Miracle-dz/UniFAR-FTUs huggingface dataset:
Delivery of tocopherol phosphate nanomaterials into the skin to protect against ultraviolet radiation

M. M. Saleh, A. Woods, R. Harvey, S.A. Jones (2017). The phosphorylation of alpha-tocopherol facilitates enhanced interactions with skin lipids (Poster presentation). The 6 FIP Pharmaceutical Sciences World Congress (PSWC 2017), Stockholm, Sweden. M. M. Saleh, A. Woods, R. Harvey, S. A. Jones (2017). Nanostructured vitamin E phosphate: Characterising its cutaneous permeation and interaction with skin lipids (Poster presentation). The Skin @ Bath Network 1 Skin Symposium. Bath, UK. M. M Saleh, A. R Young, K. P. Lawrence, S. A. Jones (2018). Vitamin E Phosphate: Characterising Its Cutaneous Permeation and Photoprotection Against Ultraviolet A Induced Cell Death (Poster presentation). The 1 Dermatology Away Day, St John Institute Of Dermatology, The Great Hall, Strand Campus, King’s College London, London, Uk. M. M. Saleh, A. R. Young, K. P. Lawrence, S. A. Jones (2018). Vitamin E Phosphate: Characterising Its Cutaneous Permeation and Photoprotection Against Ultraviolet A Induced Cell Death (Poster presentation). The 16 International Perspectives in Percutaneous Penetration Conference, La Grande Motte, France.
Soil Washing Technology for Removing Heavy
Metals from a Contaminated Soil: A Case Study

Although EDTA solution is considered an efficient soil washing liquid for extraction of heavy metals from soils, its low biodegradability may alter soil properties and suppress plant growth. Alternatively, chlorides are safer and cheaper washing liquids than EDTA. To investigate the efficiency of chlorides versus EDTA in extracting heavy metals from contaminated soils, soil samples (pH 6.14±0.11) were collected from a local agriculture soil in Australia, artificially contaminated with either Pb, Cd or Cr at three different levels of 200, 400 and 600 mg kg-1, and then packed in capped plastic flasks. Batch washing techniques were followed with either EDTA or FeCl3 solutions (prepared at 4 different concentrations of 0.05, 0.1, 0.25 and 0.5 M), and soil suspensions were agitated for different time periods (from 5.0 min. to 60.0 min); afterward, the extraction efficiencies of the investigated metals were considered. The removal efficiency of Pb from the contaminated soil (200 mg Pb kg-1) after 5 minutes of EDTA application seemed to be relatively high (≈75%). Afterward this efficiency decreased gradually with time. The efficiencies of the extracted Cd and Cr by soil washing with EDTA increased significantly with increases in the agitating period. Generally, the extraction efficiencies by EDTA decreased noticeably with increasing levels of soil contamination. On the other hand, the efficiencies of Pb, Cd and Cr extractions were high – especially when increasing both the concentrations of applied FeCl3 solution and the time of agitation. The results also highlighted that soil washing with FeCl3 seemed to be more favourable over EDTA for rapid extraction of heavy metals from contaminated soils. The efficiencies of extracting heavy metals by soil washing with 0.5M FeCl3 for only one hour were 93.79±2.35%, 97.4±2.45% and 81.75±7.86% for Pb, Cd and Cr, respectively.
A Preliminary Study on Response of Iron Content and Yield of Corn to Different Rates of Sewage Sludge

OZ: Aritma camurlarinin tehlikeli agir metaller ve toksik maddeler icermemesi durumunda, topraklarda kullanilabilirligi mumkundur. Bu on calismada misir bitkisine aritma camuru ile birlikte uygulanan demirli gubrelemenin bu bitkinin verim ve bitki besin elementi icerigi uzerine olan etkisi belirlenmeye calisilmistir. Arastirma saksi denemesi olarak 3 tekrarlamali olarak tesaduf parselleri deneme desenine gore yurutulmus ve saksilarda C-955 silajlik hibrit misir cesidi yetistirilmistir. Denemede saksilara temel gubreleme sabit dozda 200 mg kg –1 N, 100 mg kg –1 P ve 150 mg kg –1 K olarak uygulanmistir. Deneme konulari olarak; (K) % 100 toprak, (T1) 5 mg kg –1 Fe (aritma camuru demiri) + 10 mg kg –1 Fe (FeSO 4 .7H 2 O), (T2) 10 mg kg –1 Fe (aritma camuru demiri) + 5 mg kg –1 Fe (FeSO 4 .7H 2 O), (T3) 15 mg kg –1 Fe (aritma camuru demiri) ve (T4) 15 mg kg –1 Fe (FeSO 4 .7H 2 O) belirlenmistir. Arastirma sonucunda aritma camuru ve aritma camuru demir kombinasyonlari kontrole oranla misir bitkisinin yas agirligi, kuru agirligi, bitki boyu, yaprak sayisi ve bitkinin N, K, Fe, Zn ve Cu icerigini arttirdigi belirlenmistir. Ayrica bitkinin demir ihtiyacini karsilamak icin aritma camurundaki 15 mg kg –1 Fe uygulamasinin yeterli olacagi sonucuna varilmistir. Bununla beraber, bu calismadan elde edilen sonuclar bu deneme topragi ve aritma camuru icin gecerlidir ve toprak ve iklim gibi farkli kosullar altinda degisebileceginden konuda genis kapsamli calismalar yapilmalidir.
Robust Discriminative Metric Learning for Image Representation

Metric learning has attracted significant attention in the past decades, because of its appealing advances in various real-world tasks, e.g., person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm through fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, the low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, the fast low-rank representation is implemented to mitigate the computational burden and ensure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, image clustering, and person re-identification. The experimental results verify the effectiveness of our proposed algorithm in comparison to many metric learning algorithms, even deep learning ones.
Pareto-Optimal Design and Performance Mapping of Telecom Rectifier Concepts

In the course of the design of single-phase PFC rectifiers, the demand for a high efficiency η and a high power density ρ must be met at the same time. Depending on the weight on these two design criteria different topologies could be advantageous. However, a comprehensive comparison of the topologies is difficult, since a large number of parameters must be determined and constraints in different physical domains, as e.g. magnetic or thermal properties, and EMI issues must be considered. Therefore, in this paper an approach based on relatively simple analytical equations is presented for calculating the limiting curves of conventional, bridgeless and TCM resonant-transition single-phase PFC rectifiers in the ρ-η-plane. These Pareto-Limits represent the Topology Performance Map of the considered topologies and allow a direct comparison of the achievable performance and the limitations with respect to power density and/or efficiency.
Minimum error tool path generation method and an interpolator design technique for ultra-precision multi-axis CNC machining

This thesis investigates an ultra-precision multi-axis CNC machining problem encountered in machining sculptured surfaces. Conventional multi-axis CNC machining uses straight line segments to connect consecutive data points, and uses linear interpolation technique to generate the command signals for positions between machining data points. However, due to the multi-axis simultaneous and coupled translational and rotational movements, the actual machining motion trajectory is a non-linear curve. The non-linear curve segments deviate from the linearly interpolated straight line segments, resulting in non-linearity errors, which in turn cause obstacles to ensuring high precision machining. The problem in multi-axis CNC machining is that non-linearity errors result in total machining error which is beyond the range of the machining tolerance. The problem arises from the fact that the linear interpolation technique generates commands for positions along a straight line segment, while rotational movements superimposed onto translational movements cause the cutting point moving along a curved machining motion trajectory. The machining motion trajectory depends on both multi-axis CNC machine tool configuration and the machining rotational movements. The machining rotational movements are kinematically related to cutter orientation variations. Thus, The factors causing the multi-axis CNC machining error problem are the spatially varying cutter orientations and the utilization of linear interpolation method. A novel off-line tool path generation methodology for is developed and reported in this thesis in order to solve the non-linearity error problem in ultra-precision multi-axis CNC machining. The new off-line tool path generation method reduces non-linearity errors by modifying cutter orientation changes based on machine kinematics and machining motion trajectory. A software routine for implementing the new tool path generation methodology is developed. A simulation of the process for machining a sculptured surface by applying the novel methodology illustrates that it increases machining precision considerably. A novel interpolator design technique for solving the non-linearity error problem in ultra-precision multi-axis CNC machining is also presented. A 3D circular interpolation principle is developed which is capable of tracking spherical curves with low position errors and uniform feedrates. On the basis of this (3D circular) interpolator, a combined 3D linear and circular (L&C) interpolator is proposed for five-axis CNC machining. The proposed 3D L&C interpolator is able to drive the pivot of rotational movements along a predesigned 3D curve and conduct the cutting point along a linear spatial path, so that the elimination of non-linearity errors in five-axis CNC machining is achieved. A software interpolation routine of the 3D L&C interpolator is developed, and a computer simulation illustrating the machining of a sculptured surface validates the novel technique
Wireless Communications over MIMO Channels: Applications to CDMA and Multiple Antenna Systems

Preface. Acknowledgements. List of Abbreviations. List of Symbols. 1. Introduction to Digital Communications. 1.1 Basic System Model. 1.2 Characteristics of Mobile Radio Channels. 1.3 Signal Detection. 1.3.1 Optimal Decision Criteria. 1.4 Digital Linear Modulation. 1.5 Diversity. 1.6 Summary. 2. Information Theory. 2.1 Basic Definitions. 2.2 Channel Coding Theorem for SISO Channels. 2.3 Channel Capacity of MIMO Systems. 2.4 Channel Capacity for Multi-User Communications. 2.5 Summary. 3. Forward Error Correction Coding. 3.1 Introduction. 3.2 Linear Block Codes. 3.3 Convolutional Codes. 3.4 Soft-Output Decoding of Binary Codes. 3.5 Performance Evaluation of Linear Codes. 3.6 Concatenated Codes. 3.7 Low Density Parity Check (LDPC) Codes. 3.8 Summary. 4. Code Division Multiple Access. 4.1 Fundamentals. 4.2 OFDM-CDMA. 4.3 Low Rate Channel Coding in CDMA Systems. 4.4 Uplink Capacity of CDMA Systems. 4.5 Summary. 5. Multi-User Detection in CDMA Systems. 5.1 Optimum Detection. 5.2 Linear Multi-User Detection. 5.3 Nonlinear Iterative Multi-User Detection. 5.4 Combining Linear MUD and Nonlinear SIC. 5.5 Summary. 6. Multiple Antenna Systems. 6.1 Introduction . 6.2 Spatial Diversity Concepts. 6.3 Multi-Layer Transmission. 6.4 Linear Dispersion Codes. 6.5 Information Theoretic Analysis. 6.6 Summary. Appendix A: Channel Models. A.1 Equivalent Baseband Representation. A.2 Typical Propagation Profiles for Outdoor Mobile Radio Channels. A.3 Moment Generating Function for Ricean Fading. Appendix B: Derivations for Information Theory. B.1 Chain Rule for Entropies. B.2 Chain Rule for Information. B.3 Data Processing Theorem. Appendix C: Linear Algebra. C.1 Selected Basics. C.2 Householder Reflections and Givens Rotation. C.3 LLL Lattice-Reduction. Bibliography. Index.
NANO-COPPER AS A NEW GROWTH PROMOTER IN THE DIET OF GROWING NEW ZEALAND WHITE RABBITS

Seventy two rabbits, 35 days old and weighing 622g average body weight, were used for the present study. The rabbits were randomly assigned into 4 equal treatments (n=18). Animals in treatment 1 served as a control group and were given basal diet without supplementation, while rabbits in treatments 2, 3 and 4 were given nano-copper in the diet at levels 25, 50 and 75 mg/kg diet, respectively. The experiment lasted for 8 weeks. The results revealed that, rabbits fed diets supplemented with 50 or 75 mg Cu/ kg diet as Nano-Cu recorded significantly higher (P<0.01) final body weight and performance index also, recorded better feed conversion ratio during all growth periods than the control group. The group of rabbits fed diet supplemented with 50 mg Nano-Cu/kg diet significantly achieved the best relative growth rate percentage. Dietary supplementation with 50 mg Nano-Cu /kg diet significantly recorded higher carcass, fore part, trunk and hind part percentages comparing with the control group, it also significantly reduced abdominal fat by 28.4%. The Nano- Cu supplementation significantly (P<0.05) increased activity of superoxide dismutase enzyme compared with the control group. Copper content in rabbits liver significantly increased by increasing dietary Nano-Cu. Dietary Nano-Cu supplementation significantly (P<0.05) increased the population of total bacterial count and lactolacillus counts and decreased the population of ureolytic bacteria, Escherichia coli and clostridium spp. Supplementation of Nano-Cu to rabbit diets significantly increased plasma hemoglobin, red blood cells count and lymphocytes percentage. Economical efficiency improved for rabbits fed either 50 or 75 mg Nano-Cu supplemental diet. Conclusively, Nano-Cu is considered a new substitution for high dose of inorganic copper in growing NZW rabbits feeding and can be fed up to 50 mg/kg diet without any deleterious effects on production and meat safety of rabbits.
Image classification with a new kind of shape representation

As one of the research hotspots in recent years, especially in pattern recognition, Convolutional Neural Network (CNN) is widely known for its high efficiency. However some researches show that there is a problem in the CNN which cannot learn the high-level features. In order to solve this problem, this paper proposes a new kind of image representation, which we call it “shape encoding maps”. Our experimental results show that, in most cases, the recognition accuracies obtained by inputting the shape encoded maps to a CNN are higher than that of using the original image data for a CNN to learn directly without shape encoding.
Wet‐Spinning and Carbonization of Lignin‐Polyvinyl Alcohol Precursor Fibers

Lignin is a promising bio‐based precursor for sustainable carbon fibers. Limiting factors for their development include the brittleness of lignin and the lack of large‐scale production routes. Here, a simple and economic wet‐spinning method, suitable for the fabrication of fibers based on softwood Kraft lignin (KL) and polyvinyl alcohol (PVA), is proposed. These two polymers reveal a partial miscibility in solution, and form metastable dispersions in solid state. KL‐PVA fibers are prepared at a weight ratio of 70:30 and are carbonized without thermo‐stabilization. A tailor‐made temperature program leads to a decreased microporosity on the fiber surfaces. The obtained carbon structures at 1000 °C are found to be poorly ordered, leading to only intermediate mechanical and electrical properties. However, graphitic domains appear at temperatures above 1500 °C and indicate a high potential for the system.
Most of the Time, It Works Every Time: Limitations in Refining Domain Models with Learning Curves.

Data from student learning provide learning curves that, ideally, demonstrate improvement in student performance over time. Existing data mining methods can leverage these data to characterize and improve the domain models that support a learning environment, and these methods have been validated both with already-collected data, and in close-the-loop studies that actually modify instruction. However, these methods may be less general than previously thought, because they have not been evaluated under a wide range of data conditions. We describe a problem space of 90 distinct scenarios within which data mining methods may be applied to recognize posited domain model improvements. The scenarios are defined by two kinds of domain model modifications, five kinds of learning curves, and 25 types of skill combinations under three ways of interleaving skill practice. These extensive tests are made possible by the use of simulated data. In each of the 90 scenarios, we test three predictive models that aim to recognize domain model improvements, and evaluate their performance. Results show that the conditions under which an automated method tests a proposed domain model improvement can drastically affect the method’s accuracy in accepting or rejecting the proposed improvement, and the conditions can be affected by learning curve shapes, method of interleaving, choice of predictive model, and the threshold for predictive model comparison. Further, results show consistent problems with accuracy in accepting a proposed improvement by the Additive Factors Model, made popular in the DataShop software. Other models, namely Performance Factors Analysis and Recent-Performance Factors Analysis, are much more accurate, but still struggle under some conditions, such as when distinguishing curves from two skills where students have a high rate of errors after substantial practice. These findings bear on how to evaluate proposed refinements to a domain model. In light of these results, historical attempts to test domain model refinements may need to be reexamined.
Hydrostaticity of pressure-transmitting media for high pressure infrared spectroscopy

ABSTRACT Ensuring hydrostatic conditions in high pressure experiments is of utmost importance for obtaining reproducible and comparable dataset. Here, we investigate the degree of hydrostaticity of four pressure-transmitting media (PTM), three alkali halides and polyethylene, commonly used in infrared (IR) spectroscopy using multiple rubies as pressure sensors within a diamond anvil cell (DAC) pressure cavity. We demonstrate that the pressure standard deviation is always below 0.3 GPa at pressures below 10 GPa and that sodium chloride (NaCl) provides the best quasi-hydrostatic conditions up to 21 GPa. In the 10–20 GPa pressure range, NaCl, cesium iodide (CsI), and polyethylene perform significantly better than 4:1 ethanol–methanol or silicone oil – widely used PTM in high pressure experiments. These results will enable a more reliable and quantitative comparison between different experiments where the PTM investigated here are used.
Experimental study on the removal of FCCS catalyst particles by electrostatic separation

ABSTRACT Electrostatic separation is a technology for removing catalyst particles from fluid catalytic cracking slurry (FCCS). In this study, the particle trajectory was simulated by COMSOL Multiphysics based on theories and deductions. To verify whether these theories are reasonable for an electrostatic separation simulation, a mixture of alkylbenzene and catalysts was used to characterize the FCCS, and a cold model experiment was carried out, Moreover, the effects of the raw material properties, operating conditions and material properties on the efficiency of the separation were investigated. The results showed that the separation efficiency increased with decreasing glass sphere diameter because the porosity decreased. With the increase in the size of the catalyst particles, the dielectrophoretic (DEP) force increased rapidly and played a more important role, and the separation efficiency increased. Increasing the separation time helped adsorb the catalyst particles that were far from the contact point of the glass ball, and some particles in these areas were unable to be adsorbed. The electrostatic separator has the optimal voltage, and this value decreased with decreasing glass sphere diameter. The experimental results were consistent with the theoretical analysis. Through the nonlinear fitting of a large number of experimental data points, the prediction model of the device was obtained.
Comparative bioconversion of gamma irradiated and steam sterilized ‘wawa’ sawdust (Triplochiton scleroxylon L.) by mycelia of oyster mushrooms (Pleurotus ostreatus Jacq. Ex.Fr. Kummer)

Pleurotus ostreatus was cultivated on composted ‘wawa’ (Triplochiton scleroxylon) sawdust subjected to physical pretreatment techniques of moist heat sterilization and gamma irradiation to assess their comparative mycelial growth performance. Substrates were moist heat sterilized at either 95-100oC for 2.5 hours or gamma irradiated with a Cobalt 60 source at 0 kGy, 5 kGy, 10 kGy, 15 kGy, 20 kGy, 24 kGy and 32 kGy at a dose rate of 1.7 kGy/hour using the ethanol chlorobenzene (ECB) dosimetry. Sorghum grains for spawns were also irradiated at same doses (0 kGy, 5 kGy, 10 kGy, 15 kGy, 20 kGy, 24 kGy and 32 kGy) and steamed similarly and their interactive mycelia growth effects on pretreated sawdust measured. Final linear growth, colonizing time, rate of growth, primordial emergence time, density and percentage contamination were monitored for the four weeks at 28-32oC. The fastest time taken for full colonization was 22 days by irradiated (I+I) samples of the 15 kGy set up while the fastest rate of growth of 28.3 mm/day was recorded by irradiated sorghum and steamed compost bag (I +S) of the 15 kGy set up. There was no significant difference (P>0.05) in final linear growth range of 190.2248. 8 mm recorded for all pretreated samples which also recorded very low (020%) contamination. Gamma irradiation was shown to be a good substitute and more efficient for sterilizing composted sawdust for mushroom cultivation and yield in the Ghanaian tropic conditions.
Design and Implementation of Malware Detection Scheme

Malware is a worldwide epidemic and studies suggest that with the evolution of internet it is getting even worse. There is a new virus implemented every minute and various relevant strategies and tactics have been proposed to alleviate and eradicate cyber threats. Therefore, major concern for the researchers today is to detect and mitigate such programs. In this paper an effort has been made to propose a system which will detect some dangerous viruses and some features of the recently emerged new form of malware in cyberspace known as Ransomware. Imposing serious threats to information assets protection ransomware victimizes the internet users by hijacking user files, encrypting them, then demanding a ransom to be paid in exchange of the decryption key. The proposed program aims to scan the system to get hold of all vulnerable files present on the system and to detect the malicious one’s and remove them.