Potentially Suitable Area and Change Trends of Tulipa iliensis under Climate Change

Tulipa iliensis,as a wild plant resource,possesses high ornamental value and can provide abundant parental materials for tulip breeding.The objective of this research was to forecast the worldwide geographical spread of Tulipa iliensis by considering bioclimatic,soil,and topographic variables,the findings of this research can act as a benchmark for the conservation,management,and utilization of Tulipa iliensis as a wild plant resource.Research results indicate that all 12 models have an area under curve(AUC)of the receiver operating characteristic curve(ROC)values greater than 0.968 for the paleoclimatic,current,and future climate scenarios,this suggests an exceptionally high level of predictive accuracy for the models.The distribution of Tulipa iliensis is influenced by several key factors.These factors include the mean temperature of the driest quarter(Bio9),calcium carbonate content(T_CACO3),slope,precipitation of the driest month(Bio14),Basic saturation(T_BS),and precipitation of the coldest quarter(Bio19).During the three paleoclimate climate scenarios,the appropriate habitats for Tulipa iliensis showed a pattern of expansion-contraction expansion.Furthermore,the total suitable area accounted for 13.38%,12.28%,and 13.28%of the mainland area,respectively.According to the current climate scenario,the High-suitability area covers 61.78472×10^(4)km^(2),which accounts for 6.57%of the total suitable area,The Midsuitability area covers 190.0938×10^(4)km^(2),accounting for 20.2%of the total suitable area,this represents a decrease of 63.53%~67.13%compared to the suitable area of Tulipa iliensis under the paleoclimate scenario.Under the Shared Socioeconomic Pathways(SSP)scenarios,in 2050 and 2090,Tulipa iliensis is projected to experience a decrease in the High,Mid,and Low-suitability areas under the SSP126 climate scenario by 7.10%~12.96%,2.96%~4.27%and 4.80%~7.96%,respectively.According to the SSP245 scenario,the high suitability area experienced a slight expansion of 2.26%in 2050,but a reduction of 6.32%in 2090.In the SSP370 scenario,the High-suitability areas had a larger reduction rate of 11.24%in 2050,while the Mid-suitability and Low-suitability areas had smaller expansion rates of 0.36%and 4.86%,respectively.In 2090,the High-suitability area decreased by 4.84%,while the Mid and Low-suitability areas experienced significant expansions of 15.73%and 45.89%,respectively.According to the SSP585 scenario,in the future,the High,Mid,and Low-suitability areas are projected to increase by 5.09%~7.21%,7.57%~17.66%,and 12.30%~48.98%,respectively.The research offers enhanced theoretical direction for preserving Tulipa iliensis’genetic variety amidst evolving climatic scenarios.

Age Structure and Change Trend of Tobacco Farmers:A Case Study of Shashi Town,Liuyang City,Hunan Province

Taking the tobacco-growing area of Shashi Town,Liuyang City as the object,the number of registered,newly added and resigned tobacco farmers in each year,average age,number and proportion of tobacco farmers of different ages,number and proportion of tobacco farmers born in different eras,as well as planting scale and planting history of flue-cured tobacco in the last 10 years were analyzed statistically.The results showed that the number of tobacco farmers in Shashi Town showed a rapid decline trend from 2012 to 2018,while the average age continuously increased,and the average planting scale rose in a wave like way.From 2019 to 2021,the decline rate of the number of tobacco farmers slowed down,and the average planting scale continuously increased,while the aging of tobacco farmers accelerated.Meantime,the birth years of tobacco farmers were further concentrated in the 1960s,and they were mainly at the age of 50-59.The tobacco industry in Shashi Town of Liuyang City is facing the crisis of aging tobacco farmers and no successor of flue-cured tobacco planting,and it is predicted that the next 5-10 years will continue the change trend from 2019 to 2021.

Comparative Analysis of Climatic Change Trend and Change-Point Analysis for Long-Term Daily Rainfall Annual Maximum Time Series Data in Four Gauging Stations in Niger Delta

The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.

Detecting Climate Change Trend, Size, and Change Point Date on Annual Maximum Time Series Rainfall Data for Warri, Nigeria

The study focused on the detection of indicators of climate change in 24-hourly annual maximum series (AMS) rainfall data collected for 36 years (1982-2017) for Warri Township, using different statistical methods yielded a statistically insignificant positive mild trend. The IMD and MCIMD downscaled model’s time series data respectively produced MK statistics varying from 1.403 to 1.4729, and 1.403 to 1.463 which were less than the critical Z-value of 1.96. Also, the slope magnitude obtained showed a mild increasing trend in variation from 0.0189 to 0.3713, and 0.0175 to 0.5426, with the rate of change in rainfall intensity at 24 hours duration as 0.4536 and 0.42 mm/hr.year (4.536 and 4.2 mm/decade) for the IMD and the MCIMD time series data, respectively. The trend change point date occurred in the year 2000 from the distribution-free CUSUM test with the trend maintaining a significant and steady increase from 2010 to 2015. Thus, this study established the existence of a trend, which is an indication of a changing climate, and satisfied the condition for rainfall Non-stationary intensity-duration-frequency (NS-IDF) modeling required for infrastructural design for combating flooding events.

Estimation of Drought Change Trends in Northeast China in 2020-2050 and Its Impact on Maize Yield

Based on the results of nine Coupled Model Intercomparison Project Phase 5 (CMIP5) coupling models, the temperature and precipitation data of 114 stations in Northeast China were compared and analyzed. The simulation effect of CMIP5 model on precipitation and temperature in Northeast China was evaluated. The research shows that the Geophysical Fluid Dynamics Laboratory Earth System Models (GGFDL-ESM2G) have the best simulation effect on precipitation and temperature in Northeast China. Based on the SPEI index, the relationship between the drought trend of maize growing season and the yield change rate of maize in Northeast China was analyzed, and the future drought (2020-2050) and corn yield in Northeast China were estimated. The cumulative Standardized Precipitation Evapotranspiration Index (SPEI) analysis of the northeast maize growing season (May-September) shows that the drought in the northeastern region showed an intensifying trend from 1980 to 2010, especially in the first ten years of the 21st century. The cumulative SPEI index has a significant positive correlation with the yield of maize in Northeast China, and has a certain indicator effect on the yield of maize in Northeast China. The three scenarios of GFDL-ESM2G model show that under the three scenarios of Representative Concentration Pathways (RCP), the warming in Northeast China is significant;under the RCP4.5 scenario, the precipitation in Northeast China is increasing;in the RCP2.6 and RCP8.5 climate scenarios, precipitation is presented and reduces the trend of drought. Estimates of drought trends in Northeast China show that under the RCP4.5 climate scenario, the drought in Northeast China showed a slowing trend from 2020 to 2050. Under the RCP2.6 and RCP8.5 climate scenarios, the drought in Northeast China showed an increasing trend. Under the RCP2.6 and RCP8.5 climate scenarios, the yield change rate of maize in Northeast China showed a downward trend, indicating that climate warming caused the drought in Northeast China to increase, which had a negative impact on corn yield increase. In severe drought years, drought may cause northeast corn production seriously reduced. However, under the RCP4.5 scenario, drought has little effect on corn yield.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Climate Change Trend Observations in Morocco: Case Study of Beni Mellal-Khenifra and Darâa-Tafilalt Regions

Despite the extreme events impact on various climate events frequency and intensity in many developing countries, there has been dearth information on daily climate trends and climatic extremes. This study comes to characterize the climate type and its evolution in Morocco, specially the two regions Beni Mellal-Khenifra and Daraa-Tafilalt. RClimdex software has been used to calculate pluvio-thermal and ombro-thermic indices in the studied stations to reveal climate type in the two regions, their evolutions, and the daily extreme temperatures and precipitations. A cartographic representation has been done for the calculated indices and climatic trends in the stations from 1970 to 2016. As results, the temperature trend for hot day’s number shows a significant increase, while the cold night’s numbers, warm sequences, and precipitation are gradually decreases especially at Khouribga and Midelt. These observed modifications influence negatively the annual rainfall total in the year, the consecutive wet days, the maximums rainy days, and the increase of consecutively dry days. As a conclusion, the Martonne aridity and ombro-thermic indices, show that the temperatures and precipitations evolutions do not induce a change in the climate type for the studied regions.

The Trend of Changes in Surface Wind in the Indian Ocean, in the Period from 1981 to 2015, Using Reanalysis Data, NCEP/NCAR

Knowing the pattern of surface winds on the seas and oceans and how it changes over time is of great importance. In this research, the monthly surface wind fields on the Indian Ocean have been studied and analyzed for a 35-year period (1981-2015), using NCEP/NCAR data reanalysis. The results show that transition from cold to warm pattern happens in May and that the summer monsoon pattern begins in June and continues until August. The wind speed pattern tends to the winter monsoon from November on. The maximum average wind speed in June is 13 m/s and its minimum is 2 m/s in October. Direction of prevailing winds is the southwest in the summer. The highest wind speed happens in the latitude of 10 - 15 degrees. Analysis of the wind distribution shows that the wind speed of 2 - 5 m/s happens in about 60% of the cases. There is probability of blowing 0.5 - 4 m/s wind for all months;but this probability is higher in the autumn (October and November) than that in the summer (July and August). Probability of the monthly over 5 m/s winds shows a definitely opposite distribution;that is, wind speed in July and August is higher than that in October. A long-term survey on the speed of surface water wind and sea surface temperature shows an opposite changing trend in wind speed and sea surface temperature during a 55-year statistical period. Wind speed reduced, while the sea surface temperature was increasing. The wind speed gradient in the upper levels of atmosphere graph has been increasing;this phenomenon confirms the effects of global warming and ocean warming on the monsoon system patterns in the Indian Ocean. Keywords

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Changes of coastline and tidal flat and its implication for ecological protection under human activities: Take China’s Bohai Bay as an example

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area,which is also of great significance for understanding coastal ecological environment changes and even global changes.Based on field measurements,combined with Linear Regression(LR)model and Inverse Distance Weighing(IDW)method,this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay.The shoreline faces a high erosion chance under the action of natural factors,while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities.The implication of change rule for ecological protection and recovery is also discussed.Measures should be taken to protect the coastal ecological environment.The models used in this paper show a high correlation coefficient between observed and modeling data,which means that this method can be used to predict the changing trend of shoreline and tidal flat.The research results of present study can provide scientific supports for future coastal protection and management.

Diffusion kurtosis imaging of microstructural changes in brain tissue affected by acute ischemic stroke in different locations

The location of an acute ischemic stroke is associated with its prognosis. The widely used Gaussian model-based parameter, apparent diffusion coefficient(ADC), cannot reveal microstructural changes in different locations or the degree of infarction. This prospective observational study was reviewed and approved by the Institutional Review Board of Xiamen Second Hospital, China(approval No. 2014002).Diffusion kurtosis imaging(DKI) was used to detect 199 lesions in 156 patients with acute ischemic stroke(61 males and 95 females), mean age 63.15 ± 12.34 years. A total of 199 lesions were located in the periventricular white matter(n = 52), corpus callosum(n = 14), cerebellum(n = 29), basal ganglia and thalamus(n = 21), brainstem(n = 21) and gray-white matter junctions(n = 62). Percentage changes of apparent diffusion coefficient(ΔADC) and DKI-derived indices(fractional anisotropy [ΔFA], mean diffusivity [ΔMD], axial diffusivity [ΔD_a], radial diffusivity ΔDr, mean kurtosis [ΔMK], axial kurtosis [ΔK_a], and radial kurtosis [ΔK_r]) of each lesion were computed relative to the normal contralateral region. The results showed that(1) there was no significant difference in ΔADC, ΔMD, ΔD_a or ΔD_r among almost all locations.(2) There was significant difference in ΔMK among almost all locations(except basal ganglia and thalamus vs. brain stem; basal ganglia and thalamus vs. gray-white matter junctions; and brainstem vs. gray-white matter junctions.(3) The degree of change in diffusional kurtosis in descending order was as follows: corpus callosum > periventricular white matter > brainstem > gray-white matter junctions > basal ganglia and thalamus > cerebellum. In conclusion, DKI could reveal the differences in microstructure changes among various locations affected by acute ischemic stroke, and performed better than diffusivity among all groups.

Spatial Variation and Trend of Extreme Precipitation in Africa during 1981-2019 and Its Projected Changes at the End of 21st Century

This study comprehensively examines the patterns and regional variation of severe rainfall across the African continent, employing a suite of eight extreme precipitation indices. The analysis extends to the assessment of projected changes in precipitation extremes using five General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs) scenarios at the long-term period (2081-2100) of the 21st century. Furthermore, the study investigates potential mechanisms influencing precipitation extremes by correlating extreme precipitation indices with oceanic system indices, specifically Ni?o 3.4 for El Ni?o-Southern Oscillation (ENSO) and Dipole Mode Index (DMI) for the Indian Ocean Dipole (IOD). The findings revealed distinct spatial distributions in mean trends of extreme precipitation indices, indicating a tendency toward decreased extreme precipitation in North Africa, Sahel region, Central Africa and the Western part of South Africa. Conversely, West Africa, East Africa and the Eastern part of South Africa exhibit an inclination toward increased extreme precipitation. The changes in precipitation extreme indices indicate a general rise in both the severity and occurrence of extreme precipitation events under all scenarios by the end of the 21st century. Notably, our analysis projects a decrease in consecutive wet days (CWD) in the far-future. Additionally, correlation analysis highlights significant correlation between above or below threshold rainfall fluctuation in East Africa and South Africa with oceanic systems, particularly ENSO and the IOD. Central Africa abnormal precipitation variability is also linked to ENSO with a significant negative correlation. These insights contribute valuable information for understanding and projecting the dynamics of precipitation extreme in Africa, providing a foundation for climate adaptation and mitigation efforts in the region.

The assessment of time dependent flow of Williamson fluid with radiative blood flow against a wedge

The present pagination reports both Brownian diffusion and thermophoresis aspects subject to magneto hydrodynamic Williamson fluid model.Assuming the flow is unsteady and blood is treated as Williamson fluid over a wedge with radiation.The governing equations are transformed into ordinary differential equations by using similarity variables.The analytical solutions of the transformed governing equations are obtained by using the RK 4th order method along with shooting technique solver.The effects of various physical parameters such as Hartmann number,local Weissenberg number,radiation parameter,unsteadiness parameter,Prandtl number,Lewis number,Brownian diffusion,thermophoresis,wedge angle parameter,moving wedge parameter,on velocity,temperature,concentration,skin friction,heat transfer rate and mass transfer rate have been discussed in detail.The velocity and temperature profile deprives for larger We and an opposite trend is observed for concentration.The radiation parameter is propositional to temperature and a counter behaviour is observed for Pr.

Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

Review on the Impact of Climate Change on Great Lakes Region’s Agriculture and Water Resources

This study investigates the multifaceted impacts of climate change on the Midwest region of the United States, particularly the rising temperatures and precipitation brought about by hot weather activities and technological advances since the 19th century. From 1900 to 2010, temperatures in the Midwest rose by an average of 1.5 degrees Fahrenheit, which would also lead to an increase in greenhouse gas emissions. Precipitation is also expected to increase due to increased storm activity and changes in regional weather patterns. This paper explores the impact of these changes on urban and agricultural areas. In urban areas such as the city of Chicago, runoff from the increasing impervious surface areas poses challenges to the drainage system, and agriculture areas are challenged by soil erosion, nutrient loss, and fewer planting days due to excessive rainfall. Sustainable solutions such as no-till agriculture and the creation of grassland zones are discussed. Using historical data, recent climate studies and projections, the paper Outlines ways to enhance the Midwest’s ecology and resilience to climate change.

Climate change trend and its effects on reference evapotranspiration at Linhe Station, Hetao Irrigation District

Linhe National Meteorological Station, a representative weather station in the Hetao Irrigation District of China, was selected as the research site for the present study. Changes in climatic variables and reference evapotranspiration （ET0 ） （estimated by the Penman-Monteith method） were detected using Mann-Kendall tests and Sen＇s slope estimator, respectively. The authors analyzed the relationship between the ET0 change and each climatic variable＇s change. From 1954 to 2012, the air temperature showed a significant increasing trend, whereas relative humidity and wind speed decreased dramatically. These changes resulted in a slight increase in ETo. The radiative component of total ET0 increased from 50% to 57%, indicating that this component made a greater contribution to the increase in total ETo than the aerodynamic component, especially during the crop growing season （from April to October）. The sensitivity analysis showed that ETo in Hetao is most sensitive to mean daily air temperature （11.8%）, followed by wind speed （-7.3%） and relative humidity （4.8%）. Changes in sunshine duration had only a minor effect on ET0 over the past 59 years.

Climate change and finger millet: Perception, trend and impact on yield in different ecological regions in Central Nepal

The views of local people on climate change along different ecological regions are relatively unexplored in Nepal. This study was conducted in 13 villages in central Nepal at different altitudes to document the views of small holder farmers and compare their perception with trends of climatic variables, finger millet yield, natural disasters, plant phenology(flowering and fruiting), status of forest and wild life, as well as the spread of diseases and pests. Analysis on the climatic data of stations for 36-41 years between 1975 and 2016 showed significant increases in the minimum temperature in lower tropical climatic region(<500 m), upper tropical to subtropical climatic region(500-2000 m) and temperate climatic region(2000-3000 m) by 0.01, 0.026 and 0.054℃/year, respectively, and an increase of maximum temperature by 0.008, 0.018, and 0.019℃/year, respectively. Rainfall showed a strongly significant decreasing trend in all elevation regions. This result matches with the views of respondents except 38% respondent from temperate climatic region. People from the temperate climatic region also mentioned that current onset of snowfall is delayed but amount of snowfall remained the same. From the documented records, except events of wild fire, frequency of natural disasters events have increased in the recent years, which was in harmony with the views of local people. Multi-linear regression analysis showed that contribution of climatic variables on finger millet yield in lower tropical climatic region and upper tropical to subtropical regions was 23% and 57.3%, respectively, which was supported by increasing trend on average growing degree day(GDD) temperature at the rate of 0.01℃ in upper tropical to subtropical region and 0.007℃ in lower tropical climatic region yearly. Finger millet yield has been increasing at the rate of 7.39 and 36.9 kg/ha yearly in lower tropical climatic region and upper tropical to subtropical climatic region, respectively. This result provides deeper understanding of people's perception of causes and effects of climate change on diverse variables along different elevation and related magnitude which can contribute to policy making in Nepal.

An Analysis of Liberia’s Vulnerability to Climate Change in the Context of Least Developed Countries (LDCs): A Review

Climate change is an alarming global challenge, particularly affecting the least developed countries (LDCs) including Liberia. These countries, located in regions prone to unpredictable temperature and precipitation changes, are facing significant challenges, particularly in climate-sensitive sectors such as mining and agriculture. LDCs need more resilience to adverse climate shocks but have limited capacity for adaptation compared to other developed and developing nations. This paper examines Liberia’s susceptibility to climate change as a least developed country, focusing on its exposure, sensitivity, and adaptive capacity. It provides an overview of LDCs and outlines the global distribution of carbon dioxide emissions. The paper also evaluates specific challenges that amplify Liberia’s vulnerability and constrain sustainable adaptation, providing insight into climate change’s existing and potential effects. The paper emphasizes the urgency of addressing climate impacts on Liberia and calls for concerted local and international efforts for effective and sustainable mitigation efforts. It provides recommendations for policy decisions and calls for further research on climate change mitigation and adaptation.

Polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes syndrome with dilated cardiomyopathy: A case report

BACKGROUND Polyneuropathy,organomegaly,endocrinopathy,M-protein,skin changes(POEMS)syndrome is a rare paraneoplastic syndrome that encompass multiple systems.The most common clinical symptoms of POEMS syndrome are pro-gressive sensorimotor polyneuropathy,organ enlargement,endocrine disorders,darkening skin,a monoclonal plasma cell proliferative disorder,and lymph node hyperplasia.The organomegaly consists of hepatosplenomegaly and/or lym-phadenopathy;cases of cardiomyopathy are rare.Diagnoses are often delayed because of the atypical nature of the syndrome,exposing patients to possibly severe disability.Therefore,identifying atypical symptoms can improve the prognosis and quality of life among POEMS syndrome patients.lenalidomide and dexamethasone.CONCLUSION When patients with cardiomyopathy have systemic manifestations such as numb limbs and darkening skin,the POEMS syndrome is the most possible diagnosis.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.