Análisis del concepto de liderazgo y sus aplicaciones en el contexto empresarial

El presente trabajo se realizó con el objetivo de tener una visión completa de las teorías del liderazgo, teniendo de este una concepción como proceso y poder examinar las diversas formas de aplicación en las organizaciones contemporáneas. El tema es enfocado desde la perspectiva organizacional, un mundo igualmente complejo, sin desconocer su importancia en otros ámbitos como la educación, la política o la dirección del estado. Su enfoque tiene que ver con el estudio académico del cual es la culminación y se enmarca dentro de la perspectiva constitucional de la Carta Política Colombiana que reconoce la importancia capital que tienen la actividad económica y la iniciativa privada en la constitución de empresas. Las diversas visiones del liderazgo han sido aplicadas de distintas maneras en las organizaciones contemporáneas y han generado diversos resultados. Hoy, no es posible pensar en una organización que no haya definido su forma de liderazgo y en consecuencia, confluyen en el campo empresarial multitud de teorías, sin que pueda afirmarse que una sola de ellas permita el manejo adecuado y el cumplimiento de los objetivos misionales. Por esta razón se ha llegado a concebir el liderazgo como una función compleja, en un mundo donde las organizaciones mismas se caracterizan no solo por la complejidad de sus acciones y de su conformación, sino también porque esta característica pertenece también al mundo de la globalización. Las organizaciones concebidas como máquinas que en sentido metafórico logran reconstituirse sus estructuras a medida que están en interacción con otras en el mundo globalizado. Adaptarse a las cambiantes circunstancias hace de las organizaciones conglomerados en permanente dinámica y evolución. En este ámbito puede decirse que el liderazgo es también complejo y que es el liderazgo transformacional el que más se acerca al sentido de la complejidad.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality fig-ure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge rep-resentation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases re-trieval one. On the other hand, and aiming at an improvement of the CBR theo-retical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were re-written, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of one`s confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

Chest Breadths to Predict Individuals' Age - A Case Based View

It is well known that rib cage dimensions depend on the gender and vary with the age of the individual. Under this setting it is therefore possible to assume that a computational approach to the problem may be thought out and, consequently, this work will focus on the development of an Artificial Intelligence grounded decision support system to predict individual’s age, based on such measurements. On the one hand, using some basic image processing techniques it were extracted such descriptions from chest X-rays (i.e., its maximum width and height). On the other hand, the computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters for the handling of incomplete, unknown, or even contradictory information. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process. The accuracy of the proposed model is satisfactory, close to 90%.

An Assessment of Pharmacological Properties of Schinus Essential Oils: A Soft Computing Approach

Plants of genus Schinus are native South America and introduced in Mediterranean countries, a long time ago. Some Schinus species have been used in folk medicine, and Essential Oils of Schinus spp. (EOs) have been reported as having antimicrobial, anti-tumoural and anti-inflammatory properties. Such assets are related with the EOs chemical composition that depends largely on the species, the geographic and climatic region, and on the part of the plants used. Considering the difficulty to infer the pharmacological properties of EOs of Schinus species without a hard experimental setting, this work will focus on the development of an Artificial Intelligence grounded Decision Support System to predict pharmacological properties of Schinus EOs. The computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters to the handling of incomplete, unknown, or even self-contradictory information. New clustering methods centered on an analysis of attribute’s similarities were used to distinguish and aggregate historical data according to the context under which it was added to the Case Base, therefore enhancing the prediction process.

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process.

Advanced decision-support methods for the design, control, and optimization of perishable products life cycle

In the last decades, global food supply chains had to deal with the increasing awareness of the stakeholders and consumers about safety, quality, and sustainability. In order to address these new challenges for food supply chain systems, an integrated approach to design, control, and optimize product life cycle is required. Therefore, it is essential to introduce new models, methods, and decision-support platforms tailored to perishable products. This thesis aims to provide novel practice-ready decision-support models and methods to optimize the logistics of food items with an integrated and interdisciplinary approach. It proposes a comprehensive review of the main peculiarities of perishable products and the environmental stresses accelerating their quality decay. Then, it focuses on top-down strategies to optimize the supply chain system from the strategical to the operational decision level. Based on the criticality of the environmental conditions, the dissertation evaluates the main long-term logistics investment strategies to preserve products quality. Several models and methods are proposed to optimize the logistics decisions to enhance the sustainability of the supply chain system while guaranteeing adequate food preservation. The models and methods proposed in this dissertation promote a climate-driven approach integrating climate conditions and their consequences on the quality decay of products in innovative models supporting the logistics decisions. Given the uncertain nature of the environmental stresses affecting the product life cycle, an original stochastic model and solving method are proposed to support practitioners in controlling and optimizing the supply chain systems when facing uncertain scenarios. The application of the proposed decision-support methods to real case studies proved their effectiveness in increasing the sustainability of the perishable product life cycle. The dissertation also presents an industry application of a global food supply chain system, further demonstrating how the proposed models and tools can be integrated to provide significant savings and sustainability improvements.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Innovation Diffusion and Biostimulant knowledge in Moroccan agriculture

Our study focused on Morocco investigating the dissemination of PBs amongst farmers belonging to the first pillar of the GMP, located in the Fès-Meknès region. As well as to assess how innovation adoption is influenced by the network of relationships that various farmers are involved in. We adopted an “ego network” approach to identify the primary stakeholders responsible for the diffusion of PBs. We collected data through “face-to-face” interviews with 80 farmers in April and May 2021. The data were processed with the aim of: 1) analysing the total number of main and specific topics discussed between egos and egos’ alters regarding the variation of some egos attributes; 2) analysing egos’ network characteristics using E-Net software, and 3) identifying the significant variables that influence farmers to access knowledge, use and reuse of PBs a Binary Logistic Regression (LR) was applied. The first result disclosed that the main PBs topics discussed were technical positioning, the need to use PBs, knowledge of PBs, and organic PBs. We noted that farmers have specific features: they have a high school diploma and a bachelor's degree; they are specialised in fruits and cereals farming, and they are managers and members of a professional organisation. The second result showed results of SNA: 1) PBs seem to become generally a common argument for farmers who have already exchanged fertiliser information with their alters; 2) we disclosed a moderate heterogeneity in the networks, farmers have access to information mainly from acquaintances and professionals, and 3) we revealed that networks have a relatively low density and alters are not tightly connected to each other. Farmers have a brokerage position in the networks controlling the flow of information about the PBs. LR revealed that both the farmers’ attributes and the networks’ characteristics influence growers to know, use and reuse PBs.

Monitoring of Mediterranean marine benthic biodiversity through novel, standardized, and integrated approaches

Marine healthy ecosystems support life on Earth and human well-being thanks to their biodiversity, which is proven to decline mainly due to anthropogenic stressors. Monitoring how marine biodiversity changes trough space and time is needed to properly define and enroll effective actions towards habitat conservation and preservation. This is particularly needed in those areas that are very rich in species compared to their low surface extension and are characterized by strong anthropic pressures, such as the Mediterranean Sea. Subtidal rocky benthic Mediterranean habitats have a complex structural architecture, hosting a panoply of tiny organisms (cryptofauna) that inhabit crevices and caves, but that are still unknown. Different artificial standardized sampling structures (SSS) and methods have been developed and employed to characterize the cryptofauna, allowing for data replicability and comparability across regions. Organisms growing on these artificial structures can be identified coupling morphological taxonomy and DNA barcoding and metabarcoding. The metabarcoding allows for the identification of organisms in a bulk sample without morphological analysis, and it is based on comparing the genetic similarities of the assessed organisms with barcoding sequences present in online barcoding repositories. Nevertheless, barcoded species nowadays represent only a small portion of known species, and barcoding reference databases are not always curated and updated on a regular basis. In this Thesis I used an integrative approach to characterize benthic marine biodiversity, specifically coupling morphological and molecular techniques with the employment of SSS. Moreover, I upgraded the actual status of COI (cytochrome c oxidase subunit I) barcoding of marine metazoans, and I built a customized COI barcoding reference database for metabarcoding studies on temperate biogenic reefs. This work implemented the knowledge about diversity of Mediterranean marine communities, laying the groundworks for monitoring marine and environmental changes that will occur in the next future as consequences of anthropic and climate threats.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.

The River And The Sea: Fieldwork In Human Ecology And Ethnobiology.

This article is a commentary on the experiences that motivated my decision to become a human ecologist and ethnobiologist. These experiences include the pleasure of studying and of having the sense of being within nature, as well as the curiosity towards understanding the world and minds of local people. In particular, such understanding could be driven by addressing the challenging questions that originate in the interactions of such individuals with their natural surroundings. I have been particularly interested in the sea and the riverine forests that are inhabited by coastal or riverine small-scale fishers. Sharing the distinctive world of these fishers enjoyably incited my curiosity and challenged me to understand why fishers and their families 'do as they do' for their livelihoods including their beliefs. This challenge involved understanding the rationality (or the arguments or views) that underlies the decisions these individuals make in their interaction with nature. This curiosity was fundamental to my career choice, as were a number of reading interests. These reading interests included political economy and philosophy; evolution and sociobiology; evolutionary, human, and cultural ecology; cultural transmission; fisheries; local knowledge; ecological economics; and, naturally, ethnobiology.

Monte Carlo Simulation Of The Response Functions Of Cdte Detectors To Be Applied In X-ray Spectroscopy.

In this work, the energy response functions of a CdTe detector were obtained by Monte Carlo (MC) simulation in the energy range from 5 to 160keV, using the PENELOPE code. In the response calculations the carrier transport features and the detector resolution were included. The computed energy response function was validated through comparison with experimental results obtained with (241)Am and (152)Eu sources. In order to investigate the influence of the correction by the detector response at diagnostic energy range, x-ray spectra were measured using a CdTe detector (model XR-100T, Amptek), and then corrected by the energy response of the detector using the stripping procedure. Results showed that the CdTe exhibits good energy response at low energies (below 40keV), showing only small distortions on the measured spectra. For energies below about 80keV, the contribution of the escape of Cd- and Te-K x-rays produce significant distortions on the measured x-ray spectra. For higher energies, the most important correction is the detector efficiency and the carrier trapping effects. The results showed that, after correction by the energy response, the measured spectra are in good agreement with those provided by a theoretical model of the literature. Finally, our results showed that the detailed knowledge of the response function and a proper correction procedure are fundamental for achieving more accurate spectra from which quality parameters (i.e., half-value layer and homogeneity coefficient) can be determined.

Evaluation Of Characteristic-to-total Spectrum Ratio: Comparison Between Experimental And A Semi-empirical Model.

Primary X-ray spectra were measured in the range of 80-150kV in order to validate a computer program based on a semiempirical model. The ratio between the characteristic and total air Kerma was considered to compare computed results and experimental data. Results show that the experimental spectra have higher first HVL and mean energy than the calculated ones. The ratios between the characteristic and total air Kerma for calculated spectra are in good agreement with experimental results for all filtrations used.

Association of insulin resistance and GLP-2 secretion in obesity: a pilot study

OBJECTIVE: The objective of this pilot study was to determine whether glugagon-like peptide 2 (GLP-2) secretion relates to insulin sensitivity (IS) in obese subjects. SUBJECTS AND METHODS: Twenty four obese subjects [body mass index (BMI) 40.0 ± 3.0 kg/m² (mean ± standard deviation)] were included, nine of which were male, age 43 ± 8 years. Twelve subjects had type 2 diabetes, all treated with oral anti-diabetic agents only. The subjects were submitted to standard meal tolerance test (MTT) for dosage of the curves: glucose, insulin, and GLP-2. Insulin sensitivity was measured by HOMA-IR, and OGIS was derived from the MTT. Spearman linear correlations and partial correlations were obtained. RESULTS: There was an inverse relationship between the GLP-2 secretion and IS: HOMA-IR correlated with GLP-2 AUC (R = 0.504; p = 0.012), and OGIS correlated with GLP-2 incremental AUC (R = -0.54; p = 0.054). The correlation persisted after controlling for BMI. CONCLUSION: We found an association of GLP-2 secretion and insulin resistance (IR). The understanding of the underlying mechanisms may provide future directions in the pharmacological manipulation of incretins, and in the treatment of obesity and related metabolic disorders.

A estimulação da inteligencia corporal cinestesica no contexto na educação fisica escolar

Universidade Estadual de Campinas. Faculdade de Educação Física