Abstract: Introduction: Artisanal pineapple juice is widely consumed in Guinea, yet data on its physicochemical quality and compliance with international standards remain limited. Methods: A cross-sectional analytical study was conducted on 60 artisanal pineapple juice samples collected in Maferinyah (n = 30) and Friguiagbé (n = 30). Organoleptic characteristics (taste, color, stability) were assessed, and physicochemical parameters (pH, density, total soluble solids (°Brix), titratable acidity, dry matter, and sulfur dioxide (SO₂)) were determined using standard methods. Results: All samples showed acceptable sensory quality, with 100% presenting pleasant taste and stability at rest. pH values were within the acceptable Codex range in 83% of Maferinyah samples and 100% of Friguiagbé samples. Density was the main deviation, with only 20% compliance in Maferinyah and none in Friguiagbé. Total soluble solids exhibited moderate variability between sites, while titratable acidity and SO₂ levels were within recommended limits for all samples. Discussion: Overall, artisanal pineapple juices from the two areas displayed satisfactory organoleptic quality and generally acceptable chemical stability. However, consistent deviations in density and partial variability in °Brix suggest heterogeneity in artisanal processing practices, underscoring the need for improved standardization to enhance consistency and align with Codex expectations.

Abstract: Life Cycle Assessment (LCA) is extensively employed to support sustainability evaluation in waste management and manufacturing systems; however, outcomes are highly sensitive to methodological decisions, particularly end-of-life (EoL) allocation approaches. This study examines how different allocation methods—primarily cut-off and substitution approaches—affect the interpretation of energy performance and decarbonisation potential in plastic waste management and injection moulding systems. The analysis applies cut-off logic to open-loop scenarios to establish a baseline impact, while substitution-based modelling is utilised for semi-closed and fully closed-loop configurations to quantify environmental credits and avoided burdens. A dual framework is adopted: first, a literature review examines methodological sensitivities in EoL modelling, focusing on allocation logic and system boundaries; second, a quantitative case study assesses open-loop, semi-closed, and fully closed-loop injection moulding scenarios for polyethylene (PE) products using LCA and hot-spot analysis. The results demonstrate that allocation choices can significantly influence calculated energy savings and greenhouse gas reduction potentials, sometimes reversing the relative ranking of configurations. Substitution-based approaches tend to report higher decarbonisation benefits by crediting avoided primary production, whereas cut-off approaches provide more conservative estimates. In the case study, increased internal material and water looping lead to measurable reductions in energy demand, although trade-offs across impact categories persist. These findings highlight that circular economy (CE) evaluations are strongly shaped by methodological assumptions, with direct implications for energy policy and decarbonisation pathways. The study emphasises the need for transparent allocation decisions and robust frameworks to ensure reliable decision-making in the transition toward low-carbon and energy-efficient systems.

Abstract: Automated cellular detection using deep learning is a key strategy for optimising cervical cancer screening by reducing the healthcare workload and inter-observer variability. However, analyzing Whole Slide Image (WSI) patches presents challenges like annotation scarcity, morphological complexity, and class imbalance. This study conducts a systematic evaluation of YOLOv11 (n, s, and m variants) to assess the impact of target variable granularity and training paradigms on performance. Four strategies were analyzed: independent and multi-class models, each evaluated at both specific cell label and diagnostic macro-group levels. To ensure clinical robustness, patient-level data partitioning was implemented to prevent information leakage. Performance was measured using precision, recall, and mAP (0.5 and 0.5:0.95). The results reveal critical trade-offs between fine-grained discrimination and model generalization when varying architectural complexity and labeling strategies. Findings indicate that diagnostic aggregation improves stability, while single-class training optimizes specialized detection. These results provide methodological guidelines for designing AI-assisted screening systems and establish a foundation for integrating YOLOv11 detectors into Multiple Instance Learning (MIL) frameworks at the WSI scale.

Abstract: During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (Na) and chlorophyll (Chla) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is vital for accurate prediction of crop photosynthetic capacity. We conducted a two-year field study on winter wheat and paddy rice in Eastern China, measuring leaf maximum carboxylation rate (Vc_max25), maximum electron transport rate (J_max25), Na, and Chla every 7–10 days from greening to maturity. We analyzed vertical variations of these parameters in upper (T-1), middle (T-2), and lower (T-3) canopy layers and explored relationships between Na/Chla and Vc_max25. Results showed significant vertical variations: Vc_max25 and J_max25 in T-1 were higher than T-2, and T-2 higher than T-3. The vertical distribution of Na and Vc_max25 was more pronounced than Chla. Correlation between Na and Vc_max25 increased from T-1 to lower layers, while Vc_max25-Chla correlation decreased. A single Vc_max25 estimation model based on Na performed well across layers (R²=0.619, RMSE=15.751 µmol m⁻² s⁻¹). Differentiating T-1 from T-2/T-3 improved Chla-based models. Na was better than Chla for characterizing Vc_max25 vertical variation, with Chla-based models requiring separation of T-1 from T-2/T-3. This study provides key insights for remote sensing of photosynthetic parameters and improves understanding of crop canopy photosynthesis.

Abstract: A reverse-phase high-performance liquid chromatographic (RP-HPLC) technique capable of indicating stability was established and validated for the concurrent measurement of Acetylsalicylic acid and Omeprazole in combined pharmaceutical products. Separation was carried out on a Luna Phenyl Hexyl column (250 × 4.6 mm, 5 µm) with a mobile phase composed of Acetonitrile and 0.1% Perchloric acid mixed at 20:80 (v/v), delivered at 1.0 mL/min. The detection wavelength was set at 249 nm, representing the isosbestic point for both substances, with an overall analysis duration of 5 minutes. The retention times recorded were 2.038 min for Acetylsalicylic acid and 2.995 min for Omeprazole, showing a resolution factor of 4.63. The procedure was validated following ICH criteria for selectivity, response linearity, recovery, repeatability, intermediate precision, durability, detection limit, and quantitation limit. The calibration plots exhibited outstanding linearity, with recovery percentages and %RSD values falling within prescribed limits. Stress degradation experiments involving acidic, basic, oxidative, reductive, thermal, photolytic, and hydrolytic conditions verified the method's ability to separate intact drugs from breakdown products, as purity angles remained below purity thresholds. The method showed strong sensitivity, with LOD/LOQ values calculated as 0.49/1.62 µg/mL for Acetylsalicylic acid and 0.24/0.80 µg/mL for Omeprazole. This validated procedure is straightforward, reproducible, accurate, and appropriate for routine quality monitoring.

Abstract: Canine osteosarcoma (OSA) is a highly aggressive primary bone tumor and a valuable model in comparative oncology. Nevertheless, commonly used canine in vitro models remain incompletely and inconsistently characterized, while exhibiting substantial biological heterogeneity affecting experimental outcomes. This study aimed to comparatively characterize three canine osteosarcoma cell lines (OSCA8, OSCA29, and D17) in reference to canine hTERT fibroblasts, and with a focus on functional properties and selected molecular features, namely including miR-27b-3p and IGF2BP3 expression. The cytophysiological profile of the cells was evaluated in relation proliferation and migratory capacity. In turn gene expression was determined with RT-qPCR, and proteins detected with Western blotting. The D17 cell line showed the highest metabolic activity and the largest fraction of S-phase cells, whereas OSCA8 cells demonstrated the greatest clonogenic potential and the highest migratory activity in the wound healing assay. OSCA29 cells displayed an intermediate functional profile, while all OSA cell lines exhibited comparable migratory capacity in transwell assay. At the molecular level, miR-27b-3p expression was significantly higher in OSCA8 and D17 cells than in OSCA29 cells. In turn, IGF2BP3 transcript levels were lower in OSCA29 cells, whereas protein analysis revealed distinct immunoreactive forms. Together, these findings highlight the functional heterogeneity of commonly used canine osteosarcoma cell lines and broaden their current characterization.

Abstract: Insect meals are promising alternatives to conventional protein sources in aquafeeds, but comparative evidence in Nile tilapia (Oreochromis niloticus) remains limited. This study evaluated the effects of partially replacing an insect-free control diet with larvae meals from Tenebrio molitor and Hermetia illucens on growth performance, nutrient digestibility, haematological profile, and resistance to Streptococcus agalactiae in juvenile Nile tilapia. Fish were fed isonitrogenous and isoenergetic diets for 82 days: a control diet without insect meal and diets containing 100 g kg⁻¹ dry matter of T. molitor, H. illucens, or a 1:1 mixture. Growth performance and somatic indices were not affected by dietary treatment. Lipid digestibility remained high and similar among diets, whereas protein digestibility differed among diets and insect ingredients. Erythrocyte and total leukocyte counts were unchanged, but neutrophil and lymphocyte proportions differed among treatments. After intraperitoneal challenge with S. agalactiae, cumulative mortality was numerically lower in fish fed T. molitor or H. illucens than in the control group, although survival curves did not differ significantly. These findings indicate that both insect meals can be included at 10% in juvenile Nile tilapia diets without impairing growth, while influencing protein digestibility and leukocyte distribution.

Abstract: Background: Dental caries remain a major public health issue among Italian children, with prevalence exceeding 60% in specific subgroups and marked socioeconomic gra-dients. Objectives: This multicenter study aimed to describe caries experience, malocclusions, and oral hygiene status in pediatric populations residing in three Italian regions and to develop and preliminarily evaluate the feasibility of an integrated care pathway for the prevention and management of caries and malocclusions. Materials and Methods: Within the Italian Centre for Diseases Control and Prevention (CCM) 2024 program (ID 10), 795 children aged 6–11 years were examined in school settings and via mobile dental units. Caries experience was assessed using the dmft/DMFT indices and International Caries Detection and Assessment System (ICDAS) criteria. Malocclusions were evaluated using the Index of Orthodontic Treatment Need (IOTN). Oral hygiene was assessed through standardized clinical indices. The proposed care pathway comprises three tiers: (1) universal, school based oral health education; (2) targeted clinical preventive and interceptive interventions; and (3) telemedici-ne/AI supported follow up for high risk children. Descriptive and multivariable statistical analyses were performed. Results: Overall caries burden was low. No statistically significant differences in dmft/DMFT were observed between males and females. A non significant trend toward higher caries indices was found among children with a positive breastfeeding history. By contrast, oral hygiene level was strongly associated with caries indices: children with insufficient hygiene had the highest dmft/DMFT, those with mediocre hygiene showed intermediate values, and those with optimal hygiene presented the lowest caries expe-rience. In multivariable models, oral hygiene emerged as the main independent pre-dictor of dmft/DMFT. Conclusions: In this low caries cohort, oral hygiene was confirmed as the principal mo-difiable determinant of caries risk. A tiered, school and community based care pathway focused on hygiene promotion, early screening, and minimally invasive clinical inter-ventions appears feasible and potentially scalable, with the aim of reducing the burden of caries and malocclusions and improving equity in pediatric oral health.

Abstract:

This study uses Autoregressive Integrated Moving Average (ARIMA) forecasting models and regression analysis to explore the impact of three government funding mechanisms on financial sustainability in Singapore’s arts and heritage sector. Based on data obtained from the Ministry of Culture, Community and Youth (MCCY) for FY (FY refers to “Financial Year”, which is generally from 1 April to 31st March of the following year) 2022-2024, we modelled three funding scenarios: direct organisational grants (Scenario A), citizen-directed cultural vouchers (Scenario B), and a hybrid model combining both approaches (Scenario C). The results showed that while direct funding provides the most significant immediate capacity increase, a hybrid model provides a better balance between organisational stability and demand, thereby offering a more sustainable pathway for sector development. Our study makes a methodological contribution by illustrating the application of ARIMA forecasting to cultural policy evaluation, and compared the outcome of supply-side and demand-side interventions in the cultural sector.

Abstract: The Muški bunar old-growth forest on Mount Psunj represents one of the rare preserved mixed ecosystems of sessile oak (Quercus petraea) and European beech (Fagus sylvatica) in Southeastern Europe, providing an important reference for understanding natural forest dynamics. This study aimed to analyse stand structure, age distribution, growth dynamics, and disturbance regime based on repeated field surveys conducted in 1979 and 2021. The results revealed pronounced structural heterogeneity, a wide range of tree sizes and ages, and clear interspecific differences. European beech dominates smaller and medium diameter and age classes, whereas sessile oak is primarily present in older and larger diameter classes. A very high growing stock (1155.81 m³ ha⁻¹) indicates exceptional stand productivity, with maximum cambial ages of 295 years for oak and 253 years for beech. Basal area increment analysis showed that even old trees maintain substantial growth. Although both species exhibit positive long-term growth trends, recent decades show divergence, with increasing growth in beech and stagnation or decline in oak. Stand dynamics are mainly driven by low-intensity disturbances, while recent windthrows have further increased structural heterogeneity and regeneration. These findings highlight the importance of old-growth forests as reference systems for close-to-nature forest management.

Abstract: In recent years, Artificial Intelligence (AI) applied to the electrocardiogram (ECG) has shown increasing potential in improving diagnosis, prognostic stratification, and cardiovascular screening through the identification of electrophysiological patterns not detectable with conventional interpretation. This narrative review, incorporating elements of a scoping review, summarizes the main available evidence on the use of AI-ECG across the entire continuum of cardiovascular disease, including arrhythmia detection, early identification of structural heart disease, decision support in acute coronary syndromes, prediction of clinical outcomes, and applications in population screening using wearable devices. Deep learning models applied to both the standard 12-lead ECG and simplified re-cordings have demonstrated high diagnostic performance in identifying atrial fibrillation (AF), left ventricle (LV) dysfunction, hypertrophic cardiomyopathy (HCM), cardiac amyloidosis, and heart failure with preserved ejection fraction (HFpEF), as well as a potential role in cardiovascular risk stratification and in the identification of systemic digital biomarkers. Despite these promising results, the clinical adoption of AI-ECG is still limited by the need for prospective multicenter validation and by challenges related to model interpretability and their integration into clinical workflows. Overall, AI-ECG represents an emerging diagnostic platform with potential applications in predictive, preventive, and personalized cardiology.

Abstract: Precise classification of landslide types is essential for effective hazard mitigation; however, many existing landslide inventories lack type-specific information, limiting their applicability in risk management. This study presents a transferable machine learning framework to identify rainfall-induced cliff-type landslides—commonly corresponding to shallow landslides in Japan—from unclassified inventories across both seismic and non-seismic environments. Using Forest-based and Boosted Classification and Regression (FBCR) tools in ArcGIS Pro, the model was developed based on 25 landslide conditioning factors using balanced datasets of cliff-type and non-landslide samples derived from Tokushima and Wakayama Prefectures, Japan.The model achieved strong predictive performance in the training regions, with accuracy and sensitivity exceeding 0.84, an F1 score of approximately 0.84–0.85, and a Matthews correlation coefficient (MCC) ranging from 0.68 to 0.71. Transferability was evaluated by applying the trained model to the Kegalle District in Sri Lanka, where it achieved an accuracy of approximately 80% against available inventory data. Variable importance analysis revealed that rainfall consistently ranks among the most influential triggering factors for cliff-type (shallow) landslides, even in earthquake-prone regions, where seismic-related variables exhibited comparatively lower influence. Key controlling factors included rainfall, slope, elevation, proximity to infrastructure, and hydrological indices.These findings highlight that rainfall remains a dominant trigger for shallow landslides across different tectonic settings. The proposed framework provides a practical approach for complementing missing landslide type information in existing inventories, thereby improving hazard zonation and supporting risk-informed planning in diverse environmental conditions.

Abstract: Tropical wetlands are highly sensitive to climatic and anthropogenic disturbances, and the composition of their plant communities can reflect the capacity of these ecosystems to respond to environmental perturbations. This study evaluated the relationship between aquatic macrophyte richness, community structure, and habitat vulnerability to climate change in aquatic ecosystems located in the San Luis rural district, Barrancabermeja mu-nicipality (Santander, Colombia). Macrophyte communities were characterized at 47 monitoring sites distributed across six mesohabitats: floodplain depressions, swamp la-goons, wetlands, artificial ponds (jagüeyes), naturalized ponds, and stream riparian zones. A total of 63 species belonging to 30 families and 51 genera were recorded. The re-lationships among species richness, abundance, and mesohabitat types were assessed using multivariate analyses and statistical models, including principal component anal-ysis (PCA) and generalized linear models. Results revealed clear differences in vegetation community structure among mesohabitats and dominance patterns associated with an-thropogenic disturbance. Ecosystems with higher macrophyte diversity and greater rep-resentation of native species exhibited lower levels of climatic vulnerability, whereas hab-itats dominated by eutrophication-tolerant species and subjected to greater anthropogenic pressure showed higher susceptibility. These findings highlight the ecological importance of aquatic macrophytes as indicators of environmental change and as key functional components contributing to the resilience of tropical wetlands under climate change.

Abstract: Background/Objectives: The global spread of multidrug-resistant pathogens complicates the treatment of ventilator-associated pneumonia (VAP), causing significant hospi-tal-acquired morbidity and mortality. This study was designed to evaluate the efficacy and safety of the non-traditional antibacterial drug Fluorothiazinone (FT) to prevent VAP caused by Gram-negative bacteria in patients on mechanical ventilation. Methods: We conducted a multicentre, randomised, double-blind, placebo-controlled, parallel-group, phase 2 pilot trial at 14 hospitals in Moscow and St. Petersburg. Treatment arm patients received FT at a dose: 2400 mg/day for the first 2 days and 1800 mg/day from the third day and further (but no more than 14 days) until the occurrence of VAP caused by gram-negative bacteria, confirmed clinically and microbiologically, or until the participant completed the study for other reasons. Results: Statistically significant differences were observed between the FT and placebo groups in the proportion of patients without clinically and microbiologically confirmed VAP caused by Gram-negative bacteria that developed 72-120 hours after tracheal intuba-tion and initiation of mechanical ventilation, as well as throughout the entire treatment period. Treatment with FT was associated with a 56% reduction in the risk of developing VAP, following adjustment for relevant clinical and demographic variables. Safety out-comes in the group receiving the study drug FT were not different from those in the place-bo group. Conclusions: The possibility of antibacterial prophylaxis with FT, to which resistance does not develop, which has a broad spectrum of action, a high degree of tissue distribu-tion, and a favorable safety profile, was demonstrated.

Abstract: N-Methyl-D-aspartate (NMDA) receptors are ligand- and voltage-gated ion channels that are critical for synaptic plasticity, learning, and memory. These receptors are variously composed of GluN1, GluN2A–D, and GluN3A/B subunits. They are widely expressed in the central nervous system and are implicated in several neurological, neurodegenerative, and psychiatric disorders. The GluN2B subunit has garnered particular interest due to its high expression in the forebrain and spinal cord, role in pathophysiological processes, and potential as a therapeutic target. Consequently, there is continuing strong interest in developing radioligands for imaging brain NMDA receptors with positron emission tomography (PET). We report the synthesis of nineteen 3-alkylaryl derivatives of 7-methoxy-2,3,4,5-tetrahydro-1H-benzo[d]azepin-1-ol and some of their enantiomers as prospective GluN2B PET radioligands. The absolute configuration of one core ligand was determined with vibrational circular dichroism and infrared spectroscopy, allowing the determinations of the absolute configurations of enantiomers of other ligands derived from this parent ligand. The GluN2B binding pocket showed generally broad tolerance for alkyl tether chain length and for alterations of both bulk and substitution in the terminal aryl group. No relationship between GluN2B affinity and computed compound lipophilicity was observed. Enantiomers of two prepared ligands, L3 (NR2B-SMe) and L6 (NR2B-Me), have desirably strong affinity for GluN2B, moderate lipophilicity, and amenability for labeling with carbon-11 (t_1/2 = 20.4 min). In subsequent and separate PET studies, [S-methyl-¹¹C](S)-L3 and [C-methyl-¹¹C](R)-L6 have shown strong specific binding to GluN2B in animal brain. Synthesis methods and other data from this study can guide and support the further development of candidate GluN2B PET radioligands for clinical applications.

Abstract: Cyberloafing is increasingly recognised as a common yet motivationally complex workplace behaviour. Drawing on the Job Demands–Resources (JD-R) framework, this study examined whether toxic leadership is associated with cyberloafing through burnout syndromes and whether individual-level perceived psychosocial safety climate (PSC) buffers this health-impairment process. Using a cross-sectional online survey design, data were collected from 199 working adults across multiple industries, primarily from South Asia. A first stage moderated parallel mediation model was tested using Hayes’s PROCESS v5.0 Model 7 with 5000 bootstrap resamples. Toxic leadership was positively associated with all four burnout subdimensions, and significant indirect effects on cyberloafing emerged via exhaustion, cognitive impairment, and emotional impairment, whereas mental distance did not mediate the relationship. Individual-level perceived PSC did not significantly buffer the links between toxic leadership and burnout. Overall, the findings suggest that, in toxic supervisory contexts, cyberloafing may be better understood as a maladaptive coping response to burnout-related impairment than as a simple retaliatory behaviour. These results extend leadership and burnout research by locating toxic leadership within the JD-R health-impairment pathway and by highlighting the limited protective role of perceived PSC when the source of harm is the immediate supervisor. Practical implications support an integrated intervention strategy.

Abstract: The current implementation of pneumonia diagnosis remains challenging to achieve better performance and improve results. The aim of this research is to propose an innovative framework for pediatric pneumonia diagnosis that unites three fine-tuned pre-trained CNN models through feature fusion at the EfficientNetB0, RestNet50, and MobileNetV2 to achieve better performance and results. The mixed-model architecture framework provides an ideal solution for time-sensitive clinical applications operating in resource-constrained environments. This research experiment used the Chest X-Ray Images (Pneumonia) dataset, which contains 5863 high-resolution anterior-posterior (AP) chest radiographs sampled from children aged 1 to 5 years old. This study presents four key novelties. Firstly, we systematically evaluated five CNN (Convolutional Neural Networks) combinations with seven different individual base models to identify the optimal ensemble configuration. Each base model was initialized with ImageNet pre-trained weights, with top classification layers replaced by global average pooling. Secondly, the proposed ensemble approach of MobileNetV2, ResNet50, and Efficient-NetB0 achieved superior performance with accuracy: 96.14%, precision: 94.10%, recall: 96.92%, and F1-score: 94.97%, outperforming all individual models and alternative ensemble combinations. Thirdly, this study compared the experiment results with several existing studies related to pneumonia classification. Fourthly, this study validated the proposed model on an external NIH pediatric dataset (94.73% ac-curacy) without fine-tuning, demonstrating true clinical transportability beyond benchmark dataset performance.

Abstract: One way to construct a generalist architecture for computational agents is to assemble different modules for different functions. Yet merely designing this using a top-down approach does not capture how the agent continually produces behavioral states and interacts with the world. A better approach is to evolve a variety of components with a relational history, and then combine the best candidate components into a modular system. We use agentic coding techniques to build a pipeline that implements an evolutionary process of diversification, recombination, and selection. As an initial demonstration of our pipeline, we utilize a toy synthetic dataset of simple shapes and a dataset based on Braitenberg’s Vehicles. In each case, the approach to phylogenetic mixing is to generate variety, select the most viable forms, and then composing an architecture. The resulting components are phylogenetically mixed in that the best components often do not share the same evolutionary history. This assembly process occurs through hypergraph construction: hypergraphs can be used to identify nested or categorical relationships. This generalist architecture could then perform a wide variety of tasks with the ability to connect between domains.

Abstract: The energy balance model (EBM) and its operational form, calories-in-calories-out (CICO), have dominated obesity research and clinical practice for decades. While these frameworks have yielded valuable public health insights, they rely on indirect conversions between mass and energy and rest on misconceptions about thermodynamic principles. This Perspective argues that a mass balance model (MBM) provides a conceptually simpler, mathematically consistent, and biologically more faithful paradigm. By tracking macronutrient mass directly – without intermediate energy-unit conversions or misapplications of thermodynamic laws – the MBM aligns analysis with physiological reality and better predicts body composition dynamics. Clarifying that the first law of thermodynamics concerns only energy (not mass), that calories cannot be eaten or oxidized, and that E=mc² has no relevance to human metabolism paves the way for more precise translational interventions in metabolic medicine.

Abstract: Soft robotic grippers excel in unstructured manipulation but suffer catastrophic failure rates (72%) when grasping deformable organics, fabrics, and mixed debris due to hyperchaotic pneumatic dynamics. This paper introduces the first Lyapunov stability controller for soft robotics, deploying real-time maximal Lyapunov exponent estimation (λ_MLE) from fibre-optic strain sensor arrays running at 100Hz on Intel Loihi 2 neuromorphic chips. The system reconstructs 12D phase space embeddings via Takens theorem, detecting chaos onset 187ms early during dual-material transitions (tomato → bolt), enabling pre-emptive damping that transforms strange attractors into stable limit cycles. Experimental validation across USDA organic datasets (tomatoes, grapes, leafy greens) and MRF waste streams demonstrates 94.2% grasp success 3.7× improvement over PID baselines with 2.3× faster cycles (2.1 grips/second) and 67% energy savings. Neuromorphic acceleration achieves 187μs latency for 12D divergence computation, 28× faster than GPU methods. Field deployments confirm robustness, agricultural harvesting sustains 3 clusters/minute, waste sorting handles mixed-material chaos, and medical tissue manipulation achieves sub-micron precision under arterial pulpability. Theoretical contributions include event-triggered Lyapunov redesign guaranteeing exponential stability (λ_1<-0.1) despite 24dB vibration and 47% moisture variance. Phase space visualization reveals Kaplan-Yorke dimension collapsing from 8.2D hyper chaos to 2.1D stable manifolds, providing online stability margins. This work establishes chaos quantification as a foundational primitive for next-generation soft robotics, transforming nonlinearity from failure mode to control parameter across agriculture, recycling, and minimally-invasive surgery.