The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This rev...The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.展开更多
Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflo...Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.展开更多
Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods a...Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the A...The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.展开更多
Microbial catalysts offer compelling advantages for oxygen reduction reaction(ORR)in microbial fuel cell(MFC)cathodes,including reduced costs and extended operational lifespans.However,their practical application rema...Microbial catalysts offer compelling advantages for oxygen reduction reaction(ORR)in microbial fuel cell(MFC)cathodes,including reduced costs and extended operational lifespans.However,their practical application remains limited by insufficient intrinsic activity at catalytic protein sites and restricted charge accessibility,both of which constrain ORR kinetics.Here,we report the development of an efficient trifunctional bioendogenous system based on menaquinone-7(MK-7),enriched from Bacillus subtilis natto(natto digester strain(ND))through a straightforward fermentation strategy.The engineered MK-7 simultaneously performs three critical functions:(i)facilitating mediated electron transfer between bacteria and electrodes,(ii)regulating the in-situ formation of size-controlled conductive polydopamine nanostructures that enhance direct electron transfer pathways,and(iii)modulating the electronic structure of cytochrome c(Cyt c)to activate its catalytic center and optimize O_(2)adsorption capacity.Through these synergistic effects,our engineered nano-hybrid ND-FM@sPDA(FM is fermentation and sPDA is size-controlled conductive polydopamine)achieves an oxygen reduction current density of 3.83 mA·cm^(-2),representing a 1.54-fold enhancement over pristine ND(2.48 mA·cm^(-2)).MFCs constructed with the ND-FM@sPDA biocathode deliver a peak power density of 412μW·cm^(-2),surpassing previously reported microbial catalysts for similar applications.This work elucidates novel regulatory mechanisms for optimizing biocatalysts at the molecular level and provides critical insights for advancing sustainable bioelectrocatalytic technologies with enhanced performance.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) underg...The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.展开更多
Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, ...Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.展开更多
Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen e...Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.展开更多
AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on...AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.展开更多
Following votes by the Valanginian Working Group,the International Subcommission on Cretaceous Stratigraphy and the International Commission on Stratigraphy,the Executive Committee of the International Union of Geolog...Following votes by the Valanginian Working Group,the International Subcommission on Cretaceous Stratigraphy and the International Commission on Stratigraphy,the Executive Committee of the International Union of Geological Sciences unanimously approved in December 2024 the Global Stratotype Section and Point(GSSP)for the Valanginian Stage(Cretaceous System).展开更多
AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. ...AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.展开更多
The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by ...The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.展开更多
In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-sp...In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.展开更多
Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays...Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.展开更多
Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contam...Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contamination,leading to elevated levels of disinfection byproducts(DBPs).Epidemiological studies have associated increased risks of adverse health effects with frequent exposure to DBPs in swimming pools.Zhang et al.(2023b)investigated the occurrence of trihalomethanes(THMs),haloacetic acids(HAAs),haloacetonitriles(HANs),and haloacetaldehydes(HALs)in eight swimming pools and the corresponding input water in a city in Eastern China.The concentrations of THMs,HAAs,HANs,and HALs in swimming poolswere 1–2 orders of magnitude higher than those detected in the input water.The total lifetime cancer and non-cancer health risks of swimmers through oral,dermal,inhalation,buccal,and aural exposure pathways were assessed using the United States Environmental Protection Agency’s(USEPA)standard model and Swimmer Exposure Assessment Model(SWIMODEL).The results showed that dermal and inhalation pathways were the most significant for the associated cancer and non-cancer risks.This article provides an overview and perspectives of DBPs in swimming pools,the benefits of swimming,the need to improve the monitoring of DBPs,and the importance of swimmers’hygiene practices to keep swimming pools clean.The benefits of swimming outweigh the risks from DBP exposure for the promotion of public health.展开更多
Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve ...Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.展开更多
Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regard...Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.展开更多
文摘The integration of Artificial Intelligence(AI)and Machine Learning(ML)into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges.This review explores key AI and ML concepts,methodologies,and their applications in hydrology,focusing on groundwater potential mapping,water quality prediction,and groundwater level forecasting.It discusses various data acquisition techniques,including remote sensing,geospatial analysis,and geophysical surveys,alongside preprocessing methods that are essential for enhancing model accuracy.The study highlights AI-driven solutions in water distribution,allocation optimization,and realtime resource management.Despite their advantages,the application of AI and ML in water sciences faces several challenges,including data scarcity,model reliability,and the integration of these tools with traditional water management systems.Ethical and regulatory concerns also demand careful consideration.The paper also outlines future research directions,emphasizing the need for improved data collection,interpretable models,real-time monitoring capabilities,and interdisciplinary collaboration.By leveraging AI and ML advancements,the water sector can enhance decision-making,optimize resource distribution,and support the development of sustainable water management strategies.
文摘Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.
文摘Rapid evolutions of the Internet of Electric Vehicles(IoEVs)are reshaping and modernizing transport systems,yet challenges remain in energy efficiency,better battery aging,and grid stability.Typical charging methods allow for EVs to be charged without thought being given to the condition of the battery or the grid demand,thus increasing energy costs and battery aging.This study proposes a smart charging station with an AI-powered Battery Management System(BMS),developed and simulated in MATLAB/Simulink,to increase optimality in energy flow,battery health,and impractical scheduling within the IoEV environment.The system operates through real-time communication,load scheduling based on priorities,and adaptive charging based on batterymathematically computed State of Charge(SOC),State of Health(SOH),and thermal state,with bidirectional power flow(V2G),thus allowing EVs’participation towards grid stabilization.Simulation results revealed that the proposed model can reduce peak grid load by 37.8%;charging efficiency is enhanced by 92.6%;battery temperature lessened by 4.4℃;SOH extended over 100 cycles by 6.5%,if compared against the conventional technique.By this way,charging time was decreased by 12.4% and energy costs dropped by more than 20%.These results showed that smart charging with intelligent BMS can boost greatly the operational efficiency and sustainability of the IoEV ecosystem.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘The Arno River Basin(Central Italy)is affected by a considerable anthropogenic pressure due to the presence of large cities and widespread industrial and agricultural practices.In this work,26 water samples from the Arno River and its main tributaries were analyzed to assess the water pollution status.The geochemical composition of the Arno River changes from the source(dominated by a Ca-HCO_(3) facies)to the mouth(where a Na-Cl(SO4)chemistry prevails)with an increasing quality deterioration,as suggested by the Chemical Water Quality Index,due to anthropogenic contributions and seawater intrusion before flowing into the Ligurian Sea.The Ombrone and Usciana tributaries introduce anthropogenic pollutants into the Arno River,whilst Elsa tributary supplies significant contents of geogenic sulfate.The concentrations of dissolved nitrate and nitrite(up to 63 and 9 mg/L,respectively)and the respective isotopic values of𝛿15N and𝛿18O were also determined to understand origin and fate of the N-species in the Arno River Basin surface waters.The combined application of𝛿15N-NO_(3) and𝛿18O-NO_(3) and N-source apportionment modelling allowed the identification of soil organic nitrogen and sewage and domestic wastes as primary sources for dissolved NO_(3)-.The𝛿15N-NO_(2) and𝛿18O-NO_(2) values suggest that the nitrification process affects the ARB waters,thus controlling the abundances and proportion of the N-species.Our work indicates that additional efforts are needed to improve management strategies to reduce the release of nitrogenated species to the surface waters of the Arno River Basin,since little progress has been made from the early 2000s.
基金supported by the National Natural Science Foundation of China(Nos.52472200 and 52271176)the 111 Project(No.D17007)+1 种基金the Henan Center for Outstanding Overseas Scientists(No.GZS2022017)Henan Province Key Research and Development Project(No.231111520500).
文摘Microbial catalysts offer compelling advantages for oxygen reduction reaction(ORR)in microbial fuel cell(MFC)cathodes,including reduced costs and extended operational lifespans.However,their practical application remains limited by insufficient intrinsic activity at catalytic protein sites and restricted charge accessibility,both of which constrain ORR kinetics.Here,we report the development of an efficient trifunctional bioendogenous system based on menaquinone-7(MK-7),enriched from Bacillus subtilis natto(natto digester strain(ND))through a straightforward fermentation strategy.The engineered MK-7 simultaneously performs three critical functions:(i)facilitating mediated electron transfer between bacteria and electrodes,(ii)regulating the in-situ formation of size-controlled conductive polydopamine nanostructures that enhance direct electron transfer pathways,and(iii)modulating the electronic structure of cytochrome c(Cyt c)to activate its catalytic center and optimize O_(2)adsorption capacity.Through these synergistic effects,our engineered nano-hybrid ND-FM@sPDA(FM is fermentation and sPDA is size-controlled conductive polydopamine)achieves an oxygen reduction current density of 3.83 mA·cm^(-2),representing a 1.54-fold enhancement over pristine ND(2.48 mA·cm^(-2)).MFCs constructed with the ND-FM@sPDA biocathode deliver a peak power density of 412μW·cm^(-2),surpassing previously reported microbial catalysts for similar applications.This work elucidates novel regulatory mechanisms for optimizing biocatalysts at the molecular level and provides critical insights for advancing sustainable bioelectrocatalytic technologies with enhanced performance.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金supported by the National Key Research and Development Program of Chinathe National Natural Science Foundation of China (Grant Nos.2024YFA1408000,12474097,and2023YFA1406001)+2 种基金the Guangdong Provincial Quantum Science Strategic Initiative (Grant No.GDZX2201001)the Center for Computational Science and Engineering at Southern University of Science and Technology,the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen(for J.L.Z.and Y.L.)the Chinese funding sources applied via HPSTAR。
文摘The magnetic properties and Kondo effect in Ce3TiBi5 with a quasi-one-dimensional structure were investigated using in situ high-pressure resistivity measurements up to 48 GPa.At ambient pressure,Ce_(3)TiBi_(5) undergoes an antiferromagnetic(AFM)transition at T_(N)∼5 K.Under high pressures within 8.9 GPa,we find that Kondo scattering contributes differently to the high-temperature resistance,R(T),depending on the applied current direction,demonstrating a significantly anisotropic Kondo effect.The complete P–T phase diagram has been constructed,in which the pressure dependence of T_(N) exhibits a dome-like shape.The AFM order remains robust under pressure,even when the coherence temperature T^(*) far exceeds 300 K.We attribute the observed anisotropic Kondo effect and the robust AFM to the underlying anisotropy in electronic hybridization under high pressure.
文摘Grid-level large-scale electrical energy storage(GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.
基金Supported by the National Natural Science Foundation of China(No.52273056)the Science and Technology Development Program of Jilin Province,China(No.YDZJ202501ZYTS305)。
文摘Electrochemical water splitting represents a sustainable technology for hydrogen(H_(2))production.However,its large-scale implementation is hindered by the high overpotentials required for both the cathodic hydrogen evolution reaction(HER)and the anodic oxygen evolution reaction(OER).Transition metal-based catalysts have garnered significant research interest as promising alternatives to noble-metal catalysts,owing to their low cost,tunable composition,and noble-metal-like catalytic activity.Nevertheless,systematic reviews on their application as bifunctional catalysts for overall water splitting(OWS)are still limited.This review comprehensively outlines the principal categories of bifunctional transition metal electrocatalysts derived from electrospun nanofibers(NFs),including metals,oxides,phosphides,sulfides,and carbides.Key strategies for enhancing their catalytic performance are systematically summarized,such as heterointerface engineering,heteroatom doping,metal-nonmetal-metal bridging architectures,and single-atom site design.Finally,current challenges and future research directions are discussed,aiming to provide insightful perspectives for the rational design of high-performance electrocatalysts for OWS.
文摘AIM:To explore the prevalence of autoimmune gastritis in chronic hepatitis C virus (HCV) patients and the influence of α-interferon (IFN) treatment on autoimmune gastritis. METHODS:We performed a prospective study on 189 patients with positive anti-HCV and viral RNA enrolled in a 12-month IFN protocol.We evaluated:a) the baseline prevalence of autoimmune gastritis,b) the impact of IFN treatment on development of biochemical signs of autoimmune gastritis (at 3,6 and 12 months),c) the evolution after IFN withdrawal (12 months) in terms of anti-gastric-parietal-cell antibodies (APCA),gastrin,anti-thyroid,and anti-non-organ- specific antibodies. RESULTS:APCA positivity and 3-fold gastrin levels were detected in 3 (1.6%) and 9 (5%) patients,respectively,at baseline,in 25 (13%) and 31 (16%) patients at the end of treatment (both P<0.001,vs baseline),and in 7 (4%) and 14 (7%) patients 12 months after withdrawal (P=0.002 and P=0.01 respectively,vs baseline;P=not significant vs end of treatment).The development of autoimmune gastritis was strictly associated with the presence of autoimmune thyroiditis (P=0.0001),no relationship was found with other markers of autoimmunity. CONCLUSION:In HCV patients,IFN frequently precipitates latent autoimmune gastritis,particularly in females.Following our 12-month protocol,the phenomenon generally regressed.Since APCA positivity and high gastrin levels are associated with the presence of antithyroid antibodies, development of autoimmune thyroiditis during IFN treatment may provide a surrogate preliminary indicator of possible autoimmune gastritis to limit the need for invasive examinations.
基金support of a project(2021-2023)funded by the Polish and French Ministries of foreign affairs(Partenariat Hubert Curien,cf.the current palaeomagnetic study that also includes the contribution of Chmielewski Andrezj,Iwanczuk Jolanta and Ploch Izabela from the Polish Geological Institute of Warsaw,colleagues who are hereby thanked)the CNRS SYSTER program(WOAW SYSTER French project,with the contribution of two students Lea Baubant and Durel Franchevel who are acknowledged),Project APVV-20-0079(Slovakian National Grant Agency,thanks to Diana Olveczka)and various grants from the Laboratoire de Geologie de Lyon.
文摘Following votes by the Valanginian Working Group,the International Subcommission on Cretaceous Stratigraphy and the International Commission on Stratigraphy,the Executive Committee of the International Union of Geological Sciences unanimously approved in December 2024 the Global Stratotype Section and Point(GSSP)for the Valanginian Stage(Cretaceous System).
基金Supported by the Grants From Ligue Nationale Contre le Cancer,Comités Départementaux de la Manche,de l'Orne et du Calvados and from Université de Metz
文摘AIM:To evaluate the association between CYP1A1 and GSTs genetic polymorphisms and susceptibility to esophageal squamous cell carcinoma(SCC)and esophageal adenocarcinoma(ADC)in a high risk area of northwest of France. METHODS:A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (CYPIAI*2C and GSTP1 exon 7 Val alleles,GSTMI*2/*2 and GSTTl *2/*2 null genotypes).A total of 79 esophageal cancer cases and 130 controls were recruited. RESULTS:GSTMI*2/*2 and CYPIAI*IA/*2C genotype frequencies were higher among squamous cell carcinomas at a level dose to statistical significance(OR =1.83,95% CI 0.88-3.83,P=0.11;OR=3.03,95% CI 0.93-9.90,P=0.07, respectively).For GSTP1 polymorphism,no difference was found between controls and cases,whatever their histological status.Lower frequency of GSTT1 deletion was observed in ADC group compared to controls with a statistically significant difference(OR=13.31,95% CI 1.66-106.92,P<0.01). CONCLUSION:In SCC,our results are consistent with the strong association of this kind of tumour with tobacco exposure.In ADC,our results suggest 3 distinct hypotheses: (1)activation of exogenous procarcinogens,such as small halogenated compounds by GSTT1;(2)contribution of GSTT1 to the inflammatory response of esophageal mucosa,which is known to be a strong risk factor for ADC, possibly through leukotriene synthesis;(3)higher sensitivity to the inflammatory process associated with intracellular depletion of glutathione.
文摘The retrospective study by Edwar et al reinforces the role of therapeutic penetrating keratoplasty(PK)as a vital intervention in severe,treatment-resistant infectious keratitis.In advanced cases—often complicated by trauma,delayed presentation,and corneal perforation—PK restores globe integrity and provides limited visual recovery.However,its application is constrained by graft-related complications and donor shortages,particularly in low-resource settings.These limitations highlight the need for earlier,globe-sparing strategies to prevent progression and reduce surgical demand.Photoactivated chromophore for infectious keratitis-corneal collagen cross-linking(PACK-CXL)has emerged as a promising adjunct or alternative.With both antimicrobial and tissue-stabilizing effects,PACK-CXL may control infection and preserve corneal structure in earlier stages.A layered treatment framework that incorporates PACK-CXL as an initial intervention and reserves PK for refractory cases may help improve clinical outcomes.Further studies are needed to define their best use in practice.
基金the National Science Foundation of China (Grant No. NSFC: 92162213)the Geology Department Faculty of Science of Al-Azhar University (Assiut Branch)+2 种基金the China Scholarship CouncilChang'an UniversityIstanbul Technical University's Scientific Research Project (BAP Project ID: 45396, code: FHD-2024-45396)
文摘In the Fatira(Abu Zawal)mine area,located in the northern Eastern Desert of Egypt,fieldwork and mineralogical analysis,integrated with machine learning techniques applied to Landsat-8 OLI,ASTER,and Sentinel-2 multi-spectral imagery(MSI)data delineate gold-sulfide mineralization in altered rocks.Gold(Au)anomalies in hydrothermal breccias and quartz veins are associated with NE-oriented felsite dykes and silicified granitic rocks.Two main alteration types are identified:a pyrite-sericite-quartz and a sulfide-chlorite-carbonate assemblage,locally with dispersed free-milling Au specks.Dimensionality reduction techniques,including principal component analysis(PCA)and independent component analysis(ICA),enabled mapping of alteration types.Sentinel-2 PC125 composite images offered efficient lithological differentiation,while supervised classifications,i.e.,the support vector machine(SVM)of Landsat-8 yielded an accuracy of 88.55%and a Kappa value of 0.86.ASTER mineral indices contributed to map hydrothermal alteration mineral phases,including sericite,muscovite,kaolinite,and iron oxides.Results indicate that post-magmatic epigenetic hydrothermal activity significantly contributed to the Au-sulfide mineralization in the Fatira area,distinguishing it from the more prevalent orogenic gold deposits in the region.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2022YFA1404204 and 2022YFA1403700)the National Natural Science Foundation of China (Grant Nos. 12274086, 11534001 and 11925402)+5 种基金funding from the National Science Foundation of China (Grant Nos. 12274046, 11874094, 12147102, and 12347101)Chongqing Natural Science Foundation (Grant No. CSTB2022NSCQ-JQX0018)the Fundamental Research Funds for the Central Universities (Grant No. 2021CDJZYJH-003)Xiaomi Foundation/Xiaomi Young Talents Programthe supports of the start-up funding of Westlake Universitysupport from the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants。
文摘Simulating U(1) quantum gauge theories with spatial dimensions greater than one is of great physical significance. Here we propose a simple realization of U(1) gauge theory with Rydberg and Rydberg-dressed atom arrays. Within the experimentally accessible range, we find that the various aspects of the U(1) gauge theory can be well simulated, such as the emergence of topological sectors, incommensurability, and the Rokhsar–Kivelson point that hosts deconfined charge excitations and degenerate topological sectors. Our proposal is promising to implement experimentally and exhibits pronounced quantum dynamics.
文摘Disinfection of swimming pool water is critical to ensure the safety of the recreational activity for swimmers.However,swimming pools have a constant loading of organic matter from input water and anthropogenic contamination,leading to elevated levels of disinfection byproducts(DBPs).Epidemiological studies have associated increased risks of adverse health effects with frequent exposure to DBPs in swimming pools.Zhang et al.(2023b)investigated the occurrence of trihalomethanes(THMs),haloacetic acids(HAAs),haloacetonitriles(HANs),and haloacetaldehydes(HALs)in eight swimming pools and the corresponding input water in a city in Eastern China.The concentrations of THMs,HAAs,HANs,and HALs in swimming poolswere 1–2 orders of magnitude higher than those detected in the input water.The total lifetime cancer and non-cancer health risks of swimmers through oral,dermal,inhalation,buccal,and aural exposure pathways were assessed using the United States Environmental Protection Agency’s(USEPA)standard model and Swimmer Exposure Assessment Model(SWIMODEL).The results showed that dermal and inhalation pathways were the most significant for the associated cancer and non-cancer risks.This article provides an overview and perspectives of DBPs in swimming pools,the benefits of swimming,the need to improve the monitoring of DBPs,and the importance of swimmers’hygiene practices to keep swimming pools clean.The benefits of swimming outweigh the risks from DBP exposure for the promotion of public health.
基金the Ontario Ministry of Agriculture,Food and Rural Affairs,Canada,who supported this project by providing updated soil information on Ontario and Middlesex Countysupported by the Natural Science and Engineering Research Council of Canada(No.RGPIN-2014-4100)。
文摘Conventional soil maps(CSMs)often have multiple soil types within a single polygon,which hinders the ability of machine learning to accurately predict soils.Soil disaggregation approaches are commonly used to improve the spatial and attribute precision of CSMs.The approach disaggregation and harmonization of soil map units through resampled classification trees(DSMART)is popular but computationally intensive,as it generates and assigns synthetic samples to soil series based on the areal coverage information of CSMs.Alternatively,the disaggregation approach pure polygon disaggregation(PPD)assigns soil series based solely on the proportions of soil series in pure polygons in CSMs.This study compared these two disaggregation approaches by applying them to a CSM of Middlesex County,Ontario,Canada.Four different sampling methods were used:two sampling designs,simple random sampling(SRS)and conditional Latin hypercube sampling(cLHS),with two sample sizes(83100 and 19420 samples per sampling plan),both based on an area-weighted approach.Two machine learning algorithms(MLAs),C5.0 decision tree(C5.0)and random forest(RF),were applied to the disaggregation approaches to compare the disaggregation accuracy.The accuracy assessment utilized a set of 500 validation points obtained from the Middlesex County soil survey report.The MLA C5.0(Kappa index=0.58–0.63)showed better performance than RF(Kappa index=0.53–0.54)based on the larger sample size,and PPD with C5.0 based on the larger sample size was the best-performing(Kappa index=0.63)approach.Based on the smaller sample size,both cLHS(Kappa index=0.41–0.48)and SRS(Kappa index=0.40–0.47)produced similar accuracy results.The disaggregation approach PPD exhibited lower processing capacity and time demands(1.62–5.93 h)while yielding maps with lower uncertainty as compared to DSMART(2.75–194.2 h).For CSMs predominantly composed of pure polygons,utilizing PPD for soil series disaggregation is a more efficient and rational choice.However,DSMART is the preferable approach for disaggregating soil series that lack pure polygon representations in the CSMs.
文摘Aging is an inevitable process that is usually measured by chronological age,with people aged 65 and over being defined as"older individuals".There is disagreement in the current scientific literature regarding the best methods to estimate glomerular filtration rate(eGFR)in older adults.Several studies suggest the use of an age-adjusted definition to improve accuracy and avoid overdiagnosis.In contrast,some researchers argue that such changes could complicate the classification of chronic kidney disease(CKD).Several formulas,including the Modification of Diet in Renal Disease,CKD-Epidemiology Collaboration,and Cockcroft-Gault equations,are used to estimate eGFR.However,each of these formulas has significant limitations when applied to older adults,primarily due to sarcopenia and malnutrition,which greatly affect both muscle mass and creatinine levels.Alternative formulas,such as the Berlin Initiative Study and the Full Age Spectrum equations,provide more accurate estimates of values for older adults by accounting for age-related physiological changes.In frail older adults,the use of cystatin C leads to better eGFR calculations to assess renal function.Accurate eGFR measurements improve the health of older patients by enabling better medication dosing.A thorough approach that includes multiple calibrated diagnostic methods and a detailed geriatric assessment is necessary for the effective management of kidney disease and other age-related conditions in older adults.
