Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Liming on Dithionate and Oxalate Extractable Aluminium in Acid Soils

Liming and phosphorus (P) applications are recommended practices for improving crop production in acid soils of the tropics. Although considerable work has been done to establish liming rates for acid soils in many parts of the world, information on the effects of lime on the forms of aluminium which actively sorb P in such soils is minimal. A greenhouse pot experiment was conducted at Waruhiu Farmers Training Centre, Githunguri to evaluate the effect of liming on oxalate and dithionate extractable aluminium in acid soils. Extremely (pH 4.48) and strongly (pH 4.59) acidic soils were evaluated. Four liming (CaO) rates namely 0, 2.2, 5.2 and 7.4 tonnes ha -1 for extremely acidic and 0, 1.4, 3.2, and 4.5 tonnes ha -1 for strongly acidic soils were evaluated. The experiment was laid out in a Randomized Complete Block Design (RCBD) and replicated three times. Data collected included: initial soil chemical properties, oxalate (Alo) and dithionate (Ald) aluminium levels. The tested soils had high exchangeable Al (> 2 cmol Al kg -1 ), Al saturation of (> 20% Al) and low extractable P values (< 15 mg P kg -1 soil). Liming significantly (p=.05) reduced Alo by 70% and 68% in extremely and strongly acidic soils respectively and Ald by 78% in both extremely and strongly acidic soils compared to control. Use of 7.4 tonnes ha -1 of lime in extremely acidic soils and 4.5 tonnes ha -1 of lime in strongly acidic soils significantly (p=.05) reduced both Alo and Ald by > 68% compared to no lime. It was, therefore, concluded that liming contributes to the reduction of soluble Alo and Ald in acid soils of the Kenya highlands leading to increased soluble P availability. Studies are required to provide short and long term optimal liming rates that reduce Alo and Ald without distabilizing availability of other nutrients in field conditions under wide range of acid soils. Original Research Article Muindi; AJSSPN, 5(3): 1-9, 2019; Article no.AJSSPN. 54010 2
The Role of Board Competencies on Voluntary Risk Disclosure of Malaysian Listed Companies

: This study aims to examine the effects of board competencies as enhanced governance mechanism in enhancing transparency and accountability. A quantitative investigation is adopted to capture the extent of voluntary risk disclosure disclosed in the annual reports of 163 listed firms. This study identifies three proxies of board competencies: leaders’ commitment and support as well as duality and international experience are significant in influencing management voluntary risk disclosure decisions. Competent board members can be a valuable mechanism in complementing the commitments of regulators to promote higher corporate transparency. Findings on the three characteristics of board competencies in this study provide insights on the improvements of effective board of directors.
Development and Evaluation of Ketoconazole Loaded Nano-sponges for Topical Drug Delivery

Beta-cyclodextrin nanosponges (NS) based hydrogel had been studied as a topical delivery of ketoconazole (KTZ) for effective eradication of cutaneous fungal infection. The purpose of the present study was to develop KTZ loaded NS for topical drug delivery with skin targeting to minimizing the adverse side effects and providing a controlled release. The four types of NS were synthesized by varying the molar ratios of β-cyclodextrin (β-CD) to diphenylcarbonate (DPC) as a cross linker viz. 1:2, 1:4, 1:6, and 1:8. The KTZ loaded NS shows particle size 274.6-367 nm and high loading efficacy was obtained, FTIR, DSC, XRD studies confirmed the complexation of KTZ with NS. Hydrogel were evaluated comparatively with commercial product with respect to physicochemical properties, ex-vivo skin permeation and skin retention on human cadaver skin and antifungal activity. Ex-vivo study of KTZ-NS hydrogel exhibited controlled drug release up to 8 hrs whereas skin retention studies show avoidance of the systemic uptake and better accumulative uptake of the drug compared to marketed formulation. The zone of inhibition of KTZ-NS hydrogel was higher in comparison with commercial formulation against Candida albicans. These results indicate that the KTZ-NS is having controlled drug release, potential of skin targeting with enhanced antifungal activity.
Learning for Forecasting in High Dimensions: Contributions from the International Workshop on Industry Practices for Forecasting

In this paper, we discuss the problem of short-term electricity load forecasting by regarding electricity load on each day as a curve. The dependence between successive daily loads and other relevant factors such as temperature, is modelled via curve linear regression where both the response and the regressor are functional (curves). The key ingredient of the proposed method is the dimension reduction based on the singular value decomposition in a Hilbert space, which reduces the curve linear regression problem to several ordinary (i.e. scalar) linear regression problems. This method has previously been adopted in the hybrid approach proposed by [6] for the same purpose, where the curve linear regression modelling was applied to the data after the trend and the seasonality were removed by a generalised additive model fitted at the weekly level. We show that classifying the successive daily loads prior to curve linear regression removes the necessity of such a two-stage approach as well as resulting in reducing the forecasting error by a great margin. The proposed methodology is illustrated using the electricity load dataset collected between 2007 and mid-2012, on which it is compared to the hybrid approach and other available competitors. Finally, various ways for improving the forecasting performance of the curve linear regression technique are discussed.
CORRELATION ANALYSIS OF ENDOCRINE HORMONES AND OPERATION EFFECT OF PITUITARY TUMOR

[Objective]Through the detection of endocrine hormone levels,to analyze the correlation the endocrine hormone and long-term curative effect of pituitary adenoma patients and significance. [Methods]274 pituitary adenoma patients with microsurgical operation in our hospital from January 2008 to January 2010 were retrospectively analyzed and dynamicly observed the serum prolactin,thyroid function(adrenocorticotropic hormone and cortisol)and changes of growth hormone level before and after operation,and all the patients were given a year observation,analysis of its long-term curative effect. [Results]The serum levels of prolactin,growth hormone levels of 274 cases of pituitary tumor within a week after operation were significantly decreased compared to before the operation.And the postoperative follow-up observation had been maintained.In 274 cases,68 cases were pituitary growth hormone adenoma patients,22 patients' growth hormone was down to 0.34nmol/L,decreased level of 46 cases' growth hormone did not reach to 0.34nmol/L.26 patients were with recurrent performance after operation. [Conclusion]The patients with growth hormone down to below 0.34nmol/L after the operation are cured,and endocrine hormone level can effectively identify the effect of pituitary tumor patients after treatment,which has very high diagnostic significance and value in the clinic.
Topologically Densified Distributions

We study regularization in the context of small sample-size learning with over-parameterized neural networks. Specifically, we shift focus from architectural properties, such as norms on the network weights, to properties of the internal representations before a linear classifier. Specifically, we impose a topological constraint on samples drawn from the probability measure induced in that space. This provably leads to mass concentration effects around the representations of training instances, i.e., a property beneficial for generalization. By leveraging previous work to impose topological constraints in a neural network setting, we provide empirical evidence (across various vision benchmarks) to support our claim for better generalization.
Noradrenergic function in NK1+/+ and NK1-/- mice

NK1 receptor antagonists represent an emerging class of putative antidepressants. These compounds may act through an interaction with 5-HT and / or noradrenaline (NA), the targets of established antidepressants. Pilot microdialysis studies in our laboratory have demonstrated that cortical NA efflux is 2-fold higher in halothane-anaesthetised NK1-/- mice compared with NK1+/+ mice. These current studies were, therefore, primarily aimed at investigating the autoregulatory 012-adrenoceptor, which controls the firing-rate and release of NA from noradrenergic neurones. NA efflux was monitored following systemic administration of the -adrenoceptor antagonist RX821002, during anaesthetised (0.3 mg /kg i.p.), and freely-moving (0.3, 1.0 and 3.0 mg / kg i.p.), in vivo microdialysis. NA efflux and the behavioural response to an aversive, naturalistic stimulus were investigated using the light / dark exploration box (LDEB), in naive and RX821002 pre-treated mice. Localisation and density of -adrenoceptors were examined using immunohistochemistry, Western blot analysis and 3H RX821002 autoradiography. Adrenaline-stimulated 35S GTPyS binding analysed the functional status of the a2-adrenoceptors. Basal NA efflux was 4-5-fold higher in halothane-anaesthetised NK1-/- mice compared with NK1+/+ mice. No difference in NA efflux between genotypes was found during freely-moving mouse microdialysis. Treatment with RX821002, increased NA efflux in NK1+/+ mice, only. Of the 12 behaviours scored in the LDEB, 5 were genotype dependent. With the exception of rearing activity, these were not dependent on the genotype difference in locomotor activity. In the LDEB pre-treatment with RX821002 modified the behavioural response in 4 of the 12 behaviours, and increased NA efflux in NK1+/+ mice, only. However, no difference in net NA efflux was found between groups. No difference between NK1+/+ and NK1-/- mice in the localisation, density or functional activity of the a2-adrenoceptors was found. These results suggest that genetic disruption of the NK1 receptor, modifies the regulation of the noradrenergic system, which can, at least in part, be attributed to a decreased sensitivity of the -adrenoceptor.
Measuring privacy in smart metering anonymized data

In recent years, many proposals have arisen from research on privacy in smart metering. In one of the considered approaches, referred to as anonymization, smart meters transmit fine-grained electricity consumption values in such a way that the energy supplier can not exactly determine procedence. This paper measures the real privacy provided by such approach by taking into account that at the end of a billing period the energy supplier collects the overall electricity consumption of each meter for billing purposes. An entropy-based measure is proposed for quantifying privacy and determine the extent to which knowledge on the overall consumption of meters allows to re-identify anonymous fine-grained consumption values.
Analysis Of Multi Field Of View Cnn And Attention Cnn On H&E Stained Whole-slide Images On Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size.
Average-case Acceleration Through Spectral Density Estimation

We develop a framework for the average-case analysis of random quadratic problems and derive algorithms that are optimal under this analysis. This yields a new class of methods that achieve acceleration given a model of the Hessian's eigenvalue distribution. We develop explicit algorithms for the uniform, Marchenko-Pastur, and exponential distributions. These methods are momentum-based algorithms, whose hyper-parameters can be estimated without knowledge of the Hessian's smallest singular value, in contrast with classical accelerated methods like Nesterov acceleration and Polyak momentum. Through empirical benchmarks on quadratic and logistic regression problems, we identify regimes in which the the proposed methods improve over classical (worst-case) accelerated methods.
Rowmotion Orbits of Trapezoid Posets

Rowmotion is an invertible operator on the order ideals of a poset which has been extensively studied and is well understood for the rectangle poset. In this paper, we show that rowmotion is equivariant with respect to a bijection of Hamaker, Patrias, Pechenik and Williams between order ideals of rectangle and trapezoid posets, thereby affirming a conjecture of Hopkins that the rectangle and trapezoid posets have the same rowmotion orbit structures. Our main tools in proving this are $K$-jeu-de-taquin and (weak) $K$-Knuth equivalence of increasing tableaux. We define almost minimal tableaux as a family of tableaux naturally arising from order ideals and show for any $\lambda$, the almost minimal tableaux of shape $\lambda$ are in different (weak) $K$-Knuth equivalence classes. We also discuss and make some progress on related conjectures of Hopkins on down-degree homomesy.
Topology Classification from Chiral Symmetry: Chiral Phase Index and Spin Correlations in Graphene Nanoribbons

Topology concepts have significantly deepened of our understanding in recent years of the electronic properties of one-dimensional (1D) nano structures such as the graphene nanoribbons. Controlling topological electronic properties of GNRs has been demonstrated in both theoretical studies and experimental realization. Most previous works rely on classification theory requiring both time reversal and spatial symmetry of a unit cell in the 1D bulk material that is commensurate to its boundary. To access boundary structures that lead to unit cell with no spatial symmetry and to generalize the theory, we propose here another classification scheme, using chiral symmetry, to arrive at a Z classification that is not only applicable to GNRs with arbitrary terminations, but also to any general 1D chiral structures. This theory, combining with Lieb's theorem, moreover enables access to the electron's spin degree of freedom, allowing for investigation of spin physics.
Learning Spatio-Temporal Representations With Temporal Squeeze Pooling

In this paper, we propose a new video representation learn¬ing method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set of few im¬ages, named Squeezed Images. By embedding the Tempo¬ral Squeeze pooling as a layer into off-the-shelf Convolution Neural Networks (CNN), we design anew video classification model, named Temporal Squeeze Network (TeSNet). The re¬sulting Squeezed Images contain the essential movement in¬formation from the video frames, corresponding to the op¬timization of the video classification task. We evaluate our architecture on two video classification benchmarks, and the results achieved are compared to the state-of-the-art.
Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Ramps

In this paper, we present an optimization framework for cooperative merging of platoons of connected and automated vehicles at highway on-ramps. The framework includes (1) an optimal scheduling algorithm, through which, each platoon derives the sequence and time to enter the highway safely, and (2) the formulation of an optimal control problem the solution of which allows each platoon to derive its optimal control input (acceleration/deceleration) in terms of fuel consumption. We evaluate the efficacy of the proposed optimization framework through VISSIM-MATLAB simulation environment. The proposed framework significantly reduces the crossing time and fuel consumption of platoons at the highway on-ramps compared to the baseline scenario where the vehicles on the minor road yield to the vehicles on the highway.
Active suspensions of bacteria and passive objects: a model for the near field pair dynamics.

Near field hydrodynamic interactions are essential to determine many important emergent behaviors observed in active suspensions, but have not been successfully modeled so far. In this work we propose an effective model capable of efficiently capturing the essence of the near field hydrodynamic interactions, validated numerically by a pedagogic model system consisting of an E. coli and a spherical tracer. The proposed model effectively captures all the details of near field hydrodynamics through only a tensorial coefficient of resistance, which is fundamentally different from, and thus cannot be replaced by, an effective interaction of conservative nature. In a critical test case that studies the scattering angle of the bacterium-tracer pair dynamics, calculations based on the proposed model reveals a region in parameter space where the bacterium is trapped by the spherical tracer, a phenomenon that is regularly observed in experiments but cannot be explained by any existing model.
A Zero-Shot based Fingerprint Presentation Attack Detection System

With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization ability always astrict existing PAD methods' actual performances. Therefore, we propose a novel Zero-Shot Presentation Attack Detection Model to guarantee the generalization of the PAD model. The proposed ZSPAD-Model based on generative model does not utilize any negative samples in the process of establishment, which ensures the robustness for various types or materials based presentation attack. Different from other auto-encoder based model, the Fine-grained Map architecture is proposed to refine the reconstruction error of the auto-encoder networks and a task-specific gaussian model is utilized to improve the quality of clustering. Meanwhile, in order to improve the performance of the proposed model, 9 confidence scores are discussed in this article. Experimental results showed that the ZSPAD-Model is the state of the art for ZSPAD, and the MS-Score is the best confidence score. Compared with existing methods, the proposed ZSPAD-Model performs better than the feature-based method and under the multi-shot setting, the proposed method overperforms the learning based method with little training data. When large training data is available, their results are similar.
Think Global, Act Local: Relating DNN generalisation and node-level SNR

The reasons behind good DNN generalisation remain an open question. In this paper we explore the problem by looking at the Signal-to-Noise Ratio of nodes in the network. Starting from information theory principles, it is possible to derive an expression for the SNR of a DNN node output. Using this expression we construct figures-of-merit that quantify how well the weights of a node optimise SNR (or, equivalently, information rate). Applying these figures-of-merit, we give examples indicating that weight sets that promote good SNR performance also exhibit good generalisation. In addition, we are able to identify the qualities of weight sets that exhibit good SNR behaviour and hence promote good generalisation. This leads to a discussion of how these results relate to network training and regularisation. Finally, we identify some ways that these observations can be used in training design.
A powerful MAF-neutral allele-based test for case-control association studies

In a case-control study aimed at locating autosomal disease variants for a disease of interest, association between markers and the disease status is often tested by comparing the marker minor allele frequencies (MAFs) between cases and controls. For most common allele-based tests the statistical power is highly dependent on the actual values of these MAFs, where associated markers with low MAFs have less power to be detected compared to associated markers with high MAFs. Therefore, the popular strategy of selecting markers for follow-up studies based primarily on their p-values is likely to preferentially select markers with high MAFs. We propose a new test which does not favor markers with high MAFs and improves the power for markers with low to moderate MAFs without sacrificing performance for markers with high MAFs and is therefore superior to most existing tests in this regard. An explicit formula for the asymptotic power function of the proposed test is derived theoretically, which allows for fast and easy computation of the corresponding p-values. The performance of the proposed test is compared with several existing tests both in the asymptotic and the finite sample size settings.
Classical analogies for the force acting on an impurity in a Bose–Einstein condensate

We study the hydrodynamic forces acting on a small impurity moving in a two-dimensional Bose–Einstein condensate at non-zero temperature. The condensate is modelled by the damped-Gross Pitaevskii (dGPE) equation and the impurity by a Gaussian repulsive potential coupled to the condensate. For weak coupling, we obtain analytical expressions for the forces acting on the impurity, and compare them with those computed through direct numerical simulations of the dGPE and with the corresponding expressions for classical forces. For non-steady flows, there is a time-dependent force dominated by inertial effects and which has a correspondence in the Maxey–Riley theory for particles in classical fluids. In the steady-state regime, the force is dominated by a self-induced drag. Unlike at zero temperature, where the drag force vanishes below a critical velocity, at low temperatures the impurity experiences a net drag even at small velocities, as a consequence of the energy dissipation through interactions of the condensate with the thermal cloud. This dissipative force due to thermal drag is similar to the classical Stokes’ drag. There is still a critical velocity above which steady-state drag is dominated by acoustic excitations and behaves non-monotonically with impurity’s speed.
Classification of punctures on complete flat surfaces

We investigate the behavior of a complete flat metric on a surface near a puncture. We call a puncture on a flat surface regular if it has a neighborhood which is isometric to that of a point at infinity of a cone. We prove that there are punctures which are not regular if and only if the curvature at the puncture is $4\pi$.