Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK)

Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co‐kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten‐fold cross‐validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring.
KALP HIZI DEĞİŞKENLİĞİ VE EGZERSİZE KRONİK YANITLARI

Bu derleme çalışmanın amaçlarından biri; kalp hızı değişkenliği kavramı ve bileşenlerini, kalp hızı değişkenliği ölçüm yöntemlerini ve kalp hızı değişkenliği üzerinde etkili olan parametreleri incelemekti. Çalışmanın diğer amacı da sağlığa ilişkin birçok faydası olduğu bilinen egzersizin, sağlıklı yetişkinlerde kalp hızı değişkenliği üzerindeki kronik etkilerinin tartışılması idi. Bu amaçla otonom sinir sistemi, kalp hızı değişkenliği ve egzersiz/düzenli fiziksel aktivite/fitness sözcükleri belirli bilimsel indekslerde incelenmiş ve konuyla ilgili çalışmalar ele alınmaya çalışılmıştır.
Laminar flow in regular polygonal shaped ducts with circular centered cores

The method of least-squares fitting of harmonic functions to known boundary conditions has been employed to obtain closed-form solutions for laminar flow of an incompressible fluid through the constant area annulus between a regular polygonal duct and a centered circular core. The product of Fanning friction factor and Reynolds number has been determined for the entire range between the extreme case of no core and the other extreme case where the core touches the outerwalls. For the latter case, it is shown that when the number of sides of the polygon approaches infinity, f.Re approaches a minimum value of 6.22. The analysis of this case is further extended to corner angles less than 60°, the minimum imposed by an equilateral triangular duct. On a adapte la methode des moindres carres de fonctions harmoniques a des conditions-limites connues pour obtenir des solutions de genre ferme dans le cas de l'ecoulement laminaire d'un fluide incompressible par un anneau a surface constante, entre un conduit en forme de polygone regulier et un noyau circulaire et centre. On a determine le produit facteur de friction de Fanning et du nombre de Reynolds sur une echelle complete comprise entre deux extrěmes, a savoir l'absence de noyau et le contact du noyau avec les parois exterieures; on a demontre dans ce dernier cas que, lorsque le nombre des cǒtes du polygone approche l'infini, f. Re approche une valeur minimum de 6.22. On a etendu l'analyse de ce cas a des angles de coin inferieurs a 60°, qui est le minimum requis pour un conduit en forme de triangle equilateral.
A “Floatilla” of Airborne Seismometers for Venus

Barometers floating on high‐altitude balloons in the relatively clement cloud layer on Venus could detect and characterize acoustic waves generated by seismic activity, avoiding the need for high‐temperature electronics required for surface seismology. Garcia et al. (2022, https://doi.org/10.1029/2022GL098844) recently demonstrated the detection of low‐frequency sound (infrasound) caused by earthquakes of magnitudes 7.3 and 7.5 from stratospheric balloons nearly 3,000 km away from the epicenter. They provided a preliminary demonstration of earthquake magnitude and location inversion, and the determination of S‐ and Rayleigh wave velocities using only their acoustic signature. Large earthquakes produce low‐frequency seismic waves that penetrate the interiors of planets; their detection at continental‐scale distances from a high‐vantage point demonstrates the feasibility of balloon‐based investigations of Venus' interior. We contextualize these results within the effort to perform seismology on Venus from balloons, discuss its limitations, and share perspectives on open research questions in this area.
Evaluation of Sustainable Business Model Innovation in Increasing the Penetration of Renewable Energy in The Ghana Power Sector

 Abstract— Ghana's primary energy supply is heavily reliant on petroleum, biomass, and hydropower. Currently, Ghana gets its energy from hydropower (Akosombo and Bui), thermal power plants powered by crude oil, natural gas, and diesel, solar power, and imports from La Cote d'Ivoire. Until the early 2000s, large hydroelectric dams dominated Ghana's electricity generation. Due to the unreliable weather patterns, Ghana increased its reliance on thermal power. Thermal power contributes the highest percentage in terms of electricity generation in Ghana and is predominantly supplied by Independent Power Producers (IPPs). Ghana's electricity industry operates the corporate utility model as its business model. This model is typically 'vertically integrated', with a single corporation selling the majority of power generated by its generation assets to its retail business, which then sells the electricity to retail market consumers. The corporate utility model has a straightforward value proposition that is based on increasing the number of energy units sold. The unit volume business model drives the entire energy value chain to increase throughput, locking system users into unsustainable practices. This report uses the qualitative research approach to explore the electricity industry in Ghana. There is the need for increasing renewable energy such as wind and solar in the electricity generation. The research recommends two critical business models for the penetration of renewable energy in Ghana's power sector. The first model is the peer-to-peer electricity trading model which relies on a software platform to connect consumers and generators in order for them to trade energy directly with one another. The second model is about encouraging local energy generation, incentivizing optimal time-of-use behaviour, and allow any financial gains to be shared among the community members.
Wave-front reconstruction by digital phase retrieval method in high power laser

A phase retrieval algorithm which only needs to measure the intensity distribution at two positions was used to reconstruct laser wavefront. It was further applied in high power laser. Results were obtained from the phase retrieval algorithm in the visible band, and the effects of measurement error on the phase retrieval process have been simulated. This algorithm is not sensitive to measurement error, but sensitive to the relative distribution of light intensity.
Erratum The effective temperature scale of giant stars (F0-K5) - II. Empirical calibration of $T_{\mathsf {eff}}$ versus colours and [Fe/H]

The sign of coefficient $a(3)$ in the general formula of Table 2 should be plus instead of minus. Thus, the formula should read: $$ \theta_{\rm eff}=a_{0} + a_{1} X + a_{2} X^{2} + a_{3} X {\rm [Fe/H]} + a_{4} {\rm [Fe/H]} + a_{5} {\rm [Fe/H]}^{2}. $$
Implementation of Practicum Worksheets Based on Guided Inquiry on the Topic of Colloids to Improve Students' Science Process Skills

Currently, a lot of learning focuses on cognitive achievement, which results in less involvement of students in learning. Whereas in the renewable curriculum students are required to have ability incognitive, affective and psychomotor. One of these psychomotor aspects is science process skills by applying inquiry model. This study aims to determine the effect of the implementation of guided inquiry-based practicum worksheets on colloid topics on students' science process skills. The research method used mixed research method research with concurrent triangulation designs. The research was conducted in Cimahi, involving 33 students of class XII and 5 validators consisting of two lecturers and three teachers. The research instruments were used self-assessment, observation sheets for practicum activities using guided inquiry-based worksheets, assessment of student worksheet’s assignments, and restricted-response essays. The enhancement of science process skills was calculated by the N-gain formula using pretest and posttest score and characterize into high, medium and low. The results showed that the improvement of student science process skills on the topic of emulsifiers was in the medium category with an N-gain value of 0.6. The implementation of the practicum using guided inquiry-based worksheets on the result of self-assessment obtained 82.0%, the results of observation obtained 82.9%, and the assessment of student’s assignment in the worksheets obtained 81.6% in which all in the very good category
Discovering misannotated lncRNAs using deep learning training dynamics

Abstract Motivation Recent experimental evidence has shown that some long non-coding RNAs (lncRNAs) contain small open reading frames (sORFs) that are translated into functional micropeptides, suggesting that these lncRNAs are misannotated as non-coding. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (Ribo-Seq) and mass-spectrometry experiments, which are cell-type dependent and expensive. Results Here, we propose a computational method to identify possible misannotated lncRNAs from sequence information alone. Our approach first builds deep learning models to discriminate coding and non-coding transcripts and leverages these models’ training dynamics to identify misannotated lncRNAs—i.e. lncRNAs with coding potential. The set of misannotated lncRNAs we identified significantly overlap with experimentally validated ones and closely resemble coding protein sequences as evidenced by significant BLAST hits. Our analysis on a subset of misannotated lncRNA candidates also shows that some ORFs they contain yield high confidence folded structures as predicted by AlphaFold2. This methodology offers promising potential for assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs and for curating better datasets for building coding potential predictors. Availability and implementation Source code is available at https://github.com/nabiafshan/DetectingMisannotatedLncRNAs. Supplementary information Supplementary data are available at Bioinformatics online.
Common subexpression elimination algorithm for low-cost multiplierless implementation of matrix multipliers

The design of multiplierless implementations (which use only adders, subtracters and binary shifts) of fixed-point matrix multipliers is considered and a new common subexpression elimination method is described that recursively extracts signed two-term common subexpressions. Examples are given that show that the resulting adder-cost is significantly lower than for existing algorithms.
Physical interpretations for radiative corrections in the non-relativistic limit

A detailed physical discussion of the contribution of non-relativistic modes of the radiation field to electron radiative corrections (Lamb shift, g-2) is presented. It is shown that these corrections can be described by a simple effective Hamiltonian, derived from a single-particle theory, and that two main physical effects are involved: the vibration of the electron charge and spin moment due to vacuum fluctuations, and the radiation reaction of the charge. It is found that the positive sign of g-2 is entirely due to the radiation reaction which slows down the cyclotron motion, whereas the Lamb shift results from the averaging of the Coulomb potential by the vibrating electron (Welton's picture). Many-particle effects and the contributions of relativistic modes are briefly discussed. They do not seem to alter these conclusions.
The motion of an ellipsoid in tube flow at low Reynolds numbers

The motion of a rigid ellipsoidal particle freely suspended in a Poiseuille flow of an incompressible Newtonian fluid through a narrow tube is studied numerically in the zero-Reynolds-number limit. It is assumed that the effect of inertia forces on the motion of the particle and the fluid can be neglected and that no forces or torques act on the particle. The Stokes equation is solved by a finite element method for various positions and orientations of the particle to yield the instantaneous velocity of the particle as well as the flow field around it, and the particle trajectories are determined for different initial configurations. A prolate spheroid is found to either tumble or oscillate in rotation, depending on the particle–tube size ratio, the axis ratio of the particle, and the initial conditions. A large oblate spheroid may approach asymptotically a steady, stable configuration, at which it is located close to the tube centreline, with its major axis slightly tilted from the undisturbed flow direction. The motion of non-axisymmetric ellipsoids is also illustrated and discussed with emphasis on the effect of the particle shape and size.
Focused bulk ultrasonic waves generated by ring‐shaped laser illumination and application to flaw detection

Focused bulk ultrasonic waves have been generated in aluminum plates by surface irradiation with ring‐shaped laser light. The waves are detected by a piezoelectric transducer. Compression and shear peak amplitudes drop quickly when the detector is moved away from the epicenter. This shows that strong focusing exists at the epicenter as the result of constructive interference of the waves generated by different parts of the ring. The focusing persists when the radius of the laser light is scanned over a large range, indicating that the elastic disturbance concentrates in depth along the ring’s central axis. Numerical simulations are presented for comparison. The ‘‘pencil‐like’’ acoustic wave structure is used to observe a sample plate with an artificial flaw. Strong new features including compress‐shear mode conversion at the site of the flaw are observed. These features are used to locate the flaw within the sample.
Effect of non-thermal plasma on cellulose crystallinity and lignin content in corn stalks

Lignocellulosic biomass is a cheap raw material that, thanks to its high carbohydrate content, can be used in fermentation to produce biofuels, biogas and other compounds. Its complex structure, including cellulose, hemicellulose and lignin, requires prior treatment of the biomass to facilitate hydrolysis to simple sugars. Today, biomass is only partially utilized and generates about 14% of the world's energy. This is because the most commonly used physical, chemical and physicochemical treatments are not sustainable. They are energy-consuming but still low in productivity and toxic inhibitors formed during these treatments could hinder later steps of fermentation. Biomass treatment with advanced oxidation techniques has great potential as an environmentally friendly, so-called "green" treatment. These processes generate reactive species (radicals, electrons, ions and peroxides) that attack cellulose, hemicellulose, and lignin components. In this work, the effects of non-thermal plasma, the Fenton process, and the combined treatment of corn stalks with non-thermal plasma/Fenton were compared. Grounded biomass of corn stalks was mixed with Fenton reagent and hydrogen peroxide at different ratios and subjected to non-thermal plasma treatment. Carbohydrate content was decreased in non-thermal plasma treated samples both with and without Fe2+. However, a specific biomass: Fe2+:H2O2 ratio was required to achieve the highest rate of lignocellulose decomposition. The cellulose and hemicellulose fractions were affected and reduced by the treatments studied but resulted in almost no changes in the cellulose crystallinity index. The lower lignin content and cellulose crystallinity allow for more efficient enzyme hydrolysis of the treated lignocellulose and new options for valorization in fermentations.
Invasion by an exotic grass species homogenizes native freshwater plant communities

A growing body of evidence has shown that biological invasions cause shifts in species composition of communities in space and time. Although biological invasions are considered a major driver of biotic homogenisation worldwide, most previous studies were conducted at small spatial scales and over short time periods, which may have underestimated the impacts of exotic species on native communities. Using a unique dataset of aquatic plants sampled in 235 sites over 12 years (2007–2010 and 2015–2019) in a large reservoir (Itaipu Reservoir; 1350 km2), we analysed how the invasion of a non‐native grass Urochloa arrecta affects the species richness, ecological uniqueness (i.e. local contribution to beta diversity—LCBD) and temporal β–diversity of native plant communities. From 3934 surveyed plant communities, U. arrecta was recorded in 2888 samples and it was absent from 1046 samples. Overall, species richness and ecological uniqueness of native plant communities were markedly lower in sites invaded than non‐invaded by U. arrecta. From 2007 to 2019, the ecological uniqueness of native plants was 60% lower in the invaded than non‐invaded sites. Whereas in invaded sites species loss was the dominant process driving native communities over time, in non‐invaded sites gain of new native species was the primary process underlying community trajectories. Moreover, comparing native plant communities before and after the invasion of U. arrecta, species richness, ecological uniqueness and species gains of native plant communities decreased, whereas species losses increased after the invasion of U. arrecta. Finally, the positive relationship between native biodiversity and precipitation was stronger in non‐invaded than invaded sites. Synthesis. Our findings provide comprehensive evidence that an invasive plant is decreasing the spatial and temporal β–diversity of native plant communities through declining species richness, rather than simply correlating with them. This suggests that U. arrecta is driving native plants to become less diverse and homogeneous after the invasion, both spatially and temporally. Our findings illustrate that at broad scales, aquatic plant communities may become increasingly homogeneous with the increasing number of biological invasion events taking place worldwide.
Prevalence of common adverse events experienced following COVID‐19 vaccination and its associated factors in Ghana: Cross‐sectional study design

The novel severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) (COVID‐19) pandemic devasted the general life of people and various human activities across the globe, and Ghana is of no exception. This led to development of vaccines within record time to combat morbidity and mortality associated with the virus. In Ghana, COVID‐19 vaccines were introduced in addition to existing COVID‐19 protocols. However, the vaccines have adverse events among those who received them. In this study, we determined the prevalence of some common adverse events of the COVID‐19 vaccines and its associated sociodemographic factors in Ghana.
Evaluation of the treatment efficacy of titanium elastic nail in addition to external fixator in humeral diaphyseal fractures: Comparison of three treatment methods

Objectives: This study aimed to compare the clinical and radiological outcomes of combined titanium elastic nailing and external fixator application (TEN+EFA) with EFA and plate osteosynthesis in patients with humerus diaphysis fractures. Material and Methods: Data of the patients who underwent surgical treatment for a humerus diaphysis fracture were retrospectively reviewed. The patients were divided into three groups based on the surgical technique. Group 1 consisted of the patients who underwent plate osteosynthesis, Group 2 included those who underwent EFA, and Group 3 included those who went through TEN+EFA. Results: Eighty patients were included. The number of patients in the study groups was 32, 25, and 23 in Group 1, 2, 3, respectively. The duration of surgery was statistically significantly shorter in Groups 2 and 3 than in Group 1 (p=0.02). Nonunion was diagnosed in 3 patients (9.3%) and in 1 patient (4%) in Groups 1 and 2, respectively. The presence of nonunion was not statistically significant. (p=0.28) Malunion was encountered in 2 patients (4%) in Group 2, while no patients experienced malunion in Groups 1 and 3. The presence of malunion was not statistically significant. (p=0.10) Conclusions: The EFA+TEN is a practical, safe and fast procedure providing a minimally invasive surgical treatment option for patients with humerus diaphysis fractures.
Deep Neural Network Classifier for Alzheimer’s Disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by dementia and, eventually, a loss of cognitive abilities. Two histopathological features are associated with AD, neurofibrillary tangles, and amyloid-beta plaque. Both contribute to neuron cell death, neuron dysfunction, and AD pathogenesis. Current methods to diagnose AD remain reliant on symptomatic diagnosis with interviews that can be time-consuming, costly, and inaccurate. Alternative methods such as brain imaging are expensive and require extensive laboratory setup for accurate results. Thus molecular-level quantitative approaches are necessary. Omics datasets and machine learning technology advancements have opened new avenues to diagnose AD. This paper proposes using statistical methods such as principal component analysis, t-distributed stochastic neighbor embedding, and Kolmogorov-Smirnov test combined with Benjamini-Hochberg correction through feature selection and dimensionality reduction to isolate significant features associated with AD. Furthermore, we developed machine learning models based on logistic regression, random forest classifier, and deep neural network (DNN) classifier to predict AD diagnosis. Eight unique genes (TGM2, NKIRAS1, SYK, GABARAPL2, ABCC12, NDEL1, TEP1) were identified as significant biomarkers of AD and confirmed previous works identifying prognoses' roles in AD. After extensive hyperparameter tuning, the DNN model showed the best prediction performance for AD diagnosis among the three machine learning algorithms. The DNN model and preprocessed dataset demonstrated a 5-fold cross-validation accuracy of 0.823 and AUC-ROC of 0.940. Its code is publicly available at https://www.kaggle. com/neobrando/ml-dnn.
Analyzing Volatility Forecasting Capabilities of Neural Network Enhanced ARCH Models

Examines the capabilities of Autoregressive-Conditional-Heteroskedasticity (ARCH) family models (with Artificial Neural Networks) to predict volatility of thirty equities from a five-year fiscal-period. The models underwent the maximization of its parameters through Hessian matrices and were used to predict volatility by maximizing the log-likelihood function. Trained Long-Short-Term-Memory models using Neural-Net-Enhanced-ARCH algorithms and calculated the Root-Mean-Square-Error. Found the RMSE value of the traditional ARCH/GARCH models as 1.1695 as opposed to the algorithm’s 0.8763.