Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Groundwater Suitability for Domestic and Irrigational Purposes: A Case Study from Mingoa River Basin, Yaounde, Cameroon

A baseline study involving analyses of subsurface water samples from the Mingoa river basin (longitude: 11°30′E; latitude: 3°52′N) in migmatitic complex in Yaounde Cameroon (central Africa) was carried out to assess their suitability for drinking, domestic and agricultural purposes. Study results show that pH is ranged between 5.1≤ pH ≤ 5.8 and then, induces acidic waters. Groundwater samples are generally characterized by low conductivity values, of which 100% are within the range (55 ≤ EC ≤ 1500 uS/cm). The mean values of the major cations (Ca2+, Mg2+, Na+, K+) and anions (SO42– , Cl–, HCO3– ) are all within the World Health Organization (WHO) standards. Two of the springs sampled have nitrate (NO3– ) contamination. Even though contamination and acidic waters exist in some of the springs, the majority of the springs are excellent for agricultural and domestic purposes. Assessment of the groundwater for agricultural irrigation revealed two main categories. These are low salinity-low sodicity (C1-S1) and medium salinity-low sodicity (C2-S1), using the US Salinity Laboratory (USSL) classification scheme. As much as all of the samples plotted in the “excellent to good” and “good to permissible” categories on the Wilcox diagram. The groundwater in the study area may therefore be regarded as good for irrigation activities. The major identifiable geochemical processes responsible for the evolution of the various ions are mineral weathering, chemical reactions and anthropogenic activities.
Micro level analysis of AQM's in multi-hop wireless environment

Last decade seen an astronomical growth in the development of the multi-hop wireless technologies such as mobile ad-hoc network, vehicular ad-hoc network and cognitive radio network, the network congestion poses a serious threat in full scale deployment of wireless multi-hop technologies towards internet architecture. An active queue management (AQM) is a proactive, effective congestion control mechanism deployed at the intermediate gateway. Random Early Detection (RED) is the earliest and most widely used Active Queue Management (AQM) mechanism in backbone gateways, which in turn reduce global synchronization and bias against burst traffic. The traditional RED and its variants such GRED, AGRED, ARED, NL-RED, REM, RIO, etc. has more parameters to be tuned each time for the arrival of the packet. The tuning of more parameters leads to computation load, so it is unsuitable for low memory less devices such as laptops, handset, etc. In this paper, a micro level analysis was made on standard AQM's under multi-hop wireless environment. The results were analyzed based on the metrics such as average queue length, packet loss, queuing delay, end to end delay and throughput. This work forms a base work in designing a new queuing algorithm over heavily tailed multi-hop networks.
Robust repetitive controller design and its application on the track-following control system in optical disk drives

A repetitive controller has been applied owing to its prominent capability for attenuating periodic disturbances and/or tracking periodic reference commands. A repetitive controller is generally added on the existing feedback control system to improve the tracking performance. However, the repetitive controller has been designed without utilizing the effective information such as the performance weighting function used in the design of the feedback controller. In this paper, we deal with the problem of a robust repetitive controller design for an uncertain feedback control system using its explicit performance information. We first show that a robust stability condition of repetitive control systems is closely related with the well-known robust performance condition of general feedback control systems. It is also shown that the steady-state tracking error of the repetitive control system is described in a simple form without time-delay element. From this result, we explain how different loop properties of the repetitive control system are from those of the feedback control system. Moreover, sufficient conditions are provided, which ensure that the power of the steady-state tracking error generated by the repetitive control system is less than or equal to that only by the feedback control system. Based on the obtained results, we present repetitive controller design criteria. Finally, to show the validity of the proposed method, application studies on the track-following control system of optical disk drives are performed.
Using Curvature Information for Fast Stochastic Search Improving Stochastic Search

We present an algorithm for fast stochastic gradient descent that uses a nonlinear adaptive momentum scheme to optimize the late time convergence rate. The algorithm makes eeective use of curvature information, requires only O(n) storage and computation, and delivers convergence rates close to the theoretical optimum. We demonstrate the technique on linear and large nonlinear back-prop networks. Learning algorithms that perform gradient descent on a cost function can be formulated in either stochastic (on-line) or batch form. The stochastic version takes the form ! t+1 = ! t + t G(! t ; x t) (1) where ! t is the current weight estimate, t is the learning rate, G is minus the instantaneous gradient estimate, and x t is the input at time t 1. One obtains the corresponding batch mode learning rule by taking constant and averaging G over all x. Stochastic learning provides several advantages over batch learning. For large datasets the batch average is expensive to compute. Stochastic learning eliminates the averaging. The stochastic update can be regarded as a noisy estimate of the batch update, and this intrinsic noise can reduce the likelihood of becoming trapped in poor local optima 1, 2]. 1 We assume that the inputs are i.i. d. This is achieved by random sampling with replacement from the training data.
The new generation of the photoelectric measurement methods of mos structure parameters

New generation of MOS (metal-oxide-semiconductor) system photoelectric measurement methods has been developed. These methods are based on a new approach to the photoelectric phenomena occurring at low electric fields in the dielectric layer. Basic features of this approach were outlined, with the emphasis on its practical applications. Principles underlying some of the new measurement methods were presented, underscoring the importance of the effective contact potential difference (ECPD or PhiMS) determination method. This method is the most sensitive and accurate of the existing methods of PhiMS determination. These measurement methods have been recently improved, allowing for the first time, to determine distributions of local PhiMS values over the gate area of MOS structures. Distributions of PhiMSvalues were determined for metal-gate and silicon-gate MOS structures. Comparison of the results obtained was presented. Methods were also developed to determine distributions of potential barrier height local values at gate-dielectric and semiconductor-dielectric interfaces. Examples of such distributions were given and their causes, as well as consequences were discussed
Type-Aware Concolic Testing of JavaScript Programs

Conventional concolic testing has been used to provide high coverage of paths in statically typed languages. While it has also been applied in the context of JavaScript (JS) programs, we observe that applying concolic testing to dynamically-typed JS programs involves tackling unique problems to ensure scalability. In particular, a naive type-agnostic extension of concolic testing to JS programs causes generation of large number of inputs. Consequently, many executions operate on undefined values and repeatedly explore same paths resulting in redundant tests, thus diminishing the scalability of testing drastically. In this paper, we address this problem by proposing a simple yet effective approach that incorporates type-awareness intelligently in conventional concolic testing to reduce the number of generated inputs for JS programs. We extend our approach inter-procedurally by generating preconditions for each function that provide a summary of the relation between the variable types and paths. Employing the function preconditions when testing reduces the number of inputs generated even further. We implement our ideas and validate it on a number of open-source JS programs (and libraries). For a significant percentage (on average 50%) of the functions, we observe that type-aware concolic testing generates a minuscule percentage (less than 5%) of the inputs as compared to conventional concolic testing approach implemented on top of Jalangi. On average, this approach achieves over 97% of line coverage and over 94% of branch coverage for all the functions across all benchmarks. Moreover, the use of function preconditions reduces the number of inputs generated by 50%. We also demonstrate the use of function preconditions in automatically avoiding real crashes due to incorrectly typed objects.
Substitution of Nonresponding Units in Probability Sampling.

The substitution of a nonresponding unit with one not originally selected in the sample is a commonly used method for dealing with unit nonresponse. Although frequently used in practice, substitution is largely neglected in the survey sampling literature. To date, few studies have attempted to develop a formal framework for describing and evaluating substitution methods, and little research has been done to improve estimates obtained through the use of substitution as a nonresponse adjustment procedure. This dissertation presents the results from three research studies conducted to enhance our understanding of substitution methods and develop new procedures to improve them.
Kalman filter based multiple sensor data fusion in systems with time delayed state

Obtaining accurate data in any system is a challenging problem. Multi-sensor data fusion is a widely used technique to improve the accuracy. In this paper, measurement level fusion, covariance union fusion, and state vector fusion based on Kalman filters for systems with delayed states is presented. The obtained Kalman filter based data fusion algorithms for time delayed systems are then modified to handle the missing measurements which is a common problem in wireless sensor networks. The simulation results of the data fusion algorithms obtained using MATLAB are compared. The performances of the fusion filters are found to be similar and the choice of a fusion algorithm would only depend on the particular application.
Proteomic and Bioinformatics Analyses of Mouse Liver Microsomes

Microsomes are derived mostly from endoplasmic reticulum and are an ideal target to investigate compound metabolism, membrane-bound enzyme functions, lipid-protein interactions, and drug-drug interactions. To better understand the molecular mechanisms of the liver and its diseases, mouse liver microsomes were isolated and enriched with differential centrifugation and sucrose gradient centrifugation, and microsome membrane proteins were further extracted from isolated microsomal fractions by the carbonate method. The enriched microsome proteins were arrayed with two-dimensional gel electrophoresis (2DE) and carbonate-extracted microsome membrane proteins with one-dimensional gel electrophoresis (1DE). A total of 183 2DE-arrayed proteins and 99 1DE-separated proteins were identified with tandem mass spectrometry. A total of 259 nonredundant microsomal proteins were obtained and represent the proteomic profile of mouse liver microsomes, including 62 definite microsome membrane proteins. The comprehensive bioinformatics analyses revealed the functional categories of those microsome proteins and provided clues into biological functions of the liver. The systematic analyses of the proteomic profile of mouse liver microsomes not only reveal essential, valuable information about the biological function of the liver, but they also provide important reference data to analyze liver disease-related microsome proteins for biomarker discovery and mechanism clarification of liver disease.
Draft Genome Sequence of Sphingobium sp. Strain C100, a Polycyclic Aromatic Hydrocarbon-Degrading Bacterium from the Deep-Sea Sediment of the Arctic Ocean

ABSTRACT Sphingobium sp. strain C100 was isolated from a polycyclic aromatic hydrocarbon (PAH)-degrading consortium from the deep-sea sediment of the Arctic Ocean. It can degrade two- to four-ring PAHs at 25°C. Here we present the draft genome sequence of this strain, which is 4,776,810 bp with a G+C content of 63.9%.
Energy-Aware Scheduling for Real-Time Systems

This article presents a survey of energy-aware scheduling algorithms proposed for real-time systems. The analysis presents the main results starting from the middle 1990s until today, showing how the proposed solutions evolved to address the evolution of the platform's features and needs. The survey first presents a taxonomy to classify the existing approaches for uniprocessor systems, distinguishing them according to the technology exploited for reducing energy consumption, that is, Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Power Management (DPM), or both. Then, the survey discusses the approaches proposed in the literature to deal with the additional problems related to the evolution of computing platforms toward multicore architectures.
How Prior Choices Affect Subsequent Valuations How Prior Choices Affect Subsequent Valuations

This paper develops and tests a model of sequential decision making where a first stage of ranking a set of alternatives is followed by a second stage of determining the value of these same alternatives. The model assumes a boundedly rational Bayesian decision maker who is uncertain about his/her underlying preferences over the relevant attributes, and who has to exert costly cognitive effort to resolve this uncertainty. Compared to when only valuation takes place, the analysis reveals that ranking a set of alternatives prior to determining their value has three primary effects: a) the spread (or dispersion) of valuations between most and least preferred alternatives increases, b) decision makers will, on expectation, exert more effort in the valuation phase, and c) the more each attribute contributes to overall utility the greater the relative impact of ranking is on valuation spread. The analysis also sheds light on how prior ranking impacts the demand for a product. These results are then corroborated in a series of controlled lab experiments with actual prizes. The findings have implications for many real life decision making situations ranging from auctions, where there is a tendency to prioritize items before determining a bid, to the ranking of job candidates prior to determining wages and benefits to be offered. More generally, the results bear on our understanding of how past decisions can affect future related decisions.
Theory of traveltime shifts around compacting reservoirs: 3D solutions for heterogeneous anisotropic media

Time-lapse traveltime shifts of reflection events recorded above hydrocarbon reservoirs can be used to monitor production-related compaction and pore-pressure changes. Existing methodology, however, is limited to zero-offset rays and cannot be applied to traveltime shifts measured on prestack seismic data. We give an analytic 3D description of stress-related traveltime shifts for rays propagating along arbitrary trajectories in heterogeneous anisotropic media. The nonlinear theory of elasticity helps to express the velocity changes in and around the reservoir through the excess stresses associated with reservoir compaction. Because this stress-induced velocity field is both heterogeneous and anisotropic, it should be studied using prestack traveltimes or amplitudes. Then we obtain the traveltime shifts by firstorder perturbation of traveltimes that accounts not only for the velocity changes but also for 3D deformation of reflectors. The resulting closed-form expression can be used efficiently for numerical modeling of traveltime shifts and, ultimately, for reconstructing the stress distribution around compacting reservoirs. The analytic results are applied to a 2D model of a compacting rectangular reservoir embedded in an initially homogeneous and isotropic medium. The computed velocity changes around the reservoir are caused primarily by deviatoric stresses and produce a transversely isotropic medium with a variable orientation of the symmetry axis and substantial values of the Thomsen parameters and . The offset dependence of the traveltime shifts should play a crucial role in estimating the anisotropy parameters and compaction-related deviatoric stress components.
Abstract Line Drawings from 2D Images Based on Thinning

A novel method for turning an image into simple line drawings is presented. Our technique consists of three parts: line extraction, line rendering and style translation. First it proposes a novel line extraction method based on mathematical morphology thinning and assembles the discrete pixels into stroke paths. Then it considers the feature scale with which the level-of-detail of lines is controlled in the rendering. Finally it gives out a method to generate exaggerated-style line drawings. Experimental results show that our technique can generates vivid line drawings from images.
Psycho politics, neoliberal governmentality and austerity

Benefit claimants have been at the focal point of neoliberal economic policy under successive governments for nearly twenty-five years, but the banking crisis of 2008 reinvigorated government attempts to cut benefit spending. This has deepened divisions and inequalities in British society, as disabled people and those with mental health problems unable to work, are coerced by an increasingly authoritarian regime to seek low-paid work or unsuitable jobs based in zero hours contracts. One consequence of these developments is a resurgence of interest in the ideas of Peter Sedgwick, whose book Psycho Politics, set out a Marxist critique of antipsychiatry (including Foucault's early work) and the consequences of neoliberalism for people with mental health problems.This paper outlines Sedgwick's main arguments, and together with Foucault's later work, questions the underlying principles of neoliberalism. Of particular significance is a shift in the governmental function of psychology and psychotherapy from the .
Secure Routing in Directional Sensor Networks

We describe our current research efforts in the area of secure routing for directional sensor networks (DSNs) in order to overcome the challenge of purely directional links encountered in DSNs. We outline two of our three main area of focus: (1) a probabilistic connectivity analysis on physical network properties (number of nodes, communication radius and beam angle) that guarantee a connected network; and (2) the design of an efficient and security aware routing protocol. Preliminary simulation results illustrate the applications and performance of our research.
SINGLE-MACHINE SCHEDULING WITH AN ACTUAL TIME-DEPENDENT LEARNING CONSIDERATION

This paper deals with single-machine scheduling problems with an actual time-dependent learning consideration. First, we provide a mathematical description of learning effects in scheduling environment. Then we introduce an actual time-dependent learning model, in which the learning effect is defined a function of the ratio of sum of actual processing times of the jobs previously scheduled to total normal processing time of all jobs. We incorporate it into single-machine scheduling problems and show by examples that the optimal schedule for the classical version of the problem is not optimal in the presence of this new actual time-dependent learning effect for the following objective functions: the makespan, the sum of kth power of the completion times, the total weighted completion times, the maximum lateness and the number of tardy jobs. But for some special cases, we show that the shortest processing time first (SPT) rule, the weighted shortest processing time first (WSPT) rule, the earliest due date (EDD) rule and Moore’s Algorithm can also construct an optimal schedule for the problem of minimizing these objective functions, respectively. We also use these rules as heuristics for the general cases and analyze their worst-case error bounds.
LOIND: An illumination and scale invariant RGB-D descriptor

We introduce a novel RGB-D descriptor called local ordinal intensity and normal descriptor (LOIND) with the integration of texture information in RGB image and geometric information in depth image. We implement the descriptor with a 3-D histogram supported by orders of intensities and angles between normal vectors, in addition with the spatial sub-divisions. The former ordering information which is invariant under the transformation of illumination, scale and rotation provides the robustness of our descriptor, while the latter spatial distribution provides higher information capacity so that the discriminative performance is promoted. Comparable experiments with the state-of-art descriptors, e.g. SIFT, SURF, CSHOT and BRAND, show the effectiveness of our LOIND to the complex illumination changes and scale transformation. We also provide a new method to estimate the dominant orientation with only the geometric information, which can ensure the rotation invariance under extremely poor illumination.
Automatic Evaluation of Karaoke Singing Based on Pitch, Volume, and Rhythm Features

This study aims to develop an automatic singing evaluation system for Karaoke performances. Many Karaoke systems in the market today come with a scoring function. The addition of the feature enhances the entertainment appeal of the system due to the competitive nature of humans. The automatic Karaoke scoring mechanism to date, however, is still rudimentary, often giving inconsistent results with scoring by human raters. A cause of blunder arises from the fact that often only the singing volume is used as the evaluation criteria. To improve on the singing evaluation capabilities on Karaoke machines, this study exploits various acoustic features, including pitch, volume, and rhythm to assess a singing performance. We invited a number of singers having different levels of singing capabilities to record for Karaoke solo vocal samples. The performances were rated independently by four musicians, and then used in conjunction with additional Karaoke Video Compact Disk music for the training of our proposed system. Our experiment shows that the results of automatic singing evaluation are close to the human rating, where the Pearson product-moment correlation coefficient between them is 0.82.