Background: Despite the improvement in therapies, pancreatic cancer represents one of the most cancer-related deaths. In our hypothesis, we propose that Hyperthermic Intraperitoneal Chemotherapy with gemcitabine after pancreatic cytoreductive surgery could reduce tumor progression by reducing residual neoplastic volume and residual pancreatic cancer stem cells. Materials and methods: A randomized trial involving 42 patients. All patients were diagnosed with pancreatic ductal adenocarcinoma. Group I: R0 resection. Group II. R0 resection and HIPEC with gemcitabine (120 mg/m² for 30 min). Effectiveness was measured with analysis of overall survival, disease-free survival, distant recurrence, locoregional recurrence, and measuring of pancreatic cancer stem cells (EpCAM⁺CXCR4⁺CD133⁺). Results: From 2017 to 2023, 63 patients were recruited for our clinical trial; 21 patients were included in each group, and 21 were excluded. Locoregional recurrence, p-value: 0.022, was lower in the experimental group. There were no significant differences between the two groups in hospital mortality, perioperative complications, or hospital costs. We found a significant decrease in pancreatic cancer stem cells in patients in the experimental group after treatment, p -value of 0.018. Conclusions: The use of HIPEC with gemcitabine after surgery in patients with resectable pancreatic ductal adenocarcinoma reduces locoregional recurrence and may be associated with a significant decrease in pancreatic cancer stem cells. Full article

Fishing tourism, a niche yet burgeoning sector within the broader tourism industry, presents a unique opportunity to explore the dynamics of the Experience Economy. This study delves into the intricacies of fishing tourism through the lens of data science, leveraging web crawling techniques to gather extensive data from reviewer profiles on TripAdvisor. By employing natural language processing (NLP) techniques, the study investigates the correlation between Experience Economy dimensions and user profiling aspects within this domain. The findings of this study shed light on the predominant dimensions of the Experience Economy within fishing tourism. “Entertainment” emerges as the primary dimension, closely followed by “Aesthetic”, “Educational”, and “Escapist” elements. Notably, the study reveals frequent co-occurrences of certain dimension pairs, such as “Entertainment”–“Aesthetic” and “Educational”–“Entertainment”, underscoring the multifaceted nature of the fishing tourism experience. The implications of this research extend beyond academic discourse to practical considerations for stakeholders in the fishing tourism sector. By highlighting the socioeconomic benefits inherent in fishing tourism for local communities and fishers, the study emphasizes the importance of governmental support. This support, in the form of infrastructure development, effective leadership, legislative measures, and financial backing, is deemed essential for fostering sustainable growth and development in this sector. This study stands as a pioneering endeavor within the realm of fishing tourism research, particularly in its focus on Experience Economy dimensions and user profiling. By drawing data from both business pages and user profiles on TripAdvisor, it provides a comprehensive understanding of the intricate interplay between tourist experiences and the broader economic and social landscape of fishing tourism destinations. Full article

Polymer-plugging agents are widely employed in profile control and water-plugging measures, serving as a crucial component for efficient reservoir development. However, quantitatively monitoring the emplacement depth of polymer-plugging agents in low-permeability and high-permeability layers remains a challenging bottleneck. Presently, insufficient attention on shear thinning, a critical rheological property for water shut-off and profile control, has limited our understanding of polymer distribution laws. In this study, polymer shear-thinning experiments are firstly conducted to explore polymer variations with flow rate. The novelty of the research is that varying polymer viscosity is implemented instead of the fixed-fluid viscosity that is conventionally used. The fitted correlation is then integrated into the 2D and 3D heterogeneous numerical models for simulations, and a multivariate nonlinear regression analysis is performed based on the simulation results. The results show that lower polymer emplacement depth ratios corresponded to higher viscosity loss rates under the same flow rate. An increase in the initial permeability ratio corresponds to a decrease in the emplacement ratio, along with a reduction in the fraction of the plugging agent penetrating the low permeability formations. The model was applied to the Kunan Oilfield and demonstrated a polymer reduction of approximately 3000 tons compared to traditional methods. Despite the slightly complex nature of the multivariate nonlinear mathematical model, it presents clear advantages in controlling plugging agent distribution and estimating dosage, laying good theoretical ground for the effective and efficient recovery of subsurface resources. Full article

This paper experimentally and analytically investigated the damage and seismic behavior of concrete walls reinforced by low-bond ultra-high-strength (LBUHS) bars. To this end, four half-scale rectangular concrete walls were fabricated and tested under reversed cyclic loading and constant axial compression. The test variables were the shear span ratio and the axial load ratio. Based on the test results, the propagation of cracks on the wall surface, the maximum strain capacity of concrete, the hysteresis loops and envelope curves, the residual drifts, and the strain distributions of LBUHS rebars were presented and discussed. The experimental results showed that all the test walls could exhibit drift-hardening capability until at least a 2.0% drift ratio if LBUHS rebars were anchored by nuts at their ends. The test results also indicated that the maximum strain capacity of concrete was above 0.86%, much larger than the currently recommended 0.4%. After unloading from the transient drift ratios of 2.0% and 2.5% for the walls with shear span ratios of 1.5 and 2.0, respectively, the measured residual drift ratios were controlled below 0.4%, which is less than the critical drift ratio (0.5%) having 98% repairable probability recommended in the FEMA document (P-58) for general concrete structures. Furthermore, a numerical method was presented to evaluate the cyclic response of the test walls, and a comparison between the experimental and the calculated results verified the reliability and accuracy of the proposed numerical method. Full article

The non-tectonic deformation caused by hydrological loads is an important influencing factor in GNSS vertical displacement. Limited by the temporal and spatial resolution of global models and model errors, the hydrological load results calculated by traditional methods are difficult to meet the high temporal and spatial resolution requirements of small to medium-scale regions. This paper introduces the idea of the remove–restore method, assimilates regional high-resolution hydrological data, and obtains higher temporal and spatial-resolution hydrological load results. Subsequently, utilizing data from 12 CORS observed in the western Yunnan region between January 2018 and December 2020, the quantitative relationship and variation characteristics between GNSS vertical displacement and hydrological load displacement were analyzed in detail. Furthermore, the annual signals of both were extracted using the SSA method for comparative analysis. After removing the effects of atmospheric load and non-tidal ocean load, the average correlation coefficient between GNSS vertical displacement and hydrological load displacement is 0.84, with an average reduction of WRMS (%) reaching 37.17%. The average correlation coefficient of the annual signals between GNSS vertical displacement and hydrological load deformation is 0.94, with an average reduction of WRMS (%) reaching 46.5%, indicating that the contribution of hydrological load to the GNSS non-tectonic vertical displacement annual signal is close to 50%. The research results provide scientific support and important references for studying surface tectonic deformation by removing non-tectonic deformations such as hydrological loads from GNSS vertical displacement. Additionally, it helps to explore the mechanisms of interaction between water storage migration and surface deformation. Full article

Using the aldehyde amine condensation procedure and the triphenylamine group as the skeleton structure, the new triphenylamine-aromatic aldehyde-succinylhydrazone probe molecule DHBYMH was created. A newly created acylhydrazone probe was structurally characterized by mass spectrometry (MS), NMR, and infrared spectroscopy (FTIR). Fluorescence and UV spectroscopy were used to examine DHBYMH’s sensing capabilities for metal ions. Notably, DHBYMH achieved a detection limit of 1.62 × 10⁻⁷ M by demonstrating exceptional selectivity and sensitivity towards Cu²⁺ ions in an optimum sample solvent system (DMSO/H₂O, (v/v = 7/3); pH = 7.0; cysteine (Cys) concentration: 1 × 10⁻⁴ M). NMR titration, high-resolution mass spectrometry analysis, and DFT computation were used to clarify the response mechanism. Ultimately, predicated on DHBYMH’s reversible identification of Cu²⁺ ions in the presence of EDTA, a molecular logic gate was successfully designed. Full article

Grain size has an important effect on rice yield. Although several key genes that regulate seed size have been reported in rice, their molecular mechanisms remain unclear. In this study, a rice small grain size 2 (sms2) mutant was identified, and MutMap resequencing analysis results showed that a 2 bp insertion in the second exon of the LOC_Os02g01590 gene resulted in a grain length and width lower than those of the wild-type Teqing (TQ). We found that SMS2 encoded vacuolar acid invertase, a novel allele of OsINV3, which regulates grain size. GO and KEGG enrichment analyses showed that SMS2 was involved in endoplasmic reticulum protein synthesis, cysteine and methionine metabolism, and propionic acid metabolism, thereby regulating grain size. An analysis of sugar content in young panicles showed that SMS2 reduced sucrose, fructose, and starch contents, thus regulating grain size. A haplotype analysis showed that Hap2 of SMS2 had a longer grain and was widely present in indica rice varieties. Our results provide a new theoretical basis for the molecular and physiological mechanisms by which SMS2 regulates grain size. Full article

This study delves into the impact of ship noise on seafarer well-being, emphasizing fatigue—a significant contributor to maritime accidents due to human error. The investigation, centered around the hypothesis that IMO ship construction standards may not adequately minimize noise levels in seafarer cabins, seeks to establish whether these levels are sufficient to ensure seafarer security and prevent sleep disturbances. According to current IMO regulations, noise levels are set at 55 dB for vessels under 10,000 gross tonnage and 60 dB for those over 10,000, yet WHO guidelines recommend a maximum of 40 dB in bedrooms to avoid sleep disruption. A comprehensive survey involving 221 cadets demonstrates that 79.6% of participants experience sleep disturbances, work disruptions, and stress due to noise, indicating that the present noise standards are insufficient. This paper argues that reducing noise levels in individual cabins to below 40 dB is critical for enhancing seafarer health and safety and could significantly reduce human error-related maritime accidents. The findings advocate for more stringent noise control measures and regulatory reforms to bridge the knowledge gaps and improve labor protection in the maritime industry. Full article

The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users’ historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including “MovieLens-1M” and “Douban dataset” from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems. Full article

Cardiovascular disease has become a common ailment that endangers human health, having garnered widespread attention due to its high prevalence, recurrence rate, and sudden death risk. Ginseng possesses functions such as invigorating vital energy, enhancing vein recovery, promoting body fluid and blood nourishment, calming the nerves, and improving cognitive function. It is widely utilized in the treatment of various heart conditions, including palpitations, chest pain, heart failure, and other ailments. Although numerous research reports have investigated the cardiovascular activity of single ginsenoside, there remains a lack of systematic research on the specific components group that predominantly contribute to cardiovascular efficacy in ginseng medicinal materials. In this research, the spectrum–effect relationship, target cell extraction, and BP neural network classification were used to establish a rapid screening system for potential active substances. The results show that red ginseng extract (RGE) can improve the decrease in cell viability and ATP content and inhibit the increase in ROS production and LDH release in OGD-induced H9c2 cells. A total of 70 ginsenosides were identified in RGE using HPLC-Q-TOF-MS/MS analysis. Chromatographic fingerprints were established for 12 batches of RGE by high-performance liquid chromatography (HPLC). A total of 36 common ingredients were found in 12 batches of RGE. The cell viability, ATP, ROS, and LDH of 12 batches RGE were tested to establish gray relationship analysis (GRA) and partial least squares discrimination analysis (PLS-DA). BP neural network classification and target cell extraction were used to narrow down the scope of Spectral efficiency analysis and screen the potential active components. According to the cell experiments, RGE can improve the cell viability and ATP content and reduce the oxidative damage. Then, seven active ingredients, namely, Ginsenoside Rg1, Rg2, Rg3, Rb1, Rd, Re, and Ro, were screened out, and their cardiovascular activity was confirmed in the OGD model. The seven ginsenosides were the main active substances of red ginseng in treating myocardial injury. This study offers a reference for quality control in red ginseng and preparations containing red ginseng for the management of cardiovascular diseases. It also provides ideas for screening active ingredients of the same type of multi-pharmacologically active traditional Chinese medicines. Full article

The need for public fast electric vehicle charging station (FEVCS) infrastructure is growing to meet the zero-emission goals of the transportation sector. However, the large charging demand of the EV fleet may adversely impact the grid’s stability and reliability. To improve grid stability and reliability, the development of a DC microgrid (MG) leveraging renewable energy sources to supply the energy demands of FEVCSs is the sustainable solution. Balancing the intermittent EV charging demand and fluctuating renewable energy generation with the stable DC bus voltage of a DC MG is a challenging objective. To address this objective, a piece-wise droop control strategy is proposed in this work. The proposed scheme regulates DC bus voltage and power sharing with droop value updating in a region-based load current distribution. Voltage compensation in individual regions is carried out to further improve the degree of freedom. In this paper, the performance of the proposed strategy is evaluated with the consideration of real-time solar PV dynamics and EV load dynamics. Further, to showcase the effectiveness of the proposed strategy, a comparative analysis with a maximum power point tracking (MPPT) controller against various dynamic EV load scenarios is carried out, and the results are validated through a hardware-in-loop experimental setup. Despite the intermittent source and EV load dynamics, the proposed piece-wise droop control can maintain voltage regulation with less than 1% deviation. Full article

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the $R^2$ value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set $R^2$ value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits. Full article

This study employs a systematic and predictive modelling approach to investigate the structure and properties of multi-component borate glasses. In particular, this work is focused on understanding the individual and interaction effects of multiple constituents on several material properties. By leveraging advanced modeling techniques, this work examines how the inclusion and variation of B₂O₃, CaF₂, TiO₂, ZnO, and Na₂CO₃ influence the glass network, with particular attention to modifier fractions ≥ 30 mol%. This research addresses the gap in knowledge regarding the complex behavior of borate glasses in this high modifier fraction range, known as the borate anomaly, where prediction of glass structure and properties becomes particularly challenging. The use of a design of mixtures (DoM) approach facilitated the generation of polynomial equations indicating the influence of mixture components on various responses, enabling the prediction and optimization of glass properties over broad compositional ranges despite being within the anomalous region. This methodical approach not only advances our understanding of borate glass systems but also underscores the importance of predictive modelling in the accelerated design and development of novel glass materials for diverse applications. Full article

An exploding foil initiator system (EFIs) is essential in modern weaponry for its safety and reliability. As the main component of EFIs, the performance of the switch is critical to EFIs. In this study, a planar three-electrode trigger switch was designed and fabricated using the Flexible Printed Circuits (FPC) process. Subsequently, the performance of the FPC switch was tested. The results show that the self-breakdown voltage of the FPC switch is stable. In addition, an FPF switch with a 0.6 mm main electrode gap demonstrated consistency, with delay times below 31.75 ns, and a jitter ranging from 1.7 ns to 10.94 ns at 900 V to 1200 V, evidencing the FPC switches’ reliability and uniform performance across various voltages. Compared to the Micro-Electro-Mechanical Systems (MEMS) switches of similar dimensions, the FPC switches achieved a faster high-current attainment with less inductance, showing a 5% reduction in loop inductance. The repetitive testing results demonstrate that the FPC switch maintains consistent output performance, with stable peak currents, peak current time, and delay time over 50 action cycles, highlighting its repeatability. The FPC switch was assembled with an EFI chip and capacitor into an integrated system, which was subsequently able to successfully detonate HNS-IV at 1000 V/0.22 μF, proving the FPC switch’s potential in low inductance applications. Full article

In the Spanish context, Sports Sciences education is evolving to emphasize competency-based learning, crucial for adapting to the dynamic global landscape and labor market. This opinion article highlights the shift towards integrating generic and specific competencies, essential for automation and artificial intelligence, aligning with Sustainable Development Goal (SDG) 8’s focus on sustainable economic growth and employment. Despite the recognized importance of these competencies for economic sustainability and job readiness, the literature on this framework, particularly within the context of physical activity and Sports Sciences in Spain, remains underexplored. This paper is structured to first address the current state of the problem, followed by a conceptualization of competencies, including types of competencies. It then analyzes professional competencies within the realm of Physical Activity and Sports Sciences in Spain, moving towards the implementation and evaluation of these competencies in the classroom setting. By bridging the gap between educational outcomes and market demands, this work calls for ongoing research and pedagogical innovation to equip future professionals with the necessary skills for success. This approach not only prepares students for the future labor market but also contributes to the broader economic and sustainable development goals envisioned by SDG 8. Full article

Extreme temperature during summer may lead to heat stress in cattle and compromise their productivity. It also poses detrimental impacts on the developmental capacity of bovine budding oocytes, which halt their fertility. To mitigate the adverse effects of heat stress, it is necessary to investigate the mechanisms through which it affects the developmental capacity of oocytes. The primary goal of this study was to investigate the impact of heat stress on the epigenetic modifications in bovine oocytes and embryos, as well as on oocyte developmental capacity, reactive oxygen species, mitochondrial membrane potential, apoptosis, transzonal projections, and gene expression levels. Our results showed that heat stress significantly reduced the expression levels of the epigenetic modifications from histone H1, histone H2A, histone H2B, histone H4, DNA methylation, and DNA hydroxymethylation at all stages of the oocyte and embryo. Similarly, heat stress significantly reduced cleavage rate, blastocyst rate, oocyte mitochondrial-membrane potential level, adenosine-triphosphate (ATP) level, mitochondrial DNA copy number, and transzonal projection level. It was also found that heat stress affected mitochondrial distribution in oocytes and significantly increased reactive oxygen species, apoptosis levels and mitochondrial autophagy levels. Our findings suggest that heat stress significantly impacts the expression levels of genes related to oocyte developmental ability, the cytoskeleton, mitochondrial function, and epigenetic modification, lowering their competence during the summer season. Full article

In this paper, we constructed a new and robust fixed point iterative scheme called the UO iterative scheme for the approximation of a contraction mapping. The scheme converges strongly to the fixed point of a contraction mapping. A rate of convergence result is shown with an example, and our scheme, when compared, converges faster than some existing iterative schemes in the literature. Furthermore, the stability and data dependence results are shown. Our new scheme is applied in the approximation of the solution to the oxygen diffusion model. Finally, our results are applied in the approximation of the solution to the boundary value problems using Green’s functions with an example. Full article

Bismuth ferrite BiFeO₃ (BFO)-based ferroelectrics have great potential as inorganic perovskite-like oxides for future solar cells applications due to their unique physical properties. In this work, La and Mn co-doped BFO thin films with compositions Bi_0.9La_0.1(Fe_1−xMn_x)O₃ (x = 0, 0.05, 0.1, 0.15) (denoted as BLF, BLFM5, BLFM10, BLFM15, respectively) were prepared via a sol–gel technique on indium tin oxide (ITO) glass. All the films are monophasic, showing good crystallinity. The optical bandgap E_g was found to decrease monotonously with an increase in the Mn doping amount. Compared with other compositions, the BLFM5 sample exhibits a better crystallinity and less oxygen vacancies as indicated by XRD and XPS measurements, thereby achieving a better J–V performance. Based on BLFM5 as the light absorbing layer, the ITO/ZnO/BLFM5/Pt and ITO/ZnO/BLFM5/NiO/Pt heterostructure devices were designed and characterized. It was found that the introduction of the ZnO layer increases both the open circuit voltage (V_oc) and the short circuit current density (J_sc) with V_oc = 90.2 mV and J_sc = 6.90 μA/cm² for the Pt/ BLFM5/ZnO/ITO device. However, the insertion of the NiO layer reduces both V_oc and J_sc, which is attributed to the weakened built-in electric field at the NiO/BLFM5 interface. Full article

This study focuses on understanding the association of rare earth elements (REE; lanthanides + yttrium + scandium) with organic matter from the Middle Devonian black shales of the Appalachian Basin. Developing a better understanding of the role of organic matter (OM) and thermal maturity in REE partitioning may help improve current geochemical models of REE enrichment in a wide range of black shales. We studied relationships between whole rock REE content and total organic carbon (TOC) and compared the correlations with a suite of global oil shales that contain TOC as high as 60 wt.%. The sequential leaching of the Appalachian shale samples was conducted to evaluate the REE content associated with carbonates, Fe–Mn oxyhydroxides, sulfides, and organics. Finally, the residue from the leaching experiment was analyzed to assess the mineralogical changes and REE extraction efficiency. Our results show that heavier REE (HREE) have a positive correlation with TOC in our Appalachian core samples. However, data from the global oil shales display an opposite trend. We propose that although TOC controls REE enrichment, thermal maturation likely plays a critical role in HREE partitioning into refractory organic phases, such as pyrobitumen. The REE inventory from a core in the Appalachian Basin shows that (1) the total REE ranges between 180 and 270 ppm and the OM-rich samples tend to contain more REE than the calcareous shales; (2) there is a relatively higher abundance of middle REE (MREE) to HREE than lighter REE (LREE); (3) there is a disproportionate increase in Y and Tb with TOC likely due to the rocks being over-mature; and (4) the REE extraction demonstrates that although the OM has higher HREE concentration, the organic leachates contain more LREE, suggesting it is more challenging to extract HREE from OM than using traditional leaching techniques. Full article

The progress of incorporating deep learning in the field of medical image interpretation has been greatly hindered due to the tremendous cost and time associated with generating ground truth for supervised machine learning, alongside concerns about the inconsistent quality of images acquired. Active learning offers a potential solution to these problems of expanding dataset ground truth by algorithmically choosing the most informative samples for ground truth labeling. Still, this effort incurs the costs of human labeling, which needs minimization. Furthermore, automatic labeling approaches employing active learning often exhibit overfitting tendencies while selecting samples closely aligned with the training set distribution and excluding out-of-distribution samples, which could potentially improve the model’s effectiveness. We propose that the majority of out-of-distribution instances can be attributed to inconsistent cross images. Since the FDA approved the first whole-slide image system for medical diagnosis in 2017, whole-slide images have provided enriched critical information to advance the field of automated histopathology. Here, we exemplify the benefits of a novel deep learning strategy that utilizes high-resolution whole-slide microscopic images. We quantitatively assess and visually highlight the inconsistencies within the whole-slide image dataset employed in this study. Accordingly, we introduce a deep learning-based preprocessing algorithm designed to normalize unknown samples to the training set distribution, effectively mitigating the overfitting issue. Consequently, our approach significantly increases the amount of automatic region-of-interest ground truth labeling on high-resolution whole-slide images using active deep learning. We accept 92% of the automatic labels generated for our unlabeled data cohort, expanding the labeled dataset by 845%. Additionally, we demonstrate expert time savings of 96% relative to manual expert ground-truth labeling. Full article

Ethyl carbamate (EC) is a natural by-product in the production of fermented food and alcoholic beverages and is carcinogenic and genotoxic, posing a significant food safety concern. In this study, Clavispora lusitaniae Cl-p with a strong EC degradation ability was isolated from Daqu rich in microorganisms by using EC as the sole nitrogen source. When 2.5 g/L of EC was added to the fermentation medium, the strain decomposed 47.69% of ethyl carbamate after five days of fermentation. It was unexpectedly found that the strain had the ability to produce aroma and ester, and the esterification power reached 30.78 mg/(g·100 h). When the strain was added to rice wine fermentation, compared with the control group, the EC content decreased by 41.82%, and flavor substances such as ethyl acetate and β-phenylethanol were added. The EC degradation rate of the immobilized crude enzyme in the finished yellow rice wine reached 31.01%, and the flavor substances of yellow rice wine were not affected. The strain is expected to be used in the fermented food industry to reduce EC residue and improve the safety of fermented food. Full article