Search Results (623)

Crisp meat, a traditional Chinese food, is widely consumed due to its convenience and long frozen shelf life. However, conventional preparation methods lead to excessive oil absorption during frying and ice crystal formation during freezing, causing coating softening and reduced crispiness after reheating. This study aimed to enhance the quality of crisp meat before and after freezing by incorporating modified starches into the batter. Four types—oxidized starch, hydroxypropyl distarch phosphate, starch acetate, and acetylated distarch phosphate—were tested at replacement levels of 10–40% for natural potato starch (NS). Results showed that all modified starches improved batter rheology by 20%, increased viscosity and stability during frying, and delayed retrogradation during freezing compared to NS. Among them, 20% acetylated starch has a better effect on improving the quality of frozen small crisp meat for enhancing water-holding capacity, increasing immobilized water content, reducing oil uptake by 12–18%, and improving product texture. Specifically, they helped maintain a crispier coating after reheating, addressing a key drawback of traditional crisp meat. In conclusion, modified starches significantly improved frying performance and minimized quality loss during freezing compared to NS. This study provides practical insights for the food industry in optimizing batter formulations for better-quality crisp meat products. Full article

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

Frying native potato chips produces snacks that are widely accepted, although they are associated with high fat content and the formation of potentially undesirable compounds. This study evaluated the effect of pretreatment with ultrasound at 28 and 40 kHz on the physicochemical, structural, and sensory properties of chips made from the Sempal and Agustina varieties. The chips were immersed in water and treated with ultrasound for 10 min before frying at 175 °C. Parameters such as moisture, fat content, water activity, color, reducing sugars, FTIR spectroscopy, SEM microscopy, and sensory acceptance by consumers were analyzed. Treatment with 40 kHz significantly reduced fat content (up to 22.07%), improved crispness, and promoted a more porous microstructure. A lower concentration of reducing sugars, greater brightness, and less darkening were also observed. Sensory evaluation showed that chips treated with 40 kHz were the most preferred and best rated in terms of texture and flavor. Finally, it was demonstrated that pretreatment with ultrasound at 40 kHz improved the technological and sensory quality of native potato chips, which would promote the value of these resources in healthy products. Full article

Diospyros kaki Thunb. ‘Taishuu’ is novel fruit cultivar known for its excellent mouthfeel properties and high economic value. This study aimed to identify the ecological adaptability and potential suitable cultivating regions of this persimmon in China. In addition, key ecological factors influencing fruit mouthfeel were also investigated. Differences between key metabolites and mouthfeel properties of 35 persimmon samples from 13 provinces were compared. Subsequently, ecological factors were evaluated to explore interactions among dominant ecological factors, habitat suitability, and fruit quality. An adaptive segmentation map was ultimately created to highlight variations in mouthfeel properties of the persimmon. The findings were summarized as follows: The core ecological suitability zones encompass most warm, temperate and typically subtropical regions of China, spanning 116,200 square kilometers. Habitat suitability influences fruit size but does not affect mouthfeel properties. Key factors affecting mouthfeel properties of D. kaki ‘Taishuu’ include precipitation during the growing period, high temperature during the fruit ripening stage, and low temperatures during dormancy. Persimmons from coastal areas and Yunnan province were characterized by a lusciously sweeter and richer taste, a satisfying crisp texture, and an overall distinctly superior mouthfeel. In contrast, samples from central cultivation areas exhibited higher density, greater firmness, reduced crispness, and inferior flavor quality Based on zoning results, extensive regions show significant potential for high-quality production, making them highly promising for D. kaki ‘Taishuu’ cultivation. For marginally suitable habitats, appropriate cultivation measures should be implemented to mitigate limiting factors such as temperature and soil moisture. Full article

The concept of fuzzy modeling and fuzzy system design has opened new horizons of research in functional analysis, having a significant impact on major fields such as data science, machine learning, and so on. In this research, we use fuzzy set theory to analyze the global dynamics and oscillatory behavior of nonlinear fuzzy difference equations with exponential decay. We discuss the stability, oscillatory patterns, and convergence of solutions under different initial conditions. The exponential structure simplifies the analysis while providing a clear understanding of the system’s behavior over time. The study reveals how fuzzy parameters influence growth or decay trends, emphasizing the method’s effectiveness in handling uncertainty. Our findings advance the understanding of higher-order fuzzy difference equations and their potential applications in modeling systems with imprecise data. Using the characterization theorem, we convert a fuzzy difference equation into two crisp difference equations. The g-division technique was used to investigate local and global stability and boundedness in dynamics. We validate our theoretical results using numerical simulations. Full article

Acanthophis antarcticus, commonly known as the death adder, is a venomous Australian snake and a member of the Elapidae family. Due to its robust body and triangular head, it was historically misclassified as a viper. Its venom is known for neurotoxic, hemorrhagic, and hemolytic effects but displays low anticoagulant activity. Although key toxins such as three-finger toxins (3FTxs) and phospholipase A₂ (PLA₂) have been previously described, no study has integrated proteomic and functional analyses to date. In this study, we conducted a comprehensive characterization of A. antarcticus venom. Reverse-phase high-performance liquid chromatography (RP-HPLC) followed by LC-MS/MS enabled the identification of nine toxin families, with 3FTxs and PLA₂ as the most abundant. Less abundant but functionally relevant toxins included Kunitz-type inhibitors, CRISP, SVMP, LAAO, NGF, natriuretic peptides, and nucleotidases, the latter being reported here for the first time based on proteomic evidence. Hydrophilic interaction chromatography (HILIC) coupled with MALDI-TOF was used to analyze polar, non-retained venom components, revealing the presence of low-molecular-weight peptides (2–4 kDa). Functional assays confirmed the enzymatic activity of HYAL, PLA₂, and LAAO and, for the first time, demonstrated inhibitory activity on serine peptidases and fibrinogenolytic activity in the venom of this species. These findings expand our understanding of the biochemical and functional diversity of this venom. Full article

To assist stakeholders in selecting appropriate social media influencers (SMIs), this study proposes a multi-attribute decision-making framework for influencer evaluation based on their key performance metrics and engagement characteristics. This study introduces a new modification of the Evaluation Based on Distance from Average Solution (EDAS) under an interval-valued Fermatean fuzzy (IVFF) environment, addressing the limitations of the conventional EDAS method. In addition, a conceptual framework for the static and dynamic evaluation of SMIs is developed, integrating various crisp and fuzzy multi-criteria decision-making (MCDM) approaches. Empirical validation through two practical case studies demonstrates the effectiveness and applicability of the proposed framework, resulting in recommendations for marketers seeking to optimize their influencer-based marketing strategies. Full article

We introduce I-fp convergence (ideal convergence in fuzzy paranormed spaces) and develop its core theory, including stability results and an equivalence to $I^{*}$-fp convergence under the AP Property. Building on this foundation, we design an adaptive base-stock policy for a single-echelon inventory system in which weekly demand is expressed as triangular fuzzy numbers while holiday or promotion weeks are treated as ideal-small anomalies. The policy is updated by a simple learning rule that can be implemented in any spreadsheet, requires no optimisation software, and remains insensitive to tuning choices. Extensive simulation confirms that the method simultaneously lowers cost, reduces average inventory and raises service level relative to a crisp benchmark, all while filtering sparse demand spikes in a principled way. These findings position I-fp convergence as a lightweight yet rigorous tool for blending linguistic uncertainty with anomaly-aware decision making in supply-chain analytics. Full article

Low glycemic index (GI) cookie bars were prepared with soft wheat flour substituted with 10–50% soybean flour and 10–50% resistant starch. The effects of increased levels of soybean flour and resistant starch on the quality of low glycemic index cookie bars were investigated (i.e., moisture, cookie spread, texture (breaking force), surface color, and in vitro starch digestibility). It was found that increasing soybean flour substitution increased the breaking force, moisture, protein content, and yellowish color of the low GI cookie bars but decreased the cookie bar spread and the lightness of the cookie bars (p < 0.05). The addition of soybean flour and resistant starch by up to 50% did not significantly change the in vitro starch digestibility of the cookie bars. The overall acceptability of the cookie bars was lower when the soybean flour blend went beyond 10%. When soft wheat flour in the cookie bar formulation was replaced at the following levels (10%, 30%, and 50%) by resistant starch, the cookie spread and lightness of the cookie bars increased but the breaking force was decreased along with the yellowish color (p < 0.05). When resistant starch was combined with soft wheat flour at levels of up to 50%, this significantly increased the content of total dietary fiber and spread ratio of cookie bars. Sensorial analysis showed that resistant starch presence had an acceptable impact on overall acceptability of the low GI cookie bars. Resistant starch represents a viable dietary fiber source when substituted for 50% of soft wheat flour in formulations. While this substitution may result in increased spread ratio and decreased crispness in cookie bars, the addition of 10% soybean flour can mitigate these textural changes. Full article

The growing demand for accurate and efficient Chest X-Ray (CXR) interpretation has prompted the development of AI-driven systems to alleviate radiologist workload and reduce diagnostic variability. This paper introduces the Intelligent Humanized Radiology Analysis System (IHRAS), a modular framework that automates the end-to-end process of CXR analysis and report generation. IHRAS integrates four core components: (i) deep convolutional neural networks for multi-label classification of 14 thoracic conditions; (ii) Grad-CAM for spatial visualization of pathologies; (iii) SAR-Net for anatomical segmentation; and (iv) a large language model (DeepSeek-R1) guided by the CRISPE prompt engineering framework to generate structured diagnostic reports using SNOMED CT terminology. Evaluated on the NIH ChestX-ray dataset, IHRAS demonstrates consistent diagnostic performance across diverse demographic and clinical subgroups, and produces high-fidelity, clinically relevant radiological reports with strong faithfulness, relevancy, and alignment scores. The system offers a transparent and scalable solution to support radiological workflows while highlighting the importance of interpretability and standardization in clinical Artificial Intelligence applications. Full article

The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R² of 78.32%, whereas the random forest regression model had an R² of 63.7%. The final model for predicting the remaining useful life had an R² of 99.6%. Full article

This paper addresses a fundamental challenge in statistical radar detection systems: optimizing the trade-off between the probability of a false alarm ($P_{FA}$) and the probability of a miss ($P_M$). These two metrics are inversely related and critical for performance evaluation. Traditional detection approaches often enhance one aspect at the expense of the other, limiting their practical applicability. To overcome this limitation, a fuzzy hypothesis testing framework is introduced that improves decision making under uncertainty by incorporating both crisp and fuzzy data representations. The methodology is divided into three phases. In the first phase, we reduce the probability of false alarm $P_{FA}$ while maintaining a constant probability of miss $P_M$ using crisp data characterized by deterministic values and classical statistical thresholds. In the second phase, the inverse scenario is considered: minimizing $P_M$ while keeping $P_{FA}$ fixed. This is achieved through parameter tuning and refined threshold calibration. In the third phase, a strategy is developed to simultaneously enhance both $P_{FA}$ and $P_M$, despite their inverse correlation, by adopting adaptive decision rules. To further strengthen system adaptability, fuzzy data are introduced, which effectively model imprecision and ambiguity. This enhances robustness, particularly in scenarios where rapid and accurate classification is essential. The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance. Full article

The composition of Australian snake venoms is the least well-known of any continent. We characterised the venom proteome of the southern death adder Acanthophis antarcticus—one of the world’s most morphologically and ecologically divergent elapids. Using a combined bottom-up proteomic and venom gland transcriptomic approach employing reverse-phase chromatographic and gel electrophoretic fractionation strategies in the bottom-up proteomic workflow, we characterised 92.8% of the venom, comprising twelve different toxin identification hits belonging to seven toxin families. The most abundant protein family was three-finger toxins (3FTxs; 59.8% whole venom), consisting mostly of one long-chain neurotoxin, alpha-elapitoxin-Aa2b making up 59% of the venom and two proteoforms of another long-chain neurotoxin. Phospholipase A₂s (PLA₂s) were the second most abundant, with four different toxins making up 22.5% of the venom. One toxin was similar to two previous non-neurotoxic PLA₂s, making up 16% of the venom. The remaining protein families present were CTL (3.6%), NGF (2.5%), CRiSP (1.8%), LAAO (1.4%), and AChE (0.8%). A. antarcticus is the first Australian elapid characterised that has a 3FTx dominant venom, a composition typical of elapids on other continents, particularly cobras Naja sp. The fact that A. antarcticus has a venom composition similar to cobra venom while having a viper-like ecology illustrates that similar venom expressions can evolve independently of ecology. The predominance of post-synaptic neurotoxins (3FTxs) and pre-synaptic neurotoxins (PLA₂) is consistent with the neurotoxic clinical effects of envenomation in humans. Full article

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts. Full article

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects was extracted from the national SECOP open-procurement platform and processed using the CRISP-DM protocol. Following the cleaning and coding of 14 project-level variables, statistical analyses were conducted using Spearman correlations, Kruskal–Wallis tests, and post-hoc Wilcoxon comparisons to identify significant bivariate relations I confirm I confirm I confirm hips. A Random Forest model was subsequently applied to determine the most influential multivariate predictors of cost and time deviations. In parallel, a directed content analysis of contract addenda reclassified 22 recorded deviation descriptors into ten internationally recognized categories of causality, enabling an integrated interpretation of both statistical and documentary evidence. The findings indicate that contract value, geographical region, and contractor configuration are significant determinants of cost and time performance. Additionally, project intensity and discrepancies between awarded and bid values emerged as key contributors to cost escalation. Scope changes and adverse weather conditions together accounted for 76% of all documented deviation triggers, underscoring the relevance of robust front-end planning and climate-risk considerations in rural infrastructure delivery. The findings provide information for stakeholders, policymakers, and professionals who aim to manage the risk of schedule and budget deviations in public infrastructure projects. Full article