Background: Cleft lip and palate can affect the development of the maxilla, which may create a midfacial deficiency as well as an interference of the facial growth pattern and dentofacial esthetics. Objective: Estimate the chronological age of complete fusion of the spheno-occipital synchondrosis in cleft lip and/or palate patient´s and a control group, using cone beam computed tomography images. Methods: In this retrospective study, 125 patients were enrolled (cleft lip and/or palate group (n=91); control group (n=34)). Age comparison was made with Chi-square test and a Kaplan-Meyer analysis determined the median time to reach complete fusion of the spheno-occipital synchondrosis (p<0.05). Results: The experimental group showed statistically significant differences in the median time for complete ossification between males and females (p=0.019). No statistically significant differences were found in the control group between males and females (p=0.104). The median time for complete ossification of the spheno-occipital synchondrosis was, for males, 15.0 years in both groups; for females, it was 14.0 years in the experimental group and 13.0 years in the control group. Conclusions: The present study showed no differences in the ossification of the spheno-occipital synchondrosis between individuals with and without cleft lip and/or palate.

Aim: To analyze a single center's experience with Short Bowel Syndrome/ Chronic Intestinal Failure (SBS/CIF) in adult patients treated with Home Parenteral Nutrition (HPN). Materials and methods: In the last thirty years, 13 patients with SBS/CIF were included in an HPN program. Results: Between 1992 and 2023, 13 patients were included in an HPN program. The primary underlying pathology was acute mesenteric ischemia. The median age of the starting HPN was 44 years old. Most are submitted to several surgeries of extensive intestinal resection with posterior intestinal reconstruction. Five of the 13 patients have died while on HPN for a median of 42 months. The causes of death HPN-related were catheter sepsis, endocarditis with cardiac failure, or hepatic failure. One patient died due to underlying pathology: pelvic abscesses and bleeding related to radiotherapy. Eight patients remain alive with a median time of HPN of 173 months. During the HPN support, the most frequent complications were venous catheter infection and venous territory thrombosis. None of the eight patients alive have hepatic failure. Two patients recently started teduglutide, with good tolerance, and need reduction of HPN support. All these eight patients have a satisfactory quality of life (parenteral support needs range between five to two nutrition bags per week). Conclusion: HPN remains the gold standard of SBS/CIF treatment, although Teduglutide may reduce HPN needs and HPN complications and provide a better quality of life. An HPN multidisciplinary healthcare group support is essential to ensure these patients' survival and quality of life. Keywords Short bowel Syndrome, Chronic Intestinal failure, Home Parenteral Nutrition, Teduglutide, Complications of Parenteral Support.

In this paper, we introduce the bicomplex Tetranacci and Tetranacci-Lucas quaternions. Moreover, we present Binet's formulas, generating functions, and the summation formulas for those bicomplex quaternions.

Background - Compared to the da Vinci platform, there is limited experience with the Hugo RAS platform for colorectal surgery in Europe. This difference is especially notable when considering a single complex procedure, such as total colectomy. Aim - To demonstrate the feasibility and safety of using the Hugo RAS for assisted total colectomy. Clinical case- Female patient: eighteen-year-old with Familial Adenomatous Polyposis (FAP). Her IBM was 19. She underwent a total colectomy with ileorectal anastomosis using the Hugo RAS platform. The procedure took 230 minutes and had no complications. The postoperative period was uneventful. She was discharged from the hospital on the third postoperative day. Conclusion - The Hugo RAS platform is one of the emergent minimally invasive robotic platforms that can be used even in total colectomy if the proper patient selection is made. The placement choice of arms and trocars was crucial to obtain a similar operative time of the single laparoscopic approach. The certification of Hugo’s new instruments, such as energy devices and staplers, will make this platform even more competitive.

Ménière's disease (MD) has an unclear cause. The microvascular dysregulation of the inner ear has been increasingly pointed as a potential contributor. This study investigates the prevalence of cardiovascular risk factors (CVRFs) in MD patients compared to those with posterior circulation cerebral infarction (POCI). CVRFs like hypertension, diabetes, dyslipidemia, obesity, coronary heart disease, and smoking were assessed in both MD and POCI patients. Brain MRI identified POCI etiology as "small vessel occlusion" (SVO) or "other etiology" (OE). The study included 64 MD and 84 POCI patients. Compared to MD, POCI OE showed a higher prevalence of CVRFs across various age groups, including hypertension, diabetes, dyslipidemia, and smoking. Notably, the odds of having POCI OE were significantly higher for individuals with hypertension and smoking. On the other hand, POCI SVO showed similar prevalence of CVRFs compared to MD. This study reveals no significant difference in CVRF prevalence between MD and smaller vessel POCI. However, a clear distinction emerges when comparing MD to POCI with involvement of larger blood vessels. Further research is needed to confirm these findings and explore potential shared risk factors between POCI (SVO) and MD.

In this paper, we define Tetranacci and Tetranacci-Lucas matrix sequences and investigate their properties.

Advanced composite materials have drawn significant interest in the last years as an alternative to traditional materials due to their higher performance. However, industry struggle to provide low-cost, higher occupational safety, lower footprint with composites, making them suitable for holistic analyses. Therefore, the material design becomes an essential element that can impact the competitiveness, particularly in terms of productivity, circularity, safety, sustainability, and quality of the value chain. The Material Design-for-eXcellence is a state-of-art methodology for material performance multi-dimensional assessment along its life cycle phases, either useful to support ma-terial selection for new products or also to new material design support optimizing resource effi-ciency. In this methodology, the material behaviour and its multiple characteristics assessment, and the manufacturing processes efficiency are evaluated. The framework considers the analogy of product design holistic approaches, as Lean Design-for-X, to organize and assess the mul-ti-dimensional performance for each “X” Material Property. In this work, it was possible to observe that the bio-based composites solutions could be a good sustainable alternative for several sectors. Every day researchers are creating more new materials, having a diversity of properties at different scales, Material Design-for-eXcellence must also in the near future consider other factors. Hence, additional studies are thus foreseen to explore and develop this new tool.

In this paper, we introduce hybrid numbers with Fibonacci and Lucas hybrid number coefficients. We give the Binet formulas, generating functions, exponential generating functions for these numbers. Then we define an associate matrix for these numbers. In addition, using this matrix, we present two different versions of Cassini identitiy of these numbers.

In this paper, by using the q-integer, we introduce a new generalization of Fibonacci and Lucas sedenions called q-Fibonacci and q-Lucas sedenions. We present some fundamental properties of these type of sedenions such as Binet formulas, exponential generating fuctions, summation formulas, Catalan's identity, Cassini's identity and d'Ocagne's identity.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Net CO2 emissions and sequestration from European forests are the result of removal and growth of flora. To arrive at aggregated measurements of these processes at a country's level, local observations of increments and harvest rates are up-scaled to national forest areas. Each country releases these statistics through their individual National Forest Inventory using their particular definitions and methodologies. In addition, five international processes deal with the harmonization and comparability of such forest datasets in Europe, namely the IPCC, SOEF, FAOSTAT, HPFFRE, FRA (definitions follow in the article). In this study, we retrieved living biomass dynamics from each of these sources for 27 European Union member states. To demonstrate the reproducibility of our method, we release an open source python package that allows for automated data retrieval and analysis, as new data becomes available. The comparison of the published values shows discrepancies in the magnitude of forest biomass changes for several countries. In some cases, the direction of these changes also differ between sources. The scarcity of the data provided, along with the low spatial resolution, forbids the creation or calibration of a pan-European forest dynamics model, which could ultimately be used to simulate future scenarios and support policy decisions. To attain these goals, an improvement in forest data availability and harmonization is needed.

High-throughput screening technologies continues to produce large amounts of multiomics data from different populations and cell types for various diseases, such as cancer. However, analysis of such data encounters difficulties due to cancer heterogeneity, further exacerbated by human biological complexity and genomic variability. There is a need to redefine the drug discovery development pipeline, bringing an Artificial Intelligence (AI)-powered informational view that integrates relevant biological information and explores new ways to develop effective anticancer approaches. Here, we show SynPred, an interdisciplinary approach that leverages specifically designed ensembles of AI-algorithms, links omics and biophysical traits to predict synergistic anticancer drug synergy. SynPred exhibits state-of-the-art performance metrics: accuracy – 0.85, precision – 0.77, recall – 0.75, AUROC – 0.82, and F1-score - 0.76 in an independent test set. Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application available online at http://www.moreiralab.com/resources/synpred/ was constructed to predict synergistic anticancer drug combinations requiring only the upload of the two drug SMILES to be tested, allowing easy access by non-expert researchers.

This work aims to produce a cartographic model that identifies areas with a greater probability of finding karstic caves - the endokarst potential - in the northern sector of Santo António Plateau (Estremadura Limestone Massif, Central Portugal). Geological, topographic, hydrogeological, and vegetation cover data were collected, processed, and integrated into a spatial database using a geographic information system (GIS). The location of known cave entrances in the study area was also identified from the records of local public institutions and speleological teams. Four conditioning factors were extracted from the collected data, including lithostratigraphic units, fracture density, relief energy, and land cover. In a multi-criteria decision-making analysis framework, each previously chosen conditioning factor and respective classes/categories were weighted using the analytic hierarchy process (AHP). With an agreement of 81.9%, the results of the cartographic model constructed in GIS seem to be promising, considering that the entrances of the known caves are mainly located in areas classified as having high to very high endokarst potential. This prototype of the endokarst potential map for the study area can be used in strategic and operational environmental planning (at least on a local scale), as it can assist decision-makers, competent authorities, and local speleological teams in a more accurate and thoughtful definition of the areas that should be investigated, providing a substantial reduction in times and costs field prospecting.

Spontaneity and its connections to mental health and wellbeing are a central issue in both theory and practice of psychodrama, and the specialized literature sometimes even associates the presence of pathology to a lack of spontaneity. This paper describes spontaneity in Moreno's theory, its assessment, its association with other measures and concepts, and scientific advances in the field. Specifically, we present results obtained with the English and Portuguese-language versions concerning its assessment. The main purpose of this work was to study the spontaneity measure (SAI-R) in a 3 first-order factors version for a Portuguese sample of 719 subjects, divided into two independent samples. The participants were aged 18 to 69. The mean age of participants was 28.5 years (SD = 7.51), and ages ranged between 18 and 69 years. Data was collected through an online platform of a Portuguese market research company. The results revealed that the 3-factor model has acceptable validity when items 1 (“Creative”) and 7 (“Euphoric”), confirming the composite reliability, the convergent validity, but not the discriminant validity. Through multigroup analysis, the model showed to be stable. Notwithstanding the need for complementary studies, including clinical samples, the SAI-R is a short and valid instrument in clinical and non-clinical contexts when evaluating spontaneity.

Multi-receiver encryption enables a sender to encrypt a message and transmit the ciphertext to a set of authorized users, while no one out this group of authorized users can decrypt the message. Multi-receiver encryption is of great importance in many sectors such as broadcast communication, cloud computing, wireless communications, networking applications, e-voting, lottery, and medical applications. This paper proposes an efficient multi-receiver public key encryption scheme based on Lucas sequences. The results of the computational analysis show that, projected scheme is better against renown attacks and prevailing anonymous multireceiver algorithms

Changes brought about by the Internet to Scholarly Communication and the spread of Open Access movement, have made it possible to increase the number of potential readers of published research dramatically. This two-phase study aims, at first, to assert the satisfaction of the potential for increased open access to articles published by authors at the University of Coimbra, in a context when there was no stimulus for the openness of published science other than an institutional mandate set by the University policy on Open Access (“Acesso Livre”). The satisfaction of the access openness was measured by observing the actual archiving behavior of researchers (either directly or through their agents). We started by selecting the top journal titles used to publish the STEM research of the University of Coimbra (2004-2013) by using Thomson Reuters’ Science Citation Index (SCI). These titles were available at the University libraries or through online subscriptions, some of them in open access (21%). By checking the journals' policy at the time regarding self-archiving at the SHERPA/RoMEO service, we found that the percentage of articles in Open Access (OA) could rise to 80% if deposited at Estudo Geral, the Institutional Repository of the University of Coimbra, as prescribed by the Open Access Policy of the University. As we concluded by verifying the deposit status of every single paper of researchers of the University that published in those journals, this potential was far from being fulfilled, despite the existence of the institutional mandate and favorable editorial conditions. We concluded, therefore, that an institutional mandate was not sufficient by itself to fully implement an open access policy and to close the gap between publication and access. The second phase of the study, to follow, will rescan the status of published papers in a context where the Portuguese public funding agency, the Fundação para a Ciência e a Tecnologia, introduced in 2014 a new significant stimulus for open access in science. The FCT Open Access Policy stipulates that publicly funded published research must be available as soon as possible in a repository of the Portuguese network of scientific repositories, RCAAP, which integrates the Estudo Geral.

(1) Background: Hand hygiene practice by using alcohol-based hand sanitizers was generalized in all sectors of activity, due the COVID-19 pandemic. The aim of this study was to organize and analyze the scientific data regarding occupational exposure to hand sanitizers, with a particular focus on their use and health effects on workplace settings. (2) Methods: A rapid literature review was carried out searching on the main scientific database, namely: Web of Science Core Collection, Medline, Scielo and Current Content Connect. (3) Results: All studies analyzed included samples from healthcare professionals and eczema as well as irritant contact dermatitis were the most reported skin lesions. (4) Conclusions: The adequate implementation of hygiene and disinfection programs in workplaces is essential to guarantee the workers safety and health. Further research is needed on the effectiveness of contingency plans in different sectors of activity, particularly, in industry.

Soil amendments with composted organic materials are recommended to increase soil organic matter (SOM), promote soil fertility, and the Circular Economy. Growing areas of hedged olive groves in southern Iberia peninsula generate huge amounts of olive leaves whose potential as soil organic amendment is not fully studied. An experimental field trial in a hedged olive grove (cv. “Cobrançosa”) was set up near Portalegre, Portugal, to test a compost of olive leaves plus sheep manure (2:1) applied in the row, at soil surface. Three nominal rates (0 or control, 2.5 and 5.0 kg m-2) were used, in a complete randomized block setup (3 treatments, 3 replicas, 9 plots) and soil chemical properties of layers 0-5, 5-15, and 15-30 cm deep were annually monitored. Results (mainly in layer 0-5 cm) showed increases in total N, carbon of the particulate organic matter (POM-C), permanganate oxidizable carbon (POX-C), extractable phosphorus, and zinc, after one year, and increases in soil organic carbon (SOC), C-stock, pHKCl, extractable phosphorus, and zinc, after two years. POX-C was the most sensitive SOM-related indicator, showing increases up to 30 cm deep after one year. This compost improved soil fertility, but should be monitored over longer periods of time, namely for SOC and extractable zinc content.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

Laser powder bed fusion (LPBF) is an additive manufacturing process employed in many industries, for example for aerospace, automotive and medical applications. In these sectors, mainly nickel-, aluminum- and titanium-based alloys are used. In contrast, the mechanical engineering industry is interested in more wear-resistant steel alloys with higher hardness, both of which can be achieved with a higher carbon content, like in high-speed steels. Since these steels are susceptible to cracking, preheating needs to be applied during processing by LPBF. In a previous study, we applied a base plate preheating temperature of 500 °C for HS6-5-3-8 with 1.3 % carbon content. We were able to manufacture dense (p > 99.9 %) and crack-free parts from HS6-5-3-8 with a hardness > 62 HRC (as built) by LPBF. In this study, we investigate the influence of preheating temperatures up to 600 °C on hardness and microstructure dependent on part height for HS6-5-3-8. The microstructure was studied by light optical microscopy (LOM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The analysis of hardness and microstructure at different part heights is necessary because state-of-the-art preheating systems induce heat only into the base plate. Consequently, parts are subjected to temperature gradients and different heat treatment effects depending on part height during the LPBF process.

The purpose of this work is to describe and explain the characteristics of pedagogy as a science of education, as an object, concepts and research method. The methodology assumed was the review of the history of pedagogy, especially the classics of pedagogy, those who built it with their experiences in laboratory schools. Here are listed the main concepts of pedagogy, its laws and principles; she establishes the opposition that exists between teaching-learning and education as formation of the personality; she also delimits the fields of the pedagogical method, of didactics or teaching-learning, and the methodology of educational labor that organizes the process and the educational context, the regime and the lifestyle, such as those experienced in the "colonies" or "communes" of children and adolescents, organized and directed by Neill and Makarenko. Since the present article does not derive from a primary source or from field work, as an empirical or experimental research, the results constitute the content of the article, as conclusions.

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

In this paper, inspired by recent articles of G. Anatriello and G. Vincenzi (see \cite{Ana}), and G. Cerda-Morales (see \cite{Cer1,Cer2}), we will introduce the third-order $\overline{h}$-Jacobsthal and third-order $\overline{h}$-Jacobsthal--Lucas sequences and their associated quaternions. The new results that we have obtained extend most of those obtained in \cite{Cer2}.

Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

In this paper, we define and investigate modified p-Leonardo, p-Leonardo-Lucas and p-Leonardo sequences as special cases of the generalized Leonardo sequence. We present Binet's formulas, generating functions, Simson formulas, and the summation formulas for these sequences. Moreover, we give some identities and matrices related with these sequences. Furthermore, we show that there are close relations between modified p-Leonardo, p-Leonardo-Lucas, p-Leonardo numbers and Fibonacci, Lucas numbers.

There is a restricted knowledge about the potential impact of the use of different wood species on color and anthocyanin changes during the red wine aging process. This lack of knowledge is even greater when no oak wood species are used. Thus, the aim of this study was to carry out a comparative analysis of the impact of wood chip extracts from oak, acacia and cherry species on the chromatic characteristics and anthocyanins changes by the use of model wine solutions. In this context, several methodologies were used to quantified, color and anthocyanins changes during the aging time studied. The results indicated that the contact between wood chip extracts and grape skin isolated anthocyanin extracts induced a decrease of color intensity, particularly red color, and also the anthocyanin content in the different experimental model wine solutions studied. All chromatic modifications are potentially detected by human eyes because ΔE values were much higher than 3 CIELab units. These tendencies seems to be independent of the wood species used, but more pronounced for higher contact time between wood chip extracts and anthocyanins. The obtained results may contribute to a better understanding of the chromatic changes of red wines when aged in contact with different wood chips species.

Considering the importance of assessing the impacts resulting from a sport career, this study aimed at developing and validating a semi-structure interview guide suitable for quantifying the socio-demographic and epidemiological profile of former professional football players. Based on the theoretical frameworks and several methodological procedures, an interview guide was de-veloped, consisting of 3 areas of conceptual organization (A1. Biographical data; A2. Career data; and A3. Post-Career Transition data) and 8 data collection categories (C1. Personal data; C2. Pro-fessional data; C3. Socio-demographic pathway in the post-career; epidemiological pathway; C4. Epidemiological pathway; C5. Moment of leaving the career; C6. Socio-demographic pathway in post-career; C7. Epidemiological pathway in the post-career; and C8. Perceptions on post-career planning). The construction and validation of the interview guide following 4 steps: Ad hoc con-struction of the interview guide (step 1); Review of the interview guide by a panel of experts (step 2); Definition of procedures and protocol for the application of the interview (step 3); Application of the pilot study for face validation of the interview guide (step 4). Thus, in procedural terms, four stages were considered for the construction and validation of the interview guide, namely the Ad hoc construction of the interview guide (i), the review of the in-terview guide by a panel of experts (ii), definition of procedures and protocol for the application of the interview (iii), and the application of the pilot study for the face validation of the interview guide (iv). The results showed that the interview guide for the study of the impacts of the sports careers on the quality of life of Portuguese former football players shows reliability for the collec-tion of qualitative and quantitative information from the respective content analysis. The use of the interview guide characterizes the path of former player, providing information and knowledge on the socio-demographic and epidemiological impact factors resulting from their post-career (sports retirement).

Using a humanistic and qualitative approach, the present study aims to (1) bring light on 1 the impact of clown doctors’ artistic work in pediatric oncology and, as a consequence, (2) contribute 2 to the refinement and improvement of the clown doctors’ intervention quality, in the context of 3 pediatric oncology, bringing hypotheses of reflection hardly met with quantitative approaches. We 4 are interested in the subjective experience of the artists, and the perceived subjective experience of 5 the child as reported by the clown doctors. The present analysis was developed from the clown 6 doctors’ final reports, their narrative, after visiting the pediatric oncology ward in a Portuguese public 7 hospital, for a continuous period of six months. The visits were performed by a clown doctor dyad, 8 and the audience was a young adolescent girl, with cancer, and her constant mother.9

Augmented and Virtual Reality have been experiencing a rapidly growth in recent years, but there is not still a deep knowledge on their capabilities and where they could be explored. In that sense, this paper presents a study on the accuracy and repeatability of the Microsoft's HoloLens 2 (Augmented Reality device) and HTC Vive (Virtual Reality device) using an OptiTrack system as ground truth. For the HoloLens 2, the method used was hand tracking, while in HTC Vive, the object tracked was the system's hand controller. A series of tests in different scenarios and situations were performed to explore what could influence the measures. The HTC Vive obtained results in the millimetre scale, while the HoloLens 2 revealed not so accurate measures (around 2 centimetres). Although the difference can seem to be considerable, the fact that HoloLens 2 was tracking the user's hand and not an inherit controller made a huge impact. The results were considered a significant step for the on going project of developing a human-robot interface to program by demonstration an industrial robot using Extended Reality, which shows great potential to succeed based on this data.

This paper presents a simple drone survey and image processing approach to identifying St. John's wort (Hypericum perforatum) on grazing land in the Hunter Valley NSW, Australia. St John’s wort is an invasive species (weed) that competes with pasture, poisons livestock, can downgrade wool with ‘vegetable fault’, and decreases property values. Identifying the locations of St John wort from the ground can be difficult due to topography, limited access, and/or larger land areas of mixed vegetation. In this study, a drone was used to survey a 174-ha grazing property in the Hunter Valley NSW (Australia). The images were stitched together using commercially available software. A unique Visual Atmospheric Resistance Index (VARI) attribute was identified and used to highlight the presence of St John’s wort in the survey area. These sub-areas were then digitised onto high resolution maps for future planning use by land managers.

This prospective observational cohort pilot study included 22 cats diagnosed with partial traumatic brachial plexus injury (PTBPI), aiming to explore the response of an early intensive neurorehabilitation protocol in a clinical setting. This protocol included functional electrical stimulation (FES), locomotor treadmill training and kinesiotherapy exercises, starting at the time with highest probability of nerve repair. The synergetic benefits of this multimodal ap-proach were based on the potential structural and protective role of proteins and release of neurotrophic factors. Furthermore, parametrization of FES was according to the presence or absence of deep pain. Results have shown that 72.6% (16/22) cats achieved ambulation, with 9 cats within 15 days, 2 cats until 30 days and 5 cats until 60 days. During the 4 years follow-up, there was evidence of improvement on both muscle mass and muscle weakness, in addition to the disappearance of neuropathic pain. Notably, after the 60 days of neurorehabilitation, 3 cats improved ambulation after arthrodesis of the carpus. Thus, early rehabilitation, with FES ap-plied at the first weeks after injury and accurate parametrization according to the deep pain perception, is suggestive to help in functional recovery and ambulation, reducing probability of amputation.

If environmental exposures are shown to cause an adverse health outcome, reducing exposure should reduce the disease risk. Links between exposures and outcomes are typically based on ‘associations’ derived from observational studies, and causality may not be clear. Randomised controlled trials to ‘prove’ causality are often not feasible or ethical. Here the history of evidence that tobacco smoking causes lung cancer – in observational studies – is compared to that of low sun exposure and/or low vitamin D status as causal risk factors for the autoimmune disease, multiple sclerosis. Evidence derives from in vitro and animal studies, as well as ecological, case-control and cohort studies, in order of increasing strength. For smoking and lung cancer, the associations are strong, consistent, and biologically plausible – the evidence is coherent or ‘in harmony’. For low sun exposure/vitamin D as risk factors for MS, the evidence is weaker, with smaller effect sizes, but coherent across a range of sources of evidence, and biologically plausible. The association is less direct – smoking is directly toxic and carcinogenic to the lung, but sun exposure/vitamin D modulate the immune system, which in turn may reduce the risk of immune attack on self-proteins in the central nervous system. Opinion about whether there is sufficient evidence to conclude that low sun exposure/vitamin D increase the risk of multiple sclerosis, is divided. General public health advice to receive sufficient sun exposure to avoid vitamin D deficiency (<50nmol/L) should also ensure any benefits for multiple sclerosis.

There is now evidence that, based on cytokine profiles, bipolar disorder (BD) is accompanied by simultaneous activation of the immune-inflammatory response system (IRS) and the compensatory immune-regulatory system (CIRS), and that both components may be associated with the staging of illness. Nevertheless, no BD studies have evaluated the IRS/CIRS ratio using CD (cluster of differentiation) molecules expressed by peripheral blood activated T effector (Teff) and T regulatory (Treg) subpopulations. This study examined T cell subsets both before and after ex vivo anti CD3/CD28 stimulation using flow cytometric immunophenotyping in 25 euthymic BD patients and 21 healthy controls as well as human cytomegalovirus (HCMV)-specific IgG antibodies. BD is associated with a significantly lowered frequency of baseline (unstimulated) CD3+CD8+CD71+ and CD4+CD25+FOXP3 and increased CD4+CD25+FOXP3+CD152+ frequencies and with lowered stimulated frequencies of CD3+CD8+CD71+, CD4+CD25+FOXP3+CD152+ and CD4+CD25+FOXP3+GARP cells and, consequently, by an increased stimulated Teff/Treg ratio. Moreover, the number of manic, but not hypomanic or depressive episodes, is significantly and negatively associated with the stimulated proportions of CD3+CD4+CD154+, and CD69+ and CD71+ expression on CD4+ and CD8+ cells, while duration of illness (≥ 10 years) is accompanied by a depleted frequency of stimulated CD152+ Treg, and CD154+ and CD71+ CD4+ T cells. BD and anti-human cytomegalovirus (HCMV) IgG levels significantly interact to decrease the expression of CD4+CD25+FOXP+GARP T phenotypes. In conclusion, BD is characterized by deficits in immune-regulatory functions while the staging of illness is characterized by additional impairments is Teff and Treg activation. HCMV seropositivity may contribute to an immune-risk phenotype associated with BD.

In this paper, we study sums of finite products of Chebyshev polynomials of the first kind and Lucas polynomials and represent each of them in terms of Chebyshev polynomials of all kinds. Here the coefficients involve terminating hypergeometric functions ₂F₁ and these representations are obtained by explicit computations.

The alien invasive insect pest Spodoptera frugiperda Smith (Lepidoptera: Noctuidae), commonly referred to as Fall Armyworm (FAW), is a polyphagous insect feeding on more than 350 host plants in addition to maize in its native habitat. Due to the voracious nature of FAW, significant yield losses on maize production were reported across the African continent since its detection in 2016. Despite being a polyphagous insect, little is known about its alternative host plants in the new habitat including Mozambique. This study aimed to assess the host range of FAW in the central province of Manica, Mozambique. A field survey was conducted from May to August 2019 (dry season of 2018/2019 cropping season) and in December 2019 and January 2020 (rainy season of 2019/2020 cropping season) in maize fields and crops often mixed with maize or located in the vicinity of maize fields. A total of 1291 fields were surveyed. In each field, 20 plants were selected in a “W” pattern and checked for the presence of FAW egg masses and/or larvae. At the time of the sampling, no evidence was found suggesting that in Manica province FAW feeds in crops other than maize because out of 35 different crops surveyed, FAW was only recorded on maize. Results from this study suggest that the strain of FAW occurring in Manica province might be the one specialized in maize or the continuous availability of maize fields throughout the year is influencing the choices of the host plants of this invasive insect pest.

A large set of undrained compression triaxial tests was carried out on different types of cohesionless soils, from sands to silty-sands and silts. Shear wave velocity measurements were carried out alongside. These tests exhibited distinct state transitions ranging from flow liquefaction to strain softening or strain hardening. With the purpose of defining a framework to assess soil liquefaction, it was found that the ratio between the shear wave velocity (VS0) and the peak undrained deviatoric stress, qpeak, VS0/qpeak, could be accurately used to define a boundary between liquefaction and strain hardening for sands, and between strain softening and strain hardening for silty-sands and silts. Since this ratio is a function of the tested material, it was also discovered that the prediction of these boundaries could be made as a function of soil grading, namely via the coefficient of uniformity, CU. Despite not being regarded as a strong geomechanical parameter, CU is easily determined from a grain-size distribution test and it has an empirically proven correlation with critical state parameters.

Incorporating renewables in the power grid presents challenges for stability, reliability, and operational efficiency. Integrating Energy Storage Systems (ESS) offers a solution by managing unpredictable loads, enhancing reliability, and serving the grid. Hybrid storage solutions have gained attention for specific applications, suggesting higher performance in some respects. This paper compares the performance of hybrid energy storage systems (HESS) to single battery, evaluating their energy supply cost and environmental impact through optimization problems. The model generates an optimized dispatch, minimizing cost or environmental impact of supplying energy to a generic load while considering battery degradation. Seven technologies are assessed, with an example applied to an industrial site contrasting vanadium redox flow (VRFB) and lithium batteries considering the demand of a local load (building). The results also indicates that efficiency and degradation curves have the highest impact in the final costs and environmental functions on the various storage technologies assessed. For the simulations of the example case, a single system only outperforms the hybrid system in cases where lithium efficiency is higher than approximately 87% and vanadium is lower approximately 82%.

This article presents an in-depth review of Virtual Power Plant (VPP), its organization in the energy system and its prospects in the face of the promising future of the increasing bidirectional complexity exposed in the current scenario. A new paradigm for the operations of modern power distribution and transmission systems requires greater grid flexibility that is accompanying an extensive change in the structure of electricity markets, the fruit of the development of the renewable industry and of the growing photovoltaic systems popularized, but in contrast due to the intermittent nature cause variable uncertainties in the power system. Innovative concepts like VPP are becoming a reality, establishing an efficient and effective mechanism. The objectives and components are described in a comprehensive way, and some of the most important are pointed out and presented in detail to contribute with a description of the energy systems and the implicit research needed for sustainability and resilience in the eminent energy scenario with this technology. In addition, the literature and studies of this technology already indicate a direction of this new tool as a promising solution to manage the uncertainties of the renewable energies.

Earth is a complex material with a mechanical and physical behavior that differs from modern building materials. The conservation of rammed earth (RE) constructions presents specific difficulties that are challenging to overcome. The lack of knowledge about RE due to its abandonment for decades and attempts to adopt materials and repair methodologies designed for new building products has led to inadequate interventions. In the case of historic defensive constructions, the doubts and technical difficulties are even greater due to the nature of the so called military RE (with physical and mechanical characteristics significantly different from those of civil, more common, RE) and, not least, due to the historical and cultural heritage value of these constructions. Some important interventions have been carried out recently and others are underway or in the planning stage, and there is a constant lack of information and technical data on the best ways to intervene. To fill this gap, the state of conservation of the RE defensive structures and the results of interventions carried out throughout history in the southwest of the Iberian Peninsula are being assessed. This article sets out the framework of the subject, identifies the material and construction techniques, and recognizes the main causes of its deterioration and decay. With special focus on the most frequent damages detected in historic military defensive structures built with RE, it analyses and discuss the most common techniques that have been used for the repair and conservation of these particular structures.

The application of optimization techniques to improve the performance of polymer processing technologies is of great practical consequence, since it may result in significant savings of materials and energy resources, assist recycling schemes and generate products with better properties. The present review aims at identifying and discussing the most important characteristics of polymer processing optimization problems in terms of the nature of the objective function, optimization algorithm, and process modelling approach that is used to evaluate the solutions and the parameters to optimize. Taking into account the research efforts developed so far, it is shown that several optimization methodologies can be applied to polymer processing with good results, without demanding important computational requirements. Also, within the field of artificial intelligence, several approaches can be reach significant success. The first part of this review demonstrated the advantages of the optimization approach in polymer processing, discussed some concepts on multi-objective optimization and reported the application of optimization methodologies in single and twin screw extruders, extrusion dies and calibrators. This second part focus on injection molding, blow molding and thermoforming technologies.

Given the global economic and societal importance of the polymer industry, the continuous search for improvements in the various processing techniques is of practical primordial importance. This review evaluates the application of optimization methodologies to the main polymer processing operations. The most important characteristics related with the usage of optimization techniques, such as the nature of the objective function, the type of optimization algorithm, the modelling approach used to evaluate the solutions, and the parameters to optimize, are discussed. The aim is to identify the most important features of an optimization system for polymer processing problems, and define the best procedure for each particular practical situation. For this purpose, a state-of-the-art of the optimization methodologies usually employed is first presented, followed by an extensive review of the literature dealing with the major processing techniques, the discussion being completed by considering both the characteristics identified and the available optimization methodologies. This first part of the review focus on extrusion, namely extruders, extrusion dies and calibrators. It is concluded that there is a set of methodologies that can be confidently applied in polymer processing, with a very good performance and without the need of demanding computation requirements.

Purpose To analyse diagnostic and therapeutic impact of molecular imaging TNM (miTNM) stage obtained with 18F-DCFPyL versus 18F-Fluorocholine in head to head comparison in biochemical recurrence (BCR) prostate cancer (PCa). Material and methods Patients with BCR PCa after radical treatment with previous 18F-Fluorocholine-PET/CT (negative or oligometastatic disease) were derived to 18F-DCFPyL-PET/CT. Patients were classified attending to: grade group, EAU classification, PSA, PSA doubling time (PSAdt) and PSA velocity (PSAvel). The overall detection rate (DR) and miTNM stage according to PROMISE criteria were assessed for both radiotracers and also correlation (Kappa). The influence of PSA and kinetics on both PET/CT (DR and miTNM) and predictive value of unfavourable kinetics on miTNM were determined. Cut-off PSA, PSAdt and PSAvel values able to predict PET/CT results were determined. Change in miTNM and treatment derived of 18F-DCFPyL information with respect to 18F-Fluorocholine was also evaluated. Results We studied 138 patients. 18F-DCFPyL showed a higher DR than 18F-Fluorocholine (64.5% vs 33.3%). 18F-DCFPyL and 18F-Fluorocholine detected T in 33.3% versus 19.6%, N in 27.5% versus 13.8%, M in 30.4% versus 8.7%. Both tracers DR showed significant associations with PSA and PSAvel. Significant association was only found between miTNM and PSA on 18F-Fluorocholine-PET/CT (p=0.033). For 18F-Fluorocholine and 18F-DCFPyL PET/CT a PSAdt cut-off of 4.09 and 5.59 months, respectively, were able to predict M stage. 18F-DCFPyL changed therapeutic management in 40/138 patients. Conclusion 18F-DCFPyL provides a higher DR and superior miTNM staging than 18F-Fluorocholine in restaging BCR, especially with high PSA and unfavourable PSA kinetics, showing a fair agreement to 18F-Fluorocholine.

In this age of mass media and, in particular, social media-driven perception of reality, coupling disease and prophylactic opinion dynamics models can provide better insights into disease evolution than using a disease model alone. We develop in this work two disease-opinion dynamics models based on the epidemiology of the new coronavirus disease (COVID-19) and the availability or not of imperfect vaccines. We assume that susceptibility to infection decreases with the level of prophylactic attitude (personal hygiene, social distancing), and changes in prophylactic attitudes of susceptible individuals occur in response to perceived disease prevalence and vaccination coverage and efficacy in the population. We derive and discuss the disease-free equilibriums and reproduction numbers in the introduced models. We further assess the impacts of the distribution of opinions at disease introduction, the ability to detect presymptomatic, asymptomatic and symptomatic positive COVID-19 cases, the behavioural responses to the outbreak and the introduction of vaccination, and the effects of distortions of disease prevalence by public policy and mass media on disease dynamics. The insights highlighted from the proposed models are expected to make informative contributions to public policy in a context of opinion fluxes in response to perceived disease prevalence.

: Rasmussen’s encephalitis (RE) stands as a rare neurological disorder marked by progressive cerebral hemiatrophy and epilepsy resistant to medical treatment. Despite extensive study, the primary cause of RE remains elusive, while its histopathological features encompass cortical inflammation, neuronal degeneration, and gliosis. The underlying molecular mechanisms driving disease progression remain largely unexplored. In this case study, we present a patient with RE who underwent hemispherectomy and has remained seizure-free for over six months, experiencing gradual motor improvement. Further-more, we conducted molecular analysis on the excised brain tissue, unveiling a decrease in the expres-sion of cell cycle-associated genes coupled with elevated levels of BDNF and TNF-α proteins. These findings suggest potential involvement of cell cycle regulators in the progression of RE.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

In this article, metallic additive manufacturing (AM) processes were classified and demonstrated. AM technology can be applied to a wide range of industrial areas because of its great feasibility in the design and manufacturing of various complex parts. The main factor that distinguishes various metallic AM processes from each other is the deposition method. Deposited metal can be imported in the form of melt or semi-solid. AM technology also covers a wide range of materials like aluminum, titanium, and many other alloys. Some of the common applications of metallic AM processes are the quick manufacturing of the parts which utilized in various industries like medical, automotive, aerospace, and jewelry industries. AM can help to fabricate parts that are impossible to be manufactured by other processes.

One of the most common solutions to access underground is through the construction of circular shafts. At preliminary design stage analytical expressions are often used to estimate the horizontal earth pressure acting on the lining. However, most of these solutions are based on active state conditions and do not consider the restrictions imposed on the shaft lateral displacements by the rigidity of its lining or by the construction method employed. To overcome this limitation a new methodology to account with the shaft lateral displacements in the determination of the horizontal earth pressure acting on circular shafts is developed and validated against the results of relevant physical scale models. The obtained results show good agreement with the measured data, reproducing adequately the reduction of the horizontal earth pressure from its initial value to an active state condition with the increase of the shaft lateral displacement. Following this approach more realistic predictions of the earth pressure acting on the lining of circular shafts can be performed.

In the present study, a numerical model was developed to predict the flow pattern inside a broiler building. The model intends to predict the velocities fields inside the domain and ammonia (NH3) emitted or released by litter from poultry housing. The numerical model developed in Computational Fluid Dynamics (CFD) commercial code, intends to represent a commercial broiler building, and intends to simulate the 3D and heat transfer, in steady state flow. The evaporative cooling pads were also included in the model. The validation of the model was based in experimental measurements obtained in previous studies. The simulations were focused on Summer, Winter and also Mid-Season situation. The numerical results of NH3 concentration were compared with the experimental measurements, and a quite good agreement was verified. The numerical results allowed the characterization of: the inside flow pattern developed for the summer and winter situation; the inside NH3 distribution, and the velocity field distribution inside the broiler building. It was found that NH3 concentration increases along the tunnel, especially in low flow rate imposed from the exhaust fan. Also, it was verified that the low velocities inside domain are no sufficient to remove the gaseous pollutants.

Scientific literature shows increased interest in the aged and the aging phenomenon. The Aging Attitudes Questionnaire - AAQ was validated for the Portuguese population to understand the importance of attitudes towards old age and their impact on the subjective well-being of the elderly. A sample of 400 subjects (from 18 to 93 years) answered a socio-demographic questionnaire, and the AAQ was composed of three subscales (psychosocial losses, physical change, and psychological growth). The CFA confirmed the tri-factorial structure with very good adjustment of the model to the data with the Cronbach alpha of the total scale scoring .84 and ranging from .65 to .77 for each factor. A total of 9 items were omitted both for poor factor loadings (<0.50. Notwithstanding, 3 items below the criteria were maintained, as they conceptually fit into the factor. Of the final 15 AAQp items, 5 belong to the Psychosocial Loss factor, 6 to Physical Change, and 4 to Psychosocial Growth. This tree factor model explained 50.1 % of the total variance. In conclusion, this study supports that AAQ has acceptable validity, confirming the composite reliability and the discriminant validity, but not the convergent validity. Through multi-group analysis, the invariance of the scale was confirmed. This validation is of pivotal importance once it allows measuring the attitudes towards aging, thus facilitating the promotion of wellbeing across the lifespan.

With the continuous growth of electric and electronic appliances’ usage, the waste produced with obsolete material (e-waste) has an increasing environmental impact. Also, the production of such appliances bears to increased consumption of natural resources and produces a multitude of toxic and hazardous substances, which typically are not properly treated. One of the approaches that may be adopted to reduce such problems relies on the circularization of the current linear model, commonly adopted in the EEE value chain. This includes recovering eol products and reintroducing its parts, components, or raw materials into the value chain (e.g. semiconductors, circuit boards, raw metals, etc.), thus contributing to a more sustainable value chain. In this article, we present a state-of-art review that focuses on approaches and solutions for the EEE value chain traceability, and analyses the technologies that may be beneficial for promoting and implementing the CE model in this value chain.

Buildings’ thermal energy needs are inherently linked to climate conditions. Consequently, it is crucial to evaluate how climate changes affect these energy demands. Despite extensive analysis, a comprehensive assessment involving a diverse range of building types has not been consistently conducted. The primary objective of this research is to perform a coherent evaluation of the influence of climate changes, construction element properties, and the Heating, Ventilation, and Air Conditioning (HVAC) system type of control on the energy requirements of six buildings (residential, services, and commercial). The buildings are considered located in a temperate Mediterranean climate. Our focus is on the year 2070, considering three distinct climatic scenarios: (i) maintaining the current climate without further changes, (ii) moderate climate changes, and (iii) extreme climate changes. The buildings are distributed across three different locations, each characterized by unique climatic conditions. Buildings’ envelope features a traditional External Thermal Insulation Composite System (ETICS) and expanded polystyrene (EPS) serves as thermal insulation material. Two critical design factors are explored: EPS thickness ranging from 0 (no insulation) to 12 cm; and horizontal external fixed shading elements varying lengths from 0 (absence) to 150 cm. Six alternative setpoint ranges are assessed for the HVAC system control: three based on the Predicted Mean Vote (PMV) and three based on indoor air temperature (Tair). Results were obtained with a validated in-home software tool. They show that, even under extreme climate conditions, the application of thermal insulation remains energetically favorable, however its relative importance diminishes as climate severity increases. Then, proper insulation design remains important for energy efficiency. The use of external shading elements for glazing (e.g., overhangs, louvers) proves beneficial in specific cases. As climate changes intensify, the significance of shading elements grows. Thus, strategic placement and design are necessary for good results. The HVAC system’s energy consumption depends on the level of thermal comfort requirements, on the climate characteristics and on the building’s type of use. As climate change severity intensifies, energy demands for cooling increase, while energy needs for heating decrease. However, it’s essential to recognize that the impact of climate changes on HVAC system energy consumption significantly depends on the type of building.

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN—Facebook and Foursquare) and on the awareness of the use of voluntary geographic information generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) & Inverse Distance Weighting (IDW) Interpolation.

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap where human factor was seen as an important source of errors and disruptions. Today the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised the awareness about the central role humans have to play in manufacturing systems, to the design of which they must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different Human dimensions associated with CPS and IoT and focuses on their conceptual evolution of automatization to improve the sociability of such automated production systems and consequently puts again the human in the loop. Hereby, our aim is to take stock of current research trends, and to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. As results, different models of sociability as way to integrate human into the broad sense and/or the development of future automated production systems, were identified from the literature and analysed.

The purpose of this article is to analyze urban form through the mapping of morphological indices, namely impervious surface fraction, building density, verticality, height/width ratio, roughness length, and porosity, to support urban planning in the city of João Pessoa, PB, in northeastern Brazil. The application of this study identifies and calculates such significant indices for the city's urban space from a Geographic Information System (GIS) model. The spatial indices play notable roles in climate at different scales, developing guidelines to maximize environmental quality, promote improvements to thermal comfort, minimize the urban heat island in the city of João Pessoa, and provide relevant data (considering microclimate aspects), guiding decisions related to the planning process.

(S)-Atenolol ((S)-2-(4-(2-Hydroxy-3-(isopropylamino)propoxy)phenyl)acetamide) has been synthesized in >99% ee with use of Candida antarctica lipase B from Syncozymes (Shanghai, China), in a kinetic resolution of the corresponding racemic chlorohydrin. Deprotonation of the starting phenol building block was base catalyzed. The enantiopurity of the chlorohydrin building block remained unchanged upon subsequent amination. All four steps in the synthesis protocol have been optimized compared to previously reported methods, which makes this new protocol more sustainable and in accordance with green chemistry principles compared to previously reported protocols.

In order to fully perform their functions and be durable, mortars for renders and plasters are requested to have a set of characteristics that can vary with the type of exposure to external environmental actions and the type of substrate. Generally, they need moderate strength, high deformability, some water protection capability, good adhesion to the substrate, and compatibility with the pre-existent materials. The presence of water and its movement inside the pore structure of the mortars are among the most significant causes of degradation. Moreover, several authors consider that the main factors for durability and good performance of lime-based mortars are mostly related with the good quality of the binder and the use of adequate aggregates. This paper intends to study the effect of ageing on the properties and durability of air lime mortars, using aggregates of different mineralogy. For this purpose, different mortars compositions are exposed to an accelerated weathering test under defined conditions. The obtained characteristics are discussed and compared with the results obtained with the same mortars tested in laboratory conditions. The effects of the aggregate type on the durability of mortars seem to be linked to their effects on the mortars porous structure.

Keywords: emerging pollutants; pharmaceuticals; plant uptake; soil behavior; soil remediation.

Filter Media (FM) sourced from recycled organic and mineral material offers a low cost and effective means of treating urban stormwater. Using recycled materials rather than from an increasingly scarce source of virgin materials (typically sandy loam soil) can ensure a sustainable long-term economy and environment. This paper presents results from the laboratory analysis and mathematical modeling to highlight the performance of recycled organic and mineral material in removing nutrients and metals from stormwater. Analysis included physical and chemical characterisation such as particle size distribution, saturated hydraulic conductivity (Ksat), bulk density, effective cation exchange capacity, and pollutant removal performance. Design mixes (DM), comprising a combination of organic and mineral materials, were characterised and used to develop/derive modelling design within the Model for Urban Stormwater Improvement Conceptualisation (MUSIC v6) [1]. Comparison is made to the Adoption Guidelines for Stormwater Biofiltration Systems - Summary Report [2] which were based on the Facility for Advancing Water Biofiltration (FAWB) guidelines to assist in the development of biofiltration systems, including the planning, design, construction and operation of those systems. An observed outcome from over two decades of biofiltration guideline development has been the exclusion of alternative biofilter materials due to claims of excessive leaching. Results from this study indicate that high nutrient and metal removal rates can be achieved over a range of hydraulic conductivities using design mixes of recycled organic and mineral materials that have a demonstrated equivalence to existing guideline specifications.

Numerous elements, such as the composition and characteristics of carbon nanomaterials, the composition and characteristics of the matrix material, moisture levels, temperature, and loading circumstances, influence the piezoresistive behaviour of self-sensing cementitious composites. While some past research has explored the impact of some of these factors on the performance of self-sensing cementitious composites, additional investigations need to be conducted to delve into how loading conditions affect the sensitivity of self-sensing cement-stabilized composites. Therefore, this study explores the influences of various loading conditions (i.e., location of loading regarding the location of recording electrodes, and loading level) on the electromechanical performance of self-sensing cement-stabilized sand. To this end, firstly, the evaluation of the percolation threshold based on 10% cement-stabilized sand specimens containing various multiwall carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) was performed. Then, 10% cement-stabilized sand containing 4% MWCNTs/GNPs was tested under various cyclic compressive stresses. The results suggested that the distance between the loading area and the electrode location used for recording the electrical resistance significantly impacts the sensitivity of cement-stabilized sand. Optimal sensitivity was achieved when the electrodes were positioned directly beneath the loading area. Moreover, the study yielded that the stress sensitivity of self-sensing cement-stabilized sand increases proportionally with the stress level. Examination through scanning electron microscopy (SEM) demonstrated that the loading condition influences the bridging characteristics of carbon nanomaterials in cement-stabilized sand, leading to diverse electromechanical behaviors based on the loading condition. This study underscores the importance of considering specific parameters when designing the application of self-sensing cement-stabilized sand for practical field use.

Carnivorous waterbirds have been reported to enhance wetlands diversities, control pests, bio-indicators of ecological conditions, and act as indicators of potential disease outbreaks. They also provide important ecosystem services to indigenous communities residing along wetlands. Regardless of their importance, global waterbirds populations are declining. Despite their ecological contribution to wetlands functions and ecosystem services, piscivore waterbirds have been re-ported to compete with piscivorous fish and fishermen. The aim of this study was to compare piscivorous waterbirds species and functional diversity between fish protected areas (FPA) and non-fish protected area (NFPA) in two tributaries of Zambezi River in Namibia. We also measured the degree to which patterns of guild structure between piscivore waterbirds and fish were similar between FPA and non-FPA. Species diversities were determined by calculating the Shannon Diversity Index. We calculated taxonomic diversity, functional diversity and community-based trait diversity indices for each tributary. Generally, species diversities were not significantly different between FPA and NFPA (P > 0.05). However, all functional diversities indices calculated were significantly different between FPA an NFPA (P < 0.05 for all comparisons). This was partly due to food item, weight and movement type that were associated with each site.

Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of literature review and applied research. This 4-phased strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities are avoided. Furthermore, early incompatibility detection can ensure that a system remains permanently operational. Based on a discussion, additionally strategies are identified to consolidate the robustness and applicability of the presented concept.

Traumatic cardiac arrest has a high mortality. We report the case of a 65 years old women who presented a cardiac arrest following traffic accident. Examinations concluded to a tension gastro thorax with a delayed diagnosis. Naso gastric tube insertion was for many authors a temporary care while waiting for surgery. We report on the diagnostic difficulties found in the literature and a recent review of published cases.

Wound infection is a common complication of chronic wounds. It can impair healing, which may not occur without external help. Antimicrobial dressings (AMDs) are a type of external help to infected chronic wounds. In this study, highly porous membranes made of only chitosan and containing the antiseptic polyhexanide (poly(hexamethylene biguanide); PHMB) were prepared by cryogelation, aiming to be used in AMDs. These membranes exhibited high water swelling capacity, fast water absorption and high water vapor transmission. The best drug loading method involved simultaneous loading by soaking in a PHMB solution and sterilization by autoclaving, resulting in sterilized, drug-loaded membranes. The drug release kinetics was comparable to that of a commercial PHMB-releasing AMD assayed under the same conditions. These membranes exhibited high antibacterial activity against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa, which are bacterial species commonly found in infected wounds, and blood clotting activity. The obtained results suggest that these membranes may be suitable for use in the development of AMDs.

The Brazilian economy, the rural in particular, was until the end of the 19th century based on slave labour. In this research, it was intended to obtain, through a review of historical descriptive studies, a detailed picture of the occupational hygiene conditions related to the slaves’ work and their interactions with climatic and environmental conditions. The search was done in the following databases: Science Direct, Scopus, Web of Science, Criminal Justice, Ebsco, Business Source Supplement, as well as original historical documents. Descriptive studies, without the restriction of language, were selected that involved the rural work of slaves in colonial and imperial Brazil. Working environmental conditions have been evaluated: environmental and occupational hygiene conditions to which the captive workers were exposed, as well as their accommodation and clothing. The analysed studies registered the existence of accommodation and similar dresses all over Brazil, regardless of the region's climate. In addition to these accommodation conditions, slaves were still exposed in a similar way to physical, chemical and biological agents throughout the country. Finally, it was also possible to identify a clear similarity with the occupational exposure conditions of the modern slaves in the 21^st century.

The Textile and Clothing (T\&C) value chain is one of the most polluting in the world and one that produces the most waste. It is, therefore, important to encourage the Circular Economy (CE) model in this sector, to reduce pollution and mitigate the effects of waste production, and consequently increase environmental sustainability. For that, the involvement of the final consumer is essential. And, the final consumer's use of an Eco-gamified application for registering and promoting Consumer-to-Consumer (C2C) and Consumer-to-Business (C2B) activities, which extends the life time of textile products, is of utmost importance. In this article, we survey gamification frameworks for analyzing system design level techniques that enable engaging the final consumer in the CE process. Then, we select and use one of such frameworks, Gameful Design Heuristics (GDH), for defining the gamification structure needed to implement on a Business-to-Consumer-to-Consumer (B2C2C) context of a circular economy. As result, we present a B2C2C circular business process model for the T\&C value chain, and propose the design model of a gamified platform for the final consumers, which allows them to register the C2B and C2C activities, from the circular value chain's business process, and benefit from a game-like experience. All model features have been mapped to GDH framework heuristics, validating that it is possible to support a set of defined heuristics of applied gamification for promoting CE in the T\&C value chain.

In this work we explore the utilization of plasmonic resonance (PR) in silver nanowires to enhance the performance of organic solar cells. Plasmonic resonance is a phenomenon in which nanoscale conductive materials exhibit oscillation of conduction electrons, resulting in the creation of an electric field. Enhancing light absorption is crucial for improving organic solar cell efficiency and incorporating metallic nanostructures to induce surface plasmon resonance (SPR) shows promise in achieving this goal. We discuss the two key mechanisms of plasmonic effects: far-field scattering and near-field resonance modes. Far-field scattering extends the optical path of incident light, while near-field plasmonic effects involve localized surface plasmon resonance (LSPR) and plasmonic cavity modes, enhancing absorption by strengthening electric fields near the nanostructures. Silver nanowires are the focus of this study, and finite-difference time-domain (FDTD) simulation software is used to investigate their plasmonic resonance behavior in a ZnO/Silver nanowires/ZnO (ZAZ) electrode structure. The simulations reveal the dominance of LSPR in this configuration, with intense electric fields inside the nanowire and propagation into the surrounding medium, offering opportunities for enhanced light absorption in the organic solar cell's active layer.

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.

Quantification of erythrocytes and leukocytes is an essential aspect of hemogram diagno- 23 sis in Veterinary Medicine. Flow cytometry analysis, laser scattering, and impedance detection are 24 standard laboratory techniques, verified by manual microscopy counting. Although single-cell scat- 25 tering is already used as a standard technology for differentiating cell counts in flow cytometry, it 26 requires capillary cell separation. The current study investigates the scattering characteristics of 27 whole blood to identify correlations with erythrocytes and leukocytes counts. The scattering infor- 28 mation present in blood samples can be classified into three types: i) geometrical scattering, which 29 occurs when non-absorbed light is reflected and scattered, ii) Mie scattering, which happens when 30 light is scattered by particles of a similar size to the wavelength, and iii) Rayleigh scattering, which occurs when light is scattered by particles that are smaller than the incident light wavelength. In 32 this study, we investigate the scattering correction coefficients of dog blood absorption spectra in 33 the visible-near infrared range, to establish direct correlations with erythrocytes and leukocytes 34 counts, using multivariate linear regression. Our findings demonstrate the possibility of using the 35 scattering properties of dog blood, which is a step towards the existence of a portable and miniatur- 36 ized hemogram diagnosis in Veterinary Clinics worldwide.

Following the development of tyrosine kinase inhibitors (TKI), the survival of patients with chronic myeloid leukaemia (CML) drastically improved. With the introduction of these agents, CML is now considered a chronic disease, for some patients. Taking into consideration the side effects, toxicity, and high cost, discontinuing TKIs became a goal for patients with chronic phase CML. Patients who achieved deep molecular response (DMR) and discontinued TKI, remained in treatment-free remission (TFR). Currently, the data from the published literature demonstrate that 40-60% of patients achieve TFR, with relapses occurring within the first six months. In addition, almost all patients who relapsed regained a molecular response upon re-treatment, indicating TKI discontinuation is safe. However, there is still a gap in the understanding the mechanisms behind TFR, and whether there are prognostic factors that can predict the best candidates who qualify for TKI discontinuation with a view to keeping them in TFR. Furthermore, the information about a second TFR attempt and the role of gradual de-escalation of TKI before complete cessation is limited. This review highlights the factors predicting success or failure of TFR. In addition, it ex-amines the feasibility of a second TFR attempt after the failure of the first one, and the current guidelines concerning TFR in clinical practice.

Oral health involves physiological functions related to mouth, teeth and orofacial structures, but also includes psychological and social dimensions. In this sense, oral health is an integrated part of human general health and well-being. Our aim was to access and evaluate the general oral health status of a high- and elite-level sample of Portuguese athletes from different sports. One hundred and sixteen participants were examined regarding their dental relationships, teeth and periodontal health, the presence of parafunctional activities (bruxism), and current or past orthodontic treatment. Our sample were predominantly adolescents and young adults, including 54 high-level and 62 elite subjects (71 males and 45 males) from 11 different sports, but mainly from swimming and athletics. Most sport performers presented with normocclusion (77%), despite the presence of other sagittal, transverse and vertical malocclusions. Twenty-three participants were affected by dental caries and six had missing teeth. Gingivitis (~40%) and dental calculus (~30%) were presented in our sample. Bruxism signs (47%) and current or past orthodontic treatment (~22%) were also found. Oral health conditions are not under control among high- and elite-level athletes, supporting the importance of healthcare needs and oral health promotion strategies in sports.

Within the framework of multiblock data analysis, a unified approach of supervised methods is discussed. It encompasses multiblock redundancy analysis (MB-RA) and multiblock partial least squares (MB-PLS) regression. Moreover, we develop new supervised strategies of multiblock data analysis, which can be seen as variants of one or the other of these two methods. They are respectively refered to as multiblock weighted redundancy analysis (MB-WRA) and multiblock weighted covariate analysis (MB-WCov). The four methods are based on the determination of latent variables associated with the various blocks of variables. They are derived from clear optimization criteria whose aim is to maximize either the sum of the covariances or the sum of squared covariances between the latent variable associated with the response block of variables and the block latent variables associated with the various explanatory blocks of variables. We also propose indices to help better interpreting the outcomes of the analyses. The methods are illustrated and compared based on simulated and real datasets.

Heat pumps are increasingly becoming a key solution in the quest for decarbonization, offering a significant advantage over traditional boilers due to their superior energy efficiency. Not only can heat pumps provide both heating and cooling, but they also achieve thermal efficiencies exceeding 500%, which can seem to defy conventional expectations. The outdoor unit of a heat pump consists of about twenty main components, one of which is a complex network of copper pipes and sheets welded to valves, connectors, and other fittings. This set involves over thirty welded joints, highlighting a significant opportunity for reducing manufacturing time through process optimization. Copper is a critical material extensively used in various applications due to its excellent thermal and electrical conductivity. However, these very properties make welding copper particularly challenging, especially when aiming to automate and streamline industrial processes. Traditionally, connecting copper pipes and fittings is done through brazing, yet, alternative methods such as LASER welding, electron beam welding, resistance welding, friction welding, and ultrasonic welding also exist. This study focuses on the ultrasonic welding of copper, aiming to identify the optimal parameters that maximize the mechanical strength of overlapping joints and ensure effective tube sealing. Experimental results showed that for copper sheets, the best outcomes were achieved with a welding time of 3 s and a pressure of 6 bar. For tube sealing, the optimal conditions were a welding time of 3 s and a pressure of 4 bar. For both scenarios, the same variables were used, retention time of 0.62 s, initial pressure of 2 bar, retention pressure of 2 bar and sonotrode rising pressure of 3 bar. The analysis reveals that ultrasonic welding of copper can achieve both robust mechanical strength and reliable sealing, making it a promising technique for industrial applications.

Background: The importance of knowing the workload of family nurses lies essentially in the possibility of improving health outcomes, care processes and the nurse’s professional life. There is a lack of studies that fully describe nursing workload in primary care, particularly, in the context of family health nursing, and the ideal metrics to be used remains unknown, making it impossible to characterize and therefor provide the necessary insight to acknowledge the different contributions of several aspects that embodies the global workload of family nurses. The objective of this scoping review was to map the known evidence and characterize the instruments used to assess the workload of family nurses. Methods: Scoping review, according the Joanna Briggs Institute proposed methodology for scoping reviews, consisting of 3 research stages: 1) initial research in Medline and CINHAL; 2) extended search, using keywords and search terms, in the following databases: JBI, CINAHL Complete, MEDLINE, Cochrane and Scopus; 3) Search the reference list of the selected articles. No time limit was defined. Results: Fourteen studies referring to ten as-sessment instruments were included. Nine of them analyze workload as a dimension of a broader instrument, and two studies refer to an instrument that focuses exclusively on workload. Con-clusions: The diversity of professional competencies and contexts, the conceptual complexity of workload and the absence of an theoretical framework, makes it difficult to identify consensual instruments to assess the workload of family nurses. This study was registered in Open Science Framework® with the following registration (https://osf.io/3k6vr).

: Polymer single screw extrusion is a major industrial processing technique used to obtain plastics products. To assure high outputs, tight dimensional tolerances, and excellent product performance, extruder screws may show different design characteristics. Barrier screws, which contain a second flight in the compression zone, have become quite popular as they promote and stabilize polymer melting. Therefore, it is important to design efficiently extruder screws and decide whether a conventional screw will do the job efficiently, or a barrier-screw should be considered instead. This work uses multi-objective evolutionary algorithms to design conventional and barrier screws (Maillefer screws will be studied) with optimized geometry. Processing of two polymers, Low Density Polyethylene and Polypropylene, is analyzed. A methodology based on the use of Artificial Intelligence (AI) techniques, namely data mining, decision making and evolutionary algorithms, is presented and utilized to obtain results with practical significance, based on relevant performance measures (objectives) used in the optimization. For the various case studies selected, Maillefer screws were generally advantageous for processing LDPE, while for PP the use of both types of screws would be feasible.

The normal and Poisson distribution assumptions in the normal-Poisson mixed effects regression model are often too restrictive for many real count data. Several works have independently relaxed the Poisson conditional distribution assumption for counts or the normal distribution assumption for random effects. This work couples some recent advances in these two regards to develop a skew t–discrete gamma regression model in which the count outcomes have full dispersion flexibility and random effets can be skewed and heavy tailed. Inference in the model is achieved by maximum likelihood using pseudo-adaptive Gaussian quadature. The use of the proposal is demonstrated on a popular owl sibling negotiation data. It appears that, for this example, the proposed approach outperforms models based on normal random effects and the Poisson or negative binomial count distribution.

Most existing flexible count regression models allow only approximate inference. Balanced discretization is a simple method to produce a mean-parametrizable flexible count distribution starting from a continuous probability distribution. This makes easy the definition of flexible count regression models allowing exact inference under various types of dispersion (equi-, under- and overdispersion). This study describes maximum likelihood (ML) estimation and inference in count regression based on balanced discrete gamma (BDG) distribution and introduces a likelihood ratio based latent equidispersion (LE) test to identify the parsimonious dispersion model for a particular dataset. A series of Monte Carlo experiments were carried out to assess the performance of ML estimates and the LE test in the BDG regression model, as compared to the popular Conway-Maxwell-Poisson model (CMP). The results show that the two evaluated models recover population effects even under misspecification of dispersion related covariates, with coverage rates of asymptotic 95% confidence interval approaching the nominal level as the sample size increases. The BDG regression approach, nevertheless, outperforms CMP regression in very small samples (n = 15 − 30), mostly in overdispersed data. The LE test proves appropriate to detect latent equidispersion, with rejection rates converging to the nominal level as the sample size increases. Two applications on real data are given to illustrate the use of the proposed approach to count regression analysis.

Shallow Geothermal Energy (SGE) exchanges heat with the ground. In continuous long-term operation, the initial temperature field can be disturbed, and subsurface thermal plumes can be developed. In this paper, the thermal impact of a SGE system under Mediterranean climate is handled. Temperature recordings from 104 thermal probes placed in depth along 15 monitoring boreholes are analysed. Those boreholes were drilled 1-2m from thermos-active boreholes of the case study system installed in a university building. The analysis handles one year of SGE system operation. Temperature depth profiles, reaching up to 140 m depth, were registered with a 10-minute time-step resulting in a large amount of data. Ground thermal conductivity was estimated experimentally and semi-empirically allowing to compute the initial undisturbed ground temperature profiles and compare it with the monitored values. Climate data was recorded by the university meteorological station. Globally, the measured and computed data were coherent and a non-negligible impact of the SGE system operation in the first year was observed. The building orientation as well as the nearby departments had significant impacts on shallow ground temperature. Maximum ground temperature changes, ranging from 2 to 3º C as observed in different boreholes indicating that the system is operating efficiently.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

In recent years, the abundance of streaming data has posed significant challenges for traditional machine learning algorithms due to the dynamic nature and high volume of data. To address these challenges, this paper presents an adaptive supervised learning framework designed for data streams, leveraging the power of reproducing kernel Hilbert spaces (RKHS) while incorporating a data sparsity constraint. The proposed framework aims to adaptively update the model to efficiently handle the continuous arrival of streaming data while ensuring accurate and reliable predictions. The primary objective is to exploit the RKHS framework, which provides a rich mathematical structure for learning in high-dimensional feature spaces. By utilizing RKHS, the model can capture complex patterns and nonlinear relationships in the streaming data. Furthermore, the framework incorporates a data sparsity constraint to address the issue of limited resources and computational efficiency. The constraint promotes the selection of a subset of relevant features, reducing the dimensionality of the problem and enhancing the scalability of the learning algorithm. This constraint not only improves computational efficiency but also mitigates the effects of noisy or irrelevant features, leading to more robust and accurate predictions. To achieve adaptability, the proposed framework employs an online learning approach that incrementally updates the model parameters as new data arrives. This allows the model to adapt to concept drift and changing data distributions, ensuring its relevance and effectiveness over time. The adaptation process is guided by a mechanism that balances the exploitation of current knowledge with the exploration of new information, enabling the model to gradually evolve and refine its predictions. Experimental evaluations on various benchmark datasets demonstrate the efficacy of the proposed framework in handling streaming data. The results indicate that the adaptive supervised learning approach in RKHS with a data sparsity constraint outperforms traditional batch learning methods and exhibits superior accuracy, scalability, and adaptability in dynamic data stream scenarios. Overall, this research contributes to the development of adaptive supervised learning methods for data streams, highlighting the effectiveness of RKHS and the significance of incorporating a data sparsity constraint. The proposed framework offers a promising solution to handle the challenges posed by streaming data, paving the way for real-time, efficient, and accurate learning in diverse application domains.

Tyrosinases (TYR) play an important role in oxidizing phenols and catechols, leading to the formation of catechols and ortho-quinones, respectively. In mammals, TYR catalyzes the conversion of L-Tyrosine to quinone, a pivotal step in melanogenesis, which generates melanin pigments responsible for protecting against UV radiation and oxidative stress. Given TYR's importance in melanin-related disorders and cosmetic applications, the inhibition of TYR has garnered attention for controlling melanin production. However, current inhibitors like hydroquinone, arbutin, and kojic acid (KA) have limitations in terms of side effects and effectiveness. A novel class of TYR inhibitors featuring arylpiperidine and arylpiperazine components has demonstrated potent inhibitory activity. This study presents a computational analysis of these inhibitors using molecular docking, molecular dynamics (MD) simulations, and the Linear Interaction Energy (LIE) method was used to estimate binding free energies. The arylpiperidine and arylpiperazine inhibitors exhibit remarkable stability within the TYR active site during simulations. The van der Waals interactions are notably sustained, indicating effective inhibition mechanisms. The results from LIE calculations closely align with experimental binding affinities. In summary, these results contribute to the understanding of TYR inhibition and underscore the potential of arylpiperidine and arylpiperazine derivatives as effective agents for modulating melanin production in both cosmetic and pharmaceutical applications. Overall, this study sheds light on the intricate interactions between inhibitors and TYR, opening avenues for the development of novel therapies and interventions.

A 3D graphene foam made of interconnected multilayer graphene flakes was produced on optical fibres (OF) by laser-induced transformation of a polyimide (PI) film coated on the OF cladding. This material, known as la-ser-induced graphene (LIG), was explored in the electrochemical detection and quantification of dopamine (DA) at physiologically relevant concentrations in the presence of the most relevant interfering molecules in biological fluids, ascorbic acid (AA) and uric acid (UA). The measured limit of detection is 100 nM, the linear range is 0.1 to 5 μM and a maximum sensitivity of 5.0 µA µM−1 cm−2 was obtained for LIG decorated with Pt nanoparticles (NPs). Moreover, immunity to AA and UA interference and to fouling was attained by decorating the LIG elec-trode with Pt NPs and coating with Nafion. These figures of merit underlines the potential of these sensors for the quantification of physiologically relevant concentrations of DA in biological fluids, paving the way for the development of hybrid electrochemical/optical sensing actuating platforms in a lab-on-fibre configuration, with relevant applications in biomedical engineering. The advantages of this hybrid arrangement include the possi-bility of in-situ counterproofing, extended measuring ranges, photoelectrochemical detection and the probing of inaccessible places. This elegant approach can also provide a simple and cost-effective way to fabricate biomed-ical devices with extended functionality, such as medical optical probes with added electrochemical capabilities, optogenetics combined with local electrochemical detection, among others.

Regional heterogeneity of microglia was first described a century ago by Pio del Rio Hortega. Currently, new hints on microglia heterogeneity across central nervous system (CNS) regions are still being unraveled by high-throughput techniques. It remains unclear whether these spatial specificities translate into different microglial behaviors in vitro. We cultured microglia isolated from the cortex and spinal cord and analyzed the effect of the CNS spatial source on in vitro behavior by applying the same experimental protocol and culture conditions. We analyzed microglial cell numbers, function, and morphology and found a distinctive in vitro phenotype. We found that microglia were present in higher numbers in spinal cord-derived glial cultures, presenting different expression of inflammatory genes and a lower phagocytosis rate under basal conditions or after activation with LPS and IFN-. Morphologically, cortical microglial cells are more complex and present longer ramifications, which were also observed in vivo in CX3CR1+/GFP transgenic reporter mice. Collectively, our data demonstrated that microglial behavior in vitro is defined according to specific spatial characteristics acquired in the tissue. Thus, our study highlights the importance of microglia as a source of CNS for in vitro studies.

Premature failure and degradation of layers are the main problems for transportation infrastruc-ture. Addressing these issues necessitates implementing structural health monitoring (SHM) for transportation layers. This strategic approach is instrumental in mitigating maintenance expenses, prolonging the operational lifespan, and preventing accidents resulting from abrupt layer col-lapse. To this end, this research investigated the stress/strain and damage detection capabilities of a self-sensing cementitious composite developed for potential utilization in the construction of an intelligent subgrade layer. The prepared self-sensing cementitious composite consisted of 10% cement and hybrid conductive fillers, including multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in sand. Initial findings reveal that the electrical resistivity of the composite is significantly affected by the concentration of MWCNTs/GNPs, with a minimum concentration of more than 0.5% needed to achieve a responsive cementitious composite. More-over, the piezoresistive analysis indicates that an increase in the concentration of MWCNTs/GNPs and stress levels leads to an improvement in the stress/strain sensing perfor-mance. When the self-sensing cementitious composite is subjected to equivalent stress levels, variations in the FCR exhibit an increasing trend with decreasing resilient modulus, stemming from a decrease in stiffness due to the increased concentration of MWCNTs/GNPs. Additionally, the EIS analysis demonstrates a contraction for the Nyquist curves under compressive ramp loading prior to failure, followed by the expansion of these curves postfailure. Scanning electron microscopy (SEM) images visually showcase the bridging effects of MWCNTs and the filling ef-fects of GNPs within the composite structure.

(1) Background: This text emphasizes concerns regarding the safety of Spanish police officers at-tributed to inadequate training, employing biomechanics for the analysis and mitigation of risks during Use of Force training. The elevated global prevalence of musculoskeletal disorders under-scores the imperative for effective occupational health measures. The study concentrates on evalu-ating injury risks in novice police officers during Use of Force training. (2) Research method: The movements of police officers were captured using a Rokoko Smartsuit Pro equipped with 19 sen-sors. The Biomechanics of Bodies software processed the data, providing REBA and Marras risk scores. Statistical tests were conducted, and Operational Tactical Procedures (OTP) were inte-grated into police training. A diverse group of officers participated, categorized by training level and equipped with the Rokoko Smartsuit. (3) Results: The study evaluates police force training us-ing REBA and Marras methods. REBA scores indicate a moderate to high injury risk, suggesting effective technique design. ANOVA reveals no significant differences between training levels. Marras analysis, characterized by non-normal distribution, unveils varied risk perceptions among groups via the Kruskal-Wallis test. (4) Conclusions: The analysis of both the REBA and Marras methods implies the effectiveness of the OTP technique in reducing injury risks across dif-ferent training levels. The ANOVA test indicates no significant differences in REBA Maximum Scores between the groups. However, the Kruskal-Wallis test for Marras Probability of high-risk values suggests distinctions in medians between training levels. The combined analysis visually illustrates variations in membership regions between expert, intermediate, and novice groups.

Electric Transformers make up some of the most critical components of a power system. Their reliable operation is critical for an efficient power grid as well as economic viability for the transformer operators and owners. Power transformers, like other High Voltage (HV) electrical equipment experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial Dischargers (PD) are localized electrical discharges that develop within insulation systems of HV electrical equipment such as transformers. In transformers, PD occurs in different forms and various locations and insulation both internal and external of the transformer. PD originates as small pulses but tends to increase in size and intensity which can result in complete insulation failure. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of the power transformer, allowing transformer operators to make calculated decisions for the maintenance and life of plant plans. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator to allow for future research in the field. The systematic review surveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine (SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.

Disorders associated with substance abuse are a major public health crisis with few treatment options. According to World Health Organization (WHO) ethanol is the most widely used drug in the world, and it represents a risk factor for the advent of disease, disability, and eventually death. Foetal Alcoholic Spectrum Disorders (FASD) is a diagnostic term to describe the range of effects that can occur in an individual whose mother drank alcohol during pregnancy. These effects encompass both physical, mental, behavioural and further lifelong disabilities. Besides, ethanol can harm the gut microbiota. Gut microbiome is firstly acquired from the mother and it is crucial for intestinal homeostasis during hosts’ lifetime. It is responsible for producing metabolites that benefits and protects the host from harm microbial colonization. Knowledge about the interactions between human gut microbes and the developing nervous system is still scarce. Nevertheless, animal models have shown that gut bacteria and microbial metabolites are strongly associated with Central Nervous System (CNS) homeostasis. Endotoxins such as Lipopolysaccharides (LPS) are hypothesized to have a major role in neurodegeneration, however, conclusions must be taken with care due to differences in sensitivity between humans and mice. In this review we focus on the role of gut microbiota on the neurodevelopment of mice when ethanol consumption is one of the major stressors during prenatal period. We detail the range of the endotoxin hypothesis in describing endotoxins’ contribution to neurodegeneration and the influence that kynurenine pathway has on the process.

This paper introduces the exponential time differencing (ETD) technique as a numerical method to solve efficiently American vulnerable options pricing. We address several challenges, including removing cross-derivative terms through appropriate transformations, treating early-exercise opportunities using the penalty method, and substituting fixed boundary conditions with corresponding one-sided finite differences. The proposed method is shown to be both accurate and efficient through numerical experiments, which also compare the results with existing methods and analyse the numerical stability and convergence rate.

Entering University is an important transition for students. Requires adaptation and can influence academic success. Psychological disorders might emerge when difficulties like anxiety occur due to insecurity about emotions in a student's life. We are currently living in post-pandemic conditions where pressure is increasing. The project "Toolbox: University Student – Web-interactive Platform", a platform for learning anxiety management, was developed to address this problem among university students. The main objective is to understand whether using the platform (based on cognitive behavioral theory) has effectively managed anxiety among students. It is a case study with a sample of 31 students from two Portuguese Universities, aged between 19 and 47 years. They were divided into two groups: intervention (n=17) and control (n=13). The results show that participants subjected to intervention with the interactive platform decreased their anxiety.

Angiotensin-converting enzyme 2 (ACE2) is an important player of the renin-angiotensin-aldosterone system (RAAS) in regulating the conversion of angiotensin II into angiotensin (1-7). While expressed on the surface of human cells, such as lung, heart, kidney, neurons, and endothelial cells (EC), ACE2 is the entry receptor for SARS-CoV-2. Here, we would like to highlight that ACE2 is predominant on the EC membrane. Many of coronavirus disease 2019 (COVID-19) symptoms have been associated with the large recruitment of immune cells, directly affecting EC. Additionally, cytokines, hypoxia, and complement activation can trigger the activation of EC leading to the coagulation cascade. The EC dysfunction plus the inflammation due to SARS-CoV-2 infection may lead to abnormal coagulation, actively participating in thrombo-inflammatory processes resulting in vasculopathy and indicating poor prognosis in patients with COVID-19. Considering the intrinsic relationship between EC and the pathophysiology of SARS-CoV-2, EC-associated therapies such as anticoagulants, fibrinolytic drugs, immunomodulators, and molecular therapies have been proposed. In this review, we will discuss the role of EC in the lung inflammation and edema, in the disseminate coagulation process, ACE2 positive cancer patients, and current and future EC-associated therapies to treat COVID-19.

Electronic devices are sensitive to electromagnetic (EM) emissions requiring electromagnetic shielding protection to assure good operation, preventing noise, malfunctioning, or even burning. To assure protection, it is important to develop suitable material and design solutions for electronic enclosures. Most common enclosures are made with metal alloys by traditional manufacturing methods. However, resourcing to thermoplastic composites combined with additive manufac-turing (AM) technologies emerges as an alternative that enables to fabricate complex parts that are lightweight, consolidated and oxidation and corrosion resistant. In this research, an AM technique based in material extrusion was used to print 2 mm thick specimens with a multi-material made of micro-carbon fiber (CF) filled polyamide, reinforced, at specific layers, by continuous carbon fibers stacked with a 90° rotation to each other. Specimens electromagnetic shielding effectiveness (EMSE) was evaluated in the frequency band of 0.03 – 3 GHz by coaxial transmission line method. De-pending on the number of CF layers, the EM shielding obtained can up to 70 dB, with a specific shielding up to 60 dB.cm3/g, predominantly by absorption mechanism, being 22 times higher that without the CF layers. These findings fundament this innovative approach for lightweight cus-tomizable solutions for EM shielding applications.

The gold nanoparticle (AuNP) was synthesized using the well-known Turkevich method. This article explains didactic step-by-step of the synthesis, showing pictures of the entire process, including a well-explained mechanism and characterization study. The synthesis includes the reduction of NaAuCl4 using sodium citrate in high temperature (around 90 ⁰C). The two main mechanisms used to explain the AuNP synthesis via Turkevich method were explained. The first mechanism considerate that a nanowire intermediary and the other proposed that aggregate intermediates are not formed at any time during the synthesis. The materials (NaAuCl4 and AuNP) were characterized by UV-Vis, SEM, AFM, XRD and DLS. The UV-Vis exhibit an absorption maximum in 521 nm because the surface plasmon resonance (SPR) absorption band of AuNP. The SEM images of NaAuCl4 presents crystals with cubic shapes, while the AuNP presents average particle size of about 16-25 nm and particles that appear mainly spherical. To confirm the particles shapes, the AFM was conducted and it was possible to observe clearly the individual spherical nanoparticles. The XRD of AuNP showed the four main characteristic peaks for silver corresponding to (111), (200), (220) and (311) planes, confirming the cubic (FCC) silver. The DLS presented the average particle size of 3.3 ± 0.9 nm and the polydispersity index (PDI) of 0.574. In summary, the AuNP was synthesized using a simple and fast method. The result was a spherical and ultrasmall particle, that can be used is several applications.

This paper aims to quantify the influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters. This model has 4 parameters which have been obtained by tensile tests on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. Application of the (W-X) model has been done to get the local resistance used to predict failure by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement. The effect of this displacement is then assimilated to the shearing of the straight section of the pipe. The pipe shows a corrosion defect considered a semi-elliptical crack. The influence of hydrogen embrittlement on local strain resistance and safety factors is discussed.

Increasingly measured data in the context of smart cities can be used to develop new and innovative business models to increase efficiency and the value of life. A time-series classification algorithm can support to automatize many different processes such as forecasting services. In order to ensure data security and privacy, Federated Learning trains a global model collaboratively on multiple clients. Having different data-distributions and data-quantities across participating clients, neural networks suffer from slow convergence and overfitting. Based on different data-distributions, data-quantities and number of clients, we develop and evaluate different data-clustering strategies to update global model weights in comparison to the state of the art. We use public time-series data, generate various synthetic datasets and train a Relational-Regularized Autoencoder for classification purposes. Our results show an improvement of model performance concerning generalization.

When a limb suffers a fracture, rupture, or dislocation, it is traditionally immobilized with plaster. This may induce discomfort in the patient, excessive itching and sweating, which creates the growth of bacteria, leading to an unhygienic and difficulty to keep clean from treatment. Furthermore, if the plaster remains for a long period, it may cause lesions in the joints and ligaments. To overcome all these disadvantages, orthoses have emerged as important medical devices to help patients in rehabilitation, as well as for self-care of deficiencies in clinics and daily life. Traditionally, these devices are produced manually, which becomes time-consuming and error prone. From another point-of-view, it is possible to use imageology (X-ray or computed tomography) to scan the human body; a process that may help orthoses manufacturing but induces radiation to the patient. To overcome this great disadvantage, several types of 3D scanners, without any kind of radiation have emerged. This article describes the use of various types of scanners capable of digitizing the human body, to produce custom orthoses. Studies have shown that photogrammetry is the most used and most suitable 3D scanner for the acquisition of the human body in 3D. With this evolution of technology, it is possible to decrease the scanning time and it will be possible to introduce this technology in clinical environment.

The isotopic and nuclear techniques discussed, namely: Fallout Radionuclide, Fingerprint, and Isotope Hydrology, are linked to the evaluation of soil and water degradation processes. A traditional literature review was conducted to gain the most comprehensive understanding of both the current state of the art and the subject matter, with the aim of proposing a hypothesis that integrates the synergistic convergence of these techniques in order to offer results that are more reliable. A case study is presented as a synthesis of the preceding discussion.

Background: The relationship between adherence to the Mediterranean Diet (MD) and both physical fitness (PF) and physical activity (PA) level has been analysed in several studies. The aim of this research was to describe, compare and analyse the level of PF and PA in schoolchildren aged 6-13 in the Region of Murcia, according to adherence to the MD. Methods: A descriptive and cross-sectional study was performed. A total of 370 schoolchildren (44.9% girls) aged 6-13 (8.7±1.8) from six primary schools in the Region of Murcia (Spain). Results: Only 25.9% of the schoolchildren had optimal adherence to the MD. Regarding the scores of the different PF tests in MD groups, only statistically significant differences were found for CRF (p=0.048) in boys. PA level, showed statistically significant differences in both boys (p=0.040) and girls (p=0.016). A positive relationship was found between the KIDMED index and CRF (ρ=.127), standing broad jump (ρ=.133) and PA level (ρ=.235). A higher probability of having a greater CRF (OR=1.17; CI95%=1.02–1.34) and PA level (OR=7.84; CI95%=2.84–21.60) was found in High MD group. Conclusion: These results suggest that an optimal adherence to the MD is associated with higher CRF and PA level in the selected schoolchildren.