Search Results (9,303)

An intrusion into the operational network (OT) of a production site can cause serious damage by affecting productivity, reliability, and quality. The presence of embedded neural networks (NNs), such as classifiers, in physical devices opens the door to new attack vectors. Due to the stochastic behavior of the classifier and the difficulty of reproducing results, the Artificial Intelligence (AI) Act requires the NN’s behavior to be explainable. For this purpose, the platform HistoTrust enables tracing NN behavior, thanks to secure hardware components issuing attestations registered in a blockchain ledger. This solution helps to build trust between independent actors whose devices perform tasks in cooperation. This paper proposes going further by integrating a mechanism for detecting tampering of embedded NN, and using smart contracts executed on the blockchain to propagate the alert to the peer devices in a distributed manner. The use case of a bit-flip attack, targeting the weights of the NN model, is considered. This attack can be carried out by repeatedly injecting very small messages that can be missed by the Intrusion Detection System (IDS). Experiments are being conducted on the HistoTrust platform to demonstrate the feasibility of our distributed approach and to qualify the time required to detect intrusion and propagate the alert, in relation to the time it takes for the attack to impact decisions made by the AI. As a result, the blockchain may be a relevant technology to complement traditional IDS in order to face distributed attacks. Full article

In the context of the era where the maritime power strategy converges with the wave of the digital economy, the digital economy provides a critical transformational opportunity for marine fisheries to break through the traditional extensive model and achieve high-quality development. Based on panel data from 41 coastal cities in China from 2003 to 2022, this study empirically examines the enabling effect of the digital economy on marine fisheries from the perspective of total factor productivity. The findings are as follows: First, the development of the digital economy promotes the improvement of total factor productivity in marine fisheries, but this is primarily achieved through “innovation-driven” expansion of the production frontier, while its potential in “efficiency catch-up” has not yet been fully realized. Second, the enabling effect exhibits distinct spatial heterogeneity, with its positive impact concentrated in cities in the South China Sea region, where industrial foundations and policy environments are more aligned. Third, the influence of the digital economy demonstrates nonlinear threshold characteristics; when technology promotion and industrial collaboration surpass specific thresholds, the enabling effect significantly strengthens, but as innovation capability improves, its marginal contribution shows a diminishing trend. Accordingly, it is recommended to deepen the application of digital technologies in core processes, transitioning from “isolated applications” to “systematic integration.” Simultaneously, tailored regional development strategies should be formulated to align with the resource endowments and development stages of each maritime region. On this basis, efforts should be made to improve technology promotion and industrial support systems, construct a collaborative and efficient digital fishery ecosystem, and facilitate the sustainable transition of marine fisheries from factor-driven to innovation-driven growth. Full article

This study investigates the Lower Cretaceous Madongshan Formation in the Liupanshan Basin, a classic saline lacustrine succession, to elucidate the key mechanisms for high-quality source rock development. An integrated approach combining organic geochemistry (Rock-Eval, Gas Chromatography–Mass Spectrometry [GC-MS], δ¹³C) and inorganic elemental geochemistry (X-ray Fluorescence [XRF] ) was applied to a well-characterized outcrop section. The results reveal that the Madongshan Formation contains mature, oil-prone source rocks dominated by Type II₁ and II₂ kerogen. Geochemical proxies consistently indicate deposition within an arid to semi-arid climate, which drove the formation of a stratified, saline-to-hypersaline water column with persistent bottom-water anoxia (Pristane/Phytane [Pr/Ph] < 0.5). Isotopic and biomarker data confirm a mixed source input, with an average contribution of approximately 55% from aquatic organisms supplemented by a significant terrestrial influx. Based on these findings, we propose a “Salinity-Driven Preservation” model. This model posits that climate-induced salinity played a critical role in establishing a persistent halocline, leading to an intensely anoxic “preservation factory” at the lake bottom. Current evidence suggests that this exceptional preservation efficiency was a pivotal factor compensating for moderate productivity to control organic matter enrichment. This study provides a robust framework for predicting source rock quality in the Liupanshan Basin and serves as a valuable analogue for other saline lacustrine systems. Full article

The shift toward digital and smart manufacturing requires an accurate prediction of cutting behavior, such as cutting forces. Controlling cutting forces in machining is important for maintaining product quality, particularly in steels such as X5CrNi18-10. This steel has high toughness, which resists cutting, thereby increasing overall cutting forces. Proper selection of machining parameters and conditions can help reduce cutting forces during machining. Several studies have been dedicated to understanding the influence of cutting parameters on cutting forces. However, limited attention is given to the influence of the cutting conditions on cutting forces. The primary objective of this study is to understand the behavior of cutting forces in chromium-nickel alloy steel by varying machining parameters, specifically cutting conditions (dry and wet), using a full factorial (3¹ × 2²) design of experiments (DoE). The secondary objective is to develop a multilinear regression model to predict cutting forces. The root mean square (RMS) values of the cutting force components were calculated from the acquired data and analyzed using OriginPro 2025b. In addition, this study analyzes the effects of cutting parameters and cutting forces on root mean square (RMS) surface roughness (R_q) to understand their impact on quality using the AltiSurf 520 profilometer. The results suggest a significant effect of the selected machining parameters and conditions on cutting force reduction and on improved surface quality when cutting forces are low. This research provides a valuable insight into optimizing the machining process for hard steels. Full article

Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp²) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products. Full article

For high-value-added products such as matcha, scientific confirmation of the origin is essential for quality assurance and fraud prevention. In this study, three nondestructive analytical techniques, specifically fluorescence (FF), near-infrared (NIR), and Fourier transform infrared (FT-IR) spectroscopy, were combined with machine learning algorithms to accurately identify the origin of Japanese matcha. FF data were analyzed using convolutional neural networks (CNNs), whereas NIR and FT-IR spectral data were analyzed using k-nearest neighbors (KNNs), random forest (RF), logistic regression (LR), and support vector machine (SVM) models. The FT-IR–RF model demonstrated the highest accuracy (99.0%), followed by the NIR–KNN (98.7%) and FF–CNN (95.7%) models. Functional group absorption in FT-IR, moisture and carbohydrates in NIR, and amino acid and polyphenol fluorescence in FF contributed to the identification. These findings indicate that the selection of an algorithm appropriate for the characteristics of the spectroscopic data is effective for improving accuracy. This method can quickly and nondestructively identify the origin of matcha and is expected to be applicable to other teas and agricultural products. This new approach contributes to the verification of the authenticity of food and improvement in its traceability. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

The maturity of Lentinus edodes directly affects its quality, taste, and market value. Currently, maturity assessment primarily relies on manual experience, making it difficult to ensure efficiency and consistency. To achieve efficient and accurate detection of Lentinus edodes maturity, this study proposes an improved lightweight object detection model, YOLOv8n-CFS. Based on YOLOv8n, the model integrates the SegNeXt Attention structure to enhance key feature extraction capabilities and optimize feature representation. A Feature Diffusion Propagation Network (FDPN) is designed to improve the expressive ability of objects at different scales through cross-layer feature propagation, enabling precise detection. The CSFCN module combines global cue reasoning with fine-grained spatial information to enhance detection robustness and generalization performance in complex environments. The CWD method is adopted to further optimize the model. Experimental results demonstrate that the proposed model achieves 97.34% mAP50 and 84.5% mAP95 on the Lentinus edodes maturity detection task, representing improvements of 2.02% and 4.92% compared to the baseline method, respectively. It exhibits excellent stability in five-fold cross-validation and outperforms models such as Faster R-CNN, YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv10n, YOLOv11n, and YOLOv12. This study provides efficient and reliable technical support for Lentinus edodes maturity detection and holds significant implications for the intelligent production of edible fungi. Full article

Oak (Quercus spp.), traditionally used for aging alcoholic bever ages, is not native in many countries, which increases production costs and environmental impact. During the aging process of alcoholic beverages, complex physical and chemical transformations occur that determine their chemical composition and sensory quality, many of which are unique depending on the type of wood used in the process. In this context, Maclura tinctoria (Tajuva), a native Brazilian species rich in phenolic compounds, was evaluated based on its phenolic composition and extraction behavior as a sustainable alternative for beverage aging. Wood chips were subjected to three toasting levels (untoasted, medium, and high) and aged for up to 360 days in two hydroethanolic model systems (10% and 14% v/v ethanol). The total and individual phenolic compounds were determined using the Folin–Ciocalteu method and HPLC–DAD/LC–MS/MS analysis. Results showed that toasting level, ethanol concentration, and aging time significantly influenced phenolic extraction. Untoasted Tajuva released the highest amounts of phenolic acids and flavonoids, particularly gallic and caffeic acids, and quercetin, respectively; while medium toasting favored the formation of thermally derived aromatic compounds, such as vanillic acid. The 14% ethanol system enhanced extraction efficiency for most analytes. Overall, Tajuva wood exhibited higher phenolic yields than French oak under comparable conditions, highlighting its chemical richness and extraction reactivity. These findings support the use of M. tinctoria as an eco-efficient and functional alternative to oak for the maturation of alcoholic beverages. Full article

Shiitake mushrooms (Lentinula edodes) are widely consumed as a key health food in the Republic of Korea. However, they face declining production value and consumption, necessitating a shift from production-focused research to an understanding of consumer demand. The aim of this study was to quantify Korean consumers’ trade-offs among key shiitake attributes and to derive actionable marketing strategies to expand domestic consumption. We conducted an online survey (n = 500) to quantify consumer utility for four key attributes: cap size (two levels), cap color (two levels), origin (two levels: domestic (Korean) and imported (Chinese)), and price (four levels per 500 g). The results identified price as the most important attribute (relative importance = 46.41%), followed by origin (19.85%), cap color (17.10%), and cap size (16.64%). Utility analysis (part-worths) revealed a distinct dual preference: consumers value both low-priced shiitake (KRW 4000 (USD 2.9)/500 g) for personal consumption and high-priced options (KRW 13,000 (USD 9.5)/500 g) for gifting. Consumers showed a clear preference for dark-colored caps, while the aggregate-level utility difference between origin levels was small. A Logit model simulation indicated the highest predicted shares for profiles priced at KRW 13,000 (15.9%) and KRW 4000 (15.7%), consistent with a polarized value–premium structure. These findings indicate that Korean producers should adopt a dual strategy: developing low-cost products to stimulate general consumption while simultaneously marketing high-quality, dark-colored, domestically produced shiitake as premium gift items, thereby establishing effective food choice strategies in a competitive market. Although the empirical setting is the Republic of Korea (with ‘Chinese’ included only as an imported-origin level representing the main foreign competitor), the findings speak to broader specialty-food contexts where import competition and dual-purpose purchasing (everyday use vs. gifting) shape attribute trade-offs. Full article

The use of natural antimicrobials and advanced sensor technologies is increasingly explored to improve the safety and quality of dairy products like cheese. The current work evaluated the effect of sodium alginate edible films enriched with oregano essential oil (EO) on the microbial spoilage of Feta cheese and the fate of Escherichia coli O157:H7 and Listeria monocytogenes during storage. Samples were inoculated with approximately a 4 log CFU/g of pathogens and subsequently wrapped with edible films containing EO or left without, serving as controls. Samples were stored under aerobic and vacuum conditions at 4 and 12 °C. Microbiological analyses, pH, and sensory attributes were monitored during storage, while multispectral imaging (MSI) devices were used for rapid, non-invasive quality assessment. EO films moderately suppressed spoilage and pathogen survival, particularly under aerobic conditions. The MSI spectral data coupled with machine learning models provided reasonable results for the estimation of yeast and mould populations, with the best models coming from aerobic conditions, from benchtop-MSI data, with R² = 0.726 and RMSE = 0.426 from the Neural Networks model, and R² = 0.661 and RMSE = 0.696 from the LARS model. The results highlight the combined potential of natural antimicrobial films and MSI-based sensors for extending Feta cheese shelf life and enabling rapid, non-destructive monitoring, respectively. Full article

Based on survey data from 320 Azerbaijani enterprises operating across the organic product value chain—including producers, sellers, and key supporting firms such as logistics, financial, and ICT service providers—this study investigates how digital platform capability influences firms’ innovation and performance outcomes and their perceived high-quality economic development within an emerging digital economy context. Four constructs—Digital Platform Capability, Enterprise Digital Transformation, Innovation and Performance Outcomes, and Perceived High-Quality Economic Development—are measured using multi-item Likert scales. Confirmatory factor analysis and Structural Equation Modeling (SEM) are employed to test the proposed relationships. The results show that Digital Platform Capability exerts a strong positive effect on Innovation and Performance Outcomes (β = 0.574) and on Perceived High-Quality Economic Development (β = 0.512). In addition, Innovation and Performance Outcomes have a direct positive impact on Perceived High-Quality Economic Development (β = 0.313). Mediation analysis further indicates that Enterprise Digital Transformation partially mediates this relationship, transmitting approximately 52% of the total effect of Innovation and Performance Outcomes on Perceived High-Quality Economic Development. These findings underscore digital transformation as a key structural mechanism through which firm-level innovation and performance contribute to broader perceptions of high-quality economic development. The study provides novel empirical evidence from Azerbaijan and offers practical implications for digital policy design and enterprise strategies aimed at promoting innovation-driven, inclusive, and sustainable growth. Full article

Livestock production in the high Andes is vital for rural livelihoods and food security but is limited by poor pasture quality, environmental variability, and restricted resources. Pasture improvement, achieved through management practices and particularly through fertilization, may enhance productivity and sustainability in high-Andean livestock systems. This study aimed to characterize mixed herds composed of domestic sheep (Ovis aries), alpacas (Vicugna pacos), llamas (Lama glama), and domestic cattle (Bos taurus) and to evaluate the role of pasture fertilization on herd composition and livestock size. Primary data were collected through structured questionnaires administered to 88 randomly selected livestock producers, complemented by direct field observations of grazing areas, corrals, shelters, and water sources. The survey documented herd structure, grazing management, pasture conservation, fertilization practices, and farm infrastructure. Data from multiple farms were analyzed using a clustering approach to group production units with similar characteristics, and statistical models were applied to assess the effects of fertilization, pasture area, and water sources. Three distinct clusters were identified: one dominated by alpacas, another by sheep, and a third by llamas with the most uniform stocking density. Pasture fertilization was most common in the sheep-dominated cluster and was significantly associated with higher sheep numbers, while no significant effects were detected for alpacas, llamas, or cattle. Farms without fertilization showed slightly higher overall livestock size; however, a strong negative interaction between pasture area and lack of fertilization indicated that expanding grazing land alone could not offset low forage quality. These findings suggest that targeted fertilization, when combined with sustainable grazing practices, may contribute to improved herd performance and long-term resilience in heterogeneous Andean livestock systems. Full article

Research laboratories in universities face a complex challenge: they must manage multiple projects, diverse teams, and tight deadlines, often with limited resources. While the business world has long used agile and quality management tools to navigate such complexity, these methods are surprisingly rare in academic research. In this study, we set out to bridge this gap. We implemented a combined management model, blending agile Scrum practices with proven quality tools like the Ishikawa diagram and PDCA cycle, within a pharmaceutical sciences research lab. Over a six-month period, we diagnosed key issues, created a structured action plan, and introduced an online platform to monitor progress continuously. Our approach led to a significant increase in productivity, with 65% of targeted articles being published or submitted and 75% of general lab activities completed. Perhaps just as importantly, communication improved dramatically, and the lab successfully met all its institutional deadlines. We conclude that this hybrid framework is not just a theoretical idea but a practical and powerful innovation. It provides a tangible blueprint for other research groups looking to enhance their productivity, streamline communication, and build a more adaptive and effective research culture in the face of academic complexity. Full article

The present study aimed to model and optimize the fortification of corn-based extruded flips with quinoa flour to improve their nutritional, functional, and sensory quality while maintaining desirable technological properties. Corn flour was partially replaced with quinoa flour at levels of 0, 10, 20, and 30%, and the mixtures were processed using a twin-screw extruder at three screw speeds (350, 500, and 650 rpm). The influence of formulation and mechanical energy input on product quality was evaluated through comprehensive characterization, including bulk density, expansion index, texture, color, chemical composition, mineral profile, amino acid and fatty acid composition, and descriptive sensory attributes. Response surface methodology (RSM) was applied to model the effects of quinoa addition and screw speed on 56 quality responses. The Z-score approach was employed to identify optimal processing conditions. The results showed that from a technological and nutritional perspective, formulations containing 20–30% quinoa processed at medium to high screw speeds (500–650 rpm) provided the most balanced products. Z-score optimization identified that the sample with 20% quinoa extruded at 650 rpm showed a balanced combination of enhanced nutritional characteristics and preserved physical and sensory quality. Full article