Search Results (509)

Digital transformation in the interior design industry has opened new opportunities for innovation; however, many cost-conscious homeowners still face difficulties in selecting and customizing design packages that achieve a balance between overall cost and sustainable quality. Existing interior design platforms lack seamless support and often require homeowners to invest considerable time and effort to tailor services to their needs while staying within budget. To address these challenges, this paper explores the use of machine learning to build a predictive modelling framework that supports personalized and value-driven interior design recommendations. The proposed approach uses a hybrid recommendation system that combines content-based and collaborative filtering. It also incorporates lightweight techniques such as TF–IDF (Term Frequency–Inverse Document Frequency) and logistic regression to more effectively capture user preferences, budget limits, and several interior-design service categories. Primary data was collected from small to medium-sized interior design companies. To demonstrate the proposed approach, a user-friendly web application tool is developed to integrate machine learning-enabled recommendation services. The resulting solution provides access to professional interior design services, enhancing customization and customer satisfaction while reducing the time and effort required from homeowners. To validate and compare the performance of the proposed approach, several machine learning models including Random Forest, XGBoost and KNN (K-Nearest Neighbors) were tested using standard metrics such as accuracy, precision, recall, and ROC-AUC (Receiver Operating Characteristic-Area Under the Curve). The proposed logistic regression hybrid model achieved the strongest overall results, with an accuracy of 83.62%. These findings demonstrate the significant contribution of this work to enhancing personalization and accessibility in the interior design sector via machine learning-enabled recommendation systems. The proposed approach bridges the gap between expert-level services and financial limits, making it a practical choice for cost-conscious homeowners. Full article

Background/Objectives: Magnesium is an intracellular cation that plays important roles in metabolism and insulin signaling. The evidence of association between magnesium levels and diabetic retinopathy is limited by small study effects. Therefore, this systematic review and meta-analysis aim to update the current evidence. Methods: A comprehensive search of PubMed, ScienceDirect, Google Scholar, and Scopus was conducted from database inception to January 2026 to identify studies examining magnesium levels and diabetic retinopathy. The pooled standardized mean difference in magnesium levels between type 2 diabetic patients with retinopathy and those without retinopathy was estimated using the “meta” package in R software. Results: We included seventeen studies which assessed magnesium levels in 1100 patients with diabetic retinopathy and 1132 diabetic controls without retinopathy. The random-effects model indicated significantly lower magnesium levels in patients with diabetic retinopathy compared to diabetic controls [SMD = −1.19, 95% CI (−1.68; −0.70); p < 0.0001; I² = 95%]. Sensitivity analysis retained all studies, and no evidence of publication bias was detected. Subgroup analyses demonstrated consistent findings across geographic regions (Asian versus non-Asian), study designs (case–control versus cross-sectional), and magnesium assay methods except enzymatic method. Meta-regression analysis revealed that year of publication (coefficient = 0.061; p = 0.009) and non-Asian studies (coefficient = 2.376; p = 0.001) were positively associated with the pooled effect size, while the NOS quality score was inversely associated (coefficient = −0.709; p = 0.035). The magnesium levels were significantly lower in patients with proliferative diabetic retinopathy compared with those with non-proliferative diabetic retinopathy using a fixed effect model [SMD = −1.41, (95% CI: −1.83; −1.00); p < 0.01; I² = 32%; Cochran’s Q statistic (Q = 1.46, p < 0.23)]. The certainty of the generated evidence is rated as low certainty. Conclusions: This systematic review and meta-analysis conclude that magnesium levels are significantly lower in patients with diabetic retinopathy than in diabetic controls without retinopathy. A potential association between hypomagnesemia and the development of diabetic retinopathy in individuals with type 2 diabetes is suggested; therefore, the clinician may check and adjust magnesium levels in patients with type 2 diabetes mellitus. Full article

This study systematically investigates the effects of the final annealing temperature on the microstructural evolution and mechanical properties of an Al-Fe-Si alloy aluminum foil. Scanning electron microscopy (SEM) characterization and tensile tests are employed for analysis. As the annealing temperature is elevated from 240 °C to 360 °C, the average grain size increases monotonically from 5.2 μm to 9.6 μm. Continuous recrystallization is identified as the predominant grain growth mechanism. Tensile deformation exhibits the homogeneous plastic behavior without localized necking. The tensile strength decreases significantly in the range of 240–300 °C and subsequently undergoes a recovery stage at 300–360 °C. Significant elongation anisotropy is observed. The maximum elongation reaches 30–34% in the 45° direction, relative to the rolling direction (RD), which is approximately 1.5 times that along the RD (0°). Comparative analysis of the anisotropy indices demonstrates that the aluminum foil annealed at 240 °C achieves the minimal tensile strength anisotropy (13.0 MPa) and elongation anisotropy (−4.2%). This indicates an optimal comprehensive mechanical performance. These findings provide a theoretical rationale for the industrial optimization of the annealing processes for Al-Fe-Si alloy foils. They are particularly valuable for balancing microstructural regulation and mechanical property enhancement in lithium-ion battery soft-packaging applications. Full article

Cardiovascular-kidney-metabolic (CKM) syndrome integrates excess adiposity, metabolic dysfunction, kidney impairment, subclinical cardiovascular diseases, and clinical events along a staged continuum that invites unified prevention and treatment. Ultra-processed foods (UPFs) are a complex, high-prevalence exposure that may influence risk across CKM stages through nutrient profiles, additives, processing-induced compounds, and packaging-related contaminants. This review synthesizes epidemiologic, mechanistic, and translational evidence with attention to exposure definition and analytic rigor. We summarize NOVA-based UPF operationalization across dietary assessment tools, highlighting misclassification of mixed dishes, brand heterogeneity, and energy under-reporting, and we propose further examination of energy-adjusted models, calibration, and harmonized metrics. Observational studies consistently associate higher UPF intake with adiposity, diabetes, chronic kidney disease, cardiovascular events, and mortality, with modest to moderate effect sizes that are heterogeneous across populations. Mechanistic data from metabolomics, lipidomics, proteomics, and the gut microbiome converge on pathways of inflammation, lipid metabolism, oxidative and metabolic stress, and intestinal barrier dysfunction; in selected cohorts, multi-omics modules account for a substantial minority of UPF-outcome associations. We outline quality-control pipelines, batch-effect prevention/correction, and multiple-testing control necessary for reproducible diet-omics. Translationally, targeted lipidomic and proteomic panels show promise for CKM risk stratification and monitoring but require validation, clinical thresholds, and guideline endorsement. Equity and global context, including differences in product mix, food systems, and care capacity, modify population impact. We conclude with a research agenda prioritizing harmonized exposure metrics, error-aware modeling, standardized multi-omics workflows, and adequately powered, stage-specific interventions capable of testing mediation and prognostic utility. Full article

The uniaxial tensile test is a common and fundamental test in materials science and engineering, in which a specimen is subjected to controlled tension until failure. From this, the stress–strain curve and many property parameters of the material can be calculated, such as tensile strength, ultimate strength, maximum elongation, Young’s modulus, Poisson’s ratio, and yield strength. As fibrous materials, such as paper and paperboard, become more popular, accurately measuring their mechanical properties becomes essential for developing and applying these materials, especially in packaging. However, since they are anisotropic and inherently inhomogeneous due to the arrangement of the fibers, accurately determining their mechanical properties is not straightforward. This study investigated how several key factors influence the results of tensile tests on fiber-based materials: sample size and deformation measurement techniques using three fiber materials. This study also compared three different strain recording methods: digital image correlation (DIC), video extensometer, and conventional extensometer (Traverse). The DIC technique emphasized the effect of the inherent inhomogeneity of the paperboard on the overall mechanical properties obtained from tensile tests. The results indicated that sample size has a negligible effect on the stress–strain curve, and any apparent influence likely stems from slip at the grips during tensile testing. However, sample size does affect paperboard fracture to some extent. The study also provided recommendations for optimal specimen geometry and deformation recording methods to improve the accuracy and repeatability of tensile testing of fiber-based materials. Full article

Food waste is an issue that affects human and environmental health around the planet. At colleges and universities, food waste poses a serious concern, as its impact can be compared to that of mini-cities or large corporations. Identifying an institution’s capacity to reduce and redistribute food waste is critical to decreasing its carbon footprint and maintaining sustainability. Understanding the nature of waste produced at a university’s buildings is the first step in establishing effective waste management plans; however, campus cafeterias, being the primary source of food waste, are typically the focus. Limited research emphasis has been placed on assessing food waste generated in campus dormitories. This project tests the hypothesis that food waste generated from dormitories at the main campus of Adelphi University, a private liberal arts institution in New York, is a significant component of waste. To analyze post-consumer trash disposal patterns, garbology methods were utilized. Trash collected at dormitories between 2022 and 2024 was sorted and weighed. This mixed-methods analysis included student interviews of waste perceptions. Food waste was the primary waste type generated in the halls, followed by food and beverage packaging, including containers, napkins, and utensils. In particular, food waste comprised 32% of sampled dormitory waste. Interview results integrated with these quantitative results demonstrated student perceptions of food led to food waste, such as perceived level of cooking, portion sizes, and home context. These results suggest that any efforts to improve campus sustainability through management of food waste–such as composting or anaerobic digestion–must encompass dormitories as well as cafeterias. As the world’s population continues to rise at a rapid pace, primarily in metropolitan areas, the volume of waste generated by this growth must be managed to address planetary health. Full article

Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints. Full article

Lignin nanoparticles (LNPs) offer sustainable alternatives to petroleum-derived nanofillers, yet their industrial application remains limited by poor dispersion control and trade-offs between loading, optical clarity, and mechanical performance. Here, we present a molecular architecture-driven design framework that systematically decouples polymer network physics from nanoparticle dispersion in poly(vinyl alcohol)/LNP nanocomposites. Through eco-friendly self-precipitation, we synthesize uniform LNPs with size tunability, overcoming persistent reproducibility challenges. Systematic investigation across PVA molecular weights and LNP loadings reveals entanglement-controlled dispersion behavior. Combined rheological and small-angle X-ray scattering analyses demonstrate that macroscopic suspension rheology is governed exclusively by polymer chain overlap, remaining invariant across LNP loadings. Conversely, the nanoscale LNP microstructural organization—ranging from depletion-driven clustering in weakly entangled networks to network-confinement stabilization in densely entangled systems—fundamentally dictates the film’s optical clarity and mechanical toughness. This rheology-microstructure decoupling establishes critical processing windows for industrial formulations, where polymer entanglement ensures suspension processability while the LNP dispersion state enables optical–mechanical tunability. The entangled network’s structure-filtering effect provides robust protocols for fabricating sustainable, transparent bio-composites suitable for packaging, optics, and functional films. Our quantitative composition–structure–performance framework advances fundamental understanding of entanglement-mediated interfacial phenomena while delivering practical design rules for next-generation sustainable bio-composites. Full article

Product visual strategies (PVS) on food packaging influence how consumers visually inspect products at the point of purchase. However, evidence comparing transparent windows and product images remains mixed, particularly regarding how these strategies interact with food unit size (FUS) and shape visual attention patterns. Moreover, few studies have examined these effects using objective eye-tracking measures within controlled experimental designs. This study employed a 2 × 2 between-subjects quasi-experiment to investigate the effects of PVS (transparent window and product image) and FUS (large unit and small unit) on visual attention and subsequent product-related evaluations. A total of 160 participants viewed realistic chocolate package stimuli that varied only in visual strategy and unit size. Eye movements were recorded using Tobii Pro Glasses 2. Visual attention was assessed through Time to First Fixation (TFF) and Fixation Duration (FD), while expected tastiness, expected quality, and purchase intention were measured using standardized self-report scales. The results showed that transparent-window packaging attracted visual attention more rapidly and sustained longer fixations than product-image packaging. These attention differences were accompanied by higher expected tastiness, expected quality, and purchase intention. While food unit size alone showed limited effects on eye-movement measures, a significant interaction was observed: small-unit designs elicited greater visual attention and more favorable evaluations only when the product was directly visible through a transparent window. Overall, the findings demonstrate how product visual strategies and food unit size jointly shape visual attention allocation during packaging inspection. By integrating eye-tracking measures with evaluation and behavioral intention outcomes, this study contributes to applied eye-movement research in food packaging contexts. Full article

This review summarizes scientific advances about the sonochemical synthesis of starch nanoparticles (St-NPs) for the food industry, as well as nutraceutical and drug delivery applications. High-intensity ultrasonication (HIU) has been explored as a versatile and environmentally friendly alternative to conventional methods for synthesizing St-NPs with high yields (>90%), controlled size (~100 nm), and minimal effluent generation. Thus, HIU has been explored (pre- or post-treatment) to mitigate the inherent disadvantages (high-cost, low yields, and environmental impact) of hydrothermal gelatinization, acid/alkaline hydrolysis, enzymatic hydrolysis, enzyme branching, water-in-oil and oil-in-water emulsions, non-solvent nanoprecipitation, extrusion, high-pressure homogenization, high-energy milling, and cold plasma. Conventional sources of starch (corn [normal, waxy, high-amylose] and potato) and other unconventional sources (tubers [cassava, yam, malanga], seeds and grains [sorghum, barley, quinoa, lotus], breadfruit, pinhao seed, Araucaria angustifolia) have been subjected to single or assisted sonochemical protocols to obtain St-NPS with unique structural, physicochemical, and technological properties. The physical–mechanical effects of ultrasonication (cavitation, heat, and pressure) directly promote surface functionalization (i.e., esterification, pore formation) and impact the St-NPS’s particle size, double-helix structure, enzymatic-resistance properties, crystallinity, and intra- and intermolecular arrangements. Pickering additives in food systems, colloids in beverages, nanocomposites in biofilms for food packaging, and nanocarriers for drug and nutraceutical delivery (oral and transdermal) have been the most reported applications. Full article

Polymer powder bed fusion (PBF) is strongly influenced by powder chemistry and powder state, yet many studies discuss the materials and processing conditions in isolation. This review synthesises the literature using a powder-centred framework that connects polymer chemistry and powder production history to measurable powder descriptors, and then links these descriptors to processing windows, defect mechanisms, and application outcomes. Key descriptors include crystallinity and thermal transitions, additive packages, particle size distribution, morphology, and surface texture. Environmental sensitivities are also considered, including moisture uptake, temperature effects, and optical response. These factors are related to powder spreading, energy absorption, and melt solidification or sintering to explain how flowability, packing density, and melt dynamics govern porosity, lack of fusion, distortion, and degradation. Powder qualification is discussed together with lot-to-lot variability and lifecycle effects, including ageing, reuse, and refresh, using the indicators commonly reported in laboratory and production settings and supported by emerging in situ monitoring. Application case studies are consolidated to illustrate how powder state and process control translate into repeatable qualification targets as polymer PBF moves toward a predictable and transferable manufacturing practice. Full article

Background: Polyphenols—bioactive compounds abundant in plant-based foods—are increasingly recognised for their capacity to modulate the gut microbiota. As the gut microbiome plays a central role in metabolic regulation, immune function, and disease prevention, understanding how specific polyphenol subclasses influence microbial diversity and functionality remains essential. Despite growing evidence of their benefits, the precise effects of flavonoids, phenolic acids, and anthocyanins on gut microbial composition are not yet fully clarified. Objective: This study aimed to evaluate the impact of dietary polyphenols on gut microbiota composition and function, with a particular focus on the abundance of Bifidobacterium, a key beneficial genus associated with metabolic and immune health. It was hypothesised that polyphenol-rich interventions were associated with increases in Bifidobacterium abundance and enhance overall microbial diversity. Design: A systematic review and meta-analysis were conducted following PRISMA guidelines. Human intervention studies published between January 2015 and February 2025 were retrieved from PubMed, Scopus, and Web of Science. A predefined PICO framework guided study selection. Twenty-two studies were synthesised using thematic analysis, and four of these were eligible for quantitative meta-analysis. The meta-analysis was performed in R (version 4.4.1) using the metafor and meta packages, calculating standardised mean differences (SMD) under a random-effects model to account for heterogeneity. Extracted data included study design, population characteristics, polyphenol subclass, intervention type, microbiome assessment method, and key outcomes. Results: Across the 22 reviewed studies, polyphenols—particularly flavonoids and phenolic acids from foods such as berries, grape pomace, and green tea—consistently increased beneficial microbial taxa including Bifidobacterium, Faecalibacterium, and Lactobacillus. These microbial shifts were associated with improved metabolic markers, reduced inflammation, and enhancements in gut barrier integrity. Polyphenol-rich dietary patterns also showed benefits in conditions such as NAFLD, prediabetes, and depression. However, findings were influenced by interindividual variability, short intervention durations, and inconsistent methodologies. The meta-analysis revealed a significant positive effect of polyphenol intake on Bifidobacterium abundance (SMD = 0.81; 95% CI: 0.18–1.44; p = 0.0114), corresponding to a moderate-to-large effect size. Substantial heterogeneity (I² = 77.4%) suggested considerable variation in intervention types, dosage, study design, and microbiome analysis methods. Conclusions: Polyphenol-rich diets were associated with increased Bifidobacterium abundance and favourable modulation of gut microbiota composition, supporting their potential as a nutritional strategy to enhance gut and metabolic health. However, interstudy variability highlights the need for more standardised, long-term, and mechanistically focused human trials. Future research should incorporate multi-omics approaches, personalised nutrition frameworks, and consistent microbiome analysis methods to better understand the pathways linking polyphenol intake and host health outcomes. Full article

Background: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized enterprises, mediated by competitiveness, and forecasts future trends to inform transitions aligned with net-zero goals. Methods: A mixed-methods design integrates structural equation modeling with the gray model. Primary data were collected via Likert-scale questionnaires administered to 350 managers to measure latent variables. Secondary financial metrics (revenue, costs, assets, profits) from 15 firms spanning 2021–2024 enabled forecasting. Results: SEM, employing bootstrapping for path estimation, revealed positive direct effects, with the strongest effects for green transportation and weaker effects for technology, packaging, and warehousing. Mediation via competitiveness yielded mixed indirect effects: positive for warehousing and transportation, but negative for technology. GM(1,1) projected moderate performance growth under conditions of data uncertainty. Conclusions: The hybrid framework advances the resource-based view in emerging market contexts, recommending prioritization of transportation and technology initiatives alongside policy incentives to align with sustainable development goals and enhance resilience in Vietnam’s logistics sector. Full article

The new European Regulation (EU) 2025/40 includes provisions on modern packaging and packaging waste. It defines the use of image QR codes on packaging (items 71 and 161) and in personal documents, making line barcodes a thing of the past. The definition of a QR code is precisely specified in ISO/IEC 18004:2024. However, their implementation in printing systems is not specified and remains an important factor for their future application. Digital foil printing is a completely new hybrid printing process for applying information to highly precise applications such as QR codes, security printing, and packaging printing. The technique is characterized by a combination of two printing techniques: drop-on-demand UV inkjet followed by thermal transfer of black foil. Using a matte-coated printing substrate (Garda Matt, 300 g/m²), Konica Minolta KM1024 LHE Inkjet head settings, and a transfer temperature of 100 °C, the size of the square printing elements in QR codes plays a decisive role in the quality of the decoded information. The aim of this work is to investigate the possibility of realizing the basic elements of the QR code image (the profile of square elements and the success of realizing a precisely defined surface) with a variation in the thickness of the UV varnish coating (7, 14 and 21 µm), realized using the MGI JETvarnish 3DS digital machine. The most commonly used rectangular elements with a surface area of 0.01 cm² were tested: 0.06 cm², 0.25 cm², 1 cm², 4 cm², and 16 cm². The results showed that the imprint quality is uneven for the smallest elements (square elements with base lengths of 0.1 cm and 0.25 cm). The effect is especially visible with a minimum UV varnish application of 7 μm (1 drop). By increasing the amount of UV varnish and the application thickness to 14 μm (2 drops) and 21 μm (3 drops), respectively, a significantly more stable, even reproduction of the achromatic image is achieved. The highest technical precision was achieved with a UV varnish thickness of 21 μm. Full article

Mediation analysis is a widely used statistical technique for identifying the mechanisms underlying the relationship between an exposure and an outcome. However, accurate power analysis and sample size determination for mediation models that involve non-normal distributions or mixtures of continuous and binary variables are challenging. We propose a computationally efficient simulation-based approach for general mediation analysis. By applying monotone smoothing splines to estimate empirical critical values derived from extensive simulations, our method enables accurate power calculations without the need for real-time simulation. We validated the method across varying scenarios, including continuous, binary variables and time-to-event outcome with strict Type I error control. The method-quantified large effects (0.35) yielded >80% power at minimal sample sizes (n = 25–50) across all settings, while small effects (0.02) required larger samples. Continuous models achieved 80% power for small effects at n = 410, whereas fully binary models required n > 500. For medium effects (0.15), the power was >0.80 at n = 75 with binary mediators. This study presents a robust framework that combines the flexibility of simulation-based inference with the speed of analytical approximations. We provide an accompanying R package to facilitate efficient sample size planning for mediation models. Full article