Exploration Of Resources Research Articles

Optimization of Rate of Penetration and Mechanical Specific Energy Using Response Surface Methodology and Multi-Objective Optimization

Optimizing the drilling process is critical for the exploration of natural resources. However, there are several mechanic parameters that continuously interact with formation properties, hindering the optimization process. Rate of penetration (ROP) and mechanical specific energy (MSE) are considered two key performance indicators that allow the identification of ideal conditions to enhance the drilling process. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a 2D analysis of parameters as a function of depth, response surface methodology (RSM), and multi-objective optimization. The results show that the RSM method and multi-objective optimization provide better results when compared with 2D analysis of parameters as a function of depth. The RSM method can be used as a tool to analyze the effects of the independent drilling mechanical parameters (WOB, RPM, FLOW, and TOR) on the response variables (ROP and MSE) with a 95% confidence level. Through multi-objective optimization, it was possible to concomitantly achieve an ROP of approximately 22 ft/h and MSE of nearly 11 kpsi using the values of WOB, RPM, FLOW, and TOR of about 11 klb, 109 rev/min, 803 gpm, and 3 klb-ft, respectively. Using high WOB values, i.e., from the mean value up to the maximum value of approximately 43 klb, reflects a low ROP and most likely indicates an operation beyond the foundering point. High FLOW promotes a more efficient hole cleaning and higher rates of cuttings transport, thus preventing eventual in situ drill-bit sticking. Flow adjustment also ensures an adequate balance of dynamic bottom hole pressure, in addition to controlling the force impact force of the drilling fluid in contact with the rock being drilled, expressing importance in terms of efficiency and rock penetration. Finally, it is important to mention that the results of this research are not only applicable to hydrocarbon exploration but also to geothermal and natural hydrogen exploration. Values analyzed and presented with decimal precision should be logically focused as integers when in industrial application.

Reservoir Fluid Identification Based on Bayesian-Optimized SVM Model

Tight sandstone reservoirs are characterized by fine-grained rock particles, a high clay content, and a complex interplay between the electrical properties and gas content. These factors contribute to low-contrast reservoirs, where the logging responses of the gas and water layers are similar, resulting in traditional logging interpretation charts exhibiting a low accuracy in the fluid-type classification. This inadequacy fails to meet the fluid identification needs of the study area’s reservoirs and severely restricts the exploration and development of unconventional oil and gas resources. To address this challenge, this study proposes a fluid identification method based on Bayesian-optimized Support Vector Machine (SVM) to enhance the accuracy and efficiency of the fluid identification in low-contrast reservoirs. Firstly, through a sensitivity analysis of the logging responses, sensitive logging parameters such as the natural gamma, compensated density, compensated neutron, and compensated sonic logs are selected as input data for the model. Subsequently, Bayesian optimization is employed to automatically search for the optimal combination of hyperparameters for the SVM model. Finally, an SVM model is established using the optimized hyperparameters to classify and identify the following four fluid types: water layers, gas layers, gas–water layers, and dry layers. The proposed method is applied to fluid identification in the study area, and comparative experiments are conducted with the K-Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost models. The classification performance of each model is systematically evaluated using metrics such as the accuracy, recall, and F1-score. The experimental results indicate that the SVM model outperforms the other models in fluid identification, achieving an average accuracy of 91.41%. This represents improvements of 16.94%, 4.39%, and 8.30% over the KNN, RF, and AdaBoost models, respectively. These findings validate the superiority of the SVM model for fluid identification in the study area and provide an efficient and feasible solution for fluid identification in tight sandstone reservoirs.

Plastid genome comparison and phylogenetic analyses of the Chinese group of medicinal species and related taxa within Asparagus genus

BackgroundAsparagus L. is a large genus widely distributed across the continents of the Old World. Among its members, approximately 14 species found in China are recognized as popular herbal medicines. However, accurate authentication of these medicinal species and their phylogenetic relationships with related taxa remains unresolved.MethodsTo identify simple sequence repeats (SSRs) and divergence hotspot regions appropriate for future authentication studies, as well as to infer the phylogenetic relationships among Asparagus species, we employed a plastid genome (plastome) dataset consisting of 25 Asparagus species (21 newly sequenced and four retrieved from GenBank), encompassing 12 Chinese medicinal species, for comparative and phylogenetic analyses.ResultsAll Asparagus plastomes displayed a typical quadripartite structure with sizes ranging from 155,948 bp to 157,128 bp and harbored 114 unique genes (80 protein-coding genes, 30 tRNA genes, and four rRNA genes). IRscope and Mauve analyses indicated minimal structural variation among Asparagus plastomes. We detected between 79 to 95 SSRs across the plastomes; most were located in the large single-copy (LSC) region and primarily consisted of mono-nucleotide repeat sequences (especially A and T repeats). The genus displayed mono-, di-, tri-, tetra-, penta-, and hexa-nucleotide repeats, but with variations in types and numbers among different species. Additionally, we identified 12 special SSR motifs and seven divergent hotspot regions that may serve as potential molecular markers for future identification efforts. Phylogenetic analyses yielded a robust phylogeny for Asparagus taxa, which were split into Clades I, II, and III. Notably, medicinal Asparagus species were mainly found in Clade III. Although the phylogenetic relationships of most Asparagus species aligned with previous study findings, the phylogenetic positions of A. munitus, A. subscandens, A. gobicus, and A. dauricus were newly determined.ConclusionsThe plastomes of Asparagus are largely conserved in terms of genome structure, size, gene content, and arrangement. Nevertheless, SSRs analyses revealed significant interspecific polymorphism within Asparagus. In addition, special SSR motifs and divergent hotspot regions identified from Asparagus plastomes provided reference for subsequent identification investigations. The plastome-based phylogeny provided preliminary insights into the relationships among the Chinese group of medicinal species and related taxa within Asparagus. Overall, this study offers a wealth of informative genetic resources pertinent to Asparagus, thereby enhancing our understanding of its evolution and laying a foundation for species identification, assessment of genetic population diversity, as well as the exploration and conservation of germplasm resources.

Seabed Depth Prediction Using Multi-Scale Gravity Anomalies and Fully Connected Deep Neural Networks: A Novel Approach Applied to the South China Sea

Accurate seabed topography is crucial for marine research, resource exploration, and engineering applications. While deep learning techniques have been widely applied in seabed inversion, existing methods often overlook the multi-scale influence of gravity anomalies, particularly the critical role of short-wavelength gravity anomalies in resolving fine-scale bathymetric features. In this study, we propose a novel Fully Connected Deep Neural Network (FCDNN) approach that systematically integrates long-wavelength, short-wavelength, and residual gravity anomaly components for seabed topography inversion. Using multi-satellite altimetry-derived gravity anomaly data (SIO V32.1) and shipborne bathymetric data (NCEI), we constructed a high-resolution (1′ × 1′) seabed topography model for the South China Sea (108°E–121°E, 6°N–23°N), termed FCD_Depth_SCS. The workflow included multi-scale decomposition of gravity anomalies, linear regression-based residual calculation, and FCDNN-based nonlinear training to capture the complex relationships between gravity anomalies and water depth. The FCD_Depth_SCS model achieved a difference standard deviation (STD) of 44.755 m and a mean absolute percentage error (MAPE) of 2.903% when validated against 160,476 shipborne control points. This performance significantly outperformed existing models, including GEBCO_2024, SIOv25.1, DTU18, and GGM_Depth (derived from the Gravity–Geologic Method), whose STDs were 82.234 m, 108.241 m, 186.967 m, and 58.874 m, respectively. Notably, the inclusion of short-wavelength gravity anomalies enabled the model to capture fine-scale bathymetric variations, particularly in open-sea regions. However, challenges remain near coastlines and complex terrains, highlighting the need for further model partitioning to address localized nonlinearity. This study highlights the benefits of integrating multi-scale gravity anomaly data with a fully connected deep neural network. Employing this innovative and robust approach enables high-resolution inversion of seabed topography with enhanced precision. The proposed method provides significant advancements in accuracy and resolution, contributing valuable insights for marine environmental research, resource management, and oceanographic studies.

Progress of Deep Exploration Artificial-Source Electromagnetic Instruments in China: A Review

As a type of widely used geophysical exploration equipment, artificial-source electromagnetic instruments are effectively used for resource and energy exploration due to their simple operation, deep-detecting depth, and higher resolution in the field, compared to natural-source electromagnetic instruments. However, after years of development, the current work is in a significant period. Due to the exploration requirement gradually shifting from shallow-surface to deep, traditional electromagnetic instruments are facing severe challenges. Over the course of the elaborate study by geophysicists in China, electromagnetic instruments have made innovative breakthroughs during recent years. The application in the deep exploration of artificial-source electromagnetic instruments has been further extended. By taking the time–frequency electromagnetic instrument, the low-temperature transient electromagnetic instrument, the surface electromagnetic prospecting instrument, and the wide-field electromagnetic instrument as examples, the purpose of this paper is to give a systematic introduction to the technical indicators and field applications. Then, an overview of the situation and progress of the instrument is given to help the readers understand the research directions. Finally, we offer the developing trends of the deep exploration artificial-source electromagnetic instrument.

Japanese space policy: An international legal perspective

Japan’s advancements in technology and its strategic focus on space policy have driven swift advancements in the field, attributed to Japan’s ability to balance scientific exploration, industrial growth, and national defense. This article examines Japan’s national legal framework governing space activities. The key pieces of legislation analyzed are the Law Concerning Japan Aerospace Exploration Agency of 2002, the Basic Space Law of 2008, the Act on Launching of Spacecraft, etc. and Control of Spacecraft of 2016, the Act on Ensuring Appropriate Handling of Satellite Remote Sensing Data of 2016, and the Act on the Promotion of Business Activities for the Exploration and Development of Space Resources of 2021. The analysis of these activities is conducted in accordance with the relevant international space legal framework. The article delves into the shift in Japan’s space policy from “non-military” to “non-aggressive” in 2008 and its impact on national legislation. An examination is provided of the fourth law in the world (following similar actions by the USA, Luxembourg, and the UAE) concerning the exploitation of natural resources of celestial bodies, enabling private entities to conduct exploration and extraction activities. This is especially noteworthy given the lack of direct international legal oversight on space resource utilization. This article also examines Japan’s international legal space policy, including the Japan-U.S. Framework Agreement on Cooperation in the Exploration and Peaceful Uses of Outer Space, including the Moon and Other Celestial Bodies 2023, in light of the particularly close Japan-U.S. cooperation in space exploration and use. Japan’s enactment of a law regarding the exploration and exploitation of natural resources of celestial bodies, along with Japan’s participation in projects such as Artemis, Lunar Gateway, and Hakuto-R, demonstrates its growing role in international space projects and its pursuit of the closest possible cooperation with the United States in ensuring national security through the use of outer space. The conclusion offers summarizing statements.

Research on RTD Fluxgate Induction Signal Denoising Method Based on Particle Swarm Optimization Wavelet Neural Network.

Aeromagnetic surveying technology detects minute variations in Earth's magnetic field and is essential for geological studies, environmental monitoring, and resource exploration. Compared to conventional methods, residence time difference (RTD) fluxgate sensors deployed on unmanned aerial vehicles (UAVs) offer increased flexibility in complex terrains. However, measurement accuracy and reliability are adversely affected by environmental and sensor noise, including Barkhausen noise. Therefore, we proposed a novel denoising method that integrates Particle Swarm Optimization (PSO) with Wavelet Neural Networks, enhanced by a dynamic compression factor and an adaptive adjustment strategy. This approach leverages PSO to fine-tune the Wavelet Neural Network parameters in real time, significantly improving denoising performance and computational efficiency. Experimental results indicate that, compared to conventional wavelet transform methods, this approach reduces time difference fluctuation by 23.26%, enhances the signal-to-noise ratio (SNR) by 0.46%, and improves sensor precision and stability. This novel approach to processing RTD fluxgate sensor signals not only strengthens noise suppression and measurement accuracy but also holds significant potential for improving UAV-based geological surveying and environmental monitoring in challenging terrains.

Underwater DVL Optimization Network (UDON): A Learning-Based DVL Velocity Optimizing Method for Underwater Navigation

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

Design and Synthesis of Crystalline Al-Doped TiO2 Buffer Layers for Enhancing Energy Conversion Efficiency of New Photovoltaic Devices

Perovskite solar cells (PSCs) characterized by high energy conversion efficiency (ECE) and low manufacturing costs, exhibit promising potential for commercialization in the near term. For commercialization, it is very important to prevent the decomposition of perovskite by ultraviolet (UV) radiation in the air environment. Also, the mesoscopic architecture of PSCs presents considerable opportunities for the solar cell industry, offering potential for recycling of spent photocatalytic materials such as TiO2, and exploration of new energy resources. To solve these problems, therefore, this study introduces a strategy to mitigate these challenges using a crystalline Al-doped TiO2 buffer layer as the electron transport layer (ETL) in conjunction with a mesoporous TiO2 layer in the fabrication of PSCs. Among various Al concentrations in the crystalline Al-doped TiO2 buffer layer fabricated via spin-coating, an optimum concentration of 7 mol% Al yielded the highest cell performance in the specific perovskite solar cell structure. These solar cells exhibited an impressive ECE of 11.87%, representing a substantial enhancement of nearly double the ECE (6.37%) achieved with the conventional ETL. This remarkable improvement can be attributed to the passivation effect of the newly developed ETL, which combines a crystalline Al-doped TiO2 buffer layer with a mesoporousTiO2 layer. Electrochemical impedance spectroscopy (EIS) analysis was performed in conjunction with theoretical calculations of charge transport parameters to substantiate this claim.

Adaptive Reuse of Industrial Heritage Sites for Sustainable Urban Development

The research aims at future urban transformation and development by rediscovering the industrial heritage in the 798 Art Zone, Beijing and the M50 Creative Plaza, Shanghai. In the assessment of the criteria, benefits and success factors of adaptive reuse projects, the research employs the use of both quantitative (questionnaires) and qualitative (case studies) methods. The following points have been identified such as government incentives, local engagement, and retaining the appearance of industrial structures. Drawing from industrial heritage architecture, the study reveals the economic, environmental, and social benefits of retrofitting industrial structures, increased economy revitalisation and cultural identity, and the mitigation of new construction. However, the research found some drawbacks of the study such as the absence of unified conceptual frameworks that cover all four dimensions of sustainability and the uncertainty associated with technical and regulatory factors. This work, therefore, points out that, while adaptive reuse projects are critical to the augmentation of urban regeneration, there exist hurdles that require deliberate effort to be surmounted. Based on these findings, suggestions are given for improvement to policies for government, political parties, urban designers and developers to improve the comprehensiveness of pro-historical building reuse policies. This work finally suggests a framework for further research on any adaptive reuse projects to inform future sustainable design efforts that take into consideration the exploration of resources of the culture and ecosystems.

Source Rock Characteristics and Petroleum‐Geological Significance of Paleogene Niubao Formation in the Nima Basin, Central Tibet, China

ABSTRACTNima Basin, as a typical Tethys area, is a Cenozoic oil‐bearing basin in the middle of Qinghai‐Tibet Plateau, which is expected to become an important target for petroleum exploration. The main controlling factors and models of organic matter enrichment are not yet clear. To identify the organic geochemical characteristics of high‐quality source rocks and the main controlling factors of their formation, this study analyzes the paleoclimate and paleo‐salinity indexes based on the analysis of the major and trace elements through systematic sampling of the muddy sediments of Niubao Formation in Well Shuangdi 1, Cebuco Depression. The results show that the organic matter of the E1‐2n source rocks in Nima Basin is differentially enriched, which shows that the organic matter of the middle E1‐2n source rocks is more enriched than that of the upper E1‐2n source rocks. Hydrocarbon source rocks of the middle E1‐2n are mainly of type I organic matter, while those of the upper E1‐2n are mainly of type Ⅱ1 organic matter, both of which are in the mature stage. Organic matter enrichment is primarily governed by a combination of climate and redox conditions, with humid climate and reducing environments emerging as the dominant factors, whereas salinity exerts a lesser influence. On the one hand, this study can supplement the accumulation mechanism of organic matter in the Tethys domain in Asia; on the other hand, it will fill the gap in the geological research of Nima Basin, contributing to the prediction and exploration of oil and gas resources in the Nima Basin.

Simultaneous tri-parameter surface plasmon resonance photonic crystal fiber sensor

Abstract This study presents a photonic crystal fiber (PCF) integrated with a surface plasmon resonance (SPR) sensor that utilizes two orthogonal polarization core modes for sensing. The PCF features a thin indium tin oxide (ITO) layer on the inner channel and lower polished surface, an upper polished surface with a thin gold film, and an inner channel filled with magnetic fluid (MF) for magnetic field detection. Additionally, a temperature and refractive index sensitive material polydimethylsiloxane (PDMS) is placed on the lower polished surface, while the upper polished surface is designed for refractive index measurement. This multifunctional PCF-SPR sensor offers a compact and efficient solution for simultaneous detection of magnetic fields, temperature, and refractive index variations. The highest magnetic field sensitivity in the range of 0~12Oe is 11.1nm/Oe, the highest temperature sensitivity in the range of 20~400℃ is -0.5nm/℃, and the highest refractive index sensitivity in the range of 1.17~1.35μm is 1000nm/RIU. The designed sensor effectively mitigates the cross-sensitivity issues during concurrent measurements of the magnetic field, temperature, and refractive index simultaneously, presenting significant potential for applications in complex environment, including mineral resource exploration, geological and environmental assessments.

An Airborne Gravity Gradient Compensation Method Based on Convolutional and Long Short-Term Memory Neural Networks.

As gravity exploration technology advances, gravity gradient measurement is becoming an increasingly important method for gravity detection. Airborne gravity gradient measurement is widely used in fields such as resource exploration, mineral detection, and oil and gas exploration. However, the motion and attitude changes of the aircraft can significantly affect the measurement results. To reduce the impact of the dynamic environment on the accuracy of gravity gradient measurements, compensation algorithms and techniques have become a research focus. This paper proposes a post-error compensation algorithm using convolutional and long short-term memory neural networks (CNN-LSTMs). By leveraging convolution feature extraction capabilities and considering the temporal dependencies of dynamic measurement parameters with LSTM, the model demonstrates a stronger ability to learn from severely coupled time series data, resulting in a significant improvement in the compensation performance. This method outperforms traditional neural networks' multi-layer perceptrons (MLPs) in terms of compensation accuracy on both simulated and measured airborne gravity gradient data from Heilongjiang Province.

Pre-concentration of Low-grade Banded Iron Formation (BIF) by Physical Beneficiation

The depletion of high-grade iron ore reserves in South Africa has necessitated the exploration of low-grade hematite resources in a bid to extend life of mine and maximize resource utilization, particularly banded iron formations (BIF) material which makes up approximately 66% of future reserves. BIF are sedimentary rocks and consists of alternating layers of silica and iron. It has been observed that for ores with Fe grades below 40%, the concentrate grades and recoveries are compromised. Thus, the aim of the research study involved production of a pre-concentrate grading at approximately 55% Fe with potential for alternative downstream processing such as cationic reverse flotation to further upgrade the pre-concentrates to final concentrates with grades higher than 63% Fe. The industrial benefits of pre-concentration are evident in upfront gangue reduction and ability to improve production capacity and reduce operational costs. With this in mind, the paper investigates the pre-concentration methodology undertaken, namely, magnetic separation versus gravity shaking table on feed material crushed to a fine grind size of − 1.18 mm for effective liberation of hematite from gangue. Comparative SLon testwork and shaking table tests yielded similar overall recoveries at approximately 50% with high Si rejection of approximately 90% at a target pre-concentrate grade of 55% Fe. Increasing the target grade further to 60% and 63% Fe could only be achieved by gravity separation at a significant reduction in recovery of 35–62%, respectively. The feasibility of pre-concentration for this ore type is thus assessed in detail.

KANDiff: Kolmogorov–Arnold Network and Diffusion Model-Based Network for Hyperspectral and Multispectral Image Fusion

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training process and exceptional image generation capabilities, have shown good application potential in this field. However, when dealing with multimodal data, it may prove challenging for the models to fully capture the intricate relationships between the modalities, which may result in incomplete information integration and a small amount of remaining noise in the generated images. To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. It helps the model understand the complex relationships between the modalities by replacing the fixed activation function in the traditional MLP with a learnable activation function. Furthermore, the image generated by the diffusion model may exhibit a small amount of the remaining noise. Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. Experimental results on the public CAVE and Harvard datasets indicate that KanDiff has achieved improvements over current high-performance methods across several metrics, particularly showing significant enhancements in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), thus demonstrating superior performance. Additionally, we have created an image fusion dataset of the lunar surface, and KANDiff exhibits robust performance on this dataset as well. This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration.

Geochemical exploration of rare earth element resources in highland karstic bauxite deposits in the Sierra de Bahoruco, Pedernales Province, Southwestern Dominican Republic.

This study investigates the geochemical characteristics of rare earth elements (REEs) in highland karstic bauxite deposits located in the Sierra de Bahoruco, Pedernales Province, Dominican Republic. These deposits, formed through intense weathering of volcanic material, represent a potentially valuable REE resource for the nation. Surface and subsurface soil samples were analyzed using portable X-ray fluorescence (pXRF) and a NixPro 2 color sensor validated with inductively coupled plasma optical emission spectrometry (ICP-OES). We employed compositional data analysis (CoDA) and machine learning models to estimate total REE concentrations, demonstrating that pXRF and the color sensor, when properly calibrated, are effective tools for remote geochemical exploration. The results reveal that REE concentrations increase with depth and elevation, with light REEs (LREEs) dominating the profiles. The correlation of REE concentrations with morphological soil development suggests that higher-altitude areas are enriched in REEs due to progressive weathering processes. The study also shows a strong relationship between REE concentrations and environmental factors such as latitude and elevation. While pXRF provided reliable estimates of total REE concentrations, to our surprise, the NixPro2 color sensor proved similarly accurate. The research emphasizes the practical value of the x-ray and color sensors for remote exploration, provided that a well-explored, robust calibration is performed to account for site-specific variability. These findings contribute to understanding the geochemical distribution of REEs in karstic bauxite deposits and highlight the potential for further exploration in remote, high-altitude regions. Future research should explore using these and other portable sensors, singly or combined, to predict REE speciation, for expediting information related to the environmentally sustainable extractability and potential economic feasibility of resources in expeditionary locations.

Underwater polarization de-scattering imaging independent of target-free regions

Underwater optical imaging technology presents broad application prospects in fields such as marine resource exploration, underwater ecological environment monitoring, and seabed topography detection. The technology employs the polarization characteristics of light, particularly those of the background and target, to achieve a clear image. However, the traditional methods rely on target-free regions to compute the backscattered light information, which is infrequently present in the actual scene captured by the camera. Then the full-space resolution of target information light and backscattered light information are required. At this time, the traditional methods may be difficult to adapt in practical application.<br>In this paper, an underwater polarization de-scattering method independent of target-free regions is proposed by combining active polarization imaging and transmittance de-scattering model. Initially, the total light intensity within the camera's field of view is decomposed into its polarized and unpolarized components. By removing the backscattered light with polarized and unpolarized information from the total light intensity, a clear underwater target can be obtained. Based on the active polarization imaging model, the backscattered light with polarization information is calculated, in which the polarization angle of the backscattered light is considered as zero in the full-space. Thus, the polarization degree of the target information light occupying the camera's entire field of view can be derived. According to the polarization correlation, the polarization degree of the backscattered light can be characterized, and the intensity of the backscattered light with polarization information in the camera's entire field of view can also be obtained. Then the unpolarized component is calculated using the minimum intensity image with Stokes vector transformation. Finally, the underwater scene is obtained by combining the transmittance de-scattering principle and introducing adjustment parameters.<br>Experimental and real-world underwater imaging results demonstrate that the proposed method can effectively remove the majority of the backscattered light and improve the image contrast and entropy, irrespective of the presence of target-free regions. Additionally, it offers a certain rate advantage, which can facilitate the real-time complex underwater imaging technology.

Broilers Litter Management: A Review of Environmental Impacts, Nutritional Value and Sustainable Utilization

The continuous demand for animal protein occasioned by increase in population has placed much pressure on researchers across the world for improved ways towards increasing animal productivity. The situation is unpalatable as efforts in meeting the needs have been met with insecurity, unregulated policy, natural disasters, unnecessary community conflicts, and problems of land grabbers most especially in developing nations. This has therefore necessitated at different fora exploration of alternative feed resources to increase animal’s productivity and arrest the ugly trernd. Broilers litter, which include poultry droppings, feed waste and also bedding materials has been identified as possible feed resource technology for the ruminant animals’ production. They have high quality nutritional value and can be manipulated and improved upon for higher animal yield. The use of the litter in ruminants business has become an increasingly popular area in ruminant animal agriculture. Although there are associated concerns for safety of meat and milk products from animals managed on such diets despite its high energy contents. Incidentally, information of its inclusion in the diets of ruminant animal agriculture has not been adequately documented. This therefore forms the basis for this work putting into consideration the consequential effects of broiler litters on animal health, its processing, sustainability, safety and its relative implication for the good of man and his environment. Keywords: Feed shortage, non-conventional feeds, poultry litter, environment

Modeling and Control Experiments of a Fishtail-Like Pneumatic Soft Actuator

As the exploration of deep-sea resources continues, underwater actuators with conventional motors as the main building blocks can no longer meet the increasingly demanding needs. Inspired by bionics, researchers have started to work on underwater actuators with bionic structures. In this study, we designed and implemented a novel Fishtail-like Pneumatic Soft Actuator (FPSA). This innovative actuator configuration is inspired by the tail structure of Body and/or Caudal Fin (BCF) mode fish. The actuator's motion is achieved by controlling the expansion and contraction of the pneumatic soft muscles on both sides. And by constructing an experimental platform, we conducted an in-depth performance characterization, revealing the existence of a frequency-dependent nonlinear hysteresis characteristic of the FPSA. In order to accurately characterize this property, we built a dynamic model of the FPSA and successfully identified the uncertain parameters in the model by applying the nonlinear least squares method. The validation results show that the constructed model can accurately describe the nonlinear hysteresis characteristics of the FPSA. Finally, we successfully realized the high-precision trajectory tracking control of the endpoint of the FPSA using a PID controller. This result provides relevant ideas for the research of novel underwater bionic actuators.

Hydrochemical Characterisation and Genesis Mechanism of Li‐Rich Geothermal Waters in the High‐Temperature Geothermal Areas of Western Sichuan, China

ABSTRACTLithium (Li) is a valuable resource with significant economic benefits and strategic importance. The extraction of Li from Li‐rich geothermal fluids has low production costs and may be an essential source of Li in the future. The Li contents in the high‐temperature geothermal systems of western Sichuan are high (most exceeding 1 mg/L) and reach the exploration standard. However, the Li source and enrichment processes of high‐temperature geothermal fluids are not well known. Therefore, 30 groups of natural hot springs with Li ≥ 1 mg/L from Batang, Litang, and Kangding high‐temperature geothermal systems were selected to analyse the Li enrichment mechanism in high‐temperature geothermal water. The average exposed temperatures of Batang, Litang, and Kangding geothermal waters were 82.4°C, 53.7°C, and 61.9°C, respectively, and the hydrochemical types were HCO3‐Na. The average concentrations of Li in the geothermal waters of Batang, Litang, and Kangding were 2.32, 3.29, and 3.54 mg/L. Based on the δD and δ18O characteristics, the geothermal waters in the study area originated from meteoric water and snow‐melt water. Magmatic water was also mixed during circulation, with Kangding geothermal water being the most mixed (25.0%). Strong water–rock interactions occurred during geothermal water runoff ascent, including silicate mineral dissolution, geothermal gas dissolution, and cation exchange. The deep reservoir temperatures in the geothermal systems of Batang, Litang, and Kangding were estimated to be 239°C, 200°C, and 242°C, and the shallow reservoir temperatures were 175°C, 86°C, and 116°C. Finally, two Li enrichment mechanisms were proposed: (1) Li in the geothermal waters of Batang and Litang geothermal systems mainly came from the leaching of lepidolite and spodumene during water–rock interactions. (2) Li in the Kangding geothermal system mainly originated from the input of magmatic water. This research deepens the understanding of Li enrichment mechanisms in high‐temperature geothermal systems, which will be helpful for the exploration of geothermal Li resources.