In vitro and in vivo parameters for identification of landrace pigs with low reproductive performance

In pig farming, measurements of production parameters play a fundamental role in the success of the activity. Minimal differences in fertility between breeders can lead to less reproductive efficiency and, less productivity. However, assessing the fertility of each male and the early identification of subfertile males is a difficult task to be performed. Thus, the aim of this study was to evaluate the use of in vitro and in vivo parameters in the identification of subfertile males of the Landrace breed, aiming to collaborate with genetic improvement programs, routine optimization in the Genetic Diffusion Units (GDUs) and the results of performance. In experiment 1, an approach to identify males with subfertility was evaluated based on retrospective data. For this, the results (averages of birth rates, number of total births and average percentages of female and male piglets per litter) were evaluated for a total of 996 matings and 847 parturitions. The inseminations came from ejaculates of 32 males, who had at least 19 females inseminated with homospermic doses in the concentration of 2.5 x 109 total sperm from the same male. As for the birth rate (BR), an average of 85.47% ± 6.05 was observed with a group of median males, seven males that stood out and one individual (M32) with a performance of 58.06% ± 9.0. For the total number of piglets born (PB) the average was 13.41 ± 0.56, with three males with better performance and one (M32) with very poor performance (8.62 ± 0.59). In experiment 2, it was verified whether evaluations of inseminating doses (ID) of semen in vitro (motility and sperm morphology) after 96 hours of storage had correlations with fertility in vivo, which can be used to identify subfertile males. The evaluations were performed on 30 ejaculates regarding the means of BR and PB, considering only those who had at least 7 females inseminated. There were no correlations between the motility assessments and semen morphological changes and the reproductive parameters evaluated. The results obtained in vivo, referring to BR and PB, demonstrated that it was possible to identify differences between males, the individual (M32) had the worst results for the percentages of BR and PB. It is concluded that there are males of high and low fertility and that only the in vitro analyzes carried out in this study are not enough to categorize them, however, the evaluation of retrospective data was efficient for this purpose.

Dante's Gluttons

Dante’s Gluttons: Food and Society from the Convivio to the Comedy explores how in his work medieval Italian poet Dante Alighieri (1265-1321) uses food to articulate, reinforce, criticize, and correct the social, political, and cultural values of his time. Combining medieval history, food studies, and literary criticism, Dante’s Gluttons historicizes food and eating in Dante, beginning in his earliest collected poetry and arriving at the end of his major work. For Dante, the consumption of food is not a frivolity, but a crux of life in the most profound sense of the term, and gluttony is the abdication of civic and spiritual responsibility and a danger to the individual body and soul as well as to the collective. This book establishes how one of the world’s preeminent authors uses the intimacy and universality of food as a touchstone, communicating through a gastronomic language rooted in the deeply human relationship with material sustenance.

Fiction Reading and Empathy Capacity of Selected Filipino Adolescents and Young Adults

This study aimed to discover if adolescents’ and young adults’ empathy capacity is higher when they engage more in fiction reading. A total of 301 students, aged 16-22, completed a self-administered questionnaire. More than half (56%) of the respondents consider reading as a hobby with books as the preferred reading material. Around 38% have moderate fiction reading scores and around 77% have high empathy capacity scores. Findings showed that the older the respondent is, the less likely they would read fiction and the lower their empathy level. Females are more likely to read fiction and are more empathic than males. Also, results revealed that the more the individual reads fiction, the more empathic they can become. Home and school interventions can be created to increase opportunities and desire for reading fiction and enhancing empathy capacity.

Smart Healthcare Security Device on Medical IoT Using Raspberry Pi

This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.

AIgean : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters

AIgean , pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos , for multi-FPGA deployment, and hls4ml , for generating ML kernels synthesizable using Vivado HLS. AIgean provides a full end-to-end multi-FPGA/CPU implementation of a neural network. The user supplies a high-level neural network description, and our tool flow is responsible for the synthesizing of the individual layers, partitioning layers across different nodes, as well as the bridging and routing required for these layers to communicate. If the user is an expert in a particular domain and would like to tinker with the implementation details of the neural network, we define a flexible implementation stack for ML that includes the layers of Algorithms, Cluster Deployment & Communication, and Hardware. This allows the user to modify specific layers of abstraction without having to worry about components outside of their area of expertise, highlighting the modularity of AIgean . We demonstrate the effectiveness of AIgean with two use cases: an autoencoder, and ResNet-50 running across 10 and 12 FPGAs. AIgean leverages the FPGA’s strength in low-latency computing, as our implementations target batch-1 implementations.

Early Design Space Exploration Framework for Memristive Crossbar Arrays

For memristive crossbar arrays, currently, no high-level design validation and early space exploration tools exist in the literature. Such tools are essential to quickly verify the design functionality as well as compare design alternatives in terms of power and performance. In this work, we propose a VHDL-based framework that enables us to quickly perform behavioral simulation as well as estimate dynamic energy consumption and speed of any large memristive crossbar array. We propose a high-level (VHDL) model of a memristor based on which crossbar architectures can be modeled. The individual memristor model is embedded with power and delay numbers obtained from a detailed memristor model. We demonstrate the framework for MAGIC-style memristive crossbars. We validate the framework against detailed Verilog-A based model on fifteen combinational benchmarks. For the single row model, we obtained 153x simulation speedup over HSPICE, average estimation errors of 6.64% and 0% for dynamic energy consumption and cycle-time, respectively. For the transpose model, we obtained average estimation errors of 5.51% and 10.90% for dynamic energy consumption and cycle-time, respectively. We also extend our framework to support another prominent logic style and validate through a case study. The proposed framework can be easily extended to other emerging technologies.

Personalized and Explainable Employee Training Course Recommendations: A Bayesian Variational Approach

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this article, we present a focused study on the explainable personalized online course recommender system for enhancing employee training and development. Specifically, we first propose a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN), to jointly model both the employees’ current competencies and their career development preferences in an explainable way. In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Furthermore, for handling the employees with sparse or missing skill profiles, we develop an improved version of DCBVN, called the Demand-aware Collaborative Competency Attentive Network (DCCAN) framework , by considering the connectivity among employees. In DCCAN, we first build two employee competency graphs from learning and working aspects. Then, we design a graph-attentive network and a multi-head integration mechanism to infer one’s competency information from her neighborhood employees. Finally, we can generate explainable recommendation results based on the competency representations. Extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of both of our frameworks, as well as their robustness on sparse and cold-start scenarios.

Chronic Patients’ Emotions toward Self-managing Care IT

Patients’ emotions toward health IT can play an important role in explaining their usage of it. One form of health IT is self-managing care IT, such as activity trackers that can be used by chronic patients to adopt a healthy lifestyle. The goal of this study is to understand the factors that influence the arousal of emotions in chronic patients while using these tools. Past studies, in general, tend to emphasize how IT shapes emotions, underplaying the role of the individual user’s identity and, specifically, how central health is to the user’s self in shaping emotions. In this research, the authors argue that patients’ health identity centrality (i.e., the extent to which they consider health as central to their sense of self) can play an important role in forming their dependence on health IT by affecting their use of it directly and shaping their emotions around it.

A Survey of Automated Programming Hint Generation: The HINTS Framework

Automated tutoring systems offer the flexibility and scalability necessary to facilitate the provision of high-quality and universally accessible programming education. To realise the potential of these systems, recent work has proposed a diverse range of techniques for automatically generating feedback in the form of hints to assist students with programming exercises. This article integrates these apparently disparate approaches into a coherent whole. Specifically, it emphasises that all hint techniques can be understood as a series of simpler components with similar properties. Using this insight, it presents a simple framework for describing such techniques, the Hint Iteration by Narrow-down and Transformation Steps framework, and surveys recent work in the context of this framework. Findings from this survey include that (1) hint techniques share similar properties, which can be used to visualise them together, (2) the individual steps of hint techniques should be considered when designing and evaluating hint systems, (3) more work is required to develop and improve evaluation methods, and (4) interesting relationships, such as the link between automated hints and data-driven evaluation, should be further investigated. Ultimately, this article aims to facilitate the development, extension, and comparison of automated programming hint techniques to maximise their educational potential.