Spatialised fate factors for nitrate in catchments: modelling approach and implication for LCA results

The challenge for environmental assessment tools, such as Life Cycle Assessment (LCA) is to provide a holistic picture of the environmental impacts of a given system, while being relevant both at a global scale, i.e., for global impact categories such as climate change, and at a smaller scale, i.e., for regional impact categories such as aquatic eutrophication. To this end, the environmental mechanisms between emission and impact should be taken into account. For eutrophication in particular, which is one of the main impacts of farming systems, the fate factor of eutrophying pollutants in catchments, and particularly of nitrate, reflects one of these important and complex environmental mechanisms. We define this fate factor as: the ratio of the amount of nitrate at the outlet of the catchment over the nitrate emitted from the catchment's soils. In LCA, this fate factor is most often assumed equal to 1, while the observed fate factor is generally less than 1. A generic approach for estimating the range of variation of nitrate fate factors in a region of intensive agriculture was proposed. This approach was based on the analysis of different catchment scenarios combining different catchment types and different effective rainfalls. The evolution over time of the nitrate fate factor as well as the steady state fate factor for each catchment scenario was obtained using the INCA simulation model. In line with the general LCA model, the implications of the steady state fate factors for nitrate were investigated for the eutrophication impact result in the framework of an LCA of pig production. A sensitivity analysis to the fraction of nitrate lost as N(2)O was presented for the climate change impact category. This study highlighted the difference between the observed fate factor at a given time, which aggregates both storage and transformation processes and a "steady state fate factor", specific to the system considered. The range of steady state fate factors obtained for the study region was wide, from 0.44 to 0.86, depending primarily on the catchment type and secondarily on the effective rainfall. The sensitivity of the LCA of pig production to the fate factors was significant concerning eutrophication, but potentially much larger concerning climate change. The potential for producing improved eutrophication results by using spatially differentiated fate factors was demonstrated. Additionally, the urgent need for quantitative studies on the N(2)O/N(2) ratio in riparian zones denitrification was highlighted.     

Joint physical and link layer error control analysis for nanonetworks in the Terahertz band

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. The limited capabilities of individual nanomachines and the Terahertz (THz) band channel behavior lead to error-prone wireless links. In this paper, a cross-layer analysis of error-control strategies for nanonetworks in the THz band is presented. A mathematical framework is developed and used to analyze the tradeoffs between Bit Error Rate, Packet Error Rate, energy consumption and latency, for five different error-control strategies, namely, Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), two types of Error Prevention Codes (EPC) and a hybrid EPC. The cross-layer effects between the physical and the link layers as well as the impact of the nanomachine capabilities in both layers are taken into account. At the physical layer, nanomachines are considered to communicate by following a time-spread on-off keying modulation based on the transmission of femtosecond-long pulses. At the link layer, nanomachines are considered to access the channel in an uncoordinated fashion, by leveraging the possibility to interleave pulse-based transmissions from different nodes. Throughout the analysis, accurate path loss, noise and multi-user interference models, validated by means of electromagnetic simulation, are utilized. In addition, the energy consumption and latency introduced by a hardware implementation of each error control technique, as well as, the additional constraints imposed by the use of energy-harvesting mechanisms to power the nanomachines, are taken into account. The results show that, despite their simplicity, EPCs outperform traditional ARQ and FEC schemes, in terms of error correcting capabilities, which results in further energy savings and reduced latency.     

Effect of pH and zeta potential of Pickering stabilizing magnetite nanoparticles on the features of magnetized polystyrene microspheres

Styrene as a monomer was emulsified in water using several magnetite nanoparticles concentration and pH values. Emulsified styrene drops were used as templates for polymerization, in presence of water soluble free radical initiator, and formation of composite particles. Styrene template drops stabilization was verified by light as well as scanning electron microscopy imaging, which ensured the participation of the particles in building up a mechanical barrier to stop oil drops coalescence. Furthermore, the produced polystyrene composites were strongly attracted to an external magnet. The difference in particles size as a function of pH was elucidated using zeta potential measurements, which indicated dominance of pH on the hydrophilicity of the particles and consequently the extent of emulsification, which in turn affected the size of the obtained microspheres. Under some circumstances, capsules were formed instead of particles. Thereby, it can be concluded that the magnetic microspheres are optimally formed at pH 2.3 independently of the magnetite content used.     

Developing Iris Recognition System for Smartphone Security

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.     

Performance dependency of retinal image quality assessment algorithms on image resolution: analyses and solutions

Retinal image quality assessment (RIQA) is the first step performed in retinal image processing systems necessary to assure that the processed images are suitable for analysis and medical diagnosis. RIQA algorithms created for controlled environments can result in degraded performance for cross-dataset experiments in which the train and test images have different resolutions. The effect of image resolution on the performance of four different RIQA algorithms, chosen to include generic, segmentation, and transform-based quality features, is studied using datasets of various resolutions. Analyses showed that for cross-dataset classifications, the performance of some RIQA algorithms was reduced by up to 50% in cases where the train and test dataset image resolutions were significantly different. A statistical analysis was conducted to study how the retinal image quality features are affected by image resolution which resulted in their categorization into resolution-dependent and resolution-independent features. Feature scaling was then introduced to overcome the transform-based RIQA algorithm’s cross-dataset performance degradation resulting in a 100% performance enhancement. Based on this study, the investigation and enhancement of the cross-dataset performance of RIQA algorithms are recommended as a standard part of their design in order to assure their performance reliability in processing images of various resolutions.     

Using Haar classifiers to detect driver fatigue and provide alerts

Drowsiness is a transition state between being awake and asleep and can have serious consequences when occurring in tasks that require sustained attention such as driving. During the state of drowsiness, reaction time is slower, vigilance is reduced, and information processing is less efficient, which may cause accidents. The proposed Driver Fatigue Detection System (called FDS) aims to monitor the alertness of drivers to prevent them from falling asleep at the wheel. The system monitors the driver’s face using Haar feature classifiers with an increased training set to detect changes in the face of the driver quickly. A correlation matching algorithm is used to accurately provide the target’s position and track the target’s eyes according to the intensity, shape, and size of the pupils. FDS uses an IR illuminator to produce the desired bright pupil effect when the driver is wearing sunglasses. The resulting system operates in real-time, and is more accurate and less intrusive to the driver than other systems currently available.     

Supramolecular Structure,Mode of Coordination,Thermal and Potentiometric Studies of Polymer Complexes: A Review

Chelating compounds derived from acryloyl monomers are being utilized as suitable ligands. They enable the formation of rigid coordination polymers. This review gives information about preparation and structurally characterization the polymer complexes by analyses, spectral techniques like IR, NMR, ESR thermal and potentiometric methods. The ESR spectral data of the Cu(II) complexes showed that the metal–ligand bonds have considerable covalent character. The thermal stability was investigated using thermogravimetric analysis. The thermal behavior of these chelates shows that the hydrated complexes lose water molecules of hydration in the first step followed immediately by decomposition of the anions and ligand molecules in the subsequent steps. The results showed that the polymer complexes are more stable than the homopolymer. Proton ligand dissociation and metal ligand stability constants of acryloyl monomers with some transition metal ions in monomeric and polymeric forms potentiometrically were determined.     

Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

Interactive learning tools can facilitate the learning process and increase student engagement, especially tools such as computer programs that are designed for human-computer interaction. Thus, this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback, visibility, affordance, consistency, and constraints. It also compares these methods by the number of iterations and time required to display the result. This study helps students learn these methods using interactive learning tools instead of relying on traditional teaching methods. The tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with root-finding. Users with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed tool. The positive results of the user evaluation showed that the tool can be recommended to other users.     

Nourin-Associated miRNAs: Novel Inflammatory Monitoring Markers for Cyclocreatine Phosphate Therapy in Heart Failure

Background: Cyclocreatine phosphate (CCrP) is a potent bioenergetic cardioprotective compound known to preserve high levels of cellular adenosine triphosphate during ischemia. Using the standard Isoproterenol (ISO) rat model of heart failure (HF), we recently demonstrated that the administration of CCrP prevented the development of HF by markedly reducing cardiac remodeling (fibrosis and collagen deposition) and maintaining normal ejection fraction and heart weight, as well as physical activity. The novel inflammatory mediator, Nourin is a 3-KDa formyl peptide rapidly released by ischemic myocardium and is associated with post-ischemic cardiac inflammation. We reported that the Nourin-associated miR-137 (marker of cell damage) and miR-106b-5p (marker of inflammation) are significantly upregulated in unstable angina patients and patients with acute myocardial infarction, but not in healthy subjects. Objectives: To test the hypothesis that Nourin-associated miR-137 and miR-106b-5p are upregulated in ISO-induced “HF rats” and that the administration of CCrP prevents myocardial injury (MI) and reduces Nourin gene expression in “non-HF rats”. Methods: 25 male Wistar rats (180–220 g) were used: ISO/saline (n = 6), ISO/CCrP (0.8 g/kg/day) (n = 5), control/saline (n = 5), and control/CCrP (0.8 g/kg/day) (n = 4). In a limited study, CCrP at a lower dose of 0.4 g/kg/day (n = 3) and a higher dose of 1.2 g/kg/day (n = 2) were also tested. The Rats were injected SC with ISO for two consecutive days at doses of 85 and 170 mg/kg/day, respectively, then allowed to survive for an additional two weeks. CCrP and saline were injected IP (1 mL) 24 h and 1 h before first ISO administration, then daily for two weeks. Serum CK-MB (U/L) was measured 24 h after the second ISO injection to confirm myocardial injury. After 14 days, gene expression levels of miR-137 and miR-106b-5p were measured in serum samples using quantitative real-time PCR (qPCR). Results: While high levels of CK-MB were detected after 24 h in the ISO/saline rats indicative of MI, the ISO/CCrP rats showed normal CK-MB levels, supporting prevention of MI by CCrP. After 14 days, gene expression profiles showed significant upregulation of miR-137 and miR-106b-5p by 8.6-fold and 8.7-fold increase, respectively, in the ISO/saline rats, “HF rats,” compared to the control/saline group. On the contrary, CCrP treatment at 0.8 g/kg/day markedly reduced gene expression of miR-137 by 75% and of miR-106b-5p by 44% in the ISO/CCrP rats, “non-HF rats,” compared to the ISO/Saline rats, “HF rats.” Additionally, healthy rats treated with CCrP for 14 days showed no toxicity in heart, liver, and renal function. Conclusions: Results suggest a role of Nourin-associated miR-137 and miR-106b-5p in the pathogenesis of HF and that CCrP treatment prevented ischemic injury in “non-HF rats” and significantly reduced Nourin gene expression levels in a dose–response manner. The Nourin gene-based mRNAs may, therefore, potentially be used as monitoring markers of drug therapy response in HF, and CCrP—as a novel preventive therapy of HF due to ischemia.     

Deep Learning-Based Approach for Arabic Visual Speech Recognition

Lip-reading technologies are rapidly progressing following the breakthrough of deep learning. It plays a vital role in its many applications, such as: human-machine communication practices or security applications. In this paper, we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning algorithms. The Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native speakers. The primary purpose is to provide a high-performance model in terms of enhancing the preprocessing phase. Firstly, we extract keyframes from our dataset. Secondly, we produce a Concatenated Frame Images (CFIs) that represent the utterance sequence in one single image. Finally, the VGG-19 is employed for visual features extraction in our proposed model. We have examined different keyframes: 10, 15, and 20 for comparing two types of approaches in the proposed model: (1) the VGG-19 base model and (2) VGG-19 base model with batch normalization. The results show that the second approach achieves greater accuracy: 94% for digit recognition, 97% for phrase recognition, and 93% for digits and phrases recognition in the test dataset. Therefore, our proposed model is superior to models based on CFIs input.