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Chemical engineering research and design 187, 164-173 (2022). doi:10.1016/j.cherd.2022.08.061 special issue: "100 years of IChemE", Published by Elsevier, Amsterdam

This study evaluated the effects of ischemic conditioning on power output and bar velocity in the bench press exercise. Ten healthy males (age: 25 ± 2 years; body mass: 92 ± 8 kg; bench press one repetition maximum −1RM: 145 ± 13 kg), took part in two experimental sessions (with and without ischemia), 1 week apart in random and counterbalanced order. In the ischemic condition, cuffs placed around the upper part of the arms were inflated to 80% of arterial occlusion pressure before each set, while in the control condition there was no blood flow restriction. The exercise protocol included 5 sets of three repetitions each, against a resistance equal to 60% 1RM, with 5 min recovery intervals between sets. There was a main effect of condition for mean power output (MP) and mean bar velocity (MV) (p = 0.01), with overall MP being higher in ischemia than in control by 5.6 ± 4.1% (mean ± 90% compatibility limits), a standardized effect size (ES) of 0.51. Overall MV was also higher by 5.5 ± 4.0%, ES = 0.63. Peak power output (PP) and peak bar velocity (PV) were similar in set 1 of the control and ischemia condition (1039 ± 105 vs. 1054 ± 82 W; 684 ± 74 vs. 696 ± 53 W; 1.09 ± 0.07 vs. 1.12 ± 0.09 m/s; 0.81 ± 0.05 vs. 0.82 ± 0.05 m/s, p = 0.67 to 0.99, mean ± standard deviation). However, from set 3 onward (p = 0.03 to 0.001), PP and PV were higher in ischemia compared with control, with the highest difference observed in set 5 (10.9 ± 5.9%, ES = 0.73 for PP and 8.6 ± 4.6%; ES = 0.89 for PV). These results indicate that ischemia used before each set of the bench press exercise increases power output and bar velocity and this may be used as performance-enhancing stimulus during explosive resistance training. © Copyright © 2021 Wilk, Krzysztofik, Jarosz, Krol, Leznicka, Zajac, Stastny and Bogdanis.

This study examined changes in movement velocity and surface electromyographic (sEMG) activity of the pectoralis major (PM) and triceps brachii (TB) muscles during the bench press exercise to failure against different loads. Fourteen men performed a set to failure with maximum intended velocity, against low (40%-1 repetition maximum-RM), moderate (60%-1RM), and heavy loads (80%-1RM). Number of repetitions, volume load, mean and peak velocity, and total time increased with decreasing load (40% > 60% > 80%, p

Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with large numbers of observations, $N$, due to the cubic (in $N$) cost of matrix operations used in exact inference. Many solutions have been proposed that rely on $M \ll N$ inducing variables to form an approximation at a cost of $\mathcal{O}(NM^2)$. While the computational cost appears linear in $N$, the true complexity depends on how $M$ must scale with $N$ to ensure a certain quality of the approximation. In this work, we investigate upper and lower bounds on how $M$ needs to grow with $N$ to ensure high quality approximations. We show that we can make the KL-divergence between the approximate model and the exact posterior arbitrarily small for a Gaussian-noise regression model with $M\ll N$. Specifically, for the popular squared exponential kernel and $D$-dimensional Gaussian distributed covariates, $M=\mathcal{O}((\log N)^D)$ suffice and a method with an overall computational cost of $\mathcal{O}(N(\log N)^{2D}(\log\log N)^2)$ can be used to perform inference., Comment: Extended version of http://proceedings.mlr.press/v97/burt19a.html (arxiv version: arXiv:1903.03571 ). Published in Journal of Machine Learning Research: http://jmlr.org/papers/v21/19-1015.html. Code available at: https://github.com/markvdw/RobustGP

This study evaluated the effects of continuous and intermittent blood flow restriction (BFR) with 70% of full arterial occlusion pressure on bar velocity during the bench press exercise against a wide range of resistive loads. Eleven strength-trained males (age: 23.5 ± 1.4 years; resistance training experience: 2.8 ± 0.8 years, maximal bench press strength – 1RM = 101.8 ± 13.9 kg; body mass = 79.8 ± 10.4 kg), performed three different testing protocols in random and counterbalanced order: without BFR (NO-BFR); intermittent BFR (I-BFR) and continuous BFR (C-BFR). During each experimental session, subjects performed eight sets of two repetitions each, with increasing loads from 20 to 90% 1RM (10% steps), and 3 min rest between each set. In the C-BFR condition occlusion was kept throughout the trial, while in the I-BFR, occlusion was released during each 3 min rest interval. Peak bar velocity (PV) during the bench press exercise was higher by 12–17% in both I-BFR and C-BFR compared with NO-BFR only at the loads of 20, 30, 40, and 50% 1RM (p < 0.001), while performance at higher loads remained unchanged. Mean bar velocity (MV) was unaffected by occlusion (p = 0.342). These results indicate that BFR during bench press exercise increases PV and this may be used as an enhanced stimulus during explosive resistance training. At higher workloads, bench press performance was not negatively affected by BFR, indicating that the benefits of exercise under occlusion can be obtained while explosive performance is not impaired. © Copyright © 2020 Wilk, Gepfert, Krzysztofik, Stastny, Zajac and Bogdanis.

We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms. We eliminate this factorisation by explicitly modelling the dependence between state trajectories and the Gaussian process posterior. Samples of the latent states can then be tractably generated by conditioning on this representation. The method we obtain (VCDT: variationally coupled dynamics and trajectories) gives better predictive performance and more calibrated estimates of the transition function, yet maintains the same time and space complexities as mean-field methods. Code is available at: github.com/ialong/GPt., Comment: 10 pages, 4 figures, 3 tables. Published in the proceedings of the Thirty-sixth International Conference on Machine Learning (ICML), 2019

Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations on the input that are known to be irrelevant (e.g. translation). Commonly, this is done through data augmentation, where the training set is enlarged by applying hand-crafted transformations to the inputs. We argue that invariances should instead be incorporated in the model structure, and learned using the marginal likelihood, which correctly rewards the reduced complexity of invariant models. We demonstrate this for Gaussian process models, due to the ease with which their marginal likelihood can be estimated. Our main contribution is a variational inference scheme for Gaussian processes containing invariances described by a sampling procedure. We learn the sampling procedure by back-propagating through it to maximise the marginal likelihood.

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.

10.3767/003158513X675925 Novel species of microfungi described in the present study include the following from South Africa: Camarosporium aloes, Phaeococcomyces aloes and Phoma aloes from Aloe, C. psoraleae, Diaporthe psoraleae and D. psoraleae-pinnatae from Psoralea, Colletotrichum euphorbiae from Euphorbia, Coniothyrium prosopidis and Peyronellaea prosopidis from Prosopis, Diaporthe cassines from Cassine, D. diospyricola from Diospyros, Diaporthe maytenicola from Maytenus, Harknessia proteae from Protea, Neofusicoccum ursorum and N. cryptoaustrale from Eucalyptus, Ochrocladosporium adansoniae from Adansonia, Pilidium pseudoconcavum from Greyia radlkoferi, Stagonospora pseudopaludosa from Phragmites and Toxicocladosporium ficiniae from Ficinia. Several species were also described from Thailand, namely: Chaetopsina pini and C. pinicola from Pinus spp., Myrmecridium thailandicum from reed litter, Passalora pseudotithoniae from Tithonia, Pallidocercospora ventilago from Ventilago, Pyricularia bothriochloae from Bothriochloa and Sphaerulina rhododendricola from Rhododendron. Novelties from Spain include Cladophialophora multiseptata, Knufia tsunedae and Pleuroascus rectipilus from soil and Cyphellophora catalaunica from river sediments. Species from the USA include Bipolaris drechsleri from Microstegium, Calonectria blephiliae from Blephilia, Kellermania macrospora (epitype) and K. pseudoyuccigena from Yucca. Three new species are described from Mexico, namely Neophaeosphaeria agaves and K. agaves from Agave and Phytophthora ipomoeae from Ipomoea. Other African species include Calonectria mossambicensis from Eucalyptus (Mozambique), Harzia cameroonensis from an unknown creeper (Cameroon), Mastigosporella anisophylleae from Anisophyllea (Zambia) and Teratosphaeria terminal

10.3767/003158513X675925 Novel species of microfungi described in the present study include the following from South Africa: Camarosporium aloes, Phaeococcomyces aloes and Phoma aloes from Aloe, C. psoraleae, Diaporthe psoraleae and D. psoraleae-pinnatae from Psoralea, Colletotrichum euphorbiae from Euphorbia, Coniothyrium prosopidis and Peyronellaea prosopidis from Prosopis, Diaporthe cassines from Cassine, D. diospyricola from Diospyros, Diaporthe maytenicola from Maytenus, Harknessia proteae from Protea, Neofusicoccum ursorum and N. cryptoaustrale from Eucalyptus, Ochrocladosporium adansoniae from Adansonia, Pilidium pseudoconcavum from Greyia radlkoferi, Stagonospora pseudopaludosa from Phragmites and Toxicocladosporium ficiniae from Ficinia. Several species were also described from Thailand, namely: Chaetopsina pini and C. pinicola from Pinus spp., Myrmecridium thailandicum from reed litter, Passalora pseudotithoniae from Tithonia, Pallidocercospora ventilago from Ventilago, Pyricularia bothriochloae from Bothriochloa and Sphaerulina rhododendricola from Rhododendron. Novelties from Spain include Cladophialophora multiseptata, Knufia tsunedae and Pleuroascus rectipilus from soil and Cyphellophora catalaunica from river sediments. Species from the USA include Bipolaris drechsleri from Microstegium, Calonectria blephiliae from Blephilia, Kellermania macrospora (epitype) and K. pseudoyuccigena from Yucca. Three new species are described from Mexico, namely Neophaeosphaeria agaves and K. agaves from Agave and Phytophthora ipomoeae from Ipomoea. Other African species include Calonectria mossambicensis from Eucalyptus (Mozambique), Harzia cameroonensis from an unknown creeper (Cameroon), Mastigosporella anisophylleae from Anisophyllea (Zambia) and Teratosphaeria terminal

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Introduction. Andropause and aging are associated with neuroendocrine dysfunctions. Growth hormone and testosterone play a significant role in several processes affecting adaptation and thereby also everyday functioning. The aim of this research project was to evaluate the effects of recombinant human growth hormone and testosterone enanthate injections on body mass and body composition, aerobic and anaerobic fitness and lipid profile in middle-aged men. Materials and method. The research group was comprised of 14 men aged 45 – 60 years. Two series of laboratory analyses were performed. Independent tests were carried out at baseline and after 12 weeks of the experiment. The data were analyzed using Statistica 9.1 software. Results. A two-way repeated measures ANOVA revealed a statistically significant effect of the intervention programme on fat-free mass (η2=0.34), total body fat (η2=0.79), total cholesterol (η2=0.30), high-density lipoprotein cholesterol (η2=0.31), low-density lipoprotein cholesterol (η2=0.42), triglyceride (η2=0.28), testosterone (η2=0.52), insulin-like growth factor 1 (η2=0.47) and growth hormone (η2=0.63). Furthermore, ANOVA revealed a statistically significant effect of the rhGH and T treatment on maximal oxygen uptake (η2=0.63), anaerobic threshold (η2=0.61) and maximal work rate (η2=0.53). Conclusion. It should be emphasized that the lipid profile was affected not only by rhGH+T replacement therapy, but also by the prescribed physical activity programme. The strength and endurance fitness programme alone did not cause significant changes in body mass and composition, nor the anaerobic and aerobic capacity. On the other hand, the rhGH=T treatment stimulated these changes significantly.

Target tracking has a wide range of applications in Internet of Things (IoT), such as smart city sensors, indoor tracking, and gesture recognition. Several studies have been conducted in this area. Most of the published works either use vision sensors or inertial sensors for motion analysis and gesture recognition [1, 2]. Recent works use a combination of depth sensors and inertial sensors for 3D ranging and tracking [3, 4]. This often requires complex hardware and the use of complex embedded algorithms. Stereo cameras or Kinect depth sensors used for high precision ranging are instead expensive and not easy to use. The aim of this work is to track in 3D a hand fitted with a series of precisely positioned IR LEDs using a novel Lensless Smart Sensor (LSS) developed by Rambus, Inc. [5, 6]. In the adopted device, the lens used in conventional cameras is replaced by low-cost ultra-miniaturized diffraction optics attached directly to the image sensor array. The unique diffraction pattern enables more precise position tracking than possible with a lens by capturing more information about the scene.

Chemical engineering as a skilled profession depends on the competencies of the individuals practicing it. Formal teaching of chemical engineering at universities only started in the 19th century. Informally, the subject has been in existence at least since Roman times. Highly skilled people have been recognised as a means to ensure prosperity and allow innovation for at least several centuries, as examples like the Bergakademie Freiberg show. It can be shown that skilled people flock to prosperous regions and prosperous regions develop where skilled people aggregate, modern examples include Silicon Valley and Northwestern Europe. The competences needed by a chemical engineer continue to broaden, e.g. sustainability, digitisation force changes in the education of engineers. The sheer volume of material cannot be covered in a tertiary study period and the speed of change makes knowledge obsolete very fast, which puts more focus on new life-long learning requirements. Modern, more individualised and context specific ways of education will develop, based sometimes on efficiency and cost of teaching and more often on effectiveness of learning.

Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each component’s errors. Further, improving safety alone is not sufficient. Passengers must also feel safe to trust and use AV systems. To address such concerns, we investigate three under-explored themes for AV research: safety, interpretability, and compliance. Safety can be improved by quantifying the uncertainties of component outputs and propagating them forward through the pipeline. Interpretability is concerned with explaining what the AV observes and why it makes the decisions it does, building reassurance with the passenger. Compliance refers to maintaining some control for the passenger. We discuss open challenges for research within these themes. We highlight the need for concrete evaluation metrics, propose example problems, and highlight possible solutions.

Dysarthia is a common sequela of cerebral palsy (CP), directly affecting both the intelligibility of speech and the child's psycho-social adjustment. Speech therapy focused exclusively on the articulatory organs does not always help CP children to speak more intelligibly. The program of art therapy described here has proven to be helpful for these children.From among all the CP children enrolled in our art therapy program from 2005 to 2009, we selected a group of 14 boys and girls (average age 15.3) with severe dysarthria at baseline but no other language or cognitive disturbances. Our retrospective study was based on results from the Auditory Dysarthria Scale and neuropsychological tests for fluency, administered routinely over the 4 months of art therapy.All 14 children in the study group showed some degree of improvement after art therapy in all tested parameters. On the Auditory Dysarthia Scale, highly significant improvements were noted in overall intelligibility (p0.0001), with significant improvement (p0.001) in volume, tempo, and control of pauses. The least improvement was noted in the most purely motor parameters. All 14 children also exhibited significant improvement in fluency.Art therapy improves the intelligibility of speech in children with cerebral palsy, even when language functions are not as such the object of therapeutic intervention.

In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of object boundaries. Estimating this inherent uncertainty and predicting multiple plausible hypotheses is of great interest in many applications, yet this ability is lacking in most current deep learning methods. In this paper, we introduce stochastic segmentation networks (SSNs), an efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture. In contrast to approaches that produce pixel-wise estimates, SSNs model joint distributions over entire label maps and thus can generate multiple spatially coherent hypotheses for a single image. By using a low-rank multivariate normal distribution over the logit space to model the probability of the label map given the image, we obtain a spatially consistent probability distribution that can be efficiently computed by a neural network without any changes to the underlying architecture. We tested our method on the segmentation of real-world medical data, including lung nodules in 2D CT and brain tumours in 3D multimodal MRI scans. SSNs outperform state-of-the-art for modelling correlated uncertainty in ambiguous images while being much simpler, more flexible, and more efficient., Comment: Published at Neurips2020. 17 pages, 11 figures, 2 tables

Il lavoro consiste in un rapporto che è il risultato di una ricerca condotta utilizzando le risposte che diversi paesi europei hanno fornito ad un questionario loro somministrato riguardante il grado di attuazione delle direttive europee in tema di fiscalità ambientale. In particolare il rapporto si occupa della tassazione dei prodotti energetici e dell'elettricità, della tassazione dei trasporti, nonché dell'applicazione dei pedaggi e dei diritti di utenza, della possibilità di graduare i tributi sui veicoli in ragione del grado di emissioni CO2 e dell'inserimento del settore dell'aviazione nel sistema di scambi di quote di emissione di gas "effetto serra".

Il lavoro si occupa di analizzare la disciplina dei singoli stati membri dell'UE in tema di principi e direttive europee in tema ambientale allo scopo di valutare il grado di armonizzazione degli stessi