The influence of elevated temperatures and strain rate on the mechanical and structural properties of steel 42CrMo4 were analysed experimentally in this paper. The experiments were based on uniaxial tension and compression tests at high temperatures between 700 °C and 1100 °C and strain rates in the range 0.0018–0.1 s⁻¹. The influence of temperature and strain rate on yield stress, strain to fracture, hardness, structural changes, and fracture characteristics were analysed. The non-uniformity of deformations obtained at different values of the strain rate and temperature were also analysed. Analysis by scanning electron microscopy showed the ductile behaviour of the material. The degree of damage in the material caused by the presence of cavities increased with increasing deformation temperature. For all the presented deformation conditions, the formation of the fracture through the ductile fracture mechanism resulted from localized necking and the coalescence of microvoids. By increasing the deformation temperature and reducing the strain rate, the fracture behaviour of 42CrMo4 steel can be improved. Full article

The genus Brassica is an important source of food in the Mediterranean diet with documented nutritional and medicinal properties. However, few studies have investigated the phytochemical composition and the biological activity of wild Sicilian taxa. Thus, we aimed to study the chemical profile and the antioxidant potential, in vitro and in LPS-stimulated RAW 264.7 cells, of a methanolic extract of leaves of wild Brassica macrocarpa Guss (B. macrocarpa) (Egadi Islands; Sicily-Italy). B. macrocarpa methanolic extract showed a large amount of glucosinolates and different phenolic compounds. It exhibited antioxidant activity in the DPPH assay and in LPS-stimulated RAW 264.7 cells, being able to reduce NO and ROS levels and NOS2 mRNA expression. Our study demonstrated that Sicilian B. macrocarpa methanolic extract, in LPS-stimulated macrophages, efficiently counteracts oxidative stress and displays radical scavenging activity. Future studies are required to identify the contribution of the single phytocomponents, to characterize the action mechanism, and to reveal possible applications in human health. Full article

Unsupervised domain adaptation (UDA) aims to reapply the classifier to be ever-trained on a labeled source domain to a related unlabeled target domain. Recent progress in this line has evolved with the advance of network architectures from convolutional neural networks (CNNs) to transformers or both hybrids. However, this advance has to pay the cost of high computational overheads or complex training processes. In this paper, we propose an efficient alternative hybrid architecture by marrying transformer to contextual convolution (TransConv) to solve UDA tasks. Different from previous transformer based UDA architectures, TransConv has two special aspects: (1) reviving the multilayer perception (MLP) of transformer encoders with Gaussian channel attention fusion for robustness, and (2) mixing contextual features to highly efficient dynamic convolutions for cross-domain interaction. As a result, TransConv enables to calibrate interdomain feature semantics from the global features and the local ones. Experimental results on five benchmarks show that TransConv attains remarkable results with high efficiency as compared to the existing UDA methods. Full article

Currently, there are many enterprises involved in extracting and processing of primary raw materials. The danger of working in this industry consists in the formation of cracks in rocks of the pit side slopes, which can lead to destruction. This article discusses the existing systems for monitoring the pit collapse prevention. The most promising is the use of systems with fiber-optic sensors. However, use of these systems is associated with some difficulties due to high costs, low noise immunity, and in some cases, the requirement for additional equipment to improve the reliability of measurements. A completely new method of processing the data from a fiber-optic sensor that simplifies the design and reduces the cost of the device is proposed considering the experience of previous developments. The system uses artificial intelligence, which improves the data processing. The theoretical part is dedicated to the development of foundations, and the analysis of the nonlinear properties of the physical and mathematical model of optical processes associated with the propagation of an electromagnetic wave in a fiber-optic material was developed. The results of experimental and theoretical applied research, which are important for the development of fiber-optic systems for monitoring the pit collapse prevention, are presented. The dependences of optical losses and the number of pixels on the dis-placement were obtained. The accuracy of the method corresponds to the accuracy of the device by which it is calibrated and is 0.001 mm. The developed hardware-software complex is able to track the rate of changing the derivative of the light wave intensity in time, as well as changing the shape of the spot and transition of pixels from white to black. Full article

Fluoropyrimidine (FP) drugs are central components of combination chemotherapy regimens for the treatment of colorectal cancer (CRC). FP-based chemotherapy has improved survival outcomes over the last several decades with much of the therapeutic benefit derived from the optimization of dose and delivery. To provide further advances in therapeutic efficacy, next-generation prodrugs and nanodelivery systems for FPs are being developed. This review focuses on recent innovative nanodelivery approaches for FP drugs that display therapeutic promise. We summarize established, clinically useful FP prodrug strategies, including capecitabine, which exploit tumor-specific enzyme expression for optimal anticancer activity. We then describe the use of FP DNA-based polymers (e.g., CF10) for the delivery of activated FP nucleotides as a nanodelivery approach with proven activity in pre-clinical models and with clinical potential. Multiple nanodelivery systems for FP delivery show promise in CRC pre-clinical models and we review advances in albumin-mediated FP delivery, the development of mesoporous silica nanoparticles, emulsion-based nanoparticles, metal nanoparticles, hydrogel-based delivery, and liposomes and lipid nanoparticles that display particular promise for therapeutic development. Nanodelivery of FPs is anticipated to impact CRC treatment in the coming years and to improve survival for cancer patients. Full article

The use of secondary metabolites of rice to control pests has become a research hotspot, but little is known about the mechanism of rice self-resistance. In this study, metabolomics analysis was performed on two groups of rice (T1, with insect pests; T2, without pests), indicating that fatty acids, alkaloids, and phenolic acids were significantly up-regulated in T1. The up-regulated metabolites (p-value < 0.1) were enriched in linoleic acid metabolism, terpene, piperidine, and pyridine alkaloid biosynthesis, α-linolenic acid metabolism, and tryptophan metabolism. Six significantly up-regulated differential metabolites in T1 were screened out: N-trans-feruloyl-3-methoxytyramine (1), N-trans-feruloyltyramine (2), N-trans-p-coumaroyltyramine (3), N-cis-feruloyltyramine (4), N-phenylacetyl-L-glutamine (5), and benzamide (6). The insect growth inhibitory activities of these six different metabolites were determined, and the results show that compound 1 had the highest activity, which significantly inhibited the growth of Chilo suppressalis by 59.63%. Compounds 2–4 also showed a good inhibitory effect on the growth of Chilo suppressalis, while the other compounds had no significant effect. RNA-seq analyses showed that larval exposure to compound 1 up-regulated the genes that were significantly enriched in ribosome biogenesis in eukaryotes, the cell cycle, ribosomes, and other pathways. The down-regulated genes were significantly enriched in metabolic pathways, oxidative phosphorylation, the citrate cycle (TCA cycle), and other pathways. Eighteen up-regulated genes and fifteen down-regulated genes from the above significantly enriched pathways were screened out and verified by real-time quantitative PCR. The activities of detoxification enzymes (glutathione S-transferase (GST); UDP-glucuronosyltransferase (UGT); and carboxylesterase (CarE)) under larval exposure to compound 1 were measured, which indicated that the activity of GST was significantly inhibited by compound 1, while the activities of the UGT and CarE enzymes did not significantly change. As determined by UPLC-MS, the contents of compound 1 in the T1 and T2 groups were 8.55 ng/g and 0.53 ng/g, respectively, which indicated that pest insects significantly induced the synthesis of compound 1. Compound 1 may enhance rice insect resistance by inhibiting the detoxification enzyme activity and metabolism of Chilo suppressalis, as well as promoting cell proliferation to affect its normal growth and development process. The chemical–ecological mechanism of the insect resistance of rice is preliminarily clarified in this paper. Full article

The development of low-cost, high-performance oxygen electrocatalysts is of great significance for energy conversion and storage. As a potential substitute for precious metal electrocatalysts, the construction of efficient and cost-effective oxygen electrocatalysts is conducive to promoting the widespread application of zinc–air batteries. Herein, Co_xNi_yMOF nanoparticles encapsulated within a carbon matrix were synthesized and employed as cathode catalysts in zinc–air batteries. Co_0.5Ni_0.5MOF exhibits superior oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) performance and durability. The zinc–air battery assembled with Co_0.5Ni_0.5MOF as the air cathode exhibits a maximum power density of 138.6 mW·cm⁻². These improvements are mainly attributed to the optimized metal composition of the cobalt–nickel alloy, which increases the specific surface area of the material and optimizes its pore structure. Significantly, the optimization of the electronic structure and active sites within the material has led to amplified ORR/OER activity and better zinc–air battery performance. This study underscores the immense promise of Co_0.5Ni_0.5MOF catalysts as feasible substitutes for commercial Pt/C catalysts in zinc–air batteries. Full article

Safe landings are imperative to accomplish NASA’s Artemis goal to enable human exploration on the Moon, including sample collection missions. However, a process known as plume surface interaction (PSI) presents a significant hazard to lunar landings. PSI occurs when the engine exhaust of a lander interacts with the surface ejecting large amounts of regolith particles at high velocities that can interfere with the landing, disturb the surface, and damage hardware. To better understand PSI, the particle impact event (PIE) sensor is being developed to measure the kinetic energy and the flux of ejecta during landings, to quantify the potential damage, and to quantify the ejecta displaced. Multiple parameters were estimated to define the PIE instrument requirements. These estimates demonstrate that ejecta can travel at velocities of up to 800 m/s and impact the surrounding area with energies of up to 400 µJ. A significant amount of ejecta can be deposited several 10 s of meters away from the landing site, modifying the surface and causing dust-related challenges. The PIE sensor will be launched for the first time in an upcoming lunar lander. Then, PIE measurements will be used to improve PSI prediction capabilities and develop mitigation strategies to ensure safe landings. Full article

This study explored the positive effects of a six-week Social–Emotional and Ethical Learning^® (SEE Learning) program on resilience and social and emotional competences, adapted for elementary students in Daegu, South Korea, a region strongly affected by the first outbreak of COVID-19. A total of 348 third- and fourth-grade students from 15 elementary schools participated, and the curriculum was tailored, emphasizing key areas such as resilience, attention, kindness, attention training, and compassion. Repeated measures analysis of variance (RMANOVA) tests showed statistically significant improvements between pre- and post-tests in resilience and its subscales, including self-efficacy, tolerance of negative affect, positive support relations, power of control, and spontaneity, as well as in social and emotional competencies, including emotional regulation, social skills, empathy, and social tendencies. Despite a lack of maintenance in all areas, at follow-up, the mean scores for self-efficacy, tolerance of negative affect, and positive support relations, as well as emotional regulation, social skills, empathy, and social tendency, remained higher than pre-test levels, suggesting some lasting benefits. The findings underscore the potential of the SEE Learning program integrated with resilience, mindfulness, compassion, and ethical practices to enhance students’ resilience and social and emotional well-being. This study contributes to the growing body of evidence supporting the use of mindfulness and compassion-based SEL programs to mitigate the adverse effects of traumatic events on children’s mental health. Full article

Cattle ticks are a significant health concern in tropical livestock production due to their hematophagous behavior and potential as vectors for human and animal pathogens. In this study, we investigated the tick population present in dairy cattle production, calves, and grazing areas of livestock systems in the northwestern Colombian Amazon. Identification was based on taxonomic keys and molecular markers. Phylogenetic relationships were established using mitochondrial COX1 and 16S genes. Population structure analysis was performed considering age, racial type (B. indicus vs. B. taurus), and the influence of environmental factors and the geomorphological landscape on tick population dynamics. Our findings revealed the presence of a single tick species, with a unique haplotype identified for each mitochondrial gene assessed. Phylogenetic analysis classified the found species within Clade A of the Rhipicephalus microplus complex. Ticks were more prevalent during periods of low rainfall and high temperature, and B. taurus cows exhibited the highest tick abundance. Thus, these results provide insights into the population characteristics and distribution of the tick species present in dairy cattle production systems in the northwestern part of the Colombian Amazon. This information is fundamental for developing targeted strategies based on seasonal variation and host characteristics to mitigate tick infestation severity in the region. Full article

The localization of deformation in shear bands is a fundamental phenomenon in granular materials like soil. In this study, we focus on the characteristics of shear bands, particularly the size effect, by implementing biaxial discrete element method (DEM) modeling. Firstly, we describe the establishment of the biaxial experimental model with dense sands. Then, we implement analyses of specimens with different sizes and find that there is a clear size effect in the stress–strain curve after the peak strength point, and there is less of a size effect in the angle of the shear band; the angle is consistent with Arthur’s theory. Finally, the reason for the size effect is analyzed using the width of the shear band and the porosity inside the shear band. As the specimen size increases, the ratio between the shear band area and the whole specimen decreases. This effect reduces as the isotropic confining stress increases. The difference in the proportion of the shear band area mainly causes the size effect that affects the specimen deformation characteristics. We also find that with the increase in isotropic confining stress, the type of shear band gradually changes from cross-type to single-type. Our study provides valuable insights into understanding the behavior of granular materials. Full article

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such processes by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective by looking at ontology matching techniques. As the manual matching of different sources of information becomes unrealistic once the system scales up, the automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual non-semantically enriched relational data with the support of existing tools (pre-LLM technology) for automatic ontology matching from the scientific community. Even considering a relatively simple case study—i.e., the spatio–temporal alignment of macro indicators—outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for more generalized application. Full article

Transition metal catalysis has contributed to the discovery of novel methodologies and the preparation of natural products, as well as new chances to increase the chemical space in drug discovery programs. In the case of marine drugs, this strategy has been used to achieve selective, sustainable and efficient transformations, which cannot be obtained otherwise. In this perspective, we aim to showcase how a variety of transition metals have provided fruitful couplings in a wide variety of marine drug-like scaffolds over the past few years, by accelerating the production of these valuable molecules. Full article

This study explores the complexities of urban planning and addresses major issues by carefully weighing four options for smart city technology, community-based development, green infrastructure investment, and transit-oriented development. Unlike traditional evaluations, our study applies the novel SWARA-WASPAS model to spherical fuzzy sets (SFSs), thus identifying and navigating the uncertainty present in decision making. This methodological approach improves the accuracy of our assessment by providing detailed information about the advantages and disadvantages of each option. Our study offers useful insights for urban policymakers and planners using carefully weighted criteria and employing a methodical ranking procedure. The aim is to provide insights for decisions that promote equity, environmental consciousness, resilience, and symmetry in urban environments. The application of the SWARA-WASPAS approach not only advances the field but also provides a strong basis for informed decision making. This improves the accuracy of our evaluations and provides detailed insights into each option’s pros and cons. Our study uses weighted criteria and systematic ranking to advise urban policymakers and planners. Our main goal is to help urban populations make resilient, environmentally responsible, equitable, and symmetrical decisions. Our research aims to further the conversation on sustainable urban development by offering a framework based on data that addresses the difficulties associated with dynamic urban environments. In the end, we want our humanized viewpoint to speak to a wider audience and inspire a shared dedication to creating cities that flourish in the face of changing urban environments. Full article

This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods. Full article

Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak, and accumulated rainfall-based damage is likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index ($SWR{I}_{24-hr-S}$) derived from normalising the existing 24-hourly water resources index ($WR{I}_{24-hr-S}$) in the literature to monitor flood risk on an hourly scale. The proposed $SWR{I}_{24-hr-S}$ is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V), and peak (Q). The comprehensive result analysis establishes the practical utility of $SWR{I}_{24-hr-S}$ in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak, and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data are available, offering crucial information for flood risk monitoring and assessment and for the development of effective mitigation strategies. Full article

Insulin-loaded nanofibers were prepared using chitosan as a natural polymer. The loaded insulin with polyethylene oxide was used for preparing monolayer batch S1. Nanofiber S1 was coated by seven layers of film on both sides to form batch S2 as a sandwich containing Layer A (CS, PEG and PEO) and Layer B (PEG and PEO) using electrospinning apparatus. SEM, TEM and FT-IR techniques were used to confirm the drug loading within the composite nanofibers. The in vitro activity that provided a sustained and controlled release of the drug from the nanofiber batch was studied at different pH values spectrophotometrically using a dialysis method. In batches S1 and S2, the release of insulin from nanofiber proceeds via burst release necessary to produce the desired therapeutic activity, followed by slow step. The rate and the percentage release of insulin in batch S2 are found to be higher at all pH values. Full article

Mitochondria, essential organelles orchestrating cellular metabolism, have emerged as central players in various disease pathologies. Recent research has shed light on mitohormesis, a concept proposing an adaptive response of mitochondria to minor disturbances in homeostasis, offering novel therapeutic avenues for mitochondria-related diseases. This comprehensive review explores the concept of mitohormesis, elucidating its induction mechanisms and occurrence. Intracellular molecules like reactive oxygen species (ROS), calcium, mitochondrial unfolded proteins (UPRmt), and integrated stress response (ISR), along with external factors such as hydrogen sulfide (H₂S), physical stimuli, and exercise, play pivotal roles in regulating mitohormesis. Based on the available evidence, we elucidate how mitohormesis maintains mitochondrial homeostasis through mechanisms like mitochondrial quality control and mitophagy. Furthermore, the regulatory role of mitohormesis in mitochondria-related diseases is discussed. By envisioning future applications, this review underscores the significance of mitohormesis as a potential therapeutic target, paving the way for innovative interventions in disease management. Full article

While it is well known that the Weibull distribution is a good model for wind-speed measurements and can be explained through simple statistical arguments, how such a model holds for shorter time periods is still an open question. In this paper, we present a systematic investigation of the accuracy of the Weibull distribution to wind-speed measurements, in comparison with other possible “cousin” distributions. In particular, we show that the Gaussian distribution enables one to predict wind-speed histograms with higher accuracy than the Weibull distribution. Two other good candidates are the Nakagami and the Rice distributions, which can be interpreted as particular cases of the Weibull distribution for particular choices of the shape and scale parameters. These findings hold not only when predicting next-point values of the wind speed but also when predicting the wind energy values. Finally, we discuss such findings in the context of wind power forecasting and monitoring for power-grid assessment. Full article

Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score. Full article

The urban heat island (UHI) is a crucial factor in developing sustainable cities and societies. Appropriate data collection, analysis, and prediction are essential first steps in studying the effects of the UHI. This research systematically reviewed the papers related to the UHI that have used on-site data collection in the United States and Canada and the papers related to predicting and analyzing this effect in these regions. To achieve this goal, this study extracted 330 articles from Scopus and Web of Science and, after selecting the papers, reviewed 30 papers in detail from 1998 to 2023. The findings of this paper indicated a methodological shift from traditional sensors and data loggers towards more innovative and customized technologies. Concurrently, this research reveals a growing trend in using machine learning, moving from supportive to direct predictive roles and using techniques like neural networks and Bayesian networks. Despite the maturation of UHI research due to these developments, they also present challenges in technology complexity and data integration. The review emphasizes the need for future research to focus on accessible, accurate technologies. Moreover, interdisciplinary approaches are crucial for addressing UHI challenges in an era of climate change. Full article

The world-class Jinding deposit in SW China has ~15 Mt of Zn and Pb metals combined, in an evaporite dome containing amounts of gypsum/anhydrite. These gypsum and anhydrite are mainly located in limestone breccias (Member I), gypsum-bearing complexes (Member III), and red mélange, with some occurring as veins in clast-free sandstone (Member IV) and as fractures/vugs of host rock. The gypsum/anhydrite and dome genesis remain equivocal. The gypsum in limestone breccias and in red mélange with flow texture contains numerous Late Triassic Sanhedong limestone fragments. The δ³⁴S (14.1%–17%), δ¹⁸O (9.7%–14.6%), and ⁸⁷Sr/⁸⁶Sr ratios (0.706913–0.708711) of these gypsum are close to the S-O-Sr isotopes of the Upper Triassic Sanhedong Formation anhydrite in the Lanping Basin (δ³⁴S = 15.2%–15.9%, δ¹⁸O = 10.9%–13.1%, ⁸⁷Sr/⁸⁶Sr = 0.707541–0.707967), and are inconsistent with the Paleocene Yunlong Formation gypsum in the Lanping Basin (⁸⁷Sr/⁸⁶Sr = 0.709406–0.709845), indicating that these gypsum were derived from the Upper Triassic Sanhedong Formation evaporite but not from the Paleocene Yunlong Formation, and formed as a result of evaporite diapirism. The δ³⁴S (14.3%–14.5%), δ¹⁸O (10.1%–10.3%), and ⁸⁷Sr/⁸⁶Sr ratios (0.709503–0.709725) of gypsum as gypsum–sand mixtures in gypsum-bearing complexes are similar to the ⁸⁷Sr/⁸⁶Sr ratios of gypsum in the Yunlong Formation of the Lanping Basin and Cenozoic basins in the northern part of the Himalayan–Tibetan orogen, suggesting that the material source of this gypsum was derived from the Yunlong Formation, and formed as a result of gypsum–sand diapirism. The gypsum veins in clast-free pillow-shaped mineralized sandstone and the gypsum in host rock fractures and vugs formed after the supergene minerals such as smithsonite. The δ³⁴S (−16.3%~−12.7%) and δ¹⁸O (−9.8%~−4.7%) of this gypsum indicate that the gypsum is of supergene origin with sulfate derived from the reoxidation of reduced sulfur. We confirmed that the Jinding dome is genetically related to diapir of the Late-Triassic Sanhedong Formation evaporite. Clast-free sandstone and gypsum-bearing complexes in the dome were produced by diapir of the Paleocene Yunlong Formation unconsolidated gypsum–sand mixtures. Full article