Miracle-dz/UniFAR-FTUs huggingface dataset:
OPINION SURVEY ON SOCIO-ECONOMIC FACTORS OF JUVENILE DELINQUENCY IN KETU – ADIE OWE COMMUNITY IN OGUN STATE

Socio – economic factors play prominent roles in determining the behaviours of children in the society. This study, therefore, investigated the opinions of Ketu – Adie – Owe community in Ado – Odo / Ota Local government of Ogun State, on the socio – economic factors that cause juvenile delinquency. The study adopted descriptive survey design. Data were collected through quantitative method. This involved seventy – three copies of questionnaire administered among community residents conveniently sampled for the study. Quantitative data were analysed using table of frequency and percentage distributions. Fifty – nine percent of the respondents were female; 49.3% of them were married and 39.7% of them had family size of 2 to 3 persons. The analyses in the study show that 97.2% of the respondents affirmed poverty as a cause of delinquency; 89.0% of them opined broken homes, 83.3% concurred on low family income as a cause, 84.5% of the respondents signified lack of parental control as a cause of delinquent behaviours; 82.2% affirmed that delinquency in juveniles is caused by pressure from bad peer groups and 77.8% of the respondents affirmed lack of moral / religious instructions from parents as a factor leading children to delinquent behaviour. The study recommends speedy economy recovery; discouragement of broken homes; adequate parental control and sound moral / religious instructions to children as panaceas to the problem of juvenile delinquency in our society.
A classification rules mining method based on dynamic rules' frequency

Rule based classification or rule induction (RI) in data mining is an approach that normally generates classifiers containing simple yet effective rules. Most RI algorithms suffer from few drawbacks mainly related to rule pruning and rules sharing training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed that utilises two thresholds to minimise the items search space. Whenever a rule is generated, DRI algorithm ensures that all candidate items' frequencies are updated to reflect the deletion of the rule's training data instances. Therefore, the remaining candidate items waiting to be added to other rules have dynamic frequencies rather static. This enables DRI to generate not only rules with 100% accuracy but rules with high accuracy as well. Experimental tests using a number of UCI data sets have been conducted using a number of RI algorithms. The results clearly show competitive performance in regards to classification accuracy and classifier size of DRI when compared to other RI algorithms.
Optimized damage detection of steel plates from noisy impact test

Model‐based non‐destructive evaluation proceeds measuring the response after an excitation on an accessible area of the structure. The basis for processing this information has been established in recent years as an iterative scheme that minimizes the discrepancy between this experimental measurement and sequence of measurement trials predicted by a numerical model. The unknown damage that minimizes this discrepancy by means of a cost functional is to be found. The damage location and size is quantified and sought by means of a well‐conditioned parametrization. The design of the magnitude to measure, its filtering for reducing noise effects and calibration, as well as the design of the cost functional and parametrization, determines the robustness of the search to combat noise and other uncertainty factors. These are key open issues to improve the sensitivity and identifiability during the information processing. Among them, a filter for the cost functional is proposed in this study for maximal sensitivity to the damage detection of steel plate under the impact loading. This filter is designed by means of a wavelet decomposition together with a selection of the measuring points, and the optimization criterion is built on an estimate of the probability of detection, using genetic algorithms. Numerical examples show that the use of the optimal filter allows to find damage of a magnitude several times smaller. Copyright © 2006 John Wiley & Sons, Ltd.
Effect of food intake on energy and protein metabolism in the skin of Romney sheep

Sheep fed on either a low (500 g lucerne (Medicago sativa) chaff/d; L) or high (1100 g lucerne chaff/d; H) intake had measurements made, using arterio–venous techniques, of blood flow and energy metabolite and cysteine utilization in the skin. Sheep on the H intake had significantly increased skin blood flow (P = 0.014) and oxygen uptake (P = 0.05). Although the H sheep had higher skin blood flow they showed no difference in skin uptake of either glucose or acetate compared with the L sheep, but the H sheep had a significantly lower output of lactate (P = 0.014). Animals in each group had either [14Clglucose or [14Clacetate infused into the skin which showed that acetate was the predominant precursor of skin sterol and fatty acid synthesis in the H sheep while L sheep skin used both glucose and acetate. The H sheep showed an increase in the net uptake of cysteine by the skin (P = 0.053), and in the uptake of cysteine for protein synthesis (P = 0.078), relative to the L sheep and this increase was of a comparable magnitude to the increase in blood now to the skin. Although blood flow, protein synthesis and energy supply increased in the skin of the H sheep by 200–300%, wool production would only have increased by 10–20 %, suggesting that nutrient flux changes are not the sole level of regulation of wool production.
Towards Hybrid Dependent Tasks Assignment for Grid Computing

Computational Grids (Shupeng Wang & Yu, 2006) are emerging as next generation of parallel and distributed computing platforms for solving large-scale computational and data-intensive problems in science, engineering, and commerce. They enable the sharing, selection, and aggregation of a wide variety of geographically distributed resources including supercomputers, storage systems, databases, data sources, and specialized devices owned by different organizations. They also provide convenient mechanisms to access these resources.
Mineral composition of local tree leaves for feeding sheep and goats in Kohat district of Khyber Pakhtunkhwa.

Mineral profile of tree leaves from eight local species (Acacia modesta, Albezia lebbeck, Capparis aphyla, Prosopes Juliflora, Tecoma undulata, Olea cuspidate, Ziziphus mauritiana and Ziziphus jujuba) was evaluated for feeding to sheep and goats. Leave samples were collected from three different locations (Surgul, Shakardara and Gumbat) in Kohat district of Khyber Pakhtunkhwa, Pakistan province and analyzed for total mineral matter, Ca, P, Na, K, Cu, Zn, Fe and Mn and In vitro DM digestibility. Statistical variations due to tree species and location were inconsistent for macro and micro minerals. Except Na, the contents of Ca, P and K were significantly influenced by tree species. Calcium contents were in the optimum range (1.66 to 5.14%) in all the leaves except Capparis aphyla (0.22%). Conversely, P was in the lower range (0.05-0.14%) in all samples that resulted in wide Ca:P ratio. Na in all samples was found ten times less than the required levels. Potassium was two folds higher than the recommended dietary level. Among the four micro-minerals, only Zn and Mn responded to difference in tree species and none varied due to location. Copper contents averaged 3.32 ppm across the tree species as against the required dietary levels 4 to10 ppm. The contents of Zn. Fe and Mn were found above the optimum levels and averaged 42, 186 and 94 ppm, respectively. The low in vitro DM digestibility (25.96 - 51.28%) of the tree leaves may constrain bioavailability of minerals and requires research to overcome this limitation.
Prediction of antigenic epitopes for coat protein of Potato virus Y, Egypt

Isolated Potato virus Y Egypt (PVY- EG) obtained from naturally infected potato plants in El-Sharkia, Egypt, was identified by biological, molecular and serological assays. The aims of this study were to predict the antigenic determinants (epitopes) of the isolated PVY using immunoinformatics; to prepare antibodies then apply them for PVY screening in potato plants in the field, as well as comparison with antibodies against coat protein. The amino acid sequence of PVY isolate was expected by DNAStar protean system; in which four parameters for epitope prediction including antigenicity, surface probability, hydrophobicity and secondary structure were used. These models were applied to PVY as a case study to predict immunogenic peptides for antibodies production. The most hydrophilic regions of PVY-coat protein (PVY- CP) were; 36-40, 46-47, 67-74, 96-98, 130-133, 164-166 and 198-200, which showed high hydrophobicity of this protein. This indicated that this protein was one of the best candidates to be immunogenic, and capable of producing antibodies that cross react with PVY. The peptide was chemically synthesized and injected into a rabbit. Obtained antibodies were evaluated using Double antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA), and Immune Dot-Blot assay. These antibodies positively reacted against PVY infected potato tissues.
Weighted sum rate optimization for downlink multiuser MIMO systems with per antenna power constraint: Downlink-uplink duality approach

This paper considers weighted sum rate maximization constrained with a per base station (BS) antenna power problem for multiuser multiple-input multiple-output (MIMO) systems. For this problem, we propose new downlink-uplink duality based solution. We solve the problem as follows. First, by introducing additional optimization variables, we reformulate our problem into an equivalent problem that incorporates a weighted sum mean-square-error (MSE) term. Second, we establish novel weighted sum MSE duality. The duality is established by modifying the input covariance matrix of the dual uplink problem, and formulating the noise covariance matrix of the uplink channel as a fixed point function. Third, we optimize the introduced variables and powers in the downlink channel by a Geometric Program (GP) method. Fourth, using the duality result and the solution of GP, we apply alternating optimization technique to solve the original downlink problem. In our simulation results, we have observed that the proposed duality based solution utilizes less power than that of existing algorithms.
Region Classification with Markov Field Aspect Models

Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-of-feature approaches label each pixel or patch independently. More advanced models attempt to improve the coherence of the labellings by introducing some form of inter-patch coupling: traditional spatial models such as MRF's provide crisper local labellings by exploiting neighbourhood-level couplings, while aspect models such as PLSA and LDA use global relevance estimates (global mixing proportions for the classes appearing in the image) to shape the local choices. We point out that the two approaches are complementary, combining them to produce aspect-based spatial field models that outperform both approaches. We study two spatial models: one based on averaging over forests of minimal spanning trees linking neighboring image regions, the other on an efficient chain-based Expectation Propagation method for regular 8-neighbor Markov random fields. The models can be trained using either patch-level labels or image-level keywords. As input features they use factored observation models combining texture, color and position cues. Experimental results on the MSR Cambridge data sets show that combining spatial and aspect models significantly improves the region-level classification accuracy. In fact our models trained with image-level labels outperform PLSA trained with pixel-level ones.
Neuroprotective effect of Bacopa monniera on memory deficits and ATPase system in Alzheimer's disease (AD) induced mice

Theme of the present study is to evaluate neuroprotective properties of Bacopa monniera (Brahmi) extract (BME) on memory deficits and biochemical changes in ATPases system of AD induced mice. Mus musculus, of one month old weighing 20 ± 2 grams, were used as experimental model and were maintained in Animal House according to the ethical guidelines for animal protection and welfare, bearing the Resolution No. 02/(i)/a/CPCSEA/ IAEC/ SVU/ KY-KK/ Dt. 21-03-2011. The mice were divided in to four groups as follows: Group I: Control mice; Group II: mice treated with BME; Group III (AD induced): mice treated with D-Gal and NaNO2; Group IV: AD induced mice simultaneously treated with BME. Changes in Morphometric and Behavioural aspects of four groups were analyzed by using Morris Water Maze technique. Various constituents of ATPase system were determined in selected regions of mice brain through standard biochemical assay methods. Results revealed that BME showed positive effects on body weight, learning skills, memory and concentration, whereas D-Gal and NaNO2 caused learning and memory deficits in mice which could be ameliorated by simultaneous administration of BME. Similar, protective effects of BME were noticed on the ATPase system which could revert all the constituents of ATPase system to normal levels in AD induced mice. From these observations, it was concluded that BME had potential compounds which can prevent the learning and memory deficits effectively; and to maintain ion gradients across biological membranes, thus confer significant neuroprotection against AD by stabilizing the structural and functional integrity of the membrane.
On Boolean combinations of definitive classifiers

We consider the sample complexity of concept learning when we classify by using a fixed Boolean function of the outputs of a number of different classifiers. Here, we take into account the ‘margins’ of each of the constituent classifiers. A special case is that in which the constituent classifiers are linear threshold functions (or perceptrons) and the fixed Boolean function is the majority function. This corresponds to a ‘committee of perceptrons’, an artificial neural network (or circuit) consisting of a single layer of perceptrons (or linear threshold units) in which the output of the network is defined to be the majority output of the perceptrons. Recent work of Auer et al. studied the computational properties of such networks (where they were called ‘parallel perceptrons’), proposed an incremental learning algorithm for them, and demonstrated empirically that the learning rule is effective. As a corollary of the sample complexity result presented here, generalization error bounds are derived for this special case that provide further motivation for the use of this learning rule.
Collectivistic Values and an Independent Mindset Jointly Promote Group Creativity: Further Evidence for a Synergy Model

In an experiment involving 3-person laboratory groups (N = 56), we examined the combined effect of group members’ value orientation and their general mindset on group creativity. We induced a collectivistic (vs. individualistic) value orientation via a group-based (vs. individual-based) incentives. We also induced an independent (vs. interdependent) mindset by activating a focus on uniqueness and differentiation (vs. similarity and assimilation). Consistent with a synergy model of individualism-collectivism (Choi, Cho, Seo, & Bechtoldt, 2018), we found that groups generated more original ideas when members combined a collectivistic value orientation with an independent mindset than with an interdependent mindset. By contrast, no significant effects emerged when an individualistic value orientation was induced. We also found that this effect was mediated by the thinking process conducive to creativity (i.e., low fixation), thereby illuminating the underlying cognitive mechanism of the synergy effect. Implications of these findings for research on culture and group creativity are discussed.
20th Century trends and budget implications of trihalomethanes and dihalomethanes inferred from North GRIP firn air

Four trihalomethane (THM; CHCl3, CHBrCl2, CHBr2Cl and CHBr3) and two dihalomethane (DHM; CH2BrCl and CH2Br2) trace gases have been measured in air extracted from polar firn collected at the North Greenland Icecore Project (NGRIP) site. CHCl3 was also measured in firn air from Devon Island (DI), Canada, Dronning Maud Land (DML), Antarctica and Dome Concordia (Dome C), Antarctica. All of these species are believed to be almost entirely of natural origin except for CHCl3 where anthropogenic sources have been reported to contribute ~10% to the global burden. A 2-D atmospheric model was run for CHCl3 using reported emission estimates to produce historical atmospheric trends at the three firn sites, which were then input into a firn diffusion model to produce concentration depth profiles that were compared against the measurements. The anthropogenic emissions were modified in order to give the best model fit to the firn data at NGRIP, Dome C and DML. As a result, the contribution of CHCl3 from anthropogenic sources, mainly from pulp and paper manufacture, to the total chloroform budget appears to have been considerably underestimated and was likely to have been close to ~40% at the maximum in atmospheric CHCl3 concentrations around 1990, declining to ~19% at the beginning of the 21st Century. We also show that the atmospheric burden of the brominated THM's in the northern hemisphere have increased over the 20th Century while CH2Br2 has remained constant over time implying that it is entirely of natural origin.
The not so resting mind: Investigating Neural Activity associated with daydreaming using EEG

A recurring question since the times of classical psychology is concerned with how the human brain gives rise to a variety of experiences despite the lacking of sensory arousal (Antrobus, Singer & Greenberg, 1966). The current study sought to ascertain whether distinct brain activity could be observed across three different visual tasks (Visual, Day-dreaming & N-Back). Areas of interest included both a priori Brodmann areas (in light of previous literature) and post-hoc Brodmann areas – which primarily concern what this investigation found and how it fares with previous academic insight. Using source localization analysis via electroencephalography (EEG), distinct patterns of brain activity were observed across the three tasks. However, the major differences found were shown to be largely dependent on the cognitive-demand of the task – with the N-Back phase eliciting significantly more activity across the majority of Brodmann areas compared to the visual and day-dreaming tasks which, at points, demonstrated very similar patterns of brain activity. These results offer strength to some academic proposals as well as further questioning for others. Finally, these results are weighed in light of what contemporary academia currently proposes and may provide sound reasoning for more integrative measures to reliably assess instances of day-dreaming in a psychological setting.
TSRF: A Trust-Aware Secure Routing Framework in Wireless Sensor Networks

In recent years, trust-aware routing protocol plays a vital role in security of wireless sensor networks (WSNs), which is one of the most popular network technologies for smart city. However, several key issues in conventional trust-aware routing protocols still remain to be solved, such as the compatibility of trust metric with QoS metrics and the control of overhead produced by trust evaluation procedure. This paper proposes a trust-aware secure routing framework (TSRF) with the characteristics of lightweight and high ability to resist various attacks. To meet the security requirements of routing protocols in WSNs, we first analyze features of common attacks on trust-aware routing schemes. Then, specific trust computation and trust derivation schemes are proposed based on analysis results. Finally, our design uses the combination of trust metric and QoS metrics as routing metrics to present an optimized routing algorithm. We show with the help of simulations that TSRF can achieve both intended security and high efficiency suitable for WSN-based networks.
WaveLAN®-II: A high-performance wireless LAN for the unlicensed band

In July 1997 the Institute of Electrical and Electronics Engineers (IEEE) completed standard 802.11 for wireless local area networks (LANs). WaveLAN®-II, to be released early in 1998, offers compatibility with the IEEE 802.11 standard for operation in the 2.4-GHz band. It is the successor to WaveLAN-I, which has been in the market since 1991. As a next-generation wireless LAN product, WaveLAN-II has many enhancements to improve performance in various areas. An IEEE 802.11 direct sequence spread spectrum (DSSS) product, WaveLAN-II supports the basic bit rates of 1 and 2 Mb/s, but it can also provide enhanced bit rates as high as 10 Mb/s. This paper discusses various aspects of the system design of WaveLAN-II and characteristics of its antenna, radio-frequency (RF) front-end, digital signal processor (DSP) transceiver chip, and medium access controller (MAC) chip.
Relationships Between Non-Linguistic Variables in Second Language Acquisition and the Attained English Language Proficiency of Taiwanese University Students

Second language acquisition (SLA) research, the study of how people learn to communicate in a language other than their native language, encompasses a broad range of questions from a wide variety of perspectives. One of the most widely recognized facts about SLA was that some individuals are more successful in learning a second language than are others (Gass & Selinker, 2001). Students with the same initial linguistic abilities, who receive the same education, even in the same institution, often do not achieve the same linguistic competency levels at program completion (Dornyei, 1998). This phenomenon cannot be explained purely by linguistic factors. Rather, nonlinguistic factors also should be explored to observe what causes the difference in achievement and ways to take advantage of those factors to help second language learners achieve a better outcome (Birdsong, 1999). A great deal of research has been conducted on linguistic factors that influence SLA. Traditional linguistics and psychology have delved into the area from the perspectives of theoretical linguistics and psycholinguistics. They explored the areas based on Noam Chomsky's (1965) theory of human essence, the distinctive qualities of mind that are, so far as we know, unique to humans (Thomas & Nathan, 2001). In a similar vein, psycholinguistics has also examined SLA from different hypotheses generated from different models in hopes of explaining the century-old question: How is a second language acquired and what can be done to improve the acquisition of a second