Search Results (3,756)

Table olive processing comprises multiple stages in which physical, chemical, and biological hazards may occur. Although risk assessment is a core element of Hazard Analysis and Critical Control Points (HACCP) systems, the selection of assessment tools remains insufficiently standardized. This study compared a 4 × 4 risk matrix and Failure Mode and Effects Analysis (FMEA) for hazard evaluation in Spanish-style and Californian-style table olive processing. Hazards were assessed across 41 processing stages for Spanish-style olives and selected key stages for Californian-style olives using probability × severity in the 4 × 4 matrix and severity × occurrence × detection in FMEA. Significant hazards were further evaluated using the Codex Alimentarius decision tree to identify critical control points (CCPs) and strengthened prerequisite programs (PRPs). Both tools identified similar significant hazards, including biological hazards associated with fermentation, brine management, storage, container sealing, and heat treatment, as well as physical hazards from foreign bodies and chemical hazards related to heavy metals, pesticide residues, mycotoxins, and food-contact material migration. FMEA provided greater analytical detail through the detection parameter, whereas the 4 × 4 matrix was simpler and more practical for complex flow diagrams. Overall, both tools were suitable for HACCP-based risk assessment in table olive processing. Full article

The rapid emergence of SARS-CoV-2 variants underscores the need for accurate, rapid, and affordable diagnostic tools, particularly in resource-limited settings. An isothermal amplification-based assay was developed integrating reverse-transcriptase recombinase polymerase amplification (RT-RPA), T7 transcription, and duplex-specific nuclease (DSN)-mediated detection for variant discrimination. The assay targets three genomic regions: a conserved region within ORF1a and two variant regions, ORF1a (Δ3675–3677) and the S gene (Δ69–70), enabling differentiation between the Wuhan-Hu-1 reference isolate and the B.1.1.7 variant. The method demonstrated high specificity and a limit of detection of 200 copies per sample using low-cost instrumentation. DSN-mediated cleavage improved discrimination between matched and mismatched RNA targets while enabling signal amplification through target recycling. The assay requires minimal laboratory infrastructure, relying on a heat block and fluorescent plate reader. These results demonstrate a scalable and cost-effective strategy for SARS-CoV-2 variant screening with potential as a future strategy for pathogen screening and variant surveillance. Full article

Microbial inactivation is essential for extending the shelf life of raw milk. Radio frequency (RF) thermal pasteurization has emerged as a promising technology for small-scale dairy processing. This study aimed to determine optimal RF temperature–time conditions, evaluate their effects on milk quality across milk from different species of cows, and assess economic feasibility. Raw milk from Holstein Friesian, Jersey, and Brown Swiss cows was treated using a dielectric heating system (40.68 MHz) at 72–92 °C for 20–100 s. The results were compared with conventional low-temperature long-time (LTLT) pasteurization of untreated milk. The optimal condition was 92 °C for 50 s, reducing the aerobic plate count from 5.80 to 0.69 log CFU/mL (a 5.11 log reduction), with no detection of Staphylococcus aureus, Bacillus cereus, and Escherichia coli. RF treatment did not significantly affect milk composition (p > 0.05), and color changes remained within acceptable limits. Milk stored at 4 °C maintained quality and safety for up to 28 days. Economic analysis indicated a net present value of USD 134,721.78, a benefit–cost ratio of 3.25, and a payback period of 6.8 months, confirming economic feasibility. These findings demonstrate that RF pasteurization can improve processing efficiency and support sustainable dairy production. Full article

Metallographic microstructure analysis is essential for understanding the evolution of steel microstructures during heat treatment and mechanical processing. However, accurate analysis of optical micrographs remains difficult because of blurred grain boundaries, grayscale inhomogeneity within grains, and irregular grain morphologies. To address these issues, this work proposes an automated metallographic image-processing method based on superpixels, DPSS (dual-phase steel segmentation), with the main contribution focused on microstructure segmentation. First, image contrast and boundary visibility are enhanced by edge detection and sharpening. Then, superpixel segmentation is combined with extracted edge information to improve boundary localization and preserve irregular grain morphology, enabling more complete extraction of grain or particle regions from optical images. The proposed method is validated on optical micrographs of Mn-Si low-alloy steel, and the results show that it provides more accurate and complete segmentation than conventional ImageJ (Version: 1.54f)-based processing. Based on the segmented regions, a lightweight neural network is further used for phase identification. The final classification recognition accuracy can reach 99.91%. This classification result serves to demonstrate that the improved segmentation results can provide more reliable inputs for subsequent microstructure recognition. Overall, the proposed method offers an effective and automated solution for metallographic image segmentation and supports more accurate downstream phase analysis. Full article

Rapid and reliable detection methods are essential for routine monitoring of environmental hygiene on farms. This pilot study evaluated luminometers (LUM) and mobile flow cytometer (MFC) for assessment of surface organic contamination in farrowing units. The study was conducted on two pig farms after animal removal prior to sanitation, with sampling performed at heated pads, pen walls, and corridors. ATP measurements were carried out using three luminometers (Clean-Trace™ LM1, EnSure, and SystemSURE Plus), and residual particles were detected using a mobile flow cytometer (Cytoquant). Microbiological cultivation (TMC 36 °C) was additionally included. Significant differences in log RLU values were observed between LUM, with large effect sizes indicating a substantial influence of device type on RLU values. A high correlation was confirmed only between EnSure and SystemSURE Plus (r_s = 0.81–1.00; p < 0.05), and no relationship was confirmed between LUM and MFC (r_s = −0.49–0.77; p > 0.05). Correlations between rapid detection methods and microbiological cultivation were inconsistent. Corridors demonstrated the highest microbiological contamination, whereas MFC identified heated pads as sites with increased residual particulate contamination. The results indicate that LUM, MFC, and microbiological cultivation characterize different dimensions of environmental contamination and should therefore be interpreted as complementary rather than interchangeable methods. Full article

Street-food grilling is a common occupation in Asia, yet the occupational health risks associated with cooking-generated polycyclic aromatic hydrocarbons (PAHs) exposure, occurring alongside plausible unmeasured co-exposures such as ambient heat and physical workload, remain under-researched. This study investigated the internal dose of PAH exposure and its association with early biological effects and physiological strain among grill restaurant workers. A cross-sectional study was conducted involving grill workers and 20 age/BMI-matched controls. Urinary 1-hydroxypyrene (1-OHP) was utilized as the primary exposure biomarker. The study assessed early biological effects such as oxidative stress (8-OHdG, F₂-isoprostanes), lung epithelial integrity (CC16), and genotoxicity (BPDE-DNA adducts) via ELISA. Physiological parameters, including blood pressure and heart rate, were recorded to evaluate acute cardiovascular strain. Workers had significantly elevated urinary 1-OHP levels compared to controls (Hodges–Lehmann ratio = 3.66, 95% CI: 1.68–7.12, representing a 3.7-fold median increase), with exposure levels increasing proportionally to smoke proximity. Notably, workers demonstrated a significantly higher median resting heart rate (HL ratio = 1.13, 95% CI: 1.05–1.23; +12.9%) and systolic blood pressure (HL ratio = 1.09, 95% CI: 1.00–1.18; +8.9%) compared to their office-based peers. Although strong correlations were observed among biological effect biomarkers (r_s = 0.42–0.63), there were no significant differences between groups for 8-OHdG, CC16, or BPDE-DNA adducts, suggesting that cardiovascular parameters reflect acute short-term responses, while genomic damage markers may require higher cumulative exposure thresholds to become detectable. The study revealed that grill restaurant workers face substantial internal PAH exposure and significant cardiovascular strain, occurring alongside plausible unmeasured co-exposures including ambient heat and physical workload. The prevalence of chronic cough and elevated heart rate is a critical early warning sign for occupational health. Our findings indicate that current general ventilation is inadequate, highlighting an urgent need for localized engineering controls and comprehensive health surveillance, including cardiovascular monitoring in the service sector. Full article

The Hsp20 protein family, essential in heat stress responses across all organisms, is part of the heat shock protein (Hsp) superfamily, recognized for its conserved alpha-crystallin domain (ACD). Hsp20s are the smallest proteins in the superfamily and primarily assist in protein refolding during stress and developmental processes. We present an in silico characterization of the Hsp20 gene family in Chenopodium quinoa (2n = 4x = 36) using an integrative approach. Quinoa is well known for its global contributions to food production and tolerance to various abiotic stresses. We identified 69 CqHsp20 genes that exhibit a well-conserved evolutionary pattern, characterized by a balanced copy number distributed symmetrically across 19 homeologous pairs in both subgenomes (A and B), with localized expansions driven by tandem duplications on eight chromosomes. High sequence identity in contiguous gene pairs and Ka/Ks ratios consistently below 1 (0.14–0.84) mathematically demonstrate that strict purifying selection has maintained the structural and sequence integrity of these genes since the ancestral polyploidization event. The phylogenetic analysis grouped CqHsp20 into two main clusters, splitted into four sub-clusters based on peptides’ cellular localization, consistent with a characteristic gene structure and conserved motif analysis, which may reflect the evolutionary trajectory and functional specialization of the Hsp20 family in plants. The integration of transcriptomic data from published experiments enabled us to detect a cluster of putatively ubiquitously expressed CqHsp20, as well as other groups that showed differential responses across abiotic stress conditions. The pattern shows that more genes exhibit higher transcription abundance under drought and salinity than under heat, key adaptive traits underlying quinoa’s known ecological versatility. Some of these genes, which are undetectable or have low abundance under heat stress, encode organelle-targeting peptides, a phenomenon not reported in other model plant studies. Differential expression analysis revealed a highly transcribed sub-cluster where six out of seven of nuclear CqHsp20 genes were active in aerial tissue during initial heat stress, with a specific cohort of four genes (CQ025082, CQ031384, CQ041158, and CQ055373) maintaining significant upregulation ($|{log}_2\mathrm{Fold}\mathrm{Change}|\ge 1.0$, $\mathrm{padj}<0.05$) under prolonged and simultaneous shoot/root exposure. Varying expression within CqHsp20 homologous and paralogs supports the idea that gene duplication creates genomic diversity, facilitating adaptation to variable extreme environments. However, while theoretical and in silico analysis provide valuable insight into quinoa Hsp20 response, empirical data are essential to unequivocally understand how these gene expression variations affect quinoa response to abiotic stressors. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article

Residual volatile organic compounds (VOCs) and non-intentionally added substances (NIASs) limit the reuse of post-consumer recycled high-density polyethylene (rHDPE) in high-value applications because they generate persistent odors and may compromise product quality and regulatory acceptance. This work comparatively assesses five deodorization and purification routes for rHDPE: agitation washing, ultrasound-assisted washing, reflux heating, Soxhlet extraction, and dissolution/precipitation, by combining VOC removal performance, material characterization, and techno-economic evaluation. Ultrasound-assisted washing with SDS achieved ~96% total VOC removal, while reflux heating resulted in near-complete removal (~98%), approaching the analytical detection limit. Soxhlet extraction with ethanol reached 94% after 1 h, and the dissolution/precipitation method provided near-complete purification and removed additional impurities, but at the expense of substantially higher process complexity and cost. Mechanical and physicochemical characterization indicated that the evaluated treatments did not appreciably compromise the measured properties of the recycled polymer. In addition, equilibrium screening with representative analytes in ethanol provided qualitative support for the solvent–polymer interaction discussion. A plant-scale techno-economic assessment identified ultrasound-assisted SDS washing as the most attractive option, offering the best balance between deodorization efficiency, process simplicity, and cost. Overall, the results provide a practical basis for selecting scalable decontamination strategies to upgrade rHDPE quality and expand its use in circular plastic applications. Full article

Background/Objectives: Post-weaning diarrhea remains a major challenge in pig production worldwide. Enterotoxigenic Escherichia coli (ETEC) encoding fimbriae of the F4 type and producing the heat-stable enterotoxin, STb, are one of the important causes of this disease. The aim of the current study was to evaluate whether vaccination of pregnant sows with a novel capsid virus-like particle (cVLP)-based vaccine against F4 and STb (cVLP-FaeG/cVLP-STb) could enhance performance in piglets born after such vaccinated sows. Methods: A field trial was conducted in a commercial sow-to-finisher pig herd. Thirty-five sows were vaccinated twice with the cVLP-FaeG/cVLP-STb vaccine prior to farrowing, while thirty-five control sows were vaccinated twice with commercial vaccines normally used in the herd. Piglets were followed until eight weeks post-weaning to assess antibody responses, diarrhea and treatment incidences, pathogen shedding, and growth performance. Results: Piglets born from immunized sows receiving the cVLP vaccine showed significantly higher serum antibody levels against ETEC F4 throughout the post-weaning period (p ≤ 0.021). The frequency of pathogen detection was similar between groups, while piglets in the cVLP group exhibited significantly lower diarrhea scores at week 6 (p = 0.047), showed a trend of requiring fewer treatments (p = 0.06) and had significantly higher final body weight (p = 0.048). In addition, the cVLP group showed a significantly greater average daily gain over the study period (p = 0.037). Conclusion: Sow immunization with the cVLP vaccine enhanced passive immune protection of piglets, resulting in reduced antimicrobial treatment 2 weeks post-weaning and improved growth performance. Full article

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing large-scale metal components due to its high deposition efficiency, low material cost, and design flexibility. However, the widespread industrial adoption of WAAM is hindered by challenges in geometric accuracy, process stability, and defect control, which are closely related to two critical aspects: trajectory planning and real-time weld seam tracking. This review provides a comprehensive and critical analysis of recent advances in both fields, with an emphasis on their interconnection rather than treating them as separate research streams. Unlike existing reviews that primarily summarize path planning algorithms or image processing techniques in isolation, this paper explicitly examines the integration challenges and synergistic potential between offline trajectory optimization and online vision-based monitoring. Key topics include adaptive path strategies for sharp corners and intersections, interlayer filling methods to mitigate heat accumulation and residual stress, as well as passive and active visual sensing technologies for molten pool characterization and defect detection. The review further identifies a persistent gap in closed-loop systems that combine real-time image feedback with dynamic path replanning. Based on the analysis of representative studies, current limitations are discussed and future research directions are proposed, including the development of digital twins, multi-modal data fusion, and reinforcement learning-based adaptive control. This review offers a distinct perspective aimed at advancing intelligent, high-precision WAAM systems for complex metal components. Full article

Heat stress is a physiological challenge for dairy cattle, linked to oxidative imbalance and immune dysregulation. This study evaluated seasonal heat stress effects on systemic redox and inflammatory status in healthy dairy cows and tested saliva as an alternative diagnostic fluid to serum. A total of 114 clinically healthy Holstein cows were sampled across summer, autumn, and winter (38 cows per season). Environmental exposure was quantified using the temperature–humidity index (THI). Paired saliva and serum samples were analyzed for total oxidant status (TOS), total antioxidant capacity (TAC) and adenosine deaminase (ADA); the oxidative stress index (OSI = TOS/TAC) was calculated. In saliva, TOS, TAC and ADA were significantly lower in summer than in autumn and winter, while salivary OSI remained stable across seasons. In serum, summer was characterized by increased TOS and OSI together with reduced ADA activity, indicating systemic oxidative stress accompanied by diminished ADA-linked immune activation under heat stress. Although salivary markers correlated with THI, saliva did not mirror the systemic oxidative imbalance detected in serum. Under the conditions and biomarkers evaluated, serum provided a more reliable assessment of heat-stress-related redox and immune disturbances in dairy cattle, whereas saliva showed limited utility as a substitute for monitoring. Full article

Climate change remains among the most pressing environmental challenges confronting the world, exerting profound pressure on both ecological systems and socio-economic development. To advance understanding of the evolution patterns and driving mechanisms governing hydroclimatic systems in arid and semi-arid regions, this study employed an integrated framework encompassing trend testing, change-point detection, periodicity and persistence analysis, and machine learning-based attribution. Focusing on the Mu Us Sandy Land from 1982 to 2023, we systematically investigated the spatiotemporal evolution, periodic characteristics, and driving mechanisms of hydroclimatic factors. Furthermore, future climate risks were assessed using CMIP6 multi-model data. The results showed that: (1) All four variables exhibited positive slopes, but only soil moisture showed a statistically significant long-term wetting trend (β = 0.025 × 10⁻³, p = 0.0008) and a clear global abrupt change in 2011; the upward tendencies of precipitation (p = 0.3946), potential evapotranspiration (p = 0.4970), and surface runoff (p = 0.1097) did not reach the 0.05 significance level. (2) Meteorological elements showed weak periodicity and strong anti-persistence (mean Hurst index = 0.379 for precipitation and 0.222 for PET), whereas hydrological elements exhibited clear seasonal–interannual periods and more random future variability with greater spatial heterogeneity (mean Hurst index = 0.436 for runoff and 0.414 for soil moisture). (3) Monthly changes were mainly associated with local surface processes. Vegetation dynamics were key predictors of precipitation, runoff, and soil moisture, while potential evapotranspiration was dominated by atmospheric demand, with limited influence from large-scale climate indices. (4) Under high-emission scenarios, imbalanced water–heat increases may lead to a higher likelihood of drought conditions. Full article

The solar corona has an extremely low density, and its brightness is only about one millionth of that of the photosphere. High-dynamic-range imaging of its faint structure is therefore essential for studying coronal heating, coronal mass ejections, and space weather. Quantitative coronagraph imaging requires flat-field measurement and calibration, which underpin intensity calibration, small-scale feature detection, and long-term cyclic analysis. This paper analyzes the coronagraph imaging chain (baffle–optical system–detector) and the origins of flat-field errors, including optical aberrations, stray light, and pixel-response non-uniformity, and summarizes the resulting calibration requirements of next-generation coronagraphs. On this basis, ground-based and space-based flat-fielding methods are systematically reviewed: the ground-based methods include integrating-sphere uniform light sources, opal glass/diffuser plates, clear-sky and thin-cloud backgrounds, and solar disk scanning, while the space-based methods include internal light sources and diffuser plates, attitude-roll and off-corona offset observations, and multi-phase statistical self-consistent flat-fielding. Their accuracy, resource cost, and applicability are compared. The review shows that no single method is simultaneously high-precision, easy to update, and engineer-friendly; a hierarchical, multi-method calibration framework is therefore recommended. Finally, a new method is proposed in which lithographically generated structured light fields, combined with Fourier optics and machine learning inversion, are used to estimate the pixel-response function. Preliminary experiments show that this method achieves a lower residual error than the integrating-sphere and opal glass methods, providing a high-precision reference for future wide-band, high-resolution coronagraph calibration. Full article

Objectives: The study aimed to investigate the antibacterial, in vitro anti-inflammatory and anti-acne properties of Patchouli essential oil (PEO). Methods: PEO components were quantified by gas chromatography–mass spectrometry (GC-MS). The antibacterial activity of PEO against Cutibacterium acnes (C. acnes) and Staphylococcus epidermidis (S. epidermidis) was evaluated via MIC detection and growth curve analysis. Bacterial membrane integrity was assessed by detecting intracellular substance leakage. LPS-induced RAW264.7 cells inflammatory models and heat-inactivated C. acnes-stimulated human epidermal keratinocytes (HaCaT) cells acne models were constructed to explore the in vitro anti-inflammatory and anti-acne activities of PEO by measuring the secretion levels of pro-inflammatory cytokines. Results: PEO primarily contained α-guaiene, patchouli alcohol and α-bulnesene. It showed potent antibacterial activity with an MIC of 0.50 mg/mL, inhibiting bacterial growth and disrupting cell membrane integrity. PEO significantly alleviated cell damage and reduced the secretion of TNF-α, IL-1β and IL-6 in two inflammatory cell models, and suppressed excessive keratinocyte proliferation. Conclusions: PEO exerts anti-acne effects via dual antibacterial and anti-inflammatory pathways. This work provides valid experimental evidence for the application of PEO as a novel natural anti-acne agent. Full article