Potential Areas Research Articles

Metabarcoding expands knowledge on diversity and ecology of rare actinobacteria in the Brazilian Cerrado.

Rare and unknown actinobacteria from unexplored environments have the potential to produce new bioactive molecules. This study aimed to use 16s rRNA metabarcoding to determine the composition of the actinobacterial community, particularly focusing on rare and undescribed species, in a nature reserve within the Brazilian Cerrado called Sete Cidades National Park. Since this is an inaccessible area without due legal authorization, it is understudied, and, therefore, its diversity and biotechnological potential are not yet fully understood, and it may harbor species with groundbreaking genetic potential. In total, 543 operational taxonomic units (OTUs) across 14 phyla were detected, with Actinobacteria (41.2%), Proteobacteria (26.5%), and Acidobacteria (14.3%) being the most abundant. Within Actinobacteria, 107 OTUs were found, primarily from the families Mycobacteriaceae, Pseudonocardiaceae, and Streptomycetaceae. Mycobacterium and Streptomyces were the predominant genera across all samples. Seventeen rare OTUs with relative abundance < 0.1% were identified, with 82.3% found in only one sample yet 25.5% detected in all units. Notable rare and transient genera included Salinibacterium, Nocardia, Actinomycetospora_01, Saccharopolyspora, Sporichthya, and Nonomuraea. The high diversity and distribution of Actinobacteria OTUs indicate the area's potential for discovering new rare species. Intensified prospection on underexplored environments and characterization of their actinobacterial diversity could lead to the discovery of new species capable of generating innovative natural products.

Artificial Intelligence in MOBA Games: A Multivocal Literature Mapping

Esports - games played competitively - comprise a major sector of the global games industry. Esports has been used as a testbed for game AI and game analytics for two decades. This article presents a multivocal literature mapping of available research that focuses strictly on the use of artificial intelligence approaches in Multiplayer Online Battle Arena (MOBA) games, one of the most popular esports genres and the one most widely used for game AI and game analytics research. A mapping is performed on relevant publications published between 2011 and 2022 and systematically examines them to extract similarities, gaps, and main findings. We analyzed 124 publications to identify the most studied topics, the most commonly used techniques, and the most commonly applied evaluation methods. The results show that League of Legends and DOTA are the most studied games, with outcome prediction being the most popular research topic. Finally, we provide an analysis of the potential future flagship areas for research in the domain, considering the gaps found in the white and grey literature.

Ventricular tachycardia mapping and ablation using MIXture of well-established approaches. The MIX-VT study

Abstract Introduction Ablation for ventricular tachycardia (VT) is still challenging despite impressive technology development, especially in mid- or low-volume centers. The most frequently used approach is homogenization of scar area responsible for VT, combined with standard electrophysiological (EP) approaches such as VT inducibility and mapping. However, in real life long-term efficacy of VT ablation in such centers does not exceed 50%. We hypothesized that an approach consisting of the mixture of various well-established parameters would be more effective than scar homogenization and VT non-inducibility as the endpoints. Purpose To compare the outcome of VT ablation using the standard approach (scar homogenization) and a more complex approach, called the MIX-VT protocol, using late potential mapping + pace mapping with rapid change of VT morphology to localize the critical isthmus + core isolation. All procedures were performed using the 3D mapping system, Pentaray mapping catheter, Smart-Touch ablation catheter and intracardiac echocardiography (ICE). Methods 48 consecutive patients (41 male, mean age 71.5 years) with ischemic cardiopathy referred for urgent VT ablation were included: 23 patients underwent "standard" VT ablation between December 2018 and December 2019, whereas the next 25 patients underwent ablation using the MIX-VT protocol between January 2020 and December 2020. First step in the MIX-VT group was to perform ICE imaging of the scar area from the right ventricle. Next, left ventricular maps were created during rhythm at which spontaneous VT occurred (sinus or paced). Areas of late potentials were identified to localize the critical isthmus where the abrupt change in the paced-QRS axis was seen. Finally, areas delineated by these two above-mentioned maneuvers where ablated in order to achieve core isolation (Figure 1). The patients were followed for 12 months in the outpatient clinic during ICD examination. The composite end-point for follow-up was VT recurrences terminated by anty-tachycardia pacing (ATP) or shock, or death. Results Clinical and demographic variables were similar between the two groups (Figure 2), except for a higher prevalence of diabetes mellites and number of previous ablations in the standard group (4/25 [16%] vs 10/23 [43%], p=0.03 and 2/24 vs 8/23 [34.8], p=0.03, respectively). Three patients from the MixVT group and one patient from the standard group were lost to follow-up. During follow-up, VT recurrences were significantly less frequent in the MIX-VT group (3/22 [13.6] vs 10/22 [45.4]. p=0.02. It was mostly driven by less ATP therapy. (Figure 2). One patient from the standard approach groups died. Conclusion Ablation for VT in low- or medium volume centers using the combination of well-established various EP approaches resulted in the improvement in overall survival and arrythmia-free survival. The presented approach does not involve additional resource utilization.

Improving functional substrate delineation for ablation of ventricular tachycardia using high-density electroanatomic mapping and double ventricular extra stimuli protocol

Abstract Background A partial delineation of targets for ventricular tachycardia (VT) ablation may contribute to a suboptimal success rate. Abnormal electrograms (EGMs) with low-voltage near-field components might be concealed within high-amplitude far-field signals. Our hypothesis posited that acquiring functional mapping after double ventricular extrastimuli (S3 protocol) could reveal relevant areas of functional substrate overlooked during sinus or S1 mapping. Purpose To assess the benefit and feasibility of functional substrate mapping using a full ventricle S3 protocol and to evaluate its colocalization with conducting channels (CCs) identified in late gadolinium enhancement cardiac magnetic resonance (LGE-CMR). Methods (Figure 1) - An S3 mapping protocol with a drive train of S1 followed by S2 (effective refractory period (ERP) +30ms) and S3 (ERP+50ms) from the RV apex was conducted in 40 consecutive patients undergoing scar-related VT ablation. Deceleration zones (DZs) and areas of late potentials (LPs) were identified for all maps (S1, S2, and S3). Pre-procedural non-invasive substrate assessment was performed with LGE-CMR and post-processing using a commercially available system with automated CC identification. Results The S3 protocol was completed in 34 of the 40 procedures (85.0%). Repeated induction of ventricular arrhythmias prevented the full S3 protocol in 5 patients (15.0%). Functional substrate identified during S3 activation mapping was significantly more extensive than that identified using S2 and S1, including a higher number of DZs (2.94, 2.47, and 1.82, respectively; p&amp;lt;0.01) and a wider area of LPs (44.1cm2, 38.2cm2, and 29.4cm2, respectively; p&amp;lt;0.001). The percentage of CCs unmasked by DZs and LPs using S3 maps was significantly higher than the ones using S2 and S1 maps(78%, 65%, and 48%; p&amp;lt;0.001, and 88%, 81%, and 68%; p&amp;lt;0.01, repectively). After VT ablation based on an S3 protocol, 77.9% of patients have been VT free during a median follow-up of 13.6 months. Conclusions (Graphical abstract -Figure 2) - This novel substrate-based protocol for assessing functional substrate is feasible in most patients, enables better identification of ablation targets, and has the potential to improve patient prognosis post-ablation.Methods - The S3 protocolConclusion - Graphical abstract

Radial Behavior of Electrostatic Potentials of Atoms and Ions Revisited: Isotropy and Anisotropy.

In this paper we revisit earlier work relating to monoatomic atoms and ions published by pioneers in the area of electrostatic potentials. We include plots of the radial distributions of the electrostatic potentials for spherically symmetric atoms and cations, and for singly, doubly and triply negative anions. For atoms with anisotropy in their densities and electrostatic potentials, such as the halonium cations, it is shown how the molecular surface approach for plotting electrostatic potentials complements that achieved by directional radial distributions.

Substrate Mapping for Ventricular Tachycardia Ablation Through High-Density Whole-Chamber Double Extra Stimuli: The S3 Protocol

BackgroundA partial delineation of targets for ablation of ventricular tachycardia (VT) during a stable rhythm is likely responsible for a suboptimal success rate. The abnormal low-voltage near-field functional components may be hidden within the high-amplitude far-field signal. ObjectivesThe aim of this study was to evaluate the benefit and feasibility of functional substrate mapping using a full-ventricle S3 protocol and to assess its colocalization with arrhythmogenic conducting channels (CCs) on late gadolinium enhancement cardiac magnetic resonance. MethodsAn S3 mapping protocol with a drive train of S1 followed by S2 (effective refractory period + 30 ms) and S3 (effective refractory period + 50 ms) from the right ventricular apex was performed in 40 consecutive patients undergoing scar-related VT ablation. Deceleration zones (DZs) and areas of late potentials (LPs) were identified for all maps. A preprocedural noninvasive substrate assessment was done using late gadolinium enhancement cardiac magnetic resonance and postprocessing with automated CC identification. ResultsThe S3 protocol was completed in 34 of the 40 procedures (85.0%). The S3 protocol enhanced the identification of VT isthmus on the basis of DZ (89% vs 62%; P < 0.01) and LP (93% vs 78%; P = 0.04) assessment. The percentage of CCs unmasked by DZs and LPs using S3 maps was significantly higher than the ones using S2 and S1 maps (78%, 65%, and 48% [P < 0.001] and 88%, 81%, and 68% [P < 0.01], respectively). The functional substrate identified during S3 activation mapping was significantly more extensive than the one identified using S2 and S1, including a greater number of DZs (2.94, 2.47, and 1.82, respectively; P < 0.001) and a wider area of LPs (44.1, 38.2, and 29.4 cm2, respectively; P < 0.001). After VT ablation, 77.9% of patients have been VT free during a median follow-up period of 13.6 months. ConclusionsThe S3 protocol was feasible in 85% of patients, allows a better identification of targets for ablation, and might improve VT ablation results.

Verification of Source Activations in a 3D Brain Model Using ‘CLEVER’ Algorithm for Mental Arithmetic Conditions

Background: Living conditions are becoming challenging day by day. Mental stress on individuals is increasing due to multiple reasons. As mental stress is a major cause of mental illness, it must be detected at the earliest to prevent serious conditions such as depression and anxiety. Purpose: The focus of this study is to detect the exact location of the source which causes such damage. In this article, we analyse the mental conditions of subjects under a workload of performing mental arithmetic calculations for various frequency bands and plot the topography to understand the areas of active potentials. Methods: We propose a Novel Cluster Ensemble Verifier (CLEVER) algorithm, which combines two different techniques: clustering and source localisation. The proposed algorithm is highly efficient in identifying the exact location of the source. It is seen that the topographic plots of the independent component analysis (ICA), which has the maximum percentage of relative variance, correlates to the cluster generated. We are able to give the percentage-wise contribution of every component which is responsible for brain source activation with less time complexity. Results: Out of 72 subjects, in 67 subjects, 299 out of 433 components originate from the occipital and parietal areas of the brain with a maximum power of 43.5 µv 2 . As an example, the relative variance of one component is found to be contributing up to 74.03% to source activations. Clusters show similarity across the subjects in the parietal and occipital areas of the brain. The dataset used for experimentation is EEGMAT from Physionet’s repository. The computation time for the algorithms is 17.6 ± 3.2 minutes. Conclusion: Findings show that during mental arithmetic calculations, both occipital and parietal areas of the brain are involved. As the data is acquired by orally mentioning the mathematical problem, subjects tend to visualise the numbers while finding the solution, which is reflected in the occipital area of the brain. CLEVER algorithm verifies the origin of the activity in the occipital and parietal areas of the brain.

Development of Sustainable Minapolitan Area in Tamako District, Kepulauan Sangihe Regency

&#x0D; &#x0D; &#x0D; The aim of this study is to address the challenges facing the Dagho Beach Fishing Port Area in North Sulawesi, which plays a pivotal role in the region's fisheries sector but suffers from inefficiencies in development systems, markets, distribution, and infrastructure. This dependence on natural resources has eroded community independence, exacerbated by issues such as illegal fisheries transactions, price disparities, and the absence of processing industries. Additionally, limited accessibility for exports and regulatory hurdles impede the area's potential, with only a small fraction of the population engaged in fishing activities. To tackle these issues, the study employs a comprehensive four-stage analysis approach. Firstly, Descriptive Analysis delineates the economic, political, social, and environmental characteristics of the area. Secondly, Analytical Hierarchy Processes (AHP) identify the most influential indicators for focused development efforts. Thirdly, a combination of Scalogram and Weighted Sum methods determines suitable minapolitan zones. Finally, Descriptive Direction offers development guidelines based on previous analyses. The research yields actionable directions, including the establishment of a modern market, enhancing fishery product marketing through the "Marketing Mix" and Free Trade Agreement (FTA) implementation. It advocates for technology-based processing units and integrated infrastructure, alongside policy development to support the fisheries industry and promote sustainable practices. The study emphasizes the importance of infrastructure development, economic collaboration, and community empowerment initiatives to foster local fishery development, cultivation, and business capital.&#x0D; &#x0D; &#x0D;

Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency, and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) have been attracting considerable attention and have tremendously helped Machine Learning (ML) engineers in understand AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model’s reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs’ reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Second, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision and Natural Language Processing domains. Additional resources related to event prediction are included in the article website: https://kugaoyang.github.io/EGL/

Building Equitable Neuroscience Research Collaborations in Resource-limited Settings.

The burden of noncommunicable neurological disorders, such as stroke, dementia, and headache disorders, are on the rise in low- to middle-income countries (LMICs), while neuroinfectious diseases remain a major concern. The development of neuroscience research aimed at defining the burden of neurological diseases across the lifespan, as well as optimizing diagnosis and treatment strategies, is fundamental to improving neurological health in resource-limited settings. One of the key factors to advancing neuroscience research in LMICs is the establishment of effective collaborations based on responsible and trustworthy partnerships between local scientists in LMICs and international collaborators. LMIC researchers face many logistical, institutional, and individual level challenges as they embark on their neuroscience research journey. Despite these challenges, there are opportunities for improving LMIC investigator-led research that should focus on human and institutional infrastructure development. With regard to human capacity building, potential areas for offering support include enhancing research methodology training, offering instruction in manuscript and grant-writing, institutionalizing mentorship programs, and providing opportunities to conduct funded, mentored research to disseminate in high-impact journals. The foundational elements required for implementing and optimizing neuroscience research within an institution include an institutional review board, mentorship programs, data management, research administration, and laboratory facilities. This institutional capacity varies significantly across and within countries, and many rely on collaborations with better-resourced institutions to initiate research. Successful equitable collaborations ensure the engagement of all local and international stakeholders, as well as implementation of a self-sustaining long-term program. Building research capacity in LMICs is an essential endeavor that requires ongoing commitment to training independent scientists. As research capacity increases, LMIC institutions and governments should consider developing competitive research grant programs to support innovative studies led by local researchers, foster regional collaborations, and hence create a sustainable and independent neuroscience research environment.

Zircon provenances of the late Paleoproterozoic metasedimentary rock in the South Yangtze Block: Implications for paleogeographic reconstruction

ABSTRACT The zircon provenances of the late Paleoproterozoic metasedimentary rocks are important for assembling Proto-Yangtze and its reconstruction in the Nuna supercontinent. We present a dataset involving 363 detrital zircon U-Pb dating and 165 Hf isotope analyses from the Laochanghe Formation of the Dahongshan Group in the southwestern Yangtze Block. New detrital zircon U-Pb ages, combined with the heterogeneity of sedimentary strata and available magmatic age data, suggest that the maximum depositional age of the Laochanghe Formation cloud be younger than 1.81 Ga and older than 1.74 Ga. The zircon U-Pb-Hf isotope compositions of the volcanic and metamorphic rocks overlap with those of the late Paleoproterozoic metasedimentary rock, suggesting the southwestern Yangtze Block provided provenance at 1.9, 2.3, and 2.7 Ga. This characteristic is significantly different from the northern Yangtze Block only provided partial provenance at 1.9 and 2.7 Ga. The northern and southwestern of the Yangtze Block jointly provide material for the late Paleoproterozoic metasedimentary rocks, combining synchronous late Paleoproterozoic magmatism and metamorphism, indicative of the formation of the proto-Yangtze Block in response to the accumulation of the Nuna supercontinent. Additionally, the potential provenance areas of the late Paleoproterozoic metasedimentary rock in the southwestern Yangtze include the northwestern Laurentia and southern Siberia, excluding the northern Australian, and Cathaysia. These findings indicate that the southwestern Yangtze may have been located between northwestern Laurentia and southern Siberia during the partial fragmentation of the Nuna supercontinent, enhancing our understanding of changes in the Nuna supercontinent over time.

Analysis of morphological change of lentic water bodies by using spatial vulnerability index (SVI) in Tarai region of Rapti river plains, India

ABSTRACT Spatio-temporal analysis of waterbodies in terms of detection of morphological change could serve as a guiding tool to restore the lentic ecosystem. This research analysed the loss of lake extent within 50 years (1969–2019) through natural, anthropogenic reasons, as well as a lack of attention to protecting lakes in the Tarai region. In 1969, 75 lakes were found, but now only 46 lakes are alive. Among the 75 lakes, seventeen transformed into large ponds after reduction in extent, and twelve lakes either appeared as waterlogging zones during the rainy season or were permanently extinct and used as agricultural fields’ built-up activities. The spatial vulnerability index (SVI) measured the level of deterioration and urgency of lake restoration. 8 lakes were determined to be highly vulnerable (below 29.26%) and lost about 311.20 hectares of potential area of lake beds due to gradual increasing shallowness. Regression analysis measured a very weak correlation between the reducing percent of lake extent (during 50 years) and current lake extent (R2 = 0.1336), average depth (R2 = 0.0035), circumference (R2 = 0.0543), and volume (R2 = 0.0728) of 46 lakes. This study elucidated the restoration practices of the lakes to prevent their gradual extinction.

The advantages and short circuit characteristics of SiC MOSFETs

SiC MOSFETs have exhibited considerable benefits in high-frequency, high-voltage, and high-temperature power electronics applications with outstanding material attributes as a result of the rapid advancement of power electronics technology. SiC MOSFETs slower short-circuit tolerance and faster switching rates provide new issues for the short-circuit prevention technology. In the opening section of the study, Si and SiC MOSFETs are compared and evaluated using various models and parametric factors. It has been demonstrated that SiC MOSFETs outperform Si MOSFETs in a variety of conditions and applications. The many SiC MOSFET short-circuit failure types as well as their underlying theories are initially explained in the papers main body. In addition, it examines the fundamentals of short-circuit test procedures and SiC MOSFET test circuits. The issues and limitations of the currently available SiC MOSFET short-circuit protection technology are then explored, along with factors impacting the short-circuit of SiC MOSFETs that are thoroughly examined. Lastly, the SiC MOSFET short-circuit protection technology development trend is forecasted, and potential future areas for improvement and innovation are considered. SiC MOSFET short-circuit protection technology will be enhanced and optimized to satisfy the needs of efficient and dependable power electronic systems as technology advances and application requirements expand.

An insight into the prokaryotic diversity from a polymetallic nodule-rich region in the Central Indian Ocean Basin using next generation sequencing approach.

Deep sea is a vast, dark, and difficult-to-access terrain and is now looked upon as a unique niche harboring diverse microorganism. We used a metataxonomic approach to decipher the microbial diversity present in the water column (surface to near bottom), water overlaying the sediments, and the deep-sea sediments (up to 35 cm) from the Indian Contract Region (ICR) in the Central Indian Ocean Basin (CIOB). Samples were collected from #IRZ (Impact Reference Zone), #PRZ (Potential Reference Zone), and #BC20 (Control site, outside potential mining area) with an average water depth of 5,200 m. 16S rRNA (V3-V4 region) amplicon sequencing on the MiSeq platform resulted in 942,851 ASVs across 65 water and sediment samples. Higher prokaryotic diversity was observed below 200 m in the water column to the seafloor. Proteobacteria was the most dominant bacterial phylum among all the water samples while Firmicutes, Actinobacteria and, Bacteroidota dominated the sediments. Sediment (below 10 cm) was co-dominated by Firmicutes. Thermoplasmata was the dominant archaeal group in the water column while Crenarchaeota was in the sediments. BC20 was less diverse than IRZ and PRZ. Deep Sea microorganisms could play a vital role in the mineralization processes, nutrient cycling, and also different biogeochemical cycles.

Health care Utilization and Expenditures in Health Professional Shortage Areas.

Healthcare provider shortages are associated with adverse health outcomes, presumably stemming from the lack of access to primary care. This study examines the impact of Health Professional Shortage Areas (HPSAs) on healthcare utilization and spending across different types of care. Using the Medical Expenditure Panel Survey from 2002 to 2019, this study estimates the difference in healthcare utilization in HPSAs compared with non-HPSAs using a generalized random forest, which allows for more complex modeling of the outcome and a principled examination of heterogenous treatment effects. The results indicate HPSAs are associated with a 5% reduction in medical office visits, but no reduction in hospital-based care. These effects are concentrated in older persons living in urban areas, Black persons, and Medicaid beneficiaries. No statistically significant effects on annual spending were observed. These results offer insight into potential areas for further policy efforts aimed at reducing provider shortages.

Atmospheric CO2 in the megacity Hangzhou, China: Urban-suburban differences, sources and impact factors

Limited observation sites and insufficient monitoring of atmospheric CO2 in urban areas restrict our comprehension of urban-suburban disparities. This research endeavored to shed light on the urban-suburban differences of atmospheric CO2 in levels, diurnal and seasonal variations as well as the potential sources and impact factors in the megacity of Hangzhou, China, where the economically most developed region in China is. The observations derived from the existing Hangzhou Atmospheric Composition Monitoring Center Station (HZ) and Lin'an Regional Atmospheric Background Station (LAN) and the newly established high-altitude Daming Mountain Atmospheric Observation Station (DMS), were utilized. From November 2020 to October 2021, the annual averages of HZ, LAN and DMS were 446.52 ± 17.01 ppm, 441.56 ± 15.42 ppm, and 422.02 ± 10.67 ppm. The difference in atmospheric CO2 mole fraction between HZ and LAN was lower compared to the urban-suburban differences observed in other major cities in China, such as Shanghai, Nanjing, and Beijing. Simultaneous CO2 enhancements were observed at HZ and LAN, when using DMS observations as background references. The seasonal variations of CO2 at LAN and DMS exhibited a high negative correlation with the normalized difference vegetation index (NDVI) values, indicating the strong regulatory of vegetation canopy. The variations in boundary layer height had a larger influence on the low-altitude HZ and LAN stations than DMS. Compared to HZ and LAN, the atmospheric CO2 at DMS was influenced by emissions and transmissions over a wider range. The potential source area of DMS in autumn covered most areas of the urban agglomeration in eastern China. DMS measurements could provide a reliable representation of the background level of CO2 emissions in the Yangtze River Delta and a broader region. Conventional understanding of regional CO2 level in the Yangtze River Delta through LAN measurements may overestimate background concentration by approximately 10.92 ppm.

Geothermal target detection integrating multi-source and multi-temporal thermal infrared data

Thermal infrared remote sensing (TIRS) technology is intriguing for geothermal anomaly detection due to its time efficiency and cost-effectiveness. Land surface temperature (LST) derived from TIRS indicates potential geothermal anomalies. However, LST on different natural features, exhibiting relatively cold/hot anomalies in daytime and nighttime, affected the identification of abnormal areas. The aim of this study was to highlight geothermal anomalous areas by integrating multi-view daytime and nighttime LST data. Specifically, the winter LST time series of drillings on 2013–2023 was processed based on Landsat-8 TIRS in diurnal scenarios. An information aggregation classification method about Dempster-Shafer evidence theory was presented to target recognition at nighttime by the ASTER LST product. This day-night information fusion analysis effectively highlighted potential areas with geothermal anomalies. The results demonstrated that the geothermal anomalies were mainly distributed along the Ruili-Longling Fault and Wanding Fault in a Northeast (NE) – Southwest (SW) direction, and the NE side was more significant than the SW side. These findings suggested that the east side was closer to the magma heat source, and the presumed magma heat source location aligned closely with the detected geothermal anomalies. Geological data were employed for geological interpretation of the LST anomaly area, eliminating non-geothermal influences. Along with drilling data verification, this method was successfully used to identify geothermal anomaly areas in Ruili City, Yunnan Province. Overall, this study provided valuable insights into the detection of geothermal anomalies through TIRS, contributing to the successful development of geothermal resources.

Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.

Evaluation of the Potential for CO2 Storage and Saline Water Displacement in Huaiyin Sag, Subei Basin, East China

CO2 geological storage combined with deep saline water recovery technology (CO2-EWR) is one of the most effective ways to reduce carbon emissions. Due to the complex structural features, it is difficult to use CO2-EWR technology in Huaiyin Sag, Subei basin, East China. In this study, the multi-source information superposition evaluation technology of GIS was utilized for the selection of CO2 storage sites and water displacement potential target areas in this area, which mainly focused on the sandstone reservoirs of Cretaceous Pukou Formation. Based on the results, a three-dimensional injection–extraction model was established. Various scenarios with different production/injection well ratios (PIR) were simulated. Research has shown that the suitability of the surrounding site of Huaiyin Power Plant can be divided into two levels: relatively suitable and generally suitable; the area in the generally suitable level accounts for more than 80%. At a PIR of 1, CO2 is distributed asymmetrically, whereas at PIRs of 2 or 4, CO2 is distributed symmetrically. When the number of production wells is constant, a higher injection rate results in a faster expansion rate of the CO2 plume. This means that the time taken for the CO2 plume to reach the production wells is shorter. Reservoir pressure increases rapidly after more than 60 years of CO2 injection at lower PIR values, while at higher PIRs, reservoir pressure eventually stabilizes. Higher PIR values correspond to higher gas saturation, indicating a greater capacity for CO2 sequestration with more producing wells. When PIR = 4, the total CO2 injection increased by 55.73% compared to PIR = 1. However, the extraction of saline decreases with an increase in the number of producing wells, resulting in a decrease in replacement efficiency. This study provides a theoretical basis and technical support for the implementation of large-scale CO2-EWR engineering and technology demonstration in this region.

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint.

Assessment and prediction for the ecotoxicity of engineered nanoparticles (ENPs) at the community or ecosystem levels represents a critical step toward a comprehensive understanding of the ecological risks of ENPs. Current studies on predicting the ecotoxicity of ENPs primarily focus on the cellular and individual levels, with limited exploration at the community or ecosystem levels. Herein, we present the first of the reports for the direct prediction of aquatic ecological risk for ENPs at the community level using machine learning (ML) approaches in the field of computational toxicology. Specifically, we extensively collected the threshold concentrations of twelve ENPs including metal- and carbon-based nanoparticles for aquatic species, i.e., hazardous concentrations at which 5% of species are harmed (HC5), established by a species sensitivity distribution. Afterwards, we used eight supervised ML methods including Adaboost, artificial neural network, C4.5 decision tree, K-nearest neighbor, logistic regression, Naive Bayes, random forest, and support vector machine to develop nine classification models and four regression models, respectively, for the qualitative and quantitative prediction of HC5. The evaluation of model performance yielded the internal validation accuracy of all classification models ranging from 71.4 to 100%, and the determination coefficient of regression models ranging from 0.702 to 0.999, indicating that the developed models showed good performance. By using a cross-validation method and an application domain characterization, the selected models were further validated to have powerful predictive ability. Furthermore, the incorporation of three nanostructural descriptors (metal oxide sublimation enthalpy, zeta potential, and specific surface area) linked to toxicity mechanisms (the release of metal ions, the stability of dispersions of particles in aqueous suspensions, and the surface properties of the material) effectively enhanced the prediction power and mechanistic interpretability of the selected models. These findings would not only be beneficial in the screening of ENPs with potential high ecological risks that need to be tested as a priority but also contribute to the development of environmental regulations and standards for ENPs.