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This study examined the degree to which students indulge in six prominent misconducts in Distance Education Institutions (DEIs). The study also quantified how class size, instructional delivery and institutional policies predict students' indulgence in sharp practices using a general linear modelling approach. A sample of 871 participants was drawn from 1,742 final-year students across two DEIs in Nigeria. A structured questionnaire was used for data collection. The questionnaire had acceptable psychometric estimates of dimensionality, content and construct validity, as well as reliability. Sharp practices such as cheating, plagiarism, falsification, impersonation, and arm-twisting were more prevalent in large classes, whereas only inducement was higher in small classes. Class size influenced students' indulgence in sharp practices in DEIs. Instructional delivery and institutional policies negatively predicted students' indulgence in the six sharp practices. Almost all the six sharp practices correlated positively and significantly, except for impersonation and inducement. Therefore, distance education students who cheat, plagiarize, arm-twist, falsify records, induce lecturers, and promote impersonation are more likely to indulge in other forms of sharp practices. It was concluded that large class sizes, poor instructional delivery, and poorly implemented institutional policies promote sharp practices among distance education students. Based on this conclusion, key policy and research implications are discussed.

Ca and Mg have been implicated in causing hardness in beans resulting in relatively long cooking time. This study used potassium to replace the cations and determined the adsorption of potassium solution to bean seeds. Then, plantain peel, a natural source of potassium, was used to cook beans and its impact on the cooking time of beans was investigated. The adsorption experiments were performed using batch technique, while metal compositions of the bean seeds and plantain peel were determined by spectroscopy. Optimum removal conditions of potassium ion biosorption using bean seeds were observed at pH 10.2, 2 g bean seed dosage, 180 min agitation time, with 75 ppm as initial metal concentration. The kinetic model correlate with pseudo-second order reaction and the Langmuir adsorption model best fitted the adsorption. After cooking the beans with plantain peel, the concentration of Mg reduced in the bean seeds by about 48%, while the concentration of Ca reduced by about 22%, but the concentration of K increased by over 200% in the cooked bean seeds. Beans treated with plantain peel cooked earlier than the control experiment. This may be affected by pH, adsorbent dosage, metal concentration and contact time.

Metals used in industries are exposed to several processes such as acid cleaning, pickling, and descaling, which leads to corrosion. A good measure of corrosion control has been achieved by reducing the metal impurity content, by corrosion inhibition, the use of modification techniques, and the incorporation of alloying elements. However, the use of corrosion inhibitors seems to be the most economical and practical of the methods. Besides, the use of fibrous plant biomass as corrosion inhibitors has become important as a suitable substitute for the toxic chromates, phosphates, and arsenic-based inhibitors. The constituents in the fibrous plant biomass extracts react with the metal ions on a corroding metal surface forming organometallic complexes. The recent introduction of Density Functional Theory (DFT) in the theoretical study of the electronic structures elucidates the adsorption modes and the individual contributions of the plant extracts. The DFT has proven to be a significant contribution to the conventional methods based on gravimetry, gasometry, and electrochemical investigations for studying corrosion inhibition. This review provides insights into the use of fibrous plant extracts as corrosion inhibitors in acid solution as well as the DFT calculations.

Copyright of Die Unternehmensbesteuerung (Ubg) is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Die Unternehmensbesteuerung (Ubg) is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional method of using the coefficient of drag – Reynolds number relationship to predict cuttings settling velocity involves an implicit procedure that requires repeated, time-consuming and tedious iterations using Newtonian or mostly non-Newtonian correlations. Usually, these correlations are limited to certain fluid flow regimes. Besides, most of the explicit and direct cuttings settling velocity models that exist are based on the assumption that the cuttings are spherical particles. However, in the field, the cuttings are a mixture of various shapes and are hardly spherical, hence these models when applied to field conditions come off with huge errors. The objective of this work was to use a nature-inspired algorithm (artificial neural network - ANN) to develop a model for estimating cuttings settling velocity that would be robust and useful in the field that would take into account the shape of the cuttings. The data used for this work was obtained from research experiments in the literature. The model was then evaluated using four performance metrics namely: mean squared error (MSE), root mean square error (RMSE), sum of squares error (SSE) and goodness of fit (R2). It was found that the model's predictions obtained in this work agreed with experimental evidence. Furthermore, the developed model possesses the capacity to generalize across new input datasets and can be applied to particles of any shape, hence, defining the novelty of this research and bridging the gap between theory and practice. When compared with state-of-the-art models, the developed models show a high degree of robustness, as the ANN model performed reasonably well with an MSE of 7.5 × 10−4, an R2 of 0.978, RMSE of 0.0274 and SSE of 0.25. To generalize the results across new input datasets, the developed model was cross-validated with new data that was not part of the training process. It was found that the ANN model had an MSE value 0.00807, RMSE of 0.0898, MAE of 0.065, SSE of 2.74 and MAPE of 0.675%. To ensure the replicability of the ANN model, the weights and biases for the inputs, hidden and output layers are presented in this work unlike other artificial intelligence-based models in the literature. The range of application for the developed ANN model is 0.0001

Abstract In this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R 2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R 2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature.

In the exploration for hydrocarbons, a successful drilling operation to the desired depth hinges on the effective performance of the formulated drilling fluid. Apart from carrying drill cuttings to the surface, another major function of the fluid is to seal off the walls of the wellbore to prevent fluids from coming into and out of the wellbore while drilling a well. Numerous commercial fluid loss additives: carboxymethyl cellulose (CMC), polyanionic cellulose (PAC), among others have been in existence with their drawbacks and effect on the total drilling cost. This study evaluates the use of locally sourced materials: Detarium microcarpum, Brachystegia eurycoma and rice husk, as fluid loss control additive in the water-based drilling fluid. The materials were prepared, ground and sieved to 125 microns. Four sets of water-based drilling muds were formulated using the local materials and CMC as fluid loss control additives. The mud formulation was based on the American Petroleum Institute (API) standard of 25g bentonite to 350mL of water. Also, the filtration test of the formulated muds was performed using API recommended practice for static filtration test at low temperature - low pressure (LTLP) condition. The results obtained showed that Detarium microcarpum and rice husk fluid loss volume and filter cake thickness were comparable with that of CMC from additive content of 10g, while Brachystegia eurycoma was comparable from additive content of 15g. Furthermore, the composite additive results indicated that Detarium microcarpum-rice husk at 95% Detarium microcarpum-5% rice husk performed better than Brachystegia eurycoma-rice husk of the same combination. Additionally, the fluid loss volume and filter cake thickness of Detarium microcarpum-rice husk additive were comparable with CMC from 10g content. Also, the results revealed that the fluid loss volume and filter cake thickness obtained from the locally sourced materials were within API specification for fluid loss control agents. The mud filter cake characteristics exhibited by these materials depicted that they have slippery, smooth and soft mud cakes; thus, the characteristics of a good mud cake that will prevent differential pipe sticking.

When drilling with water based muds (WBM), significant fluid loss volumes from the mud into the formation can have adverse effects not just on the mud and its properties but also on the stability of the wellbore. Prevention of mud filter loss is one way of assessing the performance of a drilling mud. However, evaluation of the effectiveness or otherwise of a fluid loss control additive can be made by characterizing the mud cake formed. Interestingly, the mud cake characterization is one area that has been somewhat neglected in drilling fluid formulation with agro waste materials. Two cellulosic materials - rice husk and saw dust were chosen for the experimental study. The specie of the rice husk used was the African rice (Oryza glaberrima) while the dust from the saw milling of Oxystigma manni was utilized for this study. To ensure result acceptability, the rice husk and saw dust were ground and the resulting products were sieved to 1.25 × 10−4 m. The filtration characteristics of the formulated mud samples were tested using the American Petroleum Institute (API) filter press and in accordance to the API recommended practice for field testing WBMs. From the filter loss tests, it was observed that the ground rice husk prevented filter loss by an average of 77% compared to ground saw dust filtration control of 63%. In addition, it was observed that at higher concentrations, ground saw dust and rice husk prevented fluid loss to the minimum acceptable API standard. For the filter cake thickness measured in millimetres, ground rice husk exhibited thicker mud cakes when compared with the saw dust by an average amount of 14%. For the mud cake characteristics, the rice husk mud exhibited smooth and slippery cakes while the saw dust mud exhibited rough texture, sticky and firm cakes.

Ulrich Julius Lipidology and Center for Extracorporeal Therapy, Department for Internal Medicine III, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany Abstract: This review reports the current situation with respect to therapeutic options (lifestyle and drugs) reducing the concentrations of atherogenic low-density lipoprotein cholesterol (LDL-C) and lipoprotein(a) (Lp[a]). Three lipoprotein apheresis (LA) principles have been realized: precipitation, filtration, and adsorption. Available LA methods are herein described in detail – major components, pumps, extracorporeal volume, treated volume, and anticoagulation. General features of all LA methods as well as pleotropic effects are elaborated. Indications for LA therapy are quoted: homozygous familial hypercholesterolemia (HCH), severe HCH, and isolated elevation of Lp(a) and progress of atherosclerotic disease. A major focus is on the evidence of the effect of LA on cardiovascular outcome data, and the most important publications are cited in this context. The best studies have been performed in patients with elevated Lp(a) in whom cardiovascular events were reduced by more than 80%. Major adverse effects and contraindications are listed. The impact of an LA therapy on patient quality of life and the requirements they have to fulfill are also highlighted. Finally, the future role of LA in treating high-risk patients with high LDL-C and/or high Lp(a) is discussed. It is probable that the significance of LA for treating patients with elevated LDL-C will decrease (with the exception of homozygous familial HCH) due to the application of PCSK9 inhibitors. The antisense oligonucleotide against apolipoprotein(a) could replace LA in patients with high Lp(a), provided positive outcome data are generated. Keywords: LDL cholesterol, lipoprotein(a), lipid-lowering therapy, lipoprotein apheresis, cardiovascular outcome

Cd(II) and Pb(II) ions removal using adsorbents prepared from sub-bituminous coal, lignite, and a blend of coal and Irvingia gabonensis seed shells was investigated. Fourier transform infrared, scanning electron microscope and X-ray fluorescence analyses implicated hydroxyl, carbonyl, Al2O3, and SiO2 as being responsible for attaching the metal ions on the porous adsorbents. The optimum adsorption of carbonized lignite for the uptake of Cd(II) and Pb(II) ions from aqueous media were 80.93 and 87.85%, respectively. Batch adsorption was done by effect of adsorbent dosage, pH, contact time, temperature, particle size, and initial concentration. Equilibrium for the removal of Pb(II) and Cd(II) was established within 100 and 120 min respectively. Blending the lignite-derived adsorbent with I. gabonensis seed shell improved the performance significantly. More improvement was observed on modification of the blend using NaOH and H3PO4. Pb(II) was preferentially adsorbed than Cd(II) in all cases. Adsorption of Cd(II) and Pb(II) ions followed Langmuir isotherm. The adsorption kinetics was best described by pseudo-second order model. The potential for using a blend of coal and agricultural byproduct (I. gabonensis seed shell) was found a viable alternative for removal of toxic heavy metals from aqueous solutions.