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\"This prosopography is a dictionary of all the characters and personal names found in the Epigrams of Marcus Valerius Martialis, containing nearly 1,000 comprehensive entries. Each of them compiles and analyses all the relevant information regarding the characters themselves, as well as the literary implications of their presence in Martial's poems. It is an indispensable tool for researchers and studients of Ancient History and Latin Literature.\"--Back cover.

An important NK-cell inhibition with reduced TNF-α, IFN-γ and TLR2 expression had previously been identified in patients with diffuse cutaneous leishmaniasis (DCL) infected with Leishmania mexicana. In an attempt to pinpoint alterations in the signaling pathways responsible for the NK-cell dysfunction in patients with DCL, this study aimed at identifying differences in the NK-cell response towards Leishmania mexicana lipophosphoglycan (LPG) between patients with localized and diffuse cutaneous leishmaniasis through gene expression profiling. Our results indicate that important genes involved in the innate immune response to Leishmania are down-regulated in NK cells from DCL patients, particularly TLR and JAK/STAT signaling pathways. This down-regulation showed to be independent of LPG stimulation. The study sheds new light for understanding the mechanisms that undermine the correct effector functions of NK cells in patients with diffuse cutaneous leishmaniasis contributing to a better understanding of the pathobiology of leishmaniasis.

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.

The understanding of pro-sustainable behavior and its true economic implications is an important subject for tourism destination marketers and policymakers, especially given that limited research has focused on the economic implications of tourist preferences for more sustainable destinations. Following the identification of three different demand segments using the concept of “sustainable intelligence” (level of commitment, attitude, knowledge and/or behavior with regard to sustainability), this study hypothesizes that the tourist segment with high level of “sustainable intelligence” (called “pro-sustainable tourist”) is willing to pay more to visit a more sustainable destination. The main aim of this paper is to use the logistic regression model to estimate the premium price that each segment is willing to pay to visit a sustainable destination. This paper reports the result of a willingness to pay study using data from 1118 respondents visiting the Western Costa del Sol (Andalusia, Spain), a mature sun-and-sand destination that is currently facing several developmental challenges supposedly associated with sustainability. The results obtained from this research study indicate that the tourist segment with high levels of “sustainable intelligence” is willing to pay more to visit a more sustainable tourism destination. However, there is little willingness to pay if the destination’s commitment to sustainability increases the price of the tourism product (26.6% of respondents).

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

Lidar (a type of airborne laser scanning) provides a powerful technique for three-dimensional mapping of topographic features. It is proving to be a valuable tool in archaeology, particularly where the remains of structures may be hidden beneath forest canopies. Canuto et al. present lidar data covering more than 2000 square kilometers of lowland Guatemala, which encompasses ancient settlements of the Classic Maya civilization (see the Perspective by Ford and Horn). The data yielded population estimates, measures of agricultural intensification, and evidence of investment in landscape-transforming infrastructure. The findings indicate that this Lowland Maya society was a regionally interconnected network of densely populated and defended cities, which were sustained by an array of agricultural practices that optimized land productivity and the interactions between rural and urban communities. Science , this issue p. eaau0137 ; see also p. 1313 Lidar data elucidate the demography, agriculture, and political economy of Classic Lowland Maya civilization. Lowland Maya civilization flourished in the tropical region of the Yucatan peninsula and environs for more than 2500 years (~1000 BCE to 1500 CE). Known for its sophistication in writing, art, architecture, astronomy, and mathematics, Maya civilization still poses questions about the nature of its cities and surrounding populations because of its location in an inaccessible forest. In 2016, an aerial lidar survey across 2144 square kilometers of northern Guatemala mapped natural terrain and archaeological features over several distinct areas. We present results from these data, revealing interconnected urban settlement and landscapes with extensive infrastructural development. Studied through a joint international effort of interdisciplinary teams sharing protocols, this lidar survey compels a reevaluation of Maya demography, agriculture, and political economy and suggests future avenues of field research.

Faba beans are highly nutritious because of their high protein content: they are a good source of mineral nutrients, vitamins, and numerous bioactive compounds. Equally important is the contribution of faba bean in maintaining the sustainability of agricultural systems, as it is highly efficient in the symbiotic fixation of atmospheric nitrogen. This article provides an overview of factors influencing faba bean yield and quality, and addresses the main biotic and abiotic constraints. It also reviews the factors relating to the availability of genetic material and the agronomic features of faba bean production that contribute to high yield and the improvement of European cropping systems. Emphasis is to the importance of using new high-yielding cultivars that are characterized by a high protein content, low antinutritional compound content, and resistance to biotic and abiotic stresses. New cultivars should combine several of these characteristics if an increased and more stable production of faba bean in specific agroecological zones is to be achieved. Considering that climate change is also gradually affecting many European regions, it is imperative to breed elite cultivars that feature a higher abiotic-biotic stress resistance and nutritional value than currently used cultivars. Improved agronomical practices for faba bean crops, such as crop establishment and plant density, fertilization and irrigation regime, weed, pest and disease management, harvesting time, and harvesting practices are also addressed, since they play a crucial role in both the production and quality of faba bean.

A persistent challenge in digital asset management is the lack of standardized models for integrating health assessment—such as the Asset Health Index (AHI)—into Digital Twins, limiting their extended implementation beyond individual projects. Asset managers in the energy sector face challenges of digitalization such as digital environment selection, employed digital modules (absence of an architecture guide) and their interconnection, sources of data, and how to automate the assessment and provide the results in a friendly decision support system. Thus, for energy systems, the integration of Asset Assessment in virtual replicas by Digital Twins is a complete way of asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. Another challenge in this context is how to compound in a structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator easy to interpret as a level of risk. This paper tries to serve as a guide against these digital and structured assessments to integrate AHI methodologies into Digital Twins for energy converters. First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. This model has been developed fulfilling standardized practices of asset digitalization as the Reference Architecture Model for Industry 4.0 (RAMI 4.0), organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the data model for cloud Digital Twin implementation, which is deployed on Microsoft Azure Architecture using native Internet of Things (IoT) and analytics services to enable automated and real-time AHI calculation. This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. In the case study, AHI-based monitoring reduced unplanned failures by 43% and improved maintenance planning accuracy by over 30%.

Background For many nursing students, clinical training represents a stressful experience. The levels of stress and anxiety may vary during students’ educational training, depending on their ability to adopt behavioral strategies for coping with stress, and other factors. This study aimed to investigate the relationship between anxiety, perceived stress, and the coping strategies used by nursing students during their clinical training. Methods A cross-sectional correlational descriptive study. The sample consisted of 190 nursing students enrolled in the Nursing Faculty of Ciudad Real University in Spain. Participants provided data on background characteristics and completed the following instruments: the Perceived Stress Scale; the State-Trait Anxiety Inventory and the Coping Behavior Inventory. Relationships between scores were examined using Spearman’s rho. Results The mean age of participants was 20.71 ± 3.89 years (range 18–46 years). Approximately half of the students (47.92%) indicated a moderate level of stress with a mean Perceived Stress Scale score of 22.78 (±8.54). Senior nursing students perceived higher levels of stress than novice students. The results showed a significant correlation for perceived stress and state anxiety ( r  = 0.463, p  < .000) and also for trait anxiety ( r  = 0.718, p  < .000). There was also a significant relationship between the total amount of perceived stress and the following domains of the coping behavior inventory: problem solving ( r  = −.452, p  < .01), self-criticism ( r  = .408 p  < .01), wishful thinking ( r  = .459, p  < .01), social support( r  = −.220, p  < .01), cognitive restructuring ( r  = −.375, p  < .01), and social withdrawal ( r  = .388, p  < .01). In the current study, the coping strategy most frequently used by students was problem-solving, followed by social support and cognitive restructuring. Conclusions Nursing students in our study presented a moderate level of stress, in addition there was a significant correlation with anxiety. Nursing teachers and clinical preceptors/mentors should be encouraged to develop programs to help prepare nursing students to cope with the challenges they are about to face during their clinical placements.