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Evaluates the applicability of ultrasonic sensors in a welding environment and reports on experimental measurements carried out with a sensory head containing ultrasonic transducers with different frequencies. Analyses the effects on the sensors of factors such as noise, temperature and shielding gas flow and concludes by suggesting appropriate protective measures for the sensors for them to operate effectively in a welding environment.

This paper presents some aspects of the research activities on sensors for robots, developed in the Instituto de Automatica Industrial (IAI) during the last years. The demand for greater precision in the measurement of distances using US techniques, makes it necessary to compensate for alterations in the sound‐speed in the interposed medium due to temperature. In this paper we will describe a method for calculating this correction in order that no other external sensor be necessary. The method is based on the resonance‐frequency variation of the piezoelectric elements with temperature. The accuracy reached with this correction is higher than normally would be needed in robotics applications.

Analyses the effects of temperature on the behaviour of ultrasonic sensors applied to object recognition. Describes the measurement system employed, the environmental considerations of ultrasonic sensors and looks at the most commonly used parameters to analyse the echo form in the recognition process. Evaluates the performance of the parametric model of the ultrasonic echo signal with a comparative study of different ultrasonic transducers.

To examine aging Puerto Ricans’ experiences with and perceptions of depression treatment.

In-depth analysis of eight exemplary cases from ethnographic interviews with a subsample of 16 aging Puerto Ricans in the Boston area who are part of the Boston Puerto Rican Health Study.

The results show that respondents were resistant to accepting pharmacological treatment for their depression, and they often characterized antidepressants as “dope.” Moreover, they claimed that in addition to their health problems, social stressors such as financial strain, lack of jobs, housing problems, and social isolation are triggering or contributing to their depression. Because of this, they express reluctance in accepting clinical treatment only, and suggest that broader social issues and other health needs ought to be addressed as part of an effective treatment. For many, pharmacological treatment is acceptable only in the more severe forms of depression.

These results have important implications for improving the quality of depression treatment and reducing health disparities for mainland Puerto Ricans.

Even though recent studies continue to show a high frequency of depression among Puerto Ricans, issues of treatment quality are still understudied and ethnographic accounts are especially lacking. Our study offers an exploratory investigation of this unresolved research issue.

This work presents a robot prototype designed and built for a new aided fruit‐harvesting strategy in highly unstructured environments, involving human‐machine task distribution. The operator drives the robotic harvester and performs the detection of fruits by means of a laser range‐finder, the computer performs the precise location of the fruits, computes adequate picking sequences and controls the motion of all the mechanical components (picking arm and gripper‐cutter). Throughout this work, the specific design of every module of the robotized fruit harvester is presented. The harvester has been built and laboratory tests with artificial trees were conducted to check range‐finder’s localization accuracy and dependence on external conditions, harvesting arm’s velocity, positioning accuracy and repeatability; and gripper‐cutter performance. Results show excellent range‐finder and harvesting arm operation, while a bottleneck is detected in gripper‐cutter performance. Some figures showing overall performance are given.

This study aims to investigate the influences of global uncertainty indicators volatility on the domestic socioeconomic and environmental vulnerability in a sample of 54 developing countries.

The two-step system generalized method of moments estimator is recruited to deal with autoregression and endogeneity matter in our dynamic panel data. Seven different global uncertainty indicators (US trade uncertainty; world trade uncertainty; economic policy uncertainty; world commodities and oil prices; the geopolitical risk index and the world uncertainty index) have been mobilized and compared for their empirical impact on the economic (growth and GDP), social (the misery index and income inequality) and environmental (CO₂ emissions) vulnerabilities of nations.

Our empirical estimations suggest that the socioeconomic and environmental vulnerability cannot be solved through the same pattern: all decrease of a particular aspect will necessarily have a cost and an opposite influence on at least one of the other aspects of the nations' vulnerability.

The originality of this article is to combine these three dimensions of vulnerability in the same investigation. To our knowledge, our research is one of the few providing a joint analysis of the influence of global uncertainty on the economic and socioenvironmental countries' vulnerabilities – given the fact social, economic and environmental aspects are at the heart of the UN sustainable goals, our study can be seen as an investigation of the nations' capabilities to work proactively on meaningful sustainable goals in an increasingly uncertain world.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

This chapter provides preliminary evidence to show that financial accounting students would use generative artificial intelligence (AI) tools to improve their learning if given the opportunity to do so by their instructors. Most students who completed the exercises we used in the study did so diligently and modified their answers after using a generative AI tool in a manner that suggests beneficial effects. It appears that the more prior knowledge a student had about the subject matter, the more beneficial was the experience. Pitfalls still exist, however. For example, students without knowledge of the subject matter struggled with crafting queries and judging the efficacy of their answers. Moreover, although a minority, some students tended to duplicate their original answers without utilizing the responses generated by the generative AI tool. Additionally, certain students merely copied the answers generated by the AI tool without providing any additional critique or analysis. Implications for teaching and learning and opportunities for future research are discussed.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

This chapter presents a new conceptual framework of institutional research (IR). The framework refines previously studied dimensions of IR and integrates them into the higher order concept of knowledge management. Previously studied dimensions of IR include the institution’s organizational sectors (e.g., academic, human resources), the functions for which information is used (e.g., operations, strategic management), and the resources supporting IR (e.g., technology, funding). The framework innovates by specifying what competencies are required to carry out IR activities and how to assign a level of development to each competency. This operationalization permits the creation of an assessment tool enabling us to move from general and intuitive statements about development to specific and behavioral levels which are actionable. The framework formulation was validated with a group of IR experts in Chile. The framework can be used to assess one institution, to compare an institution to a peer group, or to compare groups of institutions at the regional, national, or international levels.