Miracle-dz/UniFAR-FTUs huggingface dataset:
Variation in Carbon Sequestration and Soil Properties in Relation to Stand Age in Maple and Alder Plantations

ABSTRACT Increased CO2 levels caused by human activities (e.g., fossil fuel consumption and deforestation) has become a major concern for climate change and caused environmental problems. Afforestation/reforestation can help mitigate CO2 emission in the atmosphere by sequestering atmospheric carbon (C) in tree biomass. Herein, the above- and below-ground C sequestration of two pioneer tree species, including Acer velutinum Bioss and Alnus glutinosa L plantation stands of 25 (A25) and 35 (A35) years old were studied. The mean annual growth of diameter at breast height (cm/year) was significantly higher in A35 than A25, while the mean growth of height (m/year) was significantly higher in A25. The total and mean annual growth of the tree biomass and C storage (root and trunk) was significantly higher in A35. The amount of soil C sequestration in A35 was also significantly higher than in A25. The above-ground C sequestration and the annual average of C sequestration of the trees were positively correlated with soil moisture, pH, nitrogen, and phosphorus. Our study illustrates the benefits of considering stand age in the growth and developmental patterns of forest ecosystems in estimating terrestrial C stocks.
Effective Use of Network Coding in Multi-hop Wireless Networks

Multi-hop network has been envisioned to be a key technology in the next generation wireless networks. It offers the most flexible characteristic of the networks, requires no centralized or very small control, and very little configuration, thereby enjoying the quick deployment property. Yet, it has been named as the “next generation” for so many times that its usefulness is being questioned. The difficulty mainly comes from the inferiority in the performance being poor enough to make such networks overshadowed by other type of networks, unless quick deployment is a requirement, or performance is not a major concern. In this dissertation, we investigate the performance issues in multi-hop wireless networks. Some new architectures of multi-hop wireless networks with network coding are thoroughly explored. We first revisit network coding briefly. Being the latest revolution in the wireless world, network coding has evolved into a more practical shape, and has matured to a level so as to be adopted. A general introduction of network coding (network information theory) is covered for better understanding of how this technology would change the computer networks, and why we pick this topic for our research. The first work this dissertation covers is Murco, an opportunistic framework that brings the benefits of both multi-radio multi-channel technology and network coding to the multi-hop wireless networks. This combination, though it seems natural, faces many challenges. We address them with a loose collaboration between network coding and multiradio technology coordinated by our framework. Our framework requires few changes or compromises on either side, and the simulation results demonstrate enhanced throughput.
Pre-sleep protein supplementation after an acute bout of evening resistance exercise does not improve next day performance or recovery in resistance trained men

ABSTRACT Background To evaluate the effect of pre-sleep protein supplementation after an acute bout of evening resistance training on next day performance and recovery the following day in physically active men. Methods Eighteen resistance trained men performed a single bout of resistance exercise then received either a pre-sleep protein (PRO) supplement containing 40 g of casein protein (PRO; n = 10; mean ± SD; age = 24 ± 4 yrs; height = 1.81 ± 0.08 m; weight = 84.9 ± 9.5 kg) or a non-caloric, flavor matched placebo (PLA; n = 8; age = 28 ± 10 yrs; height = 1.81 ± 0.07 m; weight = 86.7 ± 11.0 kg) 30 min before sleep (1 h after a standard recovery drink). Blood samples were obtained pre-exercise and the following morning (+12-h) to measure creatine kinase and C-reactive protein. Visual analog scales were utilized to assess perceived pain, hunger, and recovery. One-repetition maximum (1RM) tests for barbell bench press and squat were performed pre-exercise and the following morning (+12-h). Statistical analysis was performed using SPSS (V.23) and p ≤ 0.05 was considered statistically significant. Results There were no significant differences between the groups in next morning performance or muscle damage biomarkers. However, pre-sleep PRO resulted in a lower perception of hunger that approached significance the following morning when compared to PLA (PRO:43.6 ± 31.2, PLA: 69.4 ± 2.22; 95% C.I. = −53.6, 2.0; p = 0.07; d = 0.95). Conclusions Following an evening bout of exercise, pre-sleep PRO did not further improve next morning muscle damage biomarkers or maximal strength performance in resistance trained men compared to a non-caloric PLA. However, there may be implications for lower perceived hunger the next morning with pre-sleep PRO consumption compared to PLA.
Naringenin Improves Ovalbumin-Induced Allergic Asthma in Rats through Antioxidant and Anti-Inflammatory Effects

Asthma is a chronic disease with eosinophilic inflammation and oxidative damages leading to airway obstruction. Naringenin is a phytochemical possessing strong antioxidant and anti-inflammatory activities against chronic destructive conditions. The current study is devoted to evaluating naringenin's effects on the attenuation of inflammation and oxidative stress in lung tissue in a rat model of ovalbumin-induced asthma. Male Wistar rats were allocated to five groups of six: normal control (NC, receiving 1 ml/day of normal saline, orally), asthmatic (AS, receiving ovalbumin (1 mg/mL), and alum (1 mg/mL in saline) on days 0 and 14. Then, on days 21, 22, and 23, they were sensitized with the inhalation of ovalbumin), AS treated with dexamethasone (AS, 1 mg/kg/day, orally) [AS + D1], AS treated with naringenin (20 mg/kg/day, orally) [AS + N20], and AS treated with naringenin (40 mg/kg/day, orally) [AS + N40]. All the groups received associated drugs/agents for 28 days. Finally, bronchoalveolar lavage fluid (BALF) and lung tissue samples were taken off from the animals. The eosinophil count in BALF and malondialdehyde (MDA), glutathione (GSH), interleukin-13 and -4 (IL-13 and IL-4) levels were measured. Besides, the expression of urocortin (UCN) and surfactant protein-D (SP-D) were evaluated in the lung tissue using immunohistochemistry (IHC) and western blotting methods, respectively. Hematoxylin and eosin (H&E) staining were utilized to conduct histopathological analysis. Naringenin treatment significantly reduced MDA, remarkably increased GSH, and meaningfully reduced IL-4 and IL-13 levels in lung tissue. The count of eosinophils in the BALF of AS + N20 and AS + N40 was significantly reduced in comparison with the AS group. The UCN and SP-D protein levels were significantly decreased in the AS + N20 and AS + N40 groups compared to the AS group, using the IHC and western blot methods, respectively. Histopathological analysis data also confirm the results. Naringenin improves the symptoms of allergic asthma through antioxidant and anti-inflammatory effects.
Bared Microtip Arrays for ECG Measurement

Barbed dry microtips arrays are fabricated using simple and low-cost silicon wet etching techniques for ECG recording. Compared with standard wet electrodes, the proposed dry electrodes do not require skin preparation or electrolytic gel during the ECG measurement process. We also discuss parameters affecting skin-electrode impedance, such as the thickness of the conductive layer and the driving frequencies. We successfully demonstrate that the proposed dry electrodes can be used to measure ECG signals.
Let's Play: Using Gameplay Mechanics in Case Study Discussions

We used gameplay mechanics to encourage contributions during case discussions in undergraduate and graduate computer information systems classes. Based on the results of a student survey, we find that in game-based learning exercises, competitiveness and playfulness are both positively related to contribution behavior – although playfulness more so than competitiveness. Contribution and enjoyment are both positively related with learning outcomes. This suggests that instructors should design gameplay mechanics focusing more on playfulness than on competitiveness in order to increase contribution. Moreover, it is crucial that the overall experience of the gameplay is enjoyable. Given the nascent literature investigating game-based learning and the use of gameplay mechanics in higher education, this study presents several theoretical propositions that may form the basis for future research.
Flexible hybrid eu3+ doped P(VDF‐HFP) nanocomposite film possess hypersensitive electronic transitions and piezoelectric throughput

A novel red light-emitter made of Eu3+ doped self-poled electroactive poly(vinylidene fluoride–hexafluoropropylene) [P(VDF-HFP)] hybrid nanocomposite film possesses piezoelectric throughput that is suitable for flexible piezoelectric nanogenerator (PNG) fabrication. We observed that PNG is enabled to generate an open-circuit voltage of 5 V and 0.35 μA of short-circuit current under an applied pressure amplitude of ∼10.4 kPa. By simple mechanical energy scavenging, PNG demonstrates an ability to light more than ten blue commercial light emitting diodes instantly, without using an energy-storage device. Additionally, it successfully charges up capacitors by simple repeating finger touch motion, which indicates its potency as an efficient energy harvesting power source. The high performance of PNG is due to well-coated Eu3+ with P(VDF-HFP) in a hybrid nanocomposite so it displays improved dielectric permittivity and energy storage capacity. This flexible composite film also possesses a hypersensitive electronic transition as it responds by an intense red light-emission confirmed by the CIE 1931 chart. This enables applications in piezo-photonics as a high-performance, energy-saving, flexible solid-state red light emitter. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 2335–2345.
Can Ready-to-Use RNNs Generate “Good” Text Training Data?

—There is much research on the state-of-the-art techniques for generating training data through neural networks. However, many of these techniques are not easily implemented or available due to factors such as copyright of their research code. Meanwhile, there are other neural network codes currently available that are easily accessible for individuals to generate text data; this paper explores the quality of the text data generated by these ready-to-use neural networks for classiﬁcation tasks. This paper’s experiment showed that using the text data generated by a default conﬁgured RNN to train a classiﬁcation model can match closely with baseline accuracy.
Changes in the content and composition of the extractives in thermally modified tropical hardwoods

Chemical composition of wood is known to change during thermal treatments. Two species grown in Turkey, afrormosia ( Pericopsis elata ) and duka ( Tapirira guianensis ) were heat treated according to Thermowood® method. Lignin, cellulose, hemicelluloses and extractives in dichloromethane, ethanol and water were determined. Wood extracts were analysed by gas chromatography with mass detection and existing compounds were identified by NIST17 database. Results show that hemicelluloses and cellulose content decreased for both heat-treated woods along the treatment while lignin percentage increased. The analysis of extractives has shown several compounds normally associated to lignin thermal degradation that increased along the treatment. At the same time several compounds associated to carbohydrate thermal degradation were found in all the extracts for both heat-treated woods. These founding have allowed the understanding of the degradation pattern of wood during thermal modification. There was not much difference between afrormosia and duka woods structural compounds behaviour along thermal modification. However, the variation of the amount of extractives along the treatment depended on the species. due to water and ethanol extractives. While the extractives increased along the treatment for afrormosia, in duka wood there was an initial increase followed by a decrease with the increase of heating time, which was probably due to the high amount of initial ethanol extractives in duka wood that are degraded along the treatment. The new formed compounds that increased along the thermal modification found in dichloromethane extract are vanillin, syringaldehyde, vanilic acid and syringic acid. For the most severe treatment (2 h), coniferaldehyde, sinapaldehyde and acetovanillone were also detected. All these compounds have been associated to lignin heat degradation showing that although the percentage increases, there is still some lignin thermal degradation. The compounds identified as resulting from lignin degradation in dichloromethane extracts still represent a significant amount in ethanol extracts of both heat-treated woods. Additionally, several other compounds like hydroxy acids, deoxy-pentonic acids, deoxy-hexonic acids γ-lactone and levoglucosan were found in ethanol extract. On heat-treated duka some carbohydrates were also found. All these compounds have been associated to C5 and C6 carbohydrate thermal decomposition. The results have contributed to the understanding of the chemical degradation path of modified wood by monitoring extractive content transfor mations and have shown the importance of optimizing the treatment for each wood species in order to make the best utilization of this material.
A novel multipath disjoint routing to support ad hoc wireless sensor networks

Ad hoc wireless sensor networks have the potential to become a major component of the Information Technology space. Due to the characteristics of sensor devices links and communications. The existing protocols for the traditional ad hoc networks affect the performance of the system in wireless sensor network. In this paper, multipath disjoint (MPD) routing algorithm is proposed. We compare MPD with two on-demand protocols such as DSR and AODV. The simulation results show that MPD improves the packet delivery ratio and throughput, saves power and reduces delay. As a result, MPD releases the network congestion quite well. The system does not bring breakdown because of the routing failure.
Opleiding Informatica Interest Point Detectors and Descriptors: an Evaluation

Applications for interest point detectors and descriptors are just as broad as the ﬁeld of computer vision as a whole. Each technique has a slightly diﬀerent approach to cover its niche. With the growing number of new detectors and descriptors, it is crucial to evaluate in which context the detector is most potent and where its struggles lie. In this paper, we will evaluate a collection of novel and established local detectors and descriptors. First, we design a dataset so that the methods are exposed to a diverse set of conditions. With this dataset, the stability of interest point detectors is tested, after which the matching capabilities of the descriptors are put to the test. The methods will also be evaluated on their capability to detect copies from a large set of images. Finally, an existing method will be improved to make it more robust to transformations. This study aims to help researchers identify which method is best appropriate for their purposes.
Blockchain Framework For Arti�cial Intelligence Computation

Blockchain is an essentially distributed database recording all transactions or digital events among participating parties. Each transaction in the records is approved and verified by consensus of the participants in the system that requires solving a hard mathematical puzzle, which is known as proof-of-work. To make the approved records immutable, the mathematical puzzle is not trivial to solve and therefore consumes substantial computing resources. However, it is energy-wasteful to have many computational nodes installed in the blockchain competing to approve the records by just solving a meaningless puzzle. Here, we pose proof-of-work as a reinforcement-learning problem by modeling the blockchain growing as a Markov decision process, in which a learning agent makes an optimal decision over the environment’s state , whereas a new block is added and verified. Specifically, we design the block verification and consensus mechanism as a deep reinforcement-learning iteration process. As a result, our method utilizes the determination of state transition and the randomness of action selection of a Markov decision process, as well as the computational complexity of a deep neural network, collectively to make the blocks not easy to recompute and to preserve the order of transactions, while the blockchain nodes are exploited to train the same deep neural network with different data samples (state-action pairs) in parallel, allowing the model to experience multiple episodes across computing nodes but at one time. Our method is used to design the next generation of public blockchain networks, which has the potential not only to spare computational resources for industrial applications but also to encourage data sharing and AI model design for common problems.
diffvg+CLIP: Generating Painting Trajectories from Text

Given a sentence, the goal of our project is to find out the optimized trajectories for a painting robot to paint an image that illustrates the context of the sentence. We investigate two solution approaches. In the first approach, we consider two stages: in the first stage a set of candidate images are generated in a classification task to match the given sentence, and in the second stage a candidate raster image is simplified into a robot-paint-image along with a vector trajectory with a color labels. For the first stage, we implemented CLIP (Contrastive Language-Image Pre-training), a computer vision system released by OpenAI to generate candidate images. In the second stage, we proposed two different approaches: (i) using an off-the-shelf differentiable vector graphics rasterizing library named dif-fvg and (ii) applying a simple image segmentation task SLIC and backpropagating through a probability-based network to achieve a set of probability measure, which can be represented as the trajectory. In the second approach, we pair diffvg with CLIP and back-propogate through the entire chain to produce vector graphics which are described by a sentence. Additional details are provided in Section 2.
Phosphorylation of SAMHD1 in Myeloid and Are Linked by CDK6-Dependent CDK2 Cell Cycle Control and HIV-1 Susceptibility

Proliferating cells are preferentially susceptible to infection by retroviruses. Sterile a motif and HD domain–containing protein-1 (SAMHD1) is a recently described deoxynucleotide phosphohydrolase controlling the size of the intracellular deoxynucleotide triphosphate (dNTP) pool, a limiting factor for retroviral reverse transcription in noncycling cells. Proliferating (Ki67 + ) primary CD4 + T cells or macrophages express a phosphorylated form of SAMHD1 that corresponds with susceptibility to infection in cell culture. We identiﬁed cyclin-dependent kinase (CDK) 6 as an upstream regulator of CDK2 controlling SAMHD1 phosphorylation in primary T cells and macrophages susceptible to infection by HIV-1. In turn, CDK2 was strongly linked to cell cycle progression and coordinated SAMHD1 phosphorylation and inactivation. CDK inhibitors speciﬁcally blocked HIV-1 infection at the reverse transcription step in a SAMHD1-dependent manner, reducing the intracellular dNTP pool. Our ﬁndings identify a direct rela-tionship between control of the cell cycle by CDK6 and SAMHD1 activity, which is important for replication of lentiviruses, as well as other viruses whose replication may be regulated by intracellular dNTP availability. The Journal of Immunology , 2014, 193: 000–000.
A Theory for the Shear Behaviour of Cracks Providing a Basis for the Assessment of Cracked Reinforced Concrete Structures

A rational crack behaviour model capable of predicting and assessing the response of cracks in reinforced concrete (RC) elements subjected to complex stress states is presented. The model, referred to as Pure Mechanics Crack Model (PMCM), is formulated and developed in terms of first principles, namely equilibrium, compatibility and stress strain relationships and employs few empirical parameters. Instrumental in the development of the analytical model were the data collected from an extensive experimental campaign conducted as part of the research work. Eighteen pre-cracked RC panel elements were tested under combinations of cyclic or reversed cyclic shear and tension with the objective of investigating the shear behaviour of cracks in reinforced concrete elements. The data from the testing program were used to develop and verify the PMCM, and to extend the database available from the literature which is currently limited to pure shear tests. The results provided information regarding the mechanics involved in the shear response of cracks and demonstrated that rational models of such systems can be achieved.
ALGEBRAIC TOPOLOGY DONE RIGHT

This paper exposits parts of May [1] and the ﬁrst chapter of Bott and Tu [2]. These two books take two diﬀerent approaches to algebraic topol- ogy, both strikingly diﬀerent from the one taken in Hatcher [3]. The title of this paper refers to the former one, in the author’s impetuous opinion.
Geoid undulation prediction using ANNs (RBFNN and GRNN), multiple linear regression (MLR), and interpolation methods: A comparative study

The present work aimed to develop a prediction model to estimate geoid undulation and to compare its efficiency with other methods including radial basis function neural network (RBFNN), generalized regression neural network (GRNN), multiple linear regression (MLR) and, ten different interpolation methods. In this study, the k-fold cross-validation method was used to evaluate the model and its behavior on the independent dataset. With this validation method, each of a k number of groups has the chance to be divided into training and testing data. The performances of the methods were evaluated in terms of the root mean square error (RMSE) mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R2) and using graphical indicators. The evaluation of the performance of the datasets obtained using cross-validation was done in two ways. If we accept the method having the minimum error result as the most appropriate method, the natural neighbor (NN) method in the DS#5 dataset gave better results than the other methods (RMSE=0.14173 m, MAE=0.09729 m, NSE=0.98986, and R2=0.99011. On the other hand, it has been observed that, the GRNN method exhibited the best performance, on average, with RMSE=0.18539 m, MAE=0.13676 m, NSE=0.98229, and R2=0.98249.