Search Results (466)

Containerized ornamental plant production requires efficient irrigation strategies to balance plant quality with water and nutrient conservation. This study evaluated four leaching fraction (LF) levels (0%, 20%, 40%, and 60%) in a completely randomized design with three replications, each consisting of three pots, to determine their effects on plant growth, ornamental quality, gas exchange, water use efficiency (WUE), and macronutrient leaching in off-season potted Curcuma cv. ‘Jasmine Pink’. Irrigation volumes were determined using crop coefficient (Kc)-based estimates derived from evaporation pan measurements. The results showed that the highest LF level (60%) significantly improved several ornamental quality traits, including flower number per cluster, leaf greenness, specific leaf area, and compactness index, while also increasing aerial dry weight and improving gas exchange parameters during the flowering stage. These improvements were associated with reduced substrate electrical conductivity, indicating lower soluble salt accumulation in the root zone under higher LF treatments and more favorable conditions for plant growth. Leaching fraction is commonly used in containerized crop production to prevent excessive salt accumulation in the root zone by allowing excess irrigation water to drain from the substrate. However, increasing LF also resulted in greater irrigation water consumption and higher macronutrient losses through leachate, particularly potassium. In contrast, lower LF treatments (0–20%) improved water use efficiency and reduced nutrient losses but were associated with higher substrate electrical conductivity, suggesting greater soluble salt accumulation in the root zone. Overall, the results indicate that a higher LF (60%) provided the greatest improvement in plant growth and ornamental quality under the conditions of the present study for off-season potted Curcuma alismatifolia production, although integrated strategies may be required to reduce water and nutrient losses. These findings provide practical insights for optimizing irrigation management in container-grown ornamental crops. Full article

Infectious enteritis caused by bacterial pathogens are a significant global health concern, with high incidence and mortalities worldwide. The objective of this research was to explore the benefits of Lactiplantibacillus plantarum CCNH185 against Citrobacter rodentium-induced colitis in mice. Female C57BL/6J mice (n = 8 per group) were orally administered L. plantarum CCNH185 at a dose of 2 × 10⁹ CFU daily for 24 days, followed by a single oral challenge with C. rodentium (2 × 10⁹ CFU) on day 21. L. plantarum CCNH185 significantly alleviated disease symptoms including body weight loss, colon shortening and histopathological damage (p < 0.05). Treatment with L. plantarum CCNH185 also reduced pro-inflammatory cytokine levels, such as IL-1β and IL-6 (p < 0.05), while increasing anti-inflammatory IL-10 expression (p < 0.05) in the colon. Histological and immunofluorescence demonstrated that L. plantarum CCNH185 improved the intestinal barrier integrity by increasing goblet cell numbers, upregulating MUC2 expression, reducing crypt hyperplasia, and suppressing epithelial cell apoptosis. Furthermore, transcriptomic analysis revealed that L. plantarum CCNH185 suppressed excessive immune cell infiltration and inflammatory responses in the colon during C. rodentium infection. Flow cytometry analysis further confirmed that L. plantarum CCNH185 suppressed hyperactivation of innate immune cells including macrophages, dendritic cells, neutrophils to alleviate inflammation. Furthermore, L. plantarum CCNH185 reshaped the gut microbiota by increasing the abundance of beneficial genera such as Lactobacillus, Dubosiella, and Romboutsia. Correlation analysis linked these microbial shifts with improved inflammatory and apoptotic markers. These findings highlight L. plantarum CCNH185 may serve as a promising preventive probiotic candidate for ameliorating infectious colitis possibly through strengthening the gut mucus barrier, modulating immune responses, and altering gut microbiota composition. Full article

Background: Cytokine storm-like inflammation includes an imbalanced immune response, where excessive interleukin-6 (IL-6) and inadequate IL-10 play a central role in increasing tissue injury. Melaleuca cajuputi leaves are known to contain anti-inflammatory bioactive compounds. However, the potential to modulate the dysregulated cytokine response remains underexplored. Objective: This study aimed to evaluate the immunomodulatory effects of Melaleuca cajuputi subsp. cajuputi Powell Ethanolic Leaf Extract (MC-ELE) on IL-6, IL-6R, and IL-10 levels in a BALB/c mouse model of lung inflammation induced by lipopolysaccharide (LPS). Methods: Phytochemical screening was performed to identify active constituents in MC-ELE. Male BALB/c mice were intratracheally challenged with LPS (mg·kg⁻¹ BW) to induce cytokine storm-like inflammation. After 24 h, mice received oral MC-ELE at doses of 750, 1500, 3000 mg·kg⁻¹ BW, or dexamethasone (10 mg·kg⁻¹ BW), for seven consecutive days. On day eight, serum and bronchoalveolar lavage fluid (BALF) were collected for IL-6, IL-6R, and IL-10 assessment using ELISA. Furthermore, body weight changes and clinical symptoms were monitored throughout the study. Results: MC-ELE was confirmed to contain anti-inflammatory compounds. Across all groups, IL-6 concentrations in BALF were consistently higher than in serum, with the LPS-only group showing the greatest elevation. Serum IL-6R levels exceeded BALF IL-6R levels in most groups, except at 1500 mg·kg⁻¹ BW MC-ELE dose. BALF IL-10 was higher compared with serum in all MC-ELE-treated groups. Therefore, MC-ELE might preferentially enhance anti-inflammatory responses within the pulmonary microenvironment. There was no observed toxicity or weight loss at doses up to 3000 mg·kg⁻¹ BW. Conclusions: MC-ELE reported promising immunomodulatory activity by lowering IL-6 and IL-6R levels while enhancing IL-10 responses in lung inflammation induced by LPS within lung tissue. These results suggested its potential as a natural therapeutic candidate for managing severe inflammatory conditions. Full article

Reaction-bonded SiC (RBSC) ceramics exhibit limited hydrothermal corrosion resistance due to the presence of residual silicon. This study presents a strategy to enhance the corrosion resistance of RBSC through homogeneous incorporation of MoSi₂ and concurrent reduction in residual silicon content. Three material systems were fabricated via reactive melt infiltration: conventional RBSC with a SiC/C preform (SC), a SiC–MoSi₂ composite incorporating commercial Mo₂C powder via physical mixing (MC), and a SiC–MoSi₂ composite derived from a Mo₂C/C precursor synthesized by a molten salt method (MS). The Mo₂C/C composite synthesized at 1150 °C exhibited fine, uniformly distributed Mo₂C particles coated on carbon black, contrasting with the agglomerated distribution in commercial Mo₂C mixtures. During reactive sintering at 1600 °C, Mo₂C reacted with molten Si to form MoSi₂, reducing residual Si content. Sample MS achieved the lowest residual Si (8.77 ± 0.45 vol.%), followed by MC (12.43 ± 0.86 vol.%) and SC (19.17 ± 1.01 vol.%). All samples achieved near-full densification (open porosity < 0.1%), with bulk densities of 2.96 ± 0.05, 3.03 ± 0.03, and 3.07 ± 0.03 g/cm³ for SC, MC, and MS, respectively. Microstructurally, MS displayed homogeneous MoSi₂ dispersion, while MC showed partial MoSi₂ aggregation, and SC contained continuous residual Si regions. Hydrothermal corrosion tests at 345 °C and 15 MPa for 9 days demonstrated that corrosion resistance followed the order MS > MC > SC. After 9 days, weight loss was 22.3970 ± 1.2059 mg/cm² (SC), 17.6370 ± 0.8266 mg/cm² (MC), and 15.4347 ± 0.7807 mg/cm² (MS), with corrosion depths of 393.17 ± 27.46, 267.40 ± 24.44, and 224.60 ± 25.13 μm, respectively. The enhanced performance of MS arises from two synergistic factors: reduced residual Si minimizes large corrosion pores, while uniform distribution of MoSi₂ facilitates the formation of a stable, dissolution-resistant composite oxide layer composed of MoO₃ and SiO₂, in which MoO₃ restrains excessive dissolution of SiO₂ through a pinning effect. These findings demonstrate that combining residual Si reduction with homogeneous MoSi₂ incorporation via molten salt-synthesized precursors offers an effective strategy for improving hydrothermal corrosion resistance of reaction-bonded SiC-based materials for applications in high-temperature and high-pressure aqueous environments such as nuclear water reactors. Full article

Background/Objectives: Strain energy density-based algorithms are widely applied in modelling bone healing, yet their use under patient-specific geometry-based conditions remains underdeveloped. This study proposes a patient-specific geometry-based framework for fracture healing simulation and investigates how different postoperative loading conditions influence the mechanical environment of callus remodeling. Methods: Using postoperative radiographic data of a 63-year-old male patient with a distal diaphyseal tibial fracture and concomitant proximal and distal fibular fractures, a three-dimensional finite element model of the tibia was reconstructed, imported into a multiphysics simulation environment, and coupled with an iterative numerical algorithm. A uniform initial callus density of 750 kg/m³ was assumed as a simplified and homogenized representation of the healing tissue. The effects of different mechanical loading conditions (partial weight-bearing, physiological loading, and supraphysiological loading) on the mechanical response and density evolution of the callus were evaluated. Results: Partial weight-bearing resulted in insufficient mechanical stimulation and progressive density loss within the callus. Physiological loading generated strain energy density levels consistent with known osteogenic ranges and contributed to continuous cortical shell formation and overall density increase. Supraphysiological loading was associated with overload-related resorption and spatial heterogeneity, which may reduce callus stability. Conclusions: The findings suggest that loading magnitude may influence the simulated remodeling response of the callus under the assumptions of the present model. These results indicate that intermediate loading conditions were associated with a more pronounced remodeling response compared to reduced or excessive loading for the investigated case. The comparison with postoperative clinical imaging showed qualitative agreement in the spatial distribution of mineralized and less mineralized regions, supporting the feasibility of the proposed patient-specific geometry-based SED-based framework. Full article

Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, as strided operations discard critical spatial details essential for the localization of tiny objects. To address these issues, we propose RFE-YOLO, a lightweight receptive field-enhanced network specifically tailored for high-precision small object detection in UAV scenarios. First, the Cross-Scale Receptive Field Enhancement (CSRE) module is designed to mitigate intrinsic information loss by integrating space-to-depth convolution (SPD-Conv), which preserves spatial details by migrating them into the channel dimension. This module further employs an energy-based adaptive weight generation mechanism to distinguish target signals from environmental noise. Second, this paper proposes the C3k2-Dynamic Inception Mixer Block (C3k2-DIMB), which adaptively captures anisotropic features—such as slender vehicles—via dynamic kernel weighting and multi-shape inception kernels. Third, the Shuffled Upsampling for Resolution Enhancement (SURE) module is introduced to maintain spatial fidelity during resolution recovery, utilizing a channel shuffle mechanism to overcome information isolation. Finally, the Multi-feature Fusion Module (MFM) replaces conventional static concatenation with a dynamic softmax-based competition mechanism, effectively bridging the semantic gap between multi-level features while suppressing background distractors. Experimental results on the VisDrone dataset demonstrate that RFE-YOLO significantly enhances the representation capability for small objects. Specifically, the proposed model achieves a state-of-the-art $mA{P}_{50}$ of 42.70%, representing a substantial 9.3% improvement over the baseline YOLO11n. Furthermore, our architecture maintains an exceptionally lightweight profile with only 1.91 M parameters, demonstrating that high-precision detection can be achieved through structural intelligence rather than excessive parameter scaling. This makes RFE-YOLO highly suitable for real-time inference on edge-deployed UAV platforms. Full article

Obesity is currently recognized as a chronic and multifactorial disease. According to epidemiological data released by the World Health Organization in 2022, more than 2.5 billion adults were overweight and more than 890 million were affected by obesity. The aim of this narrative review is to clarify what leads to overweight and obesity, to explain the concept of energy balance, to address the limited effectiveness of dietary products marketed for weight reduction, to examine commonly promoted nutritional strategies for weight loss and to challenge claims of their superiority. The most recent, robust, and high-quality evidence available on the topic was selected, with particular emphasis on systematic reviews and meta-analyses. Overweight and obesity are characterized by an excessive accumulation of fat mass. At the basis of excessive adipose tissue accumulation lies a persistent positive energy balance. Energy balance is generally considered a central physiological determinant of body weight regulation. Approaches that do not explicitly incorporate this principle may be associated with variable or unsustained outcomes. Available evidence suggests that, when an equivalent caloric deficit is achieved, differences in the timing of energy intake or in dietary patterns—such as intermittent fasting or low-carbohydrate diets—are not consistently associated with greater weight loss compared with other guideline-based dietary strategies. Some supplements supporting weight loss, in selected cases, may offer marginal support; however, based on the current state of scientific knowledge, no product represents an effective shortcut for weight loss. Full article

Background: Long noncoding RNAs (lncRNAs) have emerged as critical regulators of hepatic metabolism and disease progression. The hepatocyte nuclear factor 1 alpha antisense 1 (HNF1A-AS1) lncRNA modulates liver-specific transcription factors; however, its physiological role in diet-dependent lipid homeostasis remains poorly defined. Methods: In this study, we investigated the mouse ortholog, Hnf1a opposite strand 1 (Hnf1aos1), using AAV-mediated knockdown in C57BL/6J mice fed either a chow diet (10% kcal from fat) or a high-fat diet (HFD; 60% kcal from fat) for 12 weeks. Metabolic phenotyping included hepatic lipid quantification, histological analysis, serum biochemistry, and quantitative gene expression profiling. Results: Loss of Hnf1aos1 produced distinct, diet-dependent alterations in hepatic lipid handling. Under chow conditions, knockdown mice exhibited selective hepatic cholesterol accumulation (6.10 ± 2.9 mg/g tissue vs. 3.51 ± 1.1 mg/g in controls), accompanied by dysregulation of cholesterol clearance pathways. In contrast, under HFD conditions, knockdown precipitated severe macrovesicular degeneration, with hepatic triglyceride levels approximately doubled relative to HFD-fed controls (51.72 ± 19.8 mg/g vs. 26.34 ± 11.9 mg/g) and a numerically elevated triglyceride-to-cholesterol ratio (TG:TC ≈ 6.1:1; p = 0.0621, trend). Chow/Kd mice gained significantly less weight than chow-fed controls, whereas HFD/Kd mice exhibited weight gain comparable to HFD controls despite severe hepatic steatosis. This paradoxical phenotype suggests impaired metabolic feedback at the post-transcriptional level, in which compensatory upregulation of Hnf1a mRNA is insufficient to suppress lipid-associated genes such as Cd36, despite profound lipid overload; however, HNF1A protein levels were not directly measured in this study. Conclusion: Collectively, these findings identify Hnf1aos1 as a regulator of hepatic lipid homeostasis whose loss produces a phenotype consistent with inappropriate lipid accumulation during nutrient excess, without defining the underlying molecular mechanism. Our results support a role for Hnf1aos1 in shaping hepatic metabolic plasticity and provide insight into lncRNA-associated MASLD phenotypes. Full article

Background: The impact of overweight and adipocyte size on the development of type 2 diabetes mellitus (T2DM) remains unclear. Aim: We studied (1) the relationship between the state of adipocytes and/or overweight/obesity, the development of T2DM and its clinical and laboratory features; and (2) weight loss effect on glycemic level, endogenous hyperinsulinism (HI), insulin resistance (IR), and T2DM. Methods: We designed a systematic review by searching Web of Science, EBSCO, Scopus/ Science-Direct, Google Scholar, PubMed, Cochrane, and Wolter Kluwer for articles published in 26 years (2000–2026). The study was based on a systematic review of 3853 articles published worldwide. Results: In total, 142 full-text articles were assessed for eligibility. As overweight increases, the size of adipose tissue, adipocytes, and cell radius increase. The increase in cell size overloads intracellular transport and internal organs. The development of IR is a conformational change in cellular receptors caused by an excessive increase in cell size. The increase in cell size with overweight gradually leads to hyperglycemia and HI with the development of IR and T2DM. Any targeted intentional weight loss in patients with T2DM improves metabolic and cardiovascular health, reduces blood pressure and blood sugar, and decreases HI, IR, and T2DM. Conclusions: IR is a protective response of cells that prevents oversaturation and overflow. Overweight is an independent risk factor for the development of HI, IR, and T2DM. Targeted weight loss leads to the cure of HI, IR and T2DM. Full article

In remote sensing image oriented object detection tasks, weakly supervised learning methods based on horizontal bounding boxes have attracted much attention due to their lower annotation costs compared to fully supervised methods. However, remote sensing images, characterized by complex backgrounds, exhibit a wide range of target scales and diverse geometric characteristics across target categories. Existing methods exhibit inadequate exploitation of background and angular information under weak supervision, resulting in compromised perception of dense and high-aspect-ratio targets. Neglecting the imbalance in angle estimation samples further leads to excessively low detection accuracy for few-shot categories. To address the aforementioned issues, this paper proposes a Geometry-Aware Enhancement Network (WSOOD-GAEN) for weakly supervised oriented object detection tasks. First, in the backbone network stage, a channel-space deformable attention module (DAE-ResNet) was constructed. Through deformable sampling and screening of key regions, feature extraction has both morphological adaptability to complex shapes and semantic discriminability of key features in complex backgrounds. Secondly, in the feature pyramid stage, an Angle-Guided Feature Pyramid Network (AG-FPN) is proposed. This module dynamically applies rotation transformation to the sampling offsets of deformable convolutions, thereby enhancing the feature representation of objects with different orientations and scales. Furthermore, an adaptive geometric perception loss (AGL) was designed. Based on the geometric characteristics of different categories, it automatically learns differentiated rotation and flip consistency weights, thereby improving the prediction accuracy of small sample categories. Experiments on the DOTA-v1.0, HRSC, and RSAR datasets validate our approach. Specifically, under the AP75 evaluation metric, the proposed method outperforms existing weakly supervised methods by 1.51%, 9.86%, and 3.28%, respectively. Full article

Background/Objectives: Influenza A virus (IAV) infection triggers robust inflammation and acute lung injury. Andrographolide, a primary active compound from Andrographis paniculata, can mitigate IAV-induced inflammation; however, its precise mechanisms remain poorly elucidated. This study aimed to define its host-directed protective effects and molecular mechanisms. Methods: We used a lethal IAV (H1N1, PR8) model in BALB/c mice and infected A549 cells. Survival, lung pathology, cytokines, and viral titers were measured. Lung RNA sequencing identified dysregulated signaling pathways. PI3K/AKT and pyroptosis pro-teins were analyzed by Western blot. The PI3K/AKT axis was functionally validated with the AKT inhibitor in vivo and AKT1 siRNA in vitro. Results: Andrographolide improved survival, attenuated body weight loss, and reduced lung pathology and inflammatory cytokine levels in IAV-infected mice, without exhibiting direct antiviral activity. Consistent with the in vivo findings, andrographolide enhanced cell viability and suppressed cytokine secretion in infected cells. RNA sequencing revealed marked upregulation of the PI3K/AKT signaling pathway in the lungs of treated mice, as confirmed by increased PI3K and AKT phosphorylation. Furthermore, andrographolide downregulated the expression of key pyroptosis-executing proteins, including cleaved caspase-3 and the gasdermin E (GSDME) N-terminal fragment. These protective effects were substantially abrogated by an AKT inhibitor and AKT1 siRNA. Conclusions: These findings reveal a novel host-directed mechanism by which andrographolide alleviates IAV-induced immunopathology by activating the PI3K/AKT pathway, thereby suppressing caspase-3/GSDME-dependent pyroptosis. Thus, this axis represents a promising target for controlling excessive inflammation in severe influenza. Full article

The problem of performance degradation in current sound source localization algorithms under reverberant and noisy environments remains a critical challenge. Consequently, this paper introduces a novel approach to estimate the 3-D position of sound sources directly from raw audio signals using an artificial neural network (ANN), which improves the performance of sound source localization algorithms under reverberant and noisy environments. Instead of relying on handcrafted features, raw audio signals recorded by a tetrahedral stereo microphone array are fed directly into the ANN. This design eliminates spatial symmetry issues found in 2-D microphone arrays and enhances 3-D localization accuracy. Inspired by human auditory systems, a convolutional layer is added after the input layer to simulate frequency analysis to search localization cues in different frequency bands. Furthermore, the proposed algorithm incorporates residual connections (RC) and squeeze-and-excitation (SE: an attention mechanisms). Residual connections introduce raw features into deeper network layers to prevent localized information loss caused by excessive network depth, while also enabling improved model training stability. The attention mechanism dynamically adjusts weights across and within channels, suppressing interference while enhancing localization-critical cues, thereby playing a pivotal role in boosting the algorithm’s reverberation and noise resistance. Experimental results demonstrate significant improvements: in semi-anechoic chambers, the method reduces localization errors by 0.2 m and increases accuracy by 10%; in conference rooms, errors decrease by 0.26 m with a 21% accuracy gain. These outcomes conclusively validate the effectiveness of the proposed approach in enhancing robustness against reverberation and noise in sound source localization systems. Full article

Obesity is a chronic, highly prevalent disease affecting nearly one-third of the global population and represents a major independent risk factor for heart failure (HF), particularly heart failure with preserved ejection fraction (HFpEF). Excess adiposity—especially visceral and epicardial adipose tissue (EAT)—acts as an active endocrine and immune organ, promoting chronic low-grade inflammation, oxidative stress, endothelial dysfunction, and adverse myocardial remodeling. Expanded EAT exerts both paracrine inflammatory effects and mechanical constraint on the myocardium, contributing to diastolic dysfunction, microvascular impairment, atrial arrhythmogenesis, and elevated filling pressures despite preserved systolic function. Evidence demonstrates a dose–response relationship between increasing body mass index and incident HF. Clinically, obesity-related HFpEF is characterized by concentric left ventricular hypertrophy, impaired relaxation, increased plasma volume, reduced exercise tolerance, and relatively low natriuretic peptide levels, complicating diagnosis. HF management includes traditional treatment with diuretics, renin-angiotensin system inhibitors, β-blockers, mineralocorticoid receptor antagonists, and angiotensin receptor-neprilysin inhibitors. These agents widely remain foundational as they primarily target hemodynamic and neurohormonal pathways in HF. In contrast, sodium–glucose cotransporter 2 inhibitors consistently reduce HF hospitalizations across the ejection fraction spectrum, while glucagon-like peptide-1 receptor agonists and dual incretin therapies (e.g., tirzepatide) promote substantial weight loss, improve symptoms, and demonstrate promising anti-remodeling effects in obesity-related HFpEF. Recognizing obesity-driven HF as a distinct cardiometabolic entity supports an integrated therapeutic strategy combining structured weight reduction with guideline-directed HF polypharmacotherapy to address both hemodynamic burden and upstream adiposity-related mechanisms. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

(1) Background: In integrated financial markets where traditional diversification often fails, analyzing sustainability-oriented investments under non-linear dynamics is critical to averting erroneous decisions. This study investigates whether corporate sustainability provides effective downside mitigation against volatility in emerging markets, using Borsa Istanbul as a case study. (2) Methods: The analysis employs US Dollar-denominated excess returns of an equal-weighted portfolio from the longest-tenured BIST Sustainability Index constituents versus the broader BIST 100 Index (2014–2025), utilizing Markov Regime Switching (MS-AR) and Regime-Switching CAPM methodologies to model non-linear dynamics. (3) Results: Empirical results reveal two distinct regimes, where market variance surges approximately 8.5-fold during crises. The sustainable portfolio exhibits a low systematic risk sensitivity (Beta: 0.76) in normal conditions, driven by its distinct structural composition without generating statistically significant Alpha. In crisis regimes, despite increased sensitivity (Beta: 0.90), the portfolio remains resilient with a beta strictly below 1.00. While BIST 100 investors suffered a massive 40.86% USD wealth erosion over the full period, the sustainability portfolio significantly mitigated this damage, limiting the total capital loss to 20.73% due to substantial compounding accumulated during normal regimes. (4) Conclusions: Consequently, sustainability proves to be not merely an ethical preference but a rational financial strategy offering diversification benefits in tranquility and acting as an effective partial hedge during turbulence in high-volatility markets. Full article