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Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Up-to-date, the simulation of pedestrian behavior is used to support the design and analysis of urban infrastructure and public facilities. The purpose of this paper is to present a microscopic model that describes pedestrian behavior in a two-dimensional space. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. In a simulation scenario, an agent represents a pedestrian with homogeneous physical characteristics such as walking speed and height. The agents are moved through a discrete space formed by a lattice of hexagonal cells, where each one can contain up to one agent at the same time. The model was validated by using a test that is composed of 17 real data sets of pedestrian unidirectional flow. Each data set has been extracted from laboratory-controlled scenarios carried out with up to 400 people walking through a corridor whose configuration changed in form of the amplitude of its entrance doors and the amplitude of its exit doors from one experiment to another. Moreover, each data set contained different groups of coordinates that compose pedestrian trajectories. The scenarios were replicated and simulated using the proposed model, obtaining 17 simulated data sets. In addition, a measurement methodology based on Voronoi diagrams was used to compute the velocity, density and specific flow of pedestrians to build a time-series graphic and a set of heat maps for each of the real and simulated data sets.

The approach consists of a multi-agent system and cellular automata theory. The obtained results were compared with other studies and a statistical analysis based on similarity measurement is presented.

A microscopic mobility model that describes pedestrian behavior in a two-dimensional space is presented. It is based on multi-agent systems and cellular automata theory. The concept of layered-intelligent terrain from the video game industry is reused and concepts such as tracing, evasion and rejection effects related to pedestrian interactive behavior are involved. On average, the simulated data sets are similar by 82 per cent in density and 62 per cent in velocity compared to the real data sets. It was observed that the relation between velocity and density from real scenarios could not be replicated.

The main limitations are presented in the speed simulations. Although the obtained results present a similar behavior to the reality, it is necessary to introduce more variables in the model to improve the precision and calibration. Other limitation is the dimension for simulating variables at this moment 2D is presented. So the resolution of cells, making that pedestrian to occupy many cells at the same time and the addition of three dimensions to the terrain will be a good challenge.

In total, 17 data sets were generated as a case study. They contain information related to speed, trajectories, initial and ending points. The data sets were used to calibrate the model and analyze the behavior of pedestrians. Geospatial data were used to simulate the public infrastructure in which pedestrians navigate, taking into account the initial and ending points.

The social impact is directly related to the behavior analysis of pedestrians to know tendencies, trajectories and other features that aid to improve the public facilities. The results could be used to generate policies oriented toward developing more consciousness in the public infrastructure development.

The general methodology is the main value of this work. Many approaches were used, designed and implemented for analyzing the pedestrians’ behavior. In addition, all the methods were implemented in plug-in for Quantum GIS. The analysis was described with heat maps and statistical approaches. In addition, the obtained results are focused on analyzing the density, speed and the relationship between these features.

The tightening of environmental measures and policies in various countries around the world is forcing manufacturing companies, particularly those that make up the automotive industry, to improve their production processes, through the implementation of approaches such as lean production (LP) and Industry 4.0 (I4.0) technologies, to reduce industrial waste. However, the literature indicates that the implementation of LP and I4.0 does not always lead to an improvement in the level of operational performance (OP). Therefore, this study analyzes the effects of the implementation of LP practices and I4.0 on a green supply chain (GSC) and the operational performance of manufacturing companies in the Mexican automotive industry.

A theoretical research framework consisting of six hypotheses was developed and validated by applying partial least squares structural equation modeling (PLS-SEM) and using a sample of 460 companies from the Mexican automotive industry.

The results show that the level of OP of manufacturing companies increases substantially with the implementation of LP and I4.0 practices, as well as a GSC.

Managers of manufacturing companies will be able to use the results of this study to improve their production systems and to demonstrate the effects of these practices on OP.

This study contributes to the literature on LP and I4.0 by providing robust empirical evidence of the positive effects of implementing these approaches on the GSC and OP of manufacturing companies.

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Social media are emerging as the ideal channel for building one-to-many communication and disseminating intellectual capital (IC) information. Their rise is bringing out new research challenges to investigate the implications of their use. However, there needs to be more research contributions relating to the financial benefits of using social media for IC disclosure (ICD). This study aims to bridge this gap by analyzing, under the lens of signaling theory, the effect of ICD through Twitter on firm value.

This study is based on a content analysis of tweets disseminated by 262 companies aimed at examining the amount of IC information disclosed and on a regression analysis aimed at analyzing the impact of this type of information on firm value.

Empirical results show that a large ICD via Twitter favors an increase in firm value. They also demonstrate that disclosing information relating to the three IC dimensions positively affects the firm value. These findings suggest that actively and comprehensively communicating IC information via Twitter can help improve the perception and evaluation of the company by investors and other stakeholders.

This study offers empirical evidence about the financial benefits associated with using social media as disclosure tools by companies. It also enriches the literature on the relationship between ICD and firm value and consolidates the goodness of the signaling theory as an ideal theoretical perspective to frame the relationship between IC information and firm value.

This study offers important managerial implications for firms and investors. In light of the significant financial benefits, firms should use social media to disclose IC information and should seek to increase their visibility on such platforms to convey the information to a greater number of users. Investors should also heed social media when gathering IC information, combining the analysis of these platforms with that of traditional corporate documents.

This study enriches the limited literature on ICD via social media and extends knowledge about the relationship between IC information and firm value. In this regard, the originality also lies in the individual analysis of the impact of the three IC dimensions on firm value.

The purpose of this paper is to identify relevant gaps in human capital (HC) related to innovation performance, which might be the basis for creation of more effective innovation strategies.

The proposed approach contains the following four steps: HC survey, assessment of HC value drivers, identification of gaps related to the HC value drivers and recommendations for an innovation strategy based on identified gaps. The HC survey includes 554 managers from Serbian companies within seven different industries.

The biggest gaps in observed Serbian industries are related to crucial HC value drivers for innovation process, such as innovativeness, education and knowledge sharing and social skills.

Although there are limitations in measuring HC and innovation drivers, this approach seems to be valid in recommending more effective innovation strategies/policies on micro and macro level.

This research reveals potentials and barriers within HC in different Serbian industries, crucial to innovation, pointing to the initiatives which might improve innovation performance across Serbian industries. The identification of HC gaps across industries is valuable for gathering sounder intelligence of the sources of innovation and fine-tuning of national innovation strategy according to specific features of industries.

The proposed approach integrates a new perspective into current innovation measurement paradigm. It includes gaps within HC in the assessment of innovation performance, which might foster intangible innovation potential.

The study examines the association between board gender diversity and Sustainable Development Goal (SDG) disclosure from an international and longitudinal perspective. It also investigates the role of the Sustainability Committee (SC) as a possible factor that can mediate the relationship between board gender diversity and SDG disclosure.

The authors focused on the annual Communication on Progress (CoP) prepared annually by a sample of 526 companies from 39 countries and ten industry sectors along the 2017–2020 period to evaluate the SDG disclosure. Baron and Kenny's (1986) three-step model is estimated to test the impact of the presence of an SC on the SDG disclosure level and the mediating effect exerted by the SC on the relationship between board gender diversity and SDG disclosure.

Findings shed light on the usefulness of the CoP as an alternative reporting tool to communicate progress against SDGs achievement, especially regarding SDGs 13 and 8. This study evidences that board gender diversity positively influences SDG disclosure. The relationship between board gender diversity and SDG disclosure is not only direct but also mediated by the presence of an SC.

Companies need to consider the role of women in enhancing the effectiveness of their governance mechanisms and their ability to meet stakeholder information needs. Establishing a specific SC represents a valid mechanism that ensures greater transparency about corporate actions tackled to contribute toward SDGs and enhances the relationship between board gender diversity and SDG disclosure among International companies.

The study's findings offer stimuli for policy-makers and regulators to reflect on the relevance of the CoP as a possible alternative communication tool to provide SDGs information and overcome the limitations of the Sustainability Reports.

This is the first study that examines companies' SDG disclosure practices focusing on CoPs. Further, to the best of the authors' knowledge, this is the first study that tests the relationship between gender diversity and SDG disclosure, considering the mediating effect of an SC committee.

Methods to assess the authenticity and traceability of wines have been extensively studied as enhancers of food quality, allowing producers to obtain market recognition and premium prices. Among analytical techniques, the volatilome profile attained by gas chromatography coupled with mass spectrometry is acquiring more and more attention by the scientific community, together with the use of chemometrics

The volatilome profile of three varieties of blanc wines from the Collio area (namely Ribolla Gialla, Malvasia and Friulano) between Italy and Slovenia, was determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhancing the carbonyl compounds identification with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine with the aim of identifying the autochthonous Friulano variety.

A two-step chemometric approach based on an unsupervised technique (PCA) followed by a supervised one (PLS-DA) allowed to identify possible markers for discriminating the Friulano Collio variety from the others, in particular two chemical classes were identified by PCA (ketones and long chain esters). PLS-DA showed 87% accuracy in classification. A correct classification (i.e. non-Friulano Collio) of a group of wines obtained from the same grape variety but produced in an extra-Collio area was obtained as well. The results confirmed the benefits of using a derivatization step prior to volatile organic compounds analysis.

Among methods to assess the authenticity and traceability of wines, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine, may have a key role in conjunction with chemometrics and, in particular with principal component analysis and partial least square discriminant analysis.

Among methods to assess the authenticity and traceability of Friulano wine, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-Pentafluorobenzyl)Hydroxylamine hydrochloride, may have a key role in conjunction with chemometrics.

Few works investigated both wine traceability with a volatilome enhancer and chemometrics of the Friulano wine variety obtaining such an improvement in this wine variety discrimination.

This study aims to investigate the relationship between corporate sustainable development goals (SDGs) disclosure and analyst forecast quality.

The study focuses on a sample of 95 Italian-listed companies preparing the mandatory non-financial declaration (NFD) according to the Global Reporting Initiative (GRI) standards over a five-year period (2017–2021), corresponding to an unbalanced sample of 438 observations. Analyst forecast quality was proxied by earnings forecast accuracy (FA) and earnings forecast dispersion (FD), built on data retrieved from the Refinitiv database. A manual content analysis was performed on NFDs to derive an SDG disclosure score (SDGD) for each sampled company.

This study provides empirical evidence suggesting that voluntary SDG disclosure matters to the capital market in that it helps enhance the information environment of companies, evidenced by improved analyst forecast quality. In particular, this study highlighted that SDG disclosure positively influences analyst FA while negatively affecting analyst FD.

This study focuses on the Italian context, which has idiosyncratic characteristics regarding the structure of the financial market, the composition of corporate ownership and experience in non-financial reporting practices.

This study indicates to corporate managers that following GRI standards may represent the right way to better integrate SDG disclosure in corporate non-financial reports and increase the relevance of such information for investors and other capital market participants.

To the best of the authors’ knowledge, this is the first study that empirically examines the association between SDG disclosure and analyst forecast quality.