Mendeley Data Showcase

Towards a Common Framework for Computational Models of Emotion: Unifying Cognitive and Emotional Processing This repository contains the reference implementation of the framework proposed in the research article: "Towards a Common Framework for Computational Models of Emotion: Unifying Cognitive and Emotional Processing". Overview Current Computational Models of Emotion (CMEs) often operate as isolated systems characterized by architectural rigidity and fixed execution cycles. This framework serves as a mediating core designed to integrate heterogeneous affective components into unified, theoretically coherent processes. By decoupling domain-specific implementations, the system employs an ontology-based semantic controller to align disparate terminologies and a dependency coordinator to enable configurable execution sequences. Key Features Ontology-Based Semantic Alignment: Resolves terminological inconsistencies using a formal affective ontology comprising 139 elements. Dynamic Execution Orchestration: Employs a dependency-aware execution planner that identifies independent component groups for parallel execution while managing sequential data flow. Machine-Readable Traceability: Enriches every affective output with formal metadata and ontological provenance, yielding a semantic trace suitable for explainability analysis. Architectural Extensibility: Supports the modular integration of diverse third-party components (e.g., appraisal evaluation, mood dynamics, behavioral generators) without requiring structural redesigns. Repository Contents FRAMEWORKV7_(English).py: The main Python implementation containing the core modules (Central Executive, Semantic Controller, and Execution Coordinator). CMEs_ontology.owl: The formal OWL ontology file containing the 139 affective elements used for semantic alignment. Supplementary_Material_Tables.pdf: This document includes: Table I: Comprehensive listings of affective labels and theories extracted per model (ALMA, EEGS, EMIA, FLAME, EMA). Table II: Unified list of variability scenarios used for the semantic matching validation in Case Study 2.

This project, titled "Good and Bad Classification of Silver Carp(Hypophthalmichthys molitrix) is designed to develop an image classification system that distinguishes between healthy (good) and unhealthy (bad) Silver Carp fish (Hypophthalmichthys molitrix). The dataset consists of approximately 2000 images, evenly distributed between good and bad samples. All images were captured using a Realme 5i mobile camera, providing high-resolution visual data suitable for machine learning applications. The fish were photographed against a black background in daylight conditions to ensure consistency, clarity, and accurate feature capture. Dataset Composition Good Samples (Healthy) The dataset includes approximately 1000 images of healthy Silver Carp fish. These images show fish with: Bright, shiny, and intact scales Clear, transparent eyes Proper body shape without deformities Natural coloration and smooth texture These samples represent the positive class and help train the model to recognize healthy fish conditions. Bad Samples (Unhealthy) The dataset also contains approximately 1000 images of unhealthy Silver Carp fish. These fish may exhibit: Dull or discolored scales Cloudy or damaged eyes Physical deformities Visible injuries or infections Poor overall physical condition These images represent the negative class, enabling the model to identify unhealthy fish accurately. Data Collection Setup All images were captured using a Realme 5i smartphone camera, known for its reliable image quality and resolution. A black background was used intentionally to: Enhance contrast between the fish and the background Reduce noise and unwanted visual distractions Highlight important visual features such as scales, eyes, and body structure Images were taken under natural daylight conditions, ensuring consistent illumination and accurate representation of color and texture. Image Characteristics The dataset includes variations in: Fish size Body orientation Color intensity Health condition This diversity improves the robustness of the machine learning model and ensures better performance in real-world scenarios. Data Annotation Each image is carefully labeled as either: "Good" (Healthy) "Bad" (Unhealthy) These labels serve as the ground truth, allowing the machine learning model to learn the differences between healthy and unhealthy fish accurately.

Genotype data, phenotype data, and summary statistics for genome-wide association study aimed to identify loci associated with dermatitis severity in a colony of laboratory beagles validated as a model for atopic dermatitis. The study population included 41 dogs from the colony and six unrelated unaffected laboratory beagles as controls. The overall severity for each dog was determined by evaluating flare severity at the peak of a standardized allergen challenge using the Canine Atopic Dermatitis Extent and Severity Index (CADESI-03). Genotyping was performed using the Illumina 230K CanineHD BeadChip array. CADESI-03 scores from the 47 dogs were transformed from a scale of zero to three to a scale of one to four by adding one to each score. Excluding values of zero prevents scores from being mistaken as missing phenotypes, commonly defined by -9 or zero in Plink.

This dataset supports the article “An aggregate price for energy services: Useful exergy as an intermediate flow in a two-sector model of the economy” published in Ecological Economics (https://doi.org/10.1016/j.ecolecon.2025.108665). It provides a harmonized set of macroeconomic and biophysical time series for Portugal covering the period 1960–2014, constructed to implement a two-sector economic model with an extended energy sector (E-Sector) and a non-energy sector (NE-Sector). The dataset combines reclassified national accounts with final and useful exergy balances in order to estimate consistent prices for useful exergy (energy services) treated as an intermediate input to production rather than as a primary factor. Macroeconomic variables are derived from official sources (AMECO, EUROSTAT, Banco de Portugal, EU KLEMS) and reallocated across sectors following the accounting framework described in the associated paper. These include sectoral consumption, investment, capital stocks, labor inputs, gross operating surplus, compensation of employees, taxes, trade flows, and gross value added, all expressed in nominal monetary units. Energy and exergy variables are based on detailed national energy balances and extended exergy accounting, including primary, final, and useful exergy flows by energy carrier, economic activity, and end-use. Useful exergy estimates incorporate second-law (final-to-useful) conversion efficiencies and cover conventional energy carriers as well as food, feed for working animals, and other non-conventional sources, following established societal exergy accounting methods. The dataset also includes derived indicators, such as aggregate final-to-useful exergy efficiency, sectoral exergy uses, intermediate payments for useful exergy, and demand-side and supply-side prices of useful exergy, enabling replication of all main quantitative results presented in the paper. All variables are documented with clear definitions, units, and sectoral allocation rules. The dataset is intended for reuse in research on ecological and biophysical economics, energy–economy interactions, growth theory, integrated assessment modeling, and long-run energy service pricing.

Official Statistics and Political Regimes: These data combine open data sources from the World Bank and The Economist. This combination allows us to study official statistics directly at the country level. Democracy and the national statistical system are all-encompassing concepts with many different aspects. Measuring them is a complex and challenging operation. About democracy, the Democracy Index (DI) by the UK group Economist takes into consideration different categories and started recently. In the Word Bank, there are the Statistical Capacity Indicator and Statistical Performance Indicator. The Statistical Capacity Indicator is done only for 146 development countries. The Statistical Performance Indicator (SPI) has been done for all the countries since 2016. Both indexes are made with similar methodology, but they take into account different aspects of the statistical system. SPI tries to estimate the different characteristics of national statistical systems. Also so as to produce indicators related to the 17 Sustainable Development Goals. Open Data Watch produced two indexes: the Open Data Inventory (ODIN) and the Gender Data Compass (GDC). Both indexes are focused on the webpages of the national statistics offices around the world. The first one focuses on open data, and the second one disaggregates data. The SPI included the ODIN data. The GDC just started in 2023. The Democracy Index assessed, from 2006 onwards, the democracy of over 167 countries. This was based on five categories – electoral process and pluralism, civil liberties, functioning of government, political participation, and political culture – thanks to experts’ assessments and the public’s opinion from multiple significant surveys. The index’s values range from 0 to 10, and these countries were within one of four types of regimes: ‘Full democracies’, ‘Flawed democracies’, ‘Hybrid regimes’ and ‘Authoritarian regimes’.The SPI by the World Bank assessed the performance of national statistical systems from 2016 to 2022 in over 174 countries. This data came from the most important international organisations, including the Open Data Inventory (ODIN) by Open Data Watch. The SPI is a framework of five pillars (data use, data services, data products, data sources, and data infrastructure) and 22 dimensions. Nevertheless, there are currently 14 dimensions that have been proven by their established methods, whereas the other 8 dimensions have no measurable indicators. I create and use "Official Statistics and Political Regimes" for this article: Di Gennaro, Splendore, Luca. Is there a quantitative relationship between democracy and official statistics? Statistical Journal of the IAOS. 2024;40(3):511-519. doi:10.3233/SJI-240012 https://journals.sagepub.com/doi/10.3233/SJI-240012

Database of 94 reviewed papers

These data enable an assessment of discontinuities and associated bias in annual trends of precipitation totals for the southeastern United States from high-resolution, gridded products and combinations of the products. The products are Daymet, gridMET, nClimGrid, PRISM AN, PRISM LT, and TerraClimate. Daymet, nClimGrid, and PRISM AN have both daily and monthly totals. PRISM LT and TerraClimate only have monthly totals. gridMET only has daily totals. The time period is 1980-2024. The data files are described below. 1. ReferenceGauges_Missing.xlsx -- Monthly precipitation totals from the 120 COOP gauges that produced the reference time series. Missing totals are blanks. 2. ReferenceGauges_SeriallyComplete.xlsx -- Monthly precipitation totals from the 120 COOP gauges that produced the reference time series. Missing totals were replaced with the mean total from the three nearest gauges. 3. ReferenceGauges_Location&Elevation.xlsx -- The latitude, longitude, and elevation of the 120 COOP gauges. 4. AnnualTotals_from_MonthlyProducts.xlsx -- Annual totals from reference-gauge network, Daymet, nClimGrid, PRISM, TerraClimate, and all possible combinations of products 5. AnnualTotals_from_DailyProducts.xlsx -- Annual totals from reference-gauge network, Daymet, gridMET, nClimGrid, PRISM, and all possible combinations of products 6. Daymet_Gauges.xlsx -- Gauges in the southeastern United States used to produce Daymet estimates 7. nClimGrid_Gauges.xlsx -- Gauges in the southeastern United States used to produce nClimGrid estimates 8. PRISM_Gauges.xlsx -- Gauges in the southeastern United States used to produce PRISM estimates 9. TerraClimate_Gauges.xlsx -- Gauges in the southeastern United States used to produce TerraClimate estimates 10. ASOS.xlsx -- ASOS (Automated Surface Observing System) gauges in the southeastern United States 11. Gauges_120.shp -- Shapefile containing the 120 COOP gauges. The files are located in the GIS archive. 12. SEUSA.shp -- Shapefile containing the southeastern United States without state borders. The files are located in the GIS archive. 13. SEUSA_NoHighElevations@SEUSA_NoHighElevations@30arcseconds.shp-- Shapefile containing the southeastern United States with high-elevation areas removed. The removal was performed using a DEM with 30 arc-second resolution. The files are located in the GIS archive. 14. SEUSA_NoHighElevations@1km.shp-- Shapefile containing the southeastern United States with high-elevation areas removed. The removal was performed using a DEM with 1-km resolution. The files are located in the GIS archive. 15. SEUSA_NoHighElevations@4km.shp -- Shapefile containing the southeastern United States with high-elevation areas removed. The removal was performed using a DEM with 4-km resolution. The files are located in the GIS archive. 16. SEUSA_40kmCells.shp -- Shapefile containing 40-km grid cells that cover the entire southeastern United States. The files are located in the GIS archive.

The motivation for investigating the issues presented in this article stemmed from a discovery that resulted from using the magnetic flux quantum, that combine the Planck's constant and the Elementary charge. It led to a new relationship between the combined expressions, and it revealed that the mass of the electron is associated with the magnitude of the square of the magnetic flux quantum. Also it revealed a novel significance of the vacuum permittivity constant (in SI units), that relies also on an analogy to the kinetic theory of gases. By using the concept of the nucleus motion around the center of mass shared with the electron in the Hydrogen atom, along with defineing the orbital angular momentum of the proton at the trajectory around the center of mass, yield a velocity of the proton at this trajectory, and also a new physical constant which fulfill a similar role like the fine structure constant. The new constant yield results for the proton and neutron masses and their radii. Another aspect presented in a briefly way, demonstrates the connection between the square of the magnetic flux quantum through the Bohr radius that provides a novel significance of the wave function in the atom. This paper presents also a new perspective on the internal structure of the proton and neutron with their quarks, and on the origin of the weak force bosons associated with this internal structure. The quark model was initially proposed independently by physicists Marie Gell-Mann and George Zweig in 1964. The Quarks were introduced as part of an ordering scheme for hadrons. In this paper the proton, neutron and all baryons consist of two energy levels on which the Up and Down quarks are in an orbit level (The Gluons are exchanged between them at their levels), and a third energy level that equal to ~ 80 [Gev], that plays a central role in the decay process via the weak force. The results are in full accordance with the results published by NIST CODATA 2018 that I’ve used, validating the results.

We analyse the evolution of market structure in India between 2000 and 2020 using a rich dataset at high levels of disaggregation. We examine the extent to which business groups – notably family-owned groups – have sustained their dominant market positions in the Indian economy. We focus on two key dimensions. The first is the extent of concentration in markets and market shares by industry. The second concerns the dynamics and the extent to which business groups have focussed on consolidating their position in specific, narrow sectors or diversified by entering new sectors. We find that while market concentration has been falling, a bloc of high concentration sectors remains. Further, diversification has been actively pursued across sectors by most business groups. While this points to greater competition among business groups, the ratio of revenues to variable costs – a measure of the markup – has shifted upwards, particularly after 2013. The weight and persistence of these large business groups in the economy, as measured by the ratio of their revenues to GDP, has also increased. Finally, we discuss possible policy options. This dataset provides data for Figures and the STATA .do file for the replication of the replication of figures and tables. Due to restrictions on sharing the data by the data service provider "CMIE Prowessdx", the authors are not at liberty to share the dataset for the analysis.