Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional cortical changes associated with shoulder instability – a systematic review

Background Glenohumeral joint instability is associated with structural deficits and/or alterations in sensory and motor processing; however, a proportion of patients with glenohumeral joint instability fail to respond to surgical and rehabilitative measures. This systematic review aimed to establish if functional cortical changes occur in patients with glenohumeral joint instability. Methods AMED, CINAHL, Cochrane Central Register of Controlled Trials, Embase, Medline, PEDro, Pubmed, PsychINFO and Scopus were searched from inception to 17 March 2021. Randomised controlled trials and non-randomised trials were included and quality was appraised using the Downs and Black tool. Results One thousand two hundred seventy-nine records were identified of which five were included in the review. All studies showed altered cortical function when comparing instability patients with healthy controls and included areas associated with higher cortical functions. Discussion The findings of this systematic review offer some insight as to why interventions addressing peripheral pathoanatomical factors in patients with glenohumeral joint instability may fail in some cases due to functional cortical changes. However, data are of moderate to high risk of bias. Further high-quality research is required to ascertain the degree of functional cortical changes associated with the type and duration of glenohumeral joint instability.
Theoretical Studies of the Vibrational Spectra and Molecular Structures of Dosulepin and Doxepin

Dosulepin and doxepin are tricyclic antidepressants. The molecular geometries, harmonic vibrational frequencies, quantum chemical parameters and thermodynamic properties of dosulepin and doxepin were calculated by Generalized Gradient Approximation methods developed by Perdew and Wang (GGA-PW91) and Becke-Lee-Yang-Parr (GGA-BLYP) in the gas phase and solution media. The local reactivity of these drugs was studied by the Fukui indices in order to predict both the reactive centers and the possible sites of nucleophilic and electrophilic attacks. Computational and chemical simulations were carried out for these drugs. Quantum chemical parameters of dosulepin and doxepin were calculated and compared. The simulation results show that dosulepin is quite a reactive drug. The fundamental modes of the vibrational frequencies were determined for dosulepin and doxepin. The BLYP/PW91 analyses of the wavenumbers show that the frequencies assigned to doxepin are higher than those assigned to dosulepin in the gas phase and solution media.
Education and learning about research methodology: Views of LIS authors in Pakistan

Research is vital for generating knowledge and methodology is a core component for the execution of research ideas. Research methodology education is considered to be a core area of the degree in LIS education programs of almost every school worldwide. The aim of the study is to explore the status of methodology education in the LIS field in Pakistan. In depth interviews were used to explore empirical data. The recorded interviews were transcribed, translated, and thematic analysis was conducted. Patronage and instruction by faculty, effective education on quantitative methodology and tools and openness of students were major strengths of library schools. Over reliance on theoretical education, absence of PhD teachers, lack of coordination and discussion, less focus on qualitative methodology and lower interests of students were major flaws in the education of methodology at library schools. Private library schools were ranked lower because students pursue education for sake of the degree and teachers’ focus was on exposure and earning. Reading, writing and publishing, enrolment in research degree programs, peer learning, advice from experts and participation in workshops were major strategies of learning used by Pakistani authors. The respondents proposed an effective role of library schools, revision of course contents, development of collaboration and promotion of reading culture for enhancing the of methodology. Research methodology is always in a state of rapid change, so the understanding of challenges related to education and learning of methodology may contribute to enhance the quality of research methodology education and its competence.
Chemical Composition of Labile Carbon Fractions in Forest Soils as Affected by Soil Parameters

Understanding how the chemical composition of dissolved and particulate organic matter (DOM and POM) is affected by environment factors is critical because these labile pools of carbon are involved in an array of biological, chemical and physical processes. In this study, the chemical composition of DOM and POM was measured in 13 forest soils using UV-Vis spectroscopy, fluorescence spectroscopy with PARAFAC modelling and FT-IR spectroscopy.There were significant differences between the soils for the SUVA indexes, PARAFAC components and relative intensities of different IR bands. Redundancy analysis (RDA) revealed that soil parameters had a great influence on the chemical composition of DOM and POM with high constrained variability (77.9 and 77.1 %, respectively). The pH of the soils proved to be an important controlling factor for both DOM and POM, regulating the concentration of the C3 PARAFAC component (low-molecular-weight compounds associated with biological activity) and the aromatic compounds of POM (aromaticity, rA1630 and rA1515). The silt content was the other main regulating factor controlling the chemical characteristics of the labile pool, having a strong negative correlation with the SUVA values of DOM due to the preferential adsorption of hydrophobic moieties. RDA analysis also revealed that, despite their different origins, there is a strong correlation between the chemical composition of POM and DOM.
Getting Time Right: Using Cox Models and Probabilities to Interpret Binary Panel Data

Abstract Logit and probit (L/P) models are a mainstay of binary time-series cross-sectional (BTSCS) analyses. Researchers include cubic splines or time polynomials to acknowledge the temporal element inherent in these data. However, L/P models cannot easily accommodate three other aspects of the data’s temporality: whether covariate effects are conditional on time, whether the process of interest is causally complex, and whether our functional form assumption regarding time’s effect is correct. Failing to account for any of these issues amounts to misspecification bias, threatening our inferences’ validity. We argue scholars should consider using Cox duration models when analyzing BTSCS data, as they create fewer opportunities for such misspecification bias, while also having the ability to assess the same hypotheses as L/P. We use Monte Carlo simulations to bring new evidence to light showing Cox models perform just as well—and sometimes better—than logit models in a basic BTSCS setting, and perform considerably better in more complex BTSCS situations. In addition, we highlight a new interpretation technique for Cox models—transition probabilities—to make Cox model results more readily interpretable. We use an application from interstate conflict to demonstrate our points.
Potential Use of Sewage Sludge Ash in Lime-Based Materials

Recycling of wastes and their by-products is attracting increasing interest worldwide because of the high environmental impact in the cement, concrete and other industries. This work deals with the study of the physico-chemical characteristics of binders based on sewage sludge ashes and lime. In a first step, we used X-ray fluorescence to determine the chemical composition of ash, lime and binders. This allowed us to see that the chemical composition of sewage sludge ash is similar to that of cement. We then used X-ray diffraction to identify the main mineralogical phases in the samples. Compressive strengths of mortars containing 20%, 40%, 60% and 80% of SSA showed that SSA has a long-term positive effect which might be related to a slight pozzolanic activity. The L4 binder consisting of 80% fine ash and 20% lime has a higher compressive strength than the others. The binder setting start time is greater than that of cement but shorter than that of lime. The study of the thermophysical properties of the L4 binder shows that it has a higher thermal resistance than cement and clay mortars. Moreover, it heats up less quickly because of its low effusivity compared to the latter two. This analysis highlighted the principal characteristics that must be taken into account to use SSA correctly in lime-based materials.
Band Gap Engineering of MnO via ZnO Alloying: A Potential New Visible-Light Photocatalyst

Practical implementation of solar-powered water splitting and CO2 reduction to fuels requires the discovery of efficient and inexpensive photocatalytic (PC) materials. One possible materials design strategy aims to tune properties of relatively inexpensive transition metal oxide catalysts to increase sunlight absorption while preserving potential redox reactivity. Here we consider MnO for PC use by alloying it with ZnO in solid solutions. A combined density functional theory and GW scheme is used to study the band gap and band edge placements as a function of composition. We predict that alloying MnO with ZnO in varying amounts reduces MnO’s band gap for more efficient light absorption while maintaining advantageous band edge placements. The 1:1 alloy of MnO and ZnO is identified as a new (2.6 eV band gap) visible-light-absorbing material with band edges suitably placed with respect to both water-oxidation and CO2-reduction reactions, making it a compelling candidate for solar PC chemistry.
Analysis and Application of Two-Layer Unconventional Windings for PM-Assisted Synchronous Reluctance Motors

This paper presents a new winding configuration theory of conventional and unconventional slot-pole combinations that can be used to configure windings with any slot-pole combination. First, various two-layer windings can be divided into conventional balanced winding (CBW), unbalanced winding (UBW), unconventional even slot winding (UESW) and unconventional odd slot winding (UOSW). In addition, the winding configuration can be divided into two steps—slot sorting and slot distributions to phases—and the rule of winding distribution and connection of the four kinds of windings is given. Moreover, there are four models established corresponding to the four kinds of windings. The air-gap flux density and harmonic of models with double layer flux-barrier PM-assisted rotor and without flux-barrier rotor are analyzed and compared using two-dimensional transient finite element analysis (FEA). The effect of stator magneto motive force (MMF) on torque performance affected by winding configuration is analyzed by comparing air gap magnetic density under two kinds of rotors. The simulation of torque shows that UBW, UESW, and UOSW have a certain reducing effect on torque ripple in PMASynRM. Meanwhile, a brief comparison is made in terms of radial forces and power losses; the effects produced by UBW, UESW, and UOSW compared to CBW in these two aspects are acceptable.
Self-Imitation Learning-Based Inter-Cell Interference Coordination in Autonomous HetNets

Recently, mobile operators have been shifting to an intelligent autonomous network paradigm, where the mobile networks are automated in a plug-and-play manner to reduce the manual intervention. Under this circumstance, serious inter-cell interference becomes inevitable which may severely deteriorate system throughput performance and users’ quality of service (QoS), especially for dense residential small base station (SBS) deployment. This paper proposes an intelligent inter-cell interference coordination (ICIC) scheme for autonomous heterogeneous networks (HetNets), where the SBSs agilely schedule sub-channels to individual users at each Transmit Time Interval (TTI) with aim of mitigating interferences and maximizing long-term throughput by sensing the environment. Since the reward function is inexplicit and only few samples can be used for prior-training, we formulate the ICIC problem as a distributed inverse reinforcement learning (IRL) problem following the POMDP games. We propose a non-prior knowledge based self-imitating learning (SIL) algorithm which incorporates Wasserstein Generative Adversarial Networks (WGANs) and Double Deep Q Network (Double DQN) algorithms for performing behavior imitation and few-shot learning in solving the IRL problem from both the policy and value. Numerical results reveal that SIL is able to implement TTI level’s decision-making to solve the ICIC problem, and the overall network throughput of SIL can be improved by up to 19.8% when compared with other known benchmark algorithms.
Multicell Power Supplies for Improved Energy Efficiency in the Information and Communications Technology Infrastructures

The rapid growth of the Information and Communications Technology (ICT) sector requires additional infrastructure, such as more micro-datacenters and telecom stations, to support the higher internet speeds and low latency requirements of 5G networks. The increased power requirements of the new ICT technologies necessitate the proposal of new power supplies in an attempt to retain the increase in energy demand and running costs. The work analyses how multicell structured power supply units can increase the energy efficiency of the power supply systems and thus minimize running costs of ICT networks. The effect of multicell structures in energy efficiency improvements is verified experimentally and through simulations in PSIM’s thermal module. A new multicell Power Supply Unit (PSU) is proposed for ICT power supplies and it is shown that using multicell structures, higher efficiency at varying load levels can be achieved.
Computational Investigation of Square Embedded Delamination of a Composite Laminate using VCCT

The fiber reinforced polymer laminates are widely implemented in aviation industry due to its advantages and applications other materials in terms of strength to weight ratio, dsign features and many more. The strength of the interface compared to longitudinal and lateral directions of the plies are comparatively less and give rise too poor transverse direction strength. Hence a failure mechanism called delamination will occur in case when tools are dropped or due to poor manufacturing which would give rise to interface delamination. In this paper, VCCT is employed at the interface between base and sub laminate to investigate for a square shape delamination geometry of 20mm buckling driven delamination growth. The computational prediction of delamination growth initiation is obtained by solving a T300/976 specimen for geometric non linearity using SC8R continuum shell elements of Abaqus CAE and by plotting the required energy release rate at the delamination geometry.
S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis

Stocks price prediction is a current hot spot with great promise and challenges. Recently, there have been many stock price prediction methods. However, the prediction accuracy of these methods is still far from satisfactory. In this paper, we propose a stock price prediction method that incorporates multiple data sources and the investor sentiment, which can be called S_I_LSTM. Firstly, we crawl multiple data sources on the Internet and preprocess them respectively. These data involve stock historical data, technical indicators, and non-traditional data sources, such as stock posts and financial news. Then, we use the sentiment analysis method based on convolutional neural network for the non-traditional data, which can calculate the investors' sentiment index. Finally, we combine sentiment index, technical indicators and stock historical transaction data as the feature set of stock price prediction and adopt the long short-term memory network for predicting the China Shanghai A-share market. The experiments show that the predicted stock closing price is closer to the true closing price than the single data source, and the mean absolute error can achieve 2.386835, which is better than traditional methods. We verified the effectiveness on the real data sets of five listed companies.
Determination of the 151Sm half-life

Abstract New measurements have been undertaken to determine the half-life of 151Sm. A pure 151Sm solution was obtained after chemical separation from a samarium solution resulting from the dissolution of an irradiated samarium sample. The concentration of 151Sm in the solution was measured by mass spectrometry, combined with the isotope dilution technique. The activity of the solution was measured by liquid scintillation counting by six European laboratories as part of an international comparison. These combined results lead to a half-life of T1/2 = 94.6(6) a.
WHALE SHARK TOURISM AND WELL-BEING: A CASE STUDY OF LABUAN JAMBU

As one of the largest whale shark attractions in Indonesia, Labuan Jambu Village still lacks the attention of academics and government in terms of research and development. As a result, it's worth taking a closer look at the people of Labuan Jambu Village's well-being before and after the Whale Shark tourism activities. The authors found that the economic benefits of tourism activities have not had a significant and unequal effect on the villagers. Before the tourism industry existed, the villagers of Labuan Jambu understood the importance of education. Aside from that, the most important problem that Labuan Jambu Village has yet to resolve is waste management. In order to create sustainable tourism in Labuan Jambu Village, various stakeholders must collaborate
Dynamic resource management algorithms for interference prediction in 5G new radio scenarios

Efficient Radio Resource Management is a key mechanism in interference management in 5G New Radio (NR) networks, specifically in the case of the presence of mobile users moving at high speed. To this end, the prediction and the evaluation of the propagation channel sensitivity requires that the radio resources allocation in NR must be efficient and powerful. Therefore, several scheduling algorithms have been developed and tested using the mmWave model of NS-3 simulator, with the aim of enhancing their contribution to improving the quality of the signal received by users. The performances have been evaluated in terms of Signal-to-Interference-and-Noise-Ratio (SINR) and signal Block Error Rate (BLER). The simulations were run for different types of data flows, and achieved satisfactory results for most schemes. The achievements clearly show the importance of scheduling algorithms in lowering received interference, but they have also demonstrated the stability and reliability of some of those strategies.
Light Confinement with Structured Beams in Gold Nanoparticle Suspensions

We carry out an experimental campaign to investigate the nonlinear self-defocusing propagation of singular light beams with various complex structures of phase and intensity in a colloidal suspension of gold nanoparticles with a plasmonic resonance near the laser wavelength (532nm). Studying optical vortices embedded in Gaussian beams, Bessel vortices and Bessel-cosine (necklace) beams, we gather evidence that while intense vortices turn into two-dimensional dark solitons, all structured wavepackets are able to guide a weak Gaussian probe of different wavelength (632.8 nm) along the dark core. The probe confinement also depends on the topological charge of the singular pump.
A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations.
Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU

Mesh quality is a critical issue in numerical computing because it directly impacts both computational efficiency and accuracy. Tetrahedral meshes are widely used in various engineering and science applications. However, in large-scale and complicated application scenarios, there are a large number of tetrahedrons, and in this case, the improvement of mesh quality is computationally expensive. Laplacian mesh smoothing is a simple mesh optimization method that improves mesh quality by changing the locations of nodes. In this paper, by exploiting the parallelism features of the modern graphics processing unit (GPU), we specifically designed a parallel adaptive Laplacian smoothing algorithm for improving the quality of large-scale tetrahedral meshes. In the proposed adaptive algorithm, we defined the aspect ratio as a metric to judge the mesh quality after each iteration to ensure that every smoothing improves the mesh quality. The adaptive algorithm avoids the shortcoming of the ordinary Laplacian algorithm to create potential invalid elements in the concave area. We conducted 5 groups of comparative experimental tests to evaluate the performance of the proposed parallel algorithm. The results demonstrated that the proposed adaptive algorithm is up to 23 times faster than the serial algorithms; and the accuracy of the tetrahedral mesh is satisfactorily improved after adaptive Laplacian mesh smoothing. Compared with the ordinary Laplacian algorithm, the proposed adaptive Laplacian algorithm is more applicable, and can effectively deal with those tetrahedrons with extremely poor quality. This indicates that the proposed parallel algorithm can be applied to improve the mesh quality in large-scale and complicated application scenarios.