Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimising CH 4 simulations from the LPJ-GUESS model v4.1 using an adaptive MCMC algorithm

The processes responsible for methane (CH 4 ) emissions from boreal wetlands are complex, and hence their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS is one such model that allows quantification and understanding of the natural wetland CH 4 fluxes at various scales ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of CH 4 production, oxidation, and transport controlled by several process parameters. 5 Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions are highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis Hastings (MH) based Markov Chain Monte Carlo (MCMC) algorithm to improve predictions of CH 4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method 10 on uncertain parameters allows greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with reduced risk of sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period 2005 to 2014 from the Siikaneva wetlands in southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH 4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related.
Warm conveyor belt characteristics and impacts along the life cycle of extratropical cyclones: Case studies and climatological analysis based on ERA5

This study presents a systematic and global investigation of the characteristics and impacts of warm conveyor belts (WCBs). For this purpose, we compile a new WCB climatology (1980–2022) of trajectories calculated with the most recent reanalysis dataset ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF). Based on this new climatology, two-dimensional masks are defined, which represent the inflow, ascent and outflow locations of WCBs. These masks are then used to objectively quantify the key characteristics (intensity, ascent rate, and ascent curvature) and impacts 5 (precipitation and potential vorticity (PV) anomalies) of WCBs in order to (i) attribute them to different stages in the life cycle of the associated cyclones and to (ii) evaluate differences in the outflow of the cyclonic and anticyclonic branches. The method is first tested and illustrated through three case studies of well-documented cyclones, revealing both the similarities and the case-to-case variability in the evolution of the WCB characteristics and impacts. We then extend the analysis to about 5’000 cyclones that occurred in winter between 1980–2022 in the North Atlantic. The case studies and the climatological 10 analysis both show that WCBs are typically most intense (in terms of air mass transported, ascent rate, precipitation rate, and volume) during the intensification period of the associated cyclone. The northward displacement along the storm track and diabatic PV production lead to an increase in low-level PV in the region of WCB ascent during the cyclone life cycle. The negative PV anomaly at upper levels, associated with the WCB outflow, remains relatively constant. The investigation of the WCB branches reveals an increasing intensity of the cyclonic WCB branch with time, linked to the increasing strength of the 15 cyclonic
An Investigation of the influence of single family type on students’ academic performance in public secondary schools in Kenya

: The study sought to investigate the influence of single family type on students’ academic performance in public secondary schools in Kangundo Sub-county. It adopted a descriptive survey research design. It targeted all (27) public secondary schools in Kangundo Sub-county, all (27) school heads, all (27) parents’ association chairpersons, all (339) teachers and all (2,663) form three students. The study employed a census technique to include all schools; 24 in the main study and 3 in the pilot study. All the 24 school heads as well as all the 24 parents’ association chairpersons of the participating schools were included in the study. It sampled 30.0% of teachers that gave 102 teachers. Stratified random sampling technique was employed to select participating teachers where a proportionate weighted sample was identified per school using proportional allocation method. Then, simple random sampling technique was adopted to select the participating teachers in each school. Yamene (1967) formula was used to give a sample of 348 students. Stratified random sampling technique was employed to identify and select weighted random samples per school. In total, 498 respondents participated in the study. Data collection instruments included questionnaires for school heads, teachers, students and an interview guide for parents’ association chairpersons. Validity of the research instruments was ascertained through expert judgment and piloting. Reliability was achieved through test re-test method where the instruments were piloted in schools at a time interval of two weeks and the two results were correlated using Pearson’s Product Moment Correlation method. Data were entered into Statistical Package for Social Sciences (version 26.0) for analysis. Further, inferential analysis was employed that involved correlational analysis at a 0.05 level of significance. The hypothesis was accepted or rejected at a 0.01 level of significance. Results showed a weak correlation between single parent family type and students’ academic performance which was not statistically significant (R=.149; p=0.508). It was recommended that the government should initiate educative and enlightenment programs on how to improve and sustain intact parenthood.
Arterial stiffness and atrial ﬁ brillation: A review

Arterial stiffness has been investigated as part of the physiopathology of arterial hypertension since the 1970s. Its role in increasing the “ pulsatile load ” imposed over the Left Ventricle (LV) has been intensely studied recently and has helped in understanding the mechanisms of Atrial Fibrillation (AF) in hypertensive patients. This paper aims to review the main evidence on this issue and establish possible mechanisms involved in the development of AF in patients with arterial stiffness. A PubMed search was performed, and selected articles were searched for references focusing on this topic. In the long term, lower blood pressure levels allow for arterial wall remodeling, leading to a lower stiffness index. To this day, however, there are no available treatments that directly promote the lowering of arterial wall stiffness. Most classes of anti-hypertensive drugs ‒ with stronger evidence for beta-blockers and diuretics ‒ could be effective in reducing arterial stiffness. There is strong evidence demonstrating an association between arterial stiffness and AF. New studies focusing on arterial stiffness and pre- ﬁ brillatory stages would strengthen this causality relation.
THE BETHESDA SYSTEM FOR REPORTING THYROID FINE NEEDLE ASPIRATES.

* Sharma A 1 , Shinde R.M 2 , Deshpande T 1 and Raghuwanshi P 1 . 1. Post Graduate student, Department of Pathology, D.Y. Patil Medical College, Kolhapur. 2. Associate Professor, Department of Pathology, D.Y. Patil Medical College, Kolhapur. Manuscript Info Abstract . Manuscript History
Process Recipes for SMD Interconnection on Aqueous Conductive Inks using Direct-Write Printing

Additive print methods for fabricating electronics eliminate the need for plating baths. Earlier inks used volatile solvents to achieve conductive traces. This work studies the realization of additively printed circuits with water-based inks. Printing, in addition to the benefits of reduced costs and waste, provides a greater range of flexible and rigid substrate options. However, there are other hurdles to printing functioning electrical devices, such as print quality, smoothness, and registration accuracy requirements that exceed those of graphic printing. Substrate parameters include smoothness, compressibility, porosity, ink receptivity, wettability, and ink properties, including ink chemistry, viscosity, rheological behavior, solvent evaporation rate, and drying, which are therefore crucial. Extensive studies are necessary before mass manufacturing can begin, but there is also the possibility of including environmental factors in the development of the technology. It is generally believed that decisions taken during the early design phase affect more than 80 percent of a product's environmental impact. Water-based silver inks, however, have been offered as viable replacements for ecologically unfriendly organic solvents and harmful chemicals due to environmental concerns and health problems. This work studies the interconnection performance of aqueous-based inks with various interconnect materials such as ECA and LTS. For frequency-based applications, assess the influence of aqueous-based ink performance under varying frequencies. The performance of the interconnects is compared to the tolerance limits of the COTs. The intermetallic at the interface of conductive adhesive and additively printed circuits has been examined using optical and SEM examination. To simulate the applications, we used OrCAD software to build and test a digital circuit. The performance of the digital circuit has been compared and evaluated to the laser ablated copper clad substrate for the various bonding materials employed.
Experimental Investigation of Heat Transfer to a Dual Jet Flow with Varying Velocity Ratio

Dual jet flow is a topical area of research due to their wide range of current and potential uses in industry. Despite this, there is still a lack of published studies which focus on the characterization of dual jet flow, particularly regarding their heat transfer capabilities. The objective of this investigation is to therefore build upon the available dual jet data and conduct an experimental study which focusses on the effect of the jet velocity ratio on heat transfer to a dual jet flow for a constant offset ratio of 3, where air is used as the working fluid. The wall and offset jet velocities are each varied between $\mathbf{5500} \leq \boldsymbol{Re}\leq \mathbf{12000}$ to create a range of velocity ratios from $\mathbf{0}. \mathbf{5}\leq \boldsymbol{V}_{\boldsymbol{r}}\leq \mathbf{2}$. A uniform heat flux of 2500 W/m2 is maintained in the bounding wall and the local Nusselt number data is derived from temperature measurements acquired through infra-red thermography. The results show a strong dependence on the velocity ratio, where, for a constant total mass flow rate, increasing the velocity ratio increased the value of a localized minimum Nusselt number and moved its location closer to the jet exit. In addition, increasing the total mass flow rate elevated $\boldsymbol{Nu}_{\boldsymbol{x}}$ across all downstream locations for each $\boldsymbol{V}_{\boldsymbol{r}}$ examined.
Corrupted Data Recovering for Electrical Capacitance Tomography Based on RPCA

Electrical capacitance tomography (ECT) may be used to visualize a cross-section distribution of solids concentration in a gas-solids flow. However, it is difficult to eliminate the effect of particle charging on capacitance measurements because it is difficult to separate the measurement noise from capacitance measurements. In this research, a method based on robust principal component analysis (RPCA) is introduced and applied to recover the corrupted data induced by electrostatic sparks. In this approach, recovering the corrupted data is recast as recovering a low-rank matrix from the noisy matrix constructed by time-series data. An alternating direction method of multipliers is applied to obtain the low-rank data matrix and the sparse error matrix. The performance of RPCA is evaluated using simulation and experimental data. It has been shown that by applying the proposed technique in the measurement space, significant improvement in image quality can be achieved.
On correct evaluation techniques of brightness enhancement effect measurement data

Abstract. This paper aims to establish confidence intervals of the quantification of brightness enhancement effects resulting from the use of pulsing bright light. It is found that the methods used so far may yield significant bias in the published results, overestimating or underestimating the enhancement effect. The authors propose to use a linear algebra method called the total least squares. Upon an example dataset, it is shown that this method does not yield biased results. The statistical significance of the results is also computed. It is concluded over an observation set that the currently used linear algebra methods present many patterns of noise sensitivity. Changing algorithm details leads to inconsistent results. It is thus recommended to use the method with the lowest noise sensitivity. Moreover, it is shown that this method also permits one to obtain an estimate of the confidence interval. This paper neither aims to publish results about a particular experiment nor to draw any particular conclusion about existence or nonexistence of the brightness enhancement effect.
A boundary value problem on the whole line to second order nonlinear differential equations

Abstract Second order nonlinear ordinary differential equations are considered, and a certain boundary value problem on the whole line is studied. Two theorems are obtained as main results. The first theorem is established by the use of the Schauder theorem and concerns the existence of solutions, while the second theorem is concerned with the existence and uniqueness of solutions and is derived by the Banach contraction principle. These two theorems are applied, in particular, to the specific class of second order nonlinear ordinary differential equations of Emden–Fowler type and to the special case of second order linear ordinary differential equations, respectively.
Unsupervised Segmentation of Smart Home Position Logs for Human Activity Analysis

Human activities represent a major source of information for smart home automation. While performing their daily activities, humans trigger sensors producing measurements that flow into a sensor log. Vast majority of techniques to recognize and exploit the occurrences of human activities are supervised, requiring the log to be manually labeled in correspondence of the onset and the end of each activity repetition. This task requires a considerable effort by the final user, resulting in imprecise labeling tampering the performance of algorithms. In this paper, we propose an unsupervised technique allowing to automatically segment smart home logs containing position sensor measurements. The proposed technique exploits information about the position of the human to automatically extract basic actions, which are then segmented on a temporal basis and clustered. The approach is evaluated against a state-of-the-art dataset.
Alzheimer's Disease Prediction by Spatio-Temporal Feature Fusion for MRI Data

Alzheimer's disease (AD) is one of the leading causes of memory loss where patients eventually die. Currently, there is no cure for patients with AD, but patients with Mild Cognitive Impairment (MCI) is the prodromal stage of AD may get diagnosed and have the chance to escape from AD. However, many people with AD remain incurable. Diagnosis of MCI is very important to alleviate complications like brain cell loss and brain shrinking. This can be achieved by developing a spatio-temporal feature fusion aiding early diagnosis of AD. This study proposes a model for spatio-temporal feature fusion using two techniques a) Hierarchical Supervised Local Canonical Correlation Analysis (HSL-CCA) and b) 2D-Convolution Neural Network (2D-CNN). This work uses Magnetic Resonance Imaging (MRI) data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database scans for the 5 stages of AD subjects, and spatial, temporal, and deep features are extracted using Gray-Level Co-occurrence Matrix (GLCM), curvelet transform and 2D-CNN. Principal Component Analysis (PCA) is applied to remove the redundant information, and Short-time Fourier transform (STFT) is used to transform the 1D information into 2D images. Finally, Feature fusion techniques such as: simple concatenation, canonical correlation analysis (CCA), HSL-CCA and 2D-CNN are incorporated to fuse extracted features. The post-processing stage which includes Random Forest (RF) based classification of fused features to classify multiclass of Alzheimer's. The outcome of different feature fusion techniques was compared with several baseline models, the proposed HSL-CCA and 2D CNN fusion approach achieves 84% and 87% in AD detection.
Experimental, Computational Fluid Dynamics and Theoretical Study of Cyclone Performance Based on Inlet Velocity and Particle Loading Rate

This paper describes experimental, Computational Fluid Dynamics (CFD) and theoretical analysis of a cyclone performance, operated 1.0 g/m solid loading rate, at two different inlet velocities (5 m/s and 10 m/s). Comparing experimental results with theoretical and CFD simulation results, it is pronounced that the influence of solid in processing flow is more significant than expected. Experimental studies based on gas-solid flows of cyclone separators are complicated as they required advanced sensitive measuring techniques, especially flow characteristics. Thus, CFD modeling and theoretical analysis are economical in analyzing cyclone separator performance but detailed clarifications of the application of these in cyclone separator performance evaluation is not yet discussed. The present study shows the limitations of influencing parameters of CFD and theoretical considerations, comparing experimental results and flow characteristics from CFD modeling. Keywords—Cyclone performance, inlet velocity, pressure drop, solid loading rate.
Enhanced Digital Interface Circuit for Three-wire Connected Resistance Thermometers

A simple digital interface circuit suitable for a three-wire resistance thermometer is reported in this article. The digitizer provides a linear transfer characteristic without dependence on the connecting wire resistances. Besides, the digital interface produces the output using two conversion cycles. The methodology of the digital interface circuit is mathematically derived and analyzed. Later, the effect of dominant error sources is also detailed. The performance of the circuit is initially verified using simulation studies as well as experimental studies. A linear output characteristic is obtained during these tests, and maximum output-nonlinearity does not exceed 0.25 %. Finally, the experimental results of the proposed circuit are compared with the theoretical and simulation results, and the prior art.
Commutators of Littlewood-Paley gκ∗ $g_{\kappa}^{*} $-functions on non-homogeneous metric measure spaces

Abstract The main purpose of this paper is to prove that the boundedness of the commutator Mκ,b∗ $\mathcal{M}_{\kappa,b}^{*} $ generated by the Littlewood-Paley operator Mκ∗ $\mathcal{M}_{\kappa}^{*} $ and RBMO (μ) function on non-homogeneous metric measure spaces satisfying the upper doubling and the geometrically doubling conditions. Under the assumption that the kernel of Mκ∗ $\mathcal{M}_{\kappa}^{*} $ satisfies a certain Hörmander-type condition, the authors prove that Mκ,b∗ $\mathcal{M}_{\kappa,b}^{*} $ is bounded on Lebesgue spaces Lp(μ) for 1 < p < ∞, bounded from the space L log L(μ) to the weak Lebesgue space L1,∞(μ), and is bounded from the atomic Hardy spaces H1(μ) to the weak Lebesgue spaces L1,∞(μ).
Generator Grouping and Voltage Corrections for REI Reduction in Power Networks

This work proposes improvements to the Radial Equivalent Independent (REI) reduction approach originally proposed by Dimo. REI equivalency is typically used for reducing external systems, although other uses have been reported. Some early literature suggests that the REI method may offer reduced accuracy compared to the Extended Ward method. The improvements suggested in this work aim to enhance accuracy and are centered on generator grouping and equivalent system voltage correction. These improvements are tested on an example system subject to typical control center applications such as contingency analysis and load change. The results show that the methods proposed offer significant improvements over the original REI approach.
Multi-Person Tracking Method Robust to Dynamic Viewport Changes for AR apps

Augmented reality (AR) devices have gained a lot of attention in recent years due to their ability to enhance people’s abilities through 2D/3D spatial sensing and recognition functions. RGB cameras are most often used as sensors in this type of spatial recognition, and a particularly important task is the detection and tracking of objects and people in physical space. However, the camera positions and orientations on AR devices such as smartphones and smart glasses, frequently change due to the user wearing them on their head, leading to non-linear and complex motion in the video frames and reducing the accuracy of tracking people. To address this issue, the proposed method combines person re-identification based on deep metric learning with trajectory prediction to estimate the person’s sequential positions in 3D space around the camera. The experimental result shows 95.45% accuracy with our dataset.
Analysis of environmental aspects affecting comfort in commercial buildings

Environmental aspects are of high priority for the identification and evaluation of the parameters that affect the design and construction of buildings. Their improvement in case of the existing European building stock while considering and maintaining the occupants’ comfort sensation in high levels, is imperative for creating an environmental friendly building. The combination of those aspects can upgrade the indoor conditions leading to the creation of an appealing workspace where the well fair of the occupants is established. In this line of approach, an integrated evaluation of the indoor environmental parameters was conducted in office buildings, located in Thessaloniki, Greece, based on the occupants’ comfort sensation. Main goal of the study is the determination of the existing correlations between the perceived comfort sensation and a variety of environmental parameters considered in building rating certification schemes. Those correlations can outline the weight of every aspect based on the occupants' view and also help the policy makers to accomplish the vision of an environmental sustainable, not only concerning the energy consumption but also the occupants, building.