Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced thermal models for improved design of counter flow microchannel heat exchangers

Theoretical models of counter flow microchannel heat exchangers subjected to scaling and secondary effects are developed in this dissertation. The scaling effects studied include axial heat conduction and viscous dissipation, while the secondary effects considered in this dissertation is that of external heat transfer via heat flux and temperature. The theoretical models developed are one-dimensional and consist primarily of ordinary governing equations that describe the axial variation of hot and cold fluid. For the case of axial heat conduction, the axial variation of wall temperature is also modeled. The models are dependent on various factors, such as Reynolds number, Prandtl number, microchannel hydraulic diameter, microchannel length, microchannel profile, substrate spacing, thermal conductivities of the fluids and wall, and fluid inlet temperature difference. The individual effect of all these parameters with respect to each of the scaling and secondary effects is studied using each model. The governing equations are solved using numerical method, specifically finite difference method. Studies are done for Reynolds number between 1 and 1500 for thermal models on axial heat conduction, and external heat transfer. On the other hand, the Reynolds number for the model studying viscous dissipation is varied between 1 and 1000. The effect of Prandtl number on the models is analyzed using air, ethylene glycol and water. The influence of profile on the thermal performance of microchannel heat exchanger is studied using rectangular, trapezoidal and triangular microchannels. The aspect ratio of
Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research. Thus, there is a high demand to estimate the individual shape of inassessible structures given only information on the geometry of the surrounding tissue. This leads to a domain adaptation problem, where the domain gap could be very large while typically only small datasets are available. Hence, classical approaches for domain adaptation are not capable of providing sufficient predictions. In this work, we present a new framework for bridging this domain gap in the scope of estimating anatomical shapes based on the surrounding tissue's morphology. Thus, we propose deep representation learning to not map from one image to another but to predict a latent shape representation. We formalize this framework and present two different approaches to solve the given problem. Furthermore, we perform a proof-of-concept study for estimating the individual shape of the aortic valve leaflets based on a volumetric ultrasound image of the aortic root. Therefore, we collect an ex-vivo porcine data set consisting of both, ultrasound volume images as well as high-resolution leaflet images, evaluate both approaches on it and perform an analysis of the model's hyperparameters. Our results show that using deep representation learning and domain mapping between the identified latent spaces, a robust prediction of the unknown leaflet shape only based on surrounding tissue information is possible, even in limited data scenarios. The concept can be applied to a wide range of modeling tasks, not only in the scope of heart modeling but also for all kinds of inassessible structures within the human body.
Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.
Research on Digital Business Model Innovation Based on Emotion Regulation Lens

Digital technologies, such as artificial intelligence, brain-computer interfaces technology and big data, enable many firms to innovate their business model. It is clearly an emotional process due to its complex and uncertain nature, and involves individuals’ emotion regulation, yet the current research lacks an effective conversion path from emotion to digital business model innovation (BMI). Drawing on theories and research on emotion regulation and business model innovation, we investigate how emotion regulation of entrepreneurs (i.e., cognitive reappraisal and expressive suppression) influence digital BMI. Data from 126 new ventures show that entrepreneurs’ reappraisal positively affects digital BMI, while entrepreneurs’ suppression exerts opposite effects on digital BMI. Moreover, we find that environmental dynamism moderates this relationship. The findings explain the emotional complexity in digital technology empowerment, which has implications for the development and design of brain computer interface applications and the literature on emotions and business model innovation.
Computational Approaches for Acute Traumatic Brain Injury Image Recognition

In recent years, there have been major advances in deep learning algorithms for image recognition in traumatic brain injury (TBI). Interest in this area has increased due to the potential for greater objectivity, reduced interpretation times and, ultimately, higher accuracy. Triage algorithms that can re-order radiological reading queues have been developed, using classification to prioritize exams with suspected critical findings. Localization models move a step further to capture more granular information such as the location and, in some cases, size and subtype, of intracranial hematomas that could aid in neurosurgical management decisions. In addition to the potential to improve the clinical management of TBI patients, the use of algorithms for the interpretation of medical images may play a transformative role in enabling the integration of medical images into precision medicine. Acute TBI is one practical example that can illustrate the application of deep learning to medical imaging. This review provides an overview of computational approaches that have been proposed for the detection and characterization of acute TBI imaging abnormalities, including intracranial hemorrhage, skull fractures, intracranial mass effect, and stroke.
Tree Species Diversity and Forest Edge Density Jointly Shape the Gut Microbiota Composition in Juvenile Great Tits (Parus major)

Despite the microbiome’s key role in health and fitness, little is known about the environmental factors shaping the gut microbiome of wild birds. With habitat fragmentation being recognised as a major threat to biological diversity, we here determined how forest structure influences the bacterial species richness and diversity of wild great tit nestlings (Parus major). Using an Illumina metabarcoding approach which amplifies the 16S bacterial ribosomal RNA gene, we measured gut microbiota diversity and composition from 49 great tit nestlings, originating from 23 different nests that were located in 22 different study plots across a gradient of forest fragmentation and tree species diversity. Per nest, an average microbiome was determined on which the influence of tree species (composition and richness) and forest fragmentation (fragment area and edge density) was examined and whether this was linked to host characteristics (body condition and fledging success). We found an interaction effect of edge density with tree species richness or composition on both the microbial richness (alpha diversity: Chao1 and Shannon) and community structure (beta diversity: weighted and unweighted UniFrac). No significant short-term impact was observed of the overall faecal microbiome on host characteristics, but rather an adverse effect of specific bacterial genera on fledging success. These results highlight the influence of environmental factors on the microbial richness as well as the phylogenetic diversity during a life stage where the birds’ microbiota is shaped, which could lead to long-term consequences for host fitness.
Analysis on Failure Causes for Cylindrical Rollers

Aiming at the surface spalling on rollers during experiment for arc edge trimming generatrix cylindrical roller bearings,the failure roller is analyzed by using macro inspection,micro analysis,metallographic and hardness test and dimension measuring method. The results show that the unsmooth transition between straight part and arc of the roller are main reason of spalling. The improved bearings work normally and perform well.
Learning of Wheelchair Racing Propulsion Skills Over Three Weeks of Wheeling Practice on an Instrumented Ergometer in Able-Bodied Novices

The acquisition of daily handrim wheelchair propulsion skill as a multi-layered phenomenon has been studied in the past. Wheelchair racing, however, is considerably different from daily handrim wheelchair propulsion in terms of propulsion technique, as well as the underlying equipment and interface. Understanding wheelchair racing skill acquisition is important from a general motor learning and skill acquisition perspective, but also from a performance and injury prevention perspective. The aim of the current lab-based study was 2-fold: to investigate the evolution of racing wheelchair propulsion skill among a sample of novices and to compare them with an experienced wheelchair racer under similar conditions. A convenience sample of 15 able-bodied novices (8 male, 7 female) completed a standardized three-week submaximal uninstructed practice protocol (3 weeks, 3 sessions per week, 3x4 min per session) in a racing wheelchair on an ergometer. Required wheeling velocity was set at 2.78 m/s (10 km/h) and a rolling friction coefficient of 0.011 (resulting in a mean target load of 21W) was used. For comparison, an experienced T54 Paralympic athlete completed one block of the same protocol. Kinetics, kinematics, and physiological data were captured. A mixed effects regression analysis was used to examine the effect of practice for the novices, while controlling for speed. All participants finished the protocol successfully. However, not all participants were able to achieve the target speed during the first few sessions. Statistically significant improvements over time were found for all outcome measures (i.e., lower metabolic strain, longer push and cycle times) with the exception of mean power and torque per push. The athlete used a significantly greater contact angle and showed “better” outcomes on most metabolic and kinetic variables. While the athlete used a semi-circular propulsion technique, most participants used a double looping over technique. Three weeks of uninstructed wheelchair racing practice significantly improved efficiency and skill among a group of novices, in line with previous studies on daily handrim wheelchair propulsion. The comparison with an experienced athlete expectedly showed that there is still a large performance (and knowledge) gap to be conquered.
Affective and Enjoyment Responses to Sprint Interval Training in Healthy Individuals: A Systematic Review and Meta-Analysis

Sprint interval training (SIT) is characterized by intensity of “all-out” effort and superior time-efficiency compared to traditional moderate-intensity continuous training (MICT) and has been proposed as one viable solution to address the commonly reported barrier of lack of time for physical activity. While substantial physiological benefits of participation in SIT have been well-documented, the psychological responses to SIT are less clear. No systematic review has been conducted thus far to respond to the assumption that its supramaximal intensity will induce adverse feelings. Therefore, the aim of the present study was to synthesize studies analyzing affective and enjoyment responses to SIT and to compare the responses to SIT with MICT and other high intensity interval training (HIIT) protocols with lower intensities. After searching relevant databases up until 22nd March 2021, twenty-five studies meeting the inclusion criteria were included in the present review. Random effect meta-analysis using the pooled data demonstrated that SIT induced similar post-exercise affective valences during the training compared to MICT and HIIT, but lower affective valences immediately post-exercise compared to MICT. Moreover, affective responses during SIT decreased to negative valences according to the results from most included studies, while low-volume SIT protocols with shorter sprint duration and repetitions induced more positive affective responses. Level of enjoyment after SIT were positive and were comparable to MICT or HIIT. Overall, the results from the existing literature indicate that SIT might cause unpleasant feelings during the training and be perceived less pleasurable than MICT immediately post training but could be a comparably enjoyable modality for healthy individuals in relation to MICT or HIIT, despite its supramaximal intensity. Low-volume SIT may be a realistic option for individuals seeking a time-efficient workout with comparable affective responses to MICT or HIIT. Systematic Review Registration [https://www.crd. york.ac. uk/PROSPERO], Identifier [CRD42021284898].
Crustal Structure and Anisotropy in the Lower Yangtze Region and its Metallogenic Implications

In this study, we performed receiver function profiling and fitted harmonic functions to the arrival time variations of Pms phases to calculate the crustal seismic anisotropy with delay time and fast polarization direction, using broadband seismic data obtained from 55 temporary stations in two linear profiles and 39 stations in the Lower Yangtze and adjacent region. Moreover, we determined the crustal thickness and Poisson’s ratio using a novel H-κ-c stacking method. Our results revealed that the Middle-Lower Yangtze Metallogenic Belt and the north east section of the Qinzhou-Hangzhou Metallogenic Belt are characterized by Moho upliftment (<32 km), a relatively high Poisson’s ratio (>0.26), local lithospheric thinning (<70 km), and a pattern of deep faults that connect the crust and asthenosphere and serve as conduits for magma upwelling. The NE-SW fast polarization direction was consistent with the SKS splitting results, and the average delay time was 0.45 s. Moreover, underplating of deep magma and upwelling along the weak zone caused local Moho uplift and ductile shear of the lower crust, resulting in the directional arrangement of amphibole and other minerals, which may be the controlling mechanism for the crustal anisotropy in the study area. The variations in crustal structure and anisotropy characteristics indicated that in the context of the northeastern Paleo-Pacific plate subduction, the existence of weak lithospheric zones and the northeastern asthenospheric flow are important conditions for metal supernormal enrichment in the Lower Yangtze region.
Molecular Characterization of a New Moniliformis sp. From a Plateau Zokor (Eospalax fontanierii baileyi) in China

In the present study, a new species of the genus Moniliformis species is described taxonomically in the mitochondrial genomic context. The parasite was found in a plateau zokor captured in a high-altitude area of Xiahe County of Gansu Province, China. The mitochondrial (mt) genome length of this new species was 14,066 bp comprising 36 genes and 2 additional non-coding regions (SNR and LNR), without atp8. The molecular phylogeny inferred by the cytochrome c oxidase subunit I gene (cox1) and the18S ribosomal RNA gene (18S rDNA) sequences showed that the parasite as a sister species to other Moniliformis spp. and was named Moniliformis sp. XH-2020. The phylogeny of the concatenated amino acid sequences of the 12 protein-coding genes (PCGs) showed Moniliformis sp. XH-2020 in the same cluster as Macracanthorhynchus hirudinaceus and Oncicola luehei confirming the cox1 and 18S rDNA phylogenetic inference. In addition, the entire mt genome sequenced in this study represents the first in the order Moniliformida, providing molecular material for further study of the phylogeny of the class Archiacanthocephala. Moreover, the species of this class, use arthropods as intermediate hosts and mammals as definitive hosts and are agents of acanthocephaliasis, a zoonosis in humans. Therefore, this study not only expands the host range among potential wild animal hosts for Archiacanthocephalans which is of great ecological and evolutionary significance but also has important significance for the research of zoonotic parasitic diseases.
Case Report: Low-Frequency Repetitive Transcranial Magnetic Stimulation to Dorsolateral Prefrontal Cortex and Auditory Cortex in a Patient With Tinnitus and Depression

Repetitive transcranial magnetic stimulation (rTMS) has been widely used as a promising therapy for tinnitus. However, the exact target and stimulation sequence of rTMS that is most effective for treating tinnitus remains unclear. Here, we report a case of a 62-year-old man with treatment-refractory tinnitus and depression whose symptoms markedly improved after undergoing low-frequency rTMS over the right-side dorsolateral prefrontal cortex and left auditory cortex area. Our report indicates that low-frequency rTMS treatment that stimulates multiple brain regions sequentially is feasible and may clinically benefit patients with tinnitus and depression.
Risk Stratification Before and During Treatment in Newly Diagnosed Multiple Myeloma: From Clinical Trials to the Real-World Setting

Multiple Myeloma (MM) is a hematologic malignancy characterized by a wide clinical and biological heterogeneity leading to different patient outcomes. Various prognostic tools to stratify newly diagnosed (ND)MM patients into different risk groups have been proposed. At baseline, the standard-of-care prognostic score is the Revised International Staging System (R-ISS), which stratifies patients according to widely available serum markers (i.e., albumin, β 2-microglobulin, lactate dehydrogenase) and high-risk cytogenetic abnormalities detected by fluorescence in situ hybridization. Though this score clearly identifies a low-risk and a high-risk population, the majority of patients are categorized as at “intermediate risk”. Although new prognostic factors identified through molecular assays (e.g., gene expression profiling, next-generation sequencing) are now available and may improve risk stratification, the majority of them need specialized centers and bioinformatic expertise that may preclude their broad application in the real-world setting. In the last years, new tools to monitor response and measurable residual disease (MRD) with very high sensitivity after the start of treatment have been developed. MRD analyses both inside and outside the bone marrow have a strong prognostic impact, and the achievement of MRD negativity may counterbalance the high-risk behavior identified at baseline. All these techniques have been developed in clinical trials. However, their efficient application in real-world clinical practice and their potential role to guide treatment-decision making are still open issues. This mini review will cover currently known prognostic factors identified before and during first-line treatment, with a particular focus on their potential applications in real-world clinical practice.
Image-Driven Modeling of Nanoscopic Cardiac Function: Where Have We Come From, and Where Are We Going?

Complementary developments in microscopy and mathematical modeling have been critical to our understanding of cardiac excitation–contraction coupling. Historically, limitations imposed by the spatial or temporal resolution of imaging methods have been addressed through careful mathematical interrogation. Similarly, limitations imposed by computational power have been addressed by imaging macroscopic function in large subcellular domains or in whole myocytes. As both imaging resolution and computational tractability have improved, the two approaches have nearly merged in terms of the scales that they can each be used to interrogate. With this review we will provide an overview of these advances and their contribution to understanding ventricular myocyte function, including exciting developments over the last decade. We specifically focus on experimental methods that have pushed back limits of either spatial or temporal resolution of nanoscale imaging (e.g., DNA-PAINT), or have permitted high resolution imaging on large cellular volumes (e.g., serial scanning electron microscopy). We also review the progression of computational approaches used to integrate and interrogate these new experimental data sources, and comment on near-term advances that may unify understanding of the underlying biology. Finally, we comment on several outstanding questions in cardiac physiology that stand to benefit from a concerted and complementary application of these new experimental and computational methods.