Model reduction of fractional impedance spectra for time–frequency analysis of batteries, fuel cells, and supercapacitors

Joint time–frequency analysis is an emerging method for interpreting the underlying physics in fuel cells,batteries,and supercapacitors.To increase the reliability of time–frequency analysis,a theoretical correlation between frequency-domain stationary analysis and time-domain transient analysis is urgently required.The present work formularizes a thorough model reduction of fractional impedance spectra for electrochemical energy devices involving not only the model reduction from fractional-order models to integer-order models and from high-to low-order RC circuits but also insight into the evolution of the characteristic time constants during the whole reduction process.The following work has been carried out:(i)the model-reduction theory is addressed for typical Warburg elements and RC circuits based on the continued fraction expansion theory and the response error minimization technique,respectively;(ii)the order effect on the model reduction of typical Warburg elements is quantitatively evaluated by time–frequency analysis;(iii)the results of time–frequency analysis are confirmed to be useful to determine the reduction order in terms of the kinetic information needed to be captured;and(iv)the results of time–frequency analysis are validated for the model reduction of fractional impedance spectra for lithium-ion batteries,supercapacitors,and solid oxide fuel cells.In turn,the numerical validation has demonstrated the powerful function of the joint time–frequency analysis.The thorough model reduction of fractional impedance spectra addressed in the present work not only clarifies the relationship between time-domain transient analysis and frequency-domain stationary analysis but also enhances the reliability of the joint time–frequency analysis for electrochemical energy devices.

Erratum to:Vibration analysis of a gear-rotor-bearing system with outer-ring spalling and misalignment

Because of an unfortunate mistake during the production of this article,Figure 13 was wrongly inserted.The corrected Figure 13 is shown as follows.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Molecular cloning,characterization and promoter analysis of LbgCWIN1 and its expression profiles in response to exogenous sucrose during in vitro bulblet initiation in lily

Lily(Lilium spp.) is an important ornamental flower, which is mainly propagated by bulbs. Cell wall invertases(CWINs), which catalyze the irreversibly conversion of sucrose into glucose and fructose in the extracellular space, are key enzymes participating in sucrose allocation in higher plants. Previous studies have shown that CWINs play an essential role in bulblet initiation process in bulbous crops, but the underlying molecular mechanism remains unclear. Here, a CWIN gene of Lilium brownii var. giganteum(Lbg) was identified and amplified from genomic DNA. Quantitative RT-PCR assays revealed that the expression level of LbgCWIN1 was highly upregulated exactly when the endogenous starch degraded in non-sucrose medium during in vitro bulblet initiation in Lbg. Phylogenetic relationship, motif, and domain analysis of LbgCWIN1 protein and CWINs in other plant species showed that all sequences of these CWIN proteins were highly conserved. The promoter sequence of LbgCWIN1 possessed a number of alpha-amylase-, phytohormone-, light-and stress-responsive cis-elements. Meanwhile, β-glucuronidase(GUS) assay showed that the 459 bp upstream fragment from the translational start site displayed maximal promoter activity. These results revealed that LbgCWIN1 might function in the process of in vitro bulblet initiation and be in the response to degradation of endogenous starch.

Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults:a competing risk analysis

Background Depressive symptoms and cognitive impairment often interact,rendering their associations controversial.To date,their joint trajectories and associations with dementia and death remain underexplored.Aims To explore the interactions between depressive symptoms and cognitive function,their developmental trajectories and the associations with all-cause dementia,Alzheimer’s disease(AD)and all-cause death in older adults.Methods Data were from the Health and Retirement Study.Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status,respectively.All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses.All-cause death was determined by interviews.The restricted cubic spline,group-based trajectory modelling and subdistribution hazard regression were used.Results Significant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found,especially in women(p for interaction<0.05).Independent trajectory analysis showed that emerging or high(vs no)depressive trajectories and poor or rapidly decreased cognitive trajectories(vs very good)from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia,AD and all-cause death.15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined,where rapidly decreased cognitive function was more common in those with no depressive symptoms.Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function,those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death,with subdistribution hazard ratios(95%confidence intervals)of 4.47(2.99 to 6.67)and 1.84(1.43 to 2.36),especially in women.Conclusions To effectively mitigate the risk of dementia and death,it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms,particularly in women.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis

BACKGROUND The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer(CRC),one of the most prevalent malignancies worldwide.In this study,multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC.By revealing the heterogeneity and functional differences of B cells in cancer immunity,we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.AIM To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC,explore the potential driving mechanism of B cell development,analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules,and search for possible regulatory pathways to promote the anti-tumor effects of B cells.METHODS A total of 69 paracancer(normal),tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database(https://portal.gdc.cancer.gov/).After the immune cells were sorted by multicolor flow cytometry,the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform,and the data were analyzed using bioinformatics tools such as Seurat.The differences in the number and function of B cell infiltration between tumor and normal tissue,the interaction between B cell subsets and T cells and myeloid cell subsets,and the transcription factor regulatory network of B cell subsets were explored and analyzed.RESULTS Compared with normal tissue,the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly.Among them,germinal center B cells(GCB)played the most prominent role,with positive clone expansion and heavy chain mutation level increasing,and the trend of differentiation into memory B cells increased.However,the number of plasma cells in the tumor microenvironment decreased significantly,and the plasma cells secreting IgA antibodies decreased most obviously.In addition,compared with the immune microenvironment of normal tissues,GCB cells in tumor tissues became more closely connected with other immune cells such as T cells,and communication molecules that positively regulate immune function were significantly enriched.CONCLUSION The role of GCB in CRC tumor microenvironment is greatly enhanced,and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level.Meanwhile,GCB has enhanced its association with immune cells in the microenvironment,which plays a positive anti-tumor effect.

Prevalence of prolonged grief disorder and its symptoms among bereaved individuals in China:a systematic review and meta-analysis

Background The prevalence of prolonged grief disorder(PGD)and its symptoms among the bereaved population in China vary considerably.Aims This meta-analysis aims to estimate the prevalence of PGD and its symptoms among bereaved individuals in China.Methods We conducted a literature search in major Chinese and English databases from their inception to 4 October 2023,for cross-sectional studies on the prevalence of PGD or its symptoms in bereaved Chinese individuals.The risk of bias of the included studies and certainty of the evidence were assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data(‘JBI checklist’)and the Grading of Recommendations,Assessment,Development and Evaluations(GRADE),respectively.The‘metaprop’package in R V.4.1.2 was used to synthesise the prevalence.Results A total of 28 studies involving 10994 bereaved individuals were included in the analysis,with JBI checklist scores between 3 and 7.The combined prevalence(95%confidence interval)of PGD and its symptoms was 8.9%(4.2%to 17.6%)and 32.4%(18.2%to 50.8%),respectively.PGD and its symptoms were most prevalent among those who had lost their only child(22.7%)and those bereaved by earthquakes(80.4%),respectively.The GRADE system assigned a very low certainty level to the evidence for the pooled prevalence of PGD and its symptoms.Conclusions The pooled prevalence of PGD and its symptoms indicate a potential high need for grief counselling services among bereaved individuals in China.This need is particularly pronounced in those who have lost their only child and those bereaved due to earthquakes.Further methodologically rigorous studies are needed to provide more accurate prevalence estimates.PROSPERO registration number CRD42023432553.

Harnessing the Market Potential of the Bamboo Industry in Central Luzon, Philippines: An Analysis of the Internal and External Environment

The bamboo industry in Central Luzon holds significant promise for economic development and environmental sustainability. This study aims to analyze the internal and external factors influencing the bamboo industry in the region through SWOT and PESTLE analyses. Based on a focus group discussion involving key industry players, the study explores the industry’s strengths, weaknesses, opportunities, and threats, as well as political, economic, social, technological, legal, and environmental factors. Findings reveal the importance of comprehensive strategies that address political stability, economic growth, consumer awareness, technological advancement, legal compliance, and environmental sustainability. Recommendations include capacity-building for production and marketing, the establishment of bamboo treatment facilities, and advocacy for supportive policies. By addressing these factors, the bamboo industry in Central Luzon can realize its potential for socio-economic development and environmental stewardship.

Dynamic contrast enhanced ultrasound in differential diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis

BACKGROUND Non-invasive differential diagnosis between hepatocellular carcinoma(HCC)and other liver cancer(i.e.cholangiocarcinoma or metastasis)is highly challenging and definitive diagnosis still relies on histological exam.The patterns of enhancement and wash-out of liver nodules can be used to stratify the risk of malignancy only in cirrhotic patients and HCC frequently shows atypical features.Dynamic contrast-enhanced ultrasound(DCEUS)with standardized software could help to overcome these obstacles,providing functional and quantitative parameters and potentially improving accuracy in the evaluation of tumor perfusion.AIM To explore clinical evidence regarding the application of DCEUS in the differential diagnosis of liver nodules.METHODS A comprehensive literature search of clinical studies was performed to identify the parameters of DCEUS that could relate to histological diagnosis.In accordance with the study protocol,a qualitative and quantitative analysis of the evidence was planned.RESULTS Rise time was significantly higher in HCC patients with a standardized mean difference(SMD)of 0.83(95%CI:0.48-1.18).Similarly,other statistically significant parameters were mean transit time local with a SMD of 0.73(95%CI:0.20-1.27),peak enhancement with a SMD of 0.37(95%CI:0.03-0.70),area wash-in area under the curve with a SMD of 0.47(95%CI:0.13-0.81),wash-out area under the curve with a SMD of 0.55(95%CI:0.21-0.89)and wash-in and wash-out area under the curve with SMD of 0.51(95%CI:0.17-0.85).SMD resulted not significant in fall time and wash-in rate,but the latter presented a trend towards greater values in HCC compared to intrahepatic cholangiocarcinoma.CONCLUSION DCEUS could improve non-invasive diagnosis of HCC,leading to less liver biopsy and early treatment.This quantitative analysis needs to be applied on larger cohorts to confirm these preliminary results.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows

Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.

Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

Gene characterization and phylogenetic analysis of four mitochondrial genomes in Caenogastropoda

Caenogastropoda is a highly diverse group,containing~60%of all existing gastropods.Species in this subclass predominantly inhabit marine environments and have a high ecological and economic value.Owing to the increase in relevant phylogenetic studies,our understanding of between species relatedness in Caenogastropoda has improved.However,the biodiversity,taxonomic status,and phylogenetic relationships of this group remain unclear.In the present study,we performed next-generation sequencing of four complete mitochondrial genomes from three families(Buccinidae,Columbellidae,and Cypraeidae)and the four mitogenomes were classical circular structures,with a length of 16177 bp in Volutharpa ampullacea,16244 bp in Mitrella albuginosa,16926bp in Mauritia arabica asiatica and 15422 bp in Erronea errones.Base composition analysis indicated that whole sequences were biased toward A and T.Then compared them with 171 complete mitochondrial genomes of Caenogastropoda.The phylogenetic relationship of Caenogastropoda derived from Maximum Likelihood(ML)and Bayesian Inference(BI)trees constructed based on CDS sequences was consistent with the results of traditional morphological analysis,with all three families showing close relationships.This study supported Caenogastropoda at the molecular level as a separate clade of Mollusca.According to our divergence time estimations,Caenogastropoda was formed during the middle Triassic period(~247.2–237 Ma).Our novel mitochondrial genomes provide evidence for the speciation of Caenogastropoda in addition to elucidating the mitochondrial genomic evolution of this subclass.

Bibliometric analysis of hotspots and trends of global myopia research

AIM:To gain insights into the global research hotspots and trends of myopia.METHODS:Articles were downloaded from January 1,2013 to December 31,2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software.RESULTS:A total of 444 institutions in 87 countries published 4124 articles.Between 2013 and 2022,China had the highest number of publications(n=1865)and the highest H-index(61).Sun Yat-sen University had the highest number of publications(n=229)and the highest H-index(33).Ophthalmology is the main category in related journals.Citations from 2020 to 2022 highlight keywords of options and reference,child health(pediatrics),myopic traction mechanism,public health,and machine learning,which represent research frontiers.CONCLUSION:Myopia has become a hot research field.China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022.The main driver of myopic research is still medical or ophthalmologists.This study highlights the importance of public health in addressing the global rise in myopia,especially its impact on children’s health.At present,a unified theoretical system is still needed.Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors.In addition,the benefits of artificial intelligence(AI)models are also reflected in disease monitoring and prediction.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R2 of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

Contribution of GIS to Soil Landscape Mapping by Multi-Criteria Analysis Using Weighting: The Case of the Square Degrees of M’Bahiakro (Centre) and Daloa (Centre-West) in Ivory Coast

As part of the drive to improve coffee and cocoa production in Ivory Coast, studies are carried out to identify soils that are favourable for these crops. It is therefore necessary to orientate soil investigations based on reliable criteria that best discriminate soil cover. With this in mind, this study is being carried out to help improve survey methods by mapping soil landscapes. It uses GIS and weighted multicriteria analysis. To do this, satellite images were processed and the geological map of the square degrees of M’Bahiakro and Daloa was reclassified. The results show that relief is the main factor in soil landscape differentiation, with respective weights of 0.58 and 0.67 for the forest and pre-forest zones. In contrast, the weight of geological formation in soil landscape differentiation remains low (0.05 for the forest zone and 0.07 for the pre-forest zone). The criteria used on the base of aggregation sum methods have made it possible to formulate soil landscape mapping prediction functions according to agro-ecological environments in the humid intertropical zone. This is essential for the orientation of soil survey work. Nevertheless, other comparative methods, such as the coding mapping method, could provide elements for discussion to validate the models.

The value of toxicological analysis in acute poisoning patients with uncertain exposure histories:a retrospective and descriptive study from an institute of poisoning

BACKGROUND:In clinical practice,some patients might not be able or unwilling to provide a thorough history of medication and poison exposure.The aim of this study was to use toxicological analysis to examine the clinical characteristics of patients with acute poisoning whose exposure history was uncertain from a toxicological analysis perspective.METHODS:This was a retrospective and descriptive study from an institute of poisoning.Patient registration information and test reports spanning the period from April 1,2020 to March 31,2022,were obtained.Patients with uncertain exposure histories and who underwent toxicological analysis were included.Clinical manifestations and categories of toxics were analyzed.RESULTS:Among the 195 patients with positive toxicological analysis results,the main causes of uncertain exposure history was disturbance of consciousness(62.6%),unawareness(23.6%)and unwillingness or lack of cooperation(13.8%).The predominant clinical manifestations were disturbed consciousness(62.6%),followed by vomiting and nausea(14.4%)and liver function abnormalities(8.7%).A comparison of clinical manifestations between patients with positive and negative(n=99)toxicological analyses results revealed significantly different proportions of disturbances in consciousness(63%vs.21%),dizziness(1.5%vs.5.1%),multi-organ failure(1.5%vs.7.1%),and local pain(0 vs 4%).The main categories of substances involved were psychiatric medications(23.1%),sedatives(20.5%),insecticides(13.8%),and herbicides(12.8%).CONCLUSION:The clinical manifestations of acute poisoning in patients with an uncertain exposure history are diverse and nonspecific,and toxicological analysis plays a pivotal role in the diagnosis and differential diagnosis of such patients.

Enhancing wood efficiency through comprehensive wood flow analysis:Methodology and strategic insights

Wood,an essential natural resource in human civilization,remains widely used despite advances in technology and material substitution.The surge in greenhouse gas emissions and environmental concerns accentuates the need for optimizing wood utilization.Material flow analysis is a powerful tool for tracking material flows and stocks,aiding resource management and environmental decision-making.However,the full extent of its methodological dimensions,particularly within the context of the wood supply chain,remains relatively unexplored.In this study,we delve into the existing literature on wood flow analysis,discussing its primary objectives,materials involved,temporal and spatial scales,data sources,units,and conversion factors.Additionally,data uncertainty,data reconciliation and crucial assumptions in material flow analysis are highlighted in this paper.Key findings reveal the significance of wood cascading and substitution effects by replacing non-wood materials,where they can reduce greenhouse gas emissions more than the natural carbon sink of forests and wood products.The immediate impact of short-term wood cascading might not be as robust as the substitution effect,with energy substitution showcasing better results than material substitution.However,it's crucial to note that these conclusions could experience significant reversal from a long-term and global perspective.Strategies for improving wood efficiency involve maximizing material use,advancing construction technologies,extending product lifespans,promoting cascade use,and optimizing energy recovery processes.The study underscores the need for standardized approaches in wood flow analysis and emphasizes the potential of wood efficiency strategies in addressing environmental challenges.