length and velocity) signals were obtained through inverse dynamics. Our findings show that InFoRM significantly outperforms control architectures across various conditions, while requiring fewer resources. The network is also able to morph to untrained movement directions, generating motor commands and predicted feedback that had not been learned. These findings demonstrate the computational advantages of integrating inverse and forward processes within a single neural network, suggesting that such unified sensorimotor models may be worthwhile to explore further.

Due to the complex geological conditions of coal-rock, the cutting head of coal mine excavation machines experiences severe fluctuations in loads, making it difficult for existing macroscopic controls to accurately capture the microscopic loads on the cutting pick. Therefore, a dynamic simulation and intelligent control model for the cutting head load of an adaptive roadheader based on multi-scale coupled simulation is developed. The study first modifies the classical load model through finite element method to accurately simulate the microscopic interaction between the cutting pick and the rock mass. The non-dominated sorting genetic algorithm II in elite strategy is used to construct a multi-objective optimization model to determine the optimal parameters for cutting head speed and swing speed. Finally, load dynamic control is achieved by combining radial basis function proportional-integral-derivative controller, and multi-body dynamics-discrete element method and proximal policy optimization are introduced to improve the adaptability to complex working conditions. Test results from different operation scenarios showed that the path planning error of the model met high-precision excavation requirements in regular roadways. During the long-term stable operation phase, the energy consumption ratio and energy utilization efficiency were significantly improved compared to traditional solutions. Faced with slight changes in coal-rock hardness, this model provided early warnings effectively. Under single-point fracture failure, load stability was quickly restored. In the constant operating condition performance test, the model demonstrated significant steady-state control accuracy with minimal mean square error and zero overshoot. Furthermore, a pilot engineering application in a high-gas coal mine roadway demonstrated that the relative error between the simulated and measured loads was controlled within 6.5%, validating the practical feasibility of the proposed system. This study can effectively reduce pick failure, improve excavation efficiency, provide core technical support for the "less manpower, unmanned" operation of coal mines, and assist in the safe and efficient upgrading of the coal industry.

Aim . This work aimed to assess the effectiveness of the Russian gasoline pricing regulation model in control of inflation and to identify its inherent distortions in the form of asymmetric pricing by comparing it with the experience of countries with liberalized markets. Objectives . The work seeks to identify and systematize the key grounds of government intervention in gasoline pricing; to analyze empirically the “rockets and feathers” hypothesis in gasoline markets in Russia and developed countries; and to assess the dynamic impact of gasoline retail price shocks on inflation rates in the countries under study. Methods . A nonlinear autoregressive model with distributed lags was used for the empirical analysis of asymmetry. The impact of gasoline price surges on inflation was assessed using vector error correction models and an impulse response function. Results . The study revealed a moderate pricing asymmetry in developed countries (3–10 %) and a radically high pricing asymmetry in Russia (over 1 000 %), which is a consequence of an institutional trap created in Russia by its specific model of gasoline market regulation. A shock transmission analysis revealed that administrative control over retail prices in Russia disrupted effectively the channel through which they influence inflation, ensuring macroeconomic and social stability in light of chronic price imbalances and the suppression of competition. Conclusions . The comparative empirical analysis can be used to draw a fundamental conclusion about the existence of asymmetric pricing in retail gasoline markets. The abnormally strong asymmetry in Russia is not a statistical anomaly, but a consequence of the specific regulatory model. Quasi-directive control of retail prices at the level of the inflation rate, combined with a damping mechanism, has created an institutional trap where gasoline prices actually cease to decline when global oil prices fall.

Background: This study investigated the effects of Laurocerasus officinalis Roem (cherry laurel; CL), a traditionally consumed fruit, on cognitive performance and selected neurobiochemical and metabolic pathways in a nontransgenic streptozotocin (STZ)-induced Alzheimer's disease (i.c.v. STZ) model and an STZ-induced type 2 diabetes mellitus (T2DM; i.p. STZ) model. Method: Fifty-seven adult male Sprague-Dawley rats were allocated to control, T2DM, and Alzheimer (ALZ) model groups, with subgroup interventions including CL supplementation and, in the T2DM model, metformin as a comparator. Spatial learning and memory were assessed using the Morris Water Maze. Serum and brain tissue levels of GSK3-β, glutathione (GSH), interleukin-1 (IL-1), GLUT4, GLP-1, β-amyloid (Aβ), and acetylcholinesterase (AChE) were quantified. Results: Serum GSK3-β levels did not differ significantly between groups, whereas brain tissue GSK3-β showed significant between-group differences. CL increased GSH levels in both models, with significant elevations in serum and brain tissue GSH in the ALZ model following CL administration; in the T2DM model, GSH increased after both CL and metformin. In the ALZ model, CL was associated with decreased serum Aβ and AChE levels and improved Morris Water Maze performance, reflected by reduced escape latencies. Conclusions: CL supplementation was associated with antioxidant enhancement and modulation of amyloid- and cholinergic-related measures, alongside improved spatial learning performance in the STZ-induced nontransgenic ALZ model. In addition, CL reduced blood glucose in the T2DM model. Given the likely contribution of fruit phytochemicals (including total phenolics), further studies are warranted to better define the bioactive composition and mechanisms underlying these effects.

Industrial production of Steamed Panax notoginseng (SPN) faces batch-to-batch variability (RSD > 15%) in rare G-Rk3, G-Rh4, 20(S) G-Rg3, and 20(R) G-Rg3 and costly quality control due to expensive reference standards (e.g., 20(S) G-Rg3 ≈ $2,500 per 5mg sample). To address this, we developed an integrated strategy: (1) A "Water Activation-Gradient Temperature Control" process optimized via orthogonal design and Arrhenius kinetics (Ea = 58.3kJ/mol) increased total rare ginsenosides by 78.6% (32.7mg/g, p < 0.01) under the following optimized parameters: particle size of 2-4mm, water impregnation of 100% w/w for 2h, and steaming at 120°C for 5h. This optimization reduced batch variability to an RSD < 5%; (2) An HPLC-QAMS method using accessible 20(R) G-Rg3 as an internal reference achieved simultaneous quantification of four ginsenosides with validated relative correction factors (G-Rk3: 0.7331, G-Rh4: 0.5015, 20(S) G-Rg3: 1.0777; RSD < 2.0%), demonstrating high accuracy (recovery: 91.95-101.34%, RSD < 1.8%), linearity (R² = 1.000), and robustness across HPLC systems (RSD < 3.5%), reducing reference standard costs by 75%. The Single-Marker Quantification (QAMS) method exhibited superior Analysis of greenness (AGREE) (score: 0.76 vs. 0.63 for ESM) and Blue Applicability Grade Index (BAGI) (score: 77.5 vs. 65.0 for ESM). Analysis of 15 batches confirmed consistency (RE% < 5% vs. ESM), while optimized extraction (60% ethanol, 5 cycles × 1.5h) achieved 85.82% transfer rate for 20(R) G-Rg3. This work resolves SPN industrialization bottlenecks by ensuring bioactive consistency and establishing a cost-effective, eco-friendly quality control model transferable to other processed botanicals.

Mediator is a central transcriptional coactivator that connects sequence-specific transcription factors with RNA polymerase II to control inducible gene expression in plants. MED16 is a Mediator tail module subunit that functions as a context-dependent integrator, helping coordinate developmental programs with environmental adaptation. This review summarizes current evidence for MED16 function from structural and evolutionary perspectives to physiological outputs, with emphasis on how MED16 interacts with transcription factors and other Mediator subunits to shape RNA polymerase II engagement at target loci. In terms of development, MED16 contributes to organ growth and root system architecture, and comparative studies have revealed that it plays conserved roles in lineage-specific wiring. Under abiotic stress, MED16 supports the efficient activation of stress-inducible transcription, including cold acclimation and nutrient stress responses such as phosphate starvation-dependent root remodeling. In immunity, MED16 modulates salicylic acid- and jasmonate/ethylene-associated defence outputs and can be targeted by plant viruses, which is consistent with its role in antiviral transcriptional responses. Mechanistically, MED16 participates in cooperative and competitive interactions within the Mediator complex that tune hormone-responsive outputs, exemplified by MED25-related competition in abscisic acid signalling. We highlight key limitations and future directions, including the need for mechanistic validation beyond Arabidopsis, clearer models of dosage control in crops, improved understanding of context-dependent tail configurations, and high-resolution mapping of MED16 interaction interfaces.

Estimating grades in small-volume, high-grade volcanogenic massive sulfide (VMS) deposits can be difficult due to sharp changes in mineralization and limited data coverage around high-grade zones. This study compares ensemble machine learning models with interpolation and geostatistical methods to compare gold estimation and grade-tonnage results. Random Forest and Gradient Boosting were trained using drillhole composites and evaluated against Inverse Distance Weighting (IDW), Simple Kriging (SK), and Ordinary Kriging (OK). The trained models were applied across the block model to generate continuous grade predictions and support grade-tonnage calculations at multiple cutoff grades. The ensemble models showed lower RMSE and higher R2 values and captured grade patterns more efficiently than traditional methods. Grade-tonnage comparison indicated that IDW generated the highest contained gold equivalent at low cutoff grades, while OK and Gradient Boosting produced more consistent and geologically reasonable estimates. Overall, the results show that machine learning methods can complement traditional estimation techniques when combined with geological domain control and appropriate model tuning.

This paper provides a comprehensive psycholinguistic analysis of language processing in multilingual societies, shifting from monolingual-centric models to viewing multilingualism as the normative human condition. It examines core mechanisms including nonselective lexical access across languages, evidenced by cognate facilitation and parallel activation in models such as the Revised Hierarchical Model (RHM), BIA+, and Multilink. Cognitive control processes, particularly inhibitory control and adaptive mechanisms in code-switching contexts, are explored through frameworks like the Inhibitory Control model and Adaptive Control Hypothesis. Neurobiological evidence highlights structural plasticity, overlapping neural networks modulated by proficiency and age of acquisition, and modality-dependent processing in diglossic environments such as Arabic. Additional topics include suprasegmental (tonal) cross-linguistic influences and models of third language (L3) acquisition emphasizing selective or typological transfer. The discussion extends to educational implications, supporting multilingual pedagogies and translanguaging to promote equity and cognitive benefits. Despite advances, persistent gaps remain in research on non-Indo-European languages and low-resource contexts, underscoring the need for inclusive, globally representative studies and equitable NLP development. Overall, multilingualism reveals fundamental interactions between language, cognition, and society, offering critical insights into the adaptive architecture of the human language system.

ABSTRACT This paper introduces the concept of fundamental phase shift to provide a unified phase-shift control strategy description for power converters. It aims to simplify system control, reduce backflow power and improve operational efficiency. A Fourier-based Optimal Modulation Strategy (FOMS) is developed by decomposing transmitted active and reactive power for Dual-Active Bridge (DAB) converters. The unified mathematical model is applicable to Single Phase Shift (SPS), Extended Phase Shift (EPS), Dual Phase Shift (DPS) and Triple Phase Shift (TPS) modulation schemes. Theoretical analysis and simulation results indicate that under FOMS control, circulating harmonic components during constant active-power transmission are eliminated, significantly reducing the RMS value of inductor current. This RMS reduction diminishes backflow, thereby increasing overall converter efficiency. In all four modulation schemes and operating modes, backflow power is markedly reduced, and the system efficiency under the proposed FOMS can reach up to 95%. The proposed method simplifies implementation across different modulation strategies, offering a practical and efficient solution for power-electronics engineering. This study delivers valuable insights for optimising DAB converters and contributes to the progress of electric energy conversion technologies.

Background The swift advancement of Internet of Things (IoT) technology has revolutionized smart home settings; the prevalent automation systems are limited by their need for specific device identification and rigid rule-based configurations. These constraints impede natural human-device interaction, especially in dynamic or communal environments where spatial context is more instinctive than predetermined naming conventions. Current solutions frequently neglect spatial reasoning and multimodal inputs, resulting in heightened cognitive demands and diminished accessibility. The proposed work develops a spatial context-aware control system aimed at facilitating intuitive, vision-driven, and language-based interaction with smart devices to overcome these problems. Methods The proposed model is a modular, multimodal framework that integrates computer vision, natural language processing, and spatial inference for context-aware smart device control. The system comprises six core components: (i) an Onboarding Inference Engine for extracting device information via natural language input, (ii) Zero-Shot Device Detection using Open-World Localization–Vision Transformer (OWL-ViT) for object identification without prior training, (iii) Metadata Refinement and Filtering for structured annotation and disambiguation, (iv) a Geospatial Device Visualizer for annotated visual feedback, (v) Spatial Topology Inference using Generative Pre-trained Transformer 4 omni (GPT-4o) for reasoning about physical layouts, and (vi) Intent-Based Command Synthesis with Gemini Flash to generate precise, executable control commands. The final Agentic Execution Module interfaces with the Tuya Smart Device Application Programming Interface (API), ensuring vendor-agnostic actuation. The system supports multilingual input and adapts to various environmental contexts, including smart homes and assisted living facilities. Results A user study involving 15 participants (aged 18–80, diverse educational backgrounds) was conducted to evaluate the effectiveness of the proposed method in comparison to the Google Home Assistant. Quantitative findings demonstrate a statistically significant reduction in cognitive workload, with NASA Task Load Index (TLX) scores decreasing by an average of 13.17 points ( p = 0.0013, Cohen’s d = 1.0381). Participants rated the proposed method higher in terms of ease of use (mean = 4.67) compared to Google Home (mean = 3.8) on a 5-point Likert scale. Qualitative feedback highlighted the intuitive nature of spatial context commands, reduced cognitive burden due to the elimination of device name memorization, and enhanced accessibility via support for regional languages. 93.3% of users preferred the proposed method over the baseline system. These results affirm the feasibility and user-centric benefits of integrating vision-language models for context-aware smart device control.

Purpose The purpose of this study is to develop an optimal production control strategy for manufacturing systems producing perishable goods under stochastic demand and machine unreliability. It aims to determine the best production rates and inventory levels that minimize total operational costs – including production, holding, shortage and disposal – while ensuring customer demand satisfaction. Additionally, the study investigates the impact of shelf-life variability and system uncertainties on operational performance, providing practical guidelines for cost-effective and resilient production planning in industries where perishability and equipment disruptions are critical concerns. Design/methodology/approach A stochastic production control model is developed for perishable goods in unreliable manufacturing systems under random demand, machine failures and limited shelf life. The model integrates production, inventory, shortage and disposal costs into a unified framework. A numerical optimization procedure is employed to determine the optimal production rates and inventory levels that minimize total operational costs while maintaining a desired service level. Sensitivity analyses are conducted to evaluate the impact of key parameters, such as shelf-life variability, demand fluctuations and shortage costs, on system performance and cost-efficiency. Findings The study demonstrates that the proposed stochastic production control model effectively minimizes total operational costs while maintaining a desired service level. Numerical results show that dynamically adjusting production based on machine status and inventory aging reduces shortages and product waste. Sensitivity analyses reveal that shelf-life duration, demand variability and shortage costs significantly influence optimal production and inventory policies. The model provides actionable insights for managing perishable goods in unreliable manufacturing systems, highlighting the importance of integrating demand uncertainty, machine failures and product perishability into production planning for cost-effective and resilient operations. Originality/value This study presents a novel stochastic production control framework that simultaneously addresses perishability, machine unreliability and stochastic demand – factors often treated separately in existing literature. By integrating production, inventory, shortage and disposal considerations into a unified model, it provides a practical tool for cost-effective and resilient production planning. The numerical optimization approach enables parameterized control policies adaptable to real-world variability. The findings offer both theoretical insights and actionable strategies for industries such as food, pharmaceuticals and biotechnology, where perishable products and system uncertainties critically impact operational efficiency and service levels.

Objective To investigate whether vitamin D modulates gut microbiota and the colorectal cancer (CRC) immune microenvironment through the vitamin D receptor (VDR)-JAK-STAT signaling pathway. Methods Thirty male SD rats were randomly divided into the control group (CG), model group (MG), and vitamin D intervention group (VDG). Protein expression levels of VDR-JAK-STAT pathway (VDR, p-JAK2/JAK2, p-STAT3/STAT3), gut microbiota composition (via 16S rRNA sequencing), and immune markers (CD4+/CD8+ T cells, regulatory T cells (Treg), interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α were compared across the groups. Results Compared to the CG, the MG exhibited a significant reduction in VDR expression (p < 0.05) and a marked increase in the p-JAK2/JAK2 and p-STAT3/STAT3 ratios (p < 0.05). The gut microbiota α-diversity (Shannon/Chao1 indices) was significantly reduced (p < 0.05), and microbial composition was abnormal, with a decrease in Bacteroidetes and an increase in Firmicutes/Proteobacteria (p < 0.05). Immune microenvironment imbalance was characterized by a reduction in CD4+ T cells (p < 0.05), an increase in Treg cells (p < 0.05), elevated pro-inflammatory cytokines IL-6/TNF-α, and decreased anti-inflammatory cytokine IL-10 (p < 0.05). Vitamin D intervention significantly reversed these abnormalities (all p < 0.05). The MG also showed a significant increase in macrophage proportion and M2 polarization, and a significant decrease in dendritic cell proportion and M1 macrophage polarization (all p < 0.01). Vitamin D intervention reversed the polarization imbalance, reducing total macrophages, increasing M1 polarization, and decreasing M2 polarization (all p < 0.05). Conclusion Vitamin D inhibits excessive activation of the JAK-STAT pathway through VDR activation, ameliorates gut microbiota dysbiosis, restores butyrate metabolism, and rebalances macrophage polarization.

Later-life transitions, such as retirement, present significant developmental milestones that challenge individuals to regulate shifting goals, identities, and resources. This longitudinal study examined the relationship between two forms of passion-harmonious passion and obsessive passion-and psychological functioning during the transition to retirement among 347 tenured university faculty members (Mage = 61.6). Guided by the dualistic model of passion and the motivational theory of lifespan development, the study tested whether adaptive primary and secondary control strategies mediate the relationship between passion and psychological functioning (well-being and ill-being) and whether these processes are moderated by perceived decline in professional expertise. Results from structural moderation and mediation analyses showed that harmonious passion facilitated greater use of secondary control strategies, particularly positive reappraisal, which was linked to enhanced well-being and reduced ill-being. These effects were magnified among individuals who perceived a decline in their expertise, suggesting that harmonious passion may foster adaptive flexibility in the face of normative losses. Obsessive passion was negatively related to self-regulation and unrelated to psychological outcomes. By integrating passion with lifespan models of control, this study highlights how motivational orientation influences self-regulatory processes and psychological functioning across key developmental transitions in adulthood. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Evaluation of iliac and fibula graft reconstructions compared to healthy mandibles: A finite element analysis study.