Abstract: Agriculture faces escalating challenges from pests, diseases, and climatic stresses that threaten global food security [1,2]. Green nanotechnology offers a sustainable approach to enhance crop protection and productivity by using plant-based methods to synthesize metallic nanoparticles (NPs), reducing chemical inputs and environmental impacts [3,4]. This review presents the framework of green nanotechnology in agriculture, focusing on biogenic sources of nanoparticle synthesis (especially plant extracts), mechanisms of nanoparticle formation and stabilization by phytochemicals, and characterization techniques for green-synthesized NPs. We examine the application of plant-derived metallic nanoparticles as nanofertilizers to improve nutrient use efficiency and crop yields, as nanopesticides to manage plant pathogens and pests, and as nano-enabled agents to enhance tolerance to abiotic stresses such as salinity and drought. Recent studies demonstrate that green-synthesized NPs can significantly increase crop growth and productivity while reducing dependence on conventional agrochemicals [5]. The review also discusses key challenges limiting large-scale adoption, including production scalability, biological variability in synthesis, potential phytotoxicity at high concentrations, regulatory uncertainties, and gaps in knowledge regarding nanoparticle fate and safety [6,7]. Overall, green-synthesized metallic nanoparticles emerge as promising tools for improving crop productivity and protection in an eco-friendly manner, supporting the transition toward more sustainable agricultural systems.

Abstract: In 2.5D/3D stacked advanced packaging, one-part additive curing silicone composites are widely employed to achieve structural bonding and efficient heat dissipation. In this study, a thermally conductive silicone adhesive was prepared using medium viscosity vinyl silicone oil, hydrogen containing silicone oil, and micron-sized alumina powder as the primary components. The results demonstrated that the adhesive exhibited excellent thermal and mechanical performance. Specifically, its thermal decomposition temperature exceeded 400 °C, the thermal conductivity reached over 1.80 W·m⁻¹·K⁻¹, and the thermal resistance was below 12.0 °C·cm²·W⁻¹. The shear strength exceeded 5.00 MPa. Furthermore, after exposure to uHAST for 384 h, 1,000 thermal cycles, and thermal aging for 1,000 h, the adhesive maintained stable thermal conductivity and mechanical properties. The thermal conductivity remained above 1.70 W·m⁻¹·K⁻¹, and the shear strength remained higher than 5.00 MPa. In addition, the tensile modulus was maintained below 100 MPa, and the coefficient of linear thermal expansion was less than 160 ppm·°C⁻¹. Overall, the comprehensive performance of the adhesive satisfies the reliability requirements for advanced packaging substrates and heat dissipation lid assemblies.

Abstract: Background/Objectives: Human and canine leishmaniasis are neglected diseases with limited therapeutic options. The nitrochalcone NAT22, a high-affinity inhibitor of the essential parasite enzyme tryparedoxin peroxidase (cTXNPx), has emerged as a promising antileishmanial candidate. Interestingly, NAT22 demonstrated superior efficacy when administered orally rather than intralesionally, suggesting metabolism-driven enhancement of activity. Since in vivo studies with chalcones have been conducted exclusively in mice, this study aimed to evaluate whether mice are suitable models for oral chalcone therapies for human and canine leishmaniasis and to identify metabolites with potential antileishmanial activity. Methods: NAT22 hepatic metabolism was investigated using in silico prediction and in vitro liver microsomal assays from rats, mice, humans, and dogs. Metabolites were identified by LC-MS/MS and NMR, and docking studies were performed against cTXNPx. Results: In silico analysis predicted metabolism mainly by CYP1A2, CYP2A6, CYP2C8, and CYP3A4. Seven metabolites (M1–M7) were identified by LC-MS/MS and NMR in all species except mice, whose microsomes did not generate M6. Structural analyses indicated preservation of the α,β-enone system and nitro-substituted B ring in all metabolites. Docking studies showed that metabolites M2 and M4 displayed stronger predicted binding energies than NAT22. Conclusions: NAT22 undergoes hepatic phase I metabolism generating two metabolites with enhanced predicted interaction with cTXNPx. The similarity between human and canine metabolic profiles supports the translational relevance of oral NAT22 therapy in leishmaniasis, while metabolites M2 and M4 emerge as candidates for validation in local treatment of cutaneous leishmaniasis.

Abstract: The gravitational constant G occupies a central yet conceptually unresolved position in modern physics. Introduced as the coupling parameter between curvature and stress–energy in Einstein’s field equations, G determines the strength of gravitational interaction but lacks a structural derivation. Its measured value remains experimentally delicate, and its physical interpretation is traditionally treated as primitive. In this work, we propose a reinterpretation of G within the framework of Viscous Time Theory (VTT), treating it not as a fundamental constant, but as an emergent informational coupling operator. Specifically, we demonstrate that G can be understood as the inverse susceptibility of emergent geometry to identity-preserving informational tension (Δ

Abstract: The FDA has approved repetitive Transcranial Magnetic Stimulation (rTMS) as a treatment for major depressive disorder that affects the dorsolateral prefrontal cortex (DLPFC). However, clinical effectiveness can often be limited by inaccuracies in target localisation using heuristic methods derived from head surface analysis. Even though manual segmentation of MRI data is accurate, it takes a long time. This article discusses a three-dimensional deep learning pipeline that can segment the DLPFC quickly and without wasting resources. We have developed a custom lightweight 3D U-Net architecture that we trained from scratch with just a few data samples (N=4). The proposed method attained an average Dice similarity coefficient of 0.53 (with a peak value of 0.64) through a patch-based learning strategy and leave-one-subject-out validation that excluded one subject, while decreasing the segmentation time from 45 minutes to under 40 seconds. The findings indicate the feasibility of implementation on consumer devices for neural navigation.

Abstract: We develop the central identities of the theory of automorphic forms centering on the Jacobi theta constants \( \vartheta_2, \vartheta_3, \vartheta_4 \), the weight-4 Eisenstein series \( E_4 \), the discriminant \( \Delta \), the j–invariant, and the modular \( \lambda \)–function. The classical theory is organized around a single minimality theorem: the pair \( (\vartheta_3(\tau), \vartheta_3(2\tau)) \) suffices to recover every primary automorphic invariant at level \( \leq 2 \) as an explicit polynomial or rational function.Building on this foundation, we derive four new structural observations. \( \textbf{(I)} \) The shifted invariant \( J(\tau) := j(\tau) - 744 \) satisfies \( J(2\tau) = J(\tau)^2 - 2 \cdot 196884 + O(q^2) \) (and in fact \( J(2\tau)=J(\tau)^2-2\cdot 196884-2\cdot 21493760\,q^2+O(q^4) \)), placing the first Monster moonshine coefficient as the \emph{leading deviation from perfect squaring} under the doubling isogeny; the corresponding quadratic fixed-point polynomial has discriminant 1575073. \( \textbf{(II)} \) The sequence \( \vartheta_3(2^n\tau)^2 \) is the arithmetic-mean sequence of the arithmetic-geometric mean (AGM) iteration initialized at \( (\vartheta_3(\tau)^2, \vartheta_4(\tau)^2) \); the unique AGM fixed-point symmetry \( \vartheta_3(\tau) = \vartheta_4(\tau) \) identifies \( j(\tau) = 1728 \) (\( \tau = i \)) as the self-dual elliptic curve. \( \textbf{(III)} \) The \( \lambda \)-ODE \( d\lambda/dt = -\pi\lambda(1-\lambda)\vartheta_3(it)^4 \) approaches a logistic regime for \( t\gg 1 \); matching the exact midpoint value \( \lambda(i)=1/2 \) produces the explicit sigmoid approximation \( \lambda(it) \approx (1 + e^{\pi(t-1)})^{-1} \) for large t. \textbf{(IV)} The quantity \( R(\tau) := 2\vartheta_3(2\tau)^2-\vartheta_3(\tau)^2 = \vartheta_4(\tau)^2 \) satisfies the square-root recursion \( R(2\tau) = \vartheta_3(\tau)\sqrt{R(\tau)} \) under doubling; equivalently, \( \vartheta_4(2^n\tau) \) lies in a nested-radical (generically quadratic) extension tower over the dyadic \( \vartheta_3 \)-field, growing by one quadratic layer at each step---an algebraic obstruction distinct from the polynomial j-isogeny ladder.

Abstract: The current study proposes a multi-KPI approach for the alignment of job offers with candidate profiles that combines semantic signals, skill coverage indicators, and behavioral/contextual dimensions into a single decision support architecture. The approach is based on a semantic-first paradigm that represents job offers and candidate profiles as text embeddings that can be compared using the cosine similarity measure to produce a scalable baseline ranking that remains robust across different languages and styles. The baseline approach can then be extended using additional KPIs that capture different dimensions of the candidate-job alignment: the Hard Skill Coverage Ratio (HSCR) and the Skill Gap Index (SGI) measure the satisfaction of hard skill requirements and the remaining skill gaps; the Hard Skill Proficiency Similarity (HSPS) measures semantic similarity in the hard skill domain; the Soft Skill Semantic Alignment (SSSA) and the Soft Skill Evidence Density (SSED) measure the semantic similarity of the candidate behavior and the quality of the corroborating evidence; the Cultural & Team Fit Score (CTFS) addresses the organizational fit; and other indicators cover the operational feasibility of the candidate-job match. The approach is implemented using Python and tested using a realistic testbed of 300 EURES job postings and 300 heterogeneous candidate profiles across different formats and languages. The results show that the semantic approach yields a stable baseline that can be used to produce coherent and well-ordered shortlists of candidate-job pairs while the additional KPIs improve the interpretability of the approach and the feasibility of constraint satisfaction. The analysis of the candidate-job scores and the separations of the rankings also reveals that there are clusters of technically equivalent candidates and that there are clear cases of dominance. Overall, the approach can support an incremental move toward multi-criteria decision making while balancing scalability, transparency, and governance-by-design requirements.

Abstract: Background/Objectives: Syncope remains a common problem in the elderly, adversely affecting quality of life, morbidity and mortality. Diagnosis is challenging due to the atypical presentation, multifactorial aetiology, overlap with non-syncoptic falls and increased prevalence of cardiac disease. This study aims to investigate the impact of cardiac syncope in this high-risk population. Methods: In this retrospective single-center observational cohort study, 171 patients ≥ 65 years-old with unexplained syncope or other falls were included. Various diagnostic tools were utilised during investigation, according to clinical judgement and the latest guidelines. Patients were classified either in the ‘high risk’ (‘cardiac’) or ‘low-risk’ (‘autonomic’) pathway. Results: Mean age was 76.4±6.6 years (range: 65-92 years-old) and the mean follow-up period was 40.5 months. Our study population was characterised by a high incidence of comorbidities and underlying heart disease, and polypharmacy. One third of the patients did not report prodromals, 81.9% had no recognizable trigger and 43.3% had various 12-lead ECG abnormalities. Overall, 67.8% of the patients were stratified in the ‘cardiac pathway’. Eventually, a final diagnosis was established in 126 patients (73.7%). The cause was cardiac syncope in 56.4%, reflex syncope in 26.2%, orthostatic hypotension in 7.9% and non-syncopal falls in 9.5%. An ILR was implanted in 90.1% with a diagnostic yield of 43%. ECG-based diagnosis occurred in 53.2% whereas time to diagnosis was 4.8±3.3 months. Conclusions: Cardiac disease, mostly arrythmias, represent a common and possibly underestimated cause of unexplained syncope in the elderly. A structured approach including a targeted use of ILRs improves investigational process.

Abstract: Background: Dentists constitute one of the most heavily burdened groups of healthcare professionals, experiencing high levels of musculoskeletal disorders, occupational stress, burnout, and diminished quality of life. Although extensive literature addresses these issues, no study has directly examined biological age or epigenetic markers of aging in this population. This narrative review, informed by systematic methodological principles seeks to fill this gap by connecting established occupational stressors with contemporary concepts of biological aging and chronomedicine, ultimately proposing a preventive well-being framework specifically for dentists.Methods: A narrative review informed by systematic methodology was conducted following PRISMA 2020 guidelines. Searches in PubMed, Scopus, and the Cochrane Library (2015–2025) used combined keywords and MeSH terms related to lifestyle factors, occupational stress, musculoskeletal disorders, quality of life, and wellness among dentists. Of the 943 records identified, 15 met the inclusion criteria and were assessed for outcomes, methodological quality, and relevant risk factors. Results: The included studies consistently indicated a significant occupational burden, with musculoskeletal pain, emotional exhaustion, anxiety, and depersonalization as frequent findings. Quality of life was generally moderate to low, especially regarding mental health. Lifestyle patterns were characterized by inadequate sleep, limited physical activity, irregular eating habits, and insufficient recovery. These conditions-chronic stress, poor sleep, inactivity, and suboptimal nutrition-are recognized accelerators of biological aging, implying that the professional demands of dentistry may adversely influence the biological clock. Although none of the studies measured biological age directly, the collective evidence underscores the need for preventive strategies informed by chronomedicine. Conclusions: This review highlights a critical gap in the dental literature: the complete absence of biological-age assessment in a professional population exposed to multiple aging accelerators. Integrating occupational health data with modern concepts of biological aging and chronomedicine, the study proposes a targeted preventive framework to regulate biological rhythms, reduce cumulative biological deterioration, and improve the long-term quality of life and professional sustainability of dentists.

Abstract: The rapid and accelerating depletion of natural resources has spurred governments and pressure groups to call for effective environmental management initiatives. One such initiative is the creation of a green organisational culture to combat environmental degradation. As a result, there has been a burgeoning of literature on the concept of green organisational culture; however, the research is still in its nascent stage. For this reason, this study investigated the mediating role of green employee behaviours on the relationship between green organisational culture and environmental performance in the mining industry. Data was systematically collected from 277 participants and analysed using SMARTPLS 4. The results revealed that, while green organisational culture significantly impacted environmental performance, green employee behaviours partially mediated this relationship. These results offer important insights for mine managers.

Abstract: Classical Be stars are key laboratories for investigating how rapid rotation, pulsations, and mass loss couple to the formation and evolution of circumstellar decretion disks. However, few studies have combined Kepler/K2 photometry with multi-epoch Hα monitoring. Here we present four previously unclassified Be-type variable stars observed by K2 (three in Campaign 11 and one in Campaign 15) and followed up with ground-based spectroscopy. We analyzed public PDC light curves and extracted variability frequencies using Lomb–Scargle periodograms and iterative prewhitening with a conservative detection threshold of S/N≥5. Optical spectra obtained at the Observatório Pico dos Dias (Brazil) over a multi-year baseline (2017–2025) include repeated Hα observations and blue-region spectra for photospheric characterization. All targets show detectable K2 variability on timescales from hours to days, with frequency spectra ranging from close multi-periodic components producing beating patterns to power dominated by low frequencies. Each star exhibits Hα emission at multiple epochs, with long-term changes in line-profile morphology and equivalent width, indicating disk variability on year-long timescales. These results demonstrate that disk evolution can occur without conspicuous photometric outbursts over the time span of space-based observations, highlighting the diagnostic value of combining high-precision space photometry with long-term spectroscopy to characterize multiscale variability in Galactic Be stars.

Abstract: Early childhood caries (ECC) is routinely described as a complex, multifactorial disease shaped by biofilm ecology, host susceptibility, diet, behavior, and social context. Yet, a growing strand of public-health messaging and implementation practice increasingly treats ECC as a one-step problem solvable by a topical “magic paint” (most prominently silver diamine fluoride, SDF) and deliverable by non-dental or minimally trained providers. This commentary argues that the core contradiction—declaring ECC polycausal while operationalizing it as monocausal—drives a harmful evidence-to-policy drift: research designs favor short-term, easily marketable surrogate endpoints (e.g., “arrest” defined partly by SDF-induced black staining) and implementation strategies shift diagnosis and management to underprepared personnel without robust guardrails.Using a journal-style critical lens anchored in ROB-2, CONSORT, and STROBE principles, I examine recent Canadian work frequently cited to justify "paint-and-go" approaches, including open-label randomized trials of SDF application intervals and microbiome-focused substudies, and I integrate the delegation axis through the Canadian Caries Risk Assessment Tool (CCRAT) and its embedding into primary care workflows. While SDF and non-dental screening can be valuable adjuncts in a continuum of care, overselling them as substitutes for dentist-led diagnosis, pulpal assessment, and definitive rehabilitation risks institutionalizing a two-tier standard for children—especially for Indigenous and remote communities. I conclude with concrete research and policy guardrails: comparator-driven trials, multilevel modeling, lesion-specific sampling where mechanistic claims are made, patient-centered outcomes, defined referral timelines, and a dental-home–anchored pathway that treats SDF as a bridge—not a destination.

Abstract:

Ecological vulnerability of coral reefs contrasts sharply with their persistence through geologic time, creating a paradox from mis-scaled assumptions of time, mortality and organismal dimensionality, namely bleaching susceptibility, mortality, and recovery are treated as linear or sequential outcomes. Recursive definitions built on such mis-scaled assumptions generate straw-man inferences by conflating vulnerability with fragility and obscuring cryptic recovery dynamics. Using post hoc meta-analyses integrating datasets on coral bleaching, life history, reproductive strategy, morphology, and taxonomy, I evaluate system behavior across matrixed categories of thermal exposure and observation timing. Susceptibility emerges as a graded physiological response with weak coupling between predictor importance and variance, whereas mortality exhibits thresholded dynamics consistent with collapse behavior. Partial overlap in predictor structure indicates that bleaching does not represent a direct trajectory toward death, but rather a regulated buffering phase preceding potential tissue-level failure. Skeletal architecture consistently appears as a strong predictor across susceptibility and mortality, while taxonomic identity shows weak and variable effects. Recovery dynamics further indicate host–symbiont restructuring consistent with recursive evolutionary filtering rather than deterministic trait replacement. Together, these findings reframe coral bleaching as a regulated physiological state decoupled from mortality and demonstrate how recursive logic frameworks resolve paradoxes of timing, scale, and resilience in coral bleaching dynamics.

Abstract: Background: Few currently available mental health group therapy programs have been co-designed with key stakeholders to meet the needs of autistic adult consumers. The current study formed part of a co-designed project with both autistic adults, and mental health clinicians. The goal of the study was to develop a fit-for-purpose mental health therapy program for autistic adults. This brief report outlines the major findings of the clinician portion of the project. Methods: Semi-structured interviews were conducted with mental health clinicians, asking about their experiences working with autistic adults and their thoughts and ideas for an autism specific group mental health therapy program. A constructivist grounded theory qualitative approach was used to analyse the qualitative data. Results: 18 mental health clinicians participated. Three main themes, and a further nine sub-themes, were identified. Main themes were: 1) capacity and experience of clinicians in identifying autistic clients; 2) how group sessions run: barriers and clinicians; 3) therapies that do/don’t work well and recommendations. Conclusions: Mental health clinicians reported varying confidence working effectively with autistic adult clients. Therapeutic alliance was discussed as key for stronger outcomes, along with a strengths-based approach and specific-skills based intervention.

Abstract: Antimicrobial resistance is globally known term in this 21st century. When antibiotic does not work against mi-crobes, then antimicrobial resistance occurs. Many people have become resistant to antibiotic because of their hap-hazard use of antibiotic. People does not maintain the proper use of antibiotic and resistance is developed. Antimi-crobial Resistance (AMR) is a broad term, has now become a global concern day by day. Antimicrobial agents are utilized to treat different microbiological infection in human and animals. Antimicrobial resistance refers to the re-sistance of antibiotics against specific microorganisms. However, there are few methods for detecting antibiotic re-sistance in the laboratory. Among of them conventional and molecular techniques are popular. This review article outlines a clear description of various techniques of antimicrobial resistance detection. Previous technology and in-novative future technology have been moderately described in this article.

Abstract:

We develop a structural bridge between relativistic Hamilton–Jacobi theory and the relativistic Schrödinger equation within the framework of tempered distributions and Schwartz linear algebra. For translation-invariant Hamiltonians, the principal functions \( S_p(x)=\langle p,x\rangle \) restricted to the mass shell form a complete integral of the Hamilton–Jacobi equation, while their exponential images \( \eta_p=\exp\!\left(\frac{i}{\hbar}S_p\right) \) constitute a Schwartz basis of the tempered state space. On each spectral fiber, both classical and quantum equations reduce to the same Einstein dispersion relation. We prove that the relativistic Schrödinger equation is precisely the Schwartz–von Neumann S–linear extension of the classical energy relation from certainty momentum states to arbitrary tempered superpositions. In the presence of scalar potentials, the Hamiltonian arises as a mixed (momentum-diagonal and position-diagonal) extension, showing that the extension principle is not restricted to the free case. We further demonstrate that exact quantum dynamics cannot, in general, be represented by a single exponential phase \( \exp\!\left(\frac{i}{\hbar}S\right) \) unless \( S \) is affine in space. Instead, quantum evolution is obtained by S–superpositions of the principal exponential family associated with a complete integral of the Hamilton–Jacobi equation. In this sense, classical elimination of parameters is replaced by linear spectral superposition. Geometrically, the exponential mapping transforms the flat affine space of Minkowski generators into a curved manifold of principal waves on which the nonlinear Hamilton–Jacobi flow pushes forward to a linear unitary Schrödinger flow. Through de Broglie–Maxwell isomorphisms, the construction extends to complex electromagnetic-like fields, preserving translation representation, dispersion relations, and polarization geometry. The results suggest that, for translation-invariant systems, quantization may be understood as an infinite-dimensional complex linearization of a classical certainty space rather than as a semiclassical approximation. Within the tempered-distribution setting, relativistic quantum dynamics emerges as the superpositional completion of a classical complete integral.

Abstract: Ambient air pollution significantly contributes to respiratory illnesses, yet little is known about how industrial emissions are linked to preventable hospitalizations across atmospheric basins in middle-income countries. This study develops a basin-based geo-matics framework to examine the spatial and temporal relationship between industrial pollutants and age- and sex-adjusted avoidable hospitalizations for community-acquired pneumonia (PQI 11) in Mexico from 2013 to 2020. Using state-level data grouped into eight macro-regions, we combine bivariate choropleth maps, Pearson correlations, linear regression, and longitudinal time-series analysis to identify spatial clusters of high risk and to estimate regional sensitivities to changes in PM2.5, SO2, NOx, and volatile organic compound emissions. The findings reveal notable regional differences: northern border states and the Mexico City metropolitan basin form persistent high–high clusters where elevated emissions coincide with high PQI 11 rates, while coastal and peninsular regions show lower hospitalization burdens despite medium emission levels. Although national industrial PM2.5 emissions decreased over the study period, several macro-regions—particularly CDMX_Edomex, Centro, and Centro Norte—experienced significant increases in avoidable hospitalizations and decoupled emission–health patterns. Correlation matrices and regression slopes suggest that the strength and even direction of links between pollutants and PQI 11 vary across macro-regions, with emission-responsive patterns in Centro Norte and weak or inverse relationships in Peninsula and Pacifico Sur. These findings demonstrate that national averages obscure critical spatial disparities and highlight the value of basin-based geomatics approaches for regional air-quality governance, spatial decision support, and primary-care planning aimed at reducing preventable respiratory hospitalizations.

Abstract: Estimating Remaining Useful Life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven bearing fault prognosis and RUL prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyzes how these approaches have been individually and implicitly combined to cope with nonstationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions.

Abstract: Plastic recycling technologies are rapidly being reoriented toward process-, operations-, and quality-centered innovation, driven by the expansion of the circular economy and digital transformation. This study uses patent data to quantify long-term trends in plastic recycling and to compare technological structures and thematic shifts before and after 2015, thereby identifying core technological axes and convergence patterns. We collected and curated 64,639 triadic patents (2005–2024) and conducted IPC portfolio analysis, IPC co-occurrence network analysis, and period-split topic modeling. The results indicate that, since 2015, technologies related to data- and AI-enabled sorting, quality assurance, and process optimization (G06), along with tracking and connectivity (H04), collection and logistics (B65), water treatment (C02), and quality modification/compounding (C09), have expanded, while the relative prominence of some synthesis- and conversion-oriented technologies has declined. Convergence has shifted from material formulation–centered combinations toward stronger linkages with downstream processing–productization–standardization and operational infrastructure. Topic trends likewise show the rising salience of reuse-oriented packaging take-back, washing and standardization, remanufacturing, and data governance in the later period. Overall, these shifts suggest that recycling technologies are evolving beyond isolated process improvements toward maximizing circularity performance across the value chain, supporting sustainability objectives such as reducing environmental burdens and carbon emissions and improving resource efficiency.

Abstract: This study develops a nonlinear macro-labor framework to analyze dynamic adjustment mechanisms in European labor markets using harmonic stability theory, panel econometric modeling, and frequency-domain propagation analysis. The research investigates whether labor market interactions exhibit partial harmonic conjugacy, asymmetric transmission structures, and regime-dependent convergence behavior. Employing panel VAR estimation, DCC-GARCH volatility modeling, threshold regression, structural break testing, and spectral coherence analysis, the study provides empirical evidence of nonlinear shock propagation and spatial heterogeneity across European economies.The results confirm the existence of partial harmonic equilibrium structures, where macro-labor variables satisfy local propagation symmetry but fail to maintain global analytic consistency under crisis conditions. Structural break analysis reveals significant regime shifts during the 2008 financial crisis and the COVID-19 shock. Convergence tests indicate fragmentation between core and peripheral economies, with peripheral regions exhibiting stronger persistence, higher crisis amplification, and slower adjustment speeds. Threshold regression results demonstrate state-dependent labor market responses, particularly under high unemployment regimes.Simulation and counterfactual policy analysis show that structural reforms and coordinated policy packages generate the largest welfare gains by reducing system instability and improving harmonic synchronization. Youth unemployment dynamics display higher volatility and stronger amplification effects, highlighting demographic vulnerability. Overall, the findings suggest that European macro-labor systems operate as nonlinear, spatially heterogeneous networks characterized by regime-switching propagation dynamics.The study contributes to nonlinear macroeconomics by introducing harmonic propagation analysis as a complementary framework for understanding labor market adjustment. Policy implications emphasize the importance of structural flexibility, institutional coordination, and crisis-response mechanisms in maintaining macroeconomic stability.