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Corruption is often a source of contentious debate, covering different areas of knowledge, such as philosophy and sociology. In this paper we assess the effects of corruption on economic activity and highlight the relevance of the size of the government. We use dynamic models and the generalised method of moments approach for a panel of 48 countries, and as a measure of corruption the Transparency International's Corruption Perceptions Index, from 2012 to 2019. We find a significant adverse effect of corruption on the level and growth of GDP per capita, but that large governments benefit less from reducing corruption. Furthermore, developing economies, regardless of government size, benefit less from reducing corruption, while government size is not sufficient to explain the influence of corruption on economic activity, although the level of effectiveness of public services is crucial. Finally, our findings suggest that private investment is a potential transmission channel for corruption.

Deforestation is a matter of pressing global concern, yet surprisingly little is known about the relative efficacy of various policies designed to combat it. This article sets out a framework for measuring the cost effectiveness of alternative policies—both command-and-control and incentive-based—in the Brazilian Amazon. First, I estimate the demand for deforestation on private properties, exploiting regional variation in transportation costs as a means to recover farmers’ responses to permanent policies. Here, rescaling transportation costs using local yields allows me to express changes in farmers’ valuations in dollars per hectare. I then use the estimated demand to infer farmers’ willingness to deforest under different counterfactual policies, such as payments to avoid deforestation and taxes on land use, along with the corresponding potential farmers’ lost surpluses. The results indicate that payment programmes and land use taxes on agricultural land can be highly effective in preserving the rainforest and also be substantially less expensive than command-and-control policies (approximately 8 times less costly). A carbon tax equal to the social cost of carbon could virtually eliminate all agricultural land in the Amazon, given the low agricultural returns there.

The Scikit-HEP project is a community-driven and community-oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The project builds on five pillars that embrace the major topics involved in a physicist’s analysis work: datasets, data aggregations, modelling, simulation and visualisation. The vision is to build a user and developer community engaging collaboration across experiments, to emulate scikit-learn’s unified interface with Astropy’s embrace of third-party packages, and to improve discoverability of relevant tools.

We derive exact closed-form expressions for Long Range (LoRa) bit error probability and diversity order for channels subject to Nakagami-m, Rayleigh and Rician fading. Analytical expressions are compared with numerical results, showing the accuracy of our proposed exact expressions. In the limiting case of the Nakagami and Rice parameters, our bit error probability expressions specialize into the non-fading case.

The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

The amount of energy generated from a photovoltaic installation depends mainly on two factors—the temperature and solar irradiance. Numerous maximum power point tracking (MPPT) techniques have been developed for photovoltaic systems. The challenge is what method to employ in order to obtain optimum operating points (voltage and current) automatically at the maximum photovoltaic output power in most conditions. This paper is focused on the structural analysis of mathematical models of PV cells with growing levels of complexity. The main objective is to simulate and compare the characteristic current-voltage (I-V) and power-voltage (P-V) curves of equivalent circuits of the ideal PV cell model and, with one and with two diodes, that is, equivalent circuits with five and seven parameters. The contribution of each parameter is analyzed in the particular context of a given model and then generalized through comparison to a more complex model. In this study the numerical simulation of the models is used intensively and extensively. The approach utilized to model the equivalent circuits permits an adequate simulation of the photovoltaic array systems by considering the compromise between the complexity and accuracy. By utilizing the Newton–Raphson method the studied models are then employed through the use of Matlab/Simulink. Finally, this study concludes with an analysis and comparison of the evolution of maximum power observed in the models.

The electric field distribution of insulator surface is nonuniform, and the maximum electric field is visible around two terminals of the insulator. Using a microvaristor layer is one of the methods of field control that can reduce the electric field stresses to prevent an extension of discharges on the insulator surface and a complete flashover caused by the subsequent development of arcing. This study targets the effect of zinc oxide (ZnO) microvaristors on the electric field distribution along the contaminated and clean composite insulators that have been investigated. In addition, the impact of the insertion of microvaristor layers on the critical flashover voltage (CFO) of the insulators through a mathematical formula has been presented for the first time. The estimation of electric field distribution is conducted through finite element method (FEM) on a 400 kV insulator using COMSOL Multiphysics, in which the optimal dimensions of the microvaristor layer were obtained using the accelerated particle swarm optimization (APSO) algorithm. Then, for the first time, the analysis of the influence of the ZnO insertion on the transient performance of the insulator, i.e., the outage rate of the network, is performed in EMTP software for the insulator with the optimized insertion of the microvaristor layer. Modelling techniques were used to simulate the components of a transmission network according to the valid models. Finally, by setting different values for CFO, Monte Carlo simulation, and linking EMTP and MATLAB software, the lightning flashover rate (LFOR) and the failure risk (F.R.) of the different insulator models are calculated. It is shown that the proposed method reduces the maximum electric field of the inside and outside of the insulator, which in turn leads to a reduction in the outage rate of the power network and the insulation risk of the insulator, and an increase in CFO of the insulator.

Online monitoring of Partial Discharges (PDs) in rotating electrical machines is an useful tool for machine prognosis, as it presents reduced costs compared to intrusive inspections and is associated with relevant problems. Although this monitoring method has been developed for almost 50 years, the recent advancements in processes automation and signal processing techniques allow improvements that are still being studied by academic and industrial researchers. To analyze the current context of PDs monitoring, this article presents a literature review based on concepts of PDs in rotating machines, data acquisition techniques, state-of-the art commercial equipment, and recent methodologies for detection and pattern recognition of PDs. The challenges identified in the literature that motivate the development of more reliable and robust PD monitoring systems are presented and discussed.

Split-spectrum amplitude decorrelation angiography for spectral-domain optical coherence tomography has enabled detailed, non-invasive assessment of vascular flow. This study evaluates choriocapillaris and retinal capillary perfusion density (CPD) in diabetic eyes using optical coherence tomography angiography (OCTA). Records of 136 eyes that underwent OCTA imaging at a single institution were reviewed. Eyes were grouped as non-diabetic controls (37 eyes), patients with diabetes mellitus (DM) without diabetic retinopathy (DM without DR, 31 eyes), non-proliferative diabetic retinopathy (NPDR, 41 eyes) and proliferative diabetic retinopathy (PDR, 27 eyes). Quantitative CPD analyses were performed on OCTA images for assessing perfusion density of the choriocapillaris and retinal plexus for all patients and compared between groups. Eyes with NPDR and PDR showed significantly decreased choriocapillaris CPD compared with controls, while DM eyes without DR did not show significant change. Choriocapillaris whole-image CPD was decreased by 8.3% in eyes with NPDR (p<0.01) and decreased by 7.1% in eyes with PDR (p<0.01). Choriocapillaris parafoveal CPD was decreased by 8.9% in eyes with NPDR (p<0.01) and decreased by 8.2% in eyes with PDR (p<0.01). Compared with controls, only eyes with PDR showed significantly decreased retinal CPD, as well as significantly increased foveal avascular zone (FAZ) area. In those patients, retinal whole-image CPD was decreased by 9.7% (p<0.01), retinal foveal CPD was decreased by 20.5% (p<0.01) and retinal parafoveal CPD was decreased by 11.4% (p<0.01). FAZ area was increased by 50.9% (p<0.01). Choriocapillaris and retinal CPD are reduced in diabetic retinopathy, while FAZ area is increased in eyes with PDR. Vascular changes captured by new imaging modalities can further characterise diabetic choroidopathy.

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.