A key concern in an ageing society is citizens’ mobility. As populations age, disability impairments can affect active ageing, health-related wellbeing and quality of life. In this paper, we present the on-going research project SeriousGiggle—Game-based learning for triggering active ageing. Its goal is to assess the potential of game-based learning for active ageing and contribute to a sense of wellbeing and quality of life. It also seeks to improve the mobility of older adults by creating a set of journey plans with route guidance that are rated in terms of safety, community support, environment and age-friendliness. Drawn on our field work with 33 co-designers, 40 end users and 10 semi-structured interviews with Subject Matter Experts, we identify a set of necessary design requirements to an Age-friendly Playable City. This study recommends the use of gamification and playful techniques to engage the end-users to provide information about local traffic signs, pavement conditions, wayfinding and, therefore, help to create route guidance and walking assistance that are personalized to older adults’ context in terms of location, travel fitness, mobility impairments and motivations.

Currently, the number of approved veterinary medicines are limited, and human medications are used off-label. These approved human medications are of too high potencies for a cat or a small dog breed. Therefore, there is a dire demand for smaller doses of veterinary medicines. This study aims to investigate the use of three semi-solid extrusion 3D printers in a pharmacy or animal clinic setting for extemporaneous manufacturing of prednisolone containing orodispersible films for veterinary use. Orodispersible films with adequate content uniformity and acceptance values defined by the European Pharmacopoeia was produced with one of the studied printers, namely, the Allevi 2 bioprinter. Smooth and flexible films, with high mechanical strength, neutral pH, and low moisture content were produced with high correlation between prepared design and obtained drug amount, indicating that the Allevi 2 printer could successfully be used to extemporaneously manufacture personalized doses for animals at the point-of-care.

A revitalized practice of natural philosophy can help people to live a better life and promote a flourishing ecosystem. Such a philosophy is natural in two senses. First, it is natural by seeking to understand the whole of nature, including mental phenomena, In particular, a comprehensive natural philosophy should address the phenomena of sentience by embracing first- and second-person methods of investigation. Moreover, to expand our understanding of the world, natural philosophy should embrace a full panoply of explanations, similar to Aristotle’s four causes. Second, such a philosophy is natural by being grounded in human nature, taking full account of human capacities and limitations. Future natural philosophers should also make use of all human capacities, including emotion and intuition as well as reason and perception, to investigate nature. Finally, since the majority of our brain's activities are unconscious, natural philosophy should explore the unconscious mind with the aim of deepening our relation to the rest of nature and enhancing well-being.

Membranes based on sulfonated synditoactic polystyrene were thoroughly characterized by contrast variation SANS over a wide Q-range in dry and hydrated states. The film samples were prepared by solid-state sulfonation that allowed a uniform sulfonation of only the amorphous phase while preserving the crystallinity of the membrane. The samples were loaded with different guest molecules in either the amorphous (fullerenes) or the crystalline (toluene) regions, in order to vary the neutron contrast or to reproduce the conditions enabling an increased resistance of the membranes to chemical decomposition. The use of uni-axially deformed film samples and contrast variation with different H2O/D2O mixtures allowed for the identification and characterization of different structural levels with sizes between nm and μm, which form and evolve in the membrane morphology in dry and hydrated states and produce scattering features on different detection sectors and at different detection distances after the sample, depending on their size and orientation.

The Particulate Matter (PM), particles of variable but small diameter could penetrate the respiratory system via inhalation, causing respiratory and/or cardiovascular diseases. This study aims to evaluate the association of environmental particulate matter (PM2.5) and black carbon (BC) with respiratory health and physical activity in users traveling by transportation modes over four roads in Bogotá. This was a mixed-method study, in 300 healthy participants, based on a convergent parallel design. Including a descriptive qualitative component focused on asserting the individual perception of air pollution by semi-structured interviews and a cross-sectional study measuring the individual exposure to PM2.5 and BC to evaluate the pulmonary function by spirometry. The analysis included concurrent triangulation and a Poisson regression. This study provides integration of air pollution exposure variables and respiratory health effects in different transport microenvironments. To our knowledge, this is the first mixed-methods study focused on PM2.5, BC, and respiratory health effects in a city above 2.000 meters above sea level.

In this paper, precise examination of multi-port antennas with large mutual coupling between the ports is studied. For this purpose, some vital parameters such as active reflection coefficients and realised gain are emphasized. The main motivation of this research is the ignorance of this issue in several recently published papers. Hence, corrections and criticisms to some recently reported results are presented. At first, a microstrip patch consisting of two inverted U-slots is re-examined, which has been proposed in a recently published paper for wideband applications. However, the authors did not present satisfactory results to indicate the performance of the proposed structure. In reverse, some important parameters such as the antenna gain appear to be incorrectly reported. In this paper, the characteristics of the antenna are compared with a simple 50 Ω microstrip line excited by two probes, indicating no significant differences. In the remainder of this paper, some corrections are provided on two examples of wide-beam circularly polarised multi-fed antennas for satellite applications. The first example is the multi-fed spiral antennas with folded arms, which have been proposed as the widebeam antennas at 435 MHz. It is indicated that the antenna height should be increased for better performance. The second example is the quadrifilar square helical antennas with folded arms, which have been proposed as the widebeam antennas for 435 MHz with minor responses at 145 MHz. Some corrections are presented on the previously reported minor responses at 145 MHz.

The active layer thickness (ALT) is affected by local soil material and surface vegetation coverage in Tibetan plateau permafrost region. The human activities and engineering construction along the Qinghai-Tibet Highway (QTH) destroyed the surface vegetation coverage, which have seriously influenced the ALT and heat exchange balance. Meanwhile, the changes of the detailed internal structure in the active layer will affect the stability of engineering infrastructure. Ground-penetrating radar (GPR) was used to detect the ALT in the permafrost region. However, researches on the detailed structure of the active layer and spatial variation information of ALT are rare, the main reason given for this is that the lack of advanced imaging techniques to enhance the GPR images. In this paper, the researchers focus on solving this problem by applying the reverse time migration (RTM) method to GPR field data acquired in the Beiluhe region. Analyses of the results led to conclusions as follows: a) The RTM algorithm is proved to be accurate for imaging the characteristics of the active layer through the dielectric constant model obtained by common midpoint and GPR data velocity analysis. b) The result of RTM profiles show the loose structure and graben-like fine internal structure. Combining with drill data and soil materials information, the researchers deduce the graben-like structure might be coarse gravel layer, which may be related to subgrade settlements in some local area. c) The 2D contour maps not only show the spatial variation feature of ALT at two sides of the highway, but also can compensate the defect of the research for single 2D survey line. The ALT is about 0.6~0.8 m in most areas, and about 0.8~0.9 m near the highway at west side of the QTH on the 2D contour maps. From the RTM interpretation profile, the closer to the plash, the deeper thawed active layer. The detailed internal structure of the active layer and 2D contour maps of ALT have great significance for the study of fine internal structure and the characteristic of ALT spatial variation in the Tibetan plateau under the background of climatic change.

This paper discusses the tectonics of the St. Peter and St. Paul Archipelago (SPSPA) in the Equato-rial Atlantic Ocean, based on the joint-system geometry which show a North-South shorten-ing/transpressional uplift tectonism, is active leading to exhumation of the sub-oceanic mantle. These islets are the summits of a sigmoidal submarine ridge formed by mantle ultramafic rocks. The ridge is crossed by the principal transform deformation zone of the northern transform fault of the St. Paul Multifault System. The South flank ridge exposes serpentinized mantle perido-tites, while the North flank exposes strongly deformed/fractured ultramylonites, recording duc-tile and brittle deformation at lithospheric conditions. The SPSPA show multiple joint systems cutting mylonitic foliation of the exposed rocks, forming three main families: high-angle paral-lel joints of tectonic origin, serpentinization-related joints with random direction and load-release low-angle parallel joints. The tectonic joints show an average direction of N31°E and N28°W, forming a conjugate system with a N1ºW compression axes, coherent with a trans-pressive stress field. Accordingly, the earthquakes focal mechanism close to the islets also shows N-S compression. The previously reported active uplift with an average rate of 1.5 mm/year and the directions of the joint system here reported agreeing with a present-day active N-S compres-sive field at a high angle with the direction of the transform fault.

The classical Rotational Isomeric State (RIS) model, originally proposed by Flory, has been used to rationalize a wide range of physicochemical properties of neutral polymers. However, many weak polyelectrolytes of interest are able to regulate their charge depending on the conformational state of the bonds. Recently, it has been shown that the RIS model can be coupled with the Site Binding (SB) model, for which the ionizable sites can adopt two states: protonated or deprotonated. The resulting combined scheme, the SBRIS model, allows to analyse ionization and conformational equilibria on the same foot. In the present work this approach is extended to include pH-dependent electrostatic Long Range (LR) interactions, ubiquitous in weak polyelectrolytes at moderate and low ionic strengths. With this aim the original LR interactions are taken into account by defining effective Short Range (SR) and pH-dependent parameters, such as effective microscopic protonation constants and rotational bond energies. The new parameters are systematically calculated using variational methods. The machinery of statistical mechanics for SR interactions, including the powerful and fast transfer matrix methods, can then be applied. The resulting technique, to which we will refer as Local Effective Interaction Parameters (LEIP) method, is illustrated with a minimal model of a flexible linear polyelectrolyte containing only one type of rotating bonds. LEIP reproduces very well the pH dependence of the degree of protonation and bond probabilities obtained by semi-grand canonical Monte Carlo simulations, where LR interactions are taken explicitly into account. The reduction in the computational time in several orders of magnitude suggests that the LEIP technique could be useful in a range of areas involving linear weak polyelectrolytes, allowing direct fitting of the relevant physical parameters to the experimental quantities.

A key goal of modern neuroscience involves understanding how connections in the brain form and function. Such a knowledge is essential to better inform how defects in the exquisitely complex steps of nervous system growth underlie neuropsychiatric and neurodevelopmental disorders. In the last 40 years, studies of the nervous system in the fruit fly Drosophila melanogaster enabled the discovery of a wealth of molecular and genetic mechanisms that drive the development of these synaptic connections – specialized cell-to-cell connections that are the essential substrate for information flow and processing in the nervous system. The major driver of knowledge focused on studies at the neuromuscular junction due to its ease of examination. Analogous studies in the central nervous system lagged behind due to a lack of genetic accessibility of specific central neuron classes, appropriate synaptic labels compatible with cell-type specific access, and high resolution, quantitative imaging strategies. However, understanding how synapses in central circuits form remains a prerequisite to understanding brain development. In the last decade, a host of new tools and techniques made possible the extension of genetic studies of synapse organization into central circuits and greatly enhanced our understanding of central synapse formation, organization, and maturation. In this review, we consider the current state-of-the-field, focusing on two major elements. We first discuss the tools, technologies, and strategies developed to visualize and quantify synapses in vivo in genetically identifiable neurons of the Drosophila CNS. Second, we explore in depth how these tools enabled a clearer understanding of synaptic development and organization in different circuits of the fly brain and discovered novel molecular mechanisms that underlie synapse formation. These studies establish multiple brain regions in the fly as powerful in vivo genetic models that offer novel insights into synaptogenic regulators and mechanisms of neural development.

The goal of this study is both to determine the opinion that professors and students at the University of (details removed for peer review) have of active methodologies and to describe the perception and opinion of the modes of organization, methodological focuses, and evaluation systems that define the teaching-learning process. On surveying the professors and the students in their classes, we found significant differences in 32 of the 92 variables in common. The content of these results shows that professors and students are believe they are making progress toward a learning-centered model, that implementation of active methodologies implies new functions in their teaching practice.

In this paper we present the numerical and experimental results of a design optimization of electromagnetic cantilevers. In particular, a cost-effective technique of evolutionary computing enabling the simultaneous minimization of multiple criteria is applied. A set of optimal solutions are subsequently fabricated and measured. The designed structures are fabricated in arrays, which makes the comparison and measurements of the sensor properties reliable. The microfabrication process, based on the silicon on insulator (SOI) technology, is proposed in order to minimize parasitic phenomena and enable efficient electromagnetic actuation. Measurements on the fabricated prototypes assessed the proposed methodological approach.

The production of instant green tea requires hot-water extraction, which is time consuming and contributes to losses in aromatic compounds. In this study, an ultrasonic-assisted technology was used to improve the extraction efficiency of green tea, thereby shortening extraction time from 45 to 15 min. In pure water, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 0.23 mg/mL and total tea polyphenol dissolution decreased by 3.2 mg/mL. In 76.2% ethanol, the dissolution of caffeine and theanine did not change significantly, but total catechin dissolution increased by 1.57mg/mL and total tea polyphenol dissolution decreased by 1.5 mg/mL. Additionally, we used microwave-assisted technology to further improve the extraction efficiency of green tea, which shortened the extraction time to 2 min. However, the extraction rate remained unchanged. In pure water, the dissolution of caffeine and theanine did not change significantly, but the dissolution of total catechins increased by 0.41 mg/mL and the dissolution of tea polyphenols decreased by 2.9 mg/mL. Ultrasonic treatment increased the proportion of 3-hydroxybutan-2-one, (5S)-5-(hydroxymethyl)oxolan-2-one and 2-phenylethanol, which were the main aroma compounds in tea. Microwave treatment changed the aroma compounds in tea, causing losses in aroma compounds with low boiling point and maintaining (5S)-5-(hydroxymethyl)oxolan-2-one. The taste and aroma of instant green tea improved based on sensory evaluation results.

Increased attention has been paid to the influence of coal mining subsidence on ecological environment. Restoration of ecosystem in damaged mining area is critical for restoring disturbed environment. The comparing of plant communities and microbial communities in the artificial restoration and natural restoration areas provides an effective method for evaluating the restoration effects. However, such studies are limited in coal mining subsidence restoration areas. Subsidence area in Shendong mining area, located in the semi-arid region of Western China, was restored from 2003 with 5 ecological restoration plant species. In July 2017, the comparison and analysis of plant and microbial communities were conducted at the artificial restoration areas (AR) and the natural remediation areas (NR). The results showed that the artificial ecological restoration in Shendong mining area has achieved some success, but it has not recovered to a similar ecosystem before the destruction. A higher plant species, coverage and bacterial community diversity were observed in AR. However, these features have lower similarity compared with those in NR sites. Potential soil factors, such as pH, moisture content, total carbon content, organic matter, nitrogen and bulk density, have a greater impact on soil bacterial community structure and diversity. In the ecological restoration of the mining area, attention should be paid to the restoration of soil properties in the mining area. This study can provide theoretical guidance for more scientific ecological restoration in the damaged mining area.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

A radical, albeit pragmatic protocol for experimental access to putative String/M-theoretic, Einstein Unified Field Mechanical (UFM) additional dimensionality (XD) of the brane-bouquet bulk is presented. If successful, results demonstrate the existence dimensionality beyond the metric of observed physical reality provided by the Standard Model (SM) of particle physics and Cosmology. Quantum Mechanics (QM), as well-known is incomplete and further, should no longer be considered the basement of reality; meaning, Locality and Unitarity – the fundaments of quantum theory are an insufficient basis for extending the representation of reality. A seminal model of Tight Bound States (TBS) below the lowest Bohr orbit in the hydrogen atom, proposed by Vigier, is extended to a Kaluza-Klein-like (KK) cyclical tier of XD hyperspherical cavities defined within a manifold of uncertainty (MOU) of finite radius up to the semi-quantum limit, predicting, within its domain, new spectral lines in Hydrogen. The proposed protocol for this process, with phase modifications, provides efficacy of large-scale additional dimensionality (LSXD) of the brane bulk; XD-LSXD incursion duality accesses nonlocal Einsteinian UFM phenomena, leading to myriad new classes of technological innovation.

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

The emotional dimension in education has become increasingly important in recent decades. Enhancing the emotional dimension of prospective teachers in science subjects is higher education stuff responsibility. The implementation of active methodologies could modify the traditional student-teacher roles that are encouraged by the educational policies implemented in the Bologna Process. The principal aim of this work is to describe a Project Based Learning methodology and to introduce it as potential resource for the emotional and cognitive improvement of 19 prospective primary teachers enrolled in a scientific subject. This is a qualitative study with a transversal sustainability approach in the context of a research line focused on Higher Education for Sustainable Development. A questionnaire was designed and filled by the students at two different times, before and after implementation of the activity. The initial feedback from students was surprisingly enthusiastic by the fact that they were working with rockets, despite of this is not a common emotion in the science field. The results show the emotional improvement of prospective teachers after the implementation. It is concluded that a correct science education is necessary during the training of teachers taking into account their emotional dimension and the social repercussion due to the future transmission.

The Amoebic Self Theory is a concept of the social psychology, which postulates that humans have a psychological boundary. As the authors of the concept propose [1], the function of the boundary is to allow psychological separation of one from the others. In this study, we examined how sen-sitivity to violation of the boundary, measured by an amoebic self scale, is connected with differ-ent types of procrastination sensu lato, measured by seven procrastination subscales. Only two of the seven procrastination aspects, i.e. the preference for pressure and outcome satisfaction, were negatively and significantly related to the spatial-symbolic domain of the amoebic self scale. The other purpose of this research was to examine the connection between the students’ grade point average (GPA) and scores obtained in the procrastination subscales. Only the non-adaptive aspect of procrastination predicted significantly the GPA. That is an important detail, because pointing out the gap between one’s self-opinion and the real, non dependent of the opinion, academic achievement. All these findings were considered in the academic context and consequences of these results were discussed.

The conversion of CO2 into more synthetically flexible CO is an effective and potential method for CO2 remediation, utilization and carbon emission reduction. In this paper, the reaction of carbon-carbon dioxide (Boudouard reaction) was performed in a microwave fixed bed reactor using semi-coke (SC) as both the microwave absorber and reactant and was systematically compared with that heated in a conventional thermal field. The effects of the heating source, SC particle size, CO2 flow rate and additives on CO2 conversion and CO output were investigated. By microwave heating (MWH), CO2 conversion reached more than 99 %, while by conventional heating (CH), the maximum conversion of CO2 was approximately 29% at 900 °C. Meanwhile, for the reaction with 5 wt% Barium Carbonate added as a promoter, the reaction temperature was significantly reduced to 750 °C with almost quantitative conversion of CO2. Further kinetic calculations showed that the apparent activation energy of the reaction under microwave heating was 46.3 kJ/mol, which was only one-third of that observed under conventional heating. The microwave-assisted Boudouard reaction with catalytic barium carbonate is a promising method for carbon dioxide utilization.

Culture exploits the acquisition of meaningful content by crafting regimes of shared attention, determining what is relevant, valuable, and salient. Culture changes the field of relevant social affordances worthy of being acted upon in a context-sensitive manner. When relevant affordances are highly weighted, their attentional capture and their salience increase the probability of them being enacted due to the associated expectation for minimizing prediction error. This process is known as active inference. In the digital era, individuals need to infer the action-related attributes of digital cues, here characterized as digital affordances. The digital affordances of digital social platforms are of particular interest here. Digital social affordances are defined as online possibilities of social interactions. By their own nature, these are salient because they are related to social interactions and relevant social cues. However, the problem of digital social platforms is that they are not equivalent to situated social interactions because their structure is built, mediated, and defined by third-parties with diverse interests. The third-parties behind the digital social platforms are using the same mechanism exploited by culture to manipulate the shared patterns of attention. Moreover, digital social platforms are deliberately designed to be hyper-stimulating, making digital social affordances highly rewarding and increasingly salient. This appropriation, for economic purposes, is an issue of great importance, especially as the COVID-19 pandemic brought deep global changes, pushing societies to an online digital way of life. Here, we examined different types of digital social affordances under an active inference view, placing them into two categories, those for self-identity formation, and those for belief-updating. This paper aims to analyze digital social affordances in light of the prediction error dynamics they might elicit to their users. Although each of the analyzed digital social affordances allows different epistemic and instrumental digital actions, they all share the characteristic of having an "easy" and a fast expected rate of error reduction. Here, we aim to provide a new hypothesis about how the design behind digital social affordances is built on our natural attractiveness to minimize prediction error and the resulting positive embodied feelings when doing so. Finally, it is suggested that because digital social affordances are becoming highly weighted in the field of affordances, this might be putting at risk our context-sensitive grip on a rich, dynamic and varied field of relevant affordances.

Traditional education, particularly at a university level isn’t necessarily very engaging1 or strong at building teamwork skills. Educational escape rooms are a recent game based learning2 approach which combines team based problem solving with a story-line and cryptic clues. In this3 paper we apply the concept of educational escape rooms to the telecommunications engineering4 classroombycreatingaseriesoftwoseparatescenarios,eachcontainingthreepuzzles. Ourevaluation5 is based on beta tester survey results which suggest that this will be an engaging and challenging6 tool for teaching telecommunications engineering. Although educational escape rooms are rapidly7 being deployed in education, these are the first educational escape rooms that specifically addresses8 the field of telecommunications engineering.

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

The most popular schemes of digital potentiometers (DP) and digital-to-analog converters (DAC) are considered. DP and DAC use how elements of parameter adjustment of active RC-filters. The circuits of the DAC on the Kelvin-Varley divider, the Kelvin divider based on the R-2R matrix, and the architecture of the DAC with a segmented circuit are shown. The internal structure of DP an Analog Devices and Intersil company is shown, and also the DP switching circuits in a tunable low-pass filter and a band-pass filter, a programmable inverting amplifier and a voltage regulator are presented.

In recent years, team-based learning (TBL) is gaining popularity as a student-centered active collaborative learning strategy in healthcare education. This paper reports the design, implementation, and impact of a "hybrid team-based learning" (H-TBL) for one respiratory lecture in year two undergraduate physiotherapy program in 2019. A retrospective study was conducted, including 136 second-year undergraduate physiotherapy students using H-TBL design for one respiratory lecture topic. Student engagement was evaluated based on the percentage of completion for pre-class work, attendance to classroom session, and submission of formative creative assignment. Student' performance on formative creative tasks was evaluated based on thinking and learning rubric. Student perceptions were assessed based on the student's feedback using "Mentimeter." 109/ 136 (80%) students attended the COPD 2 session. 90/109 (82%) students engaged in COPD 1 (web-based) and tRAT in COPD 2 session. 54/109 (50%) students provided feedback and 67/90 (74%) students submitted formal formative creative assignment on completion of COPD 2 session. This study confirms that H-TBL enhances student's active engagement, creativity, and equilibration of their subject knowledge. Future randomized studies are mandated to explore the validity and specificity of H-TBL in diverse physiotherapy curriculum to evaluate the long-term student engagement and academic performance.

Depletion of ore deposits, increasing demand for raw materials, the need to process low-grade, complex and finely disseminated ores and the reprocessing of tailings are challenges, especially for froth flotation separation technologies. Even though capable of handling relatively fine grain sizes the flotation separation of very fine and ultrafine particles faces many problems still. Further, the flotation of low-contrast semi-soluble salt-type minerals with very similar surface properties, many complex interactions between minerals, reagents and dissolved species often result in poor selectivity. This study investigates the flotation beneficiation of ultrafine magnesite rich in dolomite from de-sliming, currently reported to the tailings. The paper especially focuses on the impact of the depressant sodium hexametaphosphate (SHMP) on: (i) the froth properties using dynamic froth analysis (DFA), (ii) the separation between magnesite and dolomite/calcite and (iii) its effect on the entrainment. Furthermore, the application of 1-hydroxyethylene-1,1-diphosphonic acid (HEDP) is a more environmentally friendly and low-cost alternative to SHMP is presented and discussed. The paper contributes to understanding on the complexity of depressant responses in froth flotation.

The effect of aqueous extracts of the biomass of the adult Ulomoides dermestoides beetle on the delayed effects of the defoliant paraquat causing parkinsonism in experimental mice was evaluated. The motor activity of the animals was analyzed in behavioral tests using a rotarod and a vertical pole. The number of tyrosine hydroxylase-immunopositive neurons in the ventral part of the substantia nigra of the midbrains of experimental and control mice was studied by immunohistochemistry. In the model in vitro system with SH-SY5Y human neuroblastoma, the effect of the extracts on cell proliferation was examined in the absence and presence of the neurotoxin MPP⁺. The isolation of biologically active substances from raw biomass using cavitation effects made it possible to obtain extracts with protective properties in the model of an early stage of Parkinson's disease used in this study.

The aim was to examine the sociodemographic predictors associated with changes in movement behaviours (physical activity, screen time and sleep) among toddlers and pre-schoolers during the early stages of the coronavirus disease 2019 pandemic in Chile. Caregivers of 1- to 5-year-old children completed an online survey between March 30th and April 27th, 2020. Information about the child's movement behaviours before (retrospectively) and during the pandemic, as well as family characteristics were reported. In total, 3,157 participants provided complete data (mean children age: 3.1±1.38 years). During early stages of the pandemic, time spent in physical activity decreased, recreational screen time and sleep duration increased, and sleep quality declined. Toddlers and pre-schoolers with space to play at home and living in rural areas experienced an attenuated impact of the pandemic restrictions on their physical activity levels, screen time, and sleep quality. Older children, those whose caregivers had a higher educational level, and children living in apartments had greater changes, mainly a decrease in total physical activity and increase in screen time. This study has shown the significant impact of the pandemic restrictions on movement behaviours in toddlers and pre-schoolers in Chile.

Forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate change in recent years, other regions of our planet have also been affected by this phenomenon, never seen before. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, as well as remote sensing one, is highly concerned in developing reliable techniques to provide useful support to the competent authorities. In particular, three specific tasks have been carried out in this work: (i) fire risk prevention, (ii) active fire detection, and (iii) post-fire area assessment. To accomplish these analyses, the capability of a set of spectral indices, derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires. The spectral indices are obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and openly accessible. Moreover, the twin Sentinel-2 sensors allow to overcome some restrictions on time delivery and observation repeat time. The performance of the proposed analyses were assessed experimentally to monitor the forest fires occurred in two specific study areas during the summer of 2017: the volcano Vesuvius, near Naples, and the Lattari mountains, near Sorrento (both in Campania, Italy).

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take the London, Tokyo, Shanghai, Toronto and Nasdaq as the representative examples.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

Low-cost RGB-D cameras are increasingly used in several research fields including human-machine interaction, safety, robotics, biomedical engineering and even Reverse Engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras proved to be among the best suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e. ~160-10000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations on the RealSense D415. In particular, tests are carried out to assess the device performances in the near range (i.e. 100-1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e. the German VDI/VDE 2634 part 2 normative) with a number of literature-based strategies. Performance analysis is finally compared against latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in Reverse Engineering applications.

This study evaluated the impact of climate change on water resources in a large semi-arid urban watershed located in Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface-subsurface HydroGeoSpheremodel at a high spatial resolution. Historical (1980-2005) and projected (2020-2050) climate scenario derived from the outputs of three Regional Climate Models (RCM) under the RCP 4.5 scenario were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predict increases of up to 1.6% in annual rainfall and of 1.58°C for mean annual temperatures between the historical and projected periods. The durations of the Minimum Environmental Flow (MEF) conditions, required to supply drinking and agriculture water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater for (2020-2030), and then they will be reduced for (2030-2050). All three RCMs consistently project a rise in groundwater table of more than 10 meters in topographically high zones where the groundwater table is deep and an increase of 2 meters in the shallow groundwater table.

Lung nodule segmentation in CT images and its subsequent volume analysis can help determinethe malignancy status of a lung nodule. While several efficient segmentation schemes have beenproposed, only a few studies evaluated the segmentation’s performance for large nodules. In thisresearch, we contribute a semi-automatic system which is capable of performing robust 3-D segmen-tations on both small and large nodules with good accuracy. The target CT volume is de-noisedwith an anisotropic diffusion filter and a region of interest is selected around the target nodule ona reference slice. The proposed model performs nodule segmentation by incorporating a mean in-tensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptivetechnique using image intensity histogram to estimate the desired mean intensity of the nodule.The proposed system is validated on both lung nodules and phantoms collected from publicly avail-able diverse databases. Quantitative and visual comparative analysis of the proposed work withthe Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented.The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, themean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved resultssuggest that the proposed method can be used for volume estimations of small as well as large-sizednodules.

An extended classification of active RC- and RLC- filters with Sallen-Key architectures, including those created in the last three years, is proposed. This makes it possible to formulate new design directions for an extended family of active RC- and RLC- Sallen-Key filters (PF, LPF, HPF, RF) for the tasks of high-speed and ultra-fast analog-to-digital signal conversion.

Addition of the bulky redox-active diphosphine 1,8-bis(diphenylphosphino)naphthalene (dppn) to [Fe2(CO)6(-edt)] (1) (edt = 1,2-ethanedithiolate) affords [Fe2(CO)4(2-dppn)(-edt)] (3) as the major product, together with small amounts of a P-C bond cleavage product [Fe2(CO)5{1-PPh2(1-C10H7)}(-edt)] (2). The redox properties of 3 have been examined by cyclic voltammetry and it has been tested as a proton-reduction catalyst. It undergoes a reversible reduction at E1/2 = –2.18 V and exhibits two overlapping reversible oxidations at E1/2 = –0.08 V and E1/2 = 0.04 V. DFT calculations show that while the HOMO is metal-centred (Fe-Fe -bonding), the LUMO is primarily ligand-based but also contains an antibonding Fe-Fe contribution, highlighting the redox-active nature of the diphosphine. It is readily protonated upon addition of strong acids to afford two isomeric hydride complexes and catalyzes the electrochemical reduction of protons at Ep = –2.00 V in the presence of CF3CO2H. The catalytic current indicates that it is one of the most efficient diiron electrocatalysts for the reduction of protons, albeit operating at quite negative potential.

A new set of alkoxy alcohols were synthesised by reaction of eugenol oxirane with aliphatic and aromatic alcohols. These eugenol derivatives were evaluated against their effect upon the viability of the insect cell line Sf9 (Spodoptera frugiperda).The most promising compounds, 4-(3-(tert-butoxy)-2-hydroxypropyl)-2-methoxyphenol and 4-(2-((4-fluorobenzyl)oxy)-3-hydroxypropyl)-2-methoxyphenol were submitted to in silico assays to predict possible targets. Thought an Inverted Virtual Screening approach, 23 common pesticide targets were screened and the top 2 targets predicted were further evaluated through molecular dynamics simulations and free energy calculations. In addition, these to eugenol derivatives were subjected to encapsulation and release assays using liposome-based nanosystems of egg phosphatidylcholine/cholesterol (7:3), with encapsulation efficiencies higher than 90% and release profiles well described by both Korsmeyer-Peppas and Weibull models.

A series of Ni/KIT6 catalystprecusors with 25wt.%Ni loading amount were reduced in H₂ at 400, 450, 500, and 550ºC, respectively. The studied catalysts were investigated by XRD, Quasiin-situ XPS, BET, TEM, and H₂-TPDanalysis methods.It was found that reduction temperature is an important factor affecting the hydrodeoxygenation (HDO) performance of the studied catalysts. The reduction temperature influences mainly the content of active components, crystal size, and the abilityfor adsorbing and activatingH2. The developed pore structure and large specific surface area of the KIT-6 support favored the Ni dispersion.The RT450 catalyst, which was prepared in H₂ atmosphere at 450 ºC, has the best HDO performance. Ethyl acetate can be completely transformed, and maintain 96.8% ethane selectivity and 3.2% methane selectivity at 300 ºC. The calculated apparent activation energies of the prepared catalysts increased in the following order: RT550 > RT400 > RT500 > RT450.

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improve the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared: Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps, however FCNNs are an emerging set of algorithms within Deep Learning with sparse application towards seagrass mapping. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiscale segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with standard OBIA method used by ecologists, while also noting an increase in performance for mapping ecological features that are sparsely labelled across the study site.

Virtual Histology- Intravascular Ultrasound (VH-IVUS) image is an available method for visualizing plaque component to detect thin cap fibroatheroma. Nevertheless, this imaging modality has considerable limitations to extract the plaque component features and identifying the TCFA plaque. The aim of this paper is to improve the identification of TCFA using fusion of IVUS and VH-IVUS images. In order to generate the automatic technique for reducing the human interaction, a new method namely Active Contour based Plaque Border Detection (ACPB) is proposed. In order to perform the pixel wise classification, hybrid of K-means algorithm with Particle Swarm Optimization and Plaque based Minimum Euclidean Distance (KMPSO-PMED) method is presented to classify the plaque region as well. Moreover, to obtain more significant information of imaging modalities, fusion of two different images consisting of VH-IVUS and IVUS is performed. Therefore, geometric features are extracted from the plaque region and combine with IVUS features. Furthermore, different group of plaque features are divided by means of the histopathological studies. SVM classifiers is applied to detect the TCFA and non-TCFA plaques. The proposed method is applied on 566 in-vivo IVUS and their matching VH-IVUS images obtained from 9 patients. The best result of SVM illustrates the accuracy rates of 99.41% for classification of TCFA plaque. The results prove that the highest accuracy is achieved by integrated features of IVUS and VH-IVUS images.

Fluorine represents a privileged building block in pharmaceutical chemistry. Diethylaminosulfur-trifluoride (DAST) is a reagent commonly used for replacement of alcoholic hydroxyl groups with fluorine and is also known to catalyze water elimination and cyclic Beckmann-rearrangement type reactions. In this work we aimed to use DAST for diversity-oriented semisynthetic transformation of natural products bearing multiple hydroxyl groups to prepare new bioactive compounds. Four ecdysteroids, including a new constituent of Cyanotis arachnoidea, were selected as starting materials for DAST-catalyzed transformations. The newly prepared compounds represented combinations of various structural changes DAST was known to catalyze, and a unique cyclopropane ring closure that was found for the first time. Several compounds demonstrated in vitro antitumor properties. A new 17-N-acetylecdysteroid (13) exerted potent antiproliferative activity and no cytotoxicity on drug susceptible and multi-drug resistant mouse T-cell lymphoma cells. Further, compound 13 acted in significant synergism with doxorubicin without detectable direct ABCB1 inhibition. Our results demonstrate that DAST is a versatile tool for diversity-oriented synthesis to expand chemical space towards new bioactive compounds.

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

Over the last decades, the energy market around the world has reshaped due to accommodating the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system are performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior.

In this paper, the active front-end (AFE) converter topology for the total harmonic distortion (THD) reduction in a wind energy system (WES) is used. A higher THD results in serious pulsations in the wind turbine (WT) output power and in several power losses at the WES. The AFE converter topology improves capability, efficiency and reliability in the energy conversion devices; by modifying a conventional back-to-back converter, from using a single voltage source converter (VSC) to use pVSC connected in parallel the AFE converter is generated. The THD reduction is done by applying a different phase shift angle at the carrier of digital sinusoidal pulse width modulation (DSPWM) switching signals of each VSC. To verify the functionality of the proposed methodology, the WES simulation in Matlab-Simulink® is analyzed, and the experimental laboratory tests using the concept of rapid control prototyping and the real-time simulator Opal-RT® Technologies is achieved. The obtained results show a type-4 WT with total output power of 6MVA, generating a THD reduction up to 5.5 times at the WES.

This paper presents a steady-state and transient heat conduction analysis framework using the polygonal scaled boundary finite element method (PSBFEM) with polygon/quadtree meshes. The PSBFEM is implemented with commercial finite element code Abaqus by the User Element Subroutine (UEL) feature. The detailed implementation of the framework, defining the UEL element, and solving the stiffness/mass matrix by the eigenvalue decomposition are presented. Several benchmark problems from heat conduction are solved to validate the proposed implementation. Results show that the PSBFEM is reliable and accurate for solving heat conduction problems. Not only can the proposed implementation help engineering practitioners analyze the heat conduction problem using polygonal mesh in Abaqus, but it also provides a reference for developing the UEL to solve other problems using the scaled boundary finite element method.

Agricultural landscapes are more and more characterized by intensification and habitat losses. Landscape composition and configuration are known to mediate insect abundance and richness. In the context of global insect decline, and despite 75% of crops being under insect’s dependence, there is still a gap of knowledge about the link between pollinators and aromatic crops. Fennel (Foeniculum vulgare) is an aromatic plant cultivated in South of France, for its essential oil which is of great economic interest. Using pan-traps, we investigated the influence of the surrounding habitats at landscape scale (semi-natural habitat proportion and vicinity, landscape configuration) and local scale agricultural practices (insecticides and patch size) on fennel-flower-visitor abundance and richness and their subsequent impact on fennel essential oil yield. We found that fennel may to be a generalist plant species. We did not find any effect of intense local management practices on insect abundance and richness. Landscape configuration and the proximity to semi-natural habitat were the main drivers of flying insect’s family richness. This richness positively influenced fennel essential oil yield. Maintaining a complex configuration of patches at the landscape scale are important to sustain insect diversity and crop yield.

Fiber reinforced thermoplastics (FRTP), which is reinforced with glass or carbon fibers, are used to improve the mechanical strength of injection-molded products. However, FRTP has problems such as the formation of weld lines, the deterioration of the appearance due to the exposure of fibers on the molded product surface, and the deterioration of the strength of molded products due to the fiber orientation in the molded products. We have designed and fabricated an injection mold capable of melt flow control and induction heating and cooling that has the functions of both heating and cooling the injection mold as well as the function of controlling the melt flow direction using a movable core pin. In this study, the above-mentioned mold was used for the molding of carbon fiber reinforced semi-aromatic polyamide. As a result, we found that increasing the heating temperature of the mold and increasing melt flow control volume contribute to the prevention of the generation of a weld line and the exposure of fibers on the molded product surface, as well as to the formation of a flat surface and increased bending strength. The relationships of these results with the carbon fiber orientation in the molded products and the crystallization of semi-aromatic polyamide were also examined in this study.

The purpose of this work is to study quasi-stationary wave structure in the mid-latitude stratosphere and mesosphere (40–50°N) and its role in the formation of the annual ozone cycle. Geopotential height and ozone from Aura MLS data are used and winter climatology for January–February 2011–2020 is considered. More closely examined is the 10-degree longitude segment centered on Longfengshan Brewer station, China, and located in the region of the Aleutian Low influence associated with the quasi-stationary zonal maximum of total ozone. Annual and semi-annual oscillations in ozone were compared using units of ozone volume mixing ratio and concentration, as well as changes in ozone peak altitude and in time series of ozone at individual pressure levels between 316 hPa (9 km) and 0.001 hPa (96 km). The ozone maximum in the vertical profile is higher in volume mixing ratio (VMR) values than in concentration by about 15 km (5 km) in the stratosphere (mesosphere), in consistency with some previous studies. We found that the properties of the annual cycle are better resolved in the altitude range of the main ozone maximum: middle–upper stratosphere in VMR and lower stratosphere in concentration. Both approaches reveal SAO/AO-related changes in the of ozone peak altitudes in a range of 4–6 km during the year. In the lower-stratospheric ozone of the Longfengshan domain, an earlier development of the annual cycle takes place with a maximum in February and a minimum in August compared to spring and autumn, respectively, in zonal means. This is presumably due to the higher rate of dynamical ozone accumulation in the region of the quasi-stationary zonal ozone maximum. The “no-annual-cycle” transition layers are found in the stratosphere and mesosphere. These layers with undisturbed ozone volume mixing ratio throughout the year are of interest for more detailed future study.

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation provides also a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

Shape-memory hydrogels (SMH) are as multifunctional, actively-moving polymers of interest in biomedicine. In loosely crosslinked polymer networks gelatin chains may form triple helices, which can act as temporary netpoints in SMH, depending on the presence of salts. Here, we show programming and initiation of the shape-memory effect of such networks based on a thermomechanical process compatible with the physiological environment. The SMH were synthesized by reaction of glycidylmethacrylated gelatin with OEG α,ω-dithiols of varying crosslinker length and amount. Triple helicalization of gelatin chains is shown directly by wide-angle X-ray scattering and indirectly via the mechanical behavior at different temperatures. The ability to form triple helices increased with the molar mass of the crosslinker. Hydrogels had storage moduli of 0.27-23 kPa and Young’s moduli of 215-360 kPa at 4 °C. The hydrogels were hydrolytically degradable, with full degradation to water soluble products within one week at 37 °C and pH = 7.4. A thermally-induced shape-memory effect is demonstrated in bending as well as in compression tests, in which shape recovery with excellent shape recovery rates Rr close to 100% were observed. In the future, the material presented here could be applied e.g. as self-anchoring devices mechanically resembling the extracellular matrix.

This study examined the effect of an educational hybrid physical education (PE) intervention on cognitive performance and academic achievement in adolescents. A 9-month group-randomized controlled trial was conducted in 150 participants (age: 14.63 ± 1.38) allocated into control group (CG, n = 37) and experimental group (EG, n = 113). Inhibition, verbal fluency, planning and academic achievement were assessed. Significant differences were observed in the post-test for cognitive inhibition, verbal fluency in animals, and the average from verbal fluency in favour of the EG. With regard to the intervention, verbal fluency in animals, verbal fluency in vegetables, the average of verbal fluency, cognitive inhibition, language, the average of all subjects, the average of all subjects except PE, and the average from the core subjects) increased significantly in the EG. The last five variables (the academic ones and cognitive inhibition) also increased in the CG, in addition to mathematics. This study contributes to the knowledge by suggesting that both methodologies produced improvements in the measured variables, but the use of a hybrid program based on TPSR and gamification strategies produce improvements in cognitive performance, specifically through the cognitive inhibition and verbal fluency.

In regions with low-temperature action transverse cracks can appear in asphalt pavements as a result of thermal stresses that exceed the fracture strength of materials used in asphalt layers. To better understand thermal cracking phenomenon, strength properties of different asphalt mixtures were investigated. Four test methods were used to assess the influence of bitumen type and mixture composition on tensile strength properties of asphalt mixtures: tensile strength using the Thermal Stress Restrained Specimen Test (TSRST) and the Uniaxial Tension Stress Test (UTST), flexural strength using the Bending Beam Test (BBT) and fracture toughness using the Semi-Circular Bending Test (SCB). The strength reserve behavior of tested asphalt mixtures was assessed as well. The influence of cooling rate on strength reserve was investigated and correlations between results from different test methods were also analyzed and discussed. It was observed that the type of bitumen is a factor of crucial importance to low-temperature properties of the tested asphalt concretes. This conclusion was proved by all test methods that were used. It was also observed that the level of cooling rate influences the strength reserve and, in consequence, resistance to low-temperature cracking. It was concluded that reasonably good correlations were observed between strength results for the UTST, BBT and SCB test methods.

This paper deals with a non-standard finite difference scheme defined on a quasi-uniform mesh for approximate solutions of the Magneto-Hydro Dynamics (MHD) boundary layer flow of an incompressible fluid past a flat plate for a wide range of the magnetic parameter. We show how to improve the obtained numerical results via a mesh refinement and a Richardson extrapolation. The obtained numerical results are favourably compared with those available in the literature.

Ethnopharmacology experts face several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.

Rapid search and localization for nuclear sources can be an important aspect in preventing human harm from illicit material in dirty bombs or from contamination. In the case of a single mobile radiation detector, there are numerous challenges to overcome such as weak source intensity, multiple sources, background radiation, and the presence of obstructions, i.e., a non-convex environment. In this work, we investigate the sequential decision making capability of deep reinforcement learning in the nuclear source search context. A novel neural network architecture (RAD-A2C) based on the actor critic (A2C) framework and a particle filter gated recurrent unit for localization is proposed. Performance is studied in a randomized 20 x 20 m convex and non-convex environment across a range of signal-to-noise ratio (SNR)s for a single detector and single source. RAD-A2C performance is compared to both an information-driven controller that uses a bootstrap particle filter and to a gradient search (GS) algorithm. We find that the RAD-A2C has comparable performance to the information-driven controller across SNR in a convex environment and at lower computational complexity per action. The RAD-A2C far outperforms the GS algorithm in the non-convex environment with greater than 95% median completion rate for up to seven obstructions.

This work is concerned with the existence and uniqueness of boundary value problems defined on semi-infinite intervals. These kinds of problems seldom admit exactly known solutions and, therefore, the theoretical information on their well-posedness is essential before attempting to derive an approximate solution by analytical or numerical means. Our utmost contribution in this context is the definition of a numerical test for investigating the existence and uniqueness of solutions of boundary problems defined on semi-infinite intervals. The main result is given by a theorem relating the existence and uniqueness question to the number of real zeros of a function implicitly defined within the formulation of the iterative transformation method. As a consequence, we can investigate the existence and uniqueness of solutions by studying the behaviour of that function. Within such a context the numerical test is illustrated by two examples where we find meaningful numerical results.

The goal of newborn screening is to improve health outcomes by identifying and treating affected newborns. This manuscript provides an overview of a data tool to facilitate the longitudinal collection of health information on newborns diagnosed with a condition through NBS. The Newborn Screening Translational Research Network (NBSTRN) developed the Longitudinal Pediatric Data Resource (LPDR) to capture, store, analyze, visualize, and share genomic and phenotypic data over the lifespan of NBS identified newborns to facilitate understanding of genetic disease, and to assess the impact of early identification and treatment. NBSTRN developed a consensus-based process using clinical care experts to create, maintain, and evolve question and answer sets organized into common data elements (CDEs). The LPDR contains 24,172 core and disease-specific CDEs for 118 rare genetic diseases, and the CDEs are being made available through the NIH CDE Repository. The number of CDEs for each condition average of 2,200 with a range from 69 to 7,944. The LPDR is used by state NBS programs, clinical researchers, and community-based organizations. Case level, de-identified data sets are available for secondary research and data mining. The development of the LPDR for longitudinal data gathering, sharing, and analysis supports research and facilitates the translation of new discoveries into clinical practice.

Over the past few decades, synthetic polymer production has increased, and the production of plastics has surpassed all other man-made materials due to their versatility. Moreover, packaging plastic makes up around 40% of global plastic production. However, the excessive usage of synthetic packaging materials has led to severe environmental and health-related issues. Therefore, biobased packages are considered alternatives to substitute synthetic packaging on the market. Furthermore, the choice of packing material in the food industry is a perplexing process as it depends on various factors, such as the type of food product, its sustainability, and environmental conditions. Interestingly, due to proven gas barrier properties and biological activity, polysaccharide-based bioplastics show the potential to expand the trends in food packaging, including edible films and intelligent and active food packaging. This review discusses the structure, properties and recent developments in polysaccharide-based bioplastics as food packaging materials.

This study assesses the development of professional skills in university students using serious games (SG), from a sustainability perspective. The Sustainable Development Goals (SDGs) were set by the United Nations’ 2030 Agenda for Sustainable Development. Universities are strategic agents in the transformation process towards sustainability. This way, they should be committed to promoting such sustainable values in the students through curricular sustainability, implementing active methodologies and SG for that purpose. Transversal skills are essential for the development of future graduates. The objective of this study was to assess which professional skills should be developed through the SG called The Island, to improve the degree of student satisfaction with the incorporation of a sustainable curriculum. The data were obtained using a questionnaire, and then analysed using linear regression models, with their inference estimated through the goodness of fit and ANOVA. The first results indicated that the implementation of the SG promoted a strengthening of the students' sustainable curriculum through the development of those skills. It was concluded that the key to success in education for sustainable development is improving the development of strategic thinking, collaborative thinking, and self-awareness, in addition to encouraging systemic, critical, and problem-solving thinking.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

The military areas in Sardinia are around 234 km2, which constitutes 59.97% of the national surface affected by military easements. This situation is due to its geographic centrality in the Mediterranean. This contribution evaluates the performance of the Local Coastline Plan (LCP) and the Site Management Plan of Community Interest (SCI) in conditions of military constraint. The case study is the Municipality of Villaputzu where an important coastal military easement and the use of the coast for recreational tourism purposes coexist together through specific planning, a consequence of institutional agreements between the Municipal Administration of Villaputzu and the Ministry of Defense. The evaluation of the congruence of the specific objectives of the LCP and the SCI shows how their combined action favors the environmental enhancement of Sardinia, contributing to the formation of ecosystem services, even in particular conditions arising from military easements. These are sites that pass from the status of "anti-commons" to "semi-commons". In fact, the military release process in Sardinia, together with the promiscuous military and civil use, activates unique governance policies of their kind that find a significant field of application in Sardinia to guarantee a sustainable renewal of economic development of the ‘semi-commons’ awaiting to become ‘commons’

Durability monitoring of insecticide-treated nets (ITNs) containing a pyrethroid in combination with a second active ingredient (AI) must be adapted so that the insecticidal bioefficacy of each AI can be monitored independently. An effective way to do this is to measure rapid knock down of a pyrethroid-susceptible strain of mosquitoes to assess the bioefficacy of the pyrethroid component and to use a pyrethroid-resistant strain to measure the bioefficacy of the second ingredient. To allow robust comparison of results across tests within and between test facilities, and over time, protocols for bioefficacy testing must include either characterisation of the resistant strain, standardisation of the mosquitoes used for bioassays, or a combination of the two. Through a series of virtual meetings, key stakeholders and practitioners explored different approaches to achieving these goals. Via an iterative process we decided on the preferred approach and produced a protocol consisting of characterising mosquitoes used for bioefficacy testing before and after a round of bioassays, for example at each time point in a durability monitoring study. We present the final protocol and justify our approach to establishing a standard methodology for durability monitoring of ITNs containing pyrethroid and a second AI.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

Let $G$ be a group with identity $e$ and $R$ be a commutative $G$-graded ring with nonzero unity $1$. Graded semi-primary and graded $1$-absorbing primary ideals have been investigated and examined by several authors as generalizations of graded primary ideals. However, these three concepts are different. In this article, we characterize graded rings over which every graded semi-primary ideal is graded $1$-absorbing primary and graded rings over which every graded $1$-absorbing primary ideal is graded primary.

Carbon nanotubes (CNTs) are effective supports for nano metals and together they represent hybrids that combine unique properties of both. A microwave induced reaction was used to deposit nano palladium on carboxylated and octadecylamine functionalized multiwall CNTs, which were used to carry out C-C coupling reactions in DMF and toluene. These hybrids showed excellent catalytic activity with yield as high as 99.8% while its enhancement with respect to commercially available Pd/C catalyst reached as high as 109%, and the reactions times were significantly lower. Polarity of the functionalized form was found to be a significant factor with the polar carboxylated CNT showing better activity in DMF while the relatively nonpolar octadecyl amine was better in toluene. The results suggest the possibility of tailor making functionalized CNT when used as catalyst supports

Variable nitrogen(N) rate fertilization based on remote sensing is challenging for cotton production fields, but active crop canopy sensors (ACS) appear as an alternative to make this practical on farm since they can be used at night as well. The crop spatial variability in inherent in crop production in general, and not on-the-go solutions can be used with this type of active sensing technologies. Thus, the purpose of this study was to investigate the potential of two vegetation indices to identify the N requirement variability for cotton plants and to develop prototype algorithms for topdressing nitrogen variable rate on commercial and experimental areas, using the N-sufficiency methodology based on virtual reference. The concept of virtual reference is to use a histogram to characterize the vegetation index of properly fertilized plants without establishing an N-rich plot as a reference strip. The experiment was conducted in strips with four different N rates (0, 45, 90 and 180 kgN ha^-1) during the 2015, 2016, 2017 and 2018 crop seasons in partnership with large cotton producers in Mato Grosso and also in experimental area of Embrapa Agrosilvopastoral. Two algorithms for variable rate nitrogen fertilization for cotton were developed, namely: 1) N recommendation algorithm for cotton in commercial production system: N rate (kg.N ha^-1) = -234.79 ISN² + 49,879 ISN + 195.15; R² = 0.97; and 2) for cotton grown in experimental area: N dose (kgN ha^-1) = -174.73 ISN² - 107.21 ISN + 306.78; R² = 0.94.

Films prepared from poly(3-hydroxybutyrate-co-4-hydroxybutyrate) copolymers produced by Aneurinibacillus sp. H1 using an automatic film applicator were homogeneous and had a defined thickness, which allowed a detailed study of physicochemical properties. Their properties were compared with a poly (3-hydroxybutyrate) homopolymer film prepared by the same procedure, which proved to be significantly more crystalline by DSC and XRD. Structural differences between samples had a major impact on their properties. With increasing 4-hydroxybutyrate content, the ductility and release rate of the model hydrophilic active ingredient increased significantly. Other observed properties, such as the release of the hydrophobic active substance, the contact angle with water and ethylene glycol, or the surface morphology and roughness, were also affected by the composition. The identified properties predetermine these copolymers for wide use in areas such as biomedicine or smart biodegradable packaging for food or cosmetics. The big advantage is the possibility of fine-tuning properties simply by changing the fermentation conditions.

The purpose of this work was to evaluate the suitability of recent US Food and Drug Administration (US-FDA) approved and marketed oral liquid, powder, or granule products for children in North America, to identify the next group of Active Pharmaceutical Ingredients (APIs) that have high potential for development as commercially available FDA-approved finished liquid dosage forms , and to propose lists of compounded nonsterile preparations (CNSPs) that should developed as commercially available FDA-approved finished liquid dosage forms as well as those that pharmacists should continue to compound extemporaneously. Through this identification and categorization process, the pharmaceutical industry, government, and the professions are encouraged to continue to work together to improve the likelihood that patients will receive high quality standardized extemporaneous CNSPs and US-FDA-approved products.

The World Health Organization’s Health Economic Assessment Tool (HEAT) for walking and cycling is a user-friendly web-based tool to assess health impacts of active travel. HEAT, developed over 10 years ago, has been used by researchers, planners and policymakers alike in appraisals of walking and cycling policies of both national and more local scales. HEAT has undergone regular upgrades adopting the latest scientific evidence. This article presents the most recent upgrades of the tool. Health impacts of walking and/or cycling in a specified population are quantified in terms of premature deaths avoided (or caused). In addition to the calculation of benefits from physical activity, HEAT was recently expanded to include assessments of the burden associated with air pollution exposure and crash risks while walking or cycling. Further, impacts on carbon emissions from mode shift to active travel modes can now be assessed. Monetization of impacts using Value of Statistical Life and Social Costs of Carbon now uses country-specific values. As active travel inherently results in often substantial health benefits as well as not always negligible risks, assessments of active travel behaviour or policies are incomplete without considering health implications. The recent developments of HEAT make it easier than ever to obtain ballpark estimates of health impacts and carbon emissions related to walking and cycling.

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

According to the Cosmological Principle the Universe is isotropic and no preferred direction would be seen by an observer that might be stationary with respect to the expanding cosmic fluid. However, because of observer’s partaking in the solar system peculiar motion, there would appear in some of the observed properties of the Cosmos a dipole anisotropy, which could in turn be exploited to determine the peculiar motion of the solar system. The dipole anisotropy in the Cosmic Microwave Background Radiation (CMBR) has given a peculiar velocity vector 370 km s−1 along l=264∘,b=48∘. However, some other dipoles, for instance, from the number counts, sky brightness or redshift distributions in large samples of distant Active Galactic Nuclei (AGNs), have yielded values of the peculiar velocity many times larger than that from the CMBR, though surprisingly, in all cases the directions agreed with the CMBR dipole. Here we determine our peculiar motion from a sample of 0.28 million AGNs, selected from the Mid Infra Red Active Galactic Nuclei (MIRAGN) sample comprising more than a million sources. From this, we find a peculiar velocity, which is more than four times the CMBR value, although the direction seems to be within ∼2σ of the CMBR dipole. A genuine value of the solar peculiar velocity should be the same irrespective of the data or the technique employed to estimate it. Therefore, such discordant dipole amplitudes, might mean that the explanation for these dipoles, including that of the CMBR, might in fact be something else. But, the observed fact that the direction in all cases, is the same, though obtained from completely independent surveys using different instruments and techniques, by different sets of people employing different computing routines, might nonetheless indicate that these dipoles are not merely due to some systematics, otherwise why would they all be pointing along the same direction. It might instead suggest a preferred direction in the Universe, implying a genuine anisotropy, which would violate the Cosmological Principle, the core of the modern cosmology.

Aiming the characterization of road dusts on the surrounding villages of an active mine and to assess metal(loids) bioaccessible fractions, a dust samples survey was undertaken. To achieve these objectives, pseudototal element concentration, mineralogical composition, Enrichment factor and in vitro extraction UBM assay for both gastric (G) and gastrointestinal (GI) phases, were determined. Results obtained revealed that arsenic represents the higher risk to humans, with mean pseudototal values higher than the maximum reference value range. Enrichment factor points to As significant to very high enrichment in all villages. Particles enriched in As, Ca, Fe, Cu, Al, Ti were identified by SEM-EDS in weathered agglomerates, linked to mine wastes and long-distance transport, both by wind and/or traffic. Arsenic bioaccessibility fraction (%BAF) presents low values in the studied samples, possibly due to low Fe complexes solubility with adsorbed As, limiting arsenic release and reducing its bioaccessibility. Concentrations of Cd bioaccessible G and GI phases are within the reference values range, while for Cu are above and for Pb are lower, than reference value range. Results show that the pseudototal fraction risk is overestimated when comparing to BAF (bioaccessible fraction), nevertheless, total G and GI risk are above the carcinogenic target risk (1.00E-06) in most samples. Carcinogenic risk of the bioaccessible contaminants shows that As is the element that represents higher risk for developing cancer over a lifetime, being ingestion the main risk route.

The aim of this article is to extend the local as well as the semi-local convergence analysis of multi-point iterative methods using center Lipschitz conditions in combination with our idea, of the restricted convergence region. It turns out that this way a finer convergence analysis for these methods is obtained than in earlier works and without additional hypotheses. Numerical examples favoring our technique over earlier ones completes this article.

Medical research has suggested that the anterior-posterior (AP)-diameter of the inferior vena cava (IVC) and its associated temporal variation as imaged by bedside ultrasound is useful in guiding fluid resuscitation of the critically-ill patients. This paper develops semi-automatic active ellipse and rectangle algorithms for measurement and tracking of the AP-diameter. The proposed algorithms are compared with an expert manual measurement and the previous work based on active circle model. It is shown that regardless of the shape of the IVC, the rectangle model always outperforms the two other models and performs very close to manual measurement.

As is well known, the main contribution of the FRP strips to the strength of load-bearing walls is an improvement in the in-plane strength. This paper deals with the possibility of applying the FRP strips in way to modify the strengthening mechanism of the FRP reinforcing system, from an in-plane to an out-of-plane strengthening mechanism. In order to achieve this goal, a second reinforcement system – derived from the CAM system (Active Confinement of Masonry) – provides connections between the FRP strips placed on the opposite sides of the wall. This new strengthening technique – called the straps/strips technique – establishes a stiffness constraint that forces the opposing FRP strips to behave like two flanges of an FRP I-beam embedded in the wall. Consequently, the use of FRP strips also improves the flexural strength of the wall. The present paper briefly summarizes the results obtained in previous works with the straps/strips technique and proposes an improvement of this strengthening technique, based on some weak-points emerged in the early experimentations. The paper also shows the results of a further experimental test, performed with the improved straps/strips technique. Finally, the similarity between FRP strips with transversal connection and concrete wythes of a sandwich panel with flexible connectors leads to interpret the behavior of the ideal I-beam in terms of composite action established between the FRP strips. This paves the way for analytical modeling of the straps/strips technique.

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This article improves upon a promising extant method – Batch-mode Expected Model Change Maximization (B-EMCM) method – for selecting examples to be labeled for regression problems. Specifically, it develops and evaluates alternate strategies for adaptively selecting batch size in B-EMCM. By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified to ensure that selected candidates are the most beneficial to model learning. This new methodology is compared to B-EMCM via mean absolute error and root mean square error over ten iterations benchmarked against machine learning data sets. Using multiple data sets and metrics across all methods, one variation of AB-EMCM, the max bound of the accumulated error (AB-EMCM Max), showed the best results for an adaptive batch approach. It achieved better root mean squared error (RMSE) and mean absolute error (MAE) than the other adaptive and non-adaptive batch methods while reaching the result in nearly the same number of iterations as the non-adaptive batch methods.

Natural products endowed of biological activity represent a primary source of commodities ranging from nutrition to therapeutic agents, as well as cosmetic tools, and recreational principles. These natural means have been used by mankind since centuries if not millennia. They are commonly used all over the world and socio-economical contexts but are particularly attractive in disadvantaged area or economically emerging situations all over the world. This is very likely due to the relatively easy recovery of these bioactive principles from the environment, to the low if any cost as well as ease of administration and to the general popular compliance concerning their consumption/ingestion. In this concise review, we focus on some popular bioactive principles of botanical origin which find a wide use in the Madagascan populations. But, due to space limitations only some most common and largely diffused principles in this country are considered. Finally, a possible nanotechnological administration is discussed in the case where a potential therapeutic usage is envisaged.

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring under different physical activity levels in a postprandial state in healthy and active subjects without diabetes. Ten healthy, active subjects (26±4 years, 67±11 kg bodyweight (BW), 11±3 h) were included in the study. Within 14 days, they underwent six tests consisting of a) resting fasted (R/Fast), b) resting after intake of 1 g glucose/kg BW (R/Glc) and c) running for 60 minutes at moderate (65/Glc) and d) high (85/Glc) intensity after intake of 1 g glucose/kg BW. Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to simultaneous capillary blood glucose concentrations. Pearson’s r correlation coefficient was highest for R/Glc (.92, p<.001) compared to R/Fast (.45, p<.001), 65/Glc (.60, p<.001) and 85/Glc (.70, p<.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (R/Fast: 8±6%, R/Glc: 17±12%, 65/Glc: 22 ± 24%, 85/Glc: 18±17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. However, further data in healthy subjects need to be gathered considering physical activity and nutrition status.

Search for periodic signals from blazars has become widely discussed topic in recent years. This is because periodic signals bear imprints of the processes occurring near the innermost regions of blazars which are mostly inaccessible to our direct view. Such signals provide insights into various aspect of blazar studies including disk-jet connection, magnetic field configuration and, more importantly, strong gravity near the supermassive black holes and release of gravitational waves from the binary supermassvie black hole systems. In this work, we report detection of a periodic signal in the radio light curves of the blazar J1043+2408 spanning ~10.5 years. We performed multiple methods of time series analysis, namely, epoch folding, Lomb-Scargle periodogram, and discrete auto-correlation function. All three methods consistently reveal a repeating signal with a periodicity of ~563 days. To robustly account for the red-noise processes usually dominant in the blazar variability and other possible artifacts, a large number of Monte Carlo simulations were performed. This allowed us to estimate a high significance (99.9% local and 99.4% global) against possible spurious detection. As possible explanations, we discuss a number of scenarios including binary supermassive black hole system, Lense-Thirring precession, and jet swing and precession.

Due to the rapid development of nanotechnologies, materials, in particular , solid dispersions (SDs), which are actively introduced into the life of modern man, have been obtained. Special progress in this area is observed in industry and medicine. The use of SDs in agriculture is lagging far behind, despite the growing number of scientific papers on this topic. At the same time, the prospects for the introduction of SDs in the agro-industrial complex are obvious. The review presents the results of research on the development of innovative preparations based on SD to protect plants from diseases and pests of cultivated plants, as well as parasiticides to protect animal health based on modern achievements of nanotechnology. One of these technologies is the methods of mechanochemistry, which improve the properties of poorly soluble biologically active substances by their joint mechanical treatment with water-soluble polymers and auxiliary substances.

Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR-camera fusion and semi-supervised learning implemented in a fully-convolutional neural network. Our method is tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all-weather scenarios, featuring night and rainy weather. In this work, it is shown that LiDAR-camera fusion with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm is measured in terms of intersection over union, precision, recall and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

In this paper we compare using computational fluid dynamics the aerothermal performance of two candidate casing manifolds for supplying an impingement-actuated active tip clearance control system for an aero-engine high-pressure turbine. The two geometries are (a) single-entry: an annular manifold fed at one circumferential location; (b) multiple-entry: a casing manifold split into four annular sectors, each sector supplied separately from an annular ring main. Both the single-entry and multiple-entry systems analysed in this paper are idealised version of active clearance control systems in current production engines. Aerothermal performance is quantitatively assessed on the basis of the heat transfer coefficient distribution, driving temperature difference for heat transfer between the jet and casing wall, and total pressure loss within the high-pressure turbine active clearance control system. We predict that the mean heat transfer coefficient (defined with respect to the inlet temperature and local wall temperature) of the single-entry active clearance control system is 77% greater than the multiple-entry system; primarily because the coolant in the multiple-entry case picks up approximately 40 K of temperature from the ring main walls, and secondarily because the average jet Reynolds number of impingement holes in the single entry system is 1.2 times greater than in the multiple entry system. The multiple-entry system exhibits many local hot and cold spots, depending on the position of the transfer boxes, while the single-entry case has a more predictable aerothermal field across the system. The multiple-entry feed system uses an average of 20% of the total available pressure drop, while the feed system for the single-entry geometry uses only 2% of the total available pressure drop. From the aerothermal results of this computational study, and in consideration of holistic aero-engine design factors, we conclude that a single-entry system is closer to an optimal solution than a multiple-entry system.

Theories of quantum gravity suggest that Lorentz invariance, the fundamental symmetry of the Theory of Relativity, may be broken at the Planck energy scale. While any deviation from conventional Physics must be minuscule in particular at attainable energies, this hypothesis motivates ever more sensitive tests of Lorentz symmetry. In the photon sector, astrophysical observations, in particular polarization measurements, are a very powerful tool because tiny deviations from Lorentz invariance will accumulate as photons propagate over cosmological distances. The Standard-Model Extension (SME) provides a theoretical framework in the form of an effective field theory that describes low-energy effects due to a more fundamental quantum gravity theory by adding additional terms to the Standard Model Lagrangian. These terms can be ordered by the mass dimension d of the corresponding operator and lead to a wavelength, polarization, and direction dependent phase velocity of light. In this paper, we analyze optical polarization measurements from 63 Active Galactic Nuclei (AGN) and Gamma-ray Bursts (GRBs) in order to search for Lorentz violating signals. We derive the first set limits on each of the 10 individual birefringent coefficients of the minimal SME with d = 4, with 95% confidence limits on the order of 10⁻³⁴ on the dimensionless coefficients.

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

This paper deals with the design, simulation and implementation of shunt hybrid compensator to maintain the power quality in 3-phase distribution networks feeding different types balanced and unbalanced nonlinear loads. The configuration of the compensator consists of a selective harmonic elimination passive filter, a series connected conventional 6-pulse IGBT inverter, acting as the active filter terminated with a dc link capacitor. The theory and modelling of the compensator based on current harmonic components at the load end and their decomposition in d-q axis frame of reference are utilized in the reference current generation algorithm. Accordingly, the source current waveform is made to follow the reference current waveform using a high frequency carrier based controller. Further, this inner current control loop is supported by a slower outer voltage control loop for sustaining desirable dc link voltage. Performance of the compensator is evaluated through MATLAB simulation covering different types of loads and reduction of harmonic currents and THD at the supply side along with excellent regulation of dc link voltage are confirmed. The performance of a hybrid compensator designed and fabricated using the above principles is evaluated and corroborated with the simulation results.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

Place-based education (PBE) and active learning are effective strategies for geoscience education. However, traditional PBE via field trips requires significant resources, time, physical abilities, and expertise of teachers. We provide an alternative PBE experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location specific videos about Earth events to create a virtual PBE experience. Using Google Earth, GEGVL organizes place-based videos by locations and links pertinent non-place-based videos and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower division undergraduates show that the design logic of GEGVL is promising virtual PBE organizer for interesting students in and helping them learn about Earth sciences.

Background: A large majority of primary school pupils fail to achieve 30-minutes in-school moderate-to-vigorous physical activity (MVPA). The aim of this study was to investigate MVPA accumulation and subject frequency during academic lesson segments and the broader segmented school day. Methods: 122 children (42.6% boys; 9.9±0.3yrs) from six primary schools in North East England, wore uniaxial accelerometers for eight consecutive days. Subject frequency was assessed by teacher diaries. Multilevel models (children nested within schools) examined significant predictors of MVPA across each school-day segment (lesson one, break, lesson two, lunch, lesson three). Results: Pupils averaged 18.33±8.34 minutes of in-school MVPA and 90.2% failed to achieve the in-school 30-minute MVPA threshold. Across all school-day segments, MVPA accumulation was typically influenced at the individual level. Lesson one and two - dominated by Math and English - were less active than lesson three. Break and lunch were the most active segments. Conclusion: This study breaks new ground, revealing MVPA accumulation and subject frequency varies greatly during different academic lessons. Morning lessons were dominated by the inactive delivery of Math and English, whereas afternoon lessons involved a greater array of subject delivery that resulted in marginally higher levels of MVPA.

(1) Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study is aimed at showing the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. (2) Methods: 2173 Vacuum Assisted Breast Biopsies (VABB) documented as DCIS have been reviewed. Surgery has been performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. (3) Results: We observed 2173 cases of DCIS: 408 classified as low grade; 1262 as intermediate grade; 503 as high grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was significantly lower (8.2%) when considering patients showing mammographically documented complete removal of the lesion. (4) Conclusion: The absence of mammographically documented residual lesion following VABB is associated to a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

Low levels of physical activity (PA) and sedentarism are associated with the onset of different pathologies and health problems. Regular physical activity has been linked with being beneficial to the health of the general population. Within this framework of analysis, the aim of the present study is to analyse the existing association between the time spent doing physical activity and the expressed motives for doing so, from which the first innovative aspect of the paper emerges: the use of the time spent doing PA as a study variable of the phenomenon. The data analysed come from the latest special Eurobarometer survey about the sport and physical activity done in Europe. Using an exploratory factorial analysis and a structural equations model, we were able to find a six-dimensional factorial model that explains the reasons for doing PA, demonstrating that there is no relationship between the reasons for and time spent doing PA. Therefore, motivation is not a variable that explains the time spent doing PA and another type of variable must be used to explain the phenomenon if PA is to be incentivised.

Pandemic scenario has significantly changed several factors of life, including teaching, and learning at university. The development of suitable teaching materials to support university studies is mandatory to overcome distance learning difficulties and improve traditional teaching methodologies. This work explains a novel method for the preparation of teaching materials for medical sciences, but also plausible for other learning fields. An encephalon was extracted and prepared by using this methodology for teaching purposes. More than 200 students evaluated several factors of the material prepared this way, indicating a better understanding (up to 80%) of theoretical contents related to this human section, together with a high usability and good physical appearance

Innovative teaching strategies are designing a new and promising landscape in education. They fill up the lessons with creativity and imagination either for the students and teachers. This article addresses an attempt to make easier the approach to science in a non-scientific environment: primary education at university level. Gamification methodologies were combined with flipped classroom in order to free up in-class time and engage the students with the taught courses. A qualitative study was merged with quantitative measures of emotional and motivational parameters. These results were improved with four semi-structured interviews. The results clearly showed a raise in the students' motivational level, an acknowledgment of good teaching practice and an evident enhancement of felt positive emotions toward science teaching and scientific issues.

This paper assesses the behaviour of active distribution networks with high penetration of renewable energy sources when the control is performed in a centralised manner. The control assets are the on-load tap changers of transformers at the primary substation, the reactive power injections of the renewable energy sources and the active and reactive power exchanged between adjacent feeders when they are interconnected through a DC link. A scaled-down distribution network is used as testbed to emulate the behaviour of an active distribution system with massive penetration of renewable energy resources. The laboratory testbed involves hardware devices, real-time control, and communication infrastructure. Several key performance indices are adopted to assess the effects of the different control actions on the system operation. The experimental results demonstrate that the combination of control actions enables the optimal integration of a massive penetration of renewable energy.

It is well known that the event of hepatitis B virus reactivation can occur among patients undergoing treatment for hematological malignancies. In this paper we will present the available data regarding the risk of hepatitis B virus reactivation in this special population of immunosuppressed patients and explore the relevance of an accurate prevention and management of this condition. A computerized literature search was performed using appropriate terms arrangement, including English-written literature only or additional relevant articles. The evaluation of hepatitis B reactivation risk is a multidimensional process, which includes conducting an accurate clinical and physical history, considering the virological categories, the knowledge of the medication chosen to treat these hematological malignancies and the induced grade of immunosuppression. Adopting adequate preventive strategies and surveillance according to the current international recommendations is crucial to prevent HBVr and its dire clinical consequences (hepatitis, liver failure, interruption of lifesaving anti-neoplastic treatments). Universal HBV screening of patients scheduled to undergo treatment for hematological malignancies should be the chosen policy, and clinicians should be aware of the inherent risk of viral reactivation among the different virological categories and the classes of immunosuppressive drugs.

We considered a vacuum polarization around a massive object in the eikonal approximation and found two types of vacuum polarization. The first type has the equation of state similar to radiation and can produce a halo that increases the rotation velocity of a test particle with a radial distance. The second type of vacuum polarization has a more complicated equation of state. As a static physical effect, it produces renormalization of the gravitational constant. Besides, we demonstrate that a nonstationary polarization of the second type caused by a swift increase of the galactic nuclei mass results in a gravitational potential looking like a dark matter halo.

This paper presents an Application Based Active Learning (ABAL) methodology on Power Electronics (PE) and Electric Machines (EM) as a hybrid laboratory course for the undergraduate students to design and implement the real-world engineering problems. The ABAL is a type of active learning which is a branch of Learner-centered teaching (LCT). The DC/DC converter along with the speed control of DC separately excites the motor. In addition, a DC/AC converter is designed to control the speed of an induction motor. The results are then investigated on a hardware platform under the ABAL experimental methodology. This paper also discusses the problem identification selection of the equipment, circuit design, hardware mounting and critical analysis of the results acquired from the hybrid laboratory. The ABAL methodology was evaluated based on student satisfaction, feedback, grades and interest to solve the real-world problem rather than cramming the engineering concepts and fulfill so-called lab routine and tasks

Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

Background: Active ageing is a multidimensional, relative, and context-dependent concept with different paths and outcomes. The purpose of this qualitative study was to explore personal active ageing strategies in a specific context. Method: Following a directed content analyze procedure, we conduct semi structured individual interview with 39 elder (men and women) between the ages of 60-97 years that selected with purposeful sampling .data collection and analysis were concurrent. We analyzed the data from interviews, written narratives, and field notes using directed content analyze. The Reliability of data was fulfilled in accordance Lincoln and Guba criteria. We stopped data collection when no new concept was added and data saturation occurred. Results: Based on the experience of elders, we identified 5 categories: 1) Preventive 2) Coping 3) Internal self-control 4) Coherence maintenance strategies 5) Opportunity exploiting strategies. These described the active ageing strategies when encountering with age related change. Utilizing these strategies, the elder accompanied the life time. Conclusion: The finding suggests that active ageing is a continuous process in confronting age related change. The identified strategies can help to promote active ageing by familiarizing older with opportunities of life and training them in how to use these strategies.

Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ~100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role "tie-chains" play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.

Essential oils have demonstrated antiviral activity, but their toxicity can hinder their use as therapeutic agents. Recently, some essential oil components have been used within safe levels of acceptable daily intake limits without causing toxicity. The "ImmunoDefender," a novel antiviral compound made from a well-known mixture of essential oils, is considered highly effective in treating SARS-CoV-2 infections. The components and doses were chosen based on existing information about their structure and toxicity. Blocking the Main Protease (Mpro) of SARS-CoV-2 with high affinity and capacity is critical for inhibiting the virus's pathogenesis and transmission. In-silico studies were conducted to examine the molecular interactions between the main essential oil components in "ImmunoDefender" and SARS-CoV-2 Mpro. The screening results showed that six key components of ImmunoDefender formed stable complexes with Mpro via its active catalytic site with binding energies ranging from -8.75 to -10.30 kcal/mol, respectively for Cinnamtannin B1, Cinnamtannin B2, Pavetannin C1, Syzyginin B, Procyanidin C1, and Tenuifolin. Furthermore, three essential oil bioactive inhibitors, Cinnamtannin B1, Cinnamtannin B2, and Pavetannin C, have a significant ability to bind to the allosteric site of the main protease with binding energies of -11.12, -10.74, and -10.79 kcal/mol, These results suggest that these essential oil bioactive compounds may play a role in preventing the attachment of the translated polyprotein to Mpro, inhibiting the virus's pathogenesis and transmission. These components also had drug-like characteristics similar to approved and effective drugs, suggesting further pre-clinical and clinical studies are needed to confirm the generated in-silico outcomes.