The Role of Biosynthesized Metallic and Metal Oxide Nanoparticles in Combating Anti-Microbial Drug Resilient Pathogens

Because of their high efficiency, antibiotics have long been the primary treatment for infections, but the rise of drug-resistant pathogens has become a therapeutic concern. Nanoparticles, as novel biomaterials, are currently gaining global attention to combat them. Drug-resistant diseases may need the use of nanoparticles as a viable therapeutic option. By altering target locations and enzymes, decreasing cell permeability, inactivating enzymes, and increasing efflux by overexpressing efflux pumps, they can bypass conventional resistance mechanisms. Therefore, understanding how metal and metal oxide nanoparticles affect microorganisms that are resistant to antimicrobial drugs is the main objective of this review. Accordingly, the uses of metal and metal oxide nanoparticles in the fight against drug-resistant diseases appear promising. However, their mechanism of action, dose, and possible long-term effects require special attention and future research. Furthermore, repeated use of silver nanoparticles may cause gram-negative microorganisms to acquire resistance, necessitating additional study.

Toxicity and Molecular Mechanisms of Actions of Silver Nanoparticles

Silver nanoparticles (AgNPs) have gained popularity due to their antibacterial properties, and are therefore widely used in several applications such as wound dressings, food packaging, and water purification. However, the toxicity of AgNPs to humans and the environment is a growing concern. This review aims to summarize the current knowledge on the toxicity and molecular mechanisms of action of AgNPs. The toxicity of AgNPs can be attributed to their small size, which allows them to enter cells and interact with cellular components. Reports suggest that AgNPs can induce cell death, DNA damage, and oxidative stress in various cell types. The toxic effects of AgNPs differ based on their size, shape, surface charge, and coating. The molecular mechanisms behind the toxicity of AgNPs involve the production of reactive oxygen species, disruption of cellular membranes, and activation of proinflammatory cytokines. Overall, the toxicity of AgNPs is dependent on various factors, and more research is needed to fully understand the mechanisms behind their toxicity. This review highlights the need for proper risk assessments and regulations to minimize the adverse effects of AgNPs on human health and the environment.

Summary on Adsorption and Photocatalysis for Pollutant Remediation: Mini Review

Adsorption and photo catalysis are the most popular methods applied for the reduction of amount of pollutants that enter water bodies. The main challenge in the process of adsorption is the demonstration of the experimental data obtained from sorption processes. For many decades most of the researchers used adsorption and kinetic of adsorption as a repetitive work to describe the adsorption data by using common models such as, Langmuir and Freundlich for adsorption isotherms;PFO and PSO models for kinetics. This has been done without careful evaluation of the characteristics of adsorption process. It has been well understood that adsorption does not degrade the pollutant to eco-friendly products and photo catalysis will not degrade without adsorption of the pollutant on the catalyst. Therefore, understanding the detailed mechanism of adsorption, as well as, photo catalysis has been presented in this paper. During photo catalysis: modification towards suppression of electron-hole recombination, improving visible light response, preventing agglomeration, controlling the shape, size, morphology, etc. are the most important steps. This mini review also widely discusses the key points behind adsorption and photo catalysis.

The arbuscular mycorrhizal fungi status of selected tree nurseries in the Ethiopian highlands

We investigated the arbuscular mycorrhizal fungi(AMF)status of ten nurseries suitable for restoration of dry evergreen Afromontane forests in Ethiopia.We quantified AMF root colonization(RC)and spore abundance(SA)in seedlings of nine native tree species namely Acacia abyssinica Hochst.ex Benth.,Cordia africana Lam.,Dovyalis abyssinica(A.Rich.)Warb.,H agenia abyssinica J.F.Gmel.,Juniperus procera Hochst.ex Endl.,Millettia ferruginea(Hochst.)Baker,Olea europaea L.subsp.c uspidata(Wall.ex G.Don)Cif.,Podocarpus falcatus(Thunb.)R.Br.ex Mirb.and Prunus africana(Hook.f.)Kalkman.We used the ink and vinegar method to stain AMF in roots.RC levels ranged from 8.00 to 99.67%and were generally higher than the RC levels reported from other similar nurseries in Ethiopia.SA levels ranged from 1 to 25 spores g~(-1)and werecomparable with some reports from the field in Ethiopia but they were lower than levels reported by another similar study.RC was more affected by host species than nursery location,while the reverse was true for SA.The results also showed that nursery management could improve AMF status among seedlings.When all nursery tree species were considered,RC and SA levels were unrelated.No strong correlation existed between the nursery management variables considered and RC or SA.However,considering C.africana,J.procera and P.falcatus separately,RC-age(r_(s)=0.829,P=0.042)correlation for O.europaea and RC-pot diameter(r_(s)=0.820,P=0.046),RC-pot volume(r_(s)=0.928,P=0.008)and SA-age(r_(s)=0.943,P=0.005)correlations for C.africana,were significant,strong and positive.Generally,most of the tree species and particularly,early-mid successional tree species had sufficient AMF inoculum.Hence,only the mid-late successional tree species;J.procera,P.falcatus,and P.africana may require AMF inoculation,preferably,during filed planting.Based on our results,age and pot volume were identified to be important variables potentially affecting RC and SA.To better understand the effects of these and other nursery management variables,additional study is required.We demonstrated for the first time that black Hero ink is suitable for staining root AMF and can be used in future AMF research.

Chip Layout for Adaptive Line Enhancer Design using Adaptive Filtering Algorithms and Metrics Computation for Auscultation Signal Separation

Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.

First Principles Study of Atomic Adsorption on (111) and (100) Surfaces of Iridium

We have investigated the adsorption of nine different adatoms on the(111)and(100)surfaces of Iridium(Ir)using first principles density functional theory.The study explores surface functionalization of Ir which would provide important information for further study of its functionality in catalysis and other surface applications.The adsorption energy,stable geometry,density of states and magnetic moment are some of the physical quantities of our interest.The study reveals that the three-/four-fold hollow site is energetically the most favorable adsorption site on the(111)/(100)surface of Ir.The investigation on a wide range of coverages(from 0.04 to 1 monolayer)reveals the strong coverage dependence of adsorption energy of the adsorbate atoms.The adsorption energy is found to increase as the coverage increases,implying a repulsive interaction between the adsorbates.Strong hybridization between the adsorbates and the substrate electronic states is revealed to impact the adsorption,while the magnetic moment of the adsorbates is found to be suppressed.The Bader analysis reveals significant amount of charge transfers between the adsorbate atoms and the substrate.The binding of adsorbate atoms on the(100)surface is observed to be moderately stronger as compared to that on the(111)surface.

One-Dimensional Explicit Tolesa Numerical Scheme for Solving First Order Hyperbolic Equations and Its Application to Macroscopic Traffic Flow Model

In this paper, a new numerical scheme for solving first-order hyperbolic partial differential equations is proposed and is implemented in the simulation study of macroscopic traffic flow model with constant velocity and linear velocity-density relationship. Macroscopic traffic flow model is first developed by Lighthill Whitham and Richards (LWR) and used to study traffic flow by collective variables such as flow rate, velocity and density. The LWR model is treated as an initial value problem and its numerical simulations are presented using numerical schemes. A variety of numerical schemes are available in literature to solve first order hyperbolic equations. Of these the well-known ones include one-dimensional explicit: Upwind, Downwind, FTCS, and Lax-Friedrichs schemes. Having been studied carefully the space and time mesh sizes, and the patterns of all these schemes, a new scheme has been developed and named as one-dimensional explicit Tolesa numerical scheme. Tolesa numerical scheme is one of the conditionally stable and highest rates of convergence schemes. All the said numerical schemes are applied to solve advection equation pertaining traffic flows. Also the one-dimensional explicit Tolesa numerical scheme is another alternative numerical scheme to solve advection equation and apply to traffic flows model like other well-known one-dimensional explicit schemes. The effect of density of cars on the overall interactions of the vehicles along a given length of the highway and time are investigated. Graphical representations of density profile, velocity profile, flux profile, and in general the fundamental diagrams of vehicles on the highway with different time levels are illustrated. These concepts and results have been arranged systematically in this paper.

Prevalence of Multidrug Resistant <i>Mycobacterium tuberculosis</i>among Tuberculosis Patients Admitted to Adama Hospital Medical College, Adama, Ethiopia: A Retrospective Study

Multidrug resistant tuberculosis (MDR-TB) is an emerging challenge for TB control programs globally. Ethiopia ranks 7th among the world’s 22 high TB burden countries. According to report of WHO (2017), TB is one of the leading infectious causes of death in Ethiopia claiming the life of more than 30 thousand people annually. The surge of MDR-TB has been compounding the problem further. Facility-based MDR-TB researches have not been generated in equal pace with community-based ones. The aim of this study was to assess the prevalence of MDR-TB using clinical records of MDR-TB patients in Adama Hospital Medical College (AHMC) from 2014 to 2018. All clinical data of MDR-TB from 2014-2018 was collected from AHMC TB department. Socio-demographic and risk factor data were collected from patients using semi-structured questionnaire. Data were analyzed using Microsoft excel and SPSS version 20. Out of a total 2332 TB suspected cases admitted to AHMC from 2014 to 2018, 175 (7.5%) were confirmed MDR-TB cases or confirmed Rifampicin resistant cases. In particular, 97 (4.2%) presented presumptive MDR-TB alone and 78 (3.3%) showed confirmed Rifampicin resistance alone. Comparison among age groups showed the highest prevalence for 24 - 44 years with 1.8% and 1.5% confirmed MDR-TB and Rifampicin resistance. The overall prevalence of MDR-TB was moderate indicating for possible rise of the problem due to course of time. Further study combining both community and health facility based is recommended to highlight the need to make useful strategies for testing, surveillance and effective clinical management of MDR-TB cases.

Prediction of Groundwater Level Fluctuation towards Rainfall Induced Landslide: Case of Blue Nile Gorge, Central Ethiopia

The groundwater level fluctuation was studied in a complex geological setting region where a frequent landslide is observed in a rainy season. Steady and transient states of flow are modeled with different hydrogeological parameters. The models are calibrated to satisfy the observed field conditions and expected results from the scientific point of view. The results reveal that the groundwater level fluctuation and flow direction in the region are complex. In limited areas, the fluctuation of groundwater is significant from season to season while in others the level remains stable in all seasons of the year. Following that, the result of groundwater flow model was exported to GeoStudio to simulate the slope stability of selected slope. The factor of safety was calculated using Slope/W. The effect of pore-water on the factor of safety was cross-checked by remodeling the slope without water. The results and sensitivity analysis of slope stability confirm that the rise of groundwater level decreases the factor of safety significantly only on critical slope section.

Modeling for Inter-Basin Groundwater Transfer Identification: The Case of Upper Rift Valley Lakes and Awash River Basins of Ethiopia

Groundwater movement beneath watershed divide is one component of the hydrological cycle that is typically ignored due to difficulty in analysis. Numerical ground-water models, like TAGSAC, have been used extensively for predicting aquifer responses to external stresses. In this paper TAGSAC code was developed to identify the inter-basin groundwater transfer (IBGWT) between upper Awash River basin (UARB) and upper rift valley lakes basin (URVLB) of Ethiopia. For the identification three steady state groundwater models (for UARB, URVLB and for the two combined basins) were first created and calibrated for the 926 inventoried wells. The first two models are conceptualized by considering the watershed divide between the two basins as no-flow. The third model avoids the surface water divide which justifies IBGWT. The calibration of these three models was made by changing the recharge and hydrogeologic parameters of the basins. The goodness of fit indicators (GoFIs) obtained was better for the combined model than the model that describes the URVLB. Furthermore, the hydraulic head distribution obtained from the combined model clearly indicates that there is a groundwater flow that doesn’t respect the surface water divide. The most obvious effect of IBGWT observed in these two basins is that it diminishes surface water discharge from URVLB, and enhances discharge in the UARB. Moreover, the result of this study indicates potential for internal and cross contamination of the two adjacent groundwater.

Analysis of the Promoter Region, Motif and CpG Islands in AraC Family Transcriptional Regulator ACP92 Genes of <i>Herbaspirillum seropedicae</i>

Identification of promoters and their regulatory elements are the most important phases in bioinformatics. To understand the regulation of gene expression, identification, and analysis of promoters region, motif and CpG islands are the most important steps. The accurate prediction of promoter’s is basic for proper interpretation of gene expression patterns, construction and understanding of genetic regulatory system. Therefore, the objective of this study was to analyze the promoter region, motif such as a transcription factor and CpG islands in AraC family transcriptional regulator ACP92 genes of Herbaspirillum seropedicae. The analysis was carried out by identifying transcription start sites in ACP92 genome sequences taken from the H. seropedicae assembly of NCBI genome browser, and 29 ACP92 genes sequences. Accordingly, transcription start sites (TSS) were identified, and the result indicated that 37.9% had more than one TSS whereas only 62.1% had one TSS. In the analysis, seven motifs were identified from the thought sequences and MV6 was revealed the common promoter motif for all (100%) in H. seropedicae ACP92 gene that serves as binding sites for transcription factors which shared a minimum of 48.27%. Based on a common motif MV6 to find out similar motifs using TOMTOM from the databases of prokaryotes DNA, most of them are transcription factors of fur family. The others are bacterial histone-like protein family, matp and sigma-54 factor family also transcription factor families that are binding candidate to MV6. H. seropedicae ACP92 genes are CpG Island which implies that the regulation of gene expression plays an important role.

Review: Low Cost, Environmentally Friendly Humic Acid Coated Magnetite Nanoparticles (HA-MNP) and Its Application for the Remediation of Phosphate from Aqueous Media

Phosphate is a primary nutrient required for the normal functioning of many organisms in the ecosystem. However, presence of excess phosphate into the aquatic systems leads to eutrophication which can promote harmful algal growth and decrease the amount of dissolved oxygen in water. Municipal, industrial and agricultural run-off wastewaters are the major point sources for phosphate discharges. There are different methods to remove phosphates from water. Among these, adsorption is the most widely accepted method for phosphate removal because of its high efficiency, minimum cost, easy and simple operation and applicability at lower concentrations. The emphasis of this review, is to consolidate low cost, environmentally friendly humic acid coated magnetite nanoparticles (HA-MNP) and its application for the remediation of phosphate from aqueous media. The magnetic nanoparticles could be easily separated from the reaction mixture by using a simple hand held magnet and adsorption studies demonstrate the fast and effective separation of phosphate with maximum removal efficiency > 90% at pH 6.6. The adsorption behavior follows the Freundlich isotherm and the removal of phosphate is found higher at acidic and neutral pH compared to basic conditions. The nanoparticles exhibit good selectivity and adsorption efficiency for phosphate in the presence of co-existing ions such as Cl-, ?and??with some inhibition effect by??and finally, the effect of temperature on the adsorption reveals that the process is endothermic and spontaneous.

Fe-Oxide Nanomaterial: Synthesis, Characterization and Lead Removal

Organic solvent free iron oxide nanomaterial used for lead removal was synthesized by co-precipitation method. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopic with energy dispersive X-ray analysis (SEM-EDX), X-ray diffraction (XRD) and thermo gravimetric-differential thermal (TG-DTA) analysis were used to determine the surface characteristics and analysis of iron oxide. Optimization of solution pH, adsorbent dosage, contact time, agitation speed and initial lead ion concentration were conducted for further adsorption isotherm, kinetics, thermodynamics and desorption study. Langmuir sorption isotherm model fits the adsorption data better than Freundlich, Dubinin-Radushkevich (D-RK) and Flory-Huggins (FH) models. The mean adsorption energy and free energy obtained from D-RK and FH models guides that the mechanism was under control of physical adsorption and actuality of spontaneous reaction, respectively. From kinetics of adsorption pseudo second (PSO) model fits well than pseudo first (PFO) and Elovich adsorption-reaction models. And to test whether the reaction is under control of adsorption-diffusion or not the intra particle diffusion (IPD) model was tested, but it fails to pass through the origin. This indicates that the reaction mechanism only under control of adsorption-reaction. The maximum adsorption capacity (qmax) of the adsorbent was 70.422 mg/g.

Spectral, Thermal and Antibacterial Studies for Bivalent Metal Complexes of Oxalyl, Malonyl and Succinyl-bis-4-phenylthiosemicarbazide Ligands

The thermogravimetry (TG) and derivative thermogravimetry (DTG) have been used to study the thermal decomposition of some oxalyl (H4OxTSC), malonyl (H4MaTSC) and succinyl-bis-4-phenyl- thiosemicarbazide (H4SuTSC) ligands and their metal complexes using Horowitz-Metzger (HM) and Coats-Redfern methods. The kinetic thermodynamic parameters such as: E*, ΔH*, ΔS*and ΔG* are calculated from the DTG curves. The isolated complexes have the general composition [M2(L) (H2O)6], where M=Cu(II), Zn(II), L=MaTSC and M=Co(II), Cu(II) or Sn(II) and L=Su TSC and [M2(L) (H2O)n]·nH2O where M=Cu(II), Co(II) or Sn(II), L=OxTS or Ma TSC. The tested compounds show a good activity against four strains of bacteria Gram negative Escherichia coli, Pseudomonas aeruginosa species and gram-positive Bacillus cereus and Staphylococcus aureus.

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic,many individuals are suffering from suicidal ideation in the world.Social distancing and quarantining,affects the patient emotionally.Affective computing is the study of recognizing human feelings and emotions.This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face.Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance.In this paper,a new method is proposed for emotion recognition and suicide ideation detection in COVID patients.This helps to alert the nurse,when patient emotion is fear,cry or sad.The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm.The proposed Convolution Neural Networks(CNN)architecture with DnCNN preprocessing enhances the performance of recognition.The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras.The proposed method accuracy is more when compared to other methods.It detects the chances of suicide attempt based on stress level and emotional recognition.

Review:Investigation of Partially Pre⁃mixed Charge Ignitions(PPCI)Engine Mode

This paper embraces the key points of unpolluted internally combusted engine emissions.Core objective is focused on the recent effort to improve compression ignition(CI)and spark ignition(SI)engine to have fuel⁃efficient and minimized pollutant emissions.There are many advanced internal combustion engines to overcome the challenges of conventional compression ignition engines of the high level of particulate matter(PM)and oxides of nitrogen emission.One of the latest options on which many researchers work recently is low⁃temperature combustion which studies the engine advancement and emission at the same time.This review focuses on the released emissions,incinerations and performances features of partially premixed charge ignition with different fuel blends like n⁃butanol⁃gasoline,gasoline⁃diesel,alcohol⁃diesel,and NG⁃diesel effect on partially premixed charge ignition(PPCI)engine combustion.Therefore,PPCI is a single or a dual fuel strategy in that a pre⁃mixed low or high reactive fuel(L/HRF)is directly injected into the engine.It is one of the best low⁃temperature combustion(LTC)strategies by which emissions are minimized while thermal efficiency is acceptable.The recent PPCI of various fuels and their effect are compared.Accordingly,the initial pilot injection can extend the operating load that is a problem in diesel engine;gasoline fuel displayed fewer soot releases when compared with diesel oil in all working situations.The additional inspiring outcome for this combustion approach can be directly controlled by using the start of injection timing(SOI),which is impossible with most other LTC concepts.The diesel PPCI can overcome the NOx⁃PM tradeoff but needs more EGR rates.Another problem of diesel engine PPCI is associated with its comparatively higher boiling point and relatively lower volatility,which made use of advanced injection with a great number of fuel challenges.Thus,diesel fuel is not the best candidate with the LRF to the PPCI engine setup.

Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

In recent research from the total number of new cancer cases in Africa about 29.46% and in Ethiopia 31.85% are breast cancer cases. 25.84% of all cancer related death is from breast cancer. One of the challenges in the treatment of breast cancer is early detection. Researchers agreed that, improving the preventive mechanism of breast cancer is an early predicting and detecting model. Research efforts are continuing to present different solution approaches using advanced techniques of Artificial intelligence (AI), Machine learning (ML), Deep Learning (DL), and Computational Intelligence as well. A genetic algorithm is a hyper-parameter optimization algorithm that belongs to the class of evolutionary algorithms. Genetic Algorithm (GA) is used for complex search spaces for search and optimization. This reviewed literature paper shows the positive effect of GA in the diagnosis of breast cancer on AI algorithms.

Mathematical model and optimal control analysis of coffee berry borer with temperature and rainfall variability

This study focuses on a nonlinear deterministic mathematical model for coffee berry borer(Hypothenemus hampei)with temperature and rainfall variability.In the model analysis,CBB free and coexistence equilibria are computed.The basic reproduction numbers at a minimum and maximum temperature and rainfall are derived.The qualitative analysis of the model revealed the scenario for equilibria together with basic reproduction numbers.The local stability of equilibria is established through the Jacobian matrix and the Routh-Hurwitz criteria,while the global stability of equilibria is demonstrated using an appropriate Lyapunov function.The normalized sensitivity analysis has also been performed to observe the impact of different parameters on basic reproduction numbers.The proposed model is extended into an optimal control problem by incorporating two control variables,namely,the preventive measure variable based on the separation of susceptible coffee berries from contacting the pests based on biological control and an increase in the death rate of colonizing females of CBB based on chemical control.Optimal disease control analysis is examined using Pontryagin's minimum principle.Finally,the numerical simulations are performed based on analytical results and are discussed quantitatively.Furthermore,the cost-effectiveness of control strategies to determine the best approach to minimize the CBB burden was studied.The study is significant in providing reliable information on how one can use mathematical modeling to improve the roles of control strategies and prevention in CBB transmission in a coffee farm.The outcome of the study may guide public agriculture policymakers on optimal control strategies to control the pests.In particular,using chemical pesticides is very effective to combat pests with minimum costs.

Hydrodynamic characteristics of a typical karst spring system based on time series analysis in northern China

In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern China.Results show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,respectively.The regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu Spring.The auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological year.The delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological year.In addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are small.All these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.

Climate change under CMIP5-RCP scenarios in Ethiopia:The Bale mountains ecoregion

The Bale mountains ecoregion in Ethiopia provides a number of benefits for the local communities mainly in terms of water supply,power generation,tourism activity,and irrigation development.Notwithstanding,the ecoregion has been characterized primarily by recurring floods and droughts,as well as crop failure due to a variety of natural and human-activity-driven change factors.As a matter of fact,the purpose of this study would be to examine long-term changes and fluctuation in precipitation(PCP),maximum temperature(T_(max)),and minimum temperature(T_(min))in the Bale mountains ecoregion using ensembles of three climate models with three representative concentration pathways(RCPs)scenarios from the coupled model inter-comparison project phase five(CMIP5)dataset.Statistical downscaling model(SDSM)was applied to project PCP,T_(max),and T_(min)in the forthcoming period considering three RCPs:low emission(RCP2.6),intermediate(RCP4.5),and high emission(RCP8.5).SDSM's performance in capturing historical daily PCP,T_(max),and T_(min)has been validated using standard statistical metrics such as coefficient of determination(R^(2)),Nash Sutcliff efficiency(NSE),and root mean square error(RMSE).SDSM has the potential to generate a statistical transfer function between large-scale variables and local climate,allowing PCP,T_(max),and T_(min) to be downscaled to a point scale for the ecoregion.The magnitude of mean yearly changes in PCP,T_(max),and T_(min) were investigated throughout three thirty-year time slices,corresponding to the 2020s,2050s,and 2080s.The Mann-Kendall non-parametric test was used to analyse trends in PCP,T_(max),and T_(min) from 2011 to 2100.Inter-annual variability in PCP,T_(max),and T_(min) were investigated for the aforementioned period,taking standard deviation into account under each RCP scenarios.The results reveal that mean annual PCP,T_(max),and T_(min) are rising in all three time slices and in all three CMIP5 RCP scenarios as compared to the baseline scenario.Mean annual PCP is projected to increase within the uncertainty range of 6.68% to 17.93%(RCP2.6),7.45% to 21.94%(RCP4.5),and 19.70% to 33.69%(RCP4.5)(RCP8.5).T_(max) increases from 0.04℃ to 0.24℃(RCP2.6),0.05℃ to 0.31℃(RCP4.5),and 0.04℃ to 0.42℃(RCP8.5),whereas T_(min) increases from 0.22℃ to 0.52℃(RCP2.6),0.23℃ to 0.67℃(RCP4.5),and 0.26℃ to 1.14℃(RCP8.5)(RCP8.5).For future projections at the end of the 21^(st) century,the mean annual PCP,T_(max),and T_(min) for all three analysed climate models and RCPs have shown a positive trend.The inter-annual variability of PCP,T_(max),and T_(min) is higher in the RCP8.5 than RCP4.5 and RCP2.6 in all climate models.The findings clearly implied that prior understanding of long-term climate change and variability need to be addressed to plan effective and efficient mitigation strategies,as well as to maintain adequate quantity and quality of water supplies to the communities residing in the ecoregion.