Does forest restoration influence soil infiltrability? A case study in the restored woodland of Sierra de ávila(Central Spain)

Infiltration,as a major component of the hydrological cycle,plays an important role in ecosystems,river flooding,and soil erosion.Therefore,this process has been studied on different soils,with different vegetation cover,and under different climate conditions.However,it is still necessary to know how infiltration rates depend on land degradation,vegetation cover,forest management,and forest restoration,since soil infiltration is related to soil hydrological function and hydrological ecosystem services.The aim of our study is to analyze the way reforestation and check dam construction have helped to improve soil infiltration rates in comparison with old,degraded land,different soils and vegetation covers in Central Spain.Therefore,three infiltration tests were carried out by means of a simple methacrylate infiltrometer ring,in four sampling plots,for five types of land use:(i) native holm oak forest,(ii) 60-year-old reforested pine wood,(iii) shrubs,(iv) sediment wedges of check dams,and(v) gullies and degraded hillslopes.Our results show much higher infiltration rates in the soil of 60-yearold pine reforestation sites(1198.00 mm·h^(-1)),and in the sediment wedges of check dams(1088.00 mm·h^(-1)),than in those of degraded hillslopes(365.00 mm·h^(-1)) and shrubland(420.80 mm·h^(-1)).The rates were also shown to be close to those from the remaining patches of native holm oak woodland(770.40 mm·h^(-1)).We also found that organic matter,humus and litter depth,and height of vegetation and cover,all improve soil infiltration rates,while slope degree,presence of coarse elements,stoniness,clay content,bulk density,and electric conductivity inhibit the rates.It was additionally seen that pine reforestation and check dam construction caused degraded land to recover its hydrological conditions to a level that is quite close to that of the ancient oak holm native forest,alongside ameliorating the hydrological cycle in the watershed.This information will be very useful for decisionmaking processes related to land restoration projects,forest management,and environmental policy.

On Moment Generating Function of Generalized Order Statistics from Erlang-Truncated Exponential Distribution

In this paper explicit expressions and some recurrence relations are derived for marginal and joint moment generating functions of generalized order statistics from Erlang-truncated exponential distribution. The results for k-th record values and order statistics are deduced from the relations derived. Further, a characterizing result of this distribution on using the conditional expectation of function of generalized order statistics is discussed.

Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics.A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specic time period.A nonlinear binary mathematical programming model for the problem is formulated.The decision variables are binary ones that represent whether to choose a specic consumer,and design constraints are formulated to keep track of the chosen route.To better illustrate the problem,objective,and problem constraints,a real application case study is presented.The case study involves the optimum delivery of safeguarding substances to several hospitals in the Al-Gharbia Governorate in Egypt.The hospitals are selected to represent the consumers of safeguarding substances,as they are the rst crucial frontline for mitigation against a pandemic outbreak.A distribution truck is used to distribute the substances from the main store to the hospitals in specied required quantities during a given working shift.The objective function is formulated in order to maximize the total amount of delivered quantities during the specied time period.The case study is solved using a novel Discrete Binary Gaining Sharing Knowledge-based Optimization algorithm(DBGSK),which involves two main stages:discrete binary junior and senior gaining and sharing stages.DBGSK has the ability of nding the solutions of the introduced problem,and the obtained results demonstrate robustness and convergence toward the optimal solutions.

Financial development during COVID‑19 pandemic:the role of coronavirus testing and functional labs

The outbreak of the SARS-CoV-2 virus in early 2020,known as COVID-19,spread to more than 200 countries and negatively affected the global economic output.Financial activities were primarily depressed,and investors were reluctant to start new financial investments while ongoing projects further declined due to the global lockdown to curb the disease.This study analyzes the money supply reaction to the COVID-19 pandemic using a cross-sectional panel of 115 countries.The study used robust least square regression and innovation accounting techniques to get sound parameter estimates.The results show that COVID-19 infected cases are the main contributing factor that obstructs financial activities and decrease money supply.In contrast,an increasing number of recovered cases and COVID-19 testing capabilities gave investors confidence to increase stock trade across countries.The overall forecast trend shows that COVID-19 infected cases and recovered cases followed the U-shaped trend,while COVID-19 critical cases and reported deaths showed a decreasing trend.Finally,the money supply and testing capacity show a positive trend over a period.The study concludes that financial development can be expanded by increasing the testing capacity and functional labs to identify suspected coronavirus cases globally.

Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand

Modelling of intraday increases in peak electricity demand using an autoregressive moving average-exponential generalized autoregressive conditional heteroskedastic-generalized single Pareto (ARMA-EGARCH-GSP) approach is discussed in this paper. The developed model is then used for extreme tail quantile estimation using daily peak electricity demand data from South Africa for the period, years 2000 to 2011. The advantage of this modelling approach lies in its ability to capture conditional heteroskedasticity in the data through the EGARCH framework, while at the same time estimating the extreme tail quantiles through the GSP modelling framework. Empirical results show that the ARMA-EGARCH-GSP model produces more accurate estimates of extreme tails than a pure ARMA-EGARCH model.

Challenges and Needs of National Swimming Federations in the Promotion of Health

This study focuses on the current challenges faced by the National Swimming Federations （NFs） with respect to challenges and needs of their NFs in the promotion of health in their domestic population and elite athletes. A descriptive transversal survey was circulated among the FINA NFs requesting information regarding these challenges and needs. The response rate was 64.9% （135 of the 208 NFs）. A similarity study was conducted （pairing measures method of Rogers-Tanimoto） to obtain 8 groups of NFs organized according to the degree to which their responses were similar to those of the other NFs. The lack of financial resources （95%） was identified as the most significant barrier to health promotion among the NFs. The least common challenges faced by the NFs were the relative importance of swimming as a sport （52%） and the lack of evidence based and best practice guidelines （51%）. Conclusion： Although many NFs face some common barriers to promoting health, those barriers are more significant in some NFs. Therefore, the means to overcome them will vary from NF to NF.

Influence of the Fitted Straight Line for Confidence Bands Algorithm in Q-Q Plots

Confidence bands in a Normal Q-Q Plot allow us to detect non-normality of a data set rigorously, and in such a way that the conclusion does not depend on the subjectivity of the observer of the graph. In the construction of the graph, it is usual to fit a straight line to the plotted points, which serves both to check the hypothesis of normality (linear configuration of the plotted points) and to produce estimates of the parameters of the distribution. We can opt for dif-ferent types of lines. In this paper, we study the influence of five types of fitted straight lines in a Normal Q-Q Plot used for construction the confidence bands based on the exact distribution of the order statistics.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

An Intelligent MCGDM Model in Green Suppliers Selection Using Interactional Aggregation Operators for Interval-Valued Pythagorean Fuzzy Soft Sets

Green supplier selection is an important debate in green supply chain management(GSCM),attracting global attention from scholars,especially companies and policymakers.Companies frequently search for new ideas and strategies to assist them in realizing sustainable development.Because of the speculative character of human opinions,supplier selection frequently includes unreliable data,and the interval-valued Pythagorean fuzzy soft set(IVPFSS)provides an exceptional capacity to cope with excessive fuzziness,inconsistency,and inexactness through the decision-making procedure.The main goal of this study is to come up with new operational laws for interval-valued Pythagorean fuzzy soft numbers(IVPFSNs)and create two interaction operators-the intervalvalued Pythagorean fuzzy soft interaction weighted average(IVPFSIWA)and the interval-valued Pythagorean fuzzy soft interaction weighted geometric(IVPFSIWG)operators,and analyze their properties.These operators are highly advantageous in addressing uncertain problems by considering membership and non-membership values within intervals,providing a superior solution to other methods.Moreover,specialist judgments were calculated by the MCGDM technique,supporting the use of interaction AOs to regulate the interdependence and fundamental partiality of green supplier assessment aspects.Lastly,a statistical clarification of the planned method for green supplier selection is presented.

Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

Differentiation of bee pollen samples according to the apiary of origin and harvesting period based on their amino acid content

Bee pollen is currently one of the most widely consumed dietary supplements due to its high nutritional value and its potentially beneficial effects on health. Unfortunately, in recent years an increase in the fraudulent marketing of this product has been detected, mainly in terms of adulteration with pollen from other sources. This has made it necessary to seek new tools to ensure its authentication. Therefore, this study investigates the use of free amino acids as markers of the geographical origin and harvesting period of bee pollen. To demonstrate their potential as biomarkers, 72 samples from four apiaries (Pistacho, Tío Natalio, Monte and Fuentelahiguera), located in the same geographical area (Marchamalo, Guadalajara, Spain), were analyzed by liquid chromatography-fluorescence detection, with the data obtained undergoing canonical discriminant analysis. Variable amounts and numbers of free amino acids were found in the samples analyzed;proline predominated in all of them, in a concentration range of 298–569989 mg/kg. The differences observed in amino acid composition could be attributed to the flowering plants from which the bee pollen samples originated. In addition, it was possible to statistically assign over 75% of the samples to the corresponding apiary of origin, the best results being obtained for the Fuentelahiguera and Tío Natalio apiaries (100%);this classification was even superior in the case of the harvesting periods, as more than 90% of the samples were correctly assigned, and in one period (June) a 100% rate was obtained.

Determination of Causal Effect in Observational Studies： Analysis of Correlated Data with Binary End-Points

Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.

The role of clinical diagnosis criteria on the frequency of accommodative insufficiency

AIM: To estimate and compare the frequency of accommodative insufficiency(AI) within the same clinical population sample depending on the type of clinical criteria used for diagnosis. Comparing the frequency within the same population would help to minimize bias due to sampling or methodological variability. METHODS: Retrospective study of 205 medical records of symptomatic subjects free of any organic cause and symptoms persisting despite optical compensation evaluated. Based on the most commonly clinical diagnostics criteria found in the literature, four diagnostics criteria were established for AI(Ⅰ, Ⅱ, Ⅲ and Ⅳ) based on subjective accommodative tests: monocular accommodative amplitude two or more diopters below Hofstetter's minimum value [15-(0.25×age)](Ⅰ, Ⅱ, Ⅲ, Ⅳ); failing monocular accommodative facility with minus lens, establishing the cut-off in 0 cycles per minute(cpm)(Ⅰ) and in 6 cpm(Ⅱ, Ⅲ); failing binocular accommodative facility with minus lens, establishing the cut-off in 0 cpm(Ⅰ) and in 3 cpm(Ⅱ).RESULTS: The proportion of AI(95%CⅠ) for criteria Ⅰ, Ⅱ, Ⅲ and Ⅳ were 1.95%(0.04%-3.86%), 2.93%(0.31%-4.57%), 6.34%(1.90%-7.85%) and 41.95%(35.14%-48.76%) respectively, with a statistically significant difference shown between these values(χ2=226.7, P<0.001). A pairwise multiple comparison revealed that the proportion of AI detected for criterion Ⅳ was significantly greater than the proportion for the rest of the criteria(P-adjusted<0.05 in all cases).CONCLUSION: The prevalence of cases of AI within the same clinical population varies with the clinical diagnostic criteria selected. The variation is statistically significant when considering the monocular accommodative amplitude as the only clinical diagnostic sign.

A Stochastic Flight Problem Simulation to Minimize Cost of Refuelling

Commercial airline companies are continuously seeking to implement strategies for minimizing costs of fuel for their flight routes as acquiring jet fuel represents a significant part of operating and managing expenses for airline activities.A nonlinear mixed binary mathematical programming model for the airline fuel task is presented to minimize the total cost of refueling in an entire flight route problem.The model is enhanced to include possible discounts in fuel prices,which are performed by adding dummy variables and some restrictive constraints,or by fitting a suitable distribution function that relates prices to purchased quantities.The obtained fuel plan explains exactly the amounts of fuel in gallons to be purchased from each airport considering tankering strategy while minimizing the pertinent cost of the whole flight route.The relation between the amount of extra burnt fuel taken through tinkering strategy and the total flight time is also considered.A case study is introduced for a certain flight rotation in domestic US air transport route.The mathematical model including stepped discounted fuel prices is formulated.The problem has a stochastic nature as the total flight time is a random variable,the stochastic nature of the problem is realistic and more appropriate than the deterministic case.The stochastic style of the problem is simulated by introducing a suitable probability distribution for the flight time duration and generating enough number of runs to mimic the probabilistic real situation.Many similar real application problems are modelled as nonlinear mixed binary ones that are difficult to handle by exact methods.Therefore,metaheuristic approaches are widely used in treating such different optimization tasks.In this paper,a gaining sharing knowledge-based procedure is used to handle the mathematical model.The algorithm basically based on the process of gaining and sharing knowledge throughout the human lifetime.The generated simulation runs of the example are solved using the proposed algorithm,and the resulting distribution outputs for the optimum purchased fuel amounts from each airport and for the total cost and are obtained.

COVID-19 and Unemployment: A Novel Bi-Level Optimal Control Model

Since COVID-19 was declared as a pandemic in March 2020,the world’s major preoccupation has been to curb it while preserving the economy and reducing unemployment.This paper uses a novel Bi-Level Dynamic Optimal Control model(BLDOC)to coordinate control between COVID-19 and unemployment.The COVID-19 model is the upper level while the unemployment model is the lower level of the bi-level dynamic optimal control model.The BLDOC model’s main objectives are to minimize the number of individuals infected with COVID-19 and to minimize the unemployed individuals,and at the same time minimizing the cost of the containment strategies.We use the modified approximation Karush–Kuhn–Tucker(KKT)conditions with the Hamiltonian function to handle the bi-level dynamic optimal control model.We consider three control variables:The first control variable relates to government measures to curb the COVID-19 pandemic,i.e.,quarantine,social distancing,and personal protection;and the other two control variables relate to government interventions to reduce the unemployment rate,i.e.,employment,making individuals qualified,creating new jobs reviving the economy,reducing taxes.We investigate four different cases to verify the effect of control variables.Our results indicate that rather than focusing exclusively on only one problem,we need a balanced trade-off between controlling each.

Structural Properties of Optimal Scheduling Policies for Wireless Data Transmission

We analyze a cell with a fixed number of users in a time period network. The base station schedules to serve at most one user in a given time period based on information about the available data rates and other parameter(s) for all the users in the cell. We consider infinitely backlogged queues and model the system as a Markov Decision Process (MDP) and prove the monotonicity of the optimal policy with respect to the 'starvation age' and the available data rate. For this, we consider both the discounted as well as the long-run average criterion. The proofs of the monotonicity properties serve as good illustrations of analyzing MDPs with respect to their optimal solutions.

Reliability analysis of extended generalized inverted exponential distribution with applications

A general version of the inverted exponential distribution is introduced, studied and analyzed. This generalization depends on the method of Marshall-Olkin to extend a family of distributions. Some statistical and reliability properties of this family are studied. In addition, numerical estimation of the maximum likelihood estimate（MLE） parameters are discussed in details. As an application, some real data sets are analyzed and it is observed that the presented family provides a better fit than some other known distributions.

Reproducibility of Heart Rate Recovery in Individuals with Low Heart Rate Recovery Response

Objective: To examine the reproducibility of HRR in healthy individuals with slow HRR response undergoing routine annual checkups. Method: HRR data (>18 b/min;Group 1 and 18 b/min;Group 2) were analyzed using a fixed-effects regression model adjusted for age and gender, including random effects group-specific slopes on age. Results: One hundred and thirteen individuals (56.5 ± 9.2 y), underwent 573 cumulative ESTs with an average of 5.1 ± 1.6 tests per individual during a 21-year retrospective follow-up. No differences were found in anthropometric measurements and blood variables. All individuals achieved 94% ± 7.7% of age-predicted HR max at peak EST. Group 2 demonstrated 38% of inconsistent HRR. Regression analysis demonstrated a decrease of 0.5 b/min, on average across individuals, in HRR per each extra year of age. The random effects showed an inter-subject SD level of 9.91 b/min and an SD on the age slope of 0.40 b/min/year. Conclusion: HRR showed low reproducibility in nearly 40% of tests, which was not reflected by the variation of HR nor in the slope of age during a 21-year retrospective follow-up.

Planning failure-censored constant-stress partially accelerated life test

This article deals with the case of the failure-censored constant-stress partially accelerated life test （CSPALT） for highly reliable materials or products assuming the Pareto distribution of the second kind. The maximum likelihood （ML） method is used to estimate the parameters of the CSPALT model. The performance of ML estimators is investigated via their mean square error. Also, the average confidence interval length （IL） and the associated co- verage probability （CP） are obtained. Moreover, optimum CSPALT plans that determine the optimal proportion of the test units al- located to each stress are developed. Such optimum test plans minimize the generalized asymptotic variance （GAV） of the ML estimators of the model parameters. For illustration, Monte Carlo simulation studies are given and a real life example is provided.