Physical soil attributes of Conilon coffee (Coffea canephora) under organic and conventional management systems.

Coffea sp. is cultivated in large areas, using both conventional and organic management. However, information about the sustainability of these two management systems is still deficient. The objective of the present study was to evaluate the physical properties of soil cultivated with Conilon coffee (C. canephora) under organic and conventional management. Two areas cultivated with Conilon coffee (under organic and conventional management) and a fragment of Atlantic forest, used as a reference, were selected for the experiment. Soil granulometry, hydraulic conductivity, water retention curve, resistance to penetration, porosity, optimal hydric interval, and other physical characteristics were measured at depths of 0 to 10 and 10 to 20 cm. The data was submitted to multivariate and descriptive statistical analyses. Higher similarity was observed between the soil cultivated with Conilon coffee under organic management and the Atlantic forest soil. Soil resistance to penetration at 10, 30, 100, 500 and 1500 kPa, macro porosity, density and total porosity were the main physical properties that differentiated both management systems studied. The non-use of agricultural machinery and the addition of organic matter may be the main reasons for higher soil sustainability observed under organic management when compared with the conventional system.

Analytics and Methods for Logistic System Design and Operations Management

Analytics is the technology working with the manipulation of data to produce information able to change the world we live every day. Analytics have been largely used within the last decade to cluster people’s behaviour to predict their preferences of items to buy, music to listen, movies to watch and even electoral preference. The most advanced companies succeded in controlling people’s behaviour using analytics. Despite the evidence of the super-power of analytics, they are rarely applied to the big data collected within supply chain systems (i.e. distribution network, storage systems and production plants). This PhD thesis explores the fourth research paradigm (i.e. the generation of knowledge from data) applied to supply chain system design and operations management. An ontology defining the entities and the metrics of supply chain systems is used to design data structures for data collection in supply chain systems. The consistency of this data is provided by mathematical demonstrations inspired by the factory physics theory. The availability, quantity and quality of the data within these data structures define different decision patterns. Ten decision patterns are identified, and validated on-field, to address ten different class of design and control problems in the field of supply chain systems research.

Psychological characterization of hypertensive patients: associations with adherence to pharmacological treatment and self-management

Introduction: The role of psychosocial factors in the onset and progression of essential hypertension has been object of a large body of literature, yet findings appear to be controversial. Aims: We assessed the predictive role of psychosomatic syndromes, affective symptomatology, psychological reactance, psychological distress, well-being and quality of life on adherence to antihypertensive medications, lifestyle behaviors, hypertension severity and absolute cardiovascular risk grading, as well as their temporal stability at 1-year follow-up, in a sample of hypertensive patients. In addition, we aimed to validate the Italian version of the Hong Psychological Reactance Scale (HPRS). Methods: Eighty consecutive hypertensive outpatients treated with antihypertensive medications were compared to 80 controls. Psychosocial variables were assessed using clinical interviews and self-rating questionnaires at baseline and at 1-year follow-up. Cardiac parameters were also collected. One-hundred and fifty individuals from general population provided data for the HPRS validation. Results: Hypertensive patients reported significantly higher levels of psychological distress and lower levels of psychological well-being at baseline compared to controls. Among hypertensive patients, allostatic overload (AO) was the most frequently reported psychosomatic syndrome at baseline. Patients with AO displayed significantly greater levels of psychological distress and lower levels of well-being and quality of life than those without. Further, patients with illness denial were significantly more likely to report poor adherence to pharmacological treatment and, as well as those with higher levels of affective symptomatology, were less likely to follow a balanced diet. At follow-up, patients displayed significantly higher levels of well-being and lower levels of stress, mental pain and quality of life. Conclusions: Findings suggest the clinical relevance of psychosocial factors and psychosomatic syndromes in the progression of hypertension, with important implications for its management. As to the Italian validation of the HPRS, results support previous findings, even though a confirmatory factor analysis should be carried out.

Data-driven and data-oriented methods for materials science and technologies

The discovery of new materials and their functions has always been a fundamental component of technological progress. Nowadays, the quest for new materials is stronger than ever: sustainability, medicine, robotics and electronics are all key assets which depend on the ability to create specifically tailored materials. However, designing materials with desired properties is a difficult task, and the complexity of the discipline makes it difficult to identify general criteria. While scientists developed a set of best practices (often based on experience and expertise), this is still a trial-and-error process. This becomes even more complex when dealing with advanced functional materials. Their properties depend on structural and morphological features, which in turn depend on fabrication procedures and environment, and subtle alterations leads to dramatically different results. Because of this, materials modeling and design is one of the most prolific research fields. Many techniques and instruments are continuously developed to enable new possibilities, both in the experimental and computational realms. Scientists strive to enforce cutting-edge technologies in order to make progress. However, the field is strongly affected by unorganized file management, proliferation of custom data formats and storage procedures, both in experimental and computational research. Results are difficult to find, interpret and re-use, and a huge amount of time is spent interpreting and re-organizing data. This also strongly limit the application of data-driven and machine learning techniques. This work introduces possible solutions to the problems described above. Specifically, it talks about developing features for specific classes of advanced materials and use them to train machine learning models and accelerate computational predictions for molecular compounds; developing method for organizing non homogeneous materials data; automate the process of using devices simulations to train machine learning models; dealing with scattered experimental data and use them to discover new patterns.

Internet of healthcare (law): privacy and data protection aspects in an internet of everything

The purpose of this research study is to discuss privacy and data protection-related regulatory and compliance challenges posed by digital transformation in healthcare in the wake of the COVID-19 pandemic. The public health crisis accelerated the development of patient-centred remote/hybrid healthcare delivery models that make increased use of telehealth services and related digital solutions. The large-scale uptake of IoT-enabled medical devices and wellness applications, and the offering of healthcare services via healthcare platforms (online doctor marketplaces) have catalysed these developments. However, the use of new enabling technologies (IoT, AI) and the platformisation of healthcare pose complex challenges to the protection of patient’s privacy and personal data. This happens at a time when the EU is drawing up a new regulatory landscape for the use of data and digital technologies. Against this background, the study presents an interdisciplinary (normative and technology-oriented) critical assessment on how the new regulatory framework may affect privacy and data protection requirements regarding the deployment and use of Internet of Health Things (hardware) devices and interconnected software (AI systems). The study also assesses key privacy and data protection challenges that affect healthcare platforms (online doctor marketplaces) in their offering of video API-enabled teleconsultation services and their (anticipated) integration into the European Health Data Space. The overall conclusion of the study is that regulatory deficiencies may create integrity risks for the protection of privacy and personal data in telehealth due to uncertainties about the proper interplay, legal effects and effectiveness of (existing and proposed) EU legislation. The proliferation of normative measures may increase compliance costs, hinder innovation and ultimately, deprive European patients from state-of-the-art digital health technologies, which is paradoxically, the opposite of what the EU plans to achieve.

Dysphagia Progression And Swallowing Management In Parkinson's Disease: An Observational Study.

Dysphagia is relatively common in individuals with neurological disorders. To describe the swallowing management and investigate associated factors with swallowing in a case series of patients with Parkinson's disease. It is a long-term study with 24 patients. The patients were observed in a five-year period (2006-2011). They underwent Fiberoptic Endoscopic Evaluation of Swallowing, Functional Oral Intake Scale and therapeutic intervention every three months. In the therapeutic intervention they received orientation about exercises to improve swallowing. The Chi-square, Kruskal-Wallis and Fisher's tests were used. The period of time for improvement or worsening of swallowing was described by Kaplan-Meier analysis. During the follow-up, ten patients improved, five stayed the same and nine worsened their swallowing functionality. The median time for improvement was ten months. Prior to the worsening there was a median time of 33 months of follow-up. There was no associated factor with improvement or worsening of swallowing. The maneuvers frequently indicated in therapeutic intervention were: chin-tuck, bolus consistency, bolus effect, strengthening-tongue, multiple swallows and vocal exercises. The swallowing management was characterized by swallowing assessment every three months with indication of compensatory and rehabilitation maneuvers, aiming to maintain the oral feeding without risks. There was no associated factor with swallowing functionality in this case series.

Crystallization, data collection and data processing of maltose-binding protein (MalE) from the phytopathogen Xanthomonas axonopodis pv. citri

Maltose-binding protein is the periplasmic component of the ABC transporter responsible for the uptake of maltose/maltodextrins. The Xanthomonas axonopodis pv. citri maltose-binding protein MalE has been crystallized at 293 Kusing the hanging-drop vapour-diffusion method. The crystal belonged to the primitive hexagonal space group P6(1)22, with unit-cell parameters a = 123.59, b = 123.59, c = 304.20 angstrom, and contained two molecules in the asymetric unit. It diffracted to 2.24 angstrom resolution.

Greenhouse gas emissions from alternative futures of deforestation and agricultural management in the southern Amazon

The Brazilian Amazon is one of the most rapidly developing agricultural areas in the world and represents a potentially large future source of greenhouse gases from land clearing and subsequent agricultural management. In an integrated approach, we estimate the greenhouse gas dynamics of natural ecosystems and agricultural ecosystems after clearing in the context of a future climate. We examine scenarios of deforestation and postclearing land use to estimate the future (2006-2050) impacts on carbon dioxide (CO(2)), methane (CH(4)), and nitrous oxide (N(2)O) emissions from the agricultural frontier state of Mato Grosso, using a process-based biogeochemistry model, the Terrestrial Ecosystems Model (TEM). We estimate a net emission of greenhouse gases from Mato Grosso, ranging from 2.8 to 15.9 Pg CO(2)-equivalents (CO(2)-e) from 2006 to 2050. Deforestation is the largest source of greenhouse gas emissions over this period, but land uses following clearing account for a substantial portion (24-49%) of the net greenhouse gas budget. Due to land-cover and land-use change, there is a small foregone carbon sequestration of 0.2-0.4 Pg CO(2)-e by natural forests and cerrado between 2006 and 2050. Both deforestation and future land-use management play important roles in the net greenhouse gas emissions of this frontier, suggesting that both should be considered in emissions policies. We find that avoided deforestation remains the best strategy for minimizing future greenhouse gas emissions from Mato Grosso.

Relationships between human resource dimensions and environmental management in companies: proposal of a model

This work deals with the main contributions of human resource dimensions for the environmental management in a company. While the specialized literature concerning the technical aspects of environmental management expands, there is a gap in the bibliography: integrated approaches between human resource dimensions and environmental management. An extensive bibliographical review was undertaken in order to systematize the human resource dimensions and their contributions concerning the effectiveness of the environmental management system. A model that analyses the relationships between these dimensions and the typical phases of an environmental management system is presented, within a perspective of application for academicians and managers. (c) 2006 Elsevier Ltd. All rights reserved.

EFFECT OF THERMAL TREATMENT ON PHENOLIC COMPOUNDS AND FUNCTIONALITY LINKED TO TYPE 2 DIABETES AND HYPERTENSION MANAGEMENT OF PERUVIAN AND BRAZILIAN BEAN CULTIVARS (PHASEOLUS VULGARIS L.) USING IN VITRO METHODS

The effect of thermal treatment on phenolic compounds and type 2 diabetes functionality linked to alpha-glucosidase and alpha-amylase inhibition and hypertension relevant angiotensin I-converting enzyme (ACE) inhibition were investigated in selected bean (Phaseolus vulgaris L,) cultivars from Peru and Brazil using in vitro models. Thermal processing by autoclaving decreased the total phenolic content in all cultivars, whereas the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity-linked antioxidant activity increased among Peruvian cultivars, alpha-Amylase and alpha-glucosidase inhibitory activities were reduced significantly after heat treatment (73-94% and 8-52%, respectively), whereas ACE inhibitory activity was enhanced (9-15%). Specific phenolic acids such as chlorogenic and caffeic acid increased moderately following thermal treatment (2-16% and 5-35%, respectively). No correlation was found between phenolic contents and functionality associated to antidiabetes and antihypertension potential, indicating that non phenolic compounds may be involved. Thermally processed bean cultivars are interesting sources of phenolic acids linked to high antioxidant activity and show potential for hypertension prevention.

Making Fedora easier to implement with Fez: A free open source content model and workflow management front-end to Fedora

The University of Queensland, Australia has developed Fez, a world-leading user-interface and management system for Fedora-based institutional repositories, which bridges the gap between a repository and users. Christiaan Kortekaas, Andrew Bennett and Keith Webster will review this open source software that gives institutions the power to create a comprehensive repository solution without the hassle..