Statistical Approach In Order Research Articles

Assessment of the eutrophication status at Mediterranean sub-basin scale, within the European Marine Strategy Framework Directive

The aim of this work is to define harmonized reference conditions and assessment thresholds for selected criteria elements of the Marine Strategy Framework Directive (MSFD) Descriptor 5 (Eutrophication) in the Eastern Mediterranean Sea and to test if a tool for integrated assessment of the status of marine systems can be used as a common methodological approach. In this frame, we tested two statistical approaches in order to set threshold values for four criteria of Descriptor 5: nutrients, chlorophyll a, transparency and dissolved oxygen in the bottom waters. It is noteworthy that this work revealed the need to apply common procedures in data treatment and assessment evaluation. This is the first attempt to set common methods for the assessment of eutrophication in the Eastern Mediterranean, which is essential in marine environments, especially those shared by several countries. To this end, we have applied common criteria and metrics and established thresholds “Good” and “Moderate” for nutrients, chlorophyll a, transparency and dissolved oxygen in the bottom waters for the different Water Types of the Adriatic and Aegean Seas (I, II, IIIW, IIIE), based on datasets provided by Italy, Slovenia, Croatia and Greece. The selected criteria elements were common for all countries, providing a unified approach to GES assessment of two case study areas: the Adriatic Sea and the Saronikos Gulf. Dissolved Inorganic Nitrogen (DIN) threshold values of 15.6, 6.85, 1.61 and 2.11 μmol L−1 were set for the Water Types I, II, IIIW and IIIE, respectively.We also tested if an aggregation tool for GES assessment, such as Nested Environmental status Assessment Tool (NEAT), could be used as a common methodological approach. The comparison of NEAT with TRIX showed good comparability. In this end, NEAT can be used as a useful and much needed assessment tool for assessing eutrophication status of the marine.

Basic Statistical Methods in Determining Criteria Weights

The proliferation of technology has facilitated data accessibility, leading to an expansion in the range of criteria employed in decision problem design. This situation offers an advantage for making precise and rational decisions, but when it comes to managing spending, it becomes a disadvantage. Specifically, the expense of acquiring expert views utilized in the computation of criteria weights by subjective approaches experiences a substantial rise. Hence, decision-makers may employ objective methodologies to determine criterion weights. Nevertheless, objective methods provide a more limited range of choices compared to subjective methods. The study aims to utilize two widely recognized fundamental statistical approaches in order to enhance the capabilities of objective methods. One of the suggested approaches is the dissimilarity-based weighting method, which calculates the differentiation of values within the criteria. Another approach is the weighting method, which relies on the interquartile range. The methods were adapted as means of weighting criteria. Explanatory examples were provided, simulation-based comparisons were conducted, and ultimately applied to an actual data set. The data from each scenario were compared using the factorial analysis of variance method. The findings produced demonstrate that the proposed methods align with other objective methodologies. Furthermore, the proposed approaches were observed to take more time to finish the procedure compared to the Entropy and Standard Deviation methods, but less time compared to the Critic and Merec methods. Consequently, the suggested techniques are introduced as alternative approaches derived from established fundamental statistical procedures, which are straightforward to comprehend and valuable for professionals.

Handling Large-Scale Document Collections using Information Retrieval in the Age of Big Data

This paper's primary goal is to present an overview of big data and its analysis utilizing various methodologies, particularly evolutionary computing techniques, which improve information retrieval over standard search methods. is obtainable. Big data is defined as a huge, diverse collection of data that is difficult to handle with conventional computational methods and necessitates the use of more sophisticated statistical approaches in order to extract pertinent information from the data. Along with providing an overview of evolutionary computational approaches, this study also discusses some of the main models used for information retrieval.

Assessing a model of Pacific Northwest harmful algal bloom transport as a decision-support tool

In the Pacific Northwest, blooms of the diatom Pseudo-nitzschia (PN) sometimes produce domoic acid, a neurotoxin that causes amnesic shellfish poisoning, leading to a Harmful Algal Bloom (HAB) event. The Pacific Northwest (PNW) HAB Bulletin project, a partnership between academic, government, and tribal stakeholders, uses a combination of beach and offshore monitoring data and ocean forecast modeling to better understand the formation, evolution, and transport of HABs in this region. This project produces periodic Bulletins to inform local stakeholders of current and forecasted conditions. The goal of this study was to help improve how the forecast model is used in the Bulletin's preparation through a retrospective particle-tracking experiment. Using past observations of beach PN cell counts, events were identified that likely originated in the Juan de Fuca eddy, a known PN hotspot, and then particle tracks were used in the model to simulate these events. A variety of “beaching definitions” were tested, based on both water depth and distance offshore, to define when a particle in the model was close enough to the coast that it was likely to correspond to cells appearing in the intertidal zone and in shellfish diets, as well as a variety of observed PN cell thresholds to determine what cell count should be used to describe an event that would warrant further action. The skill of these criteria was assessed by determining the fraction of true positives, true negatives, false positives, and false negatives within the model in comparison with observations, as well as a variety of derived model performance metrics. This analysis suggested that for our stakeholders’ purposes, the most useful beaching definition is the 30 m isobath and the most useful PN cell threshold for coincident field-based sample PN density estimates is 10,000 PN cells/L. Lastly, the performance of a medium-resolution (1.5 km horizontal resolution) version of the model was compared with that of a high-resolution (0.5 km horizontal resolution) version, the latter currently used in forecasting for the PNW HAB Bulletin project. This analysis includes a direct comparison of the two model resolutions for one overlapping year (2017). These results suggested that a narrower, more realistic beaching definition is most useful in a high-resolution model, while a wider beaching definition is more appropriate in a lower resolution model like the medium-resolution version used in this analysis. Overall, this analysis demonstrated the importance of incorporating stakeholder needs into the statistical approach in order to generate the most effective decision-support information from oceanographic modeling.

Fast and Efficient Voltammetric Detection of L-Tryptophan in Dietary Supplements Using Carbon Electrodes Modified with Poly(L-Arginine)

Tryptophan (TRYP) is an essential amino acid that plays several key roles in different physiological processes, ranging from acting as a building block for protein synthesis to being involved in neurotransmission and cellular signaling.1 Humans can only obtain TRYP from food intake,1 and therefore this compound is frequently added to dietary supplements. Considering the potential toxic effects that arise from TRYP overdoses,2 an efficient quality control should be performed on products containing TRYP in their formulations. In this way, this work proposes an efficient voltammetric sensor as a cost-effective and feasible tool to detect TRYP in commercial supplements. The sensor was based on glassy carbon electrodes modified with poly(L-arginine) (GCE/p-ARG), which was electrochemically synthesized in situ from L-arginine monomers.3 The conditions for p-ARG electropolymerization were optimized through multivariate statistical approaches in order to enhance the electrode’s sensing capability. Cyclic voltammetry and electrochemical impedance spectroscopy analysis in the presence of [Fe(CN)6]3-/4- redox probe showed that p-ARG significantly improved the electron transfer at the electrode surface. TRYP was irreversibly oxidized on GCE/p-ARG displaying a peak at +0.73 V (vs. Ag/AgCl), which was more intense than the anodic peak showed by the bare GCE. This was due to the presence of p-ARG on the GCE surface, which enabled the occurrence of multiple interactions that favoured TRYP detection. The amino acid oxidation followed a diffusion-controlled mechanism and showed to be dependent on the supporting electrolyte pH. A linear relationship between the anodic peak current (obtained through square wave voltammetry) and the TRYP concentration was observed in the range from 1.0 to 7.0 μmol L-1. Limits of detection and quantification were found to be equal to 0.30 and 0.91 μmol L-1, respectively. The developed electroanalytical method was successfully applied to detect TRYP in dietary supplements, with good precision and accuracy. In conclusion, it can be stated that the GCE/p-ARG is a promising candidate for food and pharmaceutical industry to enable cheap, fast, and reliable quality control of TRYP-based commercial products. References M. Platten, E. A. A. Nollen, U. F. Röhrig, F. Fallarino, and C. A. Opitz, Nature Reviews Drug Discovery, 18, 379–401 (2019).Y. Xia, F. Zhao, and B. Zeng, Talanta, 206 (2020).J. Soleymani et al., Materials Science and Engineering C, 77, 790–802 (2017).

Social Network Data and Epidemiological Intelligence: A Case Study of Avian Influenza

Purpose - Event Based Surveillance (EBS) systems detect and monitor diseases by analysing articles from online news papers and reports from health organizations (e.g. FAO, OIE, etc.). However, they partially integrate data from social networks, even though these data are present in large quantities on the web. The purpose of this study is to exploit social network data, such as Twitter and YouTube, to provide epidemiological and additional information for Avian Influenza surveillance. Methods & Materials - In this context, we propose new text-mining approaches combining lexical rules and statistical approaches in order to normalise textual data from Social Net- work ('h5 n8' - 'H5N8') and to correct errors from YouTube transcriptions (e.g. 'birth u' - 'bird u'). Another challenge consists of extracting epidemiological events automatically by identifying spatial entities (Where?), thematic entities (What?), and temporal information (When?). For this, we extended Named Entity Recognition (NER) tools like spaCy. Results - We collected 100 automatic transcripts of YouTube videos and 268 tweets in English dealing with avian influenza with dedicated API. We obtain encouraging results (i.e. accuracy around 0:6) in order to recognise automatically epidemiological information (e.g. hosts, symptoms, etc.) in textual data con- tents. Extraction of spatial information obtains better results (i.e. accuracy around 0:7). Conclusion – The final objective of this study consists of linking social media data based on these entities with official information from health organisations for the improvement of epidemiological monitoring.

Structural Characteristics of the Main Resinous Stands from Southern Carpathians, Romania

The purpose of this study, which contains historical data recorded over a period of 40 years, was to identify the main factors that influence and control the level of wood mass production. The main reason was to optimize the management of forest areas and was driven by the necessity to identify factors that can influence most of the volume produced by coniferous forests located in southeast Europe. The data was collected between1980 and 2005 at the National Institute for Research and Development in Forestry, for forests located in the Southern Carpathians, Romania. The studied data refer to the parameters that model forest structure for spruce, fir, pine, and larch. These are the main resinous species found in the Southern Carpathians. The total area covered by these forests is 143,431 ha. At the forest species level, the analysis consists of 16,162 records (corresponding to the elements of the trees), covering an area of 45,008 ha for fir, 4711 ha for larch, 81,995 ha for spruce, and 11,717 ha for pine. The aim of this research has been to investigate and to assess the impact and magnitude of abiotic factors such as altitude and field aspect on forest structures from the main resinous stands located in the Southern Carpathians. Taking into account the size of the database as well as the duration for collecting data, a complete statistical and systematic approach was considered optimum. This resulted from our wish to emphasize and evaluate the influence of each analysed factor on the wood mass production level. The relationship between abiotic factors and forest structure has been analysed by using a systematic statistical approach in order to provide a useful theoretical reference for the improvement of forest management practices in the context of multiple climatic, environmental, and socio-economic challenges. These common characteristics have been found by applying ANOVA and multivariate statistical methods such as PCA and FA methods. A series of parameters were considered in this investigation, namely altitude (ALT), forest site type (TS), forest type (TP), consistency (CONS) etc. In order to obtain a complete image, we have also applied multivariate analysis methods that emphasize the effect size for each database parameter. At such a level of recorded data, the statistical approach ensures a factor level of p <0.001 while the accuracy in evaluating effect size is increased. As such, they influence the spreading and structure of the studied resinous stands to a higher degree, regardless of species.

How to Capture Reciprocal Communication Dynamics: Comparing Longitudinal Statistical Approaches in Order to Analyze Within- and Between-Person Effects

Abstract Choosing an appropriate statistical model to analyze reciprocal relations between individuals’ attitudes, beliefs, or behaviors over time can be challenging. Often, decisions for or against specific models are rather implicit and it remains unclear whether the statistical approach fits the theory of interest. For longitudinal models, this is problematic since within- and between-person processes can be confounded leading to wrong conclusions. Taking the perspective of the reinforcing spirals model (RSM) focusing on media effects and selection, we compare six statistical models that were recently used to analyze the RSM and show their ability to separate within- and between-person components. Using empirical data capturing respondents’ development during adolescence, we show that results vary across statistical models. Further, Monte Carlo simulations indicate that some approaches might lead to wrong conclusions if specific communication dynamics are present. In sum, we recommend using approaches that explicitly model and clearly separate within- and between-person effects.

Variability of grass pollen allergy symptoms throughout the season: Comparing symptom data profiles from the Patient's Hayfever Diary from 2014 to 2016 in Vienna (Austria)

BackgroundGrass pollen allergy is the most widespread pollen allergy in the world. It still remains unknown in which aspects and in which extent symptoms from grass pollen allergy differ throughout the grass pollen season, although individual sensitization profiles of persons concerned are known for a long time. MethodsThe crowd-sourced symptom data of users of the Patient's Hayfever Diary were filtered for significant positive correlated users to grass pollen from Vienna (Austria) during the respective grass pollen seasons from 2014, 2015, and 2016. These symptom data were the foundation for 3 statistical approaches in order to examine different sections of the grass pollen season defined either by grass pollen data, phenology (grass species determination in the field), or symptom data itself. ResultsResults from all 3 approaches are similar and come to the same major conclusion. The symptom peak of most users is observed in the second section of the grass pollen season (70%), followed by the first section (20%), and with the least user numbers (10%) the third section. The profiles from single users entering data for all 3 years under study are robust and show a comparable behavior from year to year. ConclusionGrass taxa such as Arrhenatherum, Festuca, and Lolium seem to induce the highest symptom severity in most users during the second section of the grass pollen season. Poa and Dactylis are the main triggers for the first section of the grass pollen season. The flower of Phleum und Cynodon is documented for the last section of the grass pollen season. Crowd-sourced symptom data is the prerequisite for personal pollen information to consider the individuality of grass pollen allergy sufferers. Phenological monitoring is needed to provide information on specific grass taxa of importance to allergic persons.

Thermal characterization of a multi-turn pulsating heat pipe in microgravity conditions: Statistical approach to the local wall-to-fluid heat flux

A Pulsating Heat Pipe (PHP), specifically designed to be hosted on board the Heat Transfer Host of the International Space Station, is tested in microgravity conditions during the 67th Parabolic Flight Campaign promoted by the European Space Agency. The device consists in an aluminium tube (inner/outer diameter = 3/5 mm) closed in a 14 turns loop, half filled with FC-72. The PHP external wall temperature distribution are measured within the adiabatic section by means of a high-speed infrared camera. The resulting thermographic images are used as input data for the solution of the Inverse Heat Conduction Problem (IHCP) in the channels wall to estimate local time-space heat fluxes exchanged at the wall-fluid interface. The adopted post-processing method represents one of the first attempts to estimate the local wall-to-fluid heat flux in PHPs under microgravity conditions. A comprehensive investigation of the wall-to-fluid heat fluxes is performed on the overall device by means of an original statistical approach in order to study the PHP working regimes. The results highlight that such approach is capable of providing similar information regarding the fluid motion inside the PHP to those obtained by the traditional intrusive experimental methods (e.g. direct fluid temperature-pressure measurements) or transparent inserts (e.g. sapphire tube), which perturb the original layout of the PHP and require a complex experimental set-up.

Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets.

Matrix-assisted laser desorption/ionization (MALDI)-time of flight (TOF)-mass spectrometry imaging (MSI) enables the spatial localization of proteins to be mapped directly on tissue sections, simultaneously detecting hundreds in a single analysis. However, the large data size, as well as the complexity of MALDI-MSI proteomics datasets, requires the appropriate tools and statistical approaches in order to reduce the complexity and mine the dataset in a successful manner. Here, a pipeline for the management of MALDI-MSI data is described, starting with preprocessing of the raw data, followed by statistical analysis using both supervised and unsupervised statistical approaches and, finally, annotation of those discriminatory protein signals highlighted by the data mining procedure.

A comparison between univariate and multivariate statistical techniques to determine source of pen inks using ultra-performance liquid chromatography (UPLC) chromatograms

Forensic ink analyst is frequently asked to determine the source of questioned ink entries. Ultra-performance liquid chromatography (UPLC) technique is useful for profiling organic compounds of pen inks. However, interpreting the UPLC chromatograms requires the use of statistical approaches in order to ensure the objectivity and reliability of the results. This work aims to compare the performances of univariate and multivariate statistical techniques in determining the source of ballpoint pen inks based on UPLC chromatograms. Two sets of UPLC chromatograms comprising of four blue and black ballpoint pen inks were, respectively, treated like a mini ink database for predicting unknown blue and black pen inks. The pitfalls and merits of descriptive and inferential statistics, principal component analysis (PCA) and hierarchical clustering analysis (HCA) in interpreting the source of pen inks based on the mini database were critically discussed. The results strongly indicated the superiority of HCA over the other statistical techniques. The descriptive statistics presented the worst prediction results whereas the one-way ANOVA-LSD test rarely produced definite identification. In conclusion, HCA appears to be the most robust statistical-based approach in the inferring source of pen ink based on UPLC chromatograms.

Determinant assembly approach for flat-shaped airframe components

The optimization of the aeronautical structure manufacturing is a challenging task in the development of a new aircraft. To date, aeronautical industries are funding research about new assembly approaches based on cost reduction and increased efficiency of the assembly processes. The work here presented focused on an innovative assembly method based on the integration between statistical methods of tolerance prediction and the determinant assembly approach. The coupling tolerances between airframe components are predicted through statistical approach in order to reduce the features manufactured in assembly. This aspect contributes to a reduction of the costs due non-recurring costs. The method proposed has been tested on a dedicated case study developed in the frame of the “integrated main landing gear box” project on the CleanSky2 Research program. Tests have been conducted to check the consistency of the method and its feasibility in the industrial contexts in the case of flat-shaped component. The performed experiments confirmed the analytical study.

Preliminary study on sexual dimorphism of metric traits of cranium and mandible in a modern Italian skeletal population and review of population literature.

This study aims to assess the sexual dimorphism of skull in a modern skeletal collection through a direct anthropometric methodology by using a logistic regression analysis as statistical approach in order to provide specific regression formulae for the Italian population. Thirtyfour measurements (24 from cranium, 10 from mandible; 33 linear distances, one angle) were taken on 80 individuals (40 males, 40 females). A stepwise discriminant function analysis selected the combination of variables which best discriminated between sexes, and a cross-validation assessed the accuracy rate of the original sample. Most distances resulted longer in males than in females, but differences were statistically significant only for 12 and 6 out of respectively 24 cranium and 10 mandibular distances. Five combined cranium features returned a prediction accuracy of 88.6% (y=-81.01+0.14* Maximum cranial length+0.16* Basion-bregma height -0.05* Cranial base length +0.20* Bizygomatic breadth +0.31* Nasal height; adjusted R2=47.0%). Nine mandibular features were required to reach 74.7% accuracy (y=-93.08+0.19* Bigonial width +0.14* Bicondylar breadth +0.11* Mandibular length +0.18* Height of the mandibular body at mental foramen +0.21* Chin height -0.09* Mandibular angle -0.06* Minimum ramus breadth -0.05* Maximum ramus height -0.01* Maximum ramus breadth; adjusted R2=23.92%). Current results and existing literature suggested that only few cranial measurements can be used for diagnosis of sex: the same variables showed similar accuracy in different ethnic contexts. In conclusion, some aspects of sexual dimorphism of skull seem to be independent from ancestry.

Lightning Currents on Fastening Assemblies of an Aircraft Fuel Tank, Part I: Uncertainties Assessment With Statistical Approach

This paper proposes a statistical approach in order to assess the fastening resistances uncertainties in aircraft assemblies. This method relies on the use of the maximum likelihood estimation method, several statistical tests, and/or statistical criteria. Using the Dassault-Aviation measurement database, distribution laws are established in order to characterize uncertainties in the lightning impact on the fasteners. A simple finite-difference time-domain (FDTD) fastener model is proposed with a wire and a resistance. This generic model can represent any fasteners. The statistical model established from the fastener resistances state after a lightning shot is used to define the stochastic distribution values in the fastener electric model. This model is added to a 3-D FDTD parallelepiped generic fuel tank model. In this paper, we compare the current distributions between a composite and metallic fuel tank. The results highlight not only the model uncertainties effects on the statistical approach but also the fuel tank material choice impact.

Evolution of Appendicular Specializations for Fossoriality in Euryzygomatomyine Spiny Rats across Different Brazilian Biomes (Echimyidae, Hystricognathi, Rodentia)

The evolution of subterranean and fossorial rodents has been linked to the Neogene climatic shift to xeric conditions leading to open vegetation, like prairies and grasslands; most modern subterranean rodents occur in arid and open areas. Among South American spiny rats (family Echimyidae), the subfamily Euryzygomatomyinae includes both fossorial (Clyomys and Euryzygomatomys) and ambulatorial (Trinomys) genera, some of them endemic to open vegetated areas and other ones restricted to forested regions. The closely related genus Carterodon is also a fossorial rodent endemic to open vegetated areas. If the open environments constitute a determinant factor triggering the evolution of fossoriality in these spiny rats, it is expected that the fossorial lineages evolving since the Miocene in open environments (Carterodon sulcidens and Clyomys laticeps) show morphologies more specialized for digging than those currently restricted to Atlantic Forest habitats (Euryzygomatomys spinosus). Moreover, it is likely that Trinomys species specialized for xeric environments (T. albispinus and T. yonenagae) show incipient adaptations for fossoriality. The appendicular skeleton of three fossorial and five ambulatorial echimyid species were morphometrically analyzed with multivariate statistical approaches in order to test these presuppositions. The analyses showed that the appendicular morphology of T. yonenagae and T. albispinus, in comparison with the Atlantic Forest Trinomys species, and of C. sulcidens and C. laticeps in relation to E. spinosus are more adapted to scratch-digging activities, corroborating the hypothesis that open environments favor the evolution of fossoriality in spiny rats.

Development of radioactive beams at ALTO: Part 1. Physicochemical comparison of different types of UCx targets using a multivariate statistical approach

The optimization of the microstructure of the UCx target is a key point since many years in the field of ISOL method. The ultimate goal is to facilitate the release of the fission products, especially those with short half-lives. Fourteen UCx samples were synthetized from different uranium and carbon sources using three mixing protocols. All carburized samples were systematically characterized in terms of nature and proportion phases, grain and aggregate size, open and close porosity proportion and open pore size distribution. Our results were analysed using a multivariate statistical approach in order to remove any subjective bias. Strong correlations between the physicochemical characteristics of the samples as well as the impact of the synthesis process have been highlighted. In particular, using carbon nanotubes as carbon source combined with a new method of mixing is the key parameter to limit the sintering and to obtain samples with small grains and a high porosity well distributed over small pores. Moreover the microstructure obtained proved to be stable at high temperature.

A statistical evaluation on flight operational characteristics affecting aircraft noise during take-off

Aircraft noise immission level during the initial phase of take-off is influenced by several parameters, which often produce a significant fluctuation on measured noise levels at the noise monitoring terminal. This fluctuation is not only due to the different aircraft involved in the process, but it is also strongly dependent on the operational settings and characteristics of each take-off, even when the same aircraft type is analysed. The goal of this study is to investigate the relationships between the operational characteristics of flight departure and aircraft noise by means of a statistical approach in order to identify the parameters on which pilots could take action for noise-reduction purposes. The operational settings considered in the present work include actual take-off weight and ground run distance, lift-off aircraft ground speed and ground speed during the initial climb phase. Other variables, for instance source-receiver distance and weather conditions, such as air temperature, air density and headwind were also included in the analysis. The above mentioned parameters of B737-800 flight-departures were collected respectively during 15 days of July 2015 and September 2016. The data collected during the first session is the training set, while the second sample is the test set. Each sample of both datasets was joined with the corresponding noise level provided by the noise monitoring network. Principal Component Analysis and Multiple Linear Regression were performed in order to derive a simplified predictive noise model at a specific point on the ground. This method produced a good Sound Exposure Level estimation. The findings may also be useful to point out the operational characteristics causing the noisiest aircraft flyovers. Consequently, scheduled flight departures could be re-organized by introducing departure-direction and/or departure performance restrictions in order to minimize noise impact on the urban areas.

Layer analysis of CO2 sources in the US economic supply chains: an input output LCA study

Purpose Carbon footprint assessment requires a holistic approach, where all possible lifecycle stages of products from raw material extraction to the end of life are considered. The purpose of this paper is to develop an analytical sustainability assessment framework to assess the carbon footprint of US economic supply chains from two perspectives: supply chain layers (tiers) and carbon footprint sources. Design/methodology/approach The methodology consists of two phases. In the first phase, the data were collected from EORA input output and environmental impact assessment database. In the second phase, 48 input-output-based lifecycle assessment models were developed (seven CO2 sources and total CO2 impact, and six supply chain tiers). In the third phase, the results are analyzed by using data visualization, data analytics, and statistical approaches in order to identify the heavy carbon emitter industries and their percentage shares in the supply chains by each layer and the CO2 source. Findings Vast majority of carbon footprint was found to be attributed to the power generation, petroleum refineries, used and secondhand goods, natural gas distribution, scrap, and truck transportation. These industries dominated the entire supply chain structure and found to be the top drivers in all six layers. Practical implications This study decomposes the sources of the total carbon footprint of US economic supply chains into six layers and assesses the percentage contribution of each sector in each layer. Thus, it paves the way for quantifying the carbon footprint of each layer in today’s complex supply chain structure and highlights the importance of handling CO2 source in each layer separately while maintaining a holistic focus on the overall carbon footprint impacts in the big picture. In practice, one size fits all type of policy making may not be as effective as it could be expected. Originality/value This paper provides a two-dimensional viewpoint for tracing/analyzing carbon footprint across a national economy. In the first dimension, the national economic system is divided into six layers. In the second dimension, carbon footprint analysis is performed considering specific CO2 sources, including energy production, solvent, cement and minerals, agricultural burning, natural decay, and waste. Thus, this paper contributes to the state-of-art sustainability assessment by providing a comprehensive overview of CO2 sources in the US economic supply chains.

Factorial electrochemical design for tailoring of morphological and optical properties of Cu2O

ABSTRACTThe electrodeposition of cuprous oxide (Cu2O) onto FTO-coated glass substrate was studied by using a statistical approach in order to control the Cu2O morphology and optical properties. The factorial design considered four electrodeposition conditions at two representative levels as input variables (electrolyte temperature and pH, deposition potential and duration) and the deposition charge and morphology of obtained Cu2O as the output variables. The morphology analysis showed the highest influence on crystal shape was exhibited by electrolyte temperature and pH, reaching significance levels of 95 and 98%, respectively. Temperature as low as 35°C and pH 12.2 results in cubic morphology, while other parameters result in octahedron shape. The highest absorbance was exhibited by the Cu2O with cubic morphology.