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"Castillo, Miguel"
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How Effective Are Biodiversity Conservation Payments in Mexico?
We assess the additional forest cover protected by 13 rural communities located in the southern state of Chiapas, Mexico, as a result of the economic incentives received through the country's national program of payments for biodiversity conservation. We use spatially explicit data at the intra-community level to define a credible counterfactual of conservation outcomes. We use covariate-matching specifications associated with spatially explicit variables and difference-in-difference estimators to determine the treatment effect. We estimate that the additional conservation represents between 12 and 14.7 percent of forest area enrolled in the program in comparison to control areas. Despite this high degree of additionality, we also observe lack of compliance in some plots participating in the PES program. This lack of compliance casts doubt on the ability of payments alone to guarantee long-term additionality in context of high deforestation rates, even with an augmented program budget or extension of participation to communities not yet enrolled.
Journal Article
Design of Experiments to Compare the Mechanical Properties of Polylactic Acid Using Material Extrusion Three-Dimensional-Printing Thermal Parameters Based on a Cyber–Physical Production System
The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line’s local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems.
Journal Article
Intelligent and behavioral-based detection of malware in IoT spectrum sensors
The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of operational platforms, IoT spectrum sensors are vulnerable to heterogeneous malware. The usage of behavioral fingerprinting and machine learning has shown merit in detecting cyberattacks. Still, there exist challenges in terms of (i) designing, deploying, and evaluating ML-based fingerprinting solutions able to detect malware attacks affecting real IoT spectrum sensors, (ii) analyzing the suitability of kernel events to create stable and precise fingerprints of spectrum sensors, and (iii) detecting recent malware samples affecting real IoT spectrum sensors of crowdsensing platforms. Thus, this work presents a detection framework that applies device behavioral fingerprinting and machine learning to detect anomalies and classify different botnets, rootkits, backdoors, ransomware and cryptojackers affecting real IoT spectrum sensors. Kernel events from CPU, memory, network, file system, scheduler, drivers, and random number generation have been analyzed, selected, and monitored to create device behavioral fingerprints. During testing, an IoT spectrum sensor of the ElectroSense platform has been infected with ten recent malware samples (two botnets, three rootkits, three backdoors, one ransomware, and one cryptojacker) to measure the detection performance of the framework in two different network configurations. Both supervised and semi-supervised approaches provided promising results when detecting and classifying malicious behaviors from the eight previous malware and seven normal behaviors. In particular, the framework obtained 0.88–0.90 true positive rate when detecting the previous malicious behaviors as unseen or zero-day attacks and 0.94–0.96 F1-score when classifying them.
Journal Article
Building Corporate Reputation through Sustainable Entrepreneurship: The Mediating Effect of Ethical Behavior
This article investigates how a management approach based on sustainable entrepreneurship can positively affect corporate reputation. The analysis showed that this effect is enhanced by the mediating effect of good governance based on ethical behavior. The empirical study was conducted using data for 104 large Spanish firms defined as sustainable by the Corporate Reputation Business Monitor (MERCO) ranking.
Journal Article
Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models
Quantifying patterns of deforestation and linking these patterns to potentially influencing variables is a key component of modelling and projecting land use change. Statistical methods based on null hypothesis testing are only partially successful for interpreting deforestation in the context of the processes that have led to their formation. Simplifications of cause-consequence relationships that are difficult to support empirically may influence environment and development policies because they suggest simple solutions to complex problems. Deforestation is a complex process driven by multiple proximate and underlying factors and a range of scales. In this study we use a multivariate statistical analysis to provide contextual explanation for deforestation in the Usumacinta River Basin based on partial pattern matching. Our approach avoided testing trivial null hypotheses of lack of association and investigated the strength and form of the response to drivers. As not all factors involved in deforestation are easily mapped as GIS layers, analytical challenges arise due to lack of a one to one correspondence between mappable attributes and drivers. We avoided testing simple statistical hypotheses such as the detectability of a significant linear relationship between deforestation and proximity to roads or water. We developed a series of informative generalised additive models based on combinations of layers that corresponded to hypotheses regarding processes. The importance of the variables representing accessibility was emphasised by the analysis. We provide evidence that land tenure is a critical factor in shaping the decision to deforest and that direct beam insolation has an effect associated with fire frequency and intensity. The effect of winter insolation was found to have many applied implications for land management. The methodology was useful for interpreting the relative importance of sets of variables representing drivers of deforestation. It was an informative approach, thus allowing the construction of a comprehensive understanding of its causes.
Journal Article
Cooperative Terrestrial-Underwater Wireless Optical Links by Using an Amplify-and-Forward Strategy
In this paper, we analyze a combined terrestrial-underwater optical communication link for providing high-speed optical connectivity between onshore and submerge systems. For this purpose, different transmission signaling schemes were employed to obtain performance results in terms of average bit error rate (ABER). In this sense, from the starting point of a known conditional bit-error-rate (CBER) in the absence of turbulence, the behavior of the entire system is obtained by applying an amplify-and-forward (AF) based dual-hop system: The first link is a terrestrial free-space optical (FSO) system assuming a Málaga distributed turbulence and, the second one, is an underwater FSO system with a Weibull channel model. To obtain performance results, a semi-analytical simulation procedure is applied, using a hyper-exponential fitting technique previously proposed by the authors and leading to BER closed-form expressions and high-accuracy numerical results.
Journal Article
Effects of socially responsible human resource management (SR-HRM) on innovation and reputation in entrepreneurial SMEs
This work focuses on the importance of responsible human resource management, and its link to innovation and reputation, which are deemed to be relevant intangible assets for all firms, although particularly for entrepreneurial SMEs, and which are of particular interest since they have remained relatively unexplored despite their key role in the business fabric. Specifically, we present an explanatory model comprising three variables; the latent independent variable is socially responsible human resource management (SR-HRM), and the corresponding endogenous variables are reputation and innovation. In order to empirically validate the conceptual model developed, we design a survey which has been answered by a representative sample of entrepreneurs of their own firms. Using partial least squares (PLS), we analyse both the measuring model as well as the structural model. Results prove satisfactory and allow us to confirm the direct positive and significant relation between socially responsible human resource management and reputation, as well as the causal relation when innovation acts as a mediating variable.
Journal Article
Sustained participation in a Payments for Ecosystem Services program reduces deforestation in a Mexican agricultural frontier
Payments for Ecosystem Services (PES) provide conditional incentives for forest conservation. PES short-term effects on deforestation are well-documented, but we know less about program effectiveness when participation is sustained over time. Here, we assess the impact of consecutive renewals of PES contracts on deforestation and forest degradation in three municipalities of the Selva Lacandona (Chiapas, Mexico). PES reduced deforestation both after a single 5-year contract and after two consecutive contracts, but the impacts are only detectable in higher deforestation-risk parcels. Enrollment duration increases PES impact in these parcels, which suggests a positive cumulative effect over time. These findings suggest that improved spatial targeting and longer-term enrollment are key enabling factors to improve forest conservation outcomes in agricultural frontiers.
Journal Article
Lessons from a Space Lab: An Image Acquisition Perspective
The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.
Journal Article