Abstract: This paper develops the Entropic Resonance Principle (ERP) as a unified informational framework for understanding how organized systems persist across physical, biological, cognitive, and engineered domains. ERP proposes that stability arises not from resisting entropy but from a regulated co-variation between coherence (R) and entropy (H), expressed by the proportionality dR/dH≈ λ ,where the resonance parameter λ=ln⁡φ≈0.4812 is derived from a minimal self-similar renewal model. This proportionality admits both a flux form and a variational form, δ(R- λH)=0 ,which together define persistent trajectories in an informational state space. ERP does not modify microphysical laws; rather, it functions as a meta-theoretical constraint that may emerge under appropriate coarse-graining. The paper clarifies the mathematical structure of ERP, analyzes its conceptual implications, and outlines empirical predictions that render the framework testable and falsifiable. Applications are explored in quantum decoherence, non-equilibrium chemistry, neural dynamics, adaptive computation, and complex engineered systems. A methodological protocol is proposed for estimating effective slopes dR/dH in real data using sliding-window regression, bootstrap uncertainty quantification, and model comparison. ERP is ultimately positioned as the nucleus of a research programme whose validity hinges on whether λ-like proportionalities recur across systems and scales. If supported, ERP may reveal a previously unrecognized informational invariant governing the persistence of structure; if not, it offers a precise template for evaluating how coherence and entropy jointly shape organized behavior.

Abstract: This paper proposes a novel large language model (LLM)-based approach for visual target navigation in unmanned aerial systems (UAS). By leveraging the exceptional language comprehension capabilities and extensive prior knowledge of LLM, our method significantly enhances unmanned aerial vehicles (UAVs) in interpreting natural language instructions and conducting autonomous exploration in unknown environments. To equip the UAV with planning capabilities, this study interacts with LLM and designs specialized prompt templates, thereby developing the intelligent planner module for the UAV. First, the intelligent planner derives the optimal location search sequence in unknown environments through probabilistic inference.Second, visual observation results are fused with prior probabilities and scene relevance metrics generated by LLM to dynamically generate detailed sub-goal waypoints. Finally, the UAV executes progressive target search via path planning algorithms until the target is successfully localized. Both simulation and physical flight experiments validate that this method exhibits excellent performance in addressing UAV visual navigation challenges, and demonstrates significant advantages in terms of search efficiency and success rate.

Abstract: This study investigates the interplay between insecure attachment styles, materialism, and phubbing behaviors. Phubbing, the act of ignoring a partner in favor of smartphone use, is influenced by individual differences and societal norms. We hypothesized that attachment anxiety and avoidance would be positively associated with both enacted and perceived phubbing, and that materialism would mediate these relationships. Data were collected from 213 participants using validated scales for attachment, materialism, and phubbing. Results confirmed that attachment anxiety is positively associated with both enacted and perceived phubbing, while attachment avoidance is positively associated with perceived phubbing but not enacted phubbing. Materialism was found to mediate the relationship between attachment insecurity and phubbing behaviors. Specifically, materialism significantly mediated the positive relationships between attachment anxiety and both enacted and perceived phubbing, as well as between attachment avoidance and perceived phubbing. These findings suggest that materialistic values amplify the effects of insecure attachment on phubbing, highlighting the role of materialism as a compensatory mechanism for attachment-related insecurities. Future research should explore interventions targeting materialism and attachment anxiety to mitigate phubbing behaviors and improve relationship quality.

Abstract: Background: Parents play a central role in shaping children’s eating behaviors. While previous research has documented associations between parental attitudes and feeding practices, fewer studies have examined how mothers’ own eating styles may contribute to their perceptions of their children’s eating attitudes and behaviors and how these may influence subsequent feeding practices. Objectives: To our knowledge, this is the first study to examine whether mothers’ eating styles predicted their self-reported restrictive feeding practices indirectly through their perceptions of their children’s appetite and subsequently through their concern about their children’s weight. Methods: A total of 488 mothers (M_age = 33.87 years, SD = 4.81, range = 20–49) of children aged 2–7 years (M_age = 3.85 years, SD = 1.33) completed self-report measures, including the Dutch Eating Behavior Questionnaire (DEBQ) for maternal eating styles, the Child Feeding Questionnaire (CFQ) for parental concerns and restrictive practices, and the Children’s Eating Behaviour Questionnaire (CEBQ) for perceptions of child eating attitudes. Structural equation modeling (SEM) was employed to test the hypothesized mediation model, with model fit evaluated using CFI, TLI, RMSEA, and SRMR indices. Results: Our proposed model demonstrated good fit (CFI = .94, RMSEA = .07) and showed that maternal eating styles were positively associated with perceived child appetite (β = .44, p < .001). Perceived appetite predicted both maternal concern about child weight (β = .39, p < .001) and restrictive feeding practices (β = .28, p < .001), while maternal concern strongly predicted restriction (β = .65, p < .001). The total indirect effect from maternal eating styles to restriction was significant (β = .23, p < .001), and the model explained 56% of the variance in restrictive feeding. Conclusions: Our findings suggest that maternal eating styles may bias mothers’ perceptions of their children’s appetite and indirectly influence restrictive feeding practices primarily through increased concern about child weight. Given the cross-sectional design, reliance on maternal self-report, and online convenience sampling, results should be interpreted cautiously. Nonetheless, the study provides the first evidence for a sequential pathway linking maternal eating styles, child appetite perceptions, and weight concern to restrictive feeding, highlighting cognitive and perceptual processes as intervention targets.

Abstract: Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, creating an urgent need for computational tools that can resolve overlapping phenotypes and enable timely, mechanism-based therapeutics. This narrative review synthesizes recent advances in artificial intelligence as applied to neuromuscular diseases, drawing from peer-reviewed literature from the past five years. Artificial intelligence can augment diagnosis by extracting disease-relevant patterns from imaging, electrophysiology, and multimodal clinical data, improving discrimination between clinically similar entities such as Duchenne and Becker muscular dystrophy. Artificial intelligence can also enhance early detection of amyotrophic lateral sclerosis. Artificial intelligence can utilize digital biomarkers of disease progression data from gait, voice, and wearable sensors for enhanced modeling of disease outcomes. Deep learning–based multi-omics integration, high-throughput phenotypic screening, and artificial intelligence-based protein structure predictive models are accelerating the path from causative mutation, to molecular mechanism, and on to candidate therapy. Despite these advances, significant challenges remain, including data scarcity, heterogeneous acquisition methods, limited external validation, and the need for interpretable models that can win clinician acceptance. Addressing these constraints is essential to moving high-performance research tools from the laboratory to the neuromuscular clinic. Artificial intelligence has the potential to shorten the diagnostic odyssey and accelerate the historically slow development of targeted therapeutics for rare neuromuscular diseases.

Abstract: This paper addresses a decisive anomaly identified in the Mayer (2025) report: in AI-related nightmares, 93% of cases fixate on the AI interaction interface itself rather than on narrative content. To explain this “formal fixation,” we propose a paradigm-shifting Interaction Architecture Internalization Model, which posits that the cognitive system internalizes the abstract logic and temporal structure of goal-directed interactions through the accumulation of a Learning Time Delay Dose. When this dose exceeds a critical threshold, a cognitive phase transition occurs, solidifying the interaction architecture as an internal framework. Grounded in insights from Piaget, Chomsky, Einstein, Wiener, and Landau, the model not only provides a unified explanation for phenomena from language acquisition to personality formation but also generates specific, empirically testable predictions. It forecasts, for instance, that systemic fluctuations in interaction delays (e.g., widespread server latency) will catalyze architectural internalization, a prediction corroborated by analyzed dream reports from such periods. Methodologically, the Learning Time Delay Equivalence Principle circumvents the “Problem of Other Minds,” establishing an objective foundation, while the theory’s “blinded loop” validation—stemming from an academic misunderstanding—uniquely confirms its a priori predictive power. Ultimately, we advocate for a “Statistical Mechanics of Cognition,” where time delay dose acts as an order parameter, prioritizing the dynamics of form over the semantics of content.

Abstract:

Manufacturing variability has a measurable impact on the NVH behavior of gear drives. Small deviations in tooth geometry or surface quality can increase transmission error. These effects become more pronounced in electric powertrains due to the lack of masking noise. Housing stiffness variations further shape how gear-mesh excitations are radiated as airborne sound. This review examines how common gear and housing tolerances influence NVH performance and summarizes recent advances in stochastic analysis, robust design, and measurement-driven modeling. The results show that unit-to-unit differences in gear quality and housing dynamics can shift radiated noise by several decibels. Integrating real manufacturing data, statistical tolerance assessment, and coupled structural–acoustic simulations provides a practical approach to achieving consistently quiet powertrains in series production.

Abstract: Background: Cyclic AMP (cAMP) is a conserved second messenger with established roles in microbes and animals, but its functions in plants remain poorly understood. Engineered adenylate cyclase (AC) activity can elevate cAMP and influence signaling pathways. This study investigates how sustained cAMP elevation affects transcriptomic networks and salt stress tolerance in Arabidopsis thaliana.Methods: A glucocorticoid-inducible AC transgene (pTA7001-AC) was used to increase endogenous cAMP in col-0 seedlings. RNA-Seq was performed at 1, 3, 12, 24, and 72 h post-induction. Genes consistently regulated across all time points were defined as constitutive cAMP-responsive genes (CRGs) or anchor CRGs. GO, KEGG, GSEA, k-means clustering, and mapped cAMP-regulated pathways. Salt stress assays (100 mM NaCl) and HPLC quantified physiological responses and cAMP levels.Results: A total of 292 CRGs were identified, enriched for transcription factor activity, ER protein folding, phytohormone metabolism, and stress responses. K-means clustering revealed key CRG clusters emphasizing transcriptional regulation and protein quality control. Anchor CRGs, including stress-responsive genes and transcription factors acted as game changer. AC transgenic seedlings exhibited enhanced root growth and reduced sensitivity to prolonged salinity, along with enhanced disease resistance against Pst DC3000, with HPLC confirming elevated cAMP levels.Conclusion: Elevated cAMP orchestrates transcriptional, hormonal, and protein-folding networks, improving salt stress tolerance in Arabidopsis. These results position cAMP as a central integrator of plant stress responses and provide a mechanistic foundation for engineering abiotic stress-resilient crops.

Abstract: Legumes are among the most important crop species in the world. They are the foundation of global food security and, in addition to providing protein, improve soil fer-tility and sustainable agriculture. However, they are sensitive to climate change and une-ven rainfall distribution. Three two-factor pot experiments were conducted in MI-CRO-CLIMA phytotrons. The objects of the study were three legume species: faba bean, pea and soybean. The first factor was the superabsorbent (SAP) rate (0, 2, 4, 6 g·kg-1 of sub-strate), while the second factor was the watering frequency (the subjects were watered every 1, 3, 6, 9 days). The scope of the research included measurements of chlorophyll flu-orescence (Fv/Fm, PI), leaf greenness index (SPAD) and biometric measurements. The study showed a significant effect of superabsorbent application on biometric parameters of legumes. Faba bean and pea plants were significantly taller after superabsorbent appli-cation and developed greater root mass. The highest SAP dose reduced the Fv/Fm index in soybean and the PI index in faba bean and soybean. The lowest SPAD index in pea was recorded in the control treatment (without SAP), while the highest SAP dose in soybean resulted in a decrease in relative leaf chlorophyll content compared to the other treat-ments. Plants watered daily were significantly taller, developed a greater number of nodes, and had higher dry mass of both above-ground and underground parts compared to plants watered every 3, 6, and 9 days (only in soybeans the dry mass of the under-ground part was significantly higher in the treatment watered least frequently). Higher values of the Fv/Fm index were observed in soybean, and higher values of the PI index were observed in faba bean, pea, and soybean in the least frequently watered treatments. In all species studied, SPAD index was higher under conditions of the greatest water defi-cit (watering every 9 days) compared to plants watered every 1, 3, and 6 days.

Abstract: Quantum computing poses a critical threat to existing cryptographic primitives, rendering current access control mechanisms in cloud-native infrastructures vulnerable to compromise. This paper introduces a comprehensive quantum-resilient access control framework specifically engineered for distributed, containerized, and zero-trust environments. The proposed system integrates post-quantum cryptographic (PQC) primitives—specifically lattice-based key encapsulation (Kyber) and digital signatures (Dilithium)—with a hybrid key exchange protocol to maintain crypto-agility and backward compatibility. We design a secure token issuance and verification process employing PQC-based authentication, ensuring resistance to both classical and quantum adversaries. A prototype implementation demonstrates that our hybrid PQC approach incurs a moderate computational overhead of approximately 10–30\% while preserving horizontal scalability and interoperability across Kubernetes clusters. Security analysis under the post-quantum adversary model confirms resistance to key compromise, replay, and forgery attacks. The results highlight that quantum-resilient access control protocols can be efficiently integrated into modern cloud infrastructures without sacrificing scalability, performance, or operational flexibility.

Abstract: Voice-based approaches for screening and diagnostic applications, particularly in telemedicine, often rely on patient recordings collected outside clinical environments. Establishing normative baselines is essential to advance voice analytics and clinical utility. This pilot study examined acoustic parameters in 32 healthy young adults (ages 18–24) with no history of vocal pathology, neurological disorders, or speech impediments. Participants provided paired recordings of sustained vowels (/a/, /e/, /o/, /u/) and a standardized phonetically balanced phrase (“The sun sets in Cincinnati on Saturday”). Analyses focused on features including fundamental frequency, jitter, shimmer, harmonics-to-noise ratio, formants (F1–F3), speaking rate, intensity, and spectral measures. Preliminary results revealed significant differences between healthy controls and a reference dataset of laryngitis patients, suggesting acoustic features can serve as objective markers of vocal fold inflammation. However, pathology-specific biomarker identification was constrained by the quality of available laryngitis data. Simple statistical comparisons proved insufficient, emphasizing the value of advanced measures such as cepstral peak prominence (CPP) and mel-frequency cepstral coefficients (MFCC). Challenges in non-clinical data collection highlight the need for standardized, detailed annotation of patient recordings to improve diagnostic accuracy and strengthen the predictive power of future biomarker studies.

Abstract: Cost code verification in state-funded construction projects remains a labour-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent UoMs, and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and MTR - LBR - MCR conformity with provincial Unit Price Books (UPBs). A dedicated exact-match mechanism, activated only when a code is invalid, enables the recovery of typographical errors solely when a project item’s full price vector matches a normative entry exactly. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding hallucination and maintaining full auditability. Deterministic extraction and normalisation routines facilitate robust handling of displaced headers, merged cells, and non-standard labelling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves inter-evaluator consistency, and supports compliance documentation without encroaching on human judgment. This research contributes a framework for LLM-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical, regulation-driven environments. Limitations—including narrow diagnostic scope, TT quotation exclusion, single-province UPB dependence, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost-verification practice in public-sector construction.

Abstract: The design of multifunctional biomaterials that offer both structural support and bio-chemical cues is essential for enhancing tissue regeneration. In this study, hybrid nan-ofibrous scaffolds composed of poly(L-lactide-co-ε-caprolactone) (PLCL) and bioactive factors secreted by Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) were fabricated via co-electrospinning. Nanofibers were produced in both aligned and ran-dom configurations following an optimized protocol developed at the Institute for Bi-oengineering of Catalonia (IBEC). Morphological and topographical features were characterized using light microscopy and atomic force microscopy (AFM), while fiber orientation was quantitatively assessed through Fast Fourier Transform (FFT) analysis. The controlled release kinetics of FITC-labeled bioactive compounds were evaluated, and human adipose-derived MSCs (AD-MSCs) were used as a cell model to evaluate scaffold biocompatibility, in respect to cell viability, adhesion, proliferation, and mi-gration. FFT analysis was also applied to quantify the orientation of AD-MSCs when cultured on the nanofibers, revealing higher cellular anisotropy and alignment on ori-ented scaffolds. We further show that aligned nanofibers supported cell viability and proliferation, as well as directed migration as wound closure assays showed signifi-cantly faster healing on aligned nanofibers compared to random ones. These results emphasize the synergistic effects of nanofiber alignment and biochemical functionali-zation in modulating cell behavior and promoting tissue regeneration, underscoring the potential of PLCL-based hybrid nanofibers for advanced wound healing applications.

Abstract: This study explores the spatial relationship between frequent physical distress (FPD) and socioeconomic as well as health-related factors across the contiguous United States. FPD, defined as having 14 or more physically unhealthy days within the past month, serves as an important measure of overall population health. While many studies have examined the causes of mental distress, research on the geographic variation and social context of physical distress remains limited. Using data from 2,673 U.S. counties, we analyzed how socioeconomic conditions and health indicators relate to FPD at both national and local levels. Ordinary Least Squares (OLS) multivariate regression model was first used to assess general associations, followed by Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) to identify spatially varying and scale-dependent relationships. Comparing the GWR and MGWR results revealed that several predictors of FPD operate at different spatial scales, reflecting local heterogeneity in health outcomes. Counties in the southeastern United States, particularly those with higher levels of socioeconomic disadvantage and poorer health conditions, showed elevated FPD rates. These findings highlight the importance of accounting for spatial context when addressing physical distress and suggest that locally tailored public health strategies may be more effective than uniform national approaches.

Abstract: Hepatitis C Virus (HCV) continues to be a significant worldwide health issue, particularly in resource-limited environments with inadequate diagnostic and therapeutic options. This study formulates a deterministic six-compartment model, predicated on the assumptions that the population undergoes natural birth-death dynamics, awareness initiatives transition individuals from $S_1$ to $S_2$, diagnosis advances U to I, recovery is achieved through therapy or immunity, and infection and mortality rates vary among classes. The system is described by coupled nonlinear ODEs that include three time-dependent controls. Analytical examination guarantees the positivity and boundedness of all compartments and calculates the fundamental reproduction number ($R_0$) using the next-generation matrix. Sensitivity analysis shows that $\beta_1, \beta_2, \tau_1, \tau_2$ are the most important parameters. Using Pontryagin's Maximum Principle, the forward–backwards sweep method is employed to determine the optimal controls that minimise both infection and cost. A Mamdani fuzzy logic controller is added to handle parameter uncertainty and generate adaptive responses to infection pressure, awareness level, and hospital load. Simulations reveal that fuzzy control delivers equivalent suppression to the crisp optimum with around two-thirds lower cost, enabling a stable, interpretable, and resource-efficient paradigm for dynamic HCV intervention.

Abstract: This study examines the impact of harvest-time temperature on the aroma composition and sensory expression of Hárslevelű wines from the Tokaj region, with particular re-levance to sustainable gastronomy, where freshness, aromatic precision and reduced technological inputs are increasingly prioritised. Grapes were harvested either at night under cool pre-dawn conditions (18 °C) or at midday during high-temperature (28 °C) exposure, and subsequently processed through controlled microvinifications using bi-oprotection with Metschnikowia pulcherrima, applied alone or in combination with SO₂. Volatile compounds were analysed by HS-SPME–GC–MS and interpreted through PLS-DA, while sensory evaluation followed OIV standards and was complemented by qualitative insights from fine-dining chefs. Night-harvested wines contained higher levels of fresh, floral and citrus-associated terpene- and ester-derived volatiles, whereas sun-harvested wines exhibited riper aromatic traits and higher perceived acidity. Bi-oprotection increased metabolic diversity and enhanced ester formation but did not eliminate the fundamental differences imposed by harvest temperature. Sensory results consistently showed that night-harvested wines displayed greater aromatic purity, freshness and overall harmony across treatments. These findings demonstrate that night harvesting, particularly when combined with bioprotection, supports the pro-duction of aromatically expressive, high-quality Tokaj wines in alignment with the principles of sustainable gastronomy.

Abstract: Urban waterfront regeneration driven by mega-events has played a key role in shaping contemporary public open spaces, particularly in newly developed areas within the Chinese context. However, public perceptions and their influence on the use of newly built open spaces created through mega-event-led regeneration have not been examined in existing research. To address this gap, this study establishes an integrated assess-ment framework to evaluate the quality of urban waterfront open spaces. A mixed methods approach was adopted, including direct observations and 770 online ques-tionnaires collected between July and October 2024 around core nodes along the South Bank of the Qiantang River in Hangzhou, China. Spatial analysis and Importance–Performance Analysis (IPA) were employed to determine priority improvement areas that should inform future waterfront regeneration strategies. The results indicate that inclusiveness emerged as the most important factor for enhancing waterfront open space quality, while spatial aesthetics ranked the lowest. Among the sub-sub factors, elements related to improving water accessibility, enhancing natural surveillance, providing artificial shelters and diverse seating options and shaping collective memory through digital technologies received the highest ratings. Finally, the study highlights that the intangible legacies of the Asian Games have the potential to reshape a distinc-tive new city image and collective memory, even in the lack of tangible heritage build-ings.

Abstract: Biodiesel fuel produced through transesterification is mainly used in blends with conventional diesel fuel. The analysis of combustion process parameters for each specific biodiesel fuel represents the basis for a rational approach to the utilization of available motor fuel quantities. In this study, the heat release rate and cumulative heat release during the combustion of conventional diesel fuel and blends of biodiesel fuel made from waste grape seed oil and conventional diesel fuel were analyzed. The tests were conducted on a single-cylinder, air-cooled diesel engine with direct fuel injection. The combustion of conventional diesel fuel, a blend containing 7% of biodiesel by volume (B7), and a blend containing 14% of biodiesel by volume (B14) was examined. Using blends, especially those with a higher biodiesel content (B14), results in a higher maximum heat release rate compared to conventional diesel fuel, which can have negative implications in terms of mechanical stresses and engine noise. However, the higher combustion rate of the B14 blend, particularly during the combustion of the first 50% of the fuel mass per cycle, can have a positive impact on the fuel economy of the working cycle and the engine as a whole.

Abstract: This article investigates the trajectory-tracking control of a differential-drive two-wheeled mobile robot (DDWMR) using its kinematic model. A nonlinear-to-linear transformation based on differential flatness is employed to convert the original nonlinear system into two fully decoupled linear subsystems, enabling a simple and robust controller design. Unlike conventional flatness-based methods that rely on exact feedforward linearization around a reference trajectory, the proposed approach performs plant linearization, ensuring reliable tracking across a wide range of trajectories. The resulting two-loop architecture consists of an inner nonlinear loop implementing state prolongation and static feedback, and an outer linear controller performing trajectory tracking of the linearized system. Simulation results on a circular reference trajectory demonstrate high tracking accuracy, with a maximum transient deviation of 0.28 m, a settling time of approximately 120 s, and a steady-state mean tracking error below 0.01 m. These results confirm that the plant-linearization-based framework provides superior accuracy, robustness, and practical applicability for DDWMR trajectory tracking.

Abstract: Insectary plants are used to attract and boost the multiplication of beneficial arthropods, improving biological control in greenhouses. Three insectary plants were selected for this study: alyssum (Lobularia maritima (L.) Desv.), yarrow (Achillea millefolium L.), and dill (Anethum graveolens L.). This study was performed in two years, 2021 and 2025, and focused on Orius laevigatus (Fieber) (Hemiptera, Anthocoridae), one of the most im-portant predators of Thysanoptera pests in greenhouse crops. Four ornamental crops were included to analyse the movement and installation of the predator. Alyssum and yarrow housed O. laevigatus in both years (with total mean values per sampling date of 3.0±1.3 and 2.7±1.0 on alyssum and 7.0±2.8 and 1.8±0.8 on yarrow in 2021 and 2025 re-spectively), increasing its population in the greenhouse. In contrast, dill was unsuitable to settle populations of the predator but attracted other potential pests, with the addi-tional disadvantage of its short blooming period and quick decline. Orius laevigatus adults did not show great mobility during the study, and it had small populations among the ornamental crops in the greenhouse. The ornamental plant statice (Limonium sinuatum (L.) Mill.) had the highest population of the predator.