Global Structure Research Articles

Interference with Systemic Negative Feedback Regulation as a Potential Mechanism for Nonmonotonic Dose-Responses of Endocrine-Disrupting Chemicals.

Endocrine-disrupting chemicals (EDCs) often exhibit nonmonotonic dose-response (NMDR) relationships, posing significant challenges to health risk assessment and regulations. Several molecular mechanisms operating locally in cells have been proposed, including opposing actions via different receptors, mixed-ligand heterodimer formation, and receptor downregulation. Systemic negative feedback regulation of hormone homeostasis, which is a common feature of many endocrine systems, has also been invoked as a mechanism; however, whether and how exactly such global feedback structure may underpin NMDRs is poorly understood. We hypothesize that an EDC may compete with the endogenous hormone for receptors (i) at the central site to interfere with the feedback regulation thus altering the physiological hormone level, and (ii) at the peripheral site to disrupt the hormone action; this dual-action may oppose each other, producing nonmonotonic endocrine effects. The objective here is to explore - through computational modeling - how NMDRs may arise through this potential mechanism and the relevant biological variabilities that enable susceptibility to nonmonotonic effects. We constructed a dynamical model of a generic hypothalamic-pituitary-endocrine (HPE) axis with negative feedback regulation between a pituitary hormone and a terminal effector hormone (EH). The effects of model parameters, including receptor binding affinities and efficacies, on NMDR were examined for EDC agonists and antagonists. Monte Carlo human population simulations were then conducted to systemically explore biological parameter conditions that engender NMDR. When an EDC interferes sufficiently with the central feedback action of EH, the net endocrine effect at the peripheral target site can be opposite to what is expected of an agonist or antagonist at low concentrations. J/U or Bell-shaped NMDRs arise when the EDC has differential binding affinities and/or efficacies, relative to EH, for the peripheral and central receptors. Quantitative relationships between these biological variabilities and associated distributions were discovered, which can distinguish J/U and Bell-shaped NMDRs from monotonic responses. The ubiquitous negative feedback regulation in endocrine systems can act as a universal mechanism for counterintuitive and nonmonotonic effects of EDCs. Depending on key receptor kinetic and signaling properties of EDCs and endogenous hormones, some individuals may be more susceptible to these complex endocrine effects.

Intelligent tool wear prediction based on deep learning PSD-CVT model

To ensure the reliability of machining quality, it is crucial to predict tool wear accurately. In this paper, a novel deep learning-based model is proposed, which synthesizes the advantages of power spectral density (PSD), convolutional neural networks (CNN), and vision transformer model (ViT), namely PSD-CVT. PSD maps can provide a comprehensive understanding of the spectral characteristics of the signals. It makes the spectral characteristics more obvious and makes it easy to analyze and compare different signals. CNN focuses on local feature extraction, which can capture local information such as the texture, edge, and shape of the image, while the attention mechanism in ViT can effectively capture the global structure and long-range dependencies present in the image. Two fully connected layers with a ReLU function are used to obtain the predicted tool wear values. The experimental results on the PHM 2010 dataset demonstrate that the proposed model has higher prediction accuracy than the CNN model or ViT model alone, as well as outperforms several existing methods in accurately predicting tool wear. The proposed prediction method can also be applied to predict tool wear in other machining fields.

Global Monarchy: Royal Encounters in the Age of Empire

AbstractMonarchy is one of the oldest truly global social structures. On the eve of the imperial age, most of the world was governed by monarchs. But as the European empires expanded, this order was radically transformed. During the long nineteenth century, most non-European monarchies were conquered by imperial powers. Often, European imperial powers abolished them, imprisoning, killing, or exiling local rulers; in other cases, the imperial powers incorporated the conquered monarchies into new imperial orders of indirect rule. Yet, there were some monarchies that survived Europe’s imperial expansion. In fact, every country that retained its independence in the era of high imperialism was ruled by a monarch: Ethiopia, China, Japan, the Ottoman empire, Persia (Iran), and Siam (Thailand). To some extent, these monarchs lived in the same social worlds as their European counterparts, forming a global, status-based community. This article offers some observations on the relationship between the world’s monarchs in the age of empire by focusing on encounters between European monarchs and those of the few non-European countries that retained their independence.

Flux Rope Modeling of the 2022 September 5 Coronal Mass Ejection Observed by Parker Solar Probe and Solar Orbiter from 0.07 to 0.69 au

As both Parker Solar Probe (PSP) and Solar Orbiter (SolO) reach heliocentric distances closer to the Sun, they present an exciting opportunity to study the structure of coronal mass ejections (CMEs) in the inner heliosphere. We present an analysis of the global flux rope structure of the 2022 September 5 CME event that impacted PSP at a heliocentric distance of only 0.07 au and SolO at 0.69 au. We compare in situ measurements at PSP and SolO to determine global and local expansion measures, finding a good agreement between magnetic field relationships with heliocentric distance, but significant differences with respect to flux rope size. We use PSP/Wide-Field Imager for Solar Probe images as input to the ELlipse Evolution model based on Heliospheric Imager data (or ELEvoHI), providing a direct link between remote and in situ observations; we find a large discrepancy between the resulting modeled arrival times, suggesting that the underlying model assumptions may not be suitable when using data obtained close to the Sun, where the drag regime is markedly different in comparison to larger heliocentric distances. Finally, we fit the SolO's magnetometer and PSP's FIELDS data independently with the 3D Coronal ROpe Ejection (or 3DCORE) model, and find that many parameters are consistent between spacecraft. However, challenges are apparent when reconstructing a global 3D structure that aligns with arrival times at PSP and SolO, likely due to the large radial and longitudinal separations between spacecraft. From our model results, it is clear the solar wind background speed and drag regime strongly affect the modeled expansion and propagation of CMEs and need to be taken into consideration.

Multi-Task Collaborative Pre-Training and Adaptive Token Selection: A Unified Framework for Brain Representation Learning.

Structural magnetic resonance imaging (sMRI) reveals the structural organization of the brain. Learning general brain representations from sMRI is an enduring topic in neuroscience. Previous deep learning models neglect that the brain, as the core of cognition, is distinct from other organs whose primary attribute is anatomy. Capturing the high-level representation associated with inter-individual cognitive variability is key to appropriately represent the brain. Given that this cognition-related information is subtle, mixed, and distributed in the brain structure, sMRI-based models need to both capture fine-grained details and understand how they relate to the overall global structure. Additionally, it is also necessary to explicitly express the cognitive information that implicitly embedded in local-global image features. Therefore, we propose MCPATS, a brain representation learning framework that combines Multi-task Collaborative Pre-training (MCP) and Adaptive Token Selection (ATS). First, we develop MCP, including mask-reconstruction to understand global context, distort-restoration to capture fine-grained local details, adversarial learning to integrate features at different granularities, and age-prediction, using age as a surrogate for cognition to explicitly encode cognition-related information from local-global image features. This co-training allows progressive learning of implicit and explicit cognition-related representations. Then, we develop ATS based on mutual attention for downstream use of the learned representation. During fine-tuning, the ATS highlights discriminative features and reduces the impact of irrelevant information. MCPATS was validated on three different public datasets for brain disease diagnosis, outperforming competing methods and achieving accurate diagnosis. Further, we performed detailed analysis to confirm that the MCPATS-learned representation captures cognition-related information.

Evaluating the Influence of Basel Accord on Banking Sector of Pakistan

The papers aim to investigate how Basel III affected Pakistan's entire banking sector from 2013 to 2018. The requirements and obligations of Basel III are regarded as the most efficient tools for the banking sector. When analyzing the impact of Basel III on the banking industry, three primary considerations are made: financial size, spread, and provision for non-performing loans. Relapse investigations were utilized for checking speculation. We inferred that the general pummel of Basel III on banks is enormous, yet not all banks have totally taken on Basel III. The significance of this research lies in giving important experiences into the viability of global regulatory structures in rising economies' potential. By evaluating the Basel Accord's effect on Pakistan's banking sector, this study adds to a more extensive comprehension of how worldwide guidelines can be adjusted to neighborhood settings and how they can address explicit challenges faced by banks in emerging nations. This analysis not only reveals insight into the operational and vital changes made by Pakistani banks but also gives suggestions for policymakers to upgrade regulatory practices and monetary strength. Eventually, this research aims to illuminate future regulatory changes and work on the flexibility of Pakistan's banking sector in the face of worldwide monetary uncertainties. We propose highlighting semiannually the effects of Basel on the banking industry and conducting a comparison analysis of conventional and Islamic banks.

Global lowest energy structures and electronic properties of InnM (M = Mn, Fe, Co, Ni, n = 6–9) clusters

Global lowest energy structures and electronic properties of InnM (M = Mn, Fe, Co, Ni, n = 6–9) clusters

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

Improved diffusion mapping combined with procrustes analysis for capturing local-global data structures in industrial process monitoring

BackgroundProcess monitoring, by providing early warnings of abnormal operating states resulting from process faults, facilitates the maintenance of normal production and ensures process safety. In the domain of industrial process monitoring, capturing the local-global structural features of data and acquiring an explicit mapping relationship for dimensionality reduction projection holds significant importance for online fault detection in industrial processes. MethodsThis study introduces an Improved Diffusion Mapping and Procrustes analysis (IDM-P) method for this purpose. Initially, considering the multiscale and correlation among industrial data features, the Mahalanobis distance is incorporated to improve the diffusion mapping algorithm. Utilizing this method allows for the concurrent capture of both local and global data structures, leading to a more efficient extraction of data-representative features, which enhances the accuracy of fault detection. Procrustes analysis is then used to obtain an explicit mapping matrix between high-dimensional data and low-dimensional manifolds, improving the efficiency of the key feature extraction of the new samples. Finally, this matrix is utilized to construct process monitoring statistics for fault detection. Significant FindingsThe method's effectiveness was validated through experiments on the TEP dataset and actual industrial data, demonstrating that IDM-P maintains higher accuracy and achieves optimal fault detection compared to other methods.

Critically Interrogating the Decolonization of Development: Possibilities and Pitfalls

This essay delves deep into the intricate issues pertaining to the concept of "decolonization" within the realm of international development. The study is grounded in the profound understanding that the legacy of colonialism continues to cast a long shadow over contemporary development, giving rise to pervasive inequalities and marginalization in various parts of the world. The central theme of the research revolves around critically examining the potential avenues and potential pitfalls associated with the process of decolonization. Through a meticulous analysis of relevant theories and a close examination of real-world cases, the study seeks to unravel the multifaceted dimensions of the definition, possibilities, and the formidable challenges encountered in the implementation of decolonization in the development context. The findings of the research suggest that while decolonization holds the promise of steering development towards more equitable and sustainable trajectories, it also confronts a multitude of challenges, including the stubborn inertia ingrained within institutional frameworks and the resistance posed by the entrenched global power structures. The conclusions drawn emphasize the imperative need for further in-depth research to propel the process of decolonization forward and to strive towards the realization of a just and equitable global society.

HDCCT: Hybrid Densely Connected CNN and Transformer for Infrared and Visible Image Fusion

Multi-modal image fusion is a methodology that combines image features from multiple types of sensors, effectively improving the quality and content of fused images. However, most existing deep learning fusion methods need to integrate global or local features, restricting the representation of feature information. To address this issue, a hybrid densely connected CNN and transformer (HDCCT) fusion framework is proposed. In the proposed HDCCT framework, the network of the CNN-based blocks obtain the local structure of the input data, and the transformer-based blocks obtain the global structure of the original data, significantly improving the feature representation. In the fused image, the proposed encoder–decoder architecture is designed for both the CNN and transformer blocks to reduce feature loss while preserving the characterization of all-level features. In addition, the cross-coupled framework facilitates the flow of feature structures, retains the uniqueness of information, and makes the transform model long-range dependencies based on the local features already extracted by the CNN. Meanwhile, to retain the information in the source images, the hybrid structural similarity (SSIM) and mean square error (MSE) loss functions are introduced. The qualitative and quantitative comparisons of grayscale images with infrared and visible image fusion indicate that the suggested method outperforms related works.

Glubam roof truss with riveted glubam connections adopting thin-walled steel tube: Experiment, modeling, and model-updating

Bio-based laminated structures offer a timely solution to meet the pressing demand for resource-efficient and environmentally responsible construction practices in civil engineering. Riveted connections are a kind of commonly used joint and significantly impact the mechanical behaviors of global structure. However, existing studies on the complex behavior of such joints for bamboo-based structures are insufficient. This study investigated the hysteretic behavior of riveted Glued Laminated Bamboo (glubam) connections using thin-walled steel tube and corresponding planar roof truss with thin-walled steel tube connections by experimental and numerical methods. Firstly, cyclic tests were conducted on the riveted glubam connections with thin-walled steel tube to investigate their hysteretic behavior. The test matrix encompasses four distinct connection configurations, carefully selected to assess the connection behavior of glubam. After the investigation of connection behavior, cyclic tests were further conducted on glubam planar roof truss structures to evaluate their global and local hysteretic behavior. Subsequently, the numerical hysteretic models for joints and hybrid roof trusses were developed within the OpenSeesPy framework. To accurately and efficiently calibrate the model parameters, a novel parallel genetic algorithm (PGA) was proposed to carry out an initial calibration on model parameters. Based on PGA, a more comprehensive model updating framework combining the neural network, prior knowledge and PGA was developed to obtain optimal parameters. Finally, the numerical models were successfully validated against the experimental data, demonstrating the effectiveness of the established numerical model and model-updating framework.

Comparison of gene-by-gene and genome-wide short nucleotide sequence-based approaches to define the global population structure of Streptococcus pneumoniae.

Defining the population structure of a pathogen is a key part of epidemiology, as genomically related isolates are likely to share key clinical features such as antimicrobial resistance profiles and invasiveness. Multiple different methods are currently used to cluster together closely related genomes, potentially leading to inconsistency between studies. Here, we use a global dataset of 26 306 Streptococcus pneumoniae genomes to compare four clustering methods: gene-by-gene seven-locus MLST, core genome MLST (cgMLST)-based hierarchical clustering (HierCC) assignments, life identification number (LIN) barcoding and k-mer-based PopPUNK clustering (known as GPSCs in this species). We compare the clustering results with phylogenetic and pan-genome analyses to assess their relationship with genome diversity and evolution, as we would expect a good clustering method to form a single monophyletic cluster that has high within-cluster similarity of genomic content. We show that the four methods are generally able to accurately reflect the population structure based on these metrics and that the methods were broadly consistent with each other. We investigated further to study the discrepancies in clusters. The greatest concordance was seen between LIN barcoding and HierCC (adjusted mutual information score=0.950), which was expected given that both methods utilize cgMLST, but have different methods for defining an individual cluster and different core genome schema. However, the existence of differences between the two methods shows that the selection of a core genome schema can introduce inconsistencies between studies. GPSC and HierCC assignments were also highly concordant (AMI=0.946), showing that k-mer-based methods which use the whole genome and do not require the careful selection of a core genome schema are just as effective at representing the population structure. Additionally, where there were differences in clustering between these methods, this could be explained by differences in the accessory genome that were not identified in cgMLST. We conclude that for S. pneumoniae, standardized and stable nomenclature is important as the number of genomes available expands. Furthermore, the research community should transition away from seven-locus MLST, whilst cgMLST, GPSC and LIN assignments should be used more widely. However, to allow for easy comparison between studies and to make previous literature relevant, the reporting of multiple clustering names should be standardized within the research.

Finite element mesh transition for local–global modeling of composite structures

This study presents an automatic mesh generation algorithm designed to address computational challenges in simulating small-scale defects within large composite structures. The algorithm seamlessly transitions from a coarse mesh, corresponding to the global structure, to a highly refined mesh in targeted local regions of interest. The transition element number and shape can be adjusted by the specified parameters. Tailored to complement this method for non-homogeneous composite models, which include multiple materials such as cohesive layers representing interlayer properties, a volume fraction calculator is integrated to automatically assign the mixture material property in each transition element. Entire processes are fully automated using a MATLAB script, eliminating the need to open the FEA software interface. The validation studies of the reconstructed two-dimensional models, assembled with the wrinkle-defect model, demonstrate their feasibility. The performance of the model is examined in terms of strain and displacement at the connecting boundaries, load–displacement curve, and interlayer failure prediction. The mesh transition model achieves agreeable results compared to a fully fine mesh model, and a 92% reduction in computational time in stress analysis, showing the efficiency of the mesh transition for local–global modeling of composite structures.

Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.

To develop a non-coplanar cone-beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non-coplanar radiotherapy. A generative adversarial network (GAN) was utilized to reconstruct non-coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non-coplanar projections as the input, and an encoder with a dual-branch structure was utilized to extract features from the coplanar CBCT and non-coplanar projections separately. Non-coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch-based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°,±90°) that are most commonly used for brain non-coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters. In both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that±45° couch angle models performed better than±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 58.5 HU and 0.42mm, 56.8 HU and 0.41mm, 73.6 HU and 0.48mm, and 65.3 HU and 0.46mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, -45°, 90°, and -90° were 91.2 HU and 0.46mm, 95.0 HU and 0.45mm, 114.6 HU and 0.58mm, and 102.9 HU and 0.52mm, respectively. The results show that the reconstructed non-coplanar CBCT images can potentially enable intra-treatment three-dimensional position verification for non-coplanar radiotherapy.

Nutrition and Physical Activity Education in Medical School: A Narrative Review.

This review explores the diverse landscape of integrating nutrition and physical activity education into medical school curricula, focusing on the imperative role of physicians in promoting health through lifestyle changes. By examining global medical education structures, we uncovered disparities in nutrition and physical activity training, and highlighted the need for a shared framework to address international and regional challenges. Despite acknowledging the importance of both nutrition and physical activity, studies have consistently uncovered deficiencies in medical school curricula, especially in skills related to providing lifestyle advice and behavioral counseling. Survey studies among medical students have illuminated various perceptions and knowledge gaps, emphasizing the need for more comprehensive and mandatory nutrition and physical activity training. While acknowledging progress, challenges, such as time constraints, resource availability, and faculty expertise, persist. Integrating lifestyle education results in resistance, a demand for strategic communication, and faculty buy-ins. These findings underscore the importance of a holistic approach that balances theoretical knowledge, practical skills, and confidence that medical students need to promote effective nutrition and physical activity in healthcare.

Recent Progress on In Situ Catalytic Conversion Catalysts for Oil Shale.

With the gradual depletion of oil resources, it is urgent to vigorously develop unconventional and renewable energy sources. As an unconventional resource with abundant resources, the rational and efficient development of oil shale is of great significance to alleviating the national energy crisis. The main methods of oil shale development are surface dry distillation and in situ transformation. In situ catalytic conversion technology, as an emerging mining method, has the potential to improve efficiency, reduce costs, and environmental impact and is an important direction for future oil shale development. As a key component in the in situ conversion process, catalysts play a crucial role in the rate, selectivity, and product quality of the reaction. However, the research on oil shale pyrolysis catalysts is still in the laboratory stage and is difficult to translate into practical applications. Therefore, this paper reviews recent progress of catalyst research in the in situ catalytic conversion of oil shale, including the types, design principles, and reaction mechanisms of catalysts, and looks forward to the future development direction. It provides a certain reference for the discovery of new catalysts that can improve yield and reduce environmental pollution in the process of oil shale extraction and also provides a valuable technical reference for the green development and utilization of unconventional and strategic alternative energy sources in the world and the transformation and sustainable development of the global energy structure.

Discussion On Three Optimization Plans for Photovoltaic Systems

In the context of the global energy structure, photovoltaic (PV) generation plays a critical role in reducing greenhouse gas emissions and improving energy sustainability. However, PV systems differ significantly from conventional power generation like coal and nuclear power due to their dependence on environmental factors such as weather and seasons, resulting in significant load fluctuations. To mitigate this issue, the optimization of PV systems is widely accepted, involving techniques such as energy storage and performance enhancement. Three methods are proposed to address the issue of power instability in photovoltaic systems: changing the tilt angle between photovoltaic panels and sunlight, changing the electrical parameters of photovoltaic cells, and compensation using liquid air energy storage technology. Based on these three methods, corresponding implementation directions and applicable ranges are provided for different needs. The advantages and disadvantages are also discussed.

“Knowledge Strategies” for Indigenous Studies on Intercultural Communication in Non-Western Countries in the Global Power Structure

According to Michel Foucault’s power/knowledge theory, knowledge is not produced in a vacuum; the construction of any knowledge system implicitly contains power relations. The “knowledge strategies” for Indigenous studies on intercultural communication should evolve and improve in response to shifts in the global power structure. With the development of globalization and the evolution of communication technologies, this study interprets the current global power structure as a “dual structure” in which the international society and the world society coexist and develop together. This structure leads to a complex trend of simultaneous “centralization” and “decentralization”, as well as “homogenization” and “hybridization” in the global cultural order. For scholars from non-Western countries, Indigenous studies on intercultural communication need to interpret the new global power structure, expanding their research perspectives and topics to a global dimension. This approach links Indigenous conceptual resources and methodologies with an open and diverse global cultural order. This study proposes “knowledge strategies” for Indigenous studies on intercultural communication in non-Western countries and introduces a third level of significance for intercultural communication beyond daily interaction and cultural interaction: community building. Regarding the research purpose, this study aims to provide a new perspective for the study of intercultural communication theory, promoting an equal dialogue between Western and non-Western knowledge systems of intercultural communication, and enhancing the inclusiveness and humanistic awareness of this discipline.

Geothermal Resource Assessment and Development Recommendations for the Huangliu Formation in the Central Depression of the Yinggehai Basin

As a renewable resource, geothermal energy plays an increasingly important role in global and regional energy structures. Influenced by regional tectonic activities, multi-stage thermal evolution, and continuous subsidence, the subsurface temperatures in the Yinggehai Basin has been consistently rising, resulting in the formation of multiple geothermal reservoirs. The Neogene Huangliu Formation, with its high geothermal gradients, suitable burial depths, considerable thickness, and wide distribution, provides excellent geological conditions for substantial geothermal resources. However, the thermal storage characteristics and geothermal resources of this formation have not been fully assessed, limiting their effective development. This study systematically collected and analyzed drilling, geological, and geophysical data to examine these reservoirs’ geometric structures, thermal properties, and physical characteristics. Further, we quantitatively evaluated the geothermal resource potential of the Huangliu Formation and its respective reservoirs through volumetric estimation and Monte Carlo simulations, pointing zones with high geothermal prospects and formulating targeted development strategies. The findings indicate: (1) The Yinggehai Basin exhibits an average geothermal gradient of 39.4 ± 4.7 °C/km and an average terrestrial heat flow of 77.4 ± 19.1 mW/m2, demonstrating a favorable geothermal background; (2) The central depression of the Huangliu Formation harbors considerable geothermal resource potential, with an average reservoir temperature of 140.9 °C, and a total geothermal resource quantified at approximately 2.75 × 1020 J, equivalent to 93.95 × 108 tec. Monte Carlo projections estimate the maximum potential resource at about 3.10 × 1020 J, approximately 105.9 ×108 tec. (3) Additionally, the R14 and R23 reservoirs have been identified as possessing the highest potential for geothermal resource development. The study also proposes a comprehensive utilization model that integrates offshore geothermal power generation with multiple applications. These findings provide a method for the evaluation of geothermal resources in the Yinggehai Basin and lay a foundation for the sustainable development of resources.