Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon Sequestration in Selected Grass Species in a Tropical Lowland Rainforest at Obafemi Awolowo University, Ile-Ife, Nigeria #January 26, 2017:[Expression of Concern] Plagiarism has been detected in this manuscript. JTBC staffs investigate this matter further.

Dry matter production and carbon stock in the pools of Panicum maximum, Axonopus compressus and Cynodon dactylon grass species were evaluated within the Obafemi Awolowo University, Ile-Ife, Nigeria. This was with a view to provide information on grass species serving as carbon sink and to assess their contribution to carbon stock in the terrestrial ecosystem. Four 15 m x 15 m sampling plots were established in each of the grass species sites; the plant samples were harvested by randomly placing ten 1m x 1m quadrats. The harvested plants were divided into above and below ground biomass. Floor litters (residue) samples were also collected. Ten soil samples were randomly collected from ten points in each plot at a depth of 0-20 cm, air-dried and analyzed for carbon content. Bulk density was also determined in each plot. The harvested plants were oven dried at 70Â°C to a constant weight, weighed, ground and analyzed for organic carbon content. There were significant differences (p < 0.05) in the dry matter production, carbon concentration and carbon stock across the three grass species. Panicum maximum had the highest dry matter production and carbon stock in the above and below ground biomass. It also had the highest carbon concentration in below ground biomass. The results concluded that grasslands can serve as a terrestrial carbon sink and their contribution varied across the studied grass species.
Stochastic Power System Dynamic Simulation Using Parallel-in-Time Algorithm

With increasing grid modernization efforts, future electric grids will be governed by more complex and faster dynamics with the high penetration of new components such as power electronic-based control devices and large renewable resources. These lead to the importance of developing real-time dynamic security assessment under the consideration of uncertainties, whose main tool is time-domain simulation. Though there are many efforts to improve the computational performance of time-domain simulation, its focus has been on the deterministic differential-algebraic equations (DAEs) without modeling uncertainties inherent in power system networks. To this end, this paper investigates large-scale time-domain simulation including effects of stochastic perturbations and ways for its computational enhancement. Particularly, it utilizes the parallel-in-time (Parareal) algorithm, which has shown great potentials, to solve stochastic DAEs (SDAEs) efficiently. A general procedure to compute the numerical solution of SDAEs with the Parareal algorithm is described. Numerical case studies with 10-generator 39-bus system and 327-generator 2383-bus system are performed to demonstrate its feasibility and efficiency. We also discuss the feasibility of employing semi-analytical solution methods, using the Adomian decomposition method, to solve SDAEs. The proposed simulation framework provides a general solution scheme and has the potential for fast and large-scale stochastic power system dynamic simulations.
Opportunities for the Development of Motor Skills through Music-Movement and Dance Activities of 9–10-Year-Old Pupils

The goal of the research was to determine the impact of an intervention program containing musical movement and dance activities on the level of pupils’ physical abilities at the elementary level of education in physical and sport education classes. The object of the research consisted of Ʃ63 pupils, boys (n=30) aged 10.21±0.42 and girls (n=32) aged 10.32±0.37. The content of the movement program was selected means containing music and movement, dance activities and aerobics, which were implemented for 18 weeks, twice a week, 45 min during physical and sport education. In terms of data collection methods, standardized tests for physical education practice were used. Tests were used to assess the level of motor skills: sitand- reach test, standing long jump, sit-ups in 30s, endurance shuttle run and shuttle run 4x10. The somatometry method was used. The effect of the implemented movement program was confirmed and significant positive changes in the level of the observed movement abilities were observed: the level of mobility in the joints of the trunk in boys (t=-8.471), girls (t=-9.357), explosive power of the lower limbs in boys (t=-14.459), girls (Z=-4.940), the dynamic and endurance power of the abdominal, hip and thigh muscles in boys (Z=-4.325), girls (t=-19.264), running speed in both boys (Z=-4.783), girls (t=7.095) with a large effect size, in endurance skills in both boys (Z=-4.828), girls, (Z=-5.028) also with a large effect size. Based on the results, the regular and purposeful physical activity of pupils in the school environment significantly influences the level of physical abilities, and thus physical fitness as one of the manifestations of health.
Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches

The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.
Evaluation of the Dye Extraction Using Designed Hydrogels for Further Applications towards Water Treatment

In this work, novel chitosan–silica hydrogels were synthesized and investigated by various complementary techniques. The hydrogels were obtained via the immobilization of chitosan (Ch) on the surface of mesoporous cellular foams (MCFs). The latter silica materials were obtained by a sol–gel process, varying the composition of the reaction mixture (copolymer Pluronic 9400 or Pluronic 10500) and the ageing temperature conditions (80 °C or 100 °C). The role of the silica phase in the hydrogels was the formation of a scaffold for the biopolymeric chitosan component and providing chemical, mechanical, and thermal stability. In turn, the chitosan phase enabled the binding of anionic pollutions from aqueous solutions based on electrostatic interaction mechanisms and hydrogen bonds. To provide information on structural, morphological, and surface properties of the chitosan–silica hydrogels, analyses such as the low-temperature adsorption/desorption of nitrogen, small-angle X-ray scattering (SAXS), scanning electron microscopy (SEM), atomic force microscopy (AFM), and Fourier-transform infrared spectroscopy (FTIR) were performed. Moreover, the verification of the utility of the chitosan–silica hydrogels as adsorbents for water and wastewater treatment was carried out based on kinetic and equilibrium studies of the Acid Red 88 (AR88) adsorption. Adsorption data were analyzed by applying various equations and discussed in terms of the adsorption on heterogeneous solid-surfaces theory. The adsorption mechanism for the AR88 dye–chitosan–silica hydrogel systems was proposed.
Soccer match outcome prediction with random forest and gradient boosting models

In order to accurately predict the results of soccer matches, this study introduces Machine Learning (ML) techniques in joint Random Forest (RF) and Gradient Boosting (GB) models. In order to forecast the results of the next World Cup, a model has been trained using past information from prior tournaments. The proposed model is evaluated using multiple performance criteria including precision and accuracy. The RF approach outperforms the GB approach in terms of both accuracy and precision, as concluded after the experiment. The most important features for predicting the outcome of football games are identified using feature importance scores. Football enthusiasts and analysts can use the proposed model to predict the outcome of football games with high accuracy. The implications of these findings for football teams are practical as they provide valuable insights for improving team performance and increasing their chances of winning the World Cup. By identifying the most important features for predicting the outcome of football games, teams can focus their efforts on improving these areas, increasing their chances of success. Football teams and football analysts can benefit from accurate predictions, which are enabled by machine learning techniques such as GB and RFs. Overall, this study presents a promising approach to predicting the outcome of football games, with practical implications for the field of sports analytics.
The influence of slope exposure on the yield characteristics of winter wheat and spring barley in the Oka River basin, Russia

We studied the relationship of the yield of winter wheat and spring barley with slope exposure components in the west of the Oka River basin. The size of the study area was 250 km by 360 km. The yield characteristics included the maximal yield obtained when applying the optimal dose of fertilizers, the yield without applying fertilizers (control) and the maximal addition to yield, that is, their difference. The addition is shown to be most sensitive to climatic factors. For wheat, the addition increased on the warmer south-western slopes, and for barley—on the wetter north-eastern slopes. The high sensitivity of the addition for barley to moisture is shown using it comparison with climatic water deficit. To compare slopes by the energy of incident solar radiation, we used the slope insolation in energy units. Although the difference in energy between the south-west and north-east slopes was only 2.2%, wheat addition on these slopes varied by more than a factor of two. The reasons for this are discussed. The results obtained show that when choosing locations for crop areas, it is advisable to take into account the exposure of the slopes.
Optimizing the Energy Efficiency in 5G Security Systems for Intrusion Detection with an Emphasis on DDOS Assaults

: In response to the rising demand for new and existing use cases of energy efficiency, the telecoms sector is going through a dramatic shift toward 5G technology. High data speeds, extensive coverage provided by dense base station deployment, higher capacity, improved Quality of Service (QoS), and extremely low latency are required for 5G wireless networks. New deployment methods, networking architectures, processing technologies, and storage solutions must be created to satisfy the anticipated service requirements of 5G technologies. These developments further increase the need to secure the security of 5G systems and their functionality as well as energy efficiency problems. Indeed, 5G system security is the target of intense efforts by developers and academics in this industry. Significant security concerns for 5G networks have been identified with extensive research. Attackers can make use of vulnerabilities like traffic and the flow base by introducing malicious code and performing other nefarious deeds to take advantage of the system. On 5G networks, attack techniques such as model node map (MNmap), power depletion assaults, and Man-in-the-Middle (MiTM) assaults can be effectively used. However, this study analyses 5G technology’s current energy efficiency problems. We recommend an unusual intrusion detection system (IDS) that makes use of Traffic Volume methods. Considering this investigation, we propose an enhancing training process by including statistical analysis on Distributed Denial-of-Service (DDoS) threats, which is how prior research recommended using OMNET and NS-3 on IDS for optimization. Additionally, the methodology for incorporating the suggested intrusion detection systems within a typical 5G architecture is presented by our research using NETSIM. The paper also offers a planned system’s correction method, providing a useful implementation after completing analysis.
Visualisation of natural convection in a reservoir sidearm subject to surface radiation

The natural convection in a reservoir sidearm subject to daytime surface radiation was visualised in the laboratory using a shadowgraph technique. The water temperatures along the sloping bottom were monitored simultaneously with thermistors. The experimental Grashof number based on the starting water temperature and the bulk attenuation coefficient of water was fixed at 4.16x10 7. Emphasis has been put on the understanding of the initial flow features after the radiation was applied. In particular, the instabilities originating from the thermal boundary layer along the sloping bottom have been observed and discussed.
Review on Medical Applications of Manganese Oxide (Mn2+, Mn3+, and Mn4+) Magnetic Nanoparticles

Apart from our imagination, the nanotechnology industry is rapidly growing and promises that the substantial changes that will have significant economic and scientific impacts be applicable to a wide range of areas, such as aerospace engineering, nanoelectronics, environmental remediation, and medical healthcare. In the medical field, magnetic materials play vital roles such as magnetic resonance imaging (MRI), hyperthermia, and magnetic drug delivery. Among them, manganese oxide garnered great interest in biomedical applications due to its different oxidation states (Mn2+, Mn3+, and Mn4+). Manganese oxide nanostructures are widely explored for medical applications due to their availability, diverse morphologies, and tunable magnetic properties. In this review, cogent contributions of manganese oxides in medical applications are summarized. The crystalline structure and oxidation states of Mn oxides are highlighted. The synthesis approaches of Mn-based nanoparticles are outlined. The important medical applications of manganese-based nanoparticles like magnetic hyperthermia, MRI, and drug delivery are summarized. This review is conducted to cover the future impact of MnOx in diagnostic and therapeutic applications.
Numerical Modeling of Sediment Transport and Bed Evolution in Nonuniform Open-Channel Flows

The shallow-water and multilayer hydrostatic models have been commonly used to analyze the problems of a sediment-laden, plane open-channel flow. The models are adequate to solve a quasi-hydrostatic flow problem, but their accuracy deteriorates as the e ects of the vertical acceleration gain in significance. Herein, a higher-order numerical model for treating the problems of unsteady, plane open-channel flow over a movable bed is proposed. In this model, the flow hydrodynamics is governed by the depth-averaged Boussinesq-type equations, and the bed morphodynamics is determined by an Exner-type equation and additional equations describing the non-equilibrium transports of suspended load and bed load. A hybrid finite-volume and finite-di erence scheme was used to discretize and solve the governing equations, yielding solutions that are in satisfactory agreement with the experimental data. Overall, the results of the proposed model for the temporal free-surface profile and bed evolution were fairly adequate. For the two particular cases considered, however, the quality of its results was moderately a ected by the e ects of the three-dimensional characteristics of the dam-break flow and the sliding of the dike body due to sub-surface flow. The results of this investigation highlight the importance of including a higher-order Boussinesq-type correction for refining sediment transport computations.
Fate of a dairy cow urine pulse in a layered volcanic vadose zone

Nitrate-N leaching from dairy cow urine patches has been identified as one of the major contributors to groundwater contamination and degradation of surface waters in dairying catchments. To investigate the transport and transformations of nitrogen (N) originating from urine, fresh dairy cow urine was collected, amended with the conservative tracer chloride (Cl) and applied onto a loamy sand topsoil, underlain by gritty coarse sands and pumice fragments in the lower part of the vadose zone. The fluxes of the different N components and the conservative tracer leaching from the urine application were measured at five different depths in the vadose zone using three Automated Equilibrium Tension Lysimeters (AETLs) at each depth (max. 5.1 m). The uppermost part of the saturated zone was also monitored for the leached N and Cl fractions from the urine application. Textural changes and hydrophobicity in the vadose zone materials resulted in heterogeneous flow patterns and a high variability in the N and Cl masses captured. All three forms of potentially leachable N from the urine – organic-N (org-N), ammonium-N (NH4-N) and nitrate-N – were measured at the bottom of the root zone at 0.4 m depth. At the 1.0 m depth, effectively all of the captured N was in the mobile nitrate-N form. In the lower part of the vadose zone at 4.2 m, 33% of the applied urine-N was recovered as nitrate-N. This fraction was not significantly different from the corresponding fraction measured at the bottom of the root zone, indicating that no substantial assimilation of the nitrate-N being leached from the root zone was occurring in this vadose zone.
Modeling the Natural Drainage Network of the Grand River in Southern Ontario: Agriculture May Increase Total Channel Length of Low-Order Streams

The Grand River watershed is an important agricultural area in southern Ontario, with several large and growing municipalities. Based on digital elevation models (DEMs), the natural drainage network was modelled to predict flow paths. Channel lengths and locations of the predicted network were compared with a ground-truthed channel network to determine efficacy of the models. Approximately 5% of predicted channels lay >40 m from actual channel locations. This amounted to 388 km of channel that had no corresponding channels in reality. The model was unable to predict, based on topography, 2535 km of actual channel present in the watershed. Channels not anticipated by topography were mostly first-order, with low sinuosity, were most common in areas with high agricultural land use, and are likely excavated extensions to headwater streams to facilitate drainage. In addition, this study showed that Soil and Water Assessment Tool (SWAT) models produced using different DEM resolutions did not predict significantly different stream flows, even when resolution was as low as 200 m. However, these low resolution DEMs did result in under-prediction of sediment export entering Lake Erie, most likely because the low resolution maps failed to account for small localized areas of high slope that would have relatively higher rates of erosion.
Modeling Slab Temperature: A Reevaluation of the Thermal Parameter

We reevaluate the effects of slab age, speed, and dip on slab temperature with numerical models. The thermal parameter Φ = t v sin θ, where t is age, v is speed, and θ is angle, is traditionally used as an indicator of slab temperature. However, we find that an empirically derived quantity, in which slab temperature T ∝ log (t−av−b) , is more accurate at depths <120 km, with the constants a and b depending on position within the slab. Shallower than the decoupling depth (~70–80 km), a~1 and b~0, that is, temperature is dependent on slab age alone. This has important implications for the early devolatilization of slabs in the hottest (youngest) cases and for shallow slab seismicity. At subarc depths (~100 km), within the slab mantle, a~1 and b~0 again. However, for the slab crust, now a~0.5 and b~1, that is, speed has the dominant effect. This is important when considering the generation of arc magmatism, and in particular, slab melting and the generation of slab‐derived melange diapirs. Moving deeper into the Earth, the original thermal parameter performs well as a temperature indicator, initially in the core of the slab (the region of interest for deep water cycling). Finally, varying the decoupling depth between 40 and 100 km has a dominant effect on slab temperatures down to 140‐km depth, but only within the slab crust. Slab mantle temperature remains primarily dependent on age.
Research advanced in the integration of federated learning and reinforcement learning

Reinforcement learning (RL) and federated learning (FL) are two important machine learning paradigms. Reinforcement learning is concerned with enabling intelligence to learn optimal policies when interacting with an environment, while federated learning is concerned with collaboratively training models on distributed equipment while preserving data privacy. In recent years, the fusion and complementarity of reinforcement learning, and federated learning have attracted increasing research interest, providing new directions for the development of the machine learning community. Focusing on the integration of reinforcement learning and federated learning, this paper introduces in detail the latest technological developments in the integration of reinforcement learning and federated learning, and discusses the main challenges, existing methods and future directions of this intersection. Specifically, based on the introduction of classical reinforcement learning and federated learning. In addition, this document introduces cutting-edge results on the integration of reinforcement learning and joint learning and discusses the problems and future directions of the integration.
Klasifikasi Penyakit Kanker Paru-Paru Menggunakan Algoritma Naïve Bayes dan Iterative Dichotomizer 3 (ID3)

Kanker paru-paru merupakan penyakit yang disebabkan oleh keganasan tumor dari bronkus. Pada tahun 2020 terdapat kasus kematian mencapai 30.843 jiwa. Agar menghambat angka kematian akibat kanker paru-paru, diperlukan alat untuk deteksi dini akibat kanker paru-paru. Pada penelitian ini menggunakan dataset kanker paru-paru yang memiliki 309 data. Teknik uji yang digunakan pada dataset kanker paru-paru ini adalah percentage split dan k-fold cross validation dengan algoritma naive bayes dan iterative dichotomizer 3 (ID3). Parameter penilaian yang digunakan untuk menentukan algoritma terbaik adalah akurasi, presisi dan recall. Hasil penelitian yang menggunakan teknik uji percentage split, diperoleh nilai akurasi, presisi dan recall tertinggi pada algoritma naive bayes yaitu akurasi sebesar 87%, presisi sebesar 91% dan recall sebesar 94% untuk kelas YES (positif lung cancer). Penelitian yang dilakukan dengan teknik uji menggunakan k-fold cross validation memberikan nilai terbaik pada algoritma ID3 dengan nilai akurasi 92%, presisi sebesar 94% dan recall sebesar 97% untuk kelas YES (positif lung cancer). Dengan demikian, penelitian yang dilakukan dengan metode k-fold cross validation memiliki nilai yang lebih tinggi dibandingkan nilai menggunakan teknik uji percentage split. Hal tersebut memberikan kesimpulan bahwa pada penelitian ini, diperoleh algoritma terbaik yaitu ID3 dengan teknik uji k-fold cross validation pada dataset kanker paru-paru.