Search Results (616)

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

The reliability and safety of five-axis CNC abrasive water jet machining are critical for many industries. This study employs Failure Mode and Effects Analysis (FMEA) to identify and mitigate potential failures in this machining system. Traditional FMEA, which relies on crisp numerical values, often struggles with handling uncertainty in risk assessment. To address this limitation, this paper introduces an Interval-Valued Spherical Fuzzy FMEA (IVSF-FMEA) approach, which enhances risk evaluation by incorporating membership, non-membership, and hesitancy degrees. The IVSF-FMEA method leverages the inherent rotational symmetry of interval-valued spherical fuzzy sets and the permutation symmetry among severity, occurrence, and detectability criteria, resulting in a transformation-invariant and unbiased risk assessment framework. Applying IVSF-FMEA to seven periodic failure (PF) modes in five-axis CNC water jet machining achieves a more precise prioritization of risks, leading to improved decision-making and resource allocation. The findings highlight improper fixturing of the workpiece (PF6) as the most critical failure mode, with the highest RPN value of −0.54, followed by mechanical vibrations (PF2) and tool wear and breakage (PF1). This indicates that ensuring proper fixturing stability is essential for maintaining machining accuracy and preventing defects. Comparative analysis with traditional FMEA demonstrates the superiority of the proposed fuzzy-based approach in handling subjective assessments and reducing ambiguity. The findings highlight improper fixturing, mechanical vibrations, and tool wear as the most critical failure modes, necessitating targeted risk mitigation strategies. This research contributes to advancing risk assessment methodologies in complex manufacturing environments. Full article

The postharvest quality of lettuce (Lactuca sativa) is significantly influenced by the lighting environment during storage. This study evaluated the effects of green LEDs at 500 nm and 530 nm, white LEDs (400–700 nm), and dark storage on lettuce quality over 14 days at 5 °C. All treatments were applied at 10 µmol m⁻² s⁻¹ under a 12 h photoperiod. Quality parameters measured included moisture loss, relative water content (RWC), photosynthetic rate, chlorophyll content (SPAD), total soluble solids (TSSs), electrolyte leakage (EL), color change (∆E), texture (crispness), and overall visual quality (OVQ). Lettuce stored under green LEDs, particularly 530 nm, exhibited superior postharvest quality. Compared to dark storage, 530 nm reduced moisture loss by 7.1%, increased RWC by 9.2%, and reduced transpiration rate. The green light preserved photosynthetic activity (43% decline vs. 77% in the dark), increased TSS, reduced color change by 42%, improved crispness by 46.1%, and limited EL to 54.5%. Shelf life was extended by approximately four days. The 500 nm treatment showed notable improvements, including an 8.4% reduction in moisture loss, 8.2% higher RWC, a smaller photosynthesis decline (25%), and the lowest EL (53.1%). It improved color retention (∆E reduced by 45.3%) and crispness (46.8%). Both green wavelengths effectively maintained lettuce quality during cold storage, with 530 nm being the most effective overall. These results suggest that targeted green LED lighting is a promising, energy-efficient strategy to preserve postharvest quality and extend shelf life in leafy greens. Full article

While faba bean feeding improves grass carp muscle texture via reactive oxygen species (ROS), the main bioactive compound was unclear. In this study, vicine—a pro-oxidant glycoside—was isolated from faba beans using cation-exchange column chromatography and supplemented into the feed of grass carp at 0.6%. To assess the impact of vicine on muscle texture, the grass carp were fed for 150 days with three treatments: control group, faba bean group, and vicine group. The results showed that vicine improved muscle texture similarly to faba beans but caused fewer adverse effects on muscle, liver, and intestinal health. Vicine improved grass carp muscle texture in the following ways: (1) induced ROS overproduction, activating the Caspase apoptosis pathway and downregulating Pax-7 to promote satellite cell-mediated myofiber regeneration; (2) vicine-mediated intestinal microbiota alterations increased lipopolysaccharide (LPS) levels, indirectly elevating muscle ROS via the gut–muscle axis to further affect muscle structure. This study demonstrated that vicine improved muscle texture by activating ROS-dependent myofiber regeneration but also induced oxidative stress and gut microbiota perturbation. While vicine mitigated the severe toxicity of faba beans, its application requires careful evaluation of its toxicological properties to balance benefits and risks. This study offers new insights for enhancing the quality of aquatic animals. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article

Producing seeds in the organic production system still requires a lot of information regarding the fertilization and nutritional requirements. Thus, the objective of this study was to determine the dry mass and macronutrient accumulation curve in lettuce for seed production, aiming at cultivation in an organic system. The treatments consisted of two phosphorus doses (320 and 800 kg ha⁻¹ of P₂O₅, Yoorin^® thermophosphate source). The crisp lettuce plants, cultivar Solaris, were collected at eight stages (0, 14, 28, 42, 56, 70, 84, and 98 days after transplanting—DAT) for an analysis of the proposed characteristics. A nonlinear sigmoid regression curve was fitted and the results demonstrated continuous plant growth, accompanied by a gradual increase in dry matter throughout the experimental period, regardless of the phosphorus dose studied. The vegetative part of the lettuce plant shows slow initial growth, followed by acceleration up to the beginning of flowering (70 DAT), and stabilization after this period. The reproductive part of the lettuce plant begins to grow from 56 DAT, increasing the daily nutrient demand until the end of the seed maturation and harvest at 98 DAT. The dose of 800 kg ha⁻¹ of P₂O₅, the lettuce plant accumulated 1527.7, 308.2, 2922.6, 1658.4, 416.0, and 197.6 mg per plant of N, P, K, Ca, Mg, and S, respectively. The dose of 320 kg ha⁻¹ of P₂O₅, the lettuce plant accumulated 1743.1, 256.9, 2575.7, 1210.2, 358.8, and 185.5 mg per plant of N, P, K, Ca, Mg, and S, respectively. The greatest demand for nutrients in the plant occurred between 55 and 88 DAT. Full article

The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. This study proposes an AI-powered framework that employs BERT-based sentence embeddings, semantic clustering, and classification algorithms, structured under the CRISP-DM methodology, to standardize and automate complaint analysis. Leveraging real-world interaction logs from a public sector agency, the system harmonizes heterogeneous complaint narratives, uncovers latent issue patterns, and enables early detection of technical and usability problems. The approach is deployed through a real-time dashboard, transforming complaint handling from a reactive to a proactive process. Experimental results show a 27% reduction in repeated complaint categories and a 32% increase in classification efficiency. The study also addresses ethical concerns, including data governance, bias mitigation, and model transparency. This work advances citizen-centric service delivery by demonstrating the scalable application of AI in public sector informatics. Full article

Renewable energy sources (RESs) are increasingly being recognized as sustainable and accessible alternatives for the energy future. However, their intermittent nature poses significant challenges to system reliability and stability, necessitating the integration of energy storage systems (ESSs) to ensure sustainability and dependability. This study examines various ESS alternatives, evaluating their suitability for different applications using a multi-criteria decision-making (MCDM) approach. The methodology accommodates diverse criteria types, including qualitative and quantitative factors, represented as linguistic terms, interval values, and crisp numerical data. A techno-socio-economic framework for ESS selection is proposed and applied to Jordan’s unique energy landscape. This framework integrates technical performance, economic feasibility, and social considerations to identify suitable ESS solutions aligned with the country’s renewable energy goals. The study ranks twelve energy storage systems (ESSs) based on key performance criteria. Pumped hydro storage (PHS), thermal energy storage (TES), supercapacitors (SCs), and lithium-ion batteries (Li-ion BESS) lead the ranking. These systems showed the best performance in terms of scalability, efficiency, and integration with grid-scale applications in Jordan. Key applications analyzed include renewable energy integration, grid stability, load shifting, peak load regulation, frequency regulation, and seasonal energy storage. Results indicate that Li-ion batteries are most suitable for renewable energy integration, while flywheels excel in grid stability and frequency regulation. PHS was found to be the preferred solution for load shifting, peak load regulation, and seasonal storage, with hydrogen storage emerging as a promising option for long-duration needs. These findings provide critical insights to guide policy and infrastructure planning, offering a robust model for comprehensive ESS assessment in energy transition planning for countries facing similar challenges. Full article