Miracle-dz/UniFAR-FTUs huggingface dataset:
Behaviour Therapy for Olfactory

Olfactory Reference Disorder (ORD) is preoccupation with the belief that one is emitting a foul odour. It can be a distressing and substantially impairing problem. However, very little is known about its successful treatment. This study describes the treatment of a 38-year-old female with longstanding ORD using Cognitive Behavioural Therapy (CBT). Case conceptualisation focused on identifying unhelpful beliefs and maintaining factors including safety-seeking behaviours, self-focus and avoidance. These were then targeted using a variety of techniques including discussion and behavioural experiments to facilitate belief and behaviour change. Following a 12-week treatment, measures of anxiety and depression
Multiscale Dynamics of the February 11-12, 2010, Deep South US Snowstorm Event

This study investigates the synoptic/mesoscale dynamics responsible for an unusually heavy southern US snowstorm that occurred on February 11-12, 2010, using reanalysis, observations, and numerical simulations. This record breaking snowfall event represents an example of multiple upper level and low-level jets (LLJs) and their accompanying baroclinic zones. The analysis reveals the following synoptic scale processes as significant contributors: (1) upper level jet splitting and merging, (2) advection of cold arctic air at low levels by a large anticyclone, and (3) an incoming upper level shortwave trough. In addition to the synoptic scale processes, the following mesoscale features played a major role in this snowstorm event: coexisting potential (convective) instability and conditional symmetric instability, terrain blocking, and a double LLJ development process. Sensitivity experiments including (1) limiting the orographic effects of elevated plateau in Texas and the Sierra Madre Mountains in Mexico by reducing the terrain height to 225 meters, (2) the microphysics/latent heating effects, and (3) surface fluxes on the development and intensity of the snowstorm were also conducted by turning these options off in the numerical model. Of all three experiments, the surface flux experiment displays the least amount of influence on the developing frozen precipitation bands.
Expression, Purification, and Synergistic Effects in Starch Binding of Starch-Binding Domains (SBD-SBD4) Derived from Aspergillus niger 2316 Glucoamylase

A starch-binding domain encoding region of glucoamylase (GASBD) was amplified by PCR from Aspergillus niger 2316 and sequenced (GenBank JQ814697). It consists of 324bp nucleotides and encodes a peptide of 108 amino acids. To investigate the affinities of artificial multiple-repeat GASBDs for insoluble starch in vitro , the pET28a plasmids harbouring, respectively, single GASBD and multiple-repeat GASBDs from two to four were constructed and expressed in Escherichia coli BL21(DE3). The recombinant GASBD(s) proteins were purified to homogeneity by Ni 2+ –NTA affinity chromatography and the affinities of the purified recombinant proteins for insoluble starches were determined. GASBD2, GASBD3, and GASBD4 had, respectively, approximately 5, 8 and 10-fold higher affinities for starch than single GASBD, indicating that the multiple-repeat GASBDs act in a synergistic way when binding to starches.
Flexor Tendon Repair with Fast-Absorbable Sutures: A Rupture Incidence-Focused Analysis of a Case Series and a Review

Hand flexor tendon repair with absorbable sutures has the advantage of leaving no foreign material within the tendon. However, clinical application of degradable materials in flexor tendon repair is limited to slow-absorbable PDS or Maxon sutures and commonly does not involve fast-absorbable sutures, probably, because of fear of ruptures. Meanwhile, a small number of reports have shown that in terms of tenorrha-phy failure fast-absorbable sutures are safe. It is also known that classical tendon repairs with a pull-out wire withstand intensive mobilization after wire removal. Furthermore, experimental research implies that fast-absorbable polyglycolic acid fibers seeded with tenocytes can strengthen tendon repair. The current article retrospectively analyses a series of 12 patients with predominantly complex hand injuries, in which 17 flexor digitorum profundus and four flexor pollicis longus tendon repairs with Vicryl (poly-glactin 910, copolymer of polyglycolic and polylactic acid) were performed. Early postoperative passive mobilization was employed. The postoperative results were analyzed with particular reference to tenorrhaphy failure. Ruptures occurred in only two repairs. This was statistically insignificant when compared with a hypothetical set of cases consisting of only good and excellent outcomes. These results imply that hand flexor tendon repair with fast-absorbable sutures is rupture-safe if moderate postoperative mobilization is employed. However, as the calculated postoperative dynamics of the tenorrhaphy strength predicts failure of all fast-absorbable suture repairs during active motion at six weeks, more research is needed to clarify why this was not the case in the presented and other published series of patients.
Automated co‐registration and calibration in SfM photogrammetry for landslide change detection

Landslides represent hazardous phenomena, often with significant implications. Monitoring landslides with time‐series surface observations can indicate surface failure. Unmanned aerial vehicles (UAVs) employing compact digital cameras, in conjunction with structure‐from‐motion (SfM) and multi‐view stereo (MVS) image processing approaches, have become commonplace in the geoscience research community. These methods offer relatively low‐cost, flexible solutions for many geomorphological monitoring applications. However, conventionally ground control points (GCPs) are required for registration purposes, the provision of which is often expensive, difficult or even impracticable in hazardous and inaccessible terrain.
A numerical study on TMS and tDCS: Influence of skin conductivity and anatomical head model

Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) are noninvasive brain stimulation techniques used in neuronal investigation studies as well as in the treatment of neurological disorders and psychiatric diseases [1]. TMS employs a high intensity pulsed magnetic field delivered through the scalp to induce an electric field responsible for the neurostimulatory and neuromodulatory effects on the brain. Conversely, tDCS is a purely neuromodulatory technique that injects a low intensity (260 µA – 2 mA) direct current by means of electrodes placed on the scalp of the patient. To optimize efficacy during the treatment, an accurate knowledge of the E field distribution inside the brain is necessary. Thus, understanding the reliability of numerical estimations coming from modeling becomes key factor. At the same time, assignment of the correct electric properties to different tissues is still an open issue. Particularly, there is no consensus in the literature on the conductivity value to be used for the skin, as it is highly variable with hydration level, corneum layer thickness, healthy or ill status, subject age and gender [2]. Thus, we herein numerically investigated the variability of electric field induced inside the brain by a TMS coil and by tDCS electrodes, over changes in the head anatomy and skin conductivity. Electromagnetic simulations were performed with the EM software Sim4Life (V4.4, Zurich MedTec, Zurich) on the 29-year-old female head model MIDA [3] and on the 34-year-old
Acta Sci. Pol. Hortorum Cultus, 23(1) 2024, 75–85

Rainfed olive groves in arid Tunisia face severe water scarcity and a low abundance of native arbuscular mycorrhizal fungi (AMF). We investigated if a supplement of commercial AMF-product at transplantation would improve olive plant survival and ecophysiological performance in an arid region. The commercial AMF product was added to two olive tree cultivars, ‘Meski’ and ‘Zarrazi’. There was an increase in the mycorrhizal intensity in the roots of ‘Meski’. Mycorrhizal symbiosis seems to have improved the survival of ‘Meski’ and the growth rate of ‘Zarrazi’. Plant water status and gas exchanges were enhanced in ‘Mes-ki’. Mycorrhization helped maintain a higher photosynthetic assimilation rate and stomatal conductance in ‘Zarrazi’. AMF-symbiosis exhibited an inter-cultivar difference in the enhancement of the ecophysiological performance of olive trees under aridity. It also improved pre-existent indigenous defense strategies. It reinforced the avoidance strategy of ‘Meski’ but strengthened the tolerance strategy of ‘Zarrazi’.
Dual Task Reduces Balance Score but not Joint Repositioning Error in Female Athletes with Dynamic Knee Valgus

SUMMARY Background . Dynamic knee valgus (DKV) refers to an improper movement strategy of the lower limbs involving multiple planes. Objective. The present study aimed to examine the impact of dual task conditions on joint repositioning sense in female athletes with DKV. Methods. This controlled laboratory investigation involved 31 volunteered female athletes who met specific criteria for inclusion and exclusion (17 with DKV, and 14 without DKV). The assessment of DKV was carried out through the Single-Leg Stance (SLS) test. Knee repositioning sense and dynamic balance were measured by using photogrammetric method and y-balance test, respectively. The tests were done on two conditions with and without dual task. Paired samples t-test was used to analyze data t confidence level of 95% (p < 0.05). Results. The knee JPS in the frontal (p = 0.008) planes improved while the Y-balance scores in anterior (p = 0.001), Posteromedial (p = 0.003), and composite score (p = 0.003) decreased significantly while perform dual task in female athletes with DKV. Conclusions. Our results showed that dual task training could improve joint position sense in the frontal plane, but it does not have a clear effect on the joint position sense in the sagittal plane. Moreover, dual task may impair dynamic stability in individuals with DKV.
University of Birmingham Probabilistic model checking and autonomy

Design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modelling and analysis. Probabilistic model checking is a technique to automatically verify, for a given temporal logic speciﬁcation, that a system model satisﬁes the speciﬁcation, as well as to synthesise an optimal strategy for its control. This method has recently been extended to multi-agent systems that exhibit competitive or cooperative behaviour modelled via stochastic games and synthesis of equilibria strategies. In this paper, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. We demonstrate the applicability of the framework through illustrative examples from autonomous systems. Finally, we highlight research challenges and suggest directions for future work in this area.
Astrophysically Informed Priors for Self-Attention Classification in Radio Astronomy

Automated object classification is essential in the era of big data radio astronomy in order to produce final science products from large-scale astronomical surveys in a timely fashion. However, in spite of the increasingly wide-spread adoption of deep learning for such tasks in other fields, the astronomical community still tends to be apprehensive of such solutions as they are often perceived to be "black box" methods, the results from which cannot be easily interpreted. In this work we demonstrate how deep-learning models that incorporate a self-attention mechanism can provide output meta-data showing how different structures within a radio image are used by the classification. Such outputs are commonly referred to as attention maps . Furthermore, we explore how incorporating informed priors on astrophysical structure related to equivariance over the isometries of 2-dimensional Euclidean space might further benefit such deep-learning classification models for radio astronomy. We make use of the model and MiraBest radio galaxy data set introduced in [1]. This model is a common form of convolutional neural network designed for image classification, modified with custom feature-wise self-attention gates developed specifically for radio astronomy classification. To accommodate additional equivariant priors, we then extend this model to incorporate various forms of E(2)-equivariant convolution operations [2]. As well as maintaining the translational equivariance of standard convolution operations, the additional priors introduced in this work are equivariant to user determined sets of rotations and reflections. In our experiments we evaluate multiple orders of cyclic (rotation) and dihedral (rotation and reflection) equivariance, similar to [3]. Our experiments show minor classification performance improvements from using equivariant models over the standard convolutional baseline. However, by using
Enhancing Agricultural Yield Forecasting with Deep Convolutional Generative Adversarial Networks and Satellite Data

— Ensuring food security amidst growing global population and environmental changes is imperative. This research introduces a pioneering approach that integrates cutting-edge deep learning techniques. Deep Convolutional Generative Adversarial Networks (DCGANs) and Convolutional Neural Networks (CNNs) with high-resolution satellite imagery to optimize agricultural yield prediction. The model leverages DCGANs to generate synthetic satellite images resembling real agricultural settings, enriching the dataset for training a CNN-based yield estimation model alongside actual satellite data. DCGANs facilitate data augmentation, enhancing the model's generalization across diverse environmental and seasonal scenarios. Extensive experiments with multi-temporal and multi-spectral satellite image datasets validate the proposed method's effectiveness. Trained CNN adeptly discerns intricate patterns related to crop growth phases, health, and yield potential. Leveraging Python software, the study confirms that integrating DCGANs significantly enhances agricultural production forecasting compared to conventional CNN-based approaches. Against established optimization methods like RCNN, YOLOv3, Deep CNN, and Two Stage Neural Networks, the proposed DCGAN-CNN fusion achieves 98.6% accuracy, a 3.62% improvement. Synthetic images augment model resilience by exposing it to varied situations and enhancing adaptability to diverse geographic regions and climatic shifts. Moreover, the research delves into CNN model interpretability, elucidating learnt features and their correlation with yield-related factors. This paradigm promises to advance agricultural output projections, advocate sustainable farming, and aid policymakers in addressing global food security amidst evolving environmental challenges.
Measurement of the tt̄ production cross section in pp collisions at 7 TeV in lepton+jets events using b-quark jet identification

A new measurement of the inclusive production cross section for pp to t t-bar is performed at a center-of-mass energy of 7 TeV using data collected by the CMS experiment at the LHC. The analysis uses a data sample corresponding to an integrated luminosity of 36 inverse picobarns, and is based on the final state with one isolated, high transverse momentum muon or electron, missing transverse energy, and hadronic jets. The ttbar content of the selected events is enhanced by requiring the presence of at least one jet consistent with b-quark hadronization. The measured cross section is 150 +/- 9 (stat.) +/- 17 syst. ) +/- 6 (lumi.) pb and is in agreement with higher-order QCD calculations. The combination of this measurement with a previous CMS result based on dileptons gives 154 +/- 17 (stat.+syst.) +/- 6 (lumi.) pb.
Torque based Structured Pruning for Deep Neural Network

Structured pruning is a popular way of convolutional neural network (CNN) acceleration. However, current state of the art pruning techniques require modifications to the network architecture, implementation of complex gradient update rules or repetitive training and long fine-tuning stages. Our novel physics-inspired approach for structured pruning aims to solve these issues. Analogous to ‘Torque’ we apply a force that consolidates the weights of a convolutional layer around a selected pivot point during training. Using the distance-dependency nature of torque, we can encourage high density of weights in filters around this point and increase filter sparsity as we move away. Filters away from the pivot point can be pruned, resulting in a minimum loss of information. We can control the tightness of the weights by varying the hyper-parameters, thus assisting us in creating a more compact network. Our proposed technique is jointly able to perform both filter learning and filter importance sorting. Additionally, our method is easy to implement, requires no change to model architecture and needs very little to no fine-tuning. We show that our approach reaches competitive results with previous state-of-the-art by evaluating popular networks such as VGGNet and ResNet on multiple image classification tasks. Notably, our method can reduce the parameter count of VGGNet by 96% and still maintain the accuracy achieved by the full-size model without any fine-tuning. This makes our method both latency and memory efficient for hardware deployment.
Assessments of Incident Power Density at 28 GHz Using a Near-Field Reconstruction Algorithm for Compliance Assessment of Wireless Devices to Exposure Limits

With expansion of technologies using quasi-millimeter wave and millimeter wave, e.g., 5G system, in general environment, the public concern on human exposure to electromagnetic field (EMF) increases. It is essential to assess human exposure to EMF from these wireless systems. Spatially-averaged power density incident into a human body is used as a metric of reference levels for local exposure to electromagnetic fields above 6 GHz in the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines [1]. Wireless devices, such as mobile phones, are used near by a human body, therefore power densities at proximity to the devices are required to be evaluated for the compliance assessment to the reference levels. We have focused on the measurement method using a near-field reconstruction algorithm based on the plane-wave expansion technique [2]. A feature of this technique is to back-transform/-project the electric and magnetic fields using measured electric field at some wavelength away from the radiation source. One of the benefits in this technique is that conventionally available measurement methods [3], such as the system with a waveguide probe (Fig.1), is applicable. This presentation summarizes the measured results of the incident power density radiated from phased array antennas at 28 GHz. Furthermore, the results were compared with those obtained from a different measurement system [4].
Depth-related gradient of soft-bottom crustacean distribution along the Cilician shelf

Distribution of crustaceans in 3 transects (including depths of 10, 25, 50, 75, 100, and 200 m) of oligotrophic soft bottoms of the Levantine Sea shelf located near Kumkuyu, Limonlu, and Erdemli was studied in 2000 to show its association with depth-dependent environmental parameters in representative months of each season (winter, spring, summer, and autumn). A total of 90 crustacean species was found; species richness was highest in winter and autumn and between 50 and 100 m in depth. There was a peak value in abundances in the warm season and at a depth interval of 10 to 50 m, whereas highest values of biomass were recorded in May and August and at a depth of 25 m. A seasonal variation at depths shallower than 75 m was observed for evenness values, which were more stable in deeper waters in all seasons. Two main crustacean assemblages were determined, the first corresponding to shallow bottoms (10, 25-100 m) and the other to deeper bottoms (150-200 m). Besides the grain size and total organic carbon content of the sediment, depth is the most important factor in determining the crustacean assemblage. Hydrographic characteristics of the water column made up 2 seasonal crustacean assemblages: the first in February and May, and the other in August and November. Therefore, all factors governing the crustacean distribution were found to be related to the bottom depth.
Empowering optimal transport matching algorithm for the construction of surrogate parametric metamodel

Resolving Partial Differential Equations (PDEs) through numerical discretization methods like the Finite Element Method presents persistent challenges associated with computational complexity, despite achieving a satisfactory solution approximation. To surmount these computational hurdles, interpolation techniques are employed to precompute models offline, facilitating rapid online solutions within a metamodel. Probability distribution frameworks play a crucial role in data modeling across various fields such as physics, statistics, and machine learning. Optimal Transport (OT) has emerged as a robust approach for probability distribution interpolation due to its ability to account for spatial dependencies and continuity. However, interpolating in high-dimensional spaces encounters challenges stemming from the curse of dimensionality. The article offers insights into the application of OT, addressing associated challenges and proposing a novel methodology. This approach utilizes the distinctive arrangement of an ANOVA-based sampling to interpolate between more than two distributions using a step-by-step matching algorithm. Subsequently, the ANOVA-PGD method is employed to construct the metamodel, providing a comprehensive solution to address the complexities inherent in distribution interpolation.
Injuries Associated to Subtalar Dislocation: A Systematic Review

Introduction. About 1% of all ankle dislocations are subtalar dislocations (SD), a rare kind of injury. The talocalcaneal and talonavicular joints dislocate simultaneously in SD, although the tibiotalar and calcaneocuboid joints stay intact. Ankle and foot radio-graphs taken from the front, posterior, lateral, and oblique views are typically used to diagnose subtalar dislocation. fractures of the hindfoot, such as osteochondral fractures, calcaneus fractures, fractures of the posterior process
Web-based visualization of semantic annotation of mathematical PDF documents

This paper presents a method for visualizing semantic markup of texts and formulas of mathematical PDF documents. To form the markup visualization, the document structure is analyzed, formulas and in-text variables are searched, semantic markup of text blocks with placement with the formation of links to the description of concepts from the ontology of professional mathematics OntoMathPRO [1] is carried out. Visualization of markup results is performed using web technologies (CSS, JavaScript) and is available for viewing from any device. The visualization method will be implemented in the OntoMath ecosystem as a component of the Lobachevskii-DML publishing system. We also plan to apply the results of the method in the semantic search service for formulas and concepts in PDF documents.
Creating Dynamic Simulation Environments With Unreal Engine 5

—Simulation is a vital component for many machine learning-based systems. In this abstract, we present our work using Unreal Engine 5 to create realistic environments for training a neural network used in the navigation system of a mobile robot. We explore the use of randomized textures to create dynamic environments, and we evaluate trained models in environments with both randomly changing and static textures.