Search Results (8,899)

Foreign direct investment (FDI) has often been cast as a straightforward engine of growth, yet its record across Central and Eastern Europe tells a more tangled story where outcomes hinge on the interplay of education, governance, and the timing of external shocks. This study embeds fixed effects panel econometrics within a systems framework, treating FDI as a subsystem of socio-economic dynamics. Using a long-run panel of eleven economies from 2000 to 2023, the analysis models path dependence and regime shifts through interaction terms and period-specific dummies set against a systems-thinking backdrop. The analysis shows that for the average CEE economy, FDI’s contribution has waxed and waned: it dragged on growth during the early transition years (2000–2007), settled into a neutral role after the global financial crisis, and proved unpredictable in the pandemic era. Romania stands out, however, with a marked “FDI premium” quantified as approximately 0.7 pp of growth per pp of FDI that seems to stem from reinforcing loops between rising tertiary enrolment and productivity spillovers. Mapping these feedbacks brings to light virtuous circles where human capital and resilience make or break the benefits of foreign capital. The policy message is plain: nurture the positive loops through investment in skills and firm linkages, keep institutions nimble enough to adapt, and watch for early warning signs of systemic strain. Full article

The high bioavailability and low toxicity of organic selenium underscore its potential for nutritional fortification. This study investigated the biological effects of a novel 3-selenoureidoindole derivative (3-SeU-Ind) as a dietary selenium source in the invertebrate model organism silkworm (Bombyx mori). When reared on natural mulberry leaves, supplementation with 3-SeU-Ind (4–400 mg/L) had no significant effect on larval weight, pupal weight, or cocoon production performance. However, under compound diet conditions, the highest concentration (400 mg/L) significantly reduced both larval and pupal weights. Selenium was effectively accumulated in larval tissues and the pupal body. Under high-temperature stress, supplementation with 3-SeU-Ind (100 and 400 mg/L) significantly enhanced silkworm survival, which was associated with the upregulation of key antioxidant genes, including MnSOD, CAT, GPX, and TrxR. Furthermore, the supplementation altered methionine and lysine levels in the hemolymph in a sex-specific manner. Thus, 3-SeU-Ind demonstrated potential as a safe and effective selenium supplement. Full article

To address the challenge of defect detection in tempered glass panel production rising from sample scarcity, this paper proposes a few-shot detection methodology that integrates an enhanced Stable Diffusion model with Mask R-CNN. Specifically, the approach utilizes a Mask Encoder to optimize the Stable Diffusion architecture, employing the Structural Similarity Index Measure (SSIM) to evaluate sample quality. This process generates high-fidelity virtual samples to construct a hybrid dataset for training data augmentation. Furthermore, a resource isolation strategy is adopted to facilitate online detection using an improved semi-supervised Mask R-CNN framework. Experimental results demonstrate that the proposed scheme effectively resolves detection difficulties for eight defect types, including edge chipping and scratches. The method achieves an mAP50 of 81.5%, representing a nearly 47% improvement over baseline methods relying solely on real samples, thereby realizing high-precision and high-efficiency industrial defect detection. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative condition with a multifactorial etiology, characterized by dopaminergic neurons being selectively absent in the midbrain. Clinically, PD manifests primarily with core motor symptoms of resting tremor, bradykinesia, and muscle rigidity, and is often accompanied by non-motor symptoms including depression, cognitive impairment, and gastrointestinal dysfunction. Among the extensive relevant research, few have explored the precise pathogenic mechanisms underlying PD, and no curative treatment is available. Current pharmacological therapies mainly provide symptomatic relief by enhancing central dopaminergic function or modulating cholinergic activity; however, their long-term efficacy is frequently constrained by waning therapeutic response, drug tolerance, and adverse reactions. Accumulating evidence suggests that several naturally derived neuroactive compounds—such as gastrodin, uncarin, and paeoniflorin—demonstrate significant potential in combating PD. In this systematic review, we examined original research articles published from 2010 to 2025, retrieved from PubMed, Web of Science, and CNKI databases, using predefined keywords of Parkinson’s disease, neuroprotective herbal compounds, traditional medicine, multi-target mechanisms, natural product, autophagy, neuroinflammation, and oxidative stress. Studies were included if they specifically investigated the mechanistic actions of natural compounds in PD models. Conference abstracts, review articles, publications not in English or Chinese, and studies lacking clearly defined mechanisms were excluded. Analysis of the available literature reveals that natural neuroactive compounds may exert anti-PD effects through multiple mechanisms, e.g., inhibiting pathological α-synuclein aggregation, attenuating neuronal apoptosis, suppressing neuroinflammation, mitigating oxidative stress, and restoring mitochondrial dysfunction. This review provides insights that may inform the clinical application of natural bioactive compounds and guide their further development as potential therapeutic candidates against PD. Full article

Water quality observations are vital for effectively managing coastal resources and influencing decisions from emergency beach closures to aquaculture leasing agreements. This study focuses on deriving two water quality parameters—Chlorophyll a (Chl-a) and suspended particulate matter (SPM)—through the high-resolution multispectral imager (MSI) onboard the Sentinel 2A&B satellites, specifically for the Ningde coastal region, which is a crucial aquaculture hub in China. Since more than 90% of the signals captured by satellites are affected by atmospheric interference, it is crucial to apply a process called “atmospheric correction” (AC) to isolate the water contribution, known as water leaving reflectance, from the radiance measured at the top of the atmosphere. Our research assesses five published AC models and various algorithms designed to accurately estimate Chl-a and SPM from water leaving reflectance. We determine the most effective combination by comparing these findings against in situ data gathered from eleven locations in the Ningde coastal region (POLYMER-SOLID with lowest metric RMSLE (0.29), and MAE (1.68) and POLYMER-MDN with the lowest metric RMSLE (0.59), and MAE (0.56)). Our study underscores the importance of selecting locally validated AC models and algorithms for generating water quality products, as this enhances the utility of remote sensing data in monitoring water quality. Moreover, we conduct a spatiotemporal analysis of the water quality parameters from 2016 to 2021, revealing significant interannual variability that underlines the need for continuous monitoring and robust data analysis in coastal management efforts. Full article

Microbiota-derived indoles and short-chain fatty acids (SCFAs) modulate intestinal immunity via the aryl hydrocarbon receptor (AhR) and vitamin D receptor (VDR). This review proposes an operational AhR–VDR axis—three testable models (sequential, parallel, reciprocal)—to explain how indoles (AhR) and SCFAs/vitamin D (VDR) may cooperate to drive IL-22–mediated repair, antimicrobial peptide production, autophagy, and tight-junction restoration. We critically evaluate prebiotics, probiotics, and postbiotics: prebiotics shift fermentation toward SCFAs but show context-dependent effects; probiotics can supply indole-type AhR ligands yet are strain-specific; postbiotics offer standardized ligand delivery but face formulation challenges. We distinguish Salmonella-specific findings (e.g., SCFA suppression of SPI-1) from general colitis data and prioritize molecular validation, temporal mapping, multi-omics responder stratification, and standardized postbiotic development for clinical translation. Full article

Feeding behavior in cattle affects feed efficiency, which is important for increasing the profitability of production while simultaneously reducing the environmental impact. Over a six-year period, indigenous beef cows (Afrikaner, Bonsmara, Nguni) were crossed with indigenous and exotic (Angus, Simmental) sires in a hot and arid area, to produce 15 breed groups. After weaning, the bull calves were fed in a feedlot setting wherein daily feed intake and partial body weight were measured. The serial correlations of daily feed intake and partial body weight on consecutive days were estimated for each animal. Analyses of variance for the z-transformed serial correlations of daily feed intake and partial body weight were conducted. The linear model included the fixed effect of test group comprising pen and date at the beginning of the test and a fixed breed group effect. The average serial correlation of daily feed intake (r = 0.10) was interpreted to suggest that a test period of 36 days was sufficient to achieve 80% average accuracy for the animals being tested. The average serial correlation of partial body weight was very high (r = 0.94). Thus, there seems little need to average values over days to achieve an accurate estimate of the weight of an animal at any specific point in time. Variation among animals in the serial correlation of daily feed intake indicates differences in feeding behavior over time, but this variability was not related to breed composition. The results indicate that a test period of 36 days is sufficient to achieve 80% accuracy of the mean for daily feed intake of the animals being tested. Full article

Background/Objectives: Cold-smoked salmon (CSS) is a ready-to-eat product with minimal preservation hurdles and a microbiota shaped by raw-material contamination and processing environments. Short breaks in refrigeration commonly occur during shopping and transport, yet their microbiological impact remains unclear. Here, we used ASV-resolved 16S rRNA gene metataxonomics to characterize storage-driven microbiota dynamics in CSS—quantifying ASV-level genetic diversity and phylogeny-aware (UniFrac) community structure—and to evaluate the effect of a brief, consumer-mimicking 2 h room-temperature cold-chain disruption. Methods: Three CSS types (organic, conventional Norwegian, and conventional Scottish) were stored at 5 °C for 35 days. On day 16, half of each batch was exposed to 2 h at room temperature (RT) before analysis; paired controls remained refrigerated. Culture-based counts (total mesophiles, lactic acid bacteria, Photobacterium spp.; indicator/pathogen screens) were performed per ISO methods. Community profiling used 16S rRNA (V3–V4) amplicon sequencing with QIIME 2/DADA2 and SILVA taxonomy. Linear mixed effects modelled alpha diversity; beta diversity by PERMANOVA on UniFrac distances; differential abundance by ANCOM-BC. Results: ASV-resolved 16S rRNA gene profiles of CSS were dominated by Pseudomonadota and Bacillota, with storage-driven shifts and taxon-specific trajectories (e.g., increasing Latilactobacillus). Both time and product type significantly explained phylogeny-aware community structure (unweighted and weighted UniFrac), consistent with storage-driven phylogenetic convergence across products. At day 16, ASV-level genetic diversity (Shannon/Observed features) and genus-level composition did not differ between RT-disrupted and continuously refrigerated samples. Culture-dependent counts increased from baseline to day 16 and largely plateaued by day 35, with lactic acid bacteria in Norwegian CSS continuing to rise; no systematic effect of the 2 h RT exposure was observed in culture-based comparisons. Indicator/pathogen screens detected no unexpected pathogenic species throughout the study period. Conclusions: Refrigerated storage drives pronounced, phylogeny-aware microbiota shifts and cross-product convergence in cold-smoked salmon, whereas a single 2 h RT interruption at mid-storage did not measurably alter ASV-level genetic diversity or community structure under the tested conditions. Integrating culture-based enumeration with ASV-resolved 16S rRNA gene metataxonomics provides complementary insights for shelf-life evaluation and risk assessment in ready-to-eat seafood. Full article

In skeletal muscles fibers, cellular respiration, excitation–contraction (EC) coupling (the mechanism that translates action potentials in Ca²⁺ release), and store-operated Ca²⁺ entry (SOCE, a mechanism that allows recovery of external Ca²⁺ during fatigue) take place in organelles specifically dedicated to each function: (a) aerobic ATP production in mitochondria; (b) EC coupling in intracellular junctions formed by association between transverse tubules (TTs) and sarcoplasmic reticulum (SR) named triads; (c) SOCE in Ca²⁺ entry units (CEUs), SR-TT junctions that are in continuity with membranes of triads, but that contain a different molecular machinery (see Graphical Abstract). In the past 20 years, we have studied skeletal muscle fibers by collecting biopsies from humans and isolating muscles from animal models (mouse, rat, rabbit) under different conditions of muscle inactivity (sedentary aging, denervation, immobilization by casting) and after exercise, either after voluntary training in humans (running, biking, etc.) or in mice kept in wheel cages or after running protocols on a treadmill. In all these studies, we have assessed the ultrastructure of the mitochondrial network and of the sarcotubular system (i.e., SR plus TTs) by electron microscopy (EM) and then collected functional data correlating (i) the changes occurring with aging and inactivity with a loss-of-function, and (ii) the structural improvement/rescue after exercise with a gain-of-function. The picture that emerged from this long journey points to the importance of the internal architecture of muscle fibers for their capability to function properly. Indeed, we discovered how the intracellular organization of the mitochondrial network and of the membrane systems involved in controlling intracellular calcium concentration (i[Ca²⁺]) is finely controlled and remodeled by inactivity and exercise. In this manuscript, we give an integrated picture of changes caused by inactivity and exercise and how they may affect muscle function. Full article

Arthritis in K/BxN mice is provoked by pathogenic autoantibodies to glucose-6-phosphate isomerase (G6PI), which is a ubiquitously expressed enzyme that is present in cells, in the circulation and on the articular cartilage. When G6PI autoantibodies (auto-Abs) deposit on the articular cartilage of K/BxN mice, arthritis ensues due to the activation of various components of the innate immune system. Recent studies have investigated the in vivo efficacy of recombinant fragment-crystallizable (Fc) protein-based therapeutics. Many of the recombinant Fc proteins that have been evaluated have been shown to have a protective effect in mouse models of arthritis, such as the K/BxN serum-transfer model. More recently, rFc-µTP-L309C, a recombinant human IgG1-Fc with an additional point mutation at position L309C fused to the human IgM tailpiece to form a hexamer, has been shown to ameliorate the arthritis in K/BxN mice. Additional studies have shown that rFc-µTP-L309C has multiple effects that work together to ameliorate the arthritis, including inhibition of neutrophil migration into the joint, inhibition of IL-1β production, downregulation of Th1 and Th17 cells and increases in T regulatory cells and synovial fluid IL-10. In this work, rFc-µTP-L309C was shown to effectively prevent arthritis in the K/BxN serum-transfer model, significantly downregulate inflammatory cytokines/chemokines and ameliorate the arthritis in the endogenous K/BxN model. This amelioration of the arthritis was mediated by a significant decrease in antibody levels. Interestingly, this effect seems to be independent of the neonatal Fc receptor (FcRn). rFc-µTP-L309C was shown to specifically inhibit G6PI autoantibody secretion from B-cells with a concomitant increase in TGFβ and decrease in B-cell activating factor (BAFF). These new findings suggest that rFc-µTP-L309C may provide a therapeutic benefit for other antibody-mediated autoimmune disease through its effects on B-cells. Full article

Crops grown in ecologically vulnerable oases are increasingly vulnerable to climate change, a trend that poses a severe threat to the sustainability of agricultural production in arid zones. Clarifying the relative contributions of climate change and crop management to crop phenology is critical for designing climate-resilient agricultural practices—yet this remains underexplored for wheat in Xinjiang’s oases, a major arid-region agricultural hub. Using 1981–2021 phenological and meteorological data from 26 agrometeorological stations, we integrated a first-difference multiple regression model, Pearson’s correlation, multiple linear regression, and path analysis to quantify spatiotemporal phenological dynamics; disentangle the distinct impacts of climate and management factors; and identify dominant climatic drivers regulating wheat growth. Temperature was confirmed as the dominant climatic factor regulating wheat growth in arid oasis regions. Results showed that the annual change rates of sowing, emergence, booting, flowering, and maturity dates were 0.261 (−0.027), 0.265 (−0.103), −0.272 (−0.161), −0.269 (−0.226), and −0.216 (−0.127) days/year for winter (spring) wheat, respectively. For phenological durations, the annual change rates of sowing-to-emergence, emergence-to-anthesis, anthesis-to-maturity, vegetative growth period, reproductive growth period, and whole growth period were 0.007 (−0.076), −0.537 (−0.068), 0.096 (0.099), −0.502 (−0.134), 0.068 (0.034), and −0.434 (−0.100) days/year for winter (spring) wheat, respectively. Regarding climatic effects, maximum, minimum, and mean temperatures generally exerted positive impacts on wheat phenological durations; increased precipitation prolonged growth periods; and higher sunshine hours shortened winter wheat growth periods while extending those of spring wheat. Multiple regression and path analysis were employed to clarify the relative importance of climatic and management factors, as well as their direct and indirect effects on wheat phenology and yield. Furthermore, climate change had a substantially weaker impact on wheat phenology and yield compared to crop management, with climatic driver intensity following the order of mean temperature > precipitation > sunshine hours—confirming mean temperature as the key climate-induced driver. Correlation analysis revealed a positive relationship between yield and growth period length. This study provides novel insights into region-specific climate adaptation for wheat production in arid oases, highlighting that planting longer-growth-period varieties is an effective, eco-friendly strategy to enhance climate resilience and ensure sustainable agricultural development in fragile ecosystems. Full article

Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android malware detection. However, existing public benchmark datasets do not provide paired tabular and image representations for the same samples, limiting direct comparisons between tabular models and vision-based models. This work investigates whether carefully engineered, domain-specific tabular features can match or surpass the performance of state-of-the-art deep vision models under strict false-positive-rate constraints, and whether ensemble approaches justify their additional complexity. To enable this analysis, we construct a large corpus of Android applications with paired static representations and evaluate six popular machine learning models on the exact same samples: two tabular models using EMBER features, two tabular models using extended EMBER features, and two vision-based models using malware images. Our results show that a LightGBM model trained on extended EMBER features outperforms all other evaluated models, as well as a state-of-the-art approach trained on a much larger dataset. Furthermore, we develop an ensemble model combining both tabular and vision-based detectors, which yields a modest performance improvement but at the cost of substantial additional computational and engineering overhead. Full article

The rapid growth of personal protective equipment (PPE) consumption has generated unprecedented volumes of polymer-based waste, posing a major challenge to the transition from a linear to a circular economic model. The challenges associated with PPE recycling are strongly linked to the sector of origin—including healthcare, laboratories, cleanrooms, and food processing—as this factor determines contamination levels and critically influences subsequent recycling steps. PPE waste originating from the healthcare sector requires stringent decontamination processes, which directly affect the final properties of recycled materials and their suitability for upcycling or downcycling applications. Another decisive factor is source segregation, together with labeling and sorting, given the intrinsic material heterogeneity of PPE, which commonly includes polypropylene (PP) masks, polycarbonate (PC) protective eyewear, and nitrile butadiene rubber (NBR) gloves. Mechanical and chemical recycling routes, including processes specifically developed for elastomeric materials, play a complementary role depending on the cleanliness and composition of the waste streams. The potential for downcycling and upcycling of recycled PPE is closely linked to polymer integrity and process compatibility. When appropriate segregation strategies and tailored recycling technologies are implemented, PPE waste can be effectively diverted from incineration. Under these conditions, PPE—once emblematic of single-use culture—can become a representative example of how complex polymer products may be reintegrated into sustainable material loops, contributing to resource efficiency and circular-economy objectives. Full article

Due to growing demands for quality, sustainability, and digitalization, data science and artificial intelligence are gaining importance across industries. The extensive product range in many sectors often poses considerable challenges. For example, machine learning (ML) models may struggle with limited data per production variant. The present paper proposes a methodology that integrates the fields of data science and physical simulations. The results from finite element method (FEM) simulations are utilized to transform the process data in such a manner that it can be compared across processes for different production variants and employed for machine learning (ML) methods and statistical analyses. The method is illustrated using an example of aluminum production. A key advantage of this approach is that it can effectively model even production variants with very low quantities. The following discussion will present how this method can be used to enhance production processes, specifically to identify parameters that directly influence product quality, which would not be evident using alternative approaches. Furthermore, the work explores the potential for precisely controlling these parameters using ML models and discusses some major challenges. Full article

Growing environmental concerns and regulatory pressures are prompting firms to re-examine packaging design to advance sustainability. Focusing on eco-designed retail packaging in the new-style milk tea industry, this study investigates how specific attributes of eco-designed retail packaging influence consumers’ purchase intention. Data were collected from 425 university students in Wuhan. We measured eco-designed retail packaging (ECRP) with a six-dimension scale (functional, aesthetic, eco-materials, eco-information, eco-production, and innovation) and tested the mediating role of green perceived value (GPV) using structural equation modeling (SEM). Results show differentiated effects of ECRP dimensions on GPV and purchase intention. Functional design and clear eco-information increase both GPV and purchase intention, whereas using eco-materials while directly raising purchase intention reduces GPV. Aesthetics and innovation mainly operate through direct enhancement of purchase intention rather than via GPV. GPV mediates part of the effects of functional attributes, eco-materials, and eco-information on purchase intention. The findings imply that optimizing functionality, information clarity, and material choices in eco-designed retail packaging can simultaneously elevate GPV and purchase intention. As green packaging becomes an industry imperative, this study provides theoretical and practical guidance for sustainable packaging innovation and green industry development. Full article