Empirical Procedure Research Articles

A new procedure to find the optimum deconvolution kernel to deblur satellite images

ABSTRACT The digital number of a given pixel in a satellite image does not only measure the radiance originating from the area of the Earth’s surface represented by this pixel, but also measures radiance originating from surrounding areas represented by adjacent pixels. This adjacency effect, also known as image blurring, is a fundamental limitation of satellite images, and introduces errors in all image processing techniques that quantify the properties of the Earth’s surface on a per-pixel basis. Image deblurring via deconvolution, is a traditional technique to reduce the adjacency effect, which has proven effective in a variety of remote sensing applications. For example, it has provided an almost fourfold reduction of the adjacency error, when using support vector machine classifiers to estimate land cover proportion at a subpixel level. The remote sensing literature usually assumes that satellite images should be deconvolved with a kernel defined by the sensor’s Point Spread Function PSF(x, y), overlooking the results of three seminal empirical studies which systematically demonstrated that the optimum deconvolution kernel is a shrunk PSF of the form PSF(x/β, y/β), where β < 1 is the optimum shrink factor. The current empirical procedure to find the optimum shrink factor is laborious and restrictive since it requires suitable satellite images of much higher resolution, such that their Ground Sampling Distance (GSD) is equal to the GSD of the sensor of interest divided by an integer number much greater than one. A new theoretical procedure that uses synthetic edge images to find the optimum shrink factor is proposed and applied to the same cases considered by the current empirical procedure, showing that the same results are obtained. The new procedure is much simpler to apply and more accurate and versatile, opening a variety of research paths on satellite image deblurring.

Frequentist Prediction Sets for Species Abundance using Indirect Information

Abstract Citizen science databases that consist of volunteer-led sampling efforts of species communities are relied on as essential sources of data in ecology. Summarising such data across counties with frequentist-valid prediction sets for each county provides an interpretable comparison across counties of varying size or composition. As citizen science data often feature unequal sampling efforts across a spatial domain, prediction sets constructed with indirect methods that share information across counties may be used to improve precision. In this article, we present a nonparametric framework to obtain precise prediction sets for a multinomial random sample based on indirect information that maintain frequentist coverage guarantees for each county. We detail a simple algorithm to obtain prediction sets for each county using indirect information where the computation time does not depend on the sample size and scales nicely with the number of species considered. The indirect information may be estimated by a proposed empirical Bayes procedure based on information from auxiliary data. Our approach makes inference for under-sampled counties more precise, while maintaining area-specific frequentist validity for each county. Our method is used to provide a useful description of avian species abundance in North Carolina, USA based on citizen science data from the eBird database.

A Discrimination Report Card

We develop an empirical Bayes ranking procedure that assigns ordinal grades to noisy measurements, balancing the information content of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the race and gender contact gaps of 97 US employers, the identities of which we disclose for the first time. The grades are presented alongside measures of uncertainty about each firm’s contact gap in an accessible report card that is easily adaptable to other settings where ranks and levels are of simultaneous interest. (JEL C11, D22, J15, J16, J71)

Development and Testing of Small-Scale Flash Dryer for Maize Bran

Sun drying is the most common technique used to dry various food products due to its economic convenience. However, it is not effective owing to its unreliable nature leading to microbial deterioration and aflatoxin contamination of food. In Tanzania, about 1.3 million tonnes of maize bran are at risk of deterioration annually due to improper drying. The aim of this study was to design, fabricate and test an efficient small-scale flash dryer for maize bran. Using principles of material and energy balance, a small-scale flash dryer with a throughput of 500 kg of dry product per hour was designed resulting in a nominal diameter of 387 mm and a drying length of 20 m. Design calculations and drawings were done using MS Excel 2021 and SolidWorks 2018 respectively. Empirical procedures were used to fabricate and test the dryer. The material of construction used in the flash duct was food grade (stainless steel, SS 201). A fuel blend of Waste Engine Oil (WEO) and diesel (85%-15%) was used with a novel heavy oil burner. It was observed that at an inlet air temperature of 150 °C and air velocity of 12 m/s, moist maize bran was dried from 37% to 10% w.b. The fabricated flash dryer's energy efficiency, specific energy consumption, and specific energy utilization are 74%, 3.4 MJ/kg water evaporated, and 1.71 MJ/kg dry maize bran respectively. It was concluded that the fabricated small-scale flash dryer displayed improved energy performance compared to others presented in literature. Better instrumentation was recommended for feedback control on burner operation and process monitoring which further enhances the performance of the fabricated small-scale flash dryer.

Analysis of Shift in Nil-Ductility Transition Reference Temperature for RPV Steels Due to Irradiation Embrittlement Using Probability Distributions and Gamma Process

Reactor pressure vessel (RPV) steels are highly susceptible to irradiation embrittlement due to prolonged exposure to high temperature, high pressure, and intense neutron irradiation. This leads to the shift in nil-ductility transition reference temperature—∆RTNDT. The change in ∆RTNDT follows a certain distribution pattern and is impacted by factors including chemical composition, neutron fluence, and irradiation temperature. Existing empirical procedures can estimate ∆RTNDT based on fitting extensive irradiation embrittlement data, but their reliability has not been thoroughly investigated. Probability statistical distributions and the Gamma stochastic process were performed to model material property degradation in RPV steels from a pressurized water reactor due to irradiation embrittlement, with the probability models considered being normal, Weibull, and lognormal distributions. Comparisons with existing empirical procedures showed that the Weibull distribution model and the Gamma stochastic model demonstrate good reliability in predicting ∆RTNDT for RPV steels. This provides a valuable reference for studying irradiation embrittlement in RPV materials.

Further examination of the dual-factor model: characteristics of emerging adults with non-traditional mental health profiles

Traditionally, the mental health field has focused on psychological symptoms and distress, and health is considered the absence of illness. An alternative view suggests that optimal mental health must include the presence of positive characteristics in addition to the absence of illness. Accordingly, a dual-factor model of mental health includes measures of both psychopathology and positive subjective well-being to determine an individual’s mental health status. The current study investigated this dual-factor model by examining whether subjective well-being and psychopathology combine to produce distinct mental health profiles in emerging adults. A sample of 559 university students completed self-report surveys indicating their life satisfaction and internalizing and externalizing symptoms. Cluster analysis yielded four unique mental health groups. The well-adjusted cluster included individuals with high life satisfaction and low internalizing and externalizing symptoms. The dissatisfied cluster consisted of individuals with low externalizing, but also low life satisfaction and slightly above-average levels of internalizing. The externalizing cluster had high life satisfaction but also high externalizing psychopathology, and the troubled cluster exhibited low life satisfaction and high levels of both psychopathology symptoms. The clusters also differed in their personality traits, coping responses, and automatic thoughts, with the most favorable pattern across all three dimensions exhibited by the well-adjusted cluster. These findings highlight the importance of considering positive indicators of mental health in addition to measures of psychopathology and support the use of an empirical classification procedure in determining the mental health status of emerging adults.

Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data

This study investigates data aggregation bias in estimating market power in the U.S. beef packing industry using New Empirical Industrial Organization (NEIO) models and shows empirical procedures that can alleviate the bias. Unlike many earlier studies in estimating market power exertion, our study examines the data aggregation bias when market-level data are used in place of firm-level data and show how the bias could be reduced. We first derive data aggregation bias analytically, then empirically investigate the aggregation bias by estimating both firm and aggregate industry models. Because the firm-level data are not available, we use simulated data generated from the Monte Carlo simulation method. Hybrid models, combining limited firm-level data with aggregate data, are also estimated to illustrate how the aggregation bias could be reduced. Our results show that aggregate models with industry-level data tend to underestimate market power exertion in the U.S. beef packing industry, and the aggregation bias is statistically significant at the 1% level. Comparing results from hybrid models with firm-level estimates, we find that hybrid models reduce the bias but do not remove the aggregation bias significantly. The sensitivity analysis shows that market power estimate and aggregation bias are sensitive to functional forms.

Effect of industrial steam distillation conditions on volatiles and antioxidant capacity of Cananga odorata essential oils

ABSTRACT The ylang-ylang (Cananga odorata) essential oil is valued for its medicinal and cosmetic benefits. This study compared the volatile compounds and antioxidant capacity of six essential oils from C. odorata flowers obtained industrially by steam distillation. The volatiles were identified by gas chromatography-mass spectrometry (GC/MS). The ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, and total phenolic content (TPC) were measured by ultraviolet-visible (UV-Vis) spectrophotometry. The main components included benzyl acetate (21.78–27.82%), benzyl benzoate (12.30–18.07%), linalool (9.45–14.39%), and germacrene D (9.17–14.46%). The highest antioxidant values were shown by the sample S6 (206.50 ± 7.45 mmol TE/L, 52.03 ± 0.88 mg GAE/g), followed by S5. The multivariate analysis separated S6 and S5 from the remaining essential oils, suggesting that extraction was more influenced by the steam flow rate. Notably, the antioxidant capacity of S6 revealed a positive correlation between volatiles and TPC values. These findings denote the importance of improving empirical industrial procedures.

Retrofitting a poorly designed system into a full performing nitrogen‐removal activated sludge process—a scientific challenge

AbstractBACKGROUNDThis study intended to detect the inherent deficiencies of prescription‐type empirical design procedures often adopted for nitrogen ‐removal activated sludge systems. Principles of process stoichiometry and kinetics were used to identify a series of hotspots that were likely to impair and disrupt system performance and efficiency. Available data, derived from an existing plant subject to a similar design approach, were utilized to numerically assess significant hotspots and to define a sustainable remedial action in compliance with applicable scientific principles.RESULTSCritical appraisal of the implemented design exposed a large spectrum of deficiencies affecting system efficiency and performance, including nitrification efficiency, denitrification potential, anoxic volume and sludge age, internal recycling and nitrate removal. The major role of the inorganic suspended solids as a design parameter was also ignored. The remedial strategy adopted for retrofitting corrected all the observed discrepancies and it devised a sustainable approach of flipping around the existing reactors so that they could be operated as a series of carousel/fast recycle reactors, each including anoxic and aerobic volume fractions to provide full nitrification.CONCLUSIONThe merit of the proposed remedial strategy was justified by offering two additional options of equally effective system operation with either reduced reactor volume or increased sewage flow. The study presented conclusive evidence to show why prescription‐type design should be avoided, providing numerical proof that this methodology is incompatible with stoichiometric and kinetic characteristics of sewage under local conditions. © 2024 Society of Chemical Industry (SCI).

Standardized Iterative Genome Editing Method for Escherichia coli Based on CRISPR-Cas9.

The introduction of complex biosynthetic pathways into the hosts' chromosomes is gaining attention with the development of synthetic biology. While CRISPR-Cas9 has been widely employed for gene knock-in, the process of multigene insertion remains cumbersome due to laborious and empirical gene cloning procedures. To address this, we devised a standardized iterative genome editing system for Escherichia coli, harnessing the power of CRISPR-Cas9 and MetClo assembly. This comprehensive toolkit comprises two fundamental elements based on the Golden Gate standard for modular assembly of sgRNA or CRISPR arrays and donor DNAs. We achieved a gene insertion efficiency of up to 100%, targeting a single locus. Expression of tracrRNA using a strong promoter enhances multiplex genomic insertion efficiency to 7.3%, compared with 0.76% when a native promoter is used. To demonstrate the robust capabilities of this genome editing toolbox, we successfully integrated 5-10 genes from the coenzyme B12 biosynthetic pathway ranging from 5.3 to 8 Kb in length into the chromosome of E. coli chassis cells, resulting in 14 antibiotic-free, plasmid-free producers. Following an extensive screening process involving genes from diverse sources, cistronic design modifications, and chromosome repositioning, we obtained a recombinant strain yielding 1.49 mg L-1 coenzyme B12, the highest known titer achieved by using E. coli as the producer. Illuminating its user-friendliness, this genome editing system is an exceedingly versatile tool for expediently integrating complex biosynthetic pathway genes into hosts' genomes, thus facilitating pathway optimization for chemical production.

Assessing Future Precipitation Patterns, Extremes and Variability in Major Nile Basin Cities: An Ensemble Approach with CORDEX CORE Regional Climate Models

Understanding long-term variations in precipitation is crucial for identifying the effects of climate change and addressing hydrological and water management issues. This study examined the trends of the mean and four extreme precipitation indices, which are the max 1-day precipitation amount, the max 5-day precipitation amount, the consecutive wet days, and the consecutive dry days, for historical observations (1971–2000) and two future periods (2041–2060/2081–2100) under RCP2.6 and RCP8.5 emission scenarios over the Nile River Basin (NRB) at 11 major stations. Firstly, the empirical quantile mapping procedure significantly improved the performance of all RCMs, particularly those with lower performance, decreasing inter-model variability and enhanced seasonal precipitation variability. The Mann–Kendall test was used to detect the trends in climate extreme indices. This study reveals that precipitation changes vary across stations, scenarios, and time periods. Addis Ababa and Kigali anticipated a significant increase in precipitation across all periods and scenarios, ranging between 8–15% and 13–27%, respectively, while Cairo and Kinshasa exhibited a significant decrease in precipitation at around 90% and 38%, respectively. Wet (dry) spells were expected to significantly decrease (increase) over most parts of the NRB, especially during the second period (2081–2100). Thereby, the increase (decrease) in dry (wet) spells could have a direct impact on water resource availability in the NRB. This study also highlights that increased greenhouse gas emissions have a greater impact on precipitation patterns. This study’s findings might be useful to decision makers as they create NRB-wide mitigation and adaptation strategies to deal with the effects of climate change.

Direct and indirect influence of natural resources and regional integration on green growth: Exploring the role of political risk in South Asia

Within the context of sustainability, an integral focus of ongoing research is centred on cultivating economic expansion that prioritizes environmental friendliness and reduces resource dependence. This approach is commonly referred to as green economic growth. Hence, the present study strives to explore the heterogeneous effects of regional trade and economic integration, natural resource reliance, and political risk while also examining how regional integration index combined with natural resource factors influences the green growth trajectory of selected south Asian countries. The cross-sectional dependence and slope heterogeneity of the countries have been examined. Stationarity properties are checked before proceeding to the long-run relationship and empirical estimation procedure. Method of Moments Quantile Regression is employed to understand the variables' asymmetric association. The outcomes revealed the positive influence of regional integration on green growth, while political risk and natural resources pose a negative impact. It can be observed that the negative consequences of natural resources are neutralized with higher trade and financial integration. Lastly, the causality test is used to investigate the causal association between the variables. The outcomes imply that Asian countries emphasize shaping natural resource extraction policies and stabilizing their political situation to get the maximum benefit from green growth.

Orthogonality based modal empirical likelihood inferences for partially nonlinear models

&lt;abstract&gt;&lt;p&gt;This paper explored the effective empirical likelihood inferences for partially nonlinear models. By combining the modal regression method with orthogonal projection technology, a modal empirical likelihood-based estimation procedure was proposed. The proposed empirical likelihood approach retained Wilk's theorem under mild conditions, and the confidence regions of model coefficients were constructed. Nonparametric and parametric components of the estimators were independent. Simulation results demonstrated that it is more robust and effective than the existing methods.&lt;/p&gt;&lt;/abstract&gt;

Speaking in Public Coping Scale (ECOFAP): content and response process validity evidence.

To present the content and response process validity evidence of the Speaking in Public Coping of Scale (ECOFAP). A methodological study to develop and validate the instrument. It followed the instrument development method with theoretical, empirical, and analytical procedures, based on the validity criteria of the Standards for Educational and Psychological Testing (SEPT). The process of obtaining content validity evidence had two stages: 1) conceptual definition of the construct, based on theoretical precepts of speaking in public and the Motivational Theory of Coping (MTC); 2) developing items and response keys, structuring the instrument, assessment by a committee with 10 specialists, restructuring scale items, and developing the ECOFAP pilot version. Item representativity was analyzed through the item content validity index. The response process was conducted in a single stage with a convenience sample of 30 people with and without difficulties speaking in public, from the campus of a Brazilian university, belonging to various social and professional strata. In this process, the respondents' verbal and nonverbal reactions were qualitatively analyzed. The initial version of ECOFAP, consisting of 46 items, was evaluated by judges and later reformulated, resulting in a second version with 60 items. This second version was again submitted for expert analysis, and the content validity index per item was calculated. 18 items were excluded, resulting in a third version of 42 items. The validity evidence based on the response processes of the 42-item version was applied to a sample of 30 individuals, resulting in the rewriting of one item and the inclusion of six more items, resulting in the pilot version of ECOFAP with 48 items. ECOFAP pilot version has items with well-structured semantics and syntactic, representing strategies to cope with speaking in public.

Exploring the role of financial technologies and digital trade in shaping trade-adjusted resource consumption in E7 countries

Sustainable resource management integrates inclusive prosperity and resource sustainability; however, intensified resource extraction emerged resource depletion and severe environmental consequences, particularly in emerging economies (E7). Therefore, identifying the mitigating factors of trade-adjusted mineral resources footprint (MF) is imperative to ensure resource efficiency targets. Our study evaluates the asymmetric influence of Fintech (FIN), digital trade (DT), and renewable energy (REN) on MF in E7 countries using the advanced panel method of moment quantile regression (MMQR) from 2005 to 2020. The empirical analysis reveals that FIN and REN significantly mitigate MF from middle to higher quantiles while insignificant at the lowest grid. In contrast, DT substantially instigates the MF from lower to middle quantiles while not impacting the higher MF quantiles. In the empirical procedure, alternative linear panel estimation approaches are also employed to affirm the robustness of estimates echoed by the MMQR framework. Moreover, panel causality testing is performed to ascertain the causal association among the variables, exhibiting that FIN, REN, and MF have bi-directional causality, and industrial output and MF have unidirectional causality. The findings recommend policy implications to all stakeholders in improving the progress toward efficient resource allocation.

Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR.

Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.

Debris-flow risk-to-life: Preliminary screening

In many active landscapes debris-flow hazards are largely unrecognised and, as a result, developments can be sited in locations where they are unknowingly exposed to debris-flow impact, with correspondingly unrecognised risks to lives. While modelling can represent debris-flow occurrence and behaviour, it is unsuited to the routine use at local office level that is required to identify potentially unacceptable debris-flow risks to existing and proposed developments. To address this urgent and widespread need, we develop an empirical procedure for estimating the maximum probability of debris-flow occurrence at a specified location, involving (i) calculation of catchment Melton ratio R from topographic data; (ii) a well-defined linear upper-bound envelope of field relationships between catchment R and the probability of evidence for debris-flow occurrence; and (iii) an upper bound from field data relating R to the annual probability of debris-flow occurrence. We test this procedure against a model-based risk-to-life analysis for Matatā, New Zealand and find satisfactory agreement. The procedure is intended to enable local officials to routinely identify locations where debris flow risk to life might exceed acceptable levels so that expert risk modelling and analysis can be utilised where required. Our data suggest that any development that can be impacted by debris flows is likely to require detailed risk assessment to ensure that risk-to-life is reduced to acceptable levels.

A Non-Destructive Methodology for the Viscoelastic Characterization of Polymers: Toward the Identification of the Time-Temperature Superposition Shift Law.

Polymers find widespread applications in various industries, such as civil engineering, aerospace, and industrial machinery, contributing to vibration control, dampening, and insulation. To accurately design products that are able to predict their dynamic behavior in the virtual environment, it is essential to understand and reproduce their viscoelastic properties via material physical modeling. While Dynamic Mechanical Analysis (DMA) has traditionally been used, innovative non-destructive techniques are emerging for characterizing components and monitoring their performance without deconstructing them. In this context, the Time-Temperature Superposition Principle (TTSP) represents a powerful empirical procedure to extend a polymer's viscoelastic behavior across a wider frequency range. This study focuses on replicating an indentation test on viscoelastic materials using the non-destructive Viscoelasticity Evaluation System evolved (VESevo) tool. The primary objective is to derive a unique temperature-frequency relationship, referred to as a "shift law", using characteristic curves from this non-invasive approach. Encouragingly, modifying the device setup enabled us to replicate, virtually, three tests under identical initial conditions but with varying indentation frequencies. This highlights the tool's ability to conduct material testing across a range of frequencies. These findings set the stage for our upcoming experiment campaign, aiming to create an innovative shift algorithm from at least three distinct master curves at specific frequencies, offering a significant breakthrough in non-destructive polymer characterization with broad industrial potential.

Underlying Probability Measure Approximated by Monte Carlo Simulations in Event Prognostics

The prognostic of events, and particularly of failures, is a key step towards allowing preventive decision-making, as in the case of predictive maintenance in Industry 4.0, for example. However, the occurrence time of a future event is subject to uncertainty, so it is natural to think of it as a random variable. In this regard, the default procedure (benchmark) to compute its probability distribution is empirical, through Monte Carlo simulations. Nonetheless, the analytic expression for the probability distribution of the occurrence time of any future event was presented and demonstrated in a recent publication. In this article it is established a direct relationship between these empirical and analytical procedures. It is shown that Monte Carlo simulations numerically approximate the analytically known probability measure when the future event is triggered by the crossing of a threshold.

Prospects for New Antibiotics Discovered through Genome Analysis

Abstract: Antibiotic-resistant illnesses are on the rise worldwide, and the pipeline for developing new antibiotics is drying up. As a result, researchers need to create novel compounds with antimicrobial action. Recent decades have seen a dearth of novel antibiotics because of the reliance on conventional empirical screening procedures using both natural and synthetic chemicals to find them. There is hope that the massive amount of bacterial genome sequence data that has become accessible since the sequencing of the first bacterial genome more than 20 years ago might help lead to the development of new antibiotic drugs. Genes with significant levels of conservation both within and between bacterial species can be found using comparative genomic techniques; these genes may be involved in essential bacterial functions. Bioactive chemicals found in natural products have been successfully used in treating everything from infectious diseases to cancer, but over the past 20-30 years, the effectiveness of screening methods based on fermentation has decreased. Researchers urgently need answers to the unmet demand for bacterial infection resistance. Now more than ever, with the advent of cheap, high-throughput genomic sequencing technology, natural product discovery can be revitalized. Using bioinformatics, investigators may foretell whether or not a certain microbial strain would generate compounds with novel chemical structures, which may have novel modes of action in inhibiting bacterial growth. This manuscript describes how this potential might be utilised, with a particular emphasis on manipulating the expression of dormant biosynthetic gene clusters that are hypothesised to encode new antibiotics. Additionally, it consolidates the work of the past and the present to utilise bacterial genomic data in the identification and development of new antibiotics.