Hasil untuk "Home economics"

Jianyou Gao, Cuixia Liu, Qiaosheng Jiang et al.

The utilization of the fruit of Actinidia melanandra is limited by insufficient understanding of its quality attributes and regulatory mechanisms in the fruit during soft ripening stages. In this study, we employed multi-omics approaches to investigate the post-harvest quality dynamics and the regulatory mechanisms in the fruit of A. melanandra. The results showed that the kiwifruit had better palatability at the final stage during the soft ripening stages due to its higher soluble solids content and unique flavor. Volatile profiling identified 36 aroma-active compounds that were dominated by aldehydes and terpenoids, showing progressive terpenoid diversification during maturation. Anthocyanin-specific analysis demonstrated cyanidin-3-O-galactoside as the primary pigment, co-regulating coloration with pelargonidin-3-O-galactoside. The combination of integrated omics and subsequent qRT-PCR validation identified 31 structural genes and 8 transcriptional regulators governing the metabolic pathways of sugar accumulation, monoterpene biosynthesis, and anthocyanin biosynthesis. Our study provides new insights into flavor regulation during fruit soft ripening, lays a foundation for kiwifruit flavor improvement, and guides better exploitation of A. melanandra resources.

Jiaqi Zheng, Chaowen Zhang, Siheng Xiang et al.

Abstract Fusarium wilt diseases pose a huge threat to faba bean (Vicia faba L.) production globally, with significant outbreaks in Chongqing, China. Symptomatic plants showed wilting leaves and rotten roots, ultimately perishing in the advanced stage. Morphological features, multilocus phylogenetic analyses, and pathogenicity tests demonstrated that the primary causal agent was Fusarium oxysporum. Untargeted metabolomics of faba beans revealed substantial metabolic differences in the infected faba bean roots. Plants responded to fungal biotic stress by reprogramming key metabolic pathways, including alanine, aspartate, and glutamate metabolism, the citrate cycle, arginine biosynthesis, and jasmonic acid metabolism, which collectively underscore activated defense responses. Metagenome sequencing showed that Fusarium wilt significantly reshaped the structure of the rhizosphere microbiota and affected the abundance of genes encoding element cycling in soil. This work elucidates the pathogenic mechanisms of F. oxysporum by integrating pathogen identification, host metabolism, and microbiome ecology. Our findings offer biomarkers for disease diagnosis and targets for biocontrol, advancing sustainable management of Fusarium wilt diseases in legumes.

Sukriadi Sukriadi, Andi Adawiah, Ismail Ismail

Abstract This study aims to systematically evaluate machine learning (ML) applications in rural agricultural contexts, a critical yet underrepresented area in the literature. Unlike prior reviews focusing on high-tech farming environments, this research uniquely centers on smallholder and resource-constrained systems to explore the intersection of ML, sustainable agriculture, and rural development. A Systematic Literature Review (SLR) following PRISMA guidelines was performed. After screening (N = 496 articles included), we analyzed publications from 2000 to 2024 (search conducted on 15 June 2025). Quantitative findings show a fourfold increase in publications between 2019 and 2024 and that CNN-based methods were used in approximately 8% of image-based studies (see Results for breakdown). The analytical process encompassed five structured stages: data importation, descriptive analysis, interactive visualization, linkage analysis, and insight extraction, ensuring analytical rigor and replicability. The results reveal that although ML technologies such as CNNs, SVMs, and LSTM networks are increasingly used for crop monitoring, disease detection, and irrigation management, their deployment remains predominantly confined to well-resourced agricultural systems. Rural applications face persistent challenges, including limited digital infrastructure, data scarcity, and low digital literacy. Moreover, digital systems have improved rural education processes, showing potential for broader agricultural applications. This study contributes by identifying methodological trends and context-specific gaps, offering a roadmap for developing adaptable, low-cost ML solutions. This review excluded non-English publications and restricted access content for reproducibility of reported methods, which may bias results towards English-language and open-access outlets. To quantify potential bias, we conducted an exploratory search (not included in the primary dataset) which identified 8% additional non-English records in the same query terms — suggesting a non-trivial contribution of non-English literature. Future reviews should expand to non-English sources and include more extensive searches of regional databases to reduce language bias.

Leila Cheikh Ismail, MoezAlIslam Faris, Dana N. Abdelrahim et al.

Disordered eating (DE) significantly affects both physical and mental health, contributing to morbidity, mortality, and considerable global healthcare costs. This cross-sectional study assessed the prevalence of high-risk DE and examined its associations with body composition, behavioural factors, diet quality, and perceived stress among university students in the United Arab Emirates. A total of 911 students were recruited using non-probability quota sampling (50.49% female). Body composition was measured using a TANITA BC-420MA body composition monitor. Usual dietary intake was assessed via a validated 65-item food frequency questionnaire. DE risk was assessed using the Eating Attitudes Test (EAT-26) and perceived stress using the PSS-10. Analysis included linear regression and independent-samples t-test (p < 0.05). High-risk DE (EAT-26 ≥ 20) prevalence was 30.3%. High-risk DE was significantly associated with higher body fat percentage (β = 0.121, p < 0.001), fat mass (β = 0.148, p < 0.001), fat-free mass (β = 0.079, p = 0.017), lean mass (β = 0.08, p = 0.016), total body water (β = 0.084, p = 0.011), and lower total body water percentage (β = −0.131, p < 0.001). High-risk students also reported higher intakes of fibre (β = 0.12, p = 0.018), beta-carotene (β = 0.14, p = 0.025), vitamin A (β = 0.13, p = 0.034), B12 (β = 0.15, p = 0.043), folate (β = 0.16, p = 0.006), and vitamin D (β = 0.16, p = 0.036). Compared with the low-risk group, high-risk DE was associated with higher adiposity markers and slightly higher perceived stress, and differed in selected nutrient intakes; sociodemographic characteristics were largely similar between groups except for smoking status. These findings support the implementation of targeted prevention strategies, including nutrition education, routine screening, and culturally tailored programmes, for young adults in the UAE.

Qi Liang, Hang-Hang Gou, Li-Ping Chen et al.

Wensu Li, Atin Aboutorabi, Harry Lyu et al.

This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

Bowen Jiao, Nanting Zhu, Decheng Bi et al.

Fucoxanthin (FUC), a lipid-soluble carotenoid with various bioactivities, demonstrates promise as a preventive agent against inflammatory bowel disease (IBD). Nevertheless, the inherent instability and low aqueous solubility of FUC limit its application in functional food formulations and pharmaceutical applications. In this study, a FUC-loaded pickering emulsion (G-SPI-COS/FUC emulsion) with high encapsulation efficiency and small particle size was developed by using genipin-crosslinked soy protein isolate (SPI)-chitooligosaccharide (COS) nanoparticles as stabilizers. G-SPI-COS/FUC emulsion exhibited excellent stability under varying pH values, ionic strengths, and storage conditions. In vitro digestion experiments showed that G-SPI-COS/FUC emulsion controlled FUC release and enhanced its intestinal absorption and bioavailability. Furthermore, G-SPI-COS/FUC emulsion was more effective in preventing dextran sulfate sodium (DSS)-induced colitis than free FUC and FUC-loaded pickering emulsion stabilized by native SPI. These findings indicated that G-SPI-COS/FUC emulsion could serve as a promising delivery system for FUC, providing a novel approach to the prevention of IBD.

EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP), Roberto Edoardo Villa, Giovanna Azimonti et al.

Abstract Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the efficacy of Bifidobacterium longum CNCM I‐5642 (PP102I) as a zootechnical feed additive (functional group: physiological condition stabiliser) for dogs. In a previous opinion, the FEEDAP Panel concluded that PP102I was safe for dogs and the environment. Regarding the safety for the users, the Panel concluded that the additive should be considered a skin and respiratory sensitiser. Due to the lack of data, no conclusions could be drawn on the potential for skin/eye irritancy of the additive. Due to the lack of sufficient data, the Panel could not conclude on the efficacy of the additive for dogs at the proposed conditions of use. The applicant submitted in vitro eye and skin irritation tests and one efficacy trial in dogs to address the inconclusive aspects of the previous assessment. Based on the data provided, the Panel considers that the additive is irritant to the eyes but not to the skin. The additive has the potential to be efficacious when included in feed for dogs at 1 × 109 colony forming unit (CFU)/dog per day (which would correspond approximately to 3.5 × 109 CFU/kg complete feed).

Phaik Ling Quah, Phaik Ling Quah, Daniel Wei Keong Chan et al.

IntroductionResearch on early childhood caregiver feeding practices and eating behaviors is limited, especially within Asian populations. This study examined these practices across key feeding domains of variety, autonomy, and mealtime setting and timing, stratified by three age groups: 0 to &lt;7 months, 7 to &lt;13 months, and 13 to &lt;36 months.MethodsA cross-sectional survey of 1,307 caregivers from a multi-ethnic population in Singapore captured demographic data, feeding practices, child eating behaviors, and caregivers’ knowledge, attitudes, and practices. One-way analysis of variance (ANOVA), independent T-tests and the chi-square test were used to assess feeding practices and eating behaviors across age groups.ResultsRegarding dietary variety, 14.8 and 6.1% of infants aged 7 to &lt;13 months were offered three or fewer food groups frequently and daily, respectively. Additionally, 11.9% of infants were receiving processed foods often. At this age, only 1.0% of infants were consuming sugar-sweetened beverages (SSBs) often, while 2.0% consumed them daily. Among older children (aged 13 to &lt;36 months), 8.1% were offered a limited variety of three food groups, while 4.5% were offered fewer than three. In contrast, a significantly higher proportion frequently consumed processed foods (24.0%) and sugar-sweetened beverages (25.2%; p &lt; 0.05). In terms of autonomy, only 75.4% of infants (7– &lt; 13 months) and 89.5% of older children (13– &lt; 36 months) were able to self-feed. Caregivers of older children (13– &lt; 36 months) were less likely to recognize hunger and satiety cues compared to those of infants (0–&lt; 13 months; p &lt; 0.05). Older children (13– &lt; 36 months) also more frequently required special mealtime settings (36.6%), viewed screens during meals (29.9%), and were less likely to be offered post-midnight meals nightly (22.6% compared to infants; 70.3%; 0–&lt; 13 months; p &lt; 0.05).ConclusionThese findings underscore the need for culturally tailored educational interventions to improve suboptimal feeding practices in children under three in Singapore’s multiethnic population.

Sharmishtha Raghuvanshi, Supriya Sanjay Nikam, Manisha Karne et al.

This research examines the persistent challenge of health inequalities in India, departing from the conventional focus on aggregate improvements in mortality rates. While India has achieved progress in overall health indicators since independence, the distribution of health outcomes remains uneven, a fact starkly highlighted by the COVID-19 pandemic. This study investigates the socio-economic determinants of health disparities using the National Family and Health Survey (NFHS)-5 data from 2019-20, focusing on both national and state-level analyses, specifically for Maharashtra. Employing a health economics framework, the analysis delves into individual-level data, population shares, self-reported morbidity prevalence, and treatment patterns across diverse socio-economic groups. Regression analyses, stratified by gender, are conducted to quantify the impact of socio-economic factors on reported morbidity. Furthermore, a Fairlie decomposition, an extension of the Oaxaca decomposition, is utilised to dissect the gender gap in morbidity, assessing the extent to which observed differences are attributable to explanatory variables. The findings reveal a significant burden of self-reported morbidity, with approximately one in nine individuals in India and one in eight in Maharashtra reporting morbidity. Notably, women exhibit nearly double the morbidity rate compared to men. The decomposition analysis identifies key drivers of gender disparities. In India, marital status exacerbates these differences, while insurance coverage, caste, urban residence, and wealth mitigate them. In Maharashtra, urban residence and marital status widen the gap, whereas religion, caste, and insurance coverage narrow it. This research underscores the importance of targeted policy interventions to address the complex interplay of socio-economic factors driving health inequalities in India.

Bahram Adrangi, Arjun Chatrath, Madhuparna Kolay et al.

The enterprise value (EV) is a crucial metric in company valuation as it encompasses not only equity but also assets and liabilities, offering a comprehensive measure of total value, especially for companies with diverse capital structures. The relationship between economic uncertainty and firm value is rooted in economic theory, with early studies dating back to Sandmo's work in 1971 and further elaborated upon by John Kenneth Galbraith in 1977. Subsequent significant events have underscored the pivotal role of uncertainty in the financial and economic realm. Using a VAR-MIDAS methodology, analysis of accumulated impulse responses reveals that the EV of air carrier firms responds heterogeneously to financial and economic uncertainties, suggesting unique coping strategies. Most firms exhibit negative reactions to recessionary risks and economic policy uncertainties. Financial shocks also elicit varied responses, with positive impacts observed on EV in response to increases in the current ratio and operating income after depreciation. However, high debt levels are unfavorably received by the market, leading to negative EV responses to debt-to-asset ratio shocks. Other financial shocks show mixed or indeterminate impacts on EV.

Lendy Banegas, Fredy Vides

This paper introduces a methodology for identifying and simulating financial and economic systems using stochastically structured reservoir computers (SSRCs). The framework combines structure-preserving embeddings with graph-informed coupling matrices to model inter-agent dynamics while enhancing interpretability. A constrained optimization scheme guarantees compliance with both stochastic and structural constraints. Two empirical case studies, a nonlinear stochastic dynamic model and regional inflation network dynamics, demonstrate the effectiveness of the approach in capturing complex nonlinear patterns and enabling interpretable predictive analysis under uncertainty.

Agustín García-García, Pablo Hidalgo, Julio E. Sandubete

Explainable Artificial Intelligence (XAI) is increasingly required in computational economics, where machine-learning forecasters can outperform classical econometric models but remain difficult to audit and use for policy. This survey reviews and organizes the growing literature on XAI for economic time series, where autocorrelation, non-stationarity, seasonality, mixed frequencies, and regime shifts can make standard explanation techniques unreliable or economically implausible. We propose a taxonomy that classifies methods by (i) explanation mechanism: propagation-based approaches (e.g., Integrated Gradients, Layer-wise Relevance Propagation), perturbation and game-theoretic attribution (e.g., permutation importance, LIME, SHAP), and function-based global tools (e.g., Accumulated Local Effects); (ii) time-series compatibility, including preservation of temporal dependence, stability over time, and respect for data-generating constraints. We synthesize time-series-specific adaptations such as vector- and window-based formulations (e.g., Vector SHAP, WindowSHAP) that reduce lag fragmentation and computational cost while improving interpretability. We also connect explainability to causal inference and policy analysis through interventional attributions (Causal Shapley values) and constrained counterfactual reasoning. Finally, we discuss intrinsically interpretable architectures (notably attention-based transformers) and provide guidance for decision-grade applications such as nowcasting, stress testing, and regime monitoring, emphasizing attribution uncertainty and explanation dynamics as indicators of structural change.

Pierre Bardier

We provide a formal framework accounting for a widespread idea in the theory of economic design: analytically established incompatibilities between given axioms should be qualified by the likelihood of their violation. We define the degree to which rules satisfy an axiom, as well as several axioms, on the basis of a probability measure over the inputs of the rules. Armed with this notion of degree, we propose and characterize i) a criterion to evaluate and compare rules given a set of axioms, allowing the importance of each combination of axioms to differ, and ii) a criterion to measure the compatibility between given axioms, building on a analogy with cooperative game theory.

Liang Luo, Guanglei Chen, Yan Zhou et al.

BackgroundCurrently, there is limited and inconsistent evidence regarding the risk association between daily dietary intake, antioxidants, minerals, and vitamins with Childhood Asthma (CA). Therefore, this study employs Mendelian Randomization (MR) methodology to systematically investigate the causal relationships between daily dietary intake, serum antioxidants, serum minerals, and the circulating levels of serum vitamins with CA.MethodsThis study selected factors related to daily dietary intake, including carbohydrates, proteins, fats, and sugars, as well as serum antioxidant levels (lycopene, uric acid, and β-carotene), minerals (calcium, copper, selenium, zinc, iron, phosphorus, and magnesium), and vitamins (vitamin A, vitamin B6, folate, vitamin B12, vitamin C, vitamin D, and vitamin E), using them as Instrumental Variables (IVs). Genetic data related to CA were obtained from the FinnGen and GWAS Catalog databases, with the primary analytical methods being Inverse Variance Weighting (IVW) and sensitivity analysis.ResultsFollowing MR analysis, it is observed that sugar intake (OR: 0.71, 95% CI: 0.55–0.91, P: 0.01) is inversely correlated with the risk of CA, while the intake of serum circulating magnesium levels (OR: 1.63, 95% CI: 1.06–2.53, P: 0.03), fats (OR: 1.44, 95% CI: 1.06–1.95, P: 0.02), and serum vitamin D levels (OR: 1.14, 95% CI: 1.04–1.25, P: 0.02) are positively associated with an increased risk of CA.ConclusionThis study identified a causal relationship between the daily dietary intake of sugars and fats, as well as the magnesium and vitamin D levels in serum, and the occurrence of CA. However, further in-depth research is warranted to elucidate the specific mechanisms underlying these associations.

Petri P. Karenlampi

This paper investigates the financial economics of simple periodic systems. Well-established financial procedures appear to be complicated, and lead to partially biased results. Probability theory is applied, and the focus is on the finances of simple periodic growth processes, in the absence of intermediate divestments. The expected value of the profit rate, derived from accounting measures on an accrual basis, does not depend on the capitalization path. The expected value of capitalization is path dependent. Because of the path-dependent capitalization, the return rate on capital is path-dependent, and the time-average return rate on capital differs from the expected value of the return rate on capital for the growth cycle. The internal rate of return, defined through a compounding equation, is path-independent, thereby differing from the expected value of the rate of return on capital. It is shown that within a production estate, the area-average of internal rate of return is not representative of the rate of return on capital. The growth cycle length maximizing the return rate on equity is independent of market interest rate. Leverage effect enters the microeconomics of the growth processes through a separate leverage equation, where the leverage coefficient may reach positive or negative values. The leverage effect on the internal rate of return and the net present value are discussed. Both effects are solvable, resulting in incorrect estimates.

Julio Deride

We propose a new methodology to compute equilibria for general equilibrium problems on exchange economies with real financial markets, home-production, and retention. We demonstrate that equilibrium prices can be determined by solving a related maxinf-optimization problem. We incorporate the non-arbitrage condition for financial markets into the equilibrium formulation and establish the equivalence between solutions to both problems. This reduces the complexity of the original by eliminating the need to directly compute financial contract prices, allowing us to calculate equilibria even in cases of incomplete financial markets. We also introduce a Walrasian bifunction that captures the imbalances and show that maxinf-points of this function correspond to equilibrium points. Moreover, we demonstrate that every equilibrium point can be approximated by a limit of maxinf points for a family of perturbed problems, by relying on the notion of lopsided convergence. Finally, we propose an augmented Walrasian algorithm and present numerical examples to illustrate the effectiveness of this approach. Our methodology allows for efficient calculation of equilibria in a variety of exchange economies and has potential applications in finance and economics.

Serena Ng, Susannah Scanlan

Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures that account for serial correlation yield imputed values that are closer to the observed low frequency ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices.

Xiaoling Chen, Cory Miller, Mithun Goutham et al.

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage.

Irini Logothetis, Priya Rani, Shangeetha Sivasothy et al.

Smart technologies are significant in supporting ageing in place for elderly. Leveraging Artificial Intelligence (AI) and Machine Learning (ML), it provides peace of mind, enabling the elderly to continue living independently. Elderly use smart technologies for entertainment and social interactions, this can be extended to provide safety and monitor health and environmental conditions, detect emergencies and notify informal and formal caregivers when care is needed. This paper provides an overview of the smart home technologies commercially available to support ageing in place, the advantages and challenges of smart home technologies, and their usability from elderlys perspective. Synthesizing prior knowledge, we created a structured Smart Home Goal Feature Model (SHGFM) to resolve heuristic approaches used by the Subject Matter Experts (SMEs) at aged care facilities and healthcare researchers in adapting smart homes. The SHGFM provides SMEs the ability to (i) establish goals and (ii) identify features to set up strategies to design, develop and deploy smart homes for the elderly based on personalised needs. Our model provides guidance to healthcare researchers and aged care industries to set up smart homes based on the needs of elderly, by defining a set of goals at different levels mapped to a different set of features.